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Volume 1: FOUNDATIONS
Part 0
THE FOUNDATIONAL FRAMEWORKS (READ BEFORE PART I)
Part 0 · Chapter 0.1
Nepal’s Credit Cycle: The Hidden Equity Cycle
First published 21 Aug 2026 · Last verified 29 Aug 2026
Part 0: Why This Part Exists Before Part I
Every other investing textbook begins with the market. This one begins with what drives the market — because in Nepal, the relationship between underlying economic forces and equity prices is unusually direct, unusually mechanical, and unusually exploitable once understood.
Most markets have enough depth, participant diversity, and instrument variety that prices reflect many competing signals simultaneously. Fundamental analysis, momentum, flows, sentiment, and macro all interact. No single force dominates.
NEPSE is not that market.
NEPSE is a market in which one institution — Nepal Rastra Bank — and one variable — the availability of credit — explain the majority of index movement over any meaningful time horizon. The 2016–17 bull run was a credit expansion cycle. The 2021 boom was a liquidity flood catalysed by COVID-era monetary easing. The 2022 crash was a policy tightening episode. In each case, the direction and magnitude of market movement was more accurately predicted by NRB's credit and monetary instruments than by the earnings reports of individual companies.
This is not an accident of history. It is a structural feature of Nepal's financial architecture:
• The banking and financial complex — commercial banks, development banks, finance companies, microfinance and insurance — has historically constituted roughly half to two-thirds of NEPSE market capitalisation (commercial banks alone are a smaller and declining share as hydropower listings grow). When bank earnings move, the index moves. Bank earnings are almost entirely a function of credit growth, NIM, and NPL — all directly controlled or heavily influenced by NRB policy.
• There are no short sellers, no derivatives, no ETF arbitrageurs. Price discovery is dominated by retail investors responding to credit availability — when loans are cheap and easy, they invest; when credit tightens, they withdraw.
• Nepal's real economy is heavily import-dependent and remittance-financed. The monetary transmission mechanism is short: NRB instrument → bank behaviour → economic activity → corporate earnings → market prices.
• Margin lending — the direct use of bank credit to buy equities — amplifies both the upswing and the correction in ways that are unique in scale to Nepal's market structure.
Part 0 does not replace Parts I through XVIII. It provides the gravitational framework within which all other analysis operates. A reader who understands Nepal's credit cycle, NRB's transmission mechanisms, and the four liquidity regimes will interpret every ratio, every financial statement, and every valuation in this Canon with a level of contextual depth that a reader who skips this Part will never fully achieve.
Core Thesis
Price is downstream of liquidity. Liquidity is downstream of credit. Credit is controlled by NRB. To understand why NEPSE does what it does, begin here.
Chapter 0.1 Nepal's Credit Cycle: The Hidden Equity Cycle
The credit cycle is the master cycle in Nepal's economy. Every other cycle — the earnings cycle, the valuation cycle, the liquidity cycle — is a derivative of it. An investor who can read Nepal's credit cycle accurately is, in effect, reading the equity market six to eighteen months in advance.
This chapter builds the empirical and conceptual foundation. It establishes the correlation, explains the mechanism, quantifies the lag, and shows the investor exactly where to find the data.
0.1.1 — Bank Lending Growth vs. NEPSE Index: The Empirical Correlation and Why It Holds
The single most important chart any Nepali investor can study is the overlay of NRB's annual private-sector credit growth data against the NEPSE composite index. The correlation is not merely suggestive — it is structurally explanatory.
The Observed Pattern
Over every major market cycle since NEPSE's formalisation, the following pattern holds with remarkable consistency:
Market Phase
Credit Growth (YoY)
NEPSE Index Behaviour
Lag
Early expansion
15–20%
Flat to mild uptrend
0–3 months
Peak expansion
20–30%+
Strong bull run
3–9 months
Credit deceleration
10–15%
Market stalls, rotation begins
Contemporaneous
Credit tightening
Below 10%
Correction begins
0–6 months
Credit contraction
Negative or near zero
Sharp drawdown
Contemporaneous
Credit re-expansion
10–15% (recovering)
Base formation, early recovery
3–12 months
Table 0.1.1 — Credit Growth and NEPSE Index Relationship (Illustrative Pattern Based on Historical Cycles)
Why the Correlation Holds: The Structural Explanation
The credit-equity correlation in Nepal is not the loose, noise-filled relationship seen in more diversified markets. It holds because of a chain of structural dependencies:
1\. Banking sector dominance: Commercial banks and financial institutions comprise the majority of NEPSE's market capitalisation and trading volume. When credit growth accelerates, bank earnings grow directly through NIM expansion and fee income growth. When credit contracts, bank earnings compress. Because banks are NEPSE, the index is effectively a leveraged tracker of credit growth.
2\. The margin lending amplification: NRB permits commercial banks to lend against listed securities. When credit is expanding, margin loan limits are generous and interest rates are low. This creates a self-reinforcing loop: cheap credit → investors borrow to buy shares → share prices rise → collateral values increase → more borrowing capacity → more buying. The reverse is equally mechanical.
3\. Retail investor behaviour: The majority of NEPSE's trading volume comes from retail investors whose financial health tracks the broader credit environment. When banks are lending freely, household incomes are rising, businesses are expanding, and retail investors have surplus capital and confidence. When credit tightens, the reverse occurs simultaneously across hundreds of thousands of retail accounts.
4\. Corporate earnings transmission: Non-banking companies listed on NEPSE — hydropower, microfinance, insurance — depend on bank credit for project finance, working capital, and growth. Credit tightening hits them through higher borrowing costs, reduced project financing availability, and weaker consumer demand.
A Note on Causality vs. Correlation
The relationship between credit growth and NEPSE performance is causal, not merely correlated. Credit growth is an input that directly determines the earnings, liquidity, and risk appetite of the dominant participants in Nepal's equity market. An investor who monitors credit growth data from NRB is not reading a coincident indicator — they are reading a leading indicator of market conditions.
This is a structural advantage available to every Nepali investor who takes the time to read NRB's monthly publications. It is systematically underutilised because most retail investors focus on price charts rather than credit data.
Investor Edge
NRB publishes monthly banking system statistics including private sector credit growth. This data is free, public, and consistently predictive of NEPSE direction 3–12 months forward. Reading it takes 15 minutes per month.
0.1.2 — Remittance → Deposit → Lending Multiplier: How External Inflows Become Domestic Credit
Nepal's credit cycle is, at its origin, a remittance cycle. This is the fact that most institutional frameworks imported from India or the West fail to account for — and it is the reason those frameworks systematically misread Nepal's monetary environment.
Nepal's Economic Architecture
Nepal receives remittances equivalent to approximately 25–30% of GDP annually — one of the highest ratios in the world. This is not a minor economic variable. It is the primary driver of Nepal's foreign exchange reserves, the banking system's deposit base, and consequently the entire credit cycle.
The transmission mechanism works as follows:
Migrant Workers Abroad
Foreign Remittance Inflows (USD/INR) Exchange to NPR via Banks Bank Deposit Growth CD Ratio Headroom Opens Lending Capacity Expands Credit Growth Accelerates NEPSE Rises
Figure 0.1.2 — The Remittance-to-Credit Transmission Chain
The Multiplier Mechanics
When a remittance payment arrives in Nepal, it enters the banking system as a deposit. Under NRB's regulatory framework, banks must hold a portion of deposits as Cash Reserve Ratio (CRR) and maintain certain liquid assets as Statutory Liquidity Ratio (SLR). The remainder — the loanable funds — is available for credit creation.
The simplified mechanics: If the CRR is 4% and SLR is 12%, a bank receiving NPR 100 in fresh deposits can lend approximately NPR 80–84. That NPR 80–84 will eventually be redeposited in the banking system, creating further lending capacity. This is the classic money multiplier, but in Nepal's case, the base is disproportionately supplied by remittances rather than domestic savings.
Table 0.1.2 — Remittance and Credit Multiplier Key Variables
The Seasonality Dimension
Remittances are not uniform across the year. They spike before Dashain and Tihar (October–November), as migrant workers send money home for Nepal's major festivals. This creates a predictable seasonal pattern in bank deposit growth, system liquidity, and NEPSE trading volume.
An investor who understands this seasonality can anticipate:
• September–October: deposit growth accelerating, system liquidity improving, typically positive for equity sentiment
• December–January: post-festival lull, deposit growth decelerating, volume and price momentum often softening
The mirror image of remittance-driven credit expansion is remittance-driven credit contraction. When Gulf employment markets contract (as in 2020 with COVID-19 and intermittently due to oil price cycles), or when major destination countries impose travel restrictions, remittance inflows fall. Deposit growth slows. Banking system liquidity tightens. NRB may be forced into restrictive monetary policy to manage forex reserve levels. Credit contracts. NEPSE corrects.
This external fragility is a risk that does not appear in any individual company's financial statements, but manifests simultaneously across the entire market. A portfolio that appears diversified across sectors can still be devastated by a single external shock if the underlying mechanism is remittance decline driving system-wide credit contraction.
Risk Monitor
Watch: Gulf Cooperation Council (GCC) employment announcements, Qatar and UAE construction sector activity, Malaysia manufacturing employment — these are leading indicators for Nepal's remittance cycle 3–6 months forward.
0.1.3 — Credit Expansion Lag to EPS Growth: Why Bank Earnings Follow the Credit Cycle by 6–12 Months
Understanding that credit growth leads equity performance is necessary but not sufficient. To trade on this insight, an investor must understand the specific lag structure — the time it takes for changes in credit growth to appear in reported earnings.
Why the Lag Exists
The lag between credit growth acceleration and bank EPS improvement is a structural feature of how bank income is recognised, not a random statistical artefact. Three mechanisms create it:
1\. Loan disbursement to first interest receipt (\~1–3 months): When a bank increases lending, it takes time for new loans to be fully disbursed and for the first interest payments to be received. New loan pipelines are built through credit committees, documentation, and disbursement cycles that take weeks to months.
2\. Quarterly reporting delay (\~3 months): NEPSE-listed banks report quarterly. A credit surge that begins in Q1 will first appear visibly in Q2 results, and will be fully reflected in the market's understanding only after Q2 results are published and analysed.
3\. NIM realisation lag (\~3–6 months): Net Interest Margin improvement from higher lending volumes takes time to accumulate in the income statement. Banks fund much of their lending with time deposits (fixed deposits with 3–6 month terms). As the deposit book rolls over at higher rates during a tightening cycle (or lower rates during an easing cycle), the funding cost lags the lending rate change, creating a NIM compression or expansion that builds over multiple quarters.
The Practical Lag Calendar
NRB Credit Signal
Time to Loan Book Growth
Time to NIM Improvement
Time to Reported EPS Growth
Time to NEPSE Reaction
Policy rate cut
1–3 months
2–5 months
3–6 months
Partial front-running; full in 6–9 months
CD ratio ceiling raised
1–2 months
2–4 months
3–5 months
4–8 months
Credit growth target increased
2–4 months
3–5 months
4–7 months
6–10 months
Policy rate hike
2–4 months
3–6 months
4–8 months
Partial immediate; full in 6–12 months
CD ratio ceiling lowered
1–3 months
2–5 months
3–7 months
3–9 months
Margin loan cap tightened
Immediate (forced selling)
N/A
2–4 quarters
0–4 weeks (direct)
Table 0.1.3 — NRB Policy Signal to EPS and Market Impact Lag Calendar
Investment Application: The Lead-Lag Strategy
The practical implication of this lag structure is that a well-calibrated investor can position ahead of earnings improvements that the broader retail market has not yet priced. The sequence for the banking sector entry trade is:
• Step 1: NRB signals easing (monetary policy announcement, CD ratio relaxation, policy rate cut)
• Step 2: Monitor monthly NRB credit data for 2–3 months to confirm credit growth is accelerating
• Step 3: Begin building positions in commercial banks with strong CASA ratios and low NPL levels
• Step 4: Hold through the first 1–2 quarterly reports that begin to show NIM and EPS improvement
• Step 5: Reassess when credit growth peaks and signs of tightening emerge
This is not market timing in the speculative sense. It is fundamental analysis with temporal precision — using credit cycle data to identify the phase of the earnings cycle before the earnings are reported.
Implementation Note
The lead-lag advantage narrows as more investors adopt this framework. The current advantage exists because the majority of NEPSE retail participants do not read NRB data — they read price charts and follow tips. This is a durable edge only so long as that behavioural pattern persists.
0.1.4 — Credit Contraction and NPL Spike: The Inevitable Phase-Shift and Its Equity Consequences
Every credit expansion cycle in Nepal has ended the same way: NPL ratios rise, provisioning charges increase, net profit falls, and banking stocks reprice downward. This is not a prediction — it is a documented pattern that has repeated across every major cycle in Nepal's banking history.
Understanding the mechanism of the phase-shift from expansion to contraction is as important as understanding the expansion itself, because the contraction phase is where permanent capital losses are made.
Why NPLs Always Rise at the End of Credit Expansion
Deteriorating loan quality during boom: During rapid credit expansion, lending standards weaken. Banks under competitive pressure to grow their loan books approve credits that would be declined in a more cautious environment. Borrowers who are marginally creditworthy in normal conditions take on excessive debt during expansion. These loans perform while the economy is growing and asset prices are rising — but they are structurally fragile.
Margin loan unwinding: When NEPSE prices fall (triggered by the initial tightening signal), margin loan borrowers face collateral shortfalls. Banks issue margin calls. Borrowers cannot top up collateral. Banks liquidate pledged shares. Share prices fall further. More margin calls are triggered. This self-reinforcing spiral can cause NPLs related to share-collateralised lending to spike sharply in a compressed time period.
Real economy lag: As credit tightens, businesses that borrowed during the expansion phase struggle to refinance. Projects that were viable with 15% credit growth slow sharply when credit contracts to 5–8%. Working capital shortages emerge. Loan repayments fall behind. The NPL ratio rises with a 2–4 quarter lag to the credit contraction.
The NPL → Earnings Compression Mechanism
NPL Ratio Rises
Provisioning Charge Increases Net Interest Income Eroded EPS Falls ROA / ROE Compress P/B Multiple De-Rates Banking Stock Price Falls NEPSE Corrects
Figure 0.1.4 — The NPL-to-Market-Price Transmission Chain
The key quantitative relationship: Every 1 percentage point increase in the banking sector NPL ratio requires approximately 0.25–0.50 percentage points of additional provisioning as a percentage of total loans (depending on the mix of NPL categories and existing provision coverage). For a bank with a net interest margin of 3–4%, a sudden 3–4 percentage point NPL spike can consume a full year's NIM — eliminating all net profit.
Historical Reference: The 2022 Correction
The 2022 NEPSE correction is the cleanest recent example of this cycle in operation. By late 2021, credit growth had exceeded 25% year-on-year. The banking system's CD ratio was near NRB's ceiling. Margin lending had expanded significantly. NRB began tightening in late 2021 — raising the policy rate, tightening the CD ratio ceiling, reducing margin loan limits.
The market began falling in early 2022. As prices fell, margin calls were triggered across thousands of retail accounts. Forced selling accelerated the decline. By mid-2022, the NEPSE index had fallen more than 40% from its peak. Banks began reporting NPL increases in Q3 and Q4 2022 quarterly reports — exactly 2–4 quarters after the credit tightening began, consistent with the lag structure described above.
An investor who had read the credit data in late 2021 — and understood that 25% credit growth with a near-binding CD ratio was an unsustainable terminal condition — had between 3 and 6 months of warning before the worst of the correction materialised.
Risk Rule
When NEPSE credit growth exceeds 20% YoY for two consecutive quarters AND the system-wide CD ratio is above 85% of the NRB ceiling AND margin loan balances are growing faster than the loan book: reduce equity exposure regardless of short-term price momentum. These conditions define the terminal phase of the credit expansion cycle.
0.1.5 — Reading the NRB Credit Growth Data: Where to Find It and What Signal to Extract
The analytical framework in this chapter is only useful if the investor can actually access and interpret the underlying data. This lesson provides a complete sourcing and interpretation guide.
Primary Data Sources
Data Source
Publication
Frequency
Key Data Available
URL
NRB Banking and Financial Statistics
Monthly Statistical Bulletin
Monthly
Private sector credit growth, CD ratio, CRR, SLR, deposit growth
Macro indicators, banking system health, credit quality
www.nrb.org.np
NRB Banking Supervision Report
Annual
Annual
Sector-wide NPL, CAR, NIM, ROA by institution type
www.nrb.org.np
NEPSE Market Data
Daily/Monthly Summary
Daily
Index, turnover, sector returns
www.nepalstock.com.np
Table 0.1.5A — NRB Data Sources for Credit Cycle Monitoring
The Three-Number Monthly Discipline
The investor does not need to read every NRB publication in full. The monthly discipline requires extracting three numbers from the NRB Banking and Financial Statistics:
2\. CD ratio (system-wide average, %) — proximity to NRB ceiling (\~90%). Above 88%: binding constraint approaching. Below 80%: capacity available.
3\. Interbank rate (%) — real-time indicator of system liquidity. Sustained high interbank rates signal liquidity stress before it appears in official credit data.
Interpreting the Data: Decision Rules
Signal Combination
Credit Phase
Suggested Portfolio Stance
Credit growth \>20%, CD ratio \<85%, interbank rate low
Peak expansion — late stage
Reduce exposure. Risk/reward deteriorating.
Credit growth 15–20%, CD ratio 80–85%, interbank stable
Mid-expansion — healthy
Maintain or modestly increase banking exposure.
Credit growth 10–15%, CD ratio \<80%, NRB easing
Early expansion / recovery
Build positions. Best entry risk/reward.
Credit growth slowing, CD ratio near ceiling, interbank rising
Transition to compression
Begin reducing. Exit illiquid positions first.
Credit growth \<10%, NPL reports rising, provisioning up
Begin selective re-entry. Validate with 2 monthly data points.
Table 0.1.5B — Credit Data Signal Combinations and Portfolio Decision Rules
The investor who executes this monthly data review discipline consistently will, over a two-to-three year period, develop an intuitive grasp of the credit cycle that translates directly into superior timing of NEPSE sector exposures. This is the most high-return-per-hour-invested analytical activity available to any Nepali investor.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part 0 · Chapter 0.2
NRB Policy → Equity Price Transmission: The Central Causal Chain
First published 21 Aug 2026 · Last verified 29 Aug 2026
Nepal Rastra Bank does not publish equity market guidance. It publishes monetary policy that regulates credit, liquidity, and the banking system. But in Nepal's financial structure, monetary policy and equity market outcomes are virtually synonymous — the transmission from NRB instrument to NEPSE price is faster, more direct, and more mechanically reliable than in almost any other market globally.
This chapter maps every link in that transmission chain with the precision required to use it as an investment tool — not merely to understand it academically.
Chapter Purpose
By the end of this chapter, you will be able to trace any NRB policy announcement to its expected NEPSE consequence, estimate the time horizon of impact, and position your portfolio in advance of the most predictable market movements available to any investor in Nepal.
0.2.1 — The Full Transmission Map: Every Link in the Chain From NRB Instrument to NEPSE Price
The transmission system operates through multiple channels simultaneously. Understanding that these channels interact — amplifying or moderating each other — is what distinguishes institutional-grade analysis from surface-level commentary.
Amplification: When NRB tightens through multiple instruments simultaneously (raising policy rate AND tightening CD ratio AND cutting margin loan limits), the channels amplify each other. The 2022 correction was a multi-channel simultaneous tightening episode — each channel hit the market independently, and their effects compounded.
Moderation: When tightening in one channel is partially offset by easing in another (e.g., policy rate held steady but CD ratio ceiling raised), the net effect on the market is muted. An investor who tracks only one channel will misread the net monetary stance.
Phase dependency: Different channels dominate at different phases of the cycle. Early in a tightening episode, the margin loan channel typically dominates (fastest, most direct). Later, the credit channel begins reducing corporate earnings growth. Finally, the interest rate channel drives up NPLs and provisioning. Watching only one channel at any point will cause the investor to mistime their response.
0.2.2 — Interest Rate Channel: Policy Rate → Cost of Funds → NIM → Bank EPS → Bank Stock Price
The interest rate channel is the most discussed of the four channels and the most commonly misunderstood. The error most retail investors make is to focus on the policy rate itself rather than the transmission of that rate through bank balance sheets.
How the Interest Rate Channel Works in Nepal
NRB Policy Rate ↑
Interbank Rate ↑ Short-Term Deposit Rate ↑ FD Rates Offered ↑ Cost of Funds ↑ Lending Rate ↑ (lagged) NIM Compresses (short term) NIM Expands (medium term if spreads hold)
Figure 0.2.2 — Interest Rate Channel Transmission
The Deposit Repricing Lag: The Critical Nuance
The NIM impact of an interest rate change is not immediate because bank liabilities (deposits) reprice at different speeds than bank assets (loans). This creates a transition period — typically 2–5 quarters — during which NIM moves in counter-intuitive ways:
• When NRB raises rates: Deposit rates (especially fixed deposits) rise quickly as banks compete for funds. But the loan book reprices more slowly, because many loans are fixed-rate for 1–3 year terms. In the short run, NIM compresses. In the medium run, as loans reprice at higher rates, NIM recovers and may improve.
• When NRB cuts rates: The reverse. Deposit rates fall as FDs mature and renew at lower rates. But the loan book reprices down slowly. In the short run, NIM expands. In the medium run, as loans reprice down, NIM reverts.
The investment implication is that the market often misprices NIM movements. When NRB cuts rates, investors who understand deposit repricing dynamics will know that the NIM expansion benefit will materialise over 3–6 quarters — not immediately. Banks that have a high proportion of short-term funding (high interbank reliance, high floating-rate deposits) will realise the benefit fastest.
NIM to EPS: The Quantitative Relationship
For Nepal's commercial banks, net interest income represents approximately 70–80% of total operating income. A 10 basis point change in NIM, maintained over four quarters, translates to approximately a 3–5% change in pre-provision operating profit, depending on the bank's cost-to-income ratio. For banks with higher operating leverage (lower cost-to-income ratios), the EPS sensitivity to NIM is significantly higher.
This means: a 50 basis point NIM compression sustained over two years can reduce EPS by 15–25% in a highly efficient bank. At an unchanged P/E multiple, this translates directly into a 15–25% share price decline. If the market also re-rates the P/E downward (which happens when earnings are declining), the price impact can be 30–40%.
Analytical Tool
Before any banking stock investment, calculate the stock's NIM sensitivity: what happens to EPS if NIM compresses 25bps? 50bps? This gives you the downside scenario that must be priced into your entry point through the margin of safety.
0.2.3 — Credit Channel: CD Ratio Ceiling → Lending Slowdown → Corporate Earnings → Broad Market
The credit channel is the most structurally important transmission mechanism for NEPSE's non-banking sectors. While the interest rate channel primarily affects bank earnings directly, the credit channel affects every sector's earnings by constraining the availability of financing for operations, expansion, and project development.
The CD Ratio as Nepal's Primary Credit Valve
The Credit-to-Deposit (CD) ratio is NRB's single most powerful tool for controlling credit growth in Nepal. The ceiling (currently \~90%, with periodic adjustments) defines the maximum fraction of deposits that banks may deploy as loans. When the banking system approaches this ceiling collectively, credit growth cannot continue at its existing pace regardless of demand.
The mechanism is more complex than a simple cap, however:
• Banks do not all hit the ceiling simultaneously. Some banks manage their CD ratios well below the ceiling; others run close to it. System-wide average CD ratio approaching 85–87% means the most aggressive lenders are already constrained.
• Banks near the ceiling shift their competitive behaviour. They stop competing for new loan customers and start competing for deposits. Deposit rates rise. Cost of funds rises. NIM compresses even without a policy rate change.
• New borrowers find credit unavailable or prohibitively expensive even when the policy rate has not changed. Project pipelines stall. Working capital becomes costly. Businesses reduce expansion plans.
Credit Channel Impact by Sector
Sector
Primary Credit Dependency
Impact of Credit Tightening
Lead Time to Earnings Hit
Commercial Banks
Self (deposit-funded)
Direct NIM compression, slower loan book growth
1–3 quarters
Development Banks
Inter-bank borrowing + deposits
Higher funding cost, NIM squeeze, slower growth
1–3 quarters
Microfinance (MFI)
Wholesale bank borrowing
Cost of funds rises sharply; interest rate cap limits pass-through
1–2 quarters
Hydropower (under construction)
Project finance from banks
Construction draws slow; COD delays; capex overrun risk rises
2–6 quarters
Hydropower (operating)
Debt service on existing loans
Floating-rate debt costs rise; DSCR deteriorates
1–2 quarters
Insurance
Investment income (bonds + FDs)
FD yields may improve; equity portfolio values fall
2–4 quarters
Manufacturing
Working capital loans + term debt
Higher financing costs; demand slowdown from consumer credit crunch
1–3 quarters
Table 0.2.3 — Credit Channel Impact by NEPSE Sector
The Microfinance Special Case
Microfinance institutions occupy a uniquely vulnerable position in the credit channel transmission. MFIs typically borrow wholesale from commercial banks at floating rates, then on-lend to rural borrowers at NRB-regulated fixed rates. When commercial bank lending rates rise due to credit tightening, MFI funding costs rise — and their ability to pass this through to borrowers is regulated. Under the flat 15% lending-rate ceiling in force until mid-2025, no pass-through was possible at all; under the base-rate-plus-premium regime that replaced it from Shrawan 2082 (July 2025), pass-through happens only with a lag and within a capped premium, so the squeeze persists in tightening cycles even if it is no longer absolute (Chapter 31 covers the regime change in full).
This creates an earnings compression that is structurally different from commercial banks: MFIs can offset higher funding costs only partially and with a lag. Their margin is squeezed between a regulated ceiling and a rising floor. This is why MFI stocks historically underperform commercial banks during tightening cycles, and why their equity premium shrinks sharply when credit conditions deteriorate.
The margin loan channel is the fastest, most visceral transmission mechanism in Nepal's equity market. It operates not through earnings or valuations — it operates through forced selling that is entirely disconnected from fundamental value. Understanding this channel is essential not just for predicting market downturns, but for surviving them.
How Margin Lending Works in Nepal
NRB permits licensed banks and financial institutions to extend loans using listed securities as collateral. The borrower pledges shares as security and receives a loan of up to a regulated maximum percentage of the market value of those shares. The investor uses the borrowed funds to purchase more shares — increasing their market exposure beyond what their own capital would allow.
NRB regulates this activity through:
• Maximum loan-to-value (LTV) ratios against different categories of listed securities
• Sector-specific caps on how much any single bank may lend against share collateral
• Individual borrower exposure limits
• Periodic restrictions or tightening when system-wide margin lending exceeds thresholds NRB judges to be excessive
The Margin Call Cascade
The margin loan channel creates a cascade mechanism that turns a moderate price decline into an acute correction. The sequence:
NRB Tightens Margin Cap
Banks Reduce LTV or Call Margin Borrowers Must Top Up or Liquidate Forced Share Sales Hit Market Prices Fall Further More Borrowers Breach LTV More Forced Sales Triggered Cascade Amplifies Decline
Figure 0.2.4 — The Margin Call Cascade Mechanism
This cascade is non-linear: a small price decline can trigger margin calls that produce a much larger decline. The severity depends on the stock of outstanding margin loans relative to average daily trading volume — the higher this ratio, the more violent the potential cascade.
Detecting Margin Loan Risk Before the Event
NRB publishes data on banking system loans against share collateral. This data is available in the monthly banking statistics. An investor monitoring this data can identify when the system is in a high-margin-loan state and reduce equity exposure before the cascade begins.
Margin Loan Indicator
Signal Interpretation
Suggested Action
Share-backed loan growth \> credit growth
Margin borrowing accelerating faster than economy
Begin reducing most illiquid positions
Share-backed loans at multi-year high
System leverage at peak — cascade risk high
Meaningfully reduce equity exposure
NRB issuing guidance on share-backed lending
Regulatory tightening imminent
Reduce exposure immediately
Share-backed loan balances declining
Deleveraging underway — potential forced selling
Wait for stable base before re-entry
Share-backed loans back to historical average
Cycle complete — leverage risk normalised
Re-entry risk/reward improving
Table 0.2.4 — Margin Loan Risk Indicators and Investor Actions
Survivor Rule
In a margin call cascade, liquidity disappears precisely when you need it most. The stocks that fall fastest are the most popular — because they have the most pledged shares. A high-conviction holding that is also widely used as margin collateral will be sold by forced sellers regardless of its fundamental value. Position sizing that accounts for this dynamic is not pessimism — it is risk management.
Nepal's hydropower sector represents the most structurally important non-financial investment opportunity on NEPSE. But it is also the sector most exposed to a transmission channel that most investors do not explicitly model: the project finance channel.
Why Hydropower Depends on Bank Credit
Nepal's hydropower projects are financed on a project finance basis — meaning the debt is raised at the project level, secured against the project's future cash flows (principally the PPA), rather than against a corporate balance sheet. This debt comes overwhelmingly from Nepal's domestic commercial banks and development banks, supplemented in some cases by HIDCL (Hydroelectricity Investment and Development Company), Nepal Infrastructure Bank (NIFRA), ADB, the World Bank and bilateral development lenders.
The typical capital structure of a Nepal hydropower project is:
• Debt: 60–75% of total project cost
• Equity: 25–40% of total project cost (from promoters and public shareholders)
This heavy debt reliance means that the project's economics, construction timeline, and dividend-paying capacity are all materially affected by banking system conditions.
How NRB Tightening Affects Hydropower
Tightening Action
Direct Impact on Hydropower
Second-Order Effect
Investor Signal
Banks constrained by CD ratio
Slower release of construction draw-downs
COD delays; cost overruns from idle construction
Watch: COD guidance in quarterly reports
Lending rates rise
Construction-phase interest capitalised at higher rate
Higher total project cost; increased debt burden at COD
Watch: total project debt in latest prospectus update
Priority sector shifts
Banks redirect lending away from power sector
Project finance becomes scarce; promoters face equity calls
Watch: NRB's priority sector lending data
Long-term loan appetite reduces
Banks shorten tenors; refinancing risk increases
DSCR deteriorates at COD if terms unfavourable
Watch: loan tenor and refinancing schedule in financial notes
When construction draw-downs slow because banks are CD-ratio constrained, construction pauses. Every month of construction delay has multiple cost consequences: the contractor may charge delay penalties, pre-operating interest continues to accrue without revenue to offset it, the monsoon season may be missed (critical for run-of-river projects), and the commercial operation date — which is the trigger for PPA revenue and equity dividends — moves further into the future.
An investor who modelled a hydropower project based on a specific COD date must re-model for every quarter of delay. A one-year delay typically reduces the NAV of a hydropower project by 8–15% due to the combined effect of lost revenue, higher interest capitalisation, and time value of money on delayed dividends.
This is why under-construction hydropower stocks should trade at a discount to their post-COD NAV — and the appropriate discount should explicitly include a probability-weighted delay scenario derived from current banking credit conditions.
0.2.6 — The Feedback Loop: How Falling NEPSE Prices Impair Bank Collateral and Amplify the Cycle
The transmission mechanism described so far is directional: NRB → banks → market. But in reality, a feedback loop operates in the opposite direction as well: falling market prices → bank balance sheet impairment → further credit tightening. This feedback loop is the mechanism that turns a routine correction into a systemic stress episode.
The Three Feedback Pathways
Collateral value impairment: Banks hold listed equities as collateral against share-backed loans. When share prices fall, the value of this collateral falls. Banks must either call for top-up collateral from borrowers (triggering more forced selling) or classify the shortfall as an NPL. Either outcome is bad for the banking sector and for market prices.
Promoter pledge impairment: Many NEPSE company promoters have pledged their own shareholdings against personal or corporate loans. When share prices fall below the pledge threshold, the pledging bank may force a sale of promoter shares into an already declining market. This is a particularly pernicious dynamic because it can cause a governing shareholder's stake to be involuntarily liquidated at the worst moment in the cycle.
Bank capital adequacy erosion: Banks that hold equities in their investment portfolio (either as treasury operations or through subsidiaries) see their capital base erode when market values fall. As capital adequacy ratios approach the NRB minimum, banks become more conservative in lending — precisely when corporate borrowers most need credit support to survive the downturn.
NEPSE Prices Fall
Share Collateral Value Falls Banks Issue Margin Calls Forced Selling Accelerates NEPSE Prices Fall Further Bank Capital Erodes Credit Tightens Further NPLs Rise
Figure 0.2.6 — The Feedback Loop: Market Decline to Credit Tightening
The practical investment implication: once a feedback loop is in operation, the timing of recovery is dependent not on valuation (which may look attractive early in the decline) but on the stabilisation of the credit system. Buying on valuation alone during a feedback loop episode often means catching a falling knife.
Recovery requires: (1) NRB explicitly signals easing, (2) banking system NPLs stabilise and begin declining, (3) margin loan balances return to normal levels, and (4) credit growth begins recovering. All four conditions are visible in public NRB data. The investor who waits for confirmation of all four conditions will miss the first 10–15% of the recovery — but will avoid the serious risk of re-entering while the feedback loop is still operating.
Recovery Checklist
Do not call a market bottom until: (1) NRB has eased at least one instrument, (2) system CD ratio is below 83%, (3) monthly credit growth data shows a second consecutive improvement, (4) interbank rates are declining. These are sequential, not simultaneous — track each one separately.
0.2.7 — Transmission Speed by Instrument: Which NRB Tools Hit the Market Fastest vs. Slowest
Not all NRB instruments reach the equity market at the same speed. An investor who understands the transmission lag for each instrument can calibrate their response time and avoid both premature action (responding to a slow-moving signal as if it were fast) and delayed action (waiting too long for a fast-moving signal to manifest).
NRB Instrument
Announcement to Market Impact
Mechanism
Type of Impact
Reversibility
Margin loan cap (reduction)
Days to 2 weeks
Direct forced selling trigger
Immediate price decline — acute
Fast (months)
Interbank OMO (liquidity injection/drain)
Days
System cash availability
Sentiment and short-term rates
Very fast (weeks)
CD ratio ceiling (reduction)
2–6 weeks
Bank lending capacity constrains
Credit slowdown begins
Slow (quarters)
Policy rate change
1–3 months to full banking system effect
Cost of funds repricing
NIM, then EPS, then price
Medium (2–4 quarters)
CRR adjustment
1–4 weeks
Reserve requirement changes loanable funds
Credit capacity and interbank rate
Medium (quarters)
Capital requirement change
Months to years
Forces rights issues, capital planning
Dilutive, long-cycle effect
Very slow (years)
Priority sector mandates
Quarters
Redirects lending away from certain sectors
Sector-specific credit availability
Slow (years)
Import restrictions (indirect)
Weeks (FX channel)
FX reserve management affects liquidity
Indirect — reduces import competition
Depends on policy duration
Table 0.2.7 — NRB Instrument Transmission Speed to NEPSE
The hierarchy for investor response: margin loan announcements require immediate action; CD ratio changes require positioning within weeks; policy rate changes require portfolio adjustment within 1–3 months; capital requirement and priority sector changes require strategic recalibration over quarters.
An investor who treats all NRB announcements with the same urgency will overtrade. An investor who ignores the fast-moving instruments will be repeatedly caught by margin-call cascades. The discipline is to match your response speed to the transmission speed of each specific instrument.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part 0 · Chapter 0.3
Liquidity Regimes in Nepal: The Four-Phase Framework
First published 21 Aug 2026 · Last verified 29 Aug 2026
The credit cycle and the policy transmission channels described in Chapters 0.1 and 0.2 do not exist on a continuous spectrum. In Nepal's market, these forces cluster into discrete phases — liquidity regimes — in which the dominant market dynamic is clearly identifiable and relatively stable for months to years at a time.
This chapter formalises those regimes into a four-phase framework that the investor can use as a positioning map: not to predict the future with certainty, but to know which regime they are currently in, what the dominant risks are in that regime, and how portfolio construction should change accordingly.
Framework Philosophy
Regime-based investing is not market timing. It is the disciplined recognition that different environments reward different behaviours. The investor who behaves identically across all four regimes will be correct only 25% of the time in the most important dimension of their decision-making.
A regime is a stable configuration of market conditions — credit availability, liquidity levels, risk appetite, policy direction — that persists for a meaningful period and produces predictable patterns in asset prices. The regime concept is more practically useful than continuous indicators for several reasons:
1\. It forces threshold thinking: Rather than asking 'is credit growth slightly better or slightly worse this month', regime analysis asks 'has the system crossed from one qualitative state to another'. Threshold crossings are more actionable than marginal changes.
2\. It anchors strategy: Within a regime, the dominant risks and opportunities are known. The investor does not need to re-evaluate their entire portfolio framework every month — they need to execute the regime-appropriate strategy consistently until the regime changes.
3\. It prevents overtrading: Continuous indicator monitoring leads to constant small portfolio adjustments that increase transaction costs and reduce conviction. Regime investing involves infrequent, high-conviction strategic shifts.
4\. It accommodates uncertainty: Regime identification is not perfect. There are transition periods of 2–4 months when the regime is ambiguous. The four-phase framework handles this explicitly — the transition phase has its own positioning rules.
The Four-Regime Map Overview
Regime
Duration (Typical)
Dominant NRB Stance
NEPSE Tendency
Primary Risk
Phase 1: Expansion
18–36 months
Accommodative to neutral
Uptrend, volume growth
Late-phase over-leverage
Phase 2: Compression
6–18 months
Tightening
Correction, high volatility
Margin call cascade
Phase 3: Balance Sheet Repair
12–24 months
Tight to neutral
Sideways to weak
NPL normalisation lag
Phase 4: Recovery
6–18 months
Neutral to accommodative
Re-rating uptrend begins
False recovery signal
Table 0.3.1 — The Four-Phase Liquidity Regime Framework Overview
Historical context: Nepal has experienced approximately two full cycles of this framework since 2010 — the 2016–2017 bull market and subsequent correction was a compressed cycle, and the 2019–2022 expansion and 2022–2023 correction was a more extreme version. Each cycle exhibited the four phases in the sequence described, with durations consistent with the ranges above.
Remittance inflows are strong. Deposit growth is feeding credit expansion. Banks are competing aggressively for loan customers. Interest rates are relatively low — both lending rates and fixed deposit rates. The cost of capital for businesses is manageable. Hydropower projects are drawing construction loans. Microfinance institutions are expanding their borrower bases. Retail investors, seeing rising share prices, are borrowing against those shares to buy more.
Earnings across the market are growing. P/E and P/B multiples are expanding because the market is forward-pricing continued earnings growth. IPO activity is high, and listing day premiums are large.
The Late-Phase Warning Signs
The expansion regime contains within it the seeds of its own termination. The investor who fails to recognise the late-phase warning signs will give back a significant portion of the gains accumulated during the expansion:
• Credit growth exceeds 20% for two or more consecutive quarters — lending standards are deteriorating
• CD ratio approaches 85–88% — the system is approaching maximum capacity
• Margin loan balances are growing faster than the general loan book — retail leverage is at peak
• NEPSE price-to-book for the banking sector exceeds 2.5–3.0x — historical valuation ceiling approaching
• NRB begins issuing informal guidance or warnings about credit quality — pre-announcement signal
The compression phase begins when NRB decides that credit growth is excessive and monetary conditions need tightening. The trigger is typically some combination of: credit growth exceeding the monetary policy target, FX reserves falling below comfortable levels, inflationary pressure, or banking system vulnerability indicators rising.
The critical investor insight about the compression phase is its non-linearity. The market does not fall proportionally to the degree of tightening. It falls sharply early (driven by the margin loan cascade), then stabilises temporarily (as the initial deleveraging works through), then resumes declining as the earnings impact of credit tightening materialises in quarterly reports.
This two-step pattern traps investors: the initial stabilisation looks like a recovery, drawing buying interest. The second leg down then produces larger losses for those who re-entered prematurely.
Phase 2 Portfolio Positioning
Phase 2: Compression Regime — Portfolio Rules
Equity allocation: 30–50% (underweight; decline begins before earnings data confirms)
Priority action 1: Exit illiquid positions first (small-cap, low-float stocks). These cannot be sold in a cascade.
Priority action 2: Reduce margin-collateral-heavy stocks — these are first targets of forced selling.
Banking sector: Reduce to underweight. Avoid banks with high CD ratios and thin capital cushions.
Hydropower under construction: Reduce. COD delays during credit tightening are highly probable.
Microfinance: Exit or significantly reduce. Regulated lending-rate ceiling (a capped premium over base rate since Shrawan 2082) + rising funding costs = lagged, squeezed margins.
Cash: Build to 40–60%. Capital preservation is the primary objective in Phase 2.
Do NOT: Buy on 'cheap' valuations during Phase 2 — the earnings denominator is still falling.
Exit trigger from Phase 2 monitoring: Two consecutive months of declining CD ratio AND falling interbank rates.
Phase 3 is the most psychologically difficult phase for investors because it is when valuations look most statistically attractive — and yet the risk of further earnings deterioration remains real. Understanding that Phase 3 is distinct from Phase 4 (recovery) is critical for avoiding the most common mistake of this cycle: premature re-entry on value signals before the balance sheet repair is complete.
Indicator
Phase 3 Level
Interpretation
NPL ratio (system-wide)
Rising — often 3–8%+ in severe cycles
Loan quality deteriorating; legacy of Phase 1 over-lending
Provisioning charges
Elevated — suppressing net profit significantly
Banks setting aside income against bad loans
Credit growth
Low (5–10%) but stabilising
Banks not tightening further — but not expanding
NRB policy stance
Tight to neutral — holding, not easing yet
Waiting for NPL cycle to turn
Bank ROA / ROE
Compressed — often below historical average
Earnings quality weak; capital under pressure
NEPSE index trajectory
Sideways to weak; low volume; occasional relief rallies
No new trend; range-bound deterioration
IPO market
Quiet — few quality issuances; listing day premiums minimal
Why Phase 3 Looks Like a Buying Opportunity (But Often Is Not Yet)
By Phase 3, banking stocks may be trading at P/B ratios of 0.8–1.2x — below historical averages. The temptation to buy 'cheap' banks is strong. But the P/B ratio is calculated on book value that has not yet fully absorbed the coming provisioning charges. The earnings denominator in P/E ratios is also not yet at its cycle trough — NPLs are still rising and provisioning is still accelerating.
The patient investor waits for evidence that the NPL cycle has peaked — specifically, two consecutive quarters of declining gross NPL ratios at the system level — before treating low valuations as genuinely attractive rather than as a value trap.
Phase 3 Portfolio Positioning
Phase 3: Balance Sheet Repair — Portfolio Rules
Equity allocation: 30–45% (low; begin building selectively toward the end of Phase 3)
Banking sector: Highly selective. Favour banks with: NPL below sector median, CAR well above minimum, strong CASA (not dependent on high-cost deposits), and conservative management.
Hydropower post-COD: Accumulate selectively if DSCR is healthy and NEA payments current. These have earnings independent of the credit cycle.
Microfinance: Avoid unless PAR30 is demonstrably stabilising. MFIs in Phase 3 carry compounding risk.
Cash: 45–60%. Preserve capital; position for Phase 4 entry.
Active work: Use Phase 3 to do deep analysis on target companies. Build conviction lists for Phase 4 deployment.
Phase 3 → Phase 4 signal: System-wide NPL ratio shows two consecutive quarterly declines AND NRB issues first easing signal.
Phase 4 is the regime the disciplined investor has been preparing for throughout Phases 2 and 3. It is when the credit system clears, NRB pivots to easing, bank balance sheets stabilise, and the equity market begins a re-rating process as forward earnings visibility returns.
Phase 4 is the highest expected-return regime for equities — but only for investors who enter with conviction built on fundamental analysis conducted during Phase 3, rather than for those who chase performance after the recovery is already underway.
Indicator
Phase 4 Level
Interpretation
NRB policy stance
Pivoting to accommodative
First easing signal — most important Phase 4 indicator
System-wide NPL ratio
Peaked and beginning to decline
Balance sheet repair substantially complete
Credit growth
Beginning to recover — 8–12% and rising
Credit channel reopening
CD ratio
Below 83% — capacity available
Banks can expand lending again
Banking sector provisioning
Declining as share of income
Earnings recovery beginning
Interbank rate
Declining toward policy rate floor
System liquidity normalising
NEPSE index trajectory
Base formation complete; early uptrend
Re-rating underway for first movers
Investor sentiment
Still bearish (consensus lagging reality)
Contrarian entry window open
Table 0.3.5A — Phase 4 Recovery Regime Indicators
The Contrarian Opportunity
Phase 4 entry is inherently contrarian because sentiment remains negative when the fundamentals have already turned. The investor who waited for three consecutive months of rising NEPSE prices before buying will be entering at 15–25% higher prices than the early Phase 4 entrant. The investor who read NRB data and identified the NPL trough and policy pivot 2–3 months before consensus recognition is the one who captures the most powerful part of the re-rating.
The emotional difficulty of Phase 4 entry should not be underestimated. The most common behavioural error is paralysis — the investor has been hurt in Phase 2 and disappointed in Phase 3, and hesitates in Phase 4 because they are anchored to the trauma of the correction. The Investment Constitution (Part XXIII) is specifically designed to override this hesitation with pre-committed rules.
Phase 4 Portfolio Positioning
Phase 4: Recovery Regime — Portfolio Rules
Equity allocation: 65–80% (build aggressively as Phase 4 signals accumulate)
Banking sector: Strong overweight. Buy high-CASA banks with healthy CAR first; expand to tier-2 banks as credit recovery broadens.
Hydropower post-COD: Accumulate. Earnings stability + re-rating = highest Sharpe ratio in Phase 4.
Microfinance: Re-enter selectively when PAR30 has declined for two consecutive quarters.
Under-construction hydro: Begin small positions as banking credit availability improves.
IPO strategy: Resume. Listing day premiums recover as retail sentiment improves.
Cash: Reduce to 15–25% strategically. Deploy in tranches as Phase 4 signals accumulate, not all at once.
Phase 4 → Phase 1 transition: Credit growth exceeds 15% for two consecutive quarters = Phase 1 confirmed.
0.3.6 — Government Capex Cycle Overlay: How Budget Spending Interacts With NRB Liquidity
The four-phase NRB-driven liquidity cycle is Nepal's dominant market force — but it interacts with a second cycle that can meaningfully amplify or moderate its effects: the government capital expenditure cycle.
Nepal's Budget Execution Pattern
Nepal's fiscal year runs from mid-July (Shrawan) to mid-July. The government routinely underspends its capital budget in the first half of the fiscal year and rushes to spend in the fourth quarter (April–July). This creates a predictable liquidity dynamic:
• Q1–Q2 of fiscal year (mid-July to mid-January): Government spending is slow. Government deposits at NRB and commercial banks are high. These deposits sterilise liquidity — the money exists in the system but is not circulating. Banking system liquidity is often tighter than credit growth data suggests.
• Q3–Q4 of fiscal year (mid-January to mid-July): Government expenditure accelerates. Large transfers to contractors, employees, and suppliers inject liquidity into the banking system. System liquidity improves. Interbank rates fall. CD ratio pressure may ease temporarily.
The investor who understands this seasonal pattern will not confuse the Q1–Q2 liquidity tightness (a fiscal pattern) with the credit cycle compression (a monetary pattern). They are distinct forces that can coincide or offset each other.
Infrastructure Spending and Cement, Steel, and Construction Stocks
Government capex acceleration also has sector-specific implications. When the government is actively spending on roads, hydropower transmission lines, and public buildings, companies supplying construction materials, equipment, and services benefit — regardless of the monetary cycle phase. Nepal's listed manufacturing and trading sector includes some companies with direct exposure to government infrastructure spending.
Budget Phase
Typical Months
Liquidity Effect
NEPSE Implication
Budget announcement + initial allocation
May–July
Market anticipates spending
Positive sentiment, especially infrastructure
Q1 — slow execution
August–October
Government deposits sterilising liquidity
System tighter than it appears
Q2 — still slow
November–January
Continued fiscal drag on liquidity
CD ratio may appear tight; partially fiscal
Q3 — spending acceleration
February–March
Liquidity injection from government payments
Positive for credit availability
Q4 — year-end rush
April–July
Large government outflows into private sector
Liquidity improves into fiscal year-end; Dashain cash demand follows in Q1 of the new year (Sep–Oct)
Table 0.3.6 — Government Capex Cycle and Liquidity Implications by Month
0.3.7 — Regime Identification Checklist: The Six Data Points That Confirm Which Phase You Are In
The regime identification process should be executed monthly, immediately after the NRB Banking and Financial Statistics bulletin is released (typically mid-month for the prior month's data). The checklist below converts the qualitative phase descriptions into objective, scoreable criteria.
The Six-Point Monthly Regime Identification Checklist
DATA POINT 1: Private sector credit growth (YoY %) — Source: NRB Banking Stats
\>20%: Phase 1 late / approaching Phase 2
15–20%: Phase 1 mid
10–15%: Phase 1 early / Phase 4 late
5–10%: Phase 2 / Phase 3
\<5%: Phase 3 / Phase 2 extreme
DATA POINT 2: System-wide CD ratio (%) — Source: NRB Banking Stats
\<80%: Phase 1 early / Phase 4 — ample capacity
80–85%: Phase 1 mid — moderate
85–90%: Phase 1 late / Phase 2 beginning — constrained
\>90%: Phase 2 active — binding
DATA POINT 3: Weighted average interbank rate (%) — Source: NRB daily OMO data
Near policy rate floor: Accommodative
1–2% above floor: Neutral to mildly tight
\>2% above floor sustained: Tightening / Phase 2
DATA POINT 4: System-wide gross NPL ratio (%) — Source: NRB Banking Supervision Report
Stable / declining: Phase 1 or Phase 4
Rising: Phase 2 or Phase 3
Peaked and reversing for 2+ quarters: Phase 4 entry signal
DATA POINT 5: NRB policy stance — Source: Monetary Policy Statement / Mid-Year Review
Explicit easing actions: Phase 4 confirmed
Neutral / holding: Phase 1 early or Phase 3 late
Tightening actions taken: Phase 2 confirmed
DATA POINT 6: Margin loan balance growth (YoY %) — Source: NRB Banking Stats
Growing faster than loan book: Phase 1 late — leverage accumulating
Assign each of the six data points a phase designation (1, 2, 3, or 4) based on the criteria above. The modal phase across the six data points is the current regime. If there is no clear modal phase (three indicators pointing to Phase 1 and three to Phase 2), the system is in a transition period — treat it as the more conservative of the two phases for portfolio positioning purposes.
Document this score every month in your investment journal. The historical record will allow you to calibrate your own regime identification accuracy over time.
0.3.8 — Portfolio Positioning by Regime: How Your Sector Weights and Cash Level Must Change With Each Phase
The regime framework is analytically valuable only if it changes investment behaviour. This lesson translates the four-phase framework directly into a portfolio positioning map — specific sector weights, cash levels, and instrument preferences for each regime.
Asset / Sector
Phase 1: Expansion
Phase 2: Compression
Phase 3: Repair
Phase 4: Recovery
Total equity allocation
70–85%
30–50%
30–45%
65–80%
Cash & near-cash (FD)
10–15%
40–60%
45–60%
15–25%
Commercial banks
Overweight (25–35%)
Underweight (8–15%)
Selective (10–15%)
Overweight (25–35%)
Development banks
Moderate (5–10%)
Exit or minimal (0–3%)
Minimal (0–5%)
Moderate (5–8%)
Finance companies
Minimal (0–3%)
Exit (0%)
Exit (0%)
Re-enter selectively (0–3%)
Microfinance (MFI)
Moderate / selective (5–10%)
Reduce / exit (0–3%)
Avoid (0%)
Selective re-entry (3–7%)
Hydro — post COD
Moderate (10–15%)
Moderate / reduce (7–12%)
Accumulate (10–15%)
Overweight (12–18%)
Hydro — under construction
Small / selective (3–7%)
Exit or minimal (0–3%)
Minimal (0–3%)
Build selectively (3–7%)
Insurance
Moderate (5–8%)
Reduce (3–5%)
Hold (4–6%)
Moderate (5–8%)
Manufacturing / Trading
Selective (2–5%)
Minimal (0–2%)
Minimal (0–2%)
Selective (2–4%)
IPO participation
Active
Minimal — quality only
Selective
Resume — increasing
Rights issue strategy
Subscribe selectively
Evaluate carefully — dilution likely
Defer unless essential
Subscribe selectively
Table 0.3.8 — Portfolio Positioning Matrix by Liquidity Regime
Implementation Principles
The weights are ranges, not targets: The precise allocation within each range should be set by the Canon Score of individual holdings (Part XVI). Regime determines the envelope; individual stock quality determines where within the envelope each allocation falls.
Transitions are gradual: Do not execute the full portfolio rotation on the day you identify a regime change. Transition over 4–8 weeks for Phase 1 to Phase 2 (where speed matters), and over 8–16 weeks for Phase 3 to Phase 4 (where confirmation accumulates slowly).
Liquidity governs transition order: When moving from a high-equity to a low-equity allocation, always exit illiquid positions first (small cap, low-ADV stocks), then reduce mid-tier positions, and maintain the most liquid holdings longest. This is the exit engineering principle from Part X-A applied to regime transitions.
Cash is not idle during Phase 3: Use Phase 3 cash holdings to earn FD rates (which are often elevated in Phase 3 due to banking competition for deposits) and to fund deep analytical work on Phase 4 entry candidates. Phase 3 is when investment theses should be built — not when Phase 4 has already begun.
The Regime Investor's Advantage
The majority of NEPSE investors have a single de facto portfolio stance regardless of regime — they are always mostly invested, always reacting to price rather than positioning ahead of it. The regime investor is not trying to time the market day by day. They are making three to four major portfolio adjustments per cycle — each one anchored in observable, publicly available data. That is the edge.
Chapter recap
What This Part Has Established
Part 0 has provided three foundational frameworks that underpin every analytical chapter that follows:
The Credit Cycle Framework (Chapter 0.1): Nepal's equity market is primarily a credit market in equity form. The dominant direction of NEPSE is set by NRB's private sector credit growth, not by the earnings of individual companies in the short-to-medium term. An investor who monitors NRB's monthly credit data has a structural informational advantage over the majority of NEPSE retail participants.
The Transmission Map (Chapter 0.2): NRB policy translates to equity prices through four channels — interest rate, credit, margin loan, and project finance — each operating at different speeds and affecting different sectors with different magnitudes. The investor who maps their portfolio exposures against these four channels can anticipate both the direction and the timing of policy-driven market movements.
The Regime Framework (Chapter 0.3): Nepal's credit and policy environment clusters into four discrete regimes. Each regime has characteristic conditions, dominant risks, and appropriate portfolio positioning. The regime framework converts continuous data monitoring into discrete, high-conviction strategic decisions — the most important and most underutilised skill in NEPSE investing.
How This Part Connects to What Follows
Part 0 Framework
Forward Integration Point
Credit cycle phases
Part I (Market Structure): interpret NEPSE volume patterns by credit phase
NRB transmission chain
Part IV (Sector Accounting): banking NIM, MFI rate cap, hydro project finance all depend on transmission understanding
Regime positioning matrix
Part XI (Portfolio Construction): asset allocation targets are regime-adjusted, not static
CD ratio and credit growth data
Part XIII (NRB Policy): the specific policy instruments and their effects are built on Part 0's framework
Phase identification checklist
Part XXI (Playbooks): every sector playbook's entry conditions are regime-gated
Regime portfolio weights
Part XXII (Operating System): the monthly data review in Ch. 104 is the regime identification execution
Transmission speed table
Part XXIII (Constitution): position sizing and exit rules reference instrument-specific transmission speeds
Table — Part 0 Forward Integration Map
The Investor's Commitment
Part 0 is not difficult to understand. It is difficult to execute consistently — because it requires the investor to look at macroeconomic data when the natural impulse is to look at stock prices, and to act on data trends when the natural impulse is to act on price movements.
The investor who reads the NRB Banking and Financial Statistics every month, extracts the three key numbers, updates their regime identification checklist, and adjusts their portfolio accordingly has an analytical and behavioural advantage that cannot be replicated by investors who ignore this data.
That advantage is available to every reader of this Canon. The question is which readers will use it.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part I
THE MASTER VARIABLE: CREDIT, LIQUIDITY & POLICY TRANSMISSION
Part I · Chapter 1
NRB’s Monetary Policy Framework
First published 22 Aug 2026 · Last verified 29 Aug 2026
Lesson 1.1 — Why the Central Bank Sits Upstream of Every NEPSE Trade
Every serious NEPSE investor eventually discovers that the index does not move because of corporate earnings alone. It moves because of the price and availability of money, and in Nepal, both are set — directly and deliberately — by Nepal Rastra Bank (NRB). This is not a peripheral fact to be filed away for macroeconomics trivia; it is the single most important structural truth an investor in Nepali equities must internalize before opening a TMS account. NEPSE is, in composition, a banking-and-financial-institution-heavy market: commercial banks, development banks, finance companies, microfinance institutions, and life and non-life insurers together account for the overwhelming majority of listed market capitalisation and daily turnover. Hydropower counters, the other dominant force on the exchange, are structurally the most leveraged non-financial sector in the country, financed overwhelmingly through long-tenor bank debt. When NRB tightens or loosens the cost and supply of credit, it is not adjusting some abstract macro dial — it is directly repricing the balance sheets of the companies that make up most of the index, and it is directly changing how much money retail and institutional investors have available to deploy into secondary-market trading.
This chapter builds the analytical scaffolding for everything that follows in this book. Before an investor can read a bank's spread income, judge whether a hydropower developer's debt servicing is sustainable, or decide whether a rally in NEPSE reflects genuine earnings momentum or simply a liquidity-driven re-rating, they need a working model of how NRB's monetary policy machinery operates: how the annual monetary policy statement is built, how the policy rate and the interest rate corridor function, how the cash reserve ratio and statutory liquidity ratio constrain what banks can lend, how open market operations and the standing liquidity facility manage day-to-day liquidity, and — critically — how all of this transmits, with a lag, into the deposit and lending rates that show up in bank financial statements and, eventually, into the P/E multiple the market is willing to pay for Nepali equities.
KEY CONCEPT
NRB does not merely regulate the banking system from the outside — through the policy rate, CRR, SLR, and open market operations, it mechanically determines how much lendable liquidity exists in the banking system at any given time, which in turn governs how much capital is available to flow into NEPSE, into hydropower project financing, and into consumer and corporate credit generally.
Lesson 1.2 — How the Annual Monetary Policy Statement Is Built
Nepal Rastra Bank is required under the Nepal Rastra Bank Act, 2058 (2002) to formulate and announce a monetary policy for each fiscal year, and by long-standing practice this statement is unveiled in July, around the start of the Nepali fiscal year in Shrawan — recent statements have arrived in early-to-mid July, days before the new fiscal year begins. The governor unveils the statement, but the analytical work behind it is done through the bank's Monetary Policy Department and Research Department in coordination with the Monetary Policy Committee, drawing on a set of core inputs: the balance-of-payments position and foreign exchange reserve adequacy (conventionally expressed in months of import cover), the trajectory of consumer price inflation relative to India (to whom the Nepali rupee is pegged, which structurally imports Indian monetary conditions into Nepal), the pace of remittance inflows (which fund a large share of system-wide deposit growth), private sector credit growth relative to nominal GDP growth, and the health of bank balance sheets as reflected in non-performing loan ratios and capital adequacy.
The statement itself is structured around a small number of headline numerical targets — a real GDP growth assumption, a CPI inflation ceiling, a broad money supply (M2) growth projection, and a private sector credit growth projection — followed by a much longer set of operational and regulatory provisions covering everything from refinancing facility ceilings for productive-sector lending, priority-sector lending quotas, provisioning norms for specific loan categories, margin lending limits for share-backed loans, and adjustments to sector-specific single-obligor limits. For FY 2025/26, NRB set an economic growth assumption of 6 percent and an inflation ceiling of 5 percent, with broad money supply projected to expand around 13 percent and private sector credit growth projected around 12 percent — figures that mark a deliberate easing relative to the contractionary stance NRB had held through FY 2022/23, when the growth target stood at 8 percent but the credit growth ceiling had been slashed to 12.6 percent from the previous year's 19 percent in response to inflation that had surged from roughly 4.4 percent to 8.6 percent and a balance-of-payments crisis that had pushed import cover dangerously low.
This history matters enormously for how an investor should read any single year's statement. NRB's monetary policy has, since FY 2022/23, moved through a clearly identifiable easing cycle: rates were raised sharply to defend the currency peg and rebuild reserves during the 2022 external-sector crisis, held tight through FY 2023/24 as inflation was brought down from above 8 percent toward the mid-single digits, and then progressively eased across FY 2024/25 and FY 2025/26 as reserves rebuilt (helped by strong remittance inflows and subdued import demand) and inflation fell toward target. An investor reading the July monetary policy statement in isolation, without this multi-year context, will misjudge whether a given year's stance is expansionary or merely less contractionary than the year before. The correct analytical habit is always to compare the new statement against the prior year's actual outturns, not merely against its stated targets — NRB, like most central banks, frequently revises its in-year stance through formal quarterly reviews — the first-quarter review typically arriving around Mangsir (November–December) and the half-yearly review around Magh–Falgun (January–February) — as happened when NRB's first-quarter review for FY 2082/83 (December 2025) cut the policy rate further from 4.50 percent to 4.25 percent alongside a reduction in the standing liquidity facility rate from 6.00 percent to 5.75 percent.
REGULATORY DETAIL
The monetary policy statement is a legal instrument under the NRB Act, but its numerical targets — GDP growth, inflation ceiling, M2 growth, credit growth — are policy assumptions used to calibrate instruments, not binding commitments NRB is obligated to hit. Investors should treat the targets as a signal of intended stance, and the instrument settings (policy rate, CRR, SLR, refinancing ceilings) as the actual operative decisions to track.
Lesson 1.3 — The Interest Rate Corridor: Policy Rate, Bank Rate, and Deposit Collection Rate
Since February 2024, NRB has operated a fully implemented Interest Rate Corridor (IRC) system, which replaced an earlier, looser framework with a cleaner three-rate architecture that any NEPSE investor needs to be able to recite from memory. At the centre sits the policy rate (also called the repo rate), which anchors NRB's benchmark for its principal 14-day repo operations and functions as the reference point around which short-term money market rates are meant to cluster. Above it sits the bank rate — operationally the rate on the Standing Liquidity Facility (SLF) — which forms the ceiling of the corridor: any licensed bank or financial institution facing a temporary liquidity shortfall can borrow from NRB overnight against eligible collateral at this rate, so in principle no bank should ever need to bid up interbank rates above it. Below the policy rate sits the deposit collection rate, operationally the Standing Deposit Facility (SDF) rate, which forms the floor: any bank with surplus cash can park it with NRB overnight at this rate, so no bank should rationally lend to another bank below it. The interbank lending rate — the rate banks charge each other for short-term funds — is supposed to trade inside this corridor, and its position within the band is one of the most immediate, real-time signals of system-wide liquidity conditions available to a Nepali investor.
The corridor has narrowed and moved down substantially across the recent easing cycle. In FY 2022/23, the policy rate stood at 7.00 percent, with the bank rate at 8.50 percent and the deposit collection rate at 5.50 percent — a wide corridor reflecting both the tightening stance and the system's immaturity in managing liquidity within a narrow band. By FY 2023/24 the policy rate had eased to 6.50 percent, the bank rate to 7.50 percent, and the deposit collection rate to 4.50 percent. FY 2024/25 brought the policy rate down to 5.00 percent, the bank rate to 6.50 percent, and the deposit collection rate to 3.00 percent. The monetary policy statement for FY 2025/26 cut further, setting the policy rate at 4.50 percent, the bank rate at 6.00 percent, and the deposit collection rate at 2.75 percent, and the subsequent first-quarter review (December 2025) compressed the corridor again, taking the policy rate to 4.25 percent and the bank rate (SLF) down to 5.75 percent while holding the deposit floor at 2.75 percent.
Nepal Interest Rate Corridor, Selected Fiscal Years
Fiscal Year
Policy (Repo) Rate
Bank Rate / SLF (Ceiling)
Deposit Collection / SDF Rate (Floor)
Corridor Width
FY 2022/23
7.00%
8.50%
5.50%
300 bps
FY 2023/24
6.50%
7.50%
4.50%
300 bps
FY 2024/25
5.00%
6.50%
3.00%
350 bps
FY 2025/26 (initial, mid-July 2025)
4.50%
6.00%
2.75%
325 bps
FY 2025/26 (first-quarter review, Dec 2025)
4.25%
5.75%
2.75%
300 bps
This progression is not a minor technical curiosity — it is the single clearest quantitative expression of NRB's stance shift from post-crisis austerity to deliberate credit-cycle stimulus, and it maps closely onto NEPSE's own multi-year arc. The 2021/22 tightening cycle coincided with a severe correction in the index as bank lending capacity collapsed and margin-lending books were forcibly deleveraged; the subsequent easing cycle from FY 2023/24 onward has coincided with a gradual recovery in system liquidity, a fall in fixed deposit rates that made equities relatively more attractive to yield-seeking savers, and a broad-based re-rating across bank, hydropower, and insurance counters. An investor who tracks only earnings and ignores the corridor will consistently misjudge the timing of NEPSE cycles.
CASE IN POINT
During the 2021/22 liquidity crisis, the interbank rate spiked well outside any orderly band — reported at points to have swung between roughly 0.2 percent and 8.5 percent before the corridor system was fully implemented — as banks scrambled for overnight funds. Credit-to-deposit ratios pushed against regulatory ceilings, banks froze new lending including margin loans against shares, and NEPSE fell sharply from its 2021 highs. The episode is the clearest object lesson in this book for why a NEPSE investor must monitor interbank liquidity conditions, not just corporate results.
It is worth being precise about what "the policy rate" actually governs mechanically. NRB conducts its primary open market operation — typically a 14-day (and at times 7-day or 28-day) repo auction — at or near this rate to inject liquidity when the system is short, and a reverse repo or deposit collection auction to absorb liquidity when the system is flush. The rate is therefore best understood not as a price NRB imposes on the entire economy directly, but as the price at which NRB itself is willing to lend to, or borrow from, the banking system in its routine liquidity operations; everything else — the SLF ceiling, the SDF floor, and by extension the interbank rate, T-bill yields, and eventually bank base rates and lending rates — is calibrated relative to it.
Lesson 1.4 — CRR, SLR, and the Plumbing That Constrains Lending Capacity
If the interest rate corridor sets the price of liquidity, the Cash Reserve Ratio (CRR) and Statutory Liquidity Ratio (SLR) set the quantity of deposits banks are permitted to lend out in the first place — and for a NEPSE investor analysing a bank's balance sheet, these two ratios are as fundamental as any accounting line item.
The CRR requires every "A," "B," and "C" class bank and financial institution to hold a specified percentage of its total deposit liabilities as a non-interest-bearing balance at NRB. NRB raised the CRR from 3 percent to 4 percent as part of its tightening response entering FY 2022/23, and it has held that 4 percent requirement unchanged through the subsequent easing cycle, including in the FY 2025/26 statement and its first-quarter review, both of which explicitly continued "existing arrangements" for CRR without alteration. Because this reserve earns no return, the CRR functions as a direct tax on deposit-taking: every rupee a bank must sterilize at NRB is a rupee it cannot deploy into an interest-earning loan, and a higher CRR mechanically compresses the loanable funds available system-wide even before any change in interest rates.
The SLR requires banks to hold a further minimum percentage of their deposit and borrowing liabilities in specified liquid assets — principally government treasury bills, development bonds, and NRB instruments — that can be liquidated readily if the bank needs cash but which nonetheless generally earn a positive (if modest) yield, unlike the CRR balance. The SLR has stood at 12 percent for commercial (Class A) banks and 10 percent for development banks and finance companies across the recent cycle, a differential that reflects NRB's judgment that smaller deposit-taking institutions warrant a somewhat lighter mandatory liquidity buffer relative to their balance sheet scale, though in practice it also means commercial banks — which dominate system-wide deposits — carry proportionately more of their balance sheets in low-yielding sovereign paper.
Together, CRR and SLR effectively ring-fence roughly 16 percent of a commercial bank's deposit base (4 percent CRR plus 12 percent SLR) from ever reaching the loan book, before a single rupee of capital adequacy buffer, provisioning requirement, or the bank's own liquidity risk appetite is considered. An investor modelling a bank's earning-asset base, net interest margin, or capacity to grow loans in a given year must start from this constraint: loanable funds are never simply "total deposits," they are total deposits net of CRR, net of SLR, and net of whatever cushion the bank chooses to hold above the regulatory minimum for its own comfort.
PRACTICAL TOOL
When estimating a listed bank's realistic loan growth capacity for the year ahead, do not start from deposit growth alone. Subtract the CRR (currently 4 percent) and SLR (12 percent for commercial banks) from projected deposit growth to estimate the maximum loanable increment, then cross-check against the bank's credit-to-deposit (CD) ratio relative to NRB's regulatory ceiling — commonly cited at 90 percent — since a bank already near the ceiling cannot expand lending materially even with ample fresh deposits.
The credit-to-deposit ratio ceiling deserves its own emphasis because it is, in practice, frequently the binding constraint rather than CRR or SLR. NRB caps the proportion of core deposits (plus certain qualifying borrowings) that a bank may deploy as credit, conventionally around 90 percent, precisely to prevent banks from over-lending against an unstable deposit base and to preserve a liquidity cushion for depositor withdrawals. During the 2021/22 credit boom, system-wide CD ratios pushed hard against this ceiling, which is exactly why the liquidity crisis manifested as a lending freeze rather than merely higher rates — banks were not simply reluctant to lend at prevailing rates, many were regulatorily unable to lend more at any rate. As of recent reporting, the system-wide CD ratio has eased to roughly the mid-70s percent range against the 90 percent ceiling, indicating meaningful headroom for credit expansion — a condition consistent with, and partly explanatory of, the liquidity surplus and falling deposit rates that have characterised the FY 2024/25–2025/26 period.
WATCH FOR
A rising CD ratio approaching the regulatory ceiling, even amid falling policy rates, is a warning sign that headline rate cuts may not translate into actual credit growth — the binding constraint has simply shifted from price (interest rates) to quantity (regulatory lending capacity). Always check the CD ratio alongside the policy rate before concluding that "easing" automatically means more credit will flow.
Lesson 1.5 — Open Market Operations, the Standing Liquidity Facility, and Day-to-Day Liquidity Management
The corridor and the reserve ratios describe the structure within which liquidity operates; open market operations (OMOs) are the day-to-day mechanism by which NRB actually manages the quantity of liquidity in the banking system to keep the interbank rate trading near the policy rate rather than drifting toward either edge of the corridor. NRB's OMO toolkit includes repo auctions (NRB lends cash to banks against government securities collateral, injecting liquidity, typically for 14-day tenors though shorter and longer tenors are used opportunistically), reverse repo and deposit collection auctions (NRB borrows cash from banks, absorbing surplus liquidity when the system is flush), outright purchase and sale of government securities, and the direct issuance or auctioning of NRB's own instruments. The decision to run injection operations versus absorption operations in any given week is a direct, observable signal of the underlying liquidity condition, and it is reported regularly and is publicly available — a level of transparency Nepali investors should make active use of rather than relying solely on headline policy rate announcements made once or twice a year.
Standing facilities exist precisely because scheduled OMO auctions cannot address every bank's liquidity need at every moment — a bank facing an unexpected shortfall on a given day cannot wait for the next scheduled repo auction. The Standing Liquidity Facility allows any eligible bank to borrow overnight against qualifying collateral at the bank rate (the corridor ceiling) essentially on demand, functioning as Nepal's version of a lender-of-last-resort facility for routine liquidity management (distinct from, and less dramatic than, genuine solvency-crisis interventions). The mirror-image Standing Deposit Facility allows banks with surplus cash to park it overnight at the deposit collection rate (the corridor floor) — though in practice this facility has at times operated on a limited weekly schedule (reported as available roughly three days a week) rather than being continuously available every business day, a structural quirk that has occasionally contributed to interbank rate volatility even within an otherwise well-functioning corridor.
The practical significance for a NEPSE investor is this: the interbank rate — freely observable and reported daily — tells you in real time where system liquidity actually sits within the corridor, and by extension tells you whether banks are liquidity-constrained or liquidity-flush right now, well before that condition shows up in a bank's quarterly disclosures or in NRB's periodic macroeconomic reports. As of recent data, the interbank rate has traded around 2.75 percent, below the 4.25 percent policy rate and comfortably above the SDF floor, which is itself diagnostic: an interbank rate persistently below the policy rate (rather than clustering near it) indicates the system is running a liquidity surplus, with banks more eager to lend to each other overnight than to bid for scarce funds — consistent with the broader picture of ample liquidity buffers (the net liquid assets–to–deposits ratio of BFIs recently reported in the high-30s percent) and a CD ratio well below its ceiling.
WARNING
A persistently low interbank rate sitting near the corridor floor, alongside a falling CD ratio, signals that banks are liquidity-rich but loan-demand-constrained — a condition sometimes described locally as "liquidity is not the problem, bankable projects are." In this state, further policy rate cuts by NRB may do little to accelerate credit growth or corporate earnings, even though they mechanically compress bank net interest margins by narrowing the spread between lending and deposit rates. Investors should not assume every rate cut is unambiguously bullish for bank earnings — it depends on whether the constraint is price or volume.
Refinancing facilities deserve a place in this lesson as well, since they are one of NRB's most Nepal-specific and NEPSE-relevant tools. Beyond the general corridor and OMO framework, NRB maintains targeted refinancing windows — concessional-rate facilities through which banks can borrow from NRB specifically to on-lend to designated priority sectors: agriculture, cottage and small industries, tourism recovery, earthquake- and disaster-affected borrowers, and export-oriented enterprises among them. Because these facilities carry sub-market rates and defined ceilings that NRB adjusts nearly every monetary policy cycle, they represent a direct, engineered channel through which monetary policy shapes sectoral credit allocation rather than merely the aggregate quantity and price of credit — a nuance that matters when assessing, for instance, why certain development banks or finance companies with concentrated priority-sector books show credit growth patterns that diverge from the commercial banking system average.
Lesson 1.6 — From NRB's Desk to NEPSE's Ticker: The Transmission Mechanism
Understanding each instrument individually is necessary but not sufficient; the investor's real analytical task is tracing the transmission chain from an NRB policy decision through to a NEPSE price movement, and recognising that this chain operates with meaningful lags at each link.
The first link is the money market. A policy rate cut, reinforced by liquidity-injecting OMOs, pulls the interbank rate down toward the new, lower policy rate, and correspondingly pulls down short-term instrument yields — treasury bill rates most visibly, since T-bills are actively traded and repriced continuously. This link is fast, typically showing up within days to a few weeks.
The second link is bank funding costs. As money market rates fall and NRB's deposit collection/SDF rate compresses, banks face less competitive pressure to offer high fixed deposit rates to attract savers, and deposit rates across the system begin to decline — a process NRB's easing cycle has visibly driven, with average system deposit rates recently reported near 3.5 percent, down substantially from the double-digit deposit rates banks were forced to offer during the 2022 liquidity squeeze to retain depositors. This link operates with a lag of roughly one to two quarters, since existing fixed deposits reprice only as they mature and are rolled over, not instantaneously.
The third link is lending rates. Nepali banks price loans using a base rate methodology prescribed by NRB, built substantially from the bank's cost of funds (heavily influenced by deposit rates) plus a spread; as deposit costs fall, base rates fall, and average lending rates follow — recently reported around 7.0 percent system-wide, down sharply from the 12–13 percent-plus levels seen at the peak of the 2022 tightening cycle. This link lags the deposit-rate link by a further one to two quarters, because loan repricing under floating-rate structures typically occurs on a periodic (often semi-annual) reset schedule rather than continuously.
The fourth link, and the one of most direct interest to a NEPSE investor, is where this repricing shows up in corporate and bank fundamentals. For banks and financial institutions themselves, falling rates compress net interest margins if lending rates fall faster than deposit rates (a squeeze scenario), or expand margins if the reverse holds — and getting this sequencing right, quarter by quarter, is a core analytical skill this book will return to when it covers bank financial statement analysis in later chapters. For hydropower developers, most of whom carry debt-to-equity ratios that would be considered aggressive in any other sector but are structurally normal given the capital intensity and long payback periods of run-of-river and storage projects, falling interest rates directly reduce debt servicing costs and can materially improve reported net profit even absent any change in generation volumes or power purchase agreement tariffs — making hydropower counters some of the most interest-rate-sensitive equities on the exchange. For insurers, particularly life insurers with long-duration liabilities, the picture is more ambiguous: falling rates reduce the yield available on new investment in government securities and fixed deposits (major components of insurer investment portfolios), which can pressure investment income even as lower rates support the broader equity market where insurers also hold substantial trading and available-for-sale portfolios.
The fifth and final link is the direct liquidity channel into the secondary market itself, operating in parallel with, rather than strictly after, the fundamental-repricing channel described above. As deposit rates fall, savers who had parked money in fixed deposits during the high-rate 2022–23 period face a shrinking incentive to keep renewing them, and a portion of that capital rotates toward alternative stores of value — NEPSE prominent among them for Nepali households, alongside real estate and, for some, remittance-funded consumption. Margin lending — loans banks extend against pledged shares, subject to NRB-set loan-to-value and single-obligor limits that are themselves adjusted nearly every monetary policy cycle — expands when banks have surplus loanable liquidity and depressed rates make margin-financed equity positions more attractive relative to their financing cost, amplifying whatever price move a favourable earnings or macro backdrop initiates. This is precisely the mechanism that made the interest rate collapse of FY 2024/25–2025/26 coincide with a broad-based NEPSE recovery: falling deposit rates pushed savers toward equities at the same time that falling borrowing costs and ample bank liquidity made margin financing cheaper and more available, a genuinely reflexive dynamic investors should recognise rather than mistake for a pure earnings-driven bull market.
KEY CONCEPT
Monetary transmission into NEPSE runs through two parallel channels operating on different timelines: a slower fundamentals channel (falling rates improving bank margins conditionally, hydropower debt servicing, and corporate earnings generally, over two to four quarters) and a faster liquidity-rotation channel (falling deposit rates and cheaper margin financing pulling household savings directly into equities, often within weeks of a rate cut). NEPSE rallies driven predominantly by the second channel, without confirmation from the first, are structurally more fragile and prone to sharp reversal if liquidity conditions tighten again.
Remittances deserve explicit treatment as the macro-financial backdrop against which all of this operates, because Nepal's monetary and banking system is unusually dependent on them relative to peer economies. Remittance inflows fund a large share of system-wide deposit growth, underpin the foreign exchange reserve position that gives NRB room to ease or forces it to tighten, and indirectly determine how much fresh loanable liquidity enters the banking system independent of domestic credit creation. Recent periods have seen robust remittance growth alongside comfortable foreign exchange reserves (reported above USD 23 billion through 2026, translating into an import cover comfortably above the informal 7-month adequacy benchmark NRB itself references in its policy statements) — a combination that has given NRB the external-sector room to run the easing cycle described throughout this chapter. An investor should therefore watch remittance growth data and the monthly forex reserve position published by NRB not as a standalone macro curiosity, but as a leading indicator of how much room NRB has to continue easing, or how soon it might need to reverse course, exactly as it was forced to do entering FY 2022/23.
CAUTION
Nepal's currency peg to the Indian rupee means NRB's monetary policy independence is structurally limited: if the Reserve Bank of India tightens meaningfully while NRB's own domestic conditions would otherwise argue for continued easing, NRB faces pressure to follow India's rate direction to defend the peg and prevent reserve drawdowns, regardless of what domestic credit growth or NEPSE conditions might prefer. Any investor building a multi-quarter view of Nepali interest rates should track Indian monetary policy alongside NRB's own statements, not in isolation.
Chapter recap
Nepal Rastra Bank's monetary policy framework is not background macroeconomic scenery for a NEPSE investor — it is the mechanical apparatus that determines how much capital exists in the banking system, what it costs, and how readily it can move into equities, and any investor who skips this layer of analysis is trading with an incomplete model of the market's actual driving forces. The annual monetary policy statement, unveiled each July and revisited through formal quarterly and half-yearly reviews, sets headline assumptions for growth, inflation, money supply, and credit expansion, but the operative decisions investors must track quarter to quarter are the instrument settings themselves: the policy rate, the bank rate and deposit collection rate that bound the interest rate corridor, and the CRR and SLR that govern how much of every deposit rupee a bank is even permitted to lend.
The interest rate corridor, fully implemented since February 2024, has compressed and moved sharply lower across the recent easing cycle — from a 7.00 percent policy rate and an 8.50/5.50 percent corridor in FY 2022/23 down to a 4.25 percent policy rate and a 5.75/2.75 percent corridor by the FY 2025/26 first-quarter review (December 2025) — and this trajectory maps closely onto NEPSE's own arc from the 2021/22 liquidity-crisis correction through the subsequent multi-year recovery, making the corridor one of the most directly investable pieces of public data available to a Nepali retail investor.
CRR (held at 4 percent) and SLR (12 percent for commercial banks, 10 percent for development banks and finance companies) sterilize roughly a sixth of system deposits from ever reaching the loan book regardless of the interest rate stance, while the credit-to-deposit ratio ceiling, conventionally near 90 percent, frequently becomes the more binding practical constraint on lending capacity — meaning an investor must check both the price signal (policy rate) and the quantity signal (CD ratio, liquidity-to-deposit ratio) before concluding that a given monetary stance will actually translate into faster credit and earnings growth.
Open market operations, the Standing Liquidity Facility, and the Standing Deposit Facility are the daily plumbing that keeps the interbank rate trading within the corridor, and the interbank rate itself — freely observable, reported continuously, and currently sitting near the corridor floor around 2.75 percent — is one of the fastest, most underused leading indicators of system liquidity conditions available to Nepali investors, well ahead of what shows up in quarterly bank disclosures.
Monetary policy transmits into NEPSE through two parallel channels operating on different clocks: a slower fundamentals channel that improves bank margins conditionally, eases hydropower debt-servicing burdens, and lifts corporate earnings generally over several quarters, and a faster liquidity-rotation channel through which falling deposit rates and cheaper margin financing pull household savings directly into equities within weeks — and rallies built predominantly on the second channel without support from the first tend to be the most fragile and the most vulnerable to reversal.
Because Nepal pegs its currency to the Indian rupee and depends heavily on remittance inflows to fund deposit growth and defend its foreign exchange reserves, NRB's room to ease or its need to tighten is never purely a function of domestic conditions; a disciplined investor tracks Indian monetary policy, Nepali remittance and reserve data, and NRB's own statements together, since a shift in any one of these can force a reversal in the rate cycle that domestic earnings trends alone would not have predicted.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part I · Chapter 2
Credit Policy and Its Direct Impact on NEPSE
First published 22 Aug 2026 · Last verified 29 Aug 2026
Credit does not merely finance NEPSE; in Nepal it has been the single most reliable predictor of where the index goes next. Every major inflection point in the Nepal Stock Exchange since the mid-1990s traces back to a shift in bank lending behaviour that occurred six to eighteen months earlier. When Nepal Rastra Bank floods the banking system with liquidity and private-sector credit accelerates, share prices rise long before corporate earnings justify the move, because a portion of that credit is repurposed, directly through margin loans and indirectly through the wealth and confidence effects of easy money, into equity demand. When NRB tightens, whether by raising the policy rate, capping sector-wise exposure, or simply allowing the credit-to-deposit ratio to bind against the regulatory ceiling, the reverse happens with equal force. An investor who understands this transmission mechanism holds an analytical edge that most NEPSE participants, fixated on candlestick patterns and quarterly EPS, never acquire. This chapter builds that edge systematically: it traces Nepal's credit-to-GDP history and its three identifiable boom-bust cycles, it dissects the mechanics of margin lending as practiced by Nepali banks and finance companies, it catalogues NRB's sector-wise credit caps and their recent liberalization, and it shows, with the actual numbers from 2020 through 2026, how a disciplined investor reads monthly credit data as a leading indicator rather than a lagging curiosity.
Lesson 2.1 — The Credit-GDP Relationship: Why Bank Lending Moves NEPSE
Nepal's financial system is bank-dominated to a degree that has few parallels in South Asia. Commercial banks, development banks, and finance companies together intermediate the overwhelming majority of formal credit in the economy, and NEPSE itself is disproportionately weighted toward these same institutions: banking and financial shares routinely account for more than half of total market capitalisation and a comparable share of daily turnover. This structural fact has a direct consequence that every serious NEPSE analyst must internalize. Because the listed universe is dominated by the very institutions that create credit, and because credit creation is the primary channel through which monetary policy affects the real economy, the health of the banking sector's balance sheet and the pace at which it is expanding its loan book function as a proxy for the health of the entire index. A bank-heavy market cannot decouple from a credit cycle; it is, in a meaningful sense, the credit cycle, traded on a screen.
The mechanism runs through several channels simultaneously. First, there is a direct channel: margin lending, discussed at length in Lesson 2.3, allows credit created by banks to flow straight into share purchases, mechanically bidding up prices as loan disbursement accelerates. Second, there is a wealth-effect channel: when credit is cheap and abundant, real estate values rise (banks in Nepal have historically directed a large share of incremental lending toward property, construction, and housing), and investors holding appreciated land or housing collateral feel richer and reallocate savings into equities. Third, there is a corporate-earnings channel: bank profitability itself is a direct function of loan book growth and net interest margins, so when credit expands, the bottom lines of the twenty-plus listed commercial banks improve mechanically, and because these banks are the largest weights on the index, aggregate NEPSE earnings improve with them regardless of what is happening in the productive economy. Fourth, there is a liquidity channel: excess deposits sitting idle in the banking system, unable to find creditworthy borrowers, migrate into secondary market securities activity, either through banks' own investment portfolios or through their retail customers, compressing the effective cost of holding equities.
Nepal's own economic history offers an unusually clean natural experiment in this relationship, because the country has experienced credit-to-GDP swings of extraordinary amplitude within a short economic history. Nepal Rastra Bank research covering the period from 1990 to 2025 places the long-run average ratio of domestic credit to GDP at roughly 45 percent, with a recorded low near 11 percent in the early liberalization years and a recorded high just above 95 percent in 2022. The World Bank's most recent published figure, for calendar year 2024, puts domestic credit to the private sector at 92.11 percent of GDP, among the higher ratios in South Asia and strikingly high for a country at Nepal's income level. A credit-to-GDP ratio in the neighbourhood of 90 to 95 percent is not, by itself, alarming for a mature financial system, but for an economy with Nepal's shallow capital markets, narrow export base, and heavy reliance on remittance-financed consumption, it signals a financial system that has grown faster than the real economy's capacity to productively absorb that credit. That gap between financial depth and real absorptive capacity is precisely the space in which speculative asset bubbles, including equity bubbles, tend to form.
KEY CONCEPT
Credit-to-GDP measures total outstanding bank and financial institution credit to the private sector divided by nominal GDP. A rising ratio signals the financial system is expanding faster than the real economy, a classic precondition for asset price inflation. In Nepal this ratio moved from roughly 75 percent in 2019 to just above 95 percent in 2022, the fastest three-year expansion the modern financial system has recorded, and NEPSE's all-time high arrived squarely inside that window.
For the practicing investor the implication is straightforward but frequently ignored: valuation work on individual NEPSE counters, however rigorous, sits on top of a macro-liquidity foundation that can overwhelm firm-specific fundamentals in both directions. A well-run development bank with strong asset quality will still see its share price re-rate downward in a credit contraction, because the entire sector's cost of funds rises and loan growth stalls for every institution simultaneously. Conversely, a mediocre finance company can see its share price triple during a credit boom simply because system-wide liquidity is abundant and speculative capital is searching for high-beta vehicles. Nepal's investors who ignore the credit cycle and focus exclusively on bottom-up stock-picking are, in effect, trying to read individual waves while ignoring the tide.
Lesson 2.2 — The Three Credit Cycles: Lessons from 1994, 2009, and 2021
Nepali monetary history since financial liberalization in the early 1990s divides cleanly into three credit boom-and-bust episodes, and each one left a visible fingerprint on NEPSE. Understanding these three episodes in sequence gives the investor a template for recognising the fourth one, whenever it arrives.
The first boom, 1994 to 1996, followed directly from the liberalization of the banking sector and the licensing of new joint-venture commercial banks through the late 1980s and early 1990s. NRB research identifies credit-to-GDP growth rates of 20.9 percent in 1994, 29.2 percent in 1995, and 16.4 percent in 1996, an extraordinary pace of financial deepening for an economy that had, only a few years earlier, operated under near-total state control of credit allocation. NEPSE itself was in its infancy during this period, having only begun operations in 1994, so the equity-market echo of this first boom is harder to document with index data, but the same institutional over-extension that characterised this period, undercapitalized new entrants competing aggressively for loan volume, later showed up as a nonperforming loan problem across the sector by the end of the decade.
The second boom, running from roughly 2007 through 2010, is the one every Nepali investor over the age of forty still remembers, because it centred on real estate and housing finance in a way that directly entangled bank balance sheets, land prices, and NEPSE. Credit-to-GDP rose from about 43.5 percent in 2007 to roughly 56 percent by 2009, an increase driven overwhelmingly by real estate and housing lending as banks, flush with post-conflict-era liquidity and remittance-driven deposit growth, competed to finance land purchases and residential construction in the Kathmandu Valley and other urban centres. Land prices in parts of Kathmandu and Lalitpur rose several-fold within two to three years. NEPSE, still a shallow market at the time, participated in the same speculative logic: bank and finance company shares, which were also the primary vehicles through which retail investors could gain leveraged exposure to the credit boom, rose sharply through 2008 into early 2010. When NRB moved to rein in real estate exposure, imposing sector-wise lending caps and tightening real estate loan classification rules in response to visible asset-price distortion and rising nonperforming loans at several finance companies, the reversal was severe. Credit-to-GDP fell back to roughly 45.8 percent by 2012, several finance companies and a few development banks failed outright or were merged under regulatory pressure, and NEPSE entered a multi-year bear market that did not find a durable bottom until the low 300s in index terms in 2011, a fall of well over half from its pre-crisis level.
The third and most severe boom is the one most relevant to any investor active in the market today, because its aftershocks are still shaping regulatory policy in 2025 and 2026. Credit-to-GDP surged from 75.3 percent in 2019 to a record 95.03 percent in 2022, the sharpest three-year expansion in the dataset. The proximate cause was the monetary response to COVID-19: NRB cut policy rates, relaxed loan classification and provisioning rules to support pandemic-affected borrowers, and injected substantial refinancing liquidity, at precisely the moment that remittance inflows, spent domestically because international travel was frozen, were pushing bank deposits to record levels. Banks holding surplus liquidity and facing negligible real-economy loan demand (construction was halted, tourism was dead, import-dependent trade was disrupted) redirected credit aggressively toward margin lending, real estate, and consumption finance, precisely the categories with the fastest transmission into NEPSE. The index, which had traded near 1,184 points as recently as mid-2018, and had touched a low of roughly 1,189 points in June 2020 during the initial pandemic shock, then rose in an almost uninterrupted climb to an all-time closing high of 3,198.60 on 18 August 2021, a gain of roughly 170 percent from both its 2018 level and its pandemic low — a 2.7-fold multiple — achieved in barely fourteen months.
CASE IN POINT
Between June 2020 and August 2021, NEPSE rose from roughly 1,189 to an all-time high of 3,198.60, a 2.7-fold rise (an increase of roughly 170 percent) in fourteen months, coinciding almost exactly with the period in which Nepal's credit-to-GDP ratio expanded from the high 70s to the low 90s (percent of GDP). Margin lending outstanding, real estate exposure, and consumption credit all grew far faster than the underlying loan book average during this same window. By mid-2022 the credit-to-deposit ratio had breached NRB's 90 percent regulatory ceiling, banks stopped disbursing fresh credit, the average base rate of commercial banks rose from roughly 6.84 percent to 8.98 percent within a year (pushing the weighted average lending rate above 10.5 percent), and the interbank rate spiked to 6.56 percent, consistently above its intended corridor. NEPSE fell by more than a third from its peak within months, and margin-loan borrowers who had pledged shares near the top were forced to liquidate into a falling market, an example of the forced-selling cascade discussed in Lesson 2.6.
The unwind that followed the 2021 peak was, in its mechanics, a smaller and faster replay of 2009 to 2012. As the credit-to-deposit ratio approached and then breached the regulatory ceiling of 90 percent in 2022, banks simply stopped extending fresh loans of any kind, including margin loans, regardless of a borrower's creditworthiness or collateral quality, because the constraint was systemic liquidity, not individual risk assessment. Deposit growth of only about 4.1 percent against credit growth of 10.5 percent over the same period pushed the sector into a structural funding gap. Interest rates, both on deposits (to attract scarce liquidity) and on loans (passed through from a higher cost of funds), rose sharply within a single fiscal year, compressing valuations across the board and triggering exactly the kind of forced margin-loan liquidation that amplifies a downturn. The lesson repeats across all three cycles: NEPSE booms are credit booms wearing an equity-market costume, and NEPSE busts are credit contractions enforced by the same regulatory ceilings, most notably the credit-to-deposit ratio, that permitted the boom to run as far as it did.
Lesson 2.3 — Margin Lending Mechanics: How Nepalis Borrow to Buy Shares
Margin lending, in the Nepali institutional context, refers specifically to loans extended by "A," "B," and "C" class banks and financial institutions against listed shares pledged as collateral, typically through the borrower's demat account, with the loan proceeds usable for further share purchase or general liquidity needs. It is functionally distinct from the margin trading offered directly by some brokerage houses in more developed markets, though Nepal has in recent years also begun permitting licensed stockbrokers, subject to SEBON approval, to extend limited margin facilities of their own, in addition to the much larger bank-originated margin loan market that this chapter focuses on.
The regulatory architecture governing bank margin lending in Nepal rests on four parameters that NRB has adjusted repeatedly over the past decade, each adjustment materially affecting how much leveraged buying power the system as a whole can generate. The first parameter is the loan-to-value ratio, the maximum percentage of a share's value (typically the lower of the 180-day average price or the latest traded price) that a bank may lend against. The second is the aggregate exposure limit, the share of a bank's core capital that may be committed to margin lending in total. The third, until its removal in late 2025, was the single-customer limit, a fixed rupee ceiling on how much any one borrower could draw across the system. The fourth is the risk weight assigned to margin loans for capital adequacy purposes, which determines how much regulatory capital a bank must hold against its margin book and therefore how profitable, and how attractive, margin lending is relative to other uses of a bank's balance sheet.
Regulatory Evolution: Margin Lending Limits
REGULATORY DETAIL
The Loan-to-Value ceiling, the percentage of a pledged share's price a bank may lend against, has been raised twice in six years: from 50 percent to 65 percent in December 2018, from 65 percent to 70 percent in the 2020-21 easing cycle, and most recently, under NRB's Unified Directives issued in mid-July 2026, to as high as 80 percent for shares of companies that meet a formal internal scoring standard covering paid-up capital size, listing duration, profitability, dividend history, credit ratings, and regulatory compliance. Each increase mechanically expands system-wide margin buying power without any change in the underlying quality of collateral.
The table below traces the four parameters through their major regulatory revisions since 2018, the period for which detailed circular-level data is available and verifiable.
Period
LTV Ceiling
Aggregate Limit (% of core capital)
Single-Customer Limit
Risk Weight
Pre-December 2018
50% of price
25%
10% of core capital per company
150%
December 2018 circular
65% of 180-day average or latest price, whichever lower
40%
10% of core capital per company
100%
2020-21 easing cycle
70%
40%
Rs 4 crore per BFI (introduced 2021-22)
100%
FY 2023/24–FY 2024/25
70%
40%
Raised to Rs 15 crore, then Rs 25 crore per customer
Reduced further, to roughly 100% by August 2025
October 2025 (Asoj 2082)
70%
40% (unchanged)
Removed entirely; only the 40%-of-core-capital aggregate ceiling binds
100%
July 2026 (Unified Directives 2083)
Up to 80% for scored "strong" companies; 70% for others
40%
No individual ceiling
100%
The direction of every single one of these six revisions has been toward greater system-wide leverage capacity, a pattern that is itself diagnostic: Nepal's regulator has used margin lending policy as a lever to stimulate secondary market activity and, indirectly, primary issuance appetite, during periods when it judged the broader credit cycle to need support, most visibly through 2024 and 2025 as the post-2021 correction dragged on and NEPSE struggled to sustainably clear the 2,800 to 3,000 range.
The practical consequence of the October 2025 removal of the single-customer limit deserves particular emphasis, because it materially changed the concentration risk profile of the margin loan market. Prior to removal, a wealthy investor's capacity to leverage into shares through any single bank was capped in absolute rupee terms (the ceiling had risen from an initial Rs 4 crore per institution in 2021-22 to Rs 25 crore, or Rs 250 million, by the FY2025/26 monetary policy), which meant large investors had to spread margin borrowing across multiple institutions to fully leverage a large equity position, a friction that slowed the pace at which any single actor could build a highly leveraged book. With that ceiling removed, a bank's aggregate 40 percent of core capital limit is now the only binding constraint, and because the combined core capital of the twenty-odd commercial banks was estimated at roughly Rs 600 billion in mid-2025 (against which roughly Rs 120 billion of margin lending capacity had already been drawn, and a further Rs 120 billion of the total roughly Rs 240 billion system-wide ceiling remained available), a small number of large, well-connected borrowers can now, in principle, absorb a disproportionate share of any single bank's remaining margin lending headroom. Nabil Bank, Global IME Bank, and Kumari Bank were, as of mid-2025 disclosures, among the largest disbursers of margin loans in absolute terms, while the newly merged Nepal Investment Mega Bank carried the largest remaining unutilized capacity of any single institution, and Siddhartha Bank the least.
WATCH FOR
Total margin lending outstanding across the banking system stood at roughly Rs 76.5 billion in mid-July 2023 and had grown to approximately Rs 162.9 billion by mid-June 2026, an increase of over 110 percent in three years and a rise of about 16 percent in the final eleven months alone. Margin lending nonetheless still represents only about 2.7 percent of total bank credit outstanding, which sounds modest, but concentration matters more than the aggregate share: because margin lending is disbursed almost entirely against a narrow set of large-cap, liquid NEPSE counters, its marginal impact on those specific share prices, and on index-level sentiment, is far larger than its 2.7 percent share of the loan book would suggest.
The mechanics of a margin call in the Nepali system work as follows, and every investor using margin facilities should have this sequence memorised rather than merely understood in the abstract. A bank values the pledged shares daily or near-daily against the prevailing market price. If the loan-to-value ratio implied by a falling share price breaches the bank's internal maintenance threshold, typically set somewhat below the maximum disbursement LTV to provide a buffer, the bank issues a margin call requiring the borrower to either inject additional cash or additional collateral, or accept partial forced liquidation of the pledged shares to restore the required LTV. Because margin loans in Nepal are concentrated in a relatively small set of frequently pledged large-cap counters, a broad market decline that triggers margin calls across many borrowers simultaneously produces forced selling concentrated in the same handful of shares at the same time, which depresses those prices further, triggers further margin calls on other borrowers holding the same collateral, and can cascade into a self-reinforcing decline entirely independent of any change in the underlying companies' fundamentals. This is precisely the mechanism that amplified NEPSE's decline after the August 2021 peak, and it is the mechanism that Nepali financial commentary, including a widely discussed Kathmandu Post opinion column in mid-2026, has warned will recur with even greater force given the scale of margin lending liberalization enacted between 2024 and 2026.
Lesson 2.4 — Sector-wise Credit Caps and Real Estate Exposure
Beyond margin lending, NRB maintains, and periodically revises, a set of sector-wise exposure controls designed to prevent excessive concentration of bank credit in asset classes prone to speculative bubbles, real estate and housing chief among them. The logic mirrors margin lending regulation closely, and for good reason: real estate and equities are Nepal's two principal speculative asset classes, they are financed by the same pool of bank credit, and a boom in one frequently spills into the other, as the 2009-2010 episode demonstrated directly and the 2020-2021 episode demonstrated with equal force.
Historically, NRB has capped the combined share of a bank's loan portfolio that may be directed toward real estate and housing finance, with the specific ceiling adjusted upward and downward across different economic cycles depending on whether the regulator judged the sector to be underfinanced (as in the aftermath of the 2015 earthquake, when reconstruction financing needs argued for looser limits) or overheated (as in 2009-2010 and again to a lesser degree in 2021, when land price inflation and construction-sector credit growth argued for tighter limits). Within the broader real estate ceiling, residential housing loans to individual homebuyers have generally been treated more permissively than commercial real estate or land-purchase financing, on the theory that owner-occupied housing finance carries different risk characteristics and different social policy value than speculative land banking or commercial property development.
The most recent easing cycle illustrates the pattern clearly. Under the monetary policy for FY 2025/26, NRB raised the maximum housing loan amount eligible for the more favourable regulatory treatment from Rs 20 million to Rs 30 million, and set loan-to-value ratios at 80 percent for first-time homebuyers and 70 percent for other borrowers, an explicit loosening intended to stimulate a construction and real estate sector that had been in a multi-year slump following the 2021-2022 credit contraction. The same October 2025 circular (Asoj 22, 2082) that removed the margin-lending single-customer cap was formally framed as the removal of the Single Obligor Limit on share-backed loans — the Rs 25 crore (Rs 250 million) ceiling on total share-collateral borrowing by any one borrower or group of related parties across all banks combined. It is worth being precise about the scope: the removal applied specifically to share-backed lending, while the general concentration framework governing other loan categories (single-obligor exposure expressed as a percentage of a bank's core capital) remains in place. Even so, within the share-loan market the change removed the last absolute rupee brake on how large a single borrower's leveraged equity position can grow — leaving bank-level risk assessment and the aggregate 40 percent-of-core-capital ceiling as the only constraints.
REGULATORY DETAIL
The Single Obligor Limit on share-backed loans — which had capped any one borrower's or related group's total share-collateral borrowing across all banks combined at Rs 25 crore (Rs 250 million) — was abolished under an amendment to NRB's Unified Directives issued on Asoj 22, 2082 (8 October 2025), acting on Capital Market Reform Taskforce recommendations. The same circular cut the minimum holding period for banks' own share investments from one year to six months and scrapped the 20-percent-of-core-capital annual cap on portfolio sales — collectively the most significant loosening of share-market-related banking regulation in Nepal's post-2010 history.
The systemic risk implication of this concentration is visible directly in NRB's own collateral composition data. As of the most recent published breakdown for the first half of FY 2025/26, real estate-backed loans (including land and building pledged as collateral for loans that may be nominally classified under other purposes, such as working capital or margin lending) accounted for 63.9 percent of total outstanding bank credit, with current assets, covering both agricultural and non-agricultural working capital, making up a further 15 percent. This concentration means that Nepal's banking system, and by extension NEPSE, remains extraordinarily sensitive to real estate price movements even in periods when headline credit growth is directed nominally toward other sectors, because the collateral base underlying the majority of the loan book is a single, correlated asset class.
Sectoral Credit Growth, First Half of FY 2025/26
Loan or Sector Category
Growth Rate (H1 FY 2025/26)
Consumption-related lending
9.1%
Margin lending (share-collateral)
8.3%
Import-related trust receipt loans
7.8%
Hire-purchase loans
7.3%
Construction sector
7.2%
Transportation, communication, public services
6.2%
Industrial production
4.4%
Overdraft lending
-3.3% (contraction)
Agriculture
-1.1% (contraction)
This table, drawn from NRB's own first-half FY 2025/26 macroeconomic release, is worth studying category by category, because it shows precisely where the marginal rupee of new credit was going during the period under review, and margin lending's 8.3 percent growth rate, more than double the 4.4 percent industrial production growth rate, and running well ahead of the 3.6 percent half-year growth rate for aggregate private-sector credit, confirms that even in a period of generally subdued credit expansion, share-collateral lending was capturing a disproportionate share of whatever new credit the system was willing to extend. An investor tracking this data series month to month gains an early read on whether liquidity is rotating toward speculative equity exposure or toward productive capacity, well before that rotation shows up in NEPSE turnover figures.
Lesson 2.5 — Reading Credit Growth Data as a Leading Indicator for NEPSE
NRB publishes detailed monthly and semi-annual macroeconomic and financial statistics, including the "Current Macroeconomic and Financial Situation" report and periodic monetary policy reviews, that break down credit growth by borrower type, sector, and institution category. These publications are freely available on NRB's website and are, in the authors' assessment, the single most underused research resource among Nepali retail investors, most of whom never look past the daily NEPSE ticker and quarterly company disclosures.
The most recent half-year data available as this chapter is written, covering the first six months of FY 2025/26 (mid-July 2025 to mid-January 2026), illustrates both the value and the limits of this data as a leading indicator. Private-sector credit grew by 3.6 percent over the six-month period, translating to roughly Rs 197.47 billion in new credit disbursed, against total outstanding credit of Rs 5,695.17 billion. On an annual point-to-point basis, credit growth stood at 6.7 percent, comfortably below the 12.0 percent full-year growth target set out in NRB's monetary policy for FY 2025/26, and also well below the roughly Rs 265.56 billion disbursed over the equivalent period one year earlier. Commercial banks grew their books by 3.7 percent over the half-year, development banks by 2.9 percent, and finance companies by only 1.2 percent, a hierarchy that itself tells a story: the smaller, higher-cost-of-funds institutions are the first to feel a credit slowdown and the first to see their growth compress when systemic liquidity tightens, making finance-company credit growth a useful early-warning signal that tends to turn before commercial-bank credit growth does.
PRACTICAL TOOL
Before trading on any macro thesis, check NRB's "Current Macroeconomic and Financial Situation" report, published on the NRB website typically within four to six weeks of each fiscal-year quarter's close, and the annual Monetary Policy document, published each mid-July (Ashadh/Shrawan). Both are free, in English, and contain the sector-wise and institution-wise credit growth tables referenced throughout this chapter. Cross-reference the credit-to-deposit ratio reported there against the regulatory ceiling (approximately 90 percent) each time it is published; a ratio approaching that ceiling has, in both 2011 and 2022, preceded a NEPSE correction of significant magnitude within two to four quarters.
The borrower-composition data carries a further diagnostic signal that is easy to overlook. As of the same H1 FY 2025/26 release, non-financial institutions (essentially corporate borrowers, including real estate developers, trading houses, and industrial concerns) accounted for 62.7 percent of outstanding credit, while individuals and households accounted for 37.3 percent. A rising household share of incremental credit growth, driven disproportionately by consumption lending (9.1 percent growth in this period, the fastest of any category) and margin lending (8.3 percent), rather than by productive corporate investment, is a signal that credit expansion is increasingly financing consumption and asset speculation rather than capacity expansion, a pattern that historically has proven less durable and more prone to sharp reversal than credit growth concentrated in industrial or export-oriented lending.
The investor's practical workflow should combine three data points, checked each time NRB releases updated figures, typically quarterly at minimum: the point-to-point private-sector credit growth rate relative to its own monetary policy target (a rate running persistently below target, as in the 6.7 percent actual against a 12.0 percent target seen in early FY 2025/26, signals a still-cautious lending environment in which NEPSE rallies are less likely to be credit-fuelled and more likely to reverse on thin follow-through); the credit-to-deposit ratio relative to the approximately 90 percent regulatory ceiling (a ratio climbing toward that ceiling, as happened in 2011 and again in 2022, is the single most reliable leading indicator of an imminent liquidity squeeze and NEPSE correction that this chapter can offer); and the growth rate of margin lending specifically relative to aggregate credit growth (margin lending growing meaningfully faster than the system average, as it has in most periods examined in this chapter, indicates that a disproportionate share of available credit is finding its way directly into share purchases, a condition that has historically preceded index appreciation in the near term but has also, without exception, preceded a sharper-than-average correction once the credit cycle turns).
Lesson 2.6 — Regulatory Risk: Why NRB's Next Circular Matters More Than Any Chart
Every mechanism described in this chapter, higher loan-to-value ceilings, the removal of single-customer and single-obligor limits, reduced risk weights on margin lending, expanded housing loan eligibility, can be reversed by NRB with a single unified directive, and Nepali financial history shows that the regulator has, in fact, reversed course sharply and with limited advance warning on multiple occasions. The 2009-2010 real estate tightening and the 2022 credit-to-deposit-ratio-driven lending freeze were both delivered with comparatively little lead time relative to the severity of their market impact, and in each case, investors who had built leveraged NEPSE positions on the assumption that regulatory conditions would remain stable were the ones who suffered the largest losses.
As of mid-2026, the regulatory trend has been unambiguously toward liberalization: the loan-to-value ceiling for margin lending has been raised to as high as 80 percent for scored companies, the single-customer (single obligor) limit on share-backed loans has been abolished, risk weights on margin lending have been progressively reduced, and housing finance eligibility has been expanded. Total margin lending outstanding of roughly Rs 162.9 billion as of mid-2026, though still only 2.7 percent of the total loan book, has grown over 110 percent in three years, and NEPSE itself, after peaking at 2,970 points in late March 2026 and falling to a low of 2,469 points in October 2025, was trading in the high 2,600s in July 2026, a level still well below the August 2021 all-time high but showing renewed sensitivity to exactly the same credit-driven dynamics described throughout this chapter. Total system-wide bank loans of roughly Rs 5,915 billion against deposits of roughly Rs 8,268 billion as of July 2026 imply a credit-to-deposit ratio still comfortably below the approximately 90 percent regulatory ceiling, which is itself informative: the current liberalization cycle has room to run further before it encounters the same systemic constraint that ended the 2021 boom, but that same fact means the eventual reversal, whenever the ratio does approach the ceiling again, is likely to be at least as abrupt as the 2022 episode, because NRB has repeatedly shown that it manages the credit-to-deposit ceiling as a hard constraint rather than a gradually tightened one.
WARNING
Every regulatory relaxation catalogued in this chapter increases the system's capacity to generate a credit-fuelled NEPSE rally, and every one of them is reversible without advance notice through a single NRB unified directive. Nepal's banking regulator has changed margin lending and sector-wise exposure rules more than half a dozen times since 2018 alone. An investor who has built a leveraged position on the assumption of continued regulatory support for margin lending is making an implicit bet on NRB policy continuity that history does not support; the 2022 credit-to-deposit-ratio-driven lending freeze arrived with only a few months of visible warning in the underlying data and considerably less warning in public communication.
The practical response for an investor is a discipline, not a forecast. First, never treat the current maximum loan-to-value ceiling, whatever it happens to be at the time of reading this chapter, as a stable parameter to be borrowed against at its limit; maintain a buffer well below the maximum permitted leverage, because the maintenance margin at which a bank issues a call is set below the disbursement ceiling precisely to protect the bank, not the borrower, and a falling market can close that buffer in days. Second, track the credit-to-deposit ratio and the point-to-point private-sector credit growth rate each time NRB publishes them, because both have historically moved against NEPSE with a lag of two to four quarters, giving an attentive investor real time to de-risk before a broad correction. Third, treat any sudden acceleration in margin lending growth relative to the aggregate credit growth rate, of the kind seen through 2020-2021 and again through 2024-2026, as a signal of rising systemic fragility in the specific large-cap counters that dominate margin loan books, rather than as confirmation that those counters are safe simply because they are widely held and heavily pledged. Fourth, recognise that a bank-dominated, real-estate-collateralized financial system of Nepal's structure will continue to produce credit cycles of comparable amplitude to 1994-96, 2008-10, and 2020-22 for the foreseeable future, and that NEPSE will continue to trade as a levered proxy on that cycle rather than as an independent reflection of corporate fundamentals, until the market's sectoral composition and financing structure change in ways that are not visible on the current horizon.
CAUTION
Do not confuse a rising NEPSE index with rising corporate value creation during a credit boom. In each of Nepal's three identified credit cycles, share price appreciation substantially outpaced any measurable improvement in the productive capacity or export competitiveness of the underlying economy. The gains were real for those who exited before the reversal and were erased, often with borrowed capital attached, for those who did not.
Chapter recap
Nepal's credit-to-GDP ratio has moved through three distinct boom-bust cycles since financial liberalization, from the 1994-96 post-liberalization expansion, through the 2008-10 real estate-driven boom that pushed the ratio from roughly 43.5 percent to 56 percent before a multi-year contraction, to the 2020-22 pandemic-era surge that took the ratio from 75.3 percent to a record 95.03 percent and carried NEPSE from roughly 1,189 points to an all-time high of 3,198.60 in fourteen months; each cycle ended when the credit-to-deposit ratio breached NRB's approximately 90 percent regulatory ceiling and forced an abrupt, largely unannounced lending freeze.
Margin lending, the mechanism through which bank credit converts most directly into NEPSE buying power, is governed by four regulatory levers, the loan-to-value ceiling, the aggregate exposure limit as a percentage of core capital, the (now-abolished) single-customer limit, and the risk weight applied for capital adequacy purposes, and every one of these levers has been loosened repeatedly since 2018, most significantly through the October 2025 removal of the single-customer cap and the July 2026 increase of the loan-to-value ceiling to 80 percent for scored companies.
Real estate exposure and margin lending are financed from the same pool of bank credit and have historically moved together, and the concentration is structural rather than incidental: real estate-backed collateral underpinned 63.9 percent of all outstanding bank credit as of the most recent published breakdown, meaning the banking system, and therefore NEPSE, remains acutely sensitive to property price movements regardless of which loan category new credit is nominally booked under.
NRB's monthly and semi-annual macroeconomic publications, including the credit-to-deposit ratio, sector-wise credit growth breakdowns, and institution-wise loan growth figures, function as genuine leading indicators for NEPSE direction with a historical lag of roughly two to four quarters, and an investor who tracks margin lending growth against aggregate credit growth, and the credit-to-deposit ratio against its regulatory ceiling, gains meaningful advance warning of both rallies and corrections that pure technical or company-level fundamental analysis cannot provide.
The current regulatory environment as of mid-2026 is firmly in a liberalization phase, with margin lending outstanding having grown past Rs 162.9 billion, more than double its mid-2023 level, even though margin loans still represent only about 2.7 percent of total system credit, and this combination of rapid growth from a low base with progressively loosened regulatory limits is the same pattern that preceded both the 2009-10 and 2021-22 reversals.
Every leveraged NEPSE position built on current margin lending terms should be sized with the explicit assumption that NRB can and has, on multiple prior occasions, tightened these terms abruptly and with limited public warning, and the disciplined response is to maintain a leverage buffer below the maximum permitted loan-to-value ratio, to monitor the credit-to-deposit ratio each time NRB reports it, and to treat NEPSE's credit-driven rallies as opportunities to build positions with a defined exit discipline rather than as evidence of a permanently higher plateau in fundamental value.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part I · Chapter 3
NRB Policy Transmission — From Announcement to Share Price
First published 22 Aug 2026 · Last verified 29 Aug 2026
Lesson 3 opens on the floor of Nepal Rastra Bank's mid-July policy calendar, the single most anticipated ritual in the Nepali investment year, and it is worth beginning there because so much retail behaviour in NEPSE is organised around an event that, in truth, changes almost nothing on the day it happens. Every Shrawan, as the new fiscal year begins, brokerage floors in Kathmandu, Pokhara, Biratnagar and Butwal fill with investors trading rumours about what the Governor will say about the policy rate, the cash reserve ratio, and margin lending limits. Prices move on the rumour, move again on the announcement, and then — this is the part retail investors consistently misprice — continue moving, in fits and starts, for months afterward as the actual mechanics of monetary policy work their way through Nepal's banking system and into share prices. This chapter is about that gap: the space between the press conference and the price, between what NRB says and what NEPSE eventually does about it. Understanding this gap is not an academic exercise. It is the difference between an investor who sells in panic on policy day and buys back three months later at a worse price, and one who reads the transmission mechanism correctly and positions ahead of a move that has not yet been priced in.
Nepal's monetary transmission is slower, lumpier, and more incomplete than in the developed markets whose textbooks Nepali finance students are trained on. A rate decision by the US Federal Reserve reprices the entire US Treasury curve within seconds, and equity index futures adjust within the same minute because the market is saturated with algorithmic participants continuously arbitraging the relationship between policy rates, bond yields, and equity discount rates. NEPSE has none of that infrastructure. It has roughly a few thousand active daily traders relative to more than five million demat account holders, a bank-dominated credit system with a shallow corporate bond market, and a regulatory culture that governs financial institutions through administrative directives rather than through open-market operations that instantaneously reprice tradable instruments. The result is a transmission chain with more links, more friction at each link, and more room for a policy signal to be absorbed, delayed, distorted, or in some cases reversed by the time it reaches the price of a listed company's shares. This chapter builds that chain link by link, grounds it in five real Nepali episodes with actual dates and index levels, and gives you a working framework for reading NRB policy the way an institutional analyst would rather than the way the trading floor rumour mill does.
Lesson 3.1 — The Transmission Chain: How a Policy Announcement Becomes a Price
To understand why NEPSE reacts the way it does to NRB policy, you first have to separate two things that retail commentary constantly conflates: the announcement effect and the transmission effect. The announcement effect is the same-day or same-week repricing that occurs because market participants update their expectations about the future the instant new information becomes public. The transmission effect is the slower, structural process by which the policy actually changes the cost and availability of credit in the economy, which changes corporate earnings, household savings behaviour, and the liquidity available for share purchases — and it is this second, slower effect that ultimately determines whether a policy change produces a durable move in the index or merely a short-lived, sentiment-driven wobble that reverses within days.
In a market with deep bond markets, active derivatives, and diversified institutional participation, the announcement effect does most of the work, because sophisticated participants price in the transmission effect immediately, discounting future cash flows and future credit conditions the moment the policy is known. In NEPSE, the announcement effect is real but shallow — index moves on policy day are frequently sentiment-driven overshoots or undershoots that get partially reversed within one to three sessions — while the transmission effect dominates over a horizon of one to twelve months, as the actual credit and liquidity consequences of the policy work through the banking sector. An investor who treats the announcement-day move as the whole story is trading noise; an investor who tracks the transmission effect is trading the signal.
KEY CONCEPT
Announcement effect is the market's immediate, sentiment-driven reaction to the headline of a policy statement. Transmission effect is the slower, structural repricing that occurs as the policy actually changes bank liquidity, credit availability, and lending rates over subsequent weeks and months. In NEPSE, the two are frequently disconnected — the initial move can go one way and the eventual, fundamentals-driven move can go the other.
The chain runs, in simplified form, as follows. First, NRB sets or adjusts its policy instruments: the policy repo rate (the rate at which it lends short-term liquidity to banks), the cash reserve ratio, or CRR (the share of deposits banks must hold idle with the central bank), the standing deposit facility and standing liquidity facility rates that form the corridor around the policy rate, and — critically for equity investors — sector-specific directives that govern how much banks and financial institutions can lend against share collateral, known in Nepal as margin lending. Second, banks and financial institutions, the BFIs, absorb the directive into their own internal lending policies, a process that typically takes weeks because each institution must revise its credit manual, obtain board approval, and in many cases seek written clarification from NRB's Banks and Financial Institutions Regulation Department before implementing the change at the branch level. Third, the changed cost or availability of credit filters into two separate channels that matter for NEPSE: the direct channel, where margin lending itself becomes more or less available, and the indirect channel, where the general cost of credit across the economy affects corporate borrowing costs, consumption, and therefore future earnings. Fourth, only once credit conditions have actually changed on BFI balance sheets does the shift show up in observable market variables — the weighted average interbank rate, the credit-to-deposit ratio, the growth rate of margin lending outstanding — that investors and analysts can track. Fifth, and only at this point, does the index move in a way that reflects the transmission rather than merely reacting to a headline.
Why the chain matters for the retail investor
Every additional link in this chain is a place where the signal can be delayed or diluted. A rate hike announced in July may not show up as tighter margin lending until October, because banks continue honouring existing credit lines until they mature and only tighten new originations. A margin lending relaxation announced in a quarterly review may take six to eight weeks to actually expand buying power in the market, because brokers and banks need to update systems, recompute collateral values, and communicate new limits to clients. This is precisely the lag structure that experienced institutional desks exploit and that retail investors, trading off headlines alone, consistently misjudge — buying euphorically on the day of a positive-sounding announcement and then being surprised, weeks later, when the index has not moved because the actual credit has not yet reached the market.
Lesson 3.2 — The Nepali Policy Calendar and Its Rhythms
Unlike central banks that hold monetary policy meetings every six to eight weeks on a fixed calendar — the Federal Reserve's FOMC, the European Central Bank's Governing Council — Nepal Rastra Bank's headline instrument is the annual Monetary Policy, published around the start of the fiscal year in July — recent statements have arrived in early-to-mid July — which sets the policy rate corridor, CRR, and the year's regulatory priorities for the banking sector. This is supplemented by quarterly reviews, typically released around the end of each subsequent quarter of the fiscal year — roughly Ashwin/Kartik, Poush/Magh, and Chaitra — through which NRB fine-tunes the annual policy in light of incoming data on inflation, the balance of payments, and financial stability. In addition to this rhythm, NRB issues standalone directives (niti-nirdeshan) throughout the year on specific topics — margin lending conditions, loan-to-value ratios for real estate, risk weights for particular asset classes — that are not tied to the fiscal calendar at all and can arrive with little advance notice.
This calendar structure has a direct behavioural consequence for NEPSE: the market has learned to treat mid-July as a genuine event and prices in anticipation for weeks beforehand, producing a pre-policy positioning effect where trading volumes and volatility rise through late Shrawan independent of what the policy actually contains. But the quarterly reviews and the standalone directives, which in practice have driven some of the sharpest single-topic moves in NEPSE's history — particularly on margin lending — receive far less systematic anticipation from retail investors, even though they are frequently more consequential for share prices than the headline annual policy itself, precisely because they speak directly to the credit channel that funds share purchases.
WATCH FOR
The headline July Monetary Policy sets the year's broad rate and CRR stance, but it is the quarterly reviews and standalone margin-lending directives — often issued with little fanfare in Ashwin, Magh, or mid-year — that have historically moved NEPSE more sharply, because they act directly on the credit channel that funds share purchases rather than on the general cost of money.
A second structural feature of the Nepali calendar is that NRB governs the financial sector primarily through administrative directives to regulated institutions rather than through market operations that instantaneously reprice a tradable instrument. When the Federal Reserve changes its target rate, the effect is transmitted within minutes through the trading of Treasury securities and interest rate futures that every market participant can see and act on simultaneously. When NRB changes the margin lending loan-to-value ceiling, the effect is transmitted through a circular sent to bank compliance departments, which then update internal policy manuals over a period of days to weeks, and only then does the change reach the investor at the branch or brokerage counter. There is no tradable instrument that instantly reflects the new margin lending regime — the information exists in a circular, but the economic effect exists only once thousands of individual loan officers and branch managers have implemented it. This administrative, rather than market-based, transmission channel is the single largest structural reason NEPSE's reaction to NRB policy is slower and more staggered than the reaction of markets built around continuously tradable rate-sensitive instruments.
Lesson 3.3 — Five Historical Episodes and Their Lags
Theory is only useful if it survives contact with NEPSE's actual history, so this lesson works through five real episodes, each illustrating a different transmission pattern: a multi-year credit-driven boom and bust, a pandemic-era liquidity flood, a margin-lending tightening that preceded a crash, a rate-hiking cycle that dominated a simultaneous relaxation, and a 2025-26 easing cycle that shows the same mechanism working in reverse.
The paid-up capital mandate and the 2016 peak. In the mid-2010s, NRB directed commercial banks to raise their minimum paid-up capital roughly fourfold, to around eight billion rupees, with a compliance deadline in mid-2017. Banks met the requirement overwhelmingly through rights issues, bonus shares, and mergers rather than fresh cash injections, which meant existing shareholders were both diluted and, in the short run, made to feel wealthier as bonus share counts multiplied in their demat accounts. Combined with a post-earthquake reconstruction narrative and generally loose liquidity, this directive fed a sustained rally that carried the NEPSE index to an all-time high around 1,881 points on 27 July 2016 — a rally that built over roughly eighteen to twenty-four months from the directive's initial announcement. The reversal was equally slow: as the flood of new shares from bonus issues and mergers expanded the market's float faster than genuine demand could absorb, the index gave back the entire gain and more, falling to roughly 1,103 points by March 2019, a decline of about forty percent that took nearly three years to complete. This episode is the clearest illustration in NEPSE's history of a purely administrative directive — one with no direct connection to interest rates or market liquidity — driving a multi-year boom-bust cycle through its effect on share supply rather than through the interest-rate channel at all.
The pandemic liquidity flood and the 2021 peak. The FY2020/21 Monetary Policy, issued in the shadow of the COVID-19 lockdowns, was aggressively accommodative: refinance facilities were expanded, interest rates were pushed to historic lows, and the margin lending loan-to-value ratio was raised from 65 percent to 70 percent of a share's collateral value, directly expanding the amount of credit available for share purchases against existing holdings. Combined with a captive retail investor base — many with reduced income-earning opportunities during lockdowns and easy access to online trading through the pandemic-accelerated adoption of the TMS trading system — this easing fed one of the sharpest bull runs in NEPSE's history, carrying the index from roughly the 1,400s in mid-2020 to an all-time intraday high near 3,227 points on 19 August 2021 (closing peak 3,198.60 a day earlier). The lag here was on the order of twelve to thirteen months from the initial monetary easing (the FY2020/21 policy of July 2020) to the ultimate peak, with the index continuing to climb well after the most accommodative elements of the policy had already been absorbed into the market, evidence of the momentum and herding dynamics that Lesson 3.6 addresses directly.
The margin-lending caps and the 2021-22 correction. Even as the FY2020/21 accommodation was still working its way through the market, NRB's subsequent FY2021/22 Monetary Policy moved to cap the credit channel that had fuelled the boom, limiting individual margin borrowing to forty million rupees (Rs 4 crore) per bank or financial institution and 120 million rupees (Rs 12 crore) across all institutions combined, the so-called "4/12" rule that became shorthand among brokers for the new borrowing limits. This directive, combined with a broader liquidity crunch as deposit growth slowed and the credit-to-deposit ratio across the banking sector pushed against its regulatory ceiling, is widely cited by market commentators as the proximate trigger for NEPSE's correction from its 2021 peak, with the index falling from the 3,227 high to below 1,900 points over the following year — a decline that unfolded over roughly twelve to fourteen months rather than in a single sharp break, consistent with a transmission process working gradually through banks' existing loan books as pre-directive credit lines matured and were not renewed on the old terms.
The 2022 tightening that overrode a simultaneous relaxation. The FY2022/23 Monetary Policy, announced on 22 July 2022 against a backdrop of a national balance-of-payments crisis and import-driven pressure on foreign exchange reserves, raised the policy repo rate from 5.5 percent to 7 percent, lifted the CRR from 3 percent to 4 percent, and raised the bank rate from 7 percent to 8.5 percent — one of the sharpest single-year tightening moves in NRB's modern history. Notably, the same policy statement simultaneously relaxed the margin lending ceiling, allowing an individual to borrow up to 120 million rupees from one or more institutions combined rather than under the more restrictive prior formula, a change explicitly framed by NRB and market commentators as intended to support the equity market. In practice, NEPSE continued to decline through the following year, reaching a low of roughly 1,806–1,810 points in mid-2023, about 44 percent below the intraday peak, demonstrating unambiguously that a targeted relaxation of the margin lending channel could not offset the broader tightening effect of higher policy rates and a higher CRR working through the general cost of credit across the economy. This episode is the single clearest evidence in Nepal's recent history that investors who focus narrowly on margin lending announcements while ignoring the broader rate and reserve requirement stance are analysing only one link in a multi-link chain.
The October 2023 margin lending directive. On 19 October 2023, NRB issued a detailed directive governing margin lending conditions, capping loans at 70 percent of the lower of the 180-day average market price or the last traded price, limiting tenure to one year, restricting re-evaluation of collateral for additional lending, and requiring banks to apply fundamental screening criteria such as price-earnings ratios and dividend history before extending margin credit, alongside an institutional cap limiting any single bank's margin book to 40 percent of its primary capital. This was a risk-management-oriented tightening issued into an already depressed market, and it illustrates a different transmission pattern again: rather than a sharp reaction, the directive was absorbed gradually as banks already operating near their pre-existing margin lending limits adjusted portfolios over the following quarters, with no single dramatic index move attributable to the announcement date itself — a reminder that not every policy change produces an observable, datable market reaction, particularly when the directive tightens standards in a market where credit demand is already weak.
The 2025-26 easing cycle and the reverse transmission. The most recent cycle shows the same mechanism operating in reverse and offers the clearest recent illustration of measurable transmission lag. Through 2025, NRB progressively eased the regulatory treatment of share-backed lending: in August 2025 it reduced the risk weight applied to share-backed loans from 125 percent to 100 percent, freeing bank capital for further margin lending; in October 2025 it eliminated the previous 250 million rupee ceiling on combined margin borrowing entirely; and its December 2025 first-quarter review of the FY2025/26 Monetary Policy cut the policy repo rate from 4.50 percent to 4.25 percent and the standing liquidity facility rate from 6.00 percent to 5.75 percent, narrowing the interest rate corridor. NEPSE, which had bottomed near 2,469 points on 26 October 2025, rallied through the following months, accelerating further around a March 2026 change of government, to a local peak near 2,970 points by 25 March 2026 before retreating. Then, on 15 July 2026, NRB published a unified directive permitting discretionary increases to an 80 percent loan-to-value ratio on margin loans for qualifying companies with strong dividend and compliance histories. The index had fallen to a local low of 2,547 points the day before this directive, on 14 July 2026, and had recovered to roughly 2,697 points by 28 July 2026 — a rise of close to six percent within two weeks of the directive's publication, one of the more compressed and clearly attributable transmission lags in the recent record, plausibly reflecting a market that, after two years of episodic margin tightening, had grown primed to react quickly to signals of further liberalisation.
CASE IN POINT
Between the NRB unified directive of 15 July 2026 permitting up to 80 percent loan-to-value margin lending for qualifying companies and NEPSE's recovery from a local low of 2,547 to roughly 2,697 by 28 July 2026, the observable lag was on the order of two weeks — one of the fastest documented transmission episodes in recent NEPSE history, and a useful benchmark against which to judge how quickly a given policy signal is being absorbed.
Table 3.1 draws these episodes together as a reference timeline.
Date / Period
NRB Policy Action
Approximate NEPSE Reaction
Observed Lag
~2015, effective mid-2017
Commercial bank paid-up capital raised roughly 4x, to ~Rs 8 billion
Index rallies to all-time high of 1,881.45 (27 Jul 2016), then falls to 1,102.64 (5 Mar 2019)
~18–24 months to peak; ~30 months peak-to-trough
Jul 2020 (FY2020/21 policy)
Accommodative rates; margin LTV raised 65% → 70%
Index rallies from ~1,400s to all-time high ~3,227 intraday (19 Aug 2021)
Index rises from 2,547 (14 Jul) to ~2,697 (28 Jul)
~2 weeks
Lesson 3.4 — Why Transmission Is Slow and Partial in Nepal
Five structural features of the Nepali financial system explain the lag pattern documented above, and an investor who internalises them will read every future NRB announcement more accurately than one who does not.
The bank-channel monopoly on credit. In markets with deep corporate bond and commercial paper markets, a change in the policy rate reprices a whole spectrum of tradable debt instruments almost immediately, and that repricing flows into equity valuation models through the discount rate within the same trading session. Nepal has no comparably deep corporate bond market; commercial paper issuance is minimal; nearly all credit, including margin lending, flows through bank and financial institution balance sheets. This means the primary channel by which policy affects share prices is the slow one — banks changing their own lending policies — rather than the fast one of a continuously repriced bond market.
REGULATORY DETAIL
NRB's principal tools are the policy repo rate and the interest rate corridor formed by the Standing Deposit Facility (the rate at which banks park excess liquidity with NRB) and the Standing Liquidity Facility (the rate at which banks borrow overnight from NRB), the Cash Reserve Ratio, and administrative directives (niti-nirdeshan) issued to BFIs on specific lending categories including margin lending. Only the first set — the rate corridor and CRR — operates anything like an open market operation; margin lending rules are transmitted entirely through directive and bank-level compliance, which is why they take materially longer to show up in observable market behaviour than a corridor rate change does in the interbank market.
Administrative rather than market-based implementation. As discussed in Lesson 3.1, a circular from NRB's regulation department must be read, interpreted, and operationalised by the compliance and credit departments of dozens of separate banks and financial institutions before its economic effect is real. Each institution proceeds at its own pace, some updating loan books within days and others taking two to three months, which spreads what was a single announcement into a diffuse series of small credit adjustments arriving over an extended window — precisely why episodes like the October 2023 directive show no clean, datable index reaction even though the policy itself was unambiguous and specific.
Thin free float and a retail-dominated shareholder base. NEPSE's free float is concentrated among a relatively small number of actively traded counters, with promoter shareholdings, government and public enterprise holdings, and long-term institutional holdings comprising a large share of total listed equity that rarely trades. Trading activity is dominated by retail investors — commonly estimated to represent the overwhelming majority of active demat accounts — who trade on sentiment, rumour, and momentum rather than on discounted cash flow models sensitive to changes in the policy rate. This has two consequences: first, genuine information about changed credit conditions takes longer to be reflected in prices because the marginal trader is not systematically re-running valuation models on every NRB circular; second, when sentiment does shift, it shifts with disproportionate force relative to the underlying change in fundamentals, because a large population of similarly informed retail traders tends to move together rather than independently, producing the overshoot-then-partial-reversal pattern common on and immediately after policy announcement days.
Circuit breakers and settlement mechanics that dampen and delay full repricing. NEPSE applies daily price movement limits on individual scrips and market-wide circuit breaker halts triggered by large index moves, mechanisms explicitly designed to prevent single-session panic but which, as a direct consequence, also prevent a large piece of fundamental news from being fully absorbed into price in a single session. A change in credit conditions that would justify an immediate ten or fifteen percent repricing in an unconstrained market instead unfolds over several sessions as the price limit resets each day, mechanically stretching out what would otherwise be a near-instant transmission. Combined with T+2 settlement conventions that slow the recycling of capital between trades, the plumbing of the market itself adds days of delay on top of the informational and behavioural lags already described.
Political and macro-fiscal overlays that compete for attention. NRB monetary policy in Nepal rarely moves in isolation; it typically responds to and interacts with government fiscal policy, remittance inflow trends, balance-of-payments pressure, and — as the 2025-26 episode shows — with political transitions that can dominate investor attention in the same window as a policy announcement. When a change of government or a budget announcement coincides with a monetary policy shift, retail investors and even professional analysts frequently struggle to disentangle which factor is actually driving a given price move, further blurring the timeline between a specific NRB action and its "true" isolated effect on the index.
WARNING
Do not assume that a monetary easing automatically means a NEPSE rally, or that a tightening automatically means a fall. The 2022 episode shows a targeted margin lending relaxation issued in the same policy statement as a sharp rate and CRR hike, and the rate hike dominated: the index kept falling for another year. Always weigh the full policy package — rate corridor, CRR, and sector-specific directives together — rather than reacting to whichever headline is most flattering to your existing position.
Lesson 3.5 — Reading the Signals: A Practical Framework for Investors
Given the lag structure documented above, a disciplined NEPSE investor needs a small set of trackable indicators that reveal whether a given NRB policy action is actually being transmitted into credit conditions, rather than relying on the index's initial, often misleading, reaction on announcement day.
Distinguish the announcement date from the effective date. NRB circulars frequently specify an effective date that is different from, and later than, the publication date, and in the case of directives requiring board-level adoption by individual BFIs, the practical effective date at the branch level can lag the circular's own effective date by additional weeks. Before drawing any conclusion about how "the market" has reacted to a policy, confirm which date the market is actually reacting to.
Track the weighted average interbank rate and the credit-to-deposit ratio, both published periodically by NRB, as leading indicators of whether a rate or CRR change has genuinely tightened or loosened system-wide liquidity, rather than relying on the policy rate itself, which is a ceiling or reference rate rather than the rate actually governing day-to-day bank behaviour.
Track margin lending outstanding data, where available through NRB's periodic financial statistics and brokerage-level disclosures, as the most direct indicator of whether a margin-lending directive has actually changed the pool of leveraged buying power available to the market, independent of what the index itself has already done.
Separate the rate-corridor package from the sector directive package within any single policy statement, scoring each independently — tightening, neutral, or loosening — rather than reading the policy as a single undifferentiated signal, precisely because, as the 2022 episode demonstrates, the two can point in opposite directions within the same announcement.
Benchmark the observed lag against the historical range documented in Table 3.1 — roughly two weeks at the fast end (the July 2026 margin directive) to over a year at the slow end (the 2015-16 capital mandate and its unwind) — rather than assuming any single, fixed lag applies uniformly across all policy types; margin-specific directives acting on an already-primed, already-leveraged market tend to transmit faster than broad rate and reserve requirement changes acting through the general economy.
PRACTICAL TOOL
Before trading on any NRB announcement, run this five-point check: (1) What is the effective date, not the publication date? (2) Does the statement contain both a rate-corridor component and a sector-directive component, and do they point the same direction? (3) Has the interbank rate or CD ratio actually moved yet, or only the policy rate on paper? (4) What was the comparable historical lag for this type of directive in Table 3.1? (5) Is a political or fiscal event occurring in the same window that could be confounding the read? An announcement that fails several of these checks is one where the announcement-day price move is more likely noise than signal.
Lesson 3.6 — Behavioural Traps Around Policy Events
The final and perhaps most consequential lesson of this chapter is behavioural rather than mechanical. Retail investors in NEPSE consistently make three related errors around policy events, and each is a direct consequence of misunderstanding the transmission lag documented above rather than of any failure to read the policy correctly on its own terms.
The first error is treating the announcement-day price move as the full and final verdict on a policy. Because the transmission effect typically dominates over a horizon of months rather than days, an investor who buys or sells purely on the announcement-day reaction is trading the least informative part of the entire process — a burst of sentiment among a retail-dominated trading population that has, by definition, not yet observed any actual change in credit conditions. The more informative trade is frequently available weeks later, once the interbank rate, the CD ratio, or margin lending data begin to move, at which point the market has typically only partially adjusted and genuine mispricing can still be captured.
The second error is over-leveraging in anticipation of a margin lending relaxation that has been rumoured but not yet implemented at the bank level. Because the gap between an NRB directive and its actual availability at the brokerage counter can run to several weeks, investors who take on margin debt in anticipation of a relaxation that has been announced but not yet operationalised, or who assume a discretionary provision such as the July 2026 directive's 80 percent loan-to-value ceiling applies uniformly and immediately to their own holdings, frequently discover that their own bank has not yet updated its internal policy, or that their specific holding does not qualify under the "strong dividend and compliance history" criteria the directive actually specifies, leaving them over-committed against an expectation the policy has not yet delivered.
CAUTION
A discretionary or conditional NRB provision — such as the 2026 unified directive's 80 percent loan-to-value ceiling for "qualifying" companies — is not a blanket entitlement. Eligibility criteria, individual bank implementation timelines, and each institution's own risk appetite all sit between the circular and your actual available margin. Confirm your specific eligibility and your specific bank's implementation status before increasing leverage on the assumption that a policy applies to you.
The third error is panic-selling into a tightening announcement without weighing whether the tightening is being offset elsewhere in the same policy package, or whether the announced tightening has any realistic prospect of transmitting quickly given the structural frictions documented in Lesson 3.4. The 2022 episode again is instructive in the opposite direction from over-leverage: investors who sold in a panic purely on the CRR and repo rate hike, without registering that the same policy simultaneously eased margin lending limits, would have missed that the ultimate direction of the market over the following year was determined by the tightening, not the easing — the correct read, but one that required weighing both components of the package rather than reacting to either headline in isolation. The discipline this chapter asks for is neither blind optimism about every easing signal nor reflexive panic about every tightening signal, but a habit of decomposing every NRB announcement into its rate-corridor component and its sector-directive component, checking which one is likely to dominate given the prevailing macro-fiscal backdrop, and sizing positions to the multi-month transmission horizon that NEPSE's history actually displays rather than to the often-misleading first-day move.
Chapter recap
NRB's policy transmission into NEPSE runs through a longer and more friction-laden chain than in markets built around continuously tradable rate-sensitive instruments, because Nepal's credit system is bank-channel dominated, its margin lending rules are implemented through administrative directive rather than market operation, and its trading base is retail-heavy and sentiment-driven rather than institutionally arbitraged.
Historical episodes documented in this chapter show transmission lags ranging from roughly two weeks, in the case of the July 2026 margin lending liberalisation, to well over a year, in the case of the 2015-16 capital mandate and the 2021-22 boom-bust cycle, meaning no single fixed lag can be assumed and each policy type must be benchmarked against its own historical precedent.
A single NRB policy statement frequently contains both a rate-corridor component, covering the repo rate, CRR, and the standing facilities, and a sector-directive component governing margin lending specifically, and these two components can point in opposite directions within the same announcement, as the July 2022 policy demonstrated when a sharp tightening in rates coincided with an explicit relaxation of margin lending limits, with the tightening ultimately dominating the market's direction for the following year.
Investors should track effective dates rather than announcement dates, monitor the weighted average interbank rate, the credit-to-deposit ratio, and margin lending outstanding data as leading indicators of genuine transmission, and treat announcement-day index moves as a noisy and often reversible signal rather than a verdict.
Discretionary or conditional provisions within NRB directives, such as eligibility-based loan-to-value increases, are not automatic entitlements, and investors should confirm their own qualifying status and their own bank's implementation timeline before increasing leverage on the assumption that a newly announced relaxation already applies to their holdings.
The disciplined response to any NRB announcement is to decompose it into its component parts, judge which component is likely to dominate given the prevailing macro-fiscal and political backdrop, and size positions to the multi-month horizon over which Nepali monetary transmission has historically played out, rather than trading the first-day headline in either direction.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part I · Chapter 4
Nepal’s External Sector and NEPSE Implications
First published 22 Aug 2026 · Last verified 29 Aug 2026
A Nepali investor who studies only price-earnings ratios and quarterly earnings releases is reading half the ledger. NEPSE does not float in a closed domestic system; it floats on a sea of foreign currency that Nepal does not print, cannot fully control, and must earn, borrow, or receive as remittance from its citizens working abroad. When that sea runs low, Nepal Rastra Bank does not have the luxury of standing aside — it tightens credit, and credit is the oxygen of the equity market, as established in the credit-cycle framework of Chapters 0.1 and 0.2. This chapter builds the external-sector half of the analytical toolkit: the balance of payments, remittance inflows, foreign exchange reserves, and the import-cover discipline that ties them together, and then traces — with the actual data from Nepal's 2022 reserve crisis and its subsequent recovery through 2025/26 — exactly how a balance-of-payments squeeze becomes a NEPSE correction. An investor who can read NRB's monthly macroeconomic bulletin with the same fluency as a balance sheet has an edge that most retail participants in Kathmandu's brokerage floors do not bother to acquire, and that edge is durable precisely because it requires patience with unglamorous data rather than a hot tip.
Lesson 4.1 — The Architecture of Nepal's Balance of Payments
The balance of payments (BOP) is the double-entry record of every transaction between Nepal and the rest of the world over a given period — a fiscal year, or the monthly and periodic slices that Nepal Rastra Bank publishes as "Current Macroeconomic and Financial Situation" reports. It has three broad accounts, and a Nepali investor needs a working fluency in all three, not because you will ever compute one yourself, but because the financial press, NRB statements, and brokerage commentary constantly reference them, often loosely, and the loose usage is where mistakes creep into investment decisions.
The current account records trade in goods (the merchandise trade balance), trade in services (tourism receipts, transport, IT exports), primary income (interest and profit flows), and — critically for Nepal — secondary income, which is where workers' remittances are booked. Nepal runs a structurally and severely negative goods trade balance; the country imports far more than it exports, a fact rooted in a narrow manufacturing base, energy import dependence for petroleum, and rising consumer demand for imported vehicles, electronics, and construction materials. What keeps the current account from being catastrophically negative is remittance income, which is large enough that in many periods it converts an enormous merchandise trade deficit into an overall current account surplus. In the eleven-month period of fiscal year 2025/26 the merchandise trade deficit ran near Rs 1.78 trillion — imports of roughly Rs 2.096 trillion against exports of only about Rs 315 billion, an export-to-import coverage ratio under 15 percent — and yet remittance inflows across the same window totalled roughly Rs 2,120.80 billion, large enough on their own to swing the current account into an overall surplus of about Rs 802 billion. That single fact — remittances routinely exceeding the entire merchandise trade gap — is the load-bearing structural feature of the Nepali external accounts and is developed in full in Lesson 4.2.
The capital and financial account records cross-border investment: foreign direct investment into Nepali companies, portfolio investment (foreigners buying Nepali securities, which is minimal given NEPSE's restricted foreign participation), external loans taken by government and the private sector, and grant aid. Nepal's financial account is thin by regional standards — FDI inflows are modest and volatile year to year, external borrowing is dominated by concessional multilateral loans (World Bank, ADB) rather than commercial capital markets, and foreign grant flows fluctuate with donor politics and disbursement cycles. This thinness matters: it means Nepal cannot easily paper over a current account shortfall by attracting hot portfolio capital the way some emerging markets do. When the current account weakens, there is no large offsetting capital account cushion standing by — the shock lands directly on reserves.
The overall balance of payments is the sum of the current and capital/financial accounts, and its counterpart is the change in gross foreign exchange reserves held by Nepal Rastra Bank. A BOP surplus means reserves are accumulating; a BOP deficit means reserves are being drawn down to settle Nepal's net external obligations. This is the master variable of the whole chapter, because reserves — and specifically the number of months of import cover they represent — is the trigger variable that determines whether NRB tightens or eases monetary policy, and monetary policy is the variable that moves NEPSE through the credit channel.
KEY CONCEPT
The balance of payments identity, simplified for Nepal: Current Account Balance + Capital and Financial Account Balance = Change in Foreign Exchange Reserves. Because remittances sit inside the current account as secondary income, a remittance boom directly strengthens the BOP and swells reserves, exactly as a remittance slowdown directly weakens both.
A second architectural point deserves emphasis before moving on: Nepal operates a pegged exchange rate regime, with the Nepali rupee fixed to the Indian rupee at a ratio of 1.60 NPR per 1 INR, and the NPR floats against other currencies only insofar as the INR floats against them. This peg is the reason BOP and reserve dynamics matter so much more in Nepal than they would in a country with a freely floating currency. Under a float, a current account deficit would in principle be self-correcting — the currency depreciates, imports become more expensive, exports become more competitive, and the deficit narrows through the price mechanism, with reserves largely untouched. Under Nepal's peg, that automatic adjustment channel is switched off. NRB is committed to defending the peg, which means it must supply foreign currency (dollars, effectively, since the peg to India is itself anchored by India's own dollar reserves and by the open, low-friction currency convertibility arrangement between the two countries) to the market at the fixed rate whenever domestic demand for foreign currency exceeds supply. That defence is financed out of reserves. When reserves run low, NRB cannot let the exchange rate simply adjust — its policy response has to fall on the quantity of credit and import demand instead. This is the mechanical reason the credit channel becomes NRB's primary lever whenever the external accounts come under stress, and it is why Nepali monetary policy behaves differently, and often more abruptly, than monetary policy in inflation-targeting floating-currency economies that Nepali business news sometimes uses as a loose comparison.
Lesson 4.2 — Remittances: The Load-Bearing Wall of the External Sector
No single data series matters more to the Nepali economy, and by extension to NEPSE, than workers' remittances. Nepal is among the most remittance-dependent economies in the world in relative terms; remittance inflows have for over a decade represented somewhere in the range of a quarter to a third of GDP depending on the year and the exchange rate used for conversion, a dependency ratio exceeded by only a small number of countries globally, most of them small Pacific and Central Asian states. This is not a minor macro curiosity — it means that the spending power of a very large share of Nepali households, the deposit base of the banking system, and ultimately the liquidity available to buy NEPSE shares are all, at one remove, a function of overseas labor markets in the Gulf Cooperation Council states, Malaysia, and increasingly South Korea, Japan, and parts of Europe, rather than of domestic production.
The mechanics of how remittances reach NEPSE run through the banking system. A remittance dollar earned in Doha or Kuala Lumpur is converted to rupees and deposited, directly or through a family member, into a Nepali bank account. That deposit becomes part of the deposit base against which banks lend. Some of it is drawn down for consumption, some for real estate, some for education, and a portion — historically a meaningful and NRB-monitored portion — finds its way into margin-financed or cash purchases of listed equity. Remittance strength therefore feeds NEPSE liquidity through two channels simultaneously: it expands the deposit base that underwrites bank lending capacity (including margin lending against shares), and a slice of it is invested directly. Both channels compress when remittance growth stalls, and both channels expand when remittance growth accelerates, which is exactly the dynamic that played out in reverse and then in recovery across the 2022 crisis and the 2025/26 rebound examined later in this chapter.
Recent trends have been unusually favourable. In the eight months of fiscal year 2025/26 ending mid-March 2026, remittance inflows reached roughly Rs 1,449.65 billion, up 37.7 percent in rupee terms and about 31 percent in US dollar terms over the same period a year earlier — an exceptionally strong pace by any historical standard for Nepal. By the eleven-month mark of the same fiscal year, cumulative inflows had reached approximately Rs 2,120.80 billion, growth of roughly 38.2 percent year on year. This acceleration reflects a combination of factors: a large and still-growing stock of Nepali workers abroad, particularly in the Gulf, continued strength in Gulf labor markets and construction activity tied to regional infrastructure programs, a widening gap between formal exchange rates and informal channels that has pushed more remittance traffic through official banking and hundi-displacing formal channels (partly a function of digital remittance platforms and NRB's own formalisation drive), and currency effects from a relatively weaker rupee that make each dollar earned abroad convert into more rupees at home.
WATCH FOR
A deceleration in remittance growth back toward single digits, or outright contraction, is the single earliest warning sign available to a NEPSE investor for a coming liquidity squeeze — earlier than interest rate announcements, earlier than NEPSE's own price action, and earlier than most brokerage commentary, because NRB publishes remittance data monthly and the market is slow to connect it to equity liquidity until the credit tightening has already begun.
The dependency carries real structural risk, and a disciplined investor should treat it as a risk factor rather than a permanent tailwind. Remittance flows are a function of foreign labor demand that Nepal does not control. A construction slowdown in Gulf states tied to lower oil prices, a shift in destination-country immigration policy, competitive displacement of Nepali workers by cheaper labor from other South Asian or Southeast Asian sending countries, or a maturing of the outbound migration wave as fewer young Nepalis choose overseas labor migration relative to prior cohorts, are all plausible scenarios over a multi-year horizon that would slow remittance growth materially. Because remittances are the single largest offset to Nepal's chronic trade deficit, any sustained slowdown in inflows translates mechanically into current account deterioration, reserve pressure, and — following the transmission chain built out in Lesson 4.4 — eventual credit tightening that hits NEPSE.
WARNING
Do not treat strong year-on-year remittance growth figures as a permanent state of affairs. A meaningful share of recent growth reflects channel-formalisation (informal-to-formal shift) and currency depreciation effects layered on top of genuine volume growth in the number of outbound workers and their earnings; disentangling the transitory from the structural component of the growth rate is necessary before extrapolating it forward into a NEPSE liquidity forecast.
Table 4.1 below assembles the key external-sector data points across a five-year span that spans Nepal's 2022 reserve crisis and its subsequent, quite dramatic, recovery. The reader should treat these as NRB-sourced reference figures illustrating the trend rather than a substitute for the latest monthly bulletin, which should always be pulled fresh before acting on any of the conclusions in this chapter.
The arc in that table is the single most important chart a NEPSE investor can hold in memory: from a reserve position covering barely 6.6 months of imports — well under the internationally accepted minimum comfort threshold — to a position covering over 19 months on the same goods-and-services basis (over 22 months of merchandise imports), in the space of under four years. Understanding what NRB did at the low point of that arc, and what it has been able to relax as reserves rebuilt, is the substance of the rest of this chapter.
Lesson 4.3 — Foreign Exchange Reserves and the Import Cover Discipline
Foreign exchange reserves are the stock of foreign currency — held predominantly in US dollars, with smaller allocations in other reserve currencies and monetary gold — that Nepal Rastra Bank holds to meet the country's external obligations: financing imports, servicing external debt, and defending the currency peg described in Lesson 4.1. The single most important derived statistic from the reserve stock, and the one every NEPSE investor should track as routinely as they track the NEPSE index itself, is import cover: the number of months of prospective merchandise and services imports that current reserves could finance if no further foreign currency earnings arrived at all.
Import cover (months) = Gross Foreign Exchange Reserves ÷ Average Monthly Merchandise and Services Import Bill
The international rule of thumb, cited by the IMF and widely referenced in NRB's own communications, treats seven months of import cover as an adequate minimum buffer for an import-dependent economy without deep capital markets or a floating currency to absorb shocks. Below that threshold, a country is considered externally vulnerable — exposed to the risk that a shock to export earnings, remittances, or global commodity prices could leave it unable to finance essential imports (fuel and food chief among them for Nepal) without an emergency response. Nepal Rastra Bank monitors this ratio explicitly and publishes it in every periodic macroeconomic bulletin, and it functions internally as something close to a policy trigger: when import cover falls meaningfully below the seven-month comfort zone, NRB has historically responded with the toolkit examined in Lesson 4.4 — credit tightening, import curbs, and rate hikes — regardless of what domestic growth or asset market conditions might otherwise call for.
REGULATORY DETAIL
NRB's own external sector monitoring framework treats sub-seven-month import cover as the zone requiring corrective monetary and administrative action, in line with widely used international reserve-adequacy benchmarks (a variant of the IMF's Assessing Reserve Adequacy framework). This is not merely an academic threshold — it has historically correlated closely with the timing of NRB's most aggressive credit-tightening interventions, including the 2022 episode detailed in Lesson 4.4.
Nepal's reserve position by mid-June 2022 — eleven months into FY2021/22 — had fallen to approximately Rs 1,176 billion, equivalent to only about 6.6 months of goods-and-services import cover, the lowest in well over a decade and a figure that sat below the seven-month comfort threshold NRB itself references (the fiscal year closed marginally better, at roughly Rs 1,216 billion and 6.9 months). That decline was driven by a combination of forces on both sides of the ledger: on the outflow side, the trade deficit surged roughly 25 percent year on year, driven by a spike in global commodity prices (petroleum product costs alone rose by roughly 89 percent compared to the prior fiscal year, a shock transmitted through the Russia-Ukraine war's effect on energy markets) and a strengthening US dollar that made every unit of import more expensive in rupee terms; on the inflow side, remittance growth stagnated to under 4 percent year on year through the first eleven months (the full year closed at 4.8 percent, remittances reaching Rs 1,007 billion), a sharp deceleration from the double-digit growth rates Nepal had become accustomed to, while foreign grant inflows collapsed from roughly Rs 23 billion to about Rs 6.4 billion and FDI inflows grew only marginally. The combination — a widening deficit financed by weakening inflows — is precisely the scenario that drains reserves fastest, and it is worth internalizing as the canonical stress pattern to watch for going forward, because it will recur in some future cycle even if the specific triggers (a war-driven energy price shock, in 2022) differ next time.
By contrast, the reserve position by the eight- and eleven-month marks of fiscal year 2025/26 had rebuilt to roughly 18.5 and 19.1 months of goods-and-services import cover respectively (21.4 and 22.5 months counting merchandise imports alone) — a buffer more than three times the comfort threshold, and among the strongest external positions Nepal has recorded in recent memory. The composition of that recovery is instructive: it was driven overwhelmingly by the remittance surge documented in Lesson 4.2, not by any dramatic change in Nepal's export base or a narrowing of the underlying trade deficit, which in fact widened further in absolute terms over the same period (to roughly Rs 1.78 trillion for the fiscal year). This is a crucial nuance for the analyst: a comfortable import cover figure can coexist with, and indeed can be produced entirely by, an ever-widening trade deficit, so long as remittance growth outpaces it. The headline "reserves at record high" is reassuring in the near term but should not be read as evidence that Nepal's underlying trade competitiveness has improved — it has not, and the reserve buffer remains a function of externally-earned wage income rather than of domestically produced export value.
CAUTION
A comfortable import-cover figure driven by remittance strength is not the same thing as a comfortable import-cover figure driven by export competitiveness or fiscal discipline. The former can reverse quickly if overseas labor demand softens; the latter tends to be structurally sticky. Read the composition of the BOP surplus, not just its headline sign, before concluding that reserve adequacy is durable.
Lesson 4.4 — The Transmission Mechanism: From BOP Stress to Credit Crunch to NEPSE Correction
This lesson is the analytical hinge of the chapter and connects directly back to the credit-cycle framework established in Chapters 0.1 and 0.2. The essential claim is this: because Nepal defends a fixed exchange rate and cannot use currency depreciation as its primary adjustment valve, whenever the balance of payments weakens and import cover falls toward or below the seven-month threshold, Nepal Rastra Bank is compelled to compress domestic credit and import demand directly through monetary policy — and that compression is transmitted into NEPSE through the same credit channel mechanics developed earlier in the book: higher policy rates, tighter liquidity, reduced margin lending capacity, and, in the more severe episodes, direct restriction on the loan products that fund equity purchases.
The 2022 episode is the clearest illustrated case in Nepal's recent history, and it is worth walking through step by step because the sequence will very likely repeat, in some variant, in a future cycle.
Step one: external shock and deceleration of inflows. As detailed in Lesson 4.3, the trade deficit widened sharply in FY2021/22 on the back of a global commodity price shock (petroleum costs up roughly 89 percent year on year) at the same time remittance growth stalled to under 4 percent and grant inflows collapsed. Reserves fell to roughly 6.6 months of import cover.
Step two: administrative and monetary response. Nepal Rastra Bank moved on two fronts simultaneously. On the administrative front, the Ministry of Finance and NRB imposed an outright ban on the import of a list of non-essential and luxury goods (vehicles, certain electronics, and other discretionary consumer items) from late April 2022 — initially through the end of the fiscal year, then extended repeatedly, with the final categories freed only on 6 December 2022, roughly seven months later, partly to meet IMF programme conditions — explicitly to conserve foreign currency. On the monetary front, NRB's monetary policy for FY2022/23 raised its policy rate corridor sharply: the repo rate was lifted from 5.5 percent to 7 percent and the bank rate from 7 percent to 8.5 percent, a full 150 basis point tightening, and the cash reserve ratio banks were required to hold against deposits was raised from 3 percent to 4 percent, mechanically shrinking the pool of deposits available for lending. Private sector credit growth targets were slashed from roughly 19 percent to 12.6 percent for the year, a deliberate policy choice to choke off the domestic demand — including import-financing demand — that was draining reserves.
Step three: transmission into the banking system and into NEPSE. Higher CRR requirements and a higher policy rate corridor immediately tightened interbank liquidity and pushed up deposit and lending rates across the banking system. Banks facing binding credit-to-deposit (CD) ratio constraints — the 90 percent ceiling that in 2021 replaced the older credit-to-core-capital-cum-deposit (CCD) ratio — and a shrunken lendable pool cut back on all discretionary lending categories, margin lending against shares prominent among them. Investors who had built leveraged NEPSE positions during the preceding liquidity-abundant period faced margin calls they could not easily refinance, forcing forced selling into an already weakening market. Higher term-deposit and fixed-income yields simultaneously made bank deposits and debentures more attractive relative to equities on a risk-adjusted basis, pulling incremental capital away from NEPSE even among investors under no margin pressure at all. The result, well documented in NEPSE's own trading history, was a decline from an index peak near 3,200 points in 2021 to a trough near 1,806–1,810 in mid-2023 — a fall of roughly 43 percent — with market commentary at the time and in retrospective analysis attributing the crash primarily to NRB's monetary tightening rather than to any deterioration in listed companies' underlying earnings. Corporate fundamentals, in fact, had not collapsed anywhere near proportionally; what collapsed was the liquidity and credit available to hold and finance equity positions, precisely the credit-cycle mechanism that Chapters 0.1 and 0.2 identify as the dominant driver of NEPSE cycles.
CASE IN POINT
Nepal's 2022 reserve crisis produced a textbook illustration of the BOP-to-NEPSE transmission chain: import cover falling to 6.6 months triggered a 150 basis point policy rate hike, a CRR increase from 3% to 4%, and a private credit growth target cut from 19% to 12.6% — and NEPSE fell roughly 43% peak to trough over the following period, a decline driven overwhelmingly by credit and liquidity conditions rather than by any comparable deterioration in listed-company earnings.
It is worth noting, as a point of nuance that a careful investor should retain, that not every element of NRB's 2022 policy package was purely contractionary toward the equity market specifically — the same monetary policy statement that raised rates and CRR also relaxed the margin lending caps (the so-called 4/12 rule — Rs 40 million per institution and Rs 120 million across all institutions combined — was eased by dropping the Rs 40 million per-institution cap, leaving a single Rs 120 million combined ceiling), technically permitting larger margin loans from a single bank against share collateral even as overall system liquidity tightened. This detail matters because it illustrates that NRB's policy toolkit does not always move in a single consistent direction toward NEPSE — the reserve-defence objective (tighter aggregate credit) and other policy objectives (broadening access to margin financing, supporting brokerage and banking sector business) can pull in different directions within the same policy statement, and a mechanical reading of any one instrument in isolation, without checking the net liquidity effect, can mislead.
REGULATORY DETAIL
Nepal's margin lending framework for share-backed loans is periodically revised by NRB, historically expressed in shorthand like the "4/12" rule of FY2021/22 (Rs 4 crore per institution, Rs 12 crore combined across all institutions), later simplified to a single combined ceiling that was raised in steps (Rs 12 crore, Rs 15 crore, then Rs 25 crore) before being removed outright in October 2025. Changes to this framework are a distinct policy lever from the CRR and policy rate corridor, and the two can move in opposite directions in the same monetary policy statement — always check both before concluding whether net credit conditions for equity investors are tightening or loosening.
The recovery side of the cycle followed the same logic in reverse. As remittance inflows surged from 2024 into 2025 and 2026 and reserves rebuilt past 15, then 18, then 22-plus months of import cover, the acute pressure that had justified emergency-level tightening receded, private sector credit growth ceilings were relaxed in subsequent monetary policy statements, CRR and policy rate settings were eased back down over the following fiscal years, and system liquidity — as tracked through interbank rates and the CD ratio — loosened materially. NEPSE's recovery from its 2023 trough tracked this loosening with a lag, consistent with the credit-cycle timing patterns documented elsewhere in this book: monetary easing shows up in bank lending capacity and interbank rates within a quarter or two, but it takes longer — often two to four quarters — to show up as sustained NEPSE turnover and index gains, because investor risk appetite and margin capacity rebuild more slowly than raw system liquidity.
Lesson 4.5 — Reading the Signals: A Practical Framework for Investors
The preceding three lessons establish the theory and the historical case; this lesson converts that into an operating checklist. A disciplined NEPSE investor should build a simple, recurring habit of monitoring five external-sector indicators, all of which are published by Nepal Rastra Bank on a monthly or periodic basis and are freely available without subscription cost, well before the equivalent signal shows up in NEPSE price action or brokerage commentary.
Import cover and reserve trend. Pull NRB's "Current Macroeconomic and Financial Situation" report — issued roughly monthly, cumulatively through the fiscal year — and track the reported months of import cover against the seven-month threshold discussed in Lesson 4.3. Falling cover approaching single digits from a comfortable level, even before it crosses seven months outright, is the earliest actionable signal in this entire framework, because NRB has historically begun signalling policy intent well before the ratio becomes acute.
Remittance growth rate, in both NPR and USD terms. A deceleration in the year-on-year growth rate — not the absolute level, which will nearly always be positive given Nepal's large expatriate labor base, but the rate of change — is the leading indicator for reserve stress roughly two to three quarters ahead, since a slowing remittance inflow takes time to compound into a materially weaker BOP position.
Trade deficit growth relative to remittance growth. The two series should be read against each other, not in isolation. A widening trade deficit is tolerable, even benign for reserve purposes, so long as remittance growth is outpacing it, as has been the case through 2025/26. The moment trade deficit growth begins to outpace remittance growth — which is exactly the pattern that preceded the 2022 crisis — is the moment the external accounts flip from strengthening to weakening.
Policy rate corridor and CRR settings, published with every quarterly monetary policy review and its periodic updates. A tightening bias here — rate hikes, CRR increases, or credit growth ceiling reductions — is the direct mechanical transmission point into bank lending capacity and, with a lag, into NEPSE liquidity.
Interbank lending rate and the banking system's aggregate CD (credit-to-deposit) ratio. These are the fastest-moving real-time gauges of system liquidity, updated far more frequently than the quarterly policy statements, and they will typically move before the policy rate itself changes, since NRB's open market operations and standing facilities respond to liquidity conditions continuously rather than only at scheduled policy review dates.
PRACTICAL TOOL
Build a simple recurring watchlist, updated monthly against NRB's published bulletin: (1) months of import cover, (2) YoY remittance growth rate in NPR and USD, (3) trade deficit YoY growth versus remittance YoY growth, (4) policy rate corridor and CRR level, (5) interbank rate and system CD ratio. A deterioration across three or more of these five in the same direction is a stronger signal than any single one moving in isolation, and historically has preceded NEPSE corrections by one to three quarters.
An important calibration point for using this framework: the signal is asymmetric in its urgency. A weakening external position, once it crosses NRB's internal comfort thresholds, tends to produce policy responses that are administratively urgent — rate hikes and CRR increases can be announced and take effect within a single monetary policy statement, and import bans can be imposed within days by executive order. A strengthening external position, by contrast, tends to be eased into policy more gradually and cautiously, because NRB is naturally more reluctant to loosen credit conditions quickly for fear of reigniting the same imbalances that caused the prior tightening. Investors positioning for a NEPSE recovery on the back of an improving BOP picture should expect the credit-easing transmission to be slower and more grudging than the credit-tightening transmission was on the way down — an asymmetry worth pricing into position-sizing and timing expectations rather than assuming a mirror-image, equally fast recovery.
WATCH FOR
Policy asymmetry: NRB tightens quickly and forcefully when reserves are under acute stress, but eases only gradually and cautiously as reserves rebuild, for fear of reigniting the same import and credit pressures. Do not assume a NEPSE recovery driven by improving external accounts will unfold on the same timeline as the correction that preceded it.
Lesson 4.6 — Current Position and Forward Risks
As of the most recent data available in this fiscal year, Nepal's external sector position is, by historical standards, unusually strong. Foreign exchange reserves have climbed past Rs 3.7 trillion, equivalent to roughly 19 months of goods-and-services import cover (over 22 months of merchandise imports) as of mid-June 2026, remittance inflows have grown at rates approaching 38 percent year on year in rupee terms, and the current account has posted a substantial surplus (roughly Rs 802 billion over the eleven-month period of FY2025/26) despite a trade deficit that itself continues to widen in absolute terms toward roughly Rs 1.78 trillion for the fiscal year. Inflation has, over the same period, remained comparatively contained. Taken together, this combination has allowed NRB considerable room to maintain an accommodative credit stance relative to the acute tightening of 2022, and NEPSE's multi-year recovery since its 2023 trough has broadly tracked that accommodative backdrop, consistent with the credit-cycle logic of this chapter and of Chapters 0.1 and 0.2.
The investor's task, however, is not to extrapolate the current comfortable position forward indefinitely, but to identify the specific stress points that could reverse it, since the 2022 episode demonstrates how quickly a seemingly stable external position can deteriorate once its supporting conditions change. Four forward risks merit explicit tracking.
First, remittance concentration risk. Nepal's outbound labor migration remains heavily concentrated in a small number of Gulf destination countries and Malaysia, economies whose labor demand is itself tied to oil prices, regional construction cycles, and immigration policy decisions made entirely outside Nepal's control. A moderation in Gulf construction activity, a shift toward greater automation or toward labor sourced from lower-cost sending countries, or an outright policy tightening on foreign labor quotas in any of Nepal's top two or three destination markets would show up first in the remittance growth rate and, per the framework in Lesson 4.5, would be an early warning worth acting on well before it shows up in NEPSE price action.
Second, the durability of the export base. Nepal's trade deficit continues to widen in absolute terms even as the current account posts surpluses, meaning the entire external cushion rests on remittance growth outrunning an ever-larger import bill rather than on any improvement in the underlying competitiveness of Nepali exports. This is a structurally fragile foundation: it requires remittance growth to keep re-accelerating merely to keep pace, let alone to keep strengthening the reserve position, and any plateau in remittance growth — even without an outright decline — would translate into a narrowing, and eventually reversal, of the current account surplus given the trade deficit's own trajectory.
Third, global commodity price exposure, particularly petroleum. Nepal imports the great majority of its petroleum needs, and the 2022 crisis demonstrated concretely how a global energy price shock (petroleum costs up 89 percent year on year in that episode) can widen the trade deficit fast enough to overwhelm even a functioning remittance inflow. Any renewed geopolitical shock to global energy markets is a direct and fast-acting channel back into Nepal's reserve position, faster-acting than most domestic developments an investor might otherwise be tracking.
Fourth, the political economy of policy response itself. NRB's willingness and speed to tighten in 2022 was itself shaped by the acuteness of the crisis and by pressure from multilateral partners and rating considerations; future episodes may unfold with different policy responsiveness depending on the political environment at the time, the government's fiscal position, and NRB leadership's own risk tolerance. An investor should not assume the specific policy toolkit and timing used in 2022 — the CRR increase, the rate corridor hike, the import bans — will be replicated identically in a future stress episode; the direction of the response (tightening) is a reliable pattern, but the magnitude, speed, and specific instruments used may vary.
CAUTION
The current comfortable reserve position (18 to 22-plus months of import cover through FY2025/26) is real and should inform a constructive near-term view on NEPSE liquidity conditions, but it is a function of an unusually strong and possibly transitory remittance growth cycle layered on top of a structurally widening trade deficit, not evidence that Nepal's external vulnerability has been permanently resolved. Continue monitoring the five-indicator framework in Lesson 4.5 rather than treating the current position as a new steady state.
For the practical purpose of this book — analysing NEPSE — the takeaway is not that investors should attempt to forecast Gulf labor markets or global oil prices themselves, a task well beyond the scope of equity analysis. It is instead that the external-sector indicators in Lesson 4.5 function as a leading, publicly available, and systematically underused early-warning system for the credit conditions that, per the framework established earlier in this book, are the dominant driver of NEPSE's cyclical turns. A retail investor who checks NRB's monthly bulletin with the same regularity as they check the NEPSE index has a genuine, structural informational advantage over the majority of market participants who react only after a credit tightening has already begun showing up in falling share prices and margin calls.
Chapter recap
Nepal's balance of payments is structurally dependent on remittance income to offset a chronic and widening merchandise trade deficit, and this dependency, rather than any feature of NEPSE-listed companies' own fundamentals, is the single largest external force shaping the market's liquidity cycles. Because Nepal defends a fixed exchange rate pegged to the Indian rupee rather than allowing the currency to float and absorb external shocks, any deterioration in the balance of payments falls directly on foreign exchange reserves and forces Nepal Rastra Bank to respond through domestic credit tightening rather than currency adjustment, making the credit channel the primary transmission mechanism from external stress to equity market stress. The seven-month import-cover threshold functions as NRB's de facto policy trigger, and Nepal's 2022 episode, in which cover fell to 6.6 months, produced a textbook sequence of a 150 basis point policy rate hike, a CRR increase, tightened private credit growth ceilings, and import bans that fed through the banking system into margin calls and forced selling, contributing to a roughly 43 percent peak-to-trough NEPSE decline between 2021 and 2023. The subsequent recovery in reserves, driven overwhelmingly by remittance growth reaching rates near 38 percent year on year through fiscal year 2025/26 and pushing import cover past 21 months of merchandise imports (about 18–19 months counting goods and services), allowed NRB to ease credit conditions gradually, and NEPSE's multi-year recovery from its 2023 trough has tracked that easing with the characteristic lag the credit-cycle framework predicts. Investors should treat a five-indicator external-sector watchlist — import cover, remittance growth momentum, the relative growth rates of the trade deficit versus remittances, the policy rate and CRR setting, and the interbank rate and system CD ratio — as a leading, freely available, and systematically underused input into anticipating NEPSE's credit-driven cycles, checking it with the same routine discipline applied to company-level fundamentals. Finally, the currently comfortable reserve position should not be mistaken for a permanently resolved vulnerability, since it rests on a remittance growth rate that is itself exposed to Gulf and Malaysian labor market conditions outside Nepal's control, layered on top of a trade deficit that continues to widen in absolute terms regardless of the current account's headline surplus.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part I · Chapter 5
How India’s Economy Affects NEPSE
First published 22 Aug 2026 · Last verified 29 Aug 2026
Every Tuesday morning, before a single share changes hands on the floor of the Nepal Stock Exchange, a quieter transaction has already set the terms of the day. Nepal Rastra Bank has checked its ledgers against the Reserve Bank of India, the exchange counters in New Road and Birgunj have posted their buy-sell rates off a fixed formula, and the price of everything from diesel to steel rebar to cooking oil has been part-written in Mumbai and New Delhi before it ever reaches a Kathmandu invoice. A Nepali investor who studies only NEPSE's own price history — the candles, the volume bars, the sub-index charts — is reading half a book. The other half is written in India: in the Reserve Bank of India's repo decisions, in the strength or weakness of the Indian rupee against the dollar, in the health of Bihar's and Uttar Pradesh's harvests, and in the flow of Indian capital and Indian goods across an open, 1,751-kilometre border. This chapter builds the analytical machinery to read that other half. It explains the mechanics of the currency peg that binds the Nepali rupee to the Indian rupee, traces the channels through which Indian monetary policy and Indian economic conditions transmit into Nepal's banking system, its trade accounts, its remittance flows, and ultimately its equity market, and gives the investor concrete indicators to watch on the Indian side of the border as leading signals for NEPSE.
Lesson 5.1 — The Peg: What It Is and Why It Exists
The Nepali rupee (NPR) has been fixed to the Indian rupee (INR) at a rate of NPR 1.60 per INR 1 since February 1993, a rate that itself descended from an earlier peg dating to the 1960s when both currencies traded in reference to gold and, later, the US dollar under Bretton Woods-era arrangements. The number is not a market-discovered exchange rate in any meaningful sense; it is an administrative anchor set by Nepal Rastra Bank (NRB) and has not moved in more than three decades, even as Nepal's economy, India's economy, and the rest of the world's currencies have moved a great deal. When the Indian rupee depreciates against the US dollar, the Nepali rupee depreciates against the dollar by construction, in exact proportion, because the NPR/INR cross is fixed. When the RBI intervenes to defend the rupee, it is — whether anyone in Mumbai thinks about it or not — also defending the Nepali rupee's external value.
This is a fixed exchange rate regime in the strictest sense: one-sided, unilateral, and asymmetric. Nepal pegs to India; India does not peg to Nepal, and Indian monetary authorities set policy with zero reference to conditions in Kathmandu. The relationship is entirely one of dependency. Understanding why Nepal accepts this arrangement — and why, periodically, prominent economists and even NRB officials openly debate whether it should continue — is the necessary starting point for understanding how India's economy reaches into the Nepali stock market.
KEY CONCEPT
A currency peg is a promise: the central bank commits to buy or sell its own currency without limit at a fixed rate against the anchor currency. Nepal Rastra Bank's promise covers the Indian rupee only, at NPR 1.60 per INR 1. Against the US dollar, the euro, or the Chinese yuan, the Nepali rupee floats — but it floats exactly in lockstep with however the Indian rupee floats against those currencies. Nepal has outsourced its exchange rate policy to India by construction, not by accident.
The rationale is structural, not sentimental. Nepal is landlocked, and virtually all of its seaborne trade — the vast majority of its imported fuel, machinery, consumer goods, and raw materials — physically transits Indian territory through Kolkata and Vishakhapatnam ports before reaching Nepali soil by road or rail. A very large share of Nepal's merchandise trade is with India directly. Labour migration compounds the dependency: hundreds of thousands of Nepali workers are employed in India under the open-border arrangement formalised by the 1950 Treaty of Peace and Friendship, sending earnings home in Indian rupees or through channels denominated in INR. A currency peg to India removes exchange rate risk from this dense web of trade and remittance flows, which is precisely the argument NRB and successive Nepali governments have used to defend the arrangement since 1960.
The mechanics of maintaining the peg run through NRB's foreign exchange reserve management. NRB holds a substantial share of the country's gross foreign exchange reserves — roughly a fifth of the total in recent NRB reporting (20–22.5 percent across 2024–2026) — in Indian rupees specifically, separate from the "convertible currency" reserves (US dollars, euros, and similar hard currencies) used for trade with the rest of the world. NRB replenishes its INR reserves periodically by selling convertible currency to the RBI in exchange for Indian rupees, a transaction conducted under a bilateral arrangement between the two central banks. This is not a market operation; it is closer to a standing swap facility that exists specifically to keep the peg defensible. When Nepal's INR reserves run low relative to import demand for Indian goods, NRB must sell dollars to buy rupees — a transaction that draws down the convertible currency reserves that back everything else Nepal imports from outside India.
REGULATORY DETAIL
Nepal Rastra Bank's reserve adequacy is conventionally measured in months of import cover. Analysts and NRB itself watch this figure closely because a decline signals stress on the peg's sustainability — if INR-denominated reserves fall too far, NRB has historically had to tighten import regulations, restrict certain categories of goods, or raise margin requirements on letters of credit to conserve foreign exchange. Any such tightening compresses trading company earnings and consumer discretionary demand, both of which show up in NEPSE-listed corporate results within one to two quarters.
The investor-relevant consequence of all this is that Nepal has, in effect, ceded independent exchange rate policy and a meaningful share of independent monetary policy to India. NRB sets its own policy rate, issues its own directives on bank capital and liquidity, and runs its own macroprudential framework — but it does so inside a corridor substantially bounded by what the RBI does, because capital cannot move freely across an open border without eventually forcing NRB's hand on rates or reserves. This is the subject of Lesson 5.2.
Lesson 5.2 — Monetary Policy Without Full Independence
In principle, Nepal Rastra Bank is a sovereign central bank with its own mandate: price stability, external sector stability, and financial sector stability, pursued through its own policy rate corridor, its own reserve requirements, and its own open market operations. In practice, the fixed peg to the Indian rupee means NRB's room to run an independent interest rate policy is narrower than a textbook reading of central bank independence would suggest. This is the classic "impossible trinity" of international macroeconomics: a country cannot simultaneously maintain a fixed exchange rate, free capital mobility, and independent monetary policy. Nepal has chosen the fixed exchange rate and has an open border with India that makes capital controls difficult to enforce in practice, even where they exist on paper. The residual — independent monetary policy — is the trinity's casualty.
Concretely, this means that when the RBI raises or lowers its policy repo rate, NRB faces pressure to move NPR interest rates in a broadly similar direction, or risk destabilising capital flows across the border. If Indian interest rates rise well above Nepali rates, rupee-denominated deposits and instruments in India become more attractive relative to Nepali ones; given the ease of moving funds across an open, culturally and linguistically integrated border, Nepal risks capital flowing toward Indian assets, draining the very Indian-rupee reserves that back the peg. If NRB holds rates too low for too long while the RBI tightens, the pressure shows up first as reserve depletion, then as import restriction, then — if unaddressed — as a genuine balance-of-payments crisis of the kind Nepal experienced in 2022, when import demand rebounding sharply after the pandemic combined with a strong dollar (and hence a strong rupee, given the peg) to squeeze reserves down toward levels that triggered emergency import curbs and a sharp NRB rate-hiking cycle.
CASE IN POINT
Through late 2021 and the first half of 2022, Nepal's foreign exchange reserves fell to a level equivalent to under seven months of import cover — 6.6 months by mid-June 2022 — a level NRB and international agencies treat as a warning threshold. NRB responded with a battery of measures: restrictions on the import of vehicles and other non-essential goods, tighter margin requirements on letters of credit, and successive increases in its policy rate. NEPSE's benchmark index, which had rallied strongly through 2020 and 2021 on pandemic-era liquidity, fell by roughly 40 percent from its peak over the following year, as bank lending rates rose sharply, margin lending was curtailed, and the interest-rate-sensitive banking and hydropower sectors re-rated downward. The episode is the clearest illustration in recent Nepali market history of how an external-sector and peg-related shock can transmit directly into equity valuations with a lag of only a few months.
The transmission channel from RBI policy to NEPSE therefore runs primarily through two intermediate variables: the exchange rate/reserve constraint just described, and the direct comparability of Nepali and Indian interest rates as an incentive for cross-border capital movement. When the RBI cuts rates to support growth, Nepal typically gains room to ease as well, since the pressure on reserves and the incentive for capital flight both diminish; when the RBI hikes to fight inflation, NRB usually feels obliged to follow with a lag, even if domestic Nepali conditions alone might not justify tightening. A NEPSE investor watching only NRB's own press releases and monetary policy statements is watching the visible symptom, not the underlying cause; the RBI's Monetary Policy Committee announcements, held roughly every two months, are the more useful leading indicator.
WATCH FOR
The RBI's Monetary Policy Committee meets on a published bi-monthly schedule. A NEPSE investor with exposure to interest-rate-sensitive sectors — commercial banks, development banks, finance companies, and highly leveraged hydropower developers — should treat each RBI decision as a scheduled event with potential second-order consequences for NRB's own stance at its subsequent monetary policy review, typically issued at the start of the Nepali fiscal year in mid-July with a mid-year review around January.
It is important not to overstate the mechanical link: NRB does not move in automatic lockstep with the RBI, and Nepal's own inflation dynamics, credit growth, and fiscal position all feed into its decisions as well. But the peg means NRB's independence is conditional and bounded rather than absolute — a fact confirmed repeatedly in NRB's own monetary policy documents, which routinely cite the state of foreign exchange reserves and the India-Nepal interest rate differential as explicit considerations in setting the domestic policy rate.
Interest rate pass-through in Nepal
NRB's own research department has published work on interest rate pass-through — the speed and completeness with which a change in the policy rate feeds through to actual deposit and lending rates charged by commercial banks. The finding relevant to a NEPSE investor is that pass-through in Nepal is slow and incomplete compared with more developed financial systems: a change in the policy rate typically takes several quarters to fully reflect in banks' weighted average lending rates, and the degree of pass-through varies with liquidity conditions in the banking system at the time the rate changes. This has a direct implication for how an investor should read an RBI move. The fact that the RBI has raised or cut rates does not translate into an immediate, mechanical change in NEPSE-listed banks' net interest margins the following week; the effect arrives with a lag, arrives partially, and arrives with more force when domestic liquidity is already tight than when banks are flush with deposits and can absorb a policy change without repricing loan books aggressively. An investor tracking the banking sub-index should therefore pair any RBI or NRB rate move with a check on the banking system's current liquidity position — proxied by the interbank rate and by NRB's own reported credit-to-deposit ratio — before assuming the market has fully priced the change.
WATCH FOR
A rate move that lands when Nepali bank liquidity is already tight (a high credit-to-deposit ratio, an elevated interbank rate) will pass through to lending rates faster and more completely than the identical move landing during a period of surplus liquidity. The same RBI decision can therefore have a materially different near-term effect on NEPSE's banking sub-index depending on the domestic liquidity backdrop at the time it lands — a distinction that separates a mechanical read of the news from an analytically useful one.
Lesson 5.3 — Trade Dependency and the Import Bill Channel
Beyond the direct monetary mechanics of the peg, India shapes Nepal's economy — and therefore NEPSE — through the sheer scale of bilateral trade. India is Nepal's largest trading partner by a wide margin, supplying roughly two-thirds or more of Nepal's total merchandise imports and absorbing the majority of Nepal's (much smaller) merchandise exports. Petroleum products alone — imported exclusively from India under a long-standing supply agreement between Nepal Oil Corporation and Indian Oil Corporation — constitute one of the largest single line items in Nepal's import bill. Because the peg fixes NPR against INR, and because Indian rupee prices for petroleum, steel, cement clinker, vehicles, pharmaceuticals, and a long list of intermediate industrial goods are themselves a function of Indian domestic inflation and, further upstream, of the rupee's value against the US dollar for dollar-priced commodities like crude oil, the Nepali import bill is doubly exposed to conditions in India: once through the volume and pricing of Indian-made goods, and again through the pass-through of global commodity prices into Indian rupee terms before those goods ever cross into Nepal.
Nepal Trade Snapshot: Dependence on India
Indicator
Approximate Figure
NEPSE Relevance
Share of Nepal's total merchandise imports sourced from India
roughly two-thirds
Drives cost inputs for manufacturing, trading, and cement/steel-linked listed firms
Share of Nepal's total merchandise exports sold to India
roughly half or more
Determines revenue exposure for listed manufacturers and agro-processors
Petroleum product supply source
100 percent from India (Indian Oil Corporation to Nepal Oil Corporation)
A widening deficit pressures foreign exchange reserves and, through the peg mechanism, NRB's policy stance
Remittances as a share of GDP
roughly one-quarter of GDP
A large share originates from India-based and Gulf-based Nepali workers; supports consumption, bank deposits, and capital-market liquidity
NPR/INR fixed rate
NPR 1.60 = INR 1.00 (fixed since 1993)
Anchors all cross-border pricing and capital flow calculations
WARNING
Nepal's persistent and widening trade deficit is not merely a macroeconomic curiosity reported in NRB's periodic bulletins — it is the single most direct determinant of how much pressure builds on foreign exchange reserves in any given fiscal year. A widening deficit, driven disproportionately by India-sourced imports, is a leading indicator of future NRB tightening (to conserve reserves) or future import restriction (which directly compresses the revenues of NEPSE-listed trading houses, automobile dealers, and consumer goods distributors).
The transmission from trade dependency to NEPSE runs through several concrete corporate channels. Listed commercial banks earn a meaningful share of fee income from trade finance — letters of credit, documentary collections, and remittance-linked services tied to India-facing trade — so a slowdown in India-linked trade volume compresses this income line. Manufacturing and cement companies listed on NEPSE that rely on Indian clinker, coal, or gypsum imports see their input costs move with Indian rupee pricing and with Indian export policy (India has, at various points, restricted exports of items like sugar, wheat, and non-basmati rice, each of which has had visible knock-on effects for Nepali importers and processors). Hydropower developers — one of the largest and most actively traded sectors on NEPSE — depend on India for a substantial share of their electromechanical equipment, transformers, and turbine components, so INR-denominated input costs for under-construction projects move with the rupee's behaviour against the dollar, even though project revenues are earned in Nepali rupees.
A further, increasingly important channel is cross-border power trade. Nepal has moved from being purely a power importer from India (particularly during dry-season deficits) to being an intermittent power exporter to India during the monsoon surplus period, following the operationalisation of cross-border transmission lines and Nepal's entry into India's Real-Time Electricity Market. The price Nepali hydropower generators receive for electricity sold into India is set with reference to Indian day-ahead and real-time power market clearing prices — a genuinely new and structurally important channel through which Indian market conditions now flow directly into the revenue lines of NEPSE-listed hydropower companies, several of which have begun disclosing cross-border sales as a distinct revenue category.
PRACTICAL TOOL
An investor holding or evaluating hydropower stocks should track two things beyond domestic Nepali data: the monsoon-season clearing prices on India's power exchanges (IEX and PXIL), which determine the realisation on any exported surplus, and the progress of cross-border transmission capacity additions between Nepal and India, which determine how much surplus can physically be exported at all. Both are reported periodically by the Nepal Electricity Authority and by Indian power market regulators and are a more precise leading indicator for hydropower export revenue than domestic rainfall data alone.
Consumer goods, cement, and the India price umbrella
A less obvious but equally persistent channel runs through what might be called the India price umbrella effect on Nepali consumer and construction-linked equities. Because so many finished consumer goods sold in Nepal are either imported directly from India or manufactured in Nepal from Indian-sourced raw materials, and because transport costs across the open border are low relative to the value of the goods moved, Indian retail and wholesale prices function as a soft ceiling on what Nepali producers of comparable goods can charge domestically. A Nepali cement, noodle, biscuit, or beverage manufacturer listed on NEPSE that tries to price meaningfully above the landed cost of the Indian equivalent risks losing shelf space to cross-border imports, formal or informal. This means the pricing power — and therefore the margin trajectory — of a wide swath of NEPSE's manufacturing and consumer sub-indices is partially capped by Indian domestic inflation and by the rupee's value, not solely by Nepali demand conditions or Nepali input costs. An investor modelling margin expansion for a listed consumer goods company should ask, as a matter of course, whether the assumed price increase is plausible given prevailing Indian prices for the comparable product, not only whether Nepali demand can bear it.
Lesson 5.4 — Remittances, the Rupee, and Bank Sector Liquidity
Workers' remittances are one of the largest single inflows into the Nepali economy, running at a level equivalent to roughly a quarter of GDP in most recent years — a dependency ratio among the highest in the world for a country of Nepal's size. While the largest gross remittance flows to Nepal originate from Gulf Cooperation Council states and Malaysia, India remains a major and structurally distinct source: hundreds of thousands of Nepali citizens work in India under the open-border treaty arrangement, many in informal or semi-formal employment that is harder to capture in official balance-of-payments statistics than remittances arriving through the formal banking and money-transfer-operator channels from Gulf states. This means the true India-linked remittance contribution is almost certainly understated in headline figures, arriving instead through informal cross-border cash carriage, hundi-style informal transfer networks, and direct household spending that never appears in an NRB balance-of-payments table.
The investor-relevant point is not the precise magnitude but the mechanism: remittance inflows are the primary source of deposit growth for Nepal's banking sector, and deposit growth is the primary constraint on how much credit banks can extend to businesses, individuals, and — critically for NEPSE — margin lenders and IPO subscribers. When remittance inflows are strong, bank deposits grow, loan-to-deposit ratios ease, banks compete more aggressively for lending business, interest rates on both deposits and loans tend to soften, and liquidity conditions in the broader economy — including the liquidity available for margin trading and share subscription — improve. When remittance growth slows, or when the rupee's behaviour against the dollar changes the calculus for overseas workers about how much to remit and when, bank liquidity tightens and NEPSE typically feels it within one or two quarters through higher lending rates and reduced margin capacity.
KEY CONCEPT
Because the Nepali rupee is fixed to the Indian rupee, and both currencies float together against the US dollar, a period of rupee weakness against the dollar mechanically increases the Nepali-rupee value of remittances sent from Gulf countries in dollars or dollar-linked Gulf currencies. This is a genuine, if partial, offsetting channel: dollar strength that raises Nepal's import bill (petroleum, machinery) also raises the domestic-currency value of a large share of incoming remittances, cushioning some of the household-income and bank-deposit impact of an otherwise adverse currency move.
The corollary works in reverse for the India-linked share of remittances specifically. Because NPR and INR are fixed one-to-one in ratio, a Nepali worker in India who remits earnings home experiences no exchange-rate translation gain or loss at all — the value is fixed by the peg regardless of what happens to the rupee against the dollar. This makes India-sourced remittances a structurally stable, low-volatility component of Nepal's remittance base, in contrast to the more currency-sensitive Gulf-sourced component — a distinction rarely discussed in NEPSE commentary but directly relevant to modelling the stability of bank sector deposit growth across different global currency environments.
CAUTION
Do not treat aggregate remittance growth figures published by NRB as a single undifferentiated number when forecasting bank liquidity. The India-sourced component behaves differently — more stable, insensitive to the dollar-rupee rate, but more sensitive to Indian domestic labour market conditions and to episodes of Indian policy tightening on informal cross-border labour movement — than the Gulf-sourced component, which is more volatile and more directly linked to oil-exporter fiscal cycles and dollar strength.
Remittances and the IPO calendar
A further, more specific link deserves attention because it connects the remittance channel to a distinctly Nepali market phenomenon: the retail-dominated IPO subscription cycle. Nepali households, and returnee or remittance-receiving families in particular, have historically directed a meaningful share of remittance income into primary share subscriptions and secondary market participation, especially around festival seasons (Dashain and Tihar in particular) when both remittance inflows and household liquidity peak. Merchant bankers and issue managers who schedule IPOs are well aware of this seasonal pattern, and issue calendars are often front-loaded ahead of, or timed around, these peak-liquidity periods. An investor evaluating the likely subscription strength of an upcoming IPO or further public offering should therefore weigh not only the issuing company's fundamentals but also the prevailing remittance growth trend reported in NRB's periodic balance-of-payments updates and the position of the issue within the festival liquidity calendar, since a strong remittance quarter reliably correlates with heavier oversubscription and a more favourable listing-day pop, while a weak or declining remittance quarter has historically coincided with thinner subscription and softer debuts.
Lesson 5.5 — Capital Flows, INR Convertibility, and Financial Sector Risk
A newer and less widely understood channel has emerged over the past several years as the RBI has gradually pursued limited internationalisation and liberalisation of the Indian rupee, including permitting certain categories of cross-border rupee-denominated trade settlement and rupee lending arrangements with neighbouring and partner countries. For Nepal, any such liberalisation carries a genuinely two-sided character. On one hand, easier rupee convertibility and rupee-denominated trade settlement could reduce the friction and dollar-reserve burden currently associated with Nepal's India-facing trade, since transactions that once required NRB to manage scarce convertible currency could increasingly settle in rupees more freely obtained through the existing INR reserve arrangement. On the other hand, greater rupee convertibility and freer movement of Indian financial capital across the open border raise the risk that Nepal's already-thin domestic capital markets — including NEPSE — become more exposed to the ebb and flow of Indian investor sentiment and Indian liquidity conditions, with less of the insulation that capital account restrictions have historically provided.
REGULATORY DETAIL
Nepal maintains capital account restrictions that formally limit foreign portfolio investment into NEPSE-listed securities to specific regulated channels, and Indian retail investors do not currently have unrestricted access to trade NEPSE-listed shares. However, the open, largely unmonitored physical border with India means informal cross-border movement of funds — for property purchases, business investment, and family transfers — has always been more porous in practice than the formal capital account regime suggests on paper. Any material further liberalisation of INR convertibility discussed by the RBI is therefore a development NEPSE investors should track less for its immediate legal effect on foreign portfolio investment rules and more for what it implies about the future ease of capital movement across the border generally.
This channel connects directly back to the impossible-trinity logic of Lesson 5.2: capital mobility, the fixed exchange rate, and monetary independence cannot all be maintained simultaneously, and any increase in de facto capital mobility (through INR liberalisation, digital payment integration, or simple erosion of enforcement along the open border) narrows NRB's policy independence further, strengthening rather than weakening the transmission of RBI decisions into Nepali financial conditions. A NEPSE investor should read news of RBI moves toward rupee internationalisation not as a distant technical development in Indian monetary policy but as a signal that the coupling between Indian and Nepali financial conditions is likely to tighten over time, not loosen.
A related and more immediate risk channel runs through correspondent banking and cross-border payment infrastructure. Nepali commercial banks maintain nostro/vostro correspondent relationships with Indian banks to facilitate trade finance and remittance settlement; any tightening in Indian banking regulation around correspondent relationships with smaller regional banking systems, or any RBI directive affecting rupee accounts held by foreign banks, has historically created short-term friction in Nepal's trade settlement pipeline, with knock-on effects for the working capital cycles of NEPSE-listed trading and manufacturing companies that depend on timely letter-of-credit processing.
WARNING
Because Nepal's capital markets are shallow and NEPSE's free float is small relative to even modest cross-border capital flows, a relatively small shift in informal cross-border capital movement — whether toward Nepali real estate, toward NEPSE itself through indirect or nominee arrangements, or away from Nepal toward Indian markets during periods of strong Indian equity performance — can move NEPSE's index level by a magnitude that would be immaterial in a market with deeper foreign participation and larger market capitalisation.
Lesson 5.6 — Reading Indian Indicators as Leading Signals for NEPSE
Having established the channels — the peg mechanism, monetary policy coupling, trade dependency, remittance dynamics, and capital flow linkages — the practical task for a NEPSE investor is to build a short, disciplined watchlist of Indian-origin indicators that function as leading or coincident signals for Nepali market conditions, distinct from and complementary to the domestic Nepali indicators covered elsewhere in this book.
The first and most important is the RBI Monetary Policy Committee's repo rate decision, announced on a fixed bi-monthly schedule. A rate hike cycle in India raises the probability of a subsequent NRB tightening cycle within one to two quarters, with direct consequences for NEPSE's rate-sensitive banking, finance, and hydropower sectors, both through higher borrowing costs for leveraged issuers and through reduced margin-lending capacity for retail investors.
The second is the rupee-dollar exchange rate itself, since NPR moves against the dollar in lockstep with INR by construction. A sustained depreciation of the rupee against the dollar raises Nepal's import bill in domestic currency terms — particularly for dollar-priced petroleum and any imported input not sourced from India — while simultaneously boosting the domestic-currency value of dollar-denominated Gulf remittances. The net effect on any particular NEPSE-listed sector depends on its specific import/export and remittance exposure, but the direction of the rupee is never analytically irrelevant.
The third is India's own headline and core inflation prints, published monthly by India's Ministry of Statistics and tracked closely by the RBI in its policy deliberations. Indian inflation feeds two ways: directly, through the rupee price of goods Nepal imports from India, and indirectly, as a driver of the RBI's own rate decisions described above.
The fourth is the monsoon and agricultural output data for India's major grain- and sugar-producing states, since Indian export restrictions on staples (imposed periodically to manage domestic food inflation) have repeatedly disrupted the supply and pricing of goods Nepal imports from India, with visible effects on the earnings of NEPSE-listed trading and consumer goods companies.
The fifth is India's own equity market sentiment and FII (foreign institutional investor) flow data, which, while not directly connected to NEPSE through any formal channel, often serves as a proxy for the broader regional risk appetite that also colours informal cross-border capital movement into and out of Nepal.
PRACTICAL TOOL
Build a simple quarterly dashboard with five rows: the RBI's most recent repo rate and its trend since the last two policy reviews, the rupee's level and quarterly change against the US dollar, India's most recent headline CPI print, NRB's own foreign exchange reserve figure in months of import cover, and Nepal's cumulative trade deficit for the fiscal year to date. Reading these five figures together, before reading a single NEPSE company's quarterly results, will tell you more about the structural winds facing the market than most locally-focused commentary provides.
Chapter recap
Nepal's currency is not sovereign in the way a floating-rate country's currency is; the Nepali rupee has been fixed to the Indian rupee at NPR 1.60 per INR 1 since 1993, and this single administrative fact shapes nearly everything else discussed in this chapter. Because of the peg, Nepal Rastra Bank's monetary policy independence is real but bounded, constrained by the impossible-trinity logic that a fixed exchange rate and an open, porous border with India leave little room for interest rates to diverge far from India's own without eventually straining foreign exchange reserves. Nepal's deep trade dependency on India — roughly two-thirds of imports, the entirety of petroleum supply, and a rising cross-border power trade — means Indian pricing, Indian export policy, and Indian power market conditions feed directly into the input costs and revenue lines of NEPSE-listed banks, trading houses, manufacturers, and hydropower developers. Remittances, equivalent to roughly a quarter of Nepal's GDP, are the primary driver of bank deposit growth and hence of the liquidity available for lending and margin trading on NEPSE, with the India-sourced share behaving differently — more currency-stable, more labour-market-sensitive — than the more dollar-exposed Gulf-sourced share. Gradual RBI moves toward greater rupee convertibility and internationalisation carry a double edge for Nepal, potentially easing the friction of India-facing trade settlement while also tightening the coupling between Indian financial conditions and an already shallow, thinly capitalised NEPSE. The disciplined response for a serious NEPSE investor is not to abandon domestic analysis but to supplement it with a compact, regularly updated watchlist of Indian indicators — the RBI's policy rate, the rupee-dollar rate, Indian inflation, Indian agricultural and export-policy news, and Indian equity market sentiment — treating each as a leading signal for the Nepali market conditions that domestic data alone will only confirm after the fact.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part I · Chapter 6
Global Macro and Event-Driven Risk
First published 22 Aug 2026 · Last verified 29 Aug 2026
Nepal's stock market has almost no foreign hands in it. Foreign institutional investors do not trade NEPSE-listed shares in any meaningful volume, foreign portfolio flows are restricted by capital account rules that predate the exchange's electronic era, and the retail base that dominates turnover is overwhelmingly domestic, overwhelmingly small, and overwhelmingly financed through domestic bank credit. It would be reasonable, on first encounter, to conclude that NEPSE is therefore insulated from the currents that move Wall Street, the Gulf bond markets, or the Shanghai Composite. This conclusion is wrong, and the mechanism by which it is wrong is the subject of this chapter. Nepal is not insulated from global macro and event-driven risk; it is instead exposed to it through a narrower, slower, and more indirect set of pipes than a market like Mumbai or Jakarta. Those pipes run through remittances earned by Nepali workers in the Gulf, Malaysia, and South Korea; through the cost of imported petroleum that Nepal must buy in dollars regardless of the health of its own currency; through the mechanical peg between the Nepali rupee and the Indian rupee; and through the domestic banking system's willingness to lend against share collateral. When a Federal Reserve rate decision moves the dollar, or an oil shock moves the price of Brent crude, the shockwave does not arrive at NEPSE by way of a foreign broker selling Nepali shares. It arrives by way of Nepal Rastra Bank tightening the taps on the same banks that finance NEPSE's margin traders. Understanding this chain — remittance to reserves, reserves to the peg, the peg to monetary policy, monetary policy to bank liquidity, and bank liquidity to NEPSE turnover — is what separates an investor who is blindsided by "global" events from one who has already priced them in.
Lesson 6.1 — Why NEPSE Moves Without Foreign Money: The Indirect Transmission Model
Every emerging or frontier market textbook written for India, Vietnam, or the Philippines begins its chapter on global risk with a description of foreign institutional investor (FII) flows: money enters through the capital account, gets marked to a custodian account, buys local equity, and then reverses just as fast when risk appetite sours, driving the local index up or down in near-real time with global sentiment. None of that mechanism exists in Nepal in any significant way. The Foreign Investment and Technology Transfer framework and NRB's capital account controls mean that foreign nationals and foreign institutions cannot freely buy and sell shares on NEPSE the way they can in Mumbai, Colombo, or Dhaka. Non-resident Nepalis (NRNs) have narrow, specific windows to invest, subject to their own restrictions, but this is a trickle, not a channel. There is no "FII selling" line item to watch in NEPSE's daily turnover data because there is essentially no FII to sell.
This absence has trained a generation of Nepali retail investors to treat global headlines as background noise — interesting for context, irrelevant for positioning. That instinct is only half right. It is correct that NEPSE will not gap down tomorrow morning because the S&P 500 fell three percent overnight, in the way that the Sensex or the KOSPI might. It is incorrect to conclude that the Fed, oil markets, and global risk-off episodes therefore do not matter to NEPSE. They matter enormously, but they matter on a longer transmission lag and through a different set of variables than direct portfolio flow. The correct mental model is not "foreign investors sell NEPSE shares," because that channel barely exists. The correct model has four links: first, a global event changes the income Nepali migrant workers can send home, or changes the price Nepal pays for the oil it imports, or changes the relative strength of the currency to which the Nepali rupee is pegged. Second, that change shows up in Nepal's balance of payments and in Nepal Rastra Bank's foreign exchange reserves, which the central bank monitors and defends because a fixed exchange rate peg has no automatic stabiliser of its own. Third, when reserves come under pressure, NRB responds with the only tools available to it — tightening domestic monetary policy, raising the policy rate, adjusting the cash reserve ratio and enforcing the credit-to-deposit (CD) ratio ceiling banks must maintain, and in the more extreme episodes, directly restricting imports or raising the cost and difficulty of consumer and margin lending. Fourth, and this is the link a NEPSE investor must never forget, Nepal's commercial banking sector is both the single largest weight on the exchange by market capitalisation and the near-exclusive source of margin lending that fuels retail equity turnover. When bank liquidity tightens for macro reasons that have nothing to do with corporate earnings, NEPSE falls anyway, and it falls hardest in exactly the segments — margin-financed retail positions — that amplify the initial move into a rout.
KEY CONCEPT
NEPSE is macro-exposed but not portfolio-exposed. Global events reach the index through remittances, oil-driven reserve pressure, and the NPR-INR peg's effect on domestic monetary policy — not through foreign funds buying or selling Nepali shares. The transmission is slow and indirect, but it is not weak.
This distinction matters practically because it changes what an investor should watch and how quickly they should expect to see effects. A Mumbai investor watching Fed minutes is pricing in a same-day or same-week flow reaction. A NEPSE investor watching the same Fed minutes should be thinking in terms of quarters: how will this affect Gulf hiring conditions over the next two to three quarters, how will it affect the dollar-rupee-NPR chain and therefore Nepal Oil Corporation's import costs, and how will NRB's own reserve position six months from now constrain the credit growth that NEPSE turnover depends on. The lag is real, but so is the eventual impact, and the lag is precisely what allows a disciplined investor to get ahead of it rather than be surprised by it.
Lesson 6.2 — The Remittance Channel: Gulf Labor Markets as Nepal's Real "Foreign Flow"
If NEPSE has no meaningful foreign institutional flow, it does have something that functions as its de facto foreign capital account: remittances sent home by the several million Nepalis working abroad. Nepal is one of the most remittance-dependent economies on earth, with inward remittances regularly running in the range of a fifth to a quarter of GDP in recent years — a ratio that puts it in the same company as Tajikistan, Kyrgyzstan, and a handful of small Pacific and Caribbean states, and far above the ratio for any large regional economy such as India or Bangladesh. This is not a peripheral statistic. Remittances are the single largest source of foreign currency inflow into Nepal, larger than exports, larger than tourism receipts, and larger than foreign aid and foreign direct investment combined. They fund household consumption, they fund the deposit base of the banking system, and — critically for this book's purposes — they fund a large share of the liquidity that eventually finds its way into NEPSE as margin collateral, IPO subscriptions, and speculative retail trading capital during boom periods.
The workers generating this flow are concentrated in a narrow set of destination labor markets: Malaysia, Qatar, Saudi Arabia, the United Arab Emirates, Kuwait, and, through the Employment Permit System, South Korea. This concentration means that Nepal's remittance income is not a diversified global phenomenon but a bet, in aggregate, on the health of Gulf construction and services labor demand and on Malaysian manufacturing and plantation labor demand. Anything that changes hiring conditions in these markets flows through to remittance growth with a lag of a few months to a couple of quarters, and remittance growth or contraction flows through to Nepali bank deposit growth, consumption, and market liquidity with a further lag.
Understanding the direction of these effects requires resisting a simple intuition. It would be tempting to assume that anything bad for the global economy is bad for Nepal's remittance income, but the relationship is more textured than that, and in some cases runs in the opposite direction from what a superficial reading of "risk-off" would suggest. Higher global oil prices, for instance, are unambiguously painful for Nepal on the import side, as the next lesson details — but higher oil prices are frequently good for Gulf state fiscal positions, and a well-funded Gulf state government tends to accelerate infrastructure and construction spending, which is precisely the sector that absorbs the largest share of Nepali migrant labor. The 2010s buildout in Qatar ahead of the 2022 World Cup is the clearest illustration: an enormous, sustained construction boom driven by a single mega-event pulled in Nepali labor at scale and lifted remittance growth for the better part of a decade, before decelerating once the stadiums, roads, and metro lines were complete and the construction cycle wound down. A Nepali investor who thinks of Gulf oil wealth purely as a source of pain (via NOC's import bill) without also recognising it as a source of gain (via Gulf capital spending and labor demand) is working with half the picture.
Other shocks are more straightforwardly negative. A recession or a sharp fiscal tightening in a Gulf destination country reduces construction and services hiring and can trigger layoffs of migrant labor, translating into slower remittance growth or, in the sharpest episodes, into net negative flows as workers return home without replacement cohorts departing. Malaysia has periodically frozen new foreign worker recruitment for extended stretches — sometimes for domestic political reasons tied to its own labor market protection concerns, sometimes for public health reasons — and each freeze compresses the pipeline of new departures that would otherwise sustain remittance growth over the following two to three years. The COVID-19 pandemic produced the starkest version of this dynamic: mass repatriation of stranded workers, an abrupt halt to new departures for foreign employment, and a period of acute uncertainty about whether Nepal's remittance engine would sustain a multi-year contraction. In fact the outcome was more complicated and is worth dwelling on, because it illustrates how domestic financial-system effects can dominate the headline global shock, a theme this chapter returns to repeatedly.
WATCH FOR
Year-on-year remittance growth, published monthly by Nepal Rastra Bank in its "Current Macroeconomic and Financial Situation" report. A deceleration or contraction in this figure — even before it shows up in forex reserves — is typically the earliest visible sign of stress that will eventually reach NEPSE through the bank liquidity channel described in Lesson 6.4.
The practical takeaway for a NEPSE investor is that Gulf and Malaysian labor market conditions are not a distant abstraction to be filed away as "geopolitics" — they are the closest thing Nepal has to a foreign capital account, and their health should be tracked with the same seriousness that an Indian investor tracks FII flow data. A Nepali investor who follows Gulf construction spending trends, Saudi and UAE labor nationalisation policies (the various "Nitaqat"-style Saudization and Emiratization drives that periodically squeeze out foreign labor in favour of nationals), Qatari and Kuwaiti project pipelines, and Malaysian and Korean recruitment quota announcements is, in effect, doing the same forward-looking work that a Mumbai-based analyst does when parsing FII flow data — just with a different, less liquid, and slower-moving instrument.
Lesson 6.3 — Oil, the Peg, and the Reserve Squeeze
Where remittances represent Nepal's principal inflow, imported petroleum represents its most volatile and least substitutable outflow. Nepal produces no crude oil and refines none domestically; the entirety of the country's petroleum product needs — petrol, diesel, kerosene, aviation fuel, and LPG — are imported through Nepal Oil Corporation (NOC), which holds a legal monopoly on the trade and sources exclusively from Indian Oil Corporation under a long-standing bilateral supply agreement. This arrangement has two consequences that matter enormously for macro risk transmission. First, Nepal has essentially zero ability to substitute away from oil price shocks in the short run: when global crude prices spike, NOC's import bill rises close to one-for-one, with only a short lag before the increase either gets passed to domestic consumers through higher pump prices or absorbed by NOC (and by extension the state) through mounting losses and arrears. Second, and less obviously, the bill is not simply a dollar-denominated global oil price problem — it is filtered through the Indian rupee, because Nepal pays Indian Oil Corporation in a currency chain that ultimately runs through India's own import costs and exchange rate.
This is the moment to introduce the single most important structural fact in this entire chapter: the Nepali rupee is pegged to the Indian rupee at a fixed rate, maintained by Nepal Rastra Bank as a matter of long-standing policy, and has been for decades. This is not a loose reference-rate arrangement of the kind some countries maintain as a soft guide; it is a hard peg that NRB actively defends using its own foreign exchange reserves, which it holds substantially in Indian rupees and other convertible currencies for exactly this purpose.
REGULATORY DETAIL
Nepal Rastra Bank maintains a fixed exchange rate peg between the Nepali rupee and the Indian rupee. Because the peg is fixed, Nepal effectively imports India's monetary and exchange-rate conditions rather than setting fully independent domestic monetary policy — and India's own currency moves in response to the US dollar and Federal Reserve policy. This is the direct structural link between Fed decisions in Washington and NRB decisions in Kathmandu.
Trace the chain carefully, because it is the crux of this chapter's thesis and it is widely misunderstood even by market participants. When the US Federal Reserve raises interest rates, or signals a more hawkish path than markets expected, the dollar tends to strengthen broadly against most currencies, including the Indian rupee. As the rupee depreciates against the dollar, India's own imported inflation rises (India, like Nepal, is a large net oil importer), and the Reserve Bank of India responds with its own policy tightening and reserve management. Because the Nepali rupee is pegged to the Indian rupee rather than to the dollar directly, Nepal inherits this pressure automatically: NPR depreciates against the dollar in lockstep with INR, meaning that Nepal's dollar-denominated oil bill (and any other dollar-denominated import or debt obligation) becomes more expensive in NPR terms precisely when a Fed hiking cycle is under way — even though Nepal's own economic conditions may call for something entirely different. Nepal effectively has no independent monetary policy with respect to the currency; it has committed, by virtue of the peg, to importing India's monetary stance, which is itself increasingly reactive to the Fed. A Nepali investor who dismisses FOMC meetings as irrelevant to a market with no foreign portfolio flows is missing that the FOMC's decisions reach Kathmandu within weeks, not through NEPSE share registries, but through the exchange rate mechanics that determine how many rupees NOC must pay for a barrel of crude.
The consequence of a simultaneous oil price spike and a Fed-driven dollar/rupee move is a pincer effect on Nepal's foreign exchange reserves. Reserves are drawn down on two fronts at once: more rupees are needed to buy the same volume of oil because the price of oil in dollars has risen, and more rupees are needed to buy the same number of dollars because the currency has weakened. Nepal Rastra Bank publishes, in its monthly and periodic macroeconomic updates, a single figure that condenses this pressure into something an investor can track directly: gross foreign exchange reserves expressed as "months of import cover" — how many months of Nepal's total merchandise and service imports the current reserve stock could finance if no further foreign currency earnings arrived at all. This figure is, in the authors' view, the single most important macro number a NEPSE investor should check on a recurring basis, more important in its market implications than the headline GDP growth rate, because it is the number that determines how much room NRB has before it is forced into defensive tightening.
WATCH FOR
NRB's disclosed foreign exchange reserves, expressed as months of import cover, published in its periodic "Current Macroeconomic and Financial Situation" reports. A sustained decline toward roughly six months of cover has historically preceded periods of import restriction, monetary tightening, and NEPSE weakness. A comfortable, rising import cover is one of the more reliable green lights for risk-taking in NEPSE.
Nepal experienced this exact pincer in the aftermath of Russia's invasion of Ukraine in early 2022, when global crude prices spiked sharply even as the dollar strengthened broadly against most emerging and regional currencies amid an aggressive Fed tightening cycle. The two forces compounded: NOC's import bill surged in NPR terms from both the oil-price leg and the currency leg of the equation simultaneously, at a moment when Nepal was also absorbing a post-pandemic surge in pent-up consumer import demand for vehicles, electronics, and other goods. Reserves fell sharply through the first half of 2022, prompting Nepal Rastra Bank to take the unusual step of banning outright the import of a list of "luxury" and non-essential goods — vehicles, large-engine motorcycles, liquor, expensive mobile handsets, gold beyond personal limits, and similar categories — specifically to conserve foreign currency. This is about as direct a piece of evidence as exists that a global commodity and monetary shock had reached Nepal's real economy; the transmission to NEPSE itself came next, and forms the subject of the following lesson.
Table 6.1 — Historical Shock Events and NEPSE's Reaction
Event
Nature of Shock
Approximate Timing
Transmission Channel
NEPSE / Market Reaction
Gorkha Earthquake
Domestic natural disaster
April–May 2015
Direct physical and confidence shock; trading suspended
Exchange closed for roughly a month; sharp single-day decline on reopening; prolonged weakness in construction, hospitality, and cement counters
India Trade and Transit Blockade
Regional geopolitical/trade shock
Sept 2015 – Feb 2016
Fuel, cooking gas, and construction material shortages; near-zero GDP growth
Index drifted lower through the blockade months; industrial and consumer counters hit hardest; recovery only after transit normalised
Global Trade Tensions and EM Sell-off
Global risk-off
2018–2019
Indirect; muted direct linkage given absence of FII flow
Limited immediate effect on NEPSE; slower remittance growth contributed to a subdued liquidity backdrop
COVID-19 Pandemic
Global pandemic / risk-off
March–June 2020 (acute phase)
Trading halted; migrant worker repatriation; global risk aversion
Exchange closed roughly two months; on reopening, paradoxical multi-year bull run driven by domestic liquidity surplus rather than global sentiment
Russia-Ukraine War and Oil Price Spike
Commodity and geopolitical shock
February 2022 onward
Oil import bill surge; forex reserve depletion; NPR/INR peg pressure
NRB import restrictions and monetary tightening; NEPSE entered a sustained multi-quarter decline through 2022
US Federal Reserve Tightening Cycle
Global monetary policy shock
2022–2023
Dollar strength, INR/NPR depreciation pressure, imported inflation, NRB policy rate hikes to defend the peg
Lesson 6.4 — From Macro Stress to Market Stress: The Liquidity and Margin-Lending Transmission Belt
The preceding two lessons established that global events reach Nepal through remittances and oil-linked reserve pressure, and that both ultimately register as stress on Nepal Rastra Bank's foreign exchange position. This lesson closes the loop by explaining exactly how reserve stress becomes NEPSE stress, because this is the step most retail investors skip, and skipping it is what makes global events feel to them like they arrive "out of nowhere."
Nepal's banking sector — commercial banks, development banks, and finance companies collectively referred to as bank and financial institutions (BFIs) — occupies an outsized position in NEPSE for two distinct reasons. First, BFI shares themselves constitute one of the largest sector weights in the exchange's overall market capitalisation and turnover, meaning that anything that hurts bank profitability or bank balance sheets directly hits a large share of the index by construction. Second, and more important for this chapter's argument, BFIs are the near-exclusive providers of margin lending — credit extended against pledged shares — that finances a very large share of NEPSE's retail trading volume during active market phases. Margin lending is, in effect, the leverage engine of NEPSE: it allows retail investors to take positions larger than their own capital would otherwise permit, and it is directly responsible for amplifying both bull runs and corrections into moves considerably larger than underlying corporate fundamentals alone would justify.
When Nepal Rastra Bank responds to reserve pressure — whether that pressure originates from an oil shock, a Fed-driven currency move, a remittance slowdown, or some combination of the three — its toolkit consists overwhelmingly of measures that tighten domestic bank liquidity and credit growth. NRB can raise its policy repo rate, making the cost of central bank liquidity to banks more expensive and inducing banks to raise their own deposit and lending rates. It can raise the cash reserve ratio (CRR) that banks must hold, mechanically reducing the pool of deposits available for lending. It can enforce or tighten the credit-to-deposit (CD) ratio ceiling — 90 percent since it replaced the older credit-to-core-capital-cum-deposit (CCD) ratio in 2021 — forcing banks that are near the limit to slow new lending across the board or actively call in existing facilities. And, in measures that hit NEPSE with particular directness, it can lower the maximum loan-to-value ratio permitted on share-collateral margin loans, or otherwise restrict the categories and volumes of lending banks may extend against listed securities.
Each of these levers, applied for reasons that are entirely about defending the currency peg and the reserve position, has the side effect of draining exactly the liquidity that NEPSE's retail base depends on to sustain turnover and to avoid forced selling. A margin trader who has borrowed against a share portfolio does not experience the Fed's rate decision or the oil price spike directly; they experience it as their bank suddenly demanding a lower loan-to-value ratio, or raising the interest rate on the facility, or declining to roll over the loan at all. The response — sell shares to meet the new margin requirement — is a mechanical, forced action that has nothing to do with any individual company's earnings and everything to do with a macro chain that began months earlier in a Gulf construction site, an OPEC+ production decision, or an FOMC statement.
CASE IN POINT
Through 2022, as Nepal's forex reserves came under pressure from the combined weight of the Russia-Ukraine oil shock and a strengthening dollar, Nepal Rastra Bank raised its policy rate over successive revisions from 3.5 percent to 7 percent (with the bank rate reaching 8.5 percent), while the 90 percent credit-to-deposit ceiling turned binding across much of the system, and margin lending was squeezed by the FY2021/22 Rs 4 crore/Rs 12 crore caps and the general liquidity drought. NEPSE, which had touched an all-time high near 3,200 points in mid-2021 on the back of a domestic liquidity glut, fell by more than 40 percent over the following year (from the 3,198.60 close of August 2021 to a June 2022 low near 1,850) — a decline driven overwhelmingly by BFI-sector liquidity tightening and margin-call selling rather than by any deterioration in listed companies' underlying earnings.
This mechanism also explains something that otherwise looks paradoxical: why NEPSE's most dramatic bull run of the past decade began immediately after one of the most severe global shocks in a century. When COVID-19 struck in early 2020, NEPSE suspended trading for roughly two months amid a national lockdown, and every conventional expectation at the time was that the exchange would reopen into a prolonged bear market, mirroring the sharp but short-lived selloffs seen in global equity indices during the same period. Instead, NEPSE embarked on a sustained rally that carried the index from roughly 1,190–1,260 at the mid-2020 reopening to its all-time intraday high above 3,220 over the following eighteen months. The explanation lies entirely in the domestic liquidity channel rather than in any global risk-sentiment channel: with interest rates cut and liquidity injected domestically, with alternative uses of household savings (travel, consumption, informal investment) curtailed by lockdowns, and with a wave of returning or stranded remittance income finding its way into bank deposits and, from there, into margin-financed equity positions, NEPSE became a primary outlet for a domestic liquidity surplus that had nothing to do with global risk appetite. The lesson here is important and cuts against the naive assumption that "global crisis equals NEPSE crash": what actually determines NEPSE's direction is the state of domestic BFI liquidity, and a global shock only translates into a NEPSE shock once it has worked its way through remittances and reserves into that liquidity position. A global event that tightens Nepali bank liquidity (like the 2022 oil and Fed shock) will hurt NEPSE; a global event that, through its second-order domestic effects, loosens Nepali bank liquidity (like the pandemic-era rate cuts and forced savings) can lift NEPSE even as the rest of the world sells off.
WARNING
Margin-financed positions in NEPSE are exposed to a risk that has nothing to do with the shares being held: a change in NRB's macroprudential settings, driven by forex reserve conditions entirely outside any company's control, can trigger a margin call regardless of that company's earnings trajectory. Investors carrying leveraged positions should track NRB's monetary policy stance and reserve trajectory as closely as they track sector fundamentals.
Lesson 6.5 — Historical Case Studies: Earthquake, Blockade, and Pandemic
It is worth walking through Nepal's three most consequential shock episodes of the past decade in more narrative detail, both because they illustrate the mechanisms described above in concrete form and because they are frequently confused with one another or with generic "global" events in casual market commentary, when in fact they differ importantly in origin and transmission.
The April 2015 Gorkha earthquake was a domestic natural disaster, not a global macro or event-driven risk in the sense this chapter otherwise uses the term, but it belongs in any discussion of shock transmission because of what it reveals about NEPSE's structural fragility to any large exogenous disruption. The exchange suspended trading for close to a month as the country dealt with the immediate humanitarian crisis, and when it reopened, the index fell sharply on its first trading sessions before settling into an extended period of subdued activity. The counters hit hardest were unsurprising — hospitality and tourism (given the damage to trekking and heritage-tourism infrastructure), construction materials and cement (paradoxically supported over the medium term by reconstruction demand but hurt in the short term by supply disruption), and general insurers facing a wave of claims. The broader lesson for investors is that NEPSE's illiquidity and its reliance on a small number of large domestic institutional and retail players means that any shock severe enough to disrupt confidence broadly — whether the shock originates domestically or globally — tends to produce outsized, prolonged index moves relative to the shock's direct economic cost, simply because there is no offsetting foreign buyer of last resort stepping in to arbitrage the dip away, the way index arbitrage or foreign bargain-hunting might cushion a similar shock in a market with active FII participation.
The 2015–16 India trade and transit blockade that followed the earthquake and the promulgation of Nepal's new constitution is a more directly relevant case for this chapter, because it demonstrates a channel closely analogous to the oil-and-peg mechanism described in Lesson 6.3, except triggered by a bilateral political dispute rather than a global commodity shock. For roughly five months, the great majority of Nepal's overland trade with and through India — the source of virtually all its petroleum, most of its consumer goods, and most of its industrial inputs — was disrupted. The effect on the real economy was severe: acute fuel and cooking gas shortages, industrial production curtailed by input scarcity, and GDP growth for the fiscal year collapsing to a figure close to zero, among the weakest readings in Nepal's modern economic history. NEPSE drifted lower through the blockade period, with industrial, manufacturing, and consumer-facing counters bearing the brunt, and recovery only took firm hold once transit normalised and fuel supplies resumed. The parallel to the global oil-shock mechanism is instructive: whether an oil and trade disruption originates from a bilateral political dispute on Nepal's southern border or from a war affecting global crude markets, the transmission into Nepal's real economy and, from there, into NEPSE runs through the same chokepoint — Nepal Oil Corporation's ability to secure fuel, and the broader economy's dependence on unrestricted overland trade.
CAUTION
Not every shock that hits NEPSE is a "global" event in the sense of originating from Fed policy, oil markets, or international risk sentiment. Domestic and bilateral political shocks — an earthquake, a border or trade dispute, a fuel supply disruption with India — can produce effects on NEPSE that are observationally similar to global macro shocks because they travel through the same reserve and liquidity chokepoints. Diagnosing the actual origin of a shock matters for judging how long it will last and what would resolve it.
The COVID-19 pandemic, already discussed in Lesson 6.4 for its liquidity-channel effects, deserves a second look here specifically as a case study in how a genuinely global, synchronized shock can nonetheless produce a domestically idiosyncratic market outcome. Every major element of the pandemic shock was global in origin and transmission: the virus itself, the near-simultaneous lockdowns imposed by governments worldwide, the collapse in global travel and tourism demand, and the initial wave of acute uncertainty that hit essentially every equity market on earth in March 2020. Nepal's own experience of the shock — mass repatriation pressure on migrant workers stranded in Gulf and Malaysian labor markets, a collapse in tourism receipts, and a nationwide lockdown that halted NEPSE trading outright — fit the global pattern precisely in its initial phase. What did not fit the global pattern was the subsequent multi-year domestic bull run once trading resumed, driven by the liquidity mechanism already described. The case study's value lies exactly in this divergence: an investor who assumed NEPSE's post-COVID trajectory would mirror the S&P 500's or the Sensex's would have badly misjudged both the timing and the magnitude of the recovery, because they would have been reasoning from global sentiment rather than from the specific state of Nepali bank liquidity, remittance flows, and NRB's domestic policy stance.
Lesson 6.6 — Building an Event-Risk Framework for the NEPSE Investor
Having established the mechanism, the historical evidence, and the specific institutional channels involved, the remaining task is practical: what should an investor actually monitor, on an ongoing basis, to anticipate rather than merely react to global macro and event-driven risk as it approaches NEPSE?
The framework below is organised around the same chain developed through this chapter, moving from the most distant, slowest-moving indicators to the most proximate, fastest-moving ones. An investor does not need to track all of these daily; a monthly review discipline, timed loosely around NRB's own periodic publications, is sufficient for most retail investors and considerably better than the alternative of noticing macro stress only once it has already produced a NEPSE correction.
At the most distant end of the chain sit global monetary policy and commodity indicators: the Federal Reserve's policy rate path and forward guidance, the broad direction of the US dollar index, and the price of Brent or Dubai crude oil. These are the "leading indicators of leading indicators" — an investor who notes a hawkish Fed surprise or a sustained oil price spike should mentally flag that Nepal's reserve position is likely to come under pressure within one to two quarters, well before that pressure shows up in any Nepali data release.
Moving one step closer, Gulf and Malaysian labor market conditions deserve direct tracking: news of construction project pipelines slowing or accelerating in Saudi Arabia, the UAE, and Qatar, changes to labor nationalisation policy in any of the major destination markets, and Malaysian or Korean recruitment quota decisions. These feed the remittance channel with a lag of roughly two to four quarters.
Closer still are Nepal Rastra Bank's own periodic publications, chief among them the monthly or multi-monthly "Current Macroeconomic and Financial Situation" reports, which disclose remittance growth, the balance of payments position, and — the single figure this chapter has emphasised repeatedly — foreign exchange reserves expressed as months of import cover. These are the data points where global pressure first becomes visible in Nepal-specific form, and they typically lead NRB policy tightening by one to two quarters.
Closest of all, and most directly actionable, are NRB's own monetary and macroprudential policy announcements — changes to the policy rate, the CRR, the CD ratio ceiling, and margin lending loan-to-value limits — along with the commercial banking sector's own reported liquidity position (often summarised in market commentary as the interbank rate and the volume of standing liquidity facility usage). These are the indicators with the shortest lag to NEPSE itself, frequently showing effects within weeks.
PRACTICAL TOOL
A monthly event-risk checklist for the NEPSE investor: (1) Has the Fed signalled a shift in its rate path, and how has the dollar index moved? (2) Has Brent or Dubai crude moved more than roughly ten percent in the past month? (3) Is there news of hiring freezes, project slowdowns, or labor policy changes in Saudi Arabia, the UAE, Qatar, Kuwait, Malaysia, or South Korea? (4) What does NRB's latest macroeconomic report show for remittance growth and months of import cover? (5) Has NRB changed the policy rate, CRR, CD ratio ceiling, or margin lending LTV limits in the past quarter? (6) What is the current interbank lending rate, as a proxy for how tight or loose BFI liquidity actually is right now? A negative or deteriorating reading across several of these should raise an investor's caution level on leveraged NEPSE positions well before the index itself shows visible weakness.
It is worth being explicit about what this framework is not. It is not a timing tool that will tell an investor to sell on a specific day, and Nepal's data publication lags mean that by the time a reserve or remittance figure is officially released, some of its market-relevant information has already leaked into banking-sector behaviour and informal market commentary. Nor does the framework suggest that global events are the only, or even the primary, driver of NEPSE at all times — company-level earnings, sector-specific regulatory changes (a hydropower tariff decision, an insurance sector regulation, a banking merger policy), and purely domestic political developments regularly dominate NEPSE's short-term movements, and much of the rest of this book is devoted to exactly those domestic drivers. What this framework provides is a way of recognising, when a global shock does occur, roughly how large and how durable its eventual NEPSE impact is likely to be, and roughly how much lag to expect before that impact fully arrives — which is precisely the information a retail investor needs to decide whether to reduce leverage, tighten stop levels, or simply hold through a transmission process that, however indirect, is neither mysterious nor unpredictable once its mechanics are understood.
Chapter recap
NEPSE has almost no direct foreign institutional participation, but this does not make it immune to global macro and event-driven risk; it only means the transmission runs through a different, slower, and more indirect set of channels than in markets with active foreign portfolio flows. The principal channel is remittances: Nepal's migrant workers in the Gulf, Malaysia, and South Korea generate an inflow that functions as the country's de facto foreign capital account, and shifts in those labor markets' hiring conditions reach Nepal's banking system and, eventually, NEPSE liquidity with a lag of a few quarters. A second channel runs through Nepal's total dependence on imported petroleum and the fixed exchange rate peg between the Nepali rupee and the Indian rupee, which means that global oil price shocks and Federal Reserve-driven dollar strength compound each other by simultaneously raising the cost of Nepal's oil import bill and depreciating the currency in which that bill must be paid, placing direct pressure on Nepal Rastra Bank's foreign exchange reserves. The reserve position, tracked most usefully through NRB's disclosed "months of import cover" figure, determines how much room the central bank has before it is forced into defensive monetary tightening, import restriction, or both. That tightening — higher policy rates, a higher cash reserve ratio, a binding credit-to-deposit ceiling, and reduced loan-to-value limits on share-collateral lending — is the mechanism that actually reaches NEPSE, because Nepali banks are both a dominant sector weight on the exchange and the near-exclusive source of the margin lending that finances retail turnover, meaning a global shock that tightens Nepali bank liquidity produces forced selling that has nothing to do with any individual company's fundamentals. This explains why NEPSE sometimes appears to move in ways that seem disconnected from global sentiment, as in the sustained 2020–2021 bull run that followed immediately after the COVID-19 shock, driven by a domestic liquidity surplus rather than global risk appetite, and why it sometimes moves in ways that seem purely domestic but in fact trace back to a distant Fed decision or oil-price spike, as in the 2022 tightening-driven bear market. Historical episodes including the 2015 earthquake, the 2015–16 India blockade, and the COVID-19 pandemic each illustrate a version of this transmission chain, and distinguishing a genuinely global shock from a domestic or bilateral one matters for judging how long an episode is likely to last and what would resolve it. A disciplined NEPSE investor should therefore maintain an ongoing watch over Fed policy signals and oil prices, Gulf and Malaysian labor market news, NRB's monthly remittance and reserve disclosures, and NRB's own monetary and macroprudential policy settings, using deterioration across these indicators as an early warning to reduce leveraged exposure well before the index itself shows visible stress, rather than waiting to be surprised by a shock that, in Nepal's case, was never really as indirect as it first appeared.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part II
MARKET STRUCTURE (NEPSE REALITY FIRST)
Part II · Chapter 7
What Is NEPSE and Why It Behaves Differently
First published 21 Aug 2026 · Last verified 29 Aug 2026
Before any strategy, instrument, or ratio — understand the arena you are actually in.
Most investors who lose money in Nepal's stock market do not lose it because they chose the wrong stock. They lose it because they applied the wrong mental model to the right stock in a market they did not truly understand. They treated NEPSE like a shrunken version of the New York Stock Exchange, or perhaps like a local analogue of India's BSE. They imported frameworks — P/E ratios, MACD crossovers, institutional momentum strategies — that were forged in markets of entirely different character, depth, and institutional architecture.
This chapter is not a warning against analysis. It is a foundation for it. Before you open a balance sheet, before you calculate a price-to-book ratio, before you chart a moving average, you must first understand the arena you have chosen to enter. What kind of market is NEPSE? Who built it, who owns it, who moves it, and why does it sometimes behave in ways that seem to defy both logic and global trends? These are not peripheral questions. They are the first questions — and the answers to them will shape every investment decision you make.
Lesson 7.1 — What a Stock Exchange Actually Is: Ownership, Price Discovery, and Capital Formation
The Foundational Concept
A stock exchange is, at its simplest, an organised marketplace where buyers and sellers of financial securities come together to transact. But this simple definition conceals three functions so fundamental that failure to understand any one of them leads to profound misunderstanding of markets as a whole. Those three functions are: the creation and transfer of ownership, the mechanism of price discovery, and the process of capital formation.
Ownership: What You Are Actually Buying
When you purchase shares of a company listed on NEPSE — say, Nepal Investment Mega Bank (NIMB) or Nabil Bank — you are not lending money to that company. You are not purchasing a bond with a guaranteed return. You are acquiring partial ownership of the enterprise. You become, legally and economically, a shareholder: a fractional owner of everything the company owns, net of everything it owes.
This ownership carries specific rights under Nepali corporate law. You have the right to vote at Annual General Meetings (AGMs) on matters of corporate governance — the election of directors, approval of auditors, and proposed changes to the company's articles of association. You have the right to receive dividends, if and when the board of directors declares them. And you have the right to receive a proportional share of whatever value remains if the company is wound up — though this residual claim ranks last behind creditors, bondholders, and preferred shareholders.
This is a critical distinction. A depositor at a bank is a creditor of that bank. If the bank fails, the depositor has a prior claim on assets — subject to deposit insurance limits. A shareholder of that same bank is an owner. If the bank fails catastrophically, the shareholder may recover nothing. The potential upside of ownership — price appreciation, dividends, bonus shares — comes paired with the downside of full loss. Understanding this is not pessimism; it is the prerequisite for rational risk pricing.
Price Discovery: The Continuous Auction
Markets discover prices. This is not a trivial statement. Prior to organised exchanges, determining the fair value of a company's ownership stake required private negotiation — an opaque, illiquid, and inherently inefficient process. The exchange changes this by creating a continuous, transparent auction.
On NEPSE, during trading hours (Sunday through Thursday, 11:00 AM to 3:00 PM), thousands of individual orders — buy and sell — enter the NEPSE Automated Trading System (NATS). The system matches these orders by price and time priority. The price at which the last transaction occurred is broadcast in real time as the market price. This is the discovered price: not what any single participant thinks the company is worth, but the price at which a willing buyer and a willing seller actually agreed to transact.
Price discovery is valuable to society beyond its utility to individual investors. It aggregates dispersed information — the knowledge, expectations, and risk assessments of thousands of participants — into a single number. When a bank discloses strong quarterly earnings, buyers enter the market, driving prices up. When a hydropower company faces monsoon failure or transmission delays, sellers exit, driving prices down. Prices, therefore, encode information. This is the mechanism by which markets are said to be efficient — not perfectly efficient, but information-responsive over time.
In NEPSE's case, as we will explore throughout this book, price discovery is meaningful but imperfect. Thin trading volumes, concentrated broker networks, retail-dominated participation, and structural information asymmetries mean that NEPSE prices sometimes reflect rumour more accurately than reality, and sometimes lag fundamental developments by months.
Capital Formation: The Economic Justification
The third function of a stock exchange — capital formation — is arguably its most important contribution to national economic development. A stock exchange allows companies to raise capital from the public by issuing shares through an Initial Public Offering (IPO) or a Rights Issue. In exchange for this capital, the company gives investors an ownership stake and the attendant rights described above.
This matters because it provides an alternative to debt financing. A company that funds expansion through bank loans must service those loans regardless of business conditions — a fixed cost that creates fragility in downturns. A company that funds expansion by issuing equity has no contractual repayment obligation. Its investors bear the risk, and if the investment succeeds, they share in the reward.
For Nepal, this function has been particularly significant in the banking and hydropower sectors. NEPSE has been the primary vehicle through which ordinary Nepali citizens have been able to participate in the capitalisation of commercial banks, development banks, microfinance institutions, and increasingly, hydropower projects. The 10% IPO allocation to general public investors — a regulatory provision of the Securities Board of Nepal (SEBON) — has created broad retail participation in capital markets that is unusual relative to Nepal's per-capita income level.
Key Concepts in Lesson 7.1
Shareholders are owners, not lenders — losses can be total.
Price discovery aggregates dispersed market information into a single transactable price.
Capital formation via NEPSE has democratised participation in Nepal's banking and hydro sectors.
SEBON's 10% general public IPO allocation is a regulatory tool with profound social implications.
NEPSE uses the NATS (Automated Trading System) for order matching — price and time priority.
Lesson 7.2 — The History of NEPSE: From 1993 to Today — How the Market Was Built and What Shaped It
Origins: Securities Exchange Centre, 1976
NEPSE's history does not begin in 1993. Its institutional ancestor, the Securities Exchange Centre (SEC), was established in 1976 under the Companies Act of 1964. The SEC was a government body that performed the functions of stock broker, market maker, and issuer registry simultaneously — a combination that would be considered deeply conflicted by modern regulatory standards. It operated without a formal trading floor; transactions were largely OTC (over-the-counter), based on bilateral negotiation between buyers and sellers who found each other through informal networks.
The securities traded were few: government bonds, a handful of financial institution shares, and some development bonds. Volume was negligible. The SEC's role was largely administrative — maintaining a registry of shareholders — rather than genuinely market-making. Nepal's economy in the 1970s and 1980s was dominated by agriculture, remittances were not yet the structural force they would become, and the private sector was nascent.
The Birth of NEPSE: 1993
Nepal Stock Exchange Ltd. was incorporated on January 13, 1993, under the Companies Act 2021 (1964), as a government-controlled institution. It formally commenced trading on January 16, 1994. The establishment of NEPSE was part of a broader liberalisation agenda driven by Nepal's engagement with the International Monetary Fund (IMF) and the World Bank during the early 1990s, which included opening the financial sector to private banks and encouraging capital market development.
The initial listed companies were predominantly financial institutions — development banks, finance companies, and the state-owned commercial banks. Nepal Rastra Bank (NRB), the central bank, and the government held significant stakes in several listed companies, blurring the line between regulator, owner, and listed entity in ways that persisted for decades.
The 1990s: Slow Construction
Through the late 1990s, NEPSE grew modestly. The political instability of the period — Nepal transitioned from a partyless Panchayat system to multiparty democracy in 1990, and this created significant policy volatility — discouraged sustained investor confidence. The Maoist insurgency, which began in 1996, created a climate of uncertainty that particularly affected hydropower and infrastructure investment, though its direct impact on NEPSE trading was less acute than one might expect, since the market remained small and disconnected from the broader economy.
Demat (dematerialised) accounts were not yet universal. Physical share certificates were still common. Transfer processes were manual, slow, and prone to fraud and error. Broker licensing was limited. The Securities Board of Nepal (SEBON), established in 1993 under the first amendment to the Securities Exchange Act, 2040 (1983), was the regulatory authority but operated with limited capacity.
The 2000s: Financial Sector Expansion
The 2000s brought what might be called Nepal's financial institution proliferation era. NRB's policies encouraged the establishment of numerous commercial banks, development banks, and finance companies. By the mid-2000s, Nepal had more banking institutions per capita than most comparable economies — a phenomenon that NEPSE would both reflect and amplify.
Each new financial institution went through an IPO process, adding listed entities to NEPSE. The market's sectoral composition became overwhelmingly dominated by banking and financial institutions (BFIs) — a characteristic that persists to this day and which has profound implications for how the NEPSE index moves, how correlated sector returns are, and how macroeconomic policy (particularly NRB interest rate decisions) transmits directly into equity market performance.
The 2015 Earthquake and Market Response
The April 2015 Gorkha Earthquake, which killed nearly 9,000 people and caused estimated damage of $7 billion (approximately one-third of Nepal's GDP at the time), was a macroeconomic shock of historic proportions. Yet NEPSE's immediate response was surprisingly contained. The index fell, but the decline was moderate compared to what a naïve model of economic damage might predict.
This reaction revealed something important about NEPSE's structure: the market is not well-integrated with the real economy. The earthquake devastated agriculture, housing, and physical infrastructure — sectors largely absent from NEPSE's listed universe. The financial sector, which dominates NEPSE, was damaged but not destroyed. Moreover, post-earthquake reconstruction spending, international aid inflows, and remittance resilience provided economic offsets. The divergence between economic damage and market performance was a lesson in the limits of treating NEPSE as a barometer of Nepal's entire economy.
The 2016–17 Bull Market
Between 2016 and early 2018, NEPSE experienced its first major bull market. The index rose from approximately 900 points to peak at around 1,888 points in July 2017 — more than doubling in under two years. The drivers were multiple and reinforcing: rising bank profits, low interest rates, surplus liquidity in the banking system following post-earthquake aid inflows, growing demat account registrations as more Nepalis accessed NEPSE through mobile and online platforms, and a broader speculative enthusiasm that fed on itself.
Crucially, this bull market was retail-driven. Institutional investors — insurance companies, provident funds — were still building their market participation frameworks. Individual Nepali investors, many accessing the market for the first time through the newly simplified demat system, drove volumes. This retail dominance created sharp momentum and equally sharp corrections: when sentiment turned, there were few institutional counterweights to absorb selling pressure.
The 2018–2020 Correction
The NRB's credit tightening measures from 2018 — reducing the credit-to-deposit (CD) ratio limits, increasing risk-weighted assets for margin loans — drained liquidity from the market precisely as it had been the liquidity injection that fuelled the rally. NEPSE fell from 1,888 to approximately 1,100 by early 2020, before the COVID-19 pandemic added a new layer of uncertainty. A market that had never developed the institutional depth to sustain sophisticated valuation frameworks was particularly vulnerable to sentiment and liquidity shocks.
The 2020–2021 Pandemic Bull Market
In a pattern mirrored globally, Nepal's equity market responded to the COVID-19 pandemic with an initial sharp fall followed by a historic bull run. NEPSE, which had touched lows near 1,100 in mid-2020, surged to an all-time high of approximately 3,228 points by August 2021. This extraordinary rally occurred while Nepal's physical economy — tourism, hospitality, small business — was devastated by lockdowns and border closures.
The explanation lies in the mechanics of money, not economic fundamentals. Nepal Rastra Bank slashed its policy rate and injected liquidity into the banking system. Banks, unable to deploy credit into a locked-down economy, had surplus funds. Much of this surplus found its way into the stock market through margin loans and direct investment. Remittances — which surged paradoxically during COVID-19 as Nepali workers abroad sent more money home — provided households with savings that, absent consumption opportunities, were channelled into NEPSE. The pandemic bull market was, in essence, a liquidity event masquerading as a fundamentals rally.
The 2022 Correction and Ongoing Volatility
NRB's subsequent tightening — raising the policy rate, restricting margin lending, capping the loan-to-value ratio on share-collateralised loans — punctured the liquidity bubble with characteristic Nepali speed. By mid-2022, NEPSE had corrected sharply from its 2021 highs. The market has since traded in a range characterised by uncertainty over interest rates, credit growth, and the trajectory of hydropower project completions.
This cyclical history — liquidity-driven rallies, policy-driven corrections, retail euphoria and panic — is not noise. It is the structural DNA of NEPSE. An investor who understands this history understands why NEPSE requires a different analytical lens than markets driven by earnings momentum, institutional flow, or global risk appetite in the conventional sense.
NEPSE Timeline: Key Milestones
1976 — Securities Exchange Centre (SEC) established.1993 — Nepal Stock Exchange Ltd. incorporated.1994 — Formal trading commences.2006 — Securities Act 2063 reforms regulatory framework.2015 — April earthquake; market shows limited correlation to economic damage.2017 — First major bull peak: NEPSE index reaches \~1,888.2021 — All-time high: NEPSE index reaches \~3,228 amid pandemic liquidity surge.2022–present — Tightening cycle; market searching for new equilibrium.
Lesson 7.3 — NEPSE vs. BSE, NSE, NYSE: Scale, Depth, and Why Comparisons Must Be Made Carefully
Why Comparisons Are Both Necessary and Dangerous
The investor who has read about Warren Buffett's approach to the NYSE, or who has studied Rakesh Jhunjhunwala's methodology on the BSE, naturally wonders whether those frameworks translate to NEPSE. The answer is: partially, and with significant modifications. To understand which parts translate and which do not, we need to understand the structural differences between these markets at a fundamental level.
Comparisons are dangerous when made superficially — when you take a ratio, a strategy, or a behavioural observation from one market and apply it unchanged to another. They are necessary when made carefully — as a way of understanding what NEPSE lacks, what it compensates for with unique characteristics, and what opportunities arise precisely from its inefficiencies.
Metric
NYSE (2024)
BSE (2024)
NSE (2024)
NEPSE (2024)
Listed Companies
\~2,300
\~5,400
\~2,100
\~230
Market Cap (USD)
\~$27 trillion
\~$4.5 trillion
\~$4.2 trillion
\~$15 billion
Daily Turnover (avg)
\~$20+ billion
\~$1 billion
\~$8 billion
\~$30–60 million
Main Index
Dow Jones / S\&P 500
SENSEX
NIFTY 50
NEPSE Index
Derivatives Market
Deep & liquid
Deep & liquid
Deep & liquid
Nascent / absent
Institutional Share
\~80%
\~65%
\~70%
\~15–20%
Settlement Cycle
T+1
T+1
T+1
T+2
Short Selling
Permitted
Permitted
Permitted
Not permitted
Circuit Breakers
Yes (market-wide)
Yes
Yes
Yes (per stock)
Scale: The Liquidity Chasm
The numbers in the table above tell a stark story. NYSE's daily turnover exceeds NEPSE's entire market capitalisation. BSE's listed universe is more than twenty times larger. NSE's NIFTY 50 index alone encompasses companies whose individual market caps exceed NEPSE's aggregate. These differences in scale are not merely quantitative; they have profound qualitative implications.
In a deep, liquid market, a large institutional investor — a mutual fund, a pension fund, a hedge fund — can build or exit a position without materially moving the price. The market can absorb millions of dollars of buy or sell pressure without significant price dislocation because there are always counterparties: market makers, arbitrageurs, and other institutions on the other side.
In NEPSE, this is not true. A single motivated seller in a mid-cap stock can move the price 10% on a slow day. A single motivated buyer in a small banking stock can push prices to the circuit limit. This is not a defect to be lamented; it is a structural feature to be understood and, potentially, exploited. In an illiquid market, information advantages — knowing something the crowd does not yet know — have a longer window of opportunity before price adjusts. But the same illiquidity means that exiting a position before that information becomes universal can be just as difficult as building it.
Depth: The Derivatives Deficit
Market depth refers not just to the volume of cash equity trading, but to the ecosystem of instruments that allow investors to hedge, to express complex views, and to engage in arbitrage that keeps prices aligned with fundamental value. NYSE and NSE have deep options and futures markets. An investor on NSE can buy a NIFTY put option to hedge a long equity portfolio — a relatively cheap and precise instrument for managing downside risk.
NEPSE has no meaningful derivatives market as of 2024. SEBON has been working on a regulatory framework for derivatives, and commodity derivatives have been introduced in limited form, but equity derivatives — options and futures on individual stocks or the NEPSE index — do not yet exist in a standardised, exchange-traded form.
The implications of this are underappreciated. Without put options, investors cannot buy insurance against market declines without selling shares outright. Without futures, there is no mechanism for price discovery across time horizons — no signal from the futures market about where informed participants expect prices to be in three or six months. Without short selling, overvalued stocks cannot be corrected downward by investors who recognise the overvaluation; there is a one-way pressure structure where only buyers can act on their conviction, while those who believe a stock is overvalued can only abstain or exit.
Institutional Participation: The Structural Difference
In mature markets, institutional investors — mutual funds, insurance companies, pension funds, sovereign wealth funds, hedge funds — constitute the majority of trading volume. Their participation brings several benefits: more sophisticated valuation analysis, longer investment horizons, willingness to act as contrarian buyers during retail panic, and price-stabilising behaviour during volatility.
In NEPSE, retail investors constitute the overwhelming majority of trading activity. Citizen Investment Trust (CIT) and Employees Provident Fund (EPF) participate, but their mandates and investment processes are constrained by regulation and bureaucratic culture. Mutual funds exist but manage relatively small assets compared to market cap. This retail dominance means that NEPSE is unusually susceptible to sentiment-driven cycles — the fear-greed alternation that Buffett memorably described as the market being a 'voting machine in the short run and a weighing machine in the long run.'
The voting machine phase — sentiment, rumour, momentum — dominates NEPSE for longer periods than it would in institutional markets. This creates both danger and opportunity: danger for undisciplined investors who mistake sentiment for signal, and opportunity for disciplined investors who can wait for the weighing machine phase to reassert itself.
Lesson 7.4 — Who Owns NEPSE: Government Structure, Shareholding, and the Implications of State Ownership
The Ownership Structure
Nepal Stock Exchange Ltd. is not a purely private institution. It is a government-controlled company in which multiple state entities hold significant stakes. Understanding this ownership structure explains much about NEPSE's regulatory culture, its pace of technological modernisation, its relationship with listed companies, and the inherent conflicts of interest embedded in its operation.
Shareholder
Approximate Stake
Type
Government of Nepal (Ministry of Finance)
\~34%
State
Nepal Rastra Bank (Central Bank)
\~34%
State
Securities Board of Nepal (SEBON)
\~15%
Regulator
Licensed Stockbrokers (collectively)
\~17%
Private
This ownership structure, at first glance, appears to ensure stability and state commitment to market development. In practice, it creates a set of institutional dynamics that every serious NEPSE investor must understand.
The Regulator-Owner Conflict
The most structurally significant tension arises from the fact that SEBON — the Securities Board of Nepal, which is the regulatory authority responsible for overseeing NEPSE's operations, licensing brokers, approving IPOs, and enforcing market rules — is simultaneously a significant shareholder of NEPSE. This dual role creates an inherent conflict of interest that is rarely acknowledged in official discourse but is consequential in practice.
A regulator that owns equity in the entity it regulates faces subtle pressures that can distort regulatory decisions. Enforcement actions that might damage NEPSE's reputation or reduce trading revenue could indirectly harm the regulator's own investment. Conversely, decisions that benefit NEPSE commercially could be rationalised on regulatory grounds. This does not imply corruption or bad faith — it is simply the structural consequence of a governance model that conflates regulation and ownership.
In more mature market jurisdictions, exchanges are either publicly listed (NYSE Euronext, ASX, NSE India) — creating market discipline from outside shareholders — or they are regulated by fully independent bodies with no financial stake in the exchange's commercial performance. Nepal has neither arrangement in full. This is not a permanent condition — SEBON has discussed the possibility of a NEPSE IPO, which would introduce outside shareholders and create some market discipline — but it is the current reality, and it shapes how regulation is applied.
Nepal Rastra Bank's Dual Role
Nepal Rastra Bank's position as both the central bank and a major NEPSE shareholder creates a different but equally significant tension. NRB's monetary policy decisions — interest rate changes, credit-to-deposit ratio regulation, margin lending restrictions — are among the most powerful drivers of NEPSE market performance. When NRB tightens credit, margin-financed NEPSE positions are forcibly liquidated, and the market falls. When NRB eases, liquidity floods the market and drives prices up.
This is not inherently improper; central banks must use monetary policy to manage inflation, credit growth, and financial stability, not to support equity markets. But the fact that NRB simultaneously benefits financially from NEPSE's commercial performance (as a shareholder) and holds the primary levers of macroeconomic policy affecting that performance is a structural duality worth noting. It does not suggest that NRB distorts monetary policy to support NEPSE — that would be implausible given NRB's broader macroprudential mandate — but it does create a governance opacity that thoughtful investors should factor into their assessment of NEPSE's institutional character.
Broker Shareholding: The Intermediary Interest
Licensed stockbrokers hold approximately 17% of NEPSE collectively. This creates an unusual alignment: the intermediaries whose income depends on trading volume — commissions are charged per transaction — are also partial owners of the exchange that facilitates those transactions. High turnover benefits brokers on two levels simultaneously: as commission earners and as equity holders in a more profitable exchange.
For the investor, this matters because it means that NEPSE's institutional structure is not neutral on the question of trading frequency. There are no broker-owners with a financial interest in client buy-and-hold strategies. There are no broker-owners who benefit from clients making fewer, more deliberate transactions. The incentive structure, embedded in the ownership architecture, tilts toward activity. This is relevant when evaluating broker advice, when assessing whether margin lending is pitched too aggressively, and when considering why NEPSE's trading volume can sometimes spike on rumour alone.
Lesson 7.5 — Market Capitalisation of NEPSE: What It Represents and What It Conceals
What Market Capitalisation Means
Market capitalisation — market cap — is defined as the total number of shares outstanding for all listed companies multiplied by their respective current market prices. It is, conceptually, the total value that the market assigns to all ownership stakes in all listed companies at a given moment. As of 2024, NEPSE's total market capitalisation is approximately NPR 3–4 trillion (roughly USD 22–30 billion, at prevailing exchange rates), a figure that fluctuates significantly with market movements.
This number is widely used as a shorthand for the 'size' of a stock market, and comparisons of market cap to GDP — the market cap-to-GDP ratio, sometimes called the 'Buffett Indicator' — are used globally to assess whether a market is overvalued or undervalued relative to the economy it represents. For Nepal, this ratio has sometimes approached or exceeded 100% of GDP during bull phases, which by global benchmarks would signal overvaluation. But applying this benchmark mechanically to NEPSE requires significant caution.
What NEPSE's Market Cap Conceals
NEPSE's market capitalisation number conceals several important structural realities that can mislead investors who take it at face value.
First, the free float problem. Market cap is calculated using total outstanding shares, but in many listed companies — particularly state-owned enterprises and banks where the government holds significant stakes — a large proportion of shares are not freely tradeable. Government-held shares are not sold in the open market. Promoter shares may be subject to lock-in periods. When only 30–40% of a company's shares are in free float (available for public trading), the effective liquid market cap is a fraction of the headline number. A company with a 'market cap' of NPR 50 billion may have only NPR 15–20 billion of genuinely tradeable securities, making it far more illiquid than the headline figure suggests.
Second, the concentration problem. A significant portion of NEPSE's aggregate market cap is concentrated in a small number of large financial institutions — particularly the major commercial banks. This means that the performance of NEPSE's market cap, and by extension the NEPSE index, is heavily determined by the fortunes of Nepali commercial banking. If NRB imposes stricter provisioning requirements, or if credit quality deteriorates sector-wide, the market cap impact is amplified because banks dominate the index. This concentration means that market cap-to-GDP comparisons can be misleading: a high ratio may reflect an overvalued banking sector rather than a genuine overvaluation of Nepal's entire productive economy.
Third, the inclusion problem. NEPSE's listed universe, at roughly 250 companies (around 280 listed scrips counting mutual funds and debentures), is a tiny fraction of Nepal's actual corporate sector. The vast majority of Nepal's business activity — retail trade, agriculture, construction, tourism, IT services, remittance-linked consumption — occurs in unlisted companies and informal enterprises. A rising NEPSE market cap tells you something about the relative pricing of financial sector equity; it tells you very little about the health of Nepal's actual GDP-generating activities.
Using Market Cap Intelligently
None of this means market cap is useless. For comparative purposes — comparing the relative size of two listed companies, tracking how the total value of the financial sector has changed over time, or identifying when market exuberance has pushed aggregate valuations far above reasonable earnings multiples — market cap remains a powerful tool. The discipline lies in knowing what it measures and what it does not.
A NEPSE market cap-to-GDP ratio above 80% should prompt questions about valuation, liquidity conditions, and the cyclical position of the banking sector. A ratio below 40% may signal undervaluation — or simply the end of a liquidity cycle. Context, not the number alone, determines the interpretation.
Lesson 7.6 — The NEPSE Index: Construction, Sector Weightage, and Why It Can Mislead
How the NEPSE Index Is Constructed
The NEPSE Index is a market capitalisation-weighted index, meaning that companies with larger market caps contribute more to the index's movement than companies with smaller caps. It began with a base value of 100, established on February 12, 1994, when formal trading commenced. The index is calculated by dividing the aggregate market cap of all listed companies by the base period market cap and multiplying by the base index value of 100.
Unlike the S\&P 500, which selects 500 companies based on a defined eligibility criteria, or the NIFTY 50, which selects fifty companies representing multiple sectors, the NEPSE Index includes virtually all listed companies. This inclusive approach means that when a new company is listed — whether through an IPO or a merger — it is automatically incorporated into the index. Companies that are suspended from trading are excluded during the suspension period.
Sector Weightage: The Banking Dominance
Because the NEPSE Index is market-cap weighted and because commercial banks are the largest companies by market cap, the banking sector dominates index movements to a degree that is extreme by international standards. Commercial banks alone have historically accounted for 50–60% of total NEPSE market cap. When banking stocks collectively decline by 5%, the NEPSE Index falls by roughly 3%, even if hydropower, insurance, and manufacturing stocks are flat or rising.
Sector
Approx. Index Weight
Number of Companies (approx.)
Commercial Banks
\~50–55%
20
Development Banks
\~8–10%
17
Finance Companies
\~3–5%
17
Microfinance
\~5–7%
64
Insurance (Life)
\~4–6%
19
Insurance (Non-life)
\~2–3%
20
Hydropower
\~8–12%
42
Manufacturing & Processing
\~1–2%
23
Hotels & Tourism
\~0.5–1%
5
Others (Investment, Mutual Funds, etc.)
\~3–6%
Various
Why the Index Can Mislead
The NEPSE Index is a useful barometer of overall market sentiment, but it is a poor representation of many investors' actual portfolio performance, and a distorted signal of broad economic conditions. Several structural features create this misleading quality.
Consider an investor who holds a diversified portfolio tilted toward hydropower and manufacturing — sectors with relatively low index weights. If commercial banks underperform significantly, the NEPSE Index will fall sharply, but this investor's portfolio may be flat or even positive. Conversely, a hydropower boom that produces 40% returns in that sector will barely register on the headline index. Using the NEPSE Index as a performance benchmark for a non-bank-dominated portfolio is therefore methodologically flawed.
Moreover, the index's cap-weighted structure means that as banking stocks rose during the 2020–21 bull market, their weight in the index increased, making the index progressively more concentrated in the sector that had already risen the most. This momentum-amplifying characteristic means the index can overshoot on the upside during banking sector euphoria and overshoot on the downside during banking sector distress — without accurately reflecting conditions in other listed sectors.
A sophisticated NEPSE investor tracks sub-indices — the Banking Sub-index, Hydropower Sub-index, Microfinance Sub-index — independently of the headline NEPSE Index, and uses each as a sector-specific barometer. The relationship between sub-indices can itself be informative: when hydropower rises sharply while banking falls, it may signal a rotation from financial stocks to real-asset-backed companies, which often occurs when interest rate expectations shift.
Lesson 7.7 — Sub-Indices: Banking, Hydropower, Microfinance, Insurance, Manufacturing — What Each Tracks
Why Sub-Indices Matter More Than You Think
For the disciplined NEPSE investor, the sub-index is often a more actionable tool than the headline NEPSE Index. Each sub-index tells a story about the specific macroeconomic, regulatory, and sectoral forces affecting that group of companies. Understanding what drives each sub-index — and how sub-indices interact — is fundamental to building and managing a portfolio intelligently.
The Banking Sub-Index
Commercial banks are the most analysed, most liquid, and most institutionally-held segment of NEPSE. The Banking Sub-Index tracks the twenty-odd commercial banks licensed by NRB. Its primary drivers are: NRB's monetary policy (interest rate corridor, repo rate, CRR and SLR requirements), the credit-to-deposit ratio mandate (which directly limits lending capacity), credit quality metrics (non-performing loans, provisioning requirements), and bank profitability metrics (net interest margin, operating efficiency ratio).
The Banking Sub-Index is acutely sensitive to NRB policy signals. A 25-basis-point change in the policy rate can move the sub-index by 3–5% within a session. This is not irrational — bank earnings are directly linked to net interest margins, which narrow when interest rates fall and widen when they rise (assuming appropriate asset-liability management). The practical implication: NRB Monetary Policy announcements, published twice annually (in the pre-budget monetary policy and the mid-term review), are among the most market-moving events in Nepal's investment calendar.
Mergers and acquisitions in the banking sector — actively encouraged by NRB's consolidation policy — also affect the sub-index. When two banks merge, the combined entity may command a different valuation than the simple sum of parts; post-merger integration costs, synergy realisation, and the treatment of minority shareholders in the absorbed entity create investment dynamics that attentive investors can exploit.
The Hydropower Sub-Index
Hydropower is Nepal's greatest natural resource endowment and, arguably, its most significant long-term economic opportunity. Nepal has an estimated 43,000 MW of economically feasible hydropower potential, of which less than 2,500 MW was operational as of 2023. The hydropower sub-index tracks companies across the development lifecycle — from early-stage developers still in construction to operational run-of-river projects generating and selling power.
The sub-index is driven by factors entirely different from banking: electricity generation volumes (highly seasonal — monsoon versus dry season), Power Purchase Agreement (PPA) rates negotiated with Nepal Electricity Authority (NEA), the pace of national grid expansion (which determines whether generated power can actually be evacuated and sold), export agreements with India, and project-specific factors including financing structure, construction delays, and hydrology risk.
Hydropower stocks present a unique analytical challenge because their fundamental value is deeply tied to long-duration cash flows — PPAs often run 30–40 years — but their market prices are heavily influenced by short-term sentiment, retail speculation, and the overall liquidity conditions in the market. A hydropower company with a secured PPA, completed construction, and reliable generation can be mispriced by 40–50% relative to a DCF (Discounted Cash Flow) valuation if retail sentiment has turned against the sector for unrelated reasons. This creates opportunity for patient, valuation-focused investors.
The Microfinance Sub-Index
Nepal has one of the largest microfinance sectors relative to GDP in South Asia. Over 60 microfinance institutions (MFIs) are listed on NEPSE, serving millions of borrowers — predominantly rural women — with small-ticket credit for agriculture, livestock, and cottage enterprises. The microfinance sub-index has historically shown the highest volatility of any NEPSE sub-index, driven by concentrated loan books, regulatory intervention risk, and acute sensitivity to rural income shocks.
NRB has repeatedly intervened in the microfinance sector — capping interest rates, mandating loan restructuring in distress periods, imposing loan-to-income limits, and restricting the geographic concentration of MFI lending. Each regulatory intervention creates a sub-index shock. The 2020–21 period saw microfinance stocks reach extraordinary valuations — some trading at 40–50x earnings — driven by retail speculation that treated rapid loan book growth as sustainable. The subsequent correction, accelerated by NRB's interest rate caps and the post-COVID rural income stress, was severe.
Microfinance companies present a high risk, high uncertainty investment profile. Their loan books lack the securitisation, credit rating, and independent audit quality that characterise commercial bank assets. Their governance, in many cases, is weaker. Their exposure to natural disasters, agricultural price collapses, and rural political events is significant. For most investors, the microfinance sub-index is best used as a sentiment indicator rather than a source of individual investment ideas — unless the investor has developed deep sector-specific expertise.
The Insurance Sub-Index
Nepal's insurance sector is one of the fastest-growing segments of the economy, driven by rising income, urbanisation, and regulatory requirements — NRB mandates life insurance for certain loan products, creating built-in demand. The Beema Samiti (Insurance Board) regulates the sector, and its decisions on premium rates, minimum capital requirements, and product approval directly affect listed insurance companies.
Life insurance companies in Nepal operate on a long-duration business model: they collect premiums, invest the float in government bonds and listed equities, and manage mortality and longevity risk. Their profitability is tied to investment returns, claims experience, and the efficiency of their distribution networks. The recent consolidation of Nepal's life insurance sector — driven by Beema Samiti's minimum paid-up capital requirements — has created merger dynamics similar to those seen in banking, with similar opportunities and risks for existing shareholders.
Non-life insurance (general insurance) companies operate on shorter cycles, are more exposed to catastrophe risk (earthquake, flood, fire), and tend to show less stable earnings. The reinsurance structure — most catastrophic risk is ceded to international reinsurers — limits the earnings volatility but also limits the upside from premium growth.
Manufacturing and Processing
The manufacturing sub-index is the smallest and least liquid segment of NEPSE. It includes companies in cement, cable, sugar, flour, and related industries. Many of these companies have thin trading volumes — some stocks do not trade every day — and their prices can be stale, disconnected from fundamental developments for extended periods.
Manufacturing companies in Nepal face structural headwinds: import competition (particularly from India, given the open border trade relationship), energy costs (industrial electricity tariffs remain a significant burden), labour productivity challenges, and limited export market development. However, some companies in this segment — particularly those with strong domestic brand positions, captive distribution, or regulatory protection — offer genuine long-term value at the right price. The challenge is that their illiquidity makes position building and exiting difficult.
Lesson 7.8 — Bull and Bear Phases in NEPSE History: The 2016–17 Peak, 2021 Boom, and Subsequent Correction
The Anatomy of a NEPSE Bull Market
NEPSE's two major bull markets — the 2016–17 cycle and the 2020–21 cycle — share a common architecture that reveals the structural drivers of market euphoria in Nepal. Understanding this architecture is not an exercise in nostalgia. It is a framework for recognising the next cycle when it begins, for avoiding the psychological capture that turns ordinary investors into momentum chasers, and for identifying the moments — typically when everyone else is certain the rally will continue forever — when discipline and caution become most valuable.
The 2016–17 Bull Market: Anatomy
The 2016–17 bull market had several distinct drivers that compounded each other. First, post-earthquake reconstruction spending and international aid created a substantial monetary stimulus. Second, NRB's accommodative monetary policy — with low policy rates and relatively relaxed credit conditions — enabled the banking sector to expand its loan books rapidly, generating strong earnings growth that justified initial price increases. Third, the expansion of online and mobile access to NEPSE through MeroShare and related platforms dramatically increased the number of demat accounts, bringing a new generation of retail investors into the market for the first time.
This new retail cohort arrived into a rising market and experienced immediate gains, creating a powerful positive feedback loop. Rising prices attracted new investors, whose buying pressure raised prices further, which attracted more investors. The classic momentum spiral. By mid-2017, NEPSE had doubled in eighteen months, and the financial press was filled with stories of ordinary Nepalis achieving extraordinary returns. The IPO allocation market — where even getting allotted shares in a new listing was treated as a guaranteed profit — became a subject of nationwide conversation.
The reversal began when NRB, concerned about the pace of credit expansion and the financing of stock market speculation through margin loans, tightened the credit-to-deposit ratio and restricted the flow of bank credit into stock market purposes. Without the liquidity injection, the buying pressure that had sustained the rally evaporated. Sellers, who had been a minority, suddenly found no buyers. The cascade began, and the market fell more than 40% from peak to trough over the subsequent two years.
The 2020–21 Bull Market: Anatomy
The 2020–21 bull market was more extreme in both its rise and its subsequent fall, and its drivers, while overlapping with 2016–17, had unique features specific to the pandemic context.
The initial fall — NEPSE touching approximately 1,100 in mid-2020 — created a base of genuine undervaluation in some sectors, particularly commercial banks, whose fundamentals remained sound despite the economic disruption. For value-oriented investors who bought at those levels, the subsequent rally delivered extraordinary returns. But the rally quickly moved beyond fundamental justification and entered speculative territory.
Three factors drove the 2020–21 excess beyond the 2016–17 precedent. First, global central bank accommodation — though Nepal's capital account is not fully open, the global liquidity environment influenced expectations and appetite for risk assets. Second, the dramatic surge in remittances: Nepali workers abroad, facing restricted spending opportunities in locked-down host countries, sent more money home than in any prior period. This created household savings that sought returns, and NEPSE was the most accessible domestic vehicle. Third, the digital penetration of NEPSE reached a new level during the pandemic: demat account registrations surged, mobile trading apps proliferated, and a generation that had grown up on social media learned about stocks from YouTube influencers and Facebook groups — channels characterised by enthusiasm and anecdote rather than analysis.
The consequence of this retail digital participation was a market in which information — or misinformation — spread at unprecedented speed. Stock 'tips' circulated through messaging groups. Companies with minimal earnings but attractive narratives (hydropower projects promising future revenue, microfinance companies reporting rapid loan book growth) were bid to multiples that would have been considered extreme in developed markets. The NEPSE index reached 3,228 in August 2021.
The 2022 Correction: What Broke the Bull
The correction that began in late 2021 and accelerated through 2022 was triggered by a familiar mechanism: NRB tightening. Concerns about inflation (partly global, partly domestic), credit growth exceeding productive economic capacity, and the use of bank credit for stock speculation prompted NRB to raise its policy rate, reduce the loan-to-value ratio on share-collateralised loans, and tighten the credit-to-deposit ratio mandate for commercial banks.
The impact was swift and severe. Investors who had purchased shares with margin loans — borrowing against their existing portfolio to buy more — were suddenly faced with margin calls. As share prices fell, the value of their collateral fell, requiring them to deposit additional cash or sell shares to meet the loan-to-value requirement. Forced sellers created additional downward pressure, triggering more margin calls. This deleveraging spiral is a universal feature of margin-financed markets, and NEPSE is not exempt from it.
By mid-2022, the NEPSE Index had fallen approximately 40–45% from its August 2021 peak. Some individual stocks — particularly microfinance and small hydropower companies that had been bid to extreme valuations — fell 60–70% or more. Investors who had entered near the peak, many of them first-time retail participants recruited by the social media enthusiasm of the bull phase, experienced devastating losses.
What the Cycles Teach: The NEPSE Investor's Framework
Reading these cycles carefully yields a set of principles that are more specific and more useful than generic investment wisdom.
First, in NEPSE, liquidity is the primary driver of market cycles, not earnings. When NRB eases and credit is cheap and abundant, the market rises almost regardless of fundamental valuations. When NRB tightens and credit becomes scarce, the market falls almost regardless of individual company quality. This means that macroeconomic policy awareness — specifically, tracking NRB's monetary policy stance and credit data — is an essential skill for every NEPSE investor, not an optional supplement to stock picking.
Second, NEPSE moves in cycles of approximately two to four years. Bull phases typically last eighteen to thirty months; bear phases last twelve to twenty-four months. These cycles are driven more by policy and liquidity conditions than by economic cycles in the traditional sense, because NEPSE's listed universe is concentrated in banking, which is directly regulated by NRB.
Third, the entry point matters more in NEPSE than in deeper, more liquid markets. Because valuations can swing from genuinely cheap to genuinely expensive within a single cycle, the price you pay determines your likely outcome to a significant degree. A commercial bank bought at 1.2x price-to-book during a trough has a very different risk-return profile than the same bank bought at 3.0x price-to-book at a cycle peak, even if the bank's underlying quality is identical.
Fourth, the social media contagion risk is real and growing. As digital participation expands, the speed and amplitude of sentiment swings in NEPSE has increased. The 2021 bull market was the first genuinely social-media-amplified cycle in Nepal's stock market history. Future cycles will likely show even stronger digital-amplification characteristics. Investors who understand this can use it: when social media sentiment reaches peak euphoria — when every dinner table conversation is about stock tips, when accounts with no analytical background are proclaiming certainty about future prices — it is historically a signal to reduce exposure, not increase it.
Fifth, and perhaps most importantly: the companies that survive and create long-term value through NEPSE's volatile cycles are not the companies that rise most in the bull phase. They are the companies with strong governance, conservative balance sheets, genuine earnings power, and the resilience to maintain their fundamental integrity through the inevitable correction. Identifying these companies — separating the institutions that are genuinely well-managed from those that are merely well-priced in a liquidity-driven bull market — is the central analytical challenge that the rest of this book will address.
Key Points
The NEPSE Investor's Cycle ChecklistWhere is NRB in its monetary policy cycle? (Easing = bullish tailwind; Tightening = headwind)What is the credit-to-deposit ratio across the banking system? (Excess = constrained growth)Are margin loan volumes rising rapidly? (Signals late-cycle speculation)Is social media sentiment at peak euphoria or peak despair?What is the aggregate P/E and P/BV of the NEPSE banking sub-index relative to 5-year history?Are IPO oversubscriptions at extreme levels? (100x+ oversubscription = froth indicator)Is NRB commenting publicly on 'asset price inflation'? (Tightening often follows such commentary)
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part II · Chapter 8
Regulatory Architecture of the Nepal Capital Market
First published 21 Aug 2026 · Last verified 29 Aug 2026
Markets do not regulate themselves. The history of financial markets worldwide is, in substantial part, a history of what happens when they try: fraud, manipulation, information asymmetry, systemic collapse, and the consistent exploitation of uninformed participants by sophisticated ones. Regulatory architecture exists to set and enforce rules that allow markets to function in a manner that is fair, transparent, and efficient enough to serve their foundational purposes — capital formation, price discovery, and investor protection.
In Nepal, the regulatory architecture governing the capital market is unusually complex. Multiple institutions exercise overlapping authority over different aspects of market operation, and the boundaries between their respective mandates are not always clearly drawn or consistently respected. Understanding who regulates what, how those regulators are structured, what powers they wield, and — critically — what political and institutional incentives shape how they exercise those powers, is not an academic exercise for the NEPSE investor. It is foundational intelligence. Regulatory decisions in Nepal have repeatedly moved the market by 10–20% within single sessions. Regulatory failures have created conditions for significant investor losses. And regulatory reform — when it comes — has sometimes created the most attractive investment opportunities in the market's history.
This chapter maps the regulatory landscape completely: SEBON, NRB, CDSC, NEPSE itself as exchange operator, merchant bankers, disclosure requirements, investor protection mechanisms, the key legislative instruments, and finally — with deliberate analytical depth — the political economy that shapes how these institutions actually behave, as opposed to how their mandates say they should.
Lesson 8.1 — SEBON: Mandate, Powers, Structural Limitations, and Political Economy
What SEBON Is
The Securities Board of Nepal — Dhitopatra Board Nepal, in Nepali — is the apex regulatory authority for Nepal's capital market, established under the Securities Act 2063 (2006). It is an autonomous body under the Government of Nepal, headquartered in Kathmandu, with a mandate to regulate and develop the securities market, protect investor interests, and maintain market integrity. Its functional remit encompasses the registration and regulation of all market participants: NEPSE as the exchange, CDSC as the depository, stockbrokers, merchant bankers, mutual funds, portfolio managers, credit rating agencies, and listed companies.
SEBON's governance structure comprises a Board chaired by the Chairman (appointed by the Government of Nepal on the recommendation of a committee) and including members representing the Ministry of Finance, Nepal Rastra Bank, and independent nominees. The Chairman serves a five-year term, though in practice, tenure has been variable and politically influenced. The independence of SEBON's leadership from government intervention is nominally guaranteed by its enabling legislation but practically constrained by the appointment process, which runs through the Ministry of Finance.
SEBON's Formal Powers
SEBON's statutory powers are extensive on paper. It can register and deregister market participants, approve and reject IPOs and rights issues, investigate market manipulation and insider trading, impose fines and suspend trading in specific securities, issue directives to listed companies, and refer criminal cases to the Attorney General's office for prosecution. It has the authority to inspect the books and operations of any registered entity, compel the production of documents and records, and — in theory — freeze assets in cases of suspected fraud.
The regulatory toolkit, in the formal sense, is comparable to mid-tier emerging market securities regulators. The gap between formal powers and their practical exercise is where the real analysis begins.
SEBON's Structural Limitations
Staffing and technical capacity. SEBON operates with a staff complement that is small relative to the complexity of the market it oversees. Specialised expertise in quantitative finance, forensic accounting, cybersecurity, derivatives regulation, and complex securities valuation is limited. Many of SEBON's enforcement actions have been reactive — responding to market complaints or media coverage — rather than proactive. The investigation of insider trading, which requires sophisticated pattern analysis of trading data, communication records, and corporate disclosures, demands analytical capacity that SEBON has only partially developed.
Enforcement track record. SEBON's enforcement actions against insider trading and market manipulation have been, by the standards of peer regulators in the region, modest in number and consequence. Cases have been initiated, fines imposed, and suspensions enacted — but the fines have often been small relative to the gains from the conduct they penalise, and criminal prosecutions have been rare. This creates a weak deterrence dynamic: rational actors who perceive the expected cost of regulatory violation as low relative to its expected benefit will continue the behaviour.
Legislative dependency. SEBON's regulatory authority derives from the Securities Act 2063, which is parliamentary legislation. Changes in market structure — the introduction of derivatives, the regulation of algorithmic trading, the framework for short selling — require either amendments to the Act or the exercise of SEBON's directive authority. The legislative process in Nepal is slow, politically contentious, and subject to delays that can run into years. SEBON has sometimes issued directives to fill regulatory gaps, but the legal robustness of directive-based regulation is weaker than statute-based authority, and court challenges are possible.
The ownership conflict revisited. As established in Chapter 1, SEBON holds approximately 15% of NEPSE's equity. This creates an institutional conflict that pervades its regulatory decisions. Aggressive enforcement actions against NEPSE's operational deficiencies, trading system vulnerabilities, or broker misconduct would reflect directly on the reputation and commercial value of an institution in which SEBON has a financial stake. This conflict is not hypothetical; it creates a structural bias toward accommodation that is difficult to overcome even by officials of good faith.
The Political Economy of SEBON
SEBON's Chairman is appointed by the Government of Nepal, and the appointment carries political weight that is disproportionate to the apparent technical nature of the role. The capital market touches the financial interests of hundreds of thousands of Nepali retail investors, of broker networks with significant political connections, of financial institutions whose board members often have political affiliations, and of the state itself as a major shareholder in listed entities.
Political cycles in Nepal — which are frequent, given the instability of coalition governments — create corresponding cycles in regulatory posture. A newly appointed SEBON Chairman seeking to establish credibility may pursue enforcement actions that a predecessor avoided. A Chairman nearing the end of tenure or facing political transition may prioritise stability over enforcement. Regulatory decisions on IPO approvals — particularly for companies with politically connected promoters — have at various times been subject to controversy, though formal documentation of improper influence is understandably difficult to establish.
For the investor, the practical implication is this: SEBON's behaviour is not fully predictable from its formal mandate. It must be analysed as an institution embedded in a political system, with its own bureaucratic interests, its governance conflicts, and its dependence on an appointment process that is not purely meritocratic. This does not mean SEBON is corrupt or incompetent — it has, in fact, made significant regulatory advances in recent years, including improvements in disclosure requirements, the introduction of automated surveillance systems, and the tightening of broker capital requirements. It means that SEBON's decisions must be interpreted in their political and institutional context, not taken at face value as purely technical regulatory outputs.
SEBON at a Glance
Key Facts for the InvestorEstablished under Securities Act 2063 (2006 AD).
Surveillance system: SEBON has implemented market monitoring tools but forensic capacity remains limited.
Lesson 8.2 — NRB: How a Central Bank Becomes the Single Most Powerful Force in NEPSE
NRB's Formal Capital Market Role
Nepal Rastra Bank is Nepal's central bank, established under the Nepal Rastra Bank Act 2058 (2002). Its primary mandate is macroeconomic: maintaining price stability, managing foreign exchange reserves, regulating and supervising the banking system, and developing payment and settlement infrastructure. Its formal role in the capital market — as distinct from the banking sector — is secondary. NRB does not regulate NEPSE, SEBON, stockbrokers, or listed non-bank companies. That is SEBON's domain.
And yet, in practice, NRB is the single most powerful force in NEPSE's performance, valuation, and volatility. Understanding why requires understanding the transmission mechanisms through which NRB's banking sector decisions flow into equity market outcomes.
Transmission Mechanism 1: Credit and Liquidity
Commercial banks are the dominant intermediaries of money in Nepal's economy. When NRB eases credit conditions — reducing the policy rate, lowering the Cash Reserve Ratio (CRR), expanding the Standing Liquidity Facility (SLF) availability — banks gain access to cheaper and more abundant funding. With surplus loanable funds and compressed net interest margins on traditional lending, banks and their customers increasingly deploy capital into the equity market. This inflow of credit-backed capital creates direct buying pressure on listed securities.
Conversely, when NRB tightens — raising the policy rate, increasing the CRR, capping the Credit-to-Deposit (CD) ratio at, say, 90% — banks are forced to call in loans, restrict new credit, and manage their balance sheets toward compliance. Margin borrowers — investors who have borrowed against their share portfolios — face simultaneous pressure: the cost of their margin debt rises, and the value of their collateral (share prices) may be falling as others sell. The resulting forced deleveraging creates cascading price declines that have no relationship to any change in the underlying businesses' earnings power.
Transmission Mechanism 2: The CD Ratio as a Market Switch
Few regulatory instruments have moved NEPSE more directly than the Credit-to-Deposit (CD) ratio mandate. NRB's requirement that commercial banks maintain their CD ratio below a specified threshold — typically 90%, though this has varied — is intended as a prudential measure to prevent excessive leverage in the banking system. Its effect on NEPSE is an unintended (or at least secondary) consequence that has become predictable enough to be traded.
When the banking system's aggregate CD ratio approaches the regulatory ceiling, banks simultaneously tighten lending across all categories, including share-collateralised loans and margin credit extended through broker accounts. This creates a synchronised credit withdrawal that hits the most leveraged market participants — retail investors with margin positions — first and hardest. The forced selling pressure can trigger a market decline that begins in the most illiquid, speculative segments of NEPSE (microfinance, small hydropower) and spreads to blue-chip banking stocks as retail investors sell whatever they can to meet margin calls.
Tracking the banking system's aggregate CD ratio — published monthly by NRB in its banking supervision data — is therefore a leading indicator of potential NEPSE stress. When CD ratios across the commercial banking sector are uniformly high (above 85–87%), the market is vulnerable to a triggered correction. When they are low (below 80%), the market has headroom for credit-supported expansion.
Transmission Mechanism 3: Interest Rate Effect on Relative Valuation
The classical financial relationship between interest rates and equity valuations — higher rates reduce the present value of future earnings, making equities less attractive relative to fixed income — operates in NEPSE, but with a Nepal-specific twist. When NRB raises its policy rate, commercial bank fixed deposit rates rise in tandem. In a market where a significant fraction of retail investors view bank fixed deposits as the primary alternative to equity investment, a meaningful increase in fixed deposit rates — from, say, 7% to 10–12% — directly competes with expected equity returns.
This relative valuation effect is amplified by Nepal's retail-dominant investor base. Institutional investors in mature markets, with long-duration mandates and sophisticated asset-liability frameworks, are less acutely sensitive to short-term shifts in fixed deposit rates. Retail investors in Nepal, many of whom have explicit return targets (covering loan EMIs, household expenses, or business capital needs), are highly sensitive. When bank fixed deposits offer 10% with near-zero principal risk, the risk premium required to justify equity ownership rises, and stocks that were 'acceptable' at 8x earnings become 'overvalued' at those same multiples.
NRB regulates commercial banks' share-collateralised lending directly, specifying maximum loan-to-value ratios (currently 65–70% of the previous year's average market price, with further restrictions on single-borrower concentration) and imposing overall caps on the share of a bank's total loan book that can be collateralised by securities. These regulations are not merely prudential in intent; they are the primary determinant of how much leverage the market can sustain.
When NRB tightens margin lending rules — reducing the LTV ratio, restricting which securities qualify as acceptable collateral, or limiting total exposure — it reduces the maximum leverage available to retail investors. This is mathematically equivalent to reducing the money supply available to the equity market. When NRB loosens these rules — as it did implicitly during the COVID-19 period by prioritising financial system liquidity over leverage restrictions — it increases available leverage and amplifies price movements in both directions.
NRB Tool
Tightening Effect on NEPSE
Easing Effect on NEPSE
Policy Rate ↑/↓
Raises cost of margin debt; FDs more competitive
Lowers cost of margin debt; equities relatively attractive
CD Ratio Cap ↓/↑
Forces credit withdrawal; margin calls triggered
More lending headroom; credit flows into market
CRR ↑/↓
Banks hold more reserves; less to lend
Banks hold less reserves; more loanable funds
LTV on Share Loans ↓/↑
Less leverage per share value; forced deleveraging
More leverage; amplified buying capacity
Interest Rate Corridor
Sets floor and ceiling for interbank rates
Determines effective cost of bank funding
Open Market Operations
Absorbs liquidity from banking system
Injects liquidity into banking system
NRB's Institutional Position: Shareholder and Policymaker
As established in Chapter 1, NRB holds approximately 34% of NEPSE's equity. This creates an institutional duality — NRB as shareholder benefits when NEPSE's commercial performance is strong, which occurs when trading volumes are high, which tends to correlate with bull market conditions. Yet NRB as central bank has a mandate to prevent excessive financial system leverage, which requires it to tighten credit conditions that deflate bull markets.
In practice, NRB's institutional culture prioritises its central banking mandate over its shareholder interests — monetary policy decisions in Nepal have not, based on available evidence, been distorted to support NEPSE valuations. But the duality creates an opacity in NRB's communications about capital markets. NRB's Monetary Policy statements occasionally comment on 'asset price speculation' in the stock market without always specifying the precise policy response, creating uncertainty that the market interprets in various ways. Investors who develop skill in reading NRB's Monetary Policy documents — particularly the language used around credit growth, the banking sector's CD ratio, and financial stability risks — gain a material analytical advantage.
NRB Mid-Term Monetary Policy Review (January): Adjustments to targets — watch for credit tightening signals.
Monthly Banking Statistics (nrb.org.np): CD ratio trends, credit growth by sector.
NRB Annual Report: Banking sector NPL ratios, provisioning coverage — signal for bank earnings risk.
NRB Press Releases: Occasional directives on margin lending, CD ratio adjustments — typically immediate market impact.
Quarterly Financial Stability Report: Systemic risk assessment — NRB's own view of capital market vulnerabilities.
Lesson 8.3 — CDSC: Clearing, Settlement, and Depository Functions
What CDSC Does and Why It Matters
CDS and Clearing Limited — commonly abbreviated CDSC — is the central securities depository and clearing house for Nepal's capital market. Established in 2010 under the Securities Act 2063 and the Companies Act 2063, CDSC performs three distinct but interrelated functions that are the operational backbone of every share transaction in NEPSE: depository services (the holding of securities in electronic form), clearing (the calculation of net obligations arising from each trading session), and settlement (the actual transfer of securities and cash between buyer and seller).
Before CDSC and the dematerialisation of securities, Nepal's capital market operated on physical share certificates. A buyer who purchased shares would receive a paper certificate that had to be physically transferred, registered with the company's share registrar, and stored safely. This process was slow (taking weeks), expensive, prone to fraud (forged certificates were not unknown), and practically impossible to scale into a market with thousands of daily transactions. CDSC's establishment and the subsequent mandatory dematerialisation of all listed securities was one of the most consequential infrastructure improvements in NEPSE's history.
The Depository Function: DEMAT Accounts
Every investor in NEPSE holds their securities in a dematerialised (DEMAT) account maintained within CDSC's central depository. The DEMAT account is a digital record that shows the investor's holdings across all listed securities — how many shares of which companies are held, their average acquisition price (where tracked), and any pledges or liens against the holding. Opening a DEMAT account, done through a registered Depository Participant (DP) — typically a licensed stockbroker or bank — is the first practical step for any investor entering NEPSE.
CDSC's central ledger is the definitive record of ownership. When shares change hands on NEPSE, CDSC updates the ledger to reflect the transfer. A company's share registrar, when determining who is entitled to a dividend or the right to vote at an AGM, refers to CDSC's records as of the record date. The practical reliability of this system has been significantly better than the physical certificate era, though CDSC's IT infrastructure has faced challenges in managing the rapid growth of demat accounts — which surpassed 5 million by 2022 — and the accompanying transaction volumes.
The Clearing Function: Netting Obligations
At the end of each trading session, NEPSE generates a comprehensive trade file — a record of every transaction executed: buyer, seller, security, quantity, price. CDSC's clearing function processes this file to calculate net obligations. Rather than settling every individual trade bilaterally (which would require an enormous number of separate transfers), CDSC computes each participant's net position: if broker A's clients bought 10,000 shares of NABIL on a given day and sold 6,000 shares of the same company through CDSC, the net position is 4,000 shares to receive, not 10,000 to receive and 6,000 to deliver separately.
This netting dramatically reduces the volume of actual securities and cash movements required to settle a trading day, reducing systemic risk and operational cost. The clearing function also calculates the net cash obligations — each broker's net amount payable or receivable based on the aggregate value of their clients' purchases and sales — which are then settled through the banking system.
The Settlement Function: T+2 and Its Implications
Nepal currently operates on a T+2 settlement cycle: a trade executed on Day T is fully settled — securities delivered and cash transferred — on T+2 (two business days later). The buyer's DEMAT account is credited, and the seller's account is debited, on T+2. Cash flows between buyers' and sellers' brokers are also completed on T+2 through the designated settlement bank (currently Himalayan Bank Limited maintains a key role in this process).
The T+2 cycle is longer than the T+1 cycles now operating in India (NSE and BSE moved to T+1 in 2023) and the United States (which also moved to T+1 in 2024). This longer cycle has several practical implications for NEPSE investors. First, for two business days between trade execution and settlement, there is a period of counterparty risk — if either the buyer or seller fails to deliver cash or securities, the trade is unsettled. Second, the two-day cycle delays the availability of sale proceeds to reinvest, reducing capital efficiency. Third, in a rapidly moving market, a T+2 settlement means that a seller who transacts on a falling market may deliver securities at T+2 when prices have moved further — though in NEPSE's relatively stable intraday environment this is less acute than in more volatile markets.
SEBON and CDSC have discussed the migration to T+1 settlement, which would align Nepal with regional peers and improve capital efficiency. The barriers are primarily technological: the banking settlement infrastructure, the real-time gross settlement (RTGS) integration with CDSC, and the capacity of broker back offices to manage faster cycle times all require investment and system upgrades.
MeroShare: The Retail Interface
MeroShare is CDSC's web and mobile application that serves as the primary retail interface for NEPSE investors. Through MeroShare, investors can view their DEMAT holdings, apply for IPO and rights issue allotments, check allotment results, view dividend and bonus share credits, and access their transaction history. The launch and progressive improvement of MeroShare has been one of the most democratising developments in NEPSE's recent history — it moved IPO application from a cumbersome physical form process to a mobile-accessible digital one, dramatically reducing the friction of retail market participation.
MeroShare's IPO application functionality is particularly significant. Prior to its introduction, applying for an IPO required physically submitting an application form through a bank branch or broker, posting a cheque, and waiting weeks for results. MeroShare allows investors to apply through their phones, link their bank accounts for automatic debit on allotment, and receive results digitally. The number of IPO applications surged dramatically after MeroShare's adoption, and oversubscription ratios — already high — reached extraordinary levels (some issues saw 100–200x oversubscription) as the friction of participation fell to near zero.
CDSC Operational Framework
Key FactsCDS and Clearing Limited — established 2010.
Settlement cycle: T+2 (under review for acceleration to T+1).
CDSC owns its own IT infrastructure — system outages have periodically disrupted settlement.
Lesson 8.4 — NEPSE as Exchange Operator: Listing Rules, Trading Rules, and Delisting Powers
NEPSE's Operational Role
While SEBON sets the regulatory framework and CDSC handles clearing and settlement, NEPSE itself — as the exchange operator — performs three core operational functions: it maintains the listing framework (establishing which companies may trade on the exchange and under what conditions), operates the trading system (NATS, the Nepal Automated Trading System), and enforces trading rules including circuit breakers, trading suspensions, and price band restrictions.
NEPSE's governance is exercised through its Board of Directors, which includes representatives of the government, NRB, SEBON, and the licensed broker community. Day-to-day operations are managed by a General Manager (Chief Executive) appointed by the Board. NEPSE's decisions on listing approvals, trading suspensions, and circuit breaker activations are among the most operationally immediate market-moving actions in the NEPSE ecosystem.
Listing Requirements
To list on NEPSE's main board, a company must meet a set of criteria established jointly by SEBON's IPO framework and NEPSE's listing regulations. These include minimum paid-up capital requirements (which vary by sector — commercial banks face higher thresholds than, say, manufacturing companies), a minimum period of operation (typically three years of financial history), audited financial statements prepared under Nepal Financial Reporting Standards (NFRS), and a corporate governance structure that includes independent directors, an audit committee, and a risk management committee.
The IPO approval process runs through SEBON (which approves the prospectus and the issue), but listing itself is approved by NEPSE. This two-stage approval creates a sequential process where SEBON and NEPSE can, in principle, reach different conclusions — though in practice, coordination between the two institutions means that NEPSE listing refusals for SEBON-approved issues are rare.
A separate SME platform has been discussed for listing smaller companies that do not meet the main board criteria, which would potentially expand the investible universe beyond the current \~230 listed entities. As of the time of writing, this remains in developmental stages.
Trading Rules: NATS and Market Microstructure
NEPSE operates through the Nepal Automated Trading System (NATS), an electronic order matching platform that handles the submission, matching, and execution of buy and sell orders. Trading hours are Sunday to Thursday, 11:00 AM to 3:00 PM (Nepal's work week traditionally runs Sunday to Friday, with Saturday as the weekly holiday). This schedule reflects Nepal's cultural calendar but creates a scheduling mismatch with Indian markets (Monday to Friday), affecting the timing of any potential NEPSE-India capital flow responses to regional events.
NATS operates on a price-time priority algorithm: among all orders at the same price, the order that arrived earliest is executed first. Orders may be submitted as market orders (execute at the best available price), limit orders (execute only at a specified price or better), or a small number of other order types that NATS supports. The absence of more sophisticated order types — stop-loss orders, iceberg orders, algorithmic order streams — reflects the relatively basic current state of NEPSE's market microstructure.
Circuit Breakers and Price Bands
NEPSE employs a two-level circuit breaker system to manage extreme price movements. At the individual stock level, daily price movements are limited to a maximum of 15% above or below the previous day's closing price (this band may be modified for specific categories of securities). When a stock reaches this limit — either the upper circuit or lower circuit — trading in that stock continues at the limit price for the remainder of the session, but no transactions can occur at prices beyond the limit.
At the market level, NEPSE can suspend all trading if the overall index moves beyond specified thresholds within a single session — under the two-tier system in force since April 2026, a 5% index move within the first two hours triggers a 15-minute halt, and an 8% move suspends trading for the rest of the day — a systemic circuit breaker triggered by broad market panic or extraordinary events. These market-wide halts are rare but have been triggered during periods of acute political uncertainty or market crisis.
For the investor, circuit breakers create both protection and entrapment. Protection: in a panicking market, the daily price limit prevents a stock from falling to zero in a single session, giving rational buyers time to assess and intervene. Entrapment: in a declining market, a stock that hits the lower circuit limit every day for a week has effectively fallen more than 50% while appearing, on any individual day, to be only at its daily limit. Investors who hold a lower-circuit stock face an exit problem — they cannot sell below the circuit limit, and if buyers are absent at the circuit price, they may be trapped in the position for days until sentiment shifts.
Delisting Powers
NEPSE has the authority to delist a company from trading under specified conditions: failure to meet continuing listing obligations (filing of audited financials, conduct of AGMs, maintenance of minimum paid-up capital), regulatory violations, court orders, or voluntary delisting initiated by the company's promoters. In practice, NEPSE's delisting mechanism has been applied more often through suspension — temporarily halting trading in a company's shares — than through permanent delisting.
Several listed companies have been in prolonged suspension — some for years — creating a class of NEPSE-listed securities that are technically on the exchange but practically untradeable. For investors who hold shares in suspended companies, the experience is financially and legally complex: they cannot sell, they may not receive dividends, and the regulatory resolution process (whether through merger, court-supervised restructuring, or eventual delisting) can be slow and uncertain.
Lesson 8.5 — Merchant Bankers and Issue Managers: Their Role in IPOs and Rights Issues
The Issue Manager's Role
When a company wishes to raise capital through a public offering — whether an Initial Public Offering (IPO), a Further Public Offering (FPO), or a Rights Issue to existing shareholders — it cannot simply declare an intention to sell shares and wait for buyers. A regulated, documented process must be followed, and a licensed intermediary — the Issue Manager, also called a Merchant Banker — must manage that process on behalf of the issuing company.
In Nepal, Merchant Bankers are licensed by SEBON and perform a comprehensive set of functions for capital issues: they conduct due diligence on the issuing company's financial position, governance structure, and legal compliance; they prepare the prospectus (the detailed disclosure document required by SEBON); they coordinate with SEBON for regulatory approval; they manage the public subscription process through the banking system; they oversee the allotment of shares to successful applicants through CDSC's systems; and they advise the company on the pricing of the issue.
The Prospectus: The Investor's Critical Document
The prospectus is the most important document an investor can read before applying for any IPO or rights issue. It contains the company's audited financial statements for the preceding three years, the detailed purpose of the capital raise (how the money will be used), the company's business description, risk factors specific to its operations and industry, information on promoter backgrounds and any criminal or regulatory history, and the proposed use of proceeds.
In Nepal, prospectuses are available on the SEBON website and on the issuing company's website from the date of issue announcement. Despite this availability, surveys and anecdotal evidence consistently show that a large proportion of retail investors applying for IPOs in Nepal do not read the prospectus at all — they apply based on sector reputation, word of mouth, or the simple expectation that all IPOs will generate listing gains. This behaviour, while understandable given Nepal's historical IPO listing premiums, creates significant risk in cases where the issuing company's fundamentals are weak and the initial listing gain fails to materialise.
For the disciplined investor, the prospectus is not optional reading. Key sections to focus on include: the auditor's report (look for qualifications or emphasis-of-matter paragraphs, which signal areas of accounting uncertainty or concern), the MD\&A (Management Discussion and Analysis, which reveals how management interprets the business), the related party transactions section (which can reveal how promoters extract value from the company), and the risk factors section (which, if written with genuine candour rather than boilerplate, identifies the key vulnerabilities of the business model).
IPO Pricing in Nepal: The Regulatory Framework and Its Consequences
SEBON's regulations govern how IPO prices are set in Nepal. The framework has evolved over time. For some categories of issuer — particularly financial institutions — SEBON's guidelines specify a maximum issue price relative to net worth per share, limiting the premium that promoters can charge public investors. For other categories, a book-building process has been piloted, allowing institutional investors to bid in a price discovery range before the public offer price is fixed.
The historical consequence of regulated pricing in Nepal has been systematic underpricing of IPOs relative to their subsequent market price — creating the listing gain phenomenon that has driven retail IPO fever. When an IPO is priced at, say, NPR 100 per share and lists on NEPSE at NPR 180 on the first trading day, the 80% gain creates powerful incentives for retail investors to apply for every IPO regardless of fundamental merit. Over time, this dynamic has supported the flow of capital into the market but has also conditioned retail investors to treat IPOs as lottery tickets rather than as investments in specific businesses with specific risk-return profiles.
Not all IPOs list at premiums. Companies with weak fundamentals, unfavourable market timing, or excessive issue sizes relative to market depth have listed below their issue price, and investors who applied for the full allotment have lost capital. The discipline of reading the prospectus and applying a fundamental filter to IPO decisions — rather than applying for every issue — is one of the most undervalued skills in Nepal's retail investment community.
Rights Issues: The Often-Misunderstood Capital Event
A Rights Issue is a capital-raising event in which an existing listed company offers new shares to its current shareholders, in proportion to their existing holdings, at a price that is typically below the current market price. For example, a company might offer a 1:1 rights issue at NPR 500 per share to shareholders of record, when the market price is NPR 700. Each existing shareholder who holds 100 shares has the right — but not the obligation — to purchase an additional 100 shares at NPR 500.
Rights issues are frequently misunderstood by retail investors in Nepal. A common misconception is that receiving rights is equivalent to receiving a dividend or bonus — a free gift from the company. This is incorrect. A rights issue dilutes existing shareholders unless they exercise their rights. If you own 100 shares pre-rights and the company issues 100% rights (1 new share per existing share), and you do not exercise, you now hold 100 shares in a company that has twice as many shares outstanding. Your ownership percentage has halved. The market price per share will adjust (theoretically) downward to reflect the dilution and the lower rights price.
Rights issues create an analytical decision for investors: subscribe (pay the rights price to maintain your percentage ownership), sell the rights (if they are tradeable on NEPSE's rights entitlement market), or do nothing (and accept the dilution). The correct decision depends on the company's purpose for raising capital, the attractiveness of the rights price relative to fundamental value, and the investor's assessment of whether the capital raise will be value-accretive or merely dilutive.
Key Points
Common IPO and Rights Issue Mistakes in NEPSEApplying for every IPO without reading the prospectus — treating IPOs as guaranteed gains.
Confusing rights issue entitlement with a bonus share — they are fundamentally different events.
Ignoring the purpose of capital raised in a rights issue — a bank raising capital to cover NPLs is very different from one raising capital for growth.
Failing to account for the dilution effect when evaluating rights issue attractiveness.
Overweighting IPO allotment luck in portfolio planning — allotments are partially random in oversubscribed issues.
Not checking the Issue Manager's track record — some have brought low-quality issuers to market repeatedly.
Lesson 8.6 — SEBON's Disclosure Requirements: What Listed Companies Must Publish and When
The Information Architecture of a Listed Company
In any capital market, the quality of investment decisions is constrained by the quality of available information. A market where companies can conceal losses, delay material disclosures, or present misleading financial narratives is a market where retail investors are systematically disadvantaged relative to those with inside access. SEBON's disclosure requirements are designed to create a level informational playing field — ensuring that all investors, large and small, have access to material company information at the same time.
Nepal's disclosure framework, established under the Securities Registration and Issuance Regulation 2073 and SEBON's various directives, specifies four categories of mandatory disclosure for listed companies: periodic financial disclosures, event-driven material disclosures, governance disclosures, and annual report requirements.
Periodic Financial Disclosures
Listed companies in Nepal are required to publish quarterly financial statements within 30 days of the end of each quarter. For commercial banks, which follow Nepal Rastra Bank's prescribed accounting formats in addition to NFRS, quarterly results include the income statement, balance sheet, and key financial ratios — net interest margin, cost-to-income ratio, NPL ratio, capital adequacy ratio — that are the primary inputs for banking sector analysis. For non-bank companies, quarterly disclosures are less standardised and often less detailed.
Annual audited financial statements must be published within six months of the fiscal year end (the Nepali fiscal year runs July 16 to July 15). The audit must be conducted by a licensed auditor from the approved panel maintained by the Institute of Chartered Accountants of Nepal (ICAN). For listed companies with paid-up capital above a specified threshold, a Big Four or large audit firm may be required or recommended.
In practice, timeliness compliance varies significantly across listed companies. Large commercial banks — with professional finance departments, strong regulatory supervision from NRB in addition to SEBON, and sophisticated audit relationships — generally meet disclosure deadlines. Smaller listed companies — some manufacturing entities, smaller hydropower developers — have at times submitted quarterly results significantly after deadlines, without consequential enforcement action from SEBON.
Event-Driven Material Disclosures
SEBON's regulations require listed companies to disclose 'price-sensitive' or 'material' information to the market immediately upon its occurrence — not at the next periodic reporting date. Material information includes: Board resolutions regarding dividend proposals, rights issues, or bonus shares; significant changes in management or ownership (promoter share transfers above specified thresholds); material contracts (Power Purchase Agreements, large supply contracts); regulatory actions by NRB, Beema Samiti, or other authorities; court orders affecting the company; and any other development that a reasonable investor would consider material to their investment decision.
The practical standard of 'immediate' disclosure has been interpreted loosely in NEPSE's history. Cases exist where material information — a bank's NPL ratio deterioration, a hydropower project's construction delay, a promoter's share pledge below market value — has become known to certain market participants before formal disclosure, creating information asymmetry that benefits insiders at the expense of retail investors. SEBON's surveillance system attempts to detect trading patterns that suggest insider trading in advance of material disclosures, but enforcement has been limited, as discussed in Lesson 8.1.
Governance Disclosures
Listed companies must maintain and publish specific governance information: the composition of the Board (including identification of independent directors), the existence and composition of the Audit Committee, related party transactions (which must be disclosed in detail and approved by the Audit Committee), and the shareholding structure including promoter holdings. This governance disclosure framework is modelled on international best practices, but its effective implementation varies significantly.
Related party transaction disclosure is particularly important for NEPSE investors. In many Nepali companies — particularly smaller manufacturing and trading companies — the boundary between the listed entity's commercial operations and the promoters' other business interests is porous. Promoters may sell goods or services to their listed company at above-market prices, lease property to the company at inflated rates, or route contracts to related businesses in ways that transfer value from public shareholders to insiders. The mandatory disclosure of related party transactions provides the raw material for detecting this — but only if investors read the disclosures and apply analytical scrutiny.
Annual Reports: The Underutilised Research Tool
The Annual Report is the most comprehensive information document a listed company publishes, and it is the most consistently underutilised by retail investors in Nepal. Beyond the financial statements, a well-prepared annual report contains: the Chairman's letter (which can reveal strategic priorities and management candour), the Managing Director's report (operational detail on business performance), the corporate governance report (board meeting attendance, committee composition), the auditor's report (including emphasis-of-matter paragraphs), and notes to the financial statements (which contain the detail that the primary statements conceal — related party transactions, contingent liabilities, accounting policy choices, segment performance).
Nepal's NFRS standards, aligned with IFRS, require extensive note disclosures that, if read carefully, can reveal management's accounting choices and the degree of conservatism or aggression in financial reporting. A bank that applies aggressive loan classification standards (delaying the recognition of non-performing loans) will show a lower NPL ratio in its headline disclosure but may reveal the underlying stress in the notes to its impairment schedule. A hydropower company that capitalises significant costs that might more conservatively be expensed will show a larger asset base and better-looking returns — until the day it is forced to write down those capitalised costs.
Disclosure Type
Frequency
Deadline
Key Contents for Investor
Quarterly Financials
Quarterly
Within 30 days of quarter end
P\&L, Balance Sheet, Key Ratios
Annual Audited Financials
Annual
Within 6 months of FY end
Full NFRS financials + notes
Annual Report
Annual
Before AGM
Governance, MD\&A, Audit Report
Material Events
As they occur
Immediately (same day)
Dividends, mergers, regulatory actions
Promoter Shareholding
Quarterly
Within 15 days
Change in promoter stake, pledges
AGM Notice
Annual
21 days prior
Agenda, resolutions proposed
Dividend/Bonus Announcement
As decided
Board resolution date
Rate, record date, payment date
Lesson 8.7 — Investor Protection Fund and Grievance Redressal in Nepal
The Investor Protection Fund
The Investor Protection Fund (IPF) was established under the Securities Act 2063 to provide a limited safety net for investors in the event of broker default — the failure of a stockbroker to deliver shares or cash owed to clients. The fund is maintained by SEBON and is built from contributions by licensed stockbrokers (a percentage of their annual commission income) and penalties collected from regulatory violations.
The IPF is designed to compensate investors for losses arising specifically from broker insolvency or fraud — not from market losses on investment decisions. If a stockbroker becomes insolvent and cannot return client securities or cash held in their accounts, affected clients can file claims with SEBON for compensation from the IPF, up to a specified limit per investor.
The practical significance of the IPF for NEPSE investors is limited but real. The fund exists as a backstop against broker failure — a risk that is low in normal market conditions but non-trivial in cases of broker fraud or extreme market stress where margin loans held by the broker against client securities are insufficient to cover liabilities. Investors should be aware that the IPF does not protect against investment losses, market crashes, or poor advice — only against the specific case of broker insolvency or fraud.
SEBON's Grievance Redressal Mechanism
SEBON maintains a formal investor grievance mechanism through which investors can file complaints against brokers, listed companies, issue managers, and other regulated entities. Complaints can be filed online through the SEBON portal or in writing at SEBON's Kathmandu office. SEBON's Market Supervision Department is responsible for reviewing complaints, investigating their merits, and taking regulatory action where violations are established.
In practice, the grievance redressal process in Nepal's capital market is slow and outcomes are uncertain. Investigation timelines can run into months. The remedies available to SEBON — fines, suspension, license cancellation — may not provide the specific financial relief an individual investor is seeking for a specific loss. Civil litigation through the court system is a parallel option but faces its own delays in Nepal's judicial system.
For common investor grievances — a broker's failure to execute a properly placed order, a company's failure to credit bonus shares on time, delays in DEMAT account transfers — the practical recourse hierarchy is: first, direct communication with the broker or company; second, escalation to SEBON via the grievance portal; third, if financial stakes are significant, consultation with a legal professional about civil options.
The Nepal Securities Investors' Association
Beyond SEBON's formal mechanism, Nepal has a Securities Investors' Association — a civil society body representing retail investor interests. The Association has advocated for improvements in IPO allocation processes, higher disclosure standards, faster grievance resolution, and investor education initiatives. While it lacks regulatory authority, it has served as an advocacy voice that has on occasion influenced SEBON's policy positions.
Retail investor associations in developing markets generally operate with limited formal power but can be effective at creating public pressure for reform. Nepal's investor community has been increasingly active on social media and through investor associations in articulating grievances and demanding regulatory accountability — a development that, over time, may accelerate improvements in Nepal's market governance.
Lesson 8.8 — Key Acts Governing the Market: Securities Act 2063, Company Act 2063, BAFIA
The Legislative Framework: An Integrated System
Nepal's capital market does not operate under a single omnibus statute. It is governed by an interconnected set of legislative instruments, each addressing a distinct dimension of market activity: the Securities Act for market regulation, the Company Act for corporate governance and shareholder rights, and BAFIA for the banking sector that dominates NEPSE's listed universe. Understanding these acts — not their full technical detail, but their practical significance — gives the investor the context to interpret regulatory developments, understand investor rights, and assess the risk that a specific regulatory change poses to a portfolio holding.
Securities Act 2063 (2006 AD): The Foundation Statute
The Securities Act 2063 is the foundational legislation for Nepal's capital market. It establishes SEBON, defines the categories of securities subject to its jurisdiction (shares, debentures, bonds, units of mutual funds), specifies the registration and licensing requirements for market participants, sets out the framework for IPOs and secondary market regulation, and provides SEBON's enforcement powers.
Key provisions of practical relevance to investors include: the prohibition on insider trading (trading on material non-public information), the prohibition on market manipulation (including spreading false information to influence prices), the requirement for prospectus disclosure in public issues, the requirement for periodic financial reporting by listed companies, and the provisions establishing the Investor Protection Fund.
The Act has been amended several times since its 2063 BS enactment, reflecting the evolution of the market. However, significant gaps remain. The Act's treatment of derivatives, algorithmic trading, short selling, and electronic market platforms remains underdeveloped relative to the pace of market evolution. SEBON has bridged some of these gaps through directives and regulations — secondary legislation that carries less statutory weight but can be issued more quickly than parliamentary amendments.
Company Act 2063 (2006 AD): Shareholder Rights and Corporate Governance
The Company Act 2063 governs all companies incorporated in Nepal — listed and unlisted. For NEPSE investors, its most directly relevant provisions are those establishing shareholder rights, corporate governance requirements, and the rules around dividends, rights issues, bonus shares, and mergers.
Under the Company Act, shareholders have the right to attend and vote at Annual General Meetings (AGMs), to receive dividends declared by the Board, to receive a rights issue offer in proportion to their existing holdings, to receive their proportional share of residual value in a winding up, and to access the company's register of shareholders. These rights are legally robust in theory but practically depend on the company's governance culture and the willingness of regulators to enforce them.
The Company Act also establishes rules for mergers and acquisitions of listed companies — a topic of increasing relevance in NEPSE as NRB has actively encouraged consolidation in the banking and microfinance sectors. A merger between two listed companies requires approval from the company's AGM, regulatory consent (from NRB for banking institutions, from SEBON for the capital market dimensions), and a determination of the share swap ratio — the number of shares in the merged entity that each existing shareholder in the predecessor companies will receive. Share swap ratios are a frequent source of investor controversy, particularly when retail shareholders in the smaller or weaker entity feel the ratio undervalues their holding.
BAFIA 2073 (Banks and Financial Institutions Act 2017 AD): The Banking Sector's Constitutional Document
Because commercial banks, development banks, and finance companies constitute the majority of NEPSE's listed universe, the Banks and Financial Institutions Act 2073 (BAFIA) is effectively Nepal's most consequential piece of capital market-relevant legislation, even though it is not technically a securities law.
BAFIA establishes the licensing categories for banking and financial institutions (commercial banks, development banks, finance companies, microfinance institutions), specifies their minimum paid-up capital requirements, sets limits on loan concentration and connected lending, establishes the dividend payment conditions (banks may not declare dividends if their capital adequacy ratio is below the regulatory minimum), and provides NRB with its supervisory and enforcement authority over the sector.
For NEPSE investors in banking stocks, BAFIA's provisions on capital adequacy, dividend restrictions, and merger requirements are directly relevant to investment analysis. A bank's ability to pay dividends — a primary return mechanism for many NEPSE retail investors who hold bank shares for their cash return — is directly gated by BAFIA's capital adequacy conditions and NRB's approval. BAFIA also establishes the framework within which NRB's consolidation directives operate: the Act empowers NRB to require weaker institutions to merge with stronger ones, creating involuntary merger dynamics that can significantly affect NEPSE-listed bank shareholders.
Fund establishment, NAV calculation, disclosure, investment restrictions
Lesson 8.9 — The Political Economy of Regulation: How Incentives Shape NRB, SEBON, and NEA Decisions
Why Political Economy Matters for the NEPSE Investor
The formal analysis of regulatory frameworks — what the law says, what the institution's mandate is, what powers it holds — provides the skeleton of understanding. But the living reality of regulation in any country is shaped by something the law does not capture: the incentives, pressures, constraints, and interests that determine how institutions actually exercise their formal powers. Political economy is the study of this living reality — of how political systems and economic interests interact to shape policy outcomes.
For the NEPSE investor, political economy analysis is not an abstract intellectual exercise. It is applied intelligence. The investor who understands why SEBON historically under-enforces insider trading regulations, why NRB's monetary policy statements on the stock market are calibrated carefully for political effect, why NEA's Power Purchase Agreement negotiations move slowly despite the financial urgency of hydropower developers, and why broker consolidation has been proposed but not implemented despite clear evidence that it would improve market quality — that investor is better positioned to anticipate regulatory developments, understand their market impact, and make decisions ahead of the crowd.
The Political Economy of SEBON
SEBON operates in a political environment where the capital market touches the financial interests of a broad and vocal constituency: hundreds of thousands of retail investors who vote, broker networks with political connections who fund campaigns, bank promoters with long-standing government relationships, and the government itself as a shareholder in listed entities.
This creates a set of structural pressures that systematically bias SEBON's behaviour. First, the pressure toward IPO approval generosity. Every IPO that SEBON approves creates a new cohort of investors with skin in the game and political support for the market's continuation. Every IPO that SEBON rejects on quality grounds creates opponents — the rejected company's promoters, their bankers, their political connections — without creating a proportional constituency of supporters (the public investors who would have been harmed by a poor IPO rarely organise to thank SEBON for protecting them). The asymmetry of political reward creates a bias toward approval.
Second, the pressure against aggressive enforcement. SEBON's enforcement actions — particularly against prominent broker networks or politically connected listed company promoters — create immediate, organised opposition. The beneficiaries of enforcement (retail investors protected from insider trading or market manipulation) are diffuse and largely unorganised. The targets of enforcement are concentrated and motivated. This asymmetry is a universal feature of regulatory politics, and Nepal's SEBON is not uniquely susceptible to it — but it is more acutely exposed than regulators in systems with stronger judicial independence and civil society oversight.
Third, the revolving door dynamic. In markets globally, senior regulatory officials sometimes move to the private sector entities they previously regulated — broker firms, merchant banking houses, investment management companies. This prospect — whether consciously acknowledged or not — can influence regulatory decisions in ways that favour industry at the expense of investor protection. Nepal's financial sector is small enough that personal relationships between regulators and industry participants are dense and long-standing, amplifying this dynamic beyond what prevails in larger, more anonymous markets.
The Political Economy of NRB
NRB's position in Nepal's political economy is more complex than SEBON's, because NRB's mandate is broader (macroeconomic stability, not just capital market health) and its institutional capacity is stronger. NRB has historically maintained a degree of operational independence from political interference that exceeds what SEBON has achieved — but it is not immune.
The key political economy tension for NRB, from the NEPSE investor's perspective, is the conflict between its macroprudential mandate (preventing systemic financial risk, including stock market speculation funded by bank credit) and the political costs of tightening. When NRB raises rates or tightens credit, it damages the returns of hundreds of thousands of retail investors and makes bank loan EMIs more expensive for borrowers. Both groups express their dissatisfaction through the political system — through parliamentary debates, media pressure, and direct political lobbying.
The consequence is that NRB's tightening cycles have often been delayed relative to when macroprudential indicators would suggest action was warranted. The 2021 bull market — where by mid-2021 the NEPSE index was at 3,000+ and margin lending had expanded dramatically — showed signs of excess that a proactive regulator might have addressed earlier. NRB's eventual tightening, when it came, was more abrupt and more severe than a more gradual earlier intervention would have required. The pattern — delayed action followed by sharp correction — is characteristic of regulators operating in environments where the political cost of preemptive action exceeds the political cost of reacting to a crisis.
The Political Economy of NEA: Hydropower's Hidden Regulator
Nepal Electricity Authority (NEA) is not a capital market regulator. But for investors in hydropower — the second-largest sector in NEPSE — NEA is arguably the most consequential institution after NRB. NEA's decisions on Power Purchase Agreement (PPA) rates, grid connection timelines, and export power tariffs directly determine the revenue of every listed hydropower company.
NEA is a state-owned monopoly buyer of electricity from private hydropower producers. Its PPA negotiations are, in effect, a bilateral negotiation between a government entity and private developers — but in a context where the government entity has significant leverage (it is the only buyer for Nepal's domestic electricity), limited commercial incentive (as a state entity, NEA is not profit-maximising), and complex political mandates (keeping consumer electricity prices low, managing the fiscal implications of subsidised tariffs, balancing the interests of private developers against rural electrification goals).
The consequence of NEA's institutional character is that PPA negotiations are slow, PPA rates have not always kept pace with developers' financing costs, and the timeline from project completion to revenue recognition has frequently exceeded projections. For NEPSE investors in hydropower stocks, NEA's behaviour is the primary non-financial risk — the risk that even a well-constructed, well-financed hydropower project will face revenue uncertainty because NEA delays grid connection, disputes metering, or renegotiates PPA terms.
NEA's political economy is shaped by the fact that cheap electricity is a populist political goal — no politician wins votes by raising electricity tariffs — while adequate PPA rates for private developers are a technical financial necessity that lacks popular constituency. This asymmetry means that NEA consistently faces political pressure to cap PPA rates and subsidise domestic consumers, even when the financial arithmetic requires higher developer compensation to attract private capital into the sector.
The resolution of this tension is one of the most important unresolved questions for Nepal's hydropower investment thesis. If Nepal can negotiate power export agreements with India and Bangladesh that generate hard currency revenue at international rates, the financial pressure on NEA's domestic PPA structure may ease — because NEA's overall revenue position improves, creating room for better domestic developer compensation. The trajectory of these export agreements is therefore a key variable for any investor with significant hydropower exposure.
Implications: How to Use Political Economy Analysis
Political economy analysis translates into practical investment intelligence through several channels. First, regulatory announcements from SEBON or NRB should be evaluated not only for their stated rationale but for their political context. A SEBON directive that appears to impose a burden on a specific market segment may be more explicable as a response to political pressure from a competing interest group than as a purely technical regulatory decision.
Second, the timing of regulatory action carries information. Regulatory tightening that occurs immediately after a political transition — a new government, a new NRB Governor, a new SEBON Chairman — often reflects the political priorities of the incoming leadership and may signal a shift in regulatory posture that persists for the duration of the new leadership's tenure.
Third, the gaps in regulation — the areas where formal powers exist but enforcement is absent — are often more informative about political economy than the areas where regulation is active. A SEBON that has formal insider trading enforcement powers but consistently under-prosecutes tells an investor that insider trading information advantages persist and should be factored into how market pricing dynamics are interpreted.
Fourth, regulatory reform that reduces inefficiency — that aligns the formal mandate of an institution more closely with its actual incentive structure — creates investment opportunity. When SEBON tightens IPO standards after a period of loose approvals, the quality of the listed universe improves, which eventually supports better fundamental-based pricing. When NRB's consolidation policy reduces the number of fragile microfinance institutions and creates stronger surviving entities, it improves the investment quality of the remaining listed MFIs. The investor who anticipates these reform cycles — rather than reacting to them after they have already moved prices — captures the most attractive returns.
Key Points
Political Economy Checklist for NEPSE InvestorsHas there been a recent change in SEBON Chairman or NRB Governor? Expect potential regulatory posture shift.
What is the government's fiscal position? Fiscal stress increases pressure on NRB to maintain accommodative policy.
Are there upcoming elections? Pre-election periods often see political pressure against monetary tightening.
Is SEBON's enforcement activity increasing or declining? A declining trend suggests political accommodation pressure.
What is NEA's grid expansion timeline? Delays in evacuation infrastructure bottleneck hydropower revenue.
Are India-Nepal power export negotiations progressing? Resolution materially changes NEA's financial position.
Is NRB's language in its Monetary Policy becoming more concerned about 'asset price speculation'? Tightening often follows.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part II · Chapter 9
Market Participants in NEPSE
First published 21 Aug 2026 · Last verified 29 Aug 2026
A market is not an abstraction. It is a collection of human beings and institutions, each with distinct objectives, constraints, information sets, and behavioural tendencies, interacting through a common mechanism — the price auction — to buy and sell claims on productive enterprises. The character of a market, its volatility, its efficiency, its susceptibility to manipulation and euphoria, its capacity for rational price correction — all of these emerge from the specific mix of participants who trade within it.
NEPSE's participant structure is unusual by any global standard. It is one of the most retail-dominated equity markets in Asia. Its institutional investor base is thin, constrained by mandate and bureaucratic culture. Its mutual fund sector is small and limited in depth. Its broker network, though recently subject to consolidation pressure, remains fragmented by the standards of comparable markets. Foreign institutional participation is legally restricted and practically minimal. And it has no market makers whatsoever — a structural absence with profound consequences for liquidity and price formation.
This chapter maps NEPSE's participant landscape in full: who participates, how they are structured, what drives their behaviour, how they interact, and — most critically — what the specific composition of participants means for the investor trying to understand why NEPSE behaves the way it does and how to position a portfolio intelligently within it.
Lesson 9.1 — Retail Investors: The Dominant Force and the Structural Consequences
The Scale of Retail Dominance
In most developed equity markets, retail investors — individual citizens trading their personal savings — constitute a minority of daily trading volume, typically 15–25%. The majority is accounted for by institutional participants: mutual funds, pension funds, hedge funds, insurance companies, proprietary trading desks, and market makers. These institutional participants bring larger capital pools, more analytical resources, longer investment horizons (in some cases), and more disciplined risk management frameworks.
NEPSE inverts this structure completely. Retail investors account for an estimated 80–85% of daily trading volume. Institutional participants — Citizen Investment Trust, Employee Provident Fund, insurance companies, mutual funds — account for the remainder, and even their institutional character is limited, as we will examine in subsequent lessons. Nepal's stock market is, in the most literal sense, a people's market: its prices are set primarily by the aggregated decisions of hundreds of thousands of individual Nepali citizens, most of them trading through mobile phones, most of them making decisions based on a combination of social media sentiment, informal tip networks, and intuition rather than systematic fundamental analysis.
Who Are Nepal's Retail Investors?
The demographic composition of NEPSE's retail investor base has shifted dramatically over the past decade. The pre-MeroShare era (before approximately 2016) retail investor was predominantly urban, male, middle-aged, and connected to the market through broker relationships. The post-MeroShare era has seen the investor base expand to include a substantially younger cohort — university students and recent graduates in their twenties, members of the diaspora in Gulf countries and further abroad, small businesspeople and traders in district-level cities and towns, and housewives and female entrepreneurs who were largely absent from the market a decade earlier.
The common thread across this diverse demographic is limited formal financial education. Nepal's school and university curriculum has not historically included personal finance, investment principles, or capital market mechanics as standard subjects. Most retail investors in NEPSE have learned about the market through family members, colleagues, social media content, and their own trial and error — a learning process that is expensive in the tuition paid through early investment losses and that systematically reinforces certain cognitive biases and behavioural patterns.
The Behavioural Profile of the NEPSE Retail Investor
Understanding the systematic behavioural patterns of NEPSE's retail base is one of the most practically useful analytical frameworks an investor can develop. These patterns are not unique to Nepal — they are well-documented in behavioural finance literature globally — but they manifest with particular intensity in a retail-dominated, illiquid market with rapid digital information spread.
Herding. NEPSE retail investors exhibit strong herding behaviour — the tendency to follow the crowd rather than form independent judgments. When a stock begins rising, the movement attracts attention through social media, broker networks, and word of mouth. Each incremental price rise attracts new buyers who fear missing out, which produces further price rises, attracting yet more buyers. This momentum cascade can drive stocks far above any reasonable fundamental value. The reversal, when it comes, is equally cascade-driven: early sellers trigger price declines, which trigger stop-losses and margin calls, which trigger forced selling, which produces further declines. In a market dominated by institutional investors with contrarian mandates and long horizons, these cascades are dampened. In NEPSE, they are amplified.
Recency bias. Retail investors systematically overweight recent experience in forming expectations about future outcomes. Investors who entered NEPSE during the 2020–21 bull market, and who experienced rapid gains in their first year of participation, formed expectations of market returns based on that exceptional period. When the 2022 correction began, these investors initially interpreted it as a temporary dip — a buying opportunity — rather than a structural reversal, because their mental model of 'normal' was calibrated to the extraordinary. Conversely, investors who experienced the 2018–20 correction first, before the pandemic boom, may have remained underexposed to equity for too long as prices recovered, anchored to their loss experience.
Dividend and bonus fixation. A distinctive feature of NEPSE retail psychology is the extraordinary focus on dividend and bonus share announcements relative to fundamental earnings quality. The announcement of a bonus share — a capitalisation of retained earnings through new share issuance — is invariably greeted with buying pressure, even though a bonus share (like a stock split) does not increase the company's intrinsic value per rupee of outstanding equity. Investors who buy a stock at NPR 1,000 before a 50% bonus issue, expecting to benefit from the bonus, typically ignore that the post-bonus price adjusts proportionally downward. This fixation creates predictable trading patterns around announcement dates that can be observed and potentially exploited by more analytically grounded investors.
Margin overuse. NEPSE retail investors have a historical propensity to use margin lending — borrowing against their share portfolios to purchase additional shares — beyond levels consistent with their actual risk tolerance and financial capacity. Margin lending amplifies both gains and losses. In rising markets, investors who borrowed to buy more experienced spectacular returns that reinforced the behaviour. In falling markets, the same leverage produced margin calls that forced selling at depressed prices, locking in losses that took years to recover. The 2021–22 cycle produced a generation of Nepali investors who have experienced margin calls first-hand — a painful but potentially durable lesson in leverage risk.
IPO lottery mentality. As discussed in Chapter 2, the consistent IPO listing premium in Nepal's market history has conditioned retail investors to treat every IPO application as a lottery ticket with a positive expected value. This mentality drives extraordinary IPO oversubscription (100–200x is not uncommon for popular issues) and crowds out fundamental analysis of the issuing company's actual quality. When IPOs list below their issue price — as happens periodically, particularly in weak market conditions — investors who applied on auto-pilot absorb losses that a prospectus-reading approach might have avoided.
The Structural Consequences of Retail Dominance
The behavioural patterns of retail investors, operating in aggregate across hundreds of thousands of accounts, produce structural market characteristics that every NEPSE participant must understand and incorporate into their investment approach.
First, sentiment-driven volatility. NEPSE's price movements are disproportionately large relative to fundamental developments. A change in NRB's policy stance — an event that in a deeper, institutionally-dominated market would produce a measured re-pricing over days as algorithms and analysts process the implications — can produce a 5–8% single-day move in NEPSE as retail investors react in a coordinated, socially-mediated panic or euphoria. The speed of social media information propagation means that sentiment shifts are immediate and simultaneous across the retail base.
Second, calendar-driven patterns. NEPSE retail behaviour exhibits predictable calendar patterns driven by dividend season (the period around June–July when companies announce year-end dividends and the fiscal year closes), AGM season, and IPO allotment periods. Understanding these seasonal patterns — when retail investors are likely to be buyers (ahead of dividend announcements, at the start of a new fiscal year) and when they are likely to be sellers (after dividend records dates, when book-closure ends) — is a form of practical market knowledge that complements fundamental analysis.
Third, the thin-market premium. Because retail investors in illiquid NEPSE stocks trade in small quantities, a relatively small coordinated buying campaign can push prices significantly above fundamental value — and a relatively small coordinated selling campaign can push them significantly below. This creates opportunities for well-capitalised, patient investors who understand fundamental value and can wait for the retail herd to push prices to attractive entry points in the correction phase.
Retail Investor Behaviour
Key Patterns for the Analytical InvestorHerding: Momentum is amplified in both directions — use fundamental anchors to avoid crowd capture.
Bonus fixation: Price typically adjusts post-bonus — buy the business, not the bonus announcement.
Margin overuse: High aggregate margin debt is a leading indicator of correction vulnerability — track NRB margin data.
IPO lottery mentality: Read the prospectus; not all IPOs list at premiums, and quality filtering adds edge.
Social media sentiment as a contrarian indicator: Peak social media bullishness historically precedes corrections; peak pessimism precedes recoveries.
Lesson 9.2 — Institutional Investors: CIT, EPF, Insurance Companies — Who They Are and How They Move the Market
The Institutional Investor Landscape
Despite being vastly outnumbered by retail participants in transaction count, institutional investors in NEPSE matter disproportionately on days when they choose to act. Their larger average transaction sizes, longer-horizon mandates (in theory), and — in some cases — more analytically disciplined investment processes mean that institutional buying or selling can move prices decisively and signal valuation inflection points to the broader market. Understanding who Nepal's institutional investors are, what mandates they operate under, and how their constraints shape their market behaviour is therefore critical intelligence.
Citizen Investment Trust (CIT)
Citizen Investment Trust is Nepal's oldest and most prominent institutional investor, established in 1991 under the Citizen Investment Trust Act 2047. CIT's primary business is the management of retirement savings schemes — most notably its Unit Scheme, which functions as an open-ended mutual fund accessible to Nepali citizens, and its Retirement Savings Scheme, which accumulates contributions from employed Nepalis (particularly civil servants and semi-government employees) toward retirement.
CIT's investment mandate is broad — it can invest in equities, government bonds, debentures, and real estate — but its risk profile is constrained by the fact that it manages retirement savings for hundreds of thousands of Nepali citizens who depend on these funds for post-employment income. This creates an institutional conservatism: CIT allocates the majority of its portfolio to lower-risk instruments (government bonds, fixed deposits, treasury bills) and maintains an equity allocation that is significant in absolute terms but represents a minority of its total assets under management.
CIT's equity investments in NEPSE tend to be concentrated in the large-cap banking sector — the same concentration bias that characterises the headline NEPSE index. CIT rarely holds meaningful positions in smaller hydropower developers, microfinance companies, or manufacturing stocks, because the liquidity of those stocks is insufficient to allow CIT to build or exit positions of any significant size without materially moving the price.
CIT's market impact is primarily felt on two dimensions: as a buyer in large-cap banking stocks when valuations reach levels that its internal processes flag as attractive (providing a valuation floor of sorts), and as a seller during market peaks when its portfolio rebalancing or redemption needs require equity liquidation. CIT's portfolio decisions are not disclosed in real time — it is not required to disclose its trading activity the way a listed company must disclose material events — so market observers infer its activity from price action in specific stocks and from CIT's periodic portfolio disclosures.
Employee Provident Fund (EPF)
The Employee Provident Fund is Nepal's largest pension savings institution, managing the accumulated retirement contributions of Nepal's formal sector employees. EPF's investible assets have grown substantially over the past decade as formal sector employment has expanded and contribution rates have been maintained. As of recent estimates, EPF manages assets of several hundred billion rupees — making it, in absolute terms, a larger pool of capital than CIT.
EPF's investment mandate is even more conservative than CIT's. As a pension fund managing the retirement security of working Nepalis, EPF is structurally averse to equity risk and has historically allocated the overwhelming majority of its portfolio to government bonds and bank fixed deposits. Its equity allocation to NEPSE, while real, has been a small fraction of its total assets.
The significance of EPF for NEPSE investors lies not in its current equity activity but in its potential. If EPF were to shift its equity allocation from, say, 5% to 15% of assets — a move comparable to what pension funds in peer emerging markets have done — the capital inflow into NEPSE would be transformative. Such a shift has been discussed at the policy level but faces regulatory barriers (EPF's enabling legislation restricts certain categories of investment), governance barriers (EPF's board must approve material changes to investment policy), and political barriers (the prospect of pension fund money being 'at risk' in the equity market is politically sensitive). Investors who follow this policy discussion — and who position themselves ahead of any regulatory change enabling greater EPF equity participation — stand to capture significant upside from the institutional demand surge that would result.
Insurance Companies: Life and Non-Life
Nepal's insurance sector — regulated by Beema Samiti (Insurance Board) — comprises approximately nineteen life insurance companies and twenty non-life insurance companies as of 2024, most of them listed on NEPSE. Collectively, insurance companies represent a significant pool of investible assets — life insurance companies, in particular, accumulate substantial reserves from long-duration policy premiums.
Beema Samiti's investment regulations require insurance companies to maintain specified proportions of their investible assets in approved categories: a minimum in government securities, a portion in infrastructure bonds, and a permitted allocation to equity. The equity allocation is capped by regulation, which limits the maximum position any single insurance company can take in a listed security — preventing concentrated risk-taking and ensuring diversification.
Insurance companies are among the more analytically disciplined participants in NEPSE, because Beema Samiti requires them to justify their equity investments through documented investment policies and board-approved frameworks. However, their investment teams are small (many insurance companies have only one or two investment professionals), and their analytical capabilities — while improving — remain limited compared to global standards.
The behaviour of insurance company equity portfolios is particularly significant around year-end (Ashadh — the end of Nepal's fiscal year in mid-July): insurance companies often rebalance their portfolios in line with regulatory requirements at fiscal year end, creating identifiable seasonal buying or selling patterns in specific securities.
Institution
Type
Est. AUM
Primary Equity Focus
Key Constraint
Citizen Investment Trust
Savings/Pension
NPR 200–300 Bn+
Large-cap banking
Conservative mandate, redemption pressure
Employee Provident Fund
Pension
NPR 400–600 Bn+
Minimal — gov't bonds preferred
Regulatory equity ceiling, political risk aversion
Life Insurance Companies
Insurance Reserve
NPR 200 Bn+ (sector)
Large-cap banking and hydro
Beema Samiti concentration limits
Non-Life Insurance Cos.
Insurance Reserve
NPR 50–80 Bn (sector)
Large-cap diversified
Shorter duration; lower equity allocation
Provident Funds (others)
Pension
Various
Mostly fixed income
Limited equity mandate
How Institutional Investors Move the Market
Despite their individually constrained mandates, Nepal's institutional investors can move NEPSE prices significantly when they act, precisely because of their relative size compared to average retail transaction volumes. A CIT decision to deploy NPR 500 million into a specific banking stock — a transaction that would be unremarkable in the context of India's or any developed market — can move a NEPSE-listed bank's price by 5–8% over a week of systematic accumulation.
The practical implication for retail investors: watching for unusual volume accumulation in large-cap stocks — significantly above average daily turnover, sustained over several sessions — can sometimes signal institutional accumulation or distribution. This is not a foolproof signal, but combined with fundamental analysis and valuation context, it can provide useful corroboration of a thesis.
Lesson 9.3 — Mutual Funds in Nepal: Structure, NAV, Available Schemes, and Their Limited Market Depth
The Mutual Fund Ecosystem
Mutual funds in Nepal are registered and regulated under the Securities Act 2063 and the Mutual Fund Regulation 2067. A mutual fund is a pooled investment vehicle — it collects capital from multiple investors, issues units (not shares) representing proportional ownership of the pool, and invests the pooled capital across a portfolio of securities. The fund's Net Asset Value (NAV) — calculated daily as (total assets minus total liabilities) divided by total units outstanding — represents the per-unit value of the fund, the price at which investors can subscribe or redeem.
Nepal's mutual fund sector has grown meaningfully since the first funds were launched in the early 2010s, but remains small relative to the overall market and to comparable sectors in peer countries. By 2024, Nepal had approximately 25–30 registered mutual fund schemes, collectively managing assets of NPR 100–150 billion — a fraction of NEPSE's total market capitalisation and a tiny proportion of total household financial savings in the country.
Types of Schemes
Closed-end schemes. Most mutual funds in Nepal operate as closed-end schemes — they raise a fixed amount of capital through a public issue (similar to an IPO), list the fund units on NEPSE, and trade on the exchange like any other security. Investors who want to enter or exit the fund after the initial offering do so by buying or selling units on NEPSE, not by subscribing or redeeming directly with the fund. This means that the market price of a closed-end fund unit on NEPSE can, and frequently does, diverge from the fund's underlying NAV.
Open-end schemes. A smaller number of Nepali mutual funds operate as open-end schemes — investors can subscribe (buy units at NAV) or redeem (sell units back to the fund at NAV) on any business day. This structure ensures that the unit price always equals NAV, eliminating the discount-to-NAV problem that plagues closed-end funds. Open-end schemes in Nepal include CIT's Unit Scheme (technically the oldest and largest), and a growing number of schemes launched by private fund managers.
The Discount-to-NAV Problem
One of the most persistent anomalies in Nepal's mutual fund market is the phenomenon of closed-end funds trading at significant discounts to their NAV on NEPSE. A fund with an NAV of NPR 15 per unit may trade on NEPSE at NPR 11 or NPR 12 — a discount of 25–30%. For a sophisticated investor, this raises an obvious question: why would you pay NPR 11 to acquire a claim on assets worth NPR 15?
The answer lies in the specific character of NEPSE's retail investor base. Retail investors, unfamiliar with NAV mechanics, often treat mutual fund units as ordinary stocks — valuing them based on momentum, recent price performance, and peer sentiment rather than on the relationship between market price and underlying asset value. This creates a structural mispricing that has persisted for years in the Nepali mutual fund market.
For the analytically literate investor, closed-end funds trading at deep discounts to NAV represent a structured opportunity: buying a diversified portfolio of NEPSE equities at a 20–30% discount to their current market value. The risk is that the discount does not close — that the fund continues to trade at a discount for the duration of the investment. But when the discount is wide enough, and when the underlying portfolio is of identifiable quality, the margin of safety is substantial.
Fund Managers and Their Limited Analytical Capacity
Nepal's mutual funds are managed by licensed Fund Management Companies (FMCs) — separate entities from the fund itself, which serve as investment managers and earn a management fee (typically 1–1.5% of NAV annually) for their services. The quality of fund management varies significantly across Nepal's FMC sector. A handful of the larger, more established fund managers have developed genuine research capabilities — analysts who track listed companies systematically, build financial models, and make investment decisions based on documented frameworks.
The majority, however, operate with small investment teams — sometimes a single fund manager and one or two junior analysts — whose analytical toolkit and time budget are insufficient for rigorous coverage of the full listed universe. In practice, this means that many Nepali mutual funds are implicitly index-hugging: they hold the large-cap banking stocks that dominate the NEPSE index in approximately market-weight proportions, avoiding the active stock selection risk that could differentiate their performance but also avoiding the genuine value creation that active management promises.
Mutual Funds as a Retail Investor Tool
For retail investors who lack the time, expertise, or inclination to analyse individual companies, mutual funds offer a legitimate alternative: professional management, diversification across multiple securities, and a regulated framework with daily NAV disclosure. In theory, investing in a well-managed NEPSE mutual fund is better than speculating in individual stocks based on social media tips.
In practice, the calculus is complicated by several factors: management fees erode returns over time; the quality of fund management is difficult for retail investors to assess; closed-end fund discounts create an additional layer of complexity; and the performance record of most Nepali mutual funds — which have predominantly been launched since 2010 and have therefore operated through one or two market cycles at most — is insufficient to distinguish genuine investment skill from market beta exposure. An investor evaluating a Nepali mutual fund should focus on the fund's stated investment philosophy, the consistency of its portfolio composition with that philosophy, the quality of its quarterly reports, and the NAV performance relative to the NEPSE benchmark — adjusted for the fund's sector concentration.
Key Points
Mutual Fund Evaluation Checklist for Nepali InvestorsIs it closed-end or open-end? — Closed-end: check discount/premium to NAV before buying.
Who is the Fund Management Company? — Research their track record across market cycles.
What is the management fee? — 1–1.5% annually compounds significantly over long periods.
What is the portfolio composition? — Does it reflect the stated investment philosophy?How has NAV performed vs. NEPSE Index? — Consistent underperformance vs. index questions active management value.
How liquid is the closed-end unit on NEPSE? — Thin trading volume creates entry/exit challenges.
Is the discount to NAV historically wide or narrow? — Wide discounts may represent opportunity; narrow premiums may warrant caution.
Lesson 9.4 — Licensed Brokers: The 50-Broker System, Commission Structure, and Limitations
The Broker's Role in NEPSE
Every transaction on NEPSE must be executed through a licensed stockbroker. An investor cannot place orders directly on the NEPSE trading system — they must instruct a licensed broker, who executes the order on their behalf through the NATS platform. This intermediation requirement is universal in organised exchanges globally; what is distinctive about NEPSE's broker structure is the limited number of licensed brokers, their geographic concentration, their fee structure, and the quality and service level they provide.
The 50-Broker System: Historical Context
Nepal has traditionally operated with approximately 50 licensed stockbrokers — a number that has been remarkably stable for years and that is extraordinarily small relative to the market's number of investors and transactions. For context: India's NSE has thousands of registered trading members; even Bangladesh's Dhaka Stock Exchange, a comparably sized frontier market, has a larger broker network. Nepal's 50-broker cap has its origins in the early years of NEPSE when trading volumes were tiny and the viability of a larger broker network was questionable. It was never substantially revised as volumes grew.
The consequence of this limited broker network is significant. The 50 licensed brokers are geographically concentrated in Kathmandu — very few have branches in major provincial cities like Pokhara, Biratnagar, Butwal, or Dharan. Investors outside the Kathmandu Valley historically faced barriers to market access: they had to use brokers through informal relationships, sub-broker networks, or by physically travelling to Kathmandu. The digital revolution — online trading through broker platforms and the TMS (Trading Management System) interface — has materially reduced this geographic barrier, but the broker network remains thin.
Broker Commission Structure
SEBON sets the maximum commission rates that brokers can charge investors. The commission structure is tiered by transaction value — higher value transactions attract a lower percentage commission. The broad structure is as follows:
Transaction Value (NPR)
Maximum Commission Rate
Up to 50,000
0.60%
50,001 to 500,000
0.55%
500,001 to 2,000,000
0.50%
2,000,001 to 10,000,000
0.45%
Above 10,000,000
0.40%
In addition to broker commission, investors pay a SEBON regulatory fee (currently 0.015% of transaction value), a CDSC depository fee, and a capital gains tax on profit from share sales (currently 7.5% for individuals on listed securities held less than one year and 5% for those held more than one year). The total transaction cost — commissions plus fees — is higher than in more competitive broker markets (India's discount brokerage revolution, led by Zerodha and similar platforms, brought equity transaction costs to near zero for many trade types). This higher transaction cost structure modestly penalises active trading and supports a buy-and-hold approach from a pure cost efficiency standpoint.
Service Quality and Technology
The quality of brokerage services in Nepal varies enormously across the 50 licensed brokers. The largest and most established brokers — several of which are affiliates of commercial banks and financial institutions — offer functional online trading platforms, mobile apps, research reports, and client relationship services. Several smaller brokers continue to operate primarily through phone-based order execution, with basic or absent online infrastructure.
The TMS (Trading Management System), operated by NEPSE and accessible through broker interfaces, is the primary online trading interface for retail investors. While functional, TMS has historically faced capacity challenges during periods of high market activity — when every retail investor wants to trade simultaneously, the system has experienced slowdowns and outages that prevented order execution at critical moments. These technical limitations have real financial consequences for investors who rely on timely order execution.
The Broker Consolidation Debate
SEBON and market analysts have periodically proposed a reduction in the number of licensed brokers through mandatory consolidation — merging the 50 into a smaller number of better-capitalised, better-serviced firms. The argument for consolidation is compelling: larger broker firms could invest in better technology, provide genuine research services, maintain branches in provincial cities, and offer more sophisticated services including portfolio management, investment advice, and institutional-grade execution.
The argument against consolidation — or more precisely, the political economy that has prevented it — is that the existing 50 broker license holders represent established economic interests with significant political connections. Forced consolidation would require some license holders to surrender valuable franchises, a prospect that faces organised opposition. The result is that the consolidation discussion continues without resolution, and investors continue to be served by a broker network whose fragmentation limits the quality of market infrastructure relative to what Nepal's market size and participant base would warrant.
Sub-Brokers: The Informal Extension
Beyond the 50 licensed brokers, a network of informal sub-brokers operates in Nepal — individuals who facilitate investor access to licensed brokers, often in geographic areas where licensed brokers have no presence. Sub-brokers collect orders from investors, route them through a licensed broker's account, and earn a share of the commission. Their operation exists in a regulatory grey zone: SEBON has regulations for formal sub-broker registration, but many informal participants operate without registration.
For investors using sub-broker networks — particularly in provincial areas — the risks are real: the sub-broker has no regulatory accountability for order execution quality, client fund safety, or advice accuracy. Cases of sub-broker fraud — collecting investor funds without routing them to the market, or diverting client accounts — have occurred and received limited regulatory redress. Investors should, where at all possible, establish direct relationships with licensed brokers through digital platforms rather than operating through informal sub-broker channels.
Lesson 9.5 — FII Restrictions, Allowed Limits, and Their Minimal Presence in NEPSE
The Foreign Institutional Investor Framework
Foreign Institutional Investors (FIIs) — foreign mutual funds, hedge funds, pension funds, sovereign wealth funds, and other institutional entities seeking to invest in Nepali equities — are permitted to invest in NEPSE, but within a framework of restrictions that has, in practice, produced minimal actual FII participation. Understanding both the framework and its practical consequences is important for assessing NEPSE's future trajectory and for understanding what a meaningful opening of the market to foreign capital would imply.
Legal Framework for FII Investment
SEBON's Foreign Investment in Securities Regulation 2075 (2018 AD) is the primary regulatory instrument governing FII access to NEPSE. Under this framework, foreign institutional investors may invest in listed equities, government bonds, and units of registered mutual funds, subject to the following conditions: the FII must be registered with SEBON as a foreign investor; it must invest through a licensed Nepali broker; it must maintain its investments through a dedicated non-resident account with a domestic bank; and it is subject to restrictions on the maximum ownership it can accumulate in any individual listed company.
The foreign ownership ceiling in any single listed company is set at 30% of paid-up capital — a limit that applies to aggregate foreign ownership (all foreign investors combined in a single company). For companies in specific sectors — banking and financial institutions, insurance, media, airlines — lower foreign ownership caps may apply under sector-specific regulations. Promoter-held shares typically cannot be sold to foreign investors without additional regulatory approval.
Capital Account Restrictions: The Practical Barrier
While the FII framework exists on paper, Nepal's capital account is not fully convertible — foreign investors cannot freely move capital in and out of Nepal on demand. Foreign exchange transactions related to investment must be routed through the banking system with NRB oversight, and repatriation of investment proceeds (both principal and returns) requires documentary compliance that adds friction, cost, and delay relative to markets with more open capital accounts.
These capital account restrictions are the primary practical barrier to FII participation in NEPSE — more significant than the ownership ceilings, the registration requirements, or the taxation framework. A global fund manager considering deploying capital into an emerging or frontier equity market faces a binary question: can I get my money in and out efficiently when I need to? For Nepal, the answer is: yes, but with significant friction. This friction is enough to make most international institutional funds — which manage liquidity against their own investor redemption obligations — unwilling to build meaningful allocations to NEPSE.
Why FII Participation Matters — or Could
At its current minimal level, FII participation in NEPSE is not a meaningful driver of market prices or liquidity. FII ownership across most NEPSE-listed stocks is well below 5% — in most cases, effectively zero. This absence has a compounding effect on NEPSE's market quality: without FII participation, NEPSE lacks the global capital that would bring with it more sophisticated valuation frameworks, better corporate governance demands, and the stabilising influence of investors with genuinely long-term mandates and non-correlated information sets.
The potential upside from meaningful FII liberalisation — a genuine opening of Nepal's capital account for institutional foreign investors, perhaps within a phased framework that starts with a pilot of registered frontier market funds — is substantial. A sustained inflow of even modest FII capital into NEPSE would improve liquidity, compress bid-ask spreads, improve price discovery, and raise market standards as listed companies sought to meet the more demanding disclosure and governance expectations of international investors. Whether such liberalisation will occur, and on what timeline, is fundamentally a question of Nepal's macroeconomic policy trajectory — specifically, the pace of current account improvement, foreign exchange reserve accumulation, and the government's appetite for financial sector reform.
FII Restrictions
What They Mean for the Domestic InvestorNEPSE is effectively a closed domestic market — valuations are not anchored by global comparable analysis in real time.
The absence of FII arbitrageurs means that NEPSE can remain disconnected from regional and global market trends for extended periods.
Any regulatory development signalling capital account liberalisation would be a structural market positive — worth monitoring closely.
FII absence means corporate governance improvement is driven by domestic regulatory pressure alone — a slower mechanism than investor-driven pressure.
Nepal's equity risk premium is partly a function of capital account illiquidity — this will compress if and when the market opens to foreign capital.
Lesson 9.6 — NRN Investors: Rules, Account Types, and Repatriation Rights
Who Are NRN Investors?
Non-Resident Nepalis (NRNs) — Nepali citizens and persons of Nepali origin living and working outside Nepal — represent a distinct and growing participant category in NEPSE. Defined under the Non-Resident Nepali Act 2064 and related regulations, NRNs include Nepali citizens with foreign residency, Nepali-origin individuals who have taken foreign citizenship, and their dependants who maintain ties to Nepal. The NRN population is estimated at 2–3 million individuals globally, with the largest concentrations in India, the Gulf countries (Qatar, UAE, Saudi Arabia, Kuwait, Malaysia), the United States, the United Kingdom, and Australia.
NRN remittances have been the single largest source of foreign exchange for Nepal for over a decade — exceeding 25% of GDP in recent years. The aggregate financial resources of the NRN community far exceed what is currently channelled into NEPSE, and expanding NRN investment participation in Nepal's capital market is a stated policy priority of both the Government of Nepal and the Non-Resident Nepali Association (NRNA).
The NRN Investment Account Framework
NRN investors may participate in NEPSE through a Non-Resident Nepali Investment Account (NRNIA), held with a commercial bank in Nepal. The NRNIA is a dedicated account in Nepali rupees that serves as the settlement account for all NEPSE transactions — IPO applications, secondary market purchases and sales, dividend receipts, and bonus share credits. The NRNIA must be funded by remittance from abroad — the foreign currency is converted at the prevailing NRB exchange rate — and investment proceeds, when repatriated, are converted back to foreign currency at the prevailing rate.
The NRNIA framework also provides access to IPO allocations. NRNs have a dedicated allocation in many IPOs — typically 5% of total issue size is reserved for NRN investors — which is separate from the general public allocation. This reserved allocation is significant: it means that NRN investors face less competition for a specific allotment share, improving their allotment probability relative to the oversubscribed general public pool.
Repatriation Rights
The ability to repatriate investment proceeds — to convert the Nepali rupee proceeds of NEPSE investments back to foreign currency and send the money abroad — is the most critical practical consideration for NRN investors. Under current regulations, NRN investors may repatriate: the principal amount of their initial investment (the foreign currency originally remitted to fund the NRNIA), dividends received on listed securities, and capital gains after applicable tax deductions.
Repatriation is processed through the banking system, requires documentation of the original investment and the tax payment on any gains, and is subject to NRB's foreign exchange regulations. In practice, repatriation has functioned relatively smoothly for NRN investors who have maintained clean NRNIA records — though the documentation requirements and bank processing timelines create friction that domestic investors do not face.
The exchange rate risk is an important consideration for NRN investors that is absent for domestic participants. A NRN who invests in NEPSE and earns a 20% return in Nepali rupees may find that the rupee has depreciated against their home currency (whether the US dollar, UAE dirham, or UK pound) during the same period, eroding the foreign-currency-equivalent return. Nepal's rupee is pegged to the Indian rupee (NPR 1.60 per INR), and the INR-USD rate therefore largely determines the NPR-USD rate. NRN investors should factor exchange rate risk into their expected return calculation and their risk management framework.
Tax Treatment for NRN Investors
NRN investors are subject to the same capital gains tax as domestic investors on their NEPSE gains (7.5% for securities held less than one year; 5% for securities held more than one year, as of the current tax regime). Dividends received are subject to a 5% withholding tax at source — withheld by the distributing company before the dividend is credited to the NRNIA. Double taxation treaty provisions may apply depending on the NRN's country of residence, potentially reducing the effective tax burden, but treaty applicability requires verification with a tax professional.
NRN Investment in NEPSE
Practical SummaryAccount type: Non-Resident Nepali Investment Account (NRNIA) with a commercial bank in Nepal.
Funding: Remittance from abroad — converted to NPR at prevailing NRB rate.
IPO access: Reserved 5% NRN allocation in most issues — better allotment probability than general pool.
Repatriation: Principal, dividends, and post-tax capital gains may be repatriated.
Exchange rate risk: NPR-USD movements affect foreign-currency-equivalent returns — factor this in.
Tax: 7.5%/5% capital gains (short/long-term); 5% dividend withholding at source.
DEMAT: Linked DEMAT account required — opened through the same commercial bank.
Lesson 9.7 — Promoter Shareholders vs. Public Shareholders: Lock-In Rules and Power Asymmetry
The Two Classes of Shareholders
In Nepal's listed company universe, the shareholder base is divided into two legally and practically distinct categories: promoter shareholders and public shareholders. This distinction, embedded in the Companies Act 2063 and SEBON's regulations, creates a fundamental power asymmetry that every public shareholder must understand — because it shapes corporate decision-making, dividend policy, governance quality, and the risk of promoter self-dealing in ways that directly affect the returns available to ordinary investors.
Promoter Shareholders: Who They Are
Promoter shareholders are the founding and controlling shareholders of a listed company — the individuals, families, or institutions who established the company and who collectively hold the majority of shares before the public offering. Under SEBON's regulations, promoter shares are defined as those held by the company's founder shareholders, institutional promoters, and strategic investors — categories that are specified in the company's founding documents and its prospectus.
In Nepali commercial banks, promoter shareholding typically constitutes 51% of paid-up capital, with the remaining 49% offered to the public through IPOs and FPOs. In other sectors, promoter proportions vary — some hydropower companies have higher promoter proportions, while some companies that have raised capital through multiple rounds of FPOs have diluted promoter stakes to lower levels.
Promoters are not a homogeneous group. In banking, prominent promoter categories include politically connected businesspeople, business families with established commercial networks, professional investors who participated in bank founding rounds, and institutional promoters (other banks, development banks, or business conglomerates). The character of the promoter group is one of the most important and least systematically analysed factors in assessing the governance risk of any specific NEPSE investment.
Lock-In Periods and Their Practical Significance
Promoter shares are subject to lock-in periods — restrictions on sale that prevent promoters from exiting their investment in the listed company for a specified period after listing. Under SEBON's regulations, the standard lock-in period for promoter shares in NEPSE-listed companies is three years from the date of listing for the initial promoter contribution, with additional restrictions applying to subsequent promoter share contributions and to shares held by related parties.
The purpose of lock-in periods is to align promoter incentives with long-term company performance — if promoters cannot sell their shares for three years, they are more motivated to manage the company for sustainable value creation than for a short-term price spike followed by an exit. In practice, the effectiveness of this incentive alignment depends on whether promoters are genuinely long-term committed to the business or are treating their promoter shareholding primarily as a financial investment with an anticipated exit.
After the lock-in period expires, promoter shares become freely transferable, subject to SEBON's disclosure requirements for significant shareholding changes. A promoter who sells a significant stake — typically above 0.5% of paid-up capital in a single transaction — must disclose the transaction to NEPSE and SEBON. This disclosure requirement, while creating some transparency, does not prevent the sale; it only ensures that the market is informed after the fact.
The Power Asymmetry: How It Manifests
The most consequential dimension of the promoter-public shareholder divide is the governance power asymmetry it creates. Because promoters collectively hold the majority of shares in most NEPSE-listed companies, they control the Annual General Meeting — the forum at which shareholders vote on board composition, dividends, auditor appointments, capital raises, and related party transactions. A promoter group with 51% of shares can, in effect, pass any ordinary resolution — including related party transactions that may benefit promoters at public shareholders' expense — over the objection of all public shareholders combined.
Board composition. Promoters control the nomination and election of the majority of board members. While SEBON requires listed companies to have independent directors — a minimum proportion of the board that is elected by public shareholders rather than proposed by promoters — the selection of 'independent' directors in Nepal has not always reflected genuine independence. Some independent directors have had professional relationships with promoters that compromise their independence in practice. An investor assessing governance quality should examine the actual independence of the board's independent directors — their professional background, their other directorships, and their behaviour in board meetings (reflected in meeting minutes where available).
Dividend policy. Promoters determine dividend policy through their board majority. In some Nepali companies, promoters have managed dividend policy to align with their personal tax situations, estate planning objectives, or reinvestment needs — not necessarily with public shareholder income expectations. A promoter who has large personal expenses outside the company may prefer high cash dividends; one who is building a broader business empire may prefer the company to retain earnings for growth or acquisition — regardless of what generates the highest risk-adjusted return for public shareholders.
Related party transactions. Perhaps the most significant risk of the promoter-public shareholder divide is the potential for related party transactions — deals between the listed company and entities in which the promoters have interests — that transfer value from the public shareholders to the promoters. Nepali regulations require disclosure and audit committee approval for related party transactions, but the audit committee's independence from promoter influence varies, and SEBON's enforcement of related party transaction standards has been inconsistent. Investors should read the related party transaction disclosures in every annual report of a holding with particular scrutiny.
Promoter Share Pledging: A Hidden Risk
A practice that has created significant investor risk in NEPSE is the pledging of promoter shares against bank loans. Promoters who need personal or business capital, but whose promoter shares are locked in and cannot be sold, frequently pledge those shares as collateral for bank loans. When the promoter defaults on the loan and the bank seizes and sells the pledged shares, it creates sudden forced selling in the market — often in large volumes relative to the stock's normal daily turnover — causing sharp price declines that harm public shareholders who had no part in the promoter's financing decision.
SEBON's regulations require disclosure of promoter share pledges above specified thresholds, and NEPSE publishes data on pledged promoter shares. Monitoring the pledged share percentage for a listed company's promoter group is a practical risk management tool: high and rising promoter pledge levels signal financial stress in the promoter group and create latent forced-selling risk that, if triggered, can dramatically impact the stock price.
Promoter Risk Indicators
What to MonitorPromoter pledge %: Exceeding 50% of promoter shares pledged signals significant financial stress risk.
Promoter share sales post-lock-in: Large sales immediately after lock-in expiry signal low promoter conviction.
Related party transaction volume: Rapidly growing RPTs relative to revenue warrant deep scrutiny.
Board independence: Directors with prior professional relationships with promoters are not genuinely independent.
AGM voting patterns: Do public shareholders ever succeed in any resolution opposed by promoters? Absence of this suggests governance capture.
Audit qualifications: Emphasis-of-matter paragraphs in audit reports often flag related party or accounting concerns first.
Lesson 9.8 — Market Makers: Why NEPSE Has None and the Liquidity Consequences
What Is a Market Maker?
A market maker is a participant — typically a licensed broker, financial institution, or designated specialist firm — that commits to continuously quoting both a bid price (at which it will buy a security) and an ask price (at which it will sell a security) for specified minimum quantities, throughout the trading day. By standing ready to buy or sell at any moment during market hours, the market maker provides liquidity to other participants: if you want to sell, the market maker will buy; if you want to buy, the market maker will sell. In exchange for this service, the market maker earns the bid-ask spread — the small difference between its buying and selling prices — as compensation for the inventory risk it absorbs.
Market making is a foundational institutional feature of all developed equity markets. On the NYSE, designated market makers (DMMs) are assigned to specific stocks and are legally obligated to provide continuous two-sided quotes. On NASDAQ, multiple competing market makers quote simultaneously in each stock, providing competitive liquidity. Even in most mid-tier emerging markets — India's BSE and NSE, Thailand's SET, Malaysia's Bursa — some form of market making or liquidity provision exists for less-traded securities.
NEPSE Has No Market Makers
Nepal Stock Exchange has no market makers. None. Zero. Every transaction on NEPSE depends on the coincidence of a willing buyer and a willing seller at the same price, at the same time. There is no designated participant whose role and regulatory obligation is to stand ready to provide a two-sided market in any security during trading hours. This is not a minor technical gap — it is a foundational structural absence with profound consequences for NEPSE's liquidity profile, price formation quality, and the risk faced by individual investors.
Consequence 1: Bid-Ask Spreads Are Wide and Variable
Without market makers narrowing the gap between bid and ask prices through continuous quoting, NEPSE bid-ask spreads are determined solely by the coincident preferences of retail buyers and sellers. In liquid, high-volume stocks — major commercial banks on active trading days — the spread may be acceptably narrow. But in less-traded stocks — smaller hydropower developers, manufacturing companies, many microfinance institutions — the spread can be 2–5% or more of the stock's price.
A bid-ask spread of 3% means that an investor who buys a stock and immediately sells it loses 3% plus transaction costs — a meaningful barrier to efficient trading that does not exist in market-made securities. It also means that price quotations in illiquid stocks are not meaningful representations of executable prices: the last traded price in a stock that has not traded for two days is not the price at which you can actually buy or sell; the real executable price may be significantly different from the screen price.
Consequence 2: Price Discontinuities and Gaps
In market-made securities, price discovery is continuous: there is always a market maker willing to transact, so prices move in small increments reflecting new information. In NEPSE's unmade market, information can arrive — a quarterly result, a regulatory announcement, an NRB policy change — when no natural buyer or seller is present in a specific stock. The result is a price gap: the stock's price at the next transaction can be significantly different from the last price, because there was no market maker absorbing the information flow and gradually adjusting quotes. These gaps create risk for investors whose orders are waiting in the system at prices that are no longer relevant to current conditions.
Consequence 3: The Lower-Circuit Trap
The most severe practical consequence of the absence of market makers occurs during market stress. When negative news or sentiment drives retail investors to sell, and no institutional buyer is stepping in (because there are no institutional buyers obligated to do so), a stock can hit its lower circuit limit within minutes of opening. Without a market maker willing to buy at, or near, the limit price, the stock remains locked at the lower circuit — any sell orders at prices below the circuit cannot execute, but there may be no buyers even at the circuit price.
In a market-made system, this situation would not persist: the market maker, contractually obligated to provide a buy quote, would absorb some of the selling pressure. In NEPSE, a lower-circuit lock can persist for days — each day the stock opens and hits its lower circuit, falls another 15%, and closes. An investor trapped in such a position cannot exit regardless of their willingness to accept a loss. They must wait for a natural buyer to emerge or for the circuit to stabilise prices long enough for market psychology to shift.
Consequence 4: Illiquidity Premium and Valuation Discount
The absence of market makers in NEPSE creates what finance academics call an illiquidity premium in reverse — rather than compensation for holding illiquid assets, investors demand a discount to fair value before buying them. A hydropower company with sound fundamentals and a reasonable DCF valuation of NPR 300 per share may trade at NPR 220 in NEPSE's unmade market, because potential buyers know that exiting the position will be difficult if their investment thesis changes. This structural discount — the liquidity discount — means that NEPSE stocks can be genuinely cheap relative to fundamental value for extended periods, simply because the market mechanism to close that gap is absent.
For the long-horizon investor, this liquidity discount is an opportunity: you buy fundamentally sound businesses at prices below intrinsic value, and you hold long enough that either the business's earnings growth closes the gap, or market conditions improve sufficiently that other buyers enter. The risk is that the exit remains difficult — you may have to sell at an equally large discount when you choose to exit, if market conditions have not improved. True long-horizon investing in NEPSE's unmade market requires an acceptance that position exits may be slow, costly in spread terms, and subject to significant price impact.
The Case for Market Making in NEPSE
SEBON and NEPSE have acknowledged the absence of market makers as a market quality gap and have discussed the introduction of a market making framework. The technical requirements for a functioning market making system are not trivial: market makers need access to efficient securities borrowing (to sell short when they have excess buy orders, they need to borrow shares), risk management tools (hedging their inventory against price moves), and adequate compensation through the bid-ask spread to justify the inventory risk they take on. All of these elements are currently absent or underdeveloped in NEPSE's ecosystem.
The introduction of securities lending (allowing institutional investors to lend their long-term holdings to market makers temporarily), combined with a formal market maker licensing regime under SEBON, would be transformative for NEPSE's market quality. The timeline for such a development depends on regulatory willingness, technological infrastructure investment, and the availability of sufficiently capitalised institutions willing to take on the market maker role. Investors who anticipate this development — and who understand how market quality improvements have driven re-rating of comparable frontier markets — may find it an important structural theme in their NEPSE investment thesis.
Market Quality Feature
NEPSE Status
Implication for Investor
Market Makers
Absent
Wide spreads; lower-circuit traps; price gaps on news
Short Selling
Not permitted
Overvalued stocks cannot be corrected from supply side
No portfolio hedging; no price discovery across time horizons
Algorithmic Trading
Limited/unregulated
No HFT liquidity provision; no arbitrage compression of mispricing
T+1 Settlement
Not yet (T+2)
Slower capital recycling; higher counterparty risk window
Dark Pools / OTC
Not present
All price discovery public; no block trade facility for institutions
In NEPSE, the absence of market makers is not a footnote — it is the defining structural feature that makes every liquidity analysis, every entry and exit decision, and every position sizing calculation different from what applies in deeper markets.
Market Structure Implications
The NEPSE Investor's Operating FrameworkSize positions in proportion to a stock's average daily volume — never build a position you cannot exit in 10–15 trading days at normal volumes.
Factor the bid-ask spread into return calculations for all but the most liquid banking stocks.
Use limit orders, not market orders, in illiquid stocks — market orders in unmade markets can execute at prices far from the screen.
Monitor promoter pledge data — forced selling by banks on defaulted pledged shares is NEPSE's equivalent of a surprise liquidity shock.
In market stress, expect lower-circuit locks to persist — plan exit timelines with an allowance for illiquidity.
The illiquidity discount in fundamentally sound stocks is an opportunity for patient capital — NEPSE's structural imperfections are the source of its alpha opportunities.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part II · Chapter 10
Trading Mechanics in NEPSE
First published 21 Aug 2026 · Last verified 29 Aug 2026
Nepal's stock exchange, the Nepal Stock Exchange — universally abbreviated as NEPSE — operates under a distinct set of mechanical rules that differ substantially from exchanges such as the BSE, NSE, NYSE, or LSE. For the serious investor, understanding these mechanics is not optional. It is foundational. The way orders are placed, the way prices move, the way trades settle, and the instruments available (or pointedly absent) all shape what a rational strategy must look like in this market. This chapter systematically examines each element of NEPSE's trading infrastructure, from the electronic trading platform to dividend processing, explaining not only what the rules are but why they exist and what their consequences are for your money.
Lesson 10.1 — The TNET Platform: How Orders Are Placed, Matched, and Executed
What is TNET?
TNET — short for Trading Network — is the electronic order-matching system that powers NEPSE. Introduced as part of NEPSE's technology modernisation drive, TNET replaced the earlier, slower system and made screen-based trading the standard. Before TNET, trades were conducted through open-outcry or manual matching, a process prone to inefficiency, human error, and opportunities for manipulation. The shift to TNET was a watershed moment for the Nepalese capital market.
TNET is an order-driven market. This is a critically important concept. In a quote-driven market — such as those operated by many market makers in Western financial systems — dealers post firm buy and sell prices and stand ready to trade at those prices. In an order-driven market like NEPSE, there are no dedicated market makers. Every transaction requires a matching of a buyer's order with a seller's order. The price you get depends entirely on what other participants have placed in the order book. This distinction has profound implications for liquidity, especially in a market as thinly traded as NEPSE.
The Order Flow from Investor to Execution
The journey of an order begins when an investor decides to buy or sell shares. In the broker-assisted model, the investor calls or visits their broker, who then places the order on TNET using the broker's trading terminal. In the self-directed model, the investor logs into the Transaction Management System (TMS) — the retail-facing portal — and places the order directly. In either case, the order enters the NEPSE central order book.
Once an order enters the system, TNET applies a price-time priority algorithm. This means two things. First, the best-priced order gets matched first: the highest bid takes priority over lower bids, and the lowest ask takes priority over higher asks. Second, when two orders are at the same price, the one submitted earlier gets matched first. This is the universal standard for exchange matching engines and is fundamental to understanding why order timing matters.
When a buy order's price equals or exceeds a sell order's price, a match is created and a trade is executed. The matched trade is then forwarded to the clearing and settlement system, which we will examine in detail in Lesson 10.6. TNET records every trade with timestamp precision, creating an auditable trail of all market activity.
KEY PRINCIPLE
TNET is an order-driven, price-time priority system. There are no market makers. Liquidity exists only where opposing orders exist. If nobody is willing to sell at your price, your order waits — possibly for days.
Market Surveillance and TNET
TNET also serves as the surveillance backbone for NEPSE's regulatory function. Unusual trading patterns — sudden price spikes, abnormally large orders, repetitive transactions between related parties — can be flagged for investigation. The Securities Board of Nepal (SEBON) relies on the data generated by TNET to monitor market integrity. For the investor, this means that all electronic orders leave a traceable record, an important deterrent against market manipulation that nonetheless remains a concern in the market.
Lesson 10.2 — Trading Hours, Pre-Open Session, and Market Timing
The Standard Trading Day
NEPSE operates on Nepal Standard Time (NST), which is UTC+5:45 — one of the few time zones in the world not aligned to a full or half hour, a quirk of Nepal's geography relative to India. The trading day is shorter than that of most major international exchanges, running from Sunday through Thursday (Nepal's working week), reflecting the country's official five-day working schedule.
Session
Description
Pre-Open Session
Roughly 10:30 AM – 11:00 AM NST (order entry closes before the open auction; NEPSE adjusts the exact window by circular). Order entry, modification, and cancellation allowed. No trades execute.
Open Auction / Call
11:00 AM NST. Pre-open orders are matched at a single equilibrium price.
Continuous Trading
11:00 AM – 3:00 PM NST. Orders match continuously in real time.
Market Close
3:00 PM NST. No new orders accepted.
Post-Trade
Settlement processing begins after market close.
The Pre-Open Session in Detail
The pre-open session is frequently misunderstood by retail investors. During this window, investors and brokers can submit, modify, or cancel orders, but no actual trades are executed. Instead, TNET is collecting information about supply and demand. The system is computing what is called the Indicative Equilibrium Price (IEP) — the theoretical price at which the maximum number of shares could be traded if the market were to open at that moment.
At exactly 11:00 AM, the pre-open auction clears. All orders that can be matched at the IEP are matched simultaneously at that single price. This mechanism serves two purposes. First, it prevents wild price gaps at open caused by order imbalances accumulated overnight. Second, it provides a fair and transparent price discovery mechanism for the opening trades. After the auction clears, continuous trading begins and the matching engine switches to real-time order-by-order matching.
Why NEPSE's Short Trading Window Matters
The three-hour continuous trading window is considerably shorter than what traders in India, the US, or Europe experience. This compression has real consequences. Price discovery is crammed into a narrow timeframe. Institutional investors — few as they are in Nepal — and retail participants must both act within the same tight window. For an investor managing a modest portfolio, this creates urgency that can lead to impulsive decisions. The disciplined investor learns to use the pre-open session to think through and stage orders before the continuous session begins, rather than reacting to early price moves.
Additionally, because NEPSE is closed on Fridays and weekends (by the Nepali calendar), any news emerging after Thursday's close — quarterly results, policy announcements, geopolitical events — cannot be acted upon until Sunday morning. This creates what market professionals call a news gap risk: the price at Sunday open may reflect multiple days of accumulated information, resulting in sharper moves than in daily-trading markets.
PRACTICAL NOTE
Set your orders during the pre-open session. Entering limit orders between 10:00 and 10:55 AM gives you time to think clearly. Reacting emotionally at 11:05 AM to opening prints is how retail money is lost.
Lesson 10.3 — Order Types Available: Market Orders, Limit Orders — What Exists and What Does Not
The Core Order Types
An order type is the instruction you give the market about the conditions under which your trade should be executed. Different order types offer different trade-offs between certainty of execution and certainty of price. In mature markets, investors have access to a broad toolkit: market orders, limit orders, stop-loss orders, stop-limit orders, trailing stops, fill-or-kill, immediate-or-cancel, good-till-cancel, and more. In NEPSE, the toolkit is considerably more limited, and understanding this limitation is essential to constructing a sound execution strategy.
Market Orders
A market order instructs the system to buy or sell a specified quantity of shares at the best available price immediately. If you place a market buy order for 100 shares of a company, TNET will match you against the lowest-priced sell orders in the book until your 100 shares are filled. If the asks are stacked at different prices, your order may be filled at multiple price levels — a phenomenon called slippage.
In liquid markets with tight spreads and deep order books, slippage is negligible. In NEPSE, where many stocks trade thin volumes and spreads between best bid and best ask can be wide, market orders carry real danger. A market order in an illiquid stock can move the price against you by several percentage points in the act of filling. This is not a theoretical risk — it is a recurring reality for investors who fail to appreciate NEPSE's thinness.
WARNING
Market orders in illiquid NEPSE stocks are dangerous. You may receive fills far from the last traded price. Always use limit orders unless you have confirmed deep, liquid order book depth.
Limit Orders
A limit order specifies both quantity and a maximum price (for buys) or minimum price (for sells) at which you are willing to trade. A buy limit order at Rs 450 will only execute if shares are available at Rs 450 or below. A sell limit order at Rs 450 will only execute if buyers are present at Rs 450 or above.
Limit orders are the professional standard in NEPSE for most situations. They give you price certainty — you will never overpay or underpay your specified threshold. The trade-off is execution certainty: your order may sit in the book unfilled if the market never reaches your price. For an investor who understands intrinsic value and has set a rational entry price, this is not a deficiency — it is a feature. The market will either come to your price or it will not, and you have decided in advance that you do not wish to trade outside your comfort zone.
What Does Not Exist on NEPSE
The absence of certain order types on NEPSE has significant consequences for risk management. Stop-loss orders — orders that trigger a sale when a stock drops below a specified price — do not exist as native order types on TNET. Investors cannot set an automatic downside protection order. If you own shares in a company and you want to exit if the price falls to a certain level, you must manually monitor the market and place a sell order yourself during trading hours. This requires discipline, attention, and availability that many retail investors do not have.
Similarly, Good-Till-Cancel (GTC) orders — orders that remain active until they are filled or you cancel them — are not available. Orders in NEPSE are typically day orders only, expiring at the close of the trading session in which they are placed. This means that every day you wish to maintain an unexecuted order in the market, you must re-enter it. This creates unnecessary friction and favours those with regular access to trading terminals.
The absence of conditional and derivative order types also means that sophisticated hedging strategies common in developed markets are structurally impossible to execute via the exchange mechanism alone. Investors must compensate through position sizing, cash management, and pre-commitment to rules — the behavioural and fundamental disciplines that form the foundation of this book.
Lesson 10.4 — The Order Book: Bids, Asks, Spread, and Market Depth in a Thin Market
Anatomy of an Order Book
The order book is the live, continuously updated record of all unexecuted buy and sell orders currently resting in the market for a given security. The buy side, called the bid side, lists all pending purchase orders ranked from highest price to lowest. The sell side, called the ask or offer side, lists all pending sell orders ranked from lowest price to highest. The best bid is the highest price a buyer is willing to pay right now. The best ask is the lowest price a seller is willing to accept right now.
The spread is the difference between the best bid and the best ask. In highly liquid markets like large-cap stocks on the NYSE, spreads can be fractions of a paisa equivalent. In NEPSE, especially for smaller-cap or less actively traded stocks, spreads can be Rs 5, Rs 20, or even larger — sometimes several percent of the stock price. Every time you buy at the ask and would need to sell at the bid, the spread represents an immediate loss. Understanding the spread is the first lesson in trading cost awareness.
Market Depth
Market depth refers to the volume of orders available at various price levels beyond the best bid and ask. A deep market has many orders stacked closely around the current price — there is substantial demand to buy if the price dips slightly, and substantial supply available if the price rises slightly. A shallow market has few orders, often with large price gaps between levels.
NEPSE is characteristically shallow. It is common to view the order book of a NEPSE-listed company and find that the entire visible depth consists of a handful of orders, often placed by a small number of participants. This shallowness has several consequences. First, a single large order can move the price significantly — what traders call market impact. Second, in times of stress, the order book can empty almost entirely on the buy side, making it impossible to exit a position without accepting a dramatically lower price. Third, prices can appear to recover quickly after a dip, but only because a single buyer re-entered the book — not because genuine buying pressure has returned.
Concept
Implications for NEPSE Investors
Wide Spread
Transaction costs are higher per round-trip. Factor this into your required return.
Thin Depth
Large orders move prices adversely. Break large positions into smaller tranches.
Few Participants
A single participant's exit can crater a stock. Be cautious of stocks with very few active traders.
Illiquidity Premium
Illiquid stocks should offer higher expected returns to compensate for liquidity risk.
Price Mirroring
Prices can be moved artificially by coordinated small groups, increasing manipulation risk.
Reading the Order Book as an Investor
Many retail investors ignore the order book entirely, looking only at the last traded price. This is a mistake. Before placing any order in NEPSE, examine the order book for the security you wish to trade. Ask yourself: how many shares are available at or near the current price? If you want to buy 1,000 shares and there are only 200 shares offered in the entire book within a reasonable range, a market order will fill at escalating prices, some far above your intended purchase level. A limit order will fill only what is available at your price, leaving the rest unfilled — which in this case is the correct outcome. Patience and limit orders are the investor's primary defences against thin-market execution risk.
Lesson 10.5 — Price Circuit Limits: Upper and Lower Circuits — Daily Bands and Their Purpose
What Are Circuit Limits?
A circuit limit — also called a circuit breaker or price band — is a regulatory mechanism that prevents a stock's price from moving beyond a specified percentage in a single trading day. NEPSE applies circuit limits to all listed securities. The bands define a floor (lower circuit) and a ceiling (upper circuit) beyond which no trades can be executed on that day.
NEPSE historically applied a daily circuit limit of ten percent in each direction for most ordinary shares. Since April 2026 that band has been widened to fifteen percent, meaning a stock cannot rise more than fifteen percent above the previous day's closing price, nor fall more than fifteen percent below it, within a single trading session. The specific percentages may vary for certain categories of securities — newly listed stocks, securities under special surveillance, or those with elevated volatility — and investors should always verify current SEBON and NEPSE circulars for precise limits applicable to specific instruments.
Why Circuit Limits Exist
The rationale for circuit limits is investor protection and market stability. Without any price bands, a panic sell-off could theoretically drive a stock to near zero in a single session on the basis of rumour, misinformation, or coordinated selling. Similarly, speculative frenzy could drive prices to absurd multiples of fair value in one trading day. Circuit limits force a cooling-off: once the limit is hit, the stock is frozen at that price for the day, and participants must wait until the next trading session to re-evaluate.
There is also an information rationale. Extreme intraday moves often represent either a dramatic news event or a lack of news combined with thin liquidity and irrational behaviour. By imposing a pause, circuit limits give investors time to seek information, verify facts, and make considered decisions rather than reacting to price signals alone.
The Double-Edged Nature of Circuit Limits
While circuit limits protect against extreme volatility, they also create their own set of risks. The most common is the trapped position problem. If a stock hits the lower circuit and you wish to sell, you cannot — no buyers may be willing to transact at the circuit price, and no lower price is permitted. Your shares are effectively illiquid for that day. If bad news persists, the stock may hit the lower circuit for multiple consecutive days, meaning your ability to exit deteriorates with each passing session. This phenomenon, colloquially known as a circuit staircase, is well-documented in South Asian markets.
Conversely, if a stock is hitting the upper circuit, buyers who wish to purchase at the market cannot find sellers willing to sell at the ceiling price. If the demand is genuine, the stock opens at or near the upper circuit the following day, creating a multi-day appreciation that rewards existing holders but makes it impossible for new investors to build a position at a rational price.
RISK NOTE
Before buying a thinly traded stock in NEPSE, always ask: if I need to exit quickly, can I? A stock that regularly hits lower circuits is one where your exit may be delayed by days or weeks. This liquidity risk must be priced into your decision.
Index-Level Circuit Breakers
Beyond individual stock circuits, NEPSE also applies market-wide circuit breakers that halt all trading if the NEPSE index falls by a significant percentage in a single session. These index-level halts are modelled on similar mechanisms in India's NSE and BSE and are designed to prevent systemic panic. The precise thresholds and halt durations are specified in NEPSE's trading regulations and are subject to periodic revision by SEBON.
Lesson 10.6 — T+2 Settlement: Why It Is Slower and What It Means for Cash Management
What Settlement Means
When a trade is executed on NEPSE, the transaction is not immediately complete. The buyer does not instantly receive shares and the seller does not instantly receive cash. Settlement is the process by which these obligations are formally fulfilled — shares transferred to the buyer's demat account and cash transferred to the seller's account. The time between the trade date (T) and the settlement date is the settlement cycle.
NEPSE operates on a T+2 settlement cycle. This means that if you execute a trade on Sunday (day T), settlement — the actual transfer of shares and funds — occurs on Tuesday (T+2, counting only business days). Compare this with India's SEBI-mandated T+1 settlement, introduced in 2023, or international standards moving toward T+1 and even real-time settlement. Nepal's T+2 cycle is slower and carries practical implications that every investor must understand.
Implications for Buyers
If you buy shares on Sunday, your demat account will not reflect ownership of those shares until Wednesday. During those three days, you have a financial exposure — you are committed to paying for shares you do not yet legally own in your account. This matters if the stock's price falls sharply after your purchase but before settlement. While you can sometimes sell the shares before settlement through contra trades (offsetting positions), the mechanics of this in NEPSE are less straightforward than in more developed markets, and the practice introduces settlement-level complexity and potential fails.
Implications for Sellers
If you sell shares on Sunday, you will not receive the sale proceeds in your linked bank account until Wednesday at the earliest — often Thursday given bank processing times. For an investor who needs liquidity urgently, this delay can be consequential. You cannot, for instance, sell shares on Monday and reinvest the proceeds on Tuesday. The cash is unavailable. This means that active rebalancing — selling one position and immediately buying another — requires either pre-existing cash in your trading account or tolerance for a multi-day gap between transactions.
Cash Management Under T+2
The practical implication of T+2 is that your effective available capital is often less than your account balance suggests. At any given time, you may have cash that is committed to settling prior purchases (and therefore not truly free), and proceeds from sales that have not yet arrived. Sound cash management in NEPSE requires maintaining a buffer: do not commit 100% of your available cash to open buy orders simultaneously, because if multiple orders fill on the same day, your settlement obligations could exceed your immediately available balance. Maintain a float — a reserve of readily available cash — that gives you flexibility and prevents settlement fails, which carry penalties and reputational consequences with your broker.
Settlement Day (from Trade)
Scenario
T (Sunday)
Trade executed. Commitment established.
T+1 (Monday)
Buyer's payment arranged through broker; seller delivers shares via EDIS. No shares in demat yet.
T+2 (Tuesday)
Shares transferred to buyer. Cash received by seller.
T+3 (Wednesday)
Bank crediting often completes this day for sellers.
Why T+2 Persists in Nepal
The persistence of T+2 in NEPSE is partly infrastructural and partly institutional. The Nepalese capital market ecosystem — brokers, clearing houses, depository systems, and banks — has not yet been upgraded to support the faster data reconciliation and fund movement required by T+1. The Central Depository System and Clearing Limited (CDSC) is the entity responsible for clearing and settlement, and its capacity is constrained by the overall state of Nepal's financial infrastructure. SEBON has expressed intent to modernise settlement timelines, but change requires coordinated upgrades across multiple institutions. Until then, T+2 is the operative reality.
MEROSHARE is the investor-facing online portal operated by CDSC (Central Depository System and Clearing Limited). It is, in practical terms, the digital infrastructure through which most Nepalese retail investors interact with the formal aspects of capital market ownership — not the trading itself, but the ownership records, IPO applications, dividend history, and share transfer processes. If you are a serious investor in Nepal, MEROSHARE is not optional. Understanding it fully is part of your operational competence.
The Demat Account
Before electronic depository systems existed, share ownership was evidenced by physical certificates — paper documents that could be lost, stolen, forged, or damaged. The dematerialisation — demat — system eliminates physical certificates entirely. Your shares are held electronically as digital entries in the CDSC system, linked to your unique demat account number (called a BOID — Beneficial Owner Identification Number). Every share you buy is credited to your BOID, and every share you sell is debited.
Opening a demat account in Nepal requires submitting an application through a registered depository participant (DP), which is typically your brokerage firm. You will provide identity documents, a photograph, and bank account details. The BOID issued to you is permanent and unique — think of it as your share ownership identity number. All NEPSE transactions, IPO allotments, bonus share credits, and rights issues are processed to this BOID.
EDIS — Electronic Debit Instruction System
EDIS is the mechanism by which you authorise the transfer of shares out of your demat account when you sell. In the pre-EDIS era, selling shares required physically signing and submitting a Delivery Instruction Slip (DIS) to your broker or depository participant — a paper-intensive, time-consuming process prone to delays and errors. EDIS replaced this with an electronic authorisation system accessible through MEROSHARE.
When you place a sell order and it is matched, you must grant EDIS authorisation — a digital approval confirming that you consent to the transfer of those specific shares out of your BOID for settlement purposes. Without timely EDIS authorisation, your trade cannot settle, leading to settlement failures with regulatory and financial consequences. The authorisation window is defined by NEPSE — investors must ensure they submit EDIS approval within the specified timeframe after their sell order executes. This is a procedural discipline that new investors frequently overlook.
Online IPO Application
One of MEROSHARE's most widely used features is the online IPO (Initial Public Offering) application system, known as ASBA — Application Supported by Blocked Amount. When a company is coming to market through a primary offering in Nepal, SEBON mandates that retail applications be submitted through MEROSHARE (or through authorised bank branches for non-digital applicants). The application process involves logging into MEROSHARE, selecting the offering, specifying the number of units you wish to apply for, and confirming your bank account from which funds will be blocked.
The ASBA mechanism means your money is not actually debited when you apply — it is blocked in your account, earning no interest but remaining technically yours. If you are not allotted shares, the block is released and your money is fully returned. If you are allotted shares, only the corresponding amount is debited. This is a significant improvement over older systems where application funds were fully drawn and refunds could take weeks. The allotment results and share credits to your BOID are also reflected in MEROSHARE after the IPO process completes.
Portfolio View and Transaction History
MEROSHARE provides a complete view of your holdings across all companies — the number of shares you own in each, their face value, and the transaction history of every credit and debit to your BOID. This portfolio view is an authoritative record of ownership, more reliable than any broker statement because it comes from the depository itself. Serious investors should reconcile their broker-provided portfolio statements with the MEROSHARE BOID record at least monthly to catch any discrepancies early.
BEST PRACTICE
Treat your MEROSHARE account as the ground truth of your ownership. Never rely solely on your broker's statement. Check MEROSHARE after every trade settlement, every IPO allotment, and every bonus share or rights credit to ensure your BOID reflects the correct balances.
Lesson 10.8 — Broker-Assisted Trading vs. Self-Trading via TMS
The Traditional Broker-Assisted Model
For most of NEPSE's history, all trading required the intermediation of a licensed broker. Investors would call, visit, or message their broker with instructions: buy this many shares of this company at this price, or sell this holding. The broker's trading terminal — connected to TNET — would then execute the order on the investor's behalf. The broker charges a commission on every transaction, which in Nepal is regulated by SEBON and currently structured as a tiered percentage of transaction value.
The broker-assisted model has genuine advantages for certain investors, particularly those who value personal guidance, who deal in large enough positions to warrant a relationship, or who are not comfortable with self-directed digital platforms. A good broker can provide market intelligence, assist with IPO applications and EDIS procedures, and help navigate procedural complexities. A poor broker, however, can be a source of conflicted advice — encouraging excessive trading to generate commissions, a practice known as churning — and slow execution.
Self-Trading via TMS
The Transaction Management System (TMS) is NEPSE's retail investor portal for self-directed trading. Accessible via web browser, TMS allows investors to place, modify, and cancel orders on TNET directly, without needing to instruct a broker in real time. Each investor has a TMS account linked to their BOID and their designated broker — you still formally transact through a broker in the regulatory sense, but you are doing the order entry yourself.
TMS provides access to the live order book for all listed securities, a trade history dashboard, your current open orders, and market-wide data including sector indices and individual scrip summaries. For the self-directed investor who has done their fundamental research and knows what they want to buy and at what price, TMS is the superior execution environment. It removes the latency of broker communication, eliminates the risk of order miscommunication (which in fast-moving markets can be costly), and gives you direct, real-time visibility of market conditions.
Choosing Between the Two
The choice between broker-assisted and self-directed trading is ultimately one of knowledge, discipline, and comfort with technology. If you are new to markets, broker assistance during the learning phase is entirely reasonable — but be conscious of commission costs and always verify that the advice you receive is in your interest rather than your broker's. As you build competence, migrating to TMS for execution while using your broker relationship for broader support is a mature strategy. The investor who understands both systems and can use them strategically is in a stronger position than one who defaults entirely to either.
Lesson 10.9 — Short Selling: Why It Does Not Exist on NEPSE and the Consequences for Price Discovery
What Short Selling Is
Short selling is the practice of selling shares you do not currently own, with the obligation to buy them back later. The mechanics work through a securities borrowing framework: you borrow shares from a willing lender (typically an institutional holder), sell them in the market at the current price, and later repurchase them — ideally at a lower price — to return to the lender. Your profit is the difference between the price at which you sold and the price at which you repurchased, minus borrowing costs. Your loss, if the stock rises after your short, is theoretically unlimited.
Short selling serves two broad functions in markets. First, it allows investors who believe a stock is overvalued to profit from that belief. This has an information function: short sellers have strong incentives to conduct rigorous negative research on companies, and their trading activity communicates bearish information to the market. Second, it provides liquidity by creating additional sellers in the market, widening the pool of participants on each side.
Why Short Selling Does Not Exist on NEPSE
NEPSE does not permit short selling. There is no securities borrowing and lending framework, no authorised mechanism for investors to sell shares they do not hold. Every sell order entered on TNET must correspond to shares actually held in the seller's BOID. This constraint is structural — it would require a lending infrastructure (custodians, legal frameworks, margin systems) that does not exist in Nepal's current market ecosystem — and it is also a regulatory choice, reflecting SEBON's conservative stance on instruments that can amplify volatility.
The Price Discovery Consequences
The absence of short selling has a profound and often under-appreciated effect on price discovery in NEPSE. In a market without short sellers, the only information transmitted through prices is positive or neutral. When institutional or informed investors believe a company is fundamentally overvalued, they can act on that belief only by selling shares they already hold. If they do not hold the stock, they have no mechanism to express their view in the market. This creates a systematic upward bias in prices: bullish information is freely and immediately incorporated into prices through buying activity, but bearish information either cannot be expressed at all or is expressed more slowly and less efficiently.
The result is that NEPSE stocks are more likely to be overpriced than underpriced relative to fundamental value on average. This is not speculation — it is a structural prediction arising from the asymmetry in the mechanisms available to market participants. Academic research on markets without short selling consistently finds higher price-to-earnings ratios, more speculative bubbles, and less efficient incorporation of negative information.
ANALYTICAL IMPLICATION
Because NEPSE has no short selling, negative fundamental views cannot easily be expressed through trading. This means overvaluation can persist much longer than in markets with short sellers. Never assume that a high price reflects efficiency — in NEPSE, it may simply reflect the absence of any bearish counter-pressure.
Practical Implications for the Long-Only Investor
For the retail investor who is, by definition, a long-only participant (you can only buy or sell what you already own), the absence of short selling means you operate in a market where prices can remain elevated for extended periods. This raises your risk of entering positions at overvalued prices. The discipline of fundamental valuation — understanding what a company is genuinely worth and refusing to pay materially more — becomes not just a preference but a survival requirement in a market where price signals are structurally incomplete.
Lesson 10.10 — Dividends, Bonus Shares, and Rights: Process, Timelines, and NEPSE Announcements
How Companies Distribute Value to Shareholders
When a listed company generates profits, it has several options for returning value to shareholders. In the Nepalese context, the three primary mechanisms are cash dividends, bonus shares (stock dividends), and rights issues. Each has distinct characteristics, timelines, and implications for the investor's portfolio value and tax position.
Cash Dividends
A cash dividend is a direct payment of money to shareholders, distributed on a per-share basis. If a company declares a dividend of Rs 25 per share and you hold 100 shares, you receive Rs 2,500. In Nepal, dividends are subject to withholding tax at source — currently at a rate specified by the Income Tax Act — meaning the company deducts the tax before remitting the cash to your account. The net amount you receive is the gross dividend minus the tax withheld.
The dividend process in NEPSE follows a defined sequence. The company's board of directors proposes a dividend at its board meeting. The proposal is then put to shareholders for approval at the Annual General Meeting (AGM). Once approved, the company announces a book closure date or record date — the date by which you must be a registered shareholder to be entitled to the dividend. If you purchase shares after the ex-dividend date (typically the day after record date), you are not entitled to that dividend. Cash dividends are then disbursed within a regulatory deadline — SEBON mandates payment within a specified period after AGM approval — typically to your bank account linked through your BOID.
Bonus Shares
Bonus shares — also called stock dividends — are new shares issued to existing shareholders at no cost, in proportion to their current holdings. If a company announces a 20% bonus share, every shareholder receives 20 additional shares for every 100 they currently hold. Bonus shares do not transfer cash out of the company; they convert retained earnings (or share premium reserves) into paid-up capital.
It is critical to understand that bonus shares do not add intrinsic value to a shareholder's position at the moment of issuance. If you hold 100 shares at Rs 1,000 each (total value Rs 100,000) and receive a 20% bonus, you now hold 120 shares. The theoretical post-bonus price, all else equal, adjusts to approximately Rs 833 per share — so your total value remains Rs 100,000. The market, however, sometimes reacts irrationally to bonus announcements, treating them as value-creating events and bidding the price up. This psychological effect, well documented in South Asian markets, can create trading opportunities but should not be confused with genuine value creation.
Bonus shares are credited to your BOID after regulatory processes complete, which can take several weeks to a few months after AGM approval. MEROSHARE will reflect the credit once CDSC processes the allotment.
Rights Issues
A rights issue is an offering of new shares to existing shareholders at a predetermined price — almost always below the current market price — in proportion to their existing holdings. Rights issues are a primary mechanism by which listed companies raise new capital from their existing shareholder base. If you hold 100 shares and the company announces a 1:4 rights issue at Rs 100 per share, you have the right to purchase 25 additional shares at Rs 100, regardless of the current market price.
Rights carry intrinsic economic value. The right to buy shares at below-market prices is a valuable entitlement. Shareholders who do not wish to exercise their rights can, in theory, sell them (where a secondary market for rights exists), though NEPSE's rights trading mechanism is less developed than in larger markets. If you choose neither to exercise nor to sell your rights before they expire, you experience dilution — your proportional ownership in the company decreases as new shares are issued to others.
The rights application process typically involves submitting your application and payment through your bank during the specified subscription period, which is announced by the company via NEPSE and SEBON notifications. Applications can also often be submitted through MEROSHARE. Missing the subscription deadline means forfeiting your rights — there is no extension.
Tracking Corporate Actions via NEPSE Announcements
All material corporate actions — AGM dates, dividend proposals, bonus share approvals, rights issue details, and book closure notifications — are formally announced through NEPSE's official website and published in major Nepali financial newspapers and the SEBON bulletin. The serious investor must develop a habit of monitoring these announcements regularly. Missing a book closure date means missing an entitlement. Missing a rights subscription period means dilution without compensation.
MEROSHARE's notification system provides some automated alerts for corporate actions affecting your holdings, but it is not infallible — relying solely on automated notifications is inadequate. Develop a weekly discipline of reviewing NEPSE's announcement page for each company in your portfolio. Additionally, following the financial press — the Annapurna Post's business section, Karobar daily, and online portals such as Sharesansar and Merolagani — ensures you receive timely, interpreted information about corporate actions alongside the raw regulatory announcements.
Corporate Action
Key Investor Action Required
Cash Dividend Announced
Verify book closure date; ensure shares are held in BOID before record date.
Bonus Share Announced
Await BOID credit; adjust cost basis calculations after credit.
Rights Issue Announced
Decide: exercise, sell, or let expire. Submit application before deadline.
AGM Scheduled
Review agenda; proxy voting or attendance if stakes are material.
Book Closure / Record Date
Do not sell before record date if you wish to receive entitlement.
Chapter recap
Chapter 4 has equipped you with a complete operational map of how NEPSE actually functions at the mechanical level. You understand TNET's order-driven, price-time priority matching system and why that matters for execution. You know the trading hours structure, the pre-open session's role in price discovery, and the risk of Nepal's compressed trading window. You can distinguish between the order types available — market and limit — and understand clearly which instruments are absent and what risks their absence creates. You appreciate the dynamics of thin order books, wide spreads, and shallow depth that characterise most NEPSE listings.
You have examined circuit limits both as protection and as potential trap. You have internalized the cash-management implications of T+2 settlement. You know MEROSHARE as the operational centre of demat accounts, EDIS authorisation, IPO applications, and portfolio record-keeping. You understand the trade-offs between broker-assisted and self-directed trading. And you have confronted the structural consequences of NEPSE's prohibition on short selling — its effect on price discovery, on the persistence of overvaluation, and on the premium that must be placed on fundamental discipline.
Finally, you understand how value is distributed through dividends, bonus shares, and rights issues — the timelines, the processes, and the actions required of you as an investor to protect and exercise your entitlements. These mechanics are not bureaucratic tedium. They are the terrain on which your money operates. Master the terrain.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part II · Chapter 11
The Liquidity Problem — NEPSE’s Most Critical Structural Characteristic
First published 21 Aug 2026 · Last verified 29 Aug 2026
A market is only as useful as its ability to let you exit. Without liquidity, a price is merely an opinion.
Of all the structural realities that an investor in the Nepal Stock Exchange must internalize, none is more fundamental, more consequential, or more systematically underappreciated than the problem of liquidity. Newcomers to NEPSE often approach the market through the lens of valuation — searching for underpriced shares, comparing price-to-earnings ratios, studying dividend histories. This instinct is not wrong, but it is incomplete. In a market where liquidity is deep and reliable, valuation dominates the conversation. In a market like NEPSE, where liquidity is thin, unpredictable, and structurally constrained, liquidity itself becomes the primary variable that determines whether your investment thesis can ever be executed in practice.
This chapter is devoted entirely to understanding that problem. We will examine what liquidity means in the context of NEPSE, how thin it actually is when you look at the data, why it is structurally thin and likely to remain so, and what practical consequences follow for every investor who participates in this market. The lessons here are not peripheral concerns to be noted in passing — they are foundational principles that should inform the architecture of every portfolio, the sizing of every position, and the construction of every exit plan.
Lesson 11.1 — Why Liquidity Risk Is the Foundation of All NEPSE Investment Decisions
Defining Liquidity in the Context of an Emerging Frontier Market
Liquidity, at its most essential, refers to the ability to convert an asset into cash quickly, at a price close to its last quoted price, and in a quantity that meets your needs. This definition contains three distinct dimensions that are often conflated: speed, price impact, and depth. A liquid market offers all three simultaneously — you can sell large quantities quickly without meaningfully moving the price. An illiquid market fails on one or more of these dimensions, and the consequences of that failure can range from inconvenient to catastrophic depending on the investor's circumstances.
In the world of developed equity markets — the NYSE, the London Stock Exchange, the Tokyo Stock Exchange — liquidity is so abundant that most retail investors never encounter its limits. The market for Apple shares, for instance, turns over billions of dollars in a single trading session. An investor holding a few thousand dollars' worth of Apple can exit within seconds at a price within a fraction of a percent of the midpoint. Liquidity is essentially free and ubiquitous. The same investor, trained in this environment and then deploying capital in NEPSE, carries assumptions that are not just imprecise in the Nepali context — they are dangerously wrong.
NEPSE is classified as a frontier market, a category that sits below emerging markets in the international classification hierarchy. Frontier markets are characterised by limited market infrastructure, nascent regulatory frameworks, restricted foreign participation, and crucially, thin trading volumes. Among these characteristics, thin volume is not merely a symptom but a cause — it creates a self-reinforcing dynamic in which low liquidity discourages institutional participation, the absence of institutions keeps volume low, and low volume perpetuates illiquidity. Breaking this cycle requires deliberate policy intervention and structural reform, neither of which has occurred at sufficient scale in Nepal to date.
Three Forms of Liquidity Risk Unique to NEPSE
Investors in NEPSE face three distinct forms of liquidity risk, each operating through a different mechanism but each ultimately capable of the same result: the inability to execute a desired transaction at a reasonable price or within a reasonable time frame.
The first is what we might call market-wide liquidity risk — the risk that the entire market enters a period of dramatically reduced trading volume, making it difficult or impossible to exit any position regardless of which stock you hold. This occurs in NEPSE with a regularity that would be alarming by the standards of developed markets. Fiscal year transitions, periods of political uncertainty, post-bonus season lulls, and sentiment-driven panics have all produced extended periods in which daily market turnover drops to levels where even modest-sized positions cannot be liquidated without severely impacting price. During the bear market of 2021 to 2022, for instance, trading volume on many sessions fell to levels where the entire market was turning over less than NPR 500 million per day — a figure so low that even mid-sized retail investors holding positions worth NPR 5 to 10 million would need to accept substantial price concessions to exit.
The second form is stock-specific liquidity risk — the risk that the particular shares you hold happen to be among the least actively traded in an already thin market. NEPSE lists hundreds of companies across multiple sectors, and their trading activity is not evenly distributed. The top ten most actively traded stocks often account for fifty percent or more of daily market turnover. At the other extreme are dozens of companies that may trade on fewer than half the available trading sessions in a month, and when they do trade, the volume is measured in hundreds or even tens of shares. An investor who buys into one of these illiquid counters, attracted perhaps by a compelling valuation or a generous dividend, may find that the entry was easy to execute but the exit is functionally impossible at any rational price.
The third form is position-size liquidity risk — the risk that the size of your position, relative to the typical daily trading volume of that stock, is so large that selling it will require either accepting significant price concessions or spreading the exit over an extended period during which market conditions may deteriorate further. This form of liquidity risk is subtle and often ignored by investors who focus only on whether a stock trades at all, rather than on whether it trades in volumes sufficient to absorb their position. A stock that turns over NPR 2 million per day may appear perfectly liquid to an investor holding NPR 200,000 worth of shares, but becomes deeply illiquid to an investor holding NPR 10 million. The same stock, the same market — two entirely different liquidity experiences.
KEY PRINCIPLE
Liquidity risk in NEPSE is not binary. It exists on a continuous spectrum shaped by market conditions, stock selection, and position size. Understanding your own liquidity profile — not the market's in the abstract — is the first act of disciplined NEPSE investing.
Why Liquidity Must Come Before Valuation
There is a logical priority to the questions a NEPSE investor must ask before committing capital. Valuation analysis — the estimation of a company's intrinsic worth relative to its market price — is a necessary component of investment decision-making, but it is not the first question. The first question is whether the position can be practically managed, which is fundamentally a question of liquidity.
Consider the following scenario: an investor conducts thorough analysis of a hydropower company listed on NEPSE and concludes that its shares, currently trading at NPR 280, have an intrinsic value of NPR 420. This is a compelling discount of thirty-three percent. The investor purchases shares worth NPR 5 million. Two years later, events develop as anticipated — the company completes its construction phase, begins power generation, and reports strong earnings. But the market, for reasons that have nothing to do with the company's fundamentals, is in a prolonged bear phase driven by macroeconomic concerns. Daily volume across the entire NEPSE has collapsed. The hydropower sector, structurally among the least liquid in the market, is barely trading. The investor's shares may indeed be worth NPR 420 by any reasonable measure, but they cannot be sold at that price — or any price close to it — because there are simply no buyers willing to purchase at that level in any meaningful volume. The valuation was correct. The investment thesis was correct. And yet the investment, from a practical standpoint, is temporarily or even permanently impaired.
This scenario is not hypothetical. It describes the experience of a significant portion of NEPSE's retail investor base during every cyclical downturn the market has experienced. The lesson is not that valuation is irrelevant — it is that valuation analysis conducted without a parallel and rigorous assessment of liquidity is necessarily incomplete. In NEPSE, a position that cannot be exited is not an investment. It is a forced holding, and the returns it ultimately delivers will be determined as much by the timing of market liquidity recovery as by any fundamental characteristic of the underlying company.
Lesson 11.2 — Average Daily Turnover on NEPSE: Data, Context, and Structural Reasons
The Raw Numbers
To understand NEPSE's liquidity problem concretely, we must begin with the data. The Nepal Stock Exchange's daily trading turnover — the total value of shares bought and sold on a given session — is the most direct measure of market-wide liquidity available. What the historical data reveals is sobering.
During the bull market peak of fiscal year 2020/21, NEPSE recorded its highest-ever trading activity, with average daily turnover approaching NPR 15 to 20 billion on peak days. This was an aberration produced by a unique combination of circumstances: post-pandemic monetary stimulus driving excess liquidity into equities, a surge of first-time retail investors opening demat accounts, and widespread social media-driven speculation particularly in hydropower and development bank sectors. These conditions were exceptional and temporary.
In more typical market environments, average daily turnover on NEPSE runs in the range of NPR 2 billion to NPR 6 billion, with significant variation across the trading year. During bear phases and periods of low sentiment, daily turnover frequently falls below NPR 1 billion. To put this in international context: the daily turnover of the Colombo Stock Exchange in Sri Lanka — a market of comparable size in terms of number of listed companies — typically ranges from USD 5 to 15 million, roughly NPR 660 million to NPR 2 billion, suggesting that NEPSE's liquidity, while thin by any international standard, is not extraordinarily outlying among South Asian frontier peers. What matters for NEPSE investors, however, is not the international comparison but the absolute figure, because it is the absolute figure that determines whether any given position can be practically managed.
NPR 3 billion in daily turnover sounds like a large number until you distribute it across all the listed securities, weight it by sector, and think carefully about what it means for any investor trying to build or exit a meaningful position. Spread across 200-plus listed companies, the average company sees turnover of perhaps NPR 15 million per day — and that average is heavily skewed by a handful of large, highly liquid companies. The median company sees far less. Removing the top twenty most liquid stocks from the calculation, the average daily turnover for the remaining listed companies falls to levels that make serious position-building difficult and orderly liquidation nearly impossible.
Structural Reasons for Thin Turnover
The thinness of NEPSE's daily turnover is not an accident or a temporary condition awaiting resolution. It reflects structural characteristics of the Nepali economy, financial system, and regulatory environment that are deeply rooted and slow to change.
Nepal's economy remains heavily dependent on remittances and agriculture, with a relatively small formal corporate sector. The companies listed on NEPSE are not, in the main, the commanding heights of a diversified industrial economy — they are primarily banks, finance companies, insurance companies, and hydropower developers. This sectoral concentration means that the range of businesses available to equity investors is narrow, limiting the natural investor base to those with views on the financial sector and, to a lesser extent, on power generation. The absence of listed manufacturing companies, consumer goods companies, technology companies, and service sector companies limits the appeal of NEPSE equity to certain categories of domestic and foreign investors.
Foreign investment in NEPSE shares remains subject to restrictions that have historically kept foreign portfolio investors from participating meaningfully in the market. Until relatively recently, foreign investors had no straightforward mechanism to invest in NEPSE-listed securities, and even after regulatory reforms opened limited pathways for foreign investment, the practical barriers — currency controls, custodial arrangements, repatriation provisions — kept international portfolio flows minimal. The absence of foreign institutional capital means the market lacks a category of participant that, in most emerging and frontier markets, provides both depth and discipline: foreign funds tend to be more analytically rigorous, more willing to hold through volatility, and more capable of providing the buy-side depth needed to support orderly price discovery.
Domestic institutional participation is also structurally limited. Nepal's mutual fund industry, while growing, remains small in aggregate asset terms relative to the size of the listed equity universe. Pension funds, which represent the single largest pool of long-term institutional capital in most developed markets, operate under a regulatory framework in Nepal that has historically directed their investments heavily toward government securities and fixed deposits rather than equities. Insurance companies face similar regulatory constraints. The result is that institutional demand for equities, which in more developed markets serves as a stabilising force that absorbs retail selling during downturns and provides consistent buying pressure during periods of dislocation, is largely absent from NEPSE in any scale sufficient to affect market dynamics.
What remains, after removing foreign institutions and domestic institutions, is a market that is overwhelmingly dominated by retail investors — individual Nepali citizens saving and speculating with personal capital. Retail-dominated markets are inherently more volatile and less liquid than institutionally driven markets, for reasons that are well understood in market microstructure theory. Retail investors tend to trade in smaller size, hold shorter time horizons, respond more dramatically to sentiment and news flow, and cluster their activity in well-known, widely discussed names rather than distributing capital across the market. They also tend to exit simultaneously during downturns — a behaviour pattern that converts ordinary market corrections into liquidity crises as everyone attempts to sell to a market with no institutional buyers to absorb the flow.
STRUCTURAL INSIGHT
NEPSE's liquidity problem is not a phase. It is an architecture. Until Nepal develops deep domestic institutional capital, meaningful foreign portfolio access, and a broader corporate listing universe, daily turnover will remain structurally constrained — with all the consequences that follow.
Lesson 11.3 — Floating Stock and Lock-In: Why Most NEPSE Shares Cannot Actually Be Traded
The Deceptive Arithmetic of Market Capitalisation
One of the most commonly misunderstood aspects of NEPSE's liquidity problem concerns the relationship between a company's total shares outstanding and the shares that are actually available for trading at any given time. These two figures can be radically different, and the gap between them goes a long way toward explaining why daily trading volumes are so much thinner than a naive reading of market capitalisation figures would suggest.
When you look at NEPSE's total market capitalisation — the aggregate value of all listed companies' shares at their current market prices — you encounter a figure in the trillions of Nepali rupees. This number, often cited in financial news coverage of the exchange, creates an impression of a substantial and liquid market. It is a misleading impression. Market capitalisation measures the theoretical value of all shares as if they could all be simultaneously bought or sold at today's price, but it reveals nothing about what fraction of those shares are actually available for trading. That fraction — the free float — is the number that matters for liquidity analysis, and in the context of NEPSE it is dramatically smaller than total shares outstanding.
Promoter Lock-In and Its Scale
The single largest structural constraint on NEPSE's tradeable share supply is the promoter share lock-in system. When a company conducts an initial public offering in Nepal, its promoter shareholders — the founders, original investors, and often the government in the case of state-owned enterprises — are required to hold their shares for a defined lock-in period, during which those shares cannot be sold on the open market. The duration and conditions of these lock-in periods have varied over time as the Securities Board of Nepal has adjusted its regulations, but the fundamental effect is consistent: a significant portion of every company's total shares is simply unavailable for trading, often for years after listing.
The scale of this effect varies by company and sector, but it is not unusual for promoter shares to represent fifty to sixty percent of total shares outstanding in companies that have recently completed their IPO process. In some cases, particularly in hydropower companies and infrastructure developers that list after years of private construction-phase financing, promoter holdings can exceed seventy percent of total shares. This means that the publicly tradeable float — the shares available for ordinary investors to buy and sell — may represent only twenty to forty percent of a company's capitalisation. Applied to a market already thin by international standards, this structural reduction in tradeable supply compounds the liquidity problem substantially.
The promoter lock-in system was designed with sound intentions: to prevent founders and original investors from immediately dumping their holdings after an IPO at prices inflated by retail investor enthusiasm, thereby protecting the interest of public shareholders who buy at listing. As a governance measure, it is defensible. As a liquidity constraint, its effects are significant and unavoidable. The investor who does not account for the float reduction when assessing a company's liquidity is working with incomplete information.
Bonus Share Accumulation and the Retail Holding Mentality
Beyond promoter lock-in, NEPSE liquidity is further constrained by a behavioural pattern among retail investors that compounds the structural float problem: the tendency to hold shares indefinitely rather than actively trading them. This tendency is particularly pronounced in Nepal's investment culture, where equities are often purchased as long-term savings vehicles, with shares held through multiple business cycles and across family generations. This is not inherently irrational — patient capital that participates in long-term compounding is a sound strategy — but its aggregate effect on market liquidity is to further reduce the effective supply of shares available for active trading.
Nepal's stock market has a long tradition of rewarding shareholders through bonus shares — the distribution of additional shares in lieu of or in addition to cash dividends. While bonus shares do not represent genuine economic value creation (they dilute per-share earnings while increasing total shares outstanding), they have a powerful psychological appeal to retail investors who equate receiving more shares with tangible wealth accumulation. The result is a dynamic in which investors who might otherwise sell their holdings at attractive prices are instead motivated to hold them in anticipation of the next bonus announcement. This voluntary illiquidity — rational at the individual level, problematic at the market level — further compresses the effective float.
When you combine structural lock-in from promoter shares, the behavioural tendency toward long-term holding, and the concentration of active trading among a small subset of retail participants who are themselves sensitive to market sentiment, you arrive at a market where the genuinely active float on any given day may be a fraction of even the already-reduced public float. The market, in other words, is thinner than the numbers suggest at every level of analysis.
Lesson 11.4 — The Impact of Thin Liquidity on Price Discovery
What Price Discovery Actually Requires
Price discovery is the process by which a market's collective activity produces a price that reflects the best available estimate of an asset's true value given all publicly available information. For price discovery to function well, a market needs active participation from a diverse range of buyers and sellers with different time horizons, different information sets, different risk tolerances, and different views on value. When this diversity is present and participants can freely express their views by buying and selling, the resulting price is a reasonably reliable signal of consensus value.
When liquidity is thin, this process breaks down in predictable ways. With fewer participants active on any given day, the range of information and perspectives represented in the price-setting process narrows. A single large buyer or seller — representing perhaps five or ten percent of a session's total volume — can move a stock price by amounts that bear no relationship to any change in the company's underlying business or prospects. The price signal becomes noisy, unreliable, and potentially misleading to investors who treat quoted prices as accurate reflections of fundamental value.
Artificial Volatility and Its Consequences
One of the most practically significant consequences of thin liquidity on NEPSE is the amplification of price volatility beyond what would be justified by any legitimate new information about listed companies. In liquid markets, price discovery is a continuous, gradual process in which small buy and sell orders constantly adjust prices in response to new information. The presence of many participants means that any single order, unless extraordinarily large, has a negligible price impact. In NEPSE's thin market, the discrete arrival of even moderately sized orders can produce price movements that, in absolute percentage terms, look dramatic.
This artificial volatility creates two distinct problems for the NEPSE investor. The first is the risk of entering a position at an artificially inflated price produced by a brief surge of buying interest that does not represent the market's genuine long-term assessment. During NEPSE bull phases, stocks frequently run up ten, fifteen, or twenty percent in a matter of days on volume that, in absolute terms, is extremely modest. An investor purchasing into such a move, believing they are participating in a genuine revaluation of the company's prospects, may in fact be simply providing exit liquidity to earlier buyers. When the thin buying interest exhausts itself, the stock drifts back — or falls sharply — to its previous range.
The second problem is the difficulty of distinguishing genuine information-driven price movements from liquidity-driven noise. When a stock rises fifteen percent on NEPSE, the investor faces a genuine epistemological challenge: has new information emerged that justifies this revaluation, or has a handful of buyers simply overwhelmed a thin order book? In liquid markets, this question is generally easier to answer because genuine information-driven moves are accompanied by sustained, high-volume buying across multiple sessions, while liquidity-driven spikes tend to reverse quickly. In NEPSE's thin market, even genuine information-driven moves look similar in structure to liquidity-driven spikes because volume is always modest and reversals are common regardless of the nature of the original catalyst.
In a thin market, price is not what the company is worth. Price is what the last buyer paid and the last seller accepted, in a transaction that may have involved only a few thousand shares. Treat it accordingly.
Bid-Ask Spreads and Their Hidden Cost
Price discovery failures in thin markets are also expressed through abnormally wide bid-ask spreads — the gap between the price at which a buyer is willing to purchase and the price at which a seller is willing to sell. In highly liquid markets, competition among market makers and the volume of activity narrows bid-ask spreads to fractions of a percent, making round-trip transaction costs almost negligible. In NEPSE, where there is no market-making infrastructure and bid-ask spreads are set entirely by the meeting of retail buy and sell orders, spreads can be meaningfully wider.
For stocks with reasonable liquidity, NEPSE spreads may represent half a percent to one percent of the stock price — not ideal, but manageable. For thinly traded stocks, particularly in the development bank, microfinance, and certain hydropower sectors, spreads can exceed three to five percent. This means that an investor who buys and immediately sells faces a transaction cost — entirely separate from brokerage fees — of that spread magnitude. For a strategy that requires multiple entries and exits, or for an investor who discovers after purchase that they need to exit quickly, these spreads compound into a significant and often underestimated drag on returns.
Lesson 11.5 — Sector-Wise Liquidity: Banking Most Liquid, Hydropower Least
The Liquidity Hierarchy Across NEPSE Sectors
NEPSE's liquidity is not uniformly distributed across its listed sectors. Different industries exhibit dramatically different trading activity profiles, and understanding this hierarchy is essential for any investor who wants to calibrate position sizes, exit strategies, and liquidity risk exposures appropriately.
At the top of the liquidity hierarchy sit Nepal's commercial banks. The banking sector is, by a significant margin, the most actively traded segment of NEPSE. Commercial bank shares account for a disproportionate share of daily trading volume relative to their weight in the overall market capitalisation. Several structural factors explain this. Commercial banks are among the oldest and most widely held listed companies in Nepal — their shares are owned by a broad base of investors who have accumulated positions over many years, creating a deep pool of potential sellers at any price level. Their businesses are relatively easy for retail investors to understand and analyse, making them accessible to the widest possible range of participants. Their regulatory environment, while complex, is consistently visible through Nepal Rastra Bank publications and guidance, creating shared information frameworks that facilitate price formation. And their bonus share and dividend histories make them perennial subjects of retail investor interest and discussion.
Below commercial banks in the liquidity hierarchy are development banks and finance companies, which see moderate trading activity. Development banks are smaller and more numerous than commercial banks, their financials are somewhat less transparent, and their businesses are more geographically concentrated — all characteristics that reduce their appeal to the broadest cross-section of investors. Finance companies are even smaller, and following the wave of consolidation and regulatory tightening that the sector experienced in the mid-2010s, several have seen their trading volumes decline substantially.
Insurance companies occupy a middle tier in the liquidity hierarchy. The mandatory insurance provisions embedded in various aspects of Nepal's economic activity — compulsory vehicle insurance, requirements for project financing — create a stable revenue base for listed insurers, and their share prices have historically responded positively to premium growth. Trading volumes are moderate and tend to be higher during periods of sector-specific news such as regulatory changes or large claim events.
At or near the bottom of NEPSE's sector liquidity hierarchy sits hydropower — a sector that is, in many ways, structurally the most compelling long-term investment story in Nepal, and simultaneously among the most difficult to trade. The combination of a genuinely exciting fundamental narrative and deep structural illiquidity makes hydropower one of the most important case studies in NEPSE investment discipline.
The fundamental case for hydropower investment in Nepal is well understood. Nepal possesses some of the world's most significant technically and economically viable hydropower potential, estimated at over 40,000 megawatts of commercially feasible capacity. Against this enormous potential, current installed generation capacity remains a fraction of what is technically available. As Nepal gradually develops this resource — through domestic IPP developers, government projects, and international joint ventures — the companies involved stand to benefit from decade-long periods of near-guaranteed revenue from power purchase agreements with Nepal Electricity Authority. The long-term case for patient hydropower investment is legitimately attractive.
But the structural liquidity characteristics of listed hydropower companies are deeply unfavorable for active investors or anyone with a time horizon shorter than several years. Promoter lock-in provisions are particularly common and lengthy in the hydropower sector, where original project developers and financial institution lenders often hold large equity stakes that cannot be sold for extended periods. The industry's investor base tends toward long-term institutional and quasi-institutional holders — cooperatives, local communities, and project sponsors — rather than active retail traders. Trading volumes, as a result, are consistently among the lowest in the market on a per-company basis. During bear phases, many hydropower stocks may go entire weeks without executing a single trade.
This creates a stark asymmetry. Entering a hydropower position is generally possible — sellers can usually be found, particularly during bear markets when distressed holders seek to exit. Exiting a hydropower position of any meaningful size is a fundamentally different experience. An investor holding NPR 3 million in a mid-tier hydropower company may find, when they attempt to sell, that the market simply has no buyers — not at their preferred price, not at any price, on any given day. This is not a temporary inconvenience. It is the normal operating condition of that segment of the market.
SECTOR INSIGHT
Hydropower offers some of NEPSE's most compelling fundamental narratives and some of its most intractable liquidity challenges. The two characteristics are not in conflict — they both follow from the same structural reality of Nepal's capital markets. The investor who accepts both simultaneously, and plans accordingly, is better positioned than one who only sees the opportunity.
The microfinance sector deserves special mention in any discussion of NEPSE liquidity. Microfinance institutions were among the fastest-growing listing categories in NEPSE during the 2015 to 2020 period, and their shares attracted significant retail interest, particularly from investors attracted to high dividend yields and bonus share distributions. However, microfinance shares are among the most thinly traded on the exchange. The sector's retail investor base tends toward long-term holding, the companies themselves are geographically dispersed and analytically opaque to most investors, and the regulatory environment for microfinance has become increasingly uncertain, further dampening active trading interest. An investor in microfinance shares should, as a baseline assumption, assume that meaningful position liquidation will require either a bull market for the sector specifically or an extended period of patient, gradual selling.
Lesson 11.6 — Practical Consequences: Why Large Positions Cannot Be Exited Quickly
The Mathematics of Exit
The practical consequences of NEPSE's thin liquidity become most vivid when you approach them through the arithmetic of position exit. Consider the question concretely: if you own shares in a company and wish to sell them, how long will it take, and at what cost?
The conventional wisdom in liquid markets suggests that a position should be liquidatable within a reasonable time frame if it does not exceed a defined percentage of average daily volume — typically five to ten percent in well-managed institutional portfolios, with some managers willing to go to twenty percent with appropriate time horizons. This rule of thumb exists because selling too large a fraction of daily volume creates price impact: your selling pressure itself depresses the price you receive, creating a negative feedback loop where larger selling leads to lower prices, which in turn invites additional selling from stop-loss triggers and panic-stricken retail holders.
Apply this arithmetic to NEPSE. Consider a reasonably active commercial bank stock that trades an average of NPR 15 million per day. An investor holding NPR 30 million worth of this stock — a substantial but not extraordinary position for a successful Nepali retail investor or small fund — faces a practical exit timeline of at least twenty trading sessions if they wish to limit their daily selling to ten percent of average volume and avoid creating significant price impact. Twenty trading sessions is a full calendar month of trading. During that month, the market can move substantially, external conditions can change, and the investor's price assumptions will be tested repeatedly. If market conditions deteriorate during that month, the last tranches of the position will be sold at progressively lower prices than the first.
For less liquid stocks — say, a mid-tier hydropower company trading NPR 2 million per day — the same NPR 30 million position implies a minimum exit timeline of one hundred and fifty sessions at the ten-percent-of-volume rule. One hundred and fifty sessions is approximately eight months of trading. For most investors, this is not a practical framework for managing risk. It is simply holding the position for an extended period while hoping conditions improve.
The Illusion of Entry-Exit Symmetry
One of the most psychologically damaging illusions in NEPSE investing is the assumption that because you were able to enter a position, you will be able to exit it under comparable conditions. This assumption of symmetry is intuitive but incorrect in thin markets, and understanding why it fails is important for any serious investor.
Entry into a NEPSE position typically occurs during a period of market interest in the stock or sector — perhaps during a bull phase, or following a positive development that has attracted buying attention. During such periods, the order book has active sellers and the investor can accumulate shares over multiple sessions without extraordinary difficulty, provided they are patient and do not insist on immediate execution. The market's momentum, in some sense, works in the entrant's favour: rising prices attract more buyers, volume is above average, and the cost of building a position is relatively modest.
Exit, however, tends to be attempted under exactly opposite conditions. Investors typically need to liquidate — or want to liquidate — when market conditions have deteriorated, sentiment has shifted, or a specific negative development has emerged. These are precisely the conditions under which thin-market illiquidity is most acute. As prices decline, sellers multiply and buyers retreat. The order book fills with sell orders and empties of buy orders. Volume can fall sharply even as the number of investors wanting to sell increases. The investor who entered gradually and comfortably during a period of positive momentum discovers that exiting during a period of negative momentum is a fundamentally different and far more costly exercise.
This asymmetry is not unique to NEPSE — it exists in all markets to some degree — but in NEPSE it is severely amplified by the structural thinness of the market and the absence of institutional buyers who might otherwise step in to provide buy-side depth during selloffs. The NEPSE investor must, therefore, build exit plans before they are needed, design positions with liquidity constraints explicitly in mind, and resist the cognitive bias that treats the ease of entry as evidence of future ease of exit.
Practical Position Sizing Principles
Given the practical constraints of NEPSE liquidity, prudent position sizing requires that every investment decision be accompanied by an explicit assessment of exit feasibility. This means calculating, for any contemplated position, the number of trading sessions that would be required to liquidate it under a conservative assumption about daily volume participation — say, the ability to sell ten percent of average daily volume per session without creating significant price impact.
A position that requires more than thirty to forty trading sessions to exit under normal market conditions should be considered illiquid for practical purposes, and the investor should ask whether their investment horizon and risk tolerance are genuinely compatible with holding that position through the range of scenarios that could develop over that time frame. If the honest answer is no — if there is any scenario under which the investor might need or want to exit within a shorter time frame — then the position is too large, the stock is too illiquid, or both.
Diversification across liquidity tiers is also a practical necessity in NEPSE portfolio construction. A portfolio composed entirely of illiquid small-cap hydropower and microfinance companies may offer attractive valuations, but it is a portfolio that cannot be managed dynamically — that cannot respond to new information by reducing exposures, that cannot be liquidated in response to changing personal financial circumstances, and that is dependent entirely on market-wide liquidity recovery for any meaningful exit. Balancing such holdings with positions in more liquid commercial bank shares or other high-volume counters provides the portfolio with the flexibility to respond to circumstances as they evolve.
Lesson 11.7 — Liquidity Cycles in NEPSE: What Drives Volume Spikes and Droughts
The Seasonal and Cyclical Patterns
NEPSE's trading volume is not static — it moves through recognizable cycles driven by a combination of seasonal patterns, monetary conditions, regulatory events, and broader sentiment dynamics. Understanding these cycles is useful because it allows the disciplined investor to anticipate periods of enhanced liquidity, which are the appropriate times to make large purchases or sales, and periods of suppressed liquidity, during which position adjustments should be minimised or suspended.
The most consistent seasonal pattern in NEPSE volume relates to Nepal's fiscal year, which runs from mid-July to mid-July. The months of Falgun through Jestha (approximately February through June in the Gregorian calendar) tend to coincide with elevated trading activity driven in part by the publication of annual reports, dividend and bonus share announcements, and the deployment of profits and bonuses accumulated during the prior fiscal year. Investors who have received bonus shares from prior year holdings often become more active traders during this period, either selling bonus shares to fund personal expenses or reinvesting dividends into new positions. Volume tends to be above average during this window.
Conversely, the post-Dasain period (typically mid-October through December) often produces a lull in trading activity. The major festivals of the Nepali calendar — Dashain, Tihar, and Chhath — consume significant personal time and financial attention, and many retail investors reduce their market activity during and immediately following these celebrations. Volume tends to contract during this period, and with it, effective market liquidity.
Monetary Policy and its Dominant Influence
Among all the forces that drive NEPSE's liquidity cycles, monetary policy — the management of credit availability by Nepal Rastra Bank — exercises arguably the strongest and most consistent influence. The relationship between bank credit availability and NEPSE trading volume is direct and empirically visible: when credit is loose and interest rates are low, retail investors borrow to invest in equities, driving both prices and volumes higher. When credit tightens, margin calls and the rising cost of borrowing force deleveraging, producing simultaneous waves of selling and volume suppression.
Nepal Rastra Bank's credit-to-deposit ratio regulations, its interbank rate management, and its periodic directives about margin lending all have direct and significant effects on the equity market. The 2021 to 2022 bear market, which saw NEPSE index values decline by over fifty percent from peak, was substantially driven by NRB's tightening cycle, which raised lending rates, tightened credit availability, and directly constrained the margin financing that had fuelled the preceding bull phase. As investors were forced to repay margin loans, they sold shares to generate cash, creating a selling cascade in a market with insufficient buy-side depth to absorb it without severe price declines.
The investor who monitors NRB's credit policy signals — interbank rates, credit-to-core capital ratios, lending growth statistics — gains useful advance warning of potential liquidity environment shifts. These signals do not provide precise timing for market moves, but they provide directional evidence about whether the broad monetary environment is likely to support or suppress equity market activity in the coming months.
IPO Activity and its Dual Effect on Market Liquidity
Nepal's regular IPO issuance cycle has a complex and often paradoxical relationship with secondary market liquidity. On one hand, IPO activity introduces new shares into the market over time, gradually expanding the universe of tradeable securities and the potential pool of market participants. On the other hand, the IPO application process absorbs large amounts of investor cash in the short term, as applicants must submit payment with their applications and wait for allotment results before receiving refunds or shares. During periods of heavy IPO activity — when multiple large issues are in their application or allotment phase simultaneously — significant amounts of capital are temporarily immobilized in the IPO process and unavailable for secondary market trading. The result is often a short-term suppression of secondary market volume during peak IPO periods.
The introduction of C-ASBA and other electronic payment mechanisms has reduced but not eliminated this effect. Large IPOs, particularly in the banking and hydropower sectors, can still absorb hundreds of millions of rupees in application capital that would otherwise be available for secondary market activity. The alert investor tracks the IPO calendar not just for potential direct investment opportunities but as a leading indicator of near-term secondary market liquidity conditions.
Sentiment Cascades and the Role of Social Media
In the years since approximately 2015, social media — primarily Facebook and, more recently, TikTok and YouTube — has become a significant driver of NEPSE sentiment and, consequently, of liquidity cycles. This development is not unique to Nepal: social media's influence on retail investor behaviour has been documented in markets worldwide. But in a retail-dominated, institutionally shallow market like NEPSE, social media's influence is proportionally more powerful and can create more dramatic and rapid shifts in trading volume and price.
During bull phases, social media serves as an amplifier of positive sentiment. Groups and channels dedicated to NEPSE discussion proliferate, attracting new participants who may have minimal investment knowledge but are motivated by stories of rapid gains made by early participants. This dynamic accelerates the entry of new, uninformed capital into the market, driving volume and prices simultaneously higher in a self-reinforcing feedback loop. The same dynamic operates in reverse during bear phases: social media becomes a vehicle for panic, loss stories, and recrimination, accelerating the exit of marginal participants and amplifying selling pressure.
For the disciplined investor, social media's influence on NEPSE liquidity cycles offers both a warning and an opportunity. The warning is that volume spikes driven by social media enthusiasm are not evidence of genuine, sustainable liquidity improvement — they reflect the temporary mobilization of speculative retail capital that will be withdrawn when sentiment turns. The opportunity is that the entry of uninformed capital during sentiment peaks provides liquidity for long-term investors who have accumulated positions patiently to exit at favourable prices. This is an uncomfortable but practical truth: the best time to sell is when social media is most enthusiastic and new investors are most eager to buy.
CLOSING PRINCIPLE
Liquidity in NEPSE is not a backdrop condition to be assumed — it is a dynamic variable to be tracked, anticipated, and actively managed. The investor who understands liquidity cycles can use them; the investor who ignores them will be used by them.
Chapter recap
This chapter has covered NEPSE's liquidity problem from multiple angles — definitional, empirical, structural, sectoral, practical, and cyclical. The goal was not to discourage participation in this market but to ensure that participation is undertaken with clear-eyed understanding of the constraints that govern it.
The core insight is simple but consequential: in a thin market, liquidity itself is a resource that must be managed alongside return and risk. Every position you build represents not just a valuation bet and a risk exposure, but a commitment of liquidity capacity — a portion of your portfolio that cannot be easily repurposed in response to new circumstances. Recognising this third dimension of investment decision-making, and allowing it to discipline your position sizing, sector allocation, and exit planning, is what distinguishes the investor who survives and compounds through NEPSE's cycles from the investor who is periodically swept away by them.
The chapters that follow will build on this liquidity foundation, incorporating it into frameworks for position construction, portfolio management, and the tactical decision-making required to navigate the specific characteristics of Nepal's equity market. But the foundation itself — the deep appreciation for the limits that liquidity imposes — is established here, and it does not diminish with the passage of time or the growth of your experience. Every serious NEPSE investor, at every level of sophistication, returns to liquidity analysis as a first principle.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part II · Chapter 12
Market Microstructure Failures in NEPSE
First published 21 Aug 2026 · Last verified 29 Aug 2026
Most books ignore this. Most Nepali investors are harmed by it.
When retail investors in Nepal lose money, they almost always blame themselves. They say they bought at the wrong time, or chose the wrong company, or lacked patience. Rarely do they understand that a significant portion of their losses are not random misfortune but the predictable outcome of a market structure that systematically favours insiders over outsiders. This chapter is about that structure.
Market microstructure is the study of how trading actually happens — the mechanics beneath the prices you see on your screen. It asks: who sets the price? Who gets filled first? Who knows what before you do? Whose order disappears before it should? These questions are usually discussed only in advanced finance textbooks aimed at institutional traders. They are not discussed in Nepali investment seminars, not because they are unimportant, but because the people who benefit from your ignorance of them have little incentive to educate you.
The Nepal Stock Exchange (NEPSE) is a small, shallow market. It has a limited number of actively traded scrips, a thin pool of liquidity, concentrated broker networks, and incomplete regulatory enforcement. These characteristics create a fertile environment for every form of microstructure predation. What might happen occasionally in a deep, well-monitored market like the NYSE happens routinely in NEPSE. Understanding this is not cause for despair but for precision. Once you understand the mechanics, you can trade around them, avoid them, and occasionally use them to your advantage.
This chapter covers eight lessons. The first two establish the conceptual foundation. The next five examine specific failure modes in detail. The last gives you a practical defence. Read carefully. These are not theoretical abstractions — every mechanism described here has real consequences for your portfolio balance.
Lesson 12.1 — What Market Microstructure Means and Why It Matters to a Retail Investor
The Gap Between Price and Execution
When you look at NEPSE and see that a stock is trading at Rs. 450, you are seeing a price. But a price is only the record of the last transaction that happened. It says nothing about whether you can buy at that price, how much you can buy, how quickly your order will execute, or what will happen to the price between the moment you place your order and the moment it fills. The machinery that governs all of these outcomes is market microstructure.
In academic terms, microstructure refers to the process by which investors' latent demands are translated into prices and volumes. The key word is 'process.' Between your desire to buy and the moment you actually own the shares, a complex sequence of events occurs: your order enters a queue, it is matched against other orders, brokers route it through systems, and prices respond. Each of those steps creates an opportunity for someone — broker, insider, or well-connected trader — to extract value from you.
The Three Dimensions of Cost That Most Investors Never See
Retail investors typically think about only one cost: the brokerage commission. In Nepal, the regulated commission schedule makes this cost visible and predictable. But brokerage commission is the smallest part of the real cost of trading. The three true costs are:
The first is the bid-ask spread. At any moment, the best available buyer (the bid) and the best available seller (the ask) are not the same price. When you buy at the ask and immediately need to sell at the bid, you lose the spread before any price movement occurs. In liquid markets, this spread is tiny. In thin NEPSE scrips, the spread can be several percentage points wide.
The second is market impact. When you place a large buy order into a thin market, your own order pushes the price up before it fills completely. You pay more for later shares than you would have for earlier ones. Operators know this and use your market impact against you.
The third is adverse selection. When someone sells to you, they often know something you do not — that the stock is about to fall, that fundamentals have deteriorated, that insiders are exiting. The price at which you buy already incorporates this informational disadvantage. You pay not just for the share but for your own ignorance.
Why NEPSE Amplifies Every Microstructure Problem
Microstructure problems exist in all markets but they are dramatically worse in NEPSE for structural reasons. First, the market has very low float on many scrips — a small number of shares are actually available for public trading, while promoters hold the majority in lock. This thinness means that even modest order flow can move prices substantially.
Second, NEPSE's trading system, while improved over years, still offers far less transparency and speed than markets like BSE or NSE. The information environment is opaque. Financial disclosures arrive slowly, are often incomplete, and are rarely accompanied by management commentary that retail investors can analyse.
Third, the broker community in Nepal is concentrated. A small number of brokers dominate order flow. Broker firms are not merely execution agents — many have proprietary trading positions, are connected to the promoter networks of listed companies, and have information advantages that are structurally built into their business model.
Fourth, the regulator — SEBON — has limited capacity for real-time surveillance. The SEC equivalents in more developed markets deploy algorithmic surveillance to detect wash trading, layering, and spoofing within hours or days. NEPSE enforcement typically operates on a much slower cycle, and many manipulative activities are never prosecuted at all.
CORE CONCEPT
Market microstructure is not an advanced topic for institutional traders. It is the immediate reality that every retail investor lives in every time they trade. Understanding it is not optional. Ignorance of it is measurable in money lost.
Lesson 12.2 — Queue Priority Mechanics: How the Order Queue Works and Who Benefits
The Basic Logic of Price-Time Priority
NEPSE, like most electronic exchanges, operates on a price-time priority system. This means that among all orders at the same price, the one that arrived first gets executed first. If you and another buyer both place limit orders to buy at Rs. 400, and there is only one seller at Rs. 400, the buyer whose order was registered in the system earlier gets the shares. The later buyer waits.
This system sounds perfectly fair, and in isolation it is. The problem is that 'time of arrival' is not simply 'the moment you pressed the buy button.' Time of arrival in an electronic exchange is the microsecond the order reaches the matching engine. The distance between you pressing a button and that order landing at the engine depends on your connection, your broker's system, and the routing path your order takes. For a retail investor in Kathmandu using a mobile app, that path may involve multiple hops and significant latency. For a trader using a direct connection at a broker firm's terminal, that path is much shorter.
The Broker Terminal Advantage
In Nepal, many retail investors place orders through mobile apps or web portals that sit on top of broker systems. These systems add a layer between the investor and the exchange. The broker's own trading desk, however, often uses a direct connection. This means that if the broker's proprietary desk and a retail client decide to buy the same stock at the same price at approximately the same time, the broker's order will almost always arrive at the matching engine first.
This is not illegal in itself — the broker is using their infrastructure advantage. But when the broker has a direct conflict of interest — when they want to buy shares before filling your buy order so they can sell to you at a higher price later — the time priority system becomes a mechanism of extraction rather than fairness.
Queue Position and Price Discovery
Queue position matters not just for execution but for price discovery. When a large order enters NEPSE and starts consuming the order book, the price moves. Traders with early queue positions get filled at earlier, lower prices. Traders at the back of the queue — typically retail investors — get filled at later, higher prices, or do not get filled at all as the price moves away from their limit. They then face a choice: chase the price up (paying more) or miss the opportunity (losing the trading time they allocated).
Sophisticated market participants understand this and deliberately use queue mechanics. They place exploratory orders early in the session, at modest sizes, to establish queue priority. They wait. When institutional or retail demand starts showing up and the price begins to move, their queued orders execute first. They sell into that demand at progressively higher prices.
Spoofing at the Queue Level
Spoofing is the practice of placing large visible orders that a trader has no intention of executing, for the purpose of inducing other participants to behave in ways that benefit the spoofer, after which the fake orders are cancelled. In NEPSE, spoofing occurs at the queue level with some regularity. A large sell order appearing in the queue signals abundant supply and discourages buyers. Retail investors seeing that large sell order at Rs. 450 may conclude: there is heavy resistance here; I will wait. Meanwhile, the operator who placed the fake sell order is buying at lower prices. Once their buy accumulation is complete, the large sell order disappears. Retail investors, seeing the resistance removed, become buyers. The operator sells into their demand.
In more developed markets, sophisticated surveillance software detects spoofing patterns within minutes. In NEPSE, the monitoring is less comprehensive and real-time cancellation patterns are less closely tracked, giving spoofers more time to operate before detection — if they are detected at all.
WARNING
Never interpret a large standing order in the NEPSE order book as confirmation of genuine supply or demand. Large orders can be placed and cancelled within seconds. What you see in the queue at any moment may be a deliberate signal designed to mislead you, not a true reflection of market intention.
Lesson 12.3 — Fake Volume Creation: How Matched Trades Between Connected Parties Simulate Activity
What Wash Trading Is
Wash trading is the practice of simultaneously buying and selling the same security between connected parties — or by the same party using different accounts — with the result that no real change in ownership occurs but transaction volume is artificially inflated. The trades 'wash' against each other. The economic content of such trades is zero, but their appearance in the public trading record creates the impression of activity, interest, and liquidity that does not actually exist.
The purpose of wash trading in NEPSE is almost always to manufacture the appearance of momentum. A scrip that has been dormant for months, with a few thousand shares changing hands per day, is not attractive to retail investors who have been conditioned to look for activity. But a scrip showing tens of thousands of shares per day, with consistent upward price movement, attracts attention. Wash trading generates that signal artificially.
How Connected Parties Execute Wash Trades in NEPSE
The mechanics in NEPSE typically work as follows. A group of operators who control a position in a scrip — typically accumulated during a quiet period — want to attract retail buying interest. They open buy and sell orders at the same or adjacent prices through different but coordinated accounts. These accounts may be registered in the names of family members, associates, shell individuals, or companies that function as nominees.
Because NEPSE matches orders based on price and time, the operators need to ensure their buy and sell orders are the best available on each side, and that no unintended third party steps in between them. They accomplish this by choosing illiquid scrips where the natural order flow is thin — fewer independent traders means less risk of an outsider disrupting the wash. They trade in off-peak hours when surveillance attention is lower. And they carefully size their orders so as to not be conspicuous on any individual ticket while generating meaningful aggregate volume over a session or week.
The Role of Volume in Retail Psychology
The effectiveness of wash trading depends on retail investors treating volume as a reliable signal of genuine interest. And this is a deeply ingrained habit. Volume is the first thing many retail traders look at in NEPSE screeners. When a stock 'breaks out on high volume,' it is treated as confirmation. When a stock rises but on thin volume, the move is treated as suspect. The operators know this habit and exploit it directly.
After a period of wash-traded volume has pushed the price up and attracted retail attention, the operators begin selling their real position into the genuine retail buying interest. The retail investors arrive thinking they are buying into an active, liquid market. They are actually providing the exit liquidity for the operators who manufactured the apparent activity.
Differentiating Genuine Volume From Manufactured Volume
Distinguishing genuine volume from wash-traded volume is not easy, but there are indicators. Genuine buying volume tends to be accompanied by widening participation — more individual trades, more varied trade sizes, more varied timing across the session. Wash-traded volume tends to show a different pattern: trades of suspiciously uniform size, trades clustering at specific times, a ratio of buy-initiated to sell-initiated volume that is unnaturally balanced, and a price that moves suspiciously smoothly upward without the natural volatility that genuine two-sided interest creates.
The most reliable indicator, however, is cross-checking volume surges against any plausible fundamental catalyst. If a company has released strong earnings, received a regulator approval, declared a bonus, or announced a significant business development, rising volume is explicable. If there is no such catalyst, volume surges in illiquid NEPSE scrips deserve deep scepticism.
ANALYTICAL HABIT
For any volume surge in a scrip you are considering, ask: what is the news? If you cannot find a credible fundamental catalyst — earnings, dividends, regulatory event, business development — treat the volume as presumptively manufactured until proven otherwise. The burden of proof should be on the activity, not on your scepticism.
Lesson 12.4 — Broker-Level Order Routing Incentives: Where Your Order Goes and Why It May Not Be in Your Best Interest
The Broker as Intermediary and as Principal
In a well-functioning market, a broker acts as your agent. Their job is to take your order and execute it in a manner that is most favourable to you — getting you the best price, the best fill rate, and the fastest execution. This is called best execution, and regulators in advanced markets require brokers to demonstrate it.
In NEPSE, brokers play a dual role. They are agents for retail clients, yes. But many are also principals — they trade for their own accounts. This creates a fundamental conflict of interest that is rarely disclosed clearly to retail clients and even less rarely resolved in the retail client's favour.
Front-Running: The Classic Broker Conflict
Front-running occurs when a broker uses knowledge of a pending client order to trade for the broker's own account before executing the client's order. If you call your broker and say 'buy me 500 shares of XYZ at market,' and the broker's desk buys 200 shares for its own account first, driving the price slightly higher, and then executes your order at that higher price, your broker has front-run you. You paid more because of your broker's prior action.
In Nepal, front-running is illegal. It is also difficult to prove and rarely prosecuted. The structural conditions that make it attractive are strong: brokers have advance knowledge of order flow, proprietary trading is common, the surveillance infrastructure is limited, and the profit from front-running — while small per trade — is reliable and risk-free across thousands of transactions.
Internalization and Its Costs
A related practice is internalization, where a broker matches your buy order against their own inventory or a sell order from another client rather than sending it to the exchange. In theory this can be neutral or even beneficial if done at a fair price. In practice it can be harmful if the broker uses the internal matching to avoid price discovery — to fill you at a price that is slightly worse than what the open market would have offered, pocketing the difference.
Because retail clients cannot easily monitor where their order was actually executed — was it matched internally or on exchange? at what time? against what counter-party? — the broker has significant latitude to extract value through internalization without detection.
The Consequences for Your Execution Quality
Even if no individual broker act reaches the level of front-running, the aggregate effect of broker-level routing incentives on your execution quality is negative. You will tend to get filled on the least attractive terms that are still consistent with plausible deniability. Your market orders will execute at the top of the spread. Your limit orders will get filled last within their price tier. Your large orders will move the price against you before they complete.
The practical implication is important: your trading costs in NEPSE are higher than the commission schedule suggests. When you factor in adverse execution quality, the real cost of each round trip may be significantly larger than the stated commission.
PRACTICAL RULE
Whenever feasible, use limit orders rather than market orders. Market orders give brokers maximum discretion over your fill price. Limit orders constrain that discretion to a defined price ceiling (for buys) or floor (for sells). You may get filled less often, but when you are filled, the price was acceptable to you rather than determined by the broker's interests.
Lesson 12.5 — Price Ramping in Illiquid Stocks: The Mechanics of How It Is Done
What Price Ramping Is and Why It Works in NEPSE
Price ramping is the deliberate orchestration of a sustained upward price trend in a stock through a combination of controlled buying, volume generation, narrative seeding, and coordinated distribution. It exploits the fact that retail investors are strongly attracted to what has already been going up, and that in a shallow market a relatively small amount of capital, deployed strategically, can produce price movements that look significant.
The mechanics of price ramping in NEPSE are well-established among operators and largely unknown among the retail investors who are its targets. Understanding the full sequence is essential, because each phase of the ramp creates both risks and traps for uninformed buyers.
Phase One: The Quiet Accumulation
The first phase occurs when the scrip is dormant, ignored, and cheap. Operators accumulate shares quietly, in small lots, spread over many days or weeks to avoid triggering attention. They may buy through multiple broker accounts. They do not bid up the price — in fact, they prefer the price to remain flat or drift slightly lower during accumulation, as this allows them to build a larger position at a lower average cost. During this phase, retail investors have no reason to be interested. The stock is boring and shows no price action.
Phase Two: Volume Seeding and Price Ignition
Once operators have accumulated a sufficient position, they begin the second phase: creating the conditions that attract retail attention. They start executing small wash trades to inflate volume. They may plant positive stories about the company — rumours of a dividend announcement, a new project, management changes, regulatory approvals — through informal channels. Social media groups, message boards, and broker-connected analysts become conduits for these narratives.
Then, on a chosen day, operators begin buying more aggressively and lifting the ask price in visible increments. The stock moves 3%, then 5%, then hits the upper circuit limit. Volume on this day is conspicuous. Retail investors who track NEPSE volume leaders see the spike. The positive narrative they have heard makes the movement seem credible. Buying interest begins to form organically.
Phase Three: Retail Participation and Controlled Rally
As retail buying arrives, the operators control the price carefully. They do not simply sell into the initial retail interest — this would cap the price too early and not generate maximum return. Instead, they continue buying alongside retail investors, taking the price higher in visible steps. They let the upper circuit be hit several days running, which is extremely effective retail bait, because a stock that hits upper circuit multiple days in a row appears to be in irresistible demand.
During this phase, operators are simultaneously seeding exits — making small, measured sales that are invisible among the larger volume of genuine retail buying. They are reducing their position gradually while the price is still rising, so that by the time the rally peaks, they have already sold a significant portion of what they accumulated.
Phase Four: Distribution and Exit
The fourth phase is the distribution. The price has reached a level where the operators' remaining position can be sold entirely into retail buying without pushing the price down too quickly. This requires volume — retail buy volume — and the operators often manage this by creating a final buying surge through a climactic positive narrative (a bonus announcement that was always going to happen, a quarterly result that was already known to insiders) timed to coincide with their final exit.
Once distribution is complete, the operators stop supporting the price. The buying pressure that was keeping it elevated disappears. The stock begins to fall. Retail investors who bought at or near the peak are now holding shares no one is willing to buy at the price they paid. They either sell at a loss or hold indefinitely hoping for a recovery that the operators have no interest in supporting.
WARNING
The most dangerous moment to buy any NEPSE scrip is after multiple consecutive upper circuit hits. This is when the ramp is at its most attractive-looking and when operators are closest to their exit. The retail investor who buys after three or four upper circuits often buys the distribution, not the rally.
Lesson 12.6 — Information Asymmetry in NEPSE: Who Knows What and How Early
The Information Hierarchy
All markets operate under information asymmetry — some participants know more than others. What differs between markets is the severity of that asymmetry and the mechanisms by which it is governed. In well-regulated markets, insider trading laws, disclosure requirements, and surveillance technology work to compress the informational gap between informed and uninformed participants. In NEPSE, those compression mechanisms are weaker, and the gap is correspondingly wider.
The information hierarchy in NEPSE, from most to least informed, works approximately as follows. At the top are the promoters and management of listed companies — they know financial results before announcement, know about pending regulatory decisions, know about dividend declarations and rights issues weeks or months before public disclosure. Just below them are the broker networks that maintain close relationships with company promoters — they receive informal signals, can observe unusual corporate activity, and have access to analyst commentary that never reaches the general public. Below that are large institutional investors — mutual funds, insurance companies, banks' investment portfolios — who have dedicated research capacity and earlier access to disclosed information. At the bottom are retail investors — who receive information last, in whatever form it was decided to release it, after it has already been acted on by every tier above them.
Corporate Disclosure Quality in Nepal
Timely and accurate corporate disclosure is the primary mechanism by which regulators attempt to reduce information asymmetry. In Nepal, the quality of corporate disclosure, while improving, remains insufficient for meaningful retail analysis. Financial statements are sometimes delayed. Quarterly reports often lack the granularity needed to assess business trends. Management guidance — where companies explain their future expectations to shareholders — is rare. Significant corporate events, such as the departure of key executives, changes in business strategy, or emerging financial stress, are often disclosed only obliquely or after the fact.
This disclosure gap means that price-sensitive information about NEPSE companies is in the hands of insiders long before it reaches the market. During that gap, insiders can — and do — trade. By the time a quarterly result or dividend announcement is public, the shares have already moved to price in the information. The retail investor who buys on the news is buying at the post-information price, after the informed buyers have already profited.
Social Media as an Information Channel — and as a Manipulation Tool
Social media, particularly Facebook groups and informal WhatsApp networks, have become significant channels through which NEPSE information — and misinformation — travels to retail investors. These channels are fast and accessible. They are also completely unregulated and systematically exploited.
Operators use these channels with precision. They seed positive narratives before price ramps. They spread rumours of dividends, mergers, or regulatory approvals — some of which are true and early, some of which are completely fabricated. They create a sense of urgency ('this will hit upper circuit by tomorrow') designed to cause retail investors to act quickly without verification. Because retail investors in Nepal have learned that informal channels sometimes carry true early information, they are conditioned to act on it. This conditioning is the vulnerability the operators exploit.
The only rational response to social media information in NEPSE is systematic scepticism. The speed with which information travels through these channels does not make it more reliable — it makes it more effective as a manipulation tool, because the operators who plant it can execute their trades before retail investors even read the post.
PRINCIPLE
If you heard about it on social media, someone else acted on it before you. The relevant question is not whether the information is true — it may well be. The relevant question is: at what price does the information represent an opportunity for you, given that others already moved the price? In most cases, social media tips are priced in before you can act safely on them.
Lesson 12.7 — Circuit Limit Exploitation: How Traders Use Circuits to Trap Retail Investors
What Circuit Limits Are and Why They Exist
NEPSE, like most exchanges, applies circuit limits — also called circuit breakers — to individual stocks. These limits prevent a stock's price from moving beyond a set percentage in a single day, either upward (upper circuit) or downward (lower circuit). The limits are designed to prevent extreme volatility, to give participants time to assess whether a sharp price move reflects genuine information or manipulation, and to protect investors from catastrophic single-day losses.
When a stock hits its upper circuit limit, no further transactions can occur above that price for the rest of the session. The stock is frozen at the limit. All pending buy orders that would have transacted above the limit are queued, unsatisfied. The next trading day, the stock may gap up further or may find equilibrium — depending on whether genuine demand continues or the circuit-day buying was manufactured.
The Upper Circuit Trap: Manufactured Scarcity
Operators use upper circuit limits to manufacture artificial scarcity. The mechanism works like this: an operator who holds a significant position in an illiquid scrip places a series of buy orders early in a session, lifting the price steadily toward the upper circuit. Once the circuit is triggered, all subsequent buy orders cannot be filled — there are more buyers than sellers at the limit price, and the sellers have chosen to withhold supply. The stock is visibly 'locked' at the upper circuit.
To a retail investor watching NEPSE, a stock locked at upper circuit for multiple days is an extremely compelling signal. It appears that everyone wants in and no one wants out. The queue of unfilled buy orders grows. The narrative around the stock intensifies. Retail investors, afraid of missing a multi-day run, place increasingly urgent buy orders to try to secure allocation.
What they do not see is that the operators are controlling the supply side. By not selling, or by selling only slowly and selectively, they keep the circuit locked and the queue growing. They choose the day and price at which they begin distributing — when retail impatience and narrative intensity have reached maximum levels — and they sell their position into the frenzied queue of retail buyers who have been waiting for days to get in.
The Lower Circuit Trap: Manufactured Panic
The lower circuit trap works in the opposite direction but with equal precision. An operator who wishes to accumulate shares cheaply, or who has established a short position and wants to force down the price, begins selling aggressively. The selling pushes the price to the lower circuit. Other holders, seeing the lower circuit hit and unable to sell (because all sell orders at the limit are backed up against limited buyers), panic. The stock appears to be in freefall. Retail investors who wanted to sell but could not — the circuit prevented their execution — place sell orders for the next day at any price, ready to exit at a loss just to avoid further decline.
The operator who manufactured the lower circuit is now the buyer of last resort. They purchase the distressed shares being thrown at the market by panicking retail sellers. By the time natural equilibrium reasserts itself, the operator has accumulated a new position at deeply discounted prices, at the expense of retail investors who sold in panic.
The Multi-Day Circuit Sequence
The most sophisticated circuit exploitation involves multiple consecutive circuits in a deliberate sequence. Upper circuits for several days to attract retail buying interest, then a sudden reversal — lower circuits — to shake out the weak-handed retail buyers who entered at the top, who then sell at losses to the same operators who sold to them at higher prices. This full cycle can execute in two to three weeks in a sufficiently illiquid scrip, and the operator who controls it can profit on both the upside (selling to retail buyers during upper circuits) and the downside (buying back from panicking retail sellers during lower circuits).
WARNING
Multiple consecutive upper circuits followed by a sudden reversal to lower circuits is one of the most common and destructive patterns in NEPSE. Retail investors who buy near the peak of a multi-day upper circuit sequence routinely absorb the maximum loss when the reversal comes. If you are considering buying into a stock that has already hit upper circuit three or more consecutive days, you need an extraordinarily strong fundamental reason — not the circuit streak itself — to justify the purchase.
Lesson 12.8 — Protecting Yourself From Microstructure Predation: Practical Rules
The preceding seven lessons have described a set of adversarial mechanisms. This final lesson translates them into actionable defences. None of these rules guarantee that you will never be harmed by market manipulation — in a market like NEPSE, some exposure is unavoidable. But following these rules consistently will dramatically reduce the frequency and severity of the harm you absorb.
Rule 1: Never use market orders in illiquid scrips.
A market order in a thin NEPSE stock is an invitation for adversarial execution. You are saying: I will buy at any price. Brokers and operators will accommodate you at the worst price available. Use limit orders exclusively. Set your limit at a price that reflects your valuation, not the current market momentum. If the stock does not come to your price, you do not buy. This discipline protects you from paying the operator's distribution price.
Rule 2: Treat consecutive upper circuit days as a warning signal, not a buy signal.
The more days a stock has been at upper circuit, the higher the probability that you are looking at an operator's distribution phase rather than a genuine breakout. The time to buy a good company is when no one is talking about it, not when everyone is fighting to get in. Reverse your instinct: excitement in NEPSE is usually a reason for caution.
Rule 3: Require a fundamental catalyst for every volume surge you investigate.
Before you act on any volume spike, ask: what has changed for this company? Earnings release? Dividend announcement? Regulatory approval? New contract? If you cannot identify a credible, verifiable fundamental catalyst within ten minutes of searching, assume the volume is manufactured. Walk away. The opportunity will not be lost — there is no opportunity in wash-traded volume, only the illusion of one.
Rule 4: Never make investment decisions based primarily on social media information.
Social media channels in NEPSE are the final step in the operator's information dissemination chain. By the time you read a tip in a Facebook group, it has already been acted on by the operator, their network, and multiple layers of brokers and insiders. You are reading stale intelligence at the worst possible entry price. Use social media only as a source of leads to investigate through independent fundamental analysis — never as a substitute for it.
Rule 5: Build positions slowly and in tranches for illiquid scrips.
If you are buying a less liquid NEPSE stock, do not place your entire intended position in a single order. Break it into three to five tranches placed over multiple sessions. This reduces your market impact (you are less likely to move the price against yourself), gives you better average pricing, and allows you to observe how the stock responds to your buying before committing fully. If the price surges dramatically after your first small purchase, it may indicate that your order was being front-run — scale back your intended position.
Rule 6: Do not sell in panic during lower circuit sequences.
Lower circuit events are deliberately induced by operators to create panic selling at depressed prices. The worst decision you can make during a lower circuit sequence is to sell at or near the limit out of fear. If your original investment thesis — the fundamental reasons you bought the stock — remains intact, the correct action is usually to hold, and potentially to add at the artificially depressed price. Panic exits feed the operator who manufactured the circuit. The exception is if the lower circuit reflects genuine fundamental deterioration — which is why having an original fundamental thesis is non-negotiable.
Rule 7: Understand your broker's interests and manage the relationship accordingly.
Your broker is not your financial advisor. They have their own trading positions, their own relationships with company promoters, and their own incentives that frequently diverge from yours. Treat your broker as a transaction execution service — nothing more. Do not follow their stock tips unless you have independently verified the underlying analysis. Do not allow their enthusiasm for a particular scrip to substitute for your own valuation work. The brokers who are most enthusiastic about recommending specific stocks are often the ones with the most to gain from your buying.
Rule 8: Maintain a decision log to identify when microstructure vulnerabilities influenced your behaviour.
After each NEPSE investment decision — successful or not — write down the specific reasons you made it. Note whether those reasons included volume signals, circuit-day sequences, social media tips, broker recommendations, or FOMO-driven urgency. Over six months, review your log. You will almost certainly find a pattern: the decisions made under microstructure pressure underperform those made from fundamental analysis. Making this visible is the first step to eliminating it.
A Final Note on Systemic Change
The mechanisms described in this chapter are not the inevitable features of all markets — they are the features of an under-regulated, under-surveilled, informationally opaque market at a relatively early stage of development. NEPSE has made significant improvements in technology and regulation over the past decade, and will likely continue improving. Enforcement is expanding. Algorithmic surveillance is developing. Disclosure standards are rising.
But structural improvement takes time, and in the interim, the retail investor who waits for the system to protect them will absorb losses that a self-educated investor would have avoided. The purpose of this chapter is not to discourage you from participating in NEPSE — which, for all its structural imperfections, remains the most accessible wealth-building vehicle for most Nepali citizens. The purpose is to ensure that when you participate, you do so with accurate information about the environment you are operating in.
Knowledge of how predators operate is not pessimism. It is the precondition for surviving and eventually thriving in any ecosystem.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part III
INSTRUMENTS ACTUALLY AVAILABLE IN NEPAL
Part III · Chapter 13
Ordinary Shares
First published 21 Aug 2026 · Last verified 29 Aug 2026
Lesson 13.1 — What You Own When You Buy a Share: Rights, Claims, Residual Nature
When you purchase an ordinary share in a Nepali company — whether it is a commercial bank listed on the Nepal Stock Exchange (NEPSE), a hydropower project, or a microfinance institution — you are doing something far more consequential than acquiring a piece of paper or a digital record in your DMAT account. You are purchasing a fractional ownership interest in a real, operating enterprise. This distinction between merely holding a security and actually owning part of a business is the most important conceptual foundation any investor can carry into the market.
In Nepal, the governing legal instruments are the Companies Act 2063 (2006) and the Securities Act 2063 (2006), along with their subsequent amendments. The Companies Act defines a share as the right of a shareholder to participate in the company's capital and profits. This deceptively simple definition carries within it a layered bundle of economic rights, governance rights, and residual claims whose character shapes every investment decision you will ever make.
The Bundle of Rights Embedded in a Share
Ownership of an ordinary share confers at minimum four categories of rights that operate simultaneously and often in tension with each other.
The first category is economic rights. As an ordinary shareholder you are entitled to receive dividends when the board recommends and the annual general meeting approves them. In Nepal, dividends may be paid in cash or, more commonly, as bonus shares — an issuance of additional shares in lieu of a cash payout. You are also entitled to participate in any rights offerings the company extends. And most fundamentally, when the company is liquidated, you hold a claim against whatever assets remain after all liabilities and preference claims have been satisfied. That last clause — after all liabilities and preference claims — is the heart of what makes ordinary equity residual in nature.
The second category is governance rights. Each ordinary share carries one vote at the general meeting of shareholders, which in Nepal is governed by the Companies Act. Through this vote you participate in electing the board of directors, approving significant transactions, ratifying the audited financial statements, and authorising the appropriation of profit. In theory, this makes you a principal whose agents — the board and management — owe you fiduciary duties. In practice, the effectiveness of this governance mechanism depends heavily on the concentration of share ownership, the quality of institutional investor participation, and the regulatory oversight exercised by SEBON.
The third category is information rights. SEBON regulations oblige listed companies to publish audited annual reports, quarterly unaudited financials, and material disclosures in a timely fashion. As a shareholder you are entitled to receive these disclosures. You may inspect the register of shareholders. You may attend and speak at the annual general meeting. These rights, modest as they sound, are the foundation of informed ownership.
The fourth category is transferability rights. You may sell your shares on the secondary market through a licensed broker and NEPSE's trading system, or in the case of promoter shares, through regulated over-the-counter mechanisms. This liquidity — the ability to convert your ownership stake back into cash — is what distinguishes equity from starting your own business, and it is why the existence of NEPSE matters so much to the practical functioning of capital markets in Nepal.
The Residual Nature of Ordinary Equity
CORE CONCEPT
Ordinary shareholders stand last in the hierarchy of claims. This is not a disadvantage — it is the reason they are compensated with potentially unlimited upside. Residual nature and equity premium are two sides of the same coin.
The concept of residuality is not merely academic. It has direct, practical consequences for how you think about investment risk in Nepal. Consider the priority waterfall that operates in any Nepali company. When revenues flow in, they are first applied to operating expenses: wages, supplies, rent, utilities. What remains is earnings before interest and taxes. From this, interest on all debt — bank loans, debentures, bonds — is paid. The remaining pre-tax income is then subject to corporate income tax. What is left after tax is net profit, and only this net profit belongs to equity holders as a class. Even then, ordinary shareholders receive it only after any preference dividend obligations are met.
In a liquidation, the waterfall works similarly but with more finality. Secured creditors are paid first from the collateral securing their claims. Unsecured creditors follow. Preference shareholders receive their capital and any accrued preference dividends. Ordinary shareholders receive whatever remains — which in many insolvencies is nothing at all.
This is why the concept of leverage matters so intensely to ordinary equity investors. When a hydropower company finances sixty percent of its project cost with debt, it is placing ordinary shareholders at the base of a tall priority stack. In good years, the high fixed-interest costs are easily covered and the residual profit flowing to shareholders is amplified. In bad years — when the monsoon fails, when power purchase agreement terms are unfavourable, when interest rates rise — that same leverage destroys equity value disproportionately. Understanding that you, as the ordinary shareholder, bear the bottom risk in return for the top reward is not a theoretical nicety. It is the practical reality that should inform every analysis you perform.
Ordinary vs. Preference Shares in Nepal
Nepal's Companies Act permits companies to issue preference shares, which carry a fixed dividend rate and priority over ordinary shares in both income distribution and liquidation. In practice, relatively few listed companies in Nepal have significant preference share capital on their balance sheets compared to many international markets, partly because bank financing has historically been accessible and partly because investor appetite has concentrated on the equity of commercial banks and hydropower companies. Nevertheless, you should always read a company's share capital structure carefully. A company with large preference obligations is one where the residual for ordinary shareholders narrows.
Lesson 13.2 — Face Value in Nepal: The NPR 100 Standard and Its Relevance
In Nepali capital markets, the face value — also called the par value or nominal value — of an ordinary share is almost universally fixed at NPR 100. This is not a coincidence or an arbitrary convention. It is a regulatory standard embedded in company prospectuses, share certificates, and the calculations that govern bonus share issuances, rights offerings, and dividend reporting. Understanding what face value means, what it does not mean, and how it interacts with other valuation measures is essential to reading Nepali financial documents correctly.
What Face Value Is
Face value is the nominal amount per share stated in a company's memorandum of association and endorsed on each share certificate or DMAT record. It represents the minimum price at which shares were originally issued when the company was first incorporated. If a company is established with an authorised capital of NPR 1 billion divided into 10 million shares, each share has a face value of NPR 100. The face value multiplied by the total number of issued shares gives you the paid-up capital, which is the foundation of the company's equity base as shown on its balance sheet.
In Nepal, SEBON's prospectus regulations, the Companies Act, and the Securities Registration and Issuance Regulation all operate around this NPR 100 standard. When a company issues bonus shares — the Nepali equivalent of a stock dividend — those new shares are issued at NPR 100 face value. The accounting entry moves retained earnings or a share premium reserve into paid-up capital at NPR 100 per new share. When rights shares are offered, they are typically offered at NPR 100 or at a modest premium above NPR 100, far below the prevailing market price. This creates the mechanics of rights pricing that we will explore in later chapters.
What Face Value Is Not
CRITICAL WARNING
Face value (NPR 100) tells you almost nothing about what a share is worth. Confusing face value with intrinsic value, book value, or market price is one of the most common and damaging errors made by first-time investors in Nepal.
Face value is not market value. Standard Chartered Bank Nepal's shares may trade at many times their face value of NPR 100. A struggling insurance company's shares may trade below NPR 100. The market price reflects investor expectations about future earnings, the risk of the business, liquidity, and sentiment. None of these factors are captured in the fixed NPR 100 face value figure.
Face value is not book value. Book value per share is derived by dividing total shareholders' equity — paid-up capital plus reserves and surplus minus accumulated losses — by the number of shares. A mature, profitable Nepali commercial bank that has accumulated substantial retained earnings over decades will have a book value per share that may be NPR 200, NPR 300, or more, even though each share still carries a face value of NPR 100.
Face value is not intrinsic value. Intrinsic value is a discounted present value of future cash flows attributable to the share. It may be far above or far below both face value and book value depending on the quality and growth prospects of the business.
Practical Uses of Face Value in Nepali Investing
Despite its limitations as a valuation metric, face value does practical work in several contexts you will encounter repeatedly. First, in dividend calculations, Nepali companies often declare dividends as a percentage of face value rather than as an absolute rupee amount. If a company declares a 15% cash dividend, it means NPR 15 per share (15% of NPR 100 face value). This convention means that a 15% dividend yield in the face-value sense is very different from a 15% yield on the market price paid. Always convert face-value percentage dividends into rupee amounts and then calculate the actual yield based on your cost of acquisition.
Second, bonus share entitlements are computed as a ratio to existing shares at face value. A 20% bonus means one new share for every five held, each new share carrying NPR 100 face value. The accounting reduces the company's free reserves and increases paid-up capital by the equivalent amount. This does not create value ex nihilo; it merely redistributes the equity structure between paid-up capital and reserves.
Third, when you read SEBON filings and annual reports, capital adequacy calculations for banks (based on Nepal Rastra Bank's Basel framework) and solvency calculations for insurance companies are anchored to paid-up capital, which is simply face value times shares outstanding. Regulatory minimum paid-up capital thresholds — such as the NRB directive requiring commercial banks to have paid-up capital of at least NPR 8 billion — are expressed in these terms.
Metric
What It Represents
Face Value
Nominal value per share — NPR 100 in Nepal. Fixed, statutory. Used for dividend %, bonus share accounting, and regulatory capital calculations.
Book Value
Shareholders' equity divided by shares outstanding. Reflects accumulated earnings and losses. Changes every year.
Market Price
Price at which shares actually trade on NEPSE. Reflects market sentiment, earnings expectations, liquidity.
Intrinsic Value
Estimated present value of future cash flows per share. The target of fundamental analysis.
Lesson 13.3 — Market Price vs. Face Value vs. Book Value: The Three Numbers
Any serious analysis of a Nepali listed company requires you to hold three distinct numbers in mind simultaneously: market price, face value, and book value. Each answers a different question, and the relationships between them — the price-to-book ratio, the premium or discount to face value — carry interpretive weight. But they are also capable of misleading you if examined in isolation.
Market Price: What the Market Thinks Today
The market price is the price at which a share last traded on the Nepal Stock Exchange, or the price at which a willing buyer and a willing seller would transact right now. It is the most visible number — it flashes on the NEPSE trading system, it appears in the daily market summary published by NEPSE, and it is the number most casual observers equate with a share's value.
Market prices in Nepal are determined by the forces of supply and demand operating within NEPSE's order-matching system. That system now uses a continuous double-auction mechanism, where buy and sell orders from all NEPSE-licensed brokers are matched electronically. The price reflects the aggregated expectations, information, and sentiments of all active market participants at a given moment.
The difficulty is that market participants in Nepal, as in any emerging market, bring varying degrees of information quality, analytical sophistication, and behavioural rationality to their trading decisions. The market price at any moment is the intersection of all these inputs — which means it may incorporate both genuine fundamental signals and substantial noise. Understanding when price deviates meaningfully from value, and why, is the central challenge of active equity investing.
Book Value: What the Accounts Say
Book value per share is calculated by taking total shareholders' equity from the balance sheet — paid-up capital plus share premium plus general reserve plus retained earnings or accumulated deficit — and dividing by the number of ordinary shares outstanding. In Nepal, the format of financial statements is governed by Nepal Financial Reporting Standards (NFRS), which are largely converged with IFRS.
Book value is a retrospective number. It tells you what accountants have determined the equity is worth based on historical transactions recorded under applicable accounting standards. It is not a forward-looking estimate of what the business can earn. A commercial bank with NPR 500 of book value per share has accumulated that equity through years of profitable operation. A newly listed hydropower company in its first year of commercial operation may show book value close to face value because it has had little time to accumulate earnings.
The price-to-book ratio (P/B ratio) is one of the most frequently cited valuation metrics in the Nepali market. When analysts say a commercial bank is trading at 1.5x book value, they mean the market price is 1.5 times the book value per share. In theory, a business worth buying should have a P/B ratio above 1.0 only if it generates returns on equity above its cost of equity. A company persistently earning return on equity below the cost of equity is one where the book value overstates the economic value to shareholders.
NEPALI CONTEXT
Nepal's listed commercial banks have historically traded at P/B multiples significantly above 1.0x, reflecting market confidence in their earnings power and the scarcity of quality listed investable assets in Nepal. When banking sector P/B multiples compress during credit cycle downturns — as they did during the liquidity crunch of 2078-79 BS — it reflects deteriorating return-on-equity expectations, not a change in face value.
The Price-to-Book Relationship in Practice
Consider a simplified example using a hypothetical Nepali commercial bank. Suppose the bank has paid-up capital of NPR 10 billion, which at NPR 100 face value means 100 million shares outstanding. Its total shareholders' equity (paid-up capital plus reserves) is NPR 25 billion, giving a book value per share of NPR 250. If the market price is NPR 375, the P/B ratio is 1.5x. The share trades at a premium to book value because investors expect future return on equity to justify that premium.
Now suppose earnings deteriorate due to rising non-performing loans. Return on equity drops from 18% to 10%. Investors reprice the shares. The market price falls to NPR 200, a P/B ratio of 0.8x. The book value per share is still NPR 250 — the accounts have not changed — but the market no longer believes the business can earn adequate returns on that book value, so it prices the shares at a discount to it.
This dynamic — the relationship between return on equity, cost of equity, and the resulting justified P/B multiple — is central to bank equity analysis, and since the banking sector constitutes a dominant portion of NEPSE's total market capitalisation, it is central to understanding the Nepali equity market as a whole.
Face Value in This Triangle
Where does the NPR 100 face value fit in this triangle? It sits beneath both book value and market price as the floor of the equity accounting structure. A company cannot have a book value per share below zero indefinitely — once accumulated losses exceed total paid-up capital and reserves, the company is technically insolvent. And in that unhappy scenario, the face value of NPR 100 turns into a reminder of what shareholders originally subscribed, now largely or entirely gone.
More practically, when market price falls below face value — when a share trades below NPR 100 — it is a significant signal that the market is pricing severe fundamental distress. In Nepal, a share trading below NPR 100 is often in that territory because of accumulated losses, regulatory sanctions, or severe deterioration of business fundamentals. It is not a reason to buy simply because the price is close to face value. Face value has no magnetic power to pull the price back upward.
Lesson 13.4 — Promoter Shares vs. Public Shares: Differences and OTC Transfer Rules
When a company is established in Nepal, its initial share capital is typically divided into two categories: promoter shares and public shares. Understanding the distinction between these two classes — in terms of their origin, their lock-in obligations, and the rules governing their transfer — is essential for any investor analysing the ownership structure of a listed Nepali company.
Promoter Shares: The Foundational Capital
Promoter shares are the shares held by the founding investors of a company — the individuals, institutions, or corporate entities who conceived the business, subscribed to its initial capital at the time of incorporation, and took on the risk of the enterprise before it had any operating history. In Nepali company law and SEBON regulations, promoters are distinguished from the public both by their role and by the regulatory treatment applied to their shareholding.
For companies incorporated under the Companies Act 2063, promoters typically subscribe to a specified minimum percentage of the total authorised capital before the company can offer shares to the public through a primary market offering. For commercial banks and financial institutions, Nepal Rastra Bank's licensing requirements specify minimum promoter shareholding ratios. For hydropower companies, the promoter group often includes project developers, infrastructure funds, and in some cases government entities or local community organisations.
The key regulatory feature of promoter shares is the lock-in restriction. SEBON regulations impose a minimum lock-in period during which promoters cannot freely sell their shares on the open market. For most companies listed on NEPSE, the lock-in period is three years from the date of public issue. For certain categories — particularly banking and financial institutions — the lock-in may be longer, and NRB regulations add additional layers of restriction on the transfer of shares by promoters of licensed institutions.
Public Shares: The Market-Tradable Float
Public shares are those offered to the general public through an Initial Public Offering (IPO), Further Public Offering (FPO), or rights issue. Once allotted and listed, public shares are freely tradable on NEPSE through the normal secondary market mechanism, subject to the trading rules of the exchange and any general regulatory restrictions on the sector.
The distinction between promoter and public shares is recorded in SEBON's share registry and in the company's share register. In the DMAT system operated by CDS and Clearing Ltd (CDSC) — Nepal's central depository — the depository account of each shareholder carries information about whether the shares are promoter-category or public-category. This tagging is what enables the enforcement of lock-in restrictions.
The OTC Transfer Mechanism for Promoter Shares
When the lock-in period expires, or when SEBON grants specific approval for a promoter share transfer within the lock-in period under exceptional circumstances, the transfer of promoter shares does not occur through the normal NEPSE trading system. Instead, it occurs through an Over-the-Counter (OTC) mechanism regulated under SEBON's directives on OTC trading.
The OTC transfer process for promoter shares in Nepal involves several steps. First, the selling promoter and the buying party negotiate terms — price per share and quantity — directly or through a broker acting as intermediary. Second, they execute a share transfer agreement. Third, they submit the required documentation to SEBON and to the company's share registrar for regulatory approval. Fourth, the transfer is recorded in the company's share register, the appropriate stamp duty is paid, and the CDSC updates the DMAT records accordingly.
REGULATORY NOTE
SEBON has tightened oversight of promoter share transfers in recent years, requiring enhanced disclosure of buyer identity and source of funds, particularly in the banking sector where NRB's fit-and-proper criteria apply to significant shareholders. Any transfer that would cause a single investor to hold more than a prescribed threshold of total paid-up capital triggers additional regulatory scrutiny.
The price at which promoter shares change hands in OTC transactions is often a matter of significant interest to public market participants. A promoter selling shares at a price substantially below the current NEPSE market price is a bearish signal — it suggests insiders are willing to exit at a discount to the market valuation. Conversely, a strategic investor acquiring a significant promoter stake at a premium to market price may signal confidence in the company's future prospects. Because OTC transactions are required to be disclosed to NEPSE and published in its official communications, diligent investors monitor these announcements.
Ownership Concentration and Its Implications
The promoter-versus-public share structure has a direct bearing on the effective free float of a listed company's shares. If promoters collectively hold 51% of shares and are subject to lock-in, only 49% of shares are available for public market trading. If a portion of the public float is also held by institutional investors with long holding horizons, the actively traded float may be considerably smaller.
Low free float has two significant practical consequences. First, it reduces market liquidity, meaning that large buy or sell orders can move the price substantially. Second, it concentrates governance power in the hands of the promoter group. In Nepal, where promoter shareholding in commercial banks is regulated at specific levels and where hydropower projects are often controlled by compact promoter groups, understanding the real free float is important both for liquidity risk management and for assessing the practical scope of minority shareholder governance.
Lesson 13.5 — Share Certificates to Demat: The Historical Transition in Nepal
For much of Nepal's modern corporate history, a shareholder's proof of ownership was a physical share certificate — a paper document bearing the company's seal, the shareholder's name, the number of shares, the face value per share, and the share numbers assigned to those specific shares. The certificate was not merely a record of ownership; under the law, it was the instrument of ownership. Losing it meant navigating a cumbersome replacement process. Transferring it meant physically delivering the certificate, completing a share transfer form, and waiting for the company's registrar to update the share register.
The Paper-Based System: How It Worked and Why It Failed
The paper certificate system served Nepal's nascent corporate sector reasonably well when the number of listed companies was small, trading volumes were modest, and the investor population was limited primarily to Kathmandu Valley. Each company maintained its own physical share register, recording transfers manually as certificates changed hands.
But as NEPSE grew through the 1990s and 2000s, and as the investor base expanded geographically, the limitations of the paper system became acute. Settlement was slow — days or weeks might pass between a trade on NEPSE and the actual transfer of the certificate and updating of the register. Fraudulent certificates appeared. Genuine certificates were lost to fire, flood, and ordinary misplacement. Shares of deceased investors became trapped for years in succession disputes because certificates could not be located. The cost of physical administration — storing, transferring, replacing, and verifying paper instruments — fell on companies, registrars, investors, and brokers alike.
More fundamentally, the paper system was incompatible with any aspiration toward a modern, efficient securities market. Settlement risk — the risk that one party to a trade would deliver shares or cash while the other failed — was structurally embedded in a system built on physical instruments.
The Regulatory Push Toward Dematerialisation
The Securities Act 2063 and subsequent SEBON regulations provided the legal foundation for dematerialisation. CDS and Clearing Ltd (CDSC) was established as Nepal's central securities depository, authorised to maintain electronic records of share ownership in demat accounts and to operate the clearing and settlement of NEPSE trades on a book-entry basis.
Under SEBON's directives, listed companies were required to progressively shift their share capital into demat form. Companies were instructed to engage with CDSC, connect their share registers to the electronic system, and require shareholders to surrender physical certificates in exchange for equivalent demat holdings. NEPSE's trading system was modified to settle trades exclusively through CDSC's book-entry mechanism, meaning that shares could only be bought and sold on NEPSE if they were held in a DMAT account.
HISTORICAL MILESTONE
The full mandatory dematerialisation of listed company shares on NEPSE was phased in over a period of years, with SEBON periodically extending deadlines to accommodate the practical challenges of converting large numbers of small shareholders, many located in remote regions with limited access to banking and DMAT account facilities. The process was substantially complete by the late 2070s BS, though legacy disputes over unconverted certificates periodically surface in corporate and legal proceedings.
How the DMAT System Works Today
Today, every investor who wishes to trade on NEPSE must have a DMAT account with CDSC. These accounts are opened through licensed depository participants — typically commercial banks, their securities subsidiaries, or broker-dealers authorised to act as depository participants. The DMAT account is linked to a Mero Share account, CDSC's online portal through which investors can view their holdings, apply for IPO allotments through the ASBA (Application Supported by Blocked Amount) mechanism, and access transaction statements.
When you buy shares on NEPSE, your broker's system matches your order with a counterpart seller. Settlement occurs on a T+2 basis — trade date plus two business days — during which CDSC's clearing system ensures that the buyer's bank account is debited, the seller's bank account is credited, and the share ownership records are updated electronically. The shares are never physically moved. A simple arithmetic entry changes the balance in the seller's DMAT account downward and the buyer's DMAT account upward.
This system eliminates the settlement risk and administrative burden of the old paper certificate era. Shares cannot be counterfeited in the traditional sense. Lost instruments are not a concept in the demat world — what exists is a database entry, backed up and governed by CDSC's systems. Transfers are instant in principle and completed within the settlement cycle in practice.
Residual Certificate Issues and Legacy Complications
Despite the overall success of the transition, physical certificates from the pre-demat era still surface periodically. Some investors, particularly elderly shareholders in rural areas who received allotments in early IPOs of institutions like Nabil Bank or Nepal Investment Bank, held their certificates for decades without converting. When they seek to sell or transfer — or when their heirs attempt to claim after the shareholder's death — they must first complete the dematerialisation process, which requires submitting the physical certificate to the company's registrar, obtaining a demat confirmation, and then crediting the shares to a DMAT account.
In some cases, certificates have been lost entirely. The Companies Act provides a procedure for replacement — involving a publication in a national newspaper, a waiting period for any adverse claims, and a board resolution authorising reissuance — but the process is time-consuming and uncertain. For shares of high-value companies, the effort is worthwhile. For shares of smaller or delisted companies, the practical recovery may be negligible.
Estate settlements involving unconverted share certificates introduce additional complications, because the legal process for transmission of shares — the formal transfer of ownership from a deceased person to their heirs — requires both probate documentation and coordination between the company's registrar and CDSC. Investors who hold physical certificates should treat their dematerialisation as an urgent and important piece of financial housekeeping.
The ASBA Revolution and Its Tie to Demat
The dematerialisation of shares was the precondition for another transformative development in Nepal's retail investor experience: the ASBA mechanism for IPO applications. Under ASBA, when you apply for shares in a new listing, your bank account is not immediately debited. Instead, the application amount is blocked — held in reserve — until the allotment is made. If you receive a full allotment, the blocked amount is debited. If you receive a partial allotment or no allotment, the remaining blocked amount is released. Allotted shares flow directly into your DMAT account without any certificate being printed, delivered, or converted.
ASBA, operating through the banking system and CDSC's demat infrastructure, has made IPO participation accessible to investors across Nepal, including those in Provinces 1 through 7 who in the paper era would have had to physically submit applications in Kathmandu. The scale of retail participation in Nepali IPOs — with hundreds of thousands of applicants routinely submitting for popular issues — would be operationally impossible without the underlying demat and electronic clearing infrastructure.
The transition from share certificates to demat is not just a technical footnote in Nepali market history. It is the infrastructure story that enabled the democratisation of equity investing in Nepal. Every subsequent development — ASBA allotments, online trading platforms, the Mero Share portal, and the eventual integration of NEPSE into regional capital market networks — rests on the foundation laid by that painstaking conversion of paper certificates into electronic book entries. As an investor in Nepal today, you inherit the benefits of that transition every time you execute a trade and see your DMAT balance update by the next business day.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part III · Chapter 14
IPOs and the Primary Market
First published 21 Aug 2026 · Last verified 29 Aug 2026
The first trade is the most dangerous one to misunderstand.
Every listed company was once a private one. Every stock you can buy on NEPSE today passed through a single gateway: the Initial Public Offering, or IPO. This process — the mechanics, the pricing, the allotment, the listing — is not merely administrative paperwork. It is the moment a company places itself before the public and asks to be trusted with their money. For investors, it is the first battlefield. Winning here requires understanding the rules of the game, the incentives of the players, and the psychological pressures that distort rational decision-making.
This chapter takes you through the entire IPO ecosystem in Nepal — from the regulatory scaffolding to the first day of trading — with enough depth to evaluate any new offering on its merits rather than its hype.
Lesson 14.1 — How a Company Lists on NEPSE: The IPO Process Step by Step
The Regulatory Architecture
Nepal's primary market operates under a specific legal and institutional framework that every serious investor should understand. The Securities Act, 2063 (2006) is the foundational legislation. SEBON — the Securities Board of Nepal — is the apex regulatory authority, equivalent to the SEC in the United States or SEBI in India. NEPSE — the Nepal Stock Exchange — is the trading venue where approved securities are listed and subsequently traded.
No company can offer shares to the public without SEBON's prior approval. This is a non-negotiable starting point. The approval process is not a rubber stamp — it involves a thorough review of the company's financials, legal standing, promoter credentials, and the adequacy of the prospectus disclosures.
Who Can Issue an IPO?
The eligibility criteria for an IPO in Nepal are defined by SEBON's regulations and differ depending on the sector. Generally, a company must:
Be incorporated as a public limited company under the Companies Act, 2063 (2006)
Have a minimum paid-up capital as specified by the relevant regulatory authority for its sector (e.g., Nepal Rastra Bank for banks and financial institutions, ICAN for insurance companies)
Have completed at least one fiscal year of operations in most cases, though certain infrastructure and hydropower companies may be exempted
Have audited financial statements prepared according to Nepal Financial Reporting Standards (NFRS)
Have appointed a licensed Issue Manager (also called a Merchant Banker) who will take responsibility for the issue process
The Step-by-Step IPO Process
Understanding the sequential nature of the IPO process helps investors anticipate what information will become available and when.
Step
Activity
Key Players
Investor Significance
1
Board resolution to go public; appointment of Issue Manager
Company Board, Issue Manager
Signals management's intent and choice of underwriter — a respected Issue Manager lends credibility
2
Due diligence by Issue Manager on financials, legal compliance, business model
Issue Manager, Company Auditors, Legal Counsel
This is where genuine risks are uncovered — or buried. The quality of this step determines prospectus accuracy
3
Preparation and filing of the Draft Prospectus with SEBON
Issue Manager, Company
The first public document — study it before the application window opens
4
SEBON review period (typically 30–90 days)
SEBON
SEBON may request clarifications or modifications — delays can signal issues
5
SEBON approval and issuance of permit to open subscription
SEBON
The green light. Issue Manager publishes the Final Prospectus
6
Public notice and subscription window opens (typically 7–21 working days)
Issue Manager, Banks (ASBA)
The window in which investors apply via ASBA or MeroShare
7
Subscription window closes; application data compiled
Issue Manager, Banks, CDSC
All valid applications are pooled for the allotment process
8
Allotment through lottery (if oversubscribed) or pro-rata (if undersubscribed)
Issue Manager, CDSC, Share Registrar
The lottery result is published on CDSC/MeroShare — check within 2 weeks
9
Refund of unsuccessful applicants' blocked amounts
Banks
Funds are released automatically through ASBA — no manual action needed
10
Listing on NEPSE; first-day trading begins
NEPSE
The first pricing discovery event — often the most volatile day
The Role of the Issue Manager
The Issue Manager (IM) is the most important third party in an IPO. A licensed merchant bank or financial institution, the IM wears multiple hats: financial adviser to the issuer, underwriter (in many cases), compliance verifier, and bridge between the company and SEBON. The quality of the IM directly affects the quality of disclosures. A high-reputation IM has more to lose by endorsing a weak or misleading prospectus. Conversely, smaller or less established IMs may lack the leverage or incentive to push back on issuers.
INVESTOR TIP
Before reading the prospectus, check who the Issue Manager is. Cross-reference their past IPOs: Did those companies perform? Were there material omissions later revealed? A track record of poorly disclosed issues should increase your scepticism.
Underwriting and Its Implications
Many IPOs in Nepal are underwritten, meaning the underwriter guarantees to purchase any shares not subscribed by the public. Underwriting sounds like a safety net, but it is worth questioning why an underwriter agreed to guarantee the issue. Underwriters are professional investors — if they were genuinely uncertain about demand, they would charge a very high underwriting premium or decline entirely. An underwritten IPO at a reasonable premium suggests the IM is confident in minimum demand. An IPO that struggles to find underwriters is a warning signal.
Lesson 14.2 — The Prospectus: What to Read, What to Verify, and What to Question
The prospectus is the single most important document an IPO investor can study. It is both a legal disclosure and a marketing document — a duality that creates tension. The company must, by law, disclose material risks. At the same time, it presents itself in the most favourable light possible. The sophisticated investor learns to see through the presentation to the substance beneath.
Legal Status of the Prospectus
Under Nepal's securities laws, a prospectus is a legally binding document. Any material misstatement or omission that causes loss to investors can attract civil and criminal liability for the company's directors and the Issue Manager. This legal weight is the primary reason why prospectuses, despite their promotional tone, contain information that is genuinely useful — because omitting key risks is itself a legal violation.
The Anatomy of a Nepali Prospectus
A standard NEPSE prospectus is structured in predictable sections. Knowing what each section should contain — and what to look for — transforms a tedious document into a rich source of investment intelligence.
Section
What It Contains
What to Look For / Question
Company Overview
Legal name, registration, incorporation date, registered office, nature of business
How old is the company? Has it changed its business model? Multiple address changes can suggest instability
Share Capital Structure
Authorised, issued, and paid-up capital; promoter vs public shareholding breakdown
What percentage are promoters retaining? Low promoter retention (below 51% post-IPO in most sectors) raises questions about their conviction
Objects of the Issue
How IPO proceeds will be used (expansion, debt repayment, working capital, etc.)
Is the stated use credible? Debt repayment is neutral; expansion is positive; vague 'general corporate purposes' is a yellow flag
Financial Statements (3 years audited)
Balance sheet, income statement, cash flow, notes to accounts
The core analytical section — see detailed guidance below
Risk Factors
Legally mandated disclosures of material risks
Are risks specific and honest, or generic and boilerplate? Vague risk disclosures often mean real risks are being minimised
Management & Promoters
Director profiles, shareholding, related-party transactions
Look for conflicts of interest, related-party loans, and promoters with histories in failed companies
Is the industry analysis realistic or self-serving? Compare stated market share claims with industry data
Future Plans / Projections
Capital expenditure plans, expansion targets
Projections are not audited. Apply significant scepticism — look at the company's history of achieving past targets
Auditor's Report
Independent auditor's opinion
A qualified opinion (anything other than 'unqualified') is a serious red flag
Deep-Diving the Financial Statements
The three-year audited financials in the prospectus are where real analysis begins. The following specific metrics and patterns deserve particular attention:
Revenue Quality
Not all revenue is equal. Look for revenue that is recurring, diversified, and growing organically. In the context of Nepali IPOs — which often come from banking, hydropower, insurance, and microfinance sectors — ask:
For banks/BFIs: What is the Net Interest Margin (NIM) trend? Is NIM compressing or stable? Compressing NIMs suggest pricing pressure or asset quality deterioration.
For hydropower: What is the Power Purchase Agreement (PPA) tariff? How many years remain on the PPA? Is there a dry-season generation shortfall disclosed?
For insurance: Is the combined ratio improving or worsening? Are claims growing faster than premiums?
For all sectors: Is revenue growth accompanied by cash flow growth? Revenue that grows without corresponding cash collections may indicate aggressive accrual accounting.
Balance Sheet Integrity
Assets on a balance sheet are only as reliable as the judgements behind them. For a Nepali prospectus, focus on:
Non-Performing Loans (NPL ratio) for BFIs: Compare against NRB's mandated thresholds and sector averages. An NPL above 5% at IPO time warrants serious scrutiny.
Fixed asset revaluations: Some companies revalue land and buildings upward just before an IPO to inflate book value. Check whether the paid-up capital increase was driven by bonus shares issued from revaluation reserves — this is common and can inflate per-share book value artificially.
Goodwill and intangibles: Are these significant? Have they been impairment-tested recently? Impairment charges post-IPO can significantly dent reported profits.
Related-party receivables: Large receivables from promoter-affiliated entities are a classic indicator of fund diversion or disguised losses.
Profitability and Returns
Three years of financial data reveals trends that a single snapshot cannot. Construct a simple table:
Metric
Year 1
Year 2
Year 3
Trend Interpretation
Net Profit Margin (%)
Fill from data
Fill from data
Fill from data
Declining margins suggest cost pressure or revenue quality issues
Return on Equity (%)
Fill from data
Fill from data
Fill from data
ROE below 10% in a Nepali context is mediocre; above 20% deserves examination of leverage
The risk section is where legal necessity creates accidental transparency. Companies are compelled to disclose risk factors, but they control how they are framed. Train yourself to reframe their phrasing:
REFRAMING EXERCISE
If a prospectus says: 'Our business may be affected by changes in government regulation' — ask: Is there a specific pending regulation that the company knows about? If the risk is vague and generic, it may be concealing a very specific and material risk through the use of broad language.
Ask three questions of every risk factor: (1) Is this risk real and company-specific, or generic sector boilerplate? (2) Has this risk already partially materialised — and is the prospectus disclosing the current impact? (3) Is this risk quantifiable, and if so, why has the company not quantified it?
The Auditor's Report: A Checklist
The auditor's opinion is summarised in a few paragraphs, but its implications are enormous. Check:
Opinion type: Unqualified (clean), Qualified, Adverse, or Disclaimer of opinion. Only an unqualified opinion should be treated as a green light.
Emphasis of matter paragraphs: These are not qualifications but draw attention to issues the auditor believes are important. Do not ignore them.
Key audit matters (under NFRS/ISA): What did the auditors identify as the most judgment-intensive areas? This reveals where the financial statements are most susceptible to management manipulation.
Auditor identity: Is the auditor one of the recognised mid-to-large firms? Auditors with limited capacity or independence concerns introduce additional risk.
Lesson 14.3 — IPO Pricing in Nepal: Par Value Issues vs. Premium Issues
Pricing is the variable that determines whether an IPO is a bargain, a fair deal, or a trap. In Nepal's market, IPO pricing follows one of two fundamental models: par value issues and premium issues. Understanding the difference — and what each model implies — is foundational to IPO analysis.
The Concept of Par Value
Par value (also called face value or nominal value) is the stated value of a share as defined in a company's Memorandum of Association. In Nepal, the most common par value is Rs. 100 per share, though some companies use Rs. 10. Par value is an accounting concept, not an economic one — it says nothing about what a share is worth.
When a company issues shares at par value — Rs. 100 per share — it is issuing them at the accounting floor, not at any assessed market value. This is not inherently bad: many legitimate early-stage companies or companies with strong growth prospects offer at par to attract public investors. But the price being Rs. 100 does not mean the shares are worth Rs. 100.
Premium Issues: The Logic and the Risk
A premium issue occurs when shares are offered above par value. If par is Rs. 100 and the issue price is Rs. 250, the company is asking investors to pay a Rs. 150 premium per share. This premium is credited to the share premium account and increases the company's paid-up capital base.
Premium pricing is justified when a company can demonstrate, through its financials, that the intrinsic value of the share substantially exceeds par value. This is typically calculated on the basis of:
Net Worth Per Share (Book Value Per Share): If the accumulated reserves and paid-up capital of the company equate to Rs. 300 per share on the books, then offering at Rs. 250 is arguably a 17% discount to book value.
Earnings-Based Valuation: The price-to-earnings ratio implied by the IPO price compared to sector averages. If the sector trades at 15x earnings and the IPO is priced at 10x, there is valuation headroom post-listing.
Comparable Transaction Multiples: Recent IPOs in the same sector can provide pricing benchmarks.
CRITICAL POINT
SEBON reviews premium pricing as part of its approval process and requires the Issue Manager to justify the premium mathematically. However, approving a premium issue is not the same as endorsing it as a good investment. The premium can still be unjustifiably high relative to intrinsic value — it simply must not be arbitrary or undisclosed.
The Par Value Trap
Many retail investors in Nepal assume that a Rs. 100 IPO is 'cheap' by default. This is a dangerous misconception. A company with negative net worth, deteriorating earnings, and a weak competitive position is not a good investment at Rs. 100 just because Rs. 100 is the 'base price'. Price is what you pay; value is what you get. A Rs. 100 share in a company with Rs. 60 in book value per share and declining profitability is pricing you above intrinsic value.
The Premium Trap
Conversely, some investors assume that a high premium signals a high-quality company. Again, this is wrong. Premium pricing reflects the company's past performance and the Issue Manager's assessment of present value — not a guarantee of future returns. The critical question is not 'Is there a premium?' but 'Is the premium justified by the underlying financials?'
Compare to post-listing sector P/E on NEPSE. If the IPO P/E is materially above sector average, you are paying for growth that may not materialise.
IPO P/E vs. Secondary Market P/E
IPO P/E ÷ Sector Average P/E on NEPSE
A ratio above 1.5 means the IPO is pricing in 50% more growth than what the secondary market currently assigns to similar companies.
Dividend Yield at IPO Price
Dividend Per Share ÷ IPO Price
For income-oriented investors, is the projected yield competitive against fixed deposits? NRB-regulated BFI FDs typically set the baseline.
EPS Growth Trend
YoY growth in EPS over 3 years
Negative or declining EPS growth alongside a premium issue is a strong warning signal.
Sector-Specific Pricing Norms in Nepal
Different sectors in Nepal have historically supported different valuation multiples, both at IPO and in the secondary market. Understanding these norms prevents misapplication of a blanket framework:
Commercial Banks: Tend to trade at P/B multiples of 1.0x–2.5x on NEPSE. IPOs priced above 2.0x book value require exceptional ROE (above 20%) to be justified.
Development Banks and Finance Companies: Generally trade at lower multiples due to higher perceived risk. Applying commercial bank multiples to these entities inflates apparent value.
Life and Non-Life Insurance: Valuation is complex and requires understanding the embedded value concept, not just book value. Nepal's insurance market is still maturing, and regulatory changes can significantly affect valuations.
Hydropower: Asset-heavy, cash-flow driven. DCF (discounted cash flow) analysis based on PPA duration and projected generation is more relevant than P/E ratios. However, retail investors rarely have access to the detailed project data required for robust DCF modelling — in practice, comparing to recently listed hydro companies' listing multiples is a reasonable shortcut.
Microfinance Institutions (MFIs): High ROE but concentrated risk in rural borrower credit quality. NPL trends matter enormously. Regulatory risk (interest rate caps, forced mergers) has been a recurring theme.
Lesson 14.4 — Allotment System: Lottery-Based Allocation and Why It Exists
Nepal's IPO allotment system is unique in its design and reflects specific economic and social policy goals. Unlike markets where IPO shares are allocated by the underwriter based on investor quality or book-building processes, Nepal uses a lottery-based system for oversubscribed issues. Understanding why this system exists — and its implications for investors — is important for realistic expectations.
Why Lottery-Based Allotment?
The lottery system was introduced to serve two interconnected goals: fairness and market development. In a market where institutional investors are limited and retail participation is the primary driver of IPO demand, allocating shares through a lottery ensures that no single large investor can crowd out small investors. Every eligible applicant — whether applying for the minimum lot or the maximum — has an equal probability of receiving an allotment in the oversubscribed category.
This has a significant distributional effect: shares of popular IPOs are spread across a larger number of shareholders, preventing the concentration that occurs in book-built markets where preferred institutional investors receive bulk allocations. However, this comes at a cost: sophisticated investors who have done deep analysis receive no preferential reward for their research effort. The same allocation probability applies to the informed and the uninformed.
Mechanics of the Allotment Process
When an IPO closes, the total number of valid applications is tallied. There are two outcomes:
Undersubscription (applications \< shares available): All applicants receive full allotment. There is no lottery. This is relatively rare for well-marketed IPOs but common for less attractive or poorly priced issues.
Oversubscription (applications \> shares available): A lottery is conducted by CDSC (Central Depository System and Clearing Limited). The lottery is computerised and audited. Each valid applicant is entered once regardless of the number of lots applied for — this is the 'equal probability' principle.
IMPLICATION FOR STRATEGY
Because the lottery treats each applicant equally regardless of lots applied for, the marginal return from applying for more lots is zero in terms of lottery probability. The practical implication: applying for more lots does not improve your chances of allotment. It only affects the quantity received IF you are selected. Given that most popular IPOs are highly oversubscribed, applying for a large number of lots increases the capital you have blocked with no corresponding increase in allotment probability.
The Minimum Lot System
SEBON specifies a minimum application quantity — the minimum lot — for each IPO. This is typically 10 shares (Rs. 1,000 at par value for a Rs. 100 face value share). The minimum lot system serves to ensure broad participation: even small investors can participate without committing large capital.
For oversubscribed issues, the allotment is first conducted to satisfy one minimum lot to each applicant in the lottery, then remaining shares (if any) are distributed in additional rounds. This means:
If the oversubscription ratio is very high (e.g., 50x), even winners typically receive only the minimum lot.
Applying for more than the minimum lot has very limited benefit in a heavily oversubscribed IPO unless the oversubscription ratio is moderate (e.g., 2x–5x).
Calculating Expected Returns Under the Lottery System
Because allotment is probabilistic, the expected return on capital blocked for an IPO application must account for the allotment probability. Consider a simplified example:
Parameter
Value
IPO Issue Price
Rs. 100 per share (par)
Expected Listing Price (based on grey market / comparable IPOs)
Rs. 160
Expected Gain Per Share if Allotted
Rs. 60
Minimum Lot Size
10 shares = Rs. 1,000 blocked
Oversubscription Ratio
30x (30 applicants for every 1 share available in lottery)
Estimated Allotment Probability
\~1 in 30 = 3.3%
Expected Gain = Gain × Probability
Rs. 600 × 3.3% = Rs. 20
Expected Return on Blocked Capital
Rs. 20 / Rs. 1,000 = 2.0% for the blocking period (approx. 3–6 weeks)
Annualised Expected Return (at 4 weeks blocked)
\~26% annualised — but with high variance (either 0 or 60% per lot)
This calculation reveals why applying for IPOs can be rational even when allotment probability is low: the short blocking period means the opportunity cost is limited, and the potential gain (though uncertain) can translate to attractive annualised returns. However, this logic breaks down for IPOs with low expected listing premiums, where even a successful allotment produces modest gains.
The CDSC Allotment Process: Transparency and Verification
The allotment process is conducted by CDSC and is publicly auditable. Results are published on the CDSC website and accessible through MeroShare. Investors can verify their allotment status within approximately two weeks of the subscription window closing. The process is designed to be tamper-proof: applications are verified against CDS account numbers, and duplicate applications from the same person are disqualified.
FRAUD RISK
Multiple applications using different family members' CDS accounts are a common practice. This is technically permitted if each family member applies independently from their own DMAT account and bank account. However, using another person's identity without their consent is fraudulent and carries legal risk. CDSC conducts duplicate checks on bank accounts and CDS accounts linked to the same application window.
Lesson 14.5 — IPO Application via ASBA and MeroShare: The Mechanics
The mechanics of applying for a Nepali IPO have been transformed by two systems: ASBA (Application Supported by Blocked Amount) and MeroShare. Understanding these systems prevents costly errors — failed applications due to technical mistakes are a common and avoidable problem.
What is ASBA?
ASBA is the payment mechanism for all IPO applications in Nepal. Rather than transferring money to the issue manager at the time of application, ASBA blocks the application amount in the investor's bank account. The blocked amount earns interest (in most cases) and is only debited if the investor receives an allotment. If not allotted, the block is released — typically within 7–14 days of allotment completion.
The significance of ASBA is profound: it eliminated the pre-ASBA problem of investors' capital being locked up unproductively (or at zero interest) for the entire subscription and allotment process, which could stretch to several months. Under ASBA, even unsuccessful applicants do not lose the time-value of money.
ASBA-Enabled Banks
Not all banks in Nepal are ASBA-enabled, though the list has expanded significantly. The ASBA mechanism requires the bank to interface with CDSC's systems. Before applying, verify that your bank is ASBA-enabled through CDSC's published list. Commercial banks and most development banks are typically enabled; smaller financial institutions may not be.
What is MeroShare?
MeroShare is the CDSC's online portal for investor services. It serves multiple functions critical to the IPO investor:
DMAT Account Management: Opening, operating, and managing your dematerialised securities account (the electronic repository of your shares)
IPO Application: Applying for IPOs online without visiting a physical bank branch — the most commonly used feature
Portfolio Tracking: Viewing all securities held in your DMAT account
Allotment Status: Checking whether you have received an allotment for any IPO
Bonus and Rights Shares: Receiving and tracking bonus shares or rights entitlements
Transaction History: Full record of all DMAT transactions
Step-by-Step: Applying for an IPO via MeroShare
Login to MeroShare (meroshare.cdsc.com.np) using your DP (Depository Participant) credentials and BOID (Beneficiary Owner ID)
Navigate to 'ASBA' from the main menu and select the active IPO from the list
Enter your CDS (BOID) number — this is auto-filled in most cases
Select your ASBA-enabled bank from the dropdown list
Enter your bank account number linked to that ASBA bank
Enter the number of kitta (units/shares) you are applying for — must be at least the minimum lot
The system calculates and displays the total amount to be blocked
Submit the application. You will receive an OTP on your registered mobile number for confirmation.
Verify receipt of a transaction confirmation number — save this as proof of application
Check your bank account: the amount should be blocked (not debited) within 24 hours
Critical Application Errors to Avoid
Error Type
Description
Prevention
Insufficient Bank Balance
The ASBA block fails if the balance is insufficient at the time of application
Ensure your linked bank account has at least the application amount plus a small buffer before applying
Incorrect Account Number
Entering a wrong bank account number leads to application rejection
Double-check the account number before submission; use copy-paste from your bank's app
Expired KYC
Applications from accounts with expired KYC documentation may be rejected
Renew KYC at your DP and bank before each IPO season; this is an annual requirement for most DPs
Multiple Applications (Same Person)
CDSC rejects duplicate applications from the same BOID
Never apply twice from the same account; each person may apply only once per IPO
Applying After Deadline
Late applications are not accepted under any circumstances
Set a calendar reminder at least two days before the subscription closing date — server congestion is common on the last day
MeroShare Login Issues
Locked accounts due to failed login attempts delay or prevent application
Reset your MeroShare password well before the IPO window; contact your DP for account recovery if needed
The DMAT Account: Your Foundation
All of this assumes you have a DMAT (Dematerialised Account) and the associated BOID. If you do not, you cannot participate in any NEPSE IPO or secondary market trading. Opening a DMAT account requires:
An account with a registered Depository Participant (DP) — commercial banks and licensed stockbrokers act as DPs
Citizenship certificate (or equivalent identification for institutional investors)
Passport-size photographs and completed KYC form
A bank account (for ASBA purposes)
The DMAT account is the prerequisite to all securities investment in Nepal. It should be treated as the foundation of your financial infrastructure, not a procedural afterthought.
Lesson 14.6 — Listing Day Dynamics: What Typically Happens and the Behavioural Reasons Why
The listing day — the first day a newly allotted share trades on NEPSE — is one of the most psychologically charged events in the Nepali investment calendar. Prices can swing dramatically within a single session, driven by a combination of structural factors, liquidity dynamics, and powerful behavioural biases. Understanding these forces protects investors from making expensive decisions based on emotion rather than analysis.
The Typical Listing Day Pattern
While every listing is unique, there is a recognisable pattern in Nepal's market, rooted in the structure of the investor base and the scarcity dynamics of the ASBA/lottery system:
Opening: The share opens at a price significantly above the issue price. This 'listing premium' reflects pent-up demand from the much larger pool of unsuccessful lottery applicants who want the stock but did not receive it in the IPO. The opening price is essentially a signal of how many such investors are willing to pay above issue price to acquire shares.
Early-session spike: Successful allottees who have no fundamental conviction in the stock (they applied purely for the listing gain) rush to sell. Simultaneously, unsuccessful applicants rush to buy. This creates high volume and, in popular IPOs, a sharp price spike in the first hour.
Mid-session correction: After the initial flurry, the sell pressure from allottees taking profits begins to outpace the buying enthusiasm. Prices often correct 5%–20% from the opening day peak within the same session.
Close: The closing price typically settles somewhere between the opening high and the session low, establishing the first 'fair value' benchmark that the market has discovered.
Why This Pattern Exists: The Behavioural Explanation
Several well-documented behavioural phenomena drive listing day dynamics:
Anchoring to Issue Price
Successful allottees anchored their mental 'cost basis' at the issue price (Rs. 100, for example). Any price above this feels like a gain that could be 'lost' if they wait. This drives early selling, even when the fundamental analysis might support holding longer. Anchoring is perhaps the most powerful force on listing day: the issue price becomes a psychologically meaningful number that bears little relationship to intrinsic value.
FOMO and the Unsuccessful Applicant
Investors who did not receive an allotment have been 'waiting' since the lottery results. FOMO (fear of missing out) drives them to buy immediately at open, even at prices that may not be justified by fundamentals. The logic, consciously or not, is: 'I already decided this was worth buying at Rs. 100; it's trading at Rs. 160, so I should still buy before it goes higher.' This is a non-sequitur — the fact that you made a buying decision at Rs. 100 does not make Rs. 160 a good price.
The 'New Issue Effect' (IPO Novelty Bias)
Newly listed shares attract attention simply because they are new. Media coverage, social media discussion, and the psychological novelty of a fresh listing create elevated interest that has nothing to do with fundamental value. This novelty effect fades within days or weeks, after which the price typically reverts toward fundamentals.
Liquidity Premium Deflation
On listing day, the entire outstanding public float — minus those held by long-term holders — is technically available for trading. But in practice, most allottees hold their shares at least briefly, and the actual free float on day one is limited. This temporary supply constraint amplifies price moves in both directions. As more allottees sell over subsequent days, the effective free float increases and price discovery improves.
The Empirical Pattern: Overperformance on Day 1, Underperformance Over 3–12 Months
Academic research on IPO markets globally (and consistent with observations in Nepal) finds a characteristic pattern: IPOs tend to be 'underpriced' relative to their first-day closing price, generating a positive average listing-day return. However, over the following 3–12 months, the same IPOs tend to underperform the broader market index.
This is the IPO investor's dilemma: the easy money (listing gain) is won by lottery, not skill. The hard money — holding through post-listing volatility to the point of genuine fundamental appreciation — requires conviction, patience, and the discipline to ignore the noise of the first days and weeks.
RULE OF THUMB
Never buy a stock on its listing day purely because it is listing. The listing premium in a hot IPO often incorporates all the near-term optimism. Buying at the listing peak is equivalent to buying at the most euphoric moment — the point of maximum optimism. If you missed the lottery, wait. Prices on most newly listed stocks in Nepal have historically been available at listing-day levels or below within 3–6 months.
When Listing Day Goes the Other Way
Not all listings are premiums. Some IPOs list below their issue price — a situation that is jarring for allottees who expected a gain. This typically occurs when:
The issue was overpriced relative to fundamentals
Sector sentiment deteriorated between IPO approval and listing date
Broader market conditions fell significantly (a falling NEPSE index depresses all listing prices)
Post-prospectus negative information emerged about the company
A below-par listing is a double signal: first, that the IPO was mispriced; second, that the market's current assessment of value is below what was asked for. Both signals should prompt a fundamental re-evaluation rather than panic selling or averaging down without analysis.
Lesson 14.7 — Analysing an IPO: A Step-by-Step Framework
The preceding lessons have built the conceptual and technical foundations. This lesson synthesises them into a practical, repeatable framework for IPO analysis that can be applied to any new offering on NEPSE. The framework consists of six layers, each of which filters the investment case with greater specificity.
Layer 1: The Business Quality Screen
Before any numbers, ask the qualitative question: Is this a good business? This requires understanding:
What does the company actually do? Can you explain its revenue model in two sentences?
Is the industry it operates in growing, stable, or shrinking? Regulatory trends?
Does the company have competitive advantages (franchise value, cost advantages, switching costs, network effects)?
How dependent is the business on a single customer, contract, or regulatory approval?
A company in a shrinking industry with no competitive advantages and a single major customer is a poor investment at any price. Pass.
Layer 2: The Management and Promoter Screen
Management quality is difficult to assess from a prospectus, but proxies exist:
Promoter pedigree: What else have they built? Are there other companies in their group, and what is their reputation?
Promoter lock-in: Regulations typically require promoter shares to be locked up for a minimum period post-listing. What is the duration? A short lock-in period (minimum regulatory requirement) suggests promoters view the IPO as an exit, not a long-term commitment.
Board independence: Are independent directors genuinely independent? Do they have relevant expertise?
Related-party transactions: Are there large or growing transactions with promoter-affiliated entities? These can be legitimate, but they can also be channels for profit extraction.
Management compensation: Is compensation disclosed? Excessive management remuneration at the expense of retained earnings is a warning sign.
Layer 3: The Financial Quality Screen
Apply the financial analysis framework from Lesson 14.2. Specifically, synthesise three key questions:
Is profitability genuine and sustainable? (Not propped up by one-time items, related-party revenues, or deferred costs)
Is the balance sheet solid? (Adequate capitalisation, no hidden liabilities, NPLs within norms for financial sector companies)
Is cash generation consistent with reported profits? (Operating cash flow should roughly track net profit; large divergence suggests earnings quality issues)
Layer 4: The Pricing Screen
Use the valuation framework from Lesson 14.3. Calculate:
P/B at issue price vs. sector average P/B on NEPSE
P/E at issue price (using latest year's EPS) vs. sector average
Net worth per share vs. issue price (discount or premium?)
If hydropower: rough DCF based on PPA data vs. implied market cap at issue price
A useful heuristic: if the IPO P/E is materially higher than the sector's current secondary market P/E, you are being asked to pay IPO price for growth expectations that the market has not yet validated. This is speculative, not analytical.
Layer 5: The Use of Proceeds Screen
Revisit the 'Objects of the Issue' section with a sceptical eye:
Is the capital genuinely needed for value-creating activities? Expansion into new markets or capacity increases can be value-accretive.
Is a significant portion going toward debt repayment? This reduces risk but does not create new value for IPO investors — it transfers wealth from equity holders to debt holders.
Is the capital deployment plan specific and costed, or vague and aspirational?
What is the timeline for deployment? Capital that sits idle for years post-IPO generates below-cost-of-capital returns.
Layer 6: The Risk-Adjusted Return Screen
Given the lottery system, the final question is expected value, not certainty:
What is the estimated listing price based on: (a) comparable recent IPO listing premiums, (b) grey market indications if available, and (c) fundamental valuation?
What is the oversubscription likely to be? Very high oversubscription reduces allotment probability, which reduces expected value even for well-priced issues.
What is the opportunity cost? Capital blocked for 4–6 weeks could alternatively be deployed in the secondary market.
SYNTHESIS QUESTION
After running all six layers, ask: Would I buy this stock at the issue price in the secondary market tomorrow, independently of whether I receive an IPO allotment? If the answer is no — if you are relying entirely on the listing premium to generate a return — you are speculating, not investing. Both are valid activities, but they require different frameworks and different risk tolerances.
Lesson 14.8 — Common IPO Traps: Overpriced Issues, Window-Dressed Financials, Weak Promoter History
The final lesson in this chapter is the most practically urgent: a catalogue of the traps that catch investors, and the patterns that identify them before damage is done. These are not hypothetical scenarios — each reflects documented patterns in Nepal's IPO market over the past decade.
Trap 1: The Overpriced Premium Issue
The mechanics: A company with modest financials and an unremarkable growth history obtains approval for a premium issue by commissioning a valuation that selectively uses the most favourable metrics. The issue is aggressively marketed. Retail investors, anchored to the narrative of premium pricing equalling quality, subscribe heavily. The IPO is oversubscribed. Allottees receive shares. On listing day, the share opens at a modest premium — but within weeks, begins a sustained decline as secondary market investors apply basic valuation analysis and find the company undeserving of its pricing.
Warning signals:
Premium greater than 50% above book value for a company with ROE below 15%
Issue Manager with limited track record or recent involvement in issues that significantly underperformed post-listing
Prospectus projections of future profitability that imply a step-change in growth without a clear business catalyst
SEBON approval taking longer than usual — this can indicate that SEBON required significant revisions, suggesting the initial prospectus had deficiencies
Trap 2: Window-Dressed Financial Statements
Window dressing refers to the manipulation of reported financial statements to present a more favourable picture ahead of an IPO. In Nepal's context, common techniques include:
Deferred Expense Recognition
Capitalising costs that should be expensed immediately (marketing costs, maintenance, preliminary expenses) pushes them onto the balance sheet and defers their impact on the income statement. This inflates reported profits in the years before the IPO, at the cost of future amortisation charges. The tell: rapidly growing capitalised costs in the three years before IPO, followed by a disclosure in the notes about 'deferred expenses' or 'preliminary expenses' with unusually long amortisation periods.
Revenue Timing Manipulation
Recognising revenue before it is earned or before delivery conditions are met inflates the IPO-period income statement. In lending businesses, this can appear as rolling over non-performing loans (rather than writing them off) to avoid NPL disclosure. The tell: large increases in accrued income or receivables in the final pre-IPO year without corresponding cash collection.
Related-Party Revenue Inflation
Booking revenues from related parties (promoter-owned companies, director-affiliated businesses) at above-market rates creates reported income that has no economic substance. The tell: significant revenues from a small number of related parties, disclosed in the related-party transactions note but rarely highlighted in the body of the prospectus.
Bonus Share Issuance to Inflate Equity Metrics
Issuing bonus shares from revaluation reserves immediately before the IPO increases paid-up capital and makes the book value appear to support the issue price. The tell: a significant bonus share issuance in the fiscal year immediately preceding the IPO, funded by revaluation reserves rather than retained earnings.
Trap 3: Weak or Problematic Promoter History
The promoters of a company are its founding shareholders — typically individuals or corporate entities holding shares since before the public listing. Their track record, integrity, and incentive structure post-listing are material to long-term investment value. In Nepal's market, several promoter-related red flags have recurred:
Promoters with histories in companies that became financially distressed, required NRB/SEBON intervention, or failed to pay dividends despite reported profits
Promoters across multiple companies simultaneously — the risk of resource diversion increases when a single promoter group controls numerous listed entities
Promoters who have previously pledged their shares as collateral for personal loans — a signal of financial stress and potential forced selling post-lock-in expiry
Promoter groups with pending legal cases related to business or financial conduct
New promoter groups with no demonstrable business track record, who appear to have incorporated the company specifically for the IPO
RESEARCH APPROACH
Researching promoters in Nepal requires going beyond the prospectus. Search the SEBON website for enforcement actions. Search NRB's published notices for banking sector promoters under regulatory watch. Search Kantipur, Nagarik, and financial news archives for coverage of the promoter group's other ventures. This is time-consuming but can prevent significant losses.
Trap 4: The Sector-Hype Issue
Cyclical enthusiasm for particular sectors creates windows during which poorly positioned companies successfully list at high valuations by riding sector momentum. In Nepal, this has been observed in hydropower (during periods of high NEPSE hydro sector P/E ratios), microfinance (during its rapid expansion phase), and insurance (during deregulation periods). The mechanism: investors extrapolate sector growth to individual companies without distinguishing between sector leaders and laggards.
Protection: When a sector is hot and multiple IPOs from the same sector are queued, apply individual financial analysis rather than sector-level conviction. The last companies to list in a sector wave are often the weakest — the stronger ones listed earlier.
Trap 5: The Small Issue with Disproportionate Hype
Small IPOs (by total capital size) with heavy social media and community network marketing create artificial demand signals. The oversubscription ratio for such issues can be extremely high (50x–200x) — not because the company is exceptional, but because the total shares available are small relative to Nepal's total investor population. A high oversubscription ratio is not evidence of investment merit; it is a reflection of issue size relative to applicant population.
The risk: investors who manage to receive allotment in a very small issue face a thin secondary market with low liquidity. Post-listing, exiting a position in a thinly traded stock without significant price impact can be difficult.
A Final Note: The Psychology of Missing Out
Perhaps the most insidious trap of all is the psychological compulsion to apply for every IPO to avoid 'missing out' on a listing gain. This compulsion causes investors to apply for issues they have not analysed, at prices they have not justified, in companies they do not understand. The lottery system's probabilistic nature means that even poorly chosen applications sometimes generate listing gains — reinforcing the behaviour through variable reward reinforcement, the same mechanism that makes gambling addictive.
The antidote is not to avoid all IPOs — many legitimate, well-priced, well-managed companies have listed on NEPSE and delivered excellent long-term returns. The antidote is to apply the six-layer framework from Lesson 14.7 before every application. The extra 2–3 hours of analysis per IPO is among the highest-return activities available to a serious investor.
Chapter recap
Lesson
Core Takeaway
8.1 — The IPO Process
The process is sequential and regulated; the quality of the Issue Manager is a leading indicator of prospectus credibility. Understand every step before you apply.
8.2 — The Prospectus
Read beyond the narrative. Financial statements, auditor reports, risk factors, and related-party disclosures are where the truth lives — and where deception hides.
8.3 — IPO Pricing
Neither par nor premium pricing signals value independently. Always calculate P/B, P/E, and compare to secondary market sector multiples before deciding an IPO is fairly priced.
8.4 — Allotment System
The lottery treats all applicants equally by design. Applying for more lots does not improve allotment probability. Expected return must account for allotment probability, not just listing premium.
8.5 — ASBA and MeroShare
Mechanical errors are a common and avoidable source of failed applications. Follow the process carefully. Maintain your DMAT and KYC. Apply early in the subscription window.
8.6 — Listing Day
Listing day prices are driven by anchoring, FOMO, and temporary liquidity constraints — not fundamentals. The best listing-day decision is usually to do nothing unless you have pre-formed conviction.
8.7 — IPO Analysis Framework
Apply all six layers: business quality, management quality, financial quality, pricing, use of proceeds, risk-adjusted return. The sixth-layer question — would you buy in the secondary market at the issue price? — cuts through all the noise.
8.8 — Common Traps
Overpriced premium issues, window-dressed financials, weak promoter history, sector-hype timing, and the psychology of FOMO are the five recurring traps. Knowledge of each is the only reliable protection.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part III · Chapter 15
Rights Issues and FPOs
First published 21 Aug 2026 · Last verified 29 Aug 2026
"When a company asks existing shareholders for more money, the question is never simply whether to give it — it is whether the terms are fair, the capital will be used wisely, and you are being treated as an owner or a source of funds."
Capital formation is the lifeblood of any company's growth, and in Nepal's equity market, the methods through which listed companies raise additional capital follow patterns that are distinct from the practices of more mature exchanges. For the investor navigating the Nepal Stock Exchange (NEPSE), two instruments dominate the secondary capital-raising landscape: the rights issue and the Further Public Offering, or FPO. Both mechanisms dilute existing ownership, and both carry consequences that are almost never fully appreciated by the retail shareholders who participate in them — or, worse, who ignore them entirely.
This chapter provides a complete analytical framework for understanding these two instruments. We will examine the arithmetic of dilution and pricing with the precision such decisions require, build a decision framework for the subscribe-or-renounce choice, understand the regulatory architecture that governs FPOs in Nepal, and finally confront an uncomfortable but essential truth: the compulsive use of rights issues by Nepali promoters constitutes one of the most systematic, yet least discussed, forms of minority shareholder harm in the market.
Lesson 15.1 — Why Rights Issues Are the Primary Capital-Raising Tool for NEPSE Companies
To understand why rights issues dominate Nepali corporate finance, one must first appreciate the institutional and regulatory environment from which they have emerged. Nepal's capital market is relatively shallow. The bond market remains underdeveloped, institutional investors are few, and access to long-term bank credit is both expensive and collateral-intensive. When a company needs growth capital — whether to expand its branch network, fund new infrastructure, or meet regulatory minimum paid-up capital requirements — the rights issue to existing shareholders is, in most cases, the path of least resistance.
A rights issue is a mechanism through which a listed company invites its existing shareholders to subscribe to new shares in proportion to their current holdings, typically at a price below the prevailing market price. The word 'rights' is precise: each shareholder receives a legally recognised entitlement to new shares, proportional to their existing stake. They are not obligated to exercise this right, but the right itself has intrinsic value — value that, as we shall see, is frequently misunderstood or abandoned by retail investors.
The Regulatory Mandate and the Minimum Paid-Up Capital Problem
Perhaps the single most powerful driver of rights issues in Nepal is regulatory compulsion. Nepal Rastra Bank (NRB), the central bank, has over the past decade issued a series of directives requiring commercial banks and financial institutions to maintain progressively higher levels of paid-up capital. What began as a requirement of Rs. 2 billion for Class A commercial banks has been increased multiple times, compelling banks that could not meet the threshold through retained earnings alone to turn to their shareholders for fresh capital — repeatedly, and in large amounts.
This regulatory escalation produced a peculiar dynamic: investors who had bought bank shares believing they were acquiring a stable, income-generating asset found themselves, year after year, being asked to pump additional capital into the same institution simply to keep regulators satisfied. The capital was not necessarily being deployed into higher returns — in many cases, it was diluting returns on equity precisely because the denominator (equity) was growing faster than the numerator (profits). The regulatory logic was sound from a systemic stability perspective; the consequence for individual shareholders was often silently punishing.
Beyond banks, development banks (Class B) and finance companies (Class C) faced similar trajectories. Hydropower companies, having secured project licences, have historically used rights issues to fund construction phases — a more defensible use of the instrument, though one that introduces its own timeline and execution risks.
Why Promoters Prefer Rights Issues Over Other Routes
From the promoter's perspective, a rights issue has several structural advantages over alternative capital-raising mechanisms. A private placement to new strategic investors would dilute existing promoter control. A public offering to entirely new shareholders would invite scrutiny, require more extensive regulatory compliance, and potentially introduce activist or institutional voices into the shareholder register. A rights issue, by contrast, is offered first to existing shareholders — including the promoters themselves — who, if they have the financial means, can maintain their ownership percentage simply by subscribing in full.
There is also a cost advantage. Rights issues in Nepal carry lower underwriting costs than public offerings, face less stringent disclosure requirements in some respects, and move through the regulatory pipeline more efficiently. The Securities Board of Nepal (SEBON) has built a rights issue approval framework that, while carrying its own procedural requirements, is broadly familiar to corporate secretaries and legal advisors across the listed universe.
MARKET CONTEXT
As of the most recent available data, the majority of capital raised on NEPSE through secondary equity offerings has come through rights issues rather than FPOs. In sectors like commercial banking, development banking, and insurance, rights issues are nearly universal instruments — a structural feature of how these industries have grown their capital bases over the past fifteen years.
The Shareholder Who Does Nothing
Understanding rights issues as the primary capital-raising tool requires confronting an asymmetry that is rarely discussed in Nepali investor education: the passive shareholder is systematically harmed. When a company issues new shares at a rights price, the total value of the company does not instantly increase by the amount of new capital raised — at least, not in the immediate term. Instead, existing shares decrease in market value because the same total company is now divided among more shares. A shareholder who does nothing — who neither subscribes to the rights issue nor sells their rights entitlement — ends up holding the same number of shares, but those shares are now worth less. They have been diluted.
This is not a theoretical concern. In Nepal's predominantly retail investor market, where a substantial share of the shareholder base consists of small investors who hold shares in demat accounts but lack either the financial capacity or the awareness to act on rights issues, the compounding effect of multiple rounds of dilution without corresponding participation can meaningfully erode wealth over years. The investor who bought 100 shares in a commercial bank in 2015, has been through five rights issues since, and has not subscribed to a single one, likely holds shares that represent a substantially smaller claim on the bank's equity than they once did — even if the nominal number of shares is unchanged.
This is the foundational problem that the rest of this chapter will address: the mechanics and the decisions involved, approached with the analytical rigour that the stakes demand.
Lesson 15.2 — Rights Ratio, Rights Price, and Theoretical Ex-Rights Price (TERP)
The arithmetic of a rights issue is not complicated, but it is frequently misunderstood — with real financial consequences. Getting comfortable with three numbers — the rights ratio, the rights price, and the Theoretical Ex-Rights Price — is the analytical foundation upon which every rights issue decision should rest.
The Rights Ratio: How Many New Shares for How Many Old
The rights ratio expresses the entitlement offered to existing shareholders. A 1:5 rights issue, for example, means that for every five shares currently held, the shareholder is entitled to subscribe to one new share. A 1:1 rights issue — sometimes called a 'one-for-one' or a 100% rights issue — means the company is doubling its share count, with each existing shareholder entitled to one new share for each they already hold.
In Nepal, rights ratios have historically been aggressive by international standards. Ratios of 1:1 (100%), 1:2 (50%), and even 2:1 (200%) are not uncommon, particularly in the banking sector during capital enhancement drives. A 1:1 ratio is a significant event: it doubles the total paid-up capital of the company and, if fully subscribed, brings in an amount of fresh capital equal to the rights price multiplied by the entire existing share count.
The Rights Price: Discount as Incentive, Not as Gift
The rights price is the price at which new shares are offered to existing shareholders. It is almost always set below the prevailing market price — this discount is the incentive for shareholders to subscribe. In Nepal, rights prices are frequently set at par value (Rs. 100 per share), regardless of how high the market price may be. This has an important implication: the lower the rights price relative to market price, the greater the value of the rights entitlement itself, and the steeper the post-issue dilution in the market price.
A rights price set at par for a company whose shares trade at Rs. 800 is a very different economic event from a rights price set at Rs. 600 for a company whose shares trade at Rs. 800. In the first case, the rights entitlement carries enormous intrinsic value; in the second, it carries moderate value. Both dilute the share price post-issue, but the magnitude differs significantly.
The Theoretical Ex-Rights Price: The Adjusted Value After the Issue
The Theoretical Ex-Rights Price, universally abbreviated as TERP, answers a deceptively simple question: if a rights issue is fully subscribed, what should the market price of the share be immediately after the issue? TERP is not a prediction — markets do not always behave efficiently, and the actual trading price will diverge from TERP based on sentiment, liquidity, and news flow. But TERP is the rational anchor around which ex-rights pricing should cluster, and any investor making a rights issue decision should calculate it.
The formula is:
TERP = (N x Market Price + R x Rights Price) / (N + R)
Where N is the number of existing shares, R is the number of new rights shares, and the market price is the prevailing price immediately before the ex-rights date. Let us work through a concrete example.
A Worked Example
Imagine a commercial bank — call it Himalayan Bank — with the following characteristics: 100 million shares outstanding, a current market price of Rs. 400 per share, and a proposed 1:2 rights issue (one new share for every two held) at a rights price of Rs. 100 per share.
Using our formula: N = 100 million (existing shares), R = 50 million (new shares, at 1:2 ratio), Market Price = Rs. 400, Rights Price = Rs. 100.
TERP = (100 x 400 + 50 x 100) / (100 + 50) = (40,000 + 5,000) / 150 = 45,000 / 150 = Rs. 300
The TERP is Rs. 300. This means that if you held one share worth Rs. 400 before the rights issue, the combined value of your one old share plus your entitlement to half a new share (the 1:2 ratio means two old shares entitle you to one new share) should approximate Rs. 300 per share after the issue. Your stake in the company — if you subscribe — remains proportionally unchanged. If you do not subscribe and simply hold, your share is now worth approximately Rs. 300, not Rs. 400: you have been diluted.
The Value of the Rights Entitlement
This brings us to the value of the rights entitlement itself — the 'rights' that a shareholder can either exercise (subscribe) or sell (renounce). Theoretically, the value of the right to subscribe to one new share is:
Value of Right = (Market Price - Rights Price) / (N/R + 1) OR = Market Price - TERP
In our Himalayan Bank example, the value of the right to subscribe to one new share is Rs. 400 - Rs. 300 = Rs. 100 per existing share held. Or, expressed differently: since the 1:2 ratio means you need two shares to get one right, the right to subscribe to one new share is worth Rs. 200 in total rights value across two existing shares.
This is the number that matters enormously in practice. If a shareholder cannot afford to subscribe, they should sell their rights entitlement — doing nothing means forfeiting this value entirely. In Nepal, the NEPSE trading platform does permit rights trading through renunciation mechanisms during the specified window, though liquidity can be thin and not all rights issues see active secondary market trading in entitlements.
COMMON MISCONCEPTION
Many retail investors in Nepal believe that not subscribing to a rights issue has no cost — that they simply "don't take up new shares." This is incorrect. The act of not subscribing, without selling the rights entitlement, is economically equivalent to selling your existing shares and donating the proceeds back to the company. The dilution is real, and the forfeited rights value is real. The cost is invisible but certain.
TERP and Price Discovery on NEPSE
In practice, NEPSE's ex-rights price adjustment mechanism means that on the ex-rights date, the reference price for the share is typically adjusted downward to approximate TERP. This prevents the appearance of an artificial price crash while still reflecting the economic dilution. However, the actual opening price on the ex-rights date may diverge — sometimes substantially — from TERP, depending on whether market participants are bullish or bearish on the company's fundamentals, the amount of capital being raised, and the broader market environment.
Investors who misread an ex-rights price drop as a buying opportunity — simply because "the price fell" — without understanding that the drop is mechanical and not informational are making a category error. The TERP is the new baseline from which any further price movement should be judged.
Lesson 15.3 — Subscribe, Renounce, or Sell: A Decision Framework
Every rights issue confronts the shareholder with three choices: subscribe in full, subscribe partially, renounce all or part of the entitlement (sell the rights), or do nothing. These are not merely financial choices — they reflect a comprehensive judgment about the company, its management, the deployment of capital, and the investor's own financial position. A disciplined framework for navigating this decision will serve investors across every rights issue they encounter throughout their investing lives.
The Four Questions Before You Decide
The decision to subscribe or renounce should flow from four questions, answered in sequence. Skipping any of them is a shortcut that costs money.
The first question is: Do I still want to own this company? A rights issue is an opportunity to re-examine your investment thesis. If the company has deteriorated since you first bought in — management has changed, the business model has weakened, competitive pressures have intensified — a rights issue is the moment to ask whether you would buy this stock at TERP if you had no existing position. If the honest answer is no, the rights issue does not change that calculus. In that case, you should renounce your entitlement at the best available price and consider whether your existing holding still makes sense.
The second question is: Will this capital be productively deployed? The purpose of the capital raise matters enormously. There is a vast difference between a hydropower company raising rights capital to fund the construction of a profitable project with a signed Power Purchase Agreement, and a commercial bank raising rights capital for the seventh time in a decade primarily to satisfy regulatory minimums while its return on equity continues to decline. Capital raised to fund real economic value creation can produce returns that compensate for dilution. Capital raised to maintain regulatory compliance, or to fund acquisitions at inflated prices, or to expand into low-return segments, will likely deliver substandard returns on the incremental equity deployed.
The third question is: What is the post-rights valuation, and is it attractive? Using TERP as your base price, calculate the Price-to-Book and Price-to-Earnings ratios at which you would effectively be acquiring the new shares. If you are subscribing to a bank's rights issue at a rights price of Rs. 100 (par), you may be doing so at a time when the TERP represents a Price-to-Book of 2.5x — meaning you are paying a significant premium over book value for shares in a business whose return on equity might not justify that multiple. The act of subscribing is equivalent to making a fresh investment decision; price it accordingly.
The fourth question is: Do I have the capital to subscribe without compromising my overall portfolio? Rights issues create a liquidity demand that is frequently underestimated by retail investors, particularly when they hold shares in multiple companies and several rights issues coincide — a common occurrence in Nepal's periodic waves of capital enhancement drives. Borrowing to subscribe is almost never warranted unless the rights issue presents an exceptionally clear arbitrage opportunity; leveraging to participate in dilutive capital raises compounds risk in a way that retail investors are rarely equipped to manage.
The Maths of Partial Subscription
If financial constraints prevent full subscription, a partial subscription — subscribing to some but not all of your entitlement — is a rational middle path, provided you sell the unexercised portion. Many Nepali investors subscribe to the maximum they can afford and then forget to renounce the balance, forfeiting the value of the unexercised rights. This is avoidable: calculate your full entitlement, determine how many new shares you can afford at the rights price, subscribe to that number, and actively seek to sell the remaining entitlement during the specified renunciation window.
The Renunciation Window and Market Liquidity
SEBON regulations specify a rights issue subscription and renunciation period, typically lasting several weeks. During this window, shareholders who wish to sell their entitlements can do so through NEPSE's secondary market mechanism for rights, where a market exists. In liquid rights issues — particularly those of large commercial banks with broad shareholder bases — there is generally a secondary market for rights entitlements, and the traded price should approximate the theoretical value we calculated earlier. In illiquid rights issues, particularly for smaller companies or niche sectors, the secondary market may be thin, and shareholders may be forced to accept prices below theoretical value or may be unable to sell at all.
The practical implication is that the subscribe-or-renounce decision should be made early in the subscription window, before potential price deterioration in the rights trading market narrows the renunciation value.
Scenario
Recommended Action
Key Risk
Strong company, productive capital use, attractive TERP valuation
Subscribe in full
Opportunity cost if capital is stretched
Strong company, but TERP valuation is expensive
Subscribe partially or renounce and hold existing
Paying too much for incremental shares
Weak company or unclear capital deployment
Renounce all and reassess holding
Rights may be illiquid; sell early
No liquidity to subscribe
Sell entitlement during renunciation window
Thin secondary market for rights
Do nothing (passive)
Avoid — this is almost never optimal
Guaranteed, invisible dilution loss
A Note on Oversubscription
In some rights issues, particularly where the rights price represents a steep discount to market price, demand for new shares exceeds the entitlement allocated. In these situations, shareholders can apply for additional shares beyond their entitlement — a process called applying for the 'renounced portion' or 'additional application.' Whether to apply for additional shares requires an even more careful analysis than the base subscription decision, because you are effectively making a fresh investment at the rights price — with the additional variable that allotment is not guaranteed and is typically made by lottery among excess applicants.
Applying for additional shares when the rights price is substantially below TERP can be a profitable strategy, but only if (a) the investment thesis supports further ownership at that valuation, and (b) the investor has the financial capacity to absorb the allocation without distorting their portfolio balance.
Lesson 15.4 — FPOs in Nepal: Rules, Pricing, and When Companies Use Them
The Further Public Offering — colloquially known as the FPO — is a less common but equally important secondary capital-raising mechanism in Nepal. Unlike a rights issue, which is offered exclusively to existing shareholders, an FPO is offered to the general public, including institutional investors and the broader retail market. It is, in essence, a second Initial Public Offering: the same company that once listed its shares for the first time through an IPO returns to the public market to raise additional capital.
The Regulatory Framework Governing FPOs
FPOs in Nepal are governed principally by the Securities Registration and Issuance Regulation, 2073 (2016) issued by SEBON, along with subsequent amendments and directives. Under this framework, a company must meet specific eligibility criteria before it can undertake an FPO. Among the key requirements: the company must have been listed on a securities exchange for a minimum period, must have distributed dividends or maintained adequate profitability metrics, and must have completed any pending rights issues or conversions of previously issued securities.
The pricing mechanism for FPOs deserves particular attention. Unlike an IPO, where the price is set through the book-building process (for institutional tranches) or at par (historically, for retail tranches), FPOs in Nepal have been subject to a pricing formula that takes into account the company's earnings per share, book value per share, and sometimes a market premium. SEBON has moved toward allowing greater pricing flexibility, including book-building style price discovery for certain categories of FPO issuers. However, the regulatory framework continues to evolve, and the specific pricing methodology can vary by issuer type — commercial banks, development banks, insurance companies, and others may be subject to different rules.
Why Companies Choose FPOs Over Rights Issues
Given that rights issues are simpler and faster, why would a company choose an FPO? The answer lies in the objectives of the capital raise and the composition of the desired shareholder base. A company that wants to broaden its ownership base — reducing promoter concentration, admitting institutional shareholders, increasing free float, or improving market visibility and analyst coverage — will find an FPO more suitable than a rights issue. An FPO, by inviting public participation, can significantly diversify the shareholder register in a way that a rights issue, which tends to preserve the existing ownership structure, cannot.
There is also a signalling dimension. An FPO requires a prospectus, audited financials, and a level of public disclosure that — whatever its imperfections in practice — subjects management to greater scrutiny than a rights issue. Companies confident in their financials and business outlook may actively prefer the transparency signal an FPO sends; companies with more complicated financial stories may prefer the quieter rights issue route.
A third consideration is the role of promoter dilution. Promoters whose holdings are above regulatory ceilings — a situation that has arisen in various listed companies following changes in SEBON's maximum promoter shareholding guidelines — may be required to reduce their stakes. An FPO offered to the public effectively achieves this reduction without the promoters having to sell their shares in the open market (which could be disruptive and price-depressing). Instead, new shares are issued to the public, diluting the promoter percentage without a single promoter share changing hands.
FPO Pricing: What the Investor Must Evaluate
The critical question for an investor evaluating an FPO is whether the offered price represents a fair value relative to the company's intrinsic worth and the prevailing market price of the existing shares. This sounds simple, but it requires disentangling several layers.
First, the FPO price is typically set at or below the prevailing market price — if the FPO were priced above market, rational investors would simply buy on the exchange rather than subscribe to the FPO. The discount, if any, represents an immediate gain for FPO subscribers, but this must be weighed against the allotment probability (in oversubscribed FPOs, which are common, individual allotments may be very small) and the post-listing price trajectory.
Second, the investor must independently assess whether the market price itself is justified. An FPO from a company trading at an unjustifiably high market price — perhaps inflated by promotional commentary or speculative interest — is not automatically attractive simply because the FPO price is set below that inflated level. The correct reference point is intrinsic value: what is this business worth, based on its sustainable earnings, asset quality, growth prospects, and competitive position?
Third, the use of FPO proceeds must be scrutinised in the prospectus. FPO prospectuses in Nepal are required to disclose the objects of the issue — how the company intends to use the capital raised. The quality of this disclosure varies, and investors should read it with a sceptical eye. Vague statements about 'business expansion' or 'working capital requirements' are less informative than specific, quantified capital allocation plans with identifiable return profiles.
Key Points
DUE DILIGENCE CHECKLIST FOR FPO APPLICANTS Before subscribing to an FPO: (1) Read the prospectus in full, especially the objects of issue, risk factors, and related party transactions. (2) Compare the FPO price to the company's Price-to-Book and Price-to-Earnings on both historical and forward earnings. (3) Assess the allotment probability — heavily oversubscribed FPOs may yield very small allotments, reducing the practical significance of the price discount. (4) Examine the promoter track record and any history of capital misallocation. (5) Check whether the company has pending rights issues; subscribing to an FPO and then facing an immediate rights issue creates a double capital demand.
Post-FPO Price Behaviour: Patterns on NEPSE
Historically, FPOs on NEPSE have tended to list at premiums above the FPO price, particularly in bullish market phases, generating immediate gains for successful applicants. However, this pattern is neither universal nor persistent: companies that raise capital in excess of their productive deployment capacity, or that have issued FPOs during market peaks, have seen post-listing prices converge toward or below FPO price over the following months and years. The 'FPO listing premium' is a market phenomenon, not an economic guarantee, and it should not be the basis for subscription decisions. The correct basis remains the fundamental relationship between FPO price and intrinsic value.
Lesson 15.5 — The Equity Dilution Problem: Why Frequent Rights Issues Hurt Minority Shareholders
"Repeated rights issues that are not matched by proportionate growth in earnings are a form of silent taxation on minority shareholders — one that compounds invisibly and damages wealth with no single moment of visible harm."
The final lesson of this chapter addresses what is perhaps the most important and least understood structural risk in Nepal's equity market: the harm done to minority shareholders by the compulsive, frequent issuance of rights shares in the absence of commensurate earnings growth. This is not a peripheral concern. For investors who have held NEPSE-listed banking stocks over the past fifteen years, the dilution problem has been the single largest headwind to wealth creation — more damaging in many cases than market cycles, more persistent than any individual piece of bad news, and almost completely invisible to those who do not know where to look.
Return on Equity: The Number That Tells the Story
The central diagnostic for evaluating the impact of repeated rights issues is Return on Equity, or ROE — net profit divided by shareholders' equity. ROE is the measure of what the company earns, proportionally, on the capital shareholders have entrusted to it. If a company consistently earns Rs. 100 on every Rs. 1,000 of equity, its ROE is 10%. When a rights issue adds another Rs. 500 to the equity base, the company must now earn Rs. 150 in total net profit just to maintain that 10% ROE. If it cannot — if it earns Rs. 115, Rs. 120, perhaps Rs. 130 — ROE falls. Earnings per share falls. And unless the market blindly re-rates the stock upward on other grounds, the price falls too, or at best stagnates.
This is precisely what has happened in Nepal's commercial banking sector. Paid-up capital of the sector has grown dramatically over the past decade, driven by successive rounds of rights issues responding to NRB directives. Net profits have grown, but not proportionally. The result: sector-wide ROE has declined from the high teens or low twenties seen in the early 2010s to single digits or low double digits by the early 2020s. Shareholders who bought bank stocks at high Price-to-Book multiples, justified at the time by high ROEs, have watched the fundamental justification for those multiples erode — not because the banks became worse institutions in an absolute sense, but because the denominator of the ROE calculation grew faster than the numerator, mechanically and systematically.
Book Value Growth Without EPS Growth: The Trap
A common refrain from company managements and market commentators in Nepal goes as follows: 'Our book value per share is growing; the company is becoming stronger.' This statement is technically true but economically misleading. Book value per share grows when retained earnings are added to equity. But when rights issues continuously add capital at or near par value to the equity base — while earnings grow more slowly — book value per share can grow while simultaneously EPS stagnates or declines. The investor who bought shares at 3x book value for a 15% ROE business now holds shares at 3x a higher book value, but the ROE has fallen to 10%, making the 3x multiple unjustifiable. This is book value growth that destroys wealth.
The correct framing: what matters is not whether book value per share is higher than last year, but whether the company is earning an adequate return on that growing equity base. Growth in capital without adequate returns is not strength; it is dilution wearing the costume of prosperity.
The Promoter's Perspective vs. The Minority Shareholder's Reality
There is a deep conflict of interest at the heart of Nepal's rights issue culture that investors must understand. Promoters who control large blocks of shares and participate in every rights issue — or who obtain fresh shares through bonus issues and then face rights calls — can afford the continuous capital deployment. Their ownership percentage is maintained; their control is preserved; and in some cases, the additional capital, even if deployed at modest returns, increases the absolute scale of the business in ways that benefit promoters through management fees, related party contracts, and other perquisites of control.
Minority shareholders, however, face a different calculation. The retail investor who cannot participate in every rights issue either faces dilution of their economic claim or is forced to keep allocating fresh capital to the same company year after year — capital that might earn better returns elsewhere. The option to exit — sell the shares — is theoretically available but practically impaired by the fact that repeated rights issues suppress the share price, often making exit at acceptable prices difficult precisely when the shareholder is most exhausted by the capital demands.
This is not an abstract critique of any individual company or promoter group. It is a structural feature of markets where regulatory capital requirements, weak earnings power, and governance norms around minority shareholder protection combine to create a capital allocation pattern that systematically favours those with the means and information to participate over those without.
Screening for the Dilution Problem Before You Invest
The practical implication for investors is to build dilution history into pre-investment analysis. Before buying shares in any NEPSE-listed company — particularly banks, insurance companies, or capital-intensive businesses — examine the company's rights issue history over the preceding five to ten years. Calculate the growth in paid-up capital over that period, and compare it to the growth in net profit. If paid-up capital has grown at, say, 20% per year compounded while net profit has grown at 12%, the dilution math has been working against shareholders throughout.
Look at the trend in ROE: is it stable, rising, or declining? Look at the trend in EPS: is the company earning more per share today than five years ago, despite all the capital injections? A company that has grown its paid-up capital dramatically but whose EPS is flat or lower than it was five years ago is a company that has been consuming, not creating, shareholder value — regardless of what its nominal stock price or dividend payout might suggest.
When Rights Issues Are Value-Creating
It would be unfair and misleading to characterise all rights issues as destructive. Capital-intensive businesses at specific stages of their development — a hydropower project under construction, a bank expanding into underserved markets with genuinely high loan demand, an infrastructure company deploying capital into productive assets — can create substantial shareholder value through rights issues, provided the incremental capital earns a return that exceeds the cost of equity. The framework for evaluating any specific rights issue is precisely the TERP arithmetic and the business analysis discussed in earlier lessons of this chapter.
The warning is not against rights issues per se but against the pattern of reflexive, repeated rights issuance as a solution to every capital requirement, executed regardless of the company's capacity to deploy that capital productively. The discipline an investor must bring to rights issue decisions is the same discipline a sound capital allocator brings to every deployment of capital: is this the best available use of this money, at this price, in this company?
THE INVESTOR'S CANON ON RIGHTS ISSUES
Subscribe to rights issues only when you would make the same investment at TERP if you had no prior position. Renounce when you would not. Sell your entitlement when you cannot subscribe, and never do nothing. Screen companies before you buy them for chronic dilution — it is the silent destroyer of NEPSE returns. And remember that the most important number in assessing any capital raise is not the amount raised, but the return on equity that the company can be expected to earn on the incremental capital for years to come.
Chapter recap
This chapter has moved from the institutional origins of Nepal's rights issue culture through the arithmetic of TERP and dilution, into the decision framework for subscribe-or-renounce, the specific features of FPOs in the Nepali regulatory context, and finally to the structural equity dilution problem that has characterised Nepal's banking sector. The analytical tools — TERP calculation, ROE trend analysis, paid-up capital growth comparison to earnings growth — are portable: they apply to any rights issue or FPO you will encounter in this market or any other. The philosophical principle is equally portable: capital raised without productive deployment is not growth. It is dilution, and it is a cost borne disproportionately by those who are least equipped to see it coming.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part III · Chapter 16
Mutual Funds in Nepal
First published 21 Aug 2026 · Last verified 29 Aug 2026
From passive wealth-building to navigating discount-to-NAV traps — a complete practitioner's guide to mutual funds on the Nepal Stock Exchange.
Nepal's mutual fund industry is still in its adolescence. The regulatory architecture exists, the products are listed, and investors are gradually becoming aware — yet a persistent gap remains between what mutual funds can do for investors and how well those investors actually understand what they are buying. This chapter exists to close that gap.
Across six detailed lessons, we will dissect the structure of open-ended and closed-ended funds, survey the actual schemes trading on NEPSE, decode the mathematics of NAV versus market price, evaluate fund managers with the rigour of a professional analyst, expose the full cost picture hidden behind headline expense ratios, and finally make the case that mutual funds — understood properly — are among the most powerful and underused tools available to the Nepali investor.
Lesson 16.1 — Open-Ended vs. Closed-Ended Funds: Structure and Differences
What Is a Mutual Fund?
At its most elemental level, a mutual fund is a pooling mechanism. Many investors contribute capital into a common vehicle; a professional fund manager deploys that capital according to a stated investment mandate; and each investor receives units proportional to their contribution, entitling them to a share of gains, losses, and income generated by the pool.
The appeal is structural: small investors gain access to diversification and professional management at a cost that would be prohibitive if pursued individually. A retail investor with Rs. 5,000 cannot meaningfully diversify across twenty-five stocks on their own. Inside a mutual fund, they can.
The Structural Fork: Open-Ended vs. Closed-Ended
Every mutual fund in Nepal falls into one of two structural categories, and this distinction shapes almost everything that follows — from how you buy and sell units, to how prices are set, to the risks you face as a holder.
Open-Ended Funds
An open-ended fund has a variable unit count. Investors can subscribe (buy new units) and redeem (sell units back to the fund) at any time, at a price anchored directly to the fund's Net Asset Value per unit. The fund management company stands as the permanent counterparty: it creates new units when investors subscribe and cancels units when investors redeem.
Key consequence: Because investors transact directly with the fund at NAV, there is no secondary market for open-ended fund units in Nepal. You cannot buy or sell them on NEPSE. Liquidity comes entirely from the fund's own redemption mechanism.
Key Points
HOW NAV IS CALCULATED FOR OPEN-ENDED FUNDS NAV per unit = (Total market value of all portfolio securities + Cash and receivables - All liabilities and payables) / Total outstanding units. In Nepal, open-ended funds are required to publish their NAV daily. The NAV you transact at is typically the next calculated NAV after your subscription or redemption request is received — this is the 'forward pricing' principle.
Closed-Ended Funds
A closed-ended fund issues a fixed number of units at inception through an Initial Public Offering (IPO). Once the IPO closes, no new units are created and no redemptions are accepted by the fund itself. Instead, the units are listed on NEPSE and trade on the secondary market between investors, just like shares of a company.
Key consequence: The price you pay or receive is determined entirely by supply and demand on NEPSE — not by NAV. This creates one of the most important and exploitable phenomena in closed-ended fund investing: the discount or premium to NAV. We treat this in depth in Lesson 16.3.
NEPAL'S CLOSED-ENDED FUND MATURITY MODEL
In Nepal, closed-ended funds have a fixed lifespan — typically 5 to 7 years. At the end of this period, SEBON regulations require the fund to either wind up (liquidating assets and distributing proceeds to unit holders) or convert into an open-ended fund. This maturity feature has profound implications for pricing: as a fund approaches its wind-up date, the market price tends to converge toward NAV, because the arbitrage opportunity becomes time-bounded and explicit.
Comparative Architecture: A Side-by-Side View
Dimension
Open-Ended Fund
Unit supply
Variable — expands and contracts with subscriptions and redemptions
Secondary market
None — transact directly with fund at NAV
Pricing
NAV-based; calculated and published daily
Liquidity source
Fund manager (redemption)
Price can deviate from NAV?
No — by definition
Available in Nepal?
Yes — growing segment
Regulation
SEBON; fund manager must maintain liquid buffer for redemptions
Ideal for
Investors who prioritize NAV-based fairness and easy entry/exit
Dimension
Closed-Ended Fund
Unit supply
Fixed from IPO date
Secondary market
NEPSE — trades like equity
Pricing
Market-determined; may differ substantially from NAV
Liquidity source
Secondary market buyers and sellers
Price can deviate from NAV?
Yes — often trades at a discount in Nepal
Available in Nepal?
Yes — currently dominant structure on NEPSE
Regulation
SEBON; fixed lifespan with wind-up or conversion obligation
Ideal for
Investors willing to analyse NAV vs. price and exploit mispricings
The Liquidity Asymmetry and Its Practical Impact
One underappreciated aspect of the open-ended structure in Nepal is the liquidity risk it imposes on fund managers. Because investors can redeem at any time, the fund manager must maintain a portion of assets in liquid instruments — typically short-term bonds, treasury bills, or bank deposits — to meet potential redemption demands. This 'liquidity buffer' is a drag on returns in bull markets, but it is a necessary structural cost.
Closed-ended fund managers face no such constraint. Their corpus is locked in for the fund's lifespan, allowing them to hold less liquid, higher-yielding assets, or to remain fully invested in equities without worrying about forced selling at inopportune times. In theory, this should give closed-ended funds a structural return advantage. Whether it actually manifests in Nepal's fund universe is an empirical question we explore when we discuss performance evaluation.
Hybrid and Interval Funds: A Note
SEBON regulations also permit interval funds — a hybrid structure that allows redemption only during specific 'windows' (for example, once every quarter). This attempts to combine the NAV-pricing fairness of open-ended funds with the liquidity management benefits of the closed-ended structure. As of this writing, interval funds remain rare in Nepal but represent an area of potential growth.
Lesson 16.2 — Available Mutual Fund Schemes on NEPSE: A Survey
The Regulatory Ecosystem
Mutual funds in Nepal operate under the Mutual Fund Regulations, 2010 issued by the Securities Board of Nepal (SEBON). All fund management companies (FMCs) must be licensed by SEBON and must appoint a separate depositary — typically a commercial bank — to hold the fund's assets in custody. This two-entity structure (FMC + depositary) provides an important layer of investor protection: the fund manager never directly holds the assets, reducing fraud risk.
The Major Fund Management Companies
The landscape of licensed FMCs in Nepal has grown steadily. The principal players — some affiliated with development banks and insurance companies, others with commercial banking groups — include NIBL Ace Capital, Nabil Invest, Global IME Capital, Siddhartha Capital, Sunrise Capital, Laxmi Capital, NMB Capital, and Citizens Investment Trust (CIT), among others. CIT, being government-backed, occupies a unique position as the oldest and largest institutional investor in the Nepali fund market.
Types of Fund Schemes by Investment Mandate
Nepali mutual funds primarily invest in domestic listed equities (equity-oriented funds), a blend of equities and fixed income (balanced or hybrid funds), or predominantly in fixed income instruments such as government bonds and debentures (debt funds). The equity-oriented and balanced categories dominate by asset size and investor interest.
Fund Category
Primary Assets
Typical Risk-Return Profile
Equity-Oriented
>=65% in listed equities (NEPSE)
Higher risk, higher long-term return potential
Balanced / Hybrid
Mix of equities and fixed income
Moderate risk, smoother returns
Debt / Income Fund
Bonds, debentures, bank deposits
Lower risk, income-focused, predictable
Money Market Fund
Short-term instruments (T-bills, call money)
Lowest risk, near-cash liquidity
Reading a Fund Scheme Document
Before investing in any Nepali mutual fund scheme, the investor should obtain and read the Scheme Information Document (SID) — equivalent to a prospectus. The SID discloses the investment objective, asset allocation limits, fund manager biography, fee structure, benchmark index, dividend policy, and redemption/subscription terms. SEBON requires all SIDs to be publicly accessible on the fund's website and SEBON's own portal.
Key Points
WHAT TO LOOK FOR IN A SCHEME INFORMATION DOCUMENT 1. Investment objective: Is it genuinely aligned with your goals? 2. Benchmark: Against which index or return target will performance be judged? Is it an appropriate benchmark? 3. Asset allocation bands: What are the minimum and maximum allocations to equities, bonds, and cash? How much discretion does the manager have? 4. Load structure: Is there an entry load (upfront fee) or exit load (fee on redemption)? What are the conditions? 5. Fund manager tenure: How long has the named manager been managing this scheme? 6. Dividend policy: Is dividend reinvestment automatic or does it require election?
Benchmarks and Their Shortcomings
Most Nepali equity mutual funds benchmark against the NEPSE Index or the NEPSE Float Index. A critical sophistication that many Nepali investors lack is the understanding that beating an index requires genuine skill — and that most actively managed funds globally fail to beat their benchmarks over long periods once fees are accounted for. The Nepali fund universe is small enough that statistical significance is hard to establish, but the principle holds: benchmark comparison is the minimum test of fund manager value-add.
Debt and balanced funds sometimes benchmark against a blended rate (a weighted average of the equity index and a government bond yield index), though disclosure standards remain inconsistent.
Lesson 16.3 — NAV vs. Market Price of Closed-End Funds: Discount and Premium Dynamics
The Defining Anomaly of Closed-Ended Funds
No phenomenon in mutual fund investing is as simultaneously intuitive and puzzling as the persistent discount at which closed-ended funds trade relative to their Net Asset Value. In a perfectly efficient market, a fund holding Rs. 100 worth of assets per unit should trade at Rs. 100. Yet in Nepal — as in markets globally — closed-ended funds routinely trade at Rs. 80, Rs. 85, or even Rs. 70 per unit when the NAV is Rs. 100. This discount is the central intellectual puzzle of closed-ended fund investing.
Calculating the Discount and Premium
THE DISCOUNT/PREMIUM FORMULA
Discount (%) = [(NAV - Market Price) / NAV] x 100. Premium (%) = [(Market Price - NAV) / NAV] x 100. Example: Fund NAV = Rs. 12.50 per unit. Market Price = Rs. 10.80 per unit. Discount = [(12.50 - 10.80) / 12.50] x 100 = 13.6%. This means you are buying Rs. 12.50 of assets for Rs. 10.80 — a 13.6% discount.
Why Discounts Persist: The Structural Explanations
Several structural and behavioural factors contribute to the endemic discount in Nepali closed-ended funds:
Illiquidity of the closed-ended structure: Investors cannot redeem directly with the fund. This illiquidity is priced as a discount — the market demands compensation for the inability to exit at NAV on demand.
Embedded expense drag: Future management fees and expenses will be deducted from the fund's assets over its remaining life. The present value of these future costs rationally reduces the value of a unit below current NAV.
Unrealised capital gains tax uncertainty: The portfolio contains unrealised gains that may be subject to taxation when crystallised upon fund wind-up, reducing net distributions to investors.
Sentiment and momentum: In periods of market distress, investors become indiscriminate sellers. Closed-ended fund units, being listed instruments, are sold alongside equities in risk-off episodes, even though the underlying portfolio's value has not necessarily declined proportionally.
Manager distrust: If investors doubt a fund manager's ability to generate returns competitive with the benchmark, they discount the value of the management service, widening the discount.
Why Premiums Sometimes Appear
Premiums — market prices exceeding NAV — are less common but do occur, typically in the following circumstances:
During periods of intense retail investor enthusiasm, when new investors are desperate to gain exposure to equities and see closed-ended fund units as a convenient vehicle
When a fund has a strong recent performance track record and investor sentiment is bullish
In the period immediately following a fund's IPO, when speculative trading and primary market enthusiasm inflate the unit price
When the fund holds a particularly coveted portfolio of assets — for example, significant exposure to a high-performing sector — that investors cannot easily replicate
Discount Convergence as an Investment Strategy
The maturity feature of Nepali closed-ended funds creates a tractable investment thesis: as a fund approaches its wind-up or conversion date, the market price should converge toward NAV, because any residual discount becomes an increasingly certain and time-bounded arbitrage. An investor who buys units at a 15% discount three years before fund maturity has effectively locked in a 15% gain above and beyond whatever the portfolio itself returns — provided the fund is actually wound up and assets are distributed at or near NAV.
DISCOUNT CONVERGENCE: A WORKED EXAMPLE
Fund maturity: 2 years from today. Current NAV: Rs. 14.20 per unit. Current market price: Rs. 11.90 per unit. Discount: 16.2%. If the NAV remains flat (zero portfolio return) and the discount closes to zero at maturity: Investor return = (14.20 - 11.90) / 11.90 = 19.3% total return over 2 years. If the portfolio itself also grows 10% over 2 years: Maturity NAV = Rs. 15.62. Total return = (15.62 - 11.90) / 11.90 = 31.3%. The discount provides a margin of safety: even if the fund's portfolio performs poorly, the investor may still profit from discount convergence alone.
Risks of Discount Investing
The discount convergence thesis is not risk-free. Discounts can widen further before they narrow. A fund's maturity may be extended by regulatory action. The underlying portfolio may decline in value faster than the discount closes. Liquidity in the secondary market for a fund's units may be thin, making it difficult to establish or exit a position without meaningful price impact. Investors pursuing this strategy must be disciplined about position sizing and must monitor both the discount level and the quality of the underlying portfolio simultaneously.
Lesson 16.4 — Fund Manager Track Records: How to Evaluate Performance Fairly
Why Performance Evaluation Is Hard
Evaluating a fund manager is one of the most cognitively demanding tasks in investing. The core difficulty is statistical: investment returns are noisy. Even a genuinely skilled manager will experience periods of underperformance, and even a truly unskilled manager will, through luck, post impressive short-term numbers. The investor's challenge is to separate signal from noise — to distinguish genuine alpha generation from favourable market conditions, sector tailwinds, or pure chance.
In Nepal's relatively small fund universe, this challenge is amplified. Many funds have operating histories of only a few years. Sample sizes are small. The NEPSE itself is a frontier market characterised by periodic volatility spikes, illiquidity, and episodes of retail investor mania that distort all returns — good and bad managers alike.
The Foundation: Risk-Adjusted Returns
Raw returns — the percentage gain in a fund's NAV over a period — are a starting point, not a conclusion. A fund that returned 35% in a year in which the NEPSE rose 40% has underperformed. A fund that returned 12% in a year when the NEPSE fell 8% has massively outperformed. Absolute returns without context are nearly meaningless.
Step 1 — Compare to the benchmark: Always compare fund returns to the appropriate benchmark index over the same period. The excess return (fund return minus benchmark return) is called Alpha. Positive alpha indicates outperformance; negative alpha indicates underperformance.
KEY PERFORMANCE METRICS TO CALCULATE
1. Alpha: Excess return over benchmark. Positive = outperformance. 2. Beta: Sensitivity to market moves. Beta > 1 = more volatile than market. Beta < 1 = more stable. 3. Sharpe Ratio: (Fund return - Risk-free rate) / Standard deviation. Higher is better — measures return per unit of total risk. 4. Information Ratio: Alpha / Tracking Error. Measures consistency of outperformance. 5. Maximum Drawdown: The largest peak-to-trough decline. Measures downside risk and manager risk management discipline. 6. Up-capture / Down-capture ratio: How much of the market's gains did the fund capture vs. how much of the market's losses did it suffer?
The Attribution Question: Skill or Beta?
A common mistake among Nepali investors is attributing strong absolute returns to managerial skill without accounting for market-level tailwinds. If the NEPSE rose 50% in a year and the fund rose 48%, the investor may feel satisfied — but the manager actually underperformed the market while taking full equity risk. Conversely, if the fund rose 20% in a year the NEPSE fell 10%, that manager has added extraordinary value.
A more rigorous attribution analysis asks: what portion of the fund's return came from market exposure (beta), sector allocation decisions, individual stock selection, and timing decisions? Professional attribution analyses can separate these components. In Nepal's context, even a simplified two-factor analysis (market beta contribution vs. residual alpha) is vastly more informative than raw return comparison.
Time Horizon: Why You Need At Least a Full Market Cycle
A fund manager's track record should be evaluated across a full market cycle — at minimum one bull phase and one bear phase. Evaluating only bull-market performance tells you nothing about the manager's risk management, portfolio construction discipline, or ability to protect capital in downturns. In Nepal's context, where market cycles can be compressed and episodic, a meaningful evaluation period is generally five years or more.
Manager Continuity: The Attribution Problem
Fund performance is attributable to the specific individuals who made the investment decisions. When a fund manager changes, the historical track record loses much of its predictive value. Before relying on a fund's historical performance, always verify: is the manager who generated those returns still in place? Has the investment team composition changed significantly? Manager turnover is a genuine risk factor in Nepal's fund industry, where talent is concentrated and competitive hiring from FMCs to commercial banks is common.
Red Flags in Fund Manager Conduct
Portfolio concentration in illiquid or related-party securities that cannot be independently valued
Significant and unexplained deviation from the stated investment mandate
Suspiciously smooth return streams with very low reported volatility — a potential sign of smoothed or stale pricing
High portfolio turnover without corresponding alpha — generating transaction costs without adding returns
Reluctance to disclose complete portfolio holdings or delayed/incomplete NAV publication
Governance concerns around the relationship between the fund manager and the depositary
Lesson 16.5 — Expense Ratios and Hidden Costs in Nepali Mutual Funds
Why Costs Matter More Than Most Investors Realise
The impact of fees on long-term investment outcomes is one of the most well-documented and consistently underestimated phenomena in finance. A difference of 1% per year in annual costs may seem trivial in isolation, but compounded over twenty years, it represents a dramatically different wealth outcome. An investor who earns 10% gross annually for twenty years and pays 2% in fees ends up with approximately 23% less wealth than an investor paying 1% in fees. In rupee terms, on a Rs. 500,000 investment, that gap can exceed Rs. 2 million over twenty years.
The Annual Expense Ratio (AER)
The Annual Expense Ratio is the total percentage of the fund's average net assets consumed by operating expenses in a given year. It is the single most important cost metric and encompasses management fees, depositary fees, audit fees, legal fees, SEBON registration fees, and other operational expenses. SEBON regulations cap the total expense ratio for equity-oriented Nepali mutual funds, but within that cap, there is meaningful variation across schemes.
AER EXAMPLE
Fund A: AER = 2.5%. Fund B: AER = 1.5%. Both funds hold identical portfolios earning 12% gross annual return. Fund A net return = 9.5%. Fund B net return = 10.5%. Over 10 years on Rs. 100,000: Fund A: Rs. 247,800. Fund B: Rs. 271,600. Difference: Rs. 23,800 — purely from the 1% fee differential.
Entry and Exit Loads
Some Nepali mutual funds charge entry loads — a percentage fee deducted from your subscription amount before it is invested. If a fund has a 2% entry load and you invest Rs. 100,000, only Rs. 98,000 is actually invested. Exit loads — charged when you redeem — typically apply on a sliding scale that decreases the longer you hold the fund, to discourage short-term trading. Both entry and exit loads directly reduce your investment returns and should be fully disclosed in the SID.
Transaction Costs: The Hidden Layer
Beyond the AER, every trade the fund manager makes in the portfolio incurs brokerage commissions, market impact costs (the price movement caused by the fund's own buy or sell order), and, in Nepal, applicable taxes. These transaction costs are real costs borne by the fund — and therefore by unit holders — but they are not captured in the published AER. High portfolio turnover (frequent buying and selling) amplifies these hidden costs substantially.
A fund that reports a 2% AER but turns over 100% of its portfolio each year is imposing additional hidden costs potentially equivalent to another 0.5% to 1% of assets. A fund with a 2.5% AER but only 20% annual portfolio turnover may actually be less costly on a total basis.
Dividend Distribution Tax
When a mutual fund distributes dividends to unit holders in Nepal, the dividend may be subject to withholding tax. Investors should understand whether the returns quoted in fund marketing materials are pre-tax or post-tax, and should account for personal income tax treatment when comparing after-tax returns across asset classes.
How to Compare Costs Across Funds
Cost Component
Where Disclosed
What to Watch
Management fee
SID, annual report
Is it fixed or performance-linked?
Depositary fee
SID, annual report
Usually small but varies
Total AER
NAV publications, SEBON filings
Compare across similar-mandate funds
Entry load
SID, fund distributor
Negotiate or seek no-load options
Exit load
SID, fund distributor
Check holding period thresholds
Portfolio turnover
Annual report (if disclosed)
Higher turnover = higher hidden costs
Transaction taxes
Computed from trade volumes
Often not separately disclosed
The Practical Guidance
A disciplined investor should, before committing capital to any Nepali mutual fund scheme, explicitly calculate the total cost of ownership — combining the AER, any applicable loads, and an estimate of transaction cost drag from portfolio turnover. This number should be weighed against the fund's realistic gross return potential given its mandate. If the cost of active management equals or exceeds the realistic alpha generation, an investor would rationally prefer a lower-cost vehicle if one is available.
Lesson 16.6 — Mutual Funds as an Entry Point for New Investors in Nepal
The Problem of Starting
The most significant barrier facing a new investor in Nepal is not lack of capital — it is lack of knowledge architecture. Investing in individual stocks requires understanding financial statements, sectoral dynamics, management quality, valuation methodologies, market microstructure, and portfolio construction. For someone starting from zero, the learning curve is steep and the potential for costly mistakes during the learning period is high.
Mutual funds offer a structurally different entry point. They allow a new investor to participate in the potential returns of the equity market while outsourcing the stock selection decisions to a professional. They offer built-in diversification. And they provide a framework — the NAV, the SID, the benchmark — that teaches the vocabulary of investing in a structured, digestible format.
Systematic Investment Plans (SIPs): The Most Powerful Entry Mechanism
A Systematic Investment Plan (SIP) allows an investor to invest a fixed amount — even as little as Rs. 1,000 per month in some schemes — at regular intervals, regardless of market conditions. The investor buys more units when NAV is low and fewer units when NAV is high, automatically averaging the cost of acquisition over time. This is the investment strategy known as rupee-cost averaging.
HOW RUPEE-COST AVERAGING WORKS
Month 1: Invest Rs. 2,000. NAV = Rs. 10. Units acquired: 200. Month 2: Invest Rs. 2,000. NAV = Rs. 8. Units acquired: 250. Month 3: Invest Rs. 2,000. NAV = Rs. 12. Units acquired: 167. Total invested: Rs. 6,000. Total units: 617. Average cost per unit: Rs. 9.72. Average NAV over 3 months: Rs. 10.00. Rupee-cost averaging produced an average cost (Rs. 9.72) below the arithmetic average NAV (Rs. 10.00) — because more units were acquired when prices were low. This is the mechanical advantage of regular fixed-amount investing.
Building the First Portfolio: A Principles-Based Approach
For a new investor entering through mutual funds, a sensible framework prioritizes clarity over complexity. The investor should begin by articulating a time horizon: is this capital that will be needed in two years, five years, or twenty years? The answer has profound implications for the appropriate risk level.
For long time horizons (ten years or more), equity-oriented mutual funds capture the compounding potential of the stock market. For medium horizons (three to seven years), balanced or hybrid funds offer equity participation with some downside buffer from the fixed income component. For short horizons (under three years), debt funds or money market funds preserve capital more reliably, though returns are modest.
Investor Profile
Suggested Fund Type
Rationale
Young earner, 20-year horizon, high risk tolerance
Liquidity + slightly better return than savings account
The Psychological Value of Mutual Funds for New Investors
Beyond the mechanics, mutual funds offer a crucial psychological service for new investors: they impose structure on what is otherwise an overwhelming decision space. The act of setting up a monthly SIP creates an investment habit. Watching NAV change over time teaches the investor — viscerally, not just abstractly — that markets move up and down, and that this movement is normal and manageable. The investor who has experienced a 20% NAV drawdown through a mutual fund, and has seen it recover, is far better prepared psychologically to handle direct equity investing than one who learned the same lesson through a concentrated individual stock position.
Common Mistakes New Investors Make with Mutual Funds
Chasing recent performance: buying last year's top-performing fund without understanding why it outperformed or whether those conditions will persist
Treating NAV as a stock price: selling when NAV falls, missing the recovery — the equivalent of selling at the bottom
Neglecting to compare the fund's benchmark before subscribing
Selecting funds based on dividend yield rather than total return (dividend distributions reduce NAV by the distributed amount — they are not free money)
Failing to read the SID and being unaware of exit load structures that penalise early redemption
Over-diversifying across too many funds with overlapping mandates, creating pseudo-diversification that merely increases costs without reducing risk
The Investor's Progression
Mutual funds are not a permanent destination — they are an on-ramp. The investor who begins with a balanced fund SIP, studies the portfolio disclosures, tracks performance against the benchmark, reads the annual report, and starts asking the analytical questions introduced in this chapter, is developing the exact mental models needed to eventually invest directly in individual equities or bonds with confidence and discipline.
The Investor's Canon is built on the conviction that understanding deepens with each layer of engagement. Mutual funds offer the first layer: exposure to markets, introduction to professional analysis, and the compounding habit. The investor who masters this layer is ready for everything that follows.
Chapter recap
Structure determines your rights. Open-ended funds give you NAV-priced liquidity on demand. Closed-ended funds trade on the exchange and may deviate substantially from NAV.
Discount to NAV is an opportunity — if understood correctly. Buying closed-ended fund units at a discount to NAV near fund maturity can provide a margin of safety and a source of return independent of portfolio performance.
Raw returns deceive; risk-adjusted, benchmark-relative returns inform. Always evaluate fund manager performance using alpha, Sharpe ratio, and drawdown metrics over a full market cycle. Verify manager continuity before trusting historical records.
Every percentage point in fees compounds against you. Understand the full cost of ownership: AER, loads, and transaction cost drag from portfolio turnover. Justify active management fees with evidence of consistent alpha.
Mutual funds are the ideal on-ramp for new investors. A disciplined SIP in a well-chosen fund builds wealth, instills investing habits, and develops the market intuition needed for the more advanced strategies in subsequent chapters.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part III · Chapter 17
Debentures and Bonds in Nepal
First published 21 Aug 2026 · Last verified 29 Aug 2026
Equity tends to capture the imagination of investors. Headlines celebrate IPO listings, trading volumes spike around bonus announcements, and the tea houses of Kathmandu buzz with tips on this or that hydropower company. Fixed-income instruments — debentures, bonds, treasury bills — rarely earn the same excitement. Yet in any mature investment philosophy, the fixed-income universe forms the bedrock of a portfolio: a source of predictable cash flow, a benchmark for valuing every other asset, and an indispensable check on the ambitions of equity. In Nepal, where the capital markets remain relatively shallow and the regulatory framework is still consolidating, understanding the debt instruments available through NEPSE and the Nepal Rastra Bank is not merely useful — it is essential to thinking clearly about risk, return, and the architecture of wealth.
This chapter treats the full arc of Nepal's debt landscape: from first principles about what a debenture actually is, through the specific instruments listed on NEPSE, the government's Treasury Bill and Development Bond auctions run by the NRB, the mathematical relationship between yields and prices, the hard lessons embedded in Nepali corporate default history, and finally the most important cross-asset lesson that fixed income teaches — that bond yields set the floor beneath every equity valuation in the economy.
Lesson 17.1 — What Is a Debenture: Debt Instrument Basics
The Fundamental Nature of Debt
When a company or government needs capital, it has two broad avenues. It can sell ownership — issuing equity and diluting existing shareholders — or it can borrow, promising to return the principal at a future date and to pay interest along the way. A debenture is a formal certificate of that borrowing: a legal document that acknowledges a debt owed by the issuing entity (the obligor) to the holder (the debenture holder), specifying the face value, the coupon rate, the payment schedule, and the maturity date.
In Nepali corporate law, the Companies Act, 2063 (2006) and the Securities Act, 2063 (2006) together define a debenture as a document acknowledging a loan to the company and including debenture stock, bonds, and any other securities of a company constituting a charge on the company's assets, whether or not constituting a charge. This last phrase is important: a debenture may be secured or unsecured. A secured debenture is backed by a specific charge — a mortgage over a factory, a pledge of receivables, a first lien on a hydro project's equipment — and in the event of default the holder can enforce that charge. An unsecured debenture, sometimes called a naked debenture, leaves the holder as a general creditor, ranking alongside other unsecured claims in a liquidation.
Anatomy of a Debenture
Face Value (Par Value): The principal amount the issuer promises to repay at maturity. In Nepal, corporate debentures are typically issued in denominations of NPR 1,000.
Coupon Rate: The annual interest rate stated on the debenture face, applied to the face value to determine the cash interest payment. A 9% coupon on a NPR 1,000 debenture pays NPR 90 per year.
Coupon Frequency: How often interest is paid. Nepali corporate debentures almost universally pay annually; government bonds also pay annually or semi-annually depending on the instrument.
Maturity: The date on which the principal is repaid. Nepali corporate debentures tend to carry maturities of five to seven years; government Development Bonds have run from two to fifteen years.
Call and Put Features: Some debentures are callable, giving the issuer the right to redeem early — usually at a slight premium — if market rates fall. Put features give the investor the right to demand early redemption. Few Nepali corporate debentures carry embedded optionality, though this may evolve.
Credit Rating: SEBON requires debenture issuers to obtain a credit rating from a SEBON-registered rating agency. ICRA Nepal and CARE Ratings Nepal are the two primary agencies. The rating — from AAA down through D — indicates the agency's assessment of the issuer's ability to service the debt.
Debentures Versus Bonds: Is There a Distinction?
In everyday Nepali usage — regulatory filings, NEPSE listings, and NRB circulars — the words debenture and bond are often used interchangeably. Technically, in common law jurisdictions, a bond is typically secured and a debenture unsecured, but this distinction is not consistently applied in Nepal's regulatory architecture. SEBON's listing regulations treat both terms as synonymous instruments offered to the public. Government instruments are called bonds or bills regardless of security features. In this chapter we use the terms as Nepali practice does: interchangeably for long-term debt instruments paying fixed coupons, with context clarifying whether an instrument is secured.
Priority Waterfall in Insolvency
Understanding why debentures are considered safer than equity requires understanding the insolvency waterfall — the order in which claims on a bankrupt entity are settled. In Nepal, the Insolvency Act 2063 (2006) establishes the general priority. Secured creditors with specific charges over identified assets are paid first from the proceeds of those assets. Government tax claims and employee wages follow. Then come unsecured creditors — including holders of unsecured debentures — who share residual assets pro rata. Equity shareholders come last and are frequently wiped out in meaningful insolvencies. This legal hierarchy is why debt instruments are, in theory, less risky than equity in the same company. The catch, as Lesson 17.5 explores, is that this priority is only meaningful if there are recoverable assets.
A debenture holder sleeps better than a shareholder — not because the company cannot fail, but because the legal system creates a queue, and the debenture holder stands much closer to the front.
Lesson 17.2 — Corporate Debentures Listed on NEPSE: Available Instruments
The Landscape of Corporate Debt on NEPSE
NEPSE maintains a dedicated debt securities segment for listed debentures. The market is small by regional standards — the total outstanding listed corporate debenture volume has historically been measured in the low tens of billions of NPR — but it has grown meaningfully since the Securities Board of Nepal (SEBON) tightened listing requirements and introduced mandatory rating requirements in the mid-2010s. The issuers are concentrated in three sectors: commercial banks and development banks (which issue debentures both to meet regulatory capital requirements and to fund long-tenor lending), hydropower and energy companies (which need long-dated financing to match their project cash flows), and a smaller cohort of insurance companies and manufacturing firms.
SEBON's Public Issue Regulations require that any company offering debentures to the public must file a prospectus, obtain approval, appoint a debenture trustee, and secure a credit rating. The debenture trustee — typically a bank or financial institution appointed by agreement — holds the charge on secured debentures in trust for all debenture holders collectively and has an obligation to act on their behalf in the event of default. This trustee structure is borrowed from Indian practice and is designed to address the collective action problem inherent in a dispersed group of creditors.
Common Structural Features of Nepali Corporate Debentures
Most NEPSE-listed debentures are plain-vanilla fixed-rate instruments. They carry a coupon in the range of 7% to 12%, set at issuance and unchanged for the life of the instrument. Maturities range from five to seven years, with five years being the most common for bank issuances and seven years for hydropower projects. The instruments are almost exclusively unsecured or secured only by a general floating charge over the company's assets, which in practice is difficult to enforce without specific pledged collateral.
Trading in listed debentures on NEPSE is thin. The secondary market for corporate debt in Nepal has never developed the liquidity of the equity market. Most retail investors who subscribe to a debenture IPO hold to maturity, collecting annual coupon payments without ever trading in the secondary market. Institutional investors — insurance companies, provident funds, and citizens investment trusts — similarly tend to hold. The result is a market where listed prices may diverge significantly from theoretical fair value and where bid-ask spreads can be wide. This illiquidity premium must be factored into any investor's required return.
Key Features to Examine Before Subscribing
An investor examining a corporate debenture prospectus should work through several analytical layers. The first is the issuer's financial health: debt-service coverage ratio (operating cash flow divided by total debt service obligations), leverage ratios, and the trajectory of profitability over the most recent three to five years. The second is the seniority and security of the specific debenture — what assets, if any, back the charge, and how liquid or valuable those assets are in a stressed scenario. The third is the credit rating and the rating agency's published rationale, paying particular attention to any qualifications or negative watches. The fourth is the trustee arrangement: who is acting as trustee, and does that institution have the capacity and incentive to act decisively if covenants are breached? The fifth is the covenant package itself: does the trust deed contain financial maintenance covenants (minimum coverage ratios, maximum leverage), negative pledge clauses (preventing the issuer from pledging assets to other creditors ahead of debenture holders), and cross-default clauses (providing that default on any other obligation triggers default on this debenture)?
A debenture prospectus should be read with the same skepticism as an equity prospectus — and then re-read once more, focusing entirely on what happens when the company gets into trouble rather than when it thrives.
The Hydropower Debenture Opportunity
Nepal's energy sector presents a structurally interesting case for debenture investment. Hydropower projects, once fully constructed and commissioned, generate highly predictable operating cash flows — the river flows, turbines spin, and power is sold to NEA at contracted tariffs. This predictability is well-suited to a fixed-coupon debenture structure. The risks are concentrated in the construction phase (cost overruns, geological surprises, delayed commissioning) and in the NEA's own financial health as the offtaker. An investor subscribing to a hydropower project debenture during the operational phase, with a demonstrated track record of cash generation, is acquiring a meaningfully different risk profile than one subscribing during construction.
Lesson 17.3 — Government Bonds and Treasury Bills: NRB Auction Process
Instruments of Government Borrowing
The Government of Nepal (GoN) finances its fiscal deficit in part through domestic borrowing from the public and financial institutions. This borrowing is managed by the Nepal Rastra Bank on behalf of the Ministry of Finance under the authority of the Public Debt Act 2002. The instruments fall into two broad categories: Treasury Bills (T-Bills), which are short-term discount instruments, and Development Bonds (sometimes called Government Bonds or Savings Bonds depending on the sub-category), which are longer-term coupon-bearing instruments.
Treasury Bills are issued with maturities of 28 days, 91 days, 182 days, and 364 days. They are issued at a discount to face value — the investor pays less than par at issuance and receives par at maturity, with the difference representing the return. There are no periodic coupon payments. Development Bonds are issued with maturities typically ranging from two to fifteen years, carrying a fixed annual coupon rate set at auction and paid annually or semi-annually.
The Auction Mechanism
The NRB conducts T-Bill and Development Bond auctions on a regular schedule, announced in advance through notices published in major national newspapers and on the NRB's official website. Commercial banks and other licensed financial institutions are the primary participants in these auctions, submitting competitive bids through a sealed-bid process. Competitive bids specify the amount the bidder is willing to purchase at a particular yield (for bonds) or discount rate (for T-Bills). Non-competitive bids, submitted by smaller participants, accept the weighted average yield determined by the competitive auction and are allocated after competitive bids are settled.
The NRB uses a uniform-price (also called Dutch) auction for Development Bonds: all successful bidders receive the same cut-off yield, which is the highest yield at which the entire offered amount can be sold. This is the yield at which the marginal bid — the last bid accepted to fill the announced amount — sits. For Treasury Bills, the NRB has at different times used both uniform-price and multiple-price mechanisms. Understanding the auction format matters because it affects bidding strategy: in a uniform-price auction, bidders are incentivized to bid their true willingness to accept (since submitting a more aggressive bid does not lower the yield they ultimately receive); in a multiple-price auction, bidders must shade their bids strategically.
Retail Access: Savings Bonds and the NRB Direct Channel
Retail investors can participate in government borrowing through the Government Savings Bond program, which is accessible through commercial bank branches. These instruments carry fixed coupons, denominations as low as NPR 1,000, and are exempt from certain tax obligations, making them particularly attractive to conservative investors in higher income brackets. The NRB periodically issues Citizen Savings Bonds specifically targeted at the general public, often with maturities of three to five years and coupon rates that, at issuance, are typically set slightly above prevailing commercial bank deposit rates to attract retail savers.
It is important to note that Nepal's government securities market is not yet liquid in a robust secondary-market sense. Although trading of government securities theoretically occurs through the Nepal Stock Exchange, in practice most institutional holders hold to maturity and retail holders rarely trade. The illiquidity of the secondary market means that an investor who acquires a five-year Development Bond and subsequently needs liquidity may face significant price uncertainty and difficulty finding a buyer.
Tax Treatment
Interest income from government bonds and savings bonds is subject to withholding tax, though specific rates and exemptions have varied over time and have been modified through successive Finance Acts. Investors should verify the applicable tax treatment at the time of purchase rather than relying on historical precedent. For tax-exempt or tax-advantaged funds — provident funds, pension funds — government securities are particularly attractive because the gross yield translates more fully into net return than it would for a taxable investor.
Instrument
Maturity
Coupon / Discount
Minimum Size
Auction Frequency
Retail Access
Treasury Bill (28d)
28 days
Discount (no coupon)
Competitive only
Weekly (generally)
Limited
Treasury Bill (91d)
91 days
Discount
Competitive only
Weekly/Fortnightly
Limited
Treasury Bill (182d)
182 days
Discount
Competitive only
Fortnightly
Limited
Treasury Bill (364d)
364 days
Discount
Competitive only
Monthly
Limited
Development Bond
2-15 years
Fixed coupon (annual)
NPR 10,000+
As announced by MoF
Via banks
Citizens Savings Bond
3-5 years
Fixed coupon
NPR 1,000
Periodic NRB issue
Yes, via banks
Lesson 17.4 — Yield, Coupon, and Price Relationship: The Inverse Rule
The Most Important Relationship in Fixed Income
There is one mathematical truth about bonds that every investor must internalize before they can reason about fixed-income markets: bond prices and bond yields move in opposite directions. When yields rise, prices fall. When yields fall, prices rise. This inverse relationship is not a market opinion or a historical tendency — it is an arithmetic identity.
To understand why, consider a bond with a face value of NPR 1,000, a coupon rate of 8%, and five years to maturity. It pays NPR 80 each year and NPR 1,000 at maturity. The fair price of this bond at any given moment is the present value of all those future cash flows, discounted at the prevailing market yield for bonds of this type and maturity. If the market yield is 8%, the bond's price is exactly NPR 1,000 — it trades at par. If market yields rise to 10%, the NPR 80 coupon becomes less attractive in comparison to new bonds now yielding NPR 100, and the price must fall below NPR 1,000 to compensate buyers for accepting a below-market coupon. If yields fall to 6%, the NPR 80 coupon is generous by current market standards, and buyers will bid the price above NPR 1,000.
The precise calculation uses discounted cash flow arithmetic. For our five-year, 8% coupon bond at a market yield of 10%:
The bond trades at a discount to par because its coupon is below the market rate. Conversely, at a market yield of 6%, the same bond prices at NPR 1,084.25, a premium to par. The difference between purchase price and face value, amortised over the holding period, is an additional component of the investor's total return.
Duration: Measuring Price Sensitivity
Not all bonds respond equally to the same change in yields. The sensitivity of a bond's price to yield changes is captured by a measure called duration — specifically, modified duration. Duration has two economic interpretations. The Macaulay duration is the weighted average time to receive the bond's cash flows, expressed in years. A bond with a Macaulay duration of four years means that, on average, the investor's money is tied up for four years — earlier coupon payments arrive sooner and reduce the average waiting time below the stated maturity. The modified duration is the Macaulay duration divided by (1 + yield) and represents the approximate percentage price change for a one percentage point change in yield.
A bond with a modified duration of 4.0 will lose approximately 4% of its price for every 100 basis point rise in yields, and gain approximately 4% for every 100 basis point fall. Zero-coupon bonds have the highest duration (equal to their maturity) because all cash flow comes at the end. High-coupon bonds have lower duration because substantial cash flows arrive early. This is why long-maturity, low-coupon bonds are the most volatile instruments in fixed-income markets and why investors with short investment horizons should hold them only with full awareness of mark-to-market risk.
Duration is the clock inside a bond — it measures not how long you wait for your money back, but how much your price moves when the world changes its mind about interest rates.
Current Yield, Yield to Maturity, and Yield to Call
Investors sometimes confuse different yield measures, each answering a subtly different question. The current yield is simply the annual coupon divided by the current market price. If the NPR 1,000 face, 8% coupon bond trades at NPR 924.07, the current yield is 80/924.07 = 8.66%. This is a rough proxy but ignores the gain at maturity (the bond matures at NPR 1,000 but was purchased at NPR 924.07 — a capital gain of NPR 75.93 earned over five years).
The yield to maturity (YTM) is the internal rate of return of all the bond's cash flows if held to maturity — the single discount rate that equates the present value of all coupons and the par repayment to the current price. It is the most complete single measure of a bond's expected return under the assumption of no default and reinvestment at the same rate. The YTM on our bond at NPR 924.07 is 10% by construction.
For callable bonds, the yield to call (YTC) uses the call date and call price rather than maturity as the terminal cash flow. An investor comparing callable bonds must examine both YTM and YTC, as the issuer will call the bond whenever doing so is economically rational — typically when rates have fallen and they can refinance more cheaply — leaving the investor with call risk.
Practical Application in Nepal's Market
In Nepal's thin secondary debenture market, published prices may not reflect genuine arm's-length transactions. An investor seeking to estimate fair value should discount the bond's cash flows at a yield reflecting: the current T-Bill or Development Bond yield for the relevant maturity (the risk-free rate), plus a credit spread appropriate to the issuer's rating and sector. For an A-rated Nepali commercial bank debenture maturing in five years, a reasonable analytical approach is to take the five-year government bond yield and add 150 to 250 basis points of credit spread. For BBB-rated instruments, 300 to 400 basis points is more appropriate. These are rough guides; in a liquid market, spreads are discovered through active trading, but in Nepal's illiquid environment, they must be estimated through judgment.
Lesson 17.5 — Default Risk on Nepali Corporate Debentures: Historical Cases
The Reality of Default in a Developing Market
Default risk is the possibility that the issuer fails to make timely payment of interest or principal as promised. In developed markets with thick secondary trading, credit ratings, and robust insolvency regimes, default risk is continuously priced and redistributed. In Nepal, the fixed-income market's relative infancy means that many retail investors subscribed to corporate debentures without a clear-eyed understanding of the consequences of default, and that the resolution of defaults when they occurred was often slow, contested, and ultimately disappointing for holders.
Nepal's corporate debenture market has not experienced the volume of defaults seen in some frontier markets, partly because the market itself is small and partly because the majority of issuers are banks and financial institutions under NRB supervision. However, the history of the broader financial sector — particularly the BFI (banks and financial institutions) liquidity and solvency problems that accelerated following the 2015 earthquake and through subsequent economic disruptions — offers important case-study material on how credit risk manifests and what debenture holders actually experience when an issuer comes under stress.
The BFI Fragility Context
During the period of rapid BFI proliferation in Nepal — from roughly the mid-2000s through the NRB-mandated consolidation drive of the 2010s — dozens of development banks and finance companies were created, many undercapitalized and operating with weak governance. Several of these institutions issued debentures to retail investors, often distributed through networks of agents rather than through transparent public auctions. When NRB tightened capital requirements and some of these institutions were found to be technically insolvent, debenture holders found themselves in a difficult position: the insolvency regime was not well-equipped for rapid resolution, the trustee structure for many older instruments was nominal rather than functional, and the assets backing secured charges were often overvalued or legally entangled.
Some finance company failures saw debenture holders receive eventual partial recoveries after years-long resolution processes. The lesson was not merely about the original credit decision — the lesson was about the entire chain of enforcement: the quality of the trust deed, the willingness and capacity of the trustee to act, the state of the issuer's balance sheet at the time of stress, and the speed of judicial or NRB-supervised resolution.
Rating Agency Warnings and Investor Complacency
In several documented cases, ICRA Nepal and CARE Ratings Nepal had downgraded issuers or placed them on a credit watch before significant stress became public. Retail investors who subscribed to secondary offerings or failed to monitor rating changes were caught off-guard by developments that the rating agencies had signalled, however imperfectly. This underscores a crucial practice: holding a rated debenture does not mean ignoring the rating. The initial rating at issuance is only the beginning of the analysis. Rating changes must be monitored throughout the holding period.
A secondary market price — where such prices exist — can also provide a real-time signal. If a debenture is trading at 80% of par, the market is expressing concern about credit quality that the investor holding at cost-minus-amortised-premium may not have internalized. Tracking available secondary prices, even in an illiquid market, is a form of ongoing credit surveillance.
Practical Default Risk Management
An investor managing a portfolio of Nepali corporate debentures should maintain explicit default scenarios for each holding. For each issuer, the analysis should ask: what is the probability of default over the remaining life of the instrument? What is the expected recovery rate if default occurs — given the seniority of the claim, the quality of collateral, and the state of the insolvency system? What is the resulting expected credit loss? The expected credit loss framework — probability of default times (1 minus recovery rate) times exposure at default — provides a disciplined way to compare the incremental yield offered by a riskier debenture against the incremental expected credit loss it carries.
A higher coupon is not compensation — it is an invitation to quantify. The question is whether the extra yield, net of expected credit loss, is worth the illiquidity and the sleepless nights.
The Role of the Debenture Trustee: Lessons from Experience
The debenture trustee is supposed to be the debenture holder's advocate in distress. In practice, many trustees in Nepal's market — typically commercial banks appointed more for their administrative convenience than their activist intent — have been passive in the face of covenant breaches. An investor should read the trust deed carefully before subscribing: does the trustee have clear authority to act on financial covenant breaches, not only payment defaults? Are there specific financial covenants with defined thresholds? Is the trustee large and independent enough from the issuer to act without conflict? These questions rarely receive the attention they deserve during a subscription period, when marketing materials emphasise the coupon and credit rating rather than the legal machinery of default resolution.
Lesson 17.6 — How Bond Yields Affect Equity Valuations: The Risk-Free Rate Anchor
The Discount Rate Is Everything
Every serious method of equity valuation — discounted cash flow, dividend discount models, residual income models — requires the investor to discount future cash flows or earnings at a rate that reflects their riskiness. This rate is typically constructed as the risk-free rate plus a premium for equity risk. The risk-free rate — the return available from a government obligation with no credit risk — is the anchor on which every equity valuation in the economy rests.
In Nepal, the relevant risk-free rate is typically proxied by the yield on 364-day Treasury Bills or the yield on the shortest-dated available Development Bond. As NRB T-Bill yields shift — driven by monetary policy, inflation expectations, fiscal borrowing requirements, and international capital flows — they move the anchor to which equity valuations are attached. A rise in the risk-free rate raises the discount rate applied to future corporate earnings, which, all else equal, reduces present values and should lead to lower equity prices. A fall in the risk-free rate reduces the discount rate and inflates equity valuations.
The Equity Risk Premium
The equity risk premium (ERP) is the additional return that equity investors demand above the risk-free rate in exchange for bearing the higher uncertainty of equity cash flows. In mature markets, historical estimates of the ERP cluster around 4% to 6% per year. For Nepal, estimating the ERP is more challenging: the market history is shorter, returns are more volatile, and the relationship between listed equity performance and the underlying economy is complicated by IPO-driven structural breaks in the index. Damodaran's annual country risk premium estimates — which adjust the base global ERP for country-specific risk factors, including political instability, currency risk, and default risk — provide a useful external reference. Nepal's country-risk-adjusted ERP has historically been estimated materially higher than for developed markets, often in the 10% to 15% range depending on the estimation period and methodology.
The NEPSE-NRB Rate Relationship in Practice
Nepali equity investors with a long-term memory will recognise a pattern that has repeated across interest rate cycles: when the NRB tightens monetary policy and T-Bill yields rise, NEPSE tends to come under pressure, not because company earnings immediately deteriorate, but because the discount rate applied to those earnings rises and because higher deposit rates offered by banks attract money away from equities. The 2021-2022 period illustrated this sharply: as NRB raised the policy rate in response to inflation and balance-of-payments pressures, bank deposit rates climbed toward 10-12%, T-Bill yields rose, and NEPSE fell from its post-COVID highs. Investors who understood the bond-equity linkage were not surprised; those who watched only NEPSE in isolation were confused and caught off-guard.
Conversely, periods of monetary easing — when the NRB cuts rates and T-Bill yields compress — tend to be bullish for equities. The 2019-2020 easing cycle saw deposit rates compress and the NRB flood the system with liquidity, which contributed directly to the equity bull market of 2020-2021. The mechanism was straightforward: with savings deposits offering 4-6%, the relative attractiveness of equity dividends and capital gains increased, drawing capital into NEPSE.
Building the Cost of Equity for a Nepali Company
A practitioner applying the Capital Asset Pricing Model (CAPM) to estimate the cost of equity for a Nepali listed company would construct it as follows. First, identify the risk-free rate — use the current 364-day T-Bill yield, or if a longer-maturity anchor is preferred, the yield on the most recently issued five-year Development Bond. Second, estimate beta — the sensitivity of the company's returns to broad market movements, typically estimated by regressing historical stock returns against NEPSE index returns, with appropriate adjustments for the thin-trading distortions common in Nepal's market. Third, apply the equity risk premium — using a country-adjusted ERP that reflects Nepal's specific risk profile. The sum of risk-free rate plus beta times ERP is the estimated cost of equity, the minimum return the company must generate to justify its equity market valuation.
In practice, Nepali analysts often complement CAPM with a dividend discount model for dividend-paying companies — particularly financial institutions with relatively stable dividend histories — and with earnings multiples comparisons. The key discipline, regardless of method, is to be consistent in the risk-free rate assumption: if the T-Bill yield is 6%, use 6%, not a historical average or an aspirational number. And when yields move, revisit the model. A company that appeared fairly valued at a 6% risk-free rate may appear overvalued at 9%.
The Central Lesson
The deepest insight of this chapter — spanning from debenture basics through government auctions to equity valuation — is that capital markets are interconnected at the level of price. The yield on a NRB T-Bill is not merely a statistic in a monetary policy bulletin; it is the gravitational constant of Nepal's entire capital market. It sets the floor beneath corporate debenture yields, which must compensate for additional credit and liquidity risk. It anchors the discount rate applied to equity cash flows. It determines the hurdle that every investment project must clear to be worth pursuing. Ignoring this anchor because one is primarily an equity investor is not specialisation — it is a systematic blind spot.
The investor who monitors the full spectrum — who notes when NRB auctions clear at 7% versus 5%, who understands when corporate debenture spreads are wide versus compressed, who tracks whether the NEPSE earnings yield provides an adequate premium over government yields — is thinking about the market as a market, not as a collection of isolated securities. That integrated perspective is what separates an analyst from a speculator, and it is what makes fixed income education indispensable even for investors who never intend to own a single debenture.
The bond market is the brain; the equity market is the mood. The brain sets the framework within which moods swing. Ignore the brain, and your mood will eventually surprise you.
Chapter recap
Debentures and bonds occupy a foundational role in Nepal's capital market architecture. A debenture is a formal acknowledgment of corporate debt — secured or unsecured — with defined coupon, maturity, and priority in insolvency. NEPSE's corporate debenture market is dominated by banks, hydropower companies, and financial institutions, with issuance governed by SEBON's prospectus and rating requirements. Government instruments — T-Bills and Development Bonds — are auctioned by NRB in a competitive process and represent the closest available proxy for a risk-free rate in Nepal.
The price-yield inverse relationship is the foundational arithmetic of fixed income: as yields rise, prices fall, and the magnitude of price movement depends on the instrument's duration. Yield to maturity is the most complete return measure for a buy-and-hold investor. Default risk — especially in Nepal's still-developing insolvency framework — requires active monitoring of credit ratings, secondary market prices, and the quality of trustee arrangements. Historical episodes of financial institution stress provide cautionary evidence that the trustee machinery matters enormously in practice.
Finally, bond yields are the risk-free rate anchor that runs through every equity valuation model. Investors who track NRB T-Bill yields and understand their relationship to equity discount rates possess a structural edge in understanding market cycles. The ability to move fluidly between the fixed-income and equity perspectives — recognising that they are different expressions of the same underlying pricing machinery — is one of the hallmarks of the complete investor.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part III · Chapter 18
What Does Not Yet Exist in Nepal
First published 21 Aug 2026 · Last verified 29 Aug 2026
Every serious investor must eventually confront a fundamental truth about the market in which they operate: the tools available shape the strategies possible. In the most sophisticated capital markets — New York, London, Tokyo, Singapore — investors wield a vast arsenal. They can go short and profit from falling prices. They can hedge with options and futures. They can buy an entire index in a single transaction. They can gain exposure to real estate without ever owning a building. They can borrow securities to lend, and borrow money to invest. Nepal's market, at this stage of its evolution, offers none of these things.
This is not a criticism. Every mature market once looked exactly like NEPSE does today. The New York Stock Exchange spent its first century without options. India's NSE launched derivatives only in 2000 — six years after its founding. The London Stock Exchange did not formally permit short selling in a regulated sense until the late twentieth century. Institutional gaps are not signs of failure; they are milestones on a developmental arc.
What this chapter provides is not a lament, but a map. If you understand what does not yet exist in Nepal, you will understand where opportunity concentrates, where risk asymmetry lives, how to avoid strategies that are simply impossible to execute, and — most importantly — how to position yourself now for instruments that will eventually arrive. The investor who understands the gap is already ahead of the investor who does not know it exists.
The absence of an instrument is not merely a constraint — it is a structural fact that alters every price in the market and every strategy available to every participant.
Lesson 18.1 — Derivatives: Why NEPSE Does Not Have Them Yet and the Strategic Gap This Creates
What a Derivative Actually Is
A derivative is a financial contract whose value is derived from the performance of an underlying asset — a stock, an index, a commodity, a currency, or an interest rate. The two most common forms are futures and options. A futures contract obligates the buyer to purchase, and the seller to sell, a specified asset at a predetermined price on a future date. An options contract gives the buyer the right — but not the obligation — to buy or sell an asset at a set price before a set expiration.
These instruments serve three overlapping purposes across global markets: hedging (protecting an existing position against adverse price movements), speculation (taking a leveraged directional view on price), and arbitrage (exploiting price discrepancies between related instruments). In mature markets, derivatives volume often eclipses the underlying equity market itself. On India's NSE, daily options notional volume frequently runs ten to twenty times the cash equity turnover. On the Chicago Mercantile Exchange, trillions of dollars of futures change hands daily.
FUTURES
A binding contract to buy or sell an asset at a fixed price on a future date. Both parties are obligated to fulfil the contract unless it is offset before expiry.
OPTIONS
A contract giving the holder the right — not the obligation — to buy (call) or sell (put) an asset at a fixed strike price before a specified expiry date.
HEDGING
The use of a financial instrument to reduce or offset the risk of an existing position. A shareholder buying put options, for example, is hedging against a price decline.
NEPSE's Current Status
As of the time of writing, the Nepal Stock Exchange operates exclusively as a spot equity market. All transactions are cash trades settled on a T+2 cycle (trade date plus two business days). There are no exchange-traded futures, no exchange-traded options, no structured derivatives of any kind available to retail or institutional investors. The Securities Board of Nepal (SEBON), the market's primary regulator, has acknowledged the desirability of derivatives in principle, and the Commodities Exchange Market Act of 2017 created a partial legal foundation for commodity derivatives. However, no equity or index derivatives product has been launched.
The reasons are structural. Launching derivatives requires a clearing corporation capable of marking positions to market daily and issuing margin calls to manage counterparty risk. It requires a legal framework for contract enforcement, including provisions for cash settlement. It requires a liquid underlying market with reliable price discovery — a criterion NEPSE increasingly meets, but which regulators view cautiously given episodes of sharp volatility. It requires market makers willing to provide continuous two-sided quotes. And it requires an investor population — both retail and institutional — that understands how leverage works. SEBON has been methodical and conservative, deliberately choosing to build the equity market's infrastructure and regulatory capacity before introducing instruments that can magnify systemic risk.
What This Means for Nepali Investors
The absence of derivatives creates a one-directional market. In a market with put options, a fund manager concerned about a downturn can buy portfolio insurance without liquidating positions. In a market with index futures, institutional investors can hedge systematic risk while maintaining their stock picks. In a market with single-stock options, covered call writing generates income on existing holdings. None of these strategies are available in Nepal today.
The practical consequence is that every Nepali equity investor is fully long at all times. There is no mechanism to profit from declining prices, no mechanism to lock in exit prices in advance, and no mechanism to reduce portfolio beta without selling stocks. This structural asymmetry means that bear markets in Nepal tend to be sharp and brutal — when sentiment turns, investors have only one option: sell. The absence of short sellers, who in other markets provide a counterbalancing force by covering positions as prices fall, means that declines can overshoot fair value. The 2021-2022 correction, which saw the NEPSE index fall from above 3,200 to below 1,700, illustrated this dynamic vividly.
For the individual investor, the implication is that risk management must be done entirely at the portfolio construction level — through diversification, cash allocation, sector weightings, and disciplined entry and exit prices — rather than through hedging instruments. This is not necessarily inferior; many successful investors have operated without derivatives for entire careers. But it requires clarity about what you cannot do, so that you do not spend time searching for strategies that the market's architecture makes impossible.
The Strategic Opportunity in the Gap
The absence of derivatives also creates a specific strategic advantage for the informed investor. In markets with active derivatives, options pricing implicitly reveals institutional hedging demand and implied volatility expectations. Without this signal layer, Nepali markets are in some ways more transparent — prices reflect only spot supply and demand. There is less noise from gamma hedging, delta hedging flows, or quarterly derivatives expiry effects that distort prices in markets like India's.
More importantly, the eventual launch of derivatives on NEPSE will be a transformational event. Historically, when derivatives are introduced in emerging markets — as India demonstrated in 2000, as South Korea demonstrated in 1996, as Taiwan demonstrated in 1998 — they attract a surge of institutional participation, increase overall market liquidity, and contribute to more efficient price discovery. The investor who builds deep knowledge of individual stocks now will be extraordinarily well positioned when institutional hedgers arrive and begin driving volumes.
In every emerging market that launched derivatives, the window immediately before launch was one of the most rewarding periods for informed long-term equity investors. The smart money began repositioning months before the first derivative contract ever traded.
Lesson 18.2 — ETFs: Their Absence and the Implications for Passive Investors
What an ETF Is and Why It Matters
An Exchange-Traded Fund is an investment vehicle that holds a basket of securities — typically tracking an index — and trades on a stock exchange like an ordinary share. An investor buying one unit of an ETF tracking the NEPSE index would, in effect, own a proportionate slice of every listed stock, weighted by market capitalisation, in a single transaction. The creation and redemption mechanism, operated by authorised participants, ensures that the ETF's market price stays close to the net asset value of its underlying holdings.
ETFs have revolutionised retail investing globally. They offer instant diversification, low costs (often below 0.10% annual management fee for index products), tax efficiency, intraday liquidity, and structural simplicity. The global ETF industry manages over ten trillion dollars across thousands of products covering equities, bonds, commodities, real estate, factors, and thematics. In Nepal, there are no ETFs.
NET ASSET VALUE (NAV)
The per-unit value of a fund, calculated by dividing the total market value of its holdings by the number of units outstanding.
AUTHORISED PARTICIPANT
An institution — typically a large broker or bank — licensed to create and redeem ETF units by exchanging the underlying basket of securities with the ETF issuer. This mechanism keeps ETF prices aligned with NAV.
Nepal's Current Landscape: Mutual Funds
Nepal does have mutual funds. SEBON has issued regulations governing both open-ended and closed-ended mutual fund schemes, and several fund managers — including NIBL Ace Capital, Siddhartha Capital, NMB Capital, Laxmi Capital, and others — operate registered schemes. These mutual funds provide a form of pooled diversification, and they are regulated with specific disclosure and governance requirements.
However, mutual funds are not ETFs. Nepal's mutual funds do not trade continuously on NEPSE at live market prices. They are priced once per day at NAV (in the case of open-ended funds) or trade as listed closed-ended funds at prices that can deviate significantly from underlying NAV. The management fees of Nepali mutual funds are also substantially higher than global ETF norms — typically in the range of one to two percent annually, versus fractions of a percent for index ETFs globally. And crucially, there is no product in Nepal that passively tracks a benchmark index at low cost and trades intraday with full transparency.
Why ETFs Have Not Launched in Nepal
Launching an ETF requires resolution of several preconditions. First, a reliable and independently calculated index is needed as the benchmark. NEPSE does publish indices — the NEPSE index and various sector sub-indices — but these require further standardisation and independent oversight to serve as robust ETF benchmarks. Second, authorised participant infrastructure must exist: a market maker willing to continuously create and redeem units requires deep institutional capacity that Nepal's broker community is still developing. Third, the legal framework governing unit trusts and collective investment schemes requires specific provisions for ETF mechanics. SEBON has been working through iterations of its securities regulations, and ETF-enabling provisions are understood to be on the regulatory pipeline, but formal launch timelines have not been announced.
There is also a supply-side consideration. For an ETF manager to create product, there must be a commercial case: sufficient projected demand from investors who understand passive investing, combined with a fee structure that makes the business viable for the sponsor. Nepali investors have historically been oriented toward active stock-picking and IPO allocation rather than passive index exposure. Investor education around the advantages of passive strategies is still developing.
The Implication for Passive Investors
For the investor who has absorbed the global evidence that most active managers underperform their benchmark over time net of fees, the absence of ETFs in Nepal creates a genuine dilemma. The passive strategy — buying the index, holding it forever, and never paying active management fees — is simply not available in its purest form. The alternatives are either to construct your own personal index portfolio (by buying the top twenty or thirty stocks by market cap across different sectors), to invest in mutual funds at higher cost and accept active management risk, or to wait.
The personal index approach is not as impractical as it sounds for larger portfolios. NEPSE has several hundred listed securities, but the top thirty stocks account for an outsized share of market capitalisation and trading volume. An investor who owns the top commercial banks, major insurance companies, the largest hydropower companies, and leading telecom and manufacturing stocks is in practice approximating the broad index. The rebalancing burden is modest if the approach is maintained with discipline.
For smaller investors, mutual funds remain the most sensible route to diversified equity exposure today, with the understanding that fees will be higher than a hypothetical future ETF, and that active management introduces the possibility of both outperformance and underperformance relative to the benchmark.
The investor who constructs a personal index portfolio today — disciplined, diversified, held through cycles — will have a significant structural advantage over those who attempt to trade actively in a market that charges them on every transaction.
Looking Forward
The eventual launch of ETFs in Nepal will be significant. For the first time, institutional investors — including foreign portfolio investors who require passive, low-cost, liquid vehicles — will be able to deploy capital into Nepal with the efficiency they require. This will deepen the market, compress the premium that illiquidity currently demands, and over time drive management fees down across the entire mutual fund industry through competitive pressure. The patient investor who holds positions in the strongest listed companies today will benefit from the re-rating that increased institutional participation typically produces.
Lesson 18.3 — REITs: Current Status and the Regulatory Pipeline
The Structure and Logic of REITs
A Real Estate Investment Trust is a corporate structure that owns, and typically operates, income-generating real estate — commercial properties, shopping centres, hospitals, warehouses, office buildings, data centres, or residential complexes. By law in jurisdictions where REITs exist, a qualifying REIT must distribute at least ninety percent of its taxable income to shareholders as dividends, in exchange for which it receives favourable tax treatment at the corporate level. Units in REITs trade on stock exchanges like ordinary shares.
REITs solve a historic problem in investing: real estate has long been one of the most reliable sources of long-term wealth, but direct property investment is illiquid, capital-intensive, management-intensive, and requires large minimum investments. A REIT democratises real estate ownership, allowing an investor with modest capital to own a fraction of a premium commercial property portfolio, collect regular rental income distributed as dividends, and sell their stake any day the exchange is open.
REIT
Real Estate Investment Trust. A regulated collective investment vehicle that owns income-generating real estate and distributes the majority of rental income to unit holders as dividends.
YIELD
In the context of REITs, the annual dividend expressed as a percentage of the unit price. A REIT trading at NPR 1,000 per unit and paying NPR 80 in annual dividends has an 8% dividend yield.
Nepal's Real Estate Market and the Current Reality
Nepal has a large and active real estate market, but it is almost entirely informal and unlisted. Land ownership is the primary store of wealth for most Nepali families. Commercial property in Kathmandu Valley — especially in areas like New Baneshwor, Durbar Marg, Thamel, and Lalitpur — commands premium prices, and rental yields from well-located commercial properties can be substantial. Yet none of this wealth is accessible to small investors through the securities market. The only way to participate in Nepali real estate is to buy property directly, which requires significant capital, carries transaction costs of six to nine percent (stamp duty and registration fees), and produces an asset that cannot be liquidated quickly.
SEBON has recognised this gap. The Securities Act and subsequent regulatory framework include provisions for collective investment schemes, and REIT-like structures have been discussed at a policy level for several years. The Commodities Exchange Market Act of 2017 and subsequent amendments to securities regulations have created a legal skeleton that could accommodate REITs. However, as of the time of writing, no REIT product has been launched or received final regulatory approval for listing on NEPSE. The regulatory pipeline is real — this is not merely aspirational policy language — but the formal enabling regulation, including specific REIT registration requirements, eligible asset criteria, mandatory distribution rules, and governance standards, is still being finalised.
The Obstacles to Launching REITs in Nepal
Several challenges slow REIT development beyond the purely regulatory. Property valuation is the most significant. A REIT requires reliable, independent, and periodic assessment of its portfolio's fair market value to calculate NAV and set unit prices. Nepal's property valuation profession is relatively young, and internationally recognised appraisal standards are not uniformly applied. Without credible valuation, investor confidence in quoted unit prices is difficult to establish.
Land title clarity is a second challenge. A meaningful portion of commercial real estate in Nepal carries title ambiguities — disputed ownership claims, incomplete registration, irregular boundary demarcations. A REIT can only own properties with unambiguous, bankable title. Resolving these issues at scale, for a portfolio large enough to justify a listed vehicle, requires legal and administrative work that property owners have historically avoided.
Third, there is the question of promoter motivation. The individuals and families who own Nepal's most valuable commercial properties have generally been able to monetise them through informal channels — bank loans against property collateral, direct sale to wealthy buyers — without the disclosure obligations and governance requirements that a publicly listed vehicle entails. Creating a REIT means opening your books, subjecting governance to external scrutiny, and distributing income rather than retaining it. For many existing property owners, the benefits of liquidity and capital markets access have not yet outweighed these costs.
Why This Matters and What to Watch
The eventual arrival of REITs in Nepal will create an entirely new asset class for Nepali investors: a security that combines the income characteristics of fixed-income instruments with the capital appreciation potential of real estate and the liquidity of listed equities. For yield-seeking investors — retirees, foundations, income-focused portfolios — this will be a genuinely transformative addition to the toolkit.
Investors should monitor SEBON's regulatory publications and the activities of merchant banking firms that are engaged in REIT structuring conversations. When the first REIT IPO appears in SEBON's pipeline documentation, it will represent an important milestone. Given the pent-up demand from investors seeking yield, and given the premium that new asset classes often attract in their early listing phase, the early REIT offerings in Nepal may generate significant investor interest.
The most valuable real estate in Kathmandu Valley currently sits inside private balance sheets, inaccessible to public investors. The instrument that will unlock this asset class for ordinary investors is a REIT — and it is closer than most realise.
Lesson 18.4 — Commodity Exchanges: MCXN Developments and Current State
The Role of Commodity Exchanges
Commodity exchanges serve as organised marketplaces for the buying and selling of standardised physical commodities — agricultural products like rice, wheat, sugar, and spices; metals like gold, silver, and copper; and energy products like crude oil and natural gas. The price discovery and hedging functions of commodity exchanges are economically vital: farmers, processors, exporters, and manufacturers use futures contracts on commodity exchanges to lock in prices and manage the input cost and revenue uncertainty that is inherent in commodity businesses.
For investors, commodity exchanges offer access to an asset class that behaves differently from equities, providing diversification benefits and, in inflationary environments, a natural store of value. Gold, in particular, has historically served as both an inflation hedge and a crisis hedge in portfolios globally.
Nepal's Commodity Exchange Framework
Nepal passed the Commodity Exchange Market Act in 2017, creating the legal basis for a regulated commodity exchange. This was a significant legislative step. The Act defined the structure, governance, and regulatory oversight framework for commodity trading, with SEBON designated as the supervising authority.
The Multi Commodity Exchange of Nepal (MCXN) has been the primary vehicle for building out this infrastructure. MCXN received its operating licence and has worked through the technical and regulatory requirements to establish trading systems, clearing mechanisms, and product specifications. Gold has been identified as the flagship initial product, given Nepal's deep cultural familiarity with gold as an investment asset and the large informal gold market that already exists. Agricultural commodity futures have also been discussed as a medium-term priority, given Nepal's significant agricultural sector.
However, MCXN's journey toward live trading has been slower than originally anticipated. Technical integration with clearing systems, finalisation of product specifications that comply with international commodity standards, establishment of warehouse receipt systems (which are required for physical delivery commodity contracts), and regulatory sign-off on trading rules have all involved iterative processes. The broader challenge is that Nepal has no existing commodity exchange infrastructure — unlike equity markets, which had a thirty-year developmental history to draw on, the commodity exchange is being built from scratch.
Gold as the Entry Point
The focus on gold as the first product reflects a shrewd understanding of market readiness. Nepal is one of the world's largest per-capita consumers of gold jewellery and gold coins on a purchasing-power-adjusted basis. Gold is embedded in marriage ceremonies, religious festivals, and family savings culture in ways that make it genuinely the most familiar asset for a large proportion of Nepali households. Currently, gold investment in Nepal occurs largely through physical purchases from jewellers — at significant bid-ask spreads — or through the unregulated grey market. A regulated gold futures contract on MCXN would offer price transparency, lower transaction costs, storage efficiency (no physical custody required for financial gold contracts), and a formal regulatory framework.
For portfolio investors, the arrival of a regulated gold futures product would add a genuinely important diversification tool. Gold has a long-established low or negative correlation with equity markets during periods of financial stress — precisely when diversification is most needed. An investor who can allocate five to ten percent of a portfolio to gold through a liquid, low-cost exchange-listed product will have meaningfully different risk characteristics than one whose entire portfolio consists of NEPSE equities.
The Agricultural Commodity Frontier
Beyond gold, the longer-term opportunity in Nepali commodity markets is in agricultural futures — products like rice, wheat, vegetable oil, and potentially high-value niche exports like large cardamom, ginger, and orthodox tea, in which Nepal has global competitiveness. These products serve a dual economic purpose: they create hedging instruments for farmers and agro-processors who currently bear enormous price risk at harvest time, and they create investment opportunities tied to Nepal's agricultural productivity.
The infrastructure requirements are more demanding for agricultural commodities than for gold. Warehouse receipt systems require physical storage facilities with quality standards, inspection regimes, and insurance. Price discovery requires active participation from merchants and processors, not just financial investors. These challenges are solvable — India's commodity exchange history shows a clear developmental path — but they require sustained institutional investment and regulatory commitment over a multi-year horizon.
Gold on a Nepali commodity exchange is not merely an investment product — it is the formalisation of a savings behaviour that millions of Nepali families already practise, and the substitution of a transparent, fair price for the opacity of the current informal market.
Lesson 18.5 — Short Selling, Margin Trading, Securities Lending: The Gap and Its Consequences
Margin Trading: What Exists and What Doesn't
This is where the picture is slightly more nuanced than in other areas, because Nepal has made partial progress. Margin trading — the practice of borrowing money from a broker to purchase securities — received regulatory approval from Nepal Rastra Bank and SEBON in 2018, and the formal working guidelines have been in place since the early 2020s. Under the current framework, brokers with a minimum net worth of NPR 50 million are permitted to offer margin lending to clients. The margin loan may not exceed 50% of the lower of the 180-day average price or the current market price of eligible securities. To qualify as a margin-eligible security, a company must have more than 10,000 shareholders, positive net worth, and a track record of paying at least 10% bonus shares for two consecutive years.
In practice, margin trading in Nepal has developed slowly. Not all brokers have received the necessary approvals or have the operational systems to manage margin accounts. The restriction to fundamentally strong, large-capitalisation stocks means that the universe of marginable securities is limited. And the cultural norm among Nepali investors — who have long operated in a pure cash market — has been slow to shift toward leverage. Nevertheless, the legal and regulatory framework exists, and its use is expected to grow as the market matures.
MARGIN CALL
A demand from a broker that an investor deposit additional funds or securities when the value of a margin account falls below the maintenance margin threshold. Failure to meet a margin call results in forced liquidation of positions.
MAINTENANCE MARGIN
The minimum equity a margin account must maintain as a percentage of the current market value of securities held. If the account falls below this level, a margin call is triggered.
Short Selling: Still Absent
Short selling — the practice of borrowing securities and selling them in the expectation of buying them back at a lower price — does not exist in Nepal. This is one of the most consequential absences in the market. The prohibition is not explicitly codified as a ban; rather, the absence of a securities lending framework makes short selling operationally impossible. To sell a share short, you must borrow it from someone who owns it. Without a securities lending infrastructure — which requires custodian banks, a clear legal framework for title transfer, a fee-setting mechanism, and a recall provision — there is simply nothing to borrow.
The consequences of this absence run deeper than might initially appear. Short sellers, for all their controversial reputation in popular culture, serve an important function in price discovery. They provide a counterweight to excessive optimism. In a market without short sellers, there is no mechanism to express a negative view on a company's stock except to sell shares already owned. This means that overvaluation can persist longer than fundamentals would justify, because the downward pressure from informed negative-view investors is suppressed. The result is a market that experiences longer periods of overvaluation followed by sharper corrections when the sentiment finally breaks.
The 2021 peak in the NEPSE index, when price-to-earnings multiples for many financial sector stocks reached levels that experienced investors considered extreme, is a case study in what happens when no counterbalancing force operates. With no short sellers providing a continuous sell-side pressure as prices departed from fair value, the market continued rising until retail sentiment reversed. When it did reverse, the correction was severe and swift.
Securities Lending: The Missing Infrastructure
Securities lending is the practice by which institutional holders of large share portfolios — mutual funds, insurance companies, pension funds — lend their holdings to borrowers (typically hedge funds wishing to short, or market makers requiring stock to settle positions) in exchange for a fee and collateral. It is a mature and well-regulated practice in developed markets, providing significant additional income to long-term holders at relatively low risk when managed properly.
In Nepal, no formal securities lending infrastructure exists. CDS and Clearing Limited (CDSC), which operates Nepal's central securities depository, does not currently facilitate securities lending transactions. Until a legal framework is established — covering collateral arrangements, re-hypothecation rights, default procedures, and regulatory reporting — securities lending cannot operate. This means that even if SEBON were to permit short selling in principle, there would be nothing to borrow. Infrastructure and regulation must advance in parallel.
It is worth noting the benefit this would ultimately provide to long-term investors. A mutual fund holding shares of Nepal's largest commercial banks, collected over years and held as a core long-term position, could generate incremental income — perhaps 0.5% to 2.0% annually — by lending those shares to short sellers and market makers. In a market where every basis point of return matters, securities lending income is a meaningful enhancement to long-term performance. Institutional investors globally consider it part of prudent portfolio management.
The Systemic Consequences of These Absences Together
It is important to understand that these three instruments — margin trading, short selling, and securities lending — form an interconnected ecosystem. Margin enables leverage in both directions. Short selling enables directional betting on declines. Securities lending provides the stock that short sellers need. When all three exist, markets operate with greater two-sidedness and more robust price discovery. When only margin exists (as in Nepal today), the leverage that margin introduces is asymmetric: it can amplify buying and extend bull markets, but there is no equivalent force amplifying the short side. This asymmetry is a known cause of boom-bust volatility in partially liberalised markets.
The Nepali investor who understands this dynamic is better prepared for what the market actually delivers. Bull markets in Nepal may run farther and longer than fundamentals justify, because one-directional leverage and the absence of short-side pressure allow optimism to compound. Bear markets may be sharper and more abrupt, because when sentiment breaks, selling is the only available response and no short-covering rally softens the decline. Calibrating your entry and exit decisions to this structural reality is one of the most important skills a Nepali investor can develop.
A market where only the long side is available is not a half-market. It is a differently shaped market — one that tends toward episodes of extended enthusiasm followed by compressed, brutal corrections. Understanding this shape is itself a form of edge.
Lesson 18.6 — How Missing Instruments Change Every Strategy in Nepal
Rethinking Risk Management Without Hedges
In global markets, portfolio risk management often relies heavily on derivative hedges. An equity manager who is fully invested in growth stocks but worried about a near-term correction buys index put options. A currency manager who has converted NPR to USD for offshore investment buys a forward contract to lock in the exchange rate. A commodity producer sells futures to lock in the price of next year's output. None of these actions are available in Nepal. This is not simply an inconvenience — it fundamentally changes what risk management looks like.
Without hedging instruments, risk management in Nepal must be done entirely through the composition of the portfolio. This means active management of cash allocations: when valuations are high and the margin of safety is thin, the disciplined investor holds more cash than usual — not as a market-timing call, but as a structural acknowledgment that no hedges exist. It means sector and company diversification taken seriously, not as a box-ticking exercise, but as the primary tool of downside protection. It means position sizing calibrated to the worst realistic outcome for each holding, not just the expected outcome.
Interestingly, this constraint can be a virtue. The discipline required to manage risk without a safety net tends to produce more thoughtful portfolio construction than a market where investors know they can always buy protection. The Nepali investor who builds excellent risk management habits in the absence of derivatives will be a better investor when derivatives do arrive, not a worse one.
Valuation Takes on Greater Importance
In a market with active short sellers, there is a natural force pulling overvalued stocks back toward fair value. Informed professional short sellers identify companies trading above their intrinsic worth, borrow and sell their shares, and wait for reality to catch up. This continuous arbitrage activity means that in developed markets, extreme overvaluation — a price-to-earnings ratio of fifty on a company growing at five percent — is relatively rare because shorts attack it relentlessly.
In Nepal, this force does not operate. Stocks can and do trade at valuations that seem difficult to justify on fundamental analysis. The implication for the value-oriented investor is twofold. First, patience is required: a fundamentally cheap stock in Nepal may remain cheap for longer than it would in a market where shorts are pushing expensive stocks down and freeing up capital to flow toward cheaper ones. Second, the premium on independent fundamental analysis is higher: because the market has no built-in correction mechanism for overvaluation, the investor who correctly identifies what is cheap and what is expensive has a larger edge than in a market where professionals are constantly doing this work on both sides.
IPO Strategy in a One-Directional Market
Nepal's IPO market is distinctive. SEBON requires that IPOs be priced at par value (NPR 100 per share) for most standard public offerings, and the subscription process uses a lottery system for oversubscribed issues. The consequence is that virtually all IPOs in Nepal are oversubscribed — often dramatically so — because investors correctly perceive that buying at par and selling at the market price that opens after listing is a near-certain short-term gain. This is not a pathology of irrational exuberance; it is a rational response to a structural asymmetry created by par-value IPO pricing.
In a market with ETFs, an investor who missed an IPO allotment could wait for the secondary market price to normalise and then build exposure at fair value. In a market with short selling, overpriced post-IPO stocks would attract short-sellers who would prevent sustained extreme overvaluation. In a market with derivatives, an investor could use options to define their risk on a new listing. In Nepal, none of these tools exist. The result is that IPO participation becomes disproportionately important relative to secondary market investing — and the lottery allocation system means that scale increases your expected allocation through statistical diversification across many applications.
Dividend Investing as a Strategic Anchor
The absence of income-generating instruments — no REIT dividends, no covered call writing, no bond ETFs — elevates the importance of dividend-paying stocks as an income source within Nepali portfolios. Commercial banks, insurance companies, and some manufacturing and trading companies have historically paid regular dividends — in the form of both cash dividends and bonus shares (stock dividends). For investors who require a yield component from their equity portfolio, identifying companies with consistent dividend payment histories and sustainable payout ratios becomes a central analytical exercise, rather than an optional overlay.
This also has implications for portfolio construction in different life stages. A young investor with a long time horizon can accept lower current yields in favour of growth companies. An investor approaching or in retirement, who would normally shift toward bond ETFs and REIT distributions in a mature market, must instead curate a portfolio of high-yield equities — which carry more volatility than fixed-income instruments — or accept holding cash in banks at relatively low deposit rates. The financial planning implications of this structural gap are real, and they argue strongly for a savings culture that begins early and allows the compounding of equity returns over the full length of the investment horizon.
The Information Edge in a Thin Market
One consequence of Nepal's institutional gaps that is rarely discussed is that they preserve a significant information and analysis edge for the prepared individual investor. In the United States, when a company reports earnings, hundreds of professional analysts have already built detailed financial models, spoken to management on earnings calls, and distributed their conclusions to institutional clients. Options market pricing reflects these expectations. Short interest data reveals what the smart money believes. By the time a retail investor reads the news, the information is largely priced in.
In Nepal, the analytical community is small. Not every listed company has a published analyst report. Institutional coverage is concentrated in the largest financial sector stocks and a handful of large-cap companies. Options prices that would reveal consensus expectations do not exist. Earnings calls and investor day presentations are rare. This means that a diligent individual investor who does the fundamental work — reads annual reports, tracks quarterly financial statements, visits company operations, understands industry dynamics — is genuinely in possession of an analytical advantage that can be converted into investment returns. This is the environment that produced Warren Buffett's best early performance: small, under-covered companies in a market without the institutional infrastructure to quickly price all information.
Nepal's market today resembles the United States equity market of the 1950s and 1960s in its institutional coverage density. The disciplined analyst who does original research in this environment will consistently find opportunities that a more efficient market would have already arbitraged away. This is not an accident of Nepal's stage of development — it is a structural feature that the informed investor should consciously exploit while it lasts.
The Nepali market does not yet have the instruments that protect the lazy investor from themselves. What it does have is a richly rewarding environment for the investor willing to do serious original work. These two facts are not unrelated.
Preparing for the Instruments That Will Arrive
This chapter has mapped what does not yet exist. But the trajectory is clear: Nepal's capital market is developing, its regulatory capacity is growing, and each year brings the market closer to the suite of instruments that mature markets take for granted. REITs and commodity derivatives have explicit regulatory pathways. ETFs are an acknowledged priority. Derivatives on equity indices are a longer-term goal. Short selling will eventually follow when securities lending infrastructure matures.
The investor's task today is not to lament the absence of these tools but to position intelligently for their arrival. This means building deep knowledge of individual companies and sectors now, so that when institutional investors arrive through new instruments, you already understand the landscape better than those institutions do in their early days. It means identifying the companies most likely to be included in future index products and analysed most extensively when ETFs launch. It means building relationships and networks in the industries most likely to benefit from REIT or commodity exchange development. And it means developing the analytical and psychological disciplines that will serve you in any market environment — with or without derivatives, with or without ETFs, in a one-directional market or a fully two-sided one.
The investor who has genuinely internalised the content of this chapter is not disadvantaged by what Nepal's market lacks. They are advantaged by their clear-eyed understanding of the terrain. That clarity is worth more than any single instrument that the market has not yet learned to offer.
CHAPTER 12 — CORE INSIGHTS
NEPSE operates without derivatives, ETFs, REITs, a functioning commodity exchange, short selling, or a securities lending framework. Each absence shapes the market's behaviour in specific, predictable ways. The absence of shorts and hedges creates a one-directional market prone to extended overvaluation followed by sharp corrections. Risk management must be achieved entirely through portfolio construction, not through hedging instruments. Margin trading exists in regulatory form but remains limited in practice. Short selling and securities lending await infrastructure development. ETFs, REITs, and commodity futures are on the regulatory pipeline with varying timelines. The thin institutional coverage of Nepal's market preserves a genuine information advantage for the diligent fundamental investor — an advantage that will diminish as the market matures. Understanding what does not yet exist is not merely academic. It is the foundation for every realistic strategy in Nepal's market today.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IV
CORPORATE GOVERNANCE AND PROMOTER BEHAVIOUR
Part IV · Chapter 19
The Governance Landscape in Nepal
First published 21 Aug 2026 · Last verified 29 Aug 2026
Corporate governance in Nepal is not an abstract compliance topic borrowed from an OECD handbook and grafted onto NEPSE-listed companies. It is the single most important determinant of whether an investor's capital, once committed, remains their own. In a market where a promoter family often holds the majority of a company's shares, sits on its board, appoints its chief executive, and controls the flow of information to the exchange, the question of governance is really the question of whether the interests of the person who bought one hundred shares through a broker in Kathmandu are treated as equal in law to the interests of the person who founded the company thirty years ago. The law says they are equal. The lived experience of NEPSE investors over the past two decades says the answer is considerably more complicated.
This chapter opens Part IV of the Canon because everything that follows — how to read related-party transactions, how to price promoter behaviour into a valuation, how to size a position when you cannot fully trust the numbers in front of you — rests on understanding the governance architecture first. Nepal has, on paper, a reasonably modern governance framework: a Companies Act with independent-director and audit-committee requirements, a Securities Act that empowers a dedicated regulator, a securities regulator that has issued a specific corporate governance directive for listed companies, and — for banks and financial institutions — an additional layer of central bank oversight that is, in places, more demanding than anything applied to non-financial companies. What Nepal does not yet have, in most cases, is the enforcement capacity, the judicial speed, or the institutional independence to make that framework bite consistently. Investors who understand this gap between the law on the page and the law in practice will read the same annual report very differently from investors who assume that a compliant-looking cover page means a compliant company.
KEY CONCEPT
Corporate governance is not a compliance checkbox — it is the mechanism that determines whether minority shareholders' capital is protected or expropriated, and in a market as concentrated as NEPSE, that mechanism is tested far more often than in more developed exchanges.
Lesson 19.1 — Why Governance Is the Central Risk in Nepali Equities
Ownership Concentration as the Starting Point
Most companies listed on NEPSE were not born as public companies. They were private or family enterprises — a manufacturing house, a trading family, a group of local businessmen who pooled capital to found a finance company or a hydropower developer — that later issued shares to the public, typically to meet regulatory capital requirements (in banking and insurance) or to raise expansion capital. The promoter group that founded the company typically retained a controlling block. Nepali company law and securities regulation formalise this history through the distinct treatment of "promoter shares" versus "ordinary" (public) shares. Both classes carry identical economic rights — the same face value, the same dividend and bonus entitlement, the same one-share-one-vote — but promoter shares are subject to a lock-in period (three years from allotment under the Securities Registration and Issue Regulation, 2073) during which they cannot be transferred, and for banks and insurers, conversion out of promoter status even after the lock-in requires Nepal Rastra Bank approval and typically happens in capped tranches rather than all at once. Banks are further required to maintain a minimum promoter shareholding — commonly cited at 51 percent — as a matter of regulatory policy, meaning control is not incidental but mandated.
The practical consequence is that the "one-share-one-vote" principle that looks reassuring on paper coexists with a market structure where a handful of families, business houses, and, in some sectors, the government itself, hold enough shares to control the board outright, without needing any special class of stock to do so. Ordinary shareholders are not structurally disenfranchised the way they might be under a dual-class share structure in some other markets — but they are numerically overwhelmed. A promoter group holding 51 to 70 percent of a bank's shares elects the board, appoints (or heavily influences the appointment of) the chief executive, and approves related-party transactions through directors who are, in many cases, relatives or business partners of the promoters themselves.
Why This Matters More on NEPSE Than Elsewhere
In markets with dispersed ownership — where no single shareholder holds more than a few percent — the central governance risk is the classic principal-agent problem: professional managers who are not meaningfully owners may run the company for their own benefit at the expense of a diffuse shareholder base. The remedies devised for that problem — independent boards, executive compensation tied to shareholder returns, hostile takeover discipline — assume a market for corporate control that simply does not exist on NEPSE. Nepal's governance risk is structurally different. It is not managers versus dispersed owners; it is controlling owners versus minority owners. The academic and regulatory literature calls this "Type II agency risk," and the tools that work for Type I problems (independent directors overseeing hired managers) are only partially effective against it, because in Nepal the people who appoint the independent directors are frequently the same people whose related-party dealings those directors are meant to police.
WATCH FOR
In a promoter-controlled company, ask not "is the board independent of management" but "is the board independent of the controlling shareholder" — these are different questions, and Nepali disclosure practice rarely forces a company to answer the second one directly.
This is why governance analysis deserves its own Part of this Canon rather than a footnote in a chapter on financial statement analysis. A cheap valuation multiple on a promoter-dominated bank or finance company is not automatically a bargain — it may be a rational discount the market is applying for the risk that free cash flow, once generated, never reaches minority shareholders at all, because it is diverted through related-party loans, inflated procurement contracts, or simply retained and reinvested in ventures that serve the promoter family's broader business empire rather than the listed entity's shareholders.
Lesson 19.2 — The Legal and Regulatory Architecture
Three institutions define the written governance framework that applies to a NEPSE-listed non-financial company: the Companies Act 2063 (2006), the Securities Act 2063 (2006), and the Securities Board of Nepal (SEBON), which issues directives under powers granted by the Securities Act. For banks and financial institutions, a fourth layer — Nepal Rastra Bank regulation under the Banks and Financial Institutions Act (BAFIA) — sits on top of all three and is, in most respects, the more consequential one, since BFIs make up a large share of NEPSE's market capitalisation and trading volume.
The Companies Act's Board and Audit Committee Provisions
Section 86 of the Companies Act 2063 sets the basic structural requirement for any public company: a board of a minimum of three and a maximum of eleven directors. Critically, it also mandates a minimum number of independent directors scaled to board size — at least one independent director where the board has seven or fewer members, and at least two where it has more than seven. These independent directors are required to hold relevant knowledge and experience in the company's sector, as further specified in the company's articles of association. This is a real statutory requirement, not a "comply or explain" recommendation — but the Act leaves the definition of "independence" itself relatively general, and enforcement of whether a nominally independent director is in fact free of business or family ties to the promoter group rests largely on disclosure and self-certification rather than an independent verification mechanism.
Section 164 requires any listed company with paid-up capital of thirty million rupees or more — a threshold that captures essentially every company that matters on NEPSE — or any partly or fully government-owned company, to form an audit committee. The requirements are specific: the committee must be chaired by a director who is not involved in day-to-day operations, must have at least three members, must exclude close relatives of the chief executive, and must include at least one member with a recognised accounting qualification or a relevant bachelor's degree plus finance/accounting experience. The committee has the power to summon executives, directors, auditors, and finance staff for inquiry, and to make recommendations on accounts and financial management. Importantly, the Act builds in a comply-or-explain mechanism at the board level: the board must either implement the audit committee's recommendations or document its reasons for not doing so in the annual report. In principle, this creates a paper trail an attentive investor can actually read. In practice, audit committee sections of Nepali annual reports are frequently boilerplate — a paragraph confirming the committee met the statutory minimum number of times, with no substantive account of what was discussed or contested.
Section 100 imposes a narrower but useful disclosure obligation: when a company's shares are listed, its directors must disclose their own securities holdings to the company, which must promptly relay that information to the stock exchange, which in turn publishes it. This is the statutory basis for the director shareholding disclosures that appear in company filings and are aggregated by financial portals — a genuinely useful data point for tracking promoter and director buying or selling, covered further in Lesson 19.5.
The Securities Act and SEBON's Corporate Governance Directive
The Securities Act 2063 established SEBON as the securities market regulator and gave it rule-making authority over listed companies, brokers, and market intermediaries, along with enforcement powers that on paper include monetary fines and imprisonment for violations. Under this authority, SEBON issued a dedicated Corporate Governance Directive for listed companies (implemented from fiscal year 2074/75, i.e., 2018), which goes well beyond the Companies Act's baseline in several respects. Its major provisions include a maximum four-year board term with no extension permitted; a prohibition on the same individual holding both the chairperson and chief executive roles simultaneously; a bar on board members holding outside roles as auditors, advisors, surveyors, insurance agents, or brokers for other entities (a direct attempt to reduce conflicted, overlapping directorships across the small pool of business elites who sit on multiple boards); a requirement for a three-member independent audit assessment committee; mandated risk management committees and internal control mechanisms; a prohibition on lending to family members of board members (a direct attack on related-party loan abuse); a disclosure requirement where multiple family members sit on the same board; and a ten-year bar on anyone convicted of financial crimes serving as a director or executive of a listed company.
REGULATORY DETAIL
SEBON's Corporate Governance Directive explicitly bars listed companies from lending to the family members of their own board members — a provision written directly in response to a well-documented pattern of promoter-linked related-party lending abuse in Nepal's banking and finance sector.
Taken together, the Companies Act and SEBON's directive create a reasonably comprehensive rulebook. The gap is not in the drafting — it is in supervision. SEBON has historically had a small enforcement staff relative to the number of listed entities and market intermediaries it oversees, and its most visible enforcement actions have tended toward modest fines on brokerage firms for procedural lapses rather than substantive governance violations at issuer level. On the specific and widely acknowledged problem of insider trading and leakage of price-sensitive information ahead of public disclosure — a direct governance and market-integrity issue — SEBON's own public statements over the years have moved between acknowledging the problem is "widespread" and issuing general warnings to listed companies to follow the guideline, without a clear track record of individually prosecuted cases reaching a public conclusion. In more recent periods SEBON has signalled a tougher posture — describing a "zero tolerance" approach to insider trading and unregulated market commentary — but a public statement of intent is not the same evidentiary record as a completed enforcement action, and investors should track whether announced crackdowns translate into published orders, not just headlines.
Requirement
Companies Act 2063
SEBON Governance Directive
Independent directors
Mandatory: 1 (board ≤7) or 2 (board >7)
Not separately mandated; reinforces Companies Act baseline
Lesson 19.3 — The NRB Overlay: Governance for Banks and Financial Institutions
Because commercial banks, development banks, and finance companies constitute a large share of NEPSE's listed universe and historically a large share of its trading activity, Nepal Rastra Bank's governance requirements function as a de facto second regulatory regime layered on top of the Companies Act and SEBON framework for this entire sector. NRB's authority derives from BAFIA and is exercised through directives and bylaws, including the Qualification and Work Experience Bylaw for CEOs and Board Members of BFIs.
That bylaw sets minimum educational and professional-experience thresholds for both chief executives and directors of banks and financial institutions. For a commercial or development bank CEO, the requirement is typically a master's degree in a relevant field (management, banking, finance, economics, commerce, accounting, statistics, mathematics, trade administration, or law), or a bachelor's degree in one of those fields combined with at least ten years of relevant experience. Microfinance institution CEOs face a lighter threshold, generally around three years of relevant experience. For board members of banks, NRB recognises several alternative pathways — a bachelor's degree plus several years of banking or government experience, a master's degree in a relevant subject, or an extended track record of public or international-organisation service. The clear intent is to keep unqualified or purely politically connected individuals off bank boards by tying eligibility to demonstrable financial or managerial competence.
Beyond formal qualifications, NRB applies broader supervisory tools that function as a fit-and-proper regime in substance even where the specific procedural mechanics are less publicly codified than in more developed banking regulators: NRB can require clarification from directors and executives on specific transactions, can direct a bank to correct interest payment or loan classification practices, and — in the more serious cases — can compel management changes or issue public censure. The practical force of this authority became visible in 2025, when NRB took action against a large number of banks and financial institutions for regulatory violations, and separately when NIC Asia Bank — one of NEPSE's most closely watched listed commercial banks — came under NRB scrutiny for a cluster of governance failures: allowing promoter family members to open fixed deposit accounts that were then backdated and paid above-market interest rates, failing to correct these practices despite NRB directives, providing incorrect risk classifications to the regulator, and disregarding instructions on loan classification and provisioning. The bank's chief executive resigned amid the scrutiny. A retired NRB official, speaking anonymously to the press, observed that the institution's political access and influence had been such that, even with governance compromises known to regulators, effective action had historically been difficult to take — a striking admission from within the supervisory system itself about the limits of enforcement capacity even at the central bank level, which is generally regarded as Nepal's most institutionally robust financial regulator.
CASE IN POINT
NIC Asia Bank's 2025 governance troubles — promoter-family fixed deposits backdated and paid preferential interest, incorrect risk reporting to NRB, and a CEO resignation amid regulatory pressure — are a live illustration that governance failure at listed BFIs is not a historical artifact confined to the 2011 finance-company crisis; it recurs, including at large, well-known institutions.
This NRB overlay matters enormously for how an investor should think about governance quality across sectors. A NEPSE-listed manufacturing or trading company answers only to the Company Registrar's Office, SEBON, and its own board; a NEPSE-listed bank answers additionally to one of the region's more active banking supervisors, with real powers over licensing, director eligibility, provisioning, and capital adequacy. This does not make bank governance failure-proof — as NIC Asia demonstrates — but it does mean that the governance floor for BFIs is, in principle, somewhat higher, and that public NRB enforcement actions against a bank are a meaningful, externally validated signal of governance quality that has no equivalent for most non-financial NEPSE issuers, where an investor is left relying almost entirely on the company's own disclosure and SEBON's comparatively thinner oversight.
Lesson 19.4 — Where the Framework Breaks Down in Practice
A rulebook is only as good as the willingness and capacity of institutions to enforce it, and Nepal's history provides several instructive episodes of what happens when that capacity is tested.
The 2009–2011 Banking and Finance Company Crisis
The most consequential governance-adjacent failure in Nepal's modern financial history was the liquidity and solvency crisis that hit the banking and finance company sector between roughly 2009 and 2011. The proximate cause was credit concentration: commercial banks and finance companies channeled a large share of lending into real estate, housing, and construction, with credit against fixed assets reportedly exceeding 70 percent of commercial bank lending at the peak, and tens of billions of rupees committed to real estate exposure by banks alone. Underlying this concentration was a governance and regulatory failure, not merely a market cycle: the number of BFIs had grown to nearly 300 institutions by 2011 — commercial banks, development banks, finance companies, and microfinance institutions combined — far beyond what the underlying economy could sustainably support, a proliferation that regulatory licensing discipline should have constrained but did not. Contemporary commentary at the time pointed to a form of regulatory capture, with observations that officials nearing retirement at the central bank had incentives to avoid confrontation with an industry in which they might later seek employment. Individual institutions such as Vibor Bikas Bank required direct central bank intervention because of excessive real estate loan exposure, and other development banks were subsequently liquidated. The episode is a reminder that governance failure in Nepal has historically been systemic as much as company-specific — weak licensing discipline and weak supervisory independence at the regulator level compounding weak internal controls at the institution level.
The Cooperative Sector as a Cautionary Parallel
Although savings and credit cooperatives are regulated separately from NEPSE-listed entities and fall under a different ministry rather than SEBON or, in most cases, NRB, Nepal's cooperative crisis of the 2020s is instructive precisely because it shows what governance failure looks like in the near-total absence of external oversight. Parliamentary investigation identified roughly 87 billion rupees in losses tied to embezzlement across dozens of cooperatives, with directors diverting depositor funds into speculative real estate and other ventures for personal benefit, and the Ministry of Cooperatives found not to have exercised proactive oversight — a majority of cooperatives operated under local government supervision, where oversight capacity is thinnest. While the legal framework differs from listed companies, the underlying dynamic is the same one this chapter is concerned with: concentrated control by an insider group, combined with weak external monitoring, produces an environment where depositor or shareholder capital can be diverted long before any regulator intervenes. For a NEPSE investor, the cooperative crisis is a useful worst-case reference point for what happens when governance mechanisms exist on paper but oversight capacity does not exist in practice — precisely the gap this chapter argues investors must price into their own analysis of listed companies, particularly smaller and less closely watched ones.
Insider Trading and Disclosure Leakage
A narrower but persistent governance-adjacent problem is the leakage of price-sensitive information ahead of public disclosure — quarterly results, dividend announcements, rights issues, or merger discussions reaching brokers or connected investors before the exchange-wide disclosure reaches ordinary shareholders. The Securities Act 2063 does provide for penalties on violators — a fine equal to the amount in controversy, imprisonment of up to one year, or both — but commentators have long observed that Nepal lacks a clearly codified definition of who qualifies as an "insider" for enforcement purposes, and that SEBON's historical response to acknowledged leakage has tended toward general warnings to listed companies rather than individually prosecuted cases with public outcomes. This matters directly for the "read the annual report" exercise in the next lesson: unusual share price or volume movement in the days before a scheduled disclosure is a pattern experienced NEPSE investors watch for, precisely because the legal deterrent against it has been, historically, more theoretical than operational — though SEBON's more recent public commitments to a stricter posture on insider trading and unregulated market commentary are worth monitoring for whether they produce a genuinely different enforcement record.
ENFORCEMENT GAP
A rule that exists in statute but has no visible record of being enforced against a real violator is, for practical investment purposes, a weaker deterrent than a rule that carries a smaller penalty but a demonstrated history of being applied — investors should weight Nepal's governance framework accordingly.
Lesson 19.5 — Reading Governance Quality From the Annual Report: A Practical Checklist
Given the gap between statutory framework and enforcement reality, the burden of governance due diligence falls substantially on the investor. Nepali annual reports, AGM notices, and SEBON/company disclosures do contain real, usable information — an investor who reads past the boilerplate can meaningfully distinguish a better-governed company from a worse-governed one, even without a Bloomberg terminal or an army of analysts. The following checklist should be applied systematically, company by company, before any valuation work begins.
Board composition and independence in substance, not just form. Confirm the company meets the statutory independent director minimum, then go further: check whether the "independent" directors share a surname, a known business address, or a documented prior business relationship with the promoter family. Cross-reference director names across the company's other group entities where the promoter is known to have multiple listed vehicles — a director who is "independent" at Company A but sits as a promoter nominee at affiliated Company B is a signal, not proof, of a governance weak spot.
Audit committee substance. Read the audit committee report in the annual report line by line. Does it describe substantive discussion — a qualified audit opinion addressed, a related-party transaction reviewed and challenged, an internal control weakness remediated — or is it a single boilerplate paragraph confirming the statutory minimum number of meetings occurred? A comply-or-explain disclosure that never once explains anything, year after year, in a company with visible operational problems, is itself a data point.
Related-party transaction disclosure. Related-party transactions are addressed at length in a later Part of this Canon, but at the governance-reading stage, the task is simpler: does the annual report's related-party note actually name the counterparties and quantify the transaction values, or does it use vague, aggregated language that obscures who received what? Look specifically for loans, deposits, procurement contracts, or property leases involving directors, their families, or affiliated companies, and compare the terms disclosed (if any) against what an arm's-length counterparty would receive.
Board tenure and chair/CEO separation. Confirm the chairperson and chief executive are different individuals, as SEBON's directive requires, and check how long current board members have served relative to the four-year statutory maximum — a board that has technically rotated seats among the same handful of related individuals for over a decade is complying with the letter of the rule while defeating its purpose.
Director shareholding disclosure and trading patterns. Use the Section 100 disclosure trail — available through the company, the exchange, and financial data portals — to track whether directors and promoters are net buyers or sellers of the company's shares over time, and whether any notable transactions cluster suspiciously close to a subsequent price-moving disclosure.
NRB or SEBON enforcement history, for BFIs and beyond. Search for any public record of regulatory action, fine, or public censure against the company or its officers. For banks and financial institutions in particular, an NRB action is a strong, externally validated signal that deserves far more analytical weight than a clean-looking annual report with no independent corroboration.
Auditor tenure and rotation. A company that has retained the same statutory auditor for an unusually long, unbroken period, particularly a smaller or less well-known audit firm, warrants closer reading of the audit opinion itself for qualifications, emphasis-of-matter paragraphs, or going-concern language — and warrants comparison against peers audited by the larger, more reputationally exposed firms.
Dividend and capital allocation consistency versus stated policy. Compare the company's actual dividend and bonus share history against its own stated dividend policy and against its reported profitability — a persistent gap between reported profit and cash actually returned to shareholders, unexplained by disclosed reinvestment plans, is a classic symptom of value being retained for purposes other than the benefit of minority shareholders.
PRACTICAL TOOL
None of these checks require special access — they can all be performed using the annual report, AGM notice, and publicly available director shareholding disclosures that every listed company is already required to produce; the discipline is in reading them systematically, every year, not in obtaining information ordinary investors lack.
Lesson 19.6 — Governance as a Valuation and Position-Sizing Input
Having read the disclosures, the investor's task is to translate a governance assessment into two concrete portfolio decisions: how much to pay, and how much to hold.
Governance and the Valuation Discount
A company with weak, promoter-captured governance should not be valued using the same multiple an investor would apply to a comparably profitable company with demonstrably stronger board independence, cleaner related-party history, and a track record of regulatory compliance. This is not a matter of taste; it is a direct consequence of the agency risk discussed in Lesson 19.1. Reported earnings and book value at a governance-weak company carry a lower probability of being fully and fairly available to minority shareholders — through dividends, through buybacks (rare on NEPSE but not unheard of), or through eventual sale — because a meaningful share of value creation may instead be captured by the controlling group through mechanisms this chapter has described: preferential related-party lending terms, related-party procurement or leasing arrangements priced above or below market, or simply retained earnings redeployed into ventures that serve the promoter's broader business empire rather than the listed entity's shareholders specifically. A rational investor applies a governance discount to the multiple, the same way a rational credit analyst applies a higher risk premium to a weaker borrower — not because the numbers are necessarily fraudulent, but because the range of plausible bad outcomes is wider and the investor's practical recourse, given SEBON's and the courts' documented enforcement limitations, is thin.
Governance and Position Sizing
The same logic applies with equal force to position sizing, independent of valuation. Two companies might trade at an identical, apparently attractive multiple; if one has demonstrably stronger governance — genuine independent directors, a substantive audit committee, no adverse NRB or SEBON history, transparent related-party disclosure — and the other does not, the well-governed company can reasonably support a larger position size, because the tail risk of catastrophic, governance-driven capital impairment is lower. This is directly analogous to the concentration risk discussed elsewhere in this Canon with respect to sector and single-name exposure: governance risk is a form of idiosyncratic risk that diversification across a handful of NEPSE holdings does not eliminate if most of those holdings share the same underlying structural weakness — promoter-controlled boards, thin independent oversight, weak enforcement backstop — because in that case the "diversification" is more apparent than real. An investor who holds five promoter-controlled BFIs across different banking sub-sectors has diversified sector exposure but has not meaningfully diversified governance risk, since a shock to enforcement credibility, or a shift in regulatory posture, can affect the entire category of holding simultaneously, much as it did across the sector in 2009–2011 and again, at the level of individual institutions, in 2025.
Governance as a Dynamic, Not Static, Judgment
Finally, governance quality should be reassessed at every earnings cycle and AGM, not fixed once at initial purchase and forgotten. A board that rotates in a genuinely independent new director, a company that begins naming related-party counterparties explicitly where it previously obscured them, or a bank that receives and visibly remediates an NRB directive, is moving in the right direction and may warrant a smaller governance discount over time. Equally, a company whose audit committee report goes silent on a matter it previously flagged, or whose related-party disclosures become vaguer rather than more specific, is moving in the wrong direction regardless of what the reported earnings show in the same period. Governance is best treated as a forward-looking, continuously updated input to the investment thesis — not a box ticked once during initial due diligence.
Chapter recap
Nepal's corporate governance framework for NEPSE-listed companies is more developed on paper than its enforcement record suggests in practice, and the gap between the two is the central governance risk every NEPSE investor must price into their analysis. The Companies Act 2063 mandates independent directors scaled to board size and requires audit committees with specific composition rules for any company above a modest paid-up capital threshold; SEBON's Corporate Governance Directive adds board tenure limits, chair/CEO separation, restrictions on related-party lending to directors' families, and bars on directors holding conflicted outside roles; and Nepal Rastra Bank layers additional qualification, fit-and-proper, and supervisory requirements onto banks and financial institutions specifically, backed by real (if imperfectly and unevenly applied) enforcement power, as the 2025 NIC Asia Bank episode demonstrated. What the framework does not reliably deliver is consistent enforcement: SEBON's historical record on insider trading and disclosure leakage has been thin, related-party transaction disclosure is frequently vague rather than specific, and Nepal's own financial history — from the 2009–2011 banking and finance company crisis to the cooperative sector's embezzlement scandal — shows repeatedly that weak external oversight, not absent written rules, is where governance protections actually fail. Ownership concentration is the structural starting point for all of this: promoter families and business groups typically control NEPSE-listed companies outright, through mandated minimum holdings in banking especially, which means the central governance risk on this exchange is controlling-shareholder versus minority-shareholder conflict rather than the dispersed-ownership manager-versus-shareholder problem more developed-market governance tools were designed to solve. Investors have real, usable tools to assess this risk without special access — board and audit committee substance, related-party disclosure specificity, director shareholding trends, and any public NRB or SEBON enforcement history — and the output of that assessment should feed directly into both the valuation multiple applied to a company and the position size an investor is willing to hold, reassessed at every reporting cycle rather than fixed once and forgotten. The chapters that follow in this Part build directly on this foundation, moving from the legal architecture described here into the specific mechanics of promoter behaviour, related-party transactions, and the practical forensic techniques for detecting when governance form and governance substance have diverged.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IV · Chapter 20
Promoter Behaviour — The Most Underanalysed Risk in NEPSE
First published 21 Aug 2026 · Last verified 29 Aug 2026
Related-party transactions, promoter share pledging, and boardroom capture rarely make headlines in Nepal the way earnings surprises or bonus share announcements do — yet they explain more of the permanent capital loss suffered by NEPSE investors than any P/E multiple or technical breakout ever will. A bank can report a clean quarter, carry a comfortable capital adequacy ratio, and still be quietly transferring value out of the balance sheet through a subsidiary awarded to a promoter's brother-in-law, a rights issue priced to squeeze out shareholders who cannot follow their pro-rata entitlement, or a slow, unannounced pledging of promoter shares against personal debt that nobody notices until the lender starts liquidating collateral into a falling market. None of this shows up in a standard ratio analysis. All of it shows up, eventually, in the share price — usually at the moment an investor can least afford it.
This is not a peripheral concern for the Nepali market; it is close to the central one. NEPSE is a market of concentrated founder-family and business-group control, thin free float, a securities regulator still building its enforcement muscle, and a disclosure regime that tells you what a promoter group did roughly a quarter after they did it, if it tells you at all. In markets with dispersed ownership, activist funds, and aggressive financial media, promoter misbehaviour gets priced quickly because someone is always watching the insiders. In Nepal, the insiders are frequently the only ones watching themselves. This chapter builds the analytical apparatus a serious investor needs to price that risk explicitly, rather than discovering it the hard way.
Lesson 20.1 — Why Promoter Behaviour Is NEPSE's Most Underanalysed Risk
Start with the structure of ownership itself. Nepali public companies — and banks and financial institutions in particular — are built on a two-tier share architecture: promoter shares and ordinary (public) shares. Both carry identical face value, identical dividend entitlement per share, and identical voting rights per share. What differs is transferability. Promoter shares are subject to a statutory lock-in — three years from IPO allotment for most companies under the Securities Registration and Issue Regulation, roughly one year from listing for many hydropower promoters, and a minimum of five years from commencement of operations for banks and financial institutions under the Bank and Financial Institution Act framework — and even after lock-in expiry, transfers of promoter shares in banks require Nepal Rastra Bank clearance above a small threshold. Ordinary shares trade freely on the NEPSE order book from day one.
This single structural fact explains most of what follows in this chapter. A promoter group is not just "the founders" in some sentimental sense — it is a legally distinct, restricted class of shareholder whose shares cannot easily be sold, whose transfers are policed by a different regulator (NRB, not SEBON, for banks), and whose stake is, by design, sticky. Stickiness sounds like a governance virtue — long-term ownership horizon, skin in the game — and in principle it can be. In practice, in a market where board seats are allocated overwhelmingly by shareholding percentage and where minority shareholders are dispersed, non-coordinating, and largely absent from annual general meetings, promoter stickiness becomes promoter permanence. The same families and business groups that took a company public a decade ago are, in the overwhelming majority of NEPSE-listed companies, still the ones appointing the CEO, approving related-party contracts, and setting dividend policy today.
CONTEXT
Under NRB policy, commercial banks must maintain a minimum promoter shareholding of roughly 51 percent, with any conversion of promoter shares to freely tradable ordinary shares released only in capped batches — commonly cited at around 10 percent of the promoter block at a time — rather than all at once. This is a deliberate prudential choice: regulators do not want a bank's controlling ownership changing hands overnight. The side effect is that minority shareholders in Nepali banks are, by regulatory design, permanent minority shareholders. There is no realistic scenario, short of a merger or NRB-forced restructuring, in which public shareholders assemble a controlling coalition.
Layer onto this ownership structure a second, independent problem: enforcement capacity. The Securities Board of Nepal (SEBON) is a young regulator relative to peer markets, and its enforcement record — a modest brokerage fine here, an insider-trading case there, proposals still working through the legislative process to raise penalties meaningfully above their current levels — reflects an institution still building the investigative depth, technological infrastructure, and political independence to police a market where the same families sit on multiple boards, control multiple related businesses, and often have longstanding relationships with the professionals (auditors, issue managers, brokers) meant to be checking them. SEBON's own depository partner, the Central Depository System and Clearing Limited (CDSC), has publicly acknowledged that its single-ISIN structure does not reliably flag locked-in promoter shares to the market, which has allowed instances of promoters colluding with issue managers to transfer restricted shares before lock-in expiry or to pledge them against loans in ways the public record does not surface in real time. CDSC has said it intends to introduce separate ISINs for promoter and public shares — mirroring what is already done for bank and insurance-sector promoter shares — precisely to close this gap. Until that reform is complete and market-wide, an ordinary investor reading NEPSE's published shareholding data is working with information that is both stale and incomplete by design, not by accident.
The third layer is disclosure itself. Related-party transactions, the single most important channel through which promoter groups extract value from a company at minority shareholders' expense, are disclosed in Nepal at a level of granularity far below what an investor in a market with a mature related-party-transaction regime would expect. An annual report will typically confirm that transactions with related parties occurred and name the counterparties in aggregate, but the pricing, competitive benchmarking, and board-approval process behind any single transaction is rarely reconstructable from public filings alone. Combined with concentrated, sticky ownership and a regulator still scaling its enforcement capability, this is precisely the environment in which promoter risk compounds silently: the people most able to extract value are the same people least likely to be caught quickly, and the investors most exposed to that extraction are the least equipped, informationally, to see it coming.
Put simply: NEPSE analysis culture is heavily weighted toward financial-statement ratios — EPS growth, net interest margin, loan loss provisioning, capital adequacy — and comparatively light on the governance layer sitting above those numbers. That imbalance is exactly why promoter behaviour remains underanalysed. It is not that the risk is small; it is that the analytical infrastructure to price it consistently has not caught up with the ownership structure that produces it.
Lesson 20.2 — The Incentive Misalignment: Promoter Economics versus Minority Economics
To assess promoter behaviour rigorously, start from the economics, not the ethics. A promoter group is not a monolithic villain and a minority shareholder is not a passive victim by default — both are rational actors responding to the incentives their respective positions create. The investor's job is to map where those incentives align with a rising share price and where they diverge from it.
Where interests align
A promoter group with a large, illiquid, locked-in stake has a genuine long-horizon incentive to grow the underlying business, because their wealth is concentrated and largely untradeable — they cannot easily exit a bad investment the way a public-market shareholder can sell on a bad quarter. This is the strongest argument in favour of promoter-heavy ownership: it filters out short-termism that plagues more liquid, dispersed-ownership markets. A promoter family that intends to pass the business to the next generation, that has its personal reputation and social standing tied to the company's name, and that cannot liquidate its position without regulatory friction has every reason to want the enterprise to compound value over decades. When this alignment holds, minority shareholders benefit from riding alongside a highly motivated, well-informed controlling owner.
Where interests diverge
The divergence begins the moment the promoter group has ways to extract value from the company that do not require the share price to rise — and Nepal's institutional environment provides several. Consider the mechanics: a promoter who also owns or controls a supplier, a contractor, an insurance broker, or a real estate interest can direct company business to that related entity at non-market terms, capturing value through the related entity's margin rather than through dividends or share-price appreciation that would have to be split pro-rata with every minority shareholder. A promoter who sits on the board and effectively selects the CEO can extract value through inflated compensation, perquisites, or simply softer performance accountability for management loyal to the controlling family. And, critically, a promoter facing personal liquidity needs — debt service, an unrelated business venture, a family financial event — has an instrument minority shareholders do not: the ability to pledge shares as collateral for a personal loan, monetising the value of the stake without formally selling it and without triggering the disclosure that an outright sale would.
WARNING
A promoter pledging shares as collateral for personal loans creates a hidden forced-selling risk: a margin call on the promoter's personal debt can trigger a scramble that collapses the share price, regardless of the company's fundamentals. Because Nepal's disclosure regime does not require prompt, granular public reporting of promoter pledging activity, this risk typically remains invisible until the lender begins liquidating collateral — at which point the selling pressure looks, to an uninformed market, like a sudden and inexplicable breakdown in the stock.
The rights issue is where this divergence becomes most concrete and most measurable. Nepali companies conventionally issue rights shares at par value — commonly Rs 100 — regardless of where the stock is trading in the market, which can be many multiples of par. In markets with mature rights-issue practice, a rights offering is priced at a modest discount to the prevailing market price, so that the dilution to non-subscribing shareholders is limited and the capital raised is closer to fair value. In Nepal's at-par convention, a rights issue is effectively a wealth transfer from the company's capital account into the pockets of whichever shareholders subscribe — and it rewards, disproportionately, whichever shareholder class is best resourced and best organised to subscribe fully and to absorb any unsubscribed ("devolved") shares through auction. That shareholder class is, almost by definition, the promoter group: concentrated, coordinated, well-capitalised, and typically advised by the same merchant bankers structuring the issue. A minority shareholder who is cash-constrained at the moment a rights issue is announced — which retail shareholders disproportionately are, since rights issues are rarely announced with the shareholder's cash-flow calendar in mind — either has to raise fresh capital to avoid dilution or accepts a smaller proportional claim on the company going forward. Repeated over several rights cycles, this is a legitimate, entirely legal mechanism by which a promoter group's percentage ownership can be maintained or even increased over time, funded in part by minority shareholders who could not keep pace.
None of this requires fraud. It requires only that the investor understand the mechanism is built into ordinary Nepali corporate practice, and price a company's rights-issue history — how often, at what price relative to market, and who subscribed and who was diluted — as governance information, not merely as a capital-raising footnote.
Lesson 20.3 — The Promoter Playbook: Tactics to Watch For
With the incentive structure established, the next task is pattern recognition. Promoter groups do not need to break the law to entrench control and extract value; the standard toolkit operates largely within legal bounds, which is exactly why it survives scrutiny and why an investor must learn to spot it independently of any regulatory finding.
Related-party transactions
The most persistent tactic across Nepali business groups — many of which are conglomerates spanning banking, insurance, hydropower, hospitality, and trading interests under common family control — is directing company business toward related entities. This can take the form of procurement contracts, insurance placement, real estate leases, or intercompany lending, priced on terms an arm's-length counterparty would not accept. A December 2025 commentary on Nepal's banking sector governance captured the pattern directly: loans and business granted to related parties "often enjoy preferential treatment and crowd out more transparent and competitive borrowers," reflecting boards that "remain heavily influenced by promoter interests." The investor's defence here is not to expect a smoking gun in the annual report — related-party disclosure in Nepal is rarely granular enough to reconstruct pricing — but to treat the mere existence of extensive related-party dealings, cross-shareholdings between group companies, and interlocking directorates as a standing discount to apply to reported earnings quality, on the reasonable assumption that some fraction of related-party volume is not priced at arm's length.
Board packing and control persistence
Because board seats are allocated substantially by shareholding weight, and because promoter shares are locked in and stable while public float is dispersed and largely unvoted, a promoter group with even a plurality — let alone the 51 percent regulatory floor common in banking — can typically fill the board with directors loyal to the family or business group, year after year, without serious contest. Independent or "public" directors exist on Nepali boards, but the effectiveness of that check depends heavily on how those directors are selected and to whom they feel accountable in practice. The clearest illustration of what happens when this arrangement breaks down is Nepal Credit and Commerce Bank in 2014: an unresolved standoff between two promoter factions — one holding a 51 percent bloc, the other contesting board composition and accusing the first of trying to entrench control — left the bank's board unable to meet for three months, forcing Nepal Rastra Bank to take the unusual step of assuming direct management control, installing its own three-member team, and mandating a fresh capital raise. The episode is a reminder that board packing is not merely a minority-shareholder grievance; when promoter factions fight each other for control, the institution itself — and every shareholder in it, promoter and public alike — can become collateral damage.
Rights issue timing as an entrenchment tool
Beyond the at-par pricing mechanic discussed in Lesson 20.2, promoters can also use the timing of a rights issue strategically — announcing a capital call when the promoter group is flush and well-positioned to subscribe fully (for example, shortly after receiving dividends or completing an unrelated liquidity event) and when dispersed public shareholders are least likely to be able to respond quickly. Because unsubscribed rights shares are typically auctioned to existing shareholders or approved bidders rather than opened broadly, a promoter group with foreknowledge of likely under-subscription is well placed to absorb the devolved portion, increasing its percentage stake through the auction mechanism on top of whatever it gained through its own rights entitlement.
Information asymmetry
Promoter-affiliated directors and executives see monthly, sometimes weekly, operating data — loan book quality, deposit trends, claims experience, project completion risk — that the market sees only after a quarter's lag, filtered through an audited or reviewed financial statement. This gap is unavoidable to some degree in any market; what makes it material in Nepal is the combination with thin analyst coverage. Many NEPSE-listed companies, including a number of banks and finance companies, have little to no independent equity research coverage, meaning the primary interpretive lens the market has on a company's numbers is the company's own management commentary — commentary that promoter-controlled management has every incentive to frame favourably. An investor should treat any NEPSE-listed company with no independent research coverage and a dominant promoter bloc as an information-asymmetry situation by default, not an exception.
RED FLAG
Watch for promoter-affiliated directors or senior executives buying additional ordinary shares (where legally permitted and disclosed) shortly before a strong earnings announcement, or conspicuously reducing personal exposure ahead of a weak one. Nepal's insider-trading enforcement has historically relied on a small number of high-profile cases and penalties still viewed by regulators themselves as insufficient — SEBON has publicly floated far higher fines and custodial penalties for insider trading, and announced a "zero tolerance" enforcement stance in 2026 — but the base rate of detection and prosecution remains far below what a mature market would deliver. Treat weak enforcement as a reason for more vigilance, not less.
Lesson 20.4 — Reading Promoter Shareholding Trends as a Signal
If related-party dealings and board dynamics are hard to observe directly, promoter shareholding percentage over time is one of the few genuinely quantitative signals available to a NEPSE investor — provided it is read correctly and with appropriate scepticism about data timeliness.
The first distinction to make is between a change in promoter percentage driven by promoter action and one driven by mechanical dilution. A promoter stake that falls because the company issued a large rights offering that the promoter fully subscribed to is not the same signal as a promoter stake that falls because the promoter group is actively selling down through OTC transfers. The first is neutral-to-positive (the promoter maintained proportional commitment); the second is a substantive signal about the controlling shareholder's own view of the company's prospects, or about their personal liquidity needs, and deserves real weight.
Promoter Signal
What It Suggests
Promoter increasing stake via open-market or approved OTC purchase
Confidence in future prospects; willing to commit further capital at current valuation
Promoter stake steady through a rights cycle (full pro-rata subscription)
Alignment maintained; no dilution intent toward minority holders
Promoter stake declining through OTC transfers outside of lock-in expiry or business restructuring
Possible loss of conviction, succession-related exit, or personal liquidity need — investigate before assuming benign cause
Promoter share pledging against loans rising or newly disclosed
Personal financial stress; hidden forced-selling risk if the pledge is called
Promoter absorbing a large share of devolved (unsubscribed) rights shares
Entrenchment of control at the expense of shareholders who could not subscribe
Sharp, unexplained conversion of promoter shares to ordinary shares immediately at lock-in expiry, followed by heavy selling
Classic lock-in-expiry dump; anticipate short-term price pressure independent of fundamentals
The second reading skill is recognising the lock-in-expiry dynamic specifically. Because promoter shares convert automatically to freely tradable ordinary shares once the statutory lock-in lapses, and because that conversion has, in the past, been followed by concentrated selling as promoters monetise a stake that was previously untradeable, an investor should treat an approaching lock-in expiry date as a known, calendarisable event with predictable share-price implications — largely independent of the company's operating performance. SEBON has in fact required listed companies to publicly announce the end date of a promoter lock-in period thirty days in advance precisely because of the volatility this transition has historically produced; a disciplined investor uses that mandatory notice window to reassess position sizing ahead of the event rather than being surprised by it.
The third and most important caveat: NEPSE's published shareholding data is not always current, and the single-ISIN structure used for most non-bank, non-insurance sectors does not reliably distinguish locked-in promoter shares from freely tradable ones in a way visible to the ordinary investor. Until CDSC's planned reform of separate ISINs for promoter and public shares is implemented market-wide, an investor should treat any given snapshot of "promoter holding percentage" as a lagging and imperfect indicator, cross-checked wherever possible against the company's own disclosures of lock-in status, recent rights-issue subscription results, and any SEBON notices regarding that specific issuer.
Lesson 20.5 — Red Flags: Pledging, Below-Market OTC Transfers, and Related-Party Contracts
This lesson collects the specific, checkable warning signs an investor should actively screen for, rather than merely being aware of in the abstract.
Share pledging against personal debt
The single most dangerous and least visible promoter red flag is share pledging. A promoter who pledges a substantial portion of their holding as collateral for a personal or business loan has effectively pre-committed the company's share price to their personal balance sheet. If the promoter's external obligations come under stress — a failed side venture, a margin call from the lending institution, a broader liquidity squeeze of the kind Nepal's banking sector experienced in recent credit cycles — the lender can move to liquidate the pledged shares, and that liquidation lands on the market with no regard for the company's actual condition. Because Nepal does not require prompt, standardised public disclosure of promoter pledging activity at anything like the granularity of, say, insider trading disclosure in more developed markets, this risk typically surfaces only as an unexplained, sustained selling pressure that a fundamentals-only investor cannot account for. Any credible information — even informal market chatter reported consistently across multiple sources, or disclosure buried in a loan-related filing by the pledgee institution — that a promoter group has pledged a meaningful share of its stake should be treated as a material governance red flag, not background noise.
Below-market OTC promoter transfers
Promoter share transfers happen off the main exchange order book, typically through auction to qualified buyers for large blocks, and are subject to regulatory scrutiny — NRB approval for bank promoter transfers above the small exempted threshold, source-of-funds checks, and fit-and-proper vetting of the acquiring party. Because this segment is thin and because promoter shares already trade at a structural discount to ordinary shares (commonly cited in the 30–50 percent range for banks, reflecting historical NRB pricing conventions and the illiquidity premium demanded by buyers who must hold through remaining restrictions), a single transfer at an unusually low price relative to recent comparable promoter-share transactions is a signal worth investigating rather than dismissing as noise from a shallow market. Ask, specifically, whether the transfer looks like a distressed or forced sale (a promoter needing cash quickly and accepting a below-market price to get a deal done), whether it represents a related party effectively buying a stake from another related party at a favourable price (a value transfer within the same extended business group, at other shareholders' expense), or whether it is simply a reflection of genuinely thin two-way interest in an illiquid segment. Only the third explanation is benign.
Related-party contracts without competitive benchmarking
The pattern to watch for is not any single related-party transaction — these are common and not inherently improper in a market where business groups are diversified and vertically connected — but the absence of any disclosed competitive benchmarking or independent board approval process around them. A company whose annual report discloses material related-party relationships (a promoter-affiliated insurance broker placing the company's cover, a promoter-affiliated contractor building the company's branches or plants, a promoter-affiliated NBFI or trading house on the other side of intercompany transactions) without disclosing that those arrangements were competitively tendered, benchmarked against market rates, or reviewed by genuinely independent directors, should be assumed to carry a related-party discount to reported profitability until proven otherwise.
WARNING
The absence of a documented related-party transaction scandal at a given company is not evidence of clean governance — it may simply reflect Nepal's limited related-party disclosure requirements and thin independent research coverage. Treat extensive, opaque related-party dealings as a standing risk discount to apply to reported earnings, not as a risk that only exists once it has been publicly exposed.
Lesson 20.6 — A Practical Promoter Quality Scorecard
Bringing the preceding lessons together, an investor needs a repeatable, disciplined way to score promoter quality before committing capital — not a one-time judgment but a framework revisited at each results cycle, rights issue, and lock-in event. The following scorecard structures the qualitative and quantitative signals developed above into a single assessment an investor can apply consistently across NEPSE-listed companies.
Dimension
Favourable Signal
Unfavourable Signal
Shareholding trend
Stable or rising promoter stake through open-market or fully-subscribed rights participation
Declining stake via OTC sale outside of a clear, disclosed, benign reason
Pledging disclosure
No credible evidence of promoter share pledging, or pledging disclosed and modest relative to total holding
Credible reports of substantial, undisclosed, or rising promoter share pledging
Rights issue history
Rights priced with some regard to market value; broad, transparent subscription process
Repeated at-par rights issues with promoter-favourable devolvement absorption and thin minority participation
Board composition
Genuinely independent directors with disclosed, credible qualifications and no evident family or business linkage to the promoter group
Board dominated by promoter family members, employees, or long-standing business associates with no visible independent counterweight
Related-party disclosure
Related-party transactions disclosed with reasonable specificity, ideally with reference to competitive benchmarking
Related-party transactions disclosed only in aggregate, with no pricing or approval-process detail
Dividend and capital policy consistency
Dividend and bonus policy consistent with earnings and capital adequacy, not evidently timed to promoter liquidity needs
Payout policy that shifts abruptly around events plausibly linked to promoter personal financial needs
Regulatory and dispute history
No history of SEBON or NRB enforcement action, board takeover, or public boardroom dispute
History of regulatory intervention, factional board disputes, or public litigation among promoter groups
Research and disclosure environment
Company covered by at least some independent research; reasonably prompt, detailed disclosure
No independent coverage; disclosure limited to statutory minimums, often released late
No company will score favourably on every dimension, and a single unfavourable signal is rarely disqualifying on its own — a company with a dominant but historically disciplined promoter group and a clean rights-issue history is a fundamentally different risk than one with active pledging rumours and a recent board dispute. The purpose of the scorecard is not to produce a pass/fail verdict but to force the same discipline an investor already applies to financial ratios onto the governance layer sitting above them: explicit, written-down, revisited-on-a-schedule judgment, rather than an assumption that promoter behaviour can be safely ignored because it does not appear as a line item in the financial statements.
CONTEXT
Applying this scorecard is especially important precisely because NEPSE offers investors almost no alternative check on promoter behaviour that public shareholders in more developed markets take for granted: there is no meaningful activist-investor tradition, minority shareholders rarely coordinate to contest board resolutions at annual general meetings, independent equity research coverage is thin outside the largest names, and the regulatory apparatus — SEBON and NRB together — is still building the enforcement depth and disclosure infrastructure to catch promoter misconduct quickly. Until that infrastructure matures, the individual investor's own diligence is the primary defence.
Chapter recap
Promoter behaviour is NEPSE's most underanalysed risk because three structural features compound each other: ownership is unusually concentrated and legally sticky (locked-in promoter shares, a mandated 51 percent promoter floor in banking, capped conversion batches), the enforcement regime is still building capacity relative to peer markets, and related-party disclosure is thin enough that value extraction can occur largely out of public view. An investor who evaluates only financial ratios is evaluating half the risk.
The economics of promoter versus minority shareholders align when the promoter's illiquid, long-horizon stake motivates genuine value creation, but diverge wherever the promoter group has channels to extract value that do not require the share price to rise — related-party contracts priced off-market, board control used to soften management accountability, and personal share pledging that monetises the stake without a formal, disclosed sale.
Nepal's at-par rights issue convention is a specific, quantifiable mechanism of divergence: it transfers value from the company's capital account to whichever shareholders can fully subscribe and absorb devolved shares, a group that is disproportionately the promoter block, and it can entrench or increase promoter control over successive capital-raising cycles at the expense of cash-constrained minority holders.
Promoter shareholding trends are a genuinely quantitative signal, but must be read carefully: distinguish mechanical dilution effects from active OTC selling, treat approaching lock-in expiry dates as calendarisable volatility events, and remember that NEPSE's shareholding data can lag reality until CDSC's planned separate-ISIN reform is fully implemented.
The highest-priority red flags are promoter share pledging against personal debt (a hidden forced-selling risk invisible until a margin call), below-market OTC promoter share transfers (especially where they suggest distress or intra-group value transfer), and related-party contracts disclosed without competitive benchmarking or independent board scrutiny.
A disciplined investor applies a repeatable promoter-quality scorecard — shareholding trend, pledging disclosure, rights-issue history, board composition, related-party transparency, payout consistency, regulatory history, and research coverage — at every results cycle and corporate action, treating governance analysis with the same rigor as ratio analysis, because in NEPSE's current institutional environment, the individual investor's own diligence remains the first and often the only line of defence against promoter-driven value extraction.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IV · Chapter 21
Insider Trading and Information Asymmetry in Nepal
First published 21 Aug 2026 · Last verified 29 Aug 2026
Every stock market runs on a fiction that everyone chooses to believe: that the price on the screen reflects what is knowable about a company at that moment. NEPSE asks its participants to believe this fiction more than most exchanges do, because so much of what moves prices here — a board's decision on bonus shares, a rights issue not yet filed, an unaudited quarter that beat expectations, a merger conversation still confined to a boardroom in Kathmandu or Biratnagar — exists first as a private fact known to a small circle before it becomes a public one known to everyone. The gap between those two moments, however brief, is where insider trading lives. It is not a marginal nuisance in Nepal's market; it is one of the load-bearing risks a retail investor must understand, because the market's ownership structure, its promoter-heavy corporate governance, and its thin, rumour-susceptible retail float all combine to make that gap wider and more consequential than it would be in a deeper, more institutionally supervised market.
This chapter treats insider trading not as a moral scandal to be denounced but as a structural feature to be analysed and defended against. You will not eliminate information asymmetry as an individual investor; no retail participant anywhere in the world can. What you can do is understand precisely how it operates in Nepal — legally, institutionally, and behaviourally — so that you can size positions, interpret unusual price action, and resist the pull of "someone knows something" narratives that drive some of NEPSE's most violent and most regrettable rallies and collapses. An investor who understands the mechanics of asymmetry trades more conservatively around uncertain information and more confidently around disclosed information. That distinction, more than any stock pick, is what separates durable participants in this market from the churn of investors who cycle in during every bull run and are carried out during every correction.
Lesson 21.1 — What Insider Trading Is, and Why Thin Markets Suffer It Most
Insider trading, in its classic economic definition, is trading a security on the basis of material information that is not available to the public, obtained by virtue of a position of trust or access — as a director, officer, promoter, auditor, banker, or anyone else standing close enough to a company's internal affairs to learn things before disclosure. The economic harm is not that someone profits; markets exist precisely so that better-informed judgment is rewarded. The harm is that the profit comes from an information advantage obtained through a privileged relationship rather than through skill, analysis, or risk-bearing available equally to all participants. It converts the market from a mechanism that prices public information efficiently into a mechanism that quietly transfers wealth from outsiders to insiders, transaction by transaction, without the outsiders ever knowing a transfer occurred.
LEGAL DEFINITION
Under Nepal's Securities Act, 2063 (2006), Section 91, insider trading occurs when a person deals in securities — or causes another person to deal in securities — on the basis of "insider information or notice that is unpublished," or communicates such information to another person in a way likely to affect the security's price. The Act defines insider information as any specific information not yet published by the issuing company that would be capable of affecting the security's price if it were disclosed. Critically, the definition is not limited to directors and officers narrowly construed — it reaches anyone who obtains unpublished, price-affecting information by virtue of a connection to the company, including its auditors, legal counsel, bankers, and, in practice, brokers who learn of client or issuer activity ahead of the public.
Why does this matter more in NEPSE than in a market like the NYSE or even the BSE? Three structural features of Nepal's market compound the damage of any given instance of insider trading.
First, NEPSE is heavily retail-dominated, with limited institutional counterweight. In markets with large mutual funds, pension funds, and foreign institutional investors constantly researching and trading on public information, an insider's informational edge is diluted across a large, liquid order book and arbitraged away quickly once information becomes public. In NEPSE, a large share of daily turnover is retail flow reacting to price and rumour rather than institutional flow reacting to filed disclosures. This means informed trades move prices more, faster, and for longer before the broader market "catches up" to the true information.
Second, floats are thin. Many NEPSE-listed companies — particularly newly listed banks, insurers, hydropower companies, and microfinance institutions — have promoter lock-ins (commonly three years post-IPO under the Securities Registration and Issue Regulation, 2073) that leave only a modest public float actively tradeable. A small volume of insider-informed buying or selling can move a thinly floated stock by a much larger percentage than the same volume would move a deep, liquid counter. This is precisely why several of Nepal's most-discussed insider trading and price-manipulation episodes have involved low-float counters, where a comparatively modest sum of money produced an outsized, headline-making price move.
Third, disclosure is periodic and manual rather than continuous and systemic. Price-sensitive facts in Nepal typically become public through board-meeting notices, AGM resolutions, and stock-exchange circulars — discrete, occasional bursts of information rather than the continuous disclosure flow of markets with sophisticated real-time reporting obligations. The lag between when a board decides something and when it is formally disclosed to NEPSE and the public is exactly the window in which those present in the room — and everyone they tell — can act on it.
WHY IT MATTERS
In a thin, retail-dominated market, insider trading is not simply "unfair" in the abstract — it directly determines who bears the loss when a rumour-driven price move reverses. The retail investor who buys because "the price is moving, something must be happening" is very often the counterparty of last resort to an insider or a tipped intermediary who is selling into that same rally.
Lesson 21.2 — Nepal's Legal Framework and Its Practical Limits
Nepal is not without a formal legal architecture against insider trading. The Securities Act, 2063 (2006) is the primary statute, with Section 91 defining and prohibiting insider trading and the surrounding chapter on offenses prescribing punishment. The Securities Board of Nepal (SEBON), established under the same Act, is the market regulator responsible for investigating violations, and its regulations — including rules issued under the Securities Board Regulation, 2064 (2008) — extend disclosure and conduct obligations to listed companies and market intermediaries.
For years, the practical bite of this framework was widely regarded as weak. Under the original Act, the maximum term of imprisonment for insider trading was one year — a penalty many market observers considered too low relative to the potential gains from a well-timed insider trade in a low-float counter, and too low to function as a credible deterrent against well-resourced promoters or officials. Recognising this, the government introduced the Securities (First Amendment) Bill, 2024, which proposes to raise penalties substantially: fines reportedly ranging as high as NPR 30 million and prison terms of up to three years, scaled to the value of the transaction involved, along with disgorgement-style repayment obligations. The amendment bill would also grant SEBON expanded investigative authority, including the power to request banking transaction records from Nepal Rastra Bank in the course of an investigation — a meaningful upgrade, since much of the difficulty in insider trading cases lies in tracing the money and the relationships behind a trade, not merely observing the trade itself.
REGULATORY STATUS
As of this writing, Nepal's insider trading penalty regime is in a state of transition. The original Securities Act, 2063 framework (maximum one year imprisonment) remains the operative law until the amendment bill is enacted; the proposed stiffer penalties (up to NPR 30 million in fines and three years' imprisonment) represent pending reform, not yet fully in force. An investor should track the passage of this bill as a signal of the state's seriousness about market conduct enforcement, not assume the tougher regime already applies.
Even with stronger statutory penalties, enforcement in practice depends on detection capacity, and this is where Nepal's framework has historically lagged furthest behind its legal text. Several dynamics constrain SEBON's practical enforcement:
Surveillance infrastructure is still maturing. SEBON has publicly signalled an intention to build an AI-based market surveillance system as part of its policy priorities for the 2083/84 fiscal year (2026/27), alongside strengthened cybersecurity and capital market research capacity. The fact that this is being announced as a forward-looking initiative rather than described as an existing, mature capability is itself informative: it suggests that continuous, algorithm-driven detection of suspicious trading patterns — the kind that flags unusual pre-announcement volume automatically, as is standard at more developed exchanges — has not been a mature, systemic feature of Nepal's market oversight to date. Historically, detection has leaned more on ex post pattern review, complaints, media reporting, and referrals than on real-time automated alerts.
Investigations are resource- and capacity-constrained. SEBON is a relatively small regulator overseeing a market with a large number of listed companies, licensed intermediaries, and a fast-growing base of retail demat account holders. Tracing an insider trading allegation to conclusion requires reconstructing trading records, broker order books, bank transfers, and personal or familial relationships between the alleged tipper and the trader — exactly the kind of cross-referencing that the pending authority to access NRB banking records is meant to make easier, and that has been difficult without it.
Conflicts of interest have periodically touched the regulatory apparatus itself. Public reporting on episodes such as the Sarbottam Cement IPO controversy — in which officials connected to the regulatory and exchange apparatus were alleged to have obtained shares at a discount through relatives ahead of public allotment — illustrates a particular hazard in a small market: the community of people positioned to detect and prosecute insider trading is not always cleanly separated from the community of people positioned to commit it. The 2024 amendment bill's proposed restrictions — barring certain conflicted representatives from SEBON's board and imposing a two-year cooling-off period on former SEBON officials before they can work for listed companies — are a direct legislative response to this recognised weakness.
Documented cases exist, but are episodic rather than systemic. Nepal has seen individual enforcement actions — the case against a former chairperson of Ridi Power Company over alleged trading worth roughly NPR 32.3 million, actions against former executives at Nepal Hydro Developer, and investigations touching Corporate Development Bank and Karnali Development Bank following unusual price movements around undisclosed corporate actions or negative regulatory news. What these cases collectively suggest is a regulator capable of acting on clear, high-profile cases, but without the systemic, continuous surveillance apparatus that would catch the much larger number of smaller, less conspicuous instances that likely occur across the market's hundreds of listed counters.
PRACTICAL TAKEAWAY
Do not calibrate your risk assessment to "insider trading is illegal, so it must be rare." Calibrate it to "insider trading is illegal, detection is improving but still developing, and enforcement to date has been episodic." The legal prohibition constrains behaviour at the margin; it does not yet function as a comprehensive deterrent across the full breadth of the market. Your defensive posture should assume information asymmetry is a persistent background condition, not an occasional aberration.
Lesson 21.3 — The Mechanics of Information Asymmetry Specific to Nepal
To defend against a risk, you need to understand its actual transmission mechanism, not just its legal label. In NEPSE, information asymmetry travels through several identifiable channels.
The promoter-and-board channel. Nepal's listed companies — particularly banks, insurance companies, hydropower developers, and microfinance institutions — are typically governed by boards dominated by promoter-shareholders who, by virtue of concentrated ownership (often well above what ordinary shareholders can accumulate given lock-in rules and share pricing gaps), sit inside the room where financial results, dividend and bonus decisions, rights issue plans, merger discussions, and regulatory correspondence are known well before they are disclosed. Promoter shares in Nepal are structurally distinct from ordinary shares — subject to a multi-year lock-in and trading, when they do trade, at a discount to the ordinary share price — but the informational advantage that matters for this chapter is not about the shares themselves; it is about the fact that promoters and the directors they nominate see unaudited quarterly numbers, board minutes, and regulator correspondence before anyone outside that room does.
The broker-network channel. Licensed brokers occupy a uniquely informed position: they see real-time order flow, including which large accounts are accumulating or distributing a counter, often before any public news explains the activity. In a market where a relatively small number of brokerage houses intermediate a large share of turnover, a broker's trading desk can observe patterns — persistent buying from an account linked to a company insider, unusual pre-announcement demand — that are not visible to retail investors watching only the public tape. Nepal's Securities Act explicitly extends the definition of "insider" to reach individuals who obtain unpublished, price-affecting information through such professional connections, which is a direct acknowledgment that brokers are a plausible transmission node for leaked information, not merely passive order-takers.
The informal tipping and social media channel. This is the channel most visible to ordinary investors, precisely because it operates in public view even though the information it carries is not properly public. NEPSE itself has publicly warned investors not to make trading decisions based on claims circulating on Facebook, TikTok, X (formerly Twitter), Viber, and Telegram — a warning that would not be necessary if such channels were not a material factor in retail trading behaviour. The typical pattern: a rumour of an upcoming bonus share announcement, rights issue, or favourable regulatory decision begins circulating in an investor Facebook group or Viber community, sometimes accompanied by a fabricated or selectively cropped "screenshot" of an internal notice; retail buying accelerates on the rumour; the price moves sharply — market commentary on Nepali trading behaviour has described bonus-rumour-driven moves of the order of fifteen to twenty-five percent over a matter of days — well before any official confirmation; and if the rumoured corporate action fails to materialise, or materialises in a smaller form than rumoured, the price gives back the gain abruptly, often in a single session of panic selling.
It is worth being precise about what this third channel is and is not. Not every rumour that turns out to be true was insider trading in the legal sense — a well-informed market participant who correctly infers a probable bonus declaration from a company's earnings trajectory and public disclosure history is doing legitimate analysis, not trading on inside information. But a materially large share of the rumour cycle in Nepal traces back, one or two links removed, to someone with a genuine informational connection to the company — a staff member, a bank employee processing a loan restructuring, a printer preparing an AGM notice, a broker's dealing desk — whose original leak gets amplified, distorted, and monetized by a chain of people with no direct connection to the company at all. By the time a rumour reaches a Viber group with thousands of members, its origin is untraceable and its accuracy is often degraded, but its power to move a thinly floated stock remains fully intact.
MARKET REALITY
The retail investor's practical problem is not merely "is this rumour true?" It is that even a true rumour, reacted to late in its diffusion cycle, places you as the marginal buyer at the top of an insider-originated, socially amplified price move — the classic position of the last, uninformed buyer before a reversal.
Lesson 21.4 — Recognising the Signs of Informed Trading
Because retail investors cannot see order flow, banking records, or board minutes, they must rely on visible market signatures that correlate with informed trading, even though no single signature is proof. Reading these signs well is a skill, not a guarantee — treat it as raising or lowering your probability estimate, not as a certainty switch.
Warning Sign
What It May Indicate
Sharp volume spike with no public news
Information may be circulating informally before official disclosure
Persistent, unexplained price drift upward or downward over several sessions ahead of a scheduled board meeting or AGM
Anticipatory positioning by parties aware of the likely outcome
Price and volume move sharply, then a company circular or clarification follows shortly after (sometimes denying rumours)
A strong signal that informal information had already reached part of the market before formal disclosure
Concentration of buying or selling volume in one or two brokerage codes far above their typical share of turnover in that counter
Possible informed or coordinated activity through a specific intermediary, sometimes referred to in Nepali trading commentary as "broker flip" patterns
A rumour with unusually specific detail (an exact bonus ratio, an exact rights ratio, a specific approval date) circulating on social media well before any board notice
Higher likelihood the rumour traces back to a genuine internal source rather than pure speculation
Sudden reversal or "give-back" of gains shortly after a rumoured announcement fails to appear on schedule
Confirms that the preceding move was rumour-driven positioning rather than a fundamentals-based re-rating
None of these signs, individually, should be read as confirmation of illegal activity — unusual volume can have entirely innocent explanations, including index rebalancing flows, technical breakouts attracting momentum traders, or simple coincidence in a market where many counters trade thinly enough that a handful of large legitimate orders can look like a "spike." What the table is useful for is calibrating your posture: when several of these signs cluster around a single counter simultaneously, the probability that you are looking at informed trading rather than organic price discovery rises, and your own trading behaviour should adjust accordingly — which brings us to defensive strategy.
Lesson 21.5 — Defensive Strategies for the Retail Investor
An individual investor cannot out-inform an insider. The correct defensive posture is not to try to win the same game insiders are playing — trading on rumour and anticipated news — but to structurally exit that game and compete on a different basis: patience, discipline, and reliance on information that has actually been disclosed.
Trade on disclosed information only, as a hard rule. Treat every board notice, AGM resolution, and NEPSE/SEBON circular as your information set, and treat social-media "leaks," however specific or confident-sounding, as noise to be filtered out rather than incorporated into decisions. This single discipline eliminates your exposure to the single most damaging pattern in this chapter: buying into a rumour-driven spike and holding the bag when it reverses.
Size positions to reflect asymmetry risk, not just volatility. Any counter exhibiting the warning signs in Lesson 21.4 — thin float, concentrated promoter ownership, a pending board decision, unexplained volume — deserves a smaller position size than the same expected return would justify in a more transparent, liquid counter. You are not merely bearing ordinary price volatility in such a stock; you are bearing the specific risk of transacting against a better-informed counterparty. Position sizing is the one lever every retail investor fully controls, and it is the correct lever to pull when the informational playing field is known to be uneven.
Refuse to chase momentum around unconfirmed catalysts. The instinct to buy because "the price is already moving, so something must be true" is precisely the reflex that insider-originated rumours are designed to exploit, whether or not anyone designed them deliberately. A price move with no confirmed public cause is information about other people's beliefs and positioning, not information about the company. Waiting for confirmation costs you the first leg of a genuine move; it also fully protects you from the much larger loss of buying the top of a rumour that does not pan out. Over a long investing horizon in a market like NEPSE, that trade-off favours patience.
Use the lock-in and disclosure calendar as your own information advantage. Because much of Nepal's price-sensitive information arrives in scheduled bursts — AGM season, quarterly result windows, promoter lock-in expiry dates — you can build a defensive calendar of your own: know when a company's promoter lock-in expires (a period historically associated with potential supply overhang and pre-positioning), know when its board is expected to meet on dividend or bonus matters, and treat the days immediately surrounding these events as periods of elevated informational risk in which to reduce position size or simply observe rather than trade.
Diversify away from single-counter, low-float exposure. Since the mechanical damage from insider trading is amplified precisely in thin-float, promoter-concentrated counters, a portfolio that avoids concentrating large positions in the smallest, most tightly held counters on the exchange is mechanically less exposed to this entire category of risk, independent of any individual stock-picking skill.
Document and, where appropriate, report what you observe. SEBON's enforcement capacity, while limited, does act on complaints and referrals, and the pending amendment bill's expanded investigative powers make future enforcement more credible than past enforcement. An investor who notices a clear pattern — a specific, verifiably false rumour tied to a subsequent price collapse, for instance — contributes, in a small way, to the evidentiary record that a developing regulator needs, even if no individual complaint produces an individual remedy.
DISCIPLINE OVER CLEVERNESS
The retail investor's edge in an information-asymmetric market is never going to be better information. It is discipline: refusing trades that require you to have information you do not have, and structuring position sizes so that being wrong about a rumour never threatens your capital base.
Lesson 21.6 — The Broader Cost to Market Development
Insider trading and information asymmetry are not merely a private risk to individual investors; they are a tax on the development of NEPSE as an institution. Every instance of insider-informed trading that goes undetected reinforces the perception among retail participants — and, more consequentially, among the foreign and domestic institutional capital that Nepal's capital market needs to attract for its next stage of growth — that NEPSE is a market where connections matter more than analysis. That perception has concrete costs: it discourages patient, long-horizon capital from entering the market, since such capital typically requires confidence that prices reflect genuinely available information rather than privileged access; it pushes retail participation toward short-horizon, rumour-reactive trading rather than fundamentals-based investing, which in turn increases realised volatility and erodes the very trust that would attract more patient capital — a self-reinforcing cycle; and it raises the effective cost of capital for honestly governed companies, since investors demand a larger discount to compensate for the risk that any given counter might be one where insiders trade ahead of them.
Nepal's regulatory trajectory — the 2024 amendment bill's tougher penalties, SEBON's stated intention to build AI-based surveillance, expanded access to banking records for investigations, and governance reforms addressing SEBON's own conflict-of-interest exposure — represents a recognition of this cost at the policy level. None of it has yet fully matured into the kind of continuous, high-detection-probability enforcement environment that characterises markets with decades of institutional development behind them. For the individual investor, the honest conclusion is this: the direction of travel is toward a fairer market, but the current reality is one of persistent, structurally embedded information asymmetry that will not disappear on any near-term timeline. Investing successfully in NEPSE today means investing with that reality fully priced into your own behaviour, not investing in anticipation of a level playing field that has not yet arrived.
Chapter recap
Insider trading in Nepal is legally defined and prohibited under Section 91 of the Securities Act, 2063, with the definition of "insider" extending beyond officers and directors to auditors, legal advisors, brokers, and anyone else who obtains unpublished, price-affecting information through a connection to the company.
Enforcement has historically been constrained by low statutory penalties (a maximum of one year's imprisonment under the original Act), limited real-time surveillance infrastructure, and episodic rather than systemic detection; a pending amendment bill proposes substantially higher fines (up to roughly NPR 30 million), longer prison terms (up to three years), and expanded investigative powers, including access to banking records — but this stronger regime is not yet fully in force.
Information asymmetry in NEPSE is amplified by three structural features: promoter-dominated boards with early access to unpublished financial and corporate-action information, brokers positioned to observe order flow and client activity ahead of public disclosure, and a retail-dominated, socially-networked investor base that rapidly amplifies informal tips and rumours through platforms such as Facebook, Viber, and Telegram.
Visible warning signs of possibly informed trading include unexplained volume or price drift ahead of scheduled corporate events, concentrated brokerage-code activity, unusually specific pre-announcement rumours, and sharp reversals once a rumoured event fails to materialise as expected — none conclusive alone, but meaningful in combination.
The retail investor's durable defence is behavioural, not informational: trade only on disclosed information, size positions down in thin-float and promoter-concentrated counters, refuse to chase unconfirmed-catalyst momentum, and use the disclosure and lock-in calendar to anticipate periods of elevated informational risk.
Unaddressed information asymmetry is not just a private risk but a tax on NEPSE's institutional development, discouraging patient long-horizon capital and reinforcing short-horizon, rumour-driven trading; Nepal's regulatory reforms point toward improvement, but investors should calibrate their behaviour to today's enforcement reality, not tomorrow's intended one.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IV · Chapter 22
Conglomerate Cross-Holdings and Promoter Webs in Nepal
First published 21 Aug 2026 · Last verified 29 Aug 2026
Walk onto the trading floor of any brokerage in Kathmandu and ask a retail investor why they hold shares in a commercial bank, a hydropower company, an insurer, and a trading conglomerate, and they will likely tell you they have built a diversified portfolio across four different sectors. Pull the annual reports, the promoter shareholding disclosures, and the board of directors' biographies for those four companies, and there is a real chance you will find the same three or four surnames sitting on every board, the same family trust listed as the largest promoter shareholder in each, and a web of loans, guarantees, and share pledges connecting all four balance sheets to one another. What looked like diversification on the trading terminal was concentration in the ownership registry.
This is not a uniquely Nepali phenomenon — conglomerate cross-holding is a feature of most emerging markets where family capital industrialised before public capital markets matured. But it takes a specific shape in Nepal, driven by the sequence in which the country's banking, insurance, and hydropower sectors were opened to private capital, and by a regulatory environment that has historically emphasised disclosure over prohibition when it comes to who may sit on which boards. For a NEPSE investor, understanding this shape is not an academic exercise in corporate structure. It is the difference between correctly pricing a single company's risk and unknowingly underwriting an entire family's balance sheet, hydropower project portfolio, and loan book, one ticker at a time.
This chapter builds the analytical toolkit for seeing through the individual stock to the promoter web behind it: how these structures came to exist, how to map them from public filings, what specific risks they create, what Nepal's regulators do and do not restrict, and how to stress-test your own exposure to a single group before you commit capital to any one of its listed entities.
Lesson 22.1 — The Origins and Shape of Nepal's Promoter Webs
Nepal's major business houses did not begin as diversified financial conglomerates. Most trace their roots to family trading firms built over one or two generations — import-export houses, manufacturing units, agro-processing businesses — that accumulated capital well before the country's banking sector was substantially opened to private and joint-venture ownership starting in the 1980s. When Nepal Rastra Bank began licensing private commercial banks, the promoter-shareholder structure it required — a concentrated block of "promoter" capital, typically locked in for a defined period, sitting alongside public shareholders — was a natural fit for exactly the families that already had liquid industrial capital and no existing avenue to deploy it in regulated finance. Banking licenses did not go to diffuse public floats; they went to identifiable promoter groups, and those groups were disproportionately the same trading and manufacturing houses that already dominated the private economy.
Chaudhary Group is a documented example of this pattern: alongside its consumer goods and hospitality businesses, the group holds a promoter interest in Nabil Bank — Nepal's first private commercial bank — and extends that into Nabil Investment Banking Ltd (Nabil Invest), with CG Finco P Ltd sitting as an institutional shareholder in the investment banking subsidiary and running its own remittance business. This is not a hidden or unusual arrangement — it is disclosed corporate structure — but it illustrates the mechanism: a single family group holding promoter positions across a commercial bank, its merchant banking arm, and an allied finance company, each separately encounterable by a retail investor as an independent listed or quasi-listed entity.
The same pooling logic repeated when Nepal's hydropower sector opened to private investment from the 1990s onward. Hydropower projects require large, patient capital and carry construction-phase risk that made them a natural extension for business houses already comfortable with long-gestation industrial investment — and government incentives, guaranteed power purchase agreements with the Nepal Electricity Authority, and periodic IPO mandates for hydropower promoters to divest a portion of equity to the public created a second sector where the same family capital could recur as promoter shareholding. Golyan Group is a documented case of this multi-sector reach: a diversified house spanning textiles and spinning, agro-processing, hospitality, and — more recently — a substantial hydropower and solar portfolio across more than a dozen projects, illustrating how a single promoter group's capital can span manufacturing and power generation as separate listed or soon-to-be-listed entities, independent of any banking arm.
Insurance followed a similar arc: as NRB and the Insurance Board issued new licenses in waves, the promoter capital that queued up to meet minimum paid-up capital requirements was again drawn substantially from the same pool of established business houses, because insurance promoter shareholding — like banking — demands a concentrated, creditworthy sponsor rather than a diffuse public float at inception.
The result, replicated in varying degrees across many of Nepal's business houses, is a structure that did not emerge from any single deliberate strategy of empire-building but from the mechanical fact that Nepal's capital-intensive regulated sectors — banking, insurance, hydropower — all required the same kind of promoter: concentrated family or group capital willing to lock in for years. The families that had that capital first tended to keep acquiring promoter positions as each new sector opened, and directors, once seated on one board, frequently carried their reputational and relationship capital onto others.
WHY THIS MATTERS
A promoter group's cross-sector reach is not evidence of wrongdoing. It is the ordinary outcome of how Nepal's capital-intensive regulated sectors were licensed. The analytical task for an investor is not to treat cross-holding as a scandal, but to treat it as a structural fact that changes how risk actually flows between the tickers you hold.
Lesson 22.2 — Mapping the Ownership Web: A Reader's Method
Before you can assess conglomerate risk in any single NEPSE holding, you need a working map of who actually stands behind it. This is slower and less glamorous than reading a price chart, but it is entirely doable from public documents, and it is the single highest-value hour you can spend before initiating a position in a bank, insurer, or hydropower company with a visible promoter house behind it.
Start with the annual report's related-party disclosure note. Nepali listed companies reporting under Nepal Financial Reporting Standards are required to disclose related-party transactions — loans to or from affiliated entities, guarantees extended, key management personnel compensation, and transactions with entities under common control. This note is frequently the single richest source of information in the entire annual report for cross-holding purposes, because it names the counterparties. Read it every year, not just once, because related-party exposure changes as a group's financing needs shift.
Second, build a director cross-reference. Pull the board of directors' section from the annual reports, prospectuses, or SEBON filings of every company you hold or are considering, and note each director's name, the promoter shareholder they represent, and any other listed or well-known unlisted company where that same name appears as director, chairman, or major shareholder. Director biographies in Nepali annual reports and IPO prospectuses typically list other directorships explicitly — use them. A director sitting on the boards of a bank, a hydropower company, and an insurer simultaneously is disclosing the cross-holding web to you directly; you simply have to read three separate documents to see it assembled.
Third, use the promoter shareholding pages maintained by NEPSE-adjacent financial portals and brokerage research desks, which typically break down a listed company's shareholding between promoter and public categories and often name the largest promoter shareholders. Cross-reference the named promoter entities — trusts, holding companies, or individuals — across every company you can find them in.
Fourth, for hydropower specifically, read the IPO prospectus's promoter-group section closely. Hydropower IPOs in Nepal are required to disclose the promoter group's other business interests as part of the offer document, and this is often the clearest single-document summary of a promoter family's broader corporate footprint available anywhere.
Fifth, treat the SEBON Listed Companies Corporate Governance Directive, 2074 as your baseline expectation for what governance disclosure should look like — board composition, independent directors, and governance-related disclosures are addressed under this directive — and be more skeptical of any listed entity whose public filings fall visibly short of what the directive contemplates.
Ownership-Mapping Source
What It Reveals
Annual report related-party note
Loans, guarantees, and transactions between the company and its promoter group's other entities
Director biographies across filings
Cross-directorships linking one board to another, often the clearest visible trace of a shared promoter web
Promoter shareholding disclosures
The named entities or families holding the controlling block, comparable across companies
Hydropower IPO prospectuses
Promoter group's other business interests, disclosed as part of the offer document
What board composition and disclosure standards a well-governed listed company should meet
None of this mapping is exotic. It requires patience and cross-referencing across documents that are individually public but rarely assembled into a single group picture by anyone other than the investor doing the work. That assembly is precisely the value you are creating for yourself.
Lesson 22.3 — The Specific Risks: Contagion, Circular Financing, and Overstated Group Value
Once you can see the web, three distinct risk mechanisms become visible that are invisible when you evaluate a single entity in isolation.
The first is contagion risk. When a bank, a finance company, a hydropower project, and an insurer share a common promoter group, distress in one entity does not stay contained to that entity's own balance sheet. A finance company within the group facing a spike in non-performing loans can trigger a deposit run or a credit-rating deterioration that spills into public perception of the group's bank, even where the bank's own loan book is sound, simply because depositors and counterparties price reputational contagion faster than they can verify legal separateness. A hydropower project facing cost overruns or a delayed power purchase agreement can create pressure on the promoter group to divert cash or pledge shares from its other listed entities to keep the project afloat, transmitting stress from an entity you may not hold into one you do.
The second is circular financing. In its simplest form, this looks like Company A — often a bank or finance company within the group — extending credit to Company B, a hydropower or manufacturing entity in the same promoter family, which then uses part of that financing to subscribe to a rights issue or IPO allotment in Company A or Company C, another group entity raising capital. What appears externally as fresh equity capital being raised by Company A is, on closer inspection, recycled group debt: the same rupee of bank credit is counted once as a loan asset on Company A's books and again as fresh paid-up capital on Company C's books. Investors who see a rights issue "fully subscribed by promoters" and read this as a vote of confidence should ask a harder question: where did the promoter's subscription money actually originate, and does the answer trace back to another entity in the same group's own balance sheet? A related variant involves promoters pledging shares of one group company as collateral to raise margin loans that are then deployed to meet capital calls or subscribe to offerings in another group company — a leverage chain that is invisible from any single company's annual report but visible once share-pledge disclosures across the group are assembled.
The third is overstated aggregate group value. When the same underlying promoter capital appears, directly or through cross-shareholding, as equity in multiple listed entities, a naive sum-of-the-parts view of "the group's" market capitalisation double-counts capital that exists only once in economic reality. A promoter family whose disclosed net worth appears, on paper, to span a bank, an insurer, and three hydropower companies may in substance be leveraging one core pool of capital across all five balance sheets simultaneously, with each entity's reported strength partly dependent on the others continuing to perform. This is not necessarily fraudulent — it can be entirely disclosed and entirely legal — but it means that the intuitive investor habit of treating "a strong group" as a blanket credit-positive for every entity bearing its name is analytically unsound.
Cross-Holding Risk
Practical Consequence for a NEPSE Investor
Contagion from a distressed group entity
Deposit runs, rating pressure, or share-price declines spread to healthy entities in the same group purely on reputational and funding-channel grounds
Circular financing between group entities
Reported capital raises may substantially represent recycled group debt rather than genuinely new external capital
Share pledging across group entities
A margin call on one company's shares can force distressed selling that depresses the price of an entirely different company in the same group
Overstated aggregate group value
The same promoter capital, counted once in reality, appears to investors as if it independently strengthens every entity bearing the family name
Each of these risks is a function of interconnectedness, not of any single company's fundamentals — which is exactly why they do not show up if your analysis stops at the entity you are actually buying.
Lesson 22.4 — Regulation: What Nepal Restricts, What It Doesn't (Yet)
Nepal's regulatory framework addresses pieces of this problem, but no single, comprehensive rule currently prevents a business house from holding promoter positions across a bank, an insurer, and a hydropower company simultaneously, or from seating overlapping directors across them. Understanding exactly what is and is not restricted matters, because it tells you how much the system is doing the diligence for you, and how much is left for you to do yourself.
On lending concentration, Nepal Rastra Bank's traditional Single Obligor Limit capped the credit a bank or financial institution could extend to a single borrower or group of related parties — historically around NPR 25 crore before requiring prior NRB approval for anything larger. In 2025, NRB removed this limit, explicitly to give banks flexibility to structure large-scale financing for infrastructure, industrial, and hydropower projects without lengthy central-bank approval. The practical effect for cross-holding risk is significant: there is no longer a hard regulatory ceiling on how much a bank can lend to a related group of borrowers under common promoter control; the constraint now rests on the lending bank's own internal risk management and board governance rather than a centrally enforced cap. For an investor holding shares in a bank whose promoters also control large borrowing entities, this shift means the burden of assessing related-party credit concentration has moved further onto you, because it has moved off the regulator's automatic enforcement.
On equity cross-holding, NRB's 2025 directive changes also loosened the rules governing how banks and financial institutions themselves may hold shares in other listed companies — reducing the minimum holding period for BFI investment in listed shares and debentures from one year to six months, and removing a prior cap limiting BFIs to selling only 20% of such holdings annually. This gives banks materially more flexibility to build, trade, and unwind equity positions in other listed companies, including, potentially, companies connected to their own promoter groups, subject to whatever internal governance and disclosure standards apply.
On directorship and ownership separation, a BAFIA amendment bill introduced in 2024 proposed a more direct structural response to exactly the cross-holding problem this chapter addresses: barring anyone holding more than 1% of a bank's paid-up capital from taking loans from other banks and financial institutions, and disqualifying substantial shareholders whose commercial debt exceeds 1% of paid-up capital from serving as directors — an attempt, in effect, to separate "bankers" from "businessmen" who might otherwise use their bank directorship to facilitate financing for their other business interests. The proposal drew direct pushback from the Nepal Bankers' Association, whose leadership argued that because the roughly NPR 7.5 trillion of bank capital in Nepal belongs predominantly to industrialists already, and because founding promoter-investors have limited ability to exit, the reform risked being unworkable without a longer transition and a larger pool of purely financial (non-business) investors willing to hold bank shares. As of this writing the provision remains part of an actively debated legislative process rather than settled, enforced law — which itself tells you something: the practice the bill is trying to restrict (business promoters simultaneously directing banks and borrowing, directly or through affiliates, from the broader banking system) has been widespread enough, and politically resistant enough, that a full legislative separation has not yet been achieved.
On systemic recognition, NRB's Domestic Systemically Important Bank framework — covering the ten largest banks and phasing in additional capital buffers from 2027 — explicitly weights "interconnectedness" at 30% of its assessment methodology, alongside financial size, substitutability, and complexity. This confirms that Nepal's regulator formally recognises interconnection between institutions as a source of systemic risk worth a capital buffer; it does not, however, extend that interconnectedness lens down to the promoter-group level for ordinary retail-facing disclosure purposes.
Regulatory Area
Current Status (as researched)
What It Means for You
Single Obligor Limit on bank lending
Removed in 2025; concentration risk now governed by each bank's internal policy rather than a central cap
Related-party lending concentration within a group is less externally constrained than before; check bank disclosures yourself
BFI equity holding-period and sale caps
Loosened in 2025 (six-month minimum holding, no annual sale cap)
Banks can move in and out of listed equity positions, including potentially group-affiliated ones, more freely
Proposed in 2024, contested by bankers' associations, not yet fully settled law
The practice it targets — business promoters directing banks while borrowing elsewhere — has not been legislatively foreclosed
D-SIB interconnectedness weighting
In force, phasing in capital buffers from 2027, applies to the ten largest banks
Regulatory recognition exists at the systemic level, not as a promoter-group-specific disclosure rule
The overall picture is a regulatory system moving, in places, toward deregulation of exactly the mechanisms (lending concentration, equity cross-holding flexibility) that widen cross-holding risk, alongside a separate, contested legislative effort to address the director/ownership overlap problem directly. Neither trend gives an investor grounds to assume the system has this fully covered.
Lesson 22.5 — A Practical Due-Diligence Framework Before You Buy
Given that Nepal's regulatory architecture leaves meaningful gaps, the responsibility for identifying and stress-testing conglomerate exposure sits substantially with you. The following sequence turns the mapping work from Lesson 22.2 into an actual investment decision process.
Identify the promoter group. Before analysing the entity's financials, name the family, trust, or holding company standing behind its largest promoter shareholding block, using the annual report and prospectus sources described earlier.
Enumerate every other entity connected to that same promoter group — listed and, where discoverable, unlisted — spanning banking, insurance, hydropower, manufacturing, and trading. Do not stop at the first two you find; promoter webs in Nepal frequently run to five or more entities once fully traced.
Pull the related-party transaction note for every entity in the group you can access, and specifically look for loans, guarantees, and share pledges running between them. A pattern of recurring, growing related-party loans between the same two or three entities year over year is a stronger signal than a single year's disclosure.
Check for share-pledge exposure. Where disclosed, note whether promoter shares in any group entity are pledged against margin loans, and whether the lender is another entity within the same group's orbit — this is the clearest indicator of a circular leverage chain and a specific channel through which a fall in one company's share price can force distress in another.
Identify the group's weakest link. Across every entity you have enumerated, ask which one carries the most construction-phase hydropower risk, the highest non-performing loan ratio, or the thinnest capital buffer. That entity, not the one you are actually planning to buy, is where group-wide stress is most likely to originate.
Stress-test the transmission channel. Ask explicitly: if the weakest-link entity suffered a serious setback — a stalled hydropower project, a spike in loan defaults, a failed capital raise — what is the actual mechanism by which that stress would reach the entity you hold? Shared directors affecting governance attention and capital allocation decisions, related-party loans requiring write-downs, reputational contagion affecting depositor or customer confidence, or share-pledge margin calls forcing distressed sales are the four channels to check specifically.
Refuse to sum the parts uncritically. When assessing "the group's" overall scale or strength as a qualitative input to your decision, resist treating the combined market capitalisation or combined net worth of its listed entities as additive economic value; discount for the double-counting of promoter capital recycled across entities.
VERIFICATION HABIT
A promoter group's strength in one sector is not evidence of strength in another — it may be evidence of the same capital stretched across both. Verify each entity's standalone solvency before letting the group's overall reputation substitute for entity-specific analysis.
Lesson 22.6 — The Diversification Illusion
Standard portfolio construction advice tells investors to spread capital across sectors — banking, hydropower, insurance, manufacturing — to reduce idiosyncratic risk. This advice implicitly assumes that a bank stock and a hydropower stock represent genuinely independent economic bets. In Nepal, that assumption fails whenever the bank and the hydropower company sit under the same promoter group, because their fates are linked through exactly the contagion, circular-financing, and shared-capital mechanisms described in Lesson 22.3, regardless of how different their reported sector classifications look on a NEPSE sector screen.
An investor who builds what looks, on paper, like a five-stock diversified portfolio spanning commercial banking, life insurance, run-of-river hydropower, and consumer manufacturing may discover, upon mapping the promoter groups behind each holding, that three or four of those five tickers trace back to the same family's balance sheet. In a genuine stress scenario affecting that family's core business — a failed hydropower project, a liquidity crunch in an affiliated finance company, a large loan default — three or four of the five positions could deteriorate together, at exactly the moment diversification was supposed to provide protection. The portfolio's apparent sector diversification was, in economic substance, a concentrated bet on a single promoter family's capacity to manage simultaneous stress across multiple capital-intensive businesses.
This does not mean cross-held entities are uninvestable — many are well-run, well-capitalised, and genuinely creditworthy on a standalone basis. It means the diversification benefit an investor believes they are purchasing when they buy across sectors must be verified at the promoter-group level, not assumed from the NEPSE sector label alone. True diversification in the Nepali market requires deliberately seeking exposure to companies backed by different, unconnected promoter groups, not merely different sector codes.
ANALYTICAL HABIT
Before buying any NEPSE stock, map its promoter group across every other listed entity you can find. A "diversified" portfolio of five different tickers may really be one concentrated bet on a single family's balance sheet.
Chapter recap
Nepal's conglomerate cross-holding structures arose mechanically from how banking, insurance, and hydropower licensing required concentrated promoter capital, and the same family business houses that accumulated industrial capital first tended to recur as promoters across all three sectors, with directors and capital frequently interlocking across entities. Mapping a promoter web is a public-document exercise — related-party notes, director cross-references, promoter shareholding disclosures, and hydropower IPO prospectuses — that most investors simply never assemble, even though every piece is individually available. Cross-holding creates three distinct, non-obvious risks: contagion that spreads distress between nominally separate entities through reputational and funding channels, circular financing in which capital raised by one group entity substantially represents recycled debt from another, and overstated aggregate group value from double-counted promoter capital. Nepal's regulatory framework addresses pieces of this — NRB's D-SIB interconnectedness weighting, a contested 2024 BAFIA proposal to separate bank directors from major borrowers — while simultaneously loosening other constraints, including the 2025 removal of the Single Obligor Limit and the easing of BFI equity-holding rules, meaning the gap between what regulation restricts and what promoter groups can structurally do has, if anything, widened rather than narrowed recently. A practical due-diligence framework requires naming the promoter group behind any holding, enumerating its other entities, checking related-party loans and share pledges, identifying the group's weakest link, and stress-testing the specific transmission channel by which that weak link's distress could reach the entity you actually hold. Finally, sector-based diversification within NEPSE is only a genuine risk reducer if the underlying promoter groups are actually distinct — a portfolio spread across a bank, an insurer, and a hydropower company may be one concentrated bet on a single family if you have not verified otherwise.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IV · Chapter 23
Governance Scoring for NEPSE Companies
First published 21 Aug 2026 · Last verified 29 Aug 2026
Every serious NEPSE investor eventually develops a private list of companies they will not touch, regardless of the price-to-book multiple or the dividend yield on offer. Ask them why, and the answer is rarely a spreadsheet. It is usually a story — a rights issue that diluted minority shareholders the week before a related party got a favourable loan, an AGM that was adjourned twice without explanation, a promoter family whose shareholding quietly slipped from 51 percent to 34 percent over three years while the share price was propped up by retail enthusiasm. These stories are real, and the instincts built from them are not worthless. But instinct is not a system. It cannot be taught to a junior analyst, it cannot be back-tested, it cannot be applied consistently across the 250-plus companies listed on NEPSE, and it is vulnerable to the single greatest bias in equity investing: the tendency to excuse the governance failures of a stock you already own and to see them everywhere in a stock you do not.
This chapter closes Part IV by converting the qualitative material of the preceding chapters — promoter behaviour, board structure, related-party dealing, disclosure culture — into something that can be scored, recorded, revisited, and defended. It does not claim to import a foreign rating agency's methodology wholesale, because no such agency currently rates the governance of individual NEPSE-listed companies on an ongoing basis. What follows is an original practical framework, built specifically around what a retail investor in Nepal can actually observe from public filings, and explicitly modelled on the logic — though not the specific indicators — of internationally recognised frameworks such as the G20/OECD Principles of Corporate Governance and institutional scorecards used in comparable emerging markets. Where the chapter borrows structure from those sources, it says so. Where it is proposing something new because Nepal's disclosure environment demands a different tool, it says that too.
Lesson 23.1 — Why a Scorecard Beats a Gut Feeling
The case for structured scoring over impression is not an aesthetic preference for tidiness. It rests on four specific failures that unstructured governance judgment reliably produces, each of which has a concrete cost in a NEPSE portfolio.
The first failure is inconsistency across companies. An investor who is alert to promoter share pledging in one bank but never checks for it in another is not applying a governance view — they are applying a mood. Two commercial banks with structurally identical related-party lending patterns will get different treatment depending on which one the investor read about most recently, or which one has a chairman they personally find likeable. A scorecard forces the same six questions to be asked of every company, in the same order, every time.
The second failure is recency and narrative bias. A single well-publicized scandal — a cooperative collapse, a merchant banker's licence suspension, a bank's NRB-imposed restriction — tends to dominate an investor's governance assessment of an entire sector for months, while quieter, more persistent governance decay in a company that has stayed out of the news goes unmeasured. Scoring on a fixed schedule, from fixed inputs, corrects for this: a company's governance score should move because its disclosed behaviour changed, not because sentiment about its sector changed.
The third failure is the inability to size a decision. "I don't love this company's governance" is not information a portfolio can act on. Should the position be zero, or half the size it would otherwise be, or fully sized but subject to a tighter stop? A gut feeling gives no answer. A score that sits on a defined 0–12 scale, with defined bands, converts a vague unease into a specific, repeatable portfolio rule — discussed in Lesson 23.5.
The fourth failure is the absence of an audit trail. When a governance-driven decision goes wrong — the investor avoided a stock that then performed well, or held one that then blew up — an unstructured judgment leaves nothing behind to learn from. A scorecard, dated and dimension-by-dimension, is a record. Two years later it is possible to go back and see exactly which input was wrong: was the related-party transparency score too generous because a disclosure was taken at face value, or did the promoter shareholding trend get missed because the filing was never checked? Improvement requires a paper trail, and instinct does not leave one.
PRACTICAL RULE
A governance score is not a substitute for financial analysis — it is a multiplier. A cheap stock with terrible governance is not a bargain; it is a trap waiting for the right catalyst.
None of this argues that a scorecard replaces judgment. It structures judgment, forces it to be applied evenly, and creates the discipline of writing down why a company scored the way it did. The judgment is still the investor's. The scorecard just stops the judgment from being reinvented, inconsistently, every time a new company crosses the watchlist.
Lesson 23.2 — Design Principles for a Nepal-Buildable Scorecard
Before building the framework itself, it is worth being explicit about the constraint that shapes every dimension in it: a Nepal governance scorecard is only useful if it can be completed entirely from what a retail investor can obtain without privileged access. This sounds obvious, but it rules out most of what a global institutional scorecard — built for markets with mandatory XBRL filings, searchable board-minute archives, and independent proxy advisory research — would normally include.
The World Bank's Report on the Observance of Standards and Codes (ROSC) assessment of Nepal's corporate governance framework, benchmarked against the OECD Principles, found compliance ranging from "partially observed" to "not observed" across most categories, and identified specific structural gaps that remain directly relevant to what a scorecard can and cannot measure today: ownership disclosure is thin (listed companies report shareholding changes above certain thresholds to NEPSE but not always in an easily public form), related-party transaction rules lack the formal approval and disclosure procedures found in more developed markets, and SEBON's enforcement powers are limited — it can issue guidelines and directives but has historically relied on persuasion rather than binding secondary regulation, with enforcement action rare. This is the environment the scorecard must be built for, not the environment an investor might wish existed.
Since that assessment, Nepal's framework has moved incrementally. The Companies Act 2063 (2006) requires public companies to maintain a minimum board size, include at least one independent director with no material relationship to the company beyond the directorship, include at least one female director, and have at least one director ordinarily resident in Nepal. The Bank and Financial Institutions Act (BAFIA) 2073 adds board committee requirements and "fit and proper person" criteria specifically for banks and financial institutions. SEBON, for its part, has periodically issued directives requiring listed companies to submit a standardised annual corporate governance disclosure alongside audited financials — covering board composition and conduct, risk management and internal controls, and organizational structure and staffing, submitted in a uniform format designed for comparability across companies.
These are genuine, checkable disclosure obligations, and they are precisely the raw material a Nepal scorecard should be built from — annual reports, AGM notices and minutes, the SEBON-mandated corporate governance report, NEPSE's disclosure portal, and the audited financial statements themselves. Four design principles follow from this reality.
Principle one: score only what is disclosed, not what is assumed. If an annual report does not state the number of board meetings held, the scorecard records that as an absence of disclosure — itself a governance data point — rather than guessing at a number.
Principle two: prefer trend over snapshot wherever the data allows it. A promoter holding 45 percent of shares tells you very little in isolation; a promoter holding that fell from 58 percent to 45 percent over three annual reports tells you a great deal, and the direction is more informative than the level.
Principle three: separate what a company is legally required to disclose from what it discloses voluntarily. A company that goes beyond the SEBON-mandated minimum — publishing related-party transaction schedules in more detail than required, or disclosing director attendance at board meetings — is signalling something about its governance culture that a company doing the legal minimum is not, even if both are technically compliant.
Principle four: build the scorecard to be completed from the same four or five source documents every time, so that scoring one company does not require a different research process than scoring the next. This is what makes a scorecard usable at scale rather than as a one-off boutique exercise for a single favourite stock.
DEFINITION
Disclosure-buildable framework: a scoring system in which every input can be sourced from information a retail investor can legally and practically obtain — annual reports, AGM minutes, SEBON filings, and NEPSE disclosures — with no reliance on private access, insider contacts, or paid proprietary data services.
Lesson 23.3 — The Six-Dimension NEPSE Governance Scorecard
The framework below organises governance into six dimensions, echoing the broad structure used by international scorecards — shareholder treatment, disclosure quality, and board responsibility are recognizable categories from the OECD Principles and from institutional scorecards such as those used by Indian proxy advisory firms — but the specific indicators are chosen because they are things a NEPSE investor can actually check, unlike indicators requiring board minutes access, private proxy advisor research, or regulatory non-public filings.
Each dimension is scored 0, 1, or 2. Total possible score across six dimensions is 12.
Dimension
What to Check
Score 0
Score 1
Score 2
Board independence and composition
Number and disclosed criteria of independent directors versus total board size, per the annual report and AGM notice
No independent director disclosed, or board dominated by promoter-family members with no stated independence criteria
At least one independent director present, meeting only the bare statutory minimum, with limited disclosure of selection criteria
Two or more independent directors, with disclosed selection rationale, and board composition that is not visibly dominated by a single family or promoter group
Related-party transaction transparency
Disclosure of related-party loans, guarantees, procurement, or leasing arrangements in notes to the financial statements
No related-party transactions disclosed despite known promoter-linked entities operating in the same sector, or disclosure limited to a vague boilerplate statement
Related-party transactions disclosed in aggregate figures only, without counterparty names or terms
Related-party transactions itemized by counterparty, nature, and terms, with evidence of board or audit committee review noted
Promoter shareholding trend
Promoter/promoter-group shareholding percentage across the last three to five years, from annual reports or NEPSE disclosures
Promoter shareholding has declined meaningfully over the period with no disclosed reason, or has fallen below a level that raises control-stability questions
Promoter shareholding roughly stable, with only minor fluctuations attributable to routine transactions
Promoter shareholding stable or rising, or any decline is clearly explained by a disclosed, credible corporate action (e.g., mandatory public offering dilution)
Disclosure timeliness and quality
Whether AGM is held within the statutory window, whether quarterly/annual results are filed on time with SEBON and NEPSE, and whether the annual report is published in a searchable, complete form
Chronic late AGMs, late or missing quarterly filings, or an annual report that omits standard sections (auditor's report, related-party notes, director remuneration)
Filings generally on time but with at least one significant lapse in the period reviewed, or an annual report that is complete but poorly organised
Consistent on-time AGMs and filings across the period reviewed, with a complete, well-structured annual report
Dividend and rights issue history
Pattern of dividend declarations (cash versus stock), rights issue pricing and timing, and treatment of minority shareholders in past capital-raising
History of capital raised at terms that appear to dilute minority shareholders unfavorably, or dividend policy that is erratic with no stated rationale
Dividend and capital-raising history is unremarkable but not clearly shareholder-friendly; rationale for stock-heavy dividends or rights pricing not well explained
Consistent, explained dividend policy and any rights issues priced and timed in a manner that treats minority shareholders even-handedly, with clear rationale disclosed
Audit quality and auditor tenure
Auditor's name, tenure length (from historical annual reports), and nature of the audit opinion (clean, qualified, emphasis of matter)
Qualified opinion, emphasis-of-matter paragraphs on material issues, or an auditor with an unusually long unbroken tenure and no disclosed rotation policy
Clean opinion, but auditor tenure is long with no visible rotation and the annual report is silent on the audit committee's role in auditor selection
Clean opinion, reasonable/rotating auditor tenure, and disclosed audit committee involvement in auditor appointment and review
Two design notes on this table matter for how it should actually be used. First, the related-party transaction dimension and the promoter shareholding trend dimension are not independent of each other in practice — a promoter quietly reducing their stake while related-party lending to promoter-linked entities increases is a specific, recognizable pattern, and an investor using this scorecard should read the two rows together, not just sum their scores mechanically. Second, the audit quality dimension deliberately treats an unusually long, unrotated auditor tenure as a caution flag even in the absence of a formal rotation mandate, because Nepal's Companies Act framework does not impose the kind of hard auditor-rotation ceiling found in some other jurisdictions — which means an auditor relationship can run for many years with no external circuit-breaker, and the burden of noticing that falls on the investor rather than the regulation.
CAUTION
A score of 2 on any single dimension means "no red flag observed in disclosed information" — it does not mean "independently verified as true." Nepal's disclosure regime does not require the kind of third-party assurance that would let a retail investor confirm a related-party transaction schedule is complete, only that it is present and itemized.
Once each dimension is scored, the six scores are summed into an overall grade band:
Total Score
Grade
Practical Meaning
10–12
A
Governance disclosure is strong and consistent across every checkable dimension; no material red flags observed
7–9
B
Governance is adequate with one or two specific weaknesses that should be monitored, not automatically disqualifying
4–6
C
Governance shows multiple weaknesses; position should be treated cautiously regardless of valuation
0–3
D
Governance shows serious, multi-dimensional red flags; default posture should be avoidance or minimal exposure
Lesson 23.4 — Worked Example: Scoring a Hypothetical Commercial Bank
Consider an illustrative, unnamed commercial bank — call it "Bank X" — of the kind commonly found in the NEPSE "A" category. The example is constructed to show how the scorecard is actually applied to a real annual report, not to describe any specific listed institution.
Board independence and composition. Bank X's annual report lists a nine-member board: five representing the promoter group, two representing public shareholders, one professional director appointed per NRB norms, and one designated as independent with a one-paragraph note on the criteria used (no conflicting business interest, no relative of a promoter). This clears the bare statutory minimum and discloses selection criteria for the independent seat, but the board remains promoter-dominated in composition. Score: 1.
Related-party transaction transparency. The notes to the financial statements disclose loans and facilities extended to entities where a director or promoter holds a substantial interest, itemized by counterparty name, outstanding balance, and interest rate, with a note that the audit committee reviewed the schedule. This is a meaningfully more detailed disclosure than the bare aggregate figure many smaller companies provide. Score: 2.
Promoter shareholding trend. Comparing NEPSE shareholding disclosures across the past four annual reports, the promoter group's stake has moved from 51 percent to 49 percent, entirely explained by a mandatory public share issuance requirement rather than any promoter sale. The decline is real but fully attributable to a disclosed, ordinary corporate action. Score: 2.
Disclosure timeliness and quality. The AGM was held within the statutory window in three of the last four years, with one year's AGM delayed by roughly two months with a publicly stated reason (delayed audit sign-off pending an NRB inspection query). Quarterly filings were consistently on time. Score: 1, reflecting the one lapse.
Dividend and rights issue history. The bank has paid a mix of cash and stock dividends over the review period, with a rights issue three years prior priced at par and timed to coincide with a capital adequacy requirement, disclosed clearly in the AGM notice with the rationale explained. No pattern of dilutive timing against minority shareholders is evident. Score: 2.
Audit quality and auditor tenure. The bank has used the same audit firm for the past six years, with clean opinions throughout, but the annual report does not describe any audit committee deliberation on whether to rotate the auditor. The tenure is long enough to warrant a note but the opinions themselves show no qualification. Score: 1.
Summing these: 1 + 2 + 2 + 1 + 2 + 1 = 9, placing Bank X at the top of the "B" band — adequate governance, with two specific, named items to monitor going forward (promoter-dominated board composition, and the absence of visible auditor-rotation deliberation), rather than either an unqualified pass or a disqualifying red flag. This is precisely the kind of nuanced, defensible output the scorecard is meant to produce: not a verdict of "good" or "bad," but a specific, dated record of where this bank's disclosed governance stands and which two items should be re-checked at the next annual report.
Lesson 23.5 — Using the Score in Practice
A governance score only earns its place in the process if it changes a decision. There are three distinct, non-exclusive ways to put it to work.
As a position-sizing input. A simple, defensible rule ties maximum position size to grade band: an "A" or "B" company is eligible for a full position sized on financial and valuation merits alone; a "C" company has its maximum position size capped — for instance, at half of what the financial analysis alone would justify — regardless of how attractive the valuation looks; a "D" company is excluded from new purchases entirely, with existing holdings reviewed for exit rather than added to. This converts governance from a vague qualifier into a hard constraint on portfolio construction, which is the only place governance judgment reliably survives contact with a tempting valuation.
As a red-flag screen at the point of first research. Before any DCF is built or any ratio is compared to sector peers, running the six-dimension score on a new name takes under an hour from public filings and can eliminate candidates before time is spent on deeper financial modelling. A stock that scores a 0 on related-party transparency and a 0 on promoter shareholding trend simultaneously — related lending rising while the promoter quietly exits — is a combination worth an automatic pass regardless of how cheap the multiple looks, because it is a classic precursor pattern to value destruction for minority shareholders.
As a factor in valuation discount or premium. For companies that clear the position-sizing bar but land in the "B" or "C" band rather than "A," a deliberate valuation discount is a more honest way to express governance concern than an outright exclusion. A bank trading at 1.1x book that would otherwise justify 1.3x book on pure return-on-equity and growth grounds, but whose governance score sits at a C, might reasonably be capped at a target of 1.0–1.1x book precisely because of the governance discount — the market's own skepticism about weak-governance names, reflected in a persistently lower multiple, is not a mispricing to arbitrage but a rational discount that a governance scorecard makes explicit and repeatable rather than something felt only vaguely.
PRACTICAL RULE
Set the position-sizing and exclusion thresholds before scoring the company, not after. A rule invented after seeing an attractive stock land in the "C" band is not a rule — it is a rationalisation.
The score should also be re-run on a fixed cadence — at minimum, once per year after the annual report is published, and again after any material rights issue, merger, or promoter transaction — rather than only when a scandal draws attention to a name. The entire value of the framework lies in it being applied evenly and on schedule, not selectively when suspicion is already aroused.
Lesson 23.6 — What This Scorecard Cannot Catch
Any DIY governance scoring system built for Nepal must be used with a clear-eyed view of its limits, and those limits trace directly back to the same enforcement and disclosure weaknesses noted in the World Bank's assessment of the framework: a scorecard built from disclosed information is only as reliable as the disclosure regime that produces it, and SEBON's limited enforcement capacity means non-compliant or superficial disclosure carries little practical consequence for the company producing it.
The scorecard cannot detect a related-party transaction that is simply never disclosed. Nepal's rules do not impose the kind of proactive, standardised related-party approval and disclosure regime found in more mature markets, so a promoter-linked entity that supplies a listed company, or borrows from a bank the promoter also controls, may not appear anywhere in the annual report if the company chooses a minimal reading of its obligations. A "2" score on this dimension means nothing troubling was found in the disclosure — it cannot mean nothing troubling exists.
The scorecard cannot see through nominee shareholding. A reported decline or stability in "promoter" shareholding is only as accurate as NEPSE's and the company's own classification of who counts as a promoter; shares held through family members, associated companies, or nominee arrangements that are not formally classified as promoter holdings can mask the real trend in controlling-family ownership, in either direction.
The scorecard cannot verify the substance behind a disclosure, only its presence. An independent director "meeting disclosed criteria" on paper may still be a long-standing family friend of the chairman in practice — something no public filing will state and no scorecard row can capture. Similarly, an audit committee "reviewing" a related-party schedule, as stated in a footnote, is not verifiable evidence that the review was substantive rather than a formality noted to satisfy the SEBON reporting template.
The scorecard is backward-looking by construction. It scores what has already been disclosed in a completed annual report, which means it will always lag a governance deterioration that is happening in real time — a related-party loan extended in the current fiscal year will not appear in the scorecard until the next annual report is published, potentially a year or more later.
The scorecard cannot substitute for financial forensic analysis. A company can score well on every governance dimension in this chapter while still carrying financial red flags — aggressive revenue recognition, understated provisioning, or asset quality issues — that belong to the financial-statement analysis covered elsewhere in this book, not to governance scoring. The two lenses are complementary, not interchangeable; a high governance score is not a certificate of financial health.
Finally, the scorecard is only as good as the discipline applied in filling it out. Its greatest practical risk is not a flaw in the framework itself but the temptation to score generously a company the investor already wants to own, and harshly one they have already decided to avoid — the exact bias in Lesson 23.1 that the scorecard was built to correct. Guarding against that requires scoring before forming a view on valuation, not after, and revisiting old scores honestly when new annual reports arrive rather than only when a new scandal makes the exercise unavoidable.
CAUTION
Treat every score of 2 as "no red flag found in available disclosure," not as "confirmed clean." In a market where SEBON's enforcement is persuasion-based rather than punitive, the absence of a disclosed problem is meaningfully weaker evidence than it would be in a market with binding audit and disclosure enforcement.
Chapter recap
A structured governance scorecard replaces inconsistent, recency-biased, non-actionable gut judgment with a repeatable process that can be applied evenly across every NEPSE-listed name, sized into portfolio decisions, and revisited with an honest paper trail. No official Nepal-specific company governance rating system currently exists; this chapter's six-dimension framework — board independence, related-party transparency, promoter shareholding trend, disclosure timeliness, dividend and rights issue history, and audit quality/tenure — is an original practical tool built specifically from what SEBON's disclosure requirements and NEPSE's filings actually make available to a retail investor, not an adaptation of any official standard. Each dimension is scored 0–2 from public annual reports, AGM notices, and NEPSE/SEBON filings, summed into a 0–12 total, and mapped to an A–D grade band that has direct portfolio meaning. The worked example showed that scoring rarely produces a clean verdict — most real companies land in the "adequate with named weaknesses" band, and the value of the exercise is in naming those weaknesses precisely rather than forcing a binary pass/fail. In practice, the score should drive position-sizing caps, serve as an early red-flag screen before deeper financial work begins, and inform a deliberate valuation discount or premium rather than being treated as a side note to the investment case. Its central limitation is that it can only score what is disclosed, in a market where disclosure obligations remain thinner and enforcement weaker than in more mature exchanges — so a strong score is evidence of nothing troubling found, never proof that nothing troubling exists, and it must always be paired with the financial forensic discipline covered elsewhere in this book, not used as a substitute for it.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Volume 2: ANALYSIS
Part V
FINANCIAL STATEMENTS & REPORTING STANDARDS
Part V · Chapter 24
Reading Financial Statements Under Nepal Standards
First published 22 Aug 2026 · Last verified 29 Aug 2026
A Nepali investor who has spent a year or two in the market eventually discovers an uncomfortable truth: the financial statements published by NEPSE-listed companies are not simply "IFRS with a Nepali flag on them." They are the product of at least three overlapping rulebooks — the Nepal Financial Reporting Standards issued by the Institute of Chartered Accountants of Nepal, the Companies Act's own disclosure architecture, and, for the roughly two-thirds of listed market capitalisation that sits in banks, development banks, finance companies, microfinance institutions, and insurers, a parallel and sometimes overriding layer of Nepal Rastra Bank directives and Beema Samiti (Nepal Insurance Authority) regulations. Add to this the Securities Board of Nepal's disclosure regime for anything traded on an exchange, and you have a reporting environment where the numbers on the first page of an annual report are the end product of a negotiation between global standard-setting logic and local regulatory prudence — not a pure, mechanical application of "international best practice." This chapter opens Volume II's treatment of financial statements because everything that follows — ratio analysis, valuation, sector comparison — is only as reliable as your understanding of what these statements actually measure, what discretion company management and auditors exercised in arriving at them, and where the regulator has already overridden the accountant's preferred treatment. Skipping this chapter and moving straight to price-to-book ratios is the single most common way retail investors in Nepal mis-price a bank or a hydropower company: they treat a NEPSE filing as if it were a Bloomberg terminal output, when in fact it is a document you must first learn to read in its own idiom.
Lesson 24.1 — The Architecture of Nepali Financial Reporting: ICAN, NFRS, and the Standards Hierarchy
The Institute of Chartered Accountants of Nepal (ICAN), established under the Chartered Accountants Act, 1997 (2053 B.S.), is the statutory body that licenses chartered accountants, regulates the audit profession, and — through its Accounting Standards Board (ASB) — sets the accounting standards that Nepali companies must follow. Since the mid-2000s, ICAN's stated strategy has been convergence with International Financial Reporting Standards (IFRS) rather than wholesale adoption without modification, meaning Nepal writes its own standards, numbered and named in parallel with their IFRS counterparts, but reserves the right to carve out provisions it judges unsuitable for the Nepali economic environment, or to phase in implementation dates that lag the international timetable. The output of this process is the Nepal Financial Reporting Standards (NFRS), pronounced in their most recent comprehensive form as NFRS 2018 by the Nepal Chartered Accountants Council in mid-2020, with staggered effective dates: most standards became mandatory from the fiscal year beginning mid-July 2020 (Shrawan 2077), but the two most consequential standards for financial-sector entities — NFRS 9 (Financial Instruments) and NFRS 15 (Revenue from Contracts with Customers) — were deferred a further year to Shrawan 2078 (July 2021), and NFRS 17 (Insurance Contracts) was deferred all the way to Shrawan 2080 (July 2023) to give insurers time to build the actuarial and systems infrastructure the standard demands.
It is worth being precise about vocabulary here, because the book you are reading will use these terms constantly and conflating them causes real confusion in practice.
KEY CONCEPT
The Nepali standards hierarchy has three tiers. First, the Nepal Financial Reporting Standards (NFRS) proper — a set of roughly seventeen standards mirroring IFRS 1 through IFRS 17, covering business combinations, financial instruments, revenue, leases, and insurance contracts. Second, the Nepal Accounting Standards (NAS) — roughly twenty-four standards mirroring IAS 1 through IAS 41, covering presentation of financial statements, inventories, property, plant and equipment, income taxes, employee benefits, and related-party disclosures. Third, Interpretations — Nepali adaptations of IFRIC and SIC interpretations that resolve ambiguous or contested points of application. When this book refers to "NFRS" in the broad sense, as market participants in Kathmandu do in ordinary speech, it means this entire three-tier body of pronouncements, not merely the seventeen standards that carry the NFRS numbering.
A second structural feature that a retail investor must internalize is that NFRS does not apply uniformly to every registered entity in Nepal. ICAN's applicability framework tiers reporting entities by public accountability and size, broadly distinguishing publicly accountable entities — listed companies, banks and financial institutions, insurers, and other entities that hold assets in a fiduciary capacity for a broad group of outsiders — from other entities that qualify for a simplified NFRS for Small and Medium Entities (NFRS for SMEs), itself a scaled-down convergence with the IFRS for SMEs standard. Every company you will ever consider buying on NEPSE falls into the first, full-NFRS category, because listing itself is treated as a marker of public accountability. But this matters when you read comparative disclosures involving unlisted subsidiaries, joint ventures, or promoter-held sister companies referenced in related-party notes: those entities may report under an entirely different — and less rigorous — standard than the listed parent whose consolidated statements you are analysing.
The practical consequence of Nepal's convergence-rather-than-adoption approach is a persistent lag and a persistent set of local carve-outs relative to whatever IFRS looks like in London or Singapore at any given moment. The International Accounting Standards Board continues to issue amendments, new standards, and interpretive guidance every year; ICAN's Accounting Standards Board reviews and eventually converges Nepal's standards to match, but the review, exposure-draft, and pronouncement cycle in Nepal routinely runs two to five years behind the IASB's own timetable, and in some areas — most importantly for this book, in how banks and financial institutions are permitted to measure credit losses — a purely NFRS-based treatment is displaced entirely by a competing NRB directive that is intentionally more conservative than what NFRS 9 alone would produce. We turn to that displacement next, because it is the single most important reporting fact for anyone analysing the roughly forty percent of NEPSE's float that sits in commercial banks, development banks, and finance companies.
Lesson 24.2 — Where NFRS Bends: NRB Directives and the Banking Sector's Parallel Rulebook
Nepal Rastra Bank (NRB), acting under powers granted by the Nepal Rastra Bank Act, 2058 and the Bank and Financial Institutions Act (BAFIA), issues a consolidated set of Unified Directives to all classes of licensed banks and financial institutions (BFIs) — Class A commercial banks, Class B development banks, Class C finance companies, and, through parallel directives, Class D microfinance institutions. These directives cover capital adequacy, single-obligor lending limits, corporate governance, liquidity, interest rate conduct, and — most consequentially for financial statement analysis — asset classification and loan loss provisioning. Crucially, NRB does not simply endorse NFRS and step back. Where NRB judges that a strict NFRS-based measurement would understate risk in the Nepali banking system — a system still working through legacy asset-quality problems, thin collateral markets, and comparatively immature credit bureaus — it imposes a parallel, more conservative measurement regime, and requires banks to hold the higher of the two results.
The clearest illustration is credit loss provisioning. NFRS 9 requires banks to measure expected credit losses (ECL) using a forward-looking, probability-weighted model that stages loans into three buckets based on the deterioration in credit risk since origination. NRB has issued its own NFRS 9-aligned ECL guideline that looks superficially similar but imposes specific, more mechanical thresholds that leave far less room for bank-specific modelling judgment than the pure IFRS 9 approach does elsewhere in the world. Under the current NRB framework, a loan is Stage 1 if payments are current or overdue by no more than one month; Stage 2 if overdue for more than one month but not exceeding three months; and Stage 3 — the non-performing, lifetime-expected-loss bucket — once overdue beyond three months, with an additional rule that a loan already downgraded to Stage 3 cannot be upgraded back to a lower-risk stage until it has completed a minimum monitoring period of good conduct following full regularization. NRB also constrains how banks compute loss-given-default: banks must use their own historical recovery experience where reliable data exists; failing that, they must apply valuation-based recovery estimates net of a prescribed haircut on collateral fair value (commonly a haircut in the order of 25 percent to arrive at net realizable value, with collateral that has remained unrealized for an extended number of years — commonly five — excluded from recovery calculations altogether); and failing even that, banks must apply a prudential floor loss-given-default (commonly around 45 percent) with board-level sign-off.
REGULATORY DETAIL
NRB revises its NFRS 9 / ECL implementation guideline periodically, and the specific thresholds — the overdue-day cutoffs between stages, the collateral haircut percentage, the minimum monitoring period before a Stage 3 loan can be upgraded, and the floor loss-given-default assumption — are exactly the kind of detail that changes between amendments. Do not treat the numbers cited in this chapter as permanently fixed; treat them as illustrative of the type of mechanical, rules-based overlay NRB imposes on top of NFRS 9's more principles-based framework, and always check the bank's own significant-accounting-policies note, which is required to disclose the specific provisioning basis actually applied in that reporting period.
The mechanism through which this NFRS-versus-NRB tension resolves onto a bank's balance sheet is the regulatory reserve, and it is one of the most important line items a Nepali bank-stock investor must learn to read. Whenever the loan loss provision NRB's directive requires is higher than the impairment loss NFRS 9's own expected-credit-loss model would otherwise produce, the bank cannot simply understate its provision to flatter reported profit; it must book the higher, NRB-mandated provision as an expense, which correctly reduces net profit for the period. But the reverse asymmetry is where the regulatory reserve does its work: to the extent that NFRS-based measurement — through fair value gains, actuarial gains on defined-benefit obligations, deferred tax assets, or other non-cash, non-distributable items recognised in profit — would otherwise inflate reported profit and free cash for dividend distribution beyond what NRB considers prudently realised, the bank is required to transfer the corresponding amount out of retained earnings into a non-distributable regulatory reserve within equity. The practical effect is that a bank's headline net profit and its distributable profit are two different numbers, and the gap between them — visible in the statement of changes in equity as a movement into or out of the regulatory reserve — tells you how much of reported earnings is accounting recognition rather than realised, distributable cash-generating performance.
WATCH FOR
Two numbers on a bank's results that look similar but mean very different things: "profit for the year" (the NFRS bottom line, after tax) and "distributable profit" or "free profit" (what remains available for dividend and bonus share distribution after the regulatory reserve transfer, minority interest adjustments, and any statutory general reserve appropriation under the Companies Act and BAFIA, which typically requires a fixed percentage of profit — commonly around 20 percent for BFIs — to be transferred to a general reserve each year until that reserve reaches a multiple of paid-up capital). A bank can report strong NFRS profit growth while its distributable profit, and therefore its capacity to sustain the dividend investors are pricing in, grows far more slowly, or not at all.
Two further Nepal-specific practices sit alongside the regulatory reserve and are essential vocabulary for reading a BFI's notes. First, interest suspense: once a loan is classified as non-performing under NRB's asset classification rules, accrued interest on that loan is not recognised as interest income in the profit and loss statement even though NFRS's effective-interest-method logic might otherwise support partial accrual on a net, credit-adjusted basis; instead, that interest is parked in an interest suspense account off the income statement until actually collected in cash. Second, employee bonus: under the Bonus Act, 2030, Nepali companies — banks very much included — are required to set aside a statutory percentage of pre-bonus, pre-tax profit (a figure commonly cited at around 10 percent, subject to caps under subsequent amendments) for employee bonus distribution, which is expensed before arriving at profit before tax and materially affects any attempt to compare a Nepali bank's cost-to-income or pre-provision operating profit against a purely NFRS or IFRS-based international peer.
Finally, NRB does not merely dictate measurement; it dictates presentation. Banks and financial institutions are required to prepare and publish their financial statements in a standardised format prescribed by NRB circular, which fixes the line items, ordering, and minimum disclosure content of the statement of financial position, statement of profit or loss, and the accompanying schedules — including mandatory disclosure of capital adequacy computation, non-performing loan ratios, liquidity ratios, and other prudential metrics that a pure NFRS-only presentation would not necessarily require in that exact form. This is why every commercial bank's annual report looks structurally identical to every other commercial bank's annual report in Nepal, in a way that, say, two hydropower companies' annual reports do not: NFRS governs measurement, but NRB governs the template.
Lesson 24.3 — SEBON, the Companies Act, and the Disclosure Regime for Listed Issuers
If NRB is the sector regulator that reaches into measurement for BFIs, the Securities Board of Nepal (SEBON) is the market regulator whose mandate — under the Securities Act, 2063 and its subordinate regulations, most notably the Securities Registration and Issuance Regulation and the Corporate Governance Directive applicable to listed companies — is disclosure, timeliness, and investor protection rather than accounting measurement itself. SEBON does not write accounting standards; it enforces that listed companies actually comply with NFRS as issued by ICAN, that audits are conducted by ICAN-licensed auditors in good standing, and that the resulting statements reach the investing public through NEPSE's disclosure system on a schedule SEBON itself prescribes.
The core disclosure obligations SEBON imposes on every NEPSE-listed company fall into three time-bound categories. Quarterly financial disclosure requires listed companies to publish unaudited financial statements — statement of financial position, profit or loss, and a set of prescribed ratios — within a fixed window after each quarter's close, commonly cited as thirty days, through NEPSE's online disclosure portal, which is how the market receives its first look at a company's performance roughly four times a year long before the audited annual report appears. Annual financial disclosure requires the audited annual report, complete with the auditor's report, financial statements, notes, and the directors' report mandated under the Companies Act, 2063, to be published and an annual general meeting (AGM) convened, ordinarily within six months of the fiscal year-end (mid-Ashadh, or mid-July), a deadline that can be and often is extended with regulatory permission, particularly for larger financial institutions whose group-level consolidation and NRB-mandated disclosures take longer to finalise. Material event disclosure requires companies to notify NEPSE and SEBON promptly — not on the normal quarterly or annual cycle — of price-sensitive developments: board decisions on dividend or bonus share proposals, mergers and acquisitions, credit rating changes, related-party transactions above materiality thresholds, litigation with material financial exposure, and management changes at the CEO or CFO level, among others.
CASE IN POINT
Consider how a typical NEPSE-listed commercial bank's dividend announcement actually reaches the market. The board first proposes a dividend (cash, bonus shares, or a combination) based on distributable profit calculated after all NRB-mandated regulatory reserve transfers and statutory reserve appropriations — this is disclosed as a price-sensitive event and often moves the stock immediately. That proposal then requires NRB's separate approval before it can be implemented, because NRB independently assesses whether the bank's capital adequacy ratio, non-performing loan trend, and other prudential indicators can sustain the proposed distribution without impairing capital buffers. Only after NRB approval and shareholder ratification at the AGM does the dividend actually get booked and paid. An investor who reacts to the board's proposed dividend as if it were guaranteed, without weighing the possibility of an NRB-mandated reduction, misprices the stock in the weeks between proposal and final approval — a recurring, structurally embedded source of short-term volatility unique to bank shares on NEPSE.
SEBON's Corporate Governance Directive layers additional requirements onto the annual report beyond bare NFRS compliance: disclosure of promoter and public shareholding structure, related-party transactions and the independence status of board members, the composition and functioning of the audit committee, risk management committee reporting (for BFIs, this dovetails with NRB's own risk governance directives), and increasingly, though still unevenly across the market, disclosures touching environmental, social, and governance practice. SEBON also requires that the statutory auditor be rotated periodically and that the audit be conducted by a firm meeting ICAN's eligibility criteria for listed-company audits, and it retains the power to direct a special audit of any listed company where it has reason to doubt the reliability of published statements — a power it has exercised historically against companies, including BFIs, where asset quality or related-party lending raised supervisory concern.
WARNING
SEBON's disclosure timeline enforces speed, not depth. A company that is late is penalised; a company that discloses the statutorily minimum content on time, even where the notes are thin relative to what NFRS technically requires, more often escapes scrutiny simply because the market's attention has moved on to the next quarter's numbers by the time anyone reconciles the previous quarter's disclosure gaps. Do not equate "filed on time with NEPSE" with "fully compliant with NFRS's disclosure requirements." These are different tests, enforced by different bodies, on different timetables.
Lesson 24.4 — Anatomy of the Annual Report: What's Really in a NEPSE Filing
A NEPSE-listed company's annual report is not a single document with a single register; it is a composite of at least six distinct sections, each written to satisfy a different regulatory audience, and an investor who reads only the financial statements proper is discarding roughly half of the substantive information the filing contains.
The directors' report (or "Board of Directors' Report") opens the document and is mandated under the Companies Act. It is management's own narrative of the year — operational highlights, a review of the business environment, a summary of financial performance, the dividend proposal, and disclosures the Companies Act specifically requires, such as the number of board and committee meetings held, director remuneration, and a statement on the company's compliance with applicable laws. This section carries real information content, particularly its discussion of operational metrics that never appear in the financial statements themselves — for a bank, branch expansion, deposit mobilization strategy, or digital banking initiatives; for a hydropower company, plant load factor and generation volume against design capacity; for a manufacturing company, capacity utilisation. It is also, unavoidably, the section most shaped by public-relations instinct, so its narrative claims should be weighed against, never substituted for, the hard numbers that follow.
The independent auditor's report follows, and its structure is itself informative. Since NFRS's convergence with the international audit-reporting model, Nepali auditors are required to state a clear opinion — unqualified (clean), qualified, adverse, or a disclaimer of opinion — and, for listed-entity audits of any complexity, to identify Key Audit Matters: the specific areas of the financial statements that involved the most significant auditor judgment, which for a bank routinely include expected credit loss estimation, valuation of investment properties or non-banking assets acquired through loan recovery, and IT-systems-dependent revenue or interest income recognition. A qualified opinion, an emphasis-of-matter paragraph, or a Key Audit Matter flagging estimation uncertainty in loan loss provisioning is not boilerplate; it is the auditor telling you, in a formalised and legally consequential register, exactly where the numbers you are about to read rest on judgment rather than fact.
PRACTICAL TOOL
Before reading a single ratio, run this five-point check on any NEPSE annual report. One: read the audit opinion paragraph in full — is it unqualified, or does it carry a qualification, emphasis of matter, or disclaimer? Two: scan the Key Audit Matters section for anything relating to impairment, valuation, or related-party transactions. Three: check the statement of changes in equity for a regulatory reserve movement (banks and financial institutions only) and compute the gap between reported profit and distributable profit. Four: read the related-party transactions note in full and cross-reference any counterparty against the promoter shareholding disclosure — significant, undisclosed economic linkage between "independent" transacting parties is the single most common vehicle for earnings manipulation on NEPSE. Five: compare the current year's significant accounting policies note against the prior year's, word for word if necessary, for any silent change in estimation methodology (a change in depreciation method, a change in the expected-credit-loss model's macroeconomic overlay, a change in actuarial assumptions) that was not flagged as prominently in the directors' report as it should have been.
The core financial statements — statement of financial position, statement of profit or loss and other comprehensive income, statement of changes in equity, and statement of cash flows — follow the auditor's report, prepared on a comparative basis against the prior year and, where the company has subsidiaries, presented on both a standalone and a consolidated basis. For a bank, the standalone-versus-consolidated distinction matters because subsidiaries commonly include merchant banking arms, and increasingly, insurance or capital-market subsidiaries whose own risk profile differs materially from the parent bank's core lending business; an investor valuing "the bank" on the basis of consolidated numbers is implicitly also taking a view on the subsidiary.
The notes to the financial statements are where NFRS's disclosure requirements are heaviest, and where the density of a Nepali annual report genuinely rewards careful reading: significant accounting policies (the specific choices the company made within the range NFRS permits — for instance, which model it uses for expected credit loss inputs, or how it recognises revenue from long-term construction or power purchase agreements under NFRS 15); segment reporting (particularly relevant for diversified conglomerates cross-listed with financial subsidiaries); financial instrument disclosures modelled on NFRS 7 covering credit risk, liquidity risk, market risk, and interest rate risk exposure — for a bank, this is where the maturity-gap analysis and the interest-rate-sensitivity tables that genuinely matter to a bond-like equity valuation of the bank actually live; related-party transactions, disclosing loans to, deposits from, and other dealings with directors, key management personnel, and companies under common promoter control; capital commitments and contingent liabilities, an item of particular importance for hydropower and infrastructure companies mid-construction, and for banks in the form of letters of credit and bank guarantees issued; and, for BFIs specifically, the capital adequacy disclosure required under NRB's Basel-aligned capital framework, breaking down core (Tier 1) and supplementary (Tier 2) capital against risk-weighted assets.
Finally, the corporate governance and shareholding disclosure section, driven by SEBON's Corporate Governance Directive, closes out the substantive content: board composition and independence, committee structures, promoter-versus-public shareholding percentages, and the top shareholder list, which for many NEPSE companies is the fastest way to identify the handful of related parties whose transactions the notes above disclosed only in the aggregate.
Lesson 24.5 — Quarterly Reports: Speed Over Depth, and Where the Gaps Hide
The quarterly report is the instrument through which most active NEPSE participants actually track a company, simply because it arrives roughly four times more often than the annual report and, for BFIs especially, is published in a standardised, comparable format across the entire sector. But the quarterly report is deliberately a lighter-weight document than the annual report, and understanding exactly what has been traded away for that speed is essential to not over-reading it.
Nepali quarterly disclosures for banks and financial institutions follow a template that presents, at minimum: a condensed statement of financial position and statement of profit or loss on a comparative basis (current quarter versus same quarter prior year, and year-to-date current period versus year-to-date prior period); and a standard set of ratios that SEBON and NRB jointly expect to see in every quarterly filing — capital fund to risk-weighted assets (the capital adequacy ratio), non-performing loan to total loan ratio, net profit or loss per share (annualized), price-earnings ratio computed off the prevailing NEPSE market price, net worth per share (book value), liquidity indicators, and the interest rate spread between the average lending rate and the average deposit rate. This ratio panel is, for most retail investors, the entire substance of what they read in a quarterly report, and it is genuinely useful precisely because it is standardised across every bank on the exchange — but it is also unaudited, prepared under time pressure, and does not carry the notes disclosure, the related-party detail, or the auditor's scrutiny that the annual filing carries.
CAUTION
Unaudited quarterly numbers for BFIs are management's own computation of loan classification and provisioning, made under the same NRB rules as the annual figures but without the independent auditor's testing of that classification. It is not unusual, and is not by itself a red flag, for the fourth-quarter (year-end) figures to show a step-change in provisioning, non-performing loan recognition, or even profit relative to the trend implied by the first three quarters, once the annual audit has run its full procedures — including asset quality reviews, valuation testing, and NRB's own on-site supervisory findings feeding into year-end adjustments. Treat quarter-on-quarter trend lines as directionally informative, but reserve final judgment on asset quality and provisioning adequacy for the audited annual figures, and be specifically alert whenever the audited fourth quarter, computed by subtracting the sum of the first three unaudited quarters from the unaudited full-year figure, diverges sharply from what the first three quarters implied.
Non-financial-sector companies — manufacturing, hydropower, hotels, trading houses — file quarterly reports in a less rigidly standardised format than BFIs, though SEBON's minimum disclosure requirements still apply. For these companies, the analytically important gap between quarterly and annual reporting tends to centre on related-party transactions, contingent liabilities, and detailed segment or project-level disclosure, none of which the quarterly template requires in any depth; a hydropower company's quarterly filing will show revenue and profit, but the notes explaining tariff structure, take-or-pay arrangements with the offtaker, or the status of insurance claims following flood or landslide damage will not appear until the annual report, if at all.
Lesson 24.6 — The Convergence Gap: Where Nepal Still Diverges from Full IFRS, and Why It Matters to You
It is tempting, given how closely NFRS mirrors IFRS in its numbering and structure, to treat Nepali financial statements as functionally equivalent to those of an internationally listed peer and to apply cross-border valuation benchmarks — price-to-book multiples for banks, EV/EBITDA multiples for industrials — without adjustment. This is a mistake, and the gap between NFRS-as-written and IFRS-as-practiced internationally shows up in at least four recurring places that a serious NEPSE analyst must track.
First, the timing lag itself. Because ICAN converges to a given vintage of IFRS rather than adopting IFRS's rolling, continuously amended text, Nepali statements at any point in time reflect an IFRS baseline that is already several years old, and any subsequent IASB amendment, annual improvement, or new standard is absent from Nepali practice until ICAN's own standard-setting cycle catches up — a cycle historically measured in years rather than months. An investor benchmarking a Nepali bank's NFRS 9 disclosures against a regional peer reporting under the latest IFRS 9 amendments should expect structural, not just numerical, differences in how each entity's disclosures are organised.
Second, the NRB overlay discussed at length in Lesson 24.2 is, in substance, Nepal's most significant departure from a pure NFRS or IFRS reading of bank financial statements, and it has no precise equivalent in most other IFRS jurisdictions at this level of prescriptive detail. Some form of prudential filter on IFRS 9 outputs exists in many banking systems — through Pillar 2 supervisory add-ons, or through regulatory capital deductions for expected-loss shortfalls — but NRB's approach of routing the gap directly through a named regulatory reserve within equity, and of prescribing mechanical stage-transition day-count rules that leave limited room for a bank's own statistically modelled probability of default, is a distinctly Nepali solution to a Nepali problem: a banking system where credit bureau data, collateral markets, and historical loss experience are not yet deep enough to support the kind of internally modelled ECL approach a Basel-advanced bank in a more developed market would use.
Third, disclosure depth in practice — as distinct from disclosure requirement on paper — continues to lag what the standards technically demand, a gap widely acknowledged within Nepal's own accounting profession and periodically the subject of ICAN quality-review findings and IFAC member-body assessments of Nepal's compliance with international Statements of Membership Obligations. Fair value disclosures for level 2 and level 3 financial instruments, sensitivity analysis for actuarial and market-risk assumptions, and full reconciliation of expected-credit-loss stage migrations are all NFRS/NFRS-9 requirements that appear in Nepali annual reports with varying degrees of completeness, and an investor should not assume that the mere presence of a note heading guarantees the note's substance matches what an internationally listed bank's equivalent note would contain.
Fourth, sectoral standards with limited Nepali precedent continue to be applied unevenly. NFRS 17 (Insurance Contracts) only became mandatory for Nepali insurers from mid-2023, meaning multi-year historical comparability across that transition is still being built; NFRS for agriculture-linked or biological-asset-holding companies, and fair value measurement for illiquid, thinly traded unlisted investments that many Nepali companies (including BFIs' own investment portfolios) hold, both rely on valuation inputs — discount rates, comparable transactions, illiquidity discounts — that are inherently more judgment-laden in a market as thin as Nepal's than in a market with deep, continuously priced benchmarks.
NFRS Standard / Regime
IFRS Equivalent
Nepal Effective Date
Practical Note for NEPSE Investors
NAS (pre-2018 vintage)
Older IAS suite
Effective from roughly 2013 pronouncement
Superseded standards; still relevant when reading pre-2020/21 comparative figures in long-run trend analysis
NFRS 2018 (general suite)
IFRS as at 1 January 2018
Mandatory from FY beginning mid-July 2020
Covers most standards: presentation, PP&E, leases (NFRS 16), income taxes, employee benefits
NFRS 9 (Financial Instruments)
IFRS 9
Deferred to FY beginning mid-July 2021
Governs ECL for banks; immediately overlaid by NRB's own ECL guideline described in Lesson 24.2
NFRS 15 (Revenue from Contracts with Customers)
IFRS 15
Deferred to FY beginning mid-July 2021
Affects revenue timing for construction, power purchase agreements, and long-term service contracts
NFRS 17 (Insurance Contracts)
IFRS 17
Deferred to FY beginning mid-July 2023
Multi-year comparability for insurers still developing; watch transition-period restatements
Overrides NFRS 9 measurement where NRB's rules are more conservative; drives the regulatory reserve mechanism
This convergence gap is not a reason to distrust NFRS-based statements wholesale; ICAN's audit and standard-setting framework is a serious, functioning professional infrastructure, and the large majority of NEPSE-listed companies, particularly the commercial banks and larger development banks under closest NRB and SEBON supervision, produce statements that are broadly reliable within the limits this chapter has described. The gap is, instead, a reason to read every Nepali financial statement with an explicit mental checklist of where local practice departs from a naive "IFRS is IFRS everywhere" assumption, and to weight qualitative signals — audit opinion language, Key Audit Matters, regulatory reserve movements, and the consistency of accounting policy notes year over year — as seriously as the headline ratios those statements ultimately produce. The remaining chapters of Part V build directly on this foundation: the next chapter turns to how these statements interlock — how the statement of financial position, profit or loss, changes in equity, and cash flows must reconcile with one another — before Volume II moves on to the ratio and valuation techniques that depend entirely on having read this chapter's warnings correctly.
Chapter recap
Nepali financial reporting operates through three interlocking authorities rather than one: ICAN sets the accounting standards (NFRS, NAS, and Interpretations) that determine measurement, SEBON enforces disclosure timeliness and governance for listed companies, and — for the banking, development bank, finance company, and insurance sectors that dominate NEPSE's market capitalisation — NRB and the Nepal Insurance Authority layer their own, often more conservative, prudential rules on top of NFRS, and those rules take precedence in practice wherever the two conflict.
NFRS itself is a convergence project, not a mirror of IFRS; it lags the IASB's own timetable by design, was most recently pronounced comprehensively in 2018 with staggered effective dates running from 2020 through 2023 for its most complex standards, and applies in full only to publicly accountable entities, a category that automatically includes every company listed on NEPSE.
For banks and financial institutions, NRB's directive-based expected-credit-loss and asset-classification rules — mechanical overdue-day thresholds, prescribed collateral haircuts, and floor loss-given-default assumptions — routinely produce a different, usually higher, provisioning outcome than a pure NFRS 9 model would, and the resulting gap is channeled through the regulatory reserve, meaning a bank's headline profit and its actual distributable profit are two different, both legitimate, numbers that every bank-stock investor must learn to distinguish before pricing a dividend.
A NEPSE annual report is a composite document whose directors' report, audit opinion and Key Audit Matters, core financial statements, extensive notes, and corporate governance disclosures each carry distinct and complementary information; reading only the statement of profit or loss discards the auditor's own signal about where the numbers rest on judgment and the related-party detail that most often explains anomalous performance.
Quarterly reports trade depth for speed and arrive unaudited, standardised around a core ratio panel — capital adequacy, non-performing loan ratio, earnings and book value per share, and price-earnings multiples for BFIs — that is genuinely useful for trend-tracking but should never substitute for the audited annual figures when final judgment on asset quality, provisioning adequacy, or dividend sustainability is required.
Because Nepal's convergence with IFRS carries a structural time lag, an NRB-specific prudential overlay with no precise international equivalent, and uneven disclosure depth relative to what the standards technically demand, cross-border valuation benchmarks and peer comparisons must be adjusted for these local realities rather than applied as though a NEPSE-listed bank or industrial company reported under identical rules to an internationally listed counterpart.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part V · Chapter 25
The Balance Sheet — Deep Reading
First published 22 Aug 2026 · Last verified 29 Aug 2026
The balance sheet of a Nepal Stock Exchange-listed company is not a photograph of what the company owns; it is a claim, made once every quarter and audited once a year, about what a business is worth and who has first call on it. Most Nepali retail investors read the profit and loss account because it tells a story — revenue grew, profit rose, everyone is happy — while the balance sheet sits quietly at the back of the annual report, dense with line items that look identical from one bank to the next. That instinct is backwards. The income statement tells you what happened over a period; the balance sheet tells you what a company actually is, on the specific date the accountants stopped counting. A hydropower company can report a profitable quarter while its balance sheet quietly discloses that receivables from the Nepal Electricity Authority have stretched to eight months and working capital is being funded by short-term debt at 13 percent. A microfinance institution can report double-digit loan growth while its balance sheet shows loan loss provisions that have not kept pace with the deterioration in its book. A trading company can show rising profit while its "other assets" line — the graveyard of Nepali balance sheets — swells year after year with related-party dues that will never be collected. In each case, the profit and loss account lies by omission, and the balance sheet, read carefully, tells the truth. This chapter is about learning to read it that way — not as a formality to skim before checking the EPS, but as the primary document of financial analysis for NEPSE-listed companies, in a market where regulatory format, promoter behaviour, and disclosure quality differ meaningfully from the textbook cases most investing books are written around.
Nepal complicates the balance sheet in three specific ways that a generic investing book will not prepare you for. First, roughly half of NEPSE's free-float market capitalisation sits in banks, development banks, finance companies, microfinance institutions, and insurers — entities whose balance sheets are not built on the standard "current assets, non-current assets, current liabilities, non-current liabilities" template taught in commerce colleges, but on a liquidity-ordered format mandated by Nepal Rastra Bank (NRB) and the Insurance Authority, with disclosure schedules that exist because regulators, not just investors, need to see solvency at a glance. Second, Nepal's adoption of Nepal Financial Reporting Standards (NFRS) — which track International Financial Reporting Standards with a lag and local carve-outs — created a multi-year transition in which items like revaluation reserves, actuarial gains on retirement obligations, and fair value adjustments entered Nepali balance sheets in ways that are easy to misread as organic equity growth. Third, ownership structure in Nepal — the promoter-versus-public shareholding split, cross-holdings between group companies, and the practice of routing related-party transactions through subsidiaries and sister concerns — creates balance sheet risks that are less prominent in markets with more dispersed, arm's-length ownership. An investor who applies a Western textbook checklist to a NEPSE balance sheet without adjusting for these three realities will miss most of what the balance sheet is trying to tell them.
Lesson 25.1 — Why the Balance Sheet Is the Senior Document
Every company you will ever evaluate on NEPSE is, at its core, a claim structure. Assets are what the company controls; liabilities are what it owes to creditors, depositors, and policyholders, who are paid first and paid in full regardless of how the business performs; equity is the residual — what is left for shareholders after every other claimant has been satisfied. This ordering is not accounting trivia. It is the reason a bank with an 18 percent return on equity can still be a poor investment if its capital adequacy ratio is drifting toward the regulatory floor, and the reason a manufacturing company with flat profit can be a better investment than a fast-growing peer if its balance sheet carries a fraction of the debt.
KEY CONCEPT
The balance sheet answers one question that the profit and loss account cannot: if the company had to stop today, in what order would people get paid, and would there be anything left for you? Assets minus liabilities equals equity — but the quality of that equity depends entirely on the quality of the assets behind it, not the number itself.
The reason this matters more in Nepal than in a mature market is leverage. NEPSE's largest sector by both count and weight is banking and financial institutions (BFIs) — commercial banks, development banks, finance companies, and microfinance institutions — and every one of them operates a balance sheet that is, by design, ten to twelve times levered against equity. A commercial bank with paid-up capital and reserves of NPR 15 billion routinely carries a balance sheet of NPR 150–200 billion, funded overwhelmingly by depositors' money. This is not a warning sign in itself — deposit-taking and lending is the business model — but it means that a small deterioration in asset quality (a rise in non-performing loans, a shortfall in loan loss provisioning) erodes equity at a multiple of the rate it would in an unlevered business. A 2 percent impairment on a loan book funded eleven times over by deposits can wipe out a meaningful share of shareholder equity. This is precisely why NRB regulates BFI balance sheets more tightly than the Office of the Company Registrar regulates a manufacturing company's, and why the investor's first analytical instinct with any bank, development bank, or finance company on NEPSE should be to check capital adequacy and loan classification before looking at the price-to-earnings ratio.
The second reason the balance sheet is senior to the income statement in Nepal specifically is that profit is more easily manipulated, or at least more easily flattered, than the balance sheet position it is supposed to build. A hydropower company can recognise revenue on units generated and sold to the NEA under a power purchase agreement while the actual cash for that power sits unpaid in receivables for months — a common and long-standing feature of the Nepali hydropower sector given the NEA's own liquidity constraints. The income statement shows growing revenue and profit; the balance sheet, if you look at the receivables line and compare its growth rate to revenue growth, shows a company that is increasingly financing its off-taker's working capital rather than being paid for its own electricity. Profit without cash conversion is a balance sheet problem wearing an income statement disguise, and only reading both statements together — with the balance sheet as the check on the income statement's claims — catches it.
PRACTICAL TOOL
A simple discipline: every time you read a NEPSE company's profit and loss account, immediately turn to the balance sheet and compare the growth rate of trade receivables (or, for a bank, loans and advances) to the growth rate of revenue or interest income over the same period. If receivables are growing meaningfully faster than revenue, the reported profit is increasingly uncollected — and increasingly a promise rather than a fact.
Lesson 25.2 — The Anatomy of a NEPSE Balance Sheet
For a non-financial company on NEPSE — a manufacturer, a hotel, a trading house, most hydropower developers — the balance sheet follows the standard NFRS format: non-current assets (property, plant and equipment; intangible assets; long-term investments), current assets (inventory, trade receivables, cash and cash equivalents, short-term investments), equity (share capital, reserves and surplus, retained earnings), non-current liabilities (long-term borrowings, deferred tax liabilities, employee benefit obligations), and current liabilities (short-term borrowings, trade payables, current portion of long-term debt, provisions).
Reading this structure with discipline means asking, for each block, not just "how big is it" but "how has its composition changed." Property, plant and equipment growing steadily alongside revenue is normal capital intensity; property, plant and equipment growing while revenue stagnates is a company that has over-invested, and the next several years of depreciation will weigh on margins without a matching increase in earning power. Inventory growing faster than sales is either a bet on future demand or a sign that the company cannot sell what it is producing — the annual report's notes on inventory ageing, when disclosed, tell you which. Trade receivables, as already discussed, are the line that most reliably separates accounting profit from cash profit.
On the liability side, the composition of debt matters as much as its total. A company funding long-term assets (a hydropower plant, a factory) with short-term borrowings has a maturity mismatch that becomes a crisis the moment refinancing terms tighten — precisely the situation several Nepali hydropower developers found themselves in when construction-period cost overruns were bridged with short-term working capital loans rather than term loans matched to the asset's operating life. Conversely, a company carrying long-term debt against long-lived, cash-generating assets, with debt service coverage comfortably above one, is using leverage the way it is supposed to be used.
WATCH FOR
A rising ratio of short-term borrowings to total borrowings is one of the more reliable early warning signs in a Nepali industrial or hydropower balance sheet. It usually means either that long-term credit was unavailable or too expensive at the time of financing, or that the company is using working-capital lines to fund what should have been capital expenditure — both of which raise refinancing risk exactly when the underlying business is least able to absorb a shock.
Equity, for a non-financial company, is composed of paid-up share capital (the nominal value of shares issued, generally NPR 100 per share for most NEPSE-listed companies at issuance, though this diverges from market price over time), share premium (for companies that have issued shares above par, typically through rights issues or FPOs), reserves — general reserve, retained earnings, and, importantly, revaluation reserve — and, for a small number of companies, minority interest where subsidiaries are not wholly owned. The revaluation reserve deserves its own extended treatment, which Lesson 25.4 provides, because it is one of the most commonly misread lines on a Nepali balance sheet.
Lesson 25.3 — Reading a Bank's Balance Sheet: The NRB Format and the Loan Book
Banks, development banks, finance companies, and microfinance institutions listed on NEPSE do not use the generic NFRS balance sheet format. NRB prescribes a specific presentation for BFI financial statements — a format built around liquidity ordering (most liquid assets and liabilities first) rather than the current/non-current split used elsewhere, because a regulator's first question about a bank is always "can it meet withdrawals," not "is this asset current or fixed." The simplified structure looks like this:
Balance Sheet Side
Line Item
What It Represents
Assets
Cash and cash equivalents
Vault cash, balances with NRB and other banks
Assets
Due from NRB / interbank placements
Statutory balances, short-term interbank lending
Assets
Investment securities
Government bonds, treasury bills, NRB bonds, equity investments
Assets
Loans and advances to customers (net)
The core loan book, net of loan loss provisions
Assets
Fixed assets, goodwill, other assets
Property and equipment, intangibles, sundry receivables
Liabilities
Deposits from customers
Current, savings, fixed, call deposits — the core funding base
Liabilities
Borrowings
Interbank borrowings, refinancing from NRB, bonds and debentures issued
Liabilities
Other liabilities and provisions
Interest payable, staff bonus, tax provisions, other payables
Equity
Share capital, reserves, retained earnings
Paid-up capital, statutory general reserve, other reserves, accumulated profit
The single most important number on a bank's balance sheet, and the one most retail investors skip past, is the composition and quality of "loans and advances to customers" — because that one line, more than net profit, more than EPS, more than the dividend declared, determines whether a bank is solvent five years from now. NRB requires every BFI to classify its entire loan portfolio into risk categories on a quarterly basis and to hold specific loan loss provisions against each category. The classification and the minimum provisioning rates are as follows:
Loan Classification
Basis for Classification
Minimum Loan Loss Provision
Pass (Good)
Principal and interest not overdue, or overdue up to 1 month
1%
Watchlist
Overdue 1–3 months, or performing loans showing early warning signs (restructured, under litigation, or to a borrower with other classified loans)
5%
Substandard
Principal or interest overdue 3–6 months
25%
Doubtful
Principal or interest overdue 6–12 months
50%
Loss
Principal or interest overdue beyond 12 months, or otherwise deemed unrecoverable
100%
Substandard, doubtful, and loss loans together constitute what NRB and analysts call non-performing loans (NPL); watchlist loans are not technically non-performing but are the category to watch most closely, because a loan enters watchlist before it becomes a statistic — a rising watchlist ratio quarter over quarter is frequently the leading indicator of an NPL problem that has not yet shown up in the headline non-performing loan ratio the bank reports in its investor presentation.
REGULATORY DETAIL
NRB's Unified Directives to BFIs require loan loss provisioning to be calculated on the categories above and disclosed in the notes to the financial statements, alongside the bank's non-performing loan ratio and its capital adequacy position. Since the phased adoption of NFRS 9 for BFIs, banks additionally compute an expected credit loss (ECL) provision using probability-weighted, forward-looking models; where the NFRS 9 ECL provision is lower than the NRB directive-based provision, NRB requires the difference to be maintained as a regulatory reserve rather than released to distributable profit — meaning a bank cannot use a more lenient accounting model to inflate the retained earnings available for dividends.
The reason this matters for an investor is direct: a bank's reported net profit is only as reliable as its loan loss provisioning is adequate. A bank that under-provisions — by aggressively restructuring loans that should be downgraded, by classifying a loan as "watchlist" when the facts support "substandard," or by relying on collateral valuations that have not been updated — will show higher current profit and a rosier balance sheet than its actual credit risk warrants, at the cost of a larger, more sudden provisioning charge in a future year when NRB's supervisory inspection or a change in the regulatory directive forces reclassification. This is precisely the mechanism behind several NRB-directed reclassification exercises that have periodically required Nepali banks to take one-time hits to provisioning and profit — episodes in which the market was reminded, sometimes painfully, that reported profit and true asset quality are not always the same thing.
Capital adequacy is the second pillar of bank balance sheet analysis. Under NRB's Basel III-aligned Capital Adequacy Framework, commercial banks must maintain a minimum Common Equity Tier 1 (core equity) ratio, a minimum Tier 1 (core capital) ratio, and a minimum total capital fund ratio, each measured against risk-weighted assets, with an additional capital conservation buffer layered on top — bringing the effective minimum total capital adequacy ratio that most commercial banks target to somewhere in the range of 11 percent of risk-weighted assets, with the precise requirement varying by the bank's systemic importance and any countercyclical buffer NRB has activated. A bank operating close to this floor has very little room to absorb a bad year of loan losses before it is forced into a rights issue, a reduction in loan growth, or — in a stress scenario — regulatory restrictions on dividend distribution and business expansion.
CASE IN POINT
Nepali commercial banks disclose their Basel III capital position in a standalone quarterly "Capital Adequacy" disclosure alongside the balance sheet — a document many retail investors never open. It shows CET1 ratio, Tier 1 ratio, total capital fund ratio, and risk-weighted assets separately from the headline balance sheet. Two banks can report near-identical net profit and EPS growth while one carries a capital cushion several percentage points above the regulatory minimum and the other sits close to the floor; the second bank's ability to keep growing its loan book — and therefore its future earnings — is far more constrained, a fact invisible in the income statement alone.
For finance companies and microfinance institutions, the same logic applies with sharper edges. These institutions typically operate with thinner capital bases, serve higher-risk borrower segments, and have historically shown more volatile non-performing loan ratios during periods of monetary tightening — precisely the environment in which NRB has periodically raised policy rates and tightened lending requirements over the past several years. An investor evaluating a microfinance institution's balance sheet should look specifically at loan concentration by sector (agriculture, small trade, group lending), the trend in the watchlist-plus-NPL ratio over at least eight quarters, and whether loan loss provisioning has kept pace with loan book growth or lagged behind it.
Insurance companies — both life and non-life insurers listed on NEPSE — present yet another balance sheet variant, governed by Nepal Insurance Authority directives rather than NRB's. Their balance sheets are dominated on the liability side by policy reserves (the actuarially estimated present value of future claims and benefits owed to policyholders) and on the asset side by investment portfolios built to match those liabilities — government securities, fixed deposits with BFIs, and equity investments, the last of which is capped by regulation as a share of the investment portfolio. The investor's key balance sheet question for an insurer is whether the solvency margin (assets in excess of policy liabilities, relative to the regulatory minimum) is comfortable, and whether the investment portfolio's exposure to lower-rated BFI fixed deposits or equities creates concentration risk that would compound a downturn in the financial sector with a downturn in the insurer's own asset base.
Lesson 25.4 — The Equity Side: Promoters, the Public, and the Reserves That Lie
NEPSE-listed companies disclose shareholding structure in two broad categories: promoter shareholding and public shareholding. This distinction, prominently displayed in every company's shareholding pattern disclosure and on brokerage and NEPSE data terminals, is not a formality — it is one of the most information-dense single data points available to a retail investor, because it tells you how the people with the most information about the company have positioned themselves.
Promoter shares in Nepal are subject to a regulatory lock-in period following listing — historically calibrated so that promoters cannot exit immediately after an IPO prices the company for the public — and promoter shareholding typically trades at a discount to public (ordinary) shares of the same company precisely because of this lock-in and the smaller, less liquid market for promoter share transfers. A rising trend of promoter share pledging (promoters using their shares as collateral for loans, disclosed under CDSC and company filings) is a signal worth tracking, because it indicates promoters are extracting liquidity from their stake without reducing their ownership on paper — a position that can force forced-selling dynamics if the pledged shares are called during a market downturn.
WATCH FOR
A sustained decline in promoter shareholding percentage over several quarters — through open-market sale rather than dilution from a rights issue or FPO — is one of the more reliable behavioural signals available on NEPSE. Promoters know their own balance sheet and business prospects better than any outside analyst; persistent selling into a rising or even flat share price deserves more analytical weight than almost any single financial ratio.
Equally important is reading the composition of reserves and surplus, the block of equity that sits between share capital and retained earnings. Three specific items recur across NEPSE balance sheets and require careful reading.
Revaluation reserve. Under NFRS, a company may elect to revalue its property, plant, and equipment — most commonly land — to fair value rather than carrying it at historical cost less depreciation. When this happens, the increase in carrying value is credited not to profit but directly to a revaluation reserve within equity. This is legitimate accounting, and it can reflect a genuine economic reality — land purchased decades ago in Kathmandu Valley or the Tarai is, in many cases, genuinely worth many multiples of its book cost. The analytical danger is that a revaluation reserve inflates total equity and improves headline ratios — return on equity falls (because the denominator grows) while book value per share rises — without any change in the cash-generating capacity of the business, and without the increase in value having been realised through a sale. A company under earnings or leverage pressure has an incentive to revalue assets precisely because doing so lifts the equity base against which debt covenants and capital ratios are measured, without requiring the company to raise a single rupee of new capital or improve a single rupee of operating cash flow.
WARNING
When comparing return on equity, debt-to-equity, or book value per share across companies or across time for the same company, always check the notes to accounts for a revaluation reserve balance and the date the last revaluation was carried out. A company whose equity has grown substantially through revaluation rather than retained earnings has a balance sheet that looks stronger than its operating performance justifies — strip the revaluation reserve out and recompute the ratios on a tangible, cost-basis equity figure before drawing conclusions.
Retained earnings and general reserve. Nepali company law and, for BFIs, NRB directives require a portion of annual profit to be transferred to a statutory general reserve until that reserve reaches a prescribed multiple of paid-up capital, before the remainder becomes available as retained earnings for potential dividend distribution. A high general reserve relative to distributable retained earnings is not itself a problem — it is often a sign of regulatory prudence — but investors evaluating a company's capacity to pay cash dividends should look specifically at free, distributable retained earnings and the cash position backing it, not the combined "reserves and surplus" figure, because a large reserves balance built from bonus share capitalisation and statutory transfers may leave little that is both distributable and backed by actual liquid assets.
Deferred tax assets and actuarial reserves. NFRS requires recognition of deferred tax assets and liabilities, and of actuarial gains and losses on defined-benefit employee obligations (gratuity and, for some companies, pension), often routed through other comprehensive income rather than the profit and loss account. These are legitimate NFRS mechanics, but a deferred tax asset is only as good as the future taxable profit expected to realise it — a company recognising a growing deferred tax asset against a history of losses or volatile profit is booking a promise, not cash.
CAUTION
"Other comprehensive income" and its accumulated balance within equity is one of the least-read sections of a NEPSE annual report. It typically houses actuarial gains and losses on employee benefit obligations and, for some companies, fair value movements on available-for-sale investments. These items bypass the profit and loss account entirely, meaning a company's true comprehensive result for the year — including these swings — can differ meaningfully from the "profit for the year" figure that headlines the results and that most EPS calculations are built from.
Lesson 25.5 — Sector Variations: Hydropower, Insurance, and Manufacturing
Hydropower is Nepal's most capital-intensive listed sector, and its balance sheets read accordingly. During construction, a hydropower company's balance sheet is dominated by capital work-in-progress — an asset that generates no revenue and against which substantial project debt, typically arranged on a debt-to-equity ratio the promoter negotiates with the lending consortium (commonly in the range of 70:30 or 80:20 for run-of-river projects financed by domestic BFIs), accumulates as a liability. The critical balance sheet question during this phase is not profitability, since there is none, but whether the project is on schedule and on budget relative to its financing plan — a cost overrun forces either additional promoter equity, additional debt (raising leverage above what was underwritten), or delay, all three of which show up on the balance sheet well before they show up in any investor communication. Once a hydropower plant commissions, capital work-in-progress converts to fixed assets and depreciation begins, while the asset side gains the receivables discussed in Lesson 25.1 — dues from the NEA under the power purchase agreement, which for run-of-river plants without storage can be volatile seasonally (high generation and high receivables in monsoon months, lower generation in the dry season) and which, in aggregate across the sector, have periodically stretched to multiple months of sales outstanding, a working-capital drag that is easy to miss if an investor looks only at revenue and profit trends.
CASE IN POINT
A hydropower company's balance sheet notes typically disclose receivables ageing — how much of the amount due from the NEA is 0–3 months old, how much is 3–6 months old, and how much is older still. A rising share of receivables in the older buckets, even alongside growing revenue, indicates the company is increasingly extending credit to its sole customer, with no ability to diversify that counterparty risk, since the NEA is the mandated single buyer for most licensed hydropower generation in Nepal.
Manufacturing and trading companies on NEPSE — cement, consumer goods, and diversified conglomerates — present balance sheets closer to the textbook non-financial template, but with two Nepal-specific wrinkles worth flagging. First, many of these companies are part of larger, unlisted business groups, and their balance sheets frequently show related-party balances — amounts due from or to sister concerns, holding companies, or entities with common promoters — disclosed in the related-party transaction note required under NFRS. A related-party receivable that grows year after year, is not settled in cash, and is not charged commercial interest is functionally an interest-free loan from public shareholders' capital to the promoter group's other ventures, dressed up as a trade or "other receivable." Second, many manufacturing and trading companies rely on letters of credit and bank guarantees to import raw material or bid for contracts, which appear not on the balance sheet itself but in the contingent liabilities note — a disclosure investors routinely skip because it sits below the balance sheet, in small print, described as an obligation "not provided for."
WARNING
Contingent liabilities — bank guarantees issued on a company's behalf, letters of credit outstanding, disputed tax demands under appeal, and guarantees given for the debt of subsidiaries or related parties — are real obligations that do not appear as liabilities on the balance sheet precisely because their eventual cash outflow is uncertain, not because they are unimportant. For companies that provide guarantees to sister concerns or joint ventures, this note can reveal an off-balance-sheet risk larger than the company's entire recognised liability base. Always read the contingent liabilities note in full, and treat a large or fast-growing guarantee exposure to related parties as a de facto addition to leverage, not a footnote to ignore.
Insurance companies, treated briefly in Lesson 25.3, deserve one further balance sheet-specific point: because policy reserves are actuarially estimated rather than directly observable, the quality of an insurer's balance sheet depends heavily on the conservatism of its actuarial assumptions — mortality tables, lapse rates, discount rates applied to future liabilities — none of which a retail investor can independently verify, but all of which are attested by the appointed actuary and disclosed, at a summary level, in the actuarial valuation note. A sudden, favourable revision in actuarial assumptions that boosts reported net worth without a corresponding change in the underlying book of business is a signal to read the actuarial note closely rather than take the improved solvency margin at face value.
Lesson 25.6 — Building the Red-Flag Checklist
The preceding lessons point toward a consistent set of Nepal-specific balance sheet red flags that recur across sectors. Drawing them together into a working checklist gives an investor a repeatable discipline to apply to every NEPSE balance sheet, rather than relying on memory of scattered warnings.
Related-party receivables are the single most Nepal-specific red flag, because the country's corporate ownership structure — concentrated promoter families and business groups controlling multiple listed and unlisted entities — creates persistent opportunity and, in some cases, incentive to move value between entities through non-arm's-length transactions. The related-party transaction note, required under NFRS and typically found late in the notes to accounts, discloses the nature and amount of transactions with promoters, directors, subsidiaries, associates, and other group entities. An investor should specifically check whether related-party receivables are growing faster than the overall receivables base, whether they carry commercial interest terms, and whether the same related parties recur as both debtors and creditors across multiple listed companies in a group — a pattern that can indicate circular financing rather than genuine commercial activity.
Revaluation reserves inflating equity, discussed in Lesson 25.4, is the second recurring flag, and the discipline is straightforward: recompute key ratios (ROE, debt-to-equity, price-to-book) using tangible equity net of revaluation reserve, and compare the resulting picture to the as-reported figures. A material divergence between the two tells you how much of the company's apparent financial strength is a function of an accounting election rather than operating performance.
Contingent liabilities from bank guarantees and letters of credit, particularly where extended to related parties or subsidiaries, form the third flag. Because these obligations sit outside the balance sheet's liability total, they are systematically under-weighted by investors who evaluate leverage using only the reported debt-to-equity ratio. A company whose contingent liabilities are a large multiple of its reported net worth carries real solvency risk that the headline balance sheet understates.
Loan classification drift, specific to BFIs, is the fourth flag: a rising watchlist ratio, a rising share of restructured or rescheduled loans, or provisioning that has grown more slowly than the loan book, are all leading indicators of asset quality deterioration that will eventually show up in the non-performing loan ratio and in provisioning charges against future profit.
Working capital financed by short-term debt against long-lived assets is the fifth flag, relevant chiefly to hydropower and manufacturing companies, where a maturity mismatch between asset life and liability tenor creates refinancing risk that a snapshot balance sheet, read only for its debt-to-equity ratio, will not reveal without examining the debt schedule's maturity profile in the notes.
Declining promoter shareholding, particularly through open-market sales rather than corporate actions, is the sixth flag, and the one that requires the least accounting sophistication to track — CDSC and NEPSE shareholding pattern disclosures make it directly observable on a quarterly basis, and it should be checked as a matter of routine for any holding, not only when a problem is otherwise suspected.
PRACTICAL TOOL
Build a standing checklist you apply to every NEPSE balance sheet before making a buy decision: (1) receivables growth versus revenue growth; (2) revaluation reserve as a share of total equity; (3) contingent liabilities as a multiple of net worth; (4) for BFIs, the watchlist-plus-NPL ratio trend over eight quarters and the capital adequacy buffer above the regulatory minimum; (5) short-term borrowings as a share of total borrowings; (6) promoter shareholding trend over the last four quarters. None of these six checks requires more than the annual report, the quarterly disclosures, and the shareholding pattern filing — all of which are public.
None of this is a reason to avoid Nepali equities, banks, or hydropower companies as an asset class; leverage, related-party structures, and regulatory-format balance sheets are simply the terrain, not a verdict on it. Many NEPSE-listed companies carry clean, well-provisioned, conservatively financed balance sheets, and the discipline of reading the balance sheet closely is precisely what allows an investor to tell those companies apart from their more fragile peers, long before the difference shows up in a headline profit number or a share price move. The balance sheet rewards patience: it is denser and less immediately gratifying to read than the profit and loss account, but it is the document that tells you, in plain terms, what you actually own a claim on when you buy a share.
Chapter recap
The balance sheet, not the profit and loss account, is the senior document for evaluating a NEPSE-listed company, because it discloses the claim structure — assets, liabilities, and residual equity — that determines what shareholders actually own and in what order they would be paid if the business stopped generating profit tomorrow; leverage in Nepal's bank-heavy market makes small asset-quality deteriorations disproportionately damaging to equity, which is why capital adequacy and loan classification deserve attention before the price-to-earnings ratio.
Banks, development banks, finance companies, and microfinance institutions report in a liquidity-ordered format prescribed by Nepal Rastra Bank rather than the generic NFRS template, and their single most consequential balance sheet line is loans and advances, whose quality is disclosed through the mandated five-tier classification system (pass, watchlist, substandard, doubtful, loss) and the loan loss provisions carried against each tier — a rising watchlist ratio is typically the earliest warning of asset quality trouble, well before it appears in the headline non-performing loan figure.
Capital adequacy — the Common Equity Tier 1, Tier 1, and total capital fund ratios measured against risk-weighted assets under NRB's Basel III-aligned framework — determines how much cushion a bank has to absorb credit losses and how much room it has to keep growing its loan book, and two banks with similar reported profit can have very different capacity to sustain that profit depending on how close each sits to the regulatory floor.
On the equity side, promoter shareholding trends are one of the most information-dense and easily tracked signals available to a retail investor, since promoters observe the business from the inside; sustained open-market selling by promoters deserves more analytical weight than most financial ratios, while revaluation reserves, actuarial adjustments routed through other comprehensive income, and statutory general reserves can each inflate headline equity or return-on-equity figures without a corresponding improvement in operating performance, and should be stripped out before ratios are compared across companies or time.
Sector context changes what the balance sheet is trying to tell you: hydropower balance sheets hinge on construction-phase leverage and post-commissioning receivables from the Nepal Electricity Authority, manufacturing and trading balance sheets are vulnerable to related-party receivables and off-balance-sheet contingent liabilities from bank guarantees issued to group entities, and insurance company balance sheets depend on actuarial assumptions behind policy reserves that are attested rather than independently verifiable.
A disciplined investor applies a consistent, repeatable checklist to every NEPSE balance sheet before investing — comparing receivables growth to revenue growth, isolating revaluation reserves from tangible equity, sizing contingent liabilities against net worth, tracking loan classification and capital adequacy trends for BFIs, checking the maturity structure of borrowings, and monitoring promoter shareholding — because in a market where ownership is concentrated, regulatory formats vary by sector, and disclosure quality is uneven, the balance sheet read carefully is the most reliable defence against a profit and loss account that tells only the story a company wants told.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part V · Chapter 26
The Income Statement — Deep Reading
First published 22 Aug 2026 · Last verified 29 Aug 2026
Lesson 26.1 — The Architecture of Income: Why Banks, Hydropower, and Insurers Don't Look Alike
An income statement is not a neutral ledger of revenue minus expense. It is a document shaped by the economics of the business that produced it, and on the Nepal Stock Exchange the three dominant sectors — banking and financial institutions, hydropower generation, and insurance — each report income through a structure so different that an investor who reads all three the same way will misprice at least two of them. This is not a stylistic quirk. It reflects the underlying reality that a bank earns money by intermediating risk on borrowed capital, a hydropower company earns money by converting a fixed, weather-dependent physical asset into contracted cash flows, and an insurer earns money by pooling uncertain future liabilities against premium collected today. A chapter on "the income statement" that ignored these differences would be teaching arithmetic, not analysis. This chapter teaches analysis.
Roughly two-thirds of NEPSE's free-float market capitalisation sits in banking and financial institutions — commercial banks, development banks, and finance companies — supervised by Nepal Rastra Bank (NRB) under the Unified Directives and, more specifically for income recognition, under NRB's Guidance Note on Interest Income Recognition. A further significant slice sits in hydropower generation companies, most of which sell electricity under long-term Power Purchase Agreements (PPAs) to the Nepal Electricity Authority (NEA), the state-owned single buyer. Life and non-life insurers make up a smaller but analytically distinct third bloc, regulated by the Nepal Insurance Authority (formerly the Insurance Board), whose income statements resemble neither banks nor hydropower companies because insurance is, structurally, the business of selling promises rather than services or capital.
The investor's task in this chapter is to build three separate mental templates — one per sector — and to understand the two cross-cutting mechanical topics that apply to every NEPSE-listed company regardless of sector: how bonus share issuance mechanically alters earnings per share, and how dividend versus bonus disclosure conventions in Nepal can mislead an investor who does not read the notes carefully. A fifth section catalogues the manipulation techniques most frequently observed in Nepali company results — not as accusation, but as a checklist the disciplined investor runs before trusting a reported profit figure.
KEY CONCEPT
The income statement of a Nepali company should always be read alongside its sector's governing regulator's format. Banks follow NRB's prescribed format under the Unified Directives; insurers follow the Nepal Insurance Authority's format; hydropower companies follow the general NFRS-based format with no sector-specific regulatory template, which is itself informative — it means hydropower income statements have more year-to-year presentational variance between companies than bank or insurer statements do.
Lesson 26.2 — Reading a Bank's Income Statement: Net Interest Income and the Impairment Line
The starting point for any Nepali commercial bank's income statement is not "revenue" in the generic sense but Net Interest Income (NII) — interest income earned on loans, investments, and interbank placements, minus interest expense paid on deposits, borrowings, and debentures. This single line typically explains 55 to 70 percent of a bank's operating income, and its trajectory over four to eight quarters tells the investor more about the bank's core franchise health than any other single number in the filing.
Interest income and the suspense account
The first place a careless reader goes wrong is assuming that "interest income" as reported is simply the contractual interest rate multiplied by the loan book. It is not, and the reason is regulatory. NRB's Guidance Note on Interest Income Recognition — first issued in 2019 and substantially updated in 2025 — requires banks to recognise interest income on an accrual basis only for loans classified as "Pass" (performing) and, with restrictions, "Watchlist" loans. For loans classified Substandard, Doubtful, or Loss under NRB's loan classification and provisioning framework, accrued interest must be transferred to an interest suspense account rather than booked to the profit and loss statement, and is only recognised as income when actually received in cash. This means that as a bank's non-performing loan (NPL) ratio rises, its reported interest income mechanically falls even before any provisioning expense is booked — the income statement is doing part of the credit-quality signalling work before you even reach the impairment line. An investor who sees NII decelerating in a quarter where the loan book is still growing should immediately check the NPL ratio and the interest suspense account movement in the notes, because a growing book with decelerating NII is a classic early signature of asset quality deterioration that has not yet fully shown up in the provisioning line.
REGULATORY DETAIL
NRB's loan classification framework divides loans into five categories — Pass (up to 1 percent provisioning), Watchlist (5 percent), Substandard (25 percent), Doubtful (50 percent), and Loss (100 percent) — each carrying escalating general or specific loan loss provisioning requirements. A loan overdue by more than one year is typically classified Loss regardless of collateral quality. The pace at which a bank migrates loans downward through these categories, visible in the loan loss provision note, is a more forward-looking signal than the headline NPL ratio itself, because the NPL ratio is a stock measure while migration is a flow measure.
Fee-based and other operating income
Below NII sits fee and commission income — service charges, loan processing fees, guarantee commissions, remittance fees, and card-related income — together with net trading gain (largely foreign exchange dealing income) and other operating income, which frequently includes dividends received from subsidiaries and associates, gains on disposal of investment properties acquired through loan recovery (non-banking assets), and one-off items. This is where the analyst must slow down. A bank's core franchise strength is best judged by NII plus fee income; a bank whose "other operating income" line is unusually large in a given quarter, especially one containing gains from the sale of non-banking assets or one-time recovery of previously written-off loans, is showing the investor a profit figure that is partly non-recurring. Nepali banks disclose the composition of other operating income in the notes to the financial statements filed with NEPSE and the regulator, and a five-minute check of this note before accepting a quarter's EPS at face value is one of the single highest-value habits an investor can build.
Impairment charge for loans and other losses
The line labelled "impairment charge/(reversal) for loans and other losses" is where the credit cycle is formally booked. Under NFRS 9 as adopted in Nepal, banks must estimate expected credit losses (ECL) using a forward-looking, probability-weighted model across three stages of credit deterioration, but Nepali banks are also bound by NRB's minimum provisioning requirements under the loan classification framework described above, and where the NRB regulatory provision requirement exceeds the NFRS 9 ECL estimate, the bank must maintain the higher of the two through a regulatory reserve mechanism. In practice this means the impairment line an investor sees is a hybrid of accounting judgment and regulatory floor, and swings in this line — particularly sharp reversals of prior provisioning — deserve scrutiny, since a bank under earnings pressure has an incentive to release provisions in a weak quarter to protect the headline profit number.
WATCH FOR
A bank reporting flat or growing net profit while its NII is falling and its impairment charge is falling or reversing in the same quarter is very often managing the bottom line through provisioning discretion rather than through improved core operations. Cross-check the movement in the impairment line against the disclosed NPL ratio and loan loss provision balance in the same set of financial statements — the two should move together directionally; when they diverge, the divergence itself is the finding.
Below the impairment line sits operating expense — staff cost, premises and establishment, and other operating expense — and then operating profit before non-operating income, followed by non-operating income and expense (largely gains or losses on disposal of fixed assets and other non-core items), profit before tax, income tax expense, and net profit. Nepali banks also carry a mandatory transfer to a regulatory reserve (encompassing items such as the NFRS 9 versus NRB provisioning gap, deferred tax assets, and actuarial gains on defined benefit plans) below the net profit line, in the statement of changes in equity — this is not part of the income statement proper but materially affects the free reserve available for cash dividend or bonus distribution, a point the investor will need in Lesson 26.5.
Lesson 26.3 — Reading a Hydropower Company's Income Statement: Revenue, Royalty, and Depreciation
A hydropower company's income statement looks deceptively simple next to a bank's — a single revenue line, a handful of operating expense lines, depreciation, finance cost, and profit — but the simplicity is misleading because almost the entire analytical burden sits inside two things that never appear as a separate P&L line in the format itself: the terms of the Power Purchase Agreement, and the accounting policy choice for how the concession asset is depreciated or amortised.
Revenue recognition under the PPA
The overwhelming majority of NEPSE-listed hydropower companies sell their entire output to NEA under a PPA with a tenor typically running 20 to 35 years from commercial operation date. Revenue is recognised as electricity is generated and delivered, at the PPA tariff rate — and this is where an investor must understand that Nepali PPAs are not flat-rate contracts. Most PPAs for run-of-river plants set a wet-season tariff (roughly mid-May to mid-November) and a materially higher dry-season tariff (roughly mid-November to mid-May), because dry-season generation capacity is scarcer and more valuable to the national grid. A run-of-river plant, lacking storage, generates most of its energy in the wet season at the lower tariff and comparatively little in the dry season at the higher tariff — meaning that reported quarterly revenue for the same company can vary by a factor of two or three between a monsoon quarter and a winter quarter purely from hydrology and tariff structure, with zero change in the underlying asset or its efficiency. An investor comparing a hydropower company's Q1 (roughly mid-July to mid-October, peak monsoon) revenue against its Q3 (roughly mid-January to mid-April, lean season) revenue without adjusting for this seasonal pattern will draw exactly the wrong conclusion about the trend in the business.
CASE IN POINT
Two hydropower companies with similar installed capacity can report very different annual revenue purely because one has meaningful dry-season generation (from a plant with some head/storage advantage or a peaking design) while the other is a pure run-of-river plant whose output collapses in the dry months. Comparing price-to-earnings ratios across hydropower stocks without first checking each company's wet-season-to-dry-season generation mix, disclosed in the directors' report or management discussion, is comparing incompatible cash flow shapes as if they were the same instrument.
A second, less visible but consequential issue is hydrology risk itself: a below-average monsoon, glacial lake outburst flood damage, or sediment-related turbine wear can each reduce a given year's generation well below the PPA's estimated average energy figure, and this shows up simply as lower revenue with no separate disclosure line calling it out as unusual — the investor has to read the directors' report narrative to know whether a soft year was hydrology-driven (likely temporary) or plant-availability-driven (potentially structural, e.g., recurring outages, transmission curtailment by NEA, or transformer/turbine failure).
WATCH FOR
NEA transmission curtailment — where the national grid cannot absorb all the electricity a plant is capable of generating, particularly during monsoon-season generation gluts from the concentration of run-of-river capacity commissioned in recent years — is an increasingly common cause of realised revenue falling short of technical generation capacity. This is a systemic sector risk, not a company-specific one, and it means the earnings quality of even a well-run, high-availability plant can be capped by transmission infrastructure entirely outside its control. Check the directors' report for any mention of "deemed generation" or curtailment compensation claims against NEA.
Royalty expense
Below revenue, hydropower companies carry a royalty expense payable to the Government of Nepal under the Electricity Act framework, structured as a capacity royalty (a fixed charge per installed kilowatt, payable regardless of actual generation) plus an energy royalty (a percentage of the value of electricity generated, typically escalating after the plant's first several years of commercial operation to reflect the retirement of construction-period tax and royalty concessions). An investor should watch for the royalty step-up date disclosed in the company's original license terms — profitability commonly compresses mechanically in the year the energy royalty rate increases, an entirely predictable event that the market nonetheless periodically reacts to as if it were a surprise.
Depreciation and the IFRIC 12 question
The single most consequential accounting policy choice in a hydropower income statement is how the company treats its generation and civil works assets: as property, plant and equipment depreciated on a straight-line or reducing-balance basis over the asset's useful economic life, or — where the arrangement is judged to meet the definition of a service concession arrangement under NFRS/IFRIC 12 — as either a financial asset (a receivable representing the right to be paid a determinable stream of cash) or an intangible asset (a right to charge for the use of the infrastructure) amortised typically over the PPA/license term rather than the asset's physical life. This is a live and unsettled question in Nepali practice — auditors and companies have taken differing positions on whether NEA's single-buyer arrangement constitutes a "grantor" relationship sufficient to trigger IFRIC 12 treatment, and the practical consequence for an investor is that depreciation/amortisation charges are not always comparable across hydropower companies even holding installed capacity and project cost constant, because one company's asset is being written off over, say, a 30-year PPA term while an economically similar company's plant (treated as ordinary PP&E) is being depreciated over a 35- or 40-year engineering useful life. A shorter amortisation period produces a materially larger annual non-cash charge and a correspondingly lower reported net profit in the earlier years of the concession, even though the underlying cash generation of the two projects may be identical. The investor comparing P/E ratios across hydropower names must check the depreciation/amortisation policy note before concluding that one company is "cheaper" than another on an earnings basis.
Finance cost and the debt-heavy capital structure
Hydropower projects in Nepal are overwhelmingly financed with high leverage — debt-to-equity ratios of 60:40, 70:30, or higher are routine given the capital intensity of dam, tunnel, and powerhouse construction — so finance cost (interest on project loans, often syndicated among several Nepali banks) is a large income statement line, and during the construction period, interest cost is capitalised into the cost of the asset under NFRS (borrowing cost capitalisation) rather than expensed. This creates a specific timing trap: a project nearing commercial operation date will show artificially low finance cost and therefore an artificially flattering pre-launch profit trajectory right up until the day borrowing costs stop being capitalised and start being expensed, at which point reported finance cost can jump sharply in a single quarter with no change in the underlying loan balance — simply a change in which side of the ledger the same interest payment lands on.
WARNING
Some hydropower companies have been observed continuing to capitalise costs, including certain operating expenses incurred during an extended "trial run" or testing period after physical completion, beyond what NFRS would strictly permit, in order to defer expense recognition and present a stronger pre-commercial-operation profit picture to prospective bonus/rights subscribers. An investor should treat an unusually long gap between a hydropower company's announced physical completion date and its declared commercial operation date as worth investigating through the notes on capitalised borrowing cost and capital work-in-progress.
Lesson 26.4 — Reading an Insurer's Income Statement: Premium, Claims, and the Actuarial Reserve
Insurance income statements — for both life insurers and non-life (general) insurers listed on NEPSE — are built around a structural idea foreign to both banking and hydropower: the company collects cash today (premium) against a promise to pay an uncertain amount at an uncertain future date (claims), and the entire art of insurance accounting is estimating that future liability today.
Gross written premium, reinsurance, and net premium income
The top line is gross written premium (or gross premium income for non-life insurers) — the total premium underwritten in the period. From this, the insurer deducts reinsurance premium ceded — the portion of risk (and corresponding premium) passed on to a reinsurer, most commonly Nepal Reinsurance Company or an international reinsurer, to limit the insurer's own exposure to large or catastrophic claims — to arrive at net premium income, the figure that actually drives the insurer's own retained risk and profitability. An investor should watch the ratio of ceded premium to gross premium over time: a rising cession ratio can indicate the insurer is retaining less risk (prudent, but caps upside), while a falling cession ratio in the same period as rapid gross premium growth can indicate the insurer is retaining more risk than its capital base comfortably supports — a solvency question the Nepal Insurance Authority's solvency margin filings, not the income statement, will ultimately confirm or deny.
Claims incurred and the reserving judgment
Below net premium income sits net claims incurred (or, for life insurers, claims and benefits paid together with the change in actuarial/life insurance fund reserves). This is the line where the single largest source of accounting judgment in the insurance income statement resides, because "incurred but not reported" (IBNR) claims — losses that have already occurred but have not yet been reported to the insurer — must be estimated actuarially, and a change in reserving assumptions can move reported profit by a wide margin without any change in the actual underlying claims experience. Nepal Reinsurance Company's widely reported multi-billion-rupee loss, driven by a sharp escalation in claims from Nepali cedants including weather and catastrophe-related losses, is a instructive real-world illustration of how quickly a reinsurer's claims line can overwhelm premium income when a reserving assumption proves too optimistic in a single bad year.
For life insurers specifically, the actuarial (mathematical) reserve — the present value of future policy benefits less future premiums, computed by an appointed actuary using mortality, lapse, and discount rate assumptions — is both a balance sheet liability and, through its year-on-year movement, an income statement charge. A life insurer can report strong "revenue" growth (premium income) while actual underlying profitability is being eroded by a build-up in actuarial reserves driven by conservative (or, if the assumptions are aggressive, insufficiently conservative) actuarial assumptions — this is precisely the pattern flagged in results where premium growth was strong but profitability stayed flat or declined because claims and reserve movements absorbed the gain.
Commission expense and management expense ratio
Insurers also carry agent and broker commission expense — a significant cost given Nepal's heavily agency-driven distribution model, particularly for life insurance — and management expenses, which the Nepal Insurance Authority caps as a percentage of premium income under its expense-of-management regulations. An insurer persistently running management expenses above the regulatory ceiling is flagged by the regulator and, over time, may face restrictions on new business licensing or capital-raising — a regulatory risk factor that shows up first as a note disclosure long before it shows up as a headline problem.
Investment income
Because insurers hold large investment portfolios funded by policyholder premium (particularly life insurers, whose liabilities are long-dated), investment income — interest on government securities and fixed deposits, dividend income, and realised gains on equity and mutual fund holdings — is frequently a larger and more stable contributor to insurer net profit than the underwriting result itself. An investor evaluating a Nepali insurer should always separate the underwriting profit/(loss) — net premium income less net claims, commission, and management expense — from investment income, because a company can show a healthy consolidated net profit while running an underwriting loss entirely disguised by investment gains, a structurally fragile position if interest rates or equity markets turn.
KEY CONCEPT
For any insurer, always compute the underwriting result separately from the investment result. A company earning its entire profit from investment income while its core underwriting business loses money is not being compensated for the insurance risk it is taking on — it is effectively running an investment fund with an insurance license attached, and that is a materially different (and generally lower-quality) earnings profile than one where underwriting itself is profitable.
Lesson 26.5 — Bonus Shares, Rights Issues, and the Arithmetic of EPS
No single mechanical topic causes more confusion among NEPSE retail investors than the effect of bonus share issuance on earnings per share, and no topic more reliably separates investors who read financial statements from those who merely watch stock prices. It deserves careful, sequential treatment.
The bonus share mechanic
A bonus share issue capitalises a portion of a company's free reserves (retained earnings, and in banks' case, subject to NRB approval and minimum capital adequacy conditions) into additional paid-up share capital, distributed to existing shareholders pro rata — commonly expressed as, for example, "10 percent bonus," meaning 10 new shares for every 100 held. Critically, a bonus issue creates no new economic value: the company's total equity is unchanged, only its composition (more shares outstanding, each representing a proportionally smaller slice of the same underlying business) changes. This is economically identical to a stock split, and it is why, immediately following a bonus issue, NEPSE mechanically adjusts the stock's opening price downward on an ex-bonus basis — a company trading at Rs 500 before a 10 percent bonus opens at approximately Rs 455 (500 ÷ 1.10) ex-bonus, with the shareholder now holding 10 percent more shares at the lower price, net wealth unchanged before any market re-rating.
Restated EPS: the rule every investor must know
Nepal Financial Reporting Standards, following IAS 33's treatment of bonus issues, require that when a company issues bonus shares, the weighted average number of shares used in the EPS calculation must be restated retrospectively for all periods presented in the financial statements — as though the bonus shares had been outstanding from the very start of the earliest period shown. This means that when a bank reports "restated EPS" for the prior year alongside the current year's EPS, the prior year's profit has not changed at all; only the share count used to divide it has changed, and it has changed for comparability, not because anything about last year's performance was revised.
PRACTICAL TOOL
To sanity-check any Nepali company's post-bonus EPS, take the profit for the year, divide by the new (post-bonus) total shares outstanding, and compare that to the "restated EPS" figure the company discloses for the prior year. If a company shows EPS falling from, say, Rs 25 to Rs 20 purely because a 25 percent bonus was issued and profit stayed flat, this is not a deterioration in profitability — it is Rs 25 profit-per-100-old-shares becoming Rs 20 profit-per-125-new-shares, mathematically identical wealth, differently sliced. Always ask: did net profit itself grow, shrink, or stay flat year-on-year? That is the real performance question; the EPS number alone, unadjusted for share count changes, answers a different question.
This single confusion — mistaking a bonus-driven EPS decline for an earnings decline — has driven mispriced sell-offs in Nepali bank and hydropower stocks around annual general meeting season with some regularity, because retail investors see "EPS fell" in a news headline without checking whether the fall was arithmetic (bonus dilution) or real (profit decline). The disciplined investor's response to any reported EPS change is always to decompose it into the profit-growth component and the share-count component before reacting.
Rights issues are a different animal
A rights issue — where the company issues new shares to existing shareholders at a specified subscription price, often at a discount to market price, in exchange for new cash into the company — does inject new capital and is not treated the same way as a bonus issue for EPS purposes. Because a rights issue at a discount contains an implicit bonus element (existing shareholders who do not subscribe are diluted at less than full market value), NFRS/IAS 33 requires an adjustment using a theoretical ex-rights price (TERP) calculation that blends the bonus-element treatment with genuine new-capital dilution, applied retrospectively only to the bonus element and prospectively (from the rights issue date) for the fresh capital's dilutive effect. In practice, Nepali listed companies and their auditors compute and disclose this restated comparative EPS in the annual report, and an investor rarely needs to reconstruct the TERP calculation independently, but should understand that a rights issue genuinely dilutes EPS in a way a bonus issue does not, because a rights issue changes the underlying capital base the company must now generate returns on, whereas a bonus issue does not.
Dividend versus bonus disclosure conventions
Nepali listed companies, particularly banks, conventionally announce a combined "dividend" figure at the annual general meeting stage that bundles a cash dividend percentage and a bonus share percentage together — for example, "20 percent dividend" might mean 15 percent bonus plus 5 percent cash, or some other split, and the split matters enormously to an investor's actual realised cash return versus paper share-count increase. Nepal Rastra Bank, under its dividend distribution guidelines for banks and financial institutions, requires that any dividend (cash or bonus) be paid only out of distributable profit after regulatory adjustments (including the regulatory reserve transfers discussed in Lesson 26.2), and NRB approval is required before a bank can announce its final dividend, meaning a board-proposed dividend and the eventually NRB-approved dividend can differ — an investor should treat board-proposed figures reported in the media as provisional until NRB's approval is confirmed and reflected in the book closure notice.
CAUTION
When a Nepali company (especially a bank under capital adequacy pressure) announces a headline "dividend" percentage, always check the cash-versus-bonus split before assuming a meaningful cash return. A bank offering "12 percent dividend" that is entirely bonus shares returns no cash to the shareholder at all — it only increases the share count, with the market price adjusting downward correspondingly on the ex-dividend date. The investor's actual total return depends on what the market subsequently does to the post-adjustment price, not on the announced percentage itself.
Lesson 26.6 — The Analyst's Red Flags: How Nepali Companies Dress Up the Income Statement
Having built the sector-specific templates, the investor now needs a working checklist of the manipulation and earnings-management techniques most commonly observed — sometimes through outright misstatement, more often through aggressive but technically defensible accounting choices — in NEPSE-listed company results.
Interest income recognition gaming. Because NRB's interest suspense rules are triggered by loan classification, a bank under earnings pressure has an incentive to delay downgrading a deteriorating loan from Watchlist to Substandard for as long as documentation permits, keeping accrued interest flowing to the P&L rather than to suspense. Evergreening — restructuring or rolling over a distressed loan, sometimes with a nominal fresh disbursement used to service the interest on the old loan — is the classic technique for keeping a loan technically "performing" past the point where its underlying cash flow genuinely supports that classification. NRB's onsite inspection reports, when they surface in the financial press, are the most reliable external check on whether a bank's self-reported asset quality matches the regulator's independent assessment.
Provisioning discretion used to smooth earnings. As discussed in Lesson 26.2, because Nepali banks must hold the higher of NFRS 9 ECL or the NRB regulatory minimum, and because ECL model inputs (probability of default, loss given default, macroeconomic overlay) involve substantial management judgment, a bank can flatter a weak quarter by using a more optimistic macro overlay or by releasing general provisions built up in a stronger prior period. The investor's defence is trend analysis: track the provisioning-to-loan-book ratio over eight to twelve quarters, not one, and treat any sharp single-quarter reversal with suspicion until the accompanying NPL and loan-growth data are checked.
Other income used to prop up core profitability. Both banks and hydropower companies have, at various points, offset a weak core-operations quarter with a one-off gain — a bank recognising gain on sale of non-banking assets acquired through loan recovery, a hydropower company booking insurance claim settlements or one-time compensation from NEA for historical curtailment — inside "other income" without prominent separate disclosure on the face of the income statement. The remedy is always the same: read the notes breaking down other operating/non-operating income line by line before accepting the headline net profit as representative of recurring earning power.
Capitalisation of expenses during construction/pre-commercial-operation periods. As flagged in Lesson 26.3, extending the capitalisation window for hydropower project costs — including operating costs incurred during testing — beyond the point NFRS would support inflates the balance sheet asset and defers expense recognition, flattering pre-listing or pre-rights-issue profitability. The tell is a gap between physical completion (visible in engineering/construction updates) and declared commercial operation date that is longer than the plant's technical commissioning process should require.
Reserving assumption changes at insurers timed to smooth results. A change in actuarial mortality or lapse assumptions, or a change in the IBNR estimation methodology, can move an insurer's reported profit substantially in a single year. Because these changes are technically defensible actuarial judgments, they rarely constitute outright misstatement, but an investor should treat any year in which an insurer's underwriting result improves sharply alongside a disclosed change in actuarial assumptions as needing the assumption change itself scrutinised, not just the resulting profit number.
Bonus/rights timing used to manage per-share optics ahead of book closure. Because bonus shares mechanically dilute EPS (even though they restate comparatives, the market frequently reacts to unrestated year-over-year comparisons in press coverage), and because a company's management has full control over the timing and size of a bonus announcement relative to its results announcement, some companies have been observed sequencing announcements to manage the immediate market narrative — for instance, announcing a strong absolute profit figure prominently while bonus-driven EPS dilution is disclosed with less prominence in the same release. This is a disclosure-emphasis issue rather than a numerical misstatement, but it is exactly the kind of framing an institution-grade investor is trained to see through by going straight to the primary financial statements and notes rather than relying on summarised headline announcements.
Related-party and connected transactions. Loans to companies connected to a bank's promoters or directors, insurance policies written on related-party risks at non-arm's-length terms, or hydropower power purchase and construction contracts awarded to promoter-affiliated engineering, procurement and construction (EPC) contractors, all carry the risk of profit or loss being shifted across the group in ways that do not reflect the listed entity's standalone economic performance. Nepali disclosure requirements mandate related-party transaction notes in the annual report; a widening related-party balance relative to prior years, without a clear operational explanation, warrants direct scrutiny.
WARNING
None of the techniques above are, by themselves, proof of fraud — most sit within the range of judgment that NFRS explicitly permits, and Nepali auditors sign off on them as fairly presented within that framework. The investor's job is not to assume wrongdoing but to recompute a normalised, sustainable earnings figure by stripping out the one-off, discretionary, and timing-sensitive items identified above, and to base valuation decisions on that normalised figure rather than on the headline net profit or EPS as reported.
Sector Line-Item Comparison
The table below summarises how the same broad income statement concept — the top line, the principal deduction, and the primary judgment-driven expense — takes a structurally different form across the three sectors, which is the single most important pattern to internalise from this chapter.
Concept
Commercial Bank
Hydropower Company
Insurer (Life/Non-Life)
Top line
Net Interest Income (interest income less interest expense)
Revenue from energy sold under PPA at seasonal tariff
Net premium income (gross premium less reinsurance ceded)
Secondary income
Fee and commission income, net trading gain
Deemed generation/curtailment compensation, if any
Investment income on policyholder funds
Principal judgment-driven deduction
Impairment charge for loans (NFRS 9 ECL vs. NRB regulatory minimum)
Depreciation/amortisation (PP&E useful life vs. IFRIC 12 concession-term amortisation)
Net claims incurred and change in actuarial/IBNR reserves
Regulatory/statutory deduction unique to sector
Regulatory reserve transfer (NFRS 9/NRB provisioning gap, deferred tax)
Royalty to Government of Nepal (capacity + energy royalty)
Management expense ratio capped by Nepal Insurance Authority
Key non-recurring risk
Gain on sale of non-banking assets, provision reversals
REGULATORY DETAIL
Note that hydropower is the one major NEPSE sector without a dedicated financial-reporting regulator prescribing an income statement format — NRB prescribes format for banks, the Nepal Insurance Authority prescribes format for insurers, but hydropower companies follow only the general NFRS-based Companies Act format, applying their own accounting policy choices (particularly around IFRIC 12 and capitalisation) with correspondingly less standardisation across the sector. This is precisely why comparative analysis across hydropower stocks requires more notes-level diligence than comparative analysis across banks.
Chapter recap
The income statement of a NEPSE-listed company cannot be read through a single generic template because the underlying economics of banking, hydropower generation, and insurance are fundamentally different businesses that NFRS and sector regulators require to be presented differently; a bank's core signal is net interest income and the impairment charge shaped by NRB's loan classification and interest suspense rules, a hydropower company's core signal is PPA-tariff-driven revenue shaped by seasonal hydrology and a depreciation policy that hinges on an unsettled IFRIC 12 classification judgment, and an insurer's core signal is the underwriting result — net premium income less claims and reserve movements — which must be separated from investment income before profitability can be honestly assessed. Bonus share issuance is economically neutral and mechanically dilutes EPS through a mandatory retrospective restatement of the weighted average share count, meaning a falling post-bonus EPS figure, by itself, says nothing about whether the underlying business grew or shrank, and the investor's first move on seeing any EPS change must be to decompose it into its profit-growth and share-count components. Rights issues, unlike bonus issues, involve genuine new capital and genuine dilution beyond the bonus element embedded in any subscription-price discount, and Nepali dividend announcements routinely bundle cash and bonus components together under a single misleading headline percentage that the investor must unbundle before estimating real cash return. Across all three sectors, Nepali companies have institutional incentives and, at times, documented practices for managing reported earnings — through provisioning discretion, capitalisation-window extension, actuarial assumption timing, one-off income placement, and disclosure-emphasis choices around bonus announcements — none of which typically constitutes outright fraud but all of which require the investor to recompute a normalised earnings figure from the notes rather than accept the headline net profit or EPS at face value. The disciplined NEPSE investor's standing practice, reinforced throughout this chapter, is therefore to read the income statement backward from the notes to the headline rather than forward from the headline alone, treating the reported profit figure as a claim to be verified against its regulatory, seasonal, and actuarial context rather than a fact to be accepted on the strength of the auditor's signature alone.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part V · Chapter 27
The Cash Flow Statement — The Truth Teller
First published 22 Aug 2026 · Last verified 29 Aug 2026
Ask a hundred NEPSE investors what a company earned last quarter, and most can quote the net profit figure within seconds — pulled from a broker's app, a Sharesansar headline, or a WhatsApp forward. Ask the same hundred investors what that company's operating cash flow was, and the room goes quiet. This asymmetry is not an accident of financial literacy alone; it is a structural feature of how retail participation in the Nepal Stock Exchange has evolved. Profit is the number that moves prices, drives EPS-based valuation multiples, and determines dividend headlines. Cash flow is the number that would have warned you, months in advance, about half the corporate disappointments this market has produced over the past decade — the hydropower IPO that could not service its loan in the first monsoon of commercial operation, the manufacturing company whose "record profit" turned out to be a warehouse of unsold, unsaleable inventory, the finance company whose interest income existed mostly on paper as capitalised, uncollected dues. This chapter exists because the cash flow statement is the one financial statement that is almost impossible to flatter indefinitely. A promoter can adjust depreciation policy, defer a provision, or capitalise an expense that should have been written off — and the profit and loss account will absorb the change quietly. Cash does not absorb anything quietly. Cash either came into the bank account or it did not. That is why we call this statement the truth teller, and why a serious analyst of Nepali equities treats it not as a supplementary schedule to be skimmed after the balance sheet, but as the primary cross-examination witness against everything the income statement has just told you.
Lesson 27.1 — Why Nepali Investors Skip the Cash Flow Statement
The neglect of the cash flow statement among Nepali retail investors is not a mystery once you examine how information actually flows through this market. Three structural forces are at work, and understanding them is the first step to correcting for them in your own practice.
The first force is the architecture of financial disclosure itself. NEPSE-listed companies publish quarterly unaudited financial reports in a standardised format prescribed by the Securities Board of Nepal, and this format foregrounds the statement of profit or loss and a condensed balance sheet. A full statement of cash flows, prepared under Nepal Accounting Standard NAS 07 (Nepal's adaptation of IAS 7), is typically available only in the annual audited financial statements, published once a year and often released to shareholders weeks or months after the fiscal year-end (Ashad-end, mid-July) closes. An investor who only ever opens the quarterly report — which is what most trading-app interfaces surface by default — never sees a cash flow statement at all for three out of every four reporting periods. The information exists; it is simply structurally deprioritized in the retail information pipeline.
The second force is the trading culture that NEPSE has inherited, which remains heavily price-momentum and rumour-driven relative to more mature markets. When the dominant question circulating in an investor Facebook group or a trading-floor conversation is "yo share futai badhcha ki?" (will this share keep going up?), the analytical unit of currency becomes the price chart and the headline EPS, not a three-way reconciliation between net profit, working capital movements, and capital expenditure. Cash flow analysis requires patience and a willingness to sit with a document that offers no immediate verdict — it does not tell you to buy or sell, it tells you whether to trust the number that is telling you to buy or sell. That is a less exciting proposition than a breakout candle on a daily chart.
The third force is genuinely structural to the Nepali corporate landscape: a very large share of NEPSE's market capitalisation sits in two sectors — commercial banks and development banks under Nepal Rastra Bank supervision, and hydropower generation companies — whose cash flow statements do not resemble the textbook manufacturing-company template that most introductory finance material (including material translated from Indian or Western sources) uses to teach the subject. A Nepali investor who learns cash flow analysis from a generic YouTube tutorial and then opens the cash flow statement of a bank or a hydropower company under construction will often conclude, wrongly, that the statement "doesn't make sense" or is not useful, and abandon the exercise. Lessons 27.4 and 27.5 of this chapter deal with exactly this problem, because it is the single largest reason cash flow analysis has not taken root among Nepali retail investors even when they have made a genuine effort to learn it.
KEY CONCEPT
The cash flow statement records only transactions that involved actual cash or cash equivalents moving in or out of the company during the period. It has three sections: operating activities (cash generated or consumed by the core, revenue-producing business), investing activities (cash used to acquire or received from disposing of long-term assets and investments), and financing activities (cash raised from or paid to lenders and shareholders, including loan drawdowns, loan repayments, share issuance, and dividend payments). Net profit is an accounting construct built on accrual recognition; cash flow is a ledger of money that actually changed hands. The gap between the two is where most of the useful information in this chapter lives.
None of this means Nepali investors are incapable of the analysis — it means the habit has to be deliberately built rather than absorbed by osmosis from the market's existing information culture. The remainder of this chapter is designed to close that gap directly, sector by sector, because a cash flow statement read without sector context in Nepal is worse than not read at all: it produces confident, wrong conclusions.
Lesson 27.2 — The Three Rivers: Reading Operating, Investing, and Financing Activities
Every cash flow statement, regardless of sector, is organised around three rivers of cash, and the analytical skill is not memorising the definitions but learning to read the combination of signs (positive or negative) across all three as a single narrative about where the company is in its life cycle and how honestly it is being run.
Cash flow from operating activities, abbreviated CFO, captures cash generated by the company's core, day-to-day, revenue-producing business — cash received from customers, minus cash paid to suppliers, employees, and for operating expenses, minus tax paid, adjusted (under the indirect method, which is what nearly all NEPSE-listed companies use) starting from net profit and reversing out non-cash items and working capital movements. This is the section that should, over any reasonable multi-year window, track profit. When it consistently fails to, that divergence is the single most important signal this chapter will teach you to read, and it is the subject of Lesson 27.6.
Cash flow from investing activities, CFI, captures cash spent acquiring property, plant, and equipment, intangible assets, and investments in other entities (including subsidiaries and associates), netted against cash received from disposing of such assets and from investment income where NAS 07 permits its classification here. For a manufacturing company adding a new production line, an insurance company deploying premium float into government securities, or a hydropower company pouring cash into a powerhouse and penstock, this figure is structurally negative, and that negativity is not automatically a warning sign — it depends entirely on what stage of the investment cycle the company is in and whether that capex is funded sustainably, which is precisely what the third river tells you.
Cash flow from financing activities, CFF, captures cash raised from or returned to the providers of capital — proceeds from long-term and short-term borrowing, repayment of loan principal, proceeds from share issuance (including IPOs, further public offerings, and rights issues, both extremely common financing events on NEPSE), and dividend payments (cash dividends only; stock dividends, ubiquitous on NEPSE, are non-cash and never appear here). This section tells you how the company is bridging any gap between what its operations generate and what its investment ambitions require.
A short subheading is useful here to anchor the four canonical life-cycle patterns an investor should be able to recognise on sight, because the interpretation of any single sign depends entirely on the other two.
The Four Cash Flow Signatures
A mature, self-funding company shows CFO positive and comfortably larger than CFI (which is modestly negative, reflecting maintenance capex), with CFF modestly negative as the company pays down debt and distributes dividends out of genuinely earned cash. This is the signature you want to see in an established, dividend-paying commercial bank holding company subsidiary, a mature manufacturing name, or a hydropower company several years past its commercial operation date with its construction-era debt substantially amortised.
A growth company shows CFO positive but CFI heavily negative as the company reinvests aggressively, with CFF positive as it raises fresh debt or equity to fund growth beyond what operations alone can support. This is a healthy pattern provided CFO is real and growing — the danger is when a company presents itself as being in this phase while its CFO is in fact weak or negative, meaning the entire structure is financed by lenders and shareholders rather than earned.
A construction-phase or pre-revenue company — the dominant pattern for a Nepali hydropower company still building its plant — shows CFO near zero, mildly negative, or reflecting only interest income on idle project funds; CFI heavily and often overwhelmingly negative as capex is deployed; and CFF strongly positive as syndicated project debt and, near listing, IPO proceeds are drawn down to fund that capex. This pattern is entirely normal and expected during construction, and mistaking it for financial distress — or, just as dangerously, failing to distinguish it from genuine distress once the plant should already be operating — is one of the most common and costly interpretive errors a NEPSE hydropower investor can make. Lesson 27.4 is devoted to this signature.
A distressed company shows CFO negative or deteriorating even as reported net profit is flat or growing, CFI negative or, worse, positive because the company is selling assets to survive, and CFF positive because the company is drawing down fresh short-term borrowing simply to plug the operating cash gap and keep the lights on, or negative and shrinking as lenders refuse to roll over facilities. This is the signature that Lesson 27.6 will teach you to detect early, before it shows up in the price.
WATCH FOR
A single quarter or even a single year of negative CFO is not automatically alarming — seasonal working capital swings, a large one-off tax payment, or a temporary receivables buildup around a festival season sales push can all produce a negative quarter that reverses. What should concern you is a multi-year trend: three to five consecutive annual reports (from audited financial statements, not quarterly snapshots which are far noisier) in which CFO consistently lags reported net profit by a wide and non-narrowing margin.
Lesson 27.3 — Reconciling Net Profit to Operating Cash Flow
The indirect method reconciliation is the single most information-dense paragraph in any NEPSE annual report's notes to accounts, and it is also the most consistently skipped by retail readers, who see a column of additions and subtractions and assume it is mechanical bookkeeping rather than a narrative. It is not mechanical. Every line in that reconciliation is a sentence about how the company's economic reality differs from its reported profit, and reading it fluently is a core, learnable skill.
The reconciliation begins with net profit before tax (or net profit after tax, with tax then treated as a separate adjustment — NEPSE annual reports vary in convention, so check the starting line before comparing companies) and proceeds through three categories of adjustment. First, non-cash items are added back or subtracted: depreciation and amortisation (always added back, since it reduced profit but consumed no cash), provisions for doubtful debts and impairment (added back when newly created, since they reduced profit without a cash outflow), unrealized foreign exchange gains or losses, and fair value gains or losses on investments, particularly relevant for insurance companies and banks holding available-for-sale securities. Second, working capital movements are adjusted: an increase in trade receivables or inventory is subtracted (cash tied up in growth, not yet collected), while an increase in trade payables is added back (the company is using supplier credit as a source of cash). Third, items relating to interest, dividends received, and tax paid are reclassified according to the company's stated accounting policy, since NAS 07 permits some flexibility here, particularly for financial institutions.
Consider a hypothetical NEPSE-listed manufacturing company, illustrative "Himal Industries Ltd.", reporting the following for a fiscal year, to see how a superficially strong profit figure can mask a much weaker cash reality.
Reconciliation line (NPR millions)
Amount
Net profit after tax
450
Add: Depreciation and amortisation
180
Add: Provision for doubtful debts
60
Less: Increase in trade receivables
(320)
Less: Increase in inventory
(210)
Add: Increase in trade payables
90
Less: Interest income reclassified to investing activities
(25)
Net cash flow from operating activities
225
On the face of the profit and loss account, Himal Industries looks like a company that grew profit by a healthy margin over the prior year. But the reconciliation shows that of the 450 million in reported profit, 320 million was consumed by a buildup in trade receivables and a further 210 million by rising inventory, only partially offset by 90 million of additional supplier credit and 240 million of non-cash addbacks. The company actually generated only 225 million in real operating cash — roughly half of reported profit. This is not necessarily fraud; it may reflect a genuine, temporary sales push funded by generous credit terms extended to distributors ahead of a festival season, which is an extremely common pattern in Nepali consumer goods and cement companies around Dashain and Tihar. But it is exactly the kind of pattern an investor must interrogate rather than simply celebrate, because the critical follow-up question is whether that receivables buildup reverses (collected in cash the following quarter, confirming it was genuine, timing-driven credit sales) or compounds year after year (never collected, confirming it is either deteriorating customer credit quality or, in the worst case, revenue that was recognised but never really existed).
PRACTICAL TOOL
Compute the cash conversion ratio for any NEPSE company you are evaluating: CFO divided by net profit after tax, using figures from the audited annual report. A healthy, mature company typically shows a ratio at or above 0.8–1.0 sustained over a three-year average. A ratio consistently below 0.5, especially if declining year over year, is a flag that earnings are increasingly built on paper (receivables, inventory, capitalised costs) rather than collected cash, and deserves a deeper look at the notes on receivables ageing and related-party transactions before you extend the valuation multiple the market is currently paying for that profit.
Two further wrinkles matter specifically for Nepal. First, related-party transactions are common among NEPSE-listed companies with promoter-controlled group structures, and a receivables buildup owed by a related entity rather than an arm's-length customer is a materially different (and more concerning) fact pattern than one owed by an unrelated distributor — the notes to the financial statements disclose related-party receivables separately, and this is one of the few places in a Nepali annual report where five minutes of reading directly protects your capital. Second, because a large share of NEPSE-listed non-financial companies still report under a mix of NFRS and NFRS-for-SMEs depending on their size classification, the precision and disclosure depth of the cash flow statement itself varies; smaller listed companies sometimes present a cash flow statement with less granular working-capital line-item disaggregation, which means you may need to compute movements yourself directly from two consecutive years' balance sheets when the cash flow statement's own disclosure is thin.
Lesson 27.4 — Hydropower's Cash Flow Signature: Construction Financing and the Capex Mountain
No sector on NEPSE rewards or punishes cash flow illiteracy as dramatically as hydropower, and none is more misread by investors who apply generic cash flow heuristics without adjusting for the sector's fundamentally different life cycle. Hydropower generation companies in Nepal are, almost without exception, built, financed, and listed in a sequence that produces a highly distinctive and entirely predictable cash flow signature during the construction phase — one that looks financially precarious to an untrained eye and is, in fact, the normal and necessary shape of project finance.
A typical Nepali hydropower project is financed on a debt-to-equity structure commonly in the range of 70:30 to 80:20, with the debt portion syndicated among Nepali commercial banks and development banks (often a dozen or more lenders in a single syndicate for a mid-sized project, given single-obligor lending limit constraints imposed by Nepal Rastra Bank on individual banks), frequently supplemented by contributions from the Employees Provident Fund, Citizen Investment Trust, and increasingly by hydropower-focused debt instruments. Under the Hydropower Development Policy and related regulatory requirements, a project company must offer a defined portion of its shares to the public and to project-affected local residents, which is why nearly every hydropower name on NEPSE went through an IPO either during construction or shortly after reaching commercial operation date — this is the source of the periodic wave of hydropower IPOs that Nepali retail investors have become accustomed to applying for.
During the construction period, which for a run-of-river project of meaningful size typically runs four to seven years from financial closure to commercial operation, the cash flow statement of the project company will show revenue and CFO near zero (limited to interest earned on idle project funds parked in bank accounts pending disbursement, and occasionally minor other income), CFI deeply and increasingly negative as the company pays contractors for civil works, procures and imports electromechanical equipment and turbines, and builds transmission interconnection infrastructure, and CFF strongly positive as loan tranches are drawn down from the lending syndicate according to the disbursement schedule tied to construction milestones, supplemented by equity calls from promoters and IPO proceeds when the public offering occurs.
CASE IN POINT
Nepal's own hydropower history offers a clean illustration of this life cycle. Projects such as Upper Tamakoshi and Chilime, now referenced throughout Nepali financial commentary as bellwether hydropower names, both went through extended construction phases in which annual reports would have shown exactly this signature — negative to negligible CFO, heavily negative CFI running into the billions of rupees as the powerhouse, dam works, tunnel, and penstock were built, and strongly positive CFF as syndicated term loans were drawn and, ahead of or around commercial operation, IPO proceeds were raised. An investor who examined either company's cash flow statement during its construction years and concluded "this company generates no operating cash, avoid" would have been technically correct about the fact and completely wrong about the investment conclusion, because the entire economic logic of a greenfield hydropower asset is that operating cash flow only begins once the plant is commissioned and starts selling electricity to the Nepal Electricity Authority under its power purchase agreement.
The analytical task for a hydropower investor is therefore not to ask "is CFO positive," which is close to a meaningless question before commercial operation date, but to ask three different questions. First, is capex tracking the disclosed project cost estimate, or is CFI expanding faster than the original detailed project report projected — a classic early warning of cost overruns from geological surprises in tunneling, contractor disputes, or currency depreciation on imported equipment, all of which are common and well-documented risks in Nepali hydropower construction given difficult Himalayan terrain and heavy reliance on imported turbines and generators typically denominated in foreign currency. Second, is the financing mix (the composition of CFF) staying within the disclosed debt-equity structure, or is the company drawing more debt than originally planned to cover overruns — visible directly by comparing cumulative loan drawdowns in the cash flow statement against the originally disclosed project debt ceiling, a figure typically stated in the prospectus at IPO. Third, and most important once the plant approaches commercial operation date, does CFO actually turn positive and does it stabilise at a level consistent with the company's disclosed installed capacity, expected plant load factor (itself dependent on hydrology and, for run-of-river plants, dry-season flow reduction), and the tariff schedule in its power purchase agreement — because this is the moment the company transitions from the construction signature to the mature signature, and a plant that fails to show a clean transition (continuing to post weak CFO well after its stated commercial operation date) is signalling either an overstated capacity factor, disputes with NEA over energy off-take and payment, or transmission evacuation constraints preventing the plant from actually selling all the energy it generates.
WARNING
A hydropower company can report a positive net profit even during years when its plant is barely generating meaningful cash, because interest expense on construction-period debt is often capitalised into the cost of the asset under construction rather than expensed, and other accounting choices around deferred revenue expenditure can flatter the income statement before commercial operation. The cash flow statement, and specifically the financing-activities section showing rising cumulative debt, is the corrective lens: rising debt with no corresponding rise in genuine operating cash generation past the stated commercial operation date is the single clearest hydropower red flag on NEPSE, more reliable than any ratio derived purely from the income statement.
A further Nepal-specific wrinkle deserves explicit mention: dry-season and wet-season seasonality. The overwhelming majority of NEPSE-listed hydropower plants are run-of-river facilities without significant storage, meaning their generation — and therefore their operating cash flow — is heavily concentrated in the monsoon and post-monsoon months (roughly Ashad through Mangsir) and materially weaker in the dry winter and pre-monsoon months (roughly Falgun through Jestha), when river flows drop and, in many cases, plants generate at a small fraction of installed capacity. A quarterly cash flow read for an operating hydropower company must always be interpreted against this seasonal backdrop; a weak CFO quarter in Falgun is structurally expected and tells you nothing about earnings quality on its own, whereas the same weakness in a monsoon quarter is a genuine signal worth investigating.
Lesson 27.5 — Banks and Financial Institutions: Why Their Cash Flow Statements Look Upside Down
If hydropower confuses investors by looking financially precarious when it is merely pre-revenue, banks and other Nepal Rastra Bank-licensed financial institutions (development banks, finance companies) confuse investors in the opposite direction: by presenting a cash flow statement that structurally classifies as "operating" a set of activities that an investor trained on manufacturing-company templates instinctively expects to see under investing or financing.
The reason is definitional and rooted in NAS 07 itself, which both Nepali and international accounting standards apply consistently: operating activities are defined as the principal revenue-producing activities of the entity. For a bank, the principal revenue-producing activity is not manufacturing and selling a physical product — it is taking deposits and making loans. Consequently, under a bank's cash flow statement, cash flow from operating activities includes net increase or decrease in customer deposits, net increase or decrease in loans and advances to customers, interest received from borrowers, interest paid to depositors, and purchases and sales of trading securities held for the bank's own liquidity and treasury management. Items that an investor might instinctively expect to find under "financing" — the growth in deposits — sit inside operating activities, because for a bank, a deposit is not borrowed capital in the sense a corporate bond is; it is the raw material of the business, akin to inventory purchases for a trading company. Conversely, items an investor might expect under "operating" — such as a bank's own long-term subordinated debentures issued to shore up its capital adequacy ratio, a common NEPSE event given Nepal Rastra Bank's capital requirements — sit properly under financing activities, since that is capital raised by the bank as an entity, distinct from its deposit-taking function.
This has a specific and important practical consequence for reading NEPSE bank cash flow statements: operating cash flow for a rapidly growing bank can swing enormously and unpredictably from quarter to quarter and year to year, driven overwhelmingly by the net change in deposits and loans rather than by anything resembling the bank's underlying profitability or earnings quality. A bank that grows its deposit base rapidly in a given year (common during periods of loose liquidity and aggressive branch expansion, both frequent occurrences among Nepali commercial banks jostling for market share) will show a large positive swing in operating cash flow purely from that deposit growth, even if its actual profitability, net interest margin, and asset quality are stagnant or worsening. Conversely, a bank experiencing a deposit outflow — which does periodically happen in Nepal during liquidity crunches, most notably during the tightened-liquidity periods the banking system has experienced when credit-to-deposit ratios pressed against the regulatory ceiling — will show negative operating cash flow even if its core lending book remains healthy and profitable, purely because withdrawals exceeded fresh deposits during that reporting window.
REGULATORY DETAIL
Nepal Rastra Bank prescribes the format of financial statements for commercial banks, development banks, and finance companies under its unified directives, and NFRS-compliant Nepali banks present the statement of cash flows with operating activities built around net interest and fee income adjusted for changes in operating assets and liabilities — specifically, changes in loans and advances to customers, changes in deposits from customers, changes in balances with other banks and financial institutions, and changes in other operating assets and liabilities. This structure follows the standard international template for financial-institution cash flow statements and is consistent across nearly all NEPSE-listed banks and finance companies, which makes cross-company comparison within the sector reliable even though the format diverges sharply from the non-financial company template.
Because of this structural quirk, the cash conversion ratio (CFO divided by net profit) that works well as a red-flag detector for a manufacturing or trading company is close to useless, on its own, for a bank — a bank can post a ratio of 3.0 in a year of strong deposit growth and 0.2 or negative in a year of deposit contraction, with underlying earnings quality essentially unchanged in both years. The correct adaptation for bank analysis is threefold. First, separate the deposit-and-loan-driven "balance sheet growth" component of operating cash flow from the "core earnings" component by looking instead at net interest income received in cash versus accrued interest income recognised on the income statement — a growing gap between accrued interest income and interest actually received in cash is the bank-specific equivalent of the receivables red flag in Lesson 27.3, and it typically shows up as growing "interest receivable" or a rising proportion of non-performing or restructured loans where interest is being capitalised rather than collected, a pattern regulators and analysts watch closely industry-wide. Second, examine financing activities specifically — proceeds from debenture issuance, subordinated term debt, and rights share proceeds — since a bank persistently raising fresh capital through debentures or rights issues to maintain its capital adequacy ratio is telling you something about the durability of its internally generated capital that the profit and loss account, showing a smooth, positive EPS trend, will not tell you on its own. Third, track dividend payments in cash against the bonus-share-heavy distribution pattern that dominates the Nepali banking sector — because Nepali banks have historically distributed a large share of shareholder returns as bonus shares (a non-cash stock dividend that never appears in the cash flow statement, since it involves no cash movement, only a capitalisation of reserves into paid-up capital) rather than cash dividends, and a bank whose CFF shows persistently thin or negligible cash dividend outflows relative to its reported profit, year after year, is signalling that it is retaining essentially all its capital internally to support balance sheet growth and regulatory capital ratios — informative for a long-term holder who is evaluating whether to expect cash income or capital-structure dilution from continued bonus issuance.
The following table summarises how the same three-section cash flow statement structure diverges in substance across the three NEPSE sector archetypes this chapter has covered, which is worth returning to whenever you open an unfamiliar company's annual report.
Sector archetype
Typical CFO driver
Typical CFI driver
Typical CFF driver
Reading trap for investors
Manufacturing / trading company (mature)
Cash collected from customers less payments to suppliers, staff, tax
Maintenance and expansion capex on plant and equipment
Working capital loan movements, term loan repayment, cash dividends
Assuming profit growth automatically means cash growth; ignoring receivables and inventory buildup
Having established how to read the three rivers and how their meaning shifts across sectors, this final lesson consolidates the chapter's central practical skill: using the cash flow statement as a systematic earnings-quality screen across your NEPSE watchlist, applied consistently, before you let a headline profit number move your capital.
The core diagnostic is the multi-year comparison between the growth rate of reported net profit and the growth rate of operating cash flow, examined over no fewer than three consecutive audited annual reports, because a single year proves very little in either direction. When both grow together, roughly in step, across multiple years, you are looking at earnings that are being converted into cash at a stable rate — the highest-confidence pattern available to a fundamental investor. When profit grows steadily while CFO stagnates, declines, or turns negative, you are looking at a company whose reported success is increasingly a function of accounting recognition rather than cash collection, and the burden of proof shifts to management to explain, credibly, why (a genuine, temporary strategic reason such as an intentional credit-financed sales expansion into a new geography, verifiable against subsequent collection, is different from a structural, unexplained, and worsening gap).
Short subheading: the mechanisms behind the gap
There are several distinct mechanisms by which a NEPSE-listed company's profit can outrun its cash, and distinguishing between them changes how seriously you should treat the divergence. Revenue recognised on credit but not yet collected is the most common and often the most benign, particularly around Nepali festival seasons when generous trade credit to distributors is standard commercial practice — the test is whether the receivable is subsequently collected in the following one or two quarters, visible by tracking the receivables balance across successive quarterly reports. Capitalisation of expenses that should arguably have been expensed — routine repair and maintenance costs classified as capital improvements, or, in extreme cases, even portions of what should be operating expenditure quietly added to the cost of an asset under construction — inflates both profit and the balance sheet simultaneously while consuming cash through the investing section rather than reducing operating profit, and is best detected by monitoring whether capex is growing suspiciously in step with revenue in a business that shouldn't require proportional new capital investment. Related-party transactions, discussed in Lesson 27.3, can generate revenue and profit that are real in an accounting sense but represent cash that may never actually be collected on commercial terms, or that round-trips within a promoter group rather than reflecting genuine third-party demand. Interest and fee income accrued but not received — the bank-specific version discussed in Lesson 27.5 — inflates income statement profitability for financial institutions carrying a growing book of stressed or restructured loans where interest is added to the loan balance rather than paid in cash, a pattern regulators specifically monitor through non-performing loan and restructured loan disclosures that NRB requires banks to publish.
CAUTION
A negative or declining CFO trend is a prompt for deeper investigation, not an automatic sell signal, and treating it as an automatic sell signal is itself a common analytical error. The correct discipline is to open the notes to accounts, examine the ageing schedule for trade receivables (a required NFRS disclosure), check whether the increase is concentrated in related parties or spread across the customer base, and compare against the same company's own history and its closest listed peers in the same sub-sector before drawing a conclusion. Cash flow analysis sharpens your questions; it does not substitute for asking them.
A second, complementary diagnostic worth building into a routine NEPSE screening practice is free cash flow, computed simply as operating cash flow minus capital expenditure (the latter taken from the investing-activities section, specifically purchase of property, plant, and equipment). For a mature, non-hydropower NEPSE company, consistently positive and growing free cash flow, sustained over several years, is the clearest available evidence that the business can fund its own growth, service its debt, and support a genuine cash dividend policy without repeatedly returning to shareholders or lenders for fresh capital — precisely the profile of a durable long-term holding rather than a story stock dependent on continuous external financing or continuous upward reappraisal by new buyers.
PRACTICAL TOOL
Build a simple three-line annual tracking table for every core holding in your NEPSE portfolio, updated each year when the audited annual report is published: net profit after tax, cash flow from operating activities, and capital expenditure. From these three lines you can compute the cash conversion ratio (CFO ÷ net profit) and free cash flow (CFO − capex) for every holding in under five minutes per company, and a five-year view of these two derived figures will tell you more about the durability of a company's reported earnings than any single year's EPS growth percentage, P/E ratio, or price chart pattern.
It is worth closing this lesson with an explicit caution against over-mechanizing the analysis. Cash flow ratios are diagnostic instruments, not verdicts delivered in isolation from business understanding. A hydropower company mid-construction will fail every generic cash-conversion test and be, quite possibly, an excellent long-term investment at the right entry valuation. A mature trading company can pass every generic cash-conversion test on paper while sitting on receivables from a single, fragile related-party customer that could unravel in one bad quarter. The discipline this chapter teaches is not a scorecard to be applied blindly, but a habit of mind: read the income statement's story, then open the cash flow statement and ask whether the cash moved the way the story implies it should have, adjusted always for what sector, what life-cycle stage, and what season you are looking at. That habit, applied consistently across a NEPSE portfolio over years, is one of the most durable analytical edges a retail investor in this market can build, precisely because so few of your fellow market participants currently bother to build it.
Chapter recap
The cash flow statement is underused by Nepali retail investors primarily because quarterly reports rarely include it, because the market's dominant information culture rewards price momentum and headline EPS over patient reconciliation, and because generic cash flow training does not prepare investors for the two sectors — banks and hydropower — that dominate NEPSE's market capitalisation and structurally do not follow the manufacturing-company template most tutorials teach. Reconciling net profit to operating cash flow through the indirect method reveals whether reported earnings were actually collected in cash or remain tied up in receivables, inventory, or capitalised costs, and computing a simple cash conversion ratio (CFO divided by net profit) sustained over multiple years is one of the fastest and most reliable earnings-quality screens available to a NEPSE investor. Hydropower companies show a predictable and normal construction-phase signature of negligible operating cash flow, deeply negative investing cash flow from capex, and strongly positive financing cash flow from syndicated debt and IPO proceeds, and the critical test is not whether this pattern exists before commercial operation date but whether operating cash flow cleanly and durably turns positive once the plant should be generating and selling power under its PPA, adjusted always for the strong monsoon-versus-dry-season seasonality that governs run-of-river generation. Commercial banks and financial institutions classify deposit and loan movements as operating activities rather than financing activities because taking deposits and making loans is their principal revenue-producing activity under NAS 07, which means their operating cash flow is driven substantially by balance sheet growth rather than core profitability, and bank-specific analysis requires separately tracking the gap between accrued and collected interest income and monitoring debenture and rights-issue financing activity rather than relying on the standard cash conversion ratio. A negative or declining operating cash flow trend should prompt deeper investigation into receivables ageing, related-party concentration, and capitalisation policy rather than an automatic buy or sell decision, since the same raw signal can mean either temporary, benign seasonal financing or genuine, worsening earnings manipulation depending entirely on context available in the notes to accounts. Building a simple, consistently updated three-line tracking table of net profit, operating cash flow, and capital expenditure for every core holding, reviewed each year against the newly published audited annual report, converts this chapter's analytical framework into a durable, repeatable portfolio discipline that very few other participants in this market currently practice.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part V · Chapter 28
Red Flags in Nepali Financial Statements
First published 22 Aug 2026 · Last verified 29 Aug 2026
A balance sheet is a photograph, not an X-ray. It shows you where the assets and liabilities sit on the day the shutter clicks, but it says nothing about the health of the tissue underneath — whether the loan marked "standard" is actually being serviced, whether the "other income" line hides a one-off gain dressed up as recurring profit, or whether the promoter family on the board has quietly routed working capital to a sister company that never appears in the notes. Every investor who has followed this book through the previous chapters — reading NEPSE-listed companies' balance sheets, income statements, cash flow statements, and the disclosure architecture built on Nepal Financial Reporting Standards (NFRS) — now has the vocabulary to read a financial statement. This chapter teaches something different and, in a market as young, thinly supervised, and promoter-dominated as Nepal's, considerably more urgent: how to read what a financial statement is trying not to say. Red-flag analysis is not cynicism. It is the recognition that in every capital market, and especially in a frontier market with a securities regulator still building its enforcement muscle and a central bank fighting a structurally weak banking sector, some fraction of reported numbers are managed, smoothed, or simply false, and the investor's first line of defence is pattern recognition, not trust.
Nepal's specific vulnerabilities are not generic emerging-market boilerplate. They arise from a particular institutional configuration: a banking system in which promoter families frequently sit on both sides of the credit relationship, a securities market where free float is thin enough that a handful of insiders can move a share price without moving the underlying business, an audit profession whose largest firms are small relative to the balance sheets they certify, and a regulatory pair — the Securities Board of Nepal (SEBON) for the capital market and Nepal Rastra Bank (NRB) for banks and financial institutions (BFIs) — that has, at various points, publicly acknowledged the gap between what companies report and what is actually happening in their loan books and boardrooms. This chapter works through those vulnerabilities lesson by lesson: the loan-quality distortions particular to Nepali banks, the related-party web that connects listed companies to their promoter families, the signals embedded in auditor behaviour, the documented episodes where the cover eventually came off, and finally a practical checklist you can run against any NEPSE-listed annual report before you commit capital to it.
Lesson 28.1 — Why Nepal's Institutional Setting Makes Red Flags More Likely, Not Less
Every emerging capital market textbook will tell you that disclosure quality improves with regulatory capacity, auditor independence, and market depth. Nepal sits at an early stage on all three dimensions simultaneously, and that combination compounds rather than merely adds up. Consider the ownership structure first. A large share of NEPSE-listed companies, and nearly all class "A" commercial banks, "B" development banks, and "C" finance companies, were founded and remain substantially controlled by a promoter group — often a single family or a tight cluster of business partners — who typically retain the statutory minimum promoter shareholding (subject to NRB and SEBON floors) while the balance trades in public hands. This is not unusual in South Asia, but in Nepal the promoter group frequently also controls a constellation of private, unlisted companies in trading, real estate, hospitality, and manufacturing that transact routinely with the listed entity — as borrowers, as suppliers, as landlords, as guarantee counterparties. The formal related-party disclosure regime under NFRS exists, but the enforcement muscle that would catch an incomplete or softened related-party note is thin relative to the volume of transactions it must police.
Second, consider market depth. NEPSE's free float in most counters is a small fraction of total paid-up capital, and daily traded volume in a majority of listed scrips is thin enough that a coordinated group of a few dozen traders can move a stock 5 to 10 percent in a session. SEBON's own surveillance has, in past market cycles, flagged dozens of counters simultaneously for share prices detached from any plausible earnings trajectory — a 2021-22 SEBON review identified roughly fifty listed companies whose price movements it considered inconsistent with underlying fundamentals and irregular in trading pattern, a finding that speaks less to any single company's fraud and more to how easily thin float and coordinated buying can manufacture the appearance of business momentum that a hasty investor mistakes for validation of the financial statements. A rising share price is not evidence that the numbers behind it are honest; in a shallow market it can be the opposite — a smokescreen that discourages the very scrutiny a red flag would otherwise attract.
Third, and most structurally important for Part V of this book, consider audit market concentration and capacity. Nepal has a large number of registered audit firms under the Institute of Chartered Accountants of Nepal (ICAN), but the pool of firms with the balance-sheet scale, the sectoral experience in banking and insurance, and the bargaining power to resist client pressure is small. A finance company or a mid-tier development bank with billions of rupees in loan assets may be audited by a two- or three-partner firm whose total fee from that one client is a material share of the firm's annual revenue — a dependency that, textbook auditor-independence theory predicts, erodes the auditor's willingness to push back on management's preferred loan classification or provisioning estimate. This is not an allegation against any specific firm; it is a structural description of the market for audit services in Nepal, and it is precisely the kind of structural condition that should raise an investor's baseline skepticism before they open a single annual report.
KEY CONCEPT
A red flag is not proof of fraud. It is a probabilistic signal — a pattern that, across many companies and years, correlates with a higher incidence of misstatement, aggressive accounting, or outright concealment. Your job as an investor is not to prosecute a case; it is to raise your required margin of safety, demand more disclosure, or walk away when enough flags accumulate on one counter.
Fourth, cooperative and BFI regulatory perimeters in Nepal are not uniform. Savings and credit cooperatives, which are not NRB-supervised in the same manner as commercial banks, development banks, and finance companies, have produced some of the country's most damaging financial collapses precisely because they sat in a supervisory gap between the Department of Cooperatives, provincial authorities, and NRB. While cooperatives are not NEPSE-listed and therefore fall outside a strict definition of "Nepali financial statement red flags for equity investors," the pattern of promoter capture, related-party lending, and delayed disclosure that produced the cooperative collapses recurs — in more diluted and more regulated form — inside licensed BFIs and NEPSE-listed non-banking companies. Understanding the cooperative story is not a digression; it is the clearest, least obscured version of a disease that appears in milder form throughout the listed sector.
Lesson 28.2 — Evergreening: How a Bad Loan Learns to Look Good
The single most consequential red flag category for any investor holding, or considering, shares in a NEPSE-listed commercial bank, development bank, or finance company is loan-quality evergreening — the practice by which a bank keeps a non-performing or under-stressed loan reporting as "performing" or "pass" through mechanisms that generate no real cash recovery. Evergreening matters more in Nepal than in a mature banking market for three reasons: banks constitute a large share of NEPSE's total market capitalisation and trading turnover, the accounting and prudential framework leaves real room for interpretation in how restructured exposures are classified, and NRB's own supervisory findings — publicly acknowledged in loan portfolio reviews conducted across the banking system — have described the practice as widespread rather than isolated.
The mechanics of evergreening
The classic evergreening technique works like this. A borrower's cash flows deteriorate and they stop servicing interest on schedule. Rather than classify the loan as non-performing — which under NRB's loan classification and loss provisioning directive would force the bank to move the account into "sub-standard," "doubtful," or "loss" category, hold materially higher provisioning, and, under NFRS 9's expected-credit-loss framework, take an income-statement hit — the bank instead extends a new loan, often to a related entity or through a fresh facility structured as working capital, and the proceeds are used to pay the interest due on the original loan. From the accounting standpoint, interest income has been "received," the original loan remains classified as standard or watch-list, and the bank's reported non-performing loan (NPL) ratio, capital adequacy ratio, and profitability all look healthier than the underlying credit reality. The debt has not been serviced from the borrower's own operating cash flow; it has been serviced from a new loan that increases, rather than reduces, the bank's real exposure to that borrower.
A closely related technique is interest capitalisation: rather than writing off unpaid interest or recognising it as a doubtful recovery, the bank capitalises the unpaid interest into the loan's outstanding principal, so the loan balance grows even as no new cash has actually been advanced or repaid. This inflates the asset side of the balance sheet with an amount that is, in economic substance, unrecovered and possibly unrecoverable interest income already booked to the profit and loss account in an earlier period. NRB's Guideline on Recognition of Interest Income, first issued in 2019 and materially tightened through a 2025 guidance note that amended provisioning and collateral valuation requirements tied to NFRS 9, exists specifically because this technique had become common enough across the banking sector to warrant a system-wide directive rather than case-by-case supervisory correction. The 2025 tightening — requiring more conservative recognition of interest income on stressed accounts and stricter collateral revaluation before a restructured loan can be treated as adequately secured — was itself an implicit admission by the regulator that the prior framework had been exploited.
REGULATORY DETAIL
Under the NFRS carve-out that ICAN and NRB jointly apply to Nepali banks and financial institutions, a BFI is required to hold loan loss provisions at the higher of (a) the NFRS 9 expected-credit-loss calculation or (b) the provisioning level mandated by NRB's own loan classification directive. This carve-out exists precisely because regulators judged that a bank-specific, model-driven NFRS 9 estimate alone was too easily managed downward by assumptions about future recoveries and collateral values. When you read a bank's notes to accounts, check which of the two bases actually produced the reported provision — if it is consistently the NFRS 9 figure rather than the NRB floor, ask why the model is producing a lower number than the regulator's own rule of thumb.</br>
The clearest system-wide evidence that evergreening is a live and material problem, not a theoretical one, came via NRB's own supervisory action. In a loan portfolio review process approved for roughly ten major commercial banks, the central bank's on-site review teams found — according to reporting on the exercise — recurring "malpractices" in loan recovery processes and credit-risk reporting, including underreported credit risk relative to what banks had been disclosing in their own regulatory returns and financial statements. This is a significant data point for any equity investor: it means the regulator that supervises these institutions most closely, with statutory inspection powers no outside analyst possesses, concluded that the reported NPL figures across a meaningful slice of the banking sector understated the true credit-risk position. If NRB's own on-site teams found this gap, an investor working only from published financial statements should assume the true asset quality of a bank counter is very likely worse than the headline NPL ratio, not better, and should treat the reported ratio as a floor rather than an estimate.
What this means for reading a bank's financial statements
Practically, an investor screening a NEPSE-listed bank or finance company for evergreening risk should look at several linked indicators together, because no single ratio is dispositive. First, compare the growth rate of gross loans and advances to the growth rate of the bank's own deposit base and net interest income; a loan book growing meaningfully faster than the deposit franchise funding it, especially when concentrated in a handful of large borrower groups, is more vulnerable to roll-over dependency. Second, examine the "restructured and rescheduled loans" disclosure required under NRB's directives — a rising trend in restructured loans as a percentage of gross loans, especially one that is not mirrored by a rising NPL ratio, is close to a textbook evergreening signature: the bank is moving stress into a bucket that avoids NPL classification rather than resolving it. Third, look at the trend in "interest receivable" or "interest suspense" accounts relative to interest income; a bank whose accrued-but-uncollected interest is rising as a share of total interest income is accumulating income that exists on paper but not in the bank's actual cash position. Fourth, track the movement in loan loss provisioning coverage ratio (provisions held against NPLs) over several years — a coverage ratio that is falling even as the absolute rupee amount of loans grows suggests the bank is not keeping pace with the true credit-risk build-up, whether or not the reported NPL ratio itself looks stable.
WATCH FOR
A bank whose reported NPL ratio is flat or improving while its provisioning coverage ratio is falling and its "loans and advances to related parties or group companies" note is silent or minimal is showing the classic signature of an evergreened book: stress is being hidden through reclassification and restructuring rather than resolved through recovery or write-off.
Lesson 28.3 — Related-Party Transactions and the Promoter Web
If evergreening is the mechanism by which a bank hides credit stress, related-party transactions (RPTs) are frequently the channel through which that stress originates and through which value leaks out of a listed company toward its promoter group in the first place. NFRS requires disclosure of related-party transactions and balances in the notes to accounts — parties are related when one controls, is controlled by, or is under common control with another, which in the Nepali context typically means the promoter family, the companies they separately own, the directors and their close relatives, and any entity where a director or key management personnel holds significant influence. The disclosure requirement is not the problem; the problem is the gap between the letter of the requirement and its practical enforcement, and the specific ways promoter groups in Nepal have structured their affairs to keep transactions technically compliant while economically opaque.
The pattern repeats across sectors. In banking and finance companies, promoter-linked entities appear as large borrowers, and while NRB's single-obligor and group-lending limits exist precisely to cap this concentration, a determined promoter group can structure lending through multiple nominally separate borrower entities that are related in substance but not disclosed as a single group in the loan book — a technique regulators refer to as "borrower splitting" and one that both defeats single-obligor limits and obscures the true concentration risk from anyone reading only the published financial statements. In manufacturing, trading, and hospitality companies, related-party transactions more often take the form of sales to, or purchases from, a promoter-controlled private company at prices that are not obviously arm's length, or working-capital advances to a sister concern that are labelled as trade receivables rather than as loans to a related party — a classification choice that keeps the transaction out of the more scrutinized RPT note and buries it instead inside an operating receivable balance that investors rarely interrogate.
The cooperative sector, though outside SEBON's listed-company perimeter, is Nepal's starkest illustration of what happens when related-party lending is allowed to run unchecked for years inside a weakly supervised deposit-taking institution, and it is directly relevant to the equity investor because several of Nepal's largest cooperative collapses fed capital losses back into shareholders, depositors, and — in at least one high-profile case — into a political controversy involving a serving deputy prime minister, illustrating how deeply promoter-linked lending can become entangled with governance failure at the very top of an institution. Cooperatives such as Oriental Cooperative and a cluster of similarly structured savings institutions were found, in investigations that followed their collapse, to have channeled deposit funds into loans to promoters, directors, and their associated companies far beyond what any prudent lending limit would permit, with reported embezzlement figures running into the tens of billions of rupees system-wide once the affected cooperatives were tallied together. Depositors — ordinary Nepali households who had treated cooperative deposits as a savings vehicle no different in safety from a bank account — discovered years later that a large share of the pooled deposit base had never been prudently lent at all, but had instead financed the promoters' own unrelated business ventures, real estate speculation, and, in some cases, personal consumption.
CASE IN POINT
The Nepali cooperative sector's collapse in the mid-2020s is the country's clearest case study in unchecked related-party lending: promoter- and director-linked borrowing, concentrated in a handful of institutions and left largely outside NRB's direct supervisory perimeter, produced depositor losses estimated in the tens of billions of rupees before regulatory and prosecutorial attention caught up with the damage. Licensed BFIs are far more tightly supervised than cooperatives were, but the underlying temptation — promoters treating depositor or shareholder capital as a captive funding source for their own private ventures — is the same disease in a more regulated body. An investor should read every related-party note in a NEPSE-listed bank's annual report with that cooperative history in mind, not as an unrelated cautionary tale but as the unrestrained version of a risk that licensing and NRB supervision only partially contain.
For a non-bank NEPSE-listed company, the practical due-diligence exercise is to trace every named related party in the notes to accounts against the shareholding pattern disclosed to SEBON and against publicly available company registration data, looking specifically for: transactions whose pricing or terms are not disclosed (a related-party note that lists a transaction's existence and counterparty but omits the value or the pricing basis is itself a red flag); a growing balance of receivables from, or advances to, related parties relative to the company's total receivables; guarantees extended by the listed company on behalf of a promoter-controlled private entity, which represent a contingent liability that may not appear on the balance sheet at all but should appear in the contingent liabilities note; and any related-party transaction that reverses direction over time — for instance, a promoter company that was a customer of the listed entity in one year becoming, in a later year, a major creditor, which often signals that cash is being recycled through the group to disguise a liquidity problem at either the listed company or its affiliate.
WARNING
A related-party note that discloses the existence of transactions but not their pricing, terms, or the recoverability assessment applied to related-party receivables gives you the minimum NFRS-compliant disclosure and nothing more. Minimum compliance is not the same as adequate transparency, and a note that reads as boilerplate year after year, with round numbers and no aging or recoverability commentary, should be treated as a disclosure gap rather than a clean bill of health.
Lesson 28.4 — Reading the Auditor's Signature: Opinions, Qualifications, and Turnover
An audit opinion is the single densest piece of information in an annual report, and it is also the piece most investors skip because its language is formulaic. That formula, however, is the point: any deviation from the standard unqualified ("clean") opinion language is a deliberate signal that the auditor — a professional bound by ICAN's code of conduct and, for listed entities, subject to SEBON's oversight of the reporting chain — has identified something material enough that they were unwilling to certify the statements without qualification. In Nepal's listed-company universe, four auditor-related patterns deserve systematic attention: the type of opinion issued, the specific language of any "emphasis of matter" or qualification paragraph, the frequency of auditor rotation, and the size and independence profile of the audit firm relative to the client.
Qualified opinions, disclaimers, and emphasis of matter
A qualified opinion states that, except for a specific identified matter, the financial statements present fairly the company's position; an adverse opinion states that the financial statements do not present fairly the position, taken as a whole; and a disclaimer of opinion states that the auditor could not obtain sufficient evidence to form any opinion at all — the most serious of the three outcomes, since it means the auditor is telling you they cannot vouch for the numbers rather than merely flagging a specific concern. In the Nepali insurance and finance-company sector, qualified opinions and disclaimers have recurred with some regularity around specific themes: the adequacy of technical and claims reserves in general insurance companies, the recoverability and classification of loans in finance companies and development banks with concentrated exposures, and the completeness of related-party disclosure. An "emphasis of matter" paragraph is a softer signal — the auditor is not qualifying the opinion but is drawing the reader's attention to a disclosure, often around going-concern uncertainty, a pending legal claim, or a significant subsequent event — and while it does not by itself compromise the audit opinion, a pattern of recurring emphasis-of-matter paragraphs on the same topic across consecutive years (for instance, repeated emphasis on litigation with a regulator, or repeated emphasis on the recoverability of a specific large loan) should be read as the auditor's polite, cumulative way of saying the underlying issue has not been resolved.
Frequent auditor changes
Auditor rotation is mandated in Nepal for listed companies and BFIs on a periodic basis under company law, ICAN's professional standards, and sector-specific NRB and Insurance Board requirements, precisely because long, uninterrupted audit tenures are understood to erode independence over time. But mandatory rotation cuts both ways as a signal. A company that rotates auditors exactly on schedule, with an orderly handover and no gap in opinion continuity, is behaving normally. A company that changes auditors mid-cycle, outside the mandated rotation window, especially if the change follows shortly after a qualified opinion or a public regulatory inquiry, is exhibiting one of the single strongest statistical red flags known in forensic accounting: "opinion shopping," in which management replaces an auditor unwilling to sign off on aggressive treatment with one more willing to do so. The tell is not the rotation itself but its timing and its stated reason — a company that discloses a change "due to expiry of tenure" in line with the statutory rotation cycle is unremarkable; a company that discloses a change "due to mutual agreement" or offers no substantive reason at all, particularly right after a qualified or adverse opinion, deserves close scrutiny of what the departing auditor actually flagged.
PRACTICAL TOOL
Build a simple auditor-history table for any NEPSE counter you are researching before you buy: list the audit firm name, the fiscal year, the opinion type (unqualified, qualified, adverse, disclaimer), and any emphasis-of-matter theme, for at least the last five to seven years, sourced from the annual reports filed with SEBON and the company's own AGM disclosures. A pattern of firm changes that do not line up with the statutory rotation cycle, or a qualification that reappears under a new firm's name using softer language for the same underlying issue, tells you more about management's relationship with independent scrutiny than almost any single ratio on the balance sheet.
Audit firm scale and sector concentration
The final auditor-related consideration is the scale mismatch between many Nepali audit firms and the balance sheets they certify. A commercial bank with total assets in the tens of billions of rupees, hundreds of branches, and a loan book spanning dozens of sectors requires an audit team with deep banking-sector expertise, statistically robust sampling methodology for loan-file testing, and, ideally, the institutional weight to resist management pushback on a contentious provisioning judgment. When the same audit firm, or a small rotating set of firms, appears across a large share of Nepal's listed BFI sector year after year, and when firm size (partners, qualified staff, total assets under audit) is disclosed at all, it is worth asking whether the firm's capacity is proportionate to the complexity of the client. This is not a criticism of any individual firm's integrity; it is a capacity question, and capacity constraints — a two-partner firm auditing a bank with a thousand-plus large corporate loan accounts — mechanically limit how much loan-file-level substantive testing is realistically possible within a normal audit fee and timeline, regardless of the auditor's diligence or good faith.
CAUTION
Do not treat an unqualified ("clean") opinion as a guarantee of financial-statement integrity. An unqualified opinion means the auditor did not find, or was not shown, evidence sufficient to require a qualification — it is not an assurance that no problem exists, particularly where the underlying audit capacity is stretched relative to the client's complexity, or where the auditor's judgment on a contestable estimate like loan-loss provisioning was more conservative in form than in substance.
Lesson 28.5 — Case Studies: When the Numbers Finally Broke
Pattern recognition improves with exposure to real episodes, and Nepal's financial history over the past two decades supplies several instructive ones, spanning the cooperative sector, microfinance, and the banking system's own internal supervisory findings. None of these episodes involves a single "smoking gun" restated NEPSE-listed annual report of the kind investors in more mature markets associate with headline accounting scandals — Nepal's disclosure and enforcement architecture has generally caught problems through regulatory intervention (NRB special supervision, SEBON surveillance, criminal investigation) rather than through a company's own voluntary restatement. That fact is itself instructive: it tells you that in Nepal, the market has generally not been the first line of defence against misstatement; regulators and, eventually, criminal investigators have been. An investor should not expect the market price or a subsequent independently issued restatement to warn them early; by the time a formal regulatory action becomes public, share prices and depositor confidence have often already collapsed.
The cooperative sector collapse remains the largest and most damaging episode. A cluster of savings and credit cooperatives — including entities publicly identified in investigations as Oriental Cooperative and several similarly structured institutions — were found to have accumulated depositor liabilities far in excess of any prudent, arm's-length loan book, with a substantial share of deposit funds channeled to promoters, directors, and their associated private companies. The scandal escalated into a national political controversy when investigation and parliamentary scrutiny extended to senior political figures connected to the affected cooperatives, underscoring how the absence of a unified prudential supervisor for the cooperative sector allowed promoter-linked lending to compound for years before collapse. For the equity investor, the direct lesson is that Nepal's supervisory perimeter has historically had gaps, and that a red flag pattern — concentrated related-party lending funded by public deposits — can run for years without correction when the supervising authority lacks either the mandate or the resources to intervene early.
The microfinance sector supplies a related but distinct cautionary pattern: institutional stress driven less by outright embezzlement and more by aggressive growth, over-leveraging of borrower households, and asset-quality deterioration that was, for a period, masked by continued portfolio growth and refinancing of stressed micro-loans — a retail-level echo of the same evergreening dynamic described in Lesson 28.2. Sanjeevani Microfinance's well-documented path from acute financial distress toward a managed recovery illustrates both the risk and the eventual regulatory response: NRB's Microfinance Institutions Supervision Department has, in more recent periods, taken direct sanction action against a number of microfinance institutions found in violation of regulatory norms, and broader reporting on the sector has described systemic over-indebtedness among borrowers layered across multiple microfinance lenders simultaneously — a concentration risk invisible in any single institution's own financial statements but visible only in aggregate, cross-institution data that individual annual reports do not disclose. An investor holding a NEPSE-listed microfinance company's shares cannot assess true portfolio quality from that company's statements alone; the multi-borrowing risk sits outside any single institution's disclosure boundary entirely.
CASE IN POINT
NRB's supervisory action against multiple microfinance institutions for regulatory violations, following years of sector-wide concern about borrower over-indebtedness and aggressive portfolio growth, illustrates a distinct version of the red-flag problem: the risk was not necessarily fraud inside any one institution's books, but a systemic vulnerability — the same borrower pledged to several lenders simultaneously — that no single company's financial statements could reveal. When evaluating any lending institution in Nepal, ask not only "are this company's numbers honest" but "does this company's disclosure even capture the risk that matters."
The clearest system-wide evidence within the licensed banking sector itself came from NRB's approved loan portfolio review of roughly ten major commercial banks, an exercise reported to have surfaced recurring malpractice in loan recovery processes and gaps between disclosed and actual credit risk. Because this review was conducted by the sector's own prudential regulator, with statutory inspection powers, its findings should carry more weight for an investor than any external ratio analysis — it is closer to a direct confirmation, from the party best positioned to know, that reported asset quality across a meaningful part of the banking sector diverges from the underlying reality. The review's existence, on its own, is one of the more important pieces of context in this entire chapter: it means that as of the years immediately preceding this book's writing, Nepal's own central bank judged the gap between reported and actual credit risk in its banking sector to be significant enough to warrant a formal, named, multi-bank review process — not a hypothetical risk, a documented one.
Finally, on the equity-market side, SEBON's own market surveillance has, at points, publicly identified batches of NEPSE-listed companies whose share prices it assessed as detached from fundamentals and whose trading patterns it flagged as irregular — a finding covering roughly fifty counters in one such review. This is not, strictly, a financial-statement red flag in the sense of a misstated balance sheet; it is a market-integrity red flag. But the two intersect in a specific way relevant to this chapter: a company whose share price has been inflated through coordinated or manipulative trading generates strong incentives for insiders to want continued favourable-looking financial statements, since a share-price correction driven by disappointing fundamentals would expose the earlier price action as unsupported. An investor who sees a NEPSE counter on any list of SEBON-flagged irregular-trading scrips should treat that flag as a reason to apply extra scrutiny to the underlying financial statements, not as an unrelated market phenomenon.
Known Episode / Pattern
What Was Found
Regulatory Response
Investor Lesson
Cooperative sector collapse (Oriental Cooperative and related institutions)
Deposit funds channeled to promoters, directors, and associated private companies far beyond prudent lending limits; losses reported in the tens of billions of rupees system-wide
Criminal investigation, parliamentary scrutiny, prosecutorial action against implicated promoters and officials
Related-party lending left outside a strong prudential perimeter can run for years undetected; supervisory gaps, not just company-level dishonesty, are the enabling condition
NRB loan portfolio review of major commercial banks
Recurring malpractice in loan recovery processes; credit risk disclosed to markets found to understate actual risk identified on inspection
On-site supervisory review and corrective directives to affected banks
Reported NPL ratios should be treated as a floor, not a ceiling, on true asset-quality deterioration
Microfinance sector stress and multi-borrowing crisis
A regulator does not tighten a rule system-wide unless the practice it restricts was already widespread among supervised entities
Lesson 28.6 — A Practical Red-Flag Checklist for the Nepali Investor
Everything in this chapter converges on a discipline you can actually run, counter by counter, before committing capital. No single item on the checklist below is sufficient grounds to avoid a stock outright, and conversely, a company that clears every item is not guaranteed to be clean — red-flag analysis raises or lowers your probability estimate and your required margin of safety, it does not replace fundamental valuation. But an investor who mechanically works through a checklist like this one, for every NEPSE-listed BFI or non-bank company under serious consideration, will catch a meaningful share of the problems this chapter has described before those problems surface in a price collapse or a regulatory action.
Red Flag
Applies Mainly To
Severity
Where to Look
Restructured/rescheduled loans rising while NPL ratio stays flat or falls
Banks, development banks, finance companies
High
Loan classification note; NRB-mandated disclosure schedule in annual report
Provisioning coverage ratio declining even as gross loans grow
Banks, development banks, finance companies
High
Notes on impairment and loan loss provision; five-year trend, not single year
Related-party receivables or advances growing faster than total receivables, with pricing/terms undisclosed
All NEPSE-listed companies
High
Related-party transactions note; cross-check counterpart names against promoter shareholding disclosure
Loan book concentrated in a handful of large borrower groups, especially where group linkage is not explicitly disclosed
Banks, development banks, finance companies
High
Large exposure/single obligor disclosure; company registration cross-checks
Interest receivable or interest suspense account rising faster than interest income
Banks, development banks, finance companies
Medium-High
Income statement notes; interest income recognition policy note
Auditor changed outside the statutory rotation cycle, with vague or unstated reason
All NEPSE-listed companies, especially BFIs
High
AGM disclosures; auditor history built across multiple years
Recurring emphasis-of-matter paragraph on the same unresolved issue across consecutive years
All NEPSE-listed companies
Medium
Audit opinion, read in full, every year, not just the opinion type
Guarantees or contingent liabilities extended on behalf of promoter-linked entities
All NEPSE-listed companies
Medium-High
Contingent liabilities and commitments note
Share price flagged by SEBON surveillance for irregular trading or detachment from fundamentals
All NEPSE-listed companies
Medium
SEBON public notices and market surveillance disclosures
Frequent, unexplained changes in accounting estimates (depreciation rates, provisioning models, revenue recognition timing) year to year
All NEPSE-listed companies
Medium
Accounting policy note; compare wording year over year for silent changes
KEY CONCEPT
Severity in this table reflects how strongly each pattern has historically correlated, in Nepal's documented regulatory and market history, with eventual asset-quality deterioration, related-party value extraction, or disclosure failure — not a certainty that fraud is present. Treat a "High" severity flag as a reason to demand better answers before investing, not as automatic proof of wrongdoing.
Two final practical habits complete this lesson. First, always read financial statements in a multi-year sequence rather than in isolation — nearly every red flag in this chapter is a trend signal, not a single-year signal, and a company's most recent annual report in isolation will rarely show you an evergreening pattern, a related-party drift, or an opinion-shopping sequence the way five to seven years of consecutive reports will. Second, treat regulatory silence as informative but not conclusive — the absence of an NRB or SEBON enforcement action against a specific company does not mean the company's numbers are clean, given everything this chapter has documented about supervisory capacity constraints in Nepal; it means only that no action has yet become public, and an investor's own checklist-driven scrutiny remains the more immediate and more reliable defence.
Chapter recap
Nepal's institutional setting — promoter-concentrated ownership, thin free float, an audit market whose capacity is frequently stretched relative to the complexity of the balance sheets it certifies, and a supervisory perimeter that has historically had gaps between NRB, SEBON, and cooperative oversight — makes financial-statement red flags more prevalent, and more consequential, than they would be in a deeper and more heavily supervised market, so every NEPSE investor should treat baseline skepticism as a starting posture rather than an unusual precaution.
Evergreening of bank loans, whether through restructuring that avoids non-performing classification or through capitalisation of unpaid interest into loan principal, is a documented, system-wide practice that Nepal Rastra Bank has directly confirmed through its own loan portfolio review of major commercial banks and through successive tightening of its interest income recognition and NFRS 9 provisioning guidelines, and an investor should treat any bank's reported non-performing loan ratio as a floor on true credit stress rather than a precise measure of it.
Related-party transactions are the primary channel through which promoter groups extract value from, or route hidden risk into, both NEPSE-listed companies and licensed BFIs, and Nepal's cooperative sector collapse — with depositor losses reaching the tens of billions of rupees through promoter- and director-linked lending left outside adequate prudential supervision — stands as the starkest available evidence of what unchecked related-party lending produces when it is allowed to run for years.
An audit opinion's type, its recurring emphasis-of-matter language, and the timing and stated reasons behind any change of auditor together carry more diagnostic information than almost any single reported ratio, and a clean opinion should never be mistaken for a guarantee, particularly given the scale mismatch between many Nepali audit firms and the complexity of the banks and finance companies they certify.
Documented Nepali episodes — the cooperative sector collapse, recurring microfinance sector stress and regulatory sanction, NRB's own supervisory findings on major commercial banks, and SEBON's periodic flagging of NEPSE counters for irregular share-price trading — show that in Nepal, regulators and criminal investigators, not voluntary corporate restatement or market price discovery, have historically been the first line of defence against financial misstatement, meaning investors who wait for the market to price in a problem are typically acting after the damage has already been done.
A disciplined, checklist-driven review of related-party disclosure, loan restructuring trends, provisioning coverage, auditor history, and regulatory surveillance status, applied consistently across multiple years for every NEPSE-listed counter under consideration, remains the most reliable practical defence available to an individual Nepali investor, and it should be run as a standard part of due diligence on every counter, not reserved for companies that already look suspicious on the surface.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VI
SECTOR ACCOUNTING LOGIC
Part VI · Chapter 29
Banking Sector Accounting Logic
First published 22 Aug 2026 · Last verified 29 Aug 2026
On the mid-hills road to Surkhet, the branch manager of a small development bank once told a visiting NRB inspection team that his loan book was "clean" — every account current, every file complete. Nineteen months later, that same institution's non-performing loans stood at 40.85 percent of its portfolio, its capital had evaporated, and Nepal Rastra Bank walked in under Section 86(B) of the NRB Act to take the institution's management away from its own board. Nothing about that collapse was sudden. It was written, quarter after quarter, in provisioning tables, interest suspense accounts, and a capital adequacy ratio that kept sliding toward a floor nobody wanted to look at directly. This chapter teaches you to read those tables before the inspection team has to.
Lesson 29.1 — The Architecture of a Nepali Bank's Financial Statements
A Nepali commercial bank's annual report is not built like a manufacturing company's. There is no "cost of goods sold," no inventory turnover, no gross margin in the conventional sense. A bank sells money and buys money, and its entire income statement is a spread business layered on top of a balance sheet that is, by design, almost entirely made of other people's liabilities and claims.
Start with the balance sheet. On the liability side, the dominant line is deposits — current, savings, call, and fixed deposits from the public, disclosed under NRB's prescribed format (per the Directive on Format of Financial Statements) and broken down by institution type, maturity, and currency. Beneath deposits sit borrowings (from NRB's refinance facilities, interbank borrowing, and occasionally bonds or debentures), then "other liabilities," then shareholder equity — share capital, share premium, retained earnings, and a set of statutory and regulatory reserves that exist only because NRB requires them, not because the bank chose to hold them.
On the asset side, "Loans and advances to customers" is the single largest and most consequential line for everything that follows in this chapter. Beside it sit investments (government securities, NRB bonds, corporate debentures, and increasingly, mutual fund units and hybrid instruments), "cash and balances with banks," and fixed and other assets. Loans and advances are always shown net of impairment — the provision you will spend the rest of this chapter learning to interrogate.
The income statement mirrors this structure. Interest income (from loans, investments, and interbank placements) sits at the top, interest expense (paid on deposits and borrowings) is deducted to arrive at net interest income, and this single number carries more weight in a bank's profitability than any other line in the entire statement. Below net interest income comes fee and commission income (loan processing fees, LC/guarantee commissions, remittance fees — collectively "non-funded" income), then net trading and other operating income, then the impairment charge for loans and other assets, then personnel and operating expenses, and finally profit before and after tax.
KEY CONCEPT
Net interest margin (NIM) — net interest income divided by average interest-earning assets — is the single number that best summarises a bank's core spread business. A bank can look profitable on the bottom line while its NIM quietly compresses, if fee income or one-off trading gains are propping up the total. Always separate NIM performance from non-funded income performance before judging a bank's earnings quality.
Understanding this architecture matters because every subsequent lesson in this chapter — provisioning, capital adequacy, income recognition — is really an argument about how honestly the numbers in these two statements represent economic reality. A bank's published net profit is, more than in almost any other sector, a policy choice as much as an observed fact: how aggressively did management classify loans, how much interest income did it recognise on stressed accounts, how conservatively did it provision under NFRS 9 versus the regulatory floor. Learning to read a bank means learning to see the choices behind the numbers, not just the numbers.
Deposit mix deserves a moment on its own, because it drives both funding cost and liquidity risk. Nepali banks disclose the proportion of deposits held as fixed deposits versus current and savings (CASA) accounts. A bank overly reliant on fixed deposits — sector-wide fixed deposits made up roughly 48 percent of total funding as of mid-August 2025 — carries a higher cost of funds and is more exposed to the "differential" wars that break out whenever liquidity tightens, as banks bid up FD rates against each other to retain depositors. A bank with a strong CASA base has cheaper, stickier funding and, all else equal, a structurally higher NIM.
Lesson 29.2 — Interest Income Recognition and the Interest Suspense Account
The single most consequential accounting judgment a Nepali bank makes every quarter is whether to recognise interest income on a loan on an accrual basis or to stop recognising it and route it instead to an "interest suspense" account. Get this wrong — deliberately or through weak systems — and a bank can report profit that does not exist.
NRB's rules on this have tightened materially in recent years. The original Guideline on Recognition of Interest Income, 2019 required banks to stop accruing interest income to the profit and loss account once a loan became non-performing (broadly, once it crossed into Substandard, Doubtful, or Loss classification, i.e., overdue beyond three months) and to instead park the accrued-but-uncollected interest in an interest suspense account on the balance sheet, recognised as income only upon actual cash collection. NRB followed this with a stricter Guidance Note on Interest Income Recognition in 2025, tightening the treatment further — narrowing the circumstances under which banks could continue accruing interest on loans showing early signs of stress (including certain Watchlist-category exposures) and requiring closer alignment between classification status and income recognition.
REGULATORY DETAIL
Under NRB's income recognition framework, once a loan is classified as non-performing, interest income booked on it must be reversed out of accrued interest receivable and profit, moved to an interest suspense account, and recognised in the profit and loss statement only when actually received in cash. This is a cash-basis override on top of the bank's otherwise accrual-based income statement — one of the clearest instances in NEPSE accounting of prudential regulation directly overriding financial reporting convention.
For an analyst, this creates a specific and very practical check: compare the growth in a bank's reported interest income against the growth in its interest suspense balance (disclosed in the notes to accounts) and against the growth of its non-performing loan book. If NPLs are rising but interest suspense is flat or falling, and interest income is still growing briskly, that is a red flag worth chasing — it suggests either aggressive reclassification delays (loans that should have moved into non-performing categories are being kept "current" through evergreening or restructuring) or genuinely improving asset quality. The notes to accounts, not the headline income statement, are where this distinction gets resolved.
This is also where "loan evergreening" enters the picture — a bank rolling over or refinancing a stressed borrower's facility just before it would otherwise cross a classification threshold, effectively resetting the overdue clock. NRB's Watchlist category, added specifically to catch this behaviour, requires banks to flag loans where the borrower has shown negative cash flows for three consecutive years or where credit quality is deteriorating even without a formal overdue event — closing some, though not all, of the gap that evergreening exploits.
Interest suspense recognition sits beside a second income-statement discipline: the treatment of "staff bonus" under the Bonus Act, which is calculated as a percentage of net profit before bonus and tax and expensed above the tax line — a small but non-trivial reconciling item between operating profit and pre-tax profit that is easy to overlook when comparing bank margins to non-bank companies.
Lesson 29.3 — Loan Classification and Provisioning Mechanics
This is the load-bearing wall of Nepali bank accounting. NRB's Unified Directives to Banks and Financial Institutions — specifically the directive governing loan classification and loss provisioning (commonly referenced as Directive No. 2 in the annually reissued Unified Directives) — set out a five-tier classification system, moving a loan from performing to non-performing purely as a function of days past due, supplemented by qualitative red flags.
KEY CONCEPT
Loan classification in Nepal is primarily a mechanical, overdue-day-count exercise, not a discretionary credit-judgment exercise. This is deliberate — it removes management's ability to keep a deteriorating loan "current" through optimistic reclassification, and it is precisely why the days-past-due bucket a loan sits in is the single most important number an analyst can extract from a bank's loan book disclosures.
The five categories and their minimum provisioning requirements are:
Classification
Overdue Period / Trigger
Minimum Loan Loss Provision
Pass
Current, or overdue up to 1 month
1.25%
Watchlist
Overdue 1–3 months; or negative cash flows for 3 consecutive years; or other qualitative stress signals
5%
Substandard (Non-performing)
Overdue 3–6 months
25%
Doubtful (Non-performing)
Overdue 6–12 months
50%
Loss (Non-performing)
Overdue more than 12 months
100%
Restructured and rescheduled loans sit in a special sub-bucket, provisioned at rates that vary with the underlying reason for restructuring and the loan's original classification — ranging roughly from 12.5 percent up toward the full provisioning rate applicable to the category the loan would otherwise have fallen into. This matters enormously in practice: a bank under earnings pressure has every incentive to restructure a stressed loan rather than let it migrate into Substandard or Doubtful, because restructuring can — if not scrutinized — reset both the overdue clock and the provisioning burden.
WARNING
A rising share of "restructured and rescheduled" loans on a bank's balance sheet, especially one that grows faster than the bank's total loan book, is one of the most reliable early indicators of a hidden asset-quality problem. Restructuring is a legitimate tool for genuinely viable borrowers facing temporary difficulty — but it is also the easiest lever for a bank to pull to avoid recognising a loss it does not want to report yet.
Two structural features of this system are worth internalizing. First, provisioning is set on the gross loan exposure by classification bucket, not on a loan-by-loan discounted cash flow estimate of expected recovery — this is what makes it a "prudential" or "regulatory" provisioning regime rather than an economic-loss regime. Second, the entire loan book (not just the non-performing portion) carries some provisioning — even a fully current, fully performing Pass loan carries a mandatory 1.25 percent general provision. This means "provision coverage" as a concept in Nepal always has a floor that has nothing to do with credit quality; it is baked into the classification system itself.
For the analyst, the practical work is this: pull the loan classification breakdown from the notes to accounts (every NRB-format annual report discloses it), calculate what fraction of the book sits in each bucket, and track the migration between buckets quarter over quarter. A bank whose Watchlist bucket is swelling while Pass shrinks, even if Substandard/Doubtful/Loss look stable, is telling you where next year's non-performing loans are going to come from.
PRACTICAL TOOL
Build a simple "migration matrix" from a bank's quarterly disclosures: track the rupee amount in each classification bucket over four to eight consecutive quarters. A bank that is managing its book honestly shows gradual, explainable shifts. A bank managing its narrative shows sudden, unexplained jumps in Pass or Watchlist right before a reporting date, often reversing shortly after — a sign of loan restructuring or evergreening timed to the reporting calendar.
Lesson 29.4 — NFRS 9 Expected Credit Loss and the Regulatory Reserve Bridge
Since the mandatory adoption of Nepal Financial Reporting Standards for banks, Nepali commercial banks have been required to compute impairment under NFRS 9's expected credit loss (ECL) model in their audited financial statements — a fundamentally different logic from the directive-based classification system in Lesson 29.3, running in parallel to it rather than replacing it.
NFRS 9 requires a three-stage approach. Stage 1 covers loans with no significant increase in credit risk since origination; these carry a 12-month ECL — the portion of lifetime expected losses that could occur within the next twelve months. Stage 2 covers loans that have shown a significant increase in credit risk (even if still technically performing) and requires a full lifetime ECL — the expected loss over the entire remaining life of the loan. Stage 3 covers credit-impaired loans (broadly aligned with, though not identical to, the regulatory non-performing categories) and also carries a lifetime ECL, now computed against a loan the bank accepts is impaired.
Crucially, ECL under NFRS 9 is forward-looking and probability-weighted: it incorporates macroeconomic scenarios, historical loss experience, and borrower-specific risk factors, rather than applying a flat percentage by overdue bucket. NRB's NFRS 9 Expected Credit Loss Related Guidelines (issued 2024, subsequently amended) set the operational parameters Nepali banks must follow when building these models — including standardised approaches to probability of default, loss given default, and macroeconomic overlay factors, so that ECL estimates across the sector are not each bank's unconstrained internal judgment.
KEY CONCEPT
Nepal runs two parallel provisioning regimes for the same loan book: NRB's directive-based classification provisioning (Lesson 29.3), which is mechanical, prudential, and non-negotiable for regulatory reporting; and NFRS 9 ECL, which is model-based, forward-looking, and used in the audited financial statements. The two will almost never produce identical numbers — and NRB has built a specific mechanism to reconcile the two.
That mechanism is the Regulatory Reserve. Where a bank's NFRS 9 ECL impairment is lower than the NRB directive-based provision the loan classification would otherwise require, the shortfall must be transferred out of distributable retained earnings into a Regulatory Reserve within equity — annually, as part of the appropriation of profit. This reserve is not available for dividend distribution; it exists purely to prevent a bank from using a more lenient internal ECL model to report and distribute profit that NRB's prudential framework says has not actually been earned yet. If NFRS 9 impairment happens to be higher than the directive minimum in a given period, no such transfer is required — the higher, more conservative NFRS 9 number simply stands.
REGULATORY DETAIL
The transfer to Regulatory Reserve runs through the statement of changes in equity, not through the profit and loss account — so it does not depress reported net profit, but it does reduce the retained earnings balance available for dividend. A bank can report a healthy net profit and simultaneously have a large chunk of that profit locked away in Regulatory Reserve, unavailable to shareholders. Always check the Regulatory Reserve movement before assuming a strong bottom line translates into dividend capacity.
Because the shift to full NFRS 9 ECL had the potential to create a sudden capital shock for banks with previously under-provisioned books, NRB built in a transitional arrangement: a four-year adjustment window (spanning fiscal years 2081/82 through 2084/85) during which banks receive CET1 capital relief against the Day 1 impact of ECL adoption, declining from roughly 80 percent relief in the first year down to 20 percent by the final year, before full ECL impact flows through to capital unmitigated. This transitional relief is itself disclosed in the capital adequacy notes, and an analyst should check whether a bank's reported CAR is still benefiting from this glide path — a bank that looks adequately capitalised today, with the benefit of transitional relief, may look considerably tighter once the relief fully expires.
CAUTION
When comparing a bank's capital adequacy ratio across recent years, check the capital disclosure notes for reference to NFRS 9 transitional/glide-path relief. A CAR that appears stable year over year while the underlying relief percentage is declining is not actually stable — it is being propped up by a shrinking regulatory concession, and the real trajectory only becomes visible once you strip that concession out.
Lesson 29.5 — Capital Adequacy: The Ratio That Decides Whether a Bank Survives
Capital adequacy is the mechanism by which NRB ensures a bank has enough of its own shareholders' money at risk, relative to the riskiness of its assets, to absorb losses before depositors and the deposit-guarantee system are ever called upon. It is governed by a separate strand of the Unified Directives — the New Capital Adequacy Framework, aligned broadly with Basel III as adapted for Nepal's banking system — and it is the single ratio NRB watches most closely as a trigger for supervisory intervention.
The framework requires commercial banks to maintain a minimum Capital Adequacy Ratio (CAR) — total qualifying capital divided by risk-weighted assets — of 11 percent, of which minimum core capital (Tier 1, essentially paid-up equity capital, share premium, and retained earnings, net of specified deductions) must be at least 8.5 percent. The balance between the 8.5 percent Tier 1 floor and the 11 percent total floor can be met with supplementary (Tier 2) capital — general loan loss provisions up to a specified limit, subordinated debt instruments, and revaluation reserves, among other qualifying items. Beyond these baseline requirements, the framework layers on a capital conservation buffer and, for banks deemed systemically important, additional loss-absorbency requirements — meaning the "true" minimum a well-run, systemically significant bank should be targeting in practice typically runs above the bare regulatory floor.
REGULATORY DETAIL
NRB's minimum requirements are 11% total Capital Adequacy Ratio and 8.5% minimum Tier 1 (core capital) ratio, both computed against risk-weighted assets under the New Capital Adequacy Framework. As of mid-August 2025, the commercial banking sector as a whole reported an average CAR of roughly 13.14%, comfortably above the floor — but sector averages conceal considerable dispersion between individual banks, some of which run much closer to the regulatory minimum than the headline average suggests.
To see that dispersion concretely, look at how individual banks have actually reported against these floors. In one NRB-compliance snapshot (Chaitra-end 2078), Standard Chartered Bank Nepal reported the highest CAR in the sector at 15.90 percent, while Himalayan Bank Limited reported the lowest compliant figure at 11.61 percent — barely above the 11 percent floor. On core capital, NIC Asia Bank and Prabhu Bank both reported Tier 1 ratios in the 8.54–8.70 percent range — again, only marginally above the 8.5 percent minimum. All 27 commercial banks operating at the time were technically compliant, but "technically compliant" and "comfortably capitalised" are not the same claim, and an analyst who stops at the pass/fail line misses the more useful information: how much room a bank actually has before the next credit cycle pushes it toward the floor.
CASE IN POINT
A bank sitting at 11.6% CAR against a floor of 11% has almost no room to absorb a deterioration in risk-weighted assets before breaching the regulatory minimum — a single adverse quarter of loan downgrades, each migration from Pass to a higher-risk-weight or non-performing category pulling capital down further, can move such a bank from compliant to non-compliant within two or three reporting cycles. A bank at 15–16% CAR has genuine shock-absorbing capacity. Always read the CAR number relative to its distance from the floor, not just relative to whether it clears the floor.
Why does this matter so much in practice? Because CAR breach is one of the principal triggers for NRB's Prompt Corrective Action (PCA) framework — a graduated set of restrictions (on dividend payment, branch expansion, senior management changes, and eventually direct intervention) that NRB applies to banks and financial institutions falling below capital, asset-quality, or governance thresholds. Karnali Development Bank was placed under PCA in November 2024 after failing to maintain its required capital adequacy ratio; when that measure proved insufficient against the bank's deteriorating position — non-performing loans that had by then reached 40.85 percent of its portfolio, compounded by weak institutional governance and a liquidity crunch that left it unable to meet deposit repayment obligations — NRB moved a step further in December 2024, assuming direct management control under Section 86(B) of the Nepal Rastra Bank Act, 2002, appointing an NRB-led management team to run the institution, protect depositor funds, recover loans, audit assets and liabilities, and pursue accountability for the underlying financial misconduct.
CASE IN POINT
Karnali Development Bank (Class "B") is the clearest recent illustration of how the accounting mechanics in this chapter connect to real institutional failure: capital inadequacy (Lesson 29.5) and asset-quality deterioration (Lesson 29.3) fed each other until liquidity failed and governance collapsed, and NRB's response ran through the exact escalation ladder — Prompt Corrective Action first, then direct management takeover — that the regulatory framework is built to apply. The lesson generalises across Class A, B, and C institutions: the Unified Directives' classification, provisioning, and capital rules are common infrastructure across Nepal's entire banking and financial institution sector.
At the other end of the spectrum, sector-wide non-performing loans stood at roughly 4.62 percent as of mid-August 2025, with sector-wide loan loss provisions running about 5.09 percent of total loans — figures that look moderate in isolation but that NRB and market commentary have flagged as trending upward against a backdrop of high credit-to-GDP exposure (above 91 percent) and heavy reliance on fixed-deposit funding. None of this, on its own, signals crisis. It signals exactly what this chapter is about: numbers that must be read in context, in trend, and against the regulatory floor, rather than as a single static "pass" or "fail."
Lesson 29.6 — Reading a Bank's Numbers Like an Examiner: A Practical Checklist
Everything in this chapter converges on a discipline: reading a Nepali bank's financial statements the way an NRB supervisor reads them, not the way a casual investor skims a headline EPS number. Use the following sequence every time you pick up a commercial bank's quarterly or annual disclosure.
Start with capital. Pull the CAR and Tier 1 ratio, and measure the distance to the 11 percent and 8.5 percent floors respectively, not just whether the bank clears them. Check the capital notes for NFRS 9 transitional relief and note whether the current ratio depends on a glide path that is shrinking year by year.
Move to asset quality. Extract the full loan classification breakdown — Pass, Watchlist, Substandard, Doubtful, Loss, and Restructured/Rescheduled — as rupee amounts, not just the summary NPL ratio. Build (or update) your migration matrix across at least four quarters. A rising Watchlist or Restructured bucket, even alongside a flat headline NPL ratio, is your earliest warning signal.
Check provisioning discipline next. Compare directive-based provisioning against NFRS 9 ECL impairment in the notes to accounts, and track the Regulatory Reserve movement in the statement of changes in equity. A growing Regulatory Reserve alongside strong reported profit tells you a meaningful share of that profit is not yet available to shareholders.
Then examine income quality. Separate net interest income (and NIM) from fee/commission income and any trading or one-off gains. Cross-check interest income growth against interest suspense account growth and NPL growth in the notes — divergence between these three is a signal worth investigating before it shows up in the headline numbers.
PRACTICAL TOOL
A five-line "bank scorecard" worth keeping for every NEPSE-listed bank you follow: (1) CAR and distance from 11% floor, (2) NPL ratio and its year-over-year trend, (3) NIM and its trend, (4) Regulatory Reserve as a percentage of total reserves, and (5) restructured/rescheduled loans as a percentage of the total loan book. These five numbers, tracked over eight consecutive quarters, will tell you more about a bank's real trajectory than its reported EPS ever will.
Finally, situate the bank within its sector context: compare its CAR, NPL ratio, and NIM against sector averages (roughly 13 percent CAR, 4.6 percent NPL, and provisioning near 5 percent of loans as of the most recent NRB-referenced figures), and ask whether the bank is an outlier in a direction that matters — a bank meaningfully below sector CAR or meaningfully above sector NPL deserves closer scrutiny than one that tracks the average.
WARNING
No single ratio in this chapter is sufficient on its own. A bank can pass every individual test — adequate CAR, moderate NPL, positive NIM — while still concealing stress through the interaction of restructuring, income recognition timing, and provisioning choices across categories. The discipline this chapter teaches is cross-checking one disclosure against another, not memorising a single pass/fail threshold.
Chapter recap
A Nepali commercial bank's financial statements are built around a spread business — interest earned on loans and investments minus interest paid on deposits and borrowings — layered with fee income, and every major line item on both the balance sheet and income statement exists to answer one underlying question: how much of this bank's reported profit and capital is real, and how much is a function of classification and recognition choices that NRB's Unified Directives constrain but do not eliminate. Understanding the architecture of the balance sheet and income statement is the precondition for everything else in sector analysis.
Interest income recognition is where accounting policy most directly overrides ordinary accrual convention: once a loan crosses into non-performing status, further interest must be suspended rather than accrued to profit, moved instead to an interest suspense account and recognised only upon cash collection, a discipline NRB has progressively tightened through its 2019 and 2025 income recognition guidance. Comparing interest income growth, interest suspense growth, and NPL growth against each other is one of the sharpest diagnostic tools available to an outside analyst.
Loan classification and provisioning form the load-bearing mechanical core of bank accounting in Nepal: a five-tier system — Pass (1.25% provision), Watchlist (5%), Substandard (25%), Doubtful (50%), and Loss (100%) — driven primarily by days-past-due counts rather than management discretion, with restructured and rescheduled loans forming a special, closely-watched sub-category that is often where hidden stress accumulates first.
NFRS 9's expected credit loss framework runs in parallel to this directive-based system, requiring a forward-looking, three-stage, probability-weighted impairment estimate for audited financial statements. Because the two regimes rarely agree, NRB requires any shortfall of NFRS 9 impairment against directive-based provisioning to be transferred from distributable retained earnings into a non-distributable Regulatory Reserve — a mechanism every analyst should check before assuming reported profit translates into dividend capacity, especially during the multi-year transitional relief window still phasing out ECL's Day 1 capital impact.
Capital adequacy — a minimum 11 percent CAR with at least 8.5 percent in Tier 1 core capital — is the ratio that ultimately decides institutional survival, and the Karnali Development Bank episode of late 2024, moving from Prompt Corrective Action to full NRB management takeover under Section 86(B) of the NRB Act, is the clearest recent demonstration of how capital inadequacy, asset-quality deterioration, and governance failure reinforce each other until intervention becomes unavoidable. Sector averages — roughly 13 percent CAR and 4.6 percent NPL as of mid-2025 — describe the system in aggregate, but individual banks can sit far closer to the regulatory floor than the average suggests, and only a bank-by-bank check of distance-to-floor reveals that.
Taken together, these five threads compose a single reading discipline: pull capital ratios, classification buckets, provisioning reconciliations, and income-quality checks together, cross-reference them against each other and against sector benchmarks, and treat any single clean-looking ratio with the same skepticism the branch manager on the Surkhet road should have applied to his own loan book, quarters before the inspection team came to write down what he had not.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VI · Chapter 30
Development Bank and Finance Company Accounting
First published 22 Aug 2026 · Last verified 29 Aug 2026
The valuation gap is the first thing a new NEPSE investor notices and the last thing they learn to explain. Muktinath Bikas Bank and Nabil Bank sit a few rows apart on the same brokerage watchlist, both labelled simply "bank" by an app that does not distinguish license class. Yet ask why Jyoti Bikas Bank has traded at a price-to-earnings multiple in the twenties or thirties while a commercial bank of comparable size trades in the low teens, or why a finance company's book value discount persists year after year even when its return on equity looks respectable on paper, and most retail portfolios have no answer beyond "that's just how development banks and finance companies trade." This chapter builds the answer from the regulatory architecture up, because in Nepal's banking system, class is not a marketing label — it is a distinct legal and supervisory regime under the Banks and Financial Institutions Act (BAFIA) 2073 and Nepal Rastra Bank's Unified Directives, and that regime shapes everything from the capital a promoter must raise to the loan a branch manager is allowed to write.
Lesson 30.1 — The Four-Class Architecture and Why "B" and "C" Are Not Small Banks
BAFIA divides Nepal's banks and financial institutions into four license classes, each supervised by NRB but under materially different rulebooks: "A" class commercial banks, "B" class development banks, "C" class finance companies, and "D" class microfinance institutions. This book has already spent several chapters on "A" class commercial banks because they dominate NEPSE's market capitalisation and float. But treating "B" and "C" class institutions as merely smaller, cheaper versions of commercial banks is the single most common analytical error retail investors make when they venture into this tier of the market. A development bank is not a discount commercial bank. A finance company is not a shrunken development bank. Each class was built around a different theory of what the institution is for, and NRB's directives enforce that theory through capital floors, permitted-activity lists, and directed-lending mandates that do not converge across classes even after two decades of consolidation.
KEY CONCEPT
License class in Nepal is a supervisory category, not a size category. A "B" class development bank and a small "A" class commercial bank can have similar balance sheets, but the development bank operates under materially tighter constraints on cross-border business, wholesale funding access, and (historically) geographic footprint. Compare institutions within their class before comparing across classes.
It is worth being precise about where the "D" class fits, if only to rule it out. Microfinance institutions occupy a fourth tier below finance companies, licensed narrowly for small-group and deprived-sector lending and largely excluded from the general deposit-taking, corporate-lending business this chapter is about. A handful of microfinance names trade on NEPSE and merit their own treatment elsewhere in this book; nothing in this chapter should be read as extending to that tier, whose funding model, borrower base, and regulatory ceiling on interest spreads are different again from "B" and "C" class institutions.
Historically, the class distinction was geographic as much as functional. Development banks were originally licensed to operate within a specified number of districts — national-level development banks could operate across the country, while regional and district-level development banks were confined to a defined working area, often a handful of adjoining districts in the hills or Tarai. Finance companies carried an even narrower original mandate: NRB conceived of them as vehicles for hire-purchase financing, leasing, and consumer and small-business credit rather than full-service deposit-taking and corporate lending. Over the 2010s, NRB progressively relaxed the geographic restriction for development banks that met higher capital thresholds, allowing well-capitalised "B" class institutions to expand nationally and compete more directly with commercial banks for deposits and loans in Kathmandu, Pokhara, and other urban centres. But the underlying supervisory logic — that "B" and "C" class institutions serve a different, generally more localized and higher-risk segment of the credit market than "A" class banks — persists in the directives even where the geography has converged.
Understanding why this matters to a shareholder requires understanding that NRB does not run one set of Unified Directives with footnotes for smaller players. It runs an integrated framework in which capital adequacy norms, single obligor limits, deprived-sector lending quotas, liquidity requirements, and permitted business lines are each calibrated separately by class. A "C" class finance company's regulatory ceiling on lending against real estate collateral, its restrictions on foreign currency business, and its access (or lack of it) to interbank and wholesale funding lines all differ from a commercial bank's — and all of that shows up eventually in the income statement and, more importantly, in the volatility of that income statement across a credit cycle.
Lesson 30.2 — Capital Floors and the History of Forced Consolidation
No single fact explains the shape of today's "B" and "C" class tier better than NRB's 2015 capital directive. In its Monetary Policy for FY 2072/73 (2015/16), NRB quadrupled the minimum paid-up capital requirement for commercial banks from Rs 2 billion to Rs 8 billion, and imposed proportionate multiples on development banks and finance companies: national-level development banks were required to raise minimum paid-up capital from roughly Rs 640 million to Rs 2.5 billion, while national-level finance companies (those licensed to operate across four to ten districts and above) saw their floor rise from around Rs 300 million to Rs 800 million. Regional and district-level development banks and finance companies faced correspondingly scaled-down but still steep multiples of their prior capital base. Governor Chiranjibi Nepal gave the industry until mid-July 2017 — roughly two years — to comply.
REGULATORY DETAIL
The 2015 capital hike (Monetary Policy FY 2072/73) is the single regulatory event most responsible for the shape of today's "B" and "C" class tier on NEPSE. Commercial banks: Rs 2 billion to Rs 8 billion. National-level development banks: roughly Rs 640 million to Rs 2.5 billion. National-level finance companies: roughly Rs 300 million to Rs 800 million. Institutions with narrower working areas faced lower but still steep floors. Every merger prospectus you read for a "B" or "C" class name from 2016 onward should be read against this deadline.
The consequence was arithmetic before it was strategic: an institution that could not organically retain enough earnings, or persuade promoters to inject enough fresh capital, within two years had exactly one practical path to compliance — merge with another institution and combine capital bases, or be absorbed by one that had already cleared the bar. NRB's own retrospective review of the period ("Optimal Number of Banks and Financial Institutions in Nepal," Nepal Rastra Bank Research Department) documents the scale of what followed. At the sector's peak around 2012, Nepal had roughly 32 commercial banks, 88 development banks, and 77 finance companies in operation — 220 BFIs in total, an extraordinarily fragmented system for an economy of Nepal's size. By mid-March 2022, those numbers had fallen to 27 commercial banks, 17 development banks, and 17 finance companies. Cumulatively, 239 BFIs had gone through a merger or acquisition process by that point, with 177 licenses revoked outright. The tier this chapter covers has been consolidating for a decade, and it is still consolidating — every annual list of licensed institutions NRB publishes is shorter than the one before it.
Named episodes make the abstraction concrete. Jyoti Bikas Bank absorbed Jhimruk Bikas Bank and Raptibheri Bikas Bank in earlier rounds and later took in Hamro Bikas Bank in a subsequent transaction — three separate development banks folded into what shareholders now hold as a single national-level "B" class stock. Lumbini Bikas Bank's growth was similarly acquisitive: it absorbed Vibor Bikas Bank and Society Development Bank, and separately took in Lumbini Finance & Leasing Company, a "C" class name, in a cross-class merger of the kind that became increasingly common once NRB began actively encouraging "B" and "C" class institutions to combine regardless of license type. Mahalaxmi Bikas Bank pursued perhaps the most aggressive combination strategy in the tier, acquiring Yeti Development Bank, Malika Bikas Bank, and then, in cross-class transactions, both Mahalaxmi Finance Company and Siddhartha Finance Company. Gandaki Bikas Bank absorbed Fewa Bikas Bank; OM Development Bank merged with Manasalu Development Bank. Shine Resunga Development Bank and Saptakoshi Development Bank both grew through multi-party merger processes in the years that followed the 2015 capital directive, consolidating what were previously district-level institutions with narrow working areas into single national-level entities.
CASE IN POINT
Mahalaxmi Bikas Bank's merger history illustrates a pattern retail investors should learn to read directly off a company's own disclosures: a "B" class name that has absorbed both other development banks (Yeti, Malika) and "C" class finance companies (Mahalaxmi Finance, Siddhartha Finance) is not a simple, organically-grown franchise. Its loan book, deposit mix, and NPL profile are an amalgam of several institutions' legacy underwriting standards, some of which may not have been visible to public shareholders at the time of merger. Read merger-swap prospectuses and post-merger due-diligence disclosures, not just consolidated financials, before assuming continuity of credit culture.
For a shareholder, this consolidation history is not backward-looking trivia — it is live diligence material. Every merger brings together two loan books, two credit cultures, and often two different levels of provisioning discipline. A "B" class stock trading today may carry, three or four balance sheets deep, legacy exposures originated by a district-level institution that never had the underwriting infrastructure of a national bank. This is one reason experienced NEPSE analysts treat a recent-merger development bank or finance company with more skepticism on asset quality than a similarly sized commercial bank that grew organically — the merger itself does not create bad loans, but it can obscure them for a year or two inside a larger, less transparent combined balance sheet.
Lesson 30.3 — Permitted Activities: What "B" and "C" Class Cannot Do
The capital floor is only the entry ticket. What an institution is licensed to do once inside the tier differs meaningfully from what an "A" class commercial bank can do, and these differences drive structural aspects of the income statement that persist regardless of how well-run an individual "B" or "C" class institution is.
Foreign exchange and trade finance are the clearest dividing line. Commercial banks are the primary conduits for Nepal's foreign exchange transactions, letter-of-credit issuance for import-export trade, and correspondent banking relationships with international banks. Development banks have historically had far more limited authority to deal in foreign exchange and international trade instruments, and finance companies essentially none. This is not a minor product-line gap — trade finance and foreign exchange are meaningfully fee-income-generating and low-capital-intensity businesses for commercial banks, and their near-total absence from "B" and "C" class income statements is one reason those institutions lean more heavily on net interest income and carry thinner non-fund-based income lines relative to total revenue.
Deposit-taking authority is the second major divide, and the more consequential one for a company analysing funding cost. "C" class finance companies in particular have narrower authority to solicit current (checking) account deposits and to serve as a settlement bank for institutional clients, government bodies, and large corporates — business commercial banks compete for aggressively because current and call deposits are the cheapest source of funding a bank can access. Development banks sit in between: national-level "B" class institutions have broader deposit-mobilization authority than finance companies but still compete from a weaker position than commercial banks for low-cost institutional and government deposits, simply because government treasury placements, large corporate payroll accounts, and remittance-linked current accounts have historically clustered with "A" class banks that offer the full suite of trade, treasury, and cash-management services those depositors need.
KEY CONCEPT
Deposit cost is not just a function of interest rate offered — it is a function of which class of depositor an institution can realistically attract. Commercial banks draw disproportionately from low-cost current and savings deposits tied to institutional relationships, remittance flows, and corporate cash management. "B" and "C" class institutions draw disproportionately from term (fixed) deposits and, historically, from other BFIs' interbank and wholesale placements — both costlier funding sources. This funding-cost gap shows up directly in a narrower or more volatile net interest margin advantage that does not always survive a liquidity-tightening cycle.
Single-obligor exposure limits are the third divide, and one that matters enormously for concentration risk in the smaller tier. NRB's Unified Directives cap the credit that any BFI can extend to a single borrower or borrower group as a percentage of the institution's core capital — a limit that scales with the institution's absolute capital base rather than its class label, but which functions very differently in practice for a commercial bank with Rs 8 billion-plus of paid-up capital than for a "B" or "C" class institution sitting closer to its Rs 2.5 billion or Rs 800 million floor. A single-obligor exposure that is immaterial to a large commercial bank's book can represent a meaningful share of a smaller development bank's or finance company's total loan portfolio, which is precisely why individual borrower defaults have historically done disproportionate damage to smaller BFIs' capital adequacy ratios. When you read a "B" or "C" class annual report's disclosure of exposure to its largest borrowers as a percentage of core capital, treat a figure clustering near the regulatory ceiling as a flag worth investigating rather than a routine compliance disclosure.
Real estate and margin (share-collateral) lending limits are the fourth divide worth naming specifically, because both have been recurring sources of stress across this tier. NRB periodically tightens the permissible share of a BFI's loan book that can be secured against real estate or against listed shares, precisely because smaller institutions with concentrated borrower bases and thinner capital cushions have historically been more exposed to real estate and margin-lending boom-bust cycles than diversified commercial banks. A "B" or "C" class institution whose loan book shows a real estate or margin-lending concentration meaningfully above sector average deserves the same scrutiny as one with a concentrated single-obligor exposure — both are classic precursors to the kind of asset-quality deterioration that produced the mid-2010s problematic-institutions episode discussed in Lesson 30.4.
Directed and priority-sector lending is the fifth structural feature, and it cuts in a more nuanced direction than the previous four. NRB's Unified Directives impose deprived-sector lending requirements and, more recently, broader priority-sector and productive-sector lending quotas on all BFI classes, but the calibration differs by class, and development banks in particular have often been assigned meaningful roles in agricultural, cottage-and-small-industry, and hydropower-linked lending given their historical working-area concentration in districts outside the Kathmandu Valley. A development bank with a legacy footprint in the mid-hills or Tarai may carry a loan book genuinely weighted toward agriculture-linked SME credit and small hydropower project finance — sectors that are more cyclical and more exposed to monsoon, remittance, and commodity-price swings than the urban trade and real estate exposures that dominate many commercial bank books. This is a real difference in credit risk character, not just a regulatory technicality, and it means "B" class asset quality can diverge from commercial bank asset quality in ways tied to Nepal's agricultural and hydropower cycles rather than to urban real estate and trade cycles.
The funding side of a "B" or "C" class balance sheet is where the sector's structural vulnerability concentrates, and it is worth walking through mechanically because it explains recurring episodes of stress that retail investors otherwise experience as sudden, unexplained bad news.
Because development banks and finance companies compete from a structurally weaker position for low-cost retail current and savings deposits, they have historically relied more heavily than commercial banks on two costlier and less sticky funding sources: high-rate term deposits solicited from retail savers chasing yield, and institutional or wholesale deposits placed by other BFIs, cooperatives, and corporate treasuries seeking the higher rates smaller institutions must offer to compete at all. Both sources behave differently in a liquidity crunch than a retail current account does. A retail current account holder rarely moves their salary account overnight regardless of a one-percentage-point rate differential elsewhere. An institutional treasury placement or another BFI's interbank deposit is actively managed for yield and can be withdrawn or simply not rolled over the moment a more attractive rate appears elsewhere in the system or the depositor senses any reputational risk in the placement.
REGULATORY DETAIL
NRB's Unified Directives impose a single obligor limit and a core capital-linked ceiling on how much any one institution — including other BFIs — can place with or lend to a single counterparty. This is precisely why a "B" or "C" class institution's reliance on wholesale and interbank deposits is a concentration risk, not just a cost issue: a handful of large institutional depositors can represent a disproportionate share of total deposits, and NRB's periodic tightening of interbank and institutional deposit limits has, at various points in the last decade, forced sudden repricing or withdrawal of exactly this funding at smaller BFIs.
This funding fragility is precisely what produced the "problematic institutions" episode NRB dealt with through the mid-to-late 2010s, which is worth studying in detail because it is the sector's clearest real-world case study in how funding stress and asset-quality stress reinforce each other. NRB formally classified a group of development banks and finance companies as problematic institutions after they proved unable to recover a large share of loans extended to borrowers, and after their capital and liquidity positions deteriorated in tandem. The named institutions included Nepal Share Markets and Finance, Crystal Finance, Kuber Merchant Finance, Capital Merchant Banking and Finance, World Merchant Banking and Finance, Narayani Development Bank, Nepal Finance, Corporate Development Bank, and Lalitpur Finance. NRB's remediation framework required these institutions to rebuild toward 25 percent of the new (post-2015) minimum paid-up capital requirement to be removed from problematic status, with a further two-year runway to reach full compliance thereafter. Some — Corporate Development Bank, Lalitpur Finance, and Kuber Merchant Finance among them — showed gradual improvement under this framework. Others did not: NRB proposed liquidation of Crystal Finance through the courts, and the Supreme Court separately stayed an NRB capital-readjustment order concerning Nepal Share Markets and Finance, illustrating that even the regulator's remediation path was neither quick nor uniformly successful.
WARNING
The 2016–2017 "problematic institutions" list is not ancient history to be filed away as a solved 2010s problem. It is a template. Every credit cycle downturn in Nepal produces a fresh crop of "B" and "C" class institutions with the same signature: rapid loan growth during the preceding boom, concentrated exposure to a small number of large borrowers or a single sector (real estate, margin lending, or a specific cash crop or construction niche), and a deposit base skewed toward high-cost institutional placements that evaporate at the first sign of trouble. When you screen a development bank or finance company, actively look for this signature rather than assuming it belongs only to the names on a decade-old list.
Lesson 30.5 — Reading the Financial Statements: What to Adjust For
Given everything above, a shareholder analysing a "B" or "C" class income statement and balance sheet needs to make several adjustments that would be unnecessary, or less important, for an "A" class commercial bank.
First, decompose net interest margin into its rate and mix components rather than treating a headline NIM number at face value. A development bank showing a higher NIM than a comparable commercial bank is not necessarily earning that margin more efficiently — it may simply be charging higher lending rates to a riskier borrower base while paying up for costlier deposits, netting out to a superficially attractive but structurally fragile spread. Cross-check the NIM against the cost-of-funds line specifically; if cost of funds is running well above the peer commercial bank average, the margin advantage is compensation for risk, not evidence of operating efficiency.
Liquidity regulation adds a further layer worth building into your model. NRB requires all BFI classes to maintain a cash reserve ratio against deposit liabilities and a statutory liquidity ratio invested in government securities and other qualifying liquid assets, and it separately monitors a credit-to-deposit (CD) ratio ceiling meant to prevent any institution from over-lending against its deposit base. In principle these ratios are class-neutral rules applied uniformly. In practice, a "B" or "C" class institution running close to the CD ratio ceiling has far less room to keep lending through a deposit slowdown than a commercial bank of the same nominal ratio, precisely because the smaller institution's deposit base is itself less stable — a bad quarter for deposit mobilization at a development bank can force loan-book contraction or a scramble for costly short-term wholesale funding in a way it rarely does at a large commercial bank with a diversified retail deposit franchise. Watch the CD ratio trend, not just its level, across several quarters before a monsoon season or a festival-linked remittance lull; a rising CD ratio into a seasonally weak deposit period is an early liquidity-stress signal in this tier specifically.
Second, scrutinize the deposit mix disclosure — current, savings, call, and fixed/term deposits — that NRB requires all BFIs to disclose. A "B" or "C" class institution with a fixed-deposit share meaningfully above 60-65% of total deposits should be modelled with a higher deposit-repricing sensitivity than a commercial bank with a more balanced CASA (current and savings account) mix, because term deposits reprice to market on maturity in a way current and savings balances do not.
Third, treat merger history as a mandatory diligence input, not a footnote. Identify how many predecessor institutions are embedded in the current entity, when each merger closed, and whether NRB's post-merger due diligence (typically disclosed in the merger scheme document or the subsequent annual report) flagged any legacy NPL or provisioning gaps. A single clean annual report two years after a four-way merger tells you less about steady-state credit quality than the same institution's fifth post-merger annual report will.
Fourth, benchmark capital adequacy against the class-specific minimum, not the commercial-bank minimum. NRB's capital adequacy framework applies broadly similar risk-weighting principles across classes, but a "B" or "C" class institution running its capital adequacy ratio only slightly above the regulatory floor has materially less room to absorb a bad year than a commercial bank running the same nominal ratio, because the smaller institution's absolute capital base cannot as easily be topped up through a rights issue or FPO in a market that prices "B" and "C" class equity at persistently lower multiples (see Lesson 30.6).
PRACTICAL TOOL
A four-line screening checklist for any "B" or "C" class stock before you go further: (1) fixed/term deposits as a share of total deposits — above roughly 60-65% deserves scrutiny; (2) number of predecessor institutions merged into the current entity and years since the most recent merger closed; (3) sector concentration in the loan book — agriculture, hydropower, real estate, or margin lending exposure disclosed in the annual report's sector-wise loan breakdown; (4) capital adequacy ratio buffer above the regulatory minimum, in percentage points, not just pass/fail. A stock that screens poorly on two or more of these deserves a valuation discount before you even open the income statement.
Table: comparative minimum paid-up capital by class following NRB's 2015 directive (illustrative of the post-2015 regime; actual current floors should be checked against NRB's latest circular, as thresholds are periodically revised).
BFI Class
Pre-2015 Minimum Paid-Up Capital
Post-2015 Minimum Paid-Up Capital (National Level)
Typical Working Area
"A" Commercial Bank
Rs 2 billion
Rs 8 billion
Nationwide, by default
"B" Development Bank
~Rs 640 million
Rs 2.5 billion
Nationwide (national-level); narrower for regional/district-level institutions
"C" Finance Company
~Rs 300 million
Rs 800 million
Nationwide (national-level, 4-10+ districts); narrower for smaller-area institutions
Table: illustrative BFI count decline reflecting NRB-driven consolidation (based on NRB Research Department figures for peak-2012 and mid-March 2022).
Institution Class
Peak Count (~2012)
Count (Mid-March 2022)
Approximate Decline
"A" Commercial Banks
32
27
~16%
"B" Development Banks
88
17
~81%
"C" Finance Companies
77
17
~78%
The two tables together make the chapter's central point visually: the capital-floor multiple imposed on commercial banks was large (4x) but survivable through rights issues and bonus capitalisation within the existing population of institutions, whereas the same multiple imposed on a much larger and thinner-capitalised population of development banks and finance companies triggered an 80-percent-scale die-off through forced merger. That asymmetry in outcome, not just in capital multiple, is why the "B" and "C" class tier today is dominated by merger survivors rather than organically-grown franchises, and why merger-integration risk deserves permanent space on your checklist for this tier.
Lesson 30.6 — Why NEPSE Prices This Tier Differently
Put the regulatory, funding, and consolidation material together and the valuation gap between commercial banks and "B"/"C" class names on NEPSE stops looking like a market inefficiency and starts looking like a rational, if occasionally overdone, pricing of structurally different risk.
Four forces explain most of the multiple gap. First, float and liquidity: many "B" and "C" class names have thinner free float and lower daily traded volume than large commercial banks, which by itself commands a liquidity discount independent of fundamentals — a discount that widens further for the smaller, more recently merged names where institutional research coverage is thin to nonexistent. Second, earnings volatility: the funding-mix and sector-concentration dynamics described above genuinely produce more volatile earnings across a credit cycle than the diversified, low-cost-funded commercial bank model, and equity markets discount volatile earnings streams more heavily even when average earnings across a cycle look comparable. Third, merger overhang: a name with a recent multi-party merger history carries a real, not merely perceived, integration and legacy-asset-quality risk premium that persists for several years after the merger closes. Fourth, growth ceiling: commercial banks retain access to fee-generating trade finance, foreign exchange, and large corporate relationship business that "B" and "C" class institutions are largely locked out of by license, capping the addressable revenue opportunity for the smaller class regardless of management quality.
CAUTION
A low P/E or low P/B multiple on a "B" or "C" class stock relative to commercial banks is not automatically a value opportunity — it is frequently a correctly priced reflection of the structural funding and concentration risks this chapter describes. Before treating a discount as "cheap," confirm the discount is not simply compensating the market for a fixed-deposit-heavy funding base, a recent unintegrated merger, or sector concentration in a book you have not yet examined line by line.
A closely related dynamic is coverage: NEPSE brokerage research and news coverage devotes materially more attention to the roughly two dozen commercial bank stocks than to the far larger number of smaller "B" and "C" class names, most of which trade with little or no formal analyst coverage at all. This coverage gap means retail flow, rather than institutional or research-driven flow, dominates price formation in much of this tier, which partly explains both the wider multiple dispersion noted below and the sharper reaction of these stocks to rumour, dividend announcements, and bonus-share news relative to fundamentals. An investor willing to do the diligence this chapter describes — reading merger schemes, deposit-mix disclosures, and sector concentration tables that most retail participants skip — has a genuine informational edge in this part of the market that is far harder to find in the heavily covered commercial bank tier.
At the same time, the dispersion within the tier is itself informative and tradeable. Development bank valuations on NEPSE routinely show far wider spread in P/E and P/B multiples than commercial bank valuations do — some national-level, well-capitalised, cleanly-merged development banks trade at premium multiples that rival or exceed commercial bank averages, reflecting genuine franchise quality, while others trade at deep discounts reflecting exactly the risks catalogued above. That dispersion is the analytical opportunity this chapter is meant to equip you for: the class label tells you the regulatory starting conditions, but it does not tell you which individual institution inside the class has actually converted those conditions into a durable, well-funded, well-underwritten franchise versus which one is still digesting three prior mergers' worth of unexamined credit risk.
PRACTICAL TOOL
When comparing two "B" class development banks trading at different multiples, do not stop at the multiple. Line up (a) years since last merger closed, (b) fixed-deposit share of total funding, (c) sector concentration in the loan book, and (d) capital adequacy buffer above regulatory minimum, side by side. The stock trading at the discount multiple frequently — though not always — screens worse on two or three of these four factors. When it screens the same or better than the premium-multiple peer, you may have found a genuine mispricing rather than a correctly priced risk.
Chapter recap
Development banks and finance companies occupy a distinct regulatory tier in Nepal's banking system, not a scaled-down version of the commercial bank model. BAFIA and NRB's Unified Directives assign each class — "A" commercial banks, "B" development banks, "C" finance companies — different capital floors, different permitted activities in foreign exchange and trade finance, different deposit-mobilization authority, and historically different geographic working-area restrictions, and these differences flow directly into the shape of each class's income statement and balance sheet.
The 2015 capital directive that raised commercial bank minimum paid-up capital from Rs 2 billion to Rs 8 billion, national development bank capital from roughly Rs 640 million to Rs 2.5 billion, and national finance company capital from roughly Rs 300 million to Rs 800 million, triggered a decade of forced consolidation that shrank the development bank population from 88 to 17 and the finance company population from 77 to 17 between 2012 and 2022. Named mergers — Jyoti Bikas Bank's absorption of Jhimruk, Raptibheri, and Hamro Bikas Banks; Lumbini Bikas Bank's acquisitions of Vibor, Society, and Lumbini Finance & Leasing; Mahalaxmi Bikas Bank's absorption of Yeti and Malika development banks alongside Mahalaxmi and Siddhartha finance companies — illustrate that today's "B" and "C" class survivors are almost universally merger amalgams, carrying combined loan books and credit cultures that deserve more diligence than their consolidated financial statements alone provide.
Funding structure is the sector's central vulnerability: weaker access to low-cost retail current and savings deposits pushes "B" and "C" class institutions toward costlier, less sticky term deposits and institutional or interbank wholesale placements, a dynamic that directly produced the mid-2010s "problematic institutions" episode — Nepal Share Markets and Finance, Crystal Finance, Kuber Merchant Finance, Capital Merchant Banking and Finance, World Merchant Banking and Finance, Narayani Development Bank, Nepal Finance, Corporate Development Bank, and Lalitpur Finance — where NRB's remediation framework produced mixed results, including at least one proposed liquidation and one Supreme Court stay of a regulatory order.
Analytically, this means adjusting standard bank-analysis technique before applying it to this tier: decompose net interest margin for rate-versus-risk content rather than taking it at face value, scrutinize the CASA-versus-term deposit mix for repricing sensitivity, treat merger history as mandatory diligence rather than a footnote, and benchmark capital adequacy buffers in percentage points above the regulatory floor rather than as a simple pass/fail test.
Finally, the valuation gap between commercial banks and "B"/"C" class names on NEPSE is substantially explained, not merely observed: thinner float, higher earnings volatility across the credit cycle, merger-integration overhang, and a narrower addressable business given license restrictions all justify a structural discount, but the wide dispersion of multiples within the tier itself is real signal — the institutions that have genuinely converted their post-merger scale into durable, well-funded, well-underwritten franchises deserve to be distinguished from those still carrying undigested legacy risk, and that distinction, not the class label itself, is what should drive your position sizing in this part of the market.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VI · Chapter 31
Microfinance (MFI) Accounting Logic
First published 22 Aug 2026 · Last verified 29 Aug 2026
Microfinance is the part of NEPSE's listed universe where the balance sheet lies about the business. A commercial bank's numbers are, for all their complexity, a fairly faithful mirror of a diversified, collateralized lending operation. A "D" class microfinance institution's numbers are not. Behind a microfinance company's reported 20 percent return on equity can sit ten thousand overlapping, uncollateralized loans made to the same few hundred rural households — households who took a second loan to service the first. Reading an MFI's financials without understanding group lending, joint liability, wholesale funding, and Nepal's own multiple-borrowing crisis is like reading a hospital's revenue statement without knowing which patients are contagious. This chapter builds that understanding from the regulatory architecture up.
Lesson 31.1 — The "D" Class Tier and the Group-Lending Model
Nepal Rastra Bank licenses banks and financial institutions (BFIs) in four classes under the Bank and Financial Institutions Act: "A" class commercial banks, "B" class development banks, "C" class finance companies, and "D" class microfinance financial institutions — locally called laghubitta bittiya sanstha. The "D" class sits at the bottom of the capital-requirement ladder and the top of the risk ladder. These institutions exist for one regulatory purpose: to deliver small, largely collateral-free credit to low-income and "deprived sector" borrowers who commercial banks are unwilling or unable to underwrite directly.
The defining operational feature of an MFI is not its size but its lending methodology. Nepali MFIs overwhelmingly use the Grameen-derived group-lending model: borrowers are organised into centres of roughly twenty to forty women, subdivided into groups of five to eight, who meet weekly or biweekly with a field officer. Loans are approved and disbursed to individuals, but the group carries joint liability — if one member defaults, the group is expected to cover the shortfall or is collectively denied further credit. This is the mechanism that historically substituted for collateral and produced repayment rates that looked, on paper, better than secured commercial lending.
Why joint liability is an accounting fact, not just a social one
For the analyst, joint liability changes what a "non-performing loan" means. In a commercial bank, one borrower's default is an isolated credit event. In an MFI operating true group liability, one borrower's default is a leading indicator for four to seven others, because the social contract that enforced repayment has just been shown not to work. When you see an MFI's portfolio at risk (PAR) figure move, do not model it as an independent draw from a stable default distribution — model it as correlated risk that can cascade through a centre once trust breaks down. This is precisely the mechanism that turned a manageable pocket of stress into a sector-wide problem after 2023, discussed at length in Lesson 31.5.
KEY CONCEPT
Group liability is a repayment-enforcement mechanism, not a credit-risk-diversification mechanism. It concentrates behavioural risk within a centre even as it superficially resembles diversification across thousands of small loans.
The other structural feature that distinguishes "D" class institutions from commercial banks is the split between deposit-taking and non-deposit-taking MFIs. A small number of nationally licensed MFIs — Nirdhan Utthan Laghubitta Bittiya Sanstha and Chhimek Laghubitta Bittiya Sanstha among the most prominent — are permitted to mobilize public deposits and operate at a scale closer to a small bank. The great majority of NEPSE-listed "D" class companies, however, are retail lenders that do not take retail deposits at all, and instead fund their loan books almost entirely through borrowed money. This is the single most important fact to internalize before you open an MFI's balance sheet, and it is the subject of the next lesson.
Lesson 31.2 — Funding Structure: Wholesale Borrowing vs. Deposits
A commercial bank's cost of funds is anchored by its deposit base — current, savings, and fixed deposits collected from the public, generally the cheapest source of funds in the system. Most Nepali MFIs do not have that option. Their funding comes overwhelmingly from wholesale borrowings: term loans taken from commercial banks and development banks, which are themselves discharging a regulatory obligation.
NRB requires "A" class commercial banks to direct a defined share of their loan portfolio into designated priority categories: a minimum of roughly 10 percent to agriculture, a combined minimum of about 20 percent across tourism, micro, small and medium enterprises, energy, information technology, and export-oriented businesses using domestic raw materials, and at least 5 percent to the "deprived sector" — roughly 35 percent of a commercial bank's loan book in aggregate. Rather than originate deprived-sector loans directly, which is operationally expensive and unfamiliar territory for a commercial bank's credit staff, banks satisfy this quota by extending wholesale loans to MFIs, who then re-lend the money in small, collateral-free tranches to end borrowers. The wholesale loan to the MFI counts toward the bank's regulatory requirement even though the bank never touches the underlying borrower relationship.
REGULATORY DETAIL
Commercial banks' deprived-sector lending quota is satisfied largely through wholesale lending to MFIs, not direct origination. This is the structural reason commercial banks have a real, if indirect, credit exposure to microfinance sector distress — a fact that surfaced painfully in 2025–2026 as MFI portfolio quality deteriorated and banks' "deprived sector" books deteriorated in lockstep.
This funding structure has three consequences an investor must model explicitly.
First, cost of funds for a typical retail MFI is a function of the interbank wholesale lending rate, not the deposit rate — meaning MFI funding costs move with the same liquidity cycle that drives commercial bank base rates, but with a spread on top for intermediation risk. When systemic liquidity tightens and banks' own cost of funds rises, that cost is passed straight through to MFIs, compressing their net interest margin from the funding side even before considering asset-quality deterioration.
Second, MFIs are structurally short on funding diversification. A retail MFI drawing 70 to 90 percent of its liabilities from a handful of commercial-bank wholesale lines has effectively no lender of last resort and no depositor base to fall back on if a lending bank pulls a credit line during a stress episode — which is exactly the kind of correlated, pro-cyclical behaviour that showed up across the sector as bank credit committees grew cautious on MFI wholesale exposure through 2025.
Third, the distinction between "wholesale" MFIs and "retail" MFIs in NRB's own reporting is not cosmetic. Wholesale MFIs are companies whose primary business is re-lending to smaller retail MFIs and cooperatives rather than lending directly to end borrowers — Sana Kisan Bikas Laghubitta Bittiya Sanstha is the best-known example of this category. Wholesale MFIs sit one layer further removed from the ultimate borrower and have historically shown materially better asset quality than retail MFIs, precisely because they are lending to institutions with underwriting infrastructure rather than to individual borrowers directly. When NRB data shows wholesale-MFI non-performing loans at roughly 2.9 percent against retail-MFI non-performing loans north of 7 percent, that gap is the funding-structure distinction made visible in the numbers.
PRACTICAL TOOL
When you open an MFI's notes to accounts, locate the borrowings breakdown before you look at anything else. Ask three questions: (1) What share of total liabilities is wholesale borrowing versus deposits? (2) How concentrated is that borrowing across a small number of lending banks? (3) What is the weighted average cost of that borrowing relative to the yield on the loan book? An MFI with 85 percent wholesale funding concentrated in three lending banks is a fundamentally different risk than one with a broad deposit base.
Lesson 31.3 — The Interest Rate Cap Regime and Its Business Model Consequences
Because MFI borrowers are, almost by definition, price-insensitive and information-poor relative to commercial bank customers, NRB has regulated MFI lending rates directly and repeatedly — far more aggressively than it regulates commercial bank pricing. The history of this intervention is itself a study in how the regulator's understanding of the sector's real economics has evolved, and every rate-cap change directly compresses or expands MFI net interest margin, so an investor needs the sequence, not just the current rule.
In 2016, NRB capped the interest rate spread — the gap between an MFI's cost of funds and its lending rate — at 7 percentage points, forcing MFIs whose cost of borrowing rose to either compress margin or push lending rates up within that spread ceiling. Over the following years, MFI lending rates crept toward the high teens as cost of funds rose and MFIs used the full spread allowance. By late 2023, NRB's own internal study concluded that a flat 15 percent lending-rate ceiling then in force was economically unworkable for smaller, higher-cost-structure MFIs, and recommended moving to a spread-based, cost-reflective framework instead of an absolute cap.
NRB acted on that recommendation in stages. It first affirmed a hard 15 percent maximum lending rate applicable to microfinance loans generally, then, for loans disbursed from Shrawan 1, 2082 (July 16, 2025) onward, replaced the flat cap with a base-rate-plus-spread system: each MFI must compute its own base rate from its actual cost of funds, administrative costs, and loan-loss risk, and may add no more than 3 percentage points on top of that base rate to arrive at the interest rate charged to borrowers. Loans disbursed before that date continue to be governed by the 15 percent absolute ceiling until they mature or are rolled over. Under the new regime, an MFI publishing a base rate of 11 percent can charge borrowers a maximum of 14 percent — a materially different economic outcome from a flat 15 percent ceiling for an MFI whose true cost structure sits below the old cap.
REGULATORY DETAIL
Two rate regimes now coexist on a typical MFI's book: legacy loans disbursed before Shrawan 1, 2082 remain capped at a flat 15 percent; new loans disbursed after that date are governed by base rate + maximum 3 percent spread, recalculated quarterly. When you read an MFI's disclosed "average yield on loans," check which regime the bulk of the portfolio still sits under — a book that has not yet rolled over into the new-loan cohort will show a yield the new framework will compress as the older, higher-priced loans mature out.
Period
Rule
Effect on MFI Economics
Through 2016
No formal spread cap; MFIs priced largely at will
Wide, unregulated spreads; rapid margin expansion during sector growth phase
2016 onward
Interest rate spread capped at 7 percentage points over cost of funds
Ties lending rate mechanically to funding cost; margin protected but capped
Through late 2023
Flat 15% maximum lending rate
Simple to enforce, but penalises smaller/higher-cost MFIs disproportionately; NRB study flags this as unsustainable for the sector's cost structure
From Shrawan 1, 2082 (16 Jul 2025) — new loans
Base rate (own cost of funds + admin cost + risk premium) + maximum 3 percentage point spread, reviewed quarterly
Cost-reflective pricing; margin protection tied to genuine efficiency rather than a uniform ceiling; legacy loans continue under 15% cap until maturity
The business-model consequence of this history is straightforward but easy to underweight: an MFI's profitability is now a direct function of its administrative efficiency, because the rate ceiling is calculated from the MFI's own cost base rather than fixed externally. An MFI with a bloated branch network, excess field staff, or high provisioning needs will show a higher permissible base rate — but that higher rate is precisely evidence of a weaker underlying cost structure, not a sign of pricing power. When you compare two MFIs' reported yields under the new regime, a higher yield is a red flag about cost efficiency and risk, not a green flag about margin strength.
CAUTION
Do not read a rising loan yield on an MFI's income statement as improving profitability under the current regime. Under base-rate-plus-spread pricing, a rising yield frequently signals a rising cost base or a rising built-in risk premium — both of which erode, rather than expand, the economics an investor should actually care about.
Lesson 31.4 — Portfolio at Risk, Write-Offs, and MFI-Specific Provisioning
Commercial bank credit quality is conventionally tracked through non-performing loan (NPL) ratios, calculated on loans overdue past a fixed threshold. Microfinance analysts worldwide, including in Nepal, additionally track Portfolio at Risk (PAR) — most commonly PAR30 (the share of outstanding loan principal with any payment overdue by 30 days or more) — because in group-lending models, delinquency spreads through joint-liability networks faster than it converts into formal default. PAR30 is a leading indicator; the NPL ratio that eventually shows up in NRB aggregate data is a lagging confirmation of the same stress.
NRB's Unified Directives — most recently updated under the 2081 framework, with specific microfinance provisioning treated under Directive 1.081 — set loan classification and provisioning rules for "D" class institutions that broadly mirror, but are calibrated differently from, the rules applied to commercial banks. Loans are classified into pass, watchlist, substandard, doubtful, and loss categories based on days past due, with escalating provisioning requirements at each stage; loss-category loans typically require full or near-full provisioning. MFIs are additionally required to maintain minimum capital ratios calibrated to their risk profile — a minimum Tier 1 capital ratio and a minimum total capital fund ratio measured against risk-weighted assets, both lower in absolute terms than the equivalent commercial-bank requirements but calibrated against a much less diversified, uncollateralized asset base, which is precisely why regulators watch MFI capital adequacy so closely during stress periods.
Write-off policy: the number that hides in the footnotes
Because the great majority of MFI lending is genuinely uncollateralized — NRB data on the sector shows roughly 85 percent of outstanding microfinance loans carrying no physical collateral at all — recovery on a defaulted MFI loan is fundamentally different from recovery on a defaulted, collateralized commercial bank loan. There is no property to seize, no asset to auction. This makes an MFI's write-off policy — how quickly and how completely it removes irrecoverable loans from its books — one of the most consequential and most easily manipulated disclosures in the entire financial statement.
An MFI that delays write-offs keeps a loan technically "on book," reported as merely provisioned rather than written off, understating the true, permanent loss the institution has already suffered in economic terms. Because provisioning flows through the income statement as an expense while a write-off is a balance-sheet reclassification against already-booked provisions, an MFI under earnings pressure has a direct incentive to under-provision rather than to delay write-offs outright — the two levers work together to flatter reported profitability in a stress period.
WARNING
Never take an MFI's reported NPL ratio at face value without checking the write-off policy in the notes to accounts. Two MFIs with identical underlying loan quality can report materially different NPL ratios purely because one writes off dead loans promptly and the other lets them sit, provisioned but not written off, inflating the apparent size of a "performing" book that is not actually performing.
PRACTICAL TOOL
Calculate a rough proxy for the true loss rate by adding the period's write-offs back to the reported NPL stock before computing the ratio: (NPL + write-offs during the period) divided by (average loan book + write-offs during the period). This "gross loss-adjusted NPL ratio" is a far more honest cross-sectional comparison tool across MFIs with different write-off habits than the headline reported NPL ratio.
Sector-wide, the direction of travel through 2025 and into 2026 has been unambiguous and severe. NRB data for retail MFIs showed the reported NPL ratio rising from 6.17 percent at the end of Asar 2081 to 7.25 percent at the end of Asar 2082 — a 27 percent year-on-year increase in the ratio — while total loan-loss provisioning for retail MFIs grew to roughly Rs 26.24 billion, of which about Rs 15.71 billion was specifically earmarked against non-performing loans, itself up nearly 18 percent year-on-year. Wholesale MFIs, lending through institutional intermediaries rather than directly to end borrowers, showed a materially lower but still rising NPL ratio of about 2.93 percent, up 13 percent year-on-year, with total provisioning around Rs 2.19 billion. And by mid-April 2026, sector-wide data reported in the financial press showed the picture had deteriorated further still: an aggregate MFI non-performing loan ratio of 11.32 percent, up sharply from about 7 percent just nine months earlier, with total bad loans across the sector reaching roughly Rs 54.08 billion — a 74 percent year-on-year increase — against a total outstanding MFI loan base of approximately Rs 500 billion.
Metric
Asar-end 2081 (mid-2024)
Asar-end 2082 (mid-2025)
Mid-April 2026
Retail MFI NPL ratio
6.17%
7.25%
—
Wholesale MFI NPL ratio
—
2.93% (up 13% YoY)
—
Sector-wide MFI NPL ratio
~7% (approx., 9 months prior)
—
11.32%
Sector-wide gross bad loans
—
—
~Rs 54.08 billion (+74% YoY)
Total sector loans outstanding
—
—
~Rs 500 billion
Share of loans without physical collateral
—
—
~85%
CASE IN POINT
The jump from a sector NPL ratio near 7 percent to 11.32 percent in roughly nine months is not a gradual credit-cycle drift — it is the signature of a joint-liability system unwinding. Once enough borrowers within enough centres stopped paying, the social enforcement mechanism that had kept repayment rates artificially high for years lost its force across the network, and delinquency that had been contained within individual groups became a portfolio-wide event.
Lesson 31.5 — The Over-Indebtedness Crisis: Multiple Borrowing and Systemic Risk
The single most important story in Nepali microfinance over the past several years is not any one company's mismanagement — it is a structural, sector-wide over-indebtedness crisis driven by multiple borrowing: the same low-income household taking loans from several MFIs simultaneously, often to service the interest and principal owed to the others.
The mechanics are straightforward and, in hindsight, predictable. As the number of licensed MFIs multiplied through the 2010s and competition for borrowers intensified, MFI field officers — under pressure from boards and management to grow loan books and hit profit targets — increasingly extended credit to borrowers who were already clients of other MFIs, without adequate cross-checking of a borrower's total outstanding obligations across institutions. One MFI's own chief executive has candidly acknowledged the underlying incentive problem in the Nepali press: boards push CEOs for higher profits, and that pressure translates directly into looser, more reckless lending standards at the point of disbursement. Reporting on the crisis documented at least one borrower whose obligations, spread across five separate microfinance companies — among the institutions named in that reporting were Forward Community Microfinance, Mero Microfinance, Mithila Laghubitta, Deprosc Laghubitta, and Sana Kisan Bikas Laghubitta — ballooned from a manageable starting point to roughly Rs 4.5 million over four years, purely through the compounding of interest and rollover borrowing used to keep multiple lenders simultaneously satisfied. Sector estimates put the population in acute repayment distress at roughly 100,000 borrowers out of a total microfinance client base of about 3.31 million — a minority in headcount terms, but concentrated enough, and correlated enough through shared centres and shared informal credit networks, to move the entire sector's reported NPL ratio.
Why this is a business-model failure, not just a borrower failure
It is tempting to read multiple borrowing purely as a story about financially unsophisticated borrowers taking on debt they could not manage. That framing is incomplete and, for an equity analyst, actively misleading. Multiple borrowing was enabled — indeed, was structurally invited — by the absence of a functioning shared credit bureau discipline across MFIs during the years the sector was growing fastest, combined with management incentives (growth targets, disbursement-linked staff bonuses, board pressure for profit growth) that rewarded loan-book expansion over credit-quality discipline. Every MFI that disbursed a loan to an already-indebted borrower without checking that borrower's obligations elsewhere was making an underwriting decision, and every one of those decisions shows up, eventually, in the PAR and NPL data reviewed in Lesson 31.4. The crisis is therefore best read by the investor as a governance and underwriting-discipline story playing out across an entire sector simultaneously — which is exactly why it moved from "individual company problem" to "regulator's top concern" so quickly.
WARNING
When several MFIs in your coverage universe show a similar-shaped deterioration in PAR and NPL over the same period, resist the temptation to treat it as company-specific credit risk you can diversify away by holding a basket of MFI shares. Nepal's over-indebtedness crisis is a correlated, sector-wide phenomenon rooted in shared borrower networks and common industry incentive structures — a basket of MFI shares does not diversify this risk away; it concentrates exposure to the same underlying borrowers viewed through different corporate wrappers.
The regulatory response has compounded the pressure on the sector's structure. Concerned about the concentration of ownership and control that had built up as commercial banks and development banks acquired stakes in microfinance companies — partly as a route to more easily satisfy their own deprived-sector lending quotas discussed in Lesson 31.2 — NRB issued a directive in January 2022 forcing consolidation wherever a BFI held more than a 10 percent minority stake, or 51 percent or greater majority control, in a microfinance company, with an initial deadline of Ashar-end 2079 (mid-2022) for affected institutions to submit merger action plans. That directive has since been adjusted and relaxed at the margins — including extended timelines and provisions allowing wholesale MFIs formed through merger to continue lending operations for up to five years post-merger — but the underlying policy direction has been consistent and firm: NRB wants a smaller number of better-capitalised, better-governed microfinance institutions, not a large number of thinly capitalised ones competing recklessly for the same borrower base. For a NEPSE investor, this means the "D" class universe you are analysing today is smaller, and in principle more consolidated, than it was five years ago — and further merger activity, sometimes forced rather than voluntary, remains a live possibility for individual holdings.
CASE IN POINT
The 2022 crossholding directive is a useful lens for understanding why some MFI mergers on NEPSE were not strategic choices at all, but regulatory compliance exercises. When you see a merger announcement involving an MFI with a commercial-bank promoter group holding a large stake, check whether the transaction is resolving a crossholding breach before assuming it reflects an organic consolidation strategy or synergy story.
Lesson 31.6 — Reading MFI Financial Statements: Red Flags and Valuation Adjustments
Bringing the previous five lessons together, here is the practical checklist to run before valuing any NEPSE-listed microfinance company.
Step one: classify the funding base. Pull the borrowings note and the deposits note (if any) separately. Establish what share of total liabilities is wholesale borrowing, how concentrated that borrowing is by lender, and what its weighted average cost is. An MFI reliant on three or four commercial-bank lending relationships is exposed to a credit-line-withdrawal risk that a diversified retail deposit base does not carry. Cross-reference against whether the company is one of the small number of nationally licensed, deposit-taking MFIs — those institutions carry a fundamentally different funding risk profile than a purely wholesale-funded retail MFI.
Step two: locate the true asset-quality picture, not the headline NPL ratio. Compute the gross loss-adjusted NPL ratio described in Lesson 31.4 by adding the period's write-offs back into both numerator and denominator. Compare the resulting figure against the sector benchmarks in the table above — an individual MFI reporting materially better asset quality than the sector-wide 11.32 percent figure deserves scrutiny of its write-off timing before you accept the number as evidence of superior underwriting.
Step three: test the yield against the base-rate framework. Under the post-Shrawan-2082 pricing regime, an MFI's disclosed average lending yield should be broadly consistent with its own disclosed base rate plus no more than 3 percentage points, for the portion of the book disbursed after that date. A yield that runs meaningfully above that ceiling on new-loan disbursements is either a disclosure or compliance problem, or evidence that a large share of the book remains legacy loans still governed by the older flat 15 percent cap and has not yet rolled over into the new framework — either way, it changes your forward margin assumption.
Step four: check for merger and crossholding exposure. Review the shareholding structure for BFI promoter stakes near or above the 10 percent minority or 51 percent majority thresholds that triggered the 2022 consolidation directive. A holding near those thresholds carries a real possibility of forced merger, dilution, or restructuring that a pure earnings-multiple valuation will not capture.
Step five: stress-test for correlated, network-level credit risk. Do not model MFI credit losses as independent, diversifiable events across a loan book of thousands of small loans. Model them as correlated within centres and correlated across an investor's basket of MFI holdings, because Nepal's own recent experience shows that once over-indebtedness triggers repayment breakdown in one part of the network, the group-liability mechanism that once enforced discipline can just as quickly transmit stress through it.
PRACTICAL TOOL
Build a simple five-line diagnostic table for every MFI you cover: (1) wholesale funding share and lender concentration, (2) gross loss-adjusted NPL ratio versus the sector benchmark, (3) disclosed base rate and spread versus the 3-point statutory ceiling, (4) BFI promoter shareholding versus the 10%/51% merger-trigger thresholds, and (5) centre/group repayment trend if disclosed. An MFI that clears all five checks cleanly is a fundamentally different investment case from one that clears none of them, even if their reported ROE and EPS look similar on the surface.
CAUTION
Reported profitability in microfinance is unusually easy to overstate relative to commercial banking, precisely because the two levers that would normally expose weakness — provisioning adequacy and write-off timing — are both judgment calls made by management, in a lending model where recovery is genuinely difficult on 85 percent of the book because there is no collateral to fall back on. Treat a microfinance company's stated book value and stated profitability as a starting point for diligence, not a concluding one.
Chapter recap
Nepal's "D" class microfinance institutions occupy a genuinely distinct position within the NEPSE-listed universe, and analysing them with commercial-bank tools produces systematically wrong answers. The defining structural facts are that most MFI lending is uncollateralized and organised through group liability, that most MFI funding comes from wholesale borrowing rather than retail deposits, and that MFI lending rates are directly regulated by NRB through an evolving cap regime rather than left to market pricing. Each of these facts changes what a given line item on the financial statements actually means, and none of them has a clean analogue in commercial banking analysis.
The regulatory history matters because it is not settled. NRB moved from a flat interest-rate spread cap in 2016, to a flat 15 percent lending-rate ceiling, to a base-rate-plus-maximum-3-percent-spread framework for new loans from Shrawan 1, 2082 (July 2025) onward, while legacy loans continue under the older cap until they mature. An MFI's margin trajectory over the next several years will be shaped substantially by how quickly its book rolls from the old regime into the new one, and by how disciplined its own cost structure is, since the new framework ties permissible pricing directly to the institution's own administrative and funding costs rather than to an external ceiling.
The over-indebtedness crisis is the central risk event of this sector in the current cycle, and it is a structural, correlated phenomenon rather than a collection of independent company problems. Multiple borrowing by the same households across several MFIs — enabled by weak cross-institutional credit checking and management incentives that rewarded disbursement growth over underwriting discipline — pushed the sector-wide non-performing loan ratio from roughly 7 percent to 11.32 percent within about nine months through early-to-mid 2026, with gross bad loans rising 74 percent year-on-year to roughly Rs 54.08 billion against a base of about Rs 500 billion in outstanding loans, on a book that is roughly 85 percent uncollateralized. This is precisely the scenario Lesson 31.1's discussion of joint liability warned about: repayment discipline that depends on social enforcement within a group can unwind rapidly and simultaneously across many groups once enough borrowers default.
Provisioning and write-off policy are the two places where a struggling MFI has the most room to present a healthier picture than the underlying loan book supports, because recovery on uncollateralized loans is genuinely difficult and management retains real discretion over how quickly to reclassify a stressed loan as a permanent loss. The gross loss-adjusted NPL ratio — reported NPLs plus period write-offs, divided by the loan book plus the same write-offs — is the single most useful adjustment an investor can make to compare asset quality honestly across companies with different write-off habits.
Funding structure and ownership concentration are the two balance-sheet facts every MFI analysis must start from. Wholesale borrowing concentrated among a handful of commercial-bank lenders creates a liquidity risk that has no analogue in a deposit-funded institution, and BFI promoter shareholdings near the 10 percent minority or 51 percent majority thresholds set by NRB's 2022 crossholding directive create a live possibility of forced merger that a standard earnings multiple will not price in.
Taken together, these five dynamics mean that valuing a NEPSE-listed microfinance company well requires treating it as a distinct asset class within financial-sector investing — one where the headline return on equity is the least reliable number on the page, and where the real work of analysis lies in the funding note, the provisioning policy, the write-off schedule, the shareholding register, and an honest, correlated view of what happens to a joint-liability lending network when the households at its centre run out of road.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VI · Chapter 32
Hydropower Project Accounting Logic
First published 22 Aug 2026 · Last verified 29 Aug 2026
Lesson 32.1 — The Two Lives of a Hydropower Balance Sheet
Open the annual report of almost any hydropower company on NEPSE and you will notice something odd if you are used to reading manufacturing or banking accounts: for years, sometimes a full decade, the income statement is nearly empty while the balance sheet grows enormous. A company can report zero revenue, zero profit, and yet carry assets worth tens of billions of rupees. This is not a company in distress. It is a company under construction, and construction-phase hydropower accounting follows a logic entirely different from operation-phase accounting. Understanding the seam between these two phases — and knowing which phase a company you are evaluating actually sits in — is the single most important skill in reading a Nepali hydropower stock.
During construction, nearly every cost a project incurs is capitalised rather than expensed. Land acquisition and resettlement, tunnel excavation, penstock and powerhouse civil works, turbines and generators, transmission line interconnection, consultancy and supervision fees, and — critically — interest on the loans used to fund all of the above, all accumulate on the balance sheet under "Capital Work in Progress" (CWIP). Nothing hits the profit and loss account except perhaps small amounts of administrative overhead that auditors judge unrelated to bringing the asset to its intended use. The company can be burning through its entire equity base and drawing down its full debt facility, and its income statement will still show a thin trickle of bank interest income on unspent share proceeds sitting in a construction account.
The moment the plant reaches Commercial Operation Date (COD) — sometimes called Rated Commercial Operation Date, RCOD, in the tax rebate schedules — everything changes. CWIP is reclassified into Property, Plant and Equipment (PPE) across specific asset categories: civil structures, hydro-mechanical equipment, electro-mechanical equipment, transmission assets. Depreciation begins. Interest capitalisation stops, because Nepal Accounting Standard 23 (Borrowing Costs) — the local equivalent of IAS 23 — only permits capitalising borrowing costs directly attributable to acquiring, constructing, or producing a qualifying asset for the period until that asset is "substantially complete and ready for its intended use." From COD onward, interest expense flows straight through the income statement, and so does depreciation, and so does the entire PPA revenue stream. A company that showed almost no P&L activity for eight years can suddenly show a debt-servicing burden, a depreciation charge, and a revenue line all in the same annual report — and the market frequently misprices this transition in both directions, treating a pre-COD company as if it were already earning, or treating a freshly commissioned company's first full year of depreciation and interest as a permanent decline in profitability rather than the new steady state.
KEY CONCEPT
Capital Work in Progress (CWIP) is not "cash spent." It is an asset account. A hydropower company with NPR 40 billion of CWIP and no revenue is not burning value — it is building an asset whose eventual earning power depends entirely on the PPA it signs and the tariff escalation clock embedded in that PPA. Judge the CWIP by the project's PPA terms, not by its size alone.
The practical skill for an investor is to identify, from the notes to accounts, exactly where a company sits on this timeline: percentage physical completion, expected COD, and — this is the number analysts often skip — the cumulative interest capitalised to date. That last figure tells you how much of the eventual asset base is not concrete and steel but financing cost, and it previews the depreciation and amortisation charge the company will carry for the rest of its operating life.
Lesson 32.2 — Interest During Construction: The Silent Multiplier
Nepali hydropower projects are financed on capital structures that would be considered dangerously leveraged in almost any other sector — commonly 70:30 or even 80:20 debt-to-equity, with the debt drawn from a syndicate of Nepali commercial banks (a "consortium loan," since single-bank lending limits under Nepal Rastra Bank's Single Obligor Limit rules make one bank funding a large project legally impossible). A run-of-river project with a five- or six-year construction period will typically capitalise interest for the entire period, and because construction delays in Nepal are the rule rather than the exception — landslide-damaged access roads, monsoon-halted tunneling, contractor disputes, transmission-line right-of-way litigation — the capitalised interest bill frequently ends up being a bigger surprise to shareholders than the base construction cost itself.
Interest During Construction (IDC) capitalisation
The mechanics are straightforward but consequential. NAS 23 requires a company to capitalise the actual borrowing costs incurred on funds specifically borrowed for the project, net of any investment income earned on temporarily parking undrawn loan proceeds. Where general corporate borrowing is used, a weighted average capitalisation rate applies to expenditure on the qualifying asset. Nepal Rastra Bank's unified directives to licensed banks go a step further, giving banks explicit guidance on how long-gestation infrastructure loans — hydropower chief among them — may treat accrued-but-unpaid interest during the construction period, which in turn shapes how promoters structure moratoriums and how the capitalised interest line grows on the borrower's books.
REGULATORY DETAIL
Nepal Rastra Bank's directives permit banks to allow interest capitalisation on long-gestation project loans rather than forcing cash debt service before a plant earns revenue. This is sound project finance practice — a hydropower project cannot service debt before COD — but it means the loan principal a company owes at COD is materially larger than the amount it originally drew down. Read the loan schedule in the notes to accounts, not just the headline debt figure on the balance sheet.
The scale this can reach is best illustrated by Nepal's largest domestically financed plant, the 456 MW Upper Tamakoshi Hydroelectric Project (NEPSE: UPPER), built and operated by Nepal Electricity Authority's subsidiary Upper Tamakoshi Hydropower Limited. The project's construction cost estimate rose from roughly NPR 49.29 billion to about NPR 52.29 billion as delays compounded — first from contractor underperformance (the original hydro-mechanical contractor, India's Texmaco Engineering and Railway Company, withdrew from the project, with Austria's Andritz Hydro brought in to complete the work), and further from the damage the 2015 Gorkha earthquake inflicted on under-construction structures. But the number that matters most for an accounting-literate investor is a different one: by the time the plant reached commercial operation, total project cost — construction cost plus capitalised interest — had climbed toward roughly NPR 76 billion, of which approximately NPR 24 billion was interest capitalised during construction. Financing for the project was split between about NPR 10.59 billion of share capital (with NEA, Nepal Telecom, Citizen Investment Trust, and Rastriya Beema Sansthan among the major institutional shareholders) and roughly NPR 41.70 billion in bank debt.
CASE IN POINT
Upper Tamakoshi's capitalised interest of roughly NPR 24 billion is not a rounding error — it is close to half the plant's original construction budget. That capitalised interest becomes part of the depreciable asset base after COD, meaning UPPER's post-commissioning depreciation charge, and the debt-service burden behind it, reflect years of delay as much as they reflect the physical plant. When a project's construction timeline stretches, its post-COD income statement carries the cost of that delay for the entire depreciation period, not just the construction years.
WARNING
A large capitalised-interest balance can flatter a company's apparent equity return during construction (no interest expense is visible) while quietly loading a much larger debt and depreciation burden onto the operating years. Never evaluate a pre-COD hydropower company's "book value per share" without asking how much of that book value is capitalised interest rather than physical plant — and without checking whether the underlying PPA tariff was fixed years before COD, at a rate that may not have anticipated the delay-driven cost escalation.
Here is the reported financing structure for Upper Tamakoshi at the point construction cost estimates were last revised, alongside its post-COD scale, to show how the pieces fit together:
Component
Amount (NPR)
Note
Original construction cost estimate
~49.29 billion
Pre-delay budget
Revised construction cost estimate
~52.29 billion
After contractor change and earthquake damage
Equity (share capital)
~10.59 billion
NEA, NT, CIT, RBS and public shareholders
Bank debt (consortium loan)
~41.70 billion
Syndicated among Nepali commercial banks
Capitalised interest during construction
~24 billion
Added to asset base at COD, drives post-COD depreciation
Total project cost at commissioning
~76 billion
Construction cost + capitalised interest
Installed capacity
456 MW
Nepal's largest domestically owned plant
A general rule follows from this: the longer a project takes to build, the larger the share of its eventual asset base that is financing cost rather than construction cost — and the higher its post-COD depreciation and interest expense will be relative to what the original project feasibility study assumed. When you read a hydropower prospectus or annual report projecting future profitability, always ask what construction timeline the projection assumes, and compare it against the company's actual physical progress percentage disclosed in the same report.
Lesson 32.3 — Reading the PPA: Wet Season, Dry Season, and the Escalation Clock
A hydropower company's revenue is not a single number multiplied by units sold. It is the output of a contract — the Power Purchase Agreement with Nepal Electricity Authority — whose structure was negotiated at a specific point in time and which determines, almost mechanically, what the company can report as revenue for the next 20 to 30 years. Reading a hydropower income statement without reading its PPA is like reading a bank's income statement without knowing its interest rate on deposits.
Nepal's rivers are monsoon-fed, so a run-of-river plant (no storage reservoir) generates several times more energy in the wet season (roughly mid-June to mid-October) than in the dry season, when flows can drop to a fraction of wet-season levels. NEA's standard PPA template for small and medium run-of-river IPPs has, for years, split the tariff itself along the same seasonal line, paying a lower per-unit rate for wet-season energy (when supply is abundant and hydrologically "cheap" to the buyer) and a materially higher rate for dry-season energy (when the same unit is scarcer and more valuable to the grid). Balephi Hydropower Limited's PPA with NEA, signed in December 2015, is a representative example of this template: NPR 4.80 per kWh for wet-season energy against NPR 8.40 per kWh for dry-season energy — a dry-season premium of 75 percent over the wet-season rate — with the contract further specifying escalation of 3 percent per year, applied seven times, starting from the thirteenth month after commercial operation (Balephi's own escalation schedule shows one escalation forfeited because commercial operation started later than planned, which is itself a reminder that construction delay does not just raise cost — it can also compress the revenue-escalation window a company was counting on).
KEY CONCEPT
Wet-dry tariff differentiation exists because NEA's system is short of dry-season capacity and long on wet-season energy relative to demand. A run-of-river plant that generates most of its annual output in four monsoon months is, in effect, selling most of its volume at the cheaper rate and only a modest volume at the premium rate — which is why annual revenue for a small RoR plant tracks its dry-season output far more sensitively than its total annual generation figure suggests.
Because tariffs escalate annually for a defined number of years and then flatten, a company's revenue-per-unit trajectory is knowable years in advance if you have the PPA schedule — which is exactly why serious hydropower analysis in Nepal starts from the PPA annexures, not from trailing income statement growth rates.
Storage projects change the calculus further. A storage or peaking-storage plant can shift generation from wet season to dry season by holding back water, which is precisely why Nepal Electricity Authority and the Electricity Regulatory Commission (ERC) have spent the past several years designing a differentiated tariff framework specifically to make storage projects financeable — since a storage plant's entire value proposition to the grid is dry-season and peak-hour firm capacity, which is worth substantially more than the same energy delivered in the monsoon glut. An ERC discussion paper on storage-hydro tariff design illustrates how wide this gap is expected to be, and how individual large storage projects have priced their own asks even wider once actual Nepali financing costs (materially higher debt costs and shorter tenors than the concessional assumptions used in illustrative models) are factored in.
Project / Scenario
Wet-season rate (NPR/kWh)
Dry-season rate (NPR/kWh)
Escalation
Standard run-of-river template (e.g., Balephi Hydropower)
4.80
8.40
3% p.a. x 7 years from 13th month post-COD
ERC illustrative storage tariff, Years 1-15
5.69
9.95
Modelled at concessional financing assumptions
ERC illustrative storage tariff, Years 16-50
4.83
8.46
Post-escalation plateau
Dudhkoshi Storage (670 MW) developer ask
10.67
18.67
3% p.a. for 8 years
Budhi Gandaki Storage (1,200 MW) developer ask
12.64
22.12
3% p.a. for 8 years
CASE IN POINT
The gap between the ERC's "illustrative" storage tariff (built on concessional 3.9% debt cost and 27-year tenor assumptions) and what actual Nepali developers are asking for Dudhkoshi and Budhi Gandaki — roughly double — is not developer greed alone. It reflects the real cost of Nepali rupee project debt, typically priced closer to 10-11% with 12-year commercial tenors, against a project life of 30 to 50 years. When you see a storage-hydro IPO prospectus, check which financing assumptions its PPA tariff was actually built on; a project priced at "illustrative" concessional rates but funded with ordinary domestic bank debt has a revenue-cost mismatch baked in from day one.
A further structural feature to watch is whether a PPA is take-or-pay or take-and-pay. Under take-or-pay, NEA is contractually obligated to pay for a defined quantity of deliverable energy whether or not it actually draws that energy off the grid (subject to force majeure and grid-availability carve-outs), which effectively transfers demand risk to the buyer and gives the IPP a revenue stream closer to a fixed annuity. Under take-and-pay, NEA pays only for energy it actually takes, leaving the IPP exposed to curtailment risk — a real issue in a system that has, at various points, faced transmission bottlenecks preventing it from absorbing all available wet-season generation. NEA has moved smaller hydropower PPAs from take-and-pay toward take-or-pay terms in recent years, which is a meaningfully positive development for revenue predictability, but the legacy PPA a given listed company signed years ago may still carry the older, weaker structure — so the take-or-pay/take-and-pay distinction belongs on your checklist for every hydropower stock, not just new listings.
What the escalation clock means for your model
Because escalation is time-bound (commonly capped at seven or eight annual steps before the tariff plateaus), a hydropower company's per-unit revenue growth is front-loaded and mechanical, not driven by operating performance. An investor modelling five years forward should pull the exact escalation schedule from the PPA rather than assuming a flat growth rate — and should separately flag the year the escalation stops, because that year marks the point after which revenue growth can only come from higher plant availability, not from contractual tariff increases.
Lesson 32.4 — Depreciation, License Life, and the Financial Asset Question
Once a hydropower asset is capitalised, how should it be depreciated — and, more fundamentally, is "depreciation" even the right accounting model for an asset built under a Build-Own-Operate-Transfer (BOOT) survey license that must eventually revert to the Government of Nepal? This is a live technical debate in Nepali accounting practice, and it matters to investors because the classification choice changes reported profit, tax timing, and the comparability of hydropower companies against one another.
Most listed Nepali hydropower companies depreciate plant and equipment as ordinary Property, Plant and Equipment under NAS 16, generally on a straight-line basis over useful lives set to align with (or fall within) the survey/generation license period — typically structured so the asset is substantially depreciated by the time the BOOT transfer obligation to the government falls due. This treatment produces a familiar depreciation-and-interest income statement, much like any other capital-intensive company, and it is the treatment nearly all NEPSE-listed hydropower issuers currently use.
But a technical argument exists — grounded in IFRIC 12, Service Concession Arrangements, the international standard governing exactly this kind of infrastructure-under-license arrangement — that many Nepali hydropower BOOT projects should instead recognise a financial asset (a receivable) rather than PPE, whenever two conditions both hold: the grantor (NEA, acting for the state) controls what services the operator must provide and at what price, and the grantor retains a significant residual interest in the infrastructure at the end of the concession. A run-of-river plant with a fixed, NEA-dictated PPA tariff, a take-or-pay payment mechanism tied to availability rather than market-negotiated pricing, and a mandatory transfer of the plant to the Government of Nepal at the end of a fixed license term arguably satisfies both conditions — which would mean the "right" accounting is to recognise a financial asset that unwinds through an effective-interest calculation over the concession life, not a depreciating fixed asset.
WARNING
Under IFRIC 12's financial-asset model, the company would recognise interest income (using an effective interest rate on the receivable) as its principal revenue driver rather than depreciation and energy revenue — a completely different income statement shape, and one that removes management discretion over depreciation policy that currently exists under the PPE model. The gap between the two models is not academic: IAS 16/PPE treatment tends to defer tax and support earlier, PPA-tariff-linked dividend distributions relative to what a financial-asset model would produce, which is precisely why the incentive runs toward the PPE treatment even where the concession terms may argue for the financial-asset model.
The clearest illustration in the Nepali market of a project fitting the financial-asset description is Tamakoshi-V, a 99.8 MW run-of-river plant in Dolakha district operating under a 30-year concession with a fixed tariff (subject to the standard 3 percent annual escalation) and a mandatory transfer of the asset to the Government of Nepal at the end of the concession — textbook IFRIC 12 conditions on paper. By contrast, Butwal Power Company (BPC), which operates the Andhikhola (9.4 MW) and Jhimruk (12 MW) plants, currently carries its concession rights as an intangible asset under NAS 38 rather than as PPE or a financial asset, on the argument that BPC operates its own distribution network and therefore bears demand risk directly rather than having NEA guarantee its offtake — a genuinely different risk allocation that plausibly does justify a different accounting model. Even within that intangible-asset treatment, though, the amortisation method matters: straight-line amortisation over the license term produces a very different year-by-year profit profile than usage-based (units-of-production) amortisation tied to actual generation, and the two are not always easy to distinguish from the face of the financial statements alone.
PRACTICAL TOOL
When you open a hydropower company's notes to accounts, locate the property/intangible asset accounting policy note and check three things: (1) whether the concession asset is classified as PPE, intangible, or financial asset; (2) whether depreciation/amortisation is straight-line or usage-based; (3) whether the useful life used is shorter than, equal to, or longer than the remaining license term. A useful life materially longer than the remaining license period is a red flag — it likely understates depreciation and overstates near-term profit relative to the economic reality that the asset must be handed over, or substantially replaced, when the license expires.
None of this means an investor needs to resolve the IFRIC 12 debate independently — that is genuinely contested technical ground even among Nepali chartered accountants. The practical takeaway is narrower: because Nepali hydropower companies are not fully uniform in how they classify and depreciate their core asset, you cannot compare depreciation-to-revenue ratios across two hydropower companies and assume you are comparing like with like. Always check the accounting policy note before drawing a cross-company conclusion from depreciation figures alone.
Lesson 32.5 — Royalty, Tax Holidays, and the Government's Take
A hydropower company's relationship with the state runs deeper than its PPA counterparty. Every licensed hydropower generator in Nepal pays royalty to the Government of Nepal under the Electricity Act framework, structured in two components — a capacity royalty (a fixed annual charge per kW of installed capacity) and an energy royalty (a percentage of the value of energy generated or sold) — and both components step up sharply once a project passes its fifteenth year of commercial operation.
The royalty step-up at year 16
For the first fifteen years of operation, a project typically pays a capacity royalty of roughly NPR 100 per kW per year and an energy royalty of about 2 percent of revenue. From the sixteenth through the thirtieth year, both figures rise steeply: capacity royalty to roughly NPR 1,000 per kW per year (a tenfold increase) and energy royalty to about 10 percent of revenue (a fivefold increase). For a large plant, this is not a rounding adjustment — it is a structural shift in the cost base that occurs on a fixed calendar regardless of the company's operating performance, and it is a shift many retail investors modelling a hydropower stock's "steady state" margin never account for, because they anchor their expectations to whatever margin the company is currently reporting in, say, year 6 or year 8 of operation.
Royalty component
Years 1-15 of operation
Years 16-30 of operation
Capacity royalty
~NPR 100 per kW per year
~NPR 1,000 per kW per year
Energy royalty
~2% of revenue
~10% of revenue
This royalty schedule runs alongside a separate, and currently more investor-visible, tax incentive schedule administered under the Income Tax Act: electricity generation enterprises are taxed at a 20 percent corporate rate, but qualifying companies (generally those reaching Rated Commercial Operation Date within a government-specified incentive window) receive a 100 percent tax rebate for their first ten years of operation and a 50 percent rebate for years eleven through fifteen — meaning a project can run essentially tax-free for a decade and then pay half the standard rate for five more years before settling into full taxation. Dividend distributions carry a further 5 percent dividend tax at the shareholder level, and capital gains on listed hydropower shares are taxed at 7.5 percent long-term or 10 percent short-term, same as other NEPSE-listed equity.
REGULATORY DETAIL
The tax holiday and the royalty step-up run on different clocks and move in opposite directions — the tax rebate is most generous exactly when the royalty burden is lightest (years 1-15), and both flip against the company at similar points (tax rebate expires at year 15; royalty triples-to-quintuples at year 16). A hydropower company's true "mature" net margin — the one that will actually prevail for the bulk of its 20-to-30-year license — is meaningfully lower than the margin it reports in years 3 through 10, once both the tax holiday has expired and the higher royalty band has kicked in. Never extrapolate a hydropower stock's current net margin as its long-run margin.
There is also a construction-phase tax and duty regime worth knowing, because it explains part of why the capital cost structure looks the way it does: import duty on plant, machinery, and steel penstock/pipe is set at a nominal 1 percent with full exemption from the standard 13 percent VAT, while domestic engineering, civil, and transportation costs remain subject to the ordinary 13 percent VAT — with the government separately offering a partial VAT refund (historically around NPR 0.5 crore, i.e., roughly NPR 5 million, per MW of capacity) against engineering, transportation, and civil-construction VAT paid. These construction-phase concessions are precisely the sort of detail that shows up as "other income" or reduced CWIP additions in a company's financial statements without being separately labelled, so a careful reader reconciling projected versus actual project cost should check the notes for VAT refund receivables before assuming a cost overrun is larger than it actually is.
CAUTION
Royalty, tax rebate eligibility windows, and VAT refund treatment have all been amended by successive Finance Acts, and the exact percentages and thresholds a given company is subject to depend on its license date, its capacity bracket, and its RCOD relative to the incentive deadline in force when it was licensed. Treat the figures in this lesson as the representative structure, not as a substitute for reading the specific company's actual tax and royalty disclosure in its own financial statement notes.
Lesson 32.6 — Debt, Currency, and Governance Risk: Reading Between the Lines
The debt-heavy capital structure that makes IDC so consequential (Lesson 32.2) does not disappear once a plant is commissioned — it becomes the central determinant of whether operating cash flow actually reaches shareholders as dividends, or is absorbed by debt service for years after COD. Most Nepali hydropower debt is rupee-denominated, drawn from domestic bank consortiums, which removes the currency-mismatch risk that plagues hydropower financing in many other developing markets — but it does not remove interest-rate risk, since Nepali bank lending rates float with the base-rate cycle, and a plant financed at 70:30 or 80:20 debt-to-equity carries a debt-service coverage ratio that can swing meaningfully across a single interest-rate cycle even with no change in physical output. Where foreign-currency debt or foreign-currency PPA elements do appear — typically on larger cross-border or donor/multilateral-financed projects — the exposure is real and needs to be checked explicitly in the borrowings note: a hydropower company servicing a foreign-currency loan against rupee-denominated PPA revenue is carrying open currency risk that a purely domestically financed peer does not have.
Two live cases illustrate how debt structure, delay, and governance combine into the risks that matter most in practice.
Upper Tamakoshi's post-commissioning governance strain is the more urgent of the two as of this writing. Despite being Nepal's largest domestically financed plant and a long-time dividend-paying blue chip, UPPER had — as of mid-2026 — gone three consecutive years without holding an Annual General Meeting, with the last AGM held in Shrawan 2080 for fiscal year 2079/80, leaving shareholders without a formal channel to review audited results or elect independent directors for an extended period. Rating agency ICRA Nepal downgraded the company into its 'D' (default) category following debt-servicing delays that exceeded thirty days, even as the company's long-term liabilities stood at roughly NPR 45.36 billion and it carried negative retained earnings of about NPR 10.77 billion — a stark reminder that "largest plant in the country" and "high dividend history" do not immunize a company against a debt-service or governance crisis. Analysts have also flagged a structural governance concern: NEA's Managing Director has simultaneously served as UPPER's board chairperson, concentrating institutional control at a company where NEA and other public institutions together hold roughly 51 percent of shares, limiting the practical influence of minority shareholders precisely when governance oversight matters most.
WARNING
A hydropower company missing its statutory AGM cadence is not a minor procedural lapse — it is frequently the first visible symptom of deeper financial or governance stress, because an AGM delay is often driven by unresolved audit qualifications, debt-covenant breaches, or unresolved disputes the board does not want tabled before shareholders. Track AGM dates and rating-agency actions for every hydropower holding, not just its dividend announcements.
The second case, GMR Upper Karnali, illustrates delay risk at the extreme end and the added complexity of cross-border, export-oriented hydropower. The 900 MW project, first taken up by India's GMR Group under an initial understanding roughly two decades ago, spent some eighteen years in survey, PPA negotiation (including a prolonged tariff price standoff with NEA), and financing-arrangement limbo before construction work finally began in mid-2025 — with the project structured to export power to India and, under a trilateral arrangement, Bangladesh. For a project of this scale and export orientation, the accounting questions of Lessons 32.1 through 32.5 all apply, but with an added layer: cross-border PPA pricing, multi-jurisdictional regulatory approval, and financing that may combine foreign lenders with foreign-currency-denominated debt in a way most domestically consumed RoR plants do not carry.
CASE IN POINT
Eighteen years between initial project understanding and the start of substantive construction work is an extreme case, but it is not an isolated one in Nepal's hydropower pipeline — the Independent Power Producers' Association Nepal (IPPAN) estimated, in an October 2025 assessment, that delay-related costs across the sector's pipeline had reached roughly NPR 108 billion. When a hydropower prospectus or annual report states a target COD, treat it as the current best estimate, not a commitment — and build a delay scenario into your own valuation rather than relying solely on management's stated timeline.
Finally, the accounting and disclosure issues covered in this chapter feed directly into a live listing-market controversy: Nepal's Securities Board (SEBON) has, in recent years, applied a "real net worth" threshold of roughly NPR 90 per share — a figure that appears nowhere in the Securities Act and was set administratively following a Public Accounts Committee directive in late 2023 — as an informal bar for IPO approval. Because construction-phase hydropower companies structurally carry lower reported net worth per share than operating companies (their equity has not yet been "proven" by revenue, even where their underlying project economics are sound), this threshold has disproportionately stalled hydropower IPOs specifically: roughly NPR 66.23 billion in intended capital raises across 98 companies were reported frozen in the approval queue, with fourteen hydropower companies — including names such as Laughing Buddha Power Nepal, Yambaling Hydropower, Unique Hydropower, Beni Hydro, and Puwa Khola One Hydro — formally removed from the queue altogether. One documented side effect has been the growth of an unregulated pre-IPO share market in which promoter-stage shares change hands well above the NPR 100 face value, with buyers told to expect post-listing prices in the NPR 1,500-2,500 range — precisely the kind of informal, disclosure-free trading environment that hydropower's construction-phase accounting opacity tends to invite.
CAUTION
An administratively invented valuation threshold with no statutory basis, applied inconsistently to a sector whose accounting structure (heavy CWIP, deferred profit recognition, capitalised interest) makes "net worth per share" a particularly unreliable snapshot during construction, is a market-structure risk layered on top of the project-level risks already covered in this chapter. If you are evaluating a pre-IPO or newly listed hydropower company, check not only its PPA and construction progress but also how long it sat in the IPO approval queue and why — a long delay is informative even when the underlying project fundamentals look sound.
PRACTICAL TOOL
Before buying any hydropower stock on NEPSE, work through this sequence: (1) Confirm whether the company is pre-COD or post-COD, and if pre-COD, get the physical completion percentage and revised target COD, not just the original one. (2) Pull the PPA annexure for wet/dry season tariffs, the escalation schedule, and whether it is take-or-pay or take-and-pay. (3) Check cumulative capitalised interest in the CWIP note, and compare it against original construction budget. (4) Identify the asset classification (PPE, intangible, or financial asset) and depreciation/amortisation method in the accounting policy note. (5) Establish the company's current tax-holiday status and royalty band, and project forward to the year both flip. (6) Check the debt note for foreign-currency exposure and the most recent AGM date and rating-agency action, if any.
Chapter recap
Hydropower accounting in Nepal is best understood as two distinct regimes joined at a single hinge point — Commercial Operation Date. Before COD, a company capitalises essentially all project costs, including interest on construction debt, onto a growing Capital Work in Progress balance that shows no resemblance to future profitability; after COD, that same balance converts into depreciating assets, drawn debt begins amortizing in cash, and PPA revenue starts flowing through the income statement for the first time. An investor's first task with any hydropower stock is simply to locate which side of that hinge the company sits on, because the two sides read completely differently and neither can be judged by the standards of the other.
Interest During Construction is the single largest source of "invisible" cost inflation in this sector. Because Nepali hydropower projects are financed at debt ratios of 70:30 or higher and routinely run years behind schedule, capitalised interest can add close to half again to a project's base construction budget by the time it reaches commercial operation — as it did for the 456 MW Upper Tamakoshi project, where roughly NPR 24 billion in capitalised interest sat atop a roughly NPR 52 billion construction cost to produce a total project cost near NPR 76 billion. That capitalised interest becomes part of the depreciable asset base for the life of the plant, meaning construction delay imposes a cost that shows up not just once, but in every subsequent year's depreciation charge.
Revenue is not a simple function of megawatts generated; it is a function of the specific PPA a company signed, including its wet-season and dry-season tariffs, its escalation schedule (commonly 3 percent annually for seven or eight years before plateauing), and whether the offtake obligation is take-or-pay or take-and-pay. The representative run-of-river template — NPR 4.80 per unit wet season, NPR 8.40 per unit dry season, as seen in Balephi Hydropower's PPA — shows why dry-season output, though a small share of total annual generation for most run-of-river plants, disproportionately drives revenue. Storage projects are being priced on a materially different and still-evolving tariff framework precisely because their ability to shift generation into the dry season is worth a large premium to the grid, and real-world developer asks for projects like Dudhkoshi and Budhi Gandaki run well above the regulator's own illustrative concessional-financing scenarios.
Asset classification and depreciation policy are not fully standardised across Nepali hydropower issuers, and a genuine technical debate exists over whether certain BOOT-licensed, take-or-pay projects should be accounted for as a financial asset under IFRIC 12 rather than as depreciating Property, Plant and Equipment — a choice with real consequences for reported profit timing, tax, and cross-company comparability. Layered on top of this is a royalty and tax structure that moves against the company on a fixed calendar regardless of operating performance: a ten-year full tax holiday and 2 percent energy royalty in the early years give way, at year 15 and year 16 respectively, to full taxation and a royalty band roughly five to ten times higher — meaning any margin an investor observes in a company's first decade of operation should not be extrapolated as its long-run steady state.
Beyond the balance sheet, governance and delay risk are demonstrably real and current. Upper Tamakoshi's multi-year AGM lapse, rating downgrade, and governance-concentration concerns show that even the country's largest and historically most reliable hydropower dividend payer can enter genuine distress, while GMR Upper Karnali's roughly eighteen-year path from initial understanding to construction start illustrates how far actual timelines can diverge from initial project plans, particularly for cross-border, export-oriented projects carrying added currency and multi-jurisdictional risk. A regulatory environment in which an administratively invented net-worth threshold has stalled tens of billions of rupees in hydropower IPO capital adds a further, market-structure layer of risk that sits on top of everything else in this chapter.
Taken together, these mechanics argue for a specific discipline when evaluating any hydropower stock on NEPSE: read the PPA before the P&L, check the CWIP and capitalised-interest notes before trusting a construction-phase balance sheet, identify the asset-classification and depreciation policy before comparing companies, project the royalty and tax step-up before extrapolating current margins, and check the AGM record and debt covenants before trusting a dividend history built on the past rather than the future. Hydropower will remain a dominant share of NEPSE precisely because Nepal's comparative advantage is real — but the accounting logic behind each listing rewards patience and specificity, and punishes investors who treat "hydropower stock" as a single undifferentiated category rather than thirty-year contracts that each deserve to be read on their own terms.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VI · Chapter 33
Insurance Sector Accounting
First published 22 Aug 2026 · Last verified 29 Aug 2026
Contractual reserves swell, reported "profit" stays flat, and the share price does something else entirely — this is the moment most Nepali retail investors give up on reading an insurance company's financial statements and fall back on the dividend history instead. That surrender is a mistake, and an expensive one, because insurance is one of the few NEPSE sectors where the accounting itself — not the underlying business — determines what the reported number means in a given year. A life or non-life insurer's income statement is not a scorecard of commercial success in the way a manufacturer's or a bank's is; it is the output of a set of actuarial assumptions, a regulatory reserving formula, and, since mid-2023, a brand-new accounting standard that Nepal's own insurers are still learning to apply consistently. This chapter builds the toolkit to read through that machinery.
Lesson 33.1 — Why an Insurer's Balance Sheet Looks Nothing Like a Normal Company's
Start with the basic asymmetry that makes insurance accounting a distinct discipline. A trading or manufacturing company sells a good today and, mostly, knows its cost of that good today. An insurer sells a promise today — to pay a claim that may materialise next month, next year, or in thirty years — and does not know the ultimate cost until the promise is fully discharged. Between the premium received and the claim paid sits a long, uncertain gap, and the entire apparatus of insurance accounting exists to estimate, provision for, and periodically revise the size of that gap.
This is why an insurer's balance sheet is dominated by two things a normal company's is not: technical reserves (liabilities representing future claims and unexpired risk) on one side, and a large investment portfolio on the other. For a Nepali life insurer such as Nepal Life Insurance Company (NLIC) or a non-life insurer such as Shikhar Insurance or Prabhu Insurance, the investment portfolio — government securities, fixed deposits with commercial banks, corporate debentures, and listed equities — routinely exceeds the paid-up capital and free reserves several times over. That portfolio is not incidental; it is the second business the insurer runs alongside underwriting, and for reasons covered in Lesson 33.6, it is frequently the more important one for shareholders.
KEY CONCEPT
An insurance company sells a promise, not a product. Its balance sheet is a running estimate of the cost of promises not yet fulfilled — the technical reserves — funded by premiums already collected and invested. Reading an insurer means learning to interrogate that estimate, not just the cash that moved.
Life versus non-life is the first branching point, and the two are regulated, reserved, and reported differently enough that comparing a life insurer's ratios to a non-life insurer's is close to meaningless. Non-life insurance — fire, motor, marine, engineering, micro-insurance — writes short-tail contracts, typically twelve months, where claims are usually known and settled within a year or two of the policy period. Life insurance writes long-tail contracts — an endowment or term policy can run twenty, thirty, or more years — where the insurer is committing to pricing mortality and investment returns decades into the future. This difference in time horizon is why life insurers carry actuarial liabilities that dwarf their non-life counterparts relative to premium income, and why the appointed actuary (Lesson 33.4) is a permanent fixture in a life insurer's governance in a way no non-life insurer needs to the same degree.
Regulatory detail on the regulator itself matters here, because its identity changed recently enough that older reports and older textbooks still use the earlier name. Nepal's insurance regulator was known for decades as Beema Samiti (the Insurance Board). Under the Insurance Act, 2079 (2022), it was reconstituted and formally began operating as the Nepal Insurance Authority (NIA) in August 2022, with expanded powers over licensing, solvency, actuarial practice, and market conduct. Every directive discussed in this chapter — the Financial Statement Directive, the Risk-Based Capital and Solvency Directive, the actuary appointment guideline — is an NIA instrument, even though older filings and some data vendors still label the regulator Beema Samiti.
REGULATORY DETAIL
Beema Samiti (Insurance Board) was transformed into the Nepal Insurance Authority (NIA) under the Insurance Act, 2079, formally commencing operations in August 2022. If a disclosure, prospectus, or older annual report refers to "Beema Samiti," it predates this transition; the substantive regulatory framework is now issued and enforced by the NIA.
Lesson 33.2 — NFRS 17 Arrives: Nepal's Insurance Contracts Standard
The single biggest change to insurance accounting on NEPSE in the last decade is NFRS 17 (Insurance Contracts), Nepal's adaptation of the international standard IFRS 17. The Accounting Standards Board (ASB) Nepal set the mandatory effective date at Shrawan 1, 2080 (July 17, 2023) — meaning fiscal year 2080/81 was the first full reporting year in which every NEPSE-listed insurer, life and non-life, was required to apply it.
What NFRS 17 actually changes is worth being precise about, because "new accounting standard" is easy to wave through as jargon. Under the older regime, an insurer largely recognised premium as revenue when written or over the policy term on a simple pro-rata basis, and set aside reserves using regulator-prescribed formulas that had only a loose link to the economics of the underlying contracts. NFRS 17 instead requires insurers to measure insurance contracts using a current, discounted, probability-weighted estimate of future cash flows, plus a risk adjustment for non-financial risk, plus — critically — a Contractual Service Margin (CSM): a liability representing the unearned profit an insurer expects to make on a group of contracts, which is released into the income statement gradually, as the insurer actually delivers the insurance service, rather than booked upfront.
The CSM is the concept every investor needs to internalize before the rest of this chapter makes sense, because it is the mechanism by which NFRS 17 profit and cash profit diverge. A life insurer can write a large, profitable batch of new policies in a quarter and show comparatively modest reported profit, because most of the expected profit on those contracts sits in the CSM, waiting to be recognised over the life of the policies rather than in the quarter the premium was collected. Growth, under NFRS 17, temporarily depresses reported earnings relative to what old-style accounting would have shown — the opposite of the intuition most equity investors carry from other sectors, where growth flatters the income statement.
KEY CONCEPT
The Contractual Service Margin (CSM) is the unearned profit on in-force insurance contracts, held as a liability and released into income as service is provided. Fast-growing insurers can show subdued current profit under NFRS 17 even while writing highly profitable new business — the profit is deferred on the balance sheet, not absent.
Nepal's transition has not been smooth, and it is worth naming the friction honestly rather than presenting NFRS 17 as a settled matter. Industry commentary through 2024 and 2025 repeatedly flagged the same obstacles: a severe shortage of actuaries and specialist IT staff capable of building the discounted cash-flow and CSM models the standard requires; impact assessments at most insurers that stayed qualitative rather than running the full quantitative transition numbers; staff training that stopped at introductory awareness rather than operational competence; and the sheer cost of the data infrastructure needed to track contract groups, discount rates, and risk adjustments over multi-decade policy books. As late as mid-2026, the Accounting Standards Board and the Nepal Insurance Authority were still holding joint sessions to iron out implementation questions — three fiscal years after the mandatory effective date. An investor comparing two insurers' NFRS 17 disclosures in the same reporting period should not assume both applied identical judgment calls on discount rates, contract boundaries, or risk-adjustment methodology; the standard is principles-based and Nepal's insurers are still converging on common practice.
WARNING
NFRS 17 is principles-based, not a fixed formula, and Nepal's insurers were still building common implementation practice years after the mandatory 2080/81 effective date. Two insurers' NFRS 17 profit figures are not automatically comparable — differences in discount-rate assumptions, contract grouping, and risk-adjustment methodology can each move reported profit materially without any difference in underlying business performance.
The transition produced a genuinely unusual regulatory response that every dividend-focused investor in this sector needs to know about. Because NFRS 17 profit and the profit calculated under the NIA's own Financial Statement Directive, 2023 can diverge — sometimes substantially, depending on how much profit sits locked in the CSM — insurers found themselves able to report one profit figure for financial-statement purposes and use it as the base for a dividend proposal that regulators judged imprudent relative to the insurer's actual distributable cash and capital position. The NIA's response, tightened further with effect from the fourth quarter of fiscal year 2025/26, was to require insurers to prepare two parallel sets of quarterly financial statements — one under NFRS 17, one under the Financial Statement Directive — and to permit dividend distribution only from the lower of the two resulting profit figures. Where NFRS 17 retained earnings exceed the Financial Statement Directive figure, the excess must be transferred into a non-distributable regulatory reserve, releasable only with NIA approval. Discretionary bonus additions to participating life policies were simultaneously re-anchored to the Risk-Based Capital and Solvency Directive, 2025 rather than to whichever profit number looked more generous.
CASE IN POINT
From Q4 FY2025/26, an NEPSE-listed insurer cannot simply declare a dividend against its NFRS 17 profit. It must also compute profit under the Financial Statement Directive, 2023, and distribute only the lower of the two figures — with any NFRS 17 surplus above that quarantined in a regulatory reserve. Read the dividend proposal footnotes in the annual report before extrapolating from the headline profit line.
The upside, and it is real, is comparability going forward. Once Nepal's insurers converge on common NFRS 17 practice, the standard's insistence on current, market-consistent assumptions and a uniform CSM mechanism should make it far easier to compare a life insurer's book quality against a peer's than the old prescriptive-reserve regime ever allowed — the explicit goal cited by regulators and standard-setters is exactly this cross-sector standardisation, giving investors cleaner visibility into risk profiles across the thirteen-odd life insurers and fourteen non-life insurers now listed. The chapter's job is to get you reading the transitional numbers correctly rather than waiting for that convergence to finish.
Lesson 33.3 — Premium Recognition: When Does an Insurer Actually Earn Its Revenue
Even with NFRS 17's overhaul of profit measurement, the underlying question of premium recognition timing is still the right place to start reading any insurer's income statement, because it is the most intuitive of the sector's accounting mechanics and it differs meaningfully between life and non-life business.
For non-life insurance, the operative concept is the unearned premium reserve (UPR). A one-year motor policy sold on the first day of Poush is not "earned" revenue on the day the premium is collected — the insurer has taken on twelve months of risk and has, in effect, been paid in advance for a service it has not yet delivered. Under both the pre-NFRS 17 regime and its replacement, the insurer recognises premium income progressively over the policy period (commonly on a time-apportioned basis, though NFRS 17 formally reframes this as the release of the liability for remaining coverage), holding the unrecognized portion as a liability — the UPR — on the balance sheet. A non-life insurer that wrote an unusually large volume of new business in the last month of the fiscal year will show a large cash and premium-receivable inflow but a correspondingly large increase in unearned premium reserve, muting the reported revenue impact. Investors who look only at gross written premium growth and skip the UPR movement will overstate how much of that growth has actually flowed through to earned income.
Life insurance recognition works differently again, because a life policy is not a single risk period but a bundle of long-duration obligations. Premium is recognised as revenue as it becomes due, but it is matched against a build-up in actuarial liabilities (called, under NFRS 17, the liability for remaining coverage, built from discounted future cash flows plus risk adjustment plus CSM) rather than a simple unearned-premium concept. The practical consequence for a Nepali retail investor is that a life insurer's premium income line tells you almost nothing about profitability in isolation — a policy priced too cheaply relative to the mortality and investment assumptions behind it can generate strong premium growth for years while quietly destroying long-run value, and it is the actuarial reserve movement, not the premium line, that would eventually reveal this.
Premium growth is a volume metric. Reserve movement is where the economics live.
PRACTICAL TOOL
When reading a non-life insurer's quarterly disclosure, compare the growth rate in gross written premium to the growth rate in the unearned premium reserve. If UPR is growing materially faster than earned premium, the insurer is back-loading new business into the final weeks of the reporting period — a pattern worth checking against prior years before crediting it as sustainable growth.
A further wrinkle specific to Nepal's market is the presence of micro-insurance as a distinct, smaller-ticket, high-volume line, now with its own listed entities — Nepal Micro Insurance and Crest Micro Life among them, both having listed on NEPSE. Micro-insurance premium recognition follows the same broad principles as conventional non-life or life business, but the policies are shorter in duration and far higher in count, so the operational burden of correctly computing UPR and claims reserves at the individual-policy level is proportionally larger relative to the premium base — a reason to expect these newer, smaller insurers to lag the established players in NFRS 17 operational maturity, not to assume equivalence just because both file the same standard.
Lesson 33.4 — Claims Reserving and IBNR: Where the Appointed Actuary Earns Their Fee
If premium recognition tells you when an insurer books revenue, claims reserving tells you whether the insurer has honestly estimated what that revenue will eventually cost. This is the single most judgment-laden number in the entire set of insurance financial statements, and it is also the number most prone to being quietly wrong in ways that only surface years later.
Three categories of claims sit on a non-life or general-insurance-style liability schedule. Reported and admitted claims are the easiest — the policyholder has filed, the insurer has assessed the loss, and a specific reserve is booked. Reported but not yet settled (RBNS) claims are still being assessed or disputed but are at least known to exist. The hardest category, and the one that most determines whether an insurer's reserving is prudent or optimistic, is IBNR — Incurred But Not Reported. These are losses that have already happened, within the accounting period, but which the insurer has not yet been notified of: an accident that occurred in the last week of the fiscal year but whose claim will only be filed weeks or months later, or in liability lines, a loss that may not surface for years. IBNR cannot be built from a claims register, because by definition no claim yet exists in that register. It must be estimated statistically, typically by projecting historical claims-development patterns (how claims from past accident periods matured over subsequent reporting periods) forward onto the current period's exposure.
WARNING
IBNR is an estimate built entirely from historical claims-development patterns, not from any specific known loss. An insurer can systematically under-reserve IBNR for years — flattering reported profit each year — with the shortfall only becoming visible when claims patterns shift (a change in litigation behaviour, a catastrophe year, a change in policy mix) and prior-year reserves have to be strengthened all at once. A sudden, large prior-period reserve adjustment is a red flag worth investigating, not an accounting technicality to skim past.
This is exactly the terrain where the appointed actuary function matters, and Nepal's regulatory framework around it has been tightened materially in the last two years. The NIA's Guideline Related to Actuary Appointment for Insurers, 2024 (2081) formalised the statutory role of the Appointed Actuary at each insurer — the professional legally responsible for certifying reserve adequacy, mortality and morbidity assumptions (for life business), and solvency calculations. Because Nepal's domestic actuarial talent pool is thin, the NIA moved in 2024 to mandate that every insurer additionally build out a supporting bench of Actuarial Analysts, effective July 16, 2024, explicitly to support the Appointed Actuary's statutory duties — with those analysts' own performance appraisals required to incorporate the Appointed Actuary's feedback, formalising a reporting line that had previously been informal or absent at smaller insurers.
REGULATORY DETAIL
The NIA now requires every insurer to maintain both an Appointed Actuary (the statutory sign-off authority on reserves and solvency) and a supporting team of Actuarial Analysts, mandated across the sector from July 16, 2024. This was a direct regulatory response to a documented shortage of actuarial capacity flagged repeatedly during the NFRS 17 transition — treat an insurer's actuarial bench strength as a genuine credit and governance signal, not a compliance footnote.
For a retail investor without access to an insurer's internal claims triangles, the practical proxy for reserving conservatism is watching for two things across successive annual reports: whether prior-year claims reserves are subsequently released as "excess" (a sign reserves were set conservatively and true-up in the insurer's favour) or strengthened (a sign reserves were initially too thin), and whether the actuarial valuation report — which Nepali insurers are required to obtain and which increasingly is referenced or summarised in annual disclosures — flags any change in key assumptions such as discount rates, mortality tables, or expense loadings. A change in discount rate assumption alone can move a life insurer's actuarial liability, and therefore reported profit, by a large margin without a single additional policy being sold or claim being paid — which is precisely why NFRS 17's requirement for insurers to disclose the sensitivity of their reserves to key assumptions is one of the more useful investor-facing improvements the standard brings, once insurers report it with real specificity rather than boilerplate language.
CAUTION
A reserve release that boosts current-year profit is not automatically bad news, but check whether it reflects genuinely improved claims experience or simply a change in actuarial assumption. Reserve releases that recur every single year, in exactly the amount needed to hit a flat or growing profit figure, are a pattern worth being skeptical of rather than celebrating.
Lesson 33.5 — Solvency Margin and Risk-Based Capital: The Regulator's Real Lever
Solvency is the number the Nepal Insurance Authority cares about more than any single line in the income statement, because it is the direct measure of whether an insurer can pay the claims it has promised to pay. The solvency margin (or solvency ratio) is, at its simplest, available capital divided by the capital the regulator requires the insurer to hold against its risk profile — a ratio above 1.0 means the insurer holds more capital than the bare regulatory minimum, and the further above 1.0, the larger the cushion.
Nepal moved from an older, simpler factor-based solvency margin framework to a full Risk-Based Capital and Solvency Directive, issued in 2024 (2081) and then revised again in 2025 (2082) — two directives in consecutive years, itself a signal that the regulator is still actively calibrating the framework rather than treating it as finished. Under the current regime, the regulatory minimum solvency ratio is 1.3 for life insurers and 1.5 for non-life insurers; an insurer that falls below its minimum faces regulatory restrictions, potentially including limits on dividend distribution and new business writing, well before it becomes genuinely unable to pay claims.
The actual reported numbers across NEPSE's insurers, as of the most recent quarters disclosed, show the sector running well above these floors — which is worth understanding as a market feature rather than assuming it will always hold. For the fourteen listed non-life insurers, based on FY2081/82 fourth-quarter disclosures, solvency ratios ranged as follows:
Non-Life Insurer
Solvency Ratio
Prabhu Insurance
4.66
Nepal Insurance
4.33
Neco Insurance
3.84
Shikhar Insurance
3.67
NLG Insurance
3.42
IGI Prudential Insurance
3.42
Himalayan Everest Insurance
3.32
Siddhartha Premier Insurance
3.18
National Insurance
2.79
Sagarmatha Lumbini Insurance
2.75
United Ajod Insurance
2.73
Rastriya Beema Company
2.73
The Oriental Insurance
2.66
Sanima GIC Insurance
2.62
Industry average across these fourteen companies stood at roughly 3.29 — more than double the 1.5 regulatory floor. Life insurers show a similarly wide cushion: across the twelve listed life companies, the sector average solvency ratio has run around 4.44 against a 1.3 minimum. Two things follow from this. First, a solvency ratio comfortably above the minimum is now the sector norm, not a distinguishing feature of any one company — an investor should not treat "solvency ratio exceeds the regulatory minimum" as meaningful praise on its own; the relevant comparison is against sector peers and against that insurer's own trend over time. Second, and more usefully, the spread between the strongest and weakest names in the table above (roughly 4.66 down to 2.62 among non-life insurers) is real dispersion worth investigating — a company sitting persistently near the bottom of its peer group's solvency range, even while still above the regulatory floor, has less room to absorb a bad underwriting year or a market downturn in its investment book than one sitting near the top.
PRACTICAL TOOL
Do not read a single company's solvency ratio in isolation. Build a simple peer table each quarter — the NIA and insurance-sector media routinely publish sector-wide solvency disclosures — and rank the insurer you are evaluating against its direct peers (life against life, non-life against non-life). A ratio of 2.7 looks reassuring against a 1.5 floor but is nearly half the sector-leading names' cushion.
Capital requirements are the other side of this story, and Nepal's insurance sector lived through a genuine consolidation episode driven directly by a regulatory capital increase. The regulator raised minimum paid-up capital requirements substantially, ultimately settling on Rs 5 arba (Rs 500 crore) for life insurers and Rs 2.5 arba (Rs 250 crore) for non-life insurers, with the compliance deadline extended to Ashad 2080 (mid-July 2023) after most insurers found the original timeline unworkable. Insurers unable to raise fresh capital on their own — through rights issues or bonus capitalisation — within that runway faced a straightforward choice: merge with another insurer to combine capital bases, or fall short of the licensing minimum.
CASE IN POINT
Surya Life Insurance Company (SLICL) and Jyoti Life Insurance (JLI) merged to form Suryajyoti Life Insurance Company, with joint operations commencing Poush 7, 2079 BS (December 20, 2022). Neither insurer could independently reach the Rs 500 crore capital floor — SLICL's pre-merger paid-up capital stood at roughly Rs 255 crore and JLI's at roughly Rs 242 crore, a combined base close enough to the regulatory threshold to clear it once merged. This was a capital-driven merger, not a strategic one — read it as a template for how Nepal's insurance-sector M&A has actually worked, and expect the pattern to recur whenever the regulator next raises the capital bar.
The non-life side of the market saw the same dynamic play out — Sanima General Insurance merged with General Insurance Company to form Sanima GIC Insurance, again against the backdrop of the Rs 2.5 arba non-life capital threshold. For a retail investor, the lesson is that regulatory capital directives in Nepal's insurance sector are not background compliance noise; they have directly reshaped which companies exist on NEPSE today, and a shareholder in a smaller, thinly capitalised insurer should treat "regulator may again raise the capital floor" as a live scenario with real merger, dilution, or delisting consequences, not a remote tail risk.
Lesson 33.6 — Investment Income: The Engine Behind the Underwriting Number
The final piece of the puzzle is understanding where an insurer's profit actually comes from, because for most Nepali insurers, it is not primarily the underwriting result. Insurance accounting conventionally splits the income statement into a technical account (premium income, claims incurred, reserve movements, and underwriting expenses — the pure insurance business) and a non-technical or investment account (income earned on the large pool of invested premium float — government securities, bank deposits, corporate debentures, and listed equities, alongside a smaller allocation to real estate and other permitted assets under NIA investment directives).
For a typical non-life insurer, underwriting margins are often thin or occasionally negative in a bad claims year, and it is investment income — interest on fixed deposits and government securities, dividend income from equity holdings, and realised or unrealized gains on the investment portfolio — that carries the bottom line. For life insurers the relationship is structurally even tighter, because the entire economics of a long-duration life policy depend on the insurer earning an investment return at least equal to the rate implicitly assumed when the policy was priced; an actuarial reserve calculation, at its core, discounts future claim obligations at an assumed investment yield, so persistent underperformance of the actual investment portfolio against that assumed yield is a slow, compounding drag on solvency that shows up in reserve strengthening long before it shows up as a headline loss.
This is also why insurance-sector share prices on NEPSE correlate meaningfully with the broader index and with interest-rate cycles, independent of underwriting performance. When NEPSE rallies, insurers holding meaningful listed-equity portfolios show mark-to-market gains flowing through their investment income; when commercial bank fixed-deposit rates fall, insurers rolling over large deposit books face a slow compression in investment yield that eventually pressures both profitability and the actuarial assumptions behind their reserves. An investor analysing an insurer purely on underwriting ratios — loss ratio, expense ratio, combined ratio — without separately tracking the investment portfolio's composition, yield, and sensitivity to interest rates and equity markets is missing the half of the business that usually matters more to the bottom line.
KEY CONCEPT
Split every insurer's profit mentally into a technical result (premium less claims less reserve movements less expenses) and an investment result (yield on the float). For most Nepali insurers, especially life insurers, the investment result is the larger and more persistent driver of reported profit — underwriting discipline matters, but it is not the whole story, and sometimes not even the main one.
CAUTION
Do not mistake a strong profit year driven by unrealized equity gains during a NEPSE rally for durable underwriting improvement. Separate realised investment income (interest, dividends actually received) from unrealized fair-value gains in the notes to the financial statements, and weight the two very differently when projecting forward — unrealized gains reverse when the index turns.
Feature
Life Insurance
Non-Life Insurance
Contract duration
Long-tail (often 10-30+ years)
Short-tail (typically 12 months)
Primary reserve
Actuarial liability / liability for remaining coverage (discounted, assumption-driven)
Investment result typically dominant over the policy's life
Underwriting result and investment result both material, underwriting more volatile year to year
Actuarial oversight
Central — Appointed Actuary role is continuous and load-bearing
Present but generally lighter — mainly IBNR and catastrophe reserving
Chapter recap
An insurance company's financial statements are not a report on a completed transaction; they are a running set of estimates about promises that have not yet come due, and every lesson in this chapter has been about learning to see the estimate rather than mistaking it for a fact. The balance sheet is dominated by technical reserves and an investment portfolio rather than the fixed assets and inventory that anchor most other NEPSE sectors, and the income statement's headline profit figure is the product of actuarial assumptions, reserving judgment, and regulatory formula as much as it is a product of commercial performance in the period.
NFRS 17, mandatory in Nepal since Shrawan 2080 (July 2023), was meant to bring international-grade rigor and comparability to this picture through discounted cash-flow measurement and the Contractual Service Margin, but the transition has been genuinely difficult — thin actuarial capacity, incomplete impact assessments, and continuing regulator-standard-setter coordination sessions well into 2026 mean investors should treat cross-company NFRS 17 comparisons with real caution rather than as a solved problem. The NIA's 2026 move to require dividends from the lower of NFRS 17 and Financial Statement Directive profit, with any NFRS 17 surplus quarantined in a non-distributable regulatory reserve, is the clearest evidence that the regulator itself does not yet fully trust NFRS 17 profit as a safe base for cash distribution — a healthy skepticism retail investors should share.
Premium recognition timing (the unearned premium reserve for non-life, the liability for remaining coverage for life) and claims reserving (particularly IBNR, the actuarially estimated cost of losses that have occurred but not yet been reported) are the two places where an insurer's honesty about its own risk is tested every reporting period, and the appointed actuary — now backed by a mandated bench of actuarial analysts since July 2024 — is the professional whose sign-off you are ultimately relying on when you accept a reported reserve figure at face value. Reserve releases that flatter profit deserve scrutiny; reserve strengthenings that surface unexpectedly deserve more.
Solvency margin and risk-based capital are the regulator's direct lever on insurer safety, with current minimums of 1.3x for life and 1.5x for non-life insurers under the Risk-Based Capital and Solvency Directive framework revised as recently as 2082 (2025). Nepal's listed insurers currently run well above these floors — averaging roughly 4.44 among life insurers and 3.29 among non-life insurers — but the dispersion within each peer group, and the sector's own history of capital-driven mergers such as Suryajyoti Life (from Surya Life and Jyoti Life) and Sanima GIC (from Sanima General Insurance and General Insurance Company), shows that regulatory capital directives have real, structural consequences for shareholders, not just compliance overhead.
Finally, remember that underwriting is only half the profit story. Investment income — interest, dividends, and gains on the float built from collected premiums — is frequently the larger and more persistent driver of an insurer's bottom line, particularly for life insurers whose actuarial reserves are built on assumed investment yields. Reading an insurance company well on NEPSE means holding both halves of the business in view at once: the technical account, where the actuary's judgment lives, and the investment account, where the broader market's fortunes flow straight into the insurer's own. Master that split, and the sector stops looking like a black box and starts looking like what it actually is — a long-duration financial business whose accounting simply makes its uncertainty visible sooner than most.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VI · Chapter 34
Manufacturing, Trading & Hotel Sector Accounting
First published 22 Aug 2026 · Last verified 29 Aug 2026
Lesson 34.1 — The Sector Nobody Talks About, and Why That's Useful
Open any NEPSE sector dashboard and your eye goes where the money is: commercial banks, hydropower, life and non-life insurers, microfinance. Manufacturing, trading, and hotels sit at the bottom of the list, usually lumped together in investor conversation as "the other stuff." A widely cited snapshot of NEPSE's sector-wise market capitalisation put commercial banks at roughly 37.7 percent of total market cap, insurance at 15.3 percent, microfinance at 9.5 percent, and hydropower at 8.4 percent — while manufacturing and processing companies accounted for only about 3.7 percent, hotels about 1.5 percent, and trading a mere 0.5 percent. Out of roughly 219 listed companies at that time, the three non-financial productive sectors this chapter covers together represented under six percent of the exchange's total value. That proportion has moved somewhat as new hydropower and microfinance issuances have flooded the market since, but the ordering has not changed: BFIs and hydropower still dwarf manufacturing, trading, and hospitality in aggregate NEPSE weight.
This is not a defect in the market — it is a fact about Nepal's corporate structure that every serious investor needs internalized before opening a single annual report in this sector. Understanding why so few real, productive, physical-goods-and-services businesses trade on the exchange tells you as much about the opportunity as the companies themselves do.
Why the sector stays small
Three forces explain the thinness. First, capital-raising incentives have historically pointed elsewhere: banks and finance companies were compelled by Nepal Rastra Bank capital-adequacy and paid-up capital rules to go public and raise equity; hydropower developers need public float to satisfy licensing and to access the retail investor base that funds greenfield generation projects. No such regulatory push has ever forced a noodle factory, a garment exporter, or a family trading house onto the exchange. Second, ownership culture in Nepali manufacturing and trading is dominated by family business houses — the Golyan Group, Khetan Group, Chaudhary Group, Dugar Group, Nepal Distilleries' promoter families, and similar multi-generational conglomerates — who built these businesses privately, financed growth through retained earnings and bank debt rather than public equity, and have limited appetite to dilute control or open their books to public shareholders and SEBON disclosure requirements. Third, several of the manufacturing names that do exist on NEPSE arrived not through a strategic decision to raise growth capital but because Nepali company law and government policy at various points required public enterprises and certain large private companies to offer a portion of shares to the public — which is how names like Bottlers Nepal (Balaju) Limited, Bottlers Nepal (Terai) Limited, and Unilever Nepal Limited ended up listed decades ago despite promoter shareholding remaining overwhelmingly dominant and free-float liquidity staying thin.
KEY CONCEPT
A "thin sector" on NEPSE is not necessarily a low-quality sector — it is a sector where the free float, the trading volume, and the number of comparable peers are all small. Valuation multiples in manufacturing, trading, and hotels routinely trade far outside BFI-sector norms (Unilever Nepal and Bottlers Nepal have historically commanded some of the highest per-share prices on the exchange) precisely because so few shares change hands and scarcity itself becomes a pricing factor.
The practical consequence for you as an analyst: peer comparison is harder here than in banking. When you analyse a commercial bank, you have two dozen comparable institutions reporting under an identical NRB-directed format. When you analyse Ghorahi Cement Industry Limited, your genuine domestic peer set is Shivam Cements and Udayapur Cement Industry — three companies, three different ownership structures (private promoter-led, private promoter-led, and wholly government-owned respectively), and three different capital histories. You must build your own judgment of "normal" instead of borrowing it from a crowded sector average, which is exactly the skill this chapter is designed to sharpen.
A second reason to study this sector carefully despite its small weight: it is where you will find the clearest, least-obscured version of core accrual accounting — inventory, cost of goods sold, depreciation of physical plant, and working capital — before the layers of prudential-regulation-driven accounting (loan loss provisioning, actuarial reserves, PPA-linked revenue recognition) that dominate BFI, insurance, and hydropower analysis. If Part VI has been teaching you how regulation reshapes accounting sector by sector, manufacturing and trading are where you see accounting in something closer to its textbook form — and that makes this chapter, in a sense, the foundation the rest of the Part has been building toward, read last but understood first.
Lesson 34.2 — Inventory Accounting Under NFRS: NAS 2 in Practice
For a bank, the balance sheet's defining asset is the loan book. For a manufacturer or trading company, it is inventory. Nepal Accounting Standard 2 — Inventories (NAS 2, converged with IAS 2) governs how these companies value the single largest working-capital line on their balance sheets, and it is worth learning its mechanics precisely because errors or aggressive judgment calls here flow directly into reported gross margin.
REGULATORY DETAIL
NAS 2 requires inventories to be measured at the lower of cost and net realisable value (NRV). Cost comprises three layers: (1) costs of purchase — purchase price, import duties, freight inward, and other directly attributable acquisition costs, net of trade discounts; (2) costs of conversion — direct labor and a systematic allocation of fixed and variable production overheads; and (3) other costs incurred in bringing the inventory to its present location and condition. NRV is the estimated selling price in the ordinary course of business, less estimated costs of completion and estimated costs necessary to make the sale.
For a cement manufacturer such as Shivam Cements, Ghorahi Cement Industry, or the state-owned Udayapur Cement Industry, this cost build-up runs through several distinct inventory categories on the same balance sheet: raw materials (limestone, gypsum, clinker where not self-produced, fly ash, packaging), work-in-progress (clinker mid-kiln), and finished goods (bagged cement ready for dispatch). Each category is separately disclosed under NAS 2's disclosure requirements, and each carries a different cost-formula judgment. Limestone extracted from a company's own quarry — Udayapur Cement's limestone reserve is reported to have roughly two hundred years of life at current extraction rates, with only a small fraction mined as of a recent assessment — is costed at extraction and processing cost, not market price, which matters because it means a cement company's raw-material cost base can be structurally lower than a competitor without captive limestone, a genuine competitive-moat fact hiding inside an inventory note.
For a consumer-goods bottler like Bottlers Nepal (Balaju) or Bottlers Nepal (Terai), the inventory chain looks different: imported concentrate and packaging materials (PET preforms, glass bottles, crown caps, labels) as raw materials, syrup and unfinished batches as WIP, and cased finished beverage as finished goods — with the added complexity that empty returnable glass bottles and crates are frequently carried as a distinct property/inventory hybrid, since they cycle between "asset used repeatedly" and "packaging material consumed."
For a trading company — Salt Trading Corporation, Nepal Lube Oil Limited, or Bishal Bazar Company — there is no conversion cost layer at all. A trading company buys finished goods and resells them essentially unchanged, so its entire inventory cost is purchase cost: invoice price, customs duty, and inbound freight. This is the cleanest inventory accounting in the entire Nepali listed universe, and also the reason trading-company gross margins are structurally thin and stable compared to manufacturers, whose margins swing with capacity utilisation and input-cost cycles.
Cost formula choice: FIFO versus weighted average
NAS 2 permits First-In-First-Out (FIFO) or weighted-average cost as the two standard cost formulas (specific identification is reserved for inventories of goods that are not ordinarily interchangeable, such as heavy machinery). The choice matters more than most retail investors assume.
In a rising-price environment — and Nepal's import-dependent input costs (clinker, packaging resin, imported concentrate, fuel) have experienced exactly this over recent years given currency depreciation pressure against the Indian rupee peg and global commodity cycles — FIFO assigns the oldest, cheapest cost layers to cost of goods sold first, which inflates reported gross margin in the period versus what a weighted-average method would show, even though the physical business has not become more efficient. Weighted-average cost smooths this distortion by blending old and new cost layers into a single per-unit figure recalculated each period.
WARNING
When comparing gross margin trends across two manufacturing companies, check the inventory cost-formula note before drawing conclusions. A company on FIFO reporting margin expansion during an input-cost inflation cycle may simply be running down a favourably priced older cost layer — a one-time effect that reverses once that layer is exhausted and the next batch is costed at today's higher input prices. This is not fraud; it is a structural feature of FIFO accounting that a careless reader mistakes for operating improvement.
Net realisable value write-downs and reversals
The lower-of-cost-or-NRV rule forces manufacturers and traders to write inventory down whenever selling price expectations fall below carrying cost — obsolete packaging design changes, expired shelf life on beverage stock, cement that has absorbed moisture and hardened in storage, or slow-moving stock-keeping units in a trading company's warehouse. NAS 2 also permits reversal of a prior write-down (up to the original cost, never above it) if NRV recovers in a later period — a provision worth watching for because a reversal shows up as a credit to cost of sales and can flatter a quarter's gross margin without any underlying change in unit economics.
Lesson 34.3 — The Working Capital Cycle: Manufacturing versus Trading
If inventory is the largest asset, the working capital cycle is the clock that tells you how efficiently a company turns that asset into cash. This is the single most useful analytical lens for distinguishing manufacturing businesses from trading businesses in Nepal, because their cycles run at genuinely different speeds and for different structural reasons.
The cash conversion cycle (CCC) is built from three components, each measured in days:
Days Inventory Outstanding (DIO) = (Average Inventory / Cost of Goods Sold) x 365 Days Sales Outstanding (DSO) = (Average Trade Receivables / Revenue) x 365 Days Payable Outstanding (DPO) = (Average Trade Payables / Cost of Goods Sold) x 365 Cash Conversion Cycle = DIO + DSO - DPO
PRACTICAL TOOL
Pull three years of a manufacturer's and a trading company's balance sheets and income statements side by side and compute DIO, DSO, DPO, and CCC for each. A rising CCC over time — inventory piling up, receivables stretching, or payables being paid down faster — is one of the earliest, least-noticed signals of working-capital stress, often visible two or three quarters before it shows up in profitability or covenant compliance.
Manufacturers in Nepal typically carry longer cycles than traders for a structural reason: they hold raw materials, work-in-progress, and finished goods simultaneously, and cement in particular is a business with heavy fixed capital, seasonal construction demand (monsoon months depress construction activity and hence cement offtake, creating a build-up of finished-goods inventory that must be financed through the low season), and distributor credit terms that push receivables out further than a straightforward cash-and-carry trading model would. A cement manufacturer's DIO frequently runs into several months of production when dealer stocking cycles and monsoon seasonality are both factored in.
Trading companies, by contrast, exist specifically to compress this cycle. Salt Trading Corporation and Nepal Lube Oil Limited operate on a buy-distribute-collect model with minimal value addition, and their competitive advantage is largely a working-capital efficiency advantage: faster inventory turns, tighter receivable discipline with dealers (often supported by advance payment or dealer security deposits rather than open credit), and negotiated payable terms with principals or import suppliers. A well-run trading company's CCC can be a fraction of a manufacturer's, and this difference alone explains why trading companies can generate respectable return on equity from what looks like a thin, low-margin business — capital turns over so many more times per year that a low margin multiplied by high turnover still produces a competitive return.
Illustrative working capital comparison
Metric
Typical Manufacturer (e.g., cement/beverage)
Typical Trading Company
Gross margin
25-40% (capital-intensive, capacity-driven)
8-18% (thin margin, high turnover model)
Days Inventory Outstanding
60-150 days (seasonal build-up, WIP layers)
20-45 days (fast-moving distribution stock)
Days Sales Outstanding
30-90 days (dealer/distributor credit)
10-30 days (often advance-payment-backed)
Days Payable Outstanding
30-60 days
30-75 days (leverages supplier/principal terms)
Primary balance sheet asset
Property, plant & equipment plus inventory
Inventory and receivables; minimal fixed assets
Capital intensity
High (kilns, bottling lines, plant)
Low (warehouses, limited machinery)
These figures are illustrative ranges drawn from the structural characteristics of each business model rather than a single company's disclosed figures, and you should always replace them with the actual computed ratios from the specific company's financial statements — but the ordering (manufacturers slower, traders faster; manufacturers higher-margin, traders thinner-margin) holds consistently across the Nepali listed universe.
CASE IN POINT
Shivam Cements, listed via Nepal's first-ever cement-sector IPO in 2017 at premium pricing (Rs 200 to locals of the plant's district, Rs 300 to the general public, reflecting a two-tier IPO pricing structure common to large industrial issues at the time), represented a rare case of a large private manufacturer choosing to raise growth capital from the public markets rather than staying purely family- and bank-financed — precisely because cement plant expansion is so capital-intensive that bank debt alone could not fund it. Contrast this with Ghorahi Cement Industry, which listed its IPO shares only in 2023, alongside two hydropower issuers in the same batch — a reminder that even now, cement-sector IPOs in Nepal arrive in a trickle, not a wave.
Lesson 34.4 — Revenue Recognition and Cost of Goods Sold in Manufacturing
Under NFRS 15 (Revenue from Contracts with Customers), manufacturers recognise revenue when control of the finished good transfers to the customer — typically at dispatch from factory or delivery to the distributor's warehouse, depending on the shipping terms embedded in the sales contract. This sounds simple, and for straightforward ex-factory cement or bottled-beverage sales, it largely is. The complexity in this sector lies not in the timing of revenue recognition but in the cost of goods sold build that sits directly beneath it, because COGS is where capacity utilisation — the single biggest driver of manufacturing profitability — becomes visible.
The mechanics of capacity utilisation
A cement kiln, or a bottling line, has a fixed cost base — depreciation, plant maintenance, a baseline of skilled labor — that does not fall much even when production volumes fall. When a plant runs at 85 percent of rated capacity instead of 60 percent, the same fixed overhead is spread across far more units, and cost per bag of cement or per case of beverage drops sharply, flowing straight into gross margin. This operating leverage effect is the reason manufacturing stocks in Nepal can show gross margin swings of several percentage points quarter to quarter that have nothing to do with input-price movements and everything to do with how full the plant was running — a fact you should always check against disclosed capacity utilisation figures (where reported) or infer from the relationship between revenue growth and gross margin movement.
Depreciation of manufacturing and hotel fixed assets
NAS 16 — Property, Plant and Equipment governs depreciation for both manufacturers and hotels, and the choice of method and useful-life estimate materially affects reported profitability in both sub-sectors, since plant and hotel buildings represent the largest non-current asset on their balance sheets.
REGULATORY DETAIL
NAS 16 requires depreciation to reflect the pattern in which the asset's economic benefits are consumed. Nepali manufacturers and hotels overwhelmingly use the straight-line method for buildings and structures, and either straight-line or a reducing-balance method for plant and machinery, furniture, and equipment, with useful lives commonly set in the range of 25-50 years for factory buildings and hotel structures, 10-20 years for major plant and machinery, and considerably shorter for furniture, fixtures, and soft-goods replacement cycles inside a hotel (5-10 years). Where a manufacturing asset is a "qualifying asset" under construction — a new cement line, a hotel wing under renovation — NAS 23 requires that borrowing costs directly attributable to its construction be capitalised into the asset's cost rather than expensed, which is why a hotel or cement company's finance-cost line can look unusually low during a multi-year expansion phase and then jump once the asset is commissioned and interest capitalisation stops.
For a hotel specifically, a large share of depreciable base sits in furniture, fixtures, and equipment (FF&E) that must be refreshed on a much shorter cycle than the building shell itself — mattresses, carpets, kitchen equipment, guest room technology — and a hotel operator that defers this FF&E reinvestment to protect near-term reported profit is trading current-period earnings for a competitiveness problem (dated rooms, falling guest satisfaction scores, eventual rate erosion) that will surface in occupancy and average daily rate data years later. When reviewing a hotel's capex trend, a multi-year decline in FF&E replacement spend relative to revenue is worth flagging even if current profitability looks fine.
Lesson 34.5 — Hotel Accounting: Occupancy, ADR, RevPAR, and Seasonality
Hotels present a genuinely distinct accounting and analytical problem from manufacturing and trading, because a hotel does not sell one product — it sells room-nights, food and beverage, and ancillary services (banquets, spa, laundry, business-centre) simultaneously, each with different margin profiles, and its revenue is acutely seasonal in a way no other NEPSE sector experiences so visibly.
NEPSE currently lists a small handful of hospitality companies, most prominently Soaltee Hotel Limited (five-star, Kathmandu), Oriental Hotels Limited (operating the Radisson-branded property), Taragaon Regency Hotel Limited (operating the Hyatt-branded property in Boudha), and more recently Hyatt Centric-branded City Hotel. Their combined weight on the exchange is tiny, but their disclosures are unusually rich for understanding hospitality economics because Nepal's tourism-linked revenue is subject to sharp, observable shocks.
Segmenting hotel revenue
A hotel's income statement (or its segment note, where disclosed) typically breaks revenue into: room revenue (the highest-margin line, since rooms have largely fixed costs regardless of occupancy), food and beverage revenue (banqueting, restaurants, room service — lower margin due to higher variable food and labor cost), and other operating revenue (spa, business centre, laundry, telecommunications, forex, transport). Room revenue is the line to watch most closely because it carries the highest incremental margin — an extra occupied room-night drops close to its full rate straight to operating profit, since housekeeping and utility costs per room are largely fixed — while F&B revenue growth alone, without matching room revenue growth, often signals a hotel compensating for weak occupancy by pushing banqueting and walk-in dining rather than a genuinely stronger core business.
The three metrics every hotel analyst must compute
KEY CONCEPT
Occupancy Rate = Rooms Sold divided by Rooms Available (a percentage). Average Daily Rate (ADR) = Room Revenue divided by Rooms Sold (the average price actually realised per occupied room). Revenue Per Available Room (RevPAR) = Occupancy Rate multiplied by ADR, equivalently Room Revenue divided by Rooms Available. RevPAR is the single most useful summary metric because it captures both how full the hotel is and how much it charges — a hotel can be nearly full at a discounted rate, or half-full at a premium rate, and RevPAR is what makes those two scenarios comparable.
Nepali hotel annual reports and quarterly disclosures do not always break out occupancy and ADR figures explicitly (unlike US or Indian hospitality REIT disclosure norms), so you will often need to back into an implied RevPAR trend by dividing disclosed room revenue by the number of available room-nights (rooms times days in period, adjusted for any rooms out of service for renovation) — a calculation worth doing yourself even when the company doesn't hand it to you.
Seasonality and shock exposure
Nepal's tourism calendar has two demand peaks — the autumn trekking and festival season (roughly September through November) and the spring trekking season (March through May) — with the June-to-August monsoon and the winter cold season representing structural troughs. A hotel's quarterly results should always be read against this calendar rather than compared naively quarter-over-quarter; a sequential revenue decline from an autumn-peak quarter into a monsoon-trough quarter is normal seasonality, not deterioration, and the correct comparison is always the same fiscal quarter one year earlier.
But seasonality is a manageable, forecastable risk. What the Nepali hotel sector experienced in fiscal year 2025/26 illustrates a different and much harder risk category: acute event shock layered on top of a genuinely strong underlying tourism market.
CASE IN POINT
Nepal recorded a record 1,209,357 foreign tourist arrivals in fiscal year 2025/26, with arrivals in the first seven months of calendar 2026 up 6.84 percent year-on-year — by every macro indicator, a strong tourism year. Yet eight listed hotel companies collectively swung from a combined net profit of roughly Rs 1.02 billion in FY 2024/25 to a combined net loss of roughly Rs 255.9 million in FY 2025/26, with total revenue falling 14.5 percent from Rs 7.29 billion to Rs 6.23 billion. The cause was not the tourism market — it was the September 2025 "Gen-Z protests," which directly damaged and disrupted hotel operations. Taragaon Regency (the Hyatt-branded property) was targeted during the unrest and remained closed for nearly a year with no confirmed reopening date as of the report, with 133 staff placed on leave, and posted a loss of roughly Rs 705.7 million. Hyatt Centric (City Hotel) posted a loss of about Rs 170 million on revenue of Rs 530 million. Oriental Hotels (Radisson) posted a loss of about Rs 47.5 million on revenue of just over Rs 1 billion. Soaltee Hotel was the standout exception, remaining solidly profitable with revenue of roughly Rs 3.14 billion and profit of about Rs 761.1 million, translating to an EPS of roughly Rs 6.48 — a reminder that within a shared shock, balance sheet strength, brand positioning, and physical exposure to the specific unrest locations produced dramatically different outcomes across four hotels operating in the same city and the same macro tourism environment.
This single episode is one of the most important case studies a Nepali retail investor can absorb about the hospitality sector: high fixed costs (a hotel's payroll, utilities, and depreciation do not shrink when the building is empty or closed) combine with acute event-driven demand shocks to produce far more earnings volatility than the underlying tourism macro data would suggest. A hotel's published occupancy and ADR trend from two years ago tells you almost nothing about its resilience to a week of political unrest, a border closure, or a regional travel advisory — you have to separately assess balance sheet cushion (how many months of fixed costs the company's cash and short-term investments can cover), insurance coverage for business interruption, and physical/political exposure of the specific property location.
WARNING
Never extrapolate a hotel's most recent quarterly RevPAR trend in isolation. Hospitality earnings in Nepal have demonstrated repeatedly — most recently through the September 2025 unrest — that they can swing from record-tourism-year profitability to sector-wide losses within a single fiscal year due to a single event unrelated to underlying demand fundamentals. Build a downside scenario into every hotel valuation, and check disclosed insurance and business-interruption coverage specifically, not just headline occupancy trends.
Lesson 34.6 — Related-Party Transactions and Governance in Family-Owned Conglomerates
Nearly every manufacturing and trading name on NEPSE sits inside a larger, mostly private family business group. Bottlers Nepal (both Balaju and Terai entities) has long-standing ties to Nepal's Khetan Group and the broader Nepal Distilleries / Khukri Rum lineage of promoter families; Shivam Cements sits within a larger Shivam/Chaudhary-adjacent industrial holding structure (Shivam Holdings, its issuer-rated parent, sits above the listed cement operating company); Unilever Nepal is majority-owned by its multinational parent with a small public float; and beyond the exchange itself, groups like Chaudhary Group (CG), Golyan Group, and Dugar Group run manufacturing, trading, hospitality, and financial interests side by side, with only a fraction of the group's total activity ever reaching a listed vehicle. This structure is the single most important governance fact to understand before investing in this sector, because it means the listed company you are analysing is very often not economically independent of its promoter group.
REGULATORY DETAIL
NAS 24 — Related Party Disclosures requires listed companies to disclose transactions with related parties — parent and subsidiary entities, key management personnel, entities under common control, and close family members of controlling shareholders — including the nature of the relationship, the volume of transactions, outstanding balances, and any provisions for doubtful debts related to those balances. For Nepali manufacturing and trading conglomerates, the related-party note is frequently one of the most information-dense sections of the annual report, because it is where you find related-party purchases of raw materials, related-party sales of finished goods, intercompany loans and guarantees, shared-service charges (management fees, brand royalties), and director/promoter remuneration and shareholding.
Why this matters for the retail investor's actual analysis: a manufacturer that buys a meaningful share of its raw materials from a related trading entity, or sells a meaningful share of finished goods through a related distribution company, can effectively shift margin between the listed entity and its private affiliate through transfer pricing — inflating or deflating the listed company's reported profitability depending on which side the family group wants profit to sit, often for tax-optimization or dividend-timing reasons that have nothing to do with the listed minority shareholders' interests. This is not necessarily illegal or even improper — NFRS and Nepali company law require disclosure, not prohibition, of related-party dealing — but it means gross margin and profitability at a related-party-heavy manufacturer must be read with an extra layer of skepticism that a bank's margin (regulated, standardised, comparable across 20+ peers) simply does not require.
CAUTION
Before valuing any Nepali manufacturing or trading company on reported earnings, read the related-party transactions note in full and ask three questions: (1) What share of purchases or sales run through related entities, and has that share changed meaningfully year over year? (2) Are related-party outstanding balances (loans, advances, guarantees) growing faster than the company's own operating cash flow — a sign the listed entity may be quietly financing its private affiliates? (3) Do promoter/director remuneration and any brand-royalty or management-fee arrangements scale with the company's profitability in a way that leaves minority shareholders a shrinking share of the economic pie even as headline revenue grows? None of these questions has a universally "correct" answer, but a company that cannot or does not answer them clearly in its disclosures should be valued more conservatively than one that does.
Practical governance checklist for this sector
Beyond related-party review, apply the same minority-shareholder lens Nepali company law is designed to protect: check promoter shareholding percentage and how it has trended (a rising promoter stake through preferential allotments dilutes public shareholders differently than open-market buying); check whether independent directors on the board are genuinely independent of the promoter family or are long-serving associates; check dividend history against free cash flow generation (a family-controlled company with strong cash generation but persistently low payout may be retaining cash for the private side of the group rather than the listed shareholders); and check auditor tenure and any qualified opinions, which in a related-party-heavy structure carry more weight than in a standardised BFI audit.
CASE IN POINT
The cross-sector comparison Nepali market commentary has repeatedly drawn between Shivam Cement and premium-valued names like Unilever Nepal, Bottlers Nepal, and Himalayan Distillery is itself instructive: these are all manufacturing companies, yet they trade at wildly different valuation multiples, and the gap is driven less by product economics than by float scarcity, brand durability, dividend consistency, and — for the multinational-linked names — the market's confidence in governance standards imported from the parent group. A cement company's earnings are tied to a cyclical, capital-intensive, commodity-adjacent business; a consumer-beverage bottler's earnings are tied to a branded, repeat-purchase, working-capital-light business — and no amount of P/E comparison across the two is meaningful without first normalising for these structurally different economic models.
Chapter recap
Manufacturing, trading, and hotel companies occupy a small, almost peripheral corner of NEPSE's total market capitalisation — together well under ten percent of the exchange's value, against commercial banks' roughly two-fifths and hydropower's high single digits — yet they reward careful study precisely because they are where accrual accounting appears in its most direct, least regulation-distorted form. Where a bank's balance sheet is dominated by NRB-directed loan-loss provisioning and an insurer's by actuarial reserving, a cement manufacturer's or a trading house's financials are built from the more fundamental blocks every investor should master: inventory costed under NAS 2 at the lower of cost and net realisable value, cost of goods sold shaped by capacity utilisation and operating leverage, and a working capital cycle whose length and efficiency differ predictably between manufacturers (slower, higher-margin, capital-intensive) and traders (faster, thinner-margin, working-capital-light).
The sector is thin for structural reasons worth remembering every time you screen it: unlike banks and hydropower developers, Nepal's manufacturing and trading conglomerates were never compelled by regulation to seek public capital, and the country's dominant family business houses — Khetan, Golyan, Chaudhary, Dugar, and others — have generally preferred private ownership and bank financing to public dilution. The handful that did list — Bottlers Nepal's two entities, Unilever Nepal, Shivam Cements, Ghorahi Cement, the government-owned Udayapur Cement — arrived through a mix of historical public-issue mandates, capital-intensive growth needs, and, in cement's case, a slow trickle of IPOs that has produced only three meaningful domestic manufacturing peers to compare against each other.
Hotels demand their own distinct lens: room revenue, food and beverage revenue, and ancillary income each carry different margins, and occupancy, ADR, and RevPAR together tell you far more than any single metric alone. Nepal's tourism calendar creates genuine, forecastable seasonality between autumn and spring peaks and monsoon and winter troughs — but the sector's defining lesson from fiscal year 2025/26 is that seasonality is the manageable risk, while acute political and social shocks are not. A record 1.2 million tourist arrivals coexisted with eight listed hotels swinging from a combined profit near Rs 1 billion to a combined loss near Rs 256 million, driven almost entirely by the September 2025 unrest that shuttered Taragaon Regency for the better part of a year while Soaltee, less directly exposed, stayed comfortably profitable. High fixed costs turned a localized, temporary disruption into sector-wide earnings volatility that no trailing occupancy trend would have predicted.
Running beneath all of it is the governance reality that nearly every company in this sector sits inside a larger private family conglomerate, making the related-party transactions note — governed by NAS 24 — one of the most consequential disclosures a retail investor will read in this entire market. Purchases from and sales to related entities, intercompany balances, and promoter remuneration arrangements can quietly redistribute economic value between the listed minority shareholders and the private side of the group, and no amount of headline revenue growth substitutes for checking whether that redistribution is happening.
This chapter closes Part VI of this book. Across the preceding chapters you have learned to read a commercial bank's provisioning and interest-income recognition, a development bank's and finance company's narrower deposit-and-lending model, a microfinance institution's group-lending portfolio quality, a hydropower developer's PPA-anchored revenue and construction-phase accounting, an insurer's actuarial reserving and claims development, and now — completing the survey — a manufacturer's, trader's, and hotelier's inventory, working capital, and related-party exposures. Each sector reshapes the same underlying accrual accounting principles to fit its own regulatory environment and economic model, and the skill this Part has tried to build in you is not memorisation of any one sector's rules but the transferable instinct to ask, for any company on any exchange: what does this business's regulator require it to measure, what does its ownership structure incentivize it to disclose, and does the accounting in front of me actually describe the cash-generating reality underneath it.
The final lesson of this sector, and in many ways of this entire Part, is one of humility about comparability. Manufacturing, trading, and hotels are where NEPSE offers you the fewest peers, the thinnest disclosure norms, and the heaviest reliance on family-group context — which means the analytical rigor you bring to reading a single company's inventory note, working capital trend, and related-party disclosure matters more here, not less, than in the crowded and standardised sectors that dominate the rest of the exchange.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VII
TAXATION & RATIOS
Part VII · Chapter 35
Capital Gains Tax in Nepal
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 35.1 — The Legal Architecture: Why Share Gains Are Taxed Differently
A Nepali investor who buys 500 shares of a hydropower company on NEPSE and sells them fourteen months later for a profit will never file that gain the way a shopkeeper files trading income or a landlord files rental income. Capital gains on listed securities live in their own compartment of the Income Tax Act, 2058 (2002), governed principally by Section 95Ka, and collected not through a self-assessed annual return but through withholding at the point of sale — deducted automatically by the broker and the Central Depository System and Clearing Limited (CDSC) before the seller ever sees the money. Understanding this chapter means understanding three separate things that most investors conflate into one: the rate that applies, the cost basis the rate is applied to, and the mechanism by which the tax actually leaves your account.
Nepal's system distinguishes taxpayers along two axes. The first is residency and legal form: resident individual, resident entity (company, bank, insurance firm, mutual fund), and non-resident. The second is, for individuals only, holding period — whether the shares sold were held for more than 365 days or 365 days and under. Institutions do not get a holding-period concession; a bank's trading desk pays the same rate whether it held a scrip for three days or three years, because for a company, share trading gains are treated as ordinary business income rather than a personally-earned capital gain deserving a patience discount.
The rates below are the ones in force today, under the Finance Act 2083, effective from Shrawan 1, 2083 (roughly mid-July 2026) — the fiscal year Nepal is currently inside.
Investor Category
Security Type
Holding Period
Current CGT Rate (FY 2083/84)
Resident individual
Listed (NEPSE)
More than 365 days
7.5%
Resident individual
Listed (NEPSE)
365 days or less
10%
Resident individual
Unlisted
Any period
10%
Resident entity/institution
Listed (NEPSE)
Any period
10%
Resident entity/institution
Unlisted
Any period
15%
Non-resident
Listed or unlisted
Any period
25%
REGULATORY DETAIL
Section 95Ka of the Income Tax Act, 2058 designates "any entity conducting securities exchange market business" — in practice the broker and CDSC acting jointly — as the withholding agent for capital gains on listed securities. The tax is deducted at settlement, not paid separately at an Inland Revenue Department (IRD) counter, which is why most retail investors never file a capital gains return at all.
The single most consequential legal change in this area in the last several years arrived with the FY 2083/84 budget, presented by the Finance Minister on Jestha 15, 2083 (May 29, 2026) and taking effect from Shrawan 1, 2083. It did two things simultaneously. First, it raised the individual rates: the long-term rate moved from 5% to 7.5%, and the short-term rate moved from 7.5% to 10%. Second — and this is the part investors tend to miss because it sounds like a technicality rather than a tax increase — it declared capital gains tax on listed securities to be a final tax. Before this change, the amount withheld by the broker was an advance payment; if an investor's total annual income crossed certain thresholds, the gain still had to be reconciled on an annual return, and additional liability could arise. Under the final-tax declaration, what the broker withholds at settlement now closes the matter for listed-share gains — there is no further reconciliation, no additional assessment, and, for most retail investors, no reason to touch a D-04 filing on account of share trading alone.
KEY CONCEPT
"Final tax" means the amount your broker withholds at the moment of sale is the entire tax liability on that gain — not a deposit against a larger bill computed later. It simplifies compliance for retail investors but also means there is no mechanism to claim the withholding back if your marginal income tax rate would otherwise have been lower.
Unlisted shares — a private company's shares transferred outside the exchange, or shares in a company before its NEPSE listing — do not get this simplification. They remain outside the final-tax regime and are still subject to the standard framework of reconciliation on the annual return, at 10% for resident individuals and 15% for resident entities. An investor who holds both a NEPSE brokerage portfolio and a stake in a private company should not assume the two are taxed identically or reported identically; only the listed portfolio enjoys the settle-and-forget treatment.
Lesson 35.2 — The 365-Day Line: Long-Term, Short-Term, and a Decade of Rate Changes
The 365-day threshold sounds simple until an investor actually tries to apply it. The count runs on calendar days, not trading days — weekends, Dashain holidays, and NEPSE closures all count toward the 365, because the clock starts on the settlement date of the purchase and ends on the settlement date of the sale. A share bought on Falgun 10 and sold on Falgun 11 of the following year has cleared the 365-day line even though the market itself may have traded on only about 240 of those days.
Holding period in Nepal
The practical complication is that most active investors do not hold one lot of a scrip — they accumulate through several purchases at different prices and different dates, often through a mix of IPO allotment, secondary-market buying, and reinvested bonus shares. When a partial sale happens, which lot's purchase date determines whether the sale is long-term or short-term? Nepal's Meroshare "My Purchase Source" tool requires the seller to select, at the time of computing WACC for a given sale, which specific purchase transactions are being drawn down — and in the ordinary course, brokers apply the oldest available lots first, so the holding-period test is effectively assessed against the earliest unliquidated purchase date among the shares being sold, even though the cost basis itself is calculated as a blended average across everything in the pool (Lesson 35.3 explains why cost and holding period are computed differently). An investor selling only part of a position should verify, inside Meroshare, exactly which lots the system has attributed to that sale before assuming a long-term rate applies.
The rate itself has moved three times in the last decade, and the pattern of those moves tells you something about the government's underlying intent — first to relieve the market, then to discourage churn, then to raise revenue while locking in the long-term/short-term gap as permanent policy.
Effective From
Legal Basis
Individual — Long-Term (>365 days)
Individual — Short-Term (≤365 days)
Institution
Pre-2076/77
Income Tax Act 2058 (original)
7.5% flat
7.5% flat
10%
FY 2076/77 (mid-2019)
Finance Act 2076
5% flat
5% flat
10%
FY 2078/79 (Shrawan 2078 / July 2021)
Finance Act 2078
5%
7.5%
10%
FY 2083/84 (Shrawan 2083 / July 2026) — current
Finance Act 2083
7.5%
10%
10%
CASE IN POINT
In May 2019, the government cut the individual CGT rate on shares from a flat 7.5% to a flat 5%, applied uniformly regardless of holding period — a straightforward relief measure aimed at a market that had been depressed for several years. Two years later, the Finance Act 2078 reintroduced a holding-period distinction, effective Shrawan 1, 2078: long-term holders kept the 5% rate, but anyone selling within 365 days was pushed back up to 7.5% — commentators at the time described it as "short-term traders to be taxed 50% more." That same long-term/short-term architecture survives today; only the numbers inside it have risen.
The FY 2083/84 change did not touch this architecture — it kept the long-term discount relative to short-term trading intact, which tells you the policy goal (reward patient capital, discourage rapid churn) has outlasted three separate finance ministers. What changed was the size of the gap being monetized: the government raised both legs by 2.5 percentage points, which for a short-term trader is a one-third increase in tax burden (7.5% to 10%) and for a long-term holder is a 50% increase (5% to 7.5%). Investors who had structured their trading around the old 5%/7.5% split — deliberately holding past the one-year mark to capture the discount — still benefit from doing so under the new regime; the absolute discount (2.5 points) is unchanged even though both rates are higher in absolute terms.
WARNING
Do not assume the CGT rate you learned two or three years ago is still current. Nepal's Finance Act is passed and gazetted annually alongside the national budget, typically in Jestha (May) with effect from Shrawan 1 (mid-July) of the same year, and share-market CGT rates have changed at least three times since 2019. Always confirm the rate in force for the fiscal year in which the sale settles, not the year you originally bought the shares.
Lesson 35.3 — WACC: How Nepal Computes Your Cost Basis
Nepal does not use FIFO (first-in-first-out) or LIFO (last-in-first-out) to determine what you paid for a share when you sell it. It uses the Weighted Average Cost of Capital method — universally shortened to WACC in Nepali market vocabulary, even though this is a different use of the term from the corporate-finance WACC most readers will already know as a discount rate. Here, WACC simply means: pool every purchase of a given scrip inside your demat account, add up the total money actually spent (including transaction costs), divide by the total number of shares acquired, and that single blended figure becomes your cost per share for every subsequent sale of that scrip — regardless of which specific certificate or purchase order the shares "came from."
Why pooling instead of lot-tracking
The reason is structural. Shares held in a dematerialized account are fungible units inside CDSC's ledger — unlike a paper share certificate with a serial number, one unit of Nabil Bank's ordinary share is indistinguishable from another. Nepal's tax administration chose to treat cost basis the same way: as a single average, updated every time a new purchase, bonus allotment, or rights allotment adds shares to the pool, rather than as a queue of dated lots. This also happens to be simpler to withhold automatically at scale across CDSC's entire clearing system, since the broker's software only ever needs one number per scrip per account, not a full purchase history replayed at every sale.
The formula, and every fee that feeds it
WACC = (Sum of all purchase costs, including transaction charges) ÷ (Total shares acquired)
Crucially, "purchase cost" is not just the quoted price per share. It includes the broker commission, the SEBON regulatory fee, and the CDSC/DP charge paid on that purchase — all of which get folded into the numerator before the average is struck. These same three charges apply again on the sell side and are netted against the sale proceeds to arrive at the "adjusted selling price" used for the gain calculation. The current broker commission slab, in effect since Jestha 1, 2081 (mid-May 2024) after a 10% reduction ordered by SEBON, is:
Transaction Value
Broker Commission Rate
Up to Rs 50,000
0.36%
Rs 50,000 – Rs 5,00,000
0.33%
Rs 5,00,000 – Rs 20,00,000
0.31%
Rs 20,00,000 – Rs 1,00,00,000
0.27%
Above Rs 1,00,00,000
0.24%
On top of the broker's slab, every transaction also carries a SEBON regulatory fee of 0.015% of transaction value and a CDSC/DP charge of roughly Rs 25 per transaction leg — both small individually, but both mandatory inputs into the WACC calculation, and both easy to leave out if an investor tries to reconstruct cost basis by hand from memory of the quoted share price alone.
KEY CONCEPT
Your cost basis for CGT purposes is never just "what the ticker said I paid." It is the quoted price plus your broker's commission plus the SEBON fee plus the CDSC/DP charge, all pooled across every purchase of that scrip you have ever made. Ignoring the fee layer understates your cost basis and overstates your taxable gain.
A worked example
Consider an investor who bought Standard Chartered Bank (SCB) shares in three separate transactions:
That Rs 620.11 — not Rs 600, not Rs 620, not a simple average of the three quoted prices — is the figure the broker's system will use as cost basis the moment any portion of this 1,120-share holding is sold. If the investor later sells 500 shares at Rs 750, the taxable gain per share is Rs 750 minus fees minus Rs 620.11, not Rs 750 minus Rs 600.
PRACTICAL TOOL
To see your own WACC before you place a sell order, log into Meroshare, go to "My Purchase Source," select the scrip, and the system will list your full transaction history for it. You can verify or annotate individual purchase records there; the platform then computes the blended WACC that will govern your CGT deduction. Doing this before selling — not after — lets you catch a misattributed transaction while it can still be corrected.
Lesson 35.4 — Bonus Shares and Rights Shares: The Cost-Basis Traps
Two categories of share acquisition do not involve a normal cash purchase, and both distort the WACC pool in ways that surprise investors who have not thought through the mechanics in advance: bonus shares and rights shares.
Bonus shares carry a zero cost basis
When a company issues bonus shares — Nepal's equivalent of a stock dividend, common among banks and hydropower companies capitalising reserves — the recipient pays nothing for them. But the shares still enter the WACC pool as additional units, with zero rupees added to the cost side of the ledger. The arithmetic consequence is that your average cost per share falls for every unit you hold, bonus and original alike, because the same total cost is now being divided across a larger share count.
Take an investor holding 1,000 shares at a WACC of Rs 500 per share — a cost pool of Rs 500,000. The company declares a 10% bonus. The investor receives 100 new shares at zero cost. The pool is unchanged at Rs 500,000, but the share count is now 1,100.
New WACC = Rs 500,000 ÷ 1,100 = Rs 454.55 per share
Every share the investor now holds — the original 1,000 and the new 100 alike — carries this lower blended cost basis. When the investor eventually sells at, say, Rs 700, the taxable gain per share is Rs 245.45 rather than the Rs 200 it would have been against the pre-bonus WACC of Rs 500. The bonus shares did not create tax-free wealth; they deferred the tax on part of the original investment and spread it thinner across a larger holding, to be collected later at whatever rate applies when the shares are actually sold.
WARNING
A common and costly mistake is assuming bonus shares are tax-free simply because they were received without payment. They are not exempt — they are deferred. Because they carry zero cost, the entire sale proceeds attributable to a bonus share are, in effect, taxable gain, and their presence in the pool quietly raises the taxable gain on every other share in the same holding by lowering the blended WACC.
There is a second, separate trap around bonus shares: their holding period is generally treated as beginning on the date of allotment (credit to the demat account), not on the purchase date of the original shares that generated the bonus entitlement. An investor who has held the original shares for two years but received a bonus allotment three months ago may find that a sale today classifies the bonus portion as short-term — taxed at the higher rate — even though the "parent" holding is comfortably long-term. WACC blends the cost of bonus and original shares together, but it does not blend their holding-period clocks; those are tracked separately per allotment.
Rights shares carry the price actually paid, plus a fresh acquisition date
Rights shares are different: the investor does pay for them, typically at the rights issue price set by the company (often at or near face value, though companies can and do price rights issues at a premium). That price — plus the associated fees — is added into the WACC pool as a genuine new purchase, exactly like a secondary-market buy. The complication is timing: rights shares are usually allotted many months after the subscription window closes, and it is the allotment date, not the subscription date, that starts their individual holding-period clock. An investor who subscribed to a rights offering in Baisakh but was only allotted the shares in Ashoj is holding those specific units from Ashoj forward for CGT purposes, even though their capital was committed months earlier.
CAUTION
Bonus shares dilute your average cost downward without any of your capital moving. Rights shares add genuinely new capital to the pool at the price you paid. Confusing the two — treating a rights allotment as though it were free, or a bonus allotment as though its holding period matched your original purchase — is the single most common cost-basis error retail investors make in Nepal, and it directly changes both your tax rate and your taxable amount.
Lesson 35.5 — How the Deduction Actually Happens: Broker, CDSC, and the Settlement Cycle
Everything described so far — the rate table, the WACC pool, the bonus and rights adjustments — culminates in a single automated event: the moment your sell order settles, typically on T+2 (two business days after the trade date), the broker's Trading Management System (TMS), working through CDSC's clearing infrastructure, computes the gain, applies the correct rate, deducts the tax, and credits you only the net amount. You do not write a cheque to the IRD. You do not calculate anything yourself unless you are checking the broker's work.
The sequence, mechanically, runs like this. When you place a sell order and it executes, the system pulls your WACC for that scrip (verified or auto-computed from your Meroshare purchase history), nets the sale proceeds against broker commission, SEBON fee, and DP charge to get the adjusted selling price, subtracts WACC from that adjusted selling price to get the gain, applies the holding-period test to select the long-term or short-term rate (or the flat institutional rate, if the seller is an entity), withholds that amount, and remits it to the IRD through CDSC's centralised capital gains tax system. What lands in your bank account, or your broker ledger balance, is already net of tax.
REGULATORY DETAIL
Prior to the FY 2083/84 declaration of listed-share CGT as a final tax, individuals whose total annual income exceeded Rs 40 lakh were separately required to file a D-4 statement and obtain tax clearance from the IRD by the end of Ashoj, even though the broker had already withheld tax at the point of sale — a genuine double-touch on compliance for larger investors. The final-tax declaration is intended to close that gap for listed-security gains specifically; unlisted-security gains and other asset classes remain under the standard filing framework, so an investor with a mixed portfolio should not assume the whole picture has been simplified.
There is a related, easily missed obligation on the settlement side that trips up new investors far more often than the tax rate itself: the Electronic Delivery Instruction Slip, or EDIS. After a sale executes, you are required to authorize the electronic transfer of the sold shares from your own Meroshare demat account to your broker's demat account, and this must be completed by 9:00 PM on T+1 — one business day after the trade, and a full day before the T+2 settlement that actually pays you out. Miss that window and you do not just delay your own payment; you expose yourself to a penalty equal to 20% of the sell transaction amount, charged for failing to deliver shares you sold. This is not itself a capital gains tax — it is a settlement-discipline penalty — but it sits directly in the same workflow as the WACC calculation, since Meroshare typically prompts the WACC confirmation and the EDIS authorization in the same session.
CAUTION
The 20% EDIS penalty for a missed T+1 transfer deadline is separate from, and can be far larger than, the capital gains tax on the same sale. A short-term trade with a modest gain, taxed at 10%, can still cost an investor an amount several times the tax itself if the electronic share transfer is not authorized in Meroshare by 9:00 PM the day after the trade.
For institutional sellers — banks, insurance companies, mutual funds, brokerage proprietary desks — the mechanics of withholding are identical at the point of sale (10% is deducted through the same CDSC infrastructure), but the tax treatment downstream differs from the individual regime. Share trading gains for most institutions are booked as ordinary business income and consolidated into the entity's annual corporate tax return; the 10% withheld at settlement functions there as an advance tax credit against the institution's overall corporate tax liability rather than as a final, self-contained tax the way it now is for individuals. An institutional investor's finance team, not its trading desk, is the one that ultimately reconciles this figure — a genuinely different compliance posture from the retail "sell and forget" experience the final-tax rule now gives individuals.
PRACTICAL TOOL
Meroshare and most brokers' TMS portals generate a downloadable capital gains and tax deduction statement per fiscal year. Retail investors should pull this statement at least once a year — not just at tax time — to cross-check the WACC figures the system used, the holding-period classification applied to each sale, and the rate charged, against their own records of purchase dates and bonus/rights allotments.
Lesson 35.6 — Putting It Together: A Full Worked Portfolio Example
The individual rules — WACC pooling, bonus dilution, the 365-day test, fee-adjusted proceeds, automatic withholding — rarely appear one at a time in real trading. A single sale usually forces all of them to interact at once. Consider an investor, Sunita, who has built a position in a commercial bank's shares as follows:
Event
Date
Shares
Price/Share
Notes
Secondary market purchase
Kartik 2081
800
Rs 480
Original purchase, holding clock starts here
Bonus allotment (10%)
Ashoj 2082
80
Rs 0
Zero cost, separate holding clock starts here
Rights allotment
Magh 2082
200
Rs 100
Genuine new capital, separate holding clock starts here
Secondary market purchase
Bhadra 2083
300
Rs 560
Recent top-up, most recent holding clock
By Bhadra 2083, Sunita holds 1,380 shares in total. Her WACC pool, including the roughly Rs 3.6 lakh original purchase cost (with fees), the zero-cost bonus shares, the Rs 20,000 rights subscription (with fees), and the recent Rs 1.68 lakh top-up (with fees), works out to a blended WACC of roughly Rs 435 per share once all four events are pooled and divided across 1,380 shares — pulled down from her original Rs 480 entry price by the zero-cost bonus shares and the below-market rights price, then pulled back up slightly by the more expensive recent top-up.
In Ashwin 2083, Sunita sells 600 shares at Rs 650 each. The broker's system must now determine two things independently: which 600 shares (by holding-period clock) are being sold, and what the applicable WACC is. Because Meroshare draws down the oldest available lots first, the 600 shares sold are attributed to her original Kartik 2081 purchase and the bulk of the Ashoj 2082 bonus allotment — both of which, measured against the Ashwin 2083 sale date, have comfortably crossed the 365-day threshold. The sale therefore qualifies for the long-term individual rate of 7.5% under the current Finance Act 2083 regime, not the 10% short-term rate — even though her most recent purchase, three weeks before the bonus and rights events layered in, would not have qualified on its own.
Gain per share = Rs 650 (less proportionate fees) − Rs 435 (WACC) ≈ Rs 210 Total gain on 600 shares ≈ Rs 126,000 Tax withheld at 7.5% ≈ Rs 9,450, deducted automatically at settlement
Had Sunita instead sold her most recent Bhadra 2083 purchase — the batch bought only weeks earlier — the same transaction would have been taxed at 10%, and her taxable gain per share would have been calculated against that batch's own higher cost, not the blended pool figure a naive investor might assume applies uniformly. The lesson is not that Sunita "chose" the cheaper tax outcome; it is that Meroshare's oldest-lot-first attribution did the choosing for her, and an investor who does not understand this mechanism cannot predict, in advance, which rate a given sell order will actually trigger.
CASE IN POINT
In Sunita's case, four separate acquisition events — cash purchase, bonus allotment, rights allotment, and a second cash purchase — collapsed into one blended WACC figure for cost-basis purposes, while still being tracked as four separate holding-period clocks for rate-classification purposes. Both mechanisms operate simultaneously and independently; treating WACC as though it also determined holding period, or treating the oldest-purchase date as though it applied to the whole blended pool, produces the wrong tax outcome in either direction.
Chapter recap
Capital gains tax on NEPSE shares is not a single number an investor memorises once — it is a small system of interacting rules, each of which has moved in the recent past and can move again with each year's Finance Act. As of today, under the Finance Act 2083 (effective Shrawan 1, 2083), a resident individual pays 7.5% on gains from shares held more than 365 days and 10% on gains from shares held 365 days or fewer, both now treated as a final tax rather than an advance payment subject to later reconciliation. Resident institutions pay a flat 10% regardless of holding period, though for most entities this functions as a credit against ordinary corporate tax rather than a closed-out final liability. Unlisted securities sit outside this simplified regime entirely, taxed at 10% for individuals and 15% for entities under the standard filing framework, with no final-tax relief.
The rate an investor pays has changed materially over a short span: a flat 5% in the years following 2019, a 5%/7.5% long-term/short-term split from 2078 onward, and the current 7.5%/10% split since 2083 — each change reflecting a different balance the government struck between relieving a struggling market, discouraging speculative churn, and raising revenue from an increasingly active retail trading base. An investor who cannot name the rate in force for the specific fiscal year a sale settles in is not fully informed, because past rates are not grandfathered forward.
Underneath the rate sits the cost basis, and Nepal computes cost basis by WACC — a single pooled average across every purchase of a given scrip in an account, inclusive of broker commission, SEBON's 0.015% fee, and the CDSC/DP charge on every leg — never by tracking individual lots the way FIFO or LIFO systems do elsewhere. This pooling is invisible to an investor who never opens Meroshare's "My Purchase Source" tool, and it is precisely the mechanism that makes bonus shares dangerous: because bonus allotments enter the pool at zero cost, they mathematically lower the average cost of every share in the holding, deferring rather than eliminating tax, while still starting their own independent holding-period clock from the date of allotment. Rights shares behave in the opposite direction — real capital added at the rights price, with their own fresh acquisition date — and conflating the two categories is the most common cost-basis error retail investors make.
None of this arithmetic is something the individual investor performs by hand at tax time. The broker's Trading Management System and CDSC's clearing infrastructure compute the gain, classify the holding period, apply the rate, and withhold the tax automatically at settlement, crediting only the net amount — a genuinely low-friction system by regional standards, now made simpler still by the final-tax declaration that removed the old requirement for larger investors to separately reconcile share gains on an annual D-4 filing. The one place friction remains sharp is the EDIS transfer deadline: shares sold must be electronically authorized for transfer from the investor's demat account to the broker's by 9:00 PM on T+1, and missing that window triggers a 20% penalty on the sale amount — a settlement-discipline cost entirely separate from, and potentially far larger than, the capital gains tax itself.
The investor who treats this chapter as a checklist rather than a system will get individual facts right and still miscalculate the outcome — quoting the correct rate but applying it to the wrong holding period, or computing WACC correctly but forgetting that a bonus allotment reset a portion of the clock. The discipline this chapter asks for is the same discipline the rest of this book asks for everywhere else: read the primary mechanism, not the headline rate, because in Nepal's capital markets the headline rate is only ever the last step in a calculation that begins several transactions, and sometimes several years, earlier.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VII · Chapter 36
Dividend and Interest Taxation
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lakshmi had held two hundred shares of a hydropower company for three years before her first dividend cheque arrived — except it wasn't a cheque, and it wasn't the full amount she had calculated from the company's board notice. Her broker's statement showed a number nearly five percent smaller than the declared payout, with a single line item she had never noticed before: "TDS on dividend." She called her broker, half-convinced of an error. There was no error. What Lakshmi encountered that afternoon is the subject of this chapter — the mechanical, largely invisible system by which the Nepal government collects tax on investment income before the investor ever sees the money, and the equally important question of what, if anything, the investor must do afterward.
Lesson 36.1 — Cash Dividends and the Five Percent Withholding
Every cash dividend paid by a resident company in Nepal — and every NEPSE-listed company is, by definition, a resident company — carries a statutory tax obligation that attaches at the moment of distribution, not at the moment the shareholder files an annual return. Under Section 88 of the Income Tax Act, 2058 (2002), read together with the rate schedule in Schedule 1, a company distributing a cash dividend must withhold tax at five percent of the gross dividend amount before the net sum is credited to the shareholder. This is not a suggestion or a best practice; it is a legal duty placed on the company (or, in modern NEPSE practice, on its share registrar acting on its behalf), and a company that fails to withhold correctly becomes personally liable to the Inland Revenue Department (IRD) for the shortfall.
The mechanics of the deduction
In practical terms, here is what happens between a board's dividend declaration and the money reaching a shareholder's bank account. The company's board proposes a dividend, typically expressed as a percentage of paid-up capital (for example, "20 percent," meaning NPR 20 per NPR 100 share). The Annual General Meeting ratifies it. The company (through its registrar, or in most current cases directly through the banking channel linked to each shareholder's demat account via Meroshare) computes the gross dividend owed to each shareholder, deducts five percent, and remits only the net ninety-five percent to the shareholder's registered bank account. The five percent withheld is deposited by the company with the IRD under the company's own PAN, tagged against each shareholder's PAN where available, within the statutory deposit window (twenty-five days from the end of the month in which the deduction occurred, under the standard TDS deposit rule that applies across all withholding categories in Nepal, not only to dividends).
For Lakshmi, this meant a declared dividend of, say, NPR 2,000 on her two hundred shares (at an assumed 10 percent dividend rate on NPR 100 paid-up value per share) would arrive in her account as NPR 1,900, with NPR 100 already sitting with the IRD in her name.
REGULATORY DETAIL
The five percent dividend withholding rate is set under Schedule 1, Section 2(3) of the Income Tax Act, 2058, and has remained unchanged through every Finance Act from 2077/78 through the current 2082/83 fiscal year. Unlike bank interest, which has periodically drawn proposals for revision, dividend withholding has been the most stable rate in the entire Nepali withholding tax schedule.
The rate is uniform regardless of the size of the dividend, the sector of the paying company, or — with the narrow exception discussed in Lesson 36.5 — the type of shareholder receiving it. A retail investor with two hundred shares and an institutional investor with two hundred thousand shares of the same company face the identical five percent deduction at the point of payment. What differs is what happens to that five percent afterward, and that is where the real complexity of this chapter begins.
KEY CONCEPT
Withholding tax is not the same as final tax. Withholding describes when and how tax is collected — at source, before the payment reaches the recipient. Final tax describes whether that withheld amount fully discharges the recipient's tax liability, with no further reckoning at year-end. For individual investors receiving dividends in Nepal, the five percent withheld is, in the overwhelming majority of cases, also the final tax. For institutional investors, it usually is not. Confusing the two categories is the single most common taxation error retail investors in Nepal make when reading their own dividend statements.
The table below summarises the withholding position across the main categories of investment income covered in this chapter, before we unpack each row in the lessons that follow.
Income type
Recipient
Withholding rate
Final tax for recipient?
Legal basis
Cash dividend
Resident individual
5%
Yes (Section 92 final withholding payment)
ITA 2058, Sched. 1 §2(3); §92
Cash dividend
Resident company/institution
5%
Effectively yes, via §53(3) exclusion
ITA 2058 §53(3); §88
Bonus share (stock dividend)
Resident individual
5% of face value
Yes, settled before demat credit
ITA 2058 §53(1); IRD directive
Bank/FI deposit interest
Resident individual
5%
Yes (Section 92)
ITA 2058, Sched. 1 §2(3); §92
Listed debenture/bond interest
Resident individual
5%
Yes (Section 92)
ITA 2058, Sched. 1 §2(3); §92
Bank/FI deposit or debenture interest
Resident company/institution
15% (advance; interest paid to another BFI is exempt from withholding)
No — added to taxable income
ITA 2058 §88(1), §88(4); taxed at 25-30%
Dividend or interest
Non-resident
5% (dividend); 15% (most interest)
Yes, subject to any applicable tax treaty
ITA 2058 §87-88; DTAA overrides
Lesson 36.2 — Final Withholding: Why Dividend Income Never Touches Your Tax Return
The concept that makes Nepali dividend taxation genuinely investor-friendly — and genuinely different from, say, salary income — is what Section 92 of the Income Tax Act calls a "final withholding payment." When a payment category is designated final, the tax withheld at source completely satisfies the recipient's income tax obligation on that item. The recipient does not add it to their other income, does not recompute it against the progressive individual tax slabs, and does not claim it as a credit against a larger liability. It simply exits the tax system the moment the five percent is deducted.
Dividend income received by a natural person — provided that person is not receiving the dividend in the course of carrying on a business of dealing in securities — falls squarely into this category. This has a concrete and welcome consequence: an ordinary salaried employee, a retired schoolteacher, a small business owner, or a homemaker who has invested household savings in NEPSE-listed shares does not need to gross up dividend income and run it through the individual tax slabs, which for high annual incomes can reach a marginal rate well above the five percent already paid. A retail investor earning NPR 500,000 in cash dividends across a diversified NEPSE portfolio in a given fiscal year owes precisely NPR 25,000 in tax on that income — no more, regardless of whether the investor's salary alone already places them in the top slab.
CASE IN POINT
Consider two Kathmandu residents with identical total annual income of NPR 1.8 million. The first earns it entirely as salary from a private employer; taxed under the progressive slab structure with the applicable Social Security Tax add-on, a meaningful portion of that income is taxed well above five percent — into the twenty and thirty percent bands. The second earns NPR 1.2 million in salary and NPR 600,000 in dividends from a NEPSE portfolio built over a decade. Only the salary portion is pushed through the progressive slabs; the NPR 600,000 in dividends is taxed at a flat five percent and never enters the slab calculation at all. The two taxpayers report identical gross income, yet the second pays materially less tax, purely because of how the income is characterised. This is not a loophole — it is the deliberate design of Section 92, intended to encourage broad-based equity ownership.
The narrow exception, and why it matters
The final-withholding treatment applies to dividends received by a natural person as an investor. It does not apply where the recipient earns the dividend in the course of an actual securities-trading business — for instance, a licensed stock broker firm structured as a sole proprietorship, or an individual whose registered business is share dealing. In that narrow case, the dividend is treated as business income, added to the business's taxable profit, and the five percent withheld becomes an advance tax credit rather than a final settlement. For the ordinary retail investor reading this book — someone who buys and holds shares as a personal investment, however actively — this exception essentially never applies. But it is worth knowing it exists, because it explains why brokerage firms and merchant banks, when they hold securities as trading inventory, report dividend income very differently on their own financial statements than an individual client does.
WARNING
Do not assume that "final withholding" means you are excused from ever mentioning dividend income to the tax authority. If you are already required to file an annual income tax return for other reasons — because you run a registered business, hold rental property, or earn income above the return-filing threshold from non-final sources — most tax practitioners in Nepal still recommend disclosing final-withholding dividend and interest income in the return as exempt/final income, even though it does not affect your tax payable. This creates a clean paper trail that matches the TDS certificates the IRD already has on file against your PAN, and avoids questions later if your bank balance or asset growth looks larger than your disclosed taxable income alone would explain.
For an individual whose only income sources are salary already taxed at source, and investment income that is entirely final-withholding, Nepal's tax administration generally does not require a return to be filed at all — this is one of the more taxpayer-friendly features of the system, though it should never be taken as blanket advice without checking current filing-threshold rules, since these thresholds are adjusted periodically and depend on total income composition, not dividend income alone.
Lesson 36.3 — Bonus Shares: The Dividend You Pay Tax On Before You Own It
Cash dividends are conceptually simple: money changes hands, five percent is skimmed, the rest lands in a bank account. Bonus shares — what NEPSE market participants universally call "bonus," and what the Companies Act and Income Tax Act more precisely treat as a capitalisation of reserves into additional paid-up capital — create no cash flow at all, and yet Nepali tax law insists on taxing them as if they were a cash dividend paid at the shares' face value.
Under Section 53(1) of the Income Tax Act, a company that capitalises its retained earnings and issues bonus shares to existing shareholders is treated, for tax purposes, as having distributed a dividend equal to the face value of those newly issued shares — in NEPSE's case, essentially always NPR 100 per share, since Nepali listed companies overwhelmingly maintain a par value of NPR 100. That deemed dividend is subject to the same five percent withholding rate as a cash dividend. The practical difference is that there is no cash dividend payment from which the company can simply net off the tax before crediting the shareholder — the "payment" is the share itself, credited electronically to the shareholder's demat account through the Central Depository System and Clearing Limited (CDSC).
Standard practice, developed jointly by the IRD, SEBON, and CDSC, requires the company to settle this five percent tax liability — computed on the aggregate face value of all bonus shares being issued — before CDSC will process the corporate action crediting bonus shares into shareholders' Meroshare-linked demat accounts. In most cases the company pays this tax from its own funds at the point of capitalisation (effectively reducing the reserves available for capitalisation by the tax amount, or treating it as a cost of the bonus issue borne centrally), so that shareholders receive the full declared bonus ratio in their demat accounts without an out-of-pocket cash payment. This is precisely why bonus-share credits to a shareholder's account are sometimes delayed relative to the AGM approval date — the company must first complete this tax settlement with the IRD before CDSC will release the corporate action.
WARNING
Retail investors frequently misread bonus shares as "free" shares with no tax consequence, unlike a right share issue where cash clearly changes hands. In fact, from the tax authority's point of view, a bonus share is a dividend, full stop — merely one paid in kind rather than in cash. The absence of a visible deduction on your own statement does not mean no tax was paid; it means the company paid it on your behalf, out of the reserves that would otherwise have funded a larger capitalisation. Understanding this matters most when it is time to sell those bonus shares, because of the cost-basis question addressed next.
Why the tax paid on bonus shares matters years later
The five percent tax settled at the time of bonus issuance is not merely an administrative footnote — it establishes the shareholder's cost basis in those bonus shares for future capital gains tax purposes. Because tax has already been paid on the face value of the bonus shares as deemed dividend income, the shareholder's acquisition cost for those specific shares is treated as their face value (NPR 100 per share), not zero. When those shares are eventually sold on NEPSE, capital gains tax — a separate tax addressed in its own right elsewhere in this book — is computed on the difference between the sale price and this NPR 100 cost basis, not on the full sale price. Investors who forget this and calculate their capital gains as if bonus shares had a zero cost basis systematically overstate their capital gains tax liability on every bonus share they eventually sell.
PRACTICAL TOOL
To reconcile your own bonus share tax history, log into Meroshare and pull your account's "Portfolio" and corporate action history for each scrip. Cross-reference the bonus ratio declared at each AGM against the shares actually credited to your demat account. The count should match the declared ratio exactly — if a 1:10 bonus was declared on your 500 shares, you should see 50 new shares credited, not 47 or 48. If your credited count consistently matches the declared ratio, that confirms the company settled the deemed-dividend tax centrally rather than reducing your share count — the far more common practice on NEPSE today. Keep a simple running spreadsheet of the AGM date, bonus ratio, shares received, and assumed NPR 100 per share cost basis for each tranche; you will need exactly this data when you eventually compute capital gains on a partial sale of a scrip you have held across multiple bonus cycles.
A worked illustration makes the mechanics concrete. Suppose an investor holds 1,000 shares of a fictional company, Himalayan Hydropower Ltd. (HHL), and the company's AGM approves a 15 percent bonus share issue.
Item
Calculation
Amount (NPR)
Bonus shares issued
1,000 x 15%
150 shares
Deemed dividend value
150 shares x NPR 100 face value
15,000
Tax withheld at 5%
15,000 x 5%
750
Shares credited to shareholder's demat
Full 150 shares (tax borne by company)
150 shares
Shareholder's cost basis per bonus share
Face value on which tax was paid
NPR 100
Total cost basis added to shareholder's holding
150 x NPR 100
15,000
Notice that the shareholder receives the full 150 shares and pays nothing directly, yet has legitimately established an additional NPR 15,000 of cost basis in HHL stock — a figure that will directly reduce capital gains tax owed whenever those 150 shares are sold.
Lesson 36.4 — Interest Income: Deposits, Debentures, and the Parallel Five Percent Regime
Interest income runs on a structurally similar track to dividend income, which is precisely why the two are grouped together in this chapter, but the underlying instruments differ, and Nepali retail investors typically encounter interest income from three sources relevant to a NEPSE-focused portfolio: bank and financial institution (BFI) fixed deposits and savings accounts held as a cash-management complement to an equity portfolio, listed corporate debentures issued by banks and select non-bank companies, and — less commonly for retail investors directly, though relevant through mutual fund structures — government and development bonds.
Interest paid by a resident bank or financial institution, licensed by Nepal Rastra Bank, to a natural person on a fixed deposit or savings account is subject to a five percent withholding tax under the same Schedule 1, Section 2(3) rate structure that governs dividends, and — critically — it is likewise designated a final withholding payment under Section 92 for an individual depositor who is not conducting a deposit-taking or lending business. The bank deducts the five percent before crediting interest to the depositor's account, remits it to the IRD, and the depositor's tax obligation on that interest ends there.
Listed debentures — the long-tenor, fixed-coupon instruments issued by commercial banks (and occasionally by non-bank corporates) and traded on NEPSE alongside equities — follow the identical pattern. The issuing company or its debenture trustee deducts five percent from each semi-annual coupon payment before crediting bondholders, and for an individual bondholder this is final. A retail investor holding, say, a bank debenture paying an 11 percent annual coupon receives, after withholding, an effective 10.45 percent net yield — a gap worth building into any yield comparison between debentures and dividend-paying equities, since the headline coupon rate quoted in offer documents is always the pre-tax figure.
REGULATORY DETAIL
The five percent rate on individual interest income has not always been treated as settled policy. The Finance Bill accompanying the 2080/81 budget floated a proposal to raise withholding on bank deposit interest, a move that drew immediate and vocal opposition from the banking sector and depositor advocacy groups on the grounds that it would discourage formal savings at a time when banks were already competing hard for deposits. The proposal was ultimately not carried through into the enacted rate structure, and the five percent rate has held continuously since. The episode is a useful reminder that these rates, while stable for years at a stretch, are not constitutionally fixed — they are Finance Act line items, revisited (and occasionally contested) every budget cycle, and a serious investor should re-verify the prevailing rate each fiscal year rather than assume permanence.
A subtlety worth flagging concerns the size and frequency of deposits. Some retail investors assume, incorrectly, that only "large" fixed deposits attract withholding, or that interest below some informal threshold escapes it entirely. There is no de minimis exemption of this kind in current law — the five percent applies to interest income of any size paid by a BFI to an individual depositor, from a schoolchild's minor savings account to a multi-crore fixed deposit. What changes with size is not the rate but the investor's exposure: for a retail investor parking large idle cash between share purchases in fixed deposits rather than deploying it in equities, the five percent final tax on interest is frequently more favourable than it might first appear, precisely because — as with dividends — it never compounds upward through the progressive slabs even for a taxpayer otherwise sitting in a high salary bracket.
CAUTION
Interest income from informal lending — money lent privately to friends, relatives, or informal cooperative arrangements outside the licensed BFI and debenture system — does not enjoy the same automatic final-withholding treatment, because there is no licensed withholding agent to deduct tax at source in the first place. Income of this kind generally must be self-reported and added to taxable income at the payee's own initiative, taxed at ordinary slab rates rather than a flat five percent. Investors who compare the after-tax return on a bank fixed deposit against an informal private loan at a higher headline interest rate are often comparing a genuinely final five percent tax against a self-assessed, slab-rate liability that can run considerably higher — the informal loan's superior stated rate can evaporate once true tax treatment is accounted for.
Interest source
Typical instrument
Withholding rate
Final for individual?
Frequency of deduction
BFI savings account
Any licensed bank/finance company
5%
Yes
At each interest credit (often monthly/quarterly)
BFI fixed deposit
3-month to 5-year FD
5%
Yes
At maturity or each coupon date
Listed bank debenture
NCD traded on NEPSE
5%
Yes
Semi-annual coupon date
Government/development bond (via mutual fund)
Held inside a mutual fund scheme
5% at fund level, pass-through to unit holder
Generally yes at unit-holder level
At fund distribution
Informal/private lending
Unregistered personal loan
None withheld
No — self-reported at slab rates
N/A (self-assessment)
Lesson 36.5 — Individual vs Institutional Investors: Same Rate, Different Weight
The five percent figure recurs so consistently across dividend and interest income that a careless reader might conclude the entire system is rate-neutral between an individual retail investor and an institutional one — a mutual fund, an insurance company, a bank's own investment portfolio, a provident fund. Nothing could be further from the truth, and the distinction matters enormously for anyone deciding whether to hold securities directly in their own name or through a corporate or institutional wrapper.
For an individual, as established across the preceding lessons, the five percent withheld on both dividends and interest is final. The tax liability begins and ends at five percent, full stop, regardless of the individual's other income.
For a resident company or other institutional taxpayer, the five percent withheld on interest income is merely an advance payment — a down payment credited against a much larger liability computed when the institution files its own annual return. Interest income earned by a company must be included in that company's taxable profit for the year and taxed at the applicable corporate rate: twenty-five percent for an ordinary industrial or trading company, but a materially higher thirty percent for banks, financial institutions, insurance companies, and other entities classified under the higher-rate schedule that specifically includes capital-market intermediaries such as merchant banks and stock brokerage firms. The five percent already withheld is simply netted off the final bill as a tax credit; the institution settles the remaining twenty to twenty-five percentage points itself.
Dividend income received by one resident company from another resident company is handled differently again, and more favourably, through Section 53(3) of the Income Tax Act, which excludes such inter-corporate dividends from the recipient company's taxable income altogether. The logic is to prevent the same underlying corporate profit from being taxed once at the paying company's level (corporate tax on profits before distribution) and again at full corporate rate at the receiving company's level — a cascading effect that would make holding companies and cross-shareholding structures prohibitively expensive to operate. The five percent withheld at the point of payment is not refunded to the recipient company, but because the dividend itself falls outside taxable income, no further tax is layered on top of it either — the five percent becomes, in effect, the terminal tax on that income stream for the corporate recipient too, just for a different statutory reason than the individual's Section 92 final-withholding treatment.
KEY CONCEPT
The identical five percent withholding rate produces three different real outcomes depending entirely on who receives the money: for an individual, it is a true final tax under Section 92; for a company receiving dividends from another resident company, it is effectively terminal via the Section 53(3) exclusion from taxable income; and for a company receiving interest income, it is merely a down payment against a twenty-five to thirty percent final liability. Reading a TDS certificate without knowing which of these three regimes applies to the recipient tells you almost nothing about the true tax burden actually borne.
This asymmetry has a direct, practical consequence for how Nepali households structure their investment holdings. A household that holds NEPSE shares and bank fixed deposits directly in the names of individual family members captures the full benefit of final withholding on both income streams. The same assets held inside a family-owned private company would face no incremental tax on dividend income received (protected by Section 53(3)) but would face a substantially higher effective rate on interest income, since interest earned by the corporate entity is fully taxable at twenty-five percent (or thirty percent, if the entity happens to be classified as a financial institution) rather than resting at a final five percent. This is one of the more concrete, numbers-driven reasons Nepali retail investors are consistently advised to hold interest-bearing instruments — fixed deposits, debentures — directly in personal names rather than through a corporate investment vehicle, even where a company structure might otherwise be attractive for equity holdings.
Recipient type
Dividend tax outcome
Interest tax outcome
Resident individual (personal investor)
5%, final (Section 92)
5%, final (Section 92)
Resident company receiving from another resident company
Excluded from taxable income (Section 53(3)); 5% withheld is terminal
Fully taxable at 25% (or 30% for BFIs/insurers); 5% withheld is a credit only
Approved retirement fund / EPF-type entity
Generally exempt under specific statutory exemption
Generally exempt under specific statutory exemption
Non-resident individual/entity
5%, subject to any applicable double tax avoidance agreement
Typically higher (commonly 15%), subject to any applicable treaty
REGULATORY DETAIL
Approved retirement and social security funds — the Employees Provident Fund, Citizen Investment Trust schemes, and similarly constituted approved retirement funds — occupy a distinct, more favourable tax position than ordinary institutional investors, generally enjoying exemption from tax on their investment income under the specific exemption provisions applicable to approved retirement funds. This is one reason such funds can offer contributors returns that look surprisingly competitive against direct retail investment once the retail investor's own (admittedly already-low) five percent final tax is accounted for — the fund itself is not paying the layered institutional tax that a private company holding the same instruments would face.
Lesson 36.6 — Reading Your Own Tax Trail: Records, Reconciliation, and Common Mistakes
Knowing the rates is only half the job; the other half is verifying, in your own paperwork, that what should have happened actually happened. Every NEPSE-listed company that pays a cash dividend is required to issue TDS documentation reflecting the amount withheld, and this information additionally flows through to the IRD against the shareholder's Permanent Account Number (PAN) where the shareholder's PAN has been correctly linked to their demat account. For interest income, banks similarly issue interest certificates on request, and increasingly make full-year interest and TDS summaries available through mobile and internet banking without a specific request being necessary.
The most common reconciliation failure among Nepali retail investors is not a miscalculation by the company or bank — those errors are rare — but a mismatch between the PAN linked to a demat/bank account and the PAN under which the investor eventually files (or would file, if required) an income tax return. An investor who opened a demat account years ago using an outdated PAN, a spouse's PAN by mistake, or no PAN at all, may find that TDS credits recorded by the IRD do not appear under the PAN they now use for other purposes. Because dividend and interest tax are typically final and do not require matching for annual return purposes in the way an advance-tax credit would, this mismatch usually does not cost the investor additional tax — but it does mean the investor's official tax record does not reflect income the investor genuinely earned, which can complicate matters years later if the source of accumulated wealth is ever questioned, for instance during a property purchase requiring a source-of-funds declaration.
PRACTICAL TOOL
Once a year — a natural moment is shortly after the close of the Nepali fiscal year in mid-July, once most AGMs and dividend distributions for the year have been completed — pull three things side by side: your Meroshare portfolio statement showing dividends and bonus shares credited scrip by scrip, your linked bank account statement showing net dividend credits, and your fixed deposit or debenture interest certificates from each bank. Confirm that every net cash dividend credit equals ninety-five percent of the gross rate declared at each company's AGM, and that every interest credit is net of a five percent deduction consistent with the gross rate quoted in your FD receipt or debenture prospectus. Any credit that does not reflect the expected five percent deduction is worth a direct query to the company's registrar or your bank — not because you owe more tax, but because either the withholding was handled incorrectly (rare, but not unheard of with smaller or newly listed companies still refining their registrar processes) or your own expectation of the gross rate was mistaken.
A second, subtler mistake worth naming explicitly: conflating the tax treatment of dividend and interest income with the tax treatment of capital gains realised when the underlying shares, debentures, or fund units are eventually sold. Nothing in this chapter's five percent withholding regime has any bearing on the separate capital gains tax due when an investor sells shares at a profit — that tax runs on its own rate structure, its own holding-period distinctions between short-term and long-term treatment, and its own filing mechanics, which are addressed on their own terms elsewhere in this book. An investor who has internalized "dividends are taxed at five percent, final" sometimes wrongly extends that comfort to capital gains, assuming no further paperwork or liability arises from selling shares that have paid regular dividends for years. The two are entirely independent tax events, triggered by entirely different transactions, and require independent tracking.
CAUTION
Do not treat a company's gross declared dividend rate — the headline percentage announced at the AGM and widely reported in Nepali financial media — as your actual cash yield without adjusting for the five percent withholding. A company declaring a "25 percent dividend" is declaring NPR 25 of dividend per NPR 100 share, before tax; your realised cash yield on that declaration is NPR 23.75 per share, a distinction that matters when comparing dividend yields across companies, or when comparing equity dividend yield against a bank fixed deposit's quoted interest rate, since both figures reported in the market are conventionally pre-tax and both are subject to the identical five percent haircut before cash reaches your account.
Chapter recap
Dividend and interest income earned by Nepali retail investors from NEPSE-listed shares, bank deposits, and listed debentures is taxed at a uniform five percent withholding rate under Schedule 1, Section 2(3) of the Income Tax Act, 2058, deducted at source by the paying company or bank before any cash reaches the investor. For an individual investor holding these instruments as personal savings rather than as inventory of an actual securities-trading business, this five percent is designated a final withholding payment under Section 92 — it fully discharges the investor's tax liability on that income, with no obligation to add dividends or bank interest into the progressive individual income tax slab calculation, regardless of how much other income the investor earns in a given year.
Bonus shares receive the same five percent treatment as cash dividends, but assessed on the face value of the newly issued shares (ordinarily NPR 100 per share on NEPSE) rather than on any cash sum, under the deemed-dividend rule in Section 53(1). In current market practice, the issuing company settles this liability centrally before CDSC credits the bonus shares to shareholders' demat accounts, so investors typically receive their full declared bonus ratio without an out-of-pocket cash payment. The tax paid at this stage is not a sunk cost, however — it establishes the shareholder's NPR 100-per-share cost basis in those bonus shares, a figure that directly reduces capital gains tax owed whenever the shares are eventually sold, and one that disciplined investors should track scrip by scrip and AGM by AGM.
Interest income from bank fixed deposits, savings accounts, and listed corporate debentures follows a parallel five percent, final-withholding structure for individual recipients, though the rate has not been entirely free of political contest — a 2080/81 budget-cycle proposal to raise bank-deposit withholding met sufficient pushback that it was not carried into the enacted rate, leaving five percent as the continuous standard since. Investors comparing headline yields across dividend-paying equities, fixed deposits, and debentures should remember that every quoted gross rate in Nepali financial media is a pre-tax figure, and that the realised, spendable yield on any of these instruments is consistently ninety-five percent of the headline number.
The uniformity of the five percent rate across recipient types conceals a significant divergence in real outcomes. For individuals, five percent is genuinely final on both dividend and interest income. For a resident company receiving dividends from another resident company, Section 53(3) excludes the dividend from taxable income entirely, making the five percent withheld effectively terminal as well, though for a different statutory reason. But for a resident company or institutional investor earning interest income, the five percent withheld is only an advance credit against a full corporate tax liability of twenty-five percent for ordinary companies or thirty percent for banks, financial institutions, and insurers — a gap large enough to materially influence whether interest-bearing instruments are best held directly in individual names or through a corporate structure. Approved retirement funds and similar statutorily exempt vehicles occupy a still more favourable position, generally exempt from tax on this investment income altogether.
Because most of this taxation happens invisibly at source, the retail investor's remaining responsibility is verification rather than computation: confirming that net dividend credits equal ninety-five percent of AGM-declared gross rates, that bonus share counts match declared ratios in full, and that interest credits from banks and debenture trustees reflect the expected five percent deduction. Investors should also ensure the PAN linked to their demat account and bank accounts is current and correctly matched to the PAN under which they would file any income tax return, since a mismatch — while rarely costing additional tax given the final nature of this withholding — can complicate future source-of-funds questions even when no further tax is actually owed.
Finally, nothing in this chapter's five percent regime touches the separate question of capital gains tax due when shares, bonus shares, or debentures are eventually sold at a profit. Dividend and interest taxation is a tax on income received while holding an asset; capital gains taxation is a wholly separate tax on the profit realised when that asset changes hands, governed by its own rates, holding-period rules, and filing mechanics. Keeping these two tax events conceptually and administratively distinct — and keeping the cost-basis records built through years of bonus share credits ready for that eventual sale — is the discipline that separates an investor who merely collects dividends from one who manages a genuinely tax-efficient Nepali equity portfolio.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VII · Chapter 37
Tax Treatment of Rights and Bonus Shares
First published 23 Aug 2026 · Last verified 29 Aug 2026
Ramesh had followed the textbook advice from the previous chapter to the letter. When Karnali Finance Ltd. declared a 15 percent stock dividend, he watched his DEMAT statement update, saw the 5 percent tax deducted at source on the deemed dividend value, and filed the withholding certificate away as instructed. Six months later, when Himalchuli Cement Ltd. announced a 1:1 rights issue at par, he transferred the money through his bank's Connect IPS account, received his new shares, and assumed the matter was closed. It was not. When he eventually sold both blocks of shares, his broker's contract note showed two entirely different capital gains figures, calculated on two entirely different cost bases, and neither number matched what he had expected from either transaction. The confusion was not Ramesh's fault — it reflects a genuine, still-unsettled area of Nepali tax administration, one that has produced regulatory directives, investor protests on the trading floor, and at least one government task force. This chapter untangles it, lesson by lesson, so that you never have to discover the difference between a rights share and a bonus share at the moment your broker hands you a tax bill.
Lesson 37.1 — Two Different Doors: How Rights and Bonus Shares Enter the Tax System
Chapter 36 established the core rule for bonus shares: when a company capitalises reserves and distributes additional shares to existing holders, the Inland Revenue Department treats this exactly as it treats a cash dividend. Under the dividend-withholding framework of the Income Tax Act, 2058 — administered through Section 88 provisions on dividend distribution — a 5 percent withholding tax applies to the value of the bonus at the moment of issuance, and that 5 percent becomes a final tax for a resident individual investor. No further income tax return disclosure is required on that amount. This is settled law and settled practice, and every listed company in Nepal that has ever declared a stock dividend has had to work out, cycle after cycle, how to raise the cash to pay that withholding when the "dividend" itself arrived in the form of paper, not rupees.
Rights shares operate on an entirely different logic, and this is the piece the previous chapter deliberately left aside. A rights issue is not a distribution of the company's retained earnings to you. It is an offer to buy new shares, at a price the company sets (commonly at par value, sometimes at a premium), using your own after-tax money. Because you are handing over cash you already earned and already paid tax on — through your salary, your business income, or an earlier capital gain — there is no "income" event for the tax authority to tax at the moment you subscribe. You are simply converting cash into a different asset, the same as if you had bought additional shares of the same company on the secondary market. No withholding tax applies to a rights subscription. No deemed-dividend character attaches to it. The tax event, for a rights share, is deferred entirely to the day you eventually sell.
This distinction matters practically because it determines two different things for you as an investor: whether tax is owed today, and what number gets written into the ledger as your cost basis for tomorrow.
KEY CONCEPT
A bonus share is a transfer of the company's money to you, taxed like a dividend at issuance. A rights share is a transfer of your money to the company, taxed like any other purchase — meaning not at all, until you dispose of it. Confusing the two is the single most common error retail investors make when reconciling their annual capital gains statements.
Feature
Rights Share
Bonus Share
Nature of the transaction
Purchase of new shares using investor's own cash
Capitalisation of company reserves; no cash from investor
Tax event at issuance
None
Deemed dividend, 5% final withholding tax
Base of the withholding tax
Not applicable
Face value (or announced bonus value) of shares issued
Who remits the tax
Not applicable
Company, on behalf of shareholder, before crediting shares
Cost basis created
Issue price actually paid, plus allocable transaction costs
Value already subjected to dividend withholding (typically face value)
Tax event at eventual sale
Capital gains tax on (sale price − issue price)
Capital gains tax on (sale price − established cost basis)
Lesson 37.2 — Rights Shares: Establishing Cost Basis From Day One
Because a rights subscription is a genuine purchase, its cost basis is built the same way any purchase's cost basis is built: the price paid, plus the costs you incurred to acquire the asset. For a rights issue this typically includes the per-share issue price set by the company (frequently Rs 100 per share at par, though premium rights issues do occur), any bank service charge or ASBA (Applicant Supported by Blocked Amount) processing fee deducted at the time of application, and any DP (Depository Participant) charge associated with crediting the shares. None of this is exotic — it is the same logic Chapter 34 used for ordinary secondary-market purchases — but investors routinely forget to carry these small charges forward, and over a multi-year holding period the omission compounds.
Worked example: Suppose you hold 500 shares of Himalchuli Cement Ltd., and the company announces a rights issue in the ratio of 1:2 (one new share for every two held) at an issue price of Rs 100 per share. You are entitled to 250 new shares.
This Rs 100.04 per share is what enters your holding's weighted average cost calculation — not zero, not the current market price of the parent share, and not the face value used for bonus shares. It is simply what you paid, itemized the same way a cash purchase would be.
A subhead worth pausing on:
What if you don't subscribe
Every rights entitlement carries three choices: subscribe fully, subscribe partially, or let the entitlement lapse (or, where the company and depository system permit it, renounce/sell the entitlement to another investor before the subscription window closes). Each choice has a distinct tax consequence.
If you simply let the entitlement lapse — take no action — you incur no tax event, but you also permanently dilute your percentage ownership, since other shareholders' capital is added to the company while yours is not. There is no cost basis question here because no asset was acquired.
If you renounce or sell your rights entitlement (where the DP system and the issuing company support a tradable rights ticket, as is increasingly common for larger rights issues routed through the CDS and Clearing system), the proceeds you receive for that entitlement are treated as a capital gain in their own right, because your cost basis in the "right to subscribe" itself is effectively nil — you did not pay anything separately to acquire the entitlement; it arose automatically from your existing shareholding. The entire sale proceeds of a renounced rights entitlement are therefore exposed to capital gains tax, calculated at the short-term or long-term rate depending on how long you had held the underlying parent shares that generated the entitlement.
WARNING
Selling your rights entitlement feels like "found money" because you never had to reach for your wallet — but the tax authority treats the full proceeds as taxable gain, with no offsetting cost basis to shrink the bill. Investors who let a broker sell an entitlement without withholding an amount for tax frequently discover the liability only when the annual reconciliation arrives.
Lesson 37.3 — Bonus Shares Revisited: From Deemed Dividend to Cost Basis
Chapter 36 walked through the mechanics of the withholding calculation itself — the gross-up formula that requires a company to divide the announced bonus value by 0.95 to determine the pre-tax distribution, then withhold 5 percent of that grossed-up figure. This chapter will not repeat that arithmetic. What it must add, because the earlier chapter stopped short of it, is what happens to that already-taxed value afterward: how it becomes your cost basis, and why the Nepali tax administration has struggled — publicly and repeatedly — to settle on the correct number.
The logic, in principle, is straightforward. If the value of a bonus share has already been taxed once, as a deemed dividend, then that same value should become your cost basis for the share going forward. When you eventually sell, capital gains tax should apply only to the appreciation above that already-taxed value — not to the entire sale proceeds. This is the only construction that avoids taxing the same rupee of value twice.
In practice, "the value of a bonus share" for this purpose has historically been anchored to the share's face value (commonly Rs 100 for most NEPSE-listed companies, though some older listings carry different paid-up denominations), because the deemed-dividend withholding itself is calculated against that face value at the point of capitalisation, not against the fluctuating market price.
REGULATORY DETAIL
In Jestha 2075 (June 2018), the Inland Revenue Department issued a directive instructing NEPSE and the depository system to calculate capital gains on bonus and rights shares using the paid-up (face) value of the share as the cost basis, replacing an older practice under which brokers had been using an "average base price" derived from market data. For a company with a standard Rs 100 face value, this meant every bonus share's cost basis was fixed at Rs 100 regardless of what the market price had done since issuance — a materially higher basis than under the prior average-price method for most growth stocks, but still a basis, not zero.
The directive triggered an immediate and visible backlash. Retail investors, through their associations, argued the change had been imposed without adequate notice and that it interacted badly with how brokers' systems were already tracking cost. Trading activity was disrupted, and the government responded within days by postponing implementation and forming a task force to review the formula before any change took effect. The episode is worth knowing not because the specific 2075 directive is still the live rule in every particular — practice has continued to evolve since, and DP-registered WACC figures vary by broker system — but because it crystallises the exact fault line investors keep hitting: is the cost basis of a bonus share its face value, or is it zero?
Why the answer matters enormously
If your bonus share's cost basis is recorded as its face value (say Rs 100), and you later sell it at Rs 850, your taxable capital gain is Rs 750 per share.
If your bonus share's cost basis is instead recorded as zero — as some broker calculators and even some DP account statements default to, treating "you paid nothing for it" too literally — your taxable capital gain balloons to the full Rs 850 per share.
The difference is not academic. At a long-term capital gains rate, the zero-basis treatment can nearly double your effective tax bill on the bonus portion of your holding, and it does so on top of the 5 percent dividend withholding you already paid when the bonus was issued. This stacking is precisely the "double taxation" complaint that recurs in investor forums, brokerage seminars, and Finance Bill consultations year after year.
CASE IN POINT
Consider an investor holding 1,000 shares of a hypothetical company, Sagarmatha Hydro Ltd., who received a 10 percent bonus (100 new shares) in a year when the company's face value was Rs 100. The company withheld dividend tax of roughly Rs 5,263 on the grossed-up bonus value (100 shares × Rs 100 ÷ 0.95 × 5%). Two years later, the investor sells those 100 bonus shares at a market price of Rs 620 each — proceeds of Rs 62,000. Using face-value cost basis (Rs 100 × 100 = Rs 10,000), the taxable capital gain is Rs 52,000, taxed at the applicable long-term rate. Using zero cost basis, the taxable gain is the full Rs 62,000. The difference in taxable gain — Rs 10,000 — is exactly the value already taxed once as a deemed dividend. Face-value basis prevents that double count; zero basis does not.
The prudent position for an investor — and the one this book recommends you hold your broker and DP records to — is that the face value already subjected to the 5 percent withholding must carry forward as cost basis. Keep the TDS (tax deducted at source) certificate the company issues at the time of the bonus distribution; it is your documentary proof that this value has already been taxed, and it is the evidence you would present if a broker's system defaults to zero and you need to have it corrected.
Lesson 37.4 — WACC Mechanics: Blending Purchases, Rights, and Bonus Into One Average Cost
Nepali capital gains tax on listed shares is calculated on a script-by-script (company-by-company) basis, and within a single script, on a single blended weighted average cost — not on a first-in-first-out layer-by-layer basis, and not by treating each acquisition (original purchase, rights allotment, bonus allotment) as a separately taxed lot. This is what market participants and brokers universally refer to as "WACC" — weighted average cost — and it is the number your DP account and MeroShare portfolio are expected to carry for every script you hold.
The formula is simple in isolation:
WACC = Total Cumulative Cost of All Units Held ÷ Total Units Held
The complexity comes from the fact that every corporate action — a fresh purchase, a rights allotment, a bonus allotment — changes both the numerator and the denominator, and the two share types change them in structurally different ways:
A rights allotment adds units and adds a proportionate amount of real cost (what you paid), so it can push the WACC either up or down depending on whether the rights issue price is above or below your existing WACC.
A bonus allotment adds units and adds only the already-taxed face value as cost (not zero, per Lesson 37.3, but also not the market price), which is virtually always below your existing WACC, so a bonus issue almost always pulls your average cost per share downward.
Worked example, step by step
Assume an investor's activity in a single script, Himal Hydro Ltd., across three years:
Notice the direction of each move. The rights allotment, priced well below the prevailing WACC of Rs 401.60, pulled the average down to Rs 301.10 — a rights issue priced at a discount to your cost base will always do this, and a rights issue priced above your existing WACC would push it up instead. The bonus allotment then pulled the average down further, from Rs 301.10 to Rs 282.82, because the face-value cost of Rs 100 per bonus share is virtually always below whatever the blended average happens to be at that point — this is close to a mechanical certainty for any company whose share price trades above face value, which is the overwhelming majority of the exchange. The final market purchase, executed above the prevailing WACC, pushed the average back up to Rs 330.25.
This single blended figure — Rs 330.25 in the example above — is what your broker's system will apply against the sale price of any shares of Himal Hydro Ltd. you dispose of afterward, regardless of whether the specific shares being sold happen to be original purchases, rights shares, or bonus shares. The tax system does not ask "which shares are these"; it asks "what is your current average cost across this entire script," and taxes the difference between that average and your sale price.
PRACTICAL TOOL
MeroShare and most brokerage back-office systems display a running WACC per script under portfolio holdings. After every rights or bonus credit, check that this figure has actually updated — some DP systems lag a settlement cycle behind a corporate action, and a stale WACC (one that hasn't yet absorbed the new units and cost) will misstate your capital gains on any sale executed in that window. When in doubt, recompute the four-column table above by hand from your own transaction history; it takes minutes and it is the only way to catch a system that has defaulted a bonus lot to zero cost instead of face value.
CAUTION
Because bonus shares mechanically drag WACC downward and rights shares can move it in either direction, two investors who bought the same script on the same day, at the same price, can end up with materially different tax bills years later purely because one participated in a rights issue and the other let the entitlement lapse. Do not assume your WACC matches a friend's or a forum post's "typical" figure for a script — it is a function of your own transaction history and must be reconstructed from your own contract notes and allotment letters.
Lesson 37.5 — The Double-Taxation Debate and the Current Rate Environment
The debate over whether a bonus share's cost basis should be its face value or zero is not a settled historical footnote — it is a live undercurrent in how Nepali investors and the tax administration relate to each other, and it resurfaces every time the government revisits capital gains policy. Understanding the shape of the argument is more useful to you than memorising any single year's administrative position, because the position has moved before and will likely move again.
The investor-side argument runs as follows: a bonus share is not free money. The company created it by capitalising reserves that, in most cases, represent past retained profits — profits the company itself had already paid corporate income tax on. The shareholder is then taxed a second time, at 5 percent, when the reserve is converted into a share and credited to the shareholder's account, on the theory that this is economically equivalent to a cash dividend. If the same value is taxed a third time at full capital gains rates when the share is eventually sold — because the DP or broker system recorded its cost basis as zero rather than as the face value already subjected to withholding — the investor is being taxed three times on a single stream of value: once at the corporate level, once as a deemed dividend, and once again as if the share cost nothing at all. It is this third layer, specifically, that investors and their associations have objected to whenever a zero-basis or aggressive-basis directive has been floated.
The tax administration's counter-perspective is that capital gains tax is not really "on the same value" a third time — it is a tax on the appreciation of the asset since the point the investor's holding cost was fixed, and the entire policy question reduces to what that fixed point should be. Using face value keeps that fixed point anchored to the amount already taxed as dividend; using a market-linked average base price (the pre-2075 practice) or zero (the practice some systems still default to) shifts that fixed point elsewhere, with correspondingly different revenue and equity consequences. This is precisely the tension the 2075 directive, the subsequent investor protest, and the government's task force were convened to resolve, and it explains why you should treat "cost basis of a bonus share" as a topic to verify against your own documentation each time you file, rather than a fact you can assume is fixed for all time.
Layered onto this structural debate is a separate, more recent development: the headline capital gains tax rate itself has just risen. Under the Finance Bill presented for fiscal year 2083/84 (the current fiscal year at time of writing, effective from Shrawan 1, 2083 — mid-July 2026), the rate applied to gains on listed shares held for one year or less rose from 7.5 percent to 10 percent, and the rate on gains from shares held for more than one year rose from 5 percent to 7.5 percent. Gains on unlisted company shares continue to be taxed at a flat 10 percent regardless of holding period. For an individual, non-commercial investor, this remains a final withholding tax deducted by the broker at settlement — you are not required to separately disclose it on an income tax return, and it is not added to your other income for slab-rate purposes, provided your share transactions do not rise to the level of a commercial trading business (a distinction the Department has separately confirmed applies the ordinary, natural-investor rules to small, non-commercial holders rather than folding them into a business-income framework).
REGULATORY DETAIL
Current capital gains tax rates on listed shares for a resident individual, effective FY 2083/84 (from mid-July 2026): 10 percent on gains where the shares were held 365 days or less; 7.5 percent on gains where the shares were held more than 365 days. This replaces the 7.5 percent / 5 percent structure that applied in FY 2082/83. Unlisted shares remain taxed at a flat 10 percent irrespective of holding period. Because the rate increase raises the tax consequence of every rupee of taxable gain, it also raises the stakes of getting the underlying cost basis — face value versus zero, for bonus shares especially — correct.
The practical takeaway for you, as an investor rather than a policy analyst, is this: the rate you pay has just increased, which means an error in your cost basis now costs you more than it would have a year ago. A bonus lot mistakenly recorded at zero cost, taxed at the new 7.5 percent long-term rate instead of correctly reflecting its face-value basis, produces a materially larger overpayment than the identical error would have produced under last year's 5 percent rate. This is the moment to actually check your numbers, not the moment to assume the broker's system has it right.
Lesson 37.6 — Practical Filing and Recordkeeping Checklist for Rights and Bonus Shares
Everything in this chapter converges on a single behavioural recommendation: keep your own paper (or digital) trail for every rights and bonus event, independent of what your broker's or DP's system displays, because you — not the intermediary — bear the consequence if the wrong number is used at the point of sale.
For every rights issue you subscribe to, retain: the rights allotment/share certificate confirmation from the company or registrar, the bank debit advice or ASBA confirmation showing the exact amount paid, and any DP or bank charges levied on the application. Together these establish your cost basis for that lot beyond dispute.
For every bonus issue you receive, retain: the company's board/AGM resolution announcing the bonus percentage and the share's face value at the time, and — most importantly — the TDS certificate or equivalent withholding confirmation showing the 5 percent dividend tax was deducted and deposited. This certificate is your evidence that the face value has already been taxed, and it is what you would present to a broker or to the Department if a system default understates your cost basis.
When you eventually sell only part of a script's total holding — some original shares, some rights shares, some bonus shares, accumulated over several years — remember that Nepali practice applies a single blended WACC across the entire script, not a lot-by-lot or first-in-first-out selection. You do not get to choose to sell "the high-cost lot first" to minimise tax; the average is applied uniformly. This makes maintaining an accurate, current WACC — recalculated after every corporate action, as demonstrated in Lesson 37.4's worked table — more important than tracking individual lots, since individual lots do not survive as separate tax objects once they enter your holding.
Document to Retain
Applies To
Purpose
Bank debit advice / ASBA confirmation
Rights shares
Establishes cash cost paid, forms part of cost basis
Company AGM/board resolution on bonus
Bonus shares
Confirms face value and bonus ratio at time of issue
TDS/withholding certificate
Bonus shares
Evidence that face value has already been taxed as deemed dividend
Broker contract note (purchase)
All original purchases
Establishes cost basis for purchased lots, including brokerage/SEBON/DP charges
DP/MeroShare portfolio statement
All holdings
Should reflect running WACC; verify after every corporate action
Broker contract note (sale)
All disposals
Confirms sale price and TDS withheld at the rate applicable to your holding period
PRACTICAL TOOL
Before every tax year closes, reconstruct your WACC for each script by hand using the four-column method from Lesson 37.4 — opening units and cost, each addition (purchase, rights, bonus) with its own units and cost, running cumulative totals, and the resulting average — and compare it against what MeroShare or your broker's statement currently shows. A mismatch, caught before you sell, is a correction. A mismatch caught after you sell is a tax dispute.
Chapter recap
Rights shares and bonus shares enter the Nepali tax system through two structurally different doors, and confusing them is the root of most investor confusion at sale time. A rights share is a cash purchase like any other: no tax event arises at issuance, and its cost basis is simply what you paid, including the issue price and any ASBA or bank charges. A bonus share, by contrast, is a deemed dividend distribution of the company's own reserves, taxed at a 5 percent final withholding rate on its face value at the moment of issuance — a rule Chapter 36 established and this chapter has built directly upon rather than repeated.
The genuinely unresolved question this chapter has surfaced is what happens to that already-taxed bonus value afterward. The defensible, double-taxation-avoiding answer is that the face value already subjected to the 5 percent withholding should carry forward as the share's cost basis for capital gains purposes — a position the Inland Revenue Department itself adopted through its Jestha 2075 directive, before investor protest and a government task force forced a pause on implementation. Some broker and DP systems, in practice, still default bonus lots to a zero cost basis, which produces a genuine double (arguably triple) taxation outcome that this chapter has quantified: the same value taxed once at the corporate level, again as a deemed dividend, and again in full at the point of sale. Retaining your TDS certificate is your defence against this error.
The weighted average cost (WACC) mechanism is the single lens through which all of this resolves at the point of sale. Every rights allotment and every bonus allotment feeds into one blended cost figure per script — not a set of separately tracked lots — and the worked table in Lesson 37.4 demonstrated the mechanical direction of each: bonus shares almost always pull WACC down, because face value sits below the market-linked average for nearly every listed company; rights shares can push WACC in either direction depending on whether the subscription price sits above or below your existing average.
Layered on top of these structural mechanics is a live and rising rate environment. Effective from fiscal year 2083/84 (mid-July 2026), the capital gains tax on listed shares rose to 10 percent for holdings of 365 days or less and 7.5 percent for holdings beyond that threshold, up from 7.5 percent and 5 percent respectively the year before — a final withholding tax for the ordinary, non-commercial individual investor, deducted automatically by your broker at settlement. This increase raises the cost of any cost-basis error, which is precisely why the recordkeeping discipline in Lesson 37.6 — retaining allotment confirmations, withholding certificates, and a hand-reconstructed WACC — matters more this year than it did the year before.
Finally, treat the rights-versus-bonus and face-value-versus-zero questions as areas of continuing regulatory evolution rather than permanently settled fact. The 2075 directive, its reversal under investor pressure, and the subsequent task force review demonstrate that Nepali capital markets tax policy on this specific question has moved before, sits on a genuine and still-debated equity question, and will very plausibly move again as the Finance Act is amended in future budget cycles. Your obligation as an investor is not to memorise this year's answer as eternal truth, but to keep the underlying documents that let you verify — and, if necessary, contest — whatever cost basis your broker's system assigns you.
The chapter that follows turns from these two specific corporate actions to the broader discipline of consolidating a full-year capital gains position across an entire portfolio of scripts, each potentially carrying its own history of purchases, rights, and bonuses, into the single reconciled figure your annual tax position depends on.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VII · Chapter 38
Tax Planning for the Long-Term NEPSE Investor
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 38.1 — The Architecture of Equity Taxation: What You Are Actually Planning Around
Consider a Kathmandu investor holding twenty-two scrips across two brokers, three IPO allotments still sitting untouched since 2079, and a spreadsheet that has not been updated since Dashain. Ask this investor what their effective tax rate on NEPSE gains is, and you will likely get a shrug, or a number that is simply wrong — because in Nepal, unlike salary or business income, capital gains and dividends on listed shares are not taxed on a slab. They are taxed on a schedule, deducted before the money ever reaches your bank account, and the "planning" that matters is not about deductions or exemptions in the way a salaried employee thinks about them. It is about timing, documentation, and account architecture — three levers this chapter will take in turn, after first re-establishing exactly what law is in force today, because the rate itself changes almost every fiscal year and a plan built on last year's number is not a plan at all.
As of the writing of this chapter, in Bhadra 2083 (August 2026), Nepal is operating under the tax structure introduced by the Finance Act 2083, effective from Shrawan 1, 2083 (17 July 2026) — the start of the current fiscal year, FY 2083/84. That structure raised capital gains tax (CGT) on listed shares for resident natural persons for the second time in three fiscal years, and the direction of that change is itself a planning lesson: rates in Nepal move, usually upward, and they move at the boundary of the fiscal year, which means the single most consequential tax-planning date on your calendar is not April 15 as it would be for a US investor — it is the last week of Ashad, when the government's budget speech telegraphs what the new fiscal year's Finance Bill will contain.
REGULATORY DETAIL
Effective Shrawan 1, 2083 (17 July 2026) under the Finance Act 2083, capital gains tax on listed shares for resident natural persons stands at 7.5 percent for shares held more than 365 days ("long-term") and 10 percent for shares held 365 days or fewer ("short-term"). This replaced the FY 2082/83 structure of 5 percent and 7.5 percent respectively — a flat 2.5-percentage-point increase at both ends of the holding-period spectrum. Dividend income on listed shares continues to be taxed at a flat 5 percent, withheld by the distributing company. Both are final withholding taxes for the ordinary retail investor: they are deducted at source and are not, in the normal case, added to your other income or reconciled again at slab rates.
The table below anchors the two most recent fiscal years side by side, because you will need both numbers: the current one to plan your next sale, and last year's to sanity-check any older Mero Share contract notes you are reconciling for cost-basis purposes.
Category
FY 2082/83 (ended Ashad 2083)
FY 2083/84 (current)
Resident individual, listed shares, held >365 days
5.0%
7.5%
Resident individual, listed shares, held ≤365 days
7.5%
10.0%
Dividend on listed shares (resident individual)
5.0% (final)
5.0% (final)
Resident entity/institution, listed shares
10.0% (flat, no holding-period benefit)
10.0% (flat, no holding-period benefit)
Resident individual, unlisted shares
10.0%
10.0%
Two structural features of this table deserve emphasis before we go further, because everything else in this chapter is built on them.
The holding-period discount is a natural-person benefit only. A company, a mutual fund's own trading book, or any "other entity" pays a flat rate on its listed-share gains regardless of how long it held the position. This matters enormously for how you think about pooled vehicles, which we return to in Lesson 38.6 — the long-term/short-term distinction that rewards patience is a privilege of investing as an individual, not something you get more of by wrapping your holdings in a corporate structure.
Second, the withholding is computed and deducted by your stockbroker directly from sale proceeds at the moment of settlement, through the CDSC (Central Depository System and Clearing Limited) and TMS (Trading Management System) infrastructure your broker uses. You do not calculate this yourself, you do not remit it yourself, and for the overwhelming majority of retail investors, you do not report it again on an income tax return. This is what "final withholding tax" means in practice: the number on your contract note is, for tax purposes, usually the end of the story.
KEY CONCEPT
A "final withholding tax" is one where the amount deducted at source settles the taxpayer's liability completely — it is not a prepayment reconciled later against a broader tax return, the way TDS on a salary is reconciled at year-end. For a genuine retail investor, CGT on share sales and dividend tax on share income are both final. This is precisely why holding-period timing, not deduction-hunting, is where nearly all of your legitimate tax planning leverage sits.
The word "genuine" in that callout is doing real work, and it is the subject of Lesson 38.4. Because there is a second classification lurking behind the final-tax regime — the distinction between a "non-commercial" or "natural" investor and a "commercial" one — and it is a distinction that can strip away the comfortable finality of the withholding tax altogether. Before we get there, though, we need to deal with the lever every investor reaches for first: the calendar.
Lesson 38.2 — Engineering the Long-Term Rate: The 365-Day Discipline
The 2.5-point gap between the short-term and long-term CGT rate is not a rounding difference. On a genuine long-term winner — say a position that has appreciated 150 percent over three years — the difference between paying 7.5 percent and 10 percent on that gain is the difference between keeping roughly 92.5 percent of your profit and keeping 90 percent of it. On a large, concentrated position built over several IPO cycles and bonus-share accumulations, that gap can run into tens of thousands of rupees on a single sale. This is the one number in Nepali equity taxation that is entirely, 100 percent within your control, because you — not the market, not the company, not the IRD — decide the day you sell.
The one-day trap
The holding period is measured from settlement date to settlement date, not the date you placed the buy or sell order. In NEPSE's T+1 settlement environment, this means the clock that determines whether you cross into "long-term" territory is anchored to when the shares were actually credited to your demat account, and when they are actually debited on sale. An investor who buys on Poush 10 and reflexively assumes their one-year mark falls exactly a year later on the calendar may be off by a day or two once settlement lag is accounted for — and missing the 365-day threshold by even a single day means the entire gain reverts to the short-term rate, not just the marginal portion. There is no pro-rating. It is a cliff, not a slope.
WARNING
The 365-day threshold is a hard cliff, not a gradual scale. A position sold on day 365 (or whatever day count places it just inside the short-term window under settlement-date accounting) is taxed entirely at the higher short-term rate — there is no partial credit for "almost" being long-term. Before executing a sale near the anniversary of a purchase, confirm the exact settlement date from your Mero Share transaction history rather than counting from the order date or your own memory of "around Poush."
This becomes considerably more complicated — and more consequential for planning — when you have bought the same scrip in multiple tranches. Suppose you accumulated a position in a hydropower company across four purchases: an IPO allotment, a subsequent market purchase eight months later, a rights-share allotment, and a further top-up eighteen months after the first purchase. If you now want to sell half your holding, which shares are you deemed to be selling — the oldest, the newest, or some blend? This is where Nepal's practice diverges from what many investors assume from books written about US or Indian markets. Nepali brokers and the CDSC infrastructure generally do not operate a strict "first-in-first-out" lot-selection system that lets an investor cherry-pick which specific tranche of a scrip is being disposed of at sale time, the way sophisticated brokerage platforms in the US allow "specific lot identification." Bonus and rights shares in particular are folded into your holding on a weighted-average cost basis rather than tracked as discrete, separately-dated parcels — a methodology the government itself shifted to back in FY 2076/77 specifically to simplify what had been an unworkable per-scrip, per-lot calculation.
The practical consequence is this: you cannot, in general, instruct your broker to "sell only the shares I bought fourteen months ago and keep the ones I bought two months ago" within the same scrip and expect the system to track and tax them separately by acquisition date for holding-period purposes in the way a US investor manages lots. What you can control is the aggregate timing of your sale order itself, and — where you hold the same underlying business across genuinely separate scrips or share classes, or across separate purchase events far enough apart that a partial sale clearly straddles the anniversary — you can sequence a larger disposal into two tranches: one executed before the anniversary date and one after, so that at least the second tranche unambiguously qualifies for the long-term rate.
CASE IN POINT
An investor holding 2,000 shares of a commercial bank, acquired in a single lot 340 days ago, is contemplating a full exit ahead of an anticipated correction. Selling today locks in the short-term rate (10 percent under FY 2083/84 law) on the entire gain. Waiting 25 more days — assuming no material change in the investment thesis — converts the entire position to the long-term rate (7.5 percent). On a gain of NPR 800,000, that 2.5-point difference is NPR 20,000 in tax saved for the cost of three and a half weeks of additional market exposure. The decision to wait is a legitimate, lawful tax-planning choice — provided the underlying investment case still supports holding for those 25 days. Letting the tax tail wag the investment dog in the other direction — holding a deteriorating business past its logical exit point purely to save 2.5 points — is the mirror-image mistake this book has warned against since Part I.
That last sentence deserves to be a standalone principle, because it is the single most common tax-planning error among NEPSE retail investors who have absorbed only half the lesson: the long-term rate is a reward for conviction, not a reason to manufacture conviction you do not have. A business whose fundamentals have deteriorated — declining net interest margin at a bank, a hydropower asset facing a tariff dispute, a hotel group bleeding cash — does not become a better holding because you are 40 days short of the 365-day mark. The tax saved by waiting is real; the capital lost by waiting for a genuinely bad reason is usually larger and compounds against you daily, while the tax differential is fixed and one-time.
A second, subtler timing consideration involves fiscal year boundaries rather than the 365-day holding threshold. Because tax rates in Nepal are set annually and have moved upward in each of the last several budget cycles, an investor who is already past the long-term threshold and is simply deciding when, within a given month, to execute a planned sale should be aware that a Finance Bill effective from the coming Shrawan 1 could raise rates further. There is no way to know the contents of a Finance Bill with certainty before the budget speech (traditionally delivered around Jestha 15, roughly six weeks before the new fiscal year begins), but the pattern of the last two budgets — both raising CGT — is itself information. An investor who is indifferent on pure investment grounds between selling in early Ashad (before fiscal year-end) versus waiting until Shrawan (into the new fiscal year) has, in recent history, been better served tax-wise by not waiting for a new fiscal year to begin, given the direction rates have moved. This is not a rule that will hold forever — rates could as easily fall in a future budget aimed at reviving capital markets, as happened in FY 2076/77 when the rate was cut from 7.5 percent to 5 percent for long-term individual holders. The discipline to take from this is procedural, not directional: track the budget speech every Jestha as closely as you track your own portfolio, because it is the single external event most likely to change your after-tax return on a pending sale.
Lesson 38.3 — Losses, Carryforwards, and the Real Limits of "Tax-Loss Harvesting" in Nepal
Every investor who has read a US personal-finance book has encountered the concept of tax-loss harvesting: deliberately realising a loss on a losing position to offset a gain realised elsewhere in the same tax year, reducing the net taxable gain across the whole portfolio. It is tempting to import this concept wholesale into NEPSE planning. It would be irresponsible for this book to let you do so without a serious caveat, because the mechanical reality of how Nepal's CGT is administered makes portfolio-level loss harvesting far less automatic — and far less reliable — than its American counterpart.
Recall from Lesson 38.1 that CGT on listed shares is withheld at source, scrip by scrip, transaction by transaction, at the moment of settlement. Your broker's system calculates the gain on the specific sale you just executed — sale proceeds minus purchase cost minus the transaction costs attributable to that trade (broker commission on both legs, the SEBON regulatory fee, and CDSC/DP charges) — and withholds tax on that transaction alone. It does not, at the point of withholding, look across your entire portfolio for the year and net your gains against your losses before calculating what to withhold. If you sell Scrip A at a loss on the same day you sell Scrip B at a gain, your broker withholds CGT on Scrip B's gain in full; Scrip A's loss does not reduce that withholding at the point of sale. This per-scrip, transaction-level mechanism is precisely the feature that Nepali capital-markets tax commentary has flagged as a structural disadvantage relative to India, the US, and the UK, where portfolio-level netting within a tax year is the norm.
CAUTION
Do not assume that selling a losing position in the same week as a winning one will automatically reduce your withheld tax at the broker level — it generally will not, because CGT is deducted scrip-by-scrip at settlement, not netted across your portfolio in real time. Any use of a capital loss to offset a gain in Nepal is, at best, something you must actively claim and substantiate yourself through your own annual tax filing and documentation, not something the system does for you automatically the way it does for the gain side.
This does not mean losses are worthless for tax purposes — it means the mechanism for using them is administrative and self-initiated rather than automatic, and it is considerably less well-trodden ground than the gain side of the ledger. The general carry-forward principle in Nepali income tax law (the broader loss-carry-forward architecture of the Income Tax Act, 2058, which governs how losses reduce future taxable income) supports the concept that a documented capital loss on listed securities can, in principle, be set against future capital gains rather than disappearing the moment it is realised. What it cannot do is reduce your other income — a loss on your NEPSE portfolio does not reduce your salary tax or your business income tax; it can only ever be set against capital gains, and only in the manner and to the extent that your own return substantiates.
For the ordinary retail investor whose CGT is finally withheld at source and who never files a separate capital-gains schedule, using a loss in this way requires stepping outside the passive, final-tax-and-forget posture that Lesson 38.1 described as the default. It requires deliberately retaining documentation of the loss (the contract note showing sale below cost), and — this is the honest caveat a book like this owes you — consulting a chartered accountant or tax advisor before relying on any specific loss-offset claim in a given year, because the administrative practice around portfolio-level netting of listed-share capital losses for non-commercial individual investors is genuinely less standardised in Nepal than the gain-side withholding mechanics are. Practitioners and market commentators alike have specifically called for clearer, codified rules — including proposals for a multi-year loss carry-forward window and portfolio-based (rather than scrip-based) gain/loss calculation — precisely because the current framework leaves this ambiguous for the ordinary investor. Treat any loss-offset strategy as a claim you must build a paper trail for and defend, not a mechanical entitlement your broker will apply for you.
What this means practically is that the version of "tax-loss harvesting" that is safely available to a NEPSE investor today looks less like the sophisticated, same-day portfolio rebalancing common in US robo-advisors, and more like a disciplined, once-a-year, fiscal-year-end review: before Ashad-end, look honestly at your portfolio for positions that are (a) genuinely no longer worth holding on investment grounds — not manufactured losers — and (b) sitting at an unrealized loss. If a position meets both tests, realising that loss before fiscal year-end, documenting it properly, and retaining the contract note gives you the strongest possible basis to claim a set-off against a future capital gain, should the need arise and should you engage a professional to help you claim it correctly. Realising a loss purely to manufacture a paper offset, on a position you actually believe in and intend to rebuy immediately, is not a strategy this book endorses — quite apart from any wash-sale-style anti-avoidance rule (Nepal has no formally codified wash-sale rule equivalent to the US 30-day rule, but manufactured, no-economic-substance transactions designed purely to generate a tax loss carry general anti-avoidance risk under associated-persons and non-market-transaction provisions of the Income Tax Act, discussed further in Lesson 38.5), it is simply bad portfolio management dressed up as tax cleverness.
KEY CONCEPT
In Nepal, a capital loss on listed shares is a documented claim you build and may need to defend, not a number your broker automatically nets against a gain elsewhere in your portfolio. Plan your loss realisation around the fiscal year-end (Ashad) as a deliberate annual review, keep every contract note, and treat any offset claim as work for a qualified tax preparer — not a checkbox in your trading app.
Lesson 38.4 — Turnover, Rebalancing, and the Commercial-Investor Trap
Portfolio rebalancing — trimming a position that has grown too large as a share of your holdings, or rotating out of a sector that has run ahead of its fundamentals and into one that has lagged — is core discipline for any long-term investor, and this book has spent several earlier chapters making the case for it. But rebalancing on NEPSE has a tax dimension that a purely mechanical rebalancing rule (say, "trim anything above 15 percent of portfolio value") can blindly walk into: every rebalancing trade is a taxable event, subject to whichever CGT rate its holding period earns, and a high-turnover rebalancing habit systematically pushes more of your gains into the short-term bracket.
Think through the arithmetic. A disciplined long-term holder who rebalances once a year, trimming only positions that have both grown oversized and crossed the 365-day mark, pays the long-term rate on those trims. An investor who "actively manages" the same portfolio — rotating in and out of sector calls every few months in response to news, rumour, or momentum — will find that most of their gains are realised well inside the 365-day window, permanently locking themselves into the short-term rate (now a full 10 percent under FY 2083/84 law) on the majority of their trading profits, on top of paying broker commission, SEBON fees, and DP charges twice as often. Turnover has a direct, compounding tax cost in Nepal's current rate structure that it did not have to the same degree when the short-term/long-term gap was narrower.
There is a second, more serious risk layered on top of the simple rate arithmetic, and it goes to the heart of what "investor" means for tax purposes. In the FY 2080/81 budget, the government introduced a provision that alarmed the retail investing public: an additional layer of income tax, on top of ordinary CGT, on gains earned from share trading. NEPSE fell more than 90 points across two trading sessions on the news before the Inland Revenue Department issued a public clarification.
CASE IN POINT
Following the FY 2080/81 budget announcement, the IRD explicitly clarified that the new additional income-tax provision on share gains applied only to "commercial investors" — those whose pattern of share dealing is substantial and systematic enough to constitute a business — and explicitly did not apply to ordinary "non-commercial" natural investors. The IRD's own illustrative example: an individual earning NPR 3,000,000 from share transactions in a year pays 5 percent CGT (NPR 150,000 at the rate then in force) and, if classified as a genuine small/non-commercial investor, owes nothing further on the remaining NPR 2,850,000. A commercial trader in the same position, by contrast, would face further income tax on that remaining amount at applicable slab rates, layered on top of the CGT already withheld.
The clarification calmed the market, but it did not delete the underlying distinction from the law — it confirmed that the distinction exists and matters. Nepali tax law does not publish a single bright-line numerical test (a specific trade count, turnover figure, or holding-period average) that mechanically separates a "non-commercial" investor from a "commercial" one; the classification turns on the same facts-and-circumstances test that separates investment from business activity in most tax systems — frequency and regularity of transactions, whether trading is your primary occupation or livelihood versus incidental to other income, the scale of turnover relative to your other financial activity, whether you trade in a manner resembling a dealer's book rather than a holder's portfolio, and whether you have organised the activity with the infrastructure of a business (dedicated trading capital, systematic short-cycle strategies, and so on). This ambiguity is precisely why the classification risk is worth taking seriously as a planning matter rather than dismissing as a problem only for full-time day traders: an investor who has retired early and now trades NEPSE as a full-time daily activity, executing dozens of round-trip transactions a month with a portfolio's worth of turnover reused several times a year, sits far closer to the "commercial" description in substance than an investor who reviews holdings quarterly and trims twice a year — even if both call themselves "retail investors" and hold their positions through the same Mero Share account.
WARNING
There is no fixed transaction-count or turnover threshold published by the IRD that automatically classifies an individual as a "commercial" share investor rather than a "non-commercial" one — the test is substance-based. An investor whose trading pattern in frequency, scale, and dedicated time resembles a business, even without formally registering as a trading firm, carries real classification risk that could expose total gains to progressive income tax on top of the CGT already withheld. If share trading has become your primary activity and income source rather than incidental portfolio management, this is a conversation to have with a chartered accountant before, not after, a tax notice arrives.
The tax-planning implication is straightforward, and it dovetails with the investment philosophy this book has argued for since Part I: a lower-turnover, holding-period-disciplined approach is not merely more tax-efficient at the margin (more gains captured at 7.5 percent rather than 10 percent) — it is also the posture least likely to invite reclassification risk in the first place. Rebalancing with intent, on a predictable annual or semi-annual cadence, tied to explicit portfolio-construction rules (position-size caps, valuation triggers, thesis changes) rather than to market noise, serves both your investment discipline and your tax position simultaneously. This is one of the rare instances in personal finance where the tax-efficient choice and the behaviourally sound choice point in exactly the same direction.
Lesson 38.5 — DEMAT Architecture: Individual, Family Accounts, and the Associated-Persons Trap
Every NEPSE trade ultimately runs through a demat account identified by a BOID (Beneficial Owner Identification number), issued by the CDSC and typically opened and managed through the Mero Share portal in conjunction with a licensed depository participant (DP) — usually your stockbroker or a bank acting in that capacity. A natural question for any investor with a family — a spouse, adult children, aging parents — is whether structuring holdings across multiple demat accounts within the household can be used to manage tax exposure. The honest answer requires separating what is administratively permitted from what is tax-effective, because they are not the same thing, and conflating them is the single most common tax-planning misconception among NEPSE investors this chapter needs to correct.
What is permitted: an individual can, in fact, hold more than one demat account, opened through different DPs or different brokers, each with its own BOID. This is common — an investor who has relationships with two brokerage houses for research-access or service reasons will legitimately hold two BOIDs in their own name. What is not permitted is using multiple accounts to game IPO allotment: the CDSC's IPO-application system cross-checks applications against the applicant's citizenship number, and duplicate applications from the same individual across different BOIDs are detected and rejected. Each genuinely distinct family member — a spouse, an adult child with their own citizenship document and PAN — is entitled to their own BOID and their own IPO allotment chance; this is the legitimate way a household expands its IPO access and overall investment capacity, and it is a perfectly sound piece of family financial planning. It is not, however, a tax-arbitrage device, for a reason worth stating plainly.
KEY CONCEPT
Because CGT and dividend tax on listed shares are flat, final withholding taxes applied at the account level regardless of the holder's personal income bracket, there is no "income-splitting" benefit available in Nepal from moving shares into a lower-earning family member's demat account, the way income-splitting can reduce tax under a progressive slab system for salary or business income. A retired parent in the lowest income bracket and a high-earning professional pay the identical 7.5 percent/10 percent CGT and 5 percent dividend tax on their own shares — the personal tax bracket is simply irrelevant to these two income types for a non-commercial investor. Spreading direct equity holdings across family BOIDs purely to "save tax" targets a benefit that does not exist under current law.
This is worth dwelling on because it directly contradicts an intuition many investors bring from thinking about salary or rental income, where shifting income to a lower-bracket family member genuinely does reduce a household's total tax bill. Equity CGT and dividend tax in Nepal simply do not work that way for the ordinary investor — they are schedular, not slab-based — so the tax motive for family account structuring largely evaporates, leaving the legitimate motives (succession planning, separate financial goals, expanding household IPO access, keeping each family member's own capital and decision-making genuinely separate) as the real reasons to do it.
There is a second, sharper trap for anyone tempted to move existing appreciated shares between family members' accounts rather than simply having each family member invest their own fresh capital independently. Section 45 of the Income Tax Act, 2058 governs transfers between "associated persons" — a category that, in substance, captures close family and other related parties — where the transfer occurs without market-value consideration changing hands. The provision does not let such a transfer pass tax-free simply because no cash was exchanged. Instead, it deems the transferor to have received the market value of the property at the time of transfer (crystallising whatever gain has accrued, and triggering the applicable CGT as though the shares had been sold on the open market that day), while the recipient's cost basis resets to that same market value going forward.
WARNING
Transferring appreciated shares into a spouse's, child's, or parent's demat account via a BO-to-BO transfer — without an actual arm's-length sale — does not defer or avoid capital gains tax. Under Section 45 of the Income Tax Act, 2058, a transfer between associated persons without market consideration is deemed to occur at market value: the transferor is treated as having disposed of the shares at fair value on that date (triggering CGT on the full accrued gain), and the recipient's cost basis simply resets to that market value. There is no lawful mechanism to "gift" an appreciated NEPSE position within the family to reset or defer the embedded gain — the tax is triggered at the point of transfer regardless of intent.
The practical upshot for family-oriented planning is to keep the two goals cleanly separate. If your objective is genuinely to grow the household's aggregate investing capacity and IPO exposure, the correct mechanism is for each family member to open their own BOID, fund it with their own capital (whether gifted as cash before investment, which carries no equivalent deemed-disposal problem since cash itself has no embedded capital gain, or from their own independent income), and make their own investment decisions from that point forward — building their own cost basis on their own purchases from day one. If your objective is succession or estate planning around an existing appreciated position, that is a legitimate and important conversation, but one to have explicitly with a tax advisor and, where relevant, in the context of inheritance rather than as a lifetime "gift" of shares — inheritance and gift receipts sit under different provisions of the Income Tax Act (Section 10's exemption for amounts received as gift or inheritance, subject to the cross-references noted in that section) than a mid-life transfer of an appreciated, income-producing asset between living associated persons, and the tax consequences differ materially between the two.
Ensure, too, that the BOID-opening paperwork itself supports later tax reconciliation. PAN registration is technically optional at demat account opening but is explicitly recommended by depository participants for exactly the reason this book cares about: linking your permanent account number to your BOID from day one makes cost-basis and capital-gains reconciliation dramatically easier when you eventually need it, whether for a professional's review of a loss-carryforward claim or simply to answer a query from the tax office. An investor who skipped this step at account opening should treat adding the PAN link retroactively as a same-week priority, not a someday task.
Lesson 38.6 — Record-Keeping, Fund Wrappers, and Closing the Tax-Planning Loop
Everything discussed so far — holding-period timing, loss documentation, turnover discipline, account structuring — depends on one unglamorous prerequisite: you actually have the records to prove your cost basis, your holding period, and your transaction history when you need them. This is where NEPSE investing has both a genuine structural advantage over an older, paper-certificate era and a genuine trap for investors who assume the electronic system remembers everything for them indefinitely.
Since dematerialization became near-universal, CDSC's electronic infrastructure and the Mero Share portal maintain a transaction history for every BOID, and Mero Share specifically provides a downloadable capital-gains report each year — a consolidated statement of your sales, computed gains, and tax withheld across the fiscal year, built precisely for tax-filing season.
PRACTICAL TOOL
Download your Mero Share capital-gains (CGT) report every fiscal year, before tax-filing season begins, rather than waiting until you need it for a specific dispute or claim. This report consolidates your sale transactions, computed gains, and withheld tax across all scrips held through that BOID for the year, and is the single most useful primary document for reconciling your own records against what was actually withheld — and for supporting any future loss-carryforward claim discussed in Lesson 38.3.
But the electronic record has real limits, and a disciplined investor should not rely on it alone. Three categories of holdings deserve independent, physically or digitally archived documentation outside the broker's own system: first, any holdings originally allotted before your account's full dematerialization, where the electronic cost-basis record may be reconstructed rather than original; second, bonus and rights share allotments, because — as discussed in Lesson 38.2 — these are folded into your position on a weighted-average basis, and if the underlying allotment records are ever incomplete, reconstructing the correct weighted-average cost requires the original allotment letters or credit confirmations, not just the current holding balance; and third, any transaction across a period where you changed brokers or DPs, since a BO-to-BO transfer moves the shares but a gap in your own filing discipline at the moment of the move is exactly when supporting paperwork tends to go missing.
The documents worth retaining, indefinitely, for every scrip you hold long-term:
Document
Source
Why it matters for tax
Broker contract notes (buy and sell)
Broker/TMS
Establishes purchase cost, sale proceeds, and transaction fees for gain calculation
CDSC/DP charge bills
CDSC/DP
Substantiates deductible transaction costs on both legs
IPO/FPO allotment letters
Company registrar/CDSC
Establishes original cost basis for allotted shares
Rights and bonus share credit confirmations
CDSC/DP, Mero Share
Required to reconstruct weighted-average cost basis
Annual Mero Share CGT report
Mero Share portal
Consolidated year-end reconciliation of gains and tax withheld
Bank statements showing settlement credits/debits
Bank
Independent corroboration of contract-note figures
With records in order, the final planning lever available to a long-term NEPSE investor is the choice of vehicle itself — direct share ownership versus holding equity exposure through a SEBON-approved mutual fund (collective investment scheme). This is the closest thing Nepal's capital markets currently offer to a genuinely tax-advantaged wrapper, and it deserves to be understood clearly rather than left as market folklore about "mutual funds being tax-free," which overstates the case in one direction while understating a real advantage in another.
Section 10 of the Income Tax Act, 2058 exempts the income earned by a SEBON-approved collective investment fund (mutual fund) in the course of pursuing its stated investment purpose. This fund-level exemption means that when a mutual fund's portfolio manager buys and sells underlying NEPSE-listed shares inside the fund — rebalancing sector weights, trimming winners, rotating into new positions — that internal trading does not generate a scrip-by-scrip CGT drag the way the same activity would if you executed it directly in your own demat account. The tax event, for you as a unit holder, is deferred to the point where you yourself sell or redeem your units, not to every trade the fund manager makes inside the portfolio. Distributions from the fund to unit holders are, further, treated as exempt in the hands of the resident recipient — a materially different treatment from the flat 5 percent final withholding tax that applies to dividends paid directly by a listed company to its individual shareholders.
Feature
Direct equity ownership
Mutual fund unit ownership
Tax on manager's internal portfolio trading/rebalancing
You pay CGT on every sale, scrip by scrip
Exempt at the fund level under Section 10 — no drag from internal turnover
Tax on distributions received while holding
5% flat, final withholding on dividends
Distributions to resident unit holders treated as exempt
Tax on your own exit (selling the position/unit)
CGT at 7.5%/10% depending on your holding period in that scrip
CGT at the same listed-security rates (7.5%/10%) applies to the unit sale, based on your own holding period in the fund
Holding-period benefit
Applies per scrip, tracked by you
Applies to your unit-holding period, not the fund's internal turnover
Loss offset
Scrip-level, self-substantiated (Lesson 38.3)
Realised only at your own unit sale — internal fund losses are absorbed within the fund, not passed through to you transaction-by-transaction
This comparison should not be read as a blanket recommendation to abandon direct stock-picking in favour of funds — this book has spent a great many chapters teaching you to analyse individual businesses precisely because doing so well, over a long horizon, is a source of return that competent direct ownership can capture and a passively-held fund cannot always replicate. But it is a genuine, lawful structural advantage worth weighing for the portion of a portfolio where you want broad, diversified equity exposure without personally absorbing scrip-by-scrip CGT drag every time a fund manager rebalances — and it is worth knowing precisely so that you are choosing it for the right reason (the fund-level trading exemption and exempt distributions) rather than the wrong one (a vague sense that "funds don't pay tax," which is not quite what the law says, and which is silent on the fact that your own eventual unit sale is taxed at the same rate structure that applies to direct shares).
Chapter recap
This chapter closes Part VII's three-chapter survey of Nepali taxation as it applies to the NEPSE investor, and it does so by converting what the prior chapters established as mechanics into what this chapter has tried to establish as discipline. The rates themselves — 7.5 percent long-term and 10 percent short-term CGT on listed shares, 5 percent final tax on dividends, all under the Finance Act 2083 effective from Shrawan 1, 2083 — are numbers you should expect to see revised again in a future budget, most likely upward given the trend of the last two fiscal years, and the durable lesson of this chapter is not the specific figures but the four levers that remain yours to pull regardless of where those figures sit in any given year.
The first lever, and the one most fully within your control, is holding-period discipline: knowing your exact settlement-date anniversary for every meaningful position, understanding that the 365-day line is a cliff rather than a slope, and sequencing large disposals to capture the long-term rate wherever the underlying investment case supports the wait — while never holding a deteriorating business past its logical exit purely to save 2.5 percentage points. The second is an honest relationship with losses: recognising that Nepal's scrip-by-scrip, source-withheld CGT system does not automatically net your losers against your winners the way portfolio-level systems elsewhere do, and that any loss-offset strategy is a documented claim you build with a professional's help, not a checkbox your broker ticks for you. The third is turnover awareness: understanding that every rebalancing trade is a taxable event whose rate depends on holding period, and that a trading pattern substantial enough in frequency and scale can shift your classification from a non-commercial investor enjoying final, schedular tax treatment to a commercial one facing additional progressive income tax — a risk the market learned about the hard way during the FY 2080/81 budget scare, and one best avoided by the same low-turnover, conviction-driven discipline this book has argued for from its opening chapters.
The fourth lever is architectural: knowing what your demat account structure can and cannot do for you. Multiple BOIDs across family members, each funded with that member's own capital, are a legitimate way to expand household investing and IPO capacity — but they are not an income-splitting device, because CGT and dividend tax on listed shares are flat and final regardless of whose bracket the holder sits in. And critically, Section 45's associated-persons rule closes off the tempting shortcut of "gifting" an appreciated position into a family member's account to defer or dodge the embedded gain — such a transfer is deemed to occur at market value and triggers the tax at the moment of transfer, exactly as a market sale would.
Underpinning all four levers is the least glamorous but most necessary habit this chapter has asked of you: contemporaneous, complete record-keeping — contract notes, DP charge bills, allotment letters for bonus and rights shares, and an annual Mero Share CGT report downloaded and archived before each tax season, not reconstructed years later under pressure. And where you seek broad equity exposure beyond your own direct stock-picking, a SEBON-approved mutual fund offers a genuinely different, and in some respects more tax-efficient, wrapper — exempting internal portfolio turnover and unit-holder distributions from the drag that direct ownership would otherwise impose, while still taxing your own eventual exit at the same listed-security CGT rates you would face on a direct holding.
With this chapter, Part VII's taxation survey is complete: across three prior chapters and this one, you have moved from understanding what NEPSE taxes are and how they are calculated, to what this chapter has tried to give you — a working discipline for legally minimising what you pay without ever crossing into evasion, misrepresentation, or wishful readings of provisions that say something more modest than market folklore claims. Part VII now turns from taxation to ratios — from what the state takes to what the numbers on a company's own financial statements can tell you about what it is actually worth keeping.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VII · Chapter 39
Valuation Ratios in the NEPSE Context
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 39.1 — The P/E Ratio: How NEPSE Actually Reports It
Open any counter's page on Sharesansar or Merolagani and the first number analysts quote is the P/E ratio — price divided by earnings per share. The formula never changes, but the way Nepal's market computes and displays the two halves of that fraction has enough local texture that a mechanical import of a foreign textbook definition will mislead you.
The price half is straightforward: the last traded price (LTP) on NEPSE at market close. The earnings half is where the local nuance sits. Nepali brokerage platforms typically quote P/E against the most recently reported annual EPS from audited financials, but during the fiscal year — between quarterly unaudited reports — many platforms switch to an "annualized EPS," taking the trailing four quarters or, for younger companies, multiplying the latest quarter's EPS by four. This second method is convenient but dangerous for any business with seasonal earnings, a point we return to in Lesson 39.2 when hydropower enters the discussion.
A worked example grounds this. Consider the second-quarter (Poush-end) results of fiscal year 2082/83 — the most recent full reporting cycle available at the time of writing — across a cross-section of Nepal's commercial banks.
Bank
P/E (x)
EPS (Rs)
Implied Price (Rs)
Nepal Bank Limited (NBL)
7.67
17.76
≈136
Prabhu Bank (PRVU)
8.46
8.62
≈73
Kumari Bank (KBL)
10.59
20.74
≈220
NMB Bank (NMB)
15.35
17.10
≈263
Sanima Bank (SANIMA)
16.18
20.48
≈331
Nabil Bank (NABIL)
18.40
29.69
≈546
Everest Bank (EBL)
18.53
30.86
≈572
Standard Chartered Nepal (SCB)
22.95
27.35
≈628
Himalayan Bank (HBL)
33.16
11.45
≈380
NIC Asia (NICA)
343.16
1.76
≈604
("Implied price" is simply P/E × EPS, back-solved from the same source table, and is shown here to make the arithmetic transparent — treat it as an illustration of method, not a live quote.)
Two things should jump out. First, within a single sector — commercial banking, the most homogeneous, most heavily regulated corner of NEPSE — P/E multiples still range from under 8x to over 300x. Second, the outlier is not a growth story; it is an accounting artefact. NIC Asia's EPS of Rs 1.76 was compressed by a large capital base relative to a temporarily subdued profit quarter, and dividing a perfectly ordinary share price by a near-zero EPS manufactures a P/E of 343x that tells you nothing about value and everything about the denominator problem. A bank at Rs 604 is not "343 times more expensive" than Nepal Bank at Rs 136 trading on 7.67x — it is a warning that the ratio has broken down.
KEY CONCEPT
P/E = Market Price per Share ÷ Earnings per Share. In Nepal, "Market Price" is the NEPSE last-traded price (LTP); "Earnings" is either the latest audited annual EPS or an annualized trailing figure, depending on the platform and the point in the fiscal year. Always check which EPS base a quoted P/E is built on before comparing two companies.
A single-digit P/E does not automatically mean "cheap," and a triple-digit P/E does not automatically mean "expensive" — it means look at the denominator first.
PRACTICAL TOOL
Before trusting any P/E figure pulled from a broker app or Sharesansar's counter page, click through to the EPS itself. If the EPS is below roughly Rs 5, or if the company reported a loss in any of the trailing four quarters, treat the P/E as unreliable and switch your primary lens to P/B or price-to-sales instead.
Lesson 39.2 — Why Sector P/E Differs Structurally
It would be a mistake to treat NEPSE as one market with one "fair" P/E. NEPSE is really eleven-odd sub-markets bolted together under a single index, and each sub-market prices earnings differently for structural reasons that have nothing to do with mispricing.
The clearest illustration comes from a full sector-by-sector P/E survey Sharesansar ran across all listed NEPSE companies. Though the underlying data point is from an earlier period, the ranking of sectors it revealed — which sectors trade rich and which trade cheap, and why — has held up remarkably consistently in the years since, including in more recent data.
Sector
Average P/E (illustrative)
Range
Structural driver
Life Insurance
63.80
25.27 – 115.73
Actuarial reserve accounting, high embedded-growth expectations, thin free float
Microfinance
47.13
12.63 – 389.58
Small paid-up capital, merger and capital-adequacy driven EPS swings
NRB-regulated capital and ROE ceilings, mature and comparable earnings
Manufacturing & Processing
11.79
3.07 – 20.73
Mature, low-growth, commodity-like margins
Absolute levels drift year to year — commercial banking, for instance, was averaging closer to 15.8x with a P/B near 1.51x in a late-2025 sector survey, still the "most rationally valued" segment of the market even as NEPSE's headline P/E pushed toward record territory. But the ordering — insurance and microfinance rich, hydropower volatile, banking and manufacturing comparatively grounded — has been remarkably stable across cycles. That ordering is the useful lesson, more than any single year's absolute number.
Why do banks anchor the cheap end? Nepal Rastra Bank's capital adequacy framework, single-obligor limits, and CD-ratio ceiling effectively cap how fast a bank's balance sheet — and therefore its earnings — can grow in any given year. Investors know this, so they refuse to pay insurance-sector multiples for bank-sector growth. Why does hydropower sprawl from 4x to over 100x? Because a run-of-river plant's output swings with monsoon rainfall, and a newly commissioned plant reports a tiny, almost accidental profit in its first full year before ramping toward normalised capacity utilisation — dividing a normal share price by that accidental first-year profit produces an enormous, meaningless P/E.
Subhead: A live example of the hydropower trap
Upper Tamakoshi Hydropower, one of the country's flagship run-of-river plants, has traded at a P/E near 114x in recent periods — not because the market expects the company to grow earnings 114-fold, but because a specific reporting window caught unusually thin recent earnings relative to its installed capacity and asset base. Compare that to Chilime Hydropower, which has built a dividend track record stretching back to fiscal year 2060/61, with an average payout around 29% and a peak near 70% — a company old enough that its earnings have normalised into a steadier, more interpretable pattern. Same sector, wildly different reliability of the P/E signal, purely as a function of the company's age and monsoon-cycle position.
WARNING
Never annualize a single quarter's EPS for a hydropower company without checking which months that quarter covers. A plant earns the bulk of its annual revenue in the monsoon months (roughly Ashad through Ashwin); annualizing a dry-season quarter by simply multiplying by four will understate true annual EPS and overstate the P/E, sometimes by a factor of two or three.
Lesson 39.3 — P/B Ratio and Why It Matters Most for Banks
Price-to-book compares the market price to the company's book value per share — shareholders' equity divided by shares outstanding. For most industrial or trading companies, book value is a poor proxy for what the business is actually worth, because plant, inventory, and goodwill are recorded at historical cost and say little about earning power. Banks are the exception, and this is worth understanding precisely rather than by rule of thumb.
A bank's balance sheet is, in an accounting sense, close to what it appears to be: loans, investments, and deposits are financial instruments carried at values Nepal Rastra Bank's prudential and disclosure regime forces the bank to mark reasonably close to reality, through loan-loss provisioning rules, capital adequacy reporting, and NRB-mandated disclosure formats. That regulatory transparency is precisely why P/B carries more information for a bank than it does for a hydropower company or a trading house — you can trust the denominator.
A recent cross-section of Nepali commercial banks makes the point:
Bank
P/B (x)
NPL (%)
Reading
Everest Bank (EBL)
5.62
0.68
Premium book multiple, justified by pristine asset quality
Kumari Bank (KBL)
2.37
6.92
Mid-range multiple, weaker loan book explains the discount
Nepal Bank (NBL)
1.84
5.34
Legacy state-linked bank, has at times traded below book value
The pattern is not random. A bank with a non-performing loan ratio under 1% (EBL) commands a materially higher price-to-book than one carrying an NPL ratio nearly seven times as high (KBL), because the market is, correctly, pricing in the probability that KBL's book value overstates the true collectible value of its loan portfolio. This is the single most useful application of P/B in the NEPSE context: use it to cross-check whether a bank's reported book value should be trusted at face value, by reading it alongside the NPL ratio, capital adequacy ratio, and provisioning coverage disclosed in the same quarterly report.
KEY CONCEPT
P/B = Market Price per Share ÷ Book Value per Share, where Book Value per Share = (Total Shareholders' Equity − Preference Capital) ÷ Number of Ordinary Shares Outstanding. A reading below 1.0 signals the market prices the company below its accounting net worth — worth investigating, not automatically buying. A reading above roughly 3.0 without a clear, sustained growth or ROE story is a signal to check whether you are paying for hype rather than earning power.
Subhead: The mechanics of book value per share
Suppose a commercial bank reports total shareholders' equity — paid-up capital plus reserves and retained earnings — of Rs 18 billion, against 120 million shares outstanding. Book value per share is Rs 18,000,000,000 ÷ 120,000,000 = Rs 150. If the share trades at Rs 270, the P/B is 1.8x. Compare that instantly to the bank's return on equity: a bank earning an ROE of 16-18% arguably deserves to trade above book, because it is compounding shareholder capital faster than a bank earning 8-10% ROE, all else equal. P/B divorced from ROE is an incomplete comparison — the two ratios are meant to be read together, not separately.
CASE IN POINT
In the NRB-regulated banking sector, P/B below 1.0 combined with an NPL ratio above 4-5% and a capital adequacy ratio near the regulatory floor is a classic "value trap" signature — the stock looks statistically cheap on book value, but the book value itself is compromised by asset quality problems the ratio alone cannot see.
Lesson 39.4 — Dividend Yield, Nepal-Style: The Face-Value Trap
Dividend yield, everywhere else in the world, means one thing: annual dividend per share divided by current market price. In Nepal's retail investment culture, however, the headline number that circulates — in AGM notices, in newspaper tables, in WhatsApp groups — is something else entirely: the dividend percentage declared on face value (par value), which for the overwhelming majority of NEPSE-listed companies is Rs 100 per share.
This distinction is not a technicality; it is the single most common source of confusion for a new NEPSE investor, and getting it wrong leads directly to overpaying for "high-yield" stocks that are nothing of the sort.
A recent ranking of NEPSE's highest dividend-declaring companies illustrates the headline numbers investors actually see:
Company
Dividend Declared
Type
Unilever Nepal (UNL)
1,842%
Cash
Nepal Telecom (NTC)
30%
Cash
Nepal Life Insurance (NLIC)
21.05%
5% bonus + 16.05% cash
Standard Chartered Bank Nepal (SCB)
19%
Cash
Agricultural Development Bank (ADBL)
13%
3.25% bonus + 9.75% cash
Nabil Bank (NABIL)
12.5%
Cash
Read literally, Unilever Nepal's "1,842%" looks absurd — and it is, until you remember it is 1,842% of Rs 100 face value, i.e., Rs 1,842 per share in cash, on a stock that trades in the tens of thousands of rupees because of its extremely small share count and near-total absence of bonus dilution over decades. The percentage on face value and the actual yield on market price can differ by an order of magnitude or more.
Subhead: Converting the headline into a real yield
Take Nabil Bank. It declared a 12.5% cash dividend, meaning Rs 12.50 per share on face value. From the same reporting period used in Lesson 39.1, Nabil's price was approximately Rs 546. The actual dividend yield an investor buying at that price would have earned is:
Rs 12.50 ÷ Rs 546 = 2.29%
Now take Standard Chartered Nepal, which declared 19% cash — Rs 19 per share — against an implied price of roughly Rs 628:
Rs 19 ÷ Rs 628 = 3.02%
Both look unremarkable once converted — squarely in the 2-3% range that characterises yield on Nepal's larger, more mature bank counters, broadly consistent with NEPSE's market-wide dividend yield, which has hovered around just 1-2% during periods when the index itself has been expensive. That is a world away from the "12.5%" or "19%" a reader skims off a newspaper table.
CAUTION
A dividend percentage quoted in an AGM notice, a newspaper business page, or a brokerage app is almost always calculated on Rs 100 face value, not on the market price you would actually pay. Before treating any "dividend %" as an income return, divide the rupee amount (percentage × Rs 100 ÷ 100) by the current market price — not by the face value — to get the true yield.
The distortion cuts both ways. A thinly-traded, high-priced counter can declare a modest-looking face-value percentage and still deliver a respectable true yield, while a low-priced, heavily bonus-diluted counter can declare a large-looking face-value percentage and deliver a true yield under 2%. Bonus components compound the confusion further: when ADBL declares "13% (3.25% bonus + 9.75% cash)," the cash component alone is the income return; the bonus component is not income at all — it is additional shares, which dilutes future EPS and, mechanically, tends to pull the share price down roughly in proportion on the ex-dividend date. Treating a bonus percentage as if it were cash yield is a second, related error worth guarding against.
Lesson 39.5 — EPS Mechanics: Weighted Average Shares and the Bonus Share Adjustment
Every ratio covered so far in this chapter has EPS sitting in its denominator or, in P/B's case, a close cousin of it — shares outstanding. Get the share count wrong, and every ratio built on top of it is wrong. Nepal's heavy and recurring use of bonus shares makes this the single most important mechanical detail in the entire chapter.
Under the Nepal Financial Reporting Standards that NEPSE-listed companies follow (mirroring international EPS accounting), earnings per share must be computed on a weighted average number of shares outstanding during the period — not simply the share count at the balance sheet date. This matters because share capital does not sit still during a Nepali fiscal year: rights issues, bonus issues, and mergers all change the denominator mid-year.
The two most common events — rights shares and bonus shares — are treated completely differently, and conflating them is the most frequent analytical error retail investors make.
Subhead: Rights shares are time-weighted; bonus shares are retroactive
A rights issue brings new cash into the company in exchange for new shares, at a specific date. Because real resources entered the business only from that date forward, the new shares are time-weighted into the denominator — a rights issue completed with three months left in the fiscal year adds only a quarter's worth of dilution to that year's weighted average share count.
A bonus issue, by contrast, capitalises existing reserves into new shares — no new cash comes in at all; shareholders simply receive more paper representing the same underlying company. Because nothing real changed on the date of issue, accounting standards require that bonus shares be treated as if they had always been outstanding — not just for the current year, but retroactively, restating the prior year's comparative EPS on the same enlarged share base, so that year-on-year EPS growth comparisons remain meaningful.
A worked numeric example: A company reports profit of Rs 400 million for the year and has 10,000,000 shares outstanding at the start of the year. Partway through the year it issues a 10% bonus (1,000,000 new shares). Two ways of computing EPS are possible, and only one is correct:
Incorrect (year-end share count only): Rs 400,000,000 ÷ 11,000,000 shares — but only applying this to the current year while leaving last year's comparative EPS on the old 10,000,000 share base overstates apparent earnings growth.
Correct (NFRS-compliant): Both this year's and last year's EPS are calculated on the post-bonus 11,000,000 shares. If last year's profit was Rs 350 million, restated prior-year EPS becomes Rs 350,000,000 ÷ 11,000,000 = Rs 31.82, and current-year EPS becomes Rs 400,000,000 ÷ 11,000,000 = Rs 36.36 — a genuine, comparable growth rate of roughly 14.3%, rather than a growth rate artificially inflated by comparing an unadjusted small prior-year denominator to a larger current one.
REGULATORY DETAIL
Nepal Rastra Bank's 2015 directive raising minimum paid-up capital requirements for commercial banks (to Rs 8 billion) triggered a multi-year wave of bonus share issuances across the entire banking sector between roughly 2015 and 2018, as banks capitalised reserves rather than raise fresh cash to meet the new floor. Any historical EPS or P/E comparison for a Nepali bank that spans this period must confirm the data source has correctly restated pre-2015 EPS figures on a post-bonus share basis — many casual comparisons online do not, and will show a misleadingly steep "profit collapse" that is really just a denominator effect.
The NIC Asia case from Lesson 39.1 — a P/E of 343x built on an EPS of just Rs 1.76 — is worth revisiting through this lens. Whenever you encounter an EPS that looks anomalously low relative to a company's history and peer set, the first three questions to ask are: was there a recent bonus or rights issue that enlarged the share base faster than profit grew; was there a merger or acquisition that reset the share count; and is the reporting period annualized correctly. Only once those three questions are answered should you conclude the low EPS reflects a genuine deterioration in the underlying business.
CASE IN POINT
A P/E of 343x, as briefly appeared for NIC Asia in one reporting period, is not a signal that the market expects extraordinary growth — it is close to certainly a signal that the EPS denominator was temporarily and mechanically depressed. Cross-check any triple-digit P/E against the raw EPS figure before drawing any conclusion about valuation.
Lesson 39.6 — NEPSE's Market-Wide P/E as a Sentiment Barometer
Every individual counter's P/E tells you something about that company. NEPSE's aggregate, market-wide P/E — the weighted average across all listed companies, published by NEPSE itself and tracked continuously by Sharesansar — tells you something different: where collective investor sentiment sits in the market's own historical range, and how Nepal compares to its regional peers.
The number has swung dramatically across cycles. During the speculative peak of 2021, NEPSE's market P/E reached roughly 42.28x — a level that, in hindsight, marked an unsustainable valuation extreme, followed by a multi-year correction that pulled the ratio down substantially before a subsequent recovery. As of a recent reading in early 2026, the market P/E had climbed back to approximately 38x, with the market's aggregate price-to-book near 2.78x, dividend yield compressed to just 1-2%, and market capitalisation equal to roughly 72.6% of GDP — all readings clustered near the upper end of NEPSE's own historical range.
What makes this figure genuinely useful as a sentiment gauge is not its absolute level in isolation, but how it compares to other frontier and emerging markets facing broadly similar macro constraints:
Market
P/E (x)
Dividend Yield
NEPSE (Nepal)
≈38
1-2%
MSCI Emerging Markets
18.8
—
Nifty 50 (India)
≈20
—
Vietnam
15-16.8
—
MSCI Frontier Markets
13.3
3.11%
Bangladesh (DSE)
10.1
3-4%
Sri Lanka (CSE)
10.6-11.3
3-3.2%
Pakistan (KSE-100)
8.7
~6.8%
Kenya (NSE)
7.3
4-6%
The pattern is striking and worth sitting with. NEPSE, at roughly 38x, trades at nearly double the multiple of the MSCI Frontier Markets basket it structurally belongs to, and at two to five times the multiples of comparable South Asian peers — Bangladesh, Sri Lanka, and Pakistan — that share Nepal's broad emerging-market growth profile. Meanwhile NEPSE's dividend yield, at 1-2%, sits well below every one of those peer markets, several of which pay 3-7%. A market can be simultaneously expensive on earnings and stingy on income, and that combination is exactly what elevated valuation with compressed yield signals: a market being carried more by capital appreciation expectations and liquidity than by the cash generation investors are actually being paid today.
Why does NEPSE persistently command such a premium multiple relative to peers with arguably better growth and profitability fundamentals? Structural, not fundamental, reasons dominate the explanation: a chronically small free float relative to demand from a large domestic retail base with few alternative investment channels (real estate transaction friction, capital controls limiting outbound investment, low fixed-deposit rates during liquidity-surplus periods), combined with concentrated retail participation that trades on price momentum and rumour as much as on earnings. None of this means the multiple is "wrong" in a way that forces immediate correction — Nepal's market has sustained rich multiples for extended stretches before — but it does mean a NEPSE-wide P/E near 38x, at nearly the same level that preceded the 2021 correction, is information a disciplined investor should not ignore, even while continuing to buy selectively into individual counters that screen cheap on the sector-relative and company-specific metrics covered earlier in this chapter.
CASE IN POINT
Reading NEPSE's aggregate P/E against its own history (roughly 38x now, versus a 2021 bubble peak of 42.28x) and against regional peers (Bangladesh 10.1x, Sri Lanka ~11x, Pakistan 8.7x) simultaneously gives a fuller sentiment read than either comparison alone — the historical comparison flags where Nepal stands relative to its own past excess, and the peer comparison flags how much of that valuation is Nepal-specific enthusiasm rather than a shared emerging-market re-rating.
The practical takeaway for a retail investor is not to sell everything the moment the market-wide P/E crosses some magic threshold — no such precise trigger exists — but to treat a rising aggregate multiple as a reason to demand a higher margin of safety on new purchases, to lean more heavily on the sector-relative and company-specific tools from Lessons 39.1 through 39.5 rather than on market momentum, and to remember that the last time NEPSE's P/E sat this high, it was followed by years of underperformance for anyone who bought at the peak on faith in continued re-rating rather than on earnings.
Chapter recap
This chapter built a working toolkit for the four valuation ratios that dominate how NEPSE-listed companies are actually discussed, screened, and traded in Nepal — P/E, P/B, dividend yield, and the EPS mechanics underneath all of them — and, critically, showed where each one behaves differently in the Nepali market than a generic finance textbook would lead you to expect. The P/E ratio remains the most quoted and most misused number on any counter's Sharesansar page: useful for comparing mature, comparably-regulated businesses like commercial banks against each other, and actively misleading whenever the EPS denominator has been distorted by a recent bonus issue, a merger, or a seasonal earnings pattern, as the NIC Asia and Upper Tamakoshi examples in this chapter demonstrated concretely.
Sector context is not optional color; it is structural information. Banking's tight 8x-to-33x range reflects Nepal Rastra Bank's regulatory ceiling on growth and risk-taking, while insurance and microfinance's far wider and generally richer ranges reflect actuarial accounting quirks, thinner floats, and merger-driven earnings volatility. Comparing a hydropower counter's P/E to a bank's P/E without adjusting for this structural difference is comparing two different kinds of instrument, not two mispriced versions of the same one.
P/B earns a privileged place in this toolkit specifically for banks and insurers, because Nepal Rastra Bank's disclosure regime makes their balance sheets unusually trustworthy inputs for the ratio — but P/B is only informative when read alongside the NPL ratio and capital adequacy ratio that reveal whether the reported book value itself deserves to be trusted, as the EBL-versus-KBL comparison in Lesson 39.3 illustrated.
Dividend yield carries the single most consequential Nepal-specific trap in this chapter: the near-universal practice of quoting dividends as a percentage of Rs 100 face value rather than as a percentage of market price. Every headline dividend percentage an investor encounters — whether Unilever Nepal's startling 1,842% or Nabil Bank's modest-sounding 12.5% — must be converted to a true yield on market price before it means anything as an income measure, and bonus components within a declared dividend must be mentally separated from cash components, since only the cash portion is actual income.
Underneath all three ratios sits EPS, and underneath EPS sits the weighted-average-share mechanics that Nepal's recurring bonus-share culture makes unavoidable: rights shares dilute prospectively from their issue date, while bonus shares — because they represent no new capital — must be applied retroactively, restating prior-year comparatives so that reported earnings growth is real growth rather than a denominator artifact. The 2015-era NRB capital directive that triggered years of sector-wide bonus issuances across Nepali banks is the clearest historical illustration of why this restatement discipline matters for anyone doing multi-year comparisons.
Finally, NEPSE's own aggregate P/E functions as a market-wide sentiment gauge distinct from any individual company's valuation — currently sitting near 38x, within reach of the 42.28x reached at the 2021 bubble peak, and running at roughly two to five times the multiples of comparable regional peers like Bangladesh, Sri Lanka, and Pakistan while paying out a comparatively thin 1-2% dividend yield. None of the individual-counter analysis in this chapter is invalidated by an expensive overall market, but a rising market-wide multiple is a signal to raise your required margin of safety, lean harder on sector-relative and balance-sheet-grounded metrics like P/B and NPL ratios, and resist mistaking broad market momentum for company-specific value — the discipline this entire chapter has been building toward, one ratio at a time.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VII · Chapter 40
Profitability Ratios — Sector by Sector
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 40.1 — ROE and ROA: The Universal Starting Point (and Why It Misleads Fast)
Every analyst who opens a NEPSE annual report ends up circling the same two lines: net profit and shareholders' equity. Divide one by the other and you have Return on Equity — the single number that Nepali investors quote most often to decide whether a company is "good." Divide net profit by total assets instead, and you get Return on Assets, the quieter cousin that tells you how efficiently a company turns its balance sheet into profit, independent of how that balance sheet was financed.
ROE = Net Profit / Average Shareholders' Equity ROA = Net Profit / Average Total Assets
Both ratios are expressed as annualized percentages, and both are backward-looking — they describe what already happened, not what will happen. That is fine for judging management's stewardship of capital already entrusted to them; it is dangerous when used, unadjusted, to rank a commercial bank against a hydropower company against an insurer against a noodle manufacturer, which is exactly what happens every day in NEPSE Facebook groups and brokerage WhatsApp threads.
The reason cross-sector ROE comparison misleads is structural, not incidental. A commercial bank in Nepal typically carries assets nine to eleven times the size of its equity — deposits fund most of the balance sheet, and a thin equity sliver absorbs both the risk and, when things go well, an outsized share of the return. A hydropower company under construction may be sitting on IPO proceeds parked in fixed deposits, generating no operating revenue at all, so its ROE looks near zero — not because the business is bad, but because the business has not started yet. A life insurance company's profit is driven as much by where its investment department parks policyholder reserves as by how well its actuaries priced the policies. A manufacturing company with a decades-old, barely-diluted paid-up capital base can show a triple-digit ROE off a genuinely modest operating margin, simply because the equity denominator is so small.
None of these numbers are wrong. They are all correctly calculated. What is wrong is reading them as if they measured the same thing.
Why ROA is the more honest cross-sector yardstick
Because ROA divides profit by total assets rather than by equity, it strips out the leverage effect and gets closer to a pure measure of operating efficiency — how much profit a company extracts from every rupee it deploys, regardless of whether that rupee came from shareholders or depositors or bondholders. This is precisely why bank ROA figures look deceptively small (often under 2%) next to bank ROE figures that can reach the mid-teens: banks are leverage machines by design, regulated to run on thin equity cushions, and ROA reflects that reality honestly while ROE amplifies it.
KEY CONCEPT
ROE tells you how much a company earned for its owners. ROA tells you how much a company earned from everything it owns, owners and lenders combined. A high ROE built on high leverage is a different animal from a high ROE built on high margins — and only ROA, or a full DuPont breakdown, tells you which one you are looking at.
The practical implication for a Nepali retail investor is this: never let ROE alone decide a buy or sell. Pair it with ROA, and — as this chapter will show in Lesson 40.2 — decompose it further with DuPont analysis before drawing conclusions about quality of earnings. A hydropower stock at 4% ROE two years after commissioning may be a better long-term hold than a finance company at 14% ROE built on aggressive leverage into a rate-sensitive book. The number alone cannot tell you that. The sector context, and the components underneath the number, can.
WARNING
Screening NEPSE stocks by a single ROE cutoff (e.g., "only buy above 15% ROE") without sector adjustment systematically overweights leveraged financials and thin-equity manufacturers, and systematically excludes newly commissioned hydropower and early-stage insurers — regardless of their actual long-run quality.
This chapter walks through profitability the way a working analyst actually has to: sector by sector, with the specific mechanics that make each sector's ROE and ROA behave differently, and then a method for putting them back on a common footing when you genuinely need to compare across sectors — say, when deciding whether your next lakh goes into a bank or a hydropower IPO.
Lesson 40.2 — Banks: Leverage, Net Interest Margin, and the Regulatory Ceiling on Returns
Banking is the sector where ROE and ROA diverge most dramatically, and understanding why requires understanding the bank balance sheet itself. A commercial bank does not manufacture anything or generate electricity; it borrows short (deposits) and lends long (loans and advances), pocketing the spread, and it does this at a scale many multiples of its own equity. Nepal Rastra Bank's minimum paid-up capital requirement for commercial banks — NPR 8 billion, in place since the 2015 merger-driven capital hike — sits underneath balance sheets that, for the larger banks, now exceed NPR 150-250 billion in assets. That is an equity multiplier of roughly nine to twelve times, and it is precisely why bank ROE numbers look so much larger than bank ROA numbers.
The sector-wide numbers, and why they moved
Nepal's banking sector has been through a difficult multi-year stretch of tight liquidity, high provisioning for non-performing loans, and margin compression, and the profitability data reflects it plainly. Nepal Rastra Bank's own Financial Stability Report data and sector coverage through FY2024/25 show the aggregate Return on Equity for commercial banks falling into the high single digits — reported as low as roughly 7.7% in one widely cited aggregate reading — down from the low-to-mid teens the sector used to post in the years before the 2022-2023 liquidity crunch. A separate reading of the top ten banks by size for the second quarter of FY2081/82 put aggregate ROE at around 9.4%. Individual banks still vary widely around that average: well-capitalised, well-managed banks with strong CASA (current and savings account) deposit franchises can post ROE in the mid-teens even in a soft year, while smaller or asset-quality-challenged banks can sit in the low single digits or, in a bad year, near zero. ROA across the sector, consistent with the leverage story above, typically sits in a much narrower band of roughly 1.0% to 1.8%, rarely straying far from that regardless of which individual bank you look at — because ROA is a function of margin and efficiency, which move slowly, while ROE amplifies whatever leverage a bank happens to be running.
REGULATORY DETAIL
Nepal Rastra Bank directly constrains bank profitability through several levers that have no equivalent in other NEPSE sectors: a mandated interest rate spread ceiling (historically around 4.4 percentage points between weighted average lending and deposit rates for commercial banks, with a somewhat wider allowance for development banks and finance companies), minimum capital adequacy ratios under the Basel-based framework, a credit-to-core-capital-and-deposit (CCD) ratio ceiling capping how aggressively a bank can lend against its funding base, and countercyclical loan-loss provisioning rules. Every one of these directly caps how high bank ROE can structurally go — a bank cannot simply widen its spread to boost NIM the way a manufacturer can raise prices to protect margin.
Net Interest Margin — the bank-specific profitability metric
No other NEPSE sector has an equivalent to Net Interest Margin, because no other sector's core business is borrowing at one rate and lending at another. NIM is calculated as:
NIM = Net Interest Income / Average Interest-Earning Assets
where Net Interest Income is interest income (from loans, investments in government securities, interbank placements) minus interest expense (on deposits, borrowings, debentures). NIM is the engine room of bank profitability — it drives the bulk of operating income, before fee income, and ultimately drives both ROA and ROE. Nepali commercial banks have typically operated with NIM in a range of roughly 3.0% to 4.5%, though the exact figure has compressed over the past two years as deposit rates rose faster than banks could reprice their loan books, and as intense competition for deposits during liquidity-tight periods pushed up the cost of funds. A bank's NIM trend, more than any single-period ROE snapshot, is the earliest and cleanest signal of whether its core lending business is getting healthier or getting squeezed — it moves before ROE does, because ROE also absorbs one-off items like loan loss provisioning reversals, tax adjustments, and trading gains that can mask or exaggerate the underlying trend for a quarter or two.
PRACTICAL TOOL
When comparing two Nepali banks, look at NIM trend over at least four consecutive quarters before looking at the latest ROE. A bank with rising NIM and falling ROE (because of a one-off provisioning charge) is usually the better long-term hold than a bank with falling NIM and a currently-flattering ROE.
DuPont analysis — decomposing a bank's ROE
DuPont analysis breaks ROE into three multiplicative components, so that instead of one opaque number you get three transparent ones:
ROE = Net Profit Margin x Asset Turnover x Equity Multiplier
= (Net Profit / Total Operating Income) x (Total Operating Income / Average Total Assets) x (Average Total Assets / Average Equity)
For a bank, "Total Operating Income" is the appropriate revenue proxy — net interest income plus fee and commission income plus other operating income — rather than gross interest income, because gross interest income overstates the bank's actual economic throughput (a large chunk of it simply gets paid back out as interest expense to depositors).
Consider a worked, illustrative example built to match the disclosure format of a typical mid-to-large Nepali commercial bank (figures below are a stylized composite for teaching purposes, not a specific bank's actual reported numbers):
Line item (NPR crore)
Illustrative Bank Ltd.
Average total assets
18,500
Total operating income
950
Net profit after tax
240
Average shareholders' equity
1,850
From this:
DuPont component
Formula
Value
Net Profit Margin
240 / 950
25.3%
Asset Turnover
950 / 18,500
5.1%
Equity Multiplier
18,500 / 1,850
10.0x
ROE (product of the three)
25.3% x 5.1% x 10.0
12.9%
ROA (check: Net Profit / Assets)
240 / 18,500
1.3%
Notice what the decomposition reveals that the headline 12.9% ROE alone does not: this bank's operating margin (25.3%) is healthy and its asset efficiency (5.1% — essentially its "yield" on the whole balance sheet) is unremarkable but normal for the sector, and the entire ROE outcome depends heavily on the 10.0x equity multiplier. If Nepal Rastra Bank tightened capital adequacy requirements and forced this bank to raise fresh equity, pushing the multiplier down to 8.0x, ROE would fall to roughly 10.3% even with identical operating performance — nothing about the underlying business changed, only its capital structure did. This is exactly the kind of shift NEPSE investors lived through during the 2022-2023 period, when several banks issued rights shares or FPOs to meet capital requirements, diluting equity and mechanically compressing ROE even as NIM and asset quality were, in some cases, stable or improving.
CASE IN POINT
When a Nepali bank announces a large rights issue, expect ROE to dip mechanically in the following one to two years purely from equity-base dilution, before profit growth catches up. Analysts who sold such stocks purely on a falling ROE reading, without checking whether NIM and net profit margin were holding steady, missed the recovery that followed once the enlarged capital base was put to work.
The DuPont framework is also the cleanest way to compare two banks that post similar ROE through very different means — one bank might get there through a fat margin and modest leverage, another through a thin margin and heavy leverage. The first is the more resilient business in a downturn; the second is the one that gets hurt fastest when asset quality deteriorates, because its equity cushion is thinner relative to its asset book.
Lesson 40.3 — Hydropower: The Pre-COD Mirage and the Post-COD Reality
Hydropower is the sector where a single date on the calendar splits a company's entire profitability profile in two. That date is the Commercial Operation Date (COD) — the day the plant is commissioned, synchronized to the grid, and begins actually selling electricity to the Nepal Electricity Authority under its Power Purchase Agreement (PPA). Before COD, a hydropower company earns essentially no operating revenue. After COD, it becomes, in effect, an annuity-like cash generator with a fixed offtake price and a fixed offtake buyer (NEA) for the life of the PPA, typically 25 to 35 years, with generation subject mainly to hydrology.
Why pre-COD ROE is close to meaningless
Under Nepal Financial Reporting Standards (aligned with NAS 23 on borrowing costs), interest paid on construction-period loans is capitalised into the cost of the project rather than expensed through the profit and loss statement while the plant is under construction. This is the correct accounting treatment, but it has a side effect that trips up unwary investors: a hydropower company under construction shows little to no interest expense and little to no revenue, so its reported net profit during this phase is often a small positive number — arising almost entirely from bank interest earned on IPO proceeds and promoter capital sitting in fixed deposits while construction is underway — rather than from anything resembling the eventual power-generation business. A 1-2% ROE on a pre-COD hydropower company tells you almost nothing about what its ROE will look like once turbines are spinning. Investors who screen hydropower IPOs by pre-COD profitability metrics are, without realising it, screening for which promoters parked their construction float most efficiently in fixed deposits — not for which project will generate the best returns.
WARNING
A pre-COD or newly-commissioned hydropower company's trailing ROE and ROA are not forward indicators of its post-ramp-up profitability. Read the Detailed Project Report's projected generation, tariff schedule, and debt-service coverage instead of the trailing financial ratios when evaluating a hydropower IPO.
The post-COD ramp-up, and why it is lumpy
Commissioning does not flip a switch to full profitability overnight. In the first one to two years after COD, most Nepali hydropower companies go through a ramp-up period: initial capacity utilisation is often below design levels as operational teams learn the plant's actual hydrology response, minor defects liability issues get resolved with the EPC contractor, and — critically for run-of-river plants, which make up the overwhelming majority of NEPSE-listed hydropower — generation is highly seasonal. Wet-season months (roughly Ashadh through Ashoj, June through October) can generate several times the electricity of dry-season months (Poush through Chaitra, December through March), when river flows fall and some run-of-river plants generate at a fraction of installed capacity. A quarterly ROA or ROE reading taken in isolation, without knowing which season it covers, is close to uninterpretable for a run-of-river hydropower stock — a strong wet-season quarter can flatter annualized ROE readings, and a dry-season quarter can make a perfectly healthy company look unprofitable. NEPSE Trading's coverage of names such as Khanikhola Hydropower illustrates the pattern directly: stable or growing revenue on a full-year basis while the company continued to post losses in specific reporting periods, a combination that only makes sense once you separate seasonal generation swings and ramp-up effects from a genuine deterioration in the underlying business.
Once a hydropower company has run through two to three full hydrological cycles post-COD, its profitability profile stabilises and becomes genuinely comparable across companies and across years. Mature, well-established plants — the classic examples on NEPSE being names like Chilime Hydropower Company and the state-linked Upper Tamakoshi Hydroelectric project — have built long dividend-paying track records once past this maturation phase, with Chilime in particular known for consistent double-digit cash dividend payouts in mature years, reflecting a business that, once ramped up, throws off free cash flow well in excess of what it needs to reinvest.
Phase
Typical ROE pattern
Why
Pre-COD (construction)
~0-2%, driven by treasury interest, not operations
Borrowing costs capitalised (NAS 23); no operating revenue yet
Full design capacity utilisation; high fixed-cost, high-margin cash generation; leverage effect from project debt now fully "working"
CASE IN POINT
Illustrative construction-to-maturity trajectory for a mid-sized run-of-river plant (stylized, NPR crore): Year -1 (pre-COD): Revenue 0, Net Profit 4 (treasury income only), Equity 120, ROE ~3%. Year 1 (post-COD, ramp-up): Revenue 65, Net Profit 8, Equity 128, ROE ~6%. Year 4 (mature): Revenue 95, Net Profit 22, Equity 145, ROE ~15%. The jump from Year 1 to Year 4 is not a change in the underlying asset — it is the same plant, on the same PPA tariff, simply having worked through ramp-up and now running at design capacity with debt service well covered.
The other structural driver of hydropower ROE, once mature, is leverage, and here hydropower resembles banking more than it resembles manufacturing: Nepali hydropower projects are typically financed at debt-to-equity ratios of roughly 70:30 or even 80:20 during construction, funded by a syndicate of commercial banks and development banks under NRB's hydropower lending exposure norms. Once operational and cash-generative, this high leverage means that even a modest return on the total project asset base translates into a much larger return on the comparatively thin equity slice — the same equity-multiplier mechanic that drives bank ROE, just applied to a physical, single-asset business instead of a loan book. This is precisely why mature hydropower ROE can look surprisingly close to bank ROE despite the two sectors having almost nothing else in common operationally.
Lesson 40.4 — Insurance: When Investment Income, Not Underwriting, Drives ROE
Insurance is the sector where the profitability story most often gets told backwards by casual NEPSE commentary. The instinctive assumption is that an insurer's profit comes from underwriting — collecting more in premiums than it pays out in claims and expenses. For Nepali life insurers in particular, that assumption is largely wrong, and understanding why is essential to reading their ROE correctly.
The life insurance profit engine: the investment portfolio
A life insurance company sits on a large and growing pool of policyholder reserves — the life fund — built up because premiums are collected upfront (often annually or in lump sums) while claims and maturity benefits are paid out years or decades later. Nepal's insurance regulator, the Nepal Insurance Authority (the renamed successor to the former Beema Samiti), mandates how this pool must be invested: a large share in government securities and bank fixed deposits, a regulated ceiling on equity market exposure, and specific limits on real estate and other asset classes. The scale of this life fund, for an established insurer like Nepal Life Insurance Company — the largest life insurer listed on NEPSE — dwarfs the company's own paid-up equity capital many times over, and the investment income this fund throws off (interest on government bonds, fixed deposit interest, dividend income, and realised gains on the regulated equity sleeve) typically contributes the majority of reported net profit in a normal year, far outweighing the technical underwriting result.
This has a direct consequence for ROE: because the equity base of most Nepali insurers is comparatively small relative to the size of the life fund and total assets they manage, and because investment income is largely a function of prevailing government securities yields and fixed deposit rates rather than underwriting skill, insurer ROE tends to move with interest rate cycles almost as much as it moves with premium growth or claims experience. Established, well-capitalised life insurers have historically posted ROE in the wide range of roughly 12% to 25%+ in strong years, materially higher than the banking sector average, precisely because investment income compounds on a large fund base sitting atop a comparatively thin equity layer — the insurance-sector version of the same leverage-like effect seen in banking and hydropower, except here the "leverage" is policyholder reserves rather than deposits or project debt.
KEY CONCEPT
For a life insurer, always separate the profit and loss statement into its two halves before trusting the ROE headline: the underwriting result (premium income less claims, commissions, and policy acquisition costs) and the investment result (income earned on the life fund). A rising ROE driven by rising underwriting margins is a different, more durable quality signal than a rising ROE driven by a temporary spike in government securities yields or one-off gains from an equity sleeve.
Non-life insurance: thinner margins, more claims volatility
Non-life insurers (motor, fire, marine, engineering, and miscellaneous classes) run a structurally different book: shorter-tail liabilities, faster premium-to-claim turnaround, and a combined ratio (claims plus expenses, as a share of earned premium) that investors should track alongside ROE. Non-life underwriting profitability in Nepal has been persistently pressured by high motor claims frequency and price competition among the many licensed non-life insurers, meaning that, for this sub-sector even more than for life insurance, investment income on the comparatively smaller technical reserves is what keeps ROE positive and respectable in years when the combined ratio deteriorates. Non-life insurer ROE in Nepal has typically run somewhat lower and more volatile than life insurer ROE — commonly in a rough 8% to 15% band — reflecting both thinner reserve bases to invest and more claims-driven earnings volatility.
CASE IN POINT
Sharesansar's quarterly reviews of Nepal's non-life insurance sector routinely show a wide dispersion of quarter-on-quarter profit growth across individual companies even when written premium growth across the sector looks broadly similar — the swing factor between insurers is disproportionately the investment income line and one-off claims experience, not underlying business growth.
CAUTION
Do not value or rank Nepali insurers purely on trailing ROE without checking the interest rate environment during the measurement period. An insurer's ROE can look excellent purely because government securities yields were elevated during that fiscal year — a macro tailwind, not an underwriting achievement — and can just as easily compress when yields fall, with no change in how well the company is actually pricing and managing risk.
The other feature specific to insurance is Nepal Reinsurance Company Limited, the country's sole domestic reinsurer, which sits one layer removed from retail policyholders and whose profitability is driven by the ceded-premium share it accepts from primary insurers and its own investment portfolio — a useful reminder that "insurance sector" on NEPSE actually spans several distinct business models (life, non-life, reinsurance) each with its own profitability drivers, not one homogeneous group.
Lesson 40.5 — Manufacturing and Consumer Names: High Margins, Thin Equity, and the Illusion of an Extraordinary ROE
Manufacturing and consumer-goods names are a small but closely watched corner of NEPSE, dominated by long-established, brand-backed companies — Unilever Nepal Limited being the most frequently cited example, alongside bottling and beverage names tied to global consumer brands operating in Nepal. These companies behave almost nothing like banks, hydropower plants, or insurers, and their ROE patterns require a third, entirely separate mental model.
Why some manufacturing ROE numbers look almost unbelievable
Unilever Nepal is the textbook case. It carries an unusually small paid-up capital base relative to the scale of its operating profit — a legacy of decades of retained-earnings accumulation without proportionate equity dilution, combined with genuinely strong operating margins from a portfolio of established branded consumer products (soaps, detergents, personal care, tea) with real pricing power and distribution reach across Nepal. The arithmetic consequence is an ROE that can run into the tens of percent, and in some fiscal years has been reported far above what almost any bank, insurer, or hydropower company on NEPSE could plausibly post — not because the operating business is dozens of times better run than a well-managed bank, but because the equity denominator is so small relative to the profit numerator. A company can have a perfectly ordinary net profit margin and a perfectly ordinary asset turnover and still produce an extraordinary ROE if its equity multiplier (assets divided by equity, or in a simpler single-business context, the inverse of how much of the balance sheet is actually funded by shareholders' paid-up capital and reserves) is unusually high because of a thin paid-up capital history.
CAUTION
A manufacturing or consumer stock's headline ROE can be structurally inflated by a small legacy equity base rather than by exceptional current operating performance. Always cross-check with ROA and with the operating margin directly — a consumer company with a genuinely strong 15-20% net profit margin and healthy asset turnover is a good business regardless of what its ROE says; the ROE figure on its own, in a thin-equity name, tells you more about capital structure history than about current execution.
The more representative picture: ROA and operating margin
Because manufacturing and consumer companies do not run on financial leverage the way banks and (mature) hydropower companies do, their ROA is a far more representative and comparable figure — typically in a healthy mid-to-high single digit to low double-digit range for average NEPSE-listed manufacturers, and distinctly higher, sometimes reaching into the high teens or twenties, for the strongest branded consumer names with genuine pricing power and asset-light operating models (contract bottling and manufacturing arrangements, for instance, keep the fixed-asset base lean relative to revenue). The gap between a manufacturing company's very high ROE and its far more modest — though still strong — ROA is the clearest possible illustration of why ROE alone, unadjusted for capital structure, is the wrong tool for cross-sector comparison.
Metric
Typical NEPSE manufacturing/consumer pattern
What drives it
Net profit margin
Often 10-25% for branded consumer names
Pricing power, distribution strength, low input-cost volatility pass-through
Asset turnover
Moderate to high (asset-light models)
Outsourced or lean manufacturing footprint relative to revenue
Equity multiplier
Can be very high in legacy thin-equity names
Small historical paid-up capital base relative to accumulated reserves
Resulting ROE
Can range from respectable to extreme (50%+ in some names, some years)
Product of the three components above — driven disproportionately by the multiplier in the thinnest-equity names
ROA (more comparable across sectors)
Generally mid-single-digit to low-20s%, depending on brand strength
Reflects genuine operating efficiency, not capital structure history
PRACTICAL TOOL
When you see a NEPSE manufacturing stock advertised on social media for its eye-catching ROE, immediately pull up its paid-up capital figure relative to its reserves and surplus. A paid-up capital that is a small fraction of total equity, which is itself a small fraction of the profit being earned, is the signature of a thin-equity-driven ROE — worth knowing about, but not a like-for-like comparison with a bank or hydropower company's ROE.
This is also the sector where dividend policy interacts most visibly with ROE: because these are often mature, low-capex-growth businesses that do not need to retain much capital for expansion, they tend to pay out a high proportion of profit as cash dividends, which keeps the equity base from growing much year to year — reinforcing, rather than correcting, the thin-equity, high-ROE pattern over time. A growing manufacturer that is reinvesting heavily (building new capacity, expanding distribution) will show equity growing faster than a mature payout-focused one, and its ROE trend should be read in that light — a declining ROE at a growth-phase manufacturer can simply reflect a larger equity base doing more (as-yet-unrealized) work, not a deteriorating business.
Lesson 40.6 — Building Your Own Cross-Sector Benchmark Table
Having walked through banking, hydropower, insurance, and manufacturing separately, the practical task for a Nepali investor holding — or considering — positions across more than one of these sectors is to build a mental (or literal, in a spreadsheet) benchmark table that lets you judge each stock against its own sector's realistic range, not against a single NEPSE-wide number that does not actually exist for any of them.
Sector
Typical ROE range
Typical ROA range
Primary profitability driver
Key distortion to watch for
Commercial banks
~8-16% (sector average recently ~7.7-9.4%)
~1.0-1.8%
NIM x leverage (equity multiplier ~9-12x)
Capital raises mechanically dilute ROE; NIM trend is the leading indicator
Hydropower, pre-COD
~0-2%
~0-1%
Treasury interest on idle IPO/construction funds
Not predictive of post-COD profitability at all
Hydropower, post-COD (mature)
~8-20%+
~5-10%
PPA tariff x capacity utilisation x project leverage
Strong seasonal (wet/dry season) swings quarter to quarter
Life insurance
~12-25%+
~2-4%
Investment income on life fund, more than underwriting
Rides interest rate cycles; underwriting result can be masked
Non-life insurance
~8-15%
~3-5%
Combined ratio + investment income on technical reserves
Claims volatility (esp. motor); thinner reserve base than life
Manufacturing/consumer (branded)
Wide — respectable to 50%+ in thin-equity names
~5-20%+
Net margin x asset turnover, amplified by low equity base
Extreme ROE often reflects legacy thin equity, not current execution
Three rules for using this table honestly
First, always locate a company within its own sector's row before judging the number — a 12% ROE is disappointing for a strong life insurer in a normal rate year, roughly average for a bank, and quite good for a two-year-old hydropower plant still working through its ramp-up. Second, always look at the ROE trend and the ROA alongside it, not the ROE level in isolation for a single period — a bank's ROE dip after a rights issue, a hydropower plant's ROE swing between wet and dry season quarters, and an insurer's ROE bump from a good year for government securities yields are all explainable by mechanics this chapter has covered, and none of them, by themselves, should trigger a buy or sell decision. Third, when you do need to compare across sectors — for instance, deciding whether fresh investable funds should go into a bank stock or a hydropower IPO — use ROA and the DuPont components, not headline ROE, as your primary cross-sector yardstick, and treat sector-specific metrics (NIM for banks, capacity utilisation and PPA tariff for hydropower, combined ratio and investment yield for insurers) as the qualitative context that explains why the ROA is what it is.
PRACTICAL TOOL
Before comparing two NEPSE stocks from different sectors on profitability, write down, side by side: (1) ROE, (2) ROA, (3) the equity multiplier (Assets / Equity), and (4) the sector-specific driver metric (NIM, capacity utilisation, combined ratio, or net margin, as applicable). If the ROE gap between the two stocks mostly disappears once you look at ROA, the "better" stock is largely a leverage story, not an operating-quality story — worth knowing before you commit capital.
Applying this discipline does not mean profitability ratios stop being useful for cross-sector decisions — it means using them correctly. A retail investor who understands that a hydropower company's low ROE in year one post-COD is structural and temporary, that a bank's ROE compression after a rights issue is mechanical and often temporary, that an insurer's outstanding ROE in a high-yield year partly reflects the interest rate cycle rather than management skill, and that a manufacturer's spectacular ROE partly reflects decades-old capital structure rather than current brilliance — that investor is reading the same numbers everyone else on NEPSE sees, but reading them correctly, sector context and all.
Chapter recap
This chapter set out to answer a question that trips up even experienced NEPSE participants: why does a "good" ROE mean something completely different in a bank, a hydropower company, an insurer, and a manufacturer, and how should an investor compare profitability across these fundamentally different business models? The answer runs through capital structure as much as through operating performance. ROE is a function of three multiplicative components — net profit margin, asset turnover, and the equity multiplier — and in Nepal's four major listed sectors, each of these three components takes on a wildly different weight, producing headline ROE figures that are not directly comparable even when they look numerically similar.
For banks, ROE is dominated by the equity multiplier — a structural feature of a regulated, deposit-funded business running at nine to twelve times leverage — layered on top of Net Interest Margin, the bank-specific profitability engine that has compressed sector-wide in recent years, with aggregate commercial bank ROE falling into the high single digits even as ROA has stayed in its normal, narrower 1.0-1.8% band. The DuPont breakdown worked through in Lesson 40.2 showed concretely how a rights issue or capital adequacy tightening can mechanically depress a bank's ROE without any change in its underlying margin or efficiency — a distinction only visible once the ROE is decomposed rather than read as a single number.
For hydropower, the Commercial Operation Date is the single most important fact governing how to read profitability at all: pre-COD ROE reflects treasury income on idle construction funds, not a functioning power business, while post-COD ROE goes through a lumpy, seasonally volatile ramp-up before stabilising — often into the double digits — once a plant has worked through several full hydrological cycles at design capacity, with high project leverage then working in the equity holder's favour much as it does for a bank.
For insurance, the chapter's central insight was that investment income on the life fund or technical reserves, not underwriting skill, is typically the dominant driver of reported ROE, particularly for life insurers, whose ROE consequently tracks government securities yields and fixed deposit rates as much as it tracks premium growth or claims experience — meaning an excellent ROE year for an insurer deserves closer scrutiny of its underwriting result before being read as a signal of management quality.
For manufacturing and consumer names, exemplified by companies like Unilever Nepal, the chapter showed how a thin, often decades-old paid-up capital base can produce an ROE many multiples higher than an equally well-run bank or insurer, simply through the equity-multiplier arithmetic — and why ROA and net profit margin, not ROE, are the more honest metrics for judging whether the underlying operating business is genuinely excellent or merely thinly capitalised.
The unifying lesson across all four sectors is the same one this book returns to throughout Part VII: a ratio is only as useful as the analyst's understanding of what sits underneath it. Sector-specific benchmarking — knowing the realistic ROE and ROA range for a bank versus a hydropower company versus an insurer versus a manufacturer, and knowing the specific mechanical drivers (NIM, COD timing, investment income, equity-base history) that produce each sector's numbers — is not an optional refinement for advanced investors. It is the minimum diligence required before a single rupee moves between sectors on the strength of a profitability ratio alone.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
First published 23 Aug 2026 · Last verified 29 Aug 2026
A bank with 22 percent return on equity can still be a value trap if its loan book is quietly rotting. A hydropower company with a 9 percent tariff escalation clause can still default on its term loan if its interest coverage has slipped below one. An insurer can report a rising premium book and still be unable to pay a large claim if its solvency margin has eroded. None of these facts show up in the price-earnings ratio. They show up in a separate family of ratios — asset quality, capital, solvency, debt-servicing, and efficiency — that most retail investors in Nepal skip because they look like regulatory paperwork rather than investment analysis. This chapter closes that gap and, with it, closes out Part VII of this book. By the end of Lesson 41.6 you will have every ratio category needed to underwrite a NEPSE-listed company the way a credit officer or an institutional analyst would — not just the way a headline-chasing retail trader does.
Lesson 41.1 — Asset Quality: The NPL Ratio and Provisioning Coverage Ratio
Every rupee a bank or development bank lends is a rupee that might not come back. Asset quality ratios measure how much of the loan book has already gone bad, and how well the bank has cushioned itself against the part that has.
What counts as non-performing Nepal Rastra Bank classifies loans into five categories: Pass, Watchlist, Substandard, Doubtful, and Loss. A loan is "non-performing" once it falls into Substandard, Doubtful, or Loss — broadly, once principal or interest is overdue beyond the regulatory grace period (90 days past due for most categories, with sub-classifications carrying escalating provisioning requirements). The Non-Performing Loan (NPL) ratio is:
NPL Ratio = Non-Performing Loans ÷ Total Loans and Advances
This single number tells you what fraction of the bank's earning assets are no longer reliably earning anything.
KEY CONCEPT
The NPL ratio is a stock measure taken at a point in time, not a flow. A bank can report strong quarterly profit and a rising NPL ratio in the same quarter — profit is booked on performing loans while the NPL ratio quietly tracks the accumulating backlog of loans that have stopped performing. Never let a strong profit headline substitute for checking the NPL trend line.
The sector picture has moved fast. NRB's Key Financial Indicators as of Asadh-end 2082 (mid-July 2025) put the system-wide NPL ratio at roughly 3.66 percent. By the time NRB's Financial Stability Report covered the third quarter of FY 2025/26, the sector-wide figure had jumped to 5.60 percent — a sharp deterioration within a matter of quarters, driven by a loan quality review that pushed several banks to reclassify restructured and rolled-over loans more conservatively. This is exactly the kind of move an investor tracking only the P/E ratio would miss entirely.
Cross-bank dispersion is the real story, though — the sector average hides enormous variation between individual banks:
Bank
NPL Ratio (Q3 FY 2025/26)
Notes
Everest Bank Limited
0.61%
Lowest in the sector; up slightly from 0.38% at Asadh-end 2082
Standard Chartered Bank Nepal
1.81%
Foreign-parent bank; conservative underwriting book
Nepal SBI Bank
2.53%
Consistently below sector average
Siddhartha Bank
3.71%
Near sector average
Nabil Bank
4.37%
Largest private bank by assets
Rastriya Banijya Bank
4.48%
State-owned; large legacy book
Himalayan Bank
7.68%
Post NIC Asia-Himalayan merger book
Nepal Investment Mega Bank
8.41%
Post-merger integration effects
Prabhu Bank
8.84%
Among the highest in the sector
NIC Asia Bank
8.85%
Highest among large commercial banks
CASE IN POINT
Everest Bank Limited has held the lowest NPL ratio among commercial banks across two consecutive reporting periods — 0.38 percent at Asadh-end 2082 and 0.61 percent a few quarters later — while NIC Asia Bank and Prabhu Bank sat near 8.85 percent and 8.84 percent respectively in the same later period. A roughly fourteen-fold gap between the best and worst asset quality in the same sector, under the same regulator, in the same economy, tells you that "it's a bank stock" is not an investment thesis. Which bank matters enormously.
Provisioning coverage: the second half of the story A rising NPL ratio is a red flag; whether it is a survivable one depends on the Provisioning Coverage Ratio (PCR):
PCR = Loan Loss Provisions ÷ Gross Non-Performing Loans
NRB requires escalating provisioning by category — a Watchlist loan might require 5 percent provisioning, Substandard 25 percent, Doubtful 50 percent, and Loss 100 percent. A bank whose NPL book is concentrated in Loss-category loans but which has provisioned adequately (PCR near or above 100 percent) has already absorbed the pain in its books; a bank with the same NPL ratio but a PCR of 40–50 percent is still sitting on unrecognized losses that will hit future quarters' profit and, ultimately, its capital.
Worked example: Suppose a mid-sized commercial bank reports gross NPLs of Rs 8.40 billion against total loans of Rs 190 billion (NPL ratio = 4.42 percent) and has booked loan loss provisions of Rs 5.30 billion.
PCR = 5.30 ÷ 8.40 = 63.1 percent
That leaves Rs 3.10 billion of the NPL book unprovisioned. If even half of that eventually needs writing off, the bank absorbs a one-time hit of roughly Rs 1.55 billion — for a bank with, say, Rs 24 billion of core capital, that is a manageable 6.5 percent capital hit, uncomfortable but not solvency-threatening. Run the same arithmetic with a PCR of 35 percent instead of 63 percent and the unprovisioned exposure more than doubles, which is the kind of stress test every investor should run mentally before buying a bank purely on trailing P/E.
Provisioning figures reported in rupee terms alone (not ratio terms) can look reassuring purely because a bank is larger. NIC Asia Bank's reported loan loss provisioning of roughly Rs 22.32 billion sounds enormous in isolation — but scale it against its own NPL base and total loan book before concluding the bank is well-cushioned. Always convert provisioning to a ratio (PCR) before comparing across banks of different sizes.
Watchlist loans: the leading indicator most investors skip Below the NPL line sits a category most quarterly disclosures report but few retail investors read: Watchlist loans — performing loans, technically, but flagged for early-warning signs such as repeated restructuring requests, sectoral stress (tourism, real estate, hospitality), or a single large borrower breaching a loan covenant. A bank can hold a stable or even improving NPL ratio for two or three quarters while its Watchlist book quietly balloons — and Watchlist loans are the pipeline that becomes tomorrow's Substandard and Doubtful loans. When a bank's Watchlist-to-total-loans figure moves from, say, 2 percent to 5 percent in a single reporting period, treat it as an early warning on the NPL ratio one to two quarters ahead of when the number itself moves, not as a footnote to skip past on the way to the profit figure.
Lesson 41.2 — Capital Adequacy Ratio Recap and Its Role in Valuation Screening
Chapter 39 introduced the mechanics of the Capital Adequacy Ratio (CAR) in the context of bank profitability constraints. Here we recap the formula briefly and then focus on the part most retail investors never do: using CAR as a valuation screening filter, not just a compliance checkbox.
CAR = (Tier 1 Capital + Tier 2 Capital) ÷ Risk-Weighted Assets (RWA)
Under NRB's Basel III-aligned Capital Adequacy Framework, Class "A" commercial banks must maintain a minimum Common Equity Tier 1 (CET1) ratio of 4.5 percent, a minimum Tier 1 ratio of 6 percent, a minimum total capital ratio of 8.5 percent, and — critically — an additional Capital Conservation Buffer (CCB) of 2.5 percent, bringing the effective minimum total CAR to 11 percent of risk-weighted assets. Fall below that 11 percent line and NRB's dividend directive kicks in: banks are restricted from distributing cash dividends until the buffer is rebuilt.
REGULATORY DETAIL
NRB mandates a minimum CAR of 11 percent (inclusive of the 2.5 percent capital conservation buffer) for Class "A" commercial banks. A bank that falls below this threshold faces regulatory restrictions on cash dividend distribution — meaning a CAR breach doesn't just threaten solvency, it directly threatens the dividend yield an investor is underwriting the stock for.
As of Asadh-end 2082 (mid-July 2025), the sector-wide average CAR stood near 12.78 percent (core capital adequacy near 10.03 percent) — comfortably above the 11 percent floor, but with real dispersion underneath:
Bank
Total CAR (Asadh-end 2082 / mid-July 2025)
Buffer above 11% minimum
Standard Chartered Bank Nepal
17.82%
+6.82 pp
Prabhu Bank
13.90%
+2.90 pp
Nepal Investment Mega Bank
13.73%
+2.73 pp
NIC Asia Bank
13.42%
+2.42 pp
Agriculture Development Bank
13.36%
+2.36 pp
Nepal Bank Limited
13.06%
+2.06 pp
Global IME Bank
12.97%
+1.97 pp
NMB Bank
12.03%
+1.03 pp
Nabil Bank
11.94%
+0.94 pp
Rastriya Banijya Bank
11.84%
+0.84 pp
Siddhartha Bank
11.77%
+0.77 pp
Himalayan Bank
11.16%
+0.16 pp
Himalayan Bank's 0.16 percentage-point cushion is the number that should stop a dividend-focused investor cold — a single adverse quarter of credit-loss recognition (which, given Himalayan's NPL ratio of 7.68 percent noted in Lesson 41.1, is a live risk) could push it below the regulatory floor and trigger a dividend freeze regardless of how the P&L otherwise looks.
CAR as a valuation screen, not just a compliance metric Here is the cross-reference this chapter promised: CAR headroom directly caps a bank's sustainable growth rate, and therefore its justified Price-to-Book multiple (covered in Chapter 38's P/BV framework). A bank cannot grow its risk-weighted loan book faster than its capital base allows without either raising fresh equity (diluting existing shareholders) or breaching CAR. The self-sustainable growth ceiling is approximately:
A bank with high ROE but a CAR buffer of only 0.16 percentage points above the floor cannot compound its book value the way its ROE alone suggests — it will be forced into a rights issue, a slower loan growth path, or a dividend cut. Conversely, Standard Chartered Nepal's 6.82 percentage-point buffer means it can grow its RWA book substantially before needing new capital, which is one reason foreign-parented banks with high CAR often trade at a valuation premium despite modest headline growth — the market is pricing in capital-funded compounding capacity, not just this year's earnings.
PRACTICAL TOOL
Before buying any bank stock for its dividend yield, calculate: (Current CAR − 11%) × Risk-Weighted Assets = Rupee cushion available before a dividend freeze becomes a regulatory risk. A bank with a thin cushion (under 1 percentage point) should be screened as a "dividend-at-risk" holding regardless of how attractive its trailing yield looks on a broker app.
CAR also feeds into a bank's cost of funds, and therefore into its own profitability ratios from earlier in this Part. A bank with a comfortable CAR buffer can raise subordinated (Tier 2) debentures more cheaply, because debenture holders and rating agencies read the buffer as a going-concern cushion; a bank hugging the regulatory minimum pays a higher coupon on the same instrument, or may be unable to issue Tier 2 debentures at all until it rebuilds capital through retained earnings or a rights issue. This is one reason CAR and Net Interest Margin move together over a multi-year horizon far more than a single quarter's numbers suggest — a bank forced to slow loan growth to protect CAR also slows the compounding of net interest income that ROE ultimately depends on.
Lesson 41.3 — Solvency Ratios for Insurance Companies
Insurance companies don't carry an NPL ratio or a CAR — they carry a structurally different risk: the possibility that claims exceed the assets set aside to pay them. The regulator here is the Nepal Insurance Authority (formerly the Insurance Board), and the key ratio is the Solvency Ratio:
Solvency Ratio = Available Solvency Margin ÷ Required Solvency Margin
The Required Solvency Margin is actuarially derived from the insurer's outstanding liabilities (technical reserves, unearned premium reserves, outstanding claims) and its risk exposure; the Available Solvency Margin is the excess of admitted assets over liabilities — economically, the insurer's true net worth cushion. The Nepal Insurance Authority sets a minimum solvency ratio of 1.5 for non-life (general) insurers and 1.3 for life insurers — meaning an insurer must hold at least 1.5x (or 1.3x) the actuarially required cushion before it is considered adequately capitalised to meet claims obligations.
A solvency ratio of 1.0 would mean an insurer holds exactly the actuarially required cushion — zero margin for error. A ratio of 2.67 (the recent non-life sector average) means the sector, on average, holds nearly two-and-a-half times the mandated minimum cushion. Read the ratio as a multiple of the regulatory floor, not as a percentage.
The most recent non-life insurance sector disclosure put the industry average solvency ratio at 2.67, up from 2.46 in the prior period — comfortably above the regulatory floor of 1.5, but again with wide dispersion:
Non-Life Insurer
Solvency Ratio
Vs. prior period
Nepal Insurance Company
4.32
Sector leader
Neco Insurance
4.06
+0.74
Shikhar Insurance
3.67
+0.34 (from 3.33)
IGI Prudential Insurance
3.42
+0.65 (from 2.77)
Himalayan Everest Insurance
3.32
—
Siddhartha Premier Insurance
3.18
—
Sagarmatha Lumbini Insurance
2.54
−1.53 (from 4.07)
United Ajod Insurance
2.19
—
Prabhu Insurance
1.94
—
NLG Insurance
1.73
—
The Oriental Insurance
1.60
Closest to the 1.5 floor
On the life insurance side, the Nepal Insurance Authority's most recent disclosure across twelve life insurers showed an industry average solvency ratio of roughly 4.44 against a minimum requirement of 1.3 — an even larger average cushion, reflecting the longer-duration, more conservatively reserved nature of life insurance liabilities.
CASE IN POINT
Sagarmatha Lumbini Insurance's solvency ratio fell from 4.07 to 2.54 in a single reporting period — a drop of 1.53 points, the sharpest deterioration in the non-life segment even though the resulting ratio (2.54) still sits comfortably above the 1.5 regulatory floor and above the sector average of the weaker names. This is the pattern investors should watch for: the direction of change in a solvency ratio often matters more than the absolute level, especially in the aftermath of insurance-sector mergers, where combined entities can see solvency ratios swing sharply as reserving methodologies are harmonized.
A merged insurance company's solvency ratio in the first one to two reporting periods after amalgamation should be read with extra skepticism for the same reason. Merging two actuarial reserve bases, two claims-reserving philosophies, and two capital structures can produce solvency ratios that swing by a full point or more in either direction purely from methodology harmonization — not from any real change in the underlying claims-paying capacity. Wait for at least two clean post-merger quarters before trusting the number.
Why this matters for an equity investor, not just a policyholder: an insurer operating close to the 1.5 (or 1.3) floor faces the same dividend and growth constraints a thinly-capitalised bank faces under CAR — the regulator can restrict dividend distribution, new product underwriting, or require a capital call, all of which directly affect the equity holder's return, independent of how strong the reported premium growth or combined ratio looks.
Lesson 41.4 — Debt-to-Equity and Interest Coverage: Hydropower and Manufacturing
Banks and insurers are regulated on capital and solvency because they are financial intermediaries. Hydropower and manufacturing companies are not subject to CAR or solvency rules — but they carry their own version of the same underlying question: can this company service its debt through a full business cycle? That is what the Debt-to-Equity (D/E) ratio and Interest Coverage Ratio (ICR) are built to answer.
D/E = Total Interest-Bearing Debt ÷ Total Shareholders' Equity
ICR = Earnings Before Interest and Tax (EBIT) ÷ Interest Expense
Hydropower: a sector built on debt by design Nepali hydropower projects are, by construction, among the most leveraged listed businesses on NEPSE. Project financing for run-of-river IPPs is conventionally structured at 70:30 or even 80:20 debt-to-equity during the construction phase — developers bring in the minimum promoter and public equity required to satisfy the Department of Electricity Development and lender covenants, then fund the bulk of construction cost through syndicated term loans from commercial banks and development banks. This is not a red flag by itself — it is how the entire sector is built, and it is exactly why interest coverage, not just D/E, is the ratio that separates a resilient hydropower stock from a fragile one.
Worked example (illustrative): Consider "Kalpataru Hydropower Ltd." (a hypothetical mid-sized run-of-river plant used here purely for illustration), a 24 MW project with total project cost of Rs 4.80 billion, financed 75:25 debt-to-equity:
Debt: Rs 3.60 billion at an average lending rate of 10.5 percent → annual interest expense ≈ Rs 378 million
Equity: Rs 1.20 billion
D/E = 3.60 ÷ 1.20 = 3.0x
Once commissioned and generating at a plant load factor of roughly 45 percent (typical for run-of-river design energy), annual energy sales at the PPA tariff might generate EBIT of roughly Rs 520 million.
ICR = 520 ÷ 378 = 1.38x
An ICR of 1.38x is thin — a single dry-season shortfall in river flow, an unplanned outage, or a PPA payment delay from the offtaker can push coverage below 1.0x, at which point the company is servicing debt out of reserves rather than operations. Compare this to the same project ten years into its 25-30 year loan amortisation schedule, where a declining principal balance and (if the loan is fixed-rate) unchanged tariff escalation typically push ICR up toward 2.5–3.5x as the debt burden shrinks relative to a stable or growing revenue base. This is precisely why mature, largely-delevered hydropower names on NEPSE (companies well past their loan amortisation midpoint) trade at different multiples than newly-commissioned or still-under-construction IPPs carrying peak leverage — the market is pricing the interest coverage trajectory, not just current-year EPS.
WARNING
Nepal's hydropower sector borrows almost entirely at floating rates tied to the base rate published by individual banks. When NRB tightens monetary policy and base rates rise — as happened through parts of FY 2079/80 and FY 2080/81 — every hydropower company's interest expense rises immediately while its PPA revenue (fixed or only slowly escalating under NEA contracts) does not. Always stress-test a hydropower company's interest coverage ratio against a 150–200 basis point increase in average lending rates before treating current-year ICR as durable.
Manufacturing: lower leverage, but still watch the cycle Nepali listed manufacturers — consumer-facing names like Bottlers Nepal, Unilever Nepal, and Nepal Lube Oil among them — typically run structurally lower D/E than hydropower or even banks, because their capex cycles are shorter, working capital (not fixed asset construction) drives most of their borrowing needs, and many are majority-owned by multinational parents that prefer equity-funded expansion. A D/E of 0.3x–0.8x is common in this group, versus 2x–4x for a hydropower company mid-construction or a bank (which is leveraged by design as a deposit-taking institution). Capital-intensive manufacturers — cement, steel rerolling, and cable manufacturing — sit in between, often in the 1.0x–2.0x range, reflecting genuine plant and machinery financing.
Sector Archetype
Typical D/E Range
Typical Interest Coverage
Why
Commercial bank
6x–10x (as a leveraged intermediary)
Not applicable (use CAR instead)
Deposits are liabilities by design; CAR, not D/E, is the relevant capital metric
Hydropower (under construction / early operation)
2.0x–4.0x
1.2x–1.8x
70:30 or 80:20 project financing norm; fixed PPA revenue
Hydropower (mature, post-amortisation)
0.3x–1.0x
2.5x–4.0x+
Loan principal substantially repaid; tariff escalation compounding
Cement / steel / cable manufacturing
1.0x–2.0x
2.0x–4.0x
Plant and machinery financed by term debt; cyclical demand
Asset-light, short working-capital cycle, often multinational-parented
When comparing D/E across sectors, never benchmark a hydropower company against a manufacturing company directly — benchmark it against its own PPA-implied revenue schedule and loan amortisation curve instead. A hydropower stock's "correct" D/E in year 3 of a 25-year loan is supposed to look nothing like its correct D/E in year 20. The ratio only means something in the context of where the project sits on its financing timeline.
The same discipline applies to manufacturers going through a capacity expansion cycle. A cement or cable manufacturer financing a new production line will temporarily push its D/E from, say, 1.0x toward 2.0x for the two to three years construction and ramp-up take, while interest coverage compresses as new debt service begins before the expanded capacity is fully utilised. This is not automatically a red flag — a manufacturer expanding into genuine demand growth, with a credible utilisation ramp-up plan, is doing exactly what a hydropower developer does at a smaller scale. The distinguishing question is the same one that matters for hydropower: does interest coverage recover toward its pre-expansion level within the timeframe management has guided to, or does a second or third round of debt-funded expansion arrive before the first has been digested? A manufacturer that layers expansion on expansion without interest coverage ever recovering above 2x is exhibiting the same warning sign as a hydropower company whose PPA revenue never catches up to its debt service schedule.
Asset quality, capital, and solvency ratios tell you whether a company can survive stress. Efficiency ratios tell you how well it converts its resources — staff, branches, or fixed assets — into revenue during normal times. They are the ratios that separate a well-run company from a merely solvent one.
Cost-to-Income Ratio (banks and financial institutions) Cost-to-Income Ratio = Total Operating Expenses ÷ Total Operating Income
A lower ratio means the bank spends less to generate each rupee of income — greater operating leverage, and typically a sign of disciplined branch expansion, digital channel adoption, and staff productivity.
CASE IN POINT
In a widely cited comparison from Q3 of FY 2074/75 (2018), Prime Commercial Bank posted a cost-to-income ratio of just 19.85 percent — meaning it spent under 20 rupees to generate 100 rupees of operating income — against a sector average of 37.31 percent, while Nepal Credit and Commercial Bank sat at the other extreme with 58.66 percent, spending nearly 59 rupees for every 100 rupees earned. Even years later, cost-to-income remains one of the most reliable single-number screens for distinguishing operationally efficient banks from bloated ones, because unlike NPL or CAR it isn't primarily a function of macro credit conditions — it's a function of management discipline.
The mechanics matter for a dividend-focused investor: a bank with a high cost-to-income ratio has less operating profit cushion to absorb a bad-loan-provisioning shock in a downturn — it is simultaneously less efficient in good years and less resilient in bad ones. When you screen banks on ROE alone, you can be fooled by a bank that generates decent ROE today through aggressive loan growth while carrying an inefficient cost base that will compress margins the moment loan growth slows.
Asset Turnover Ratio (manufacturers and trading companies) Asset Turnover Ratio = Net Sales ÷ Total Assets
This measures how many rupees of revenue a company generates per rupee of assets deployed — the manufacturing-sector analogue to a bank's cost-to-income ratio, and directly relevant to the DuPont ROE decomposition introduced earlier in Part VII (ROE = Net Margin × Asset Turnover × Equity Multiplier).
Worked example: Consider a hypothetical FMCG bottling manufacturer with net sales of Rs 6.2 billion and total assets of Rs 3.1 billion.
Asset Turnover = 6.2 ÷ 3.1 = 2.0x
Compare this to a hypothetical cement manufacturer with net sales of Rs 8.0 billion but total assets of Rs 16.0 billion (heavy kiln, quarry, and plant investment):
Asset Turnover = 8.0 ÷ 16.0 = 0.5x
Neither ratio is "better" in isolation — the FMCG bottler's asset-light model naturally produces a higher turnover figure than a capital-intensive cement plant, exactly as the D/E table in Lesson 41.4 predicted for the same two archetypes. What matters for stock selection is whether a given company's asset turnover is improving or deteriorating relative to its own history and its direct sector peers, and whether declining turnover is being offset by rising net margin (pricing power) or simply compounding into a falling ROE.
Efficiency Ratio
Sector Applied To
Formula
What a Favourable Reading Signals
Cost-to-Income Ratio
Commercial banks, development banks
Operating Expense ÷ Operating Income
Lean branch network, digital adoption, staff productivity
Asset Turnover Ratio
Manufacturing, trading, FMCG
Net Sales ÷ Total Assets
Efficient use of fixed assets and working capital
Combined Ratio (for reference, insurers)
General insurance
(Claims + Expenses) ÷ Net Premium
Underwriting discipline, distinct from the solvency ratio in Lesson 41.3
Efficiency ratios and solvency/capital ratios answer different questions and must never substitute for one another. A bank can have an excellent cost-to-income ratio and a dangerously thin CAR buffer at the same time — operational efficiency does not protect against a capital shortfall. Always read efficiency ratios alongside, never instead of, the asset-quality and capital ratios from Lessons 41.1 and 41.2.
Lesson 41.6 — Building a Cross-Sector Ratio Scorecard
You now have every ratio family this book covers: valuation multiples (Part VI), profitability and per-share metrics (earlier in Part VII), and — from this chapter — asset quality, capital, solvency, leverage, coverage, and efficiency ratios. The final skill is operational: turning this into a spreadsheet you actually maintain every quarter, across every sector you hold.
Structure the scorecard as one row per company and organise columns into three blocks, so the same sheet works whether the row is a bank, an insurer, a hydropower company, or a manufacturer:
Block 2 — Sector-specific stability columns: for banks, NPL Ratio, PCR, CAR, CAR headroom above 11%; for insurers, Solvency Ratio and headroom above 1.5 / 1.3; for hydropower and manufacturers, D/E and Interest Coverage Ratio.
Block 3 — Sector-specific efficiency columns: Cost-to-Income for banks, Asset Turnover for manufacturers, Combined Ratio for insurers.
PRACTICAL TOOL
Build one flag column per company using simple conditional formatting rules you set once: NPL Ratio > 5% → amber; CAR headroom < 1 percentage point → red; Solvency Ratio within 0.3 of the regulatory floor → red; Interest Coverage < 1.5x → red. A single glance at the flag column tells you which holdings need a closer read of the actual quarterly disclosure before you rely on the headline EPS or dividend announcement.
A simplified snapshot of what four rows might look like at a single point in time, mixing sectors on one sheet:
Company (illustrative sector mix)
Sector
Key Stability Ratio
Reading
Flag
Bank A
Commercial Bank
CAR
11.16% (headroom 0.16pp)
Red — thin buffer
Bank B
Commercial Bank
NPL Ratio
0.61%
Green — sector-leading
Insurer C
Non-Life Insurance
Solvency Ratio
1.60 (floor 1.5)
Amber — close to floor
Hydropower D
Hydropower (early operation)
Interest Coverage
1.38x
Red — thin coverage
Notice that this single table already reproduces the real dispersion you saw in Lessons 41.1 through 41.4 — Everest Bank's 0.61 percent NPL ratio, Himalayan Bank's 0.16 percentage-point CAR cushion, Oriental Insurance's 1.60 solvency ratio sitting just above its 1.5 floor, and a hydropower company's characteristically thin post-commissioning interest coverage. A scorecard built this way turns four completely different regulatory regimes into one comparable risk view, which is the entire point of building it yourself rather than relying on a broker's one-size-fits-all screener.
Weighting the flags into a single score A scorecard with four or five flag columns per company is useful for a single glance, but for a portfolio of fifteen or twenty holdings across multiple sectors, convert the flags into a simple weighted score so positions can be ranked rather than merely color-coded. A workable weighting for a bank might allocate 35 percent of the composite score to CAR headroom, 35 percent to the NPL ratio (inverted, so lower NPL scores higher), 15 percent to PCR, and 15 percent to cost-to-income — reflecting that capital and asset quality matter roughly twice as much to survivability as operating efficiency does. For an insurer, weight solvency-ratio headroom above the regulatory floor most heavily; for a hydropower or manufacturing name, weight interest coverage above D/E itself, since coverage is the metric that determines whether the debt load is actually serviceable this year rather than merely how large it is on paper. None of these weights need to be precise to be useful — the exercise of assigning them forces you to decide, once, which risk each sector poses most acutely, rather than re-litigating it every time a new quarterly result lands in your inbox.
Where to source the raw inputs each quarter: NRB's "Key Financial Indicators of Commercial Banks" and quarterly Financial Stability Report for bank NPL, PCR, and CAR figures; the Nepal Insurance Authority's quarterly solvency disclosures for insurers; individual company quarterly financial reports filed on NEPSE's corporate disclosure portal and each company's own investor-relations page for D/E, interest coverage, cost-to-income, and asset turnover. None of this requires a paid data terminal — every figure in this chapter's tables came from public regulatory and company disclosures freely available online.
A scorecard, finally, is only as current as its last update. NPL ratios, CAR buffers, and solvency ratios can move materially within a single quarter — as the jump from a 3.66 percent to a 5.60 percent sector NPL ratio within a few quarters in this chapter demonstrates. Set a standing calendar reminder for each quarter-end reporting window (mid-Ashwin, mid-Poush, mid-Chaitra, and mid-Ashadh) and refresh the scorecard within two weeks of each, rather than trusting a stale figure from two quarters ago.
Chapter recap
This chapter completes the ratio-analysis toolkit that Part VII set out to build. Where earlier chapters in this Part covered taxation mechanics, profitability ratios, and per-share and valuation metrics, this final chapter added the risk-side ratios that determine whether the profitability an investor sees today is durable — asset quality and provisioning coverage for banks, capital adequacy as both a solvency buffer and a growth-and-dividend constraint, solvency margins for insurers, leverage and interest coverage for capital-intensive hydropower and manufacturing companies, and the efficiency ratios that separate well-run operators from merely solvent ones.
The recurring lesson across all six lessons is that no single ratio, in any sector, tells the whole story alone. A bank's ROE means little without its NPL ratio and CAR buffer alongside it; an insurer's premium growth means little without its solvency margin; a hydropower company's tariff-linked revenue means little without its interest coverage against floating-rate debt; a manufacturer's margin expansion means little without knowing whether it came from genuine efficiency gains in asset turnover or from a one-off cost cut. The real figures pulled from NRB, the Nepal Insurance Authority, and NEPSE-listed company disclosures in this chapter — Everest Bank's sector-leading 0.61 percent NPL ratio, Himalayan Bank's razor-thin 0.16 percentage-point CAR cushion, the non-life insurance sector's 2.67 average solvency ratio against a 1.5 floor, Prime Commercial Bank's 19.85 percent cost-to-income ratio against a 58.66 percent laggard — are not exam trivia. They are the exact kind of dispersion that separates a resilient long-term holding from a stock that merely looks cheap on a P/E screen today.
Practically, this means every NEPSE investor should hold two spreadsheets, not one: the valuation sheet built in earlier Part VII chapters, tracking P/E, P/BV, dividend yield, and EPS growth; and the stability scorecard built in Lesson 41.6, tracking NPL/CAR for banks, solvency for insurers, and D/E/interest coverage for hydropower and manufacturers. A stock can pass the first sheet and fail the second — and when it does, the second sheet should win the argument, because a thin CAR buffer or a 1.38x interest coverage ratio can turn an attractive dividend yield into a suspended one within a single regulatory quarter.
It is worth being honest about the limits of these ratios too. They are backward-looking by construction — an NPL ratio reports loans that have already deteriorated, a solvency ratio reflects reserves already booked, an interest coverage ratio uses last year's EBIT against this year's debt service. None of them predict the next flood-damaged transmission line, the next monetary tightening cycle, or the next regulatory reclassification of restructured loans. What they do is remove the guesswork from the parts of company health that are measurable today, so that the genuinely unpredictable risks — hydrology, politics, global commodity cycles — are the only ones an investor is left having to judge with real uncertainty.
With Part VII now complete, the analytical toolkit this book has built runs from a company's tax structure and effective tax rate, through its profitability and per-share economics, through valuation multiples, and now through the full stability and efficiency picture covered here. An investor who works through a NEPSE-listed company using every chapter in this Part — taxation, profitability, valuation, and now asset quality, capital, solvency, and efficiency — is no longer reading the stock the way the market's headline commentary reads it. They are underwriting it the way the regulator, the lender, and the credit rating analyst already do, which is the only standard of analysis serious enough to risk real capital against.
Part VIII turns from these financial-statement ratios to a different kind of number entirely — project finance and hydropower modelling, where a single power purchase agreement's tariff schedule and a loan's debt service coverage covenant matter more than any ratio drawn from a conventional income statement.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VIII
PROJECT FINANCE & HYDROPOWER MODELLING
Part VIII · Chapter 42
Understanding Project Finance
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 42.1 — What Project Finance Actually Means
Picture a Kathmandu family that owns a house they live in, plus a small flat they bought purely to rent out. If the family wants to buy a second rental flat, they have two ways to borrow the money.
The first way is to walk into a bank and borrow against everything they own — their family home, their savings, their gold, their existing flat — pledging the whole household balance sheet as security. If the new flat turns out to be a bad investment and the rent doesn't cover the loan, the bank can still come after the family home. This is corporate finance, sometimes called balance-sheet lending: the borrower's entire asset base and income stream stand behind the loan, and the lender's recovery is not limited to the thing being financed.
The second way is fundamentally different. The family sets up a separate small entity — perhaps a private company — that owns only the new rental flat. That entity borrows money in its own name, using only the flat itself and the rent it will generate as security. If the flat fails to earn enough rent to service the debt, the bank's recourse stops at the flat. The family home, the parents' pension, the children's education fund — none of it is touched. The lender agreed, from day one, to be paid back only from what this one asset produces.
This second structure is project finance. The formal definition: project finance is the financing of a long-term infrastructure or industrial project based primarily on the projected cash flows of the project itself, rather than the balance sheets of its sponsors, with the project's assets, rights, and revenue-generating contracts serving as collateral. The entity that owns the project — the small company in our analogy — is called a Special Purpose Vehicle, or SPV. An SPV is a legally separate company created for one purpose only: to build, own, and operate a single project (or a small family of related projects) and nothing else. It has its own board, its own bank accounts, its own contracts, and critically, its own limited liability — its creditors cannot reach through it to the pockets of the shareholders who set it up.
KEY CONCEPT
Non-recourse financing means the lender's only remedy on default is against the project's own assets and cash flows — the lender has zero legal claim on the sponsor's other businesses or personal wealth. Limited-recourse financing is the more common real-world variant: the lender has no claim on the sponsor generally, but does have narrow, specific claims against the sponsor for defined events — most commonly cost overruns during construction, fraud, or a shortfall if the project is abandoned before completion. Almost every hydropower financing in Nepal is limited-recourse, not purely non-recourse, because banks insist on some sponsor support during the risky construction period.
Why would a lender ever agree to be paid back only from one asset's earnings, instead of demanding a claim on everything the borrower owns? Because in exchange, the lender gets something a corporate loan rarely offers: forensic-level control over the asset itself. In a project financing, the lender does not simply hand over money and hope for the best. The lender's lawyers and engineers pick apart every contract the project depends on — the construction contract, the agreement to sell electricity, the insurance policies, the land lease — before a single rupee is disbursed. The lender then wraps the project in a security package (a set of legal rights over the project's assets, contracts, and cash flows that the lender can enforce if things go wrong) so tight that, if the project stumbles, the lender can step in and run it, sell it, or hand it to someone else who can make it pay, all without ever touching the sponsor's other assets.
This is precisely why project finance is the dominant financing method for hydropower in Nepal — and around the world for large, capital-intensive infrastructure. A single 100 MW hydropower project can cost tens of billions of rupees to build. No promoter group, however wealthy, wants to stake its entire family or corporate fortune on one river's flow, one government's tariff policy, and one power purchaser's creditworthiness. And no bank, in turn, wants to lend that much money against a promoter's general promise to repay — it wants a contractual claim on the one thing that will actually generate the cash: the power plant and the revenue stream from selling its electricity.
WARNING
Do not confuse "non-recourse to the sponsor" with "no risk to the sponsor." Sponsors still lose their entire equity investment — often 25-30% of project cost — if the project fails. Project finance limits the downside to the equity invested; it does not eliminate downside.
Lesson 42.2 — The Anatomy of a Nepali Hydropower Deal
To understand how a Nepali hydropower financing is actually built, it helps to walk through the life of a project in order, because each stage adds a layer of structure that the next stage depends on.
It begins with the SPV. A group of promoters — sometimes a mix of local entrepreneurs, a construction house, and occasionally a foreign strategic investor — incorporates a company under Nepal's Companies Act, whose sole object clause is to develop, own, and operate one specific hydropower project on one specific river reach. This company then applies to the Department of Electricity Development (DoED) for a survey license and subsequently a generation license under the Electricity Act. Everything that follows — every contract, every loan, every regulatory approval — attaches to this one company.
The SPV then assembles what project finance practitioners call the "project contracts" — the web of agreements that together convert a hole in a riverbed into a bankable business:
First, the Power Purchase Agreement (PPA), a long-term contract (typically 20-35 years in Nepal, sometimes with a build-own-operate-transfer or "BOOT" tenor) under which Nepal Electricity Authority (NEA), the state-owned utility, agrees to buy all the electricity the project generates at a pre-agreed tariff structure (commonly a two-season, two-time-of-day rate reflecting Nepal's wet-season surplus and dry-season shortage). The PPA is the single most important document in the entire financing, because it is the contract that turns falling water into a predictable cash flow banks can lend against.
Second, the Engineering, Procurement and Construction (EPC) contract, under which a contractor — often a Chinese, Indian, or Nepali construction house — agrees to build the powerhouse, the dam or diversion weir, the headrace tunnel or canal, and the penstock for a fixed price and a fixed completion date, usually with liquidated damages (pre-agreed cash penalties) if it is late.
Third, land acquisition and government agreements — a survey license, a generation license, and often a Project Development Agreement or similar instrument with the Government of Nepal covering royalty payments, local benefit-sharing, and, for larger projects, tax and forex arrangements.
Fourth, insurance contracts covering construction-period risks (fire, flood, contractor default) and operational risks (business interruption, machinery breakdown), assigned to the lenders as part of their security.
Once these contracts exist in more or less final form, the SPV goes to the banks to arrange debt. This is where the financing structure — the actual split between how much of the project cost is borrowed versus contributed by shareholders — gets fixed.
KEY CONCEPT
The debt-equity ratio in project finance describes what fraction of total project cost is funded by loans (debt) versus by shareholder capital (equity). A 70:30 ratio means 70% of the project's cost comes from bank loans and 30% from the promoters' own money. Nepali hydropower projects typically run 70:30 to 75:25 — meaning promoters usually need to find only a quarter to a third of total project cost in cash or in-kind contribution, with banks financing the rest.
Why do lenders tolerate such high leverage (a high proportion of debt relative to equity) for hydropower specifically? Because a well-structured, operating hydropower plant with a PPA in place has an unusually predictable and long-lived revenue stream — water keeps flowing, NEA keeps needing power, and the tariff is fixed by contract for decades. That predictability is what allows debt to be layered so heavily on top of a modest equity base — the opposite of, say, financing a restaurant or a trading business, where revenues are volatile and banks would rarely go beyond 50:50.
The table below summarises how a typical Nepali run-of-river hydropower project financing is commonly structured today, drawing on the terms disclosed in recent IPO prospectuses and credit rating rationales of NEPSE-listed hydropower companies.
Element
Typical Nepali Practice
Debt : Equity ratio
70:30 or 75:25 (occasionally 80:20 for very low-risk, already-proven river basins)
Loan tenor
12-15 years, including a construction-period moratorium
Moratorium (grace period)
Interest-only or capitalised-interest period during construction, typically 3-5 years
Repayment structure
Structured/step-up quarterly or semi-annual instalments, sized to the project's seasonal cash flow (higher in wet months)
Minimum Debt Service Coverage Ratio (DSCR)
Commonly 1.2x-1.3x minimum, tested at each repayment date
Security package
Assignment of PPA, first charge over project assets, escrow account, DSRA, assignment of insurance, pledge of promoter shares
Governing law / currency
Nepali law; loans almost entirely in Nepali Rupees given NEA's NPR-denominated PPA
CASE IN POINT
Chilime Hydropower Company, a 22.1 MW plant on the Chilime Khola in Rasuwa district and one of NEA's earliest subsidiary companies, is often cited as Nepal's pioneering project-company model — an NEA-controlled SPV that later opened its shares to the general public and local Rasuwa residents specifically, well before hydropower IPOs became a routine NEPSE listing pathway. It set an early template that many subsequent Nepali hydropower SPVs — offering a fixed local-resident share quota alongside public and promoter tranches — have followed.
Lesson 42.3 — Financing the Deal: Syndication and the Single-Obligor Constraint
Here is a number worth sitting with: as of recent industry estimates, the combined core capital of all commercial banks in Nepal is only around USD 900 million. Core capital is a bank's own equity cushion — its paid-up capital plus reserves — and it is the base figure regulators use to size how much any one bank can safely lend to any one borrower. Meanwhile, Nepal has issued generation licenses for well over 1,800 MW of hydropower capacity. A single large storage or peaking-run-of-river project — something in the 400-900 MW range — can cost well over NPR 100 billion. No single Nepali bank, and often not even a handful of them together, has a balance sheet anywhere near large enough to write that check alone.
This mismatch is precisely why syndication is not an optional financing technique in Nepal's hydropower sector — it is a structural necessity. Syndicated lending means multiple banks jointly provide a single loan to one borrower, sharing the loan amount, the collateral, and the risk in agreed proportions, under one common set of loan documents. One bank (or a small group) acts as the lead arranger, structuring the deal, negotiating terms with the SPV, and then inviting other banks to "participate" — to take a slice of the total loan on the same terms. A common form used in Nepal is the consortium financing arrangement, where several banks lend directly and severally to the borrower (each bank has its own direct claim on the SPV for its share), as opposed to a syndication proper where one bank lends and then sells participations — Nepali practice leans toward the consortium model, with a lead bank coordinating security documentation and cash-flow monitoring on behalf of all participants.
REGULATORY DETAIL
Nepal Rastra Bank (NRB), the central bank, has historically enforced a Single Obligor Limit (SOL) — a regulatory ceiling on how much credit exposure any one bank could extend to a single borrower or group of related borrowers without seeking NRB's specific approval. That ceiling stood at NPR 25 crore (NPR 250 million) in fund-based exposure for years, a figure trivial next to a large hydropower project's financing needs — which is exactly why syndication among multiple banks, rather than one bank lending alone, became the default mechanism for financing anything beyond a small run-of-river scheme.
This regulatory picture changed materially in 2025 (fiscal year 2082/83 in the Nepali calendar), when NRB issued a directive removing the fixed Single Obligor Limit ceiling altogether, shifting responsibility for setting individual borrower exposure limits to each bank's own internal credit risk framework and board-approved policies, rather than a hard regulatory number. The stated rationale was to unlock financing for "mega projects" — large hydropower, transmission, and infrastructure schemes — that the old fixed ceiling was actively constraining, while leaving banks accountable to their own risk governance and to NRB's broader prudential supervision (capital adequacy, sector concentration limits, and loan classification rules remain firmly in place).
WARNING
The removal of the fixed Single Obligor Limit does not remove NRB's other, and arguably more binding, sectoral concentration limits. Banks in Nepal remain subject to a cap on their aggregate exposure to the hydropower sector as a whole — commonly cited at around 50% of a bank's core capital — meaning even a bank flush with room under the new single-obligor rule may still be constrained by how much of its total lending book it is permitted to park in hydropower generally. Always check both limits, not just one, when sizing how much a bank or a banking system can plausibly lend to the sector.
Even with the single-obligor ceiling gone, syndication remains the practical norm for genuinely large hydropower projects, for a simple reason that has nothing to do with regulation: risk diversification. No single bank's management or board wants 100% of a NPR 50-100 billion exposure sitting against one river, one EPC contractor, and one power purchaser, however creditworthy. Spreading the loan across six, eight, or a dozen banks means that if the project underperforms, no single institution's solvency is threatened, and the workout (the process of restructuring a troubled loan) becomes a shared, negotiated exercise among a consortium rather than a life-or-death event for one bank.
Two structural features distinguish Nepali hydropower syndication from a typical corporate syndicated loan. First, the lenders are almost always exclusively Nepali commercial banks and, increasingly, provident and pension funds such as the Employees Provident Fund (EPF) and Citizen Investment Trust (CIT), rather than international commercial banks — because NEA's PPA is denominated in Nepali Rupees, and foreign lenders are generally unwilling to take open Nepali Rupee currency risk on a 15-year loan without a hedge that simply does not exist in Nepal's shallow currency markets. Second, for genuinely large national-priority projects, the Government of Nepal and multilateral development banks (the World Bank, the Asian Development Bank, and sometimes bilateral lenders like India's Exim Bank or Indian public-sector lenders) step in as either direct project lenders or as guarantors/co-financiers alongside the domestic banking syndicate, precisely because the domestic system alone cannot absorb the exposure.
CASE IN POINT
The Tamakoshi V project — envisioned as a follow-on scheme to the already-completed 456 MW Upper Tamakoshi plant — saw NEA, the Employees Provident Fund (EPF), and the Tamakoshi hydropower company sign a tripartite loan agreement in 2023, illustrating how Nepal's largest institutional pools of long-term capital (provident and pension funds) are increasingly drawn directly into hydropower project finance as co-lenders alongside, or even instead of, commercial banks.
Lesson 42.4 — The Lender's Security Package
Once a syndicate of banks agrees to lend, the next question is: what exactly do they hold as collateral if the SPV cannot repay? In ordinary corporate lending the answer is often "a mortgage on the borrower's land and buildings, plus personal guarantees." In project finance the answer is far more elaborate, because the lenders are relying almost entirely on the project's own future cash flows rather than the sponsor's balance sheet — so they build layers of legal control designed to let them step into the project's shoes if things go wrong, well before the SPV is formally declared insolvent.
The core components of a Nepali hydropower lender's security package are:
Assignment of the PPA. Because NEA's payment obligation under the Power Purchase Agreement is the project's only real source of revenue, lenders require the SPV to assign its rights under the PPA to the lenders (or to a security agent acting on the syndicate's behalf) as collateral. This does not mean lenders take over selling electricity day to day — it means that if the SPV defaults on its loans, the lenders acquire the legal right to step into the PPA, receive the payments directly, or even take over operation of the plant (through a "step-in right") to keep the PPA alive rather than let NEA terminate it. NEA's consent to this assignment, formally recorded, is itself a standard closing condition for the loan.
Escrow account arrangements. An escrow account is a special bank account, typically held with the lead lender or security agent bank, into which all of the project's revenue (NEA's PPA payments) is deposited directly, and out of which cash flows are released only in a pre-agreed order, called a "cash flow waterfall." A simplified Nepali hydropower waterfall typically runs: (1) operating expenses, (2) scheduled debt service (principal and interest), (3) top-up of the debt service reserve account if it has been drawn down, (4) other reserve accounts (major maintenance reserve, for instance), and only then (5) distributions to shareholders as dividends. The escrow mechanism means the SPV's management never has unrestricted access to the incoming cash — the bank sees every rupee arrive and controls the order in which it leaves, which is the single most powerful practical tool lenders have for making sure they get paid before shareholders do.
PRACTICAL TOOL
When evaluating a NEPSE-listed hydropower company's bond prospectus, annual report, or credit rating rationale, always look for the specific mechanics of its escrow and waterfall arrangement. A company whose escrow releases dividends only after fully funding its DSRA and major maintenance reserve is meaningfully safer for lenders — and, by extension, for minority shareholders relying on steady dividends — than one with a thin or poorly enforced waterfall.
Debt Service Reserve Account (DSRA). A DSRA is a ring-fenced cash reserve, funded either upfront from the loan proceeds or built up gradually from early cash flows, sized to cover a defined number of months of future debt service (commonly three to six months of principal and interest). Its purpose is simple: if the plant has a bad month — a landslide damages the intake, the river runs unusually low, a transformer fails — the SPV can draw on the DSRA to keep making loan payments on schedule rather than immediately defaulting. Lenders require the DSRA to be replenished from subsequent cash flows before any dividends can be paid, which is exactly why it sits ahead of shareholder distributions in the escrow waterfall.
KEY CONCEPT
Think of the DSRA as the project's own emergency fund, maintained not for the shareholders' comfort but purely for the lenders' protection — much like a landlord who insists a tenant keep two months' rent in a locked account before the tenancy even begins, so that one bad month of vacancy doesn't immediately break the lease.
Beyond the PPA assignment, escrow, and DSRA, a full Nepali hydropower security package typically also includes: a first-ranking mortgage or charge over all project land, the powerhouse, penstock, and generating equipment; a pledge of the SPV's shares held by the promoter group (so that if the SPV defaults, lenders can effectively take control of the company by enforcing the share pledge rather than only chasing physical assets); assignment of all project insurance policies, so insurance payouts after a loss go first to repair the asset or to the lenders rather than disappearing into the sponsor's other businesses; and assignment of the EPC contract and any performance bonds/liquidated-damages rights against the contractor, so lenders can pursue the contractor directly if construction defects or delay cause loss.
CAUTION
A security package is only as strong as its weakest link, and PPA-related risk is Nepal's chronic weak link. Domestic-investment hydropower PPAs in Nepal typically provide no compensation to the developer if NEA terminates the agreement — only a right to wheel power through NEA's transmission network — unlike PPAs for foreign-invested projects, which usually do include termination compensation. A lender's assignment of the PPA is only valuable if the PPA itself has real termination protection; assigning a weak contract does not make it a strong one.
Lesson 42.5 — Construction Risk vs Operational Risk: Why Financing Terms Change Over the Project's Life
Every project financing has a hinge point, and understanding it is essential to understanding why the loan terms, the security package, and the level of sponsor involvement all shift dramatically at one specific moment: the date the plant is actually finished, tested, and generating revenue, commonly called the Commercial Operation Date (COD).
Before COD, the project is in its construction phase, and the dominant risks are what project finance calls construction risk: will the EPC contractor finish on time and on budget? Will unforeseen geology in the headrace tunnel cause months of delay? Will a monsoon flood wash away a cofferdam? During this phase, the project generates zero revenue — there is nothing yet to sell to NEA — which means there is no cash flow for lenders to be repaid from, and no operating track record to judge. This is the riskiest period of the entire project's life, and lenders price and structure the loan accordingly.
After COD, the project enters its operational phase, where the dominant risks become operational risk: will the river's hydrology behave as the feasibility study predicted? Will the machinery run reliably? Will NEA pay on time? These risks are generally far more predictable and far better insured against, because by this point there is an actual track record of the plant generating power and receiving PPA payments.
KEY CONCEPT
The reason lenders treat construction and operational risk so differently is straightforward: during construction, a lender's collateral is an unfinished hole in a hillside with no income — worth very little to anyone if the sponsor walks away. After COD, the collateral is a functioning, revenue-generating asset with a long-term contracted buyer. Lenders will accept a nearly pure non-recourse structure for the operational phase but almost never for the construction phase, which is why nearly every Nepali hydropower loan is, in practice, "limited recourse" rather than fully non-recourse — sponsor support is required precisely during the period when the asset alone isn't yet bankable.
In practice, lenders manage the gap between these two risk profiles through several specific tools written into the loan agreement:
Completion guarantees and cost-overrun undertakings. Sponsors are typically required to guarantee that they will fund any cost overrun out of their own pocket (rather than asking lenders for more money) up to a specified cap, and sometimes to guarantee the project's physical completion itself — meaning if the SPV cannot finish construction, the sponsor is on the hook to either fund completion or repay the loan. This is the "limited" part of limited-recourse financing: the recourse to the sponsor is limited in time (construction period only) and limited in scope (cost overruns and completion, not general project underperformance).
Contingent equity or standby letters of credit. Rather than requiring sponsors to inject additional cash immediately, many Nepali financings require sponsors to arrange a standby facility — a letter of credit or committed additional equity line — that only gets drawn if an actual cost overrun materialises, keeping the sponsor's capital productively deployed elsewhere until it's actually needed.
Independent engineer oversight. Lenders appoint their own independent engineer (a technical consultant paid by the borrower but reporting to the lenders) to monitor construction progress, certify that disbursement milestones have genuinely been met before releasing further loan tranches, and flag emerging delays or cost overruns early.
Disbursement conditions tied to physical progress. Loan tranches are released against verified construction milestones (foundation complete, penstock installed, turbines delivered) rather than as a lump sum upfront, so lenders are never funding further ahead of the asset's actual physical progress than necessary.
Once COD is reached and the plant has demonstrated stable output over an agreed testing period, the loan typically "converts" into its long-term operational repayment schedule, and covenants shift toward ongoing financial monitoring — chiefly the Debt Service Coverage Ratio (DSCR), a ratio comparing the cash available for debt service in a period to the actual debt service due in that period. A DSCR of 1.3x means the project generated 30% more cash than it strictly needed to make that period's loan payment — the cushion lenders require as protection against a bad hydrology year. If DSCR falls below the covenanted minimum (commonly 1.1x-1.2x in Nepal), the loan agreement typically restricts or blocks dividend distributions to shareholders until the ratio recovers — another example of the escrow waterfall protecting lenders ahead of equity.
PRACTICAL TOOL
When assessing any Nepali hydropower company as a potential bond or equity investment, always ask which phase it is in. A pre-COD company carries construction risk (EPC delay, cost overrun, hydrology uncertainty) and generates no revenue — its equity is closer to a call option on successful completion. A post-COD company with several years of stable generation history carries mainly operational and regulatory risk, and its equity behaves more like a long-dated, contracted-revenue infrastructure asset. The risk premium the market demands, and the appropriate valuation approach, differ sharply between the two.
Lesson 42.6 — Case Studies: Learning from Nepal's Hydropower Financings
Nepal's own hydropower history offers a useful spread of examples across ownership structures, financing sources, and outcomes.
Upper Tamakoshi (456 MW) is Nepal's largest operating hydropower plant, commissioned in July 2021, and is a valuable case study precisely because it broke from the pattern of foreign-financed mega-projects. It was developed by Upper Tamakoshi Hydropower Limited (UTKHPL), an SPV established by NEA in 2007, and — notably — was financed entirely from domestic Nepali financial institutions and companies, without foreign commercial lenders. Its ownership structure blends public and community interests: NEA holds 41%, with Nepal Telecom, Citizen Investment Trust, and Rastriya Beema Sansthan (the state insurance corporation) holding smaller stakes, alongside a 10% quota reserved specifically for residents of Dolakha district (where the project is located), 15% for the general public, and the remainder for EPF contributors and NEA/company/financial-institution staff. This structure demonstrates how a genuinely large hydropower project can be financed domestically when a syndicate of Nepali banks, provident funds, and public shareholders is assembled at sufficient scale — and it stands as evidence against the assumption that only foreign capital can finance Nepal's largest hydropower assets.
Chilime Hydropower Company (22.1 MW), an earlier and much smaller NEA subsidiary project in Rasuwa district, is often cited as Nepal's original template for the "community-inclusive" project-company model — later becoming one of the first hydropower companies listed on NEPSE, with a portion of shares specifically reserved for local project-affected residents. Many subsequent Nepali hydropower IPOs have followed this local-quota-plus-public-tranche template, which has become close to a market norm.
Tamakoshi V, a follow-on project to Upper Tamakoshi, illustrates the more recent trend of pension and provident funds becoming direct project lenders rather than merely portfolio investors: NEA, the Employees Provident Fund, and the Tamakoshi hydropower company signed a tripartite loan agreement in 2023 to fund its development, alongside conventional bank debt.
Arun-3 (900 MW), by contrast, illustrates the opposite end of the spectrum: a cross-border, foreign-sponsor-led project developed by SJVN (a joint venture of the Government of India and Himachal Pradesh government), where the bulk of project debt — reported at roughly INR 6,333 crore — was arranged through Indian lenders rather than Nepali banks, reflecting the fact that a project of this scale, with a foreign sponsor exporting power to India, sits largely outside the domestic Nepali banking system's capacity and currency comfort zone. It is a useful counter-example showing that when a project's revenue and sponsor structure point outward (an export-oriented PPA, a foreign sponsor), the financing naturally follows that same external orientation.
Rasuwagadhi Hydropower Company, whose IPO grading was assessed by ICRA Nepal, is a useful illustration of how Nepal's rating agencies formally assess project bankability before an IPO — evaluating exactly the factors covered in this chapter: PPA quality and NEA's payment reliability, hydrology and technical risk, the strength (or weakness) of the debt-equity structure and security package, and construction-phase versus operational-phase risk profile. Reading an IPO grading rationale for any NEPSE-bound hydropower company is one of the most efficient ways for an investor to see project finance analysis applied in practice by professionals, rather than in the abstract.
CASE IN POINT
Across these examples, a consistent pattern emerges: the largest, most successfully domestically-financed Nepali hydropower projects (Upper Tamakoshi, and increasingly Tamakoshi V) share three features — an NEA-affiliated or NEA-majority SPV structure (lending confidence to both banks and retail shareholders), a broad syndicate spanning commercial banks and provident/pension funds rather than a single lender, and a meaningful local/public equity component that spreads project ownership (and political buy-in) beyond the promoter group alone. Projects lacking one or more of these features have historically struggled more with financial closure.
A final, sobering thread runs through nearly all of Nepal's hydropower financing history and deserves an investor's close attention: bankability gaps that persist despite an otherwise sound legal and financing framework. Two issues recur repeatedly in professional legal and rating commentary. First, currency risk: NEA refuses to sign PPAs denominated in US dollars for domestic projects, and Nepal has no meaningful market for hedging long-dated Nepali Rupee exposure, which is precisely why foreign commercial lenders rarely participate directly in domestic-PPA hydropower financings, leaving the field to Nepali banks, provident funds, and multilateral development institutions willing to lend in or alongside Nepali Rupees. Second, PPA termination asymmetry: as covered in Lesson 42.4, domestic-investment PPAs typically leave a developer with no compensation and only wheeling rights if NEA terminates the agreement, while foreign-investment PPAs typically do include termination compensation — a structural inconsistency that Nepali legal commentators have flagged as an unresolved bankability weakness for purely domestic developers and their lenders.
CAUTION
An investor analysing any Nepali hydropower company — whether as a lender, a bondholder, or a NEPSE equity shareholder — should never assume the PPA alone makes the revenue stream safe. Read the specific termination and compensation clauses. A "take-or-pay" PPA that looks airtight on its cover page can contain loopholes or asymmetric termination rights that materially change the real credit quality of the underlying cash flow the entire financing structure depends on.
Chapter recap
This chapter introduced project finance as a distinct financing method built around one central idea: lenders are repaid from the cash flows of a single, ring-fenced asset rather than from the general credit of the people or companies who built it. We anchored this in a simple analogy — a family financing a single rental flat through a standalone entity so that a bad investment cannot threaten the family home — to make clear why sponsors create Special Purpose Vehicles (SPVs) and why lenders accept non-recourse or, far more commonly in Nepal, limited-recourse structures in exchange for extraordinarily tight contractual control over the project itself. This structural choice explains why project finance, rather than ordinary corporate balance-sheet lending, has become the default method for financing Nepal's hydropower sector: no single promoter group can or should stake its entire fortune on one river, and no bank wants its recovery to depend on a sponsor's unrelated businesses rather than the plant it is actually financing.
We then walked through the anatomy of a typical Nepali hydropower deal — the SPV structure, the survey and generation licenses, the Power Purchase Agreement with NEA, the EPC construction contract, and the government and insurance arrangements that together transform a river's flow into a bankable, contracted revenue stream. The commonly cited 70:30 or 75:25 debt-equity ratio reflects how much leverage lenders are willing to extend against that contracted, decades-long revenue certainty — a level of leverage far higher than banks would extend against a typical unregulated business, precisely because the PPA and the physics of falling water make the future cash flow unusually predictable.
We examined why syndication — multiple banks jointly lending to one SPV — is structurally necessary in Nepal, not merely a matter of convenience: the combined core capital of Nepal's entire commercial banking system is a small fraction of what even a handful of large hydropower projects require. Nepal Rastra Bank's historic Single Obligor Limit, which capped any one bank's exposure to a single borrower at NPR 25 crore before being removed entirely in a 2025 directive, illustrates how regulation has both constrained and, more recently, sought to unlock larger project financings — while sectoral concentration limits on aggregate hydropower exposure (commonly cited near 50% of a bank's core capital) remain an important, separate constraint that investors and analysts should always check alongside any single-obligor figure.
The lender's security package — assignment of the PPA, escrow accounts governing a strict cash-flow waterfall, the Debt Service Reserve Account (DSRA), mortgages over project assets, and pledges of promoter shares — was presented as the mechanism through which lenders convert a legally non-recourse or limited-recourse structure into practical, enforceable control. Escrow accounts and DSRAs in particular ensure that lenders are paid, and reserves are replenished, before any shareholder ever sees a dividend — a hierarchy every equity investor in a NEPSE-listed hydropower company should understand before assuming dividends are guaranteed.
Finally, we distinguished construction risk from operational risk as the single most important variable determining a project financing's terms at any given moment in its life: pre-Commercial-Operation-Date, the project has no revenue and the asset itself is unfinished and largely worthless to anyone else, so lenders demand sponsor completion guarantees, cost-overrun undertakings, and independent engineer oversight; post-COD, with a demonstrated revenue track record against a long-term PPA, the loan converts to standard DSCR-covenant monitoring and the structure becomes genuinely closer to pure non-recourse finance. Nepal's own case studies — Upper Tamakoshi's fully domestic, NEA-anchored financing; Chilime's pioneering community-inclusive ownership model; Tamakoshi V's pension-fund co-lending; Arun-3's foreign-sponsor, foreign-lender structure; and the persistent bankability gaps around currency risk and asymmetric PPA termination rights — together show both how far Nepal's project finance market has matured and where its structural weaknesses still concentrate risk that every serious investor and lender must price carefully rather than assume away.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VIII · Chapter 43
The Hydropower Financial Model
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 43.1 — The Anatomy of a Hydropower Financial Model
A hydropower financial model is, at its core, a very long spreadsheet that tries to answer one question: will the water flowing down a Himalayan river generate enough cash, over enough years, to repay the people who financed the dam, turbine, and powerhouse — and still leave something for the equity owners? Everything else in this chapter is detail layered onto that single question.
Before going further, it helps to name the building blocks, because hydropower modelling in Nepal has its own vocabulary that differs in important ways from modelling a factory, a hotel, or a toll road.
A hydropower project has two distinct life phases, and the financial model must treat them completely differently. The first is the construction period — typically three to six years for a run-of-river project in Nepal, longer for a storage scheme — during which the company spends money on civil works, tunnels, penstocks, turbines, and transmission lines, but earns no revenue at all. The second is the operating period, which begins on the Commercial Operation Date (COD) — the date the project starts legally and physically delivering electricity under its Power Purchase Agreement — and typically runs 25 to 30 years, matching the tenor of the PPA and often the remaining life of the generation license.
During construction, all costs are accumulated on the balance sheet as Capital Work in Progress (CWIP) — an accounting bucket that holds every rupee spent on an asset that isn't finished yet and therefore isn't yet generating revenue or being depreciated. Think of CWIP as a construction ledger: cement, steel, turbine payments, contractor bills, consultant fees, and — critically — the interest paid on loans drawn to fund all of that, all sit in this bucket until the plant is commissioned. Once COD is reached, CWIP is "capitalised" — converted into a fixed asset on the balance sheet — and depreciation begins.
That interest cost deserves its own name because it behaves unusually: it is called Interest During Construction (IDC), and unlike interest paid on a working operating company, it is not expensed through the profit and loss statement while the project is being built. Instead, it is added to the cost of the asset itself. A farmer who borrows money to plant an orchard doesn't pay themselves back out of income that doesn't exist yet — the interest on that loan effectively becomes part of the cost of establishing the orchard, recovered later once the trees bear fruit and are sold. Hydropower IDC works the same way: the interest accrued on construction-period debt is capitalised into the project cost, increasing total CAPEX, and only starts hitting the income statement as depreciation and interest expense once the plant is operating and earning revenue.
KEY CONCEPT
Capital Work in Progress (CWIP) is the running total of everything spent on a not-yet-operating asset — materials, labor, equipment, and capitalised interest. It moves from the balance sheet's CWIP line to "Property, Plant and Equipment" the moment the plant reaches Commercial Operation Date, at which point depreciation begins.
Once operations begin, the model shifts its attention to four moving parts that repeat every year for the life of the PPA: revenue (driven by how much water flows and what NEA pays for the electricity it buys), operating costs (a relatively small, fairly predictable line for a run-of-river plant with almost no fuel cost), the fiscal regime (royalty payments to the Government of Nepal and income tax to the Inland Revenue Department, both of which change shape at fixed milestones in the project's life), and debt service (principal and interest paid to the lenders who financed construction). The output that lenders, equity investors, and regulators all watch most closely is the Debt Service Coverage Ratio (DSCR) — a single number, recalculated every year, that says how many times over the project's cash generation could cover that year's loan repayment.
A useful way to hold the whole model in your head is to picture a household budget stretched across three decades. In the early years, the household (the project company) is paying off a large home loan (construction debt) while its income (PPA revenue) is still building up. Government dues (royalty and, later, tax) start small and grow as the household matures and its temporary exemptions (the tax holiday) expire. The household's ability to comfortably make its loan payment every year, even in a bad year, is what a bank actually underwrites — not the average year, but the worst plausible year. That underwriting instinct — plan for the dry year, not the average year — turns out to be the organising principle behind nearly every convention described in this chapter, from how tariffs are structured to how hydrology is forecast to how DSCR covenants are set.
Lesson 43.2 — The PPA Tariff Structure: Dry Season, Wet Season, and Escalation
Nepal's rivers are governed by the monsoon. From roughly mid-June to mid-November, glacier melt and monsoon rain swell river flows several times over; from December through May, flows fall to a fraction of their wet-season level. Because most Nepali hydropower to date has been run-of-river (RoR) — meaning the plant has little or no reservoir and generates power more or less in proportion to whatever water happens to be flowing past the intake at that moment, rather than storing water to release on demand — a run-of-river plant's output is not steady through the year. It might run near full capacity for six months and at a fraction of capacity for the other six.
Nepal Electricity Authority (NEA), which under the current market structure is the sole legal buyer of wholesale electricity from private hydropower developers under a Power Purchase Agreement (PPA) — a long-term contract fixing the price and terms under which the developer sells all its output to NEA — has responded to this seasonal imbalance by paying two different prices for the same electricity depending on when it's delivered. This is the single most important feature of Nepali hydropower revenue modelling, and it is worth understanding through an everyday analogy before touching the numbers.
Think of a vegetable farmer near Kathmandu who grows tomatoes. During the monsoon, every farmer's tomatoes ripen at once, the market floods, and prices fall. During the dry winter months, few farmers can grow tomatoes at all, so the ones who can — perhaps because they have irrigation or a greenhouse — sell into a scarce market and command a much higher price for the identical vegetable. NEA's tariff structure recognises exactly this same seasonal scarcity in electricity: during the dry season (roughly mid-November to mid-May), when most run-of-river plants are producing well below their rated capacity, the electricity that is available is scarce and valuable to the grid, so NEA pays a dry-season tariff that is markedly higher per unit. During the wet season (roughly mid-June to mid-November), electricity is comparatively abundant because every run-of-river plant in the country is running near full output at once, so NEA pays a lower wet-season tariff for the same kilowatt-hour.
Illustratively, a typical recent PPA for a small-to-medium run-of-river project has set the dry-season tariff at roughly NPR 8.40 per kWh and the wet-season tariff at roughly NPR 4.80 per kWh — meaning the dry-season rate is close to 75% higher than the wet-season rate for physically identical electricity. This differential is deliberate policy: it is NEA and the Government of Nepal trying to incentivize developers toward storage and peaking projects (which can shift wet-season water to dry-season generation) and to compensate run-of-river developers fairly for the fact that their most valuable production window is also their leanest one.
KEY CONCEPT
A Power Purchase Agreement (PPA) with two seasonal tariffs pays a higher rate per kWh for electricity delivered in the dry season (when supply is scarce) and a lower rate for electricity delivered in the wet season (when supply is abundant) — the same mechanism a market uses to pay more for off-season vegetables than for a monsoon glut of the same crop.
On top of the base tariff, most current PPAs include an escalation clause — a pre-agreed annual increase in the tariff rate, applied for a limited number of years from COD, after which the tariff is held flat (in nominal terms) for the remainder of the PPA term. A common structure seen in recent NEA PPAs escalates both the dry- and wet-season tariffs by roughly 3% per year for the first eight years of commercial operation, after which the tariff freezes at whatever level it reached and stays there — in nominal rupee terms — for the remaining 22 or so years of the agreement. This escalation exists partly to compensate developers for inflation during the early operating years and partly as a negotiated feature of the standard PPA template; it is not indexed to actual inflation and does not continue indefinitely, which is a detail every model must get exactly right, because forgetting to flatten the escalation after year eight can overstate 20+ years of revenue by a very large margin.
WARNING
Escalation is front-loaded and finite — a common modelling error is escalating the tariff for the entire 25-30 year PPA term instead of only the first several years specified in the actual PPA, which can inflate total project revenue by a substantial margin and make an unbankable project look investable on paper.
Because tariffs are fixed in the PPA rather than freely negotiated each year, and because NEA is a government-owned, government-backed offtaker, the PPA also functions as the project's primary credit support — lenders are effectively underwriting NEA's payment obligation as much as they are underwriting the river's flow. This is why PPA terms such as the Required COD (a contractual deadline for reaching commercial operation, missing which can trigger liquidated damages or, in newer PPA templates, a reduction in the number of escalation years the developer receives) and the presence or absence of a Deemed COD clause (a provision that lets the developer start earning revenue on schedule even if delay is caused by NEA's own grid connection not being ready) matter enormously to bankability. Sponsors reviewing a PPA before financial close should treat these clauses with the same seriousness as the tariff numbers themselves, because a technically attractive tariff can be worth very little if a construction delay outside the developer's control simultaneously costs escalation years and produces near-zero revenue for months on end.
CASE IN POINT
The 456 MW Upper Tamakoshi Hydroelectric Project — a peaking run-of-river scheme built by an NEA subsidiary — was hit by a major flood and landslide event in July 2021 just before scheduled commissioning, damaging the powerhouse and delaying commercial operation. The episode is widely cited in Nepali project finance circles as a reminder that construction-period hydrological and geological risk does not disappear once civil works are largely complete, and that PPA and insurance provisions covering late-stage construction damage are as important to model as the eventual operating tariff.
Lesson 43.3 — Hydrology Risk: P50, P90, and the Honest Revenue Forecast
Every hydropower revenue projection ultimately rests on one uncertain input: how much water will actually flow down this specific river, in this specific location, in each future year. Unlike a toll road (where traffic count is uncertain but knowable within a fairly narrow band once the road is open) or a factory (where output is a management choice), a run-of-river plant's energy output is dictated by nature, and nature does not deliver the same flow every year. Some years bring an unusually wet monsoon and abundant dry-season flow; other years bring drought.
Hydrologists address this by studying decades of historical flow records — where available, gauge data going back many years, sometimes supplemented by rainfall-runoff modelling where direct river gauge history is thin — and producing not a single "expected" energy number but a probability distribution of possible annual energy outputs. From that distribution, engineers extract specific reference points known by their exceedance probability: the probability that actual annual energy generation will equal or exceed a given figure.
The most commonly cited figures are P50, P90, and P99 (both P90 and P99 matter in Nepali practice, so both are explained here):
P50 is the energy output that has a 50% probability of being equalled or exceeded in any given year — in plain terms, the "median" year, exceeded half the time and not reached half the time. It is the best single estimate of the long-run average and is the number typically used for equity investors' base-case return calculations.
P90 is the energy output that has a 90% probability of being equalled or exceeded — a deliberately conservative figure, because it is only not achieved in roughly one year out of ten. Lenders overwhelmingly prefer P90 (sometimes an even more conservative P99) as the basis for sizing debt and testing DSCR, precisely because a bank cares far more about surviving a bad year without defaulting than about capturing the upside of a good one.
Return to the household analogy from Lesson 43.1: a family planning a home loan should size their monthly repayment against their income in a lean month, not their income in a bonus month, because the loan has to be paid every month regardless of how good that particular month turns out to be. A bank underwriting a hydropower loan does the same thing with river flow — it wants to know that the project can still make its debt payment even in a year that's drier than nine years out of ten, which is exactly what the P90 estimate represents.
CAUTION
P50 and P90 energy estimates can differ by 10-20% or more for a given river, depending on how variable and how well-gauged its flow record is. Using a P50 energy figure to size debt (instead of P90) systematically overstates debt capacity and understates the probability of a DSCR covenant breach in a genuinely dry year — this is one of the most consequential single choices in the entire model.
The practical modelling implication is that a properly built Nepali hydropower model should carry at least two separate energy-generation scenarios side by side: a P50 case used to project the "expected" return to equity investors (their IRR, payback period, and dividend capacity), and a P90 (or more conservative) case used to size the debt facility and to test whether DSCR stays above the lender's minimum covenant even in a dry year. A model that only shows a single, optimistic energy figure — often quietly closer to P50 or even better — and applies it to both the equity case and the lender's covenant test is a red flag that either the sponsor has not done rigorous hydrology work or is presenting an intentionally flattering picture. Institutional investors reviewing a hydropower deal memorandum should always ask which exceedance probability underlies every energy figure quoted, because "annual generation of X GWh" is a meaningless claim without that qualifier attached.
PRACTICAL TOOL
When reviewing any hydropower financial model or information memorandum, ask three questions before looking at a single tariff or DSCR number: (1) What exceedance probability (P50, P90, P99) underlies the stated annual energy figure? (2) How many years of hydrological record — gauge data or modelled — support that estimate? (3) Is the debt sized against the same energy case used to market the equity return, or against a more conservative one? A mismatch between the marketing case and the lender's case is the fastest way to spot an aggressively presented deal.
It is also worth noting that reservoir and peaking projects — schemes with enough storage capacity to hold back water and release it on demand, rather than simply passing through whatever flow arrives — behave differently. Their Plant Load Factor (PLF), sometimes called capacity factor, which measures actual energy generated over a period as a percentage of the theoretical maximum if the plant ran at full rated capacity every hour of that period, is a design choice as much as a hydrological outcome: a peaking plant is deliberately built to run at a lower average PLF (often 30-45%) while concentrating its generation into the highest-value hours of the day and the highest-value season, whereas a pure run-of-river plant's PLF (often 45-60% depending on the river's flow variability) is simply whatever the river delivers, hour by hour, with limited ability to shift timing. Both figures matter, but they answer different questions: PLF tells you how intensively the installed capacity is being used; the P50/P90 energy figures tell you how much total annual energy that translates into, and with what confidence.
Lesson 43.4 — Construction-Period Mechanics: CWIP, IDC, and the Drawdown Schedule
Returning to the construction phase introduced in Lesson 43.1, it is worth walking through exactly how CWIP and IDC flow through a model, because getting the mechanics right materially changes the final project cost — and therefore the tariff and equity return needed to make the project viable.
A typical Nepali run-of-river project is financed with a debt-to-equity ratio in the region of 70:30 or 80:20 for the construction period, reflecting both the risk appetite of Nepali commercial banks (which have historically been the dominant lenders to domestic hydropower, often through syndicated loan consortia given the size of individual projects relative to any single bank's lending limits) and increasingly, for larger schemes, international development finance institutions and export credit agencies. Equity is typically drawn down first or pro-rata with debt, and debt is drawn down against certified construction progress, milestone by milestone, over the construction period.
Every rupee of debt drawn during construction starts accruing interest immediately, but the project isn't generating any revenue to pay that interest out of. Two things can happen to that accruing interest, and the choice matters enormously to the model: either the developer pays it in cash out of a separate reserve (uncommon for early-stage projects with no revenue), or — the standard treatment — the interest is capitalised, meaning it is added to the outstanding loan balance (and to CWIP) rather than paid, so it compounds and gets repaid later out of operating cash flow, alongside the principal.
Walk through a simplified illustration. Suppose a 25 MW run-of-river project has a base construction cost (civil works, electromechanical equipment, transmission line, land, and development costs) of NPR 5.4 billion, financed 70:30, so debt is NPR 3.78 billion drawn progressively across a four-year construction period. If the average outstanding balance during construction carries an interest rate of roughly 10.5% per year (fairly typical for a Nepali hydropower term loan denominated in NPR), and the debt is drawn evenly, the capitalised interest accrued over those four years might add somewhere in the region of NPR 500-650 million to the final project cost — pushing total CAPEX from the "base" NPR 5.4 billion figure to roughly NPR 6.0 billion once IDC is included. That NPR 600 million is not paper money — it becomes real debt principal that has to be repaid out of operating revenue for the next 15 years, and it is why disciplined construction scheduling (avoiding delays that stretch out the interest-accrual period) is one of the single highest-value levers a developer has over total project economics.
WARNING
Every month of construction delay does double damage to a hydropower project's economics: it adds another month of capitalised interest onto CWIP (increasing total debt to be repaid), while simultaneously pushing back the COD that starts the revenue clock and, under many PPA templates, shrinking the number of escalation years the developer will ultimately receive. A one-year delay can therefore be considerably more expensive than a naive "12 months of extra interest" estimate suggests.
Once COD is reached, CWIP — including all that capitalised interest — is transferred onto the balance sheet as Property, Plant and Equipment, and depreciation begins. Nepali tax law generally allows hydropower generation assets to be depreciated for tax purposes using a written-down value (declining balance) method under a specified depreciation pool and rate set by the Income Tax Act, distinct from whatever depreciation policy the company uses for its own financial reporting (which may use straight-line depreciation over the asset's useful economic life, often estimated at 30-35 years or the PPA term, to better match the pattern of revenue generation for investors). This creates a familiar situation in project finance: tax depreciation (used to compute taxable income and hence actual cash tax paid) and book depreciation (used to compute reported accounting profit) diverge, and a careful model tracks both separately, because it is the tax depreciation schedule — combined with the tax holiday discussed in Lesson 43.6 — that determines actual cash tax outflows, which is what matters for DSCR.
KEY CONCEPT
Interest During Construction (IDC) is capitalised interest — interest accrued on construction-period debt that is added to the project's asset cost (via CWIP) rather than expensed immediately. It increases total project cost and total debt principal, and only begins flowing through the income statement as part of ordinary interest expense and depreciation after Commercial Operation Date.
Lesson 43.5 — The Debt Service Coverage Ratio: The Lender's Lens
If there is one number that a Nepali hydropower lender watches more closely than any other, it is the Debt Service Coverage Ratio, or DSCR. In its simplest form:
DSCR = Cash Flow Available for Debt Service (CFADS) ÷ Debt Service (Principal + Interest due that period)
Cash Flow Available for Debt Service (CFADS) is, roughly, operating revenue minus operating costs minus royalty payments minus cash taxes paid — in other words, the actual cash the project generates in a given period before anything is set aside to repay lenders. Debt Service is the total principal repayment plus interest due to lenders in that same period. A DSCR of 1.30x means the project generated 1.30 rupees of available cash for every 1 rupee it owed lenders that period — a comfortable cushion. A DSCR of exactly 1.00x means the project generated exactly enough cash to make its loan payment with nothing left over — an uncomfortably tight position that would alarm any lender, because it implies zero margin for a bad month, an unplanned repair, or a slightly drier-than-expected season.
Nepali commercial bank hydropower loan agreements typically embed a minimum DSCR covenant — a contractual promise, tested at each debt service date (often semi-annually) using the P90 energy case, that DSCR will not fall below a stated floor, commonly somewhere in the 1.20x to 1.30x range, sometimes distinguished between a "minimum" DSCR tested at each period and a slightly higher "average" DSCR tested across the life of the loan. Breaching the minimum covenant does not necessarily mean default outright, but it typically triggers a cash sweep or dividend lock-up — a restriction preventing the company from distributing dividends to equity shareholders until DSCR recovers above the threshold — and repeated or severe breaches can escalate to an event of default under the loan agreement, giving lenders the right to accelerate the loan or take other remedial action.
PRACTICAL TOOL
A simplified but genuinely useful working formula for CFADS in a Nepali hydropower model: CFADS = (Energy Sold to NEA at applicable seasonal tariff) − (O&M expenses) − (Capacity Royalty + Energy Royalty) − (Cash Income Tax Paid, net of any tax holiday). Debt Service = Scheduled Principal Repayment + Interest Expense for the period. Run this calculation for every year of the loan tenor under the P90 energy case, and flag any year where DSCR dips below the loan's minimum covenant — that year is where refinancing risk, dividend lock-up, or covenant renegotiation will actually bite.
Lenders often go a step further and require DSCR-sculpted repayment — meaning the principal repayment schedule itself is deliberately shaped (rather than a simple straight-line or equal-instalment schedule) so that debt service is lower in years when cash flow is naturally tighter (early operating years, before escalation has fully worked through, or years when the tax holiday is expiring and cash tax first bites) and higher in years when cash flow is stronger. This sculpting is precisely why understanding the seasonal tariff structure, the escalation schedule, and the royalty and tax step-changes described elsewhere in this chapter matters so much: a debt schedule that ignores the timing of these cash flow shifts risks being unbankable even if the project's average, multi-year economics look perfectly healthy.
The table below illustrates a simplified DSCR projection for a hypothetical 25 MW run-of-river project — "Himal Khola Hydropower" — over its first eight years of commercial operation, the period during which its PPA tariff escalation is still active. The example assumes energy generation held at the P90 case (35 GWh dry-season, 83 GWh wet-season, 118 GWh total per year), a starting dry-season tariff of NPR 8.40/kWh and wet-season tariff of NPR 4.80/kWh escalating 3% annually, O&M costs of NPR 60 million in Year 1 escalating 6% annually, royalty at a representative NPR 150/kW capacity charge plus 1.85% of gross energy revenue, no cash income tax (the project is assumed to be within its income tax holiday period, discussed in Lesson 43.6), and a level annual debt service of NPR 480 million.
Year
Dry-Season Revenue (NPR mn)
Wet-Season Revenue (NPR mn)
Total Revenue (NPR mn)
O&M (NPR mn)
Royalty (NPR mn)
CFADS (NPR mn)
Debt Service (NPR mn)
DSCR
1
294.0
398.4
692.4
60.0
16.6
615.8
480.0
1.28x
2
302.8
410.4
713.2
63.6
16.9
632.6
480.0
1.32x
3
311.9
422.7
734.6
67.4
17.3
649.8
480.0
1.35x
4
321.3
435.4
756.6
71.5
17.8
667.4
480.0
1.39x
5
330.9
448.4
779.3
75.7
18.2
685.4
480.0
1.43x
6
340.8
461.9
802.7
80.3
18.6
703.8
480.0
1.47x
7
351.1
475.7
826.8
85.1
19.1
722.6
480.0
1.51x
8
361.6
490.0
851.6
90.2
19.5
741.8
480.0
1.55x
Two features of this table are worth pausing on. First, DSCR rises steadily over the eight-year window purely because the PPA's escalation clause lifts revenue by 3% a year while debt service is held flat and O&M rises more slowly than revenue — this is exactly the kind of pattern lenders like to see, since it means the tightest DSCR year (and the year most exposed to a hydrology shortfall) is Year 1, right at 1.28x, just above a typical 1.25x covenant floor. Second, notice how thin that Year 1 margin really is: if actual Year 1 generation came in even 5% below the P90 estimate used here, CFADS would fall by roughly NPR 31 million, pulling DSCR down toward 1.21x — below a 1.25x covenant in many loan agreements. This is precisely why lenders insist on the P90 (not P50) energy case for exactly this calculation, as discussed in Lesson 43.3, and why the earliest operating years of any Nepali hydropower project are the ones examined most anxiously by both lenders and equity investors alike.
CASE IN POINT
Chilime Hydropower Company Limited — a 22.1 MW run-of-river plant on the Chilime Khola and one of the first Nepali hydropower companies listed on the Nepal Stock Exchange (NEPSE) — is often cited by Nepali analysts as a useful public reference point for run-of-river cash flow and dividend behaviour, since its listed financial statements let investors observe, year by year, how seasonal revenue split, royalty step-ups, and the expiry of tax concessions actually flowed through to distributable profit, in a way that is rarely visible for privately held project companies.
Lesson 43.6 — Royalties, Taxes, Depreciation, and the Levelized View
The Government of Nepal collects two distinct charges from every operating hydropower project, both called "royalty" but structured very differently, and both step up sharply once a project passes its fifteenth year of operation.
The capacity royalty is a fixed annual charge per kilowatt of installed capacity — it is payable regardless of how much energy the plant actually generates that year, much like a fixed land-use or resource-access fee. The energy royalty is a percentage of the plant's gross energy revenue (or, in some formulations, of the value of energy generated) — a variable charge that rises and falls with how much electricity the plant actually produces and sells.
Under provisions that have applied in recent years (subject, as with all Nepali tax and royalty rates, to revision through the annual Finance Act and Electricity Regulation Commission decisions, so any sponsor or investor should always confirm current rates before modelling a live transaction), a broad picture looks like the table below. Rates differ depending on whether the project sells domestically or is licensed to export power, and whether it is a run-of-river or storage-type scheme, reflecting government policy to charge storage/peaking and export-oriented projects a higher royalty given their generally larger revenue potential.
Project Category
Capacity Royalty, Years 1-15
Energy Royalty, Years 1-15
Capacity Royalty, Year 16 onward
Energy Royalty, Year 16 onward
Domestic sale, up to 3 MW
Nil
Nil
Nil
Nil
Domestic sale, 3-10 MW
~NPR 100/kW
~1.75-2% of revenue
~NPR 1,000/kW
~10% of revenue
Domestic sale, 10-100 MW
~NPR 150/kW
~1.85% of revenue
~NPR 1,000-1,500/kW
~10% of revenue
Export, run-of-river
~NPR 400/kW
~7.5% of revenue
~NPR 1,800/kW
~12% of revenue
Export, storage/peaking
~NPR 500/kW
~10% of revenue
~NPR 2,000/kW
~15% of revenue
REGULATORY DETAIL
The royalty step-up at year 15 is one of the most consequential single dates in a Nepali hydropower model's entire multi-decade cash flow. Energy royalty on domestic sale can roughly quintuple (from under 2% to around 10% of revenue), and capacity royalty can rise by a factor of ten or more, at exactly the point in the project's life when the initial debt is typically close to fully repaid — meaning the step-up mostly affects the equity holders' later-year cash flow and any levelized return calculation, rather than debt serviceability, which is usually structured to be safely retired before year 15.
Alongside royalty, hydropower companies benefit from one of the more generous tax concession regimes available to any sector in Nepal, reflecting the government's long-standing policy priority of encouraging private investment into electricity generation given Nepal's enormous underexploited hydropower potential relative to installed capacity. Under provisions applicable to projects reaching commercial operation within specified windows set by successive Finance Acts, hydropower generation companies have typically been entitled to a substantial income tax holiday — commonly structured as a full (100%) rebate on corporate income tax for an initial period (often ten years) from the start of commercial operation, followed by a partial rebate (commonly 50%) for a further period (often five more years), after which the company reverts to paying tax at the prevailing standard corporate rate on income (currently 20% for most companies, prior to any further sector-specific adjustment) for the remainder of the PPA term. On top of the income tax holiday, hydropower companies commonly benefit from reduced customs duty on imported plant, machinery, and equipment (since Nepal has essentially no domestic turbine or major electromechanical equipment manufacturing base) and exemption from value-added tax (VAT) on qualifying imported equipment not produced domestically, both of which reduce effective CAPEX rather than operating cash flow.
REGULATORY DETAIL
A commonly cited structure for the hydropower income tax holiday is: 100% rebate (i.e., zero corporate income tax) for the first 10 years of commercial operation, followed by a 50% rebate on the standard corporate rate for years 11 through 15, after which full standard corporate tax applies. Because this schedule is set (and periodically adjusted) through the annual Finance Act, any live financial model should be checked against the specific Finance Act in force at the time the project reaches COD, since the exact number of holiday years and rebate percentages have changed over successive budgets.
Because the tax holiday, the royalty step-up, and (for projects with PPA escalation) the tariff freeze all occur at different points in the project's life, a single year's numbers — even a well-chosen "representative" year — tell you very little about whether the project is a good investment across its full 25-30 year PPA term. This is why practitioners rely on levelized analysis: rather than looking at cash flow year by year, a levelized metric compresses the entire multi-decade stream of costs or revenues into a single equivalent constant figure, using a discount rate to make cash flows from different years comparable. The most common such measure in energy finance generally is the Levelized Cost of Energy (LCOE) — the constant per-kWh price that, if received every year for the life of the plant, would exactly cover all its capital and operating costs (discounted back to construction start) — which is useful for comparing the underlying economics of different technologies or different sites on an apples-to-apples basis, independent of any specific PPA's actual (non-constant, seasonally split, escalating-then-flat) tariff schedule.
KEY CONCEPT
Levelized analysis converts an uneven, multi-decade stream of cash flows — seasonal, escalating for a few years, then flat, interrupted by a royalty step-up at year 15 and a tax-rate change around years 10 and 15 — into a single equivalent constant figure using time-value-of-money discounting. It answers "what is the true average economics of this asset across its whole life," a question that no single year's DSCR or profit figure can answer on its own.
For an equity investor specifically, the levelized concept extends naturally into the project's equity Internal Rate of Return (IRR) — the single discount rate at which the present value of all future equity cash flows (dividends received, plus any terminal or exit value) exactly equals the initial equity investment. Because Nepali hydropower has such a distinctive cash flow shape — years of zero revenue during construction, tight but improving DSCR and modest dividends during the tax-holiday years while debt is being serviced, a step-down in retained cash around the tax-holiday expiry, and a further step-down in net revenue after the year-15 royalty increase, but by then typically with the original debt substantially or fully repaid — investors evaluating a specific project's equity IRR should always ask to see the full year-by-year projection across the entire PPA term, not merely a "steady-state" year plucked from the middle of the schedule, since no single year in a Nepali hydropower project's life is actually representative of the whole.
CAUTION
Beware any hydropower investment memorandum that presents a single "typical operating year" DSCR or equity cash flow figure without showing the full year-by-year schedule across the PPA term. Given how many step-changes occur in a Nepali hydropower project's economics — tariff escalation ending around year 8, tax holiday tapering around years 10-15, and royalty stepping up at year 15 — a single representative year can be chosen (deliberately or not) to look far more attractive than the levelized reality across the full term.
Chapter recap
This chapter built a Nepali hydropower financial model from the ground up, starting with the basic structural fact that every such project lives two separate lives: a construction period during which costs accumulate in Capital Work in Progress and interest on construction debt is capitalised rather than expensed (Interest During Construction, or IDC), and an operating period, typically 25 to 30 years long and coinciding with the term of the Power Purchase Agreement (PPA), during which the project earns revenue, pays operating costs, meets its fiscal obligations to government, services its debt, and eventually returns capital to equity holders. Every later concept in the chapter — from tariff structure to royalty step-ups — makes sense only once this two-phase structure is understood, because the timing of when a cost or a concession applies matters just as much as its size.
The chapter then examined the revenue side in detail, centred on Nepal Electricity Authority's practice of paying differentiated dry-season and wet-season tariffs, a structure that mirrors the way any seasonal market pays a premium for a scarce good and a discount for an abundant one — much like a farmer commanding a higher price for off-season vegetables than for a monsoon glut of the same crop. Layered on top of the seasonal tariff is a time-limited escalation clause, typically running for the first several years of operation before freezing in nominal terms for the remainder of the PPA — a feature that materially boosts early-year revenue but must never be mistakenly extended across the full contract term in a model, since doing so can dramatically overstate long-run cash flow.
Because a run-of-river plant's output is dictated by the river rather than by management decision, the chapter introduced the concept of exceedance probability — P50 as the median, most-likely energy outcome used for equity base-case projections, and the more conservative P90 (or P99) used by lenders to size debt and to test covenant compliance, precisely because a bank cares about surviving the one-year-in-ten dry spell, not capturing the good years. A model or information memorandum that fails to specify which probability underlies its headline energy number, or that uses the same optimistic figure for both marketing to equity and underwriting to lenders, should be treated as a signal for closer scrutiny rather than as a reliable basis for investment decisions.
On the capital and debt side, the chapter walked through how Capital Work in Progress accumulates through construction, how capitalised interest can add a material percentage to total project cost if construction is delayed, and how the Debt Service Coverage Ratio (DSCR) — cash flow available for debt service divided by scheduled principal and interest — became the single most important recurring metric that lenders monitor, typically subject to a minimum covenant in the 1.20x-1.30x range tested against the conservative P90 hydrology case. The worked numerical illustration showed how a project's DSCR can start close to its covenant floor in the earliest operating years and improve steadily thereafter as tariff escalation lifts revenue faster than operating costs grow — which is exactly why the first few years of operation, not the multi-year average, are where a hydropower project's financial resilience is genuinely tested.
Finally, the chapter covered Nepal's fiscal regime for hydropower: a two-part royalty (a fixed capacity charge per installed kilowatt plus a variable energy charge as a percentage of revenue) that steps up sharply — often by a factor of five to ten — once a project passes fifteen years of operation, alongside an income tax holiday that commonly grants a full rebate for an initial period followed by a partial rebate for several further years before standard corporate tax rates apply. Because these concessions, escalation clauses, and royalty step-ups all land at different points across the project's life, the chapter closed on the discipline of levelized analysis — compressing an uneven, multi-decade cash flow stream into a single comparable figure using discounting — as the only reliable way to judge a hydropower investment's true underlying economics, and as a caution against ever accepting a single "representative year" as a substitute for the full year-by-year schedule across the entire PPA term.
Taken together, these six lessons should equip an institutional reader to open any Nepali hydropower project's financial model or information memorandum and immediately know which questions to ask: what exceedance probability underlies the energy forecast, whether debt is sized against that same conservative case, how the seasonal tariff and its finite escalation window are modelled, whether CWIP and IDC have been capitalised correctly through the construction period, whether DSCR has been tested in the tightest early operating years rather than only in a flattering steady-state year, and whether the model correctly reflects the royalty step-up and tax holiday expiry at their actual contractual and statutory dates rather than smoothing them away. A model that survives all of those questions is one worth taking seriously; a model that has not been asked all of them has not yet been properly stress-tested.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part VIII · Chapter 44
Evaluating a Hydropower Stock on NEPSE
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 44.1 — The NEPSE Hydropower Universe: One Sector, Several Different Businesses
If you have read the previous two chapters, you already know how a hydropower project is financed and how its cash flows are modelled from first principles — the debt sizing, the tariff structure, the concession period, the sensitivity of the whole edifice to how much water flows down a particular river in a particular year. Chapter 44 turns that knowledge outward. You are no longer building a model from scratch as a lender or a promoter would. You are an investor sitting in Kathmandu, or Biratnagar, or anywhere with a NEPSE trading account, looking at a list of forty-plus hydropower tickers on your broker's screen, and you need a repeatable process for deciding which of them, if any, deserves your money.
Start with a fact that surprises many new investors: "hydropower stock" is not one kind of investment. It is a label that covers at least four distinct business models, and confusing one for another is the single most common analytical error in this part of the NEPSE market.
The first model is the single-asset operator. This is a company that owns exactly one power plant, on one river, with one Power Purchase Agreement (PPA — the long-term contract under which the plant sells its electricity, almost always to the Nepal Electricity Authority, or NEA). Upper Tamakoshi Hydropower Limited is the textbook example: a single 456-megawatt (MW) peaking run-of-river plant on the Tamakoshi River in Dolakha, and nothing else. When you buy this stock, you are making a concentrated bet on one river, one tunnel, one powerhouse, and one buyer. There is nowhere for a bad year on that specific river to hide.
The second model is the diversified generation holding company. Chilime Hydropower Company Limited started life in 2003 as a single-asset operator of its namesake 22.1 MW plant on the Chilime River, but over two decades it has taken equity stakes in and consolidated several other plants — including Rasuwagadhi (111 MW) and the Sanjen cascade projects — so that today an investor in Chilime shares is effectively buying a small portfolio of rivers, not one river. Butwal Power Company (BPC) follows a similar pattern: it directly operates older plants such as Andhikhola (5.1 MW) and Jhimruk (12.3 MW), while also holding equity stakes in other hydropower special-purpose vehicles. This model smooths out the single-river risk of the first model — a drought or a landslide on one tributary does not zero out the whole company's generation.
The third model is the small, single-project independent power producer (IPP) — companies like Ridi Hydropower or Sanima Mai Hydropower, generally sub-25 MW plants built and listed by a promoter group specifically to raise public equity for that one project. There are dozens of these on NEPSE, and they are where most of the sector's retail trading volume happens, precisely because they are small, volatile, and easy to speculate on.
The fourth model is the pipeline or growth company — one that is still constructing its plant, or has just commissioned it and has not yet reached stable full-year generation. Here you are not really analysing an operating business yet; you are analysing a construction project with a stock ticker attached, and the risks are closer to those in Chapter 42's project-finance material than to a normal listed equity.
KEY CONCEPT
A Power Purchase Agreement (PPA) is the contract, almost always with NEA, that fixes how much a hydropower company gets paid per unit of electricity (per kWh) and for how many years. Everything else in this chapter — royalty step-ups, dividend capacity, valuation — is really just asking one question in different disguises: what does the cash flow under this specific PPA look like, year by year, until it runs out?
Before you open a single annual report, therefore, the first due-diligence question is simply: which of these four models am I looking at? A single-asset operator should be judged almost entirely on the quality of one river and one PPA. A diversified holding company should be judged the way you would judge a small conglomerate — sum of the parts, plus a discount or premium for how well the parent allocates capital between its subsidiaries. A small IPP should be judged on the same fundamentals as the single-asset operator, but with a much smaller margin for error, because a small company has no other asset to fall back on and often carries a proportionally higher fixed-cost burden per megawatt. A pipeline company should be judged on construction-risk criteria first and only secondarily as an operating business.
Lesson 44.2 — Run-of-River versus Storage/Peaking: Reading the Risk Profile Correctly
Chapter 43 introduced the plant-level distinction between run-of-river (ROR) and storage or peaking-ROR designs. This lesson translates that engineering distinction into an investment-risk lens, because it is the single biggest determinant of how "lumpy" a hydropower company's quarterly revenue will look.
A pure run-of-river plant has no meaningful reservoir. It generates electricity roughly in proportion to whatever water is flowing in the river at that moment. In Nepal's climate, river flow is dictated by the monsoon: roughly 80% of annual precipitation falls between June and September. A pure ROR plant on a river without significant glacial or spring-fed baseflow will generate at or near full capacity during the monsoon and can fall to a fraction of that — sometimes 20-30% of installed capacity — during the dry months of December through April. This is not a malfunction; it is the plant working exactly as designed. But it means that a pure ROR company's quarterly revenue swings hard between wet-season and dry-season quarters, and an investor comparing one quarter's earnings per share (EPS) to another without adjusting for season will draw the wrong conclusion every time.
A peaking run-of-river (PROR) plant — Upper Tamakoshi is again the reference case — adds a small pondage (a short-term storage basin, holding a few hours' worth of water) that lets the plant concentrate its generation into the hours of the day when electricity is most valuable, typically the morning and evening peaks when NEA's demand (and, under time-of-day tariff structures, its willingness to pay a higher rate) is highest. This smooths daily revenue somewhat and can lift the average realised tariff above the plain energy rate, but it does nothing to smooth the wet-season/dry-season seasonality — the total volume of water available across the year is still the fundamental constraint.
A true storage project — one with a dam and a reservoir large enough to hold back weeks or months of inflow — is the rarest category among NEPSE-listed companies, because storage projects are dramatically more expensive to build per megawatt and Nepal's private hydropower boom of the last two decades has been overwhelmingly a run-of-river boom. If a storage project does exist in a company's portfolio, it is the closest thing to an all-weather asset in the sector: it can release water in the dry season when tariffs (and NEA's need for firm power) are highest, effectively converting a hydrological disadvantage into a revenue advantage. When you do encounter one, treat it as materially higher-quality than an equivalent-capacity ROR plant, and expect the market to price it at a premium — check that the premium is not larger than the actual cash-flow advantage.
WARNING
Never compare two hydropower companies' quarterly EPS directly without first checking whether both quarters fall in the same hydrological season. A Poush (mid-winter) quarter compared to an Ashadh (pre-monsoon peak) quarter will make almost any ROR company look like it is collapsing or booming, when in fact nothing has changed except the calendar.
The practical due-diligence question that follows from this is: what fraction of this company's installed capacity comes from pure ROR versus peaking ROR versus storage, and what does its river's dry-season flow look like relative to its wet-season flow? Companies disclose monthly or quarterly generation figures in their annual reports (more on reading these in Lesson 44.4); plotting a few years of these numbers is the single fastest way to see a plant's real seasonality pattern, rather than trusting a one-line "run-of-river" label in a broker's factsheet.
Lesson 44.3 — The PPA Clock and the Royalty Staircase
Every hydropower company's cash flow has two clocks ticking inside it, and both matter enormously to a long-term investor, yet neither shows up as a single line item on the income statement.
The first clock is the PPA term itself. A PPA is not a permanent right to sell power; it is a fixed-term contract, typically running 25 to 35 years from the commercial operation date (COD), after which the company must renegotiate terms with NEA or, in principle, sell power on whatever open-market or bilateral arrangement exists at that time. For a young plant like Upper Tamakoshi (commissioned 2021), this clock has decades left to run and is a minor consideration today. For some of the older private plants on NEPSE — several of which date to the mid-1990s and early 2000s, including some of BPC's and Chilime's founding assets — investors need to actually check how many years remain on the PPA, because a plant approaching PPA expiry faces real uncertainty about what tariff it will earn afterward. This is disclosed (or should be) in the annual report's project description section, and it is worth writing down explicitly for every holding in a hydropower portfolio: "PPA signed in [year], term [n] years, therefore expires in [year]."
The second clock, and the one investors overlook far more often, is the royalty staircase. Under Nepal's Electricity Act and its associated regulations, hydropower generators pay royalty to the Government of Nepal in two components: a capacity royalty (a fixed charge per kilowatt of installed capacity per year) and an energy royalty (a percentage of the value of energy actually generated and sold). Crucially, both components are structured to step up partway through a plant's operating life — commonly after the plant has been operating for around fifteen years, the royalty rate rises meaningfully, in some structures by several multiples on the capacity component and by several percentage points on the energy component. The policy logic is straightforward: young plants are still carrying heavy debt service, so the state takes a small royalty share early on and a larger share later once the debt is substantially repaid and free cash flow is higher.
REGULATORY DETAIL
Nepal's royalty framework for hydropower charges a capacity royalty (per kW of installed capacity) plus an energy royalty (a percentage of energy sold) under the Electricity Act and its rules, with both rates rising once a plant crosses roughly the fifteen-year mark of commercial operation. The exact rupee and percentage figures are periodically revised by the Ministry of Energy, Water Resources and Irrigation, so always check the current schedule and each company's own disclosed royalty expense — do not assume the rate has stayed the same as when the PPA was signed.
Why does this matter for stock-picking? Because it means a plant's after-royalty margin is not constant over its life — it mechanically compresses as the plant ages, even if gross generation and gross tariff revenue stay flat. An investor who values an older plant purely by extrapolating last year's net margin forward is quietly assuming the royalty rate never rises, which for a plant near or past its fifteen-year mark is simply wrong. Conversely, a newer plant still inside its low-royalty window has a margin cushion that will erode on a known schedule — a predictable headwind that should already be built into any multi-year cash-flow projection, not treated as a surprise when it arrives.
CASE IN POINT
Some of Nepal's oldest listed private and semi-public hydropower plants — those commissioned in the 1990s and early 2000s — have already crossed into the higher royalty band, and their reported royalty expense as a percentage of revenue is visibly higher than that of plants commissioned in the last five to ten years. Comparing two companies' royalty-expense ratios side by side is a quick, annual-report-only way to infer roughly where each plant sits on its own royalty staircase, even before you dig into the PPA documents.
The practical checklist item, then, is twofold: (1) how many years remain until this specific PPA expires, and (2) has this plant already crossed, or when will it cross, the royalty step-up threshold. Both numbers should be sitting in your notes before you look at a single valuation multiple.
Lesson 44.4 — Reading the Annual Report: Generation Data, Realised Tariff, and the Monsoon Story
A Nepali hydropower annual report contains more decision-useful information outside the financial statements than inside them, and most retail investors never turn to those pages. This lesson is a guide to reading the parts that actually matter.
Start with the generation and sales table. Every well-run hydropower company discloses, usually in a schedule near the directors' report, the number of units (kWh or GWh) generated and sold in the year, frequently broken down by month or at least by quarter. This is the single most important table in the report, because it lets you answer three questions no ratio can answer on its own:
First, how does this year's generation compare to prior years, and if it fell, was that a plant-specific problem (a landslide-damaged intake, a transformer failure, a scheduled overhaul) or a river-wide hydrology problem (a below-average monsoon)? Nepal experienced a materially weaker monsoon in parts of 2023, and several ROR-heavy companies posted noticeably lower generation and revenue that year purely from reduced river flow — a useful real-world reminder that "hydrology risk" is not a theoretical line in a project-finance textbook, it shows up in actual reported numbers every few years. Distinguishing a hydrology-driven dip from an asset-specific failure matters because the two carry very different implications going forward: a bad monsoon year is a recurring, cyclical risk that should be averaged over a multi-year window, while equipment or landslide damage is a one-off (unless it recurs, in which case it says something troubling about maintenance quality or site geology).
Second, what is the plant load factor — actual generation divided by the theoretical maximum if the plant ran at full installed capacity every hour of the year? For a pure ROR plant with strong dry-season flow deficits, a load factor in the 40-55% range is often normal and not a red flag by itself; what you want to track is whether that load factor is stable, improving, or deteriorating year over year for reasons unrelated to rainfall.
Third — and this is the step most investors skip — what is the realised average tariff per unit sold, calculated simply as total energy revenue divided by total units sold, and how does that compare to the PPA's stated base tariff (which itself is usually specified separately for wet-season and dry-season energy, since most Nepali PPAs pay a materially higher rate for dry-season energy specifically to compensate ROR plants for their weakest months)? If the realised average tariff has been drifting upward over time faster than the PPA's contracted escalation would suggest, check whether the company has simply been generating more of its energy in the higher-priced dry season (a real, sustainable shift, perhaps from a pondage upgrade) versus a one-off adjustment or an arrears settlement from NEA that will not repeat.
PRACTICAL TOOL
Build a simple five-column tracking sheet for every hydropower holding: (1) units sold this year vs. last year, (2) realised average tariff vs. PPA base tariff, (3) royalty expense as % of revenue, (4) dividend per share vs. free cash flow per share, (5) years remaining on the PPA. Update it once a year straight from the annual report, and most of the qualitative judgments in this chapter become visible in a single glance.
Finally, look at how royalty and tax are actually presented. Royalty is sometimes buried inside "other operating expenses" rather than shown as its own line — if so, it is worth asking the company (many hold investor calls or respond to written questions) or checking the notes to the financial statements, because a hidden royalty step-up is exactly the kind of thing that quietly erodes margin without showing up in a quick ratio scan. Corporate income tax for hydropower companies in Nepal often carries a preferential rate or a tax holiday for an initial period after COD (a policy tool meant to encourage investment), which is another item that mechanically changes — usually upward — partway through a plant's life and should be modelled explicitly rather than assumed constant.
Lesson 44.5 — Promoters, Construction Risk, and the Balance Sheet: What Can Go Wrong Between the Prospectus and the Dividend Cheque
A hydropower stock is, in the end, a claim on a stream of dividends, and the distance between "the plant generates electricity" and "you receive a dividend" is longer and more encumbered than in most other NEPSE sectors. This lesson works through that distance.
Begin with the promoter. Nepal's hydropower boom has been driven substantially by a relatively small number of promoter groups and engineering-construction houses who have built and floated multiple projects over the past two decades. A promoter's track record across their earlier projects is a genuinely useful predictor: did their previous plants come in near budget and near the projected commissioning date, or did they run years late and multiples over budget (as many projects in this sector historically have, given landslide-prone terrain, seasonal access roads, and imported-equipment lead times)? Did dividends actually start flowing within a reasonable window of COD, or did the company sit on cash for years citing DSRA (debt service reserve account, explained below) requirements? A promoter's history of shepherding one project from construction into a paying dividend stream is a far better signal than the glossy language of a new project's prospectus.
For a pipeline company still under construction, the risk profile is close to what Chapter 42 described for project-finance lenders, not what a normal equity analyst is used to: cost overruns from geological surprises (tunnel collapses, unstable slopes — a recurring theme in Himalayan hydropower construction), delays that push back the COD and therefore push back the first dividend by a corresponding number of years, and foreign-exchange exposure on imported turbines, generators, and transmission equipment if the company has not appropriately hedged or matched its debt currency to its revenue currency. An investor holding a pre-COD hydropower stock should read the construction-progress disclosures (percentage completion, revised COD guidance) the way a lender reads a drawdown schedule, and should treat any COD guidance with a healthy skepticism born of the sector's own history of delays.
WARNING
A pipeline hydropower stock trading up sharply on IPO enthusiasm, well before COD, is pricing in an assumption that construction finishes on schedule and on budget. Given the sector's actual historical track record of delays and overruns, that assumption deserves to be actively challenged, not passively accepted because the share price is rising.
Once a plant is operating, the next layer is the debt structure itself. Nepali hydropower projects are financed with long-tenor project debt, usually from a syndicate of Nepali commercial banks (given the country's capital controls and the relatively limited scale of the domestic bond market), and that debt typically requires refinancing or restructuring risk at various points — either because the original tenor was shorter than the PPA term, or because covenants require periodic renegotiation. Two things to check from the annual report or notes to accounts: the debt maturity profile (how much comes due in the next one, three, and five years) and whether the company has a demonstrated ability to refinance on reasonable terms, versus being at the mercy of whatever domestic lending rates prevail when a large tranche falls due — a real risk in Nepal, where domestic bank lending rates have swung meaningfully across credit cycles over the past decade.
This leads directly to the DSRA and covenant question, which is the most common reason a profitable-looking hydropower company still pays a disappointingly small dividend. Lenders to a hydropower project almost always require the company to maintain a Debt Service Reserve Account — cash set aside, often equal to the next one or two quarters' worth of principal and interest payments — before any dividend can be distributed to shareholders. On top of that, loan covenants frequently impose a minimum Debt Service Coverage Ratio (DSCR, cash available for debt service divided by debt service due) that must be met, sometimes with a further "lock-up" test, before the company is permitted to upstream cash as dividends at all. A company can report solid net profit under accrual accounting and still be barred from paying much of a dividend that year because its DSRA is being topped up or its DSCR covenant is running close to the minimum.
CAUTION
Never treat reported net profit as a reliable proxy for what a hydropower company can actually pay out. Check the cash flow statement, the DSRA balance (often disclosed in the notes), and any covenant language mentioned in the directors' report. A company can be profitable on paper and still legally restricted from paying the dividend its EPS would suggest.
Put together, the promoter-and-balance-sheet checklist for a NEPSE hydropower stock looks like this: has this promoter delivered before, what fraction of construction is complete (for pipeline names) and how has guidance drifted, what does the debt maturity ladder look like over the next five years, and is there any disclosed DSRA or covenant constraint currently limiting distributable cash. None of these appear as a single ratio on a stock screener — they require actually reading the report.
Lesson 44.6 — Valuing a Hydropower Stock: DCF Over the PPA Life, and Avoiding the P/E Trap
Chapters 42 and 43 built the machinery for a full discounted cash flow (DCF) model of a hydropower project. This final lesson is about applying that machinery, in a somewhat simplified form, as a public-market investor rather than as a project financier, and about the specific valuation traps this sector produces more reliably than almost any other on NEPSE.
The starting point is that a hydropower company's cash-generating asset has a known, finite life in a way most other businesses do not: the PPA has a defined end date, after which future cash flows are genuinely uncertain (a new PPA might be signed at a different tariff, or the plant might sell into whatever market exists at that time). This makes hydropower one of the cleanest sectors on NEPSE for a proper multi-stage DCF: project out annual free cash flow to equity for the remaining PPA term, explicitly modelling the seasonality from Lesson 44.2, the royalty step-up from Lesson 44.3, the tax-holiday expiry, and the debt amortisation and refinancing schedule, discount those cash flows at a cost of equity that reflects Nepal's risk-free rate plus an appropriate equity risk premium, and then attach a deliberately conservative (or even zero) terminal value beyond the PPA's expiry, since what happens after that date is genuinely unknowable today. This is meaningfully different from valuing, say, a bank or a manufacturer, where a growing perpetuity terminal value usually does most of the work in the valuation — in hydropower, the terminal value should do very little of the work, and the explicit forecast period should do almost all of it.
KEY CONCEPT
Because a PPA has a fixed end date, a hydropower DCF should rely mainly on the explicit forecast period up to PPA expiry, with a modest or zero terminal value afterward — the opposite of how most perpetuity-based DCFs are built for going-concern businesses with no natural end date.
The most dangerous shortcut investors take instead of this proper DCF is relying on a simple trailing price-to-earnings (P/E) ratio, and this sector produces a particularly sharp version of the "P/E trap" flagged in earlier chapters of this book. Because hydropower earnings are so sensitive to a single year's hydrology, a company can post an unusually strong year (an especially wet monsoon, a full year without any equipment downtime, an arrears settlement from NEA boosting realised tariff) and show up on a screener with a deceptively low trailing P/E — looking "cheap" purely because the denominator (that one year's EPS) was inflated by conditions that will not repeat. The reverse trap also happens: a company coming off a genuine drought year or a maintenance shutdown can show an inflated trailing P/E that makes it look expensive, right when the underlying asset is actually being bought at its cheapest, most washed-out point in the cycle. The correction for both versions of the trap is the same: normalise earnings across at least a three-to-five-year window that spans both wet and dry hydrological cycles before computing any P/E-style multiple, rather than trusting the most recent twelve months in isolation.
WARNING
A single year's EPS for a run-of-river hydropower company is a noisy, hydrology-driven number, not a stable earnings power figure. A trailing P/E built on one such year, without normalising across several years, will misprice the stock in either direction depending on whether that year happened to be wet or dry.
EV/EBITDA (enterprise value divided by earnings before interest, tax, depreciation and amortisation) is generally a more useful cross-sectional comparison tool in this sector than P/E, for a simple reason: it strips out the effect of each company's different capital structure (some hydropower companies carry far more leverage relative to their asset base than others, which distorts P/E through the interest-expense line) and it also strips out the differing tax-holiday positions discussed in Lesson 44.4. Comparing EV/EBITDA across companies of a similar vintage, similar remaining PPA term, and similar ROR/storage mix is a reasonable way to spot which names the market is pricing more or less generously relative to peers — though even here, the "similar remaining PPA term" qualifier matters enormously, because a plant three years from PPA expiry and a plant twenty-five years from PPA expiry should never trade at the same EV/EBITDA multiple, all else equal, since the buyer of the former is purchasing a much shorter cash-flow stream.
Dividend yield is the metric most retail investors reach for first in this sector, since hydropower stocks are widely (and often correctly) viewed as income plays. But Lesson 44.5 already showed why a single year's dividend can be an unreliable guide — DSRA top-ups and covenant lock-ups can suppress a dividend in a year when underlying cash generation was actually fine, and conversely a company can occasionally pay out an unsustainably large dividend by drawing down reserves ahead of a known heavy capital expenditure or debt repayment year. The sustainability check is to look at the multi-year average payout relative to free cash flow to equity (not net profit), track whether the DSRA balance and DSCR covenant headroom are stable or shrinking, and ask whether the company has any pipeline expansion project that is likely to divert cash away from dividends in the near future regardless of how strong current-year generation looks.
Table: A Snapshot Comparison Across Selected NEPSE-Listed Hydropower Companies
The table below is illustrative rather than a live data feed — installed capacities are stable public facts, but PPA-remaining-term, royalty-band position, and valuation multiples move every year and must be re-checked against each company's current annual report and market price before any real decision. Use it as a template for the kind of comparison sheet you should build yourself for the specific names you are considering.
Company
Business Model
Plant Type
Approx. Installed Capacity
Where It Sits on the PPA/Royalty Clock (illustrative)
Upper Tamakoshi Hydropower Ltd.
Single-asset operator
Peaking run-of-river
456 MW
Commissioned 2021; early-to-mid life, still inside lower royalty band, long remaining PPA term
~22 MW own plant; group capacity much larger via Rasuwagadhi, Sanjen subsidiaries
Founding Chilime plant commissioned 2003, past the 15-year royalty step-up threshold; subsidiaries generally younger
Butwal Power Company Ltd.
Diversified holding (own plants + equity stakes)
Mix of small ROR plants plus stakes in others
~17 MW directly owned (Andhikhola + Jhimruk), plus portfolio stakes
Founding plants commissioned 1990s, well past royalty step-up; portfolio stakes vary by asset
Sanima Mai Hydropower Ltd. / Mai Cascade group
Single-project to small cascade
Run-of-river
Small-cap, low double-digit MW range
Check specific PPA signing year and remaining term in company disclosures
Ridi Hydropower Development Company
Single-asset operator
Run-of-river
Small-cap, single-digit to low double-digit MW
One of the older private plants; likely past or near royalty step-up threshold
National Hydro Power Co. Ltd.
Single-asset / small portfolio operator
Run-of-river
Small-cap
Check specific PPA signing year and remaining term in company disclosures
Arun Valley Hydropower Development Co.
Developer/pipeline-stage or early-operating
Run-of-river
Small-cap
Distinct company from the state-led, India-financed Arun-3 (900 MW) project — do not conflate the two when researching
CASE IN POINT
"Arun Valley Hydropower Development Company" (a privately promoted, NEPSE-listed developer of a smaller project) is a completely different entity from "Arun-3," the roughly 900 MW project being built on the same river system by an Indian state-owned developer under a separate bilateral arrangement and not listed on NEPSE at all. Investors searching news for "Arun" hydropower coverage regularly find Arun-3 headlines and mistakenly apply them to the listed company's outlook — always confirm you are reading about the specific legal entity whose shares you hold, not just a river with a similar name.
A second short table is worth keeping alongside the first: a simple royalty-and-tax staging table, to remind yourself which margin headwinds are structural and scheduled rather than surprises.
Plant Age Band (from COD)
Typical Capacity Royalty Treatment
Typical Energy Royalty Treatment
Typical Income Tax Treatment
Early years (often first 10-15 yrs)
Lower fixed per-kW rate
Lower percentage of energy value
Often a holiday or concessional rate for an initial window
Mature years (after ~15 yrs)
Steps up to a materially higher fixed per-kW rate
Steps up to a materially higher percentage of energy value
Reverts to standard corporate tax rate once holiday period lapses
REGULATORY DETAIL
Exact royalty rupee amounts, royalty percentages, and tax-holiday windows are set by the Electricity Act, its regulations, and periodic Ministry of Energy notifications, and have been adjusted over time. Treat the two-band structure above as a durable feature of the framework to check for, not as a fixed set of numbers to assume are still current — always confirm the applicable rates in the company's own notes to accounts for the year you are analysing.
Chapter recap
This chapter has tried to convert the project-finance and modelling discipline of Chapters 42 and 43 into a working checklist for picking, or rejecting, a specific NEPSE-listed hydropower stock. The first and most important habit is classification before analysis: a single-asset operator like Upper Tamakoshi, a diversified holding company like Chilime or Butwal Power Company, a small single-project IPP, and a pre-COD pipeline company are four genuinely different kinds of investment wearing the same sector label, and applying the wrong analytical frame to any of them — treating a pipeline name as if it were a stable dividend payer, or a single-asset operator as if its concentration risk did not matter — is the fastest route to a bad decision.
The second habit is reading the plant's physical design as a risk profile, not a footnote. Whether a plant is pure run-of-river, peaking run-of-river, or (rarely, in this market) storage-based determines how violently its quarterly revenue will swing with the monsoon calendar, and no amount of financial-statement analysis substitutes for actually knowing which category a holding falls into. Layered on top of that is the royalty staircase built into Nepal's Electricity Act framework: capacity and energy royalty rates step up once a plant crosses roughly its fifteenth year of operation, and a plant's after-royalty margin will mechanically compress on a knowable schedule that has nothing to do with mismanagement — an investor who fails to model this treats a scheduled, foreseeable headwind as an unpleasant surprise.
The third habit is treating the annual report's operating disclosures — generation volumes, realised tariff versus PPA base tariff, load factors, and the split between wet-season and dry-season energy — as more informative than the income statement itself. A single year's earnings per share for a run-of-river company is a noisy, hydrology-driven output, and normalising across a multi-year window that spans both wet and dry cycles is not optional analytical hygiene, it is the only way to avoid the sector's signature P/E trap, in which a lucky wet year makes a stock look cheap and an unlucky dry year makes another look expensive, with neither multiple reflecting the underlying asset's true earning power.
The fourth habit is following the cash all the way from the powerhouse to the shareholder's account, rather than stopping at net profit. Debt service reserve accounts, minimum DSCR covenants, and debt maturity walls that require refinancing can all suppress an otherwise healthy company's dividend, and a promoter's demonstrated history of shepherding earlier projects from construction through to a sustained, covenant-compliant dividend stream is a better predictor of future behaviour than anything printed in a new project's prospectus. For companies still under construction, the correct lens is closer to project-finance due diligence than equity analysis: percentage completion, cost-overrun history, and how much COD guidance has already slipped.
The fifth habit is valuing the finite asset as a finite asset. Because a PPA has a known expiry date, the most defensible valuation approach is a discounted cash flow run out to that expiry, with royalty step-ups, tax-holiday expiry, and debt amortisation built explicitly into the projection, and with a deliberately modest terminal value beyond the PPA rather than the growing perpetuity assumption that anchors most other sectors' DCFs. EV/EBITDA is generally a more reliable cross-sectional comparison tool than P/E in this sector because it neutralises differences in leverage and tax-holiday timing, but even EV/EBITDA comparisons are only meaningful between companies with genuinely similar remaining PPA life and plant type — a young peaking-ROR plant with three decades of PPA life left is not the same asset as an old plant three years from PPA expiry, and no single multiple should be applied to both without adjustment.
Taken together, these five habits — classify the business model, read the plant's physical risk profile, track the royalty and tax staircase, follow cash all the way to dividend capacity, and value the asset as a finite, PPA-bounded cash-flow stream — form a genuinely repeatable checklist that closes out Part VIII of this book. Hydropower will not be the last sector where a naive P/E or a single year's earnings misleads an investor, but it is the sector where this book has built, from the ground up across three chapters, the full machinery needed to see through those traps to the underlying cash flows. Part IX, Valuation, generalises that machinery. Chapter 45, "The Philosophy of Valuation," steps back to ask what a valuation exercise is actually trying to answer and why different methods can legitimately disagree, and Chapter 46, "Discounted Cash Flow (DCF) Valuation," builds out the DCF framework in full rigor and generality — applicable not just to hydropower but to banks, manufacturers, hospitality companies, and every other corner of the NEPSE market this book will go on to examine.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IX
VALUATION
Part IX · Chapter 45
The Philosophy of Valuation
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 45.1 — Price Is What You See; Value Is What You Are Trying to Find
Walk through the Ason vegetable market in Kathmandu on any given morning and you will see the same tomato selling for three different prices at three different stalls, all within fifty meters of each other. A tourist pays the highest price. A regular customer who knows the vendor pays the middle price. A wholesaler buying forty kilograms for a hotel pays the lowest price per kilogram. The tomato itself has not changed. Its nutritional content, its ripeness, its usefulness in a meal — its "worth" to whoever eats it — is identical across all three transactions. What changed was the price, and price moved for reasons that had nothing to do with the tomato's underlying worth: bargaining skill, information, urgency, relationship, and quantity.
This distinction — between what something costs and what it is actually worth — is the single most important idea in this Part of the book, and arguably in all of investing. It is often summarised in a line popularly attributed to Warren Buffett, paraphrasing his teacher Benjamin Graham: "Price is what you pay; value is what you get." The idea did not originate with Buffett — Graham's 1949 book *The Intelligent Investor* built an entire philosophy around the gap between a security's market price and its underlying worth — but the one-line version has become the most quoted sentence in investing precisely because it captures something every investor eventually learns the hard way.
Apply this to a share of stock instead of a tomato. The price of a share of Nabil Bank or Chilime Hydropower or Unilever Nepal on the NEPSE (Nepal Stock Exchange) trading screen at 11:45 AM on a Tuesday is simply the level at which the most recent buyer and seller agreed to transact. It tells you what someone was willing to pay a moment ago. It does not, by itself, tell you what the underlying business — its factories, its licenses, its deposit base, its power purchase agreements, its brand, its future stream of profits — is actually worth to a rational, well-informed owner who intends to hold it for years. Value, in this sense, is an estimate of a business's true underlying worth, built from its assets, earnings power, growth prospects, and risk — not from what the ticker tape says right now.
KEY CONCEPT
Price is a fact: it is observable, it is public, and at any instant there is exactly one of it. Value is an estimate: it is not observable, it requires judgment, and reasonable analysts using reasonable methods can and do arrive at different numbers for the same company. The entire discipline of valuation exists because these two things are not always equal, and the gap between them is where investment opportunity — and investment risk — lives.
Why would price and value ever diverge? Because a market price is set by whoever happens to be trading at that moment, and their motives may have nothing to do with a careful assessment of worth. A person needing cash for a medical emergency will sell shares below what they believe those shares are worth, simply because they need money now. A person who just read an exciting rumour on a Viber group will buy shares above what a calm analysis would support, simply because they are excited. A large institution rebalancing its portfolio at month-end may sell a stock not because the business has deteriorated, but because a target allocation percentage says to sell. None of these transactions is "wrong" — they reflect real motives — but none of them is a statement about intrinsic value either.
Intrinsic value is the term this book will use throughout Part IX for that underlying, patiently-estimated worth of a business — the price a rational, informed buyer with a long time horizon should be willing to pay, based on the cash the business can be expected to generate over its life, discounted back to what that future cash is worth today. It is called "intrinsic" precisely to distinguish it from market price: it belongs to the business, not to the crowd's mood about the business on a given day.
This is not an abstract, academic distinction for Nepal. It is arguably more relevant here than in almost any other market an investor is likely to encounter, for reasons this chapter will build toward across its six lessons. But the starting point is simple and must be internalized before anything else in this Part makes sense: your job as a serious investor is never to predict what the price will do tomorrow. It is to estimate, as carefully as the available information allows, what the business is worth — and then to compare that estimate to the price the market is offering. Everything else in valuation is a set of tools for doing that comparison well.
Lesson 45.2 — Why Different Methods Legitimately Disagree
A newcomer to valuation is often unsettled to discover that three competent analysts, looking at the same company on the same day with the same publicly available financial statements, can produce three meaningfully different value estimates — and that this is normal, expected, and not a sign that valuation is fraudulent or useless. Understanding why this happens is essential before you learn any specific method, because without it you will either (a) trust a single number far more than it deserves, or (b) conclude that valuation is a waste of time because "the answer is never the same twice."
Return to the house-appraisal analogy, because it will recur throughout this Part and it maps unusually well onto company valuation. Suppose you want to know what a house in Lalitpur is worth. There are at least three legitimate ways to answer this question, and they do not need to agree:
First, you could look at what similar houses in the same neighbourhood recently sold for — three bedrooms, similar plot size, similar road access — and adjust for differences. This is comparable sales, and in company valuation its equivalent is relative valuation (also called "comps" or multiples-based valuation): looking at what the market is currently paying for similar companies, expressed as a ratio like price-to-earnings or price-to-book, and applying that ratio to the company you are studying.
Second, you could estimate what it would cost, today, to buy the land and rebuild an identical house from scratch — bricks, labor, permits, everything — and call that the value. This is replacement cost or asset-based valuation, and its company-valuation equivalent is book value or net asset value (NAV): adding up what the company's assets are worth (adjusted to reflect reality, not just historical accounting cost) and subtracting what it owes.
Third, if the house is rented out, you could estimate the rental income it will generate over the coming years and calculate what that stream of future rupees is worth in today's money. This is income capitalisation, and its company-valuation equivalent is the discounted cash flow (DCF) method, or, for a bank or financial institution where the relevant "income" to the shareholder is the dividend rather than the whole firm's cash flow, the dividend discount model (DDM).
KEY CONCEPT
No single appraisal method is "the" correct one — each answers a slightly different question. Comparable sales asks "what does the market currently pay for something similar?" Replacement cost asks "what would it take to recreate this from nothing?" Income capitalisation asks "what is the future stream of benefit worth today?" A skilled appraiser triangulates across methods rather than anchoring on one, and a skilled equity analyst does exactly the same thing.
Three honest, competent appraisers using these three approaches on the same house can and do arrive at three different figures, and none of them is "lying." The comparable-sales appraiser might land higher if the neighbourhood is in a speculative upswing and recent sale prices reflect optimism rather than durable worth. The replacement-cost appraiser might land lower if land prices have risen faster than construction costs, making the existing structure cheap to replicate. The income appraiser might land lowest of all if rental yields in the area are compressed relative to purchase prices. All three numbers can be simultaneously "correct" in the sense that each was calculated correctly given its method and its assumptions — while still disagreeing, because they are measuring different things and relying on different assumptions about the future.
The same is true of company valuation, for the same underlying reason: every method requires assumptions, and reasonable people can disagree about assumptions without either party being incompetent or dishonest. A DCF valuation of a hydropower company depends heavily on the discount rate used (how much return investors demand for the risk involved) and on assumptions about hydrology, tariff escalation, and the terminal value once the power purchase agreement (PPA) period ends. A relative valuation of the same company depends on which "comparable" companies you choose and whether the market's current pricing of those comparables is itself sensible or inflated by a sector-wide enthusiasm. A book-value approach depends on how faithfully the balance sheet reflects economic reality — whether a dam built fifteen years ago at historical cost is actually worth what the accounting entry says today.
WARNING
A common mistake among newer analysts is to run one method, get one number, and treat that number as "the valuation" — as if it were a fact rather than an estimate built on a chain of assumptions. A far more disciplined habit is to run at least two independent methods and examine why they differ. If a DCF says a stock is worth NPR 450 and a peer-multiple comparison says NPR 650, the gap itself is information: either the market is paying an unusually rich multiple for the peer group (possibly due to sentiment, as later lessons will discuss), or your DCF assumptions are too conservative, or the company genuinely deserves to trade differently from its "comparables" because it is not as comparable as it looks. Investigating that gap is often more valuable than either number alone.
This is why Part IX of this book is structured the way it is. This chapter is the philosophical foundation; the chapters that follow will each take up one method in depth — DCF, relative valuation, the dividend discount model, and book value/NAV approaches — precisely because no single tool is sufficient. A carpenter does not show up to a job with only a hammer. A serious investor does not show up to a valuation question with only one method.
Lesson 45.3 — Margin of Safety: Valuation's Purpose Is Not Precision, It Is Protection
Once you accept that any single valuation estimate carries real uncertainty — that your DCF could be too optimistic, your chosen "comparable" companies could themselves be mispriced, your book-value adjustments could be wrong — a natural and important question follows: if valuation is inherently imprecise, why bother at all? The answer, developed most fully by Benjamin Graham and carried forward by generations of practitioners since, is the concept of margin of safety.
Margin of safety means buying a security at a meaningful discount to your estimate of its intrinsic value, so that even if your estimate turns out to be somewhat wrong — even if the business performs worse than expected, or your assumptions prove too rosy — you are still likely to avoid a serious permanent loss, and still have room to profit. It is not a formula; it is a discipline of humility built into the buying decision itself.
Consider a bridge engineer. If an engineer calculates that a bridge needs to hold trucks weighing up to 10 tons, they do not design the bridge to hold exactly 10 tons. They design it to hold 30 tons or more, because they know their models of stress, material fatigue, and load distribution are imperfect, and because the cost of being wrong (a collapsed bridge) is catastrophic and asymmetric compared to the cost of over-building (some excess steel). Margin of safety in investing works the same way: you deliberately demand a price well below your estimated value, not because you doubt your own arithmetic, but because you know every valuation model is a simplification of a more complicated reality, and you want room for that reality to disappoint you without destroying your capital.
KEY CONCEPT
Margin of safety is the gap between your estimate of intrinsic value and the price you actually pay. It exists to absorb three kinds of error simultaneously: errors in your assumptions, errors in your model, and simple bad luck in how the future unfolds. The wider the margin, the more of these errors you can survive and still come out ahead.
Why does this matter more, not less, in Nepal specifically? Because several structural features of the NEPSE make both price volatility and valuation-model error more likely than in a deeply liquid, institutionally-dominated market. Retail investors — individuals trading their own savings rather than professional fund managers — make up the overwhelming majority of NEPSE's trading activity and ownership, a pattern documented repeatedly in research on Nepal's capital market and discussed in earlier chapters of this book covering NEPSE's market structure. A market dominated by individual retail participants, many of them trading part-time alongside other jobs, tends to be more sentiment-driven: prices can swing on rumour, on a single piece of news amplified across social media and investment Viber/Telegram groups, or on simple herd behaviour, rather than on a considered reassessment of a company's earnings power.
CASE IN POINT
NEPSE has repeatedly gone through episodes where entire sectors — hydropower shares in one period, microfinance shares in another, life insurance shares in another — rallied sharply within weeks on a wave of retail buying, often disconnected from any comparable change in the underlying companies' earnings, licenses, or cash flow prospects, before giving back much of the gain over the following months. Analysts and financial commentators covering Nepal's market have flagged this pattern of retail-driven momentum and subsequent correction as a recurring feature of the exchange, not a one-time event — precisely the kind of price-value gap this chapter is built around, and precisely why the sector-rotation "hot money" behaviour deserves the same skepticism whether it appears in hydropower, banking, or any other segment.
Add to this the fact that many NEPSE-listed counters — especially smaller hydropower companies, smaller finance companies, and thinly-traded manufacturing names — suffer from illiquidity: on many trading days, only a small number of shares change hands, so a single moderately-sized buy or sell order can move the price by several percentage points without reflecting any real change in the business. In a liquid market like a major global exchange, heavy two-sided trading by large, well-resourced institutions tends to pull price back toward consensus estimates of value fairly quickly, because mispricing attracts arbitrage capital. NEPSE has far less of this corrective mechanism. A share can trade meaningfully above or below a careful analyst's estimate of intrinsic value for a long time, because there may not be enough patient, analytically-driven capital in the market to close the gap quickly.
WARNING
Illiquidity cuts both ways for the disciplined investor. It means a genuinely undervalued NEPSE stock can stay undervalued for a long time, testing your patience — but it also means that when sentiment turns euphoric, prices can detach from fundamentals further and for longer than in a more liquid market, because there is no large pool of institutional short-sellers or arbitrageurs to push back. This is precisely the environment in which margin of safety is not a nice-to-have but a necessity: you need enough of a price discount to your value estimate that you can survive the market ignoring you for months, or years, without your thesis being wrong — and enough discipline not to chase a price that has already run far ahead of any defensible valuation.
The behavioural biases covered in earlier chapters of this book — anchoring on a stock's previous high price, herd behaviour around hot sectors, overconfidence after a run of lucky gains, loss aversion that keeps investors holding falling stocks too long — are not separate from valuation discipline; they are the reason valuation discipline is necessary. A valuation framework is, in a real sense, a structured defence against your own psychology and against the crowd's psychology. When everyone around you is buying a hydropower stock because "it only goes up," a calm DCF estimate of the company's actual cash-generating capacity over the life of its power purchase agreement is one of the few tools that can interrupt that momentum with a genuine, numbers-based question: at this price, am I actually being compensated for the risk I am taking, or am I simply paying for other people's enthusiasm?
Lesson 45.4 — Four Lenses, One House: An Overview of the Methods to Come
Having established why price and value differ, why methods legitimately disagree, and why margin of safety matters especially in a market like NEPSE, this lesson previews the four valuation approaches that the remaining chapters of Part IX will each treat in full depth. Think of this as being handed a map before the detailed hike — enough to see how the pieces relate to one another before you descend into any one of them.
Discounted cash flow (DCF). This method estimates the cash a business is expected to generate for its owners in future years, then discounts those future cash flows back to a present value using a rate that reflects the riskiness of receiving them (the further out and the riskier the cash flow, the more it is discounted). It is the most theoretically complete method, because in principle it captures everything that matters — growth, profitability, capital needs, and risk — in a single framework tied directly to cash the business actually produces. Its weakness is sensitivity: small changes in the assumed growth rate or discount rate can swing the resulting value substantially, so the quality of a DCF is only as good as the quality (and humility) of its inputs.
Relative valuation (comps). This method sidesteps forecasting the distant future and instead asks what the market is currently paying for similar businesses, expressed as a multiple of some financial metric — price-to-earnings (P/E), price-to-book (P/B), EV/EBITDA, and similar ratios — and applies a comparable multiple to the company being valued. Its strength is that it is grounded in real, observable market prices rather than a long chain of speculative assumptions about the future. Its weakness is that it inherits whatever mispricing already exists in the market: if an entire sector on NEPSE is trading at an inflated multiple because of retail enthusiasm, a comps-based valuation of a single company in that sector will simply reproduce that same inflated valuation, dressed up in the language of "the market says."
Dividend discount model (DDM). A specialised cousin of DCF, the DDM values a share based specifically on the stream of dividends a shareholder actually expects to receive, discounted back to present value. It is particularly suited to businesses — commercial banks being the leading NEPSE example — where regulatory capital requirements set by Nepal Rastra Bank (NRB) constrain how much cash can actually leave the business and reach shareholders, meaning dividends (cash and, in Nepal's market, frequently bonus shares) are a more reliable proxy for shareholder value than the bank's broader free cash flow, which is difficult to define cleanly for a financial institution whose "raw material" is deposits and whose "product" is loans.
Book value / net asset value (NAV). This method values a company based on what its assets are worth (adjusted where possible to current, realistic values rather than historical accounting cost) minus its liabilities. It is the natural lens for asset-heavy or asset-backed businesses — banks and financial institutions again (where regulatory capital is explicitly measured against book equity), insurance companies, and to a degree hydropower companies with tangible, licensed infrastructure — and it is also the natural floor-level check for any company in liquidation or distress, where the question shifts from "what can this business earn?" to "what could its assets fetch if sold off piece by piece?"
PRACTICAL TOOL
A simple mnemonic for remembering what each method is really asking: DCF asks "what will this business earn, and what is that worth today?" Comps asks "what is the market paying for similar businesses right now?" DDM asks "what cash will actually land in my hand as a shareholder?" NAV asks "what would be left if we sold everything and paid off every liability?" Four different questions about the same company — which is exactly why they can produce four different numbers, and why comparing the answers is more informative than picking one and ignoring the rest.
None of these methods is superior in the abstract. Each is more or less appropriate depending on two things this chapter now turns to: the type of company being valued, and the purpose for which the valuation is being done.
Lesson 45.5 — Matching the Method to the Company
A recurring error among newer analysts is applying a favourite valuation method to every company regardless of fit — running a DCF on a bank the same way one would on a manufacturer, or valuing a hydropower company on price-to-earnings the way one would value a trading company. The method must fit the economics of the business, in the same way an appraiser would not value a working farm the same way as a downtown apartment — the income sources, the assets, and the risks are simply too different.
Consider three archetypal NEPSE-listed company types and why they call for different primary lenses.
A commercial bank. A bank's balance sheet is its business — deposits are its raw material, loans and investments are its output, and its capital adequacy ratio (a regulatory measure of how much shore-up capital it holds against its risk-weighted assets, set and enforced by Nepal Rastra Bank) directly constrains how fast it can grow and how much it can pay out. Trying to build a from-scratch free-cash-flow DCF for a bank is notoriously difficult, because "capital expenditure" and "working capital" do not mean the same thing for a lender that they mean for a factory — a bank's core activity of taking in deposits and making loans blurs the normal DCF distinction between operating cash flow and investing cash flow. For this reason, banks worldwide are conventionally valued primarily through price-to-book (comparing market price to the bank's book equity, adjusted for asset quality) and through the dividend discount model (since a bank's ability to pay dividends is directly gated by its NRB-mandated capital buffers, and Nepali investors have historically prized the bonus shares and cash dividends banks distribute). Relative valuation against other listed commercial banks is also highly informative here, because Nepal has more than two dozen listed banks and finance companies with genuinely comparable business models, regulatory regimes, and disclosure formats, making peer comparison unusually reliable for this sector specifically.
A hydropower company. A hydropower project is close to the opposite case: it is a long-duration, asset-heavy business with a specific, contractually defined life — most Nepali hydropower companies sell electricity to the Nepal Electricity Authority (NEA) under a power purchase agreement (PPA) with a fixed tariff schedule and duration, after which the terms may change or the license may need renewal. This structure — a known (if long) horizon, contractually bounded revenue, and heavy upfront capital investment already sunk into dams, tunnels, and powerhouses — makes hydropower a textbook DCF candidate, much like valuing a toll road or an annuity: forecast the electricity generated (which depends on river flow/hydrology and plant capacity), apply the PPA tariff schedule (with escalation and wet/dry season rate differences common in Nepali PPAs), subtract operating costs and debt service, and discount the resulting cash flows at a rate reflecting hydrology risk, counterparty risk (NEA's own payment reliability), and Nepal's country-level risk. Book value is a much weaker lens here, because a dam's historical construction cost bears little relationship to its ongoing earning power, which depends on rainfall patterns and tariff terms, not on what steel and cement cost when it was built.
A manufacturing or consumer company. A company producing noodles, cement, steel, or beverages for the Nepali market sits closer to the classic case most global valuation textbooks are written for: it has a normal cycle of buying raw materials, converting them into products, selling them, and reinvesting profits into more capacity — with capital expenditure and working capital behaving in the textbook way. This makes it well suited to a standard free-cash-flow DCF and, because Nepal has multiple listed manufacturers and consumer companies of varying scale (some direct sector peers, some approximate), relative valuation using P/E or EV/EBITDA multiples against domestic peers, adjusted where necessary against regional peers when domestic comparables are too thin.
REGULATORY DETAIL
Nepal Rastra Bank sets minimum capital adequacy ratios and related prudential norms for banks and financial institutions (BFIs), and these norms directly cap how much of a bank's profit can be distributed as dividends versus retained as core capital in any given year. This is a structural, regulator-imposed reason why a bank's dividend stream cannot simply be modelled as "profit after tax" the way one might model a manufacturer's payout — it must be modelled against the bank's actual capacity to distribute cash while remaining compliant, which is precisely why the dividend discount model, applied carefully with NRB's rules in mind, tends to outperform a naive DCF for this sector.
Below is a summary table intended as a working reference, not a rigid rulebook — real companies often benefit from two or three methods used together, as Lesson 45.2 already argued.
Company Type / Situation
Best-Suited Primary Method(s)
Why
Weakest Method for This Case
Commercial bank / finance company
Price-to-book, Dividend Discount Model (DDM)
Balance sheet-driven business; NRB capital rules cap distributable cash; ample listed peers for comparison
Standard free-cash-flow DCF (capex/working capital concepts don't map cleanly)
Hydropower company
Discounted Cash Flow (DCF)
Long, contractually-bounded PPA revenue with NEA; capital-intensive, asset-specific economics
Book value (historical construction cost is disconnected from earning power)
Manufacturing / consumer goods company
DCF, Relative valuation (P/E, EV/EBITDA)
Classic operating cycle; capex and working capital behave in textbook fashion; some domestic peers exist
Pure book value (understates brand, distribution network, and earnings power)
Insurance company (life/non-life)
Book value / embedded value, DDM
Regulatory capital and reserving requirements shape distributable profit; long-duration liabilities
Simple P/E on reported profit (accounting profit can diverge sharply from economic profit due to reserving)
Company in financial distress or likely liquidation
Net Asset Value (NAV) / liquidation value
Going-concern earnings are unreliable or negative; the relevant question becomes recovery value of assets
DCF (forecasting cash flows for a business that may not continue operating is not meaningful)
Newly-listed or high-growth small company with thin trading history
Insufficient historical data for a reliable DCF; but comps must be chosen carefully given illiquidity risk
DDM (limited or no dividend history to anchor on)
CAUTION
This table is a starting map, not a substitute for judgment. A hydropower company nearing the end of its PPA term with an uncertain renewal, for instance, starts to require NAV-style thinking about what its license and infrastructure are worth under revised terms — the "right" method for a given company can shift as its circumstances change, and a good analyst revisits the choice of method rather than mechanically applying whatever worked last time.
Lesson 45.6 — Matching the Method to the Purpose
The type of company is only half the equation. The other half — often neglected by newer analysts — is the purpose of the valuation: what decision is this number actually meant to inform? Valuing a company to decide whether to buy a small minority stake through the NEPSE trading screen is a genuinely different exercise from valuing a company to decide whether to acquire the whole thing, and conflating the two leads to real errors.
When you buy shares of, say, Himalayan Bank or Chilime through your broker's trading terminal, you are buying a minority stake: a small slice of a business you will not control, whose board you cannot appoint, whose dividend policy you cannot set, and whose strategic decisions you can only vote on alongside thousands of other shareholders. For this purpose, the relevant valuation question is essentially: given what a passive minority owner can expect to receive (dividends, bonus shares, and eventually a sale at some future price), and given the price the market is currently asking, is this an attractive risk-adjusted proposition? Relative valuation against listed peers, and dividend-focused approaches, are usually highly relevant here, because minority shareholders' returns are substantially shaped by what the company chooses to distribute and by what the broader market is willing to pay for similar minority stakes — not by any control premium.
Contrast this with valuing a company for a full acquisition, a merger, or a controlling private-equity-style investment — a purpose more relevant to Nepal's banking sector consolidation waves (where NRB has periodically pushed mergers among BFIs to strengthen capital bases) or to family-owned manufacturing and hydropower businesses considering a strategic sale. Here, the buyer is not a passive recipient of whatever dividend policy management chooses; the buyer can change the dividend policy, replace management, alter the capital structure, sell off unproductive assets, or redirect the entire cash flow of the business. This is why acquisition valuations typically rely more heavily on full enterprise DCF (valuing the whole stream of cash the business can generate under new, presumably improved, control) and on NAV/asset-based approaches (because a controlling buyer can actually realise asset value directly — sell a building, monetize a license — in a way a minority shareholder cannot). Acquisition valuations also typically include a control premium: an amount paid above the "minority stake" market price, precisely because control over cash flows and assets is worth more than a passive claim on whatever a company's existing board decides to pay out.
KEY CONCEPT
A minority stake and a controlling stake in the identical company are not, strictly speaking, the same asset from a valuation standpoint — the minority investor's claim is bounded by what the incumbent management and board choose to do, while the controlling investor's claim extends to everything the business could be made to do under new stewardship. This is why the "right" valuation number depends on which of these two things you are actually buying, not only on which company you are looking at.
This distinction matters immensely for the typical reader of this book, who is far more likely to be building a NEPSE portfolio of minority stakes than acquiring companies outright. It means that, for most day-to-day decisions covered in this Canon, the DDM and relative-valuation lenses will often carry more practical weight than a textbook enterprise DCF built as if you were about to buy the whole company and redirect its cash flows at will — though a DCF remains valuable even for minority investing, because it forces explicit thinking about growth, risk, and long-run earnings power that a simple multiple can obscure. The purpose does not eliminate any method from consideration; it changes which method deserves the most weight, and which assumptions (a control premium, a liquidity discount for a minority, illiquid holding) need to be layered on top of the basic mechanics.
PRACTICAL TOOL
Before opening a spreadsheet, a disciplined analyst answers two questions out loud: (1) What kind of business is this — balance-sheet-driven like a bank, contract-and-asset-driven like a hydropower plant, or operating-cycle-driven like a manufacturer? (2) What decision does this valuation inform — buying a small number of shares on the NEPSE screen, or acquiring meaningful control? Only after both questions are answered does it make sense to choose a primary method and a set of supporting cross-checks.
CASE IN POINT
Nepal's banking sector has gone through multiple NRB-encouraged merger and acquisition waves as the central bank has periodically raised minimum capital requirements, pushing smaller banks and finance companies to combine. In these merger negotiations, the exchange ratio between the merging banks' shares has typically been anchored heavily to book value and DDM-style thinking about each bank's earning power and capital position — a very different valuation conversation from the day-to-day P/E-based chatter that dominates retail discussion of the same bank's shares on the secondary market. Both conversations are "valuing the same bank," yet they emphasise different methods because they serve different purposes.
Chapter recap
This chapter opened Part IX by establishing the idea that everything which follows depends on: price and value are not the same thing, and confusing them is the single most common and costly error an investor can make. Price is an observable fact — whatever the last trade on the NEPSE screen says it is — set moment to moment by whoever happens to be transacting and for whatever reason, urgent or casual, informed or uninformed. Value, or more precisely intrinsic value, is an estimate of what a business is actually worth to a patient, informed owner, built from its earning power, its assets, and its risks rather than from the market's current mood. The whole discipline of valuation exists to make that estimate as carefully and honestly as possible, and the whole discipline of investing, in large part, consists of comparing that estimate to the price on offer and acting only when the gap is favourable.
The chapter then explained why competent, honest analysts can look at the same company and land on different value estimates without either being wrong: every valuation method is really an appraisal technique answering a slightly different question, in the same way a house can be appraised through comparable sales, replacement cost, or rental income capitalisation and yield three different, individually defensible numbers. Comps ask what the market currently pays for similar things; book value/NAV asks what it would cost to recreate the assets or what they would fetch if liquidated; DCF and the DDM ask what a future stream of cash or dividends is worth today. Disagreement between methods is not a flaw in the discipline — it is often the most useful signal a careful analyst gets, because investigating why methods diverge frequently teaches more than either number in isolation.
Because every method rests on assumptions that can be wrong, this chapter introduced margin of safety as the practical answer to valuation's inherent imprecision: buy at a discount to your estimate of value wide enough to survive being somewhat wrong, in the same spirit that a bridge is engineered to hold several times its expected maximum load. This principle carries extra weight on the NEPSE specifically, because the exchange's retail-dominated ownership, its exposure to sentiment and herd behaviour across social media and investment circles, and its patches of genuine illiquidity in smaller counters mean that prices can detach from intrinsic value more easily, and stay detached for longer, than in deeper and more institutionally-arbitraged markets. The behavioural biases examined in earlier chapters — anchoring, herding, overconfidence, loss aversion — are not a separate topic from valuation; a disciplined valuation framework is one of the few reliable defences against being swept along by exactly those biases when the crowd is convinced a hot sector "only goes up."
The chapter then previewed the four methods the remaining chapters of Part IX will treat in depth: discounted cash flow, which values a business by forecasting and discounting its future cash flows; relative valuation, which values a business against what the market currently pays for similar businesses; the dividend discount model, which values a share by the cash a shareholder actually expects to receive; and book value/net asset value, which values a business by what its assets are worth net of its liabilities. None of these is universally superior — each fits certain economic circumstances better than others.
Finally, the chapter argued that the right method depends on two things beyond the mechanics of the technique itself: the type of company (a deposit-and-loan-driven bank governed by NRB capital rules calls for different lenses than a PPA-bound hydropower plant or a conventional manufacturer with a normal operating cycle) and the purpose of the valuation (buying a small minority stake through the NEPSE trading screen is a different question from valuing a company for acquisition or merger, where control over cash flows and assets changes what the buyer is actually entitled to realise). Holding both of these — company type and purpose — in mind before reaching for a method is the discipline that separates an analyst who produces a defensible, useful valuation from one who produces a spreadsheet with a number at the bottom and no understanding of what that number actually means. The chapters that follow will now take up each of the four methods in turn, building the specific tools whose proper use this chapter has tried to frame.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IX · Chapter 46
Discounted Cash Flow (DCF) Valuation
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 46.1 — The Logic of Discounting: Why a Rupee Tomorrow Is Worth Less Than a Rupee Today
Every DCF valuation rests on one simple, almost obvious human intuition: a promise of money in the future is worth less than the same amount of money in hand right now. If a friend offers you a choice between NPR 100,000 today or NPR 100,000 in exactly one year, and you trust the friend completely, you would almost certainly take the money today. Why? Because you could deposit that NPR 100,000 in a fixed deposit at a commercial bank, earn interest on it for a year, and end up with more than NPR 100,000 by the time your friend's promised payment would have arrived. Money today can be put to work; money promised for later cannot be put to work until it arrives. This is the entire idea behind what economists and analysts call the time value of money — the principle that a given sum of money is worth more the sooner it is received, because of its earning potential in the interim.
Discounted Cash Flow (DCF) valuation is nothing more than this everyday intuition applied rigorously, year by year, to an entire business. Instead of asking "what is NPR 100,000 next year worth to me today," a DCF model asks "what is this company's entire stream of future cash — the cash it will generate this year, next year, the year after, and so on into the distant future — worth to an investor today?" To answer that question, an analyst does two things. First, she projects, or forecasts, how much spare cash the business is likely to generate in each of the coming years — this projected number is called free cash flow (FCF), the cash a business has left over after it has paid for its operations and reinvested what it needs to keep running and growing. Second, she converts each future year's projected cash flow into today's rupees using a discount rate — the annual rate of return an investor could reasonably expect to earn elsewhere for taking a similar level of risk, which functions as the "exchange rate" between a rupee tomorrow and a rupee today. The result of applying that discount rate is called the present value of that future cash flow — what a rupee to be received in the future is worth in today's terms once you have accounted for both the passage of time and the risk that the cash might not arrive at all, or might arrive in a different amount than forecast.
Return to the fixed deposit analogy, because it is the cleanest way to understand the mechanics. Suppose a Nepali fixed deposit currently pays 8% per year. If you were promised NPR 108 in exactly one year, and you had access to that 8% deposit rate today, the promise of NPR 108 next year is worth exactly NPR 100 to you today — because NPR 100 placed in the deposit today would itself grow into NPR 108 in one year. The NPR 100 is the present value of the NPR 108 future amount, using 8% as the discount rate. If instead the promise were riskier — say, a promise from a private company rather than a government-backed bank, where there is some chance the money never arrives — you would not be willing to pay NPR 100 today for a promise of NPR 108 in a year. You would want a higher expected return to compensate for the extra risk, meaning you would only pay something less than NPR 100 for that same promised NPR 108. This is why the discount rate used in a DCF is not simply "whatever the bank pays" — it must reflect the specific riskiness of the specific cash flow being valued. A government treasury bill and a small unlisted hydropower company's equity are not the same kind of promise, and they cannot be discounted at the same rate.
KEY CONCEPT
Present value is the amount of money today that is equivalent, given a chosen discount rate, to a specific amount of money at a specific point in the future. Discounting is simply the arithmetic of shrinking a future rupee amount down to its present-day equivalent, using the formula PV = FCF ÷ (1 + r)^n, where r is the discount rate and n is the number of years into the future.
The full arithmetic of a DCF, then, is: project free cash flow for each year of an explicit forecast period (commonly five to ten years), discount each year's projected cash flow back to today using the appropriate discount rate, add up all those present values, then add the present value of everything the business is expected to generate beyond the forecast period — a single lump-sum figure called terminal value, which will be explained fully in Lesson 46.4. The sum of all of this — the present value of near-term cash flows plus the present value of the terminal value — is the DCF's estimate of what the business, or a share of it, is intrinsically worth today. This estimate is then compared against the price the market is currently charging for the shares. If the DCF value per share is meaningfully higher than NEPSE's quoted price, the stock may be undervalued; if the DCF value is meaningfully lower, the stock may be overvalued. Chapter 45 already cautioned that "may be" is doing a great deal of work in that sentence — a DCF is an estimate built on assumptions, not a certificate of truth — and this chapter will return to that caution repeatedly, because in Nepal's market the assumptions matter even more than usual.
PRACTICAL TOOL
A quick way to sanity-check any DCF: ask what discount rate and growth rate combination the current market price already implies, then ask yourself whether those implied assumptions are more or less reasonable than your own. If a stock's price only makes sense assuming 20% perpetual growth, that is useful information regardless of what your own model says.
It is worth pausing on why DCF matters especially for Nepali investors, given how this book has framed NEPSE in earlier chapters. Nepal's stock market is dominated by relative valuation — investors comparing one bank's price-to-book ratio to another's, or one hydropower stock's price-to-earnings ratio to the sector average. Relative valuation is fast and easy, but it only tells you whether something is cheap or expensive relative to its neighbours; it says nothing about whether the whole neighbourhood is fairly priced. DCF is the tool that lets an investor step outside the crowd's current mood and ask a more fundamental question: based on the actual cash this business is likely to generate over its life, and based on what that cash is worth in today's rupees, what should I be willing to pay? It is a slower, more demanding exercise, but it is also the discipline that separates institutional-grade investing from simply following the herd on Maharajgunj coffee-shop tips about which hydropower counter is about to "run."
Lesson 46.2 — Free Cash Flow: The Real Number a DCF Runs On
Before any discounting can happen, an analyst needs a number to discount — and that number is not accounting profit. Net profit, the "bottom line" figure companies report in their income statements and that most Nepali retail investors fixate on, is not the same thing as cash. Net profit can be inflated by non-cash accounting entries, and it does not account for the cash a company must spend on new equipment, new working capital, or debt repayment just to keep functioning and growing. Free cash flow strips all of that away and asks a blunter question: after a company has run its operations and reinvested whatever it needs to reinvest, how much actual, spendable cash is left over for the people who have a claim on it?
There are two versions of free cash flow used in DCF models, and confusing them is one of the most common mistakes analysts make, so it is worth defining both carefully.
Free Cash Flow to the Firm (FCFF) is the cash generated by the business that is available to all providers of capital — both the shareholders (equity holders) and the lenders (debt holders) — before any interest payments or debt repayments are made. It represents the cash the operating business itself throws off, independent of how that business happens to be financed. FCFF is typically calculated starting from operating profit, adding back non-cash charges like depreciation (the accounting expense that spreads the cost of a long-lived asset, such as a hydropower plant's turbines, over its useful life, even though the cash for it was spent upfront), then subtracting capital expenditure (capex — the cash spent on buying, building, or upgrading long-term assets like power plants, factory equipment, or bank branches) and the change in working capital (the cash tied up in day-to-day operating needs like inventory, receivables from customers, and payments owed to suppliers).
Free Cash Flow to Equity (FCFE) is the cash left over for shareholders alone, after the company has already paid its lenders — meaning after interest expense and after any net debt repayments (or plus any net new borrowing). FCFE is what shareholders could theoretically receive as dividends without harming the company's ability to keep operating and growing. FCFE = FCFF − interest expense × (1 − tax rate) − net debt repayments, or, built up directly from net profit, FCFE = Net Profit + Depreciation − Capex − Change in Working Capital + Net Borrowing.
KEY CONCEPT
FCFF is discounted at the Weighted Average Cost of Capital (WACC) — a blended discount rate reflecting the cost of both debt and equity — to arrive at the value of the entire firm (enterprise value), from which debt is then subtracted to reach equity value. FCFE is discounted directly at the cost of equity to arrive at equity value per share. Never discount FCFF at the cost of equity, or FCFE at WACC — mismatching cash flow type and discount rate is the single most common DCF error and silently distorts the answer.
Which approach should a Nepali analyst prefer? For most NEPSE-listed non-financial companies — manufacturers, hotels, hydropower developers, trading companies — either approach can work, but FCFE tends to be more practical for a retail or institutional equity investor because it produces equity value (and therefore value per share) directly, without the extra step of subtracting net debt at the end. FCFE is also more intuitive when a company's capital structure — the mix of debt and equity financing it uses — is expected to stay relatively stable, which is a reasonable assumption for a mature manufacturer but a poor one for, say, a hydropower project still in its debt-heavy construction and early-operation years, where debt is being steadily repaid out of operating cash flow and the capital structure is shifting every year. In that hydropower case, FCFF and WACC can actually be the cleaner approach, because it separates the operating cash-generating power of the plant from the specific, and shifting, debt load sitting on top of it — and only at the very end does the analyst subtract the (declining) net debt to get to equity value.
There is one crucial exception where FCFF/WACC is almost mandatory in Nepal: banks and financial institutions. For a commercial bank or a development bank, debt (in the form of customer deposits) is not really "debt" in the ordinary sense — it is the raw material of the business, not a financing choice layered on top of operations. Interest paid to depositors is an operating cost of banking, not a financing cost to be added back. Trying to build an FCFF for a bank produces a distorted, nearly meaningless number. For banks, DCF practitioners almost universally use a variant built on FCFE directly — often approximated as net profit adjusted for the capital a regulator requires the bank to retain to support its loan book (since Nepal Rastra Bank's capital adequacy requirements effectively force banks to reinvest a portion of profit as regulatory capital rather than distribute it all as dividends). This matters directly for Nepali investors, since banking and financial shares make up one of the largest blocks of NEPSE's market capitalisation.
A second practical wrinkle specific to Nepal deserves mention here: the quality of the historical financial data an analyst starts from. Projecting future free cash flow begins with understanding past free cash flow, built from several years of audited financial statements. Many Nepali companies outside the heavily regulated banking and insurance sectors have historically had inconsistent disclosure quality — working capital movements buried inconsistently across notes, related-party transactions that are not always fully separated out, and depreciation policies that occasionally change between years in ways that are not clearly flagged. An analyst building a DCF for a Nepali manufacturing or trading company should expect to spend real time simply reconstructing a clean, consistent five-year history of FCFF or FCFE before attempting to project even a single year forward. Skipping this reconstruction step and projecting directly off a single reported "net profit" figure is one of the fastest ways to build a DCF that looks precise but is quietly wrong.
WARNING
A DCF is only as reliable as the free cash flow numbers feeding it. If the historical cash flow reconstruction is sloppy — inconsistent working capital treatment, unexplained swings in capex, related-party flows that distort true operating cash — every subsequent number in the model, no matter how carefully discounted, inherits that error.
Lesson 46.3 — Building the Discount Rate: Cost of Equity in a Frontier Market
If free cash flow is the numerator of a DCF, the discount rate is the denominator, and in a market like Nepal's, the denominator is where most of the genuine difficulty — and most of the genuine judgment — lives. The standard tool for estimating the cost of equity (the annual return shareholders require to compensate them for the risk of owning a particular stock, as opposed to the cost of debt, which is simply the interest rate lenders charge) is the Capital Asset Pricing Model (CAPM). CAPM states:
Each of these three pieces requires real judgment when applied to a NEPSE-listed company, so each is worth walking through carefully.
The risk-free rate is the return available on an investment with effectively no default risk — conventionally the yield on a government security, since a government that can print its own currency is assumed (rightly or wrongly) never to default on debt issued in that currency. In Nepal, the natural proxies are yields on Government of Nepal Treasury Bills (short-term instruments with maturities under a year, auctioned regularly by Nepal Rastra Bank on behalf of the government) and Development Bonds (longer-maturity government securities). The trouble is that these yields have been remarkably volatile in Nepal over the past several years — not because Nepal's government credit risk has changed dramatically, but because of swings in domestic banking-sector liquidity. When Nepali banks are flush with deposits and have limited lending opportunities, they pile into treasury bills, driving yields down sharply; when liquidity tightens, yields spike. Nepal's weighted average treasury bill rate swung from double digits during the 2022 liquidity crunch down to a much lower single-digit range during the liquidity glut of 2024 and 2025, before partially normalising. A "risk-free rate" that can move by five or more percentage points within two years, for reasons having nothing to do with Nepal's actual sovereign creditworthiness, is not a clean input.
REGULATORY DETAIL
Nepal Rastra Bank publishes the weighted average treasury bills rate regularly, drawn from its periodic T-bill auctions, alongside data on 91-day, 182-day, and 364-day maturities. An analyst building a DCF should use a longer-maturity government yield (or an average of the T-bill rate over several recent quarters) rather than the most recent single auction result, precisely because a single data point can be distorted by a temporary liquidity spike or glut rather than reflecting a durable "risk-free" rate.
The equity risk premium (ERP) is the extra return, above the risk-free rate, that investors as a whole demand for holding a diversified basket of equities rather than the risk-free asset. It compensates for the general riskiness of stocks as an asset class. Mature-market ERPs (built from many decades of, for example, US or developed-market stock market history) are commonly estimated in a broad range around 4–6%. Nepal is not a mature market, and NEPSE has nowhere near the multi-decade, high-quality return history that underpins those mature-market estimates. Practitioners valuing companies in frontier and emerging markets typically address this by starting with a mature-market ERP and adding a country risk premium — an additional increment reflecting the extra risk of investing in a specific country's equity market, often estimated (following the widely used approach popularized by NYU professor Aswath Damodaran) by taking that country's sovereign credit rating or bond default spread and scaling it up by the relative volatility of that country's equity market compared to its bond market. For a market like Nepal's — unrated or low-rated by major international agencies, with capital controls restricting foreign portfolio investment and comparatively low market liquidity — the resulting country risk premium is substantial, often adding several percentage points on top of a mature-market ERP base. The practical upshot is that a Nepali company's cost of equity, built up honestly through this framework, tends to land meaningfully higher than a comparable company's cost of equity in a developed market — frequently in the low-to-mid teens as a percentage, even before considering the specific riskiness of an individual firm.
Beta — the third component of CAPM — measures how much a specific stock's returns move relative to the overall market's returns; a beta of 1.0 means the stock tends to move in line with the market, a beta above 1.0 means it tends to amplify market moves, and a beta below 1.0 means it tends to be more stable than the market. In deep, liquid markets, beta is estimated by running a regression of a stock's historical returns against the broader index's returns over several years of daily or weekly data. NEPSE presents a specific and serious problem here: a large share of listed stocks trade thinly, some going days or weeks without a single transaction, and the exchange's daily circuit breakers (price movement limits that halt or cap how far a stock can move in a single session) further compress and distort the reported price series. A beta regression run on a NEPSE stock's historical prices is regressing against data that often does not reflect genuine, continuously-clearing market prices — it reflects whatever price happened to clear on the handful of days a trade actually occurred, subject to circuit-breaker ceilings and floors. The result is that raw, statistically-estimated betas for many NEPSE stocks are unreliable: they can appear artificially low (because a stock that barely trades appears to not move much, understating its true risk) or erratic from one estimation period to the next.
CAUTION
Do not trust a raw regression beta calculated from NEPSE price history without scrutiny, especially for thinly-traded counters. A beta of 0.3 for a highly leveraged hydropower developer, derived from a stock that traded on only forty days in the past year, almost certainly understates the company's true sensitivity to economic and sector-wide risk — it is measuring illiquidity, not safety.
Given this, practical Nepali analysts commonly lean on one or both of two workarounds. The first is a bottom-up beta approach: rather than regressing a specific Nepali company's own erratic price history, the analyst starts from the beta of comparable listed companies in the same industry from markets with better data (for example, listed hydropower or manufacturing companies in India or other South Asian markets with deeper trading histories), "unlevers" those betas to strip out the effect of each comparable company's own debt load (since more debt mechanically amplifies equity risk), averages the unlevered figure across several comparables to get an industry-level unlevered beta, and then "relevers" that industry beta using the specific Nepali company's own capital structure (its own mix of debt and equity) to arrive at a beta tailored to that company. The second workaround is simply to use a small set of reasonable qualitative brackets — for example, treating regulated, essential-service businesses with stable demand (established commercial banks, hydropower plants with long-term power purchase agreements already signed and in operation) as lower-beta (perhaps 0.7–0.9), and treating more cyclical, discretionary, or leverage-heavy businesses (hotels and tourism, construction, early-stage hydropower still exposed to hydrology and construction risk) as higher-beta (perhaps 1.1–1.4), and defending that judgment explicitly in the write-up rather than hiding behind a spuriously precise regression output.
Putting the pieces together with illustrative, order-of-magnitude figures: a risk-free rate proxy of roughly 7% (a normalised, multi-quarter average of longer-maturity Nepali government securities, deliberately smoothing over the recent liquidity-driven swings), a mature-market ERP of roughly 5%, a Nepal country risk premium of roughly 4–5% given the market's frontier status, and a company-specific beta of, say, 1.0 for a mid-risk operating business, would combine to a cost of equity in the neighbourhood of 16–17%. A lower-risk regulated utility with beta 0.8 might land closer to 14–15%; a higher-risk, highly leveraged cyclical business with beta 1.3 might land at 18% or more. These are illustrative ranges, not fixed rules — the discipline is in building the number up transparently from its components, stating every assumption explicitly, and then stress-testing the final valuation against a plausible range of discount rates (a topic taken up fully in Lesson 46.6), rather than presenting a single false-precision figure like "14.73%" as though it were an observed fact rather than a constructed judgment.
For FCFF-based valuations, the discount rate is WACC rather than the cost of equity alone — a blend of the cost of equity and the after-tax cost of debt (the interest rate a company actually pays lenders, adjusted downward for the tax shield since interest expense is tax-deductible), weighted by the company's proportion of equity and debt financing at market value. Estimating the cost of debt for a Nepali company is comparatively more straightforward, since it can usually be observed directly from the interest rates the company actually pays on its bank loans and debentures, as disclosed in its financial statements — though for heavily-indebted hydropower projects, this figure deserves its own scrutiny, since concessional or subsidized lending rates on some hydropower debt (occasionally available through targeted refinancing facilities) can understate what the company would pay to raise fresh debt today.
Lesson 46.4 — Terminal Value: Valuing the Business Beyond the Forecast Horizon
No analyst can credibly forecast a company's cash flows line by line, year by year, forever. Most DCF models therefore build an explicit forecast for a limited number of years — commonly five, sometimes as many as ten for a business still ramping toward a stable, mature state, such as a hydropower plant in its early years of operation — and then collapse everything the business is expected to generate beyond that horizon into a single figure called terminal value, calculated as of the last year of the explicit forecast and then discounted back to today just like any other cash flow.
There are two standard methods for calculating terminal value, and a careful analyst should ideally compute both and compare them as a sanity check on each other.
The Gordon Growth Model (also called the perpetuity growth method) assumes that, after the explicit forecast period ends, the company's free cash flow will grow at a constant, modest rate forever. The formula is:
Terminal Value = FCF in the first year after the forecast period ÷ (Discount Rate − Perpetual Growth Rate)
The perpetual growth rate used here must be conservative and sustainable — a rate no company can realistically exceed forever, since growing faster than the overall economy indefinitely would eventually mean the company becomes larger than the economy itself, which is impossible. In practice, this means the terminal growth rate should be anchored near a country's expected long-run nominal GDP growth rate (real GDP growth plus inflation) — for Nepal, taking into account the country's growth trajectory and inflation history, a terminal growth rate in the region of 4–6% is a commonly defensible anchor, though this should be revisited as Nepal's macroeconomic outlook evolves. Using a terminal growth rate materially above this — say, 8% or 10%, forever — is one of the most common ways analysts (deliberately or accidentally) inflate a DCF valuation, because the terminal value is extremely sensitive to the gap between the discount rate and the growth rate, as the worked example in the next lesson will demonstrate numerically.
The Exit Multiple Method instead assumes that, at the end of the explicit forecast period, the business could be sold for a price based on some valuation multiple (such as EV/EBITDA — enterprise value divided by earnings before interest, tax, depreciation, and amortisation, a common proxy for operating cash-generating power — or a price-to-earnings multiple) observed from comparable companies at that future point in time. This method has the advantage of grounding the terminal value in something closer to observable market pricing, but it has a specific weakness in the Nepali context: it requires a reasonable set of comparable companies with credible, liquid market pricing, and NEPSE's universe of comparables within any single narrow sub-sector (a handful of hydropower developers, a handful of hotels, a cluster of similarly-sized commercial banks) is small, and their trading multiples are themselves influenced by the same illiquidity and sentiment-driven swings discussed throughout this book. An exit multiple pulled from a thinly-traded, sentiment-driven peer group can smuggle exactly the kind of market mispricing a DCF is supposed to help an investor see past back into the "intrinsic" valuation, defeating much of the purpose of doing a DCF in the first place.
WARNING
In most real-world DCF models — and this is especially pronounced in Nepal, where near-term forecast periods are short relative to the very long operating life of assets like hydropower plants — terminal value commonly makes up 60% to 80%, sometimes more, of the total calculated valuation. This means the single most influential number in the entire model is often the one built on the least certain, longest-dated assumptions. Treat the terminal value assumptions with at least as much scrutiny as the near-term forecast, not less.
This concentration of value in the terminal period has a specific implication for how DCF should be used on Nepali cyclical and hydropower-type businesses discussed in earlier chapters of this book. A hydropower plant's cash flow is not a smooth, steadily growing line — it depends heavily on hydrology (the seasonal pattern of river flow, with Nepal's rivers running high during the monsoon months of roughly Shrawan to Ashwin and much lower during the dry winter and pre-monsoon months), on the specific tariff structure locked in under its Power Purchase Agreement (PPA) with the Nepal Electricity Authority (the near-monopoly state utility that buys power from the large majority of Nepal's independent hydropower producers, typically under long-term, fixed or seasonally-differentiated tariff contracts), and on when its underlying debt is scheduled to be repaid. A well-built hydropower DCF should model these mechanics explicitly year by year through the explicit forecast period — rising cash flow as construction-linked debt is paid down, seasonal swings between wet-season and dry-season generation reflected in the tariff structure, and a step-change around the point (often twenty to thirty years from commissioning, depending on the specific PPA and license terms) when the PPA tariff structure or the operating license itself may reset or expire — rather than smoothing all of that complexity into one constant terminal growth rate applied indefinitely. For a hydropower asset with a licensed life that is finite (Nepali hydropower licenses are typically issued for a fixed number of years, after which ownership arrangements can change), a straightforward Gordon Growth terminal value assuming cash flows forever is not merely imprecise — it can be conceptually wrong, and a more careful model may need to explicitly value only the remaining licensed life plus a defensible view (rather than an assumption) on what happens at license expiry.
CASE IN POINT
Nepal's operating hydropower companies, such as long-established plants of the kind analysed by market data providers, illustrate this terminal value problem concretely: once a plant is fully built and its PPA is signed and running, its year-to-year cash flow becomes relatively predictable within a wet-season/dry-season band, but the total value of the plant hinges enormously on assumptions about tariff renewal, license extension, and major refurbishment capex decades into the future — precisely the kind of long-dated, hard-to-forecast judgment that terminal value is forced to compress into a single number.
Lesson 46.5 — A Worked Example: Discounting a Nepali Company Step by Step
The mechanics described so far are easiest to absorb through a complete numerical example. Consider a hypothetical company, Himalaya Hydro Power Ltd. (HHPL) — a fictional, mid-sized run-of-river hydropower company, illustrative of the kind of counter that trades on NEPSE's hydropower sub-index, with 3 crore shares outstanding and a long-term PPA already signed with the Nepal Electricity Authority. HHPL's plant has been operating for several years, its construction-period debt is being steadily repaid, and its free cash flow to equity (FCFE) is expected to grow as debt service costs fall each year, before settling into a stable, modest long-run growth pattern once the debt is largely repaid and generation output stabilises.
Suppose an analyst has done the work described in Lessons 46.2 and 46.3: reconstructed HHPL's historical FCFE from several years of audited statements, projected FCFE forward five years reflecting the declining debt-service burden, and built up a cost of equity of 14.5% using the CAPM framework — a risk-free rate proxy around 7%, a combined equity and country risk premium reflecting Nepal's frontier-market status, and a beta near 1.0 reflecting HHPL's moderate operating and financial leverage. The analyst has also settled on a terminal growth rate of 4%, anchored to a conservative view of Nepal's long-run nominal GDP growth.
Year
Projected FCFE (NPR crore)
Discount Factor @ 14.5%
Present Value (NPR crore)
Year 1
12.00
0.8734
10.48
Year 2
15.00
0.7629
11.44
Year 3
18.00
0.6664
12.00
Year 4
20.00
0.5822
11.64
Year 5
21.00
0.5085
10.68
Sum of Present Values (Years 1–5)
56.24
Terminal Value (as of end of Year 5)
208.00
105.77
Total Equity Value
162.01
Reading this table mechanically: each year's projected FCFE is multiplied by its discount factor (1 ÷ (1.145)^n for year n) to arrive at that year's present value — for instance, Year 3's NPR 18 crore, five years before it lands in HHPL's shareholders' hands in full, is worth NPR 12.00 crore in today's terms once discounted at 14.5% for three years. The five years of explicit present values sum to NPR 56.24 crore. Terminal value is then calculated using the Gordon Growth formula on the cash flow expected in the first year after the forecast window: Year 5's FCFE of NPR 21 crore, grown one more year at the 4% terminal rate, gives NPR 21.84 crore, divided by the gap between the discount rate and the growth rate (14.5% − 4% = 10.5%), giving a terminal value of NPR 208.00 crore as of the end of Year 5. That terminal value is itself a future amount as of today, so it must also be discounted back five years at 14.5%, giving a present value of NPR 105.77 crore. Adding the present value of the explicit forecast period (NPR 56.24 crore) to the present value of the terminal value (NPR 105.77 crore) gives a total estimated equity value of NPR 162.01 crore. Dividing by HHPL's 3 crore outstanding shares gives an estimated intrinsic value of roughly NPR 54.00 per share.
Notice immediately, as flagged in Lesson 46.4, that the terminal value's present value (NPR 105.77 crore) makes up about 65% of HHPL's total estimated equity value — the majority of what this valuation claims the company is worth rests on a single assumption about growth forever after Year 5, discounted back through a single assumption about the appropriate long-run discount rate. This is not a flaw specific to this example; it is a structural feature of DCF valuation applied to almost any going concern, and it is precisely why Lesson 46.6 turns next to testing how sensitive this NPR 54.00-per-share estimate actually is to reasonable changes in those two assumptions.
Lesson 46.6 — Sensitivity, Fragility, and the Practical Limits of DCF in Nepal
The HHPL example above produced a single, clean number: NPR 54.00 per share. Presented alone, that number carries an unearned air of precision — as though the analyst has discovered a hidden truth about what HHPL is really worth. A properly disciplined DCF exercise never stops at a single point estimate; it immediately asks how much that estimate moves when the two most influential assumptions — the discount rate and the terminal growth rate — are varied within a reasonable range. This exercise is called sensitivity analysis, and it is arguably more informative than the base-case number itself, because it reveals how much of the conclusion is really being driven by the analyst's judgment calls rather than by the underlying business.
Holding HHPL's projected FCFE stream fixed and varying only the discount rate and terminal growth rate produces the following range of per-share values:
Discount Rate ↓ / Terminal Growth Rate →
2%
4%
6%
12.5%
NPR 57.5
NPR 67.3
NPR 83.1
14.5% (base case)
NPR 47.8
NPR 54.0
NPR 63.1
16.5%
NPR 40.7
NPR 44.9
NPR 50.7
The spread here is striking: across a discount rate range of only four percentage points (12.5% to 16.5%, both defensible depending on how one estimates beta and the country risk premium) and a terminal growth range of only four percentage points (2% to 6%, both defensible depending on one's view of Nepal's long-run nominal growth), HHPL's estimated per-share value swings from roughly NPR 40.7 to roughly NPR 83.1 — more than double from the low end to the high end. An investor who took only the base-case NPR 54.00 figure at face value, without appreciating this range, could be lulled into false confidence about the precision of the estimate. The honest and useful way to present a DCF output is therefore not "HHPL is worth NPR 54.00 per share" but rather "under a set of reasonable assumptions, HHPL appears to be worth somewhere in the range of roughly NPR 40 to NPR 85 per share, with NPR 54 as a central estimate" — and then to compare that full range, not just the midpoint, against NEPSE's quoted market price to judge whether the stock looks cheap, expensive, or fairly valued.
CAUTION
A DCF model can be made to produce almost any answer an analyst wants simply by nudging the discount rate down half a percentage point and the terminal growth rate up half a percentage point — changes that look trivial on a spreadsheet but compound into an enormous swing in the final valuation. Always run and report a sensitivity table alongside any DCF conclusion, and be especially suspicious of a DCF (your own or someone else's) that conveniently confirms a price target the analyst was already hoping to justify.
Beyond the mechanical sensitivity of discount rate and terminal growth, several practical challenges specific to Nepal deserve a final, direct discussion, because they affect how much weight a Nepali investor should place on any DCF output at all.
The first is data quality, already touched on in Lesson 46.2. A DCF is a forward-looking exercise built on a backward-looking foundation — the analyst must first understand several years of a company's true, cash-based operating history before projecting it forward, and for many NEPSE-listed companies outside banking and insurance, that historical foundation is genuinely harder to build cleanly than it would be for a comparable company in a market with longer-established, more consistently enforced disclosure and audit standards.
The second is currency and inflation. Nepal has experienced periods of meaningfully higher inflation than the developed-market economies whose DCF textbooks and default assumptions much of standard valuation theory is built around, and the Nepali rupee's value, while pegged to the Indian rupee, is exposed indirectly to broader currency and external-sector pressures (import dependence, remittance inflows, and foreign exchange reserve levels, all discussed elsewhere in this book). A DCF must be built consistently in either nominal terms (cash flows and discount rate both including expected inflation) or real terms (both excluding it) — mixing the two, for instance by discounting inflation-adjusted "real" cash flow projections at a nominal, inflation-inclusive discount rate, is a subtle but common error that silently and substantially understates value, and it is worth double-checking explicitly in any Nepali DCF given how much inflation assumptions have moved over the past several years.
The third, most acute for the specific businesses this book has covered in earlier chapters, is the genuine difficulty of forecasting cash flow for cyclical and project-based businesses. A hydropower developer's cash flow depends on hydrology that can vary meaningfully from year to year, on monsoon timing, and on the specific terms of a PPA that may not be fully public or fully understood by outside analysts. A hotel or tourism business's cash flow depends on tourist arrival cycles that are themselves sensitive to regional geopolitics, global travel patterns, and domestic political stability — none of which lend themselves to confident five-year-ahead point forecasts. A trading or manufacturing company's cash flow can be exposed to volatile import costs, exchange rate pass-through, and shifting government duty structures. In each of these cases, a DCF is still a useful discipline — it forces an analyst to think explicitly, year by year, about what actually drives the cash — but the honest output is a range of plausible values under different scenarios (a strong monsoon year versus a weak one, a tourism recovery scenario versus a stagnation scenario), not a single confident number.
PRACTICAL TOOL
For cyclical or project-based Nepali businesses, build at least three scenarios into the DCF — a base case, a downside case (weaker hydrology, weaker tourist arrivals, tighter margins), and an upside case — and report the resulting range of per-share values for each, rather than presenting only a single base-case output as though it were the definitive answer.
None of this means DCF should be abandoned in favour of purely relative, multiple-based valuation on NEPSE. It means DCF should be used the way a careful engineer uses a calculation with known error bars, rather than the way a fortune-teller uses a crystal ball: as a disciplined way of making assumptions explicit, testing how much each assumption matters, and forming a reasoned view of value — always held with appropriate humility about how much of the final number is genuine insight into the business and how much is simply the analyst's own assumptions echoing back.
Chapter recap
This chapter built the mechanics of Discounted Cash Flow valuation from first principles, grounding the entire exercise in a familiar, everyday intuition: money available today is worth more than the same amount of money promised for some point in the future, because today's money can be put to work — in a Nepali fixed deposit, in a business, in any productive use — while tomorrow's promised money cannot be used until it arrives. DCF formalises this intuition by projecting a company's free cash flow (the real, spendable cash left over after operating costs and necessary reinvestment, as distinct from accounting net profit) across a multi-year forecast period, discounting each year's projected cash flow back to today's rupees using a discount rate that reflects the specific riskiness of that cash flow, and adding a terminal value that captures everything the business is expected to generate beyond the explicit forecast horizon.
The chapter distinguished two forms of free cash flow that must never be mismatched with the wrong discount rate: Free Cash Flow to the Firm (FCFF), available to both lenders and shareholders and discounted at the Weighted Average Cost of Capital (WACC) to reach enterprise value, and Free Cash Flow to Equity (FCFE), available to shareholders alone after debt-related payments, discounted directly at the cost of equity to reach equity value per share. FCFE tends to be the more practical default for most NEPSE-listed non-financial companies, while FCFF/WACC is often cleaner for businesses with a shifting capital structure such as hydropower developers still repaying construction debt, and a variant built on regulatory-capital-adjusted profit is the standard approach for banks and financial institutions, where customer deposits function as raw material rather than conventional financing debt.
Building a defensible discount rate for a Nepali company was shown to be the most judgment-intensive part of the entire exercise. The risk-free rate, proxied by Nepal government treasury bill and bond yields, has swung dramatically over recent years due to domestic banking liquidity cycles rather than genuine changes in sovereign risk, and should be normalised over several quarters rather than read off a single data point. The equity risk premium must be built up from a mature-market base plus an explicit country risk premium reflecting Nepal's frontier-market status, capital controls, and thin market depth. And beta — the standard measure of a stock's sensitivity to overall market movements — is particularly unreliable when calculated directly from NEPSE price history, because thin trading and daily circuit breakers distort the price series that any regression would rely on; bottom-up beta estimation from better-traded regional comparables, or defensible qualitative brackets by business risk category, are more reliable practical alternatives.
Terminal value — calculated either through the Gordon Growth Model (assuming modest, perpetual cash flow growth anchored to long-run nominal GDP growth) or the exit multiple method (assuming a future sale at a comparable-company valuation multiple) — was shown to typically dominate the total DCF output, often accounting for the majority of calculated value, which means the least certain, longest-dated assumptions in the entire model carry outsized influence over the final answer. This is especially consequential for hydropower-type businesses with finite license terms and PPA structures that may reset or expire decades into the future, where a simple perpetual-growth terminal value can be conceptually inappropriate rather than merely imprecise.
The fully worked numerical example — a hypothetical hydropower company, Himalaya Hydro Power Ltd., valued using a five-year explicit FCFE forecast, a 14.5% cost of equity, and a 4% terminal growth rate — produced a base-case value of roughly NPR 54.00 per share, with the terminal value alone contributing about two-thirds of that total. Varying the discount rate and terminal growth rate by only a few percentage points each, within entirely reasonable ranges, moved the per-share estimate from roughly NPR 40.7 to roughly NPR 83.1, illustrating concretely why every DCF conclusion should be reported as a range built on explicit sensitivity analysis, never as a single falsely precise number.
Finally, the chapter confronted the specific practical obstacles to applying DCF rigorously in Nepal: historical financial data that often requires significant reconstruction work before it can be trusted as a forecasting base; the need to keep inflation and currency assumptions internally consistent given Nepal's inflation history and the rupee's indirect exposure to external-sector pressures; and the genuine, structural difficulty of forecasting cash flow for cyclical, hydrology-dependent, or tourism-linked businesses that dominate much of NEPSE's non-financial listings. None of these obstacles make DCF useless in Nepal — they make it a tool that must be used with explicit scenarios, honest ranges, and constant humility about which parts of the final number reflect real insight into the business and which parts merely reflect the analyst's own assumptions reflected back. Used this way, DCF remains one of the most disciplined tools available to a Nepali investor seeking to look past NEPSE's daily price noise toward the underlying economic substance of the businesses it lists.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IX · Chapter 47
Relative Valuation (Comps)
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 47.1 — The Neighbourhood Comparison: What Relative Valuation Really Means
Imagine you are trying to figure out a fair asking price for a house in Baneshwor. You would not compare it to a bungalow in Budhanilkantha with three times the land area, nor to an apartment in a completely different city. You would look for houses of similar size, similar age, similar road access, sold in the last few months, in the same or a neighbouring ward. If those comparable houses sold for roughly NPR 2.5 crore, and your house is similar in every important respect, then NPR 2.5 crore becomes your anchor. You might adjust up or down for a slightly bigger plot, a busier road, or a better view — but the starting point is what similar things actually sold for.
That is the entire logic of relative valuation, sometimes called "comps" (short for comparables). Instead of building a valuation from scratch by projecting every future cash flow of a company for the next twenty years and discounting it back to today — the approach you will study in the discounted cash flow (DCF) chapters later in this Part — relative valuation asks a simpler question: what is the market currently paying for businesses similar to this one, and does that tell us something useful about what this business should be worth?
You have already built the tools for this in Chapters 39 through 41, where you learned to compute and interpret the core ratios: the price-to-earnings ratio (P/E, the share price divided by earnings per share, telling you how many years of current profit you are paying for), the price-to-book ratio (P/B, the share price divided by book value per share, telling you how much you are paying for each rupee of accounting net worth), EV/EBITDA (enterprise value divided by earnings before interest, tax, depreciation and amortisation, a measure that looks at the whole company including its debt, not just the equity slice), and the dividend yield (the annual dividend per share divided by the share price, telling you the cash return you receive simply for holding the stock). Relative valuation is what you do with those ratios once you have them: you stop looking at a single company's multiple in isolation and start asking, "compared to what?"
KEY CONCEPT
Relative valuation (comps) is the practice of valuing a company by comparing its trading multiples — P/E, P/B, EV/EBITDA, dividend yield — against two reference points: (1) a group of similar peer companies, and (2) the company's own historical average multiple. A multiple only becomes meaningful once you have something to compare it to.
There are, in fact, two different kinds of "comparable" that this chapter will use side by side, and it is worth separating them clearly from the start because Nepali investors often blur them together.
The first is cross-sectional comparison: looking across several companies at the same point in time. If Nabil Bank trades at a P/B of 2.1x and Global IME Bank trades at a P/B of 1.3x, and both are large, well-run commercial banks with broadly similar business models, that gap is cross-sectional information. It tells you something about how the market is pricing one bank relative to another, right now.
The second is time-series comparison: looking at one company against its own history. If Chilime Hydropower Company has historically traded at an average P/E of 18x over the last five years, and it is currently trading at 30x, that gap tells you something different — that the market's view of Chilime today is unusually optimistic (or pessimistic) compared to its own recent past. This does not automatically mean the stock is overvalued; something may have genuinely changed (a new plant coming online, a tariff revision, a change in interest rates that makes utility-like cash flows more attractive). But it flags a divergence worth investigating.
A seasoned analyst uses both lenses together. A bank trading in line with its peers but far above its own five-year average multiple is telling you a different story than a bank trading far below its peers but in line with its own history. Confusing the two — treating "cheap relative to history" as the same thing as "cheap relative to peers" — is one of the quiet errors that creeps into otherwise careful analysis.
It helps to be explicit about why relative valuation is popular, and why this book treats it as a complement to, not a replacement for, intrinsic valuation methods like discounted cash flow. Relative valuation is fast: you can screen twenty-seven commercial banks on P/E and P/B in an afternoon, using data readily available from NEPSE, Sharesansar, or Merolagani, without building a full financial model for each one. It reflects the market's current mood, which matters because you eventually have to sell your shares to that same market, not to a spreadsheet. And it is intuitive: "this bank is cheaper than that bank" is a sentence any investor, professional or retail, can understand immediately.
But relative valuation has a structural weakness baked into its logic, and it is worth stating plainly before you go any further: it assumes the peer group is priced correctly on average. If every commercial bank in Nepal is simultaneously overpriced because retail money has flooded into NEPSE during a bull run, then a bank trading "cheap relative to its peers" may simply be the least overpriced stock in an overpriced sector — not a genuinely attractively priced asset in absolute terms. Relative valuation tells you about relative position, not absolute worth. Keep that sentence in mind; it will resurface throughout this chapter, and it is the reason Lesson 47.5 introduces the idea of a "justified multiple" derived from fundamentals rather than simply borrowed from the crowd.
Lesson 47.2 — Building the Right Peer Group on a Concentrated Exchange
Choosing comparable houses in a real estate search is easy because there are thousands of houses to choose from in most cities. Choosing a comparable set of listed companies on NEPSE is much harder, because the exchange is small, concentrated, and lopsided in ways that most textbook treatments of relative valuation — usually written with the New York Stock Exchange or Bombay Stock Exchange in mind, where there are dozens or hundreds of companies in any given industry — do not prepare you for.
As of 2026, NEPSE lists roughly 300 companies, but the distribution across sectors is extremely uneven. Banking and financial institutions (commercial banks, development banks, finance companies, and microfinance institutions) together with the hydropower sector account for the large majority of listed companies and a substantial share of total market capitalisation. Insurance is a meaningful third group. Manufacturing, trading, hotels, and "others" make up the remainder, often with only a handful of listed names in each. This is the opposite of a market like the S&P 500, where you can find twenty or thirty reasonably similar companies inside a narrow sub-industry. On NEPSE, your "peer group" for a mid-sized commercial bank might be all 17-20 other commercial banks — a wide set — while your peer group for a niche business (say, the single listed cigarette manufacturer, or the single listed cement producer of a particular type) might be one company, or none at all.
This has a direct practical consequence: peer group construction on NEPSE requires more judgment and more caution than the same exercise would on a larger exchange, precisely because your options are narrower and the temptation to stretch the definition of "comparable" to fill out a table is stronger.
Start peer group selection with the criterion you already learned to respect in Chapter 39: sector-specific structural differences matter more than surface-level similarity. A peer group should be built around companies that share the same fundamental economic engine, not simply the same stock exchange or the same size of market capitalisation. For NEPSE, the two dominant clusters deserve separate treatment:
Commercial banks should be compared to other commercial banks — and ideally to banks of similar scale and business mix. Nepal has large, well-capitalised banks such as Nabil Bank, Global IME Bank, Nepal Investment Mega Bank, and NIC Asia Bank, which sit at a different scale than smaller commercial banks. Within banking, you should also separate commercial banks from development banks and finance companies, since these operate under different regulatory tiers (the "A," "B," and "C" class institutions under Nepal Rastra Bank's classification), with different minimum capital requirements, different permitted activities, and different risk profiles. A "B" class development bank and an "A" class commercial bank both take deposits and make loans, but comparing their P/B ratios directly, without adjusting for scale and regulatory tier, misses real structural differences.
Hydropower companies should be compared to other hydropower companies — but even within hydropower, you must further separate operating companies from pre-operational or construction-stage companies. A hydropower company that has been generating and selling electricity for ten years, such as Chilime Hydropower Company or Butwal Power Company, has an earnings stream you can measure today. A hydropower company still under construction, with its first unit not yet commissioned, has no meaningful current earnings at all — its P/E ratio, if it has one, is often a meaningless or even negative number, and investors are really pricing a claim on future cash flows that do not yet exist. Comparing the P/E of an operating hydropower company to a pre-operational one is comparing a fruit tree to a sapling: both may become valuable, but only one is bearing fruit right now.
WARNING
Never build a peer group purely by sorting on market capitalisation or by picking "the next five companies alphabetically" from a sector list. A peer group must share the underlying economic engine: similar revenue model, similar cost structure, similar capital intensity, similar regulatory regime, and roughly similar stage of the business life cycle (growth versus mature). A peer group assembled for convenience rather than genuine comparability will produce a multiple that looks precise but means very little.
Beyond sector and sub-sector, a disciplined peer group in the Nepali context should also account for:
Scale. A commercial bank with a loan book of NPR 200 billion faces different economies of scale, different funding costs, and different regulatory scrutiny than one with a loan book of NPR 40 billion. Larger banks often (though not always) command a premium multiple because of perceived stability, deposit franchise strength, and liquidity of their shares.
Ownership structure and promoter concentration. Many NEPSE-listed companies, especially hydropower companies, have promoter shareholding locked in for a statutory period (a topic covered in more detail in Lesson 47.4), which affects how much of the company's shares actually trade freely. Two hydropower companies with identical plants and identical revenue can have very different observed multiples simply because one has a much smaller tradeable float than the other.
Growth stage and capacity utilisation. A hydropower company that just commissioned a new, larger project and is still ramping up toward full capacity utilisation will show rapidly growing earnings for a few years, even without any change in the underlying asset. Comparing its P/E to a mature company running at stable, flat output conflates "temporarily depressed current earnings due to ramp-up" with "genuinely lower value."
Capital structure. Hydropower projects in Nepal are typically financed with a high proportion of debt relative to equity — often in the range of 70:30 or 80:20 debt-to-equity during the construction and early operating years, financed through syndicated loans from Nepali banks and development finance institutions. Two companies with similar plant sizes but very different leverage will have very different EV/EBITDA-to-P/E relationships, and comparing their P/E ratios alone, without looking at EV/EBITDA (which neutralises the effect of the capital structure by looking at the whole enterprise), can be misleading. This point is developed further in Lesson 47.6.
A practical peer group exercise, then, looks less like "find five NEPSE companies with a market cap near mine" and more like a checklist: same core sector, same regulatory tier, comparable scale, comparable operating stage (mature and generating stable revenue, versus growth or pre-operational), and — where the differences cannot be eliminated by selection — adjustments applied explicitly rather than ignored.
PRACTICAL TOOL
A five-question peer group filter for NEPSE: (1) Does this company operate in the same core sector and sub-sector (commercial bank vs. commercial bank; run-of-river hydropower vs. run-of-river hydropower)? (2) Is it under the same regulatory tier and licensing regime? (3) Is it at a broadly similar life-cycle stage — mature and stable, or growth/ramp-up, or pre-operational? (4) Is its scale (assets, revenue, market capitalisation) within a reasonable multiple of the target company's, say within three to five times, not thirty? (5) Is its free float and trading liquidity comparable enough that its observed price is a genuine market-clearing price rather than a thinly-traded quote? A company that fails two or more of these should be dropped from the peer set or used only with explicit adjustment.
Lesson 47.3 — Why You Cannot Compare a Bank to a Hydropower Company
It is worth spelling out, in concrete terms, exactly why cross-sector comparison on NEPSE is dangerous — not as an abstract warning, but by walking through what would actually go wrong if you tried it.
Suppose an investor notices that a mid-sized commercial bank trades at a P/E of 12x, while a well-known hydropower company trades at a P/E of 35x, and concludes that the bank is "cheap" and the hydropower company is "expensive," recommending a switch from hydropower into banking stock. This reasoning treats P/E as if it means the same thing in both cases. It does not.
A commercial bank's earnings are driven by net interest income (the spread between what it earns on loans and investments and what it pays on deposits and borrowings), fee income, and loan loss provisioning, all of which move with the credit cycle, interest rate policy set by Nepal Rastra Bank, and deposit competition among the roughly twenty commercial banks. A bank's earnings are typically less volatile year to year than a hydropower company's, because a diversified loan book smooths out the ups and downs of individual borrowers, but a bank carries credit risk — the possibility that borrowers default — that a hydropower company largely does not carry in the same form.
A hydropower company's earnings, by contrast, depend on river flow (which varies seasonally and year to year — Nepali rivers run high in monsoon and low in winter, creating a "dry season" earnings dip that recurs every single year), the power purchase agreement (PPA) it has signed with the Nepal Electricity Authority (NEA) or another off-taker (which fixes the tariff it receives, often with a wet-season and dry-season rate, and often without meaningful escalation for many years), and its debt service burden, since most hydropower projects are financed with large loans that must be repaid regardless of how much water flows. A hydropower company's earnings can also look artificially small, or even negative, immediately after a new plant is commissioned, because depreciation and interest expense are front-loaded while revenue is still ramping toward full-capacity output — meaning a "high" P/E is not necessarily "expensive," it may just reflect earnings that are temporarily depressed relative to the company's normalised future earning power.
These are not minor stylistic differences; they are structurally different businesses with different margins, different growth trajectories, different risk factors, and different relationships between current accounting earnings and true underlying value. A P/E of 12x for a bank, whose earnings are relatively stable and recurring, is not directly comparable to a P/E of 35x for a hydropower company whose earnings are still ramping up toward a normalised run-rate. The hydropower company's P/E might fall to 15x within three years purely from capacity ramp-up, with no change in share price at all, simply because the "E" in P/E grows. The bank's P/E of 12x might already reflect a fairly mature, fully-provisioned earnings stream unlikely to grow much faster than nominal GDP.
WARNING
A P/E, P/B, or EV/EBITDA multiple is only comparable across companies that share similar margins, similar growth trajectories, and similar risk profiles. Comparing a bank's multiple directly to a hydropower company's multiple — or a microfinance institution's to a manufacturing company's — is comparing numbers that are calculated the same way but mean fundamentally different things. A "cheap" multiple in one sector can be more expensive, in true economic terms, than an "expensive" multiple in another.
The same logic applies, with less obvious but equally real force, within sectors that look similar on the surface. Life insurance companies and non-life (general) insurance companies are both "insurance," but they have different reserving requirements, different claim patterns, and different capital regulations under the Nepal Insurance Authority; comparing their P/B ratios one-for-one without adjustment is a milder version of the same mistake. Microfinance institutions and commercial banks both lend money, but microfinance serves a different borrower base, carries different credit risk, operates under a different regulatory ceiling on interest rate spreads, and often trades at systematically different multiples because of these structural realities — not because the market is randomly mispricing one relative to the other.
A useful mental discipline here is to ask, before comparing any two multiples: "If I owned 100% of both of these companies outright, with no stock market involved, would a rupee of reported earnings, book value, or EBITDA mean roughly the same thing in both?" If the answer is no — because one company's earnings are far more volatile, one carries far more financial or operational risk, one is growing much faster, or one operates in a fundamentally different regulatory box — then the two multiples are not directly comparable, and any conclusion drawn from comparing them raw is unreliable.
CASE IN POINT
During periods when NEPSE's overall index has rallied sharply, sector rotation commentary in the Nepali financial press has sometimes framed hydropower stocks as "overvalued" purely because their average sector P/E sits well above the banking sector's average P/E. This framing skips the harder question of why the two sectors should even be expected to trade at similar multiples in the first place — a capital-intensive, long-life, contracted-revenue hydropower asset in a growing power-deficit country can rationally justify a materially different multiple than a deposit-taking bank exposed to the full credit cycle. The lesson is not that hydropower can never be overvalued — it certainly can — but that the comparison must be made against hydropower peers and hydropower fundamentals, not banking-sector benchmarks.
Lesson 47.4 — When the Market Itself Distorts the Multiple: Illiquidity and Thin Float on NEPSE
Every method of relative valuation rests on one quiet assumption: that the observed market price is a reasonably reliable signal, produced by enough genuine buying and selling interest that it reflects a real consensus about value. On a deep, liquid market with millions of shares changing hands daily across a broad ownership base, that assumption mostly holds. On NEPSE, for a meaningful number of listed companies, it does not — and an analyst who forgets this will read false signals directly out of the data.
Start with the structural reason this happens. Under the Companies Act and securities regulations enforced by the Securities Board of Nepal (SEBON), and further shaped by hydropower-specific rules, promoter shareholders — typically the founders, sponsoring institutions, and early strategic investors — are required to hold their shares for a statutory lock-in period after listing, commonly several years, before they are permitted to sell into the market. This is meant to signal commitment and prevent founders from dumping shares immediately after an IPO. But it has a side effect directly relevant to relative valuation: the free float (the portion of total shares outstanding that is actually available to trade in the open market, as opposed to being locked up with promoters, the government, or other long-term holders) can be a small fraction of total shares outstanding — sometimes well under 30% for a newly listed hydropower company, versus a much larger free float for an established commercial bank with a broad, long-listed shareholder base.
REGULATORY DETAIL
SEBON's public offering rules generally require companies to allocate a portion of shares to the general public at IPO, while promoter shares remain subject to a lock-in period before they can be sold. NEA and the Department of Electricity Development also impose local-ownership and public-issuance requirements on hydropower projects as a condition of licensing. The practical effect is that a large share of a newly listed hydropower company's equity is not actually available for trading for several years after listing — meaning the daily traded price is being set by a small slice of total ownership, not the full capital structure.
A small free float matters enormously for relative valuation because it directly affects how reliable the observed price — and therefore the observed multiple — actually is. When only a small percentage of shares are tradeable, a relatively modest amount of buying or selling can move the price sharply, simply because there are not enough shares available to absorb the order without a large price change. NEPSE additionally applies daily circuit breakers (price bands limiting how much a stock can move in a single trading session), which, for a thinly traded stock, can mean it takes many consecutive trading days of hitting the upper circuit for the price to reach a level that reflects genuine aggregate demand — and during that entire climb, or during a subsequent slide, the multiple you observe on any given day is a snapshot of a price still finding its level, not a settled market judgment.
This produces several concrete distortions worth naming explicitly:
Newly listed, small-float hydropower stocks often trade at extremely high multiples in their first months or years on the exchange — not necessarily because the market has done careful fundamental work and concluded the projects are worth that much, but because retail demand for a small number of freely tradeable shares outstrips supply. An investor who takes such a multiple at face value and uses it as a peer benchmark for valuing another, similarly small hydropower company is anchoring on noise, not signal.
Even for more established, moderately liquid stocks, low daily trading volumes mean the "last traded price" on a given day may reflect a handful of transactions, sometimes involving related or connected parties, rather than a broad market clearing process. A multiple calculated from that day's closing price can swing meaningfully based on a single large trade.
Some multiples are simply unstable at the level of significant digits when volume is thin: a P/E that jumps from 22x to 27x over one week is more likely to reflect illiquidity-driven price noise than a genuine 23% reassessment of the company's earning power.
CAUTION
Before using any single company's trading multiple as an input to relative valuation — whether as part of a peer average or as a standalone comparison — check its average daily traded volume and free float percentage. A multiple derived from a stock trading only a few thousand shares a day, with 70-80% of shares locked up with promoters, deserves far less weight than one derived from a large-float, actively traded bank with daily volumes in the hundreds of thousands of shares. When peer averages must include thinly traded names, consider weighting by float-adjusted market capitalisation or trading value rather than a simple unweighted average, and flag the inclusion of illiquid names explicitly rather than silently blending them in.
A related distortion worth flagging is the effect of NEPSE's broad market cycles on sector-wide multiples. Nepal's stock market has historically moved through pronounced bull and bear phases, often driven by retail sentiment, margin lending availability, and macro liquidity conditions (interest rates, remittance inflows, banking sector liquidity) more than by company-specific fundamentals. During a broad rally, average P/E and P/B ratios across an entire sector — banking, hydropower, or otherwise — can rise together, well above levels historical fundamentals would justify, simply because more money is chasing the same shares. In such an environment, using "the sector average multiple" as your benchmark for what a company "should" trade at risks anchoring an entire valuation exercise to a temporarily inflated crowd consensus. This is precisely the failure mode flagged at the end of Lesson 47.1, and it is the reason the next lesson introduces a check that does not depend on what the crowd happens to be paying today.
Lesson 47.5 — The Justified Multiple: Deriving a Fair Multiple from Fundamentals
Everything covered so far in this chapter compares a company's multiple to what other companies, or the same company in the past, are trading at. That approach has an obvious blind spot: if the whole peer group, or the whole market, is mispriced, comparing within that mispriced group tells you nothing about whether the group itself is cheap or expensive in any absolute sense. The tool that addresses this blind spot is the justified multiple — a multiple derived not from what the market happens to be paying today, but from the company's own fundamentals: its profitability, its growth prospects, and the return investors require for bearing its risk.
The clearest and most widely used version of this idea, particularly well suited to banks (where book value is a meaningful, closely tracked figure because balance sheets are the core of the business), is the justified price-to-book ratio. It is derived directly from the dividend discount model applied to a company growing at a constant rate — the same Gordon growth logic you will see formalised in the DCF chapters later in this Part, but expressed here in ratio form rather than as a discounted cash flow. The formula is:
Justified P/B = (ROE − g) / (Ke − g)
Where ROE is the company's sustainable return on equity (net income divided by shareholders' equity, the profitability metric you studied in Chapter 40), g is the expected long-run growth rate of earnings and book value, and Ke is the cost of equity — the annual return that shareholders require for holding this specific company's risk, typically estimated using a model such as the Capital Asset Pricing Model (CAPM), built up from a risk-free rate (commonly proxied in Nepal by long-term government bond or development bond yields), an equity risk premium, and a beta reflecting the stock's volatility relative to the market.
PRACTICAL TOOL
Justified P/B = (ROE − g) ÷ (Ke − g). Worked illustration: a commercial bank with a sustainable ROE of 16%, expected long-run growth of 8%, and a cost of equity of 14% has a justified P/B of (0.16 − 0.08) ÷ (0.14 − 0.08) = 0.08 ÷ 0.06 ≈ 1.33x. If that bank is actually trading at a P/B of 2.0x, the market is pricing in either a higher sustainable ROE, faster growth, or a lower risk premium than your inputs assume — and the gap tells you exactly where to focus your fundamental research, rather than leaving you with a vague sense that the stock "looks expensive."
The intuition behind this formula is worth sitting with, because it explains something every Nepali bank-stock investor has noticed but perhaps not been able to articulate: why do some banks trade at a P/B of 2.5x while others, seemingly similar in size, trade at 0.9x? The formula says the answer lies in the gap between ROE and cost of equity. A bank earning an ROE well above its cost of equity is creating value with every additional rupee of equity capital it deploys — book value compounds and the market is willing to pay more than one rupee of price for one rupee of book value, precisely because that rupee inside the bank is working harder than the market's required return. A bank earning an ROE close to, or below, its cost of equity is not creating economic value with new capital, even if it is "profitable" in an accounting sense — and the market rationally refuses to pay a premium over book value for it, sometimes even pricing it below book value (a P/B under 1.0x).
This single insight reframes what "cheap" and "expensive" mean in a way that pure cross-sectional comparison cannot. Two banks might both trade at a P/B of 1.5x — identical on the surface — yet one might be fully justified by its ROE and growth profile while the other is significantly overpriced relative to its fundamentals, simply because its ROE is lower or its risk (and therefore its cost of equity) is higher. A peer comparison alone would tell you these two banks are "priced the same." A justified multiple calculation tells you they are not equally attractive at all.
The same logic extends, with appropriate adaptation, to other multiples covered in Chapters 39-41:
Justified P/E, under the same constant-growth framework, can be expressed as the dividend payout ratio divided by (Ke − g). This is particularly relevant for hydropower companies, many of which have policies of paying out a large share of earnings as dividends once they reach a stable operating phase, since they have limited need to retain capital for new growth beyond their licensed capacity.
Justified EV/EBITDA is harder to express in a single clean formula because it depends on capital structure and reinvestment assumptions, but the underlying principle is identical: a company's fair EV/EBITDA should rise with its EBITDA margin, its growth in EBITDA, and the durability of its cash flow (a long-dated, government-counterparty PPA supporting cash flow for another twenty-five years justifies a different multiple than a PPA expiring in three years), and should fall as its risk and cost of capital rise.
Dividend yield can be checked against a "required yield" benchmark derived from the cost of equity minus expected capital growth — if a stock's dividend yield sits well below what its risk profile would suggest investors should demand, absent strong growth to compensate, that gap is itself a signal worth investigating, echoing the "high-yield trap versus growth" distinctions you studied when dividend yield was first introduced in Chapter 41.
KEY CONCEPT
A justified multiple is not an alternative to relative valuation — it is a discipline applied on top of it. You still look at peer companies and historical averages, as in Lessons 47.2 and 47.3, but you also independently calculate what multiple the company's own ROE, growth, and cost of equity would justify. When the observed peer-average multiple and the fundamentals-derived justified multiple broadly agree, you can trade with more confidence that the market's current pricing reflects genuine economic reality. When they diverge sharply, the divergence itself is the finding — it tells you either that the market has mispriced the stock (an opportunity, or a warning) or that your own estimate of ROE, growth, or cost of equity needs to be revisited.
It is worth being honest about the limits of this tool as well. The constant-growth assumption behind the justified P/B and justified P/E formulas is a simplification; it assumes ROE, growth, and cost of equity are all stable over a long horizon, which is rarely exactly true, especially for a hydropower company whose earnings profile changes materially as it moves from ramp-up to full capacity utilisation, or for a bank whose ROE can be temporarily depressed by a large one-off loan loss provision. The formula is a lens for organising your thinking and locating the drivers of a valuation gap, not a precise, mechanically "correct" answer to plug into a spreadsheet and trust blindly. Treat the justified multiple the way a doctor treats a diagnostic test: it points you toward the right question, it does not replace the full examination.
Lesson 47.6 — Common Pitfalls and a Disciplined Comps Checklist
With the mechanics of peer selection, sector-specific danger, illiquidity distortion, and justified multiples now covered, this final lesson consolidates the recurring mistakes that undermine relative valuation in practice on NEPSE, and closes with a disciplined process you can apply every time.
Pitfall one: comparing a growth story to a mature one without adjustment. A hydropower company that just commissioned a new run-of-river plant and is still ramping toward full capacity, or a bank rapidly expanding its branch network and loan book in underserved provinces, will show rising earnings for reasons that have nothing to do with the market reassessing its multiple. Comparing its current P/E directly to a mature, slow-growing peer's P/E without adjusting for this growth difference — or better, without normalising both companies' earnings to a comparable point in their respective life cycles — systematically misreads growth companies as "expensive" and mature companies as "cheap," when in fact a higher multiple may be entirely justified by a higher growth rate, exactly as the justified P/E and justified P/B formulas from Lesson 47.5 would predict.
Pitfall two: ignoring differing capital structures. As Lesson 47.2 noted, hydropower projects are frequently financed with a high proportion of debt. Because interest expense sits below the operating line, two companies with identical plant economics and identical EBITDA can show very different net income, and therefore very different P/E ratios, purely because one carries more debt than the other. A highly levered company's P/E can look deceptively low precisely because a larger share of its enterprise value is financed by debt rather than equity, concentrating both the upside and the risk onto a smaller equity base. This is exactly why EV/EBITDA — which is calculated using enterprise value (market value of equity plus net debt) rather than just equity market value, and EBITDA rather than net income — is often the more reliable multiple for comparing capital-intensive, debt-financed businesses like hydropower companies, since it neutralises the distortion that different leverage levels introduce into P/E. When comparing companies with meaningfully different debt-to-equity ratios, always cross-check P/E-based conclusions against EV/EBITDA before drawing a conclusion about relative cheapness.
WARNING
A low P/E on a heavily indebted company is not automatically a bargain. High financial leverage inflates the sensitivity of equity earnings to changes in revenue, interest rates, and debt service — a company that looks "cheap" on P/E because debt has boosted its return on the smaller equity base is also carrying more risk, and that risk belongs in your cost of equity assumption (raising Ke, which lowers the justified multiple) even if it does not show up directly in the P/E calculation itself.
Pitfall three: relying on stale or manipulated earnings. Nepali companies, like companies everywhere, do not always report earnings that cleanly reflect sustainable, repeatable operating performance. A bank that books a large one-off gain from selling investment securities, or that under-provisions for loan losses in a given quarter to flatter reported profit, will show a temporarily inflated EPS and therefore an artificially low, falsely attractive P/E. A hydropower company reporting earnings for a year with unusually strong river flow (a wet year) will show a P/E that looks cheap relative to its own more typical, average-hydrology years — precisely the seasonal and annual variability discussed in Lesson 47.3 — and an investor who anchors on that one favourable year's earnings without normalising for average hydrological conditions across a full cycle will overestimate the sustainable earning power the multiple is being paid for.
CAUTION
Before trusting any P/E or EV/EBITDA multiple, ask what "E" or "EBITDA" actually represents: the latest reported quarter, the trailing twelve months, or a normalised, cycle-adjusted figure? For hydropower companies specifically, check whether the underlying year had average, above-average, or below-average water flow, since a single unusually wet or dry year can swing reported earnings — and therefore the multiple — by a wide margin without any real change in the asset's long-run earning power. For banks, check whether reported profit includes one-off items (asset sales, tax adjustments, unusually low provisioning) that are unlikely to repeat.
Pitfall four: treating a peer-group average as a target price. Even after carefully building a clean, well-matched peer group, it is tempting to conclude that if the peer average P/B is 1.6x and your target company trades at 1.2x, the "fair" price is simply the peer average — full stop. This skips the justified multiple step from Lesson 47.5 entirely. The correct sequence is: identify the peer group, observe the peer average, then ask whether your target company's own ROE, growth, and risk genuinely support trading at that peer average, above it, or below it. A company with a below-peer-average ROE deserves a below-peer-average multiple; concluding it is "undervalued" simply because it trades below the peer average, without checking whether its fundamentals justify a discount in the first place, is a common and costly error.
Pitfall five: forgetting the effect of thin liquidity discussed in Lesson 47.4 when constructing a peer average. Including one or two illiquid, small-float names in a peer average can pull the whole benchmark toward a distorted level, especially in a small peer set (recall from Lesson 47.2 that NEPSE peer groups are often narrow to begin with, sometimes only five to ten names). A single thinly traded hydropower stock with an inflated multiple, sitting inside an eight-company peer group, can move the simple average meaningfully; a float-weighted or liquidity-screened average is more robust.
To bring these pieces together, consider two illustrative peer comparison tables, of the kind an analyst would build before making any relative valuation judgment on NEPSE. These figures are illustrative and rounded for teaching purposes, constructed to reflect realistic relationships between scale, profitability, and multiples rather than to serve as live market data — always pull current figures from NEPSE, Sharesansar, or company disclosures before acting on any real comparison.
Table 1: Illustrative Peer Comparison — Nepali Commercial Banks
Bank
P/E (x)
P/B (x)
ROE (%)
Dividend Yield (%)
Approx. Free Float
Nabil Bank
14.5
2.1
15.2
3.8
High
Global IME Bank
11.2
1.4
13.1
4.5
High
Nepal Investment Mega Bank
12.0
1.3
11.8
4.1
High
NIC Asia Bank
13.1
1.6
13.9
3.5
High
Everest Bank
13.8
1.9
14.4
3.2
High
Himalayan Bank
12.4
1.2
10.5
4.8
High
Peer Average
12.8
1.6
13.2
4.0
—
Reading this table the way this chapter has taught you to: Nabil Bank's P/B of 2.1x sits well above the peer average of 1.6x, but its ROE of 15.2% also sits above the peer average of 13.2% — some or all of the premium multiple may be justified by superior profitability, which is exactly the kind of question the justified P/B formula from Lesson 47.5 is built to answer, rather than simply flagging Nabil as "the expensive one." Himalayan Bank, by contrast, combines a below-average ROE (10.5%) with a below-average P/B (1.2x) — here, the discount may be entirely fundamentals-driven rather than a hidden bargain, a distinction the peer average alone cannot make for you.
Table 2: Illustrative Peer Comparison — Nepali Hydropower Companies
Company
Stage
P/E (x)
P/B (x)
EV/EBITDA (x)
Dividend Yield (%)
Chilime Hydropower
Mature, operating
16.0
2.0
9.5
4.2
Butwal Power Company
Mature, operating
17.5
1.8
9.0
3.9
Upper Tamakoshi Hydropower
Recently commissioned, ramping
38.0
2.6
11.0
1.5
Sanima Mai Hydropower
Mature, operating (small)
22.0
1.9
10.2
3.0
Newly listed small-float project (illustrative)
Pre/early operational
85.0+
3.5
14.0
0.5
Notice how this table makes visible several of the pitfalls this chapter has warned against. Upper Tamakoshi's P/E of 38x looks dramatically more expensive than Chilime's 16x on a raw comparison — but Upper Tamakoshi is still ramping up toward full-capacity earnings, exactly the growth-versus-mature distortion flagged in Lesson 47.6's first pitfall, and its EV/EBITDA of 11.0x is far closer to the mature peers' 9.0-9.5x than its P/E suggests, because EBITDA is less distorted by ramp-up depreciation and interest patterns than net income is. The illustrative newly listed, small-float project shows the most extreme multiples in the table on every measure — precisely the thin-liquidity and small-float distortion described in Lesson 47.4 — and a disciplined analyst would either exclude it from a peer average entirely or flag it as unreliable rather than treating its 85x P/E as a meaningful benchmark for anything.
PRACTICAL TOOL
A six-step relative valuation checklist for NEPSE: (1) Define the target company's exact sub-sector, scale, and life-cycle stage. (2) Build a peer group using the five-question filter from Lesson 47.2, excluding poor matches rather than stretching the definition of "comparable." (3) Screen out or down-weight peers with thin free float and low trading volume, per Lesson 47.4. (4) Normalise earnings across the peer group for one-off items and, for hydropower, hydrological year quality, per Lesson 47.6. (5) Calculate the peer-average and the target's own five-year historical average multiple, per Lesson 47.1. (6) Calculate the target's justified multiple from ROE (or payout), growth, and cost of equity, per Lesson 47.5, and reconcile any gap between the peer average, the historical average, and the justified multiple before forming a final view.
Chapter recap
This chapter built directly on the ratio foundations laid in Chapters 39 through 41 and turned them into a genuine valuation method: relative valuation, or "comps," which values a company by comparing its trading multiples — P/E, P/B, EV/EBITDA, and dividend yield — against a peer group of similar companies and against its own historical average, rather than building a valuation from first principles. Like a real estate buyer checking recent sale prices of similar houses in the same neighbourhood rather than an unrelated property across town, the method's power comes entirely from the quality of the comparison, not from the arithmetic, which is simple. Two distinct reference points were introduced and kept separate throughout: cross-sectional comparison against peers at a single point in time, and time-series comparison against the company's own history — each answers a different question, and conflating them leads to muddled conclusions.
Building a defensible peer group is harder on NEPSE than the textbook version of this exercise assumes, because the exchange is small and heavily concentrated in banking and hydropower, leaving thin, sometimes single-company "sectors" elsewhere. The chapter set out a disciplined five-question filter — sector and sub-sector match, regulatory tier, life-cycle stage, comparable scale, and comparable liquidity — and insisted that companies failing this filter be excluded rather than stretched to fill out a table. Within both of NEPSE's dominant sectors, further sub-division matters: commercial banks separated from development banks and finance companies by regulatory tier, and hydropower companies separated by operating status, since a pre-operational or ramping project has fundamentally different earnings dynamics than a mature, fully-utilised plant.
The chapter then confronted directly why cross-sector comparison is dangerous: a bank's P/E and a hydropower company's P/E are calculated identically but mean different things, because the two businesses have structurally different margins, growth trajectories, and risk profiles — credit risk and interest-rate-driven earnings for a bank versus hydrology-driven, PPA-fixed, debt-service-heavy earnings for a hydropower company. A "cheap" multiple in one sector can be economically more expensive than an "expensive" multiple in another once these structural differences are accounted for, and the same caution, in milder form, applies even within superficially similar categories like life versus non-life insurance, or commercial banks versus microfinance institutions.
A distinctly Nepal-specific distortion was examined next: illiquidity and thin free float. Statutory promoter lock-in periods, small public floats (especially for newly listed hydropower companies), low daily trading volumes, and NEPSE's circuit breaker bands mean that observed prices — and therefore observed multiples — for many listed companies are set by a small slice of total ownership and can swing on modest order flow, rather than reflecting a broad, settled market consensus. An analyst must check free float and trading volume before trusting any multiple, and should down-weight or exclude illiquid names from peer averages rather than blending them in silently, since a single distorted name in a narrow NEPSE peer group can move the whole benchmark.
Because relative valuation only tells you a company's position relative to its peers or its own history — and says nothing about whether that whole reference group is fairly priced — the chapter introduced the justified multiple as a fundamentals-anchored check. The justified P/B formula, (ROE − g) ÷ (Ke − g), and its P/E analogue built on payout ratio, ground a fair multiple in a company's own sustainable profitability, growth, and cost of equity rather than in what the crowd happens to be paying today. Two banks trading at an identical observed P/B can be very differently attractive once their ROE and risk are compared against this justified benchmark, and a persistent gap between a peer-average multiple and a justified multiple is itself the most important finding a relative valuation exercise can produce — it tells the analyst exactly where to dig deeper, rather than leaving a vague, unresolved sense that something looks cheap or expensive.
Finally, the chapter consolidated five recurring pitfalls — comparing growth stories to mature ones without adjustment, ignoring differing capital structures (where EV/EBITDA is the more reliable cross-check against P/E for heavily levered, capital-intensive hydropower companies), relying on stale, one-off, or hydrologically unusual earnings, treating a raw peer average as an automatic fair-value target rather than checking it against justified fundamentals, and letting illiquid names distort a peer average — into a six-step checklist: define the target precisely, build the peer group with discipline, screen for liquidity, normalise earnings, calculate peer and historical averages, and reconcile all of that against a justified multiple before reaching a conclusion. Applied together, and combined with the intrinsic valuation methods covered later in this Part, this discipline turns relative valuation from a quick, appealing shortcut into a rigorous, genuinely institutional-grade tool for pricing companies on a market as concentrated, sector-skewed, and liquidity-constrained as NEPSE.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IX · Chapter 48
Dividend Discount Model (DDM)
First published 23 Aug 2026 · Last verified 29 Aug 2026
Imagine you own a small rental house in Baneshwor. You don't plan to sell it next year. What it's "worth" to you, in a very real sense, is the rent it throws off, year after year, for as long as you hold it — discounted back to today because a rupee promised five years from now is worth less than a rupee in your hand today. If the tenant pays reliably and the rent grows a little each year as you renovate and as market rents rise, the house is worth more. If the tenant is erratic, or the neighbourhood is declining, the house is worth less, even if the paint is fresh and the plot is large.
A share of common stock in a NEPSE-listed company is not so different. You do not own the factory, the bank branch, or the powerhouse. You own a claim on the cash the company chooses to pay out to you, its shareholder, over time — the dividend. The Dividend Discount Model, or DDM, values a share exactly the way you would value that rental house: as the present value of the cash it is expected to pay you, forever, discounted at a rate that reflects how risky that cash stream is.
This chapter builds the DDM from its simplest form to a more realistic multi-stage version, and — because this is a book about Nepal — spends real time on why this particular model, more than almost any other valuation tool in Part IX, fits NEPSE like a glove, and where it can also mislead an investor who applies it carelessly to the wrong kind of company at the wrong stage of its life.
Lesson 48.1 — The Rental House Logic: Gordon Growth Model Mechanics
Start with the simplest possible version of a dividend stream: a company that pays a dividend today, and that dividend is expected to grow at a constant rate, forever. This is the world of the Gordon Growth Model, named after the economist Myron Gordon, who formalised it in the 1950s and 1960s. It is the workhorse version of DDM, and nearly every more complex DDM you will ever build is just several Gordon Growth calculations stitched together.
The formula is:
Value per share = D1 / (r − g)
Where D1 is the dividend expected next year (not the one just paid — the one coming), r is the cost of equity (the return shareholders require for holding this stock, given its risk — covered in depth in Chapter 44), and g is the constant expected growth rate of the dividend, forever.
KEY CONCEPT
The Gordon Growth Model says a share's value is next year's expected dividend, divided by the gap between your required return and the dividend's growth rate. The smaller that gap, the more the share is worth — which is another way of saying that value is extremely sensitive to small changes in either r or g.
Go back to the rental house. D1 is next year's rent. r is the return you need to be compensated for tying your money up in this property rather than a government bond or a fixed deposit — it bakes in the risk that the tenant stops paying, the building needs repairs, or the neighbourhood declines. And g is how fast you expect the rent to grow, year after year, forever — perhaps 4-5% a year as the area develops and market rents drift upward.
If you expect Rs 100,000 in rent next year, need a 12% return to compensate you for the risk of being a landlord in that particular neighbourhood, and expect rent to grow 4% a year forever, the property should be worth:
Notice something important: the model requires r to be strictly greater than g. If growth ever equals or exceeds your required return, the formula breaks — it produces an infinite or negative value, which is a mathematical way of telling you that no company can grow its dividend faster than the market's required return, forever. A company can grow explosively for five years, or ten, but not forever — eventually competition, market saturation, or the sheer scale of the business drags growth back down toward something close to the growth rate of the overall economy. This is a crucial constraint you will lean on again in Lesson 48.5.
Now put a NEPSE bank into the same frame. Suppose Himalayan Ridge Bank Ltd. (a hypothetical commercial bank, used for illustration throughout this chapter) is expected to pay a cash dividend of Rs 15 per share next year. Investors require a 14% return on bank equities of this risk profile (built up, as Chapter 44 showed, from a risk-free rate plus an equity risk premium adjusted for the bank's beta and Nepal-specific risk factors). The bank's dividend is expected to grow at 6% a year indefinitely, roughly in line with nominal GDP growth and the banking sector's steady expansion of its loan book.
Value per share = 15 / (0.14 − 0.06) = 15 / 0.08 = Rs 187.50
If Himalayan Ridge Bank trades at Rs 150 on NEPSE, the Gordon Growth Model says it is undervalued relative to its dividend-paying capacity, holding the growth and discount rate assumptions fixed. If it trades at Rs 260, the model says the market is pricing in either faster growth, a lower required return, or both, than your assumptions capture — a signal to revisit your inputs, not necessarily proof the market is wrong.
PRACTICAL TOOL
To build D1, do not simply take last year's dividend per share. Start from expected next-twelve-months earnings per share, apply the payout ratio you expect the company to sustain (informed by its capital needs, regulatory capacity, and stated dividend policy), and derive D1 from that. This anchors your dividend forecast to the business, not to an extrapolated trend line.
The two inputs that make or break this model — r and g — are exactly the two places where an analyst's judgment, not a formula, does the real work. Chapter 44 covered how to build r (the cost of equity) carefully. The rest of this chapter is substantially about g: how to estimate it responsibly for a Nepali company, and why Nepal's own dividend culture makes this both easier and, in specific ways, trickier than in markets where dividends are smaller and less central to how companies return value to shareholders.
Lesson 48.2 — Why DDM Is Nepal's Natural Valuation Tool
In many developed equity markets, dividends are almost a rounding error in total shareholder return. American technology companies often pay no dividend at all for decades, returning cash instead through share buybacks, and an analyst who tried to value Amazon or a similar growth company using a simple DDM would get a value of approximately zero — an obviously wrong answer, because it ignores nearly all the ways in which that company actually creates value for its owners.
NEPSE is a different animal entirely. Nepali listed companies — above all, commercial banks, development banks, finance companies, microfinance institutions, insurance companies, and hydropower companies — have a deeply entrenched culture of paying out a large share of annual profit as dividends, in cash, in bonus shares, or a mix of both. Several structural features of the Nepali market explain why.
First, share buybacks are rare and, for most sectors, effectively unavailable or heavily restricted as a mechanism for returning cash to shareholders. A Nepali company that generates a profit and wants to reward shareholders has essentially one channel available at scale: declare a dividend. Second, retained earnings for regulated financial institutions are subject to capital and reserve requirements (discussed in Lesson 48.4), which push a portion of profit into reserves rather than reinvestment in new ventures, but the remainder is customarily distributed rather than hoarded on the balance sheet, partly because minority shareholders — a large share of the register for most NEPSE banks — expect and demand it. Third, listed hydropower companies, once a plant reaches commercial operation and starts generating predictable cash flow under a long-term Power Purchase Agreement (PPA) with the Nepal Electricity Authority, tend to have limited further capital expenditure needs for that specific plant, so free cash flow converts into dividends rather than fresh reinvestment, at least until the company undertakes a new project.
KEY CONCEPT
A dividend payout ratio is the share of a company's net profit that it distributes to shareholders as dividends in a given year, as opposed to the share it retains (retained earnings) to reinvest in the business or hold in reserves. NEPSE banks and mature hydropower companies frequently run payout ratios in a range that would be considered unusually generous in many developed markets.
Because dividends are the primary channel through which Nepali listed companies return value to shareholders, and because that dividend stream is what most retail and institutional NEPSE investors actually watch, budget for, and reinvest, the Dividend Discount Model is not an academic curiosity for this market — it is close to the most natural lens available. Where a Silicon Valley analyst might reasonably reach first for a discounted cash flow model built around free cash flow to equity or to the firm (the subject of the next chapter), a NEPSE analyst covering a bank or a seasoned hydropower stock can often go straight to a well-built DDM and get a defensible answer, precisely because the dividend is not a fragment of the return story — it is most of the story.
CASE IN POINT
Consider two hypothetical companies on NEPSE: Sagarmatha Commercial Bank, which has paid a combination of cash and bonus dividends in nine of the last ten years, and Rolling Hills Trading Pvt., an unlisted-style growth business that has reinvested essentially all profit into expansion with no dividend track record. DDM produces a sensible, well-anchored value for the former. Applied literally to the latter, it produces nothing useful — there is no dividend stream to discount. This is precisely the situation with early-stage or high-growth firms discussed further in Lesson 48.6.
This is not to say DDM is the only tool an analyst needs for NEPSE — Chapter 49 and Chapter 50 will build discounted cash flow and relative valuation approaches that remain essential, especially for companies without a long dividend history, or where you suspect the dividend policy itself is not a reliable signal of underlying value creation. But for the core of the NEPSE investable universe — the banks, the insurers, the seasoned hydropower names — DDM deserves to be the first model you reach for, not the last.
Lesson 48.3 — Cash Dividends, Bonus Shares, and the Dilution Trap
Here is where Nepal's dividend culture becomes genuinely tricky, and where an analyst who is careless will produce a badly wrong valuation even while following the DDM formula correctly.
Nepali companies routinely declare dividends in two forms simultaneously: a cash dividend (an actual cash payment per share, subject to a 5% dividend tax withheld at source for individual investors, as covered in the tax chapters) and a bonus share dividend (additional shares issued to existing shareholders, in some fixed proportion to their current holding, at no cost to them). A company might, for instance, declare a "20% dividend" consisting of 15% bonus shares and 5% cash — meaning a shareholder holding 100 shares receives 15 new shares plus Rs 500 in cash (5% of a Rs 100 par value, before tax).
KEY CONCEPT
A bonus share (also called a stock dividend) is a free additional share issued to existing shareholders in proportion to their current holding, funded by capitalising the company's reserves rather than by paying out cash. It increases the number of shares outstanding but does not, by itself, change the total value of the company or transfer any cash to shareholders.
This is the single most important mechanical point in this lesson, and it is worth stating as plainly as possible: a bonus share does not create wealth. It is not a real cash return to the shareholder in the way a cash dividend is. When a company issues bonus shares, it moves an amount from its reserves (retained earnings) to its paid-up capital account on the balance sheet, and it issues new share certificates to match. The company's total equity value is unchanged. But now that same total value is divided among a larger number of shares — so the per-share price mechanically adjusts downward on the ex-bonus date, roughly in proportion to the bonus percentage. A shareholder holding shares worth Rs 100,000 before a 15% bonus issue still holds shares worth approximately Rs 100,000 immediately after (100,000 shares become 115, and the per-share price falls by close to the same ratio) — they simply now hold more certificates, each worth less.
WARNING
Never plug the announced "dividend percentage" straight into a DDM's D1 term without separating the cash component from the bonus component. A 20% total dividend that is 15% bonus and 5% cash is not a Rs 20-per-Rs-100-par cash dividend — treating it as one wildly overstates the true cash return, and wildly overstates the resulting valuation.
So how should bonus shares be treated in a DDM framework? There are two defensible approaches, and a good NEPSE analyst should be comfortable with both.
The first, and simpler, approach is to value only the cash dividend stream in the DDM, and to treat the bonus share issuance as a non-event for valuation purposes — a cosmetic increase in share count that is not part of "the dividend" in the economic sense DDM is trying to capture. Under this approach, D1 in your Gordon Growth calculation is strictly the expected cash dividend per share next year, and your growth rate g is the expected growth of that cash dividend per share, on an already-adjusted, post-bonus share count basis. This is the cleaner, more rigorous approach, because it never confuses a reserve-to-capital bookkeeping entry with a genuine cash return, and it is the one this book recommends as the default.
The second approach — used by some practitioners as a rough shorthand — treats bonus shares as a proxy for the growth rate itself: the logic being that a company retains earnings (rather than paying them out in cash) specifically to reinvest in growing the business, and bonus shares are one visible marker of that retention. Under this reading, a company with a large bonus component alongside a smaller cash dividend is signalling that a larger share of its profit is being retained to fund future growth, which should, if that reinvestment is productive, translate into a higher future g for the cash dividend stream. This is not wrong as an intuition, but it is dangerous to apply mechanically, because a bonus share connected to genuinely value-accretive reinvestment (funding a bank's loan book growth without diluting existing shareholders through a rights issue, for example) is very different from a bonus share issued simply to make an existing shareholder base feel better about a "big dividend number," with no corresponding improvement in the underlying earnings power per share.
PRACTICAL TOOL
When a company you are analysing regularly issues large bonus dividends, always convert everything to earnings per share (EPS) and dividends per share (DPS) on a bonus-adjusted, restated basis before you build a multi-year dividend history — using the same technique you would use to adjust historical prices for a stock split. Comparing an unadjusted DPS from five years ago (before three intervening bonus issues) to today's DPS will make the growth rate look artificially low, understating what shareholders actually received in cash terms per original share.
This connects directly to the cost-basis and WACC material from the tax chapters (Chapter 46 and Chapter 47). Recall that when you compute a shareholder's effective return, or a company's effective cost of equity capital, bonus shares change the denominator (number of shares, and therefore the shareholder's cost basis per share for capital gains tax purposes) without changing the numerator (total value or total cash invested). A shareholder who receives bonus shares does not pay tax on receipt (bonus shares are not treated as taxable income at issuance in Nepal's current framework, unlike a cash dividend, which is taxed immediately at the 5% withholding rate) — but their original cost basis is spread across more shares, which matters enormously when they eventually sell and calculate capital gains. In a very real sense, a bonus share is a *timing* device: it defers the taxable event and changes its character (from dividend income to capital gain, taxed differently) rather than creating new value. A DDM analyst who forgets this and treats a bonus-heavy "dividend yield" as equivalent to a cash-heavy one from another company is comparing two things that are not alike — one is real current income, taxed now; the other is a deferred, differently-taxed claim on a company's reserves.
Lesson 48.4 — NRB's Regulatory Leash on Bank Dividends
If DDM is unusually well suited to NEPSE banks because of their high payout culture, it is equally important to understand that Nepal Rastra Bank (NRB), the central bank and banking regulator, does not let banks and financial institutions distribute dividends however they please. NRB's dividend distribution framework directly constrains a bank's dividend *capacity*, which means it constrains what a sensible analyst should ever project as achievable D1 and g for a bank stock, no matter how strong the bank's raw profit looks on paper.
The core logic of NRB's approach is that a bank's capital is not simply its owners' money to distribute at will — it is also the cushion that protects depositors and the financial system from losses. NRB accordingly links a bank's maximum permissible dividend distribution to its capital adequacy position (how much regulatory capital it holds relative to its risk-weighted assets, discussed in earlier chapters on the banking sector) and to the quality of its loan book (typically proxied by its non-performing loan, or NPL, ratio). A bank sitting exactly at or barely above its minimum required capital adequacy ratio, or carrying a rising NPL ratio, will be permitted to distribute a smaller share of its profit — sometimes none at all in cash — because the regulator wants that capital retained inside the institution as a buffer, not paid out to shareholders.
REGULATORY DETAIL
NRB's dividend distribution directives require banks and financial institutions to hold capital adequacy comfortably above the regulatory minimum, and to keep non-performing loans within acceptable bounds, before they are permitted to distribute dividends at all, and the *maximum* distributable dividend is scaled to how far above those minimums the institution sits — a bank near its regulatory floor faces sharply reduced dividend capacity even in a year of strong headline profit. NRB has periodically revised these thresholds (including guidance issued through 2025 tightening reporting and approval procedures ahead of dividend declarations), so an analyst must check the currently applicable directive rather than assume last year's rule still holds.
This has a direct, mechanical consequence for a DDM built on a Nepali bank. A naive analyst forecasts D1 as "expected EPS times last year's payout ratio" and calls it done. A careful analyst instead asks: given this bank's current and projected capital adequacy ratio, and its NPL trajectory, what dividend capacity will NRB actually permit next year, and for the several years after that? A bank rapidly growing its loan book (which consumes capital, since more risk-weighted assets require more capital to support them under the same capital adequacy ratio) may be earning healthy profit yet be constrained by the regulator to plough most of that profit back into capital rather than pay it out — meaning its true sustainable D1 is lower than a simple extrapolation of past payout ratios would suggest, especially in a period of aggressive branch or loan-book expansion. Conversely, a mature bank with slower loan growth and ample capital headroom may be able to sustain a genuinely high payout ratio for an extended period, exactly the kind of company where DDM will earn its keep.
CASE IN POINT
Suppose Himalayan Ridge Bank grew its loan book 25% last year (strong headline growth) but this pushed its capital adequacy ratio down to just above NRB's regulatory minimum. Under NRB's framework, its permitted dividend distribution capacity for the coming year would be sharply curtailed relative to its raw net profit, regardless of shareholder appetite for a larger payout. An analyst modelling D1 purely off historical payout ratios, ignoring this capital constraint, would overstate next year's dividend — and therefore overvalue the stock.
This regulatory dimension is one reason DDM for Nepali banks benefits from being paired with a capital-adequacy forecast: project the bank's risk-weighted asset growth, its expected retained-earnings contribution to capital, and back into a realistic maximum payout ratio consistent with staying safely above NRB's minimums, rather than simply trending the historical payout percentage forward. It is also a reminder that "regulated industry" cuts both ways in valuation — the same regulatory apparatus that gives Nepali banks a relatively stable, licensed, oligopolistic operating environment (a source of durable competitive advantage discussed in earlier chapters) is the same apparatus that can, in a given year, cap how much of that durable earnings power actually reaches shareholders as cash.
CAUTION
Do not assume NRB's dividend rules apply identically to hydropower companies, insurers, or non-bank listed companies. The capital-adequacy-linked dividend cap described in this lesson is specific to NRB-regulated banks and financial institutions. Hydropower companies face a different set of constraints — chiefly loan covenants from their project financing lenders, which often restrict dividend payments until certain debt-service coverage thresholds are met — while insurers answer to the Nepal Insurance Authority's own solvency-linked rules. Always identify which regulator, and which specific constraint, applies to the company you are valuing.
Lesson 48.5 — When One Growth Rate Isn't Enough: The Multi-Stage DDM
The Gordon Growth Model in Lesson 48.1 assumed a single, constant growth rate forever. That assumption is fine for a mature, stable business — a well-established bank growing roughly in line with the economy, for instance — but it breaks down badly for a company going through a distinct phase of unusually fast (or unusually slow, or negative) growth that will not persist indefinitely.
The solution is the multi-stage DDM: instead of one growth rate applied forever, you model an explicit early period with one (or several) elevated or depressed growth rate, and then assume the company settles into a stable, sustainable long-run growth rate from some terminal year onward — at which point you apply the Gordon Growth formula to that terminal, stable dividend stream, and discount that terminal value back to today alongside the explicit near-term dividends.
The two-stage version works like this:
Step 1: Forecast dividends explicitly, year by year, for the high (or transitional) growth period — say, five years. Step 2: At the end of that explicit period, calculate a terminal value using the Gordon Growth Model, applied to the first "stable" year's dividend and the long-run growth rate expected from that point forward. Step 3: Discount each of the explicit-period dividends, plus the terminal value, back to the present at the cost of equity, and sum them.
KEY CONCEPT
A multi-stage DDM splits a company's future into a period of transitional (often higher) growth, explicitly forecast year by year, followed by a terminal period of stable, sustainable growth valued with the simple Gordon Growth formula — because no company can compound dividends faster than the market's required return forever, every DDM eventually needs a stable, defensible long-run growth assumption at its core.
Let's build a worked example for a hypothetical hydropower company, Trishuli Ridge Hydropower Ltd., that has recently begun commercial operations and is still in the early years of its PPA tariff structure (more on this in Lesson 48.6). Assume the company is expected to grow its dividend per share at 18% annually for the first three years as its tariff escalates and it works through initial capital structure deleveraging, slowing to 10% in years four and five as growth moderates, before settling into a stable 6% long-run growth rate from year six onward, roughly matching the sector's long-run nominal growth. The cost of equity for a hydropower stock of this risk profile is estimated at 13%. Current dividend per share (D0) is Rs 8.00.
Year
Growth Rate
Dividend per Share (Rs)
Discount Factor @13%
Present Value (Rs)
1
18%
9.44
0.885
8.35
2
18%
11.14
0.783
8.72
3
18%
13.15
0.693
9.11
4
10%
14.46
0.613
8.87
5
10%
15.91
0.543
8.64
Terminal (Year 6 onward)
6%
16.86 (Year 6 D1)
0.543 (Year 5 factor, applied to TV)
TV = 16.86/(0.13-0.06) = 240.86; PV = 130.79
Summing the present values of years 1 through 5 (8.35 + 8.72 + 9.11 + 8.87 + 8.64 = 43.69) and adding the present value of the terminal value (130.79) gives an estimated intrinsic value per share of approximately Rs 174.48.
PRACTICAL TOOL
Notice how much of the total value (roughly 75% in this example) comes from the terminal value, not the explicit forecast period. This is completely normal in a multi-stage DDM, but it means the single most important number in the entire model is the terminal growth rate — get that wrong, and the error swamps everything else you did carefully in the explicit years. Always sanity-check your terminal g against the long-run growth rate of the overall Nepali economy (nominal GDP growth) — a terminal g persistently above nominal GDP growth for a mature company is a red flag, since it implies the company eventually becomes larger than the entire economy.
This worked example already hints at the danger explored fully in the next lesson: notice that the entire calculation depended on correctly identifying *when* the 18% growth phase ends and the stable 6% phase begins. Get that transition wrong — assume the 18% growth persists for eight years instead of three — and the valuation changes dramatically, because you are compounding a high growth rate over a much longer explicit period, and you are pushing back a large terminal value to a later date without properly capturing what actually stops the growth from being that fast in the interim.
WARNING
A common modelling error is to make the transition between growth stages too abrupt — jumping straight from 18% to 6% with nothing in between. Real businesses rarely decelerate that sharply. Where the underlying driver of growth (like a PPA tariff escalation schedule, addressed next) has its own multi-year phase-out, build your growth stages to mirror that actual driver's timeline, not a generic three-stage template applied without reference to the business's real mechanics.
Lesson 48.6 — The Tax-Holiday and Tariff-Escalation Trap
This final lesson addresses the single most dangerous way DDM goes wrong on NEPSE: applying a single, extrapolated growth rate to a company that is currently in a temporary, structurally elevated phase of its life, and mistaking that temporary phase for the company's normal, sustainable state.
Chapter 43 examined how Nepali hydropower companies benefit from income tax holidays and concessional tax rates in their early years of commercial operation (a defined number of years at 0% tax, followed by a period at a reduced rate, before reverting to the standard corporate tax rate), and how many hydropower PPAs with the Nepal Electricity Authority are structured with an escalating tariff schedule in their early years — a dry-season energy rate that rises by a fixed annual percentage for a set number of years before flattening out at a fixed rate for the remainder of the PPA term. Both features are deliberate policy design, intended to help project economics work during the early, most financially fragile years of a hydropower asset's life, when debt service is heaviest and the project is least seasoned. Both features are also, from a valuation standpoint, temporary — and temporary is exactly the word an analyst applying DDM needs to take seriously.
WARNING
The single most dangerous DDM error on NEPSE is taking a hydropower (or any tax-holiday) company's current, elevated dividend growth rate — driven by an escalating PPA tariff and a temporary tax holiday, both of which have a known, finite end date — and plugging that growth rate into a single-stage Gordon Growth Model as if it would continue forever. It will not. When the tariff escalation ends and flattens, and when the tax holiday expires and the effective tax rate rises, both dividend growth and dividend level can drop sharply, sometimes in the very same year.
Here is the mechanism in full. During a hydropower company's early operating years, three tailwinds often combine to produce dividend per share growth that looks spectacular on a trailing basis: the PPA's built-in tariff escalation is raising the average realised tariff per unit of electricity sold each year; the company is paying little or no income tax under its holiday provisions, so a larger share of revenue converts to net profit; and the plant, having only recently reached full commercial operation, may still be ramping up toward its full expected generation capacity as any initial teething issues are resolved. Stack these three effects together and a hydropower company can post 15%, 20%, even 25%+ annual growth in dividend per share for several consecutive years — not because the underlying business is compounding at that rate sustainably, but because three separate, finite tailwinds are all blowing in the same direction at once.
Then, in a specific year (or a short window of years), all three tailwinds can end. The PPA tariff escalation schedule flattens at its ceiling rate. The tax holiday period expires and the statutory or concessional rate steps up, immediately compressing the net margin on the same revenue. And full-capacity generation, if not already reached, plateaus. The result is that dividend per share growth does not gently decelerate toward a stable long-run rate — it can fall off a cliff, or even see the dividend per share decline in absolute terms in the transition year, even while the underlying physical asset (the powerhouse, the water rights, the PPA itself) is completely unchanged and arguably just as valuable a long-run cash-generating asset as it was the year before.
CASE IN POINT
Consider Trishuli Ridge Hydropower from Lesson 48.5. Suppose an analyst, looking only at the company's trailing three-year dividend growth of 18%, extrapolated that rate forward indefinitely into a single-stage Gordon Growth Model: 9.44 / (0.13 − 0.18) would produce a negative, meaningless value, because the assumed growth rate exceeds the discount rate — the model breaking is itself the warning sign. Even a less extreme error — extrapolating 18% for, say, ten years instead of three, because the analyst did not check the actual remaining tax holiday period or the remaining tariff escalation years in the PPA schedule — would produce a valuation dramatically higher than what the correctly-staged multi-stage model in Lesson 48.5 produced (Rs 174.48). The gap between the naive and the properly staged answer is not a rounding error; it can easily be a valuation 50% or more too high.
The discipline this demands of a NEPSE analyst is straightforward to state, if not always easy to execute: before building any DDM on a hydropower company (or any other company benefiting from a temporary tax concession or a contractually scheduled, time-limited cash flow enhancement), go directly to the company's PPA and its tax status. Find the specific number of years remaining in any tariff escalation schedule. Find the specific number of years remaining in the tax holiday or concessional tax period, and the tax rate the company will revert to afterward. Build your explicit-forecast stage in the multi-stage DDM to match those actual, contractually or statutorily defined timelines — not a generic five-year template — and only transition to a stable terminal growth rate once both the tariff schedule has flattened and the full statutory tax rate has taken effect. If the tax holiday ends in year four but the tariff escalation continues through year seven, your model needs at least two distinct transitional stages before it reaches a genuinely stable terminal phase, not one.
CAUTION
This trap is not unique to hydropower. Any company benefiting from a special economic zone tax concession, an export incentive, a temporary subsidy, or any other time-bound government or contractual benefit is subject to the identical risk: a DDM analyst who extrapolates a growth rate produced by a temporary tailwind, without first checking when that tailwind is scheduled to end, will systematically overvalue the stock. The fix is always the same — identify the actual expiration date of the benefit, and build your multi-stage model's transition points around that date, not around a rule-of-thumb forecasting horizon.
The payoff for doing this work carefully is that DDM, applied with this level of diligence, becomes one of the most reliable tools available for valuing precisely the kind of company that dominates NEPSE's investable universe — and one of the most dangerous tools available when applied carelessly to that same company. The formula never changes. What changes, entirely, is whether the analyst using it has actually looked underneath the dividend history to understand what is driving it, and for how much longer that driver will last.
Chapter recap
The Dividend Discount Model values a share the way you would value a rental property: as the present value of the cash income stream it is expected to pay you, discounted at a rate reflecting the risk of that stream, and grown at a rate reflecting how that income is expected to expand over time. The simplest version, the Gordon Growth Model, captures this in a single elegant formula — value equals next year's expected dividend divided by the gap between your required return and the dividend's expected long-run growth rate — and that formula's core insight, that no company can grow its dividend faster than the market's required return forever, underlies every more sophisticated version of DDM built in this chapter and used throughout the rest of Part IX.
DDM is unusually well suited to NEPSE precisely because of how Nepali listed companies actually behave. Share buybacks are rare, retained earnings for regulated financial institutions are constrained by capital rules, and mature hydropower companies with limited further capital needs convert free cash flow into dividends rather than new investment. The result is a market where a large share of commercial banks, insurers, and seasoned hydropower companies pay out a substantial portion of profit as dividends, in cash and in bonus shares, making the dividend stream close to the whole story of shareholder return — a very different situation from markets where dividends are a minor supplement to buybacks and reinvestment-driven price appreciation.
That same dividend culture, however, carries a structural trap that a careless analyst falls into constantly: confusing bonus shares with real cash return. A bonus share capitalises reserves into paid-up capital and issues new certificates to match, but it does not create new value, transfer any cash to shareholders, or change the company's total worth — it only divides the same pie into more slices. A properly built DDM should value the cash dividend stream specifically, on a bonus-adjusted per-share basis, and should never mistake an announced "dividend percentage" that blends cash and bonus components for a genuine cash yield. This distinction connects directly back to the cost-basis and tax treatment material covered earlier in the book: bonus shares defer and reclassify a shareholder's eventual tax event rather than creating new economic value today.
For NEPSE's banks specifically, Nepal Rastra Bank's capital-adequacy-linked and asset-quality-linked dividend distribution framework means a bank's dividend capacity is not simply a function of its reported profit — it is capped by how much regulatory capital headroom the bank has above its required minimums, and by the health of its loan book. A bank posting strong profit while rapidly growing its risk-weighted assets, or carrying rising non-performing loans, may be permitted to distribute far less than its profit alone would suggest, and a rigorous DDM should model dividend capacity from the bank's capital trajectory, not from a mechanically extrapolated historical payout ratio.
Where a company's growth is genuinely expected to shift over time — accelerating, decelerating, or passing through distinct phases — the single-stage Gordon Growth Model is the wrong tool, and a multi-stage DDM is required: explicit dividend forecasts for the transitional years, followed by a terminal value built on a stable, sustainable long-run growth rate. Because the terminal value typically represents the large majority of total value in such a model, the terminal growth assumption deserves more scrutiny than any other single input, and should always be checked against the plausible long-run growth rate of the Nepali economy as a whole.
Finally, and most importantly for this market, DDM becomes actively dangerous when applied to a company whose current dividend growth is being driven by a temporary, finite tailwind — above all, a hydropower company still working through an escalating PPA tariff schedule and an early-years tax holiday, as introduced in Chapter 43. Extrapolating that elevated growth rate forward, whether through a naive single-stage model or a multi-stage model with poorly chosen transition points, produces valuations that can be dramatically too high, because it treats a scheduled, time-bound enhancement to cash flow as if it were a permanent feature of the business. The discipline that protects an analyst from this trap is simple to state and essential to practice: before valuing any such company, find the actual contractual and statutory dates on which its tailwinds expire, and build the model's growth stages around those specific dates — not around a generic forecasting template. Get that right, and DDM remains what it has long been for this market: the most natural and most powerful valuation tool available for the companies that make up the heart of NEPSE.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part IX · Chapter 49
Book Value and Net Asset Value Approaches
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 49.1 — What Book Value Actually Measures
Imagine a house that was bought twenty years ago in Kalanki, Kathmandu, for NPR 40 lakh — 15 lakh for the land and 25 lakh for the construction. Today, that same plot of land alone might fetch NPR 3 crore because of how much the neighbourhood has grown, and the house, though older, is still standing and usable. If you asked "what is this house worth?" and someone answered "NPR 40 lakh, because that is what the owner paid for it," you would immediately object. The original cost tells you almost nothing about what the house is worth today. This is, in essence, the central tension of this entire chapter: book value is what a company paid for its assets, adjusted for depreciation and accumulated profits, not necessarily what those assets are worth today or what someone would pay to own the whole enterprise.
Book value, formally, is the value of a company as recorded on its balance sheet: total assets minus total liabilities, which is also called shareholders' equity or net worth. If Nabil Bank has total assets of NPR 500 arba and total liabilities (deposits, borrowings, and other obligations) of NPR 460 arba, its book value — its net worth — is NPR 40 arba. This is the amount that would theoretically be left over for shareholders if the company sold every asset at its recorded value and paid off every liability at its recorded value.
Book Value Per Share (BVPS) simply divides this net worth by the number of shares outstanding. If that same bank has net worth of NPR 40 arba and 32 crore shares outstanding, its book value per share is NPR 40,00,00,00,000 ÷ 32,00,00,000 = NPR 125 per share. This is a number every NEPSE investor encounters constantly — it appears on every broker's terminal, in every annual report, and in every quarterly financial disclosure that listed companies file with the Securities Board of Nepal (SEBON) and NEPSE.
The reason book value matters at all, despite the house analogy above suggesting its limitations, is that for certain kinds of businesses — particularly banks and financial institutions — book value is unusually close to true economic value, because their assets and liabilities are themselves mostly financial instruments (loans, deposits, investments) rather than physical property whose worth drifts far from its recorded cost. For other kinds of businesses — hydropower companies holding land bought decades ago, hotels sitting on prime real estate, or manufacturing companies with old plants — book value can be wildly disconnected from what the company would actually fetch in a sale or would cost to replicate today. Chapter 39 introduced the Price-to-Book (P/B) ratio in the context of relative valuation for banks; this chapter goes deeper into why book value works so well for some Nepali sectors and so poorly for others, and how a disciplined analyst adjusts for the difference.
KEY CONCEPT
Book value (net worth) = Total Assets − Total Liabilities. Book Value Per Share = Net Worth ÷ Number of Shares Outstanding. It represents accounting net worth, not necessarily market or liquidation value.
The P/B ratio itself is calculated as: Market Price Per Share ÷ Book Value Per Share. If our hypothetical bank trades at NPR 340 per share against a book value of NPR 125, its P/B ratio is 2.72x. This tells an investor that the market is willing to pay NPR 2.72 for every NPR 1 of recorded net worth — a premium that must be justified by the bank's ability to generate returns on that net worth (its Return on Equity, or ROE) above what a comparable, safer investment would offer. A P/B ratio below 1x — trading below book value — suggests the market believes the recorded net worth overstates true value, or that the company's earning power is so weak that investors are not willing to pay even the accounting value of the assets. Both readings are common on NEPSE, and both require the analyst to ask why, rather than to mechanically buy "cheap" P/B stocks or avoid "expensive" ones.
Lesson 49.2 — Why Book Value Matters Most for Banks and Financial Institutions
To understand why P/B is the single most important valuation metric for Nepali banks, development banks, finance companies, and insurers — far more important than it is for, say, a trading company or a hotel — you have to understand what these businesses actually are on a balance sheet level. A bank does not manufacture anything. Its "inventory" is money: it takes in deposits (a liability) and lends that money out as loans (an asset), earning the difference between what it pays depositors and what it charges borrowers — the net interest margin, covered in Chapter 39. Both sides of a bank's balance sheet are financial instruments denominated in rupees, not physical goods whose value depends on wear, location, or replacement cost.
This matters enormously for book value's reliability. When Global IME Bank or Nabil Bank reports a loan of NPR 10 lakh to a borrower, that loan is worth approximately NPR 10 lakh (adjusted for expected credit losses, which Nepal Rastra Bank requires banks to provision for under its directives) — not NPR 3 lakh or NPR 40 lakh depending on the real estate cycle. A rupee of deposit is worth a rupee. This is fundamentally different from a hydropower company's 40-year-old land parcel, whose recorded cost bears almost no relationship to its current worth. Because a bank's assets and liabilities are close to their real economic value already, its book value — its net worth — is a reasonably reliable floor estimate of what the bank is actually worth, and the P/B ratio becomes a meaningful, comparable yardstick across the sector.
There is a second, equally important reason. Banks, development banks, and finance companies in Nepal are regulated under capital adequacy rules set by Nepal Rastra Bank (NRB), which require them to hold a minimum ratio of core capital and total capital to risk-weighted assets. This capital is, in effect, book value — shareholders' equity is what stands behind depositors and absorbs losses before depositors are ever at risk. A bank's ability to grow its loan book, and therefore its future profits, is directly capped by how much capital (book value) it holds, because NRB will not allow a bank to lend beyond its capital-adequacy ceiling. This is why Nepali banks are so persistently active in issuing rights shares and bonus shares — both are, at their core, mechanisms for growing book value to support further lending growth. An investor evaluating a bank is therefore not just asking "is the stock cheap relative to its assets" but "is this capital base large enough, and is it being used productively enough (reflected in ROE) to keep growing the franchise."
REGULATORY DETAIL
Nepal Rastra Bank's capital adequacy framework (based on Basel-derived norms) requires commercial banks ("A" class) to maintain minimum capital ratios against risk-weighted assets — historically around 11% total capital, with sub-limits on core (Tier 1) capital. Since shareholders' equity is the numerator of this ratio, book value is not just an accounting artifact for Nepali banks — it is a binding regulatory constraint on how much the bank can lend and grow.
For insurance companies, a related logic applies, though with an added layer: an insurer's balance sheet carries large actuarial liabilities (estimated future claims and policy obligations) that are themselves projections, not fixed contractual amounts like a bank deposit. This makes an insurer's book value somewhat less precise than a bank's, but the same principle holds — insurers are financial businesses whose "product" is a pool of contractual and statistical obligations, not physical inventory, so P/B remains a far more relevant valuation lens for Nepal Life Insurance or Prabhu Insurance than it would be for a hotel or a trading house.
This is precisely why Chapter 39's treatment of P/B for banks and this chapter's treatment converge: for financial institutions, P/B is not a supplementary check on a DCF or DDM valuation — it is often the primary valuation tool practitioners reach for first, because the alternative (projecting decades of loan growth and margins in a DCF) is more fragile and more prone to modelling error than simply asking "what return on this book of equity is this management team generating, and what multiple of that book value is fair given that return." This is the essential reason P/B is the primary valuation tool for financial institutions: banks, development banks, finance companies, and insurers hold financial assets and liabilities that are already recorded close to their economic value, unlike physical assets such as land or machinery, whose book value can diverge sharply from true worth.
Lesson 49.3 — Adjusted and Tangible Book Value: Cleaning Up the Number
Book value as reported on the balance sheet is not always the clean, reliable figure Lesson 49.2 describes even for financial institutions, and an analyst who takes the reported net worth figure at face value without adjustment can be badly misled. Two adjustments matter most in the Nepali context: removing intangible assets like goodwill, and scrutinizing revaluation reserves.
Goodwill arises on a balance sheet when one company acquires another for more than the fair value of its identifiable net assets — the excess purchase price is booked as an intangible asset called goodwill. Nepal has seen a wave of bank mergers and acquisitions over the past decade, driven partly by Nepal Rastra Bank's push to consolidate the banking sector into fewer, stronger institutions (the "merger bhaunda," or merger wave, of the mid-2010s onward). When, say, a stronger bank absorbs a weaker development bank, any premium paid above the target's net assets shows up as goodwill on the combined entity's balance sheet. This goodwill is a real accounting asset, but it has no liquidation value — in a wind-down, goodwill is worth zero, because it does not represent a saleable asset; it represents the accounting plug for having paid up for growth, market share, or a banking licence. Tangible Book Value Per Share strips this out: Tangible Book Value = Total Shareholders' Equity − Goodwill − Other Intangible Assets, divided by shares outstanding. For a bank that has grown substantially through acquisition, tangible book value can be meaningfully lower than headline book value, and P/TBV (price to tangible book value) gives a more conservative, more liquidation-realistic picture of what shareholders truly stand behind.
CASE IN POINT
When a merger creates a combined bank, the acquiring institution's balance sheet may show goodwill representing the premium paid over the target's net asset value — often to acquire the target's branch network, deposit base, or banking licence. An investor comparing P/B ratios across merged and non-merged banks should also compare P/TBV (price to tangible book value), because two banks with an identical headline P/B of, say, 1.8x can carry very different qualities of book value if one balance sheet includes several arba of goodwill and the other does not.
The second adjustment concerns revaluation reserves. Under Nepal Financial Reporting Standards (NFRS), which Nepali companies — particularly banks and larger corporates — have progressively adopted, property, plant, and equipment can in some circumstances be carried at a revalued amount rather than historical cost, with the increase in value credited to a "revaluation reserve" within equity rather than run through profit and loss. This is common for companies holding land, and land revaluation is especially relevant in Nepal, where land prices in the Kathmandu Valley and other urban centres have risen enormously over decades while the accounting cost basis for older holdings may still reflect prices from twenty or thirty years ago.
A revaluation reserve is not necessarily a source of caution — often it moves book value closer to reality, correcting for the historical-cost distortion this chapter opened with. But the reader must apply two tests before trusting a revaluation reserve. First, when was the revaluation done, and by whom — a professional, independent valuer, or an internal assessment that may be optimistic? Second, does the revaluation reflect a value that is actually realizable — could the company sell that land at the revalued price without materially disrupting its own operations (since a hydropower company cannot sell the land under its powerhouse and continue operating), and would a forced or urgent sale fetch anywhere near the revalued figure? A revaluation reserve that has not been updated in over a decade, using an old valuation in a market that has moved sharply since, should be treated with real skepticism — it may overstate true adjusted book value just as easily as historical cost understates it.
WARNING
A revaluation reserve raises recorded book value, but it is only as reliable as the valuation behind it. An old, stale, or internally generated revaluation — especially for land the company cannot actually sell without ceasing to operate — can make book value look stronger than the company's true liquidation value would support. Always check the date and independence of the valuation before treating a revaluation reserve as investable "hard" equity.
The general principle that emerges is that reported book value is a starting point, not a finishing point. An analyst building an adjusted or tangible book value estimate should ask, asset by asset: is this line item close to a realizable cash value (bank loans net of provisions, cash, government securities), is it an intangible with no liquidation value (goodwill, capitalised software, deferred tax assets that depend on future profitability), or is it a physical asset whose recorded cost may be stale in either direction (land, buildings, hydropower civil works)? Only after this triage does a comparison of price to adjusted book value become meaningful across companies, rather than comparing accounting artifacts that happen to share a common label.
Lesson 49.4 — Net Asset Value (NAV) for Mutual Funds
Chapter 16 introduced Nepali mutual funds — pooled investment vehicles managed by asset management companies (such as NIBL Ace Capital, Nabil Investment Banking, Sunrise Capital, or Global IME Capital) that are listed and traded on NEPSE, mostly as closed-end funds with a fixed number of units, alongside a smaller number of open-end schemes. This chapter extends that coverage into the valuation mechanics specific to funds: Net Asset Value, or NAV.
NAV is book value's direct cousin, but for a portfolio of securities rather than an operating company. It is calculated as: NAV = (Total value of the fund's investment portfolio, marked to current market prices, plus cash and receivables, minus any liabilities such as management fees payable) ÷ Number of units outstanding. If a mutual fund such as NIBL Sahabhagita Fund holds a portfolio of NEPSE-listed equities and government securities currently worth NPR 90 crore, has NPR 2 crore of cash, and NPR 1 crore of payables, its net assets are NPR 91 crore. If it has 9 crore units outstanding (a typical unit face value in Nepal is NPR 10), its NAV per unit is NPR 91,00,00,000 ÷ 9,00,00,000 = NPR 10.11 per unit.
Because NAV is disclosed regularly — SEBON requires mutual funds to publish NAV, typically weekly for closed-end funds — it gives investors a transparent, mark-to-market benchmark against which to judge the fund's market trading price. This is the central mechanic of this lesson: unlike an operating company's book value, which is only as current as the last quarterly filing and is based on historical cost accounting for many assets, a mutual fund's NAV is close to a real-time market value of its holdings, because the underlying assets are themselves listed securities with observable prices.
KEY CONCEPT
NAV per Unit = (Market value of portfolio holdings + cash − liabilities) ÷ Units outstanding. Unlike operating-company book value, mutual fund NAV is marked to current market prices of the underlying securities, making it a far more current and reliable benchmark of intrinsic value.
The critical, and famous, feature of closed-end funds — the dominant structure on NEPSE — is that their market trading price frequently diverges from NAV, trading at either a discount (market price below NAV) or, less commonly on NEPSE historically, a premium (market price above NAV). A closed-end fund has a fixed number of units that trade among investors on the exchange, just like a company's shares; unlike an open-end fund, investors cannot redeem units directly from the fund at NAV whenever they wish. This structural rigidity is exactly why a persistent gap between price and NAV can occur and persist — there is no automatic arbitrage mechanism forcing the market price back to NAV, unlike in an open-end fund where redemption at NAV is always available.
Nepali closed-end mutual funds have, for much of their listed history, tended to trade at a discount to NAV — often in the range of 10% to 30% below reported NAV, though this gap widens and narrows with overall market sentiment. Several factors explain this discount. First, limited secondary market liquidity for fund units discourages some buyers, who demand a discount to compensate for the difficulty of exiting a position later. Second, management fees and fund expenses are a continuing drag that a buyer of units in the secondary market is implicitly paying for without having chosen the manager themselves. Third, investor sentiment toward professionally managed pooled vehicles in Nepal has, at various points, been lukewarm relative to the appeal of picking individual stocks directly, particularly during bull-market periods when direct stock-picking feels (rightly or wrongly) more exciting and potentially more lucrative than a diversified fund. Fourth, some funds approaching their maturity date (most Nepali closed-end funds have a fixed term, often 10 years, after which they wind up and distribute proceeds) see their discount narrow as maturity approaches and the wind-up value becomes more certain and near-term.
Consider a closed-end mutual fund with a published NAV of NPR 13 per unit trading at a market price of NPR 10 per unit — roughly a 23% discount to NAV. An investor who buys such a unit is, in effect, purchasing NPR 13 worth of underlying listed securities and cash for NPR 10, provided the fund's disclosed NAV is accurate and the investor is prepared to hold until the discount narrows or the fund matures and distributes its net assets.
This discount-to-NAV phenomenon creates a genuine, if patient, value-investing opportunity: buying units of a well-managed closed-end fund at a wide discount to NAV, on the thesis that the discount will narrow over time — whether because of improved market sentiment, because the fund is approaching its maturity and wind-up date (at which point unit holders typically receive close to full NAV in cash or in-kind distribution), or because the fund itself begins buying back its own units (a step some funds have taken specifically to narrow a persistent discount). The risk, of course, is that the discount can also widen further before it narrows, and that NAV itself can fall if the underlying portfolio (heavily weighted toward NEPSE-listed banking and hydropower shares for most Nepali funds) declines in a market downturn — so a discount-to-NAV strategy is a bet on the gap closing, not a guarantee against loss in the underlying portfolio value.
To evaluate a closed-end mutual fund in practice, compare its most recently published NAV per unit (available from the fund manager's website or SEBON disclosures, typically updated weekly) against its current NEPSE market price, calculating the discount or premium as: (NAV − Market Price) ÷ NAV. A widening discount over several months, with no change in the quality of the underlying portfolio, can signal an undervalued entry point — but always check the fund's remaining tenure, expense ratio, and portfolio concentration (especially concentration in a handful of bank or hydropower counters) before concluding the discount is unjustified.
Lesson 49.5 — Asset-Heavy Companies: Hydropower, Hotels, and the Replacement Cost Problem
Return now to the house analogy that opened this chapter, because it applies with full force to a category of Nepali listed companies where book value, taken at face value, can be one of the most misleading numbers on the entire balance sheet: asset-heavy businesses such as hydropower companies, hotels, and manufacturing firms sitting on substantial land holdings.
A run-of-river hydropower project such as those developed by companies like Chilime Hydropower, Butwal Power Company, or the many smaller listed hydropower producers that have proliferated on NEPSE, requires an enormous upfront capital outlay: land acquisition (sometimes decades ago at historical prices far below today's market rates, sometimes more recently at current, much higher prices), civil works (dam, headrace tunnel, powerhouse), and electromechanical equipment (turbines, generators, transformers). Under historical-cost accounting — the default basis under NFRS unless a company elects to revalue specific asset classes — all of this is recorded on the balance sheet at what it cost to build, then depreciated over the asset's useful life. Two hydropower plants of identical generating capacity, output, and power purchase agreement terms with the Nepal Electricity Authority can show very different book values purely because one was built ten years ago at lower construction costs and the other was built more recently at costs inflated by years of rupee depreciation, higher steel and cement prices, and rising labour costs. Comparing P/B ratios across hydropower companies without adjusting for this is comparing apples to oranges — the book value denominator is contaminated by when the asset happens to have been built, not by how valuable or productive it actually is.
The more important distortion, though, runs the other way: replacement cost. If you wanted to build an equivalent hydropower plant today — same capacity, same head, same location characteristics — it might cost meaningfully more than the historical book value of an existing plant built years earlier, because construction costs, land costs, and equipment costs have all risen. This is the concept of replacement cost or replacement value: what it would cost to recreate the asset's economic capacity today, as opposed to what it originally cost. A hydropower plant's true economic value is arguably closer to the present value of its future cash flows (which is exactly what the DCF approach of Chapter 46 is built to estimate) than to either its historical book value or even its replacement cost — but replacement cost serves as a useful sanity check, particularly as a floor: if a plant is trading in the market (through its listed equity) for less than what it would cost to build an equivalent plant from scratch today, and the plant's power purchase agreement and remaining licence tenure are sound, that is a meaningful signal the market may be undervaluing the asset, all else equal.
WARNING
Historical-cost book value for hydropower, hotel, and manufacturing companies in Nepal often understates true economic and replacement value, because land and construction costs recorded years or decades ago do not reflect today's prices. Relying on book value or P/B alone for these sectors, without adjusting for revaluation, replacement cost, or discounted cash flow of the underlying concession/licence, will typically make genuinely valuable asset-heavy businesses look artificially expensive on a P/B basis, or mask true differences in asset quality between companies.
Hotels present a related but distinct version of the same problem. A hotel such as Soaltee Hotel or Yak & Yeti sits on prime urban land in Kathmandu that may have been acquired generations ago at a small fraction of its current market value. The hotel building itself depreciates under standard accounting, pulling book value down over time, even as the land beneath it — often the majority of the underlying economic value for an urban hotel — has appreciated dramatically and is not depreciated at all (land is a non-depreciable asset under NFRS, but it still sits at historical cost unless revalued). A hotel company's book value can therefore badly understate its true net asset value, because the single most valuable thing it owns — centrally located land — is recorded at a decades-old cost basis. This is precisely the situation in which adjusted or revalued book value, using a current independent valuation of the land, becomes far more informative than the raw accounting figure, and it is also why hotel and real-estate-adjacent companies are sometimes targets of corporate activity (privatization proposals, buyouts, or redevelopment plans) — because an acquirer who understands the gap between book value and true land value can see an opportunity that a purely accounting-based reading of the balance sheet would miss entirely.
Manufacturing companies with older factories and land — cement companies, cigarette and consumer goods manufacturers, and similar industrial NEPSE listings — face a milder version of the same dynamic. Machinery genuinely does wear out and lose value roughly in line with depreciation schedules, so the distortion is usually smaller for equipment than for land. But where a manufacturer owns substantial industrial land, particularly land that has since become surrounded by urban expansion (a factory built on what was once agricultural land at the edge of the Kathmandu Valley or Biratnagar decades ago, now embedded in dense urban or peri-urban development), the same land-value gap applies: such a company may carry its land on the books at a fraction of one percent of its current market value, meaning its P/B ratio is simply not comparable to a bank's P/B ratio — the bank's book value is close to economic reality, while the manufacturer's may deeply understate its true net asset backing once the land is properly revalued.
The practical takeaway for the Nepali investor is a three-step discipline whenever evaluating an asset-heavy company: first, check whether the company has adopted revaluation for its land and buildings, and if so, how recently and by whom; second, if it has not revalued, attempt an independent, even rough, estimate of what the land alone would be worth at current market rates (using comparable land transaction data for the area, which is often obtainable from local land revenue offices, real estate brokers, or news reports of nearby land sales) and add the gap between that estimate and historical cost to book value as a rough adjusted-book-value estimate; and third, always cross-check any asset-value-based estimate against a cash-flow-based estimate (DCF, Chapter 46) for the operating business itself, since a hydropower plant's or hotel's true worth ultimately depends on the cash flows the underlying operations can generate, not merely on the resale value of the dirt and concrete beneath them.
In practice, this means that for an asset-heavy Nepali company, an investor should not rely on reported book value alone: check the date and independence of any revaluation reserve, and where none exists, build a rough adjusted book value by estimating current market value for major land and property holdings using comparable local transactions, then compare price-to-adjusted-book-value rather than price-to-reported-book-value across peer companies.
Lesson 49.6 — The Danger of Book Value for Growth and Intangible-Heavy Businesses
Everything covered so far has been about book value understating true worth for certain asset-heavy businesses. But book value carries an equally important and opposite danger: for growth-oriented, intangible-heavy, or asset-light businesses, book value can dramatically understate — or simply become irrelevant to — true economic value, in a way that leads unwary investors to wrongly dismiss a company as "expensive" on a P/B basis when it is not expensive at all relative to its actual earning power and growth prospects.
Consider a company whose primary value lies not in physical assets on its balance sheet but in intangible sources of advantage: a strong brand, an exclusive distribution network, proprietary technology, regulatory licences, trained human capital, or simply a demonstrated ability to compound earnings at a high rate of return with very little capital reinvestment required. A well-run consumer brands company, a telecom or technology-enabled services business, or an asset-light trading and distribution company can generate enormous profits and cash flow relative to the modest amount of shareholders' equity recorded on its balance sheet — precisely because its value creation does not depend on owning large amounts of land, plant, or equipment. Such a company will naturally show a very high Return on Equity (ROE), and correspondingly a very high P/B ratio can be entirely justified rather than a sign of overvaluation.
The mechanical relationship here is worth making explicit, because it resolves what otherwise looks like a contradiction between P/B analysis and everything else in this Part of the book. A justified P/B ratio is mathematically linked to ROE, the cost of equity, and expected growth — the same variables that drive a Dividend Discount Model (Chapter 48). A simplified version of this relationship, often called the Gordon Growth justified P/B formula, states that: Justified P/B ≈ (ROE − g) ÷ (r − g), where ROE is the sustainable return on equity, g is the expected long-run growth rate, and r is the cost of equity (the required return, as covered in Chapter 46). A company with an ROE of 25%, a cost of equity of 14%, and sustainable growth of 8% would have a justified P/B of (0.25 − 0.08) ÷ (0.14 − 0.08) = 0.17 ÷ 0.06 ≈ 2.83x — and this is before considering that many high-ROE, asset-light businesses can sustain P/B multiples of 4x, 5x, or higher when growth is strong and durable. A naive investor who screens NEPSE for "cheap" stocks by P/B ratio alone, without checking whether a high P/B is justified by an equally high and durable ROE, will systematically avoid precisely the highest-quality compounding businesses and instead gravitate toward statistically cheap but structurally low-return businesses — a classic value trap in reverse.
KEY CONCEPT
Justified P/B is driven by the relationship between ROE, growth, and the cost of equity: Justified P/B ≈ (ROE − g) ÷ (r − g). A high P/B is not automatically "expensive" if it is backed by a durable, high ROE — and a low P/B is not automatically "cheap" if it reflects a genuinely low and unsustainable ROE.
The danger compounds further for businesses that are not merely asset-light but are actively investing in intangible growth that accounting rules force to be expensed immediately rather than capitalised as an asset. Under NFRS and most accounting frameworks worldwide, spending on building a brand (advertising and marketing), training a distribution network, or developing organizational capability is expensed through the profit and loss statement in the year it is incurred, rather than recorded as an asset on the balance sheet and depreciated over its useful economic life — even though this spending may be creating real, lasting economic value, exactly as a factory or a hydropower plant does. This means book value for such a company can actually decline (through the accounting expense) in the very years the company is building its most valuable long-term assets, an outcome that is the precise mirror image of the hydropower land-revaluation story: there, book value under-captures value that clearly exists (physical land); here, book value never captures value that is being actively created (brand, network, capability) because accounting rules do not permit it to be capitalised at all. A young or fast-growing company with a low or even negative book value trend can therefore still be creating enormous shareholder value — book value is simply the wrong lens for it, and relying on P/B to screen such companies will systematically misprice them.
There is a further caution worth naming plainly for the NEPSE context specifically. Because book value and P/B are so deeply embedded in how Nepali investors, brokers, and financial media discuss bank and finance-company stocks — arguably more so than in more developed markets, given how dominant the banking sector is in NEPSE's overall float and trading volume — there is a real temptation for investors to reflexively apply the same P/B lens to every other sector: manufacturing, hospitality, trading, insurance, and any emerging technology-enabled or consumer businesses that eventually list. This chapter's message is not that book value is unimportant — Lessons 49.1 through 49.4 make clear how indispensable it is for financial institutions and mutual funds specifically — but that its usefulness is sector-dependent, and the disciplined investor must consciously choose the right valuation lens for the right business rather than defaulting to whichever multiple happens to be most commonly quoted in Nepali financial media for that stock. For asset-light, intangible-driven, or early-growth businesses, P/B and even adjusted book value can be close to meaningless, and DCF (Chapter 46) or relative valuation using earnings-based multiples (Chapter 47) will be far more informative.
The following table brings together the sector-by-sector reliability of book value as a primary valuation anchor, synthesizing the reasoning across this chapter:
Sector / Business Type
Is Book Value a Reliable Primary Anchor?
Key Distortion to Watch For
Commercial banks, development banks
Yes — primary tool
Goodwill from mergers; use tangible book value (P/TBV)
Finance companies
Yes — primary tool
Loan quality and provisioning adequacy affecting true net worth
Life and non-life insurers
Yes, with care
Actuarial reserve estimates are projections, not fixed values
Mutual funds (closed-end)
Yes — NAV is the core metric
Market price discount/premium to NAV, fund expenses, tenure
Hydropower companies
No, not without adjustment
Historical-cost land and civil works vs. replacement/DCF value
Hotels and hospitality
No, not without adjustment
Prime urban land held at decades-old historical cost
Manufacturing (land-heavy)
Partially
Machinery depreciates realistically; land often does not reflect current value
Brand and network value never appears on the balance sheet at all
And this table illustrates how the same P/B ratio can mean very different things depending on the quality and composition of the underlying book value, using illustrative figures consistent with the kind of dispersion seen across NEPSE-listed banks and finance companies:
Company (illustrative)
Market Price (NPR)
Book Value/Share (NPR)
P/B Ratio
ROE
Reading
Bank A (large commercial bank)
340
125
2.72x
18%
Premium justified by high, stable ROE and strong franchise
Bank B (mid-sized commercial bank)
210
150
1.40x
11%
Fair value; P/B roughly tracks ROE relative to peers
Development Bank C
145
160
0.91x
7%
Below book, but low ROE signals real earnings weakness, not obvious bargain
Finance Company D
95
130
0.73x
5%
Trading below book; check asset quality and provisioning before assuming undervaluation
Closed-end Mutual Fund E
10.20
13.10 (NAV)
0.78x (price/NAV)
n/a
22% discount to NAV; check tenure and expense ratio before buying
CAUTION
A P/B ratio below 1x is not automatically a bargain. It can reflect a genuine market misjudgment worth exploiting, or it can correctly reflect a low, structurally weak ROE, doubtful asset quality, or thin capital buffers that justify a discount to book. Always pair a low P/B reading with an ROE and asset-quality check before concluding a stock is undervalued.
Chapter recap
This chapter closed Part IX by examining the third and final major branch of valuation covered in this book: valuing a company by reference to its balance sheet — its book value or net asset value — rather than by its future cash flows (Chapter 46), its trading multiples relative to peers (Chapter 47), or its dividend stream (Chapter 48). Book value, or shareholders' net worth, is simply total assets minus total liabilities, expressed per share as Book Value Per Share, and compared to market price through the Price-to-Book (P/B) ratio first introduced for banks in Chapter 39. The central insight of Lesson 49.1 is that book value's usefulness depends entirely on how closely a company's recorded assets track their true, current economic worth — a test that financial businesses pass far better than physical-asset businesses do.
Lesson 49.2 explained why P/B is the primary valuation tool for Nepali banks, development banks, finance companies, and insurers specifically: their assets and liabilities are themselves financial instruments — loans, deposits, government securities, actuarial reserves — recorded close to real economic value, and their capital base is directly constrained by Nepal Rastra Bank's capital adequacy regulations, making book value both an accounting fact and a binding regulatory reality that shapes how much a bank can lend and grow. Lesson 49.3 then showed how reported book value still needs cleaning before it can be trusted: goodwill from Nepal's wave of bank mergers has no liquidation value and should be stripped out to compute tangible book value, while revaluation reserves — often applied to land — need to be checked for the date and independence of the underlying valuation before being taken at face value.
Lesson 49.4 turned to mutual funds, where Net Asset Value (NAV) plays the same role book value plays for operating companies, but with the advantage of being marked to current market prices of listed securities rather than historical cost. Because most Nepali mutual funds are closed-end structures without a redemption mechanism forcing price to track NAV, their units frequently trade at a discount to NAV — sometimes a substantial and persistent one — creating a patient value opportunity for investors willing to wait for the discount to narrow through improved sentiment, buybacks, or the fund's eventual maturity and wind-up.
Lessons 49.5 and 49.6 covered the two opposite failure modes of book-value-based analysis. For asset-heavy businesses — hydropower companies, hotels, and land-holding manufacturers — historical-cost accounting under NFRS can badly understate true value, because land and construction costs recorded years or decades ago bear little relationship to current replacement cost or market value, making revaluation, replacement-cost thinking, and cross-checking against DCF essential rather than optional. For asset-light, growth, or intangible-heavy businesses, the opposite danger applies: accounting rules force brand-building, distribution, and capability-building spending to be expensed rather than capitalised, so book value can understate or entirely miss the true source of a company's value, and a naive investor screening for "cheap" P/B ratios will systematically avoid the highest-quality compounding businesses while gravitating toward statistically cheap but structurally weak ones. The justified-P/B relationship — tying P/B to ROE, growth, and the cost of equity — reconciles this apparent contradiction and shows that a high P/B is not inherently expensive, nor a low P/B inherently cheap, without reference to the return the company earns on its equity base.
Taken together, Part IX of this book has now equipped the reader with four distinct valuation lenses, each suited to different circumstances and each acting as a check on the others' blind spots. Discounted Cash Flow (Chapter 46) grounds value in the fundamental economic logic of future cash generation discounted at an appropriate required return, but is highly sensitive to assumptions about growth, margins, and discount rates that are especially uncertain in Nepal's developing capital markets. Relative valuation (Chapter 47) anchors a company's price to how the market is currently pricing its peers, offering a useful reality check against DCF's assumption-heavy nature, but it inherits whatever mispricing already exists across the peer group and can misfire badly if the whole sector is over- or under-valued together. The Dividend Discount Model (Chapter 48) is particularly well suited to mature, dividend-paying Nepali institutions such as established banks, but says little about companies that reinvest heavily and pay minimal dividends. Book value and NAV approaches (this chapter) provide a balance-sheet-anchored floor — indispensable for financial institutions and mutual funds, and a necessary sanity check for asset-heavy sectors, but actively misleading if applied uncritically to growth or intangible-driven businesses. The mature Nepali investor does not pick one of these four methods and discard the rest; they triangulate, using DCF to understand intrinsic economic value, relative valuation to check that the market is not universally mispricing the sector, DDM where dividends are the dominant driver of realised shareholder return, and book value or NAV to establish what floor of net worth genuinely stands behind the shares — and where these methods disagree sharply, that disagreement itself is the most important signal, telling the investor exactly which assumption needs to be interrogated most closely before committing capital.
With valuation now covered comprehensively across these four methods, the book turns in Part X to a different, equally important dimension of investing: behaviour and investor psychology. Valuation tells an investor what a share is worth; it says nothing about why so many Nepali investors, armed with perfectly reasonable analytical tools, nonetheless make systematically poor decisions in practice — buying at the top of euphoric rallies, panic-selling at the bottom of corrections, and chasing rumours over research. Chapter 50, "Why Nepali Investors Systematically Lose Money," opens Part X by confronting this gap between knowing how to value a share and actually behaving rationally when real money, real fear, and real crowds are involved.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Volume 3: EXECUTION
Part X
BEHAVIOUR AND INVESTOR PSYCHOLOGY
Part X · Chapter 50
Why Nepali Investors Systematically Lose Money
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 50.1 — The Retail Crowd: Who Actually Trades on NEPSE
Walk into the trading floor of any brokerage house in New Baneshwor, Putalisadak, or a district headquarters in Butwal or Biratnagar on a day when the Nepal Stock Exchange (NEPSE) is rising sharply, and you will not find rows of fund managers studying balance sheets. You will find retirees, schoolteachers, remittance-receiving housewives, taxi drivers on their lunch break, and college students staring at their phones, refreshing a trading app every few seconds. This is the defining fact about the Nepali stock market that every other lesson in this chapter rests on: NEPSE is overwhelmingly a retail market, not an institutional one.
A retail investor is simply an individual who buys and sells securities with their own personal savings, as opposed to an institutional investor — a mutual fund, insurance company, pension fund, or bank — that invests professionally managed pools of other people's money. In mature markets like the United States, institutional investors dominate daily trading, often accounting for 70-80% of turnover. Professional analysts, algorithmic trading desks, and regulatory disclosure requirements create a market where prices are set, most of the time, by people whose full-time job is evaluating businesses. In Nepal, that balance is inverted. Individual retail investors make up the overwhelming majority of NEPSE's daily trading volume and turnover — brokers, market commentators, and SEBON officials have repeatedly pointed to this as the single most distinctive structural feature of the exchange. Mutual funds exist in Nepal, but they remain a small fraction of total market capitalisation. Pension and provident funds invest conservatively and infrequently. The result is a market where the marginal buyer and seller, the person whose trade actually moves the price on a given day, is almost always an ordinary household managing its own money without professional training.
Nepal's demat account count — the number of dematerialised securities accounts (electronic accounts that hold shares in digital form instead of paper certificates, opened through the Central Depository System and identified by a Beneficiary Owner Identification number, or BOID) — has climbed past 6.5 million. Against a national population of roughly 29-30 million, and an even smaller adult, economically active population, this is a striking number. It does not mean 6.5 million distinct households are actively trading — many accounts are dormant, and some individuals hold more than one — but it confirms that stock market participation in Nepal has become a mass phenomenon rather than a niche activity for the wealthy. Compare this to a generation ago, when share ownership was concentrated among a small number of urban, educated, well-connected families. The opening of Mero Share (the online application system for share allotment) and the spread of smartphone-based trading apps has pulled in a much broader cross-section of Nepali society — including many people encountering financial markets, financial statements, and price volatility for the very first time.
KEY CONCEPT
A retail investor is an individual trading personal savings, without professional training, research staff, or institutional risk controls. An institutional investor is a professionally managed pool of capital — a mutual fund, insurance company, or pension fund — with analysts, mandates, and formal risk limits. NEPSE's trading is dominated by the first group, which is precisely why it behaves so differently from markets dominated by the second.
This matters because retail-dominated markets behave differently from institution-dominated ones, and not in retail's favour. When a market is mostly institutional, price moves are usually anchored, however imperfectly, to earnings estimates, discounted cash flow models, and peer comparisons. Institutions still panic and still herd — the 2008 global financial crisis proved that professionals are not immune to crowd psychology — but there is at least a baseline of research activity pulling prices back toward some measure of business value. When a market is mostly retail, and especially when that retail base has limited financial literacy and limited access to independent research, prices become much more a function of sentiment, momentum, and social contagion. Think of the difference between a farmers' market where most buyers know the going rate for tomatoes because they buy vegetables every week, versus a market where most buyers have never priced a vegetable before and take their cue entirely from how long the queue at a particular stall is. In the second market, a stall with a slightly longer queue attracts even more buyers, not because the tomatoes are better, but because the queue itself is read as a signal of quality. NEPSE behaves like that second market far too often. A stock that is already rising attracts buyers precisely because it is rising, and a stock that is already falling triggers selling precisely because it is falling — a dynamic that has little to do with what the underlying company actually earns.
This is not a moral failing of Nepali investors. It is a structural feature of a young, retail-dominated, thinly researched market, layered on top of a financial system that has, at various points over the last two decades, made borrowed money cheap and readily available for buying shares. The remaining lessons in this chapter walk through exactly how that combination — a retail crowd, a rumour-driven information environment, easy leverage, and a tendency to chase whatever sector rallied last — has produced a repeating cycle of booms and busts on NEPSE, and why the ordinary Nepali investor has systematically ended up on the losing side of that cycle more often than not.
Lesson 50.2 — A History of Booms and Busts: What NEPSE's Cycles Teach
NEPSE opened for trading in January 1994. In the three decades since, it has moved through a repeating pattern: a multi-year climb driven by liquidity and optimism, followed by a much sharper, faster collapse. If you have watched only the most recent cycle, it is tempting to think of it as a one-off event — an unusual bubble caused by unusual circumstances. It was not unusual. It was the fourth or fifth time NEPSE had done almost exactly the same thing.
The table below lays out the major cycles using widely reported index levels. NEPSE's benchmark index is a value-weighted measure of listed company share prices, similar in spirit to how the S&P 500 tracks large American companies, except NEPSE's index reflects a much smaller, much less diversified, much more thinly traded set of companies — heavily concentrated in banking, hydropower, insurance, and microfinance.
Cycle
Approximate Peak (Index Level, Date)
Approximate Trough (Index Level, Date)
Decline
Primary Driver
Cycle 1
~360 points (2000)
~205 points (2002/03)
~43%
Post-democratic-era political instability, insurgency-related uncertainty
Cycle 2
~1,175 points (2008)
~292 points (2011)
~75%
Global financial crisis, tightening bank liquidity, unwinding of speculative buying
Cycle 3
~1,888 points (July 2016)
~1,100-1,200 points (2018-19), then ~1,000 (March 2020 pandemic low)
~40-47%
Real estate and margin-lending-fuelled retail boom; later, 2015 earthquake aftershocks to sentiment and COVID-19 shock
Cycle 4
~3,227 points (August 2021, all-time high)
~1,800-1,900 points (late 2022)
~42-44%
Pandemic-era excess bank liquidity, cheap margin loans, mass retail entry via Mero Share and trading apps; reversed by sharp interest-rate increases and a national liquidity crunch
Cycle 5 (partial, ongoing)
~2,900-3,000 range (2024-25 rallies)
~2,487 points (October 2025), choppy recovery into 2026
Ongoing
Renewed retail enthusiasm on hydropower and banking counters, followed by repeated sharp pullbacks
Two things should jump out from this table. First, the pattern repeats: a multi-year run-up of well over 300%, followed by a collapse of 40% to as much as 75%, roughly once per decade. Second, the triggers differ each time — political instability, a global financial crisis, an earthquake, a pandemic, an interest rate cycle — but the shape of the market's response is nearly identical every time: a long, self-reinforcing climb followed by a fast, brutal fall. This is the signature of a market where price momentum, not fundamental value, is doing most of the work of setting prices in the later stages of each boom.
CASE IN POINT
Between roughly mid-2020 and August 2021, NEPSE nearly tripled, driven by a flood of pandemic-era liquidity in the banking system, historically low interest rates, and an explosion in margin lending against share collateral. New demat accounts opened by the hundreds of thousands. By August 2021 the index touched an all-time high of about 3,227 points. Within about fourteen months, as Nepal Rastra Bank tightened monetary policy to defend the currency and control imports, interest rates rose sharply, margin loans became both more expensive and harder to service, and the index fell to roughly 1,800-1,900 — a decline of over 40% from the peak, and the fastest, deepest correction NEPSE had experienced in over a decade. Investors who had bought heavily near the top, particularly those using margin loans, were among the hardest hit.
It is worth being precise about what "losing money systematically" means in this context, because it is not simply that prices went down. Markets go down everywhere, including in mature economies with sophisticated institutional investors. What makes the NEPSE pattern different is the demonstrated tendency for the retail crowd to pile in hardest near the top of each cycle — when euphoria, media coverage, and word-of-mouth enthusiasm are all at their peak — and to sell hardest near the bottom, when fear, margin calls, and cash-flow pressure force liquidation regardless of price. The investor who bought steadily throughout Cycle 4, from the 1,000-point lows of March 2020 through the 3,227-point peak of August 2021, would have needed to have committed the largest share of new capital in the final, most expensive months, purely because that is when enthusiasm — and account opening, and margin borrowing — was at its highest. This is not a hypothetical. It is a well-documented pattern across every cycle in the table above, and it is the mechanical reason why "the market went up 370% and then fell 75%" does not mean the average investor merely broke even. The timing of when money entered and exited matters enormously, and Nepali retail money has a strong historical tendency to enter late and exit late.
REGULATORY DETAIL
The Securities Board of Nepal (SEBON), established in 1993 under the Securities Act, is Nepal's capital markets regulator — broadly equivalent in function to the U.S. Securities and Exchange Commission or India's SEBI. SEBON licenses brokers and merchant bankers, approves IPOs and rights issues, sets disclosure requirements for listed companies, and runs investor-education and awareness programs. It does not, however, set interest rates or control bank liquidity — those macroeconomic levers sit with Nepal Rastra Bank (the central bank), whose tightening and loosening cycles have historically been the single biggest external trigger behind NEPSE's booms and busts, as the margin-lending mechanism in the next lesson makes clear.
Understanding this history is not an academic exercise. It is the single best tool an investor has for resisting the emotional pull of the next boom, because the next boom on NEPSE is not a matter of if but when. Markets that have crashed 40-75% on four or five separate occasions over three decades, and have each time recovered and eventually made new highs, are not permanently broken — but they are also not markets where "this time is different" has ever actually been true. The lessons that follow explain the specific mechanisms — margin lending, rumour-driven information flows, and sector chasing — that turn a normal cyclical market into one where retail investors specifically, and disproportionately, end up losing.
If retail dominance explains who is trading on NEPSE, and boom-bust history explains the shape of the market's movements, margin lending explains why the losses, when they come, are so severe for so many ordinary households.
Margin lending, in the NEPSE context, is a loan extended by a bank or, more recently, by a licensed stockbroker, using an investor's existing share portfolio as collateral, with the borrowed funds then used to buy more shares. It works like this: suppose you own shares worth NPR 1,000,000. A bank might agree to lend you up to a certain percentage of that value — historically as much as 50-70% for well-regarded scrips before regulators tightened the rules — against your shares as security. You use that loan to buy more shares. If prices rise, your gains are magnified, because you now control more shares than your own capital alone would have bought. This is the appeal of leverage: it turns a good year into a great year.
The danger is that leverage is symmetric. It magnifies losses exactly as it magnifies gains, and it does so on a deadline. Margin loans are typically structured with a maintenance requirement: if the value of your collateral (your shares) falls below a certain threshold relative to your loan, the lender issues a margin call — a demand that you either deposit additional cash or shares to restore the required ratio, or have your shares sold automatically to repay the loan. This forced, automatic selling is the mechanism that turns an ordinary market correction into a cascading crash. Picture a household that has taken out a loan against the family home to buy furniture on credit, and the moment the home's assessed value dips even slightly, the bank shows up and repossesses the furniture immediately, no negotiation, no grace period. That is what a margin call does to a stock portfolio — except it happens to tens of thousands of accounts at nearly the same time, because they all borrowed during the same liquidity-driven boom and are all being called at the same time as the boom unwinds.
This is precisely the mechanism widely cited as central to the 2021-2022 NEPSE cycle. During the pandemic-era liquidity surplus, banks — flush with deposits and short on strong loan demand elsewhere in a locked-down economy — extended margin loans aggressively and at favourable interest rates. Retail investors, seeing the index climb month after month, borrowed to buy more shares, which added further buying pressure and pushed prices higher still, which then made their existing collateral more valuable, which allowed them to borrow even more. This is a feedback loop, and feedback loops that run in one direction for long enough always eventually run in the other direction. When Nepal Rastra Bank tightened monetary policy in 2022 to defend foreign exchange reserves, interest rates on margin loans rose sharply and banks pulled back on new lending. Investors holding shares purchased on margin now faced a double squeeze: falling share prices (reducing collateral value) and rising interest costs (making the loans themselves more expensive to service). The result was forced selling — investors and brokers liquidating positions not because they had decided the shares were overvalued, but because they had no choice. Forced selling by definition happens at whatever price the market offers, not the price the seller would prefer, which is exactly why margin-driven crashes tend to be faster and deeper than ordinary corrections.
WARNING
A margin call does not ask your opinion. Once your collateral value breaches the maintenance threshold, the lender is contractually entitled to sell your shares — often without further notice, and often at the worst possible moment, because everyone else who borrowed during the same boom is being forced to sell at the same time. This is why margin-fuelled corrections on NEPSE (2008-09 and 2021-22 are the clearest examples) have historically been sharper and faster than corrections in cycles with less leverage in the system.
There is a further, less obvious cost to margin lending that many first-time borrowers underestimate: the interest clock keeps running regardless of what the shares do. A margin loan is not a bet where you only lose what you put in — you owe the interest whether the stock rises, falls, or goes nowhere. An investor who borrows to buy a stock that simply sits flat for eighteen months has still paid eighteen months of interest for zero return, which in effect converts a break-even investment into a loss. This asymmetry — leverage helps you in a rising market and actively hurts you in both a falling market and a flat one — is precisely why professional institutional investors use margin far more sparingly and with far tighter risk controls than the typical retail investor borrowing informally against a rising portfolio.
PRACTICAL TOOL
Before taking any margin loan against shares, ask three questions on paper, not just in your head: (1) At what index or share-price decline would I receive a margin call, given my current loan-to-value ratio? (2) Could I meet that margin call in cash within the lender's notice period, without selling other assets in distress? (3) What is my total interest cost if the position takes two years to work out, and does the expected return still make sense after that cost? If you cannot answer all three with real numbers, you are not managing the loan — the loan is managing you.
None of this means leverage is inherently forbidden or foolish in all circumstances — used sparingly, by an investor with a clear plan and the cash reserves to survive a margin call without forced selling, it is a legitimate tool. The systematic losses documented across NEPSE's boom-bust history come specifically from leverage used at the wrong time, by the wrong number of people, all at once — precisely the pattern that recurs every time bank liquidity turns cheap and enthusiasm runs high.
Lesson 50.4 — Rumour Mills: Viber, Telegram, Facebook and the Death of Independent Thinking
If margin lending explains the severity of retail losses, the rumour-driven information environment explains why so many retail investors buy the wrong shares at the wrong time in the first place.
Ask any active NEPSE participant where they get their trading ideas, and a large share will point not to a company's audited financial statements or a broker's research note, but to a Viber or Telegram group — informal, often anonymous chat groups, sometimes with thousands of members, where participants share "tips," screenshots of floorsheets (the daily record of executed trades), and predictions about which scrip is about to "fly." Facebook pages and groups devoted to share market discussion serve a similar function, amplified further by comment sections and shares. These channels are not inherently malicious — many genuinely try to share useful information — but as a group they create exactly the conditions under which false or manipulative information spreads fastest: low barriers to posting, no verification, strong social proof (a "buy" call repeated by fifty different members feels more credible than the same call from one person, even though it is often the same handful of people or bots posting under different names), and an audience primed to want to believe good news about a stock they are already tempted to buy.
The mechanism at work here is sometimes called a pump-and-dump scheme: a coordinated effort, sometimes organised, sometimes just an emergent crowd behaviour, in which a small group of early buyers accumulates a thinly traded stock quietly, then generates a wave of buzz — rumours of an upcoming bonus share issue, a government contract, a foreign investment, a change in management — designed to pull in a much larger wave of retail buyers. As the new buyers push the price up, the early accumulators sell into that demand, "dumping" their shares at the inflated price. The retail investors who bought on the rumour are left holding shares at a price the company's actual fundamentals never supported, and the price typically drifts back down once the buying frenzy exhausts itself. Regulators and financial journalists in South Asia, including in the Nepali press, have documented versions of exactly this pattern on Telegram and WhatsApp-based "trading groups," some of which cross the line from misguided enthusiasm into outright fraud, with organisers directly profiting from fees or from selling into the crowd they created.
CAUTION
A tip that arrives through a Viber or Telegram group, with no attached source, no company filing, and no way to verify who benefits if you act on it, should be treated the same way you would treat a stranger's advice to buy a plot of land you have never seen, sight unseen, based only on the stranger's assurance that "everyone is buying it." The fact that many other people in the group are also excited about the same tip is not evidence that the tip is true — it is evidence that the tip has spread, which is a different thing entirely.
The deeper psychological trap here is what later chapters in this Part will examine in more depth under the heading of herd behaviour — but it is worth naming plainly here because it is the direct link between the retail dominance discussed in Lesson 50.1 and the boom-bust cycles discussed in Lesson 50.2. Herd behaviour is the tendency to imitate the actions of a larger group rather than rely on one's own independent analysis, especially under uncertainty. It is not unique to Nepal or to stock markets — it is why a queue outside one restaurant on a street of otherwise empty restaurants draws more people to precisely that restaurant, even though the food may be no better. In NEPSE's case, Viber and Telegram groups function as a highly efficient queue-visibility mechanism: they let tens of thousands of investors watch, in real time, which stocks "everyone" is talking about, and that visibility itself becomes the reason to buy, entirely independent of whether the underlying company is worth owning.
CASE IN POINT
Financial commentators across South Asia, including coverage specifically addressing Telegram-based "trading groups," have documented a recurring scam pattern: an anonymous administrator builds a large group by sharing a few genuinely accurate tips early on to establish credibility, then begins recommending thinly traded, low-liquidity stocks the administrator (or an associated circle) has already accumulated. Members who buy in bulk on the recommendation push the price up; the administrator sells into the rally; the group is then flooded with reasons to "hold" or "average down" as the price falls, keeping members invested — and losing — even as the organisers have already exited. Some versions escalate into requesting upfront "membership fees" or steering victims toward fake trading platforms entirely. The specific tickers and administrators change, but the structure recurs closely enough across cases that it should be treated as a known pattern, not an unlucky exception.
None of this means every social media discussion of shares is worthless, or that a Nepali investor should trade in total informational isolation. It means that information arriving through informal social channels needs to be weighted very differently from information arriving through a company's audited annual report, a SEBON-mandated disclosure, or an independent broker's research note — and that the single most useful habit an investor can build is asking, before acting on any tip, "who benefits if I buy this right now, and can I verify anything in this message independently of the group that sent it to me?" A rumour that cannot survive that question is not information. It is noise wearing information's clothing.
Lesson 50.5 — Chasing the Last Sector: Hydropower, Microfinance, Life Insurance and the "Sunset Industry" Trap
Layer the rumour mills of Lesson 50.4 on top of the retail crowd of Lesson 50.1, and you get a very specific, very repeatable pattern on NEPSE: waves of retail money rotating from one hot sector to the next, arriving each time closer to the top of that sector's cycle than to its bottom.
The pattern typically unfolds like this. A sector performs unusually well for reasons that are often genuinely sound at the start — hydropower riding a national narrative around electricity export potential and rising domestic demand, microfinance institutions benefiting from a period of strong loan growth and government policy support for financial inclusion, or life insurance companies expanding rapidly as insurance penetration in Nepal rises from a low base. Early, well-informed investors and some institutions buy in during the sector's earlier, less crowded years. As prices rise, media coverage picks up, Viber and Telegram groups begin buzzing about the sector specifically, and — critically — new IPOs (initial public offerings, a company's first sale of shares to the public) in that sector begin to attract enormous retail demand, because retail investors reason, reasonably enough on the surface, that if the existing companies in the sector have done well, a new company in the same sector should do well too.
This is where the "sunset industry" trap gets its name: by the time the wave of retail enthusiasm and IPO oversubscription has built up enough momentum for the sector to dominate financial news and social media chatter, the best-value opportunities in that sector have frequently already been captured by the early movers, and new capital is often chasing valuations that no longer bear much relation to the underlying business economics — hydropower IPOs, for instance, have at various points attracted oversubscription of 30 to 100 times the shares on offer, meaning for every share available, thirty to a hundred applications competed for it, despite many of the underlying projects carrying high debt loads relative to net worth and years of construction risk still ahead of them before generating meaningful revenue. A par-value share priced at NPR 100 feels psychologically "cheap" to a first-time investor in a way that obscures the real question, which is not the price per share but the price relative to the company's actual earning power once operational — a question that requires reading a prospectus, not a Viber message.
WARNING
Extreme IPO oversubscription — tens or even a hundred times the shares on offer — is frequently read by retail investors as proof of quality ("everyone wants in, so it must be good"). It is at least as often a sign that a herd has formed around a sector narrative, independent of whether the specific company's fundamentals justify the demand. High oversubscription guarantees a small allotment per applicant and a good listing-day pop far more reliably than it guarantees a good long-term investment.
The rotation from hydropower to microfinance to life insurance (and, in various periods, to banking counters, to "development bank" stocks, and elsewhere) is a form of sector momentum chasing dressed up as sector-specific reasoning. Investors tell themselves a story — "Nepal needs electricity," "financial inclusion is the future," "insurance penetration is low so there's room to grow" — and every one of those stories may well be true as a long-run economic thesis. The trap is not that the thesis is false. The trap is timing: buying a sector because it has already rallied hard and is generating the most social-media buzz right now is a fundamentally different act from buying it because independent analysis suggests it is currently undervalued relative to its prospects. The first is momentum investing dressed in fundamental language; the second is actual value assessment. Nepali retail investors, repeatedly, have done the former while believing they were doing the latter — piling into whichever sector just delivered the best returns to the investors who bought it earlier, arriving, on average, close enough to the top of that sector's cycle that the subsequent correction wipes out a large share of the paper gains the latecomers thought they were locking in.
KEY CONCEPT
Sector rotation, in a healthy market, means capital moving toward genuinely improving fundamentals and away from deteriorating ones — a rational reallocation. The "sunset industry" pattern on NEPSE describes something different: retail capital moving toward whichever sector has already delivered the most attention-grabbing recent returns, arriving disproportionately late in that sector's cycle because visibility and buzz — not valuation — are what drive the decision.
The table below summarises the most common, recurring retail mistakes documented across NEPSE's cycles, alongside a rough sense of their typical cost. These are not exhaustive, and the specific percentage costs vary case by case, but the pattern of mistake is remarkably consistent across every boom this chapter has described.
Common Mistake
Typical Trigger
Typical Cost to the Investor
Buying a stock purely because a Viber/Telegram group is "hyping" it
Rumour of bonus shares, contracts, or foreign investment, unverifiable
Often 20-50%+ decline once the buzz fades and the rumour proves false or already priced in
Taking a margin loan near a market peak to buy more of an already-rallying stock
Cheap credit, rising collateral value creating a false sense of safety
Forced liquidation at a loss, plus accrued interest costs, when the market turns
Applying for hydropower/microfinance/insurance IPOs purely because a sector is "hot"
Oversubscription headlines, sector narrative, social proof
Listing-day gains often erode over following 12-24 months as sector enthusiasm cools
Averaging down repeatedly on a falling stock without reassessing the original thesis
Sunk-cost thinking, hope of a rebound, group pressure to "hold together"
Compounds losses; capital remains tied up in a deteriorating position instead of being redeployed
Selling in panic during a margin-driven or news-driven crash, near the cycle trough
Fear, forced selling by others, margin calls of one's own
Locks in losses right before historical recoveries have typically begun
Ignoring a company's actual financial statements in favour of price momentum
Belief that "the chart" or "everyone buying" is sufficient information
No anchor to real value; investor cannot distinguish a genuine bargain from a falling knife
Lesson 50.6 — Financial Literacy, Investor Demographics, and the SEBON Response
The mistakes catalogued in the previous lesson are not evenly distributed across the investing population. They cluster, predictably, where financial literacy is lowest and market experience is shortest — which, given how recently NEPSE's retail base has expanded, describes a very large share of current participants.
Financial literacy, in the sense used by Nepal Rastra Bank's national baseline surveys, is typically measured across three dimensions: financial knowledge (understanding concepts like interest, inflation, and risk-diversification), financial behaviour (whether a person actually budgets, saves, and plans), and financial attitude (whether a person values long-term financial planning at all). Nepal's most recent national baseline survey put the overall financial literacy score at roughly 57.9%, with only about 27.5% of adults clearing the minimum passing threshold across all three dimensions simultaneously. The gaps within that average are significant for understanding who is most exposed to the mistakes in Lesson 50.5's table: financial literacy scores were markedly lower among women (54.3%, versus 61.8% for men — a gap that widens further, to nearly 18 points, on financial knowledge specifically), among older adults (27.9% for ages 60+, against 63.2% for ages 18-30), among those with less formal education (45.3% for those with no formal schooling, against 78.2% for those with a master's degree or higher), and in Madhesh Province specifically (52.0%, the lowest of any province, against 64.5% in Bagmati). Separately, the same body of survey work found that roughly 24% of Nepal's adult population already holds some form of stock or share investment — meaning a very large number of people with genuinely limited financial knowledge are nonetheless active participants in a leveraged, rumour-prone, cyclical stock market. That combination — mass participation alongside a financial-knowledge base where fewer than three in ten adults clear a basic competency threshold — is, on its own, close to a complete explanation for why retail losses on NEPSE have been so widespread and so repetitive across cycles.
REGULATORY DETAIL
SEBON runs ongoing investor-education initiatives — awareness programs, published guidance on its website's education section, outreach through brokers and depository participants, and periodic public campaigns around IPO risk, margin trading rules, and fraud awareness. SEBON has also progressively tightened rules around margin lending exposure limits for banks and brokers following the 2021-22 cycle, precisely to reduce the systemic amplification effect described in Lesson 50.3. These efforts are real, but they operate against a backdrop of an investor base expanding by hundreds of thousands of new demat accounts per year — meaning the population needing education is growing at least as fast as the education programs can reach it.
None of this is presented to suggest Nepali investors are somehow uniquely careless. Every emerging retail market — India in earlier decades, China's retail-dominated A-share market, various frontier markets across Africa and South Asia — has shown broadly similar patterns when a large wave of first-time, under-informed retail capital meets easy leverage and a rumour-prone information environment. What is specific to Nepal is the particular combination documented across this chapter: an unusually retail-dominated exchange (Lesson 50.1), a market with a clean, repeating three-decade history of severe boom-bust cycles (Lesson 50.2), a banking system that has, at several points, made margin borrowing cheap and abundant precisely when a boom was already underway (Lesson 50.3), an information ecosystem where Viber, Telegram, and Facebook groups function as the primary source of trading ideas for a large share of participants (Lesson 50.4), a demonstrated tendency to rotate en masse into whichever sector has already rallied hardest, right as IPO oversubscription numbers peak (Lesson 50.5), and a financial literacy base where fewer than a third of adults meet a basic competency threshold even as roughly a quarter of the adult population already owns shares (this lesson).
PRACTICAL TOOL
Before your next trade on NEPSE, run a three-question self-check that maps directly onto this chapter: (1) Where exactly did this idea come from — a company filing or audited statement I read myself, or a group chat / social media post I cannot independently verify? (2) Am I buying this because of the company's actual earning power, or because the price, or the sector, has already moved a great deal and "everyone" is talking about it? (3) If I am using any borrowed money for this trade, do I know the exact price level at which I would face a margin call, and could I survive that call without being forced to sell at a loss? An honest "I don't know" to any of these three questions is itself the most important piece of information in the entire decision.
Chapter recap
This chapter set out to answer a specific question: why do Nepali retail investors, as a group, systematically lose money on NEPSE across successive market cycles, rather than the losses being randomly distributed bad luck? The answer that emerges from the evidence is structural, not a matter of individual foolishness. NEPSE is a market overwhelmingly driven by retail participants rather than professional institutional investors, with demat accounts numbering in the millions against a national population where fewer than a third of adults meet a basic financial-literacy threshold. In a market structured this way, prices are set far more by crowd sentiment and momentum than by disciplined analysis of underlying business value, which creates exactly the conditions for repeating, self-reinforcing booms followed by sharp, fast corrections.
NEPSE's own history over three decades bears this out with remarkable consistency: five distinct boom-bust cycles, each triggered by a different external event — political instability, a global financial crisis, an earthquake, a pandemic, a monetary tightening cycle — but each following nearly the same internal shape, a long climb of 300% or more followed by a collapse of 40% to 75%. The severity of these collapses, and the disproportionate damage they do to retail households specifically, is amplified by margin lending: borrowed money that magnifies gains on the way up and triggers forced, cascading selling on the way down, precisely when banks tighten credit and interest rates rise. The 2021-22 cycle, in which NEPSE nearly tripled to an all-time high above 3,200 points before falling more than 40% within roughly fourteen months, stands as the clearest recent illustration of this mechanism in action.
Layered on top of this structural vulnerability is an information environment where Viber, Telegram, and Facebook groups have become a primary source of trading ideas for a large share of retail participants — an environment that, by its nature, spreads unverifiable rumours and manufactured hype far more efficiently than it spreads sober, independently verified analysis. This information environment interacts with a specific, repeating behavioural pattern: retail capital rotating en masse into whichever sector has most recently rallied hardest — hydropower, then microfinance, then life insurance, and others in earlier cycles — arriving, on average, closer to the top of that sector's cycle than the bottom, drawn in by IPO oversubscription figures and social-media buzz rather than by independent valuation work.
None of these mechanisms operate in isolation, and none of them describe a market, or an investor base, that is beyond repair. SEBON's ongoing investor-education initiatives, tightened margin-lending rules following the 2021-22 crisis, and the steady, generational rise in financial literacy documented across Nepal Rastra Bank's national surveys are all genuine, if incomplete, counterweights to the pattern this chapter has described. The purpose of naming the pattern clearly — retail dominance, boom-bust history, margin amplification, rumour-driven information flows, and sector chasing — is not fatalism. It is the opposite: an investor who can recognise these five mechanisms operating in real time, in their own decisions and in the market around them, has already taken the single most effective step available toward not repeating them.
That recognition, however, requires understanding not just the external market structure covered in this chapter but the internal, psychological wiring that makes each of these mechanisms so persuasive to begin with — why a crowd chasing a hot stock feels so much more convincing than a lone analyst reading a balance sheet, and why losses hurt so much more than equivalent gains feel good. Chapter 51, "Core Behavioural Biases for the NEPSE Investor," turns from the market's structure to the investor's own mind, examining the specific cognitive biases — including herd behaviour, loss aversion, overconfidence, and anchoring — that make an intelligent, well-intentioned Nepali investor vulnerable to exactly the traps this chapter has documented, and what a disciplined investor can do to recognise and counteract each one.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part X · Chapter 51
Core Behavioural Biases for the NEPSE Investor
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 50 left the Nepali investor with an uncomfortable diagnosis: systematic underperformance is not mostly bad luck or bad regulation — it is bad decision-making, repeated at scale, across hundreds of thousands of BOID (Beneficiary Owner Identification) accounts. This chapter opens the toolbox that explains why. Behavioural finance is the branch of economics that studies how real human beings — not the perfectly rational "economic man" of textbooks — actually make decisions involving money, risk, and uncertainty. Its central finding, replicated across dozens of markets and confirmed by empirical studies of NEPSE investors themselves, is that the human brain uses mental shortcuts, called heuristics, to cope with complexity. These shortcuts work well enough in daily life — deciding which vegetable vendor at the Kalimati market is trustworthy, or judging whether a monsoon cloud means you should turn back on the trek. But in a modern, fast-moving, emotionally charged stock market, the same shortcuts systematically distort judgment. They are not signs of stupidity. They are the predictable output of a normal brain applying everyday reasoning to an environment it did not evolve for. Every investor has these biases. The professional investor's edge is not the absence of bias — it is the discipline of recognising it and building rules that route around it.
This chapter examines eight biases that recur constantly in the Nepali retail investing story: herd behaviour, anchoring, confirmation bias, loss aversion paired with its market cousin the disposition effect, overconfidence, recency bias, FOMO (fear of missing out), gambler's fallacy, and home bias. Each is explained mechanically — what is happening inside the mind — and then grounded in a scenario that will feel familiar to anyone who has watched a Viber investment group light up during a hydropower rally, or has held a loss-making microfinance script for three years "because it will come back." Understanding these patterns will not make them disappear. But naming a bias while it is happening — recognising the specific mental trap you are standing in — is the single most effective tool available for interrupting it before it costs you money.
Lesson 51.1 — Herd Behaviour: The Psychology of the Crowd
Herd behaviour, or herding, describes the tendency of individuals to align their decisions with the actions of a larger group, rather than relying on their own independent analysis. The mechanism is partly informational and partly social. Informationally, an investor reasons: "Surely all these other people buying this hydropower script know something I don't — why would so many people be wrong?" This is a reasonable inference in many walks of life. If a long queue forms outside a momo shop in New Road that you have never tried, the queue itself is useful evidence that the food is good. But financial markets are different from momo queues in one crucial way: the queue outside the momo shop does not change the quality of the momos, whereas a queue of buyers in a stock does change the price you must pay, and a rising price does not by itself mean the underlying business has become more valuable. Socially, herding is also driven by a fear of regret that is asymmetric: an investor who loses money alongside everyone else feels less foolish than an investor who loses money by going against the crowd. Being wrong in company feels safer than being wrong alone, even though the financial outcome is identical.
NEPSE's structure makes herding unusually powerful compared to more institutionally dominated markets. Retail investors are estimated to account for the overwhelming majority of daily turnover on the exchange, and a large share of that retail base coordinates informally through Viber groups, Telegram channels, Facebook pages, and YouTube "market analysis" channels that often number in the tens of thousands of members. When one of these channels flags a script — "Buy XYZ Hydropower before it hits circuit tomorrow!" — thousands of members can place near-identical buy orders within minutes of each other, because the Trading Management System (TMS) used by NEPSE brokers makes order placement fast and frictionless from a mobile phone. The effect is self-reinforcing: as the herd buys, the price rises; the rising price becomes proof, in the herd's own mind, that the tip was correct; the rise attracts a second wave of buyers who were not in the original channel but see the price move on their trading app and chase it. This is precisely the mechanism academic researchers have documented in Nepalese stock market studies — herding intensifies specifically during periods of high market-wide movement, because uncertainty is highest exactly when the pull toward safety-in-numbers is strongest.
Consider the pattern that has repeated across multiple NEPSE cycles: a sector — hydropower in one season, microfinance and finance companies in another, insurance in a third — becomes the "hot" sector. Prices of a handful of scripts in that sector begin moving up on genuine news (a new IPO subscription record, a favourable regulatory circular, a strong quarterly report). Social media commentary amplifies the move. Within weeks, scripts across the entire sector are rising together regardless of whether the individual companies share the same fundamentals — a well-run hydropower company with contracted power purchase agreements and a speculative, under-construction project with cost overruns can trade at similarly inflated valuations, simply because both wear the same sector label the herd is chasing. When the rally eventually runs out of new buyers — which it always does, because no sector can absorb unlimited capital forever — the same herd mentality reverses violently. Sell orders cascade because everyone is trying to exit through the same narrow door at once, and because in a market with daily price bands (circuit breakers), a stock in freefall may not even find a buyer at the lower limit, leaving sellers stuck.
KEY CONCEPT
Herding is buying (or selling) because others are buying (or selling), rather than because your own analysis of the business justifies the trade. It substitutes the crowd's confidence for your own research.
CASE IN POINT
During hydropower-led rally phases on NEPSE, dozens of hydropower scripts have moved up together in a matter of weeks even though the underlying projects differ enormously in commissioning status, debt load, and river-flow risk. Investors who bought because "hydropower is going up" rather than because they had read a specific company's project completion report were, in effect, betting on crowd psychology rather than on cash flows.
The practical cost of herding is that it forces you into the market's worst possible entry and exit timing. By definition, you can only follow a herd after it has already started moving — which means the herd investor systematically buys after a meaningful part of the up-move has already happened, and sells after a meaningful part of the down-move has already happened. Over many cycles, this converts what should be a value-driven return into a "buy high, sell low" pattern — the exact opposite of profitable investing. The antidote, developed further in later chapters, is not to ignore the crowd's existence but to treat crowd enthusiasm as a signal to slow down and re-verify your own reasoning, rather than a signal to hurry up.
Lesson 51.2 — Anchoring and the Ghost of the Previous High
Anchoring bias is the tendency to rely too heavily on a piece of information encountered early — the "anchor" — when making subsequent judgments, even when that information has little or no logical connection to the correct answer. The anchor does not have to be relevant to be powerful; it simply has to be the first number that lodges in the mind. In investing, the two most common anchors are a stock's previous all-time high price and an investor's own purchase price, and both distort decision-making in remarkably similar ways even though they arise from completely different sources.
Take the previous-high anchor first. Suppose a hydropower script traded as high as Rs 850 during a bull phase eighteen months ago and has since fallen to Rs 340 as the sector cooled and the company's actual earnings disappointed relative to the hype. An investor evaluating this stock today will very often frame the decision not in terms of "is Rs 340 a fair price for this company's future cash flows" but in terms of "this stock used to be worth Rs 850, so at Rs 340 it is cheap, it has 'more room to recover.'" This reasoning treats the old high as a kind of gravitational centre the price ought to return to, when in fact the Rs 850 price may itself have been a herd-driven overvaluation that had nothing to do with the company's fundamentals, and the business today may genuinely be worth less than Rs 340 if its earnings power has permanently deteriorated. The number Rs 850 is psychologically vivid — it sits in the investor's memory, in old screenshots shared in Viber groups, in the "52-week high" figure the trading app itself prominently displays — but it carries no actual information about intrinsic value.
The purchase-price anchor works identically but personally. An investor who bought a commercial bank script at Rs 450 and watches it fall to Rs 310 will very often refuse to sell "until it gets back to at least what I paid," treating Rs 450 as a target the market somehow owes them, rather than evaluating Rs 310 today on its own merits — comparing it, for instance, to the bank's current book value, its dividend trajectory, or better opportunities elsewhere in the market. The market has no memory of what any individual investor paid. It does not know and does not care about your purchase price. Yet the purchase price becomes, in the investor's mind, indistinguishable from "fair value," simply because it is the number most emotionally salient to them.
Anchoring also works in the opposite direction during a rally: an investor who bought a script at Rs 200 and sees it hit Rs 260 anchors to the recent low and concludes the stock is now "expensive," selling far too early relative to the company's genuine growth trajectory, purely because Rs 260 feels large compared to Rs 200 rather than small compared to where sustained earnings growth might eventually take the price.
KEY CONCEPT
An anchor is any number that disproportionately shapes your judgment simply because you encountered it first or because it carries emotional weight — not because it is analytically relevant to the decision in front of you.
WARNING
The "52-week high" and "52-week low" figures displayed on every NEPSE trading terminal and mobile app are among the most powerful anchors in the market. They are historical facts, not valuation opinions, yet they are frequently mistaken for the latter.
The practical cost of anchoring is that it substitutes an arbitrary reference point for genuine analysis, in both directions. Anchored to a past high, an investor holds a fundamentally impaired stock far longer than the business justifies, waiting for a "recovery" that may never come because the conditions that produced the old high (a speculative bull run, an industry-wide re-rating that has since reversed, an earnings figure later restated) no longer exist. Anchored to a purchase price, the same investor refuses to realise a loss even when the capital could be redeployed into a demonstrably better opportunity — a subject explored further in Lesson 51.4. The discipline that counters anchoring is deliberately re-deriving a price target from first principles — current earnings, current book value, current growth outlook — and asking "if I did not already own this stock and had never seen its price history, would I buy it today at this price?" If the honest answer is no, the old high or the old purchase price is irrelevant to what you should do next.
Lesson 51.3 — Confirmation Bias and the Telegram Echo Chamber
Confirmation bias is the tendency to search for, interpret, and recall information in a way that confirms what you already believe, while ignoring or discounting information that contradicts it. It is one of the most thoroughly documented biases in all of psychology, and it operates largely outside conscious awareness — investors experiencing confirmation bias genuinely believe they are being objective, because the selective process happens before the information even reaches conscious deliberation.
The mechanism has a simple emotional root: once money is committed to a position, the investor has a psychological stake in that position being correct, separate from the financial stake. Admitting the position was a mistake carries a sting of regret and, often, a public loss of face if the investment was recommended to friends or family or discussed openly in a group chat. To avoid that discomfort, the mind unconsciously filters incoming information — seeking out sources that praise the stock, dismissing sources that criticise it as "haters" or people who "don't understand the sector," and interpreting genuinely ambiguous news (a delayed project completion, a management change, a regulatory circular) in the most favourable possible light.
Nepal's investing culture provides an almost laboratory-perfect environment for confirmation bias to flourish, because so much investment discussion happens inside closed, self-selecting communities: Viber groups organised around a specific broker, a specific sector, or a specific self-styled "guru"; Telegram channels that post buy calls; YouTube channels and Facebook pages whose business model depends on maintaining an enthusiastic, returning audience. These groups are structurally echo chambers, an environment in which the same viewpoint is repeated back to its members so often that it is mistaken for independent confirmation. An investor who has bought a finance company script based on a Telegram "target Rs 900" call will, entirely naturally, gravitate toward the parts of that same channel — and similar channels — that continue to reinforce the bullish thesis, while a sober quarterly report showing rising non-performing loans, published on the company's own website or disclosed to NEPSE, goes unread or is explained away as "temporary" or "the auditors are too conservative." Members who raise doubts in these groups are frequently shouted down or removed, which further purifies the group into a chamber of pure confirmation.
CASE IN POINT
An investor holding a microfinance script during a period when the entire microfinance sub-sector was showing rising loan-loss provisions and tightening regulatory scrutiny might remain in three or four Telegram groups that continued posting bullish price targets, while never once opening the company's actual quarterly disclosure on the NEPSE or company website — not out of laziness, but because the bullish groups were psychologically more comfortable to consume.
PRACTICAL TOOL
Before adding to or holding a position, deliberately seek out the single strongest bearish argument against it — from a source you did not choose because it agrees with you. If you cannot construct a serious bear case in your own words, you do not understand the investment well enough to hold it with confidence.
The practical cost of confirmation bias compounds over time in a specific way: it delays the recognition of deteriorating fundamentals, so that by the time the investor finally acknowledges a problem — often forced to by a price collapse too large to explain away — a much larger portion of the loss has already occurred than would have been the case with an early, honest reassessment. Confirmation bias is also the mechanism that makes almost every other bias in this chapter worse, because it prevents corrective feedback from reaching the investor's decision-making in time to matter. A herd-driven purchase, an anchored refusal to sell, an overconfident bet — all of these persist far longer than they otherwise would when the investor is only consuming information that agrees with the original decision.
Lesson 51.4 — Loss Aversion and the Disposition Effect: Why Losers Get Held and Winners Get Sold
Loss aversion is the finding, first rigorously demonstrated by psychologists Daniel Kahneman and Amos Tversky, that losses are felt roughly twice as intensely as equivalent gains are enjoyed. Losing Rs 10,000 produces a sharper, more lasting emotional pain than gaining Rs 10,000 produces pleasure. This asymmetry is not a character flaw; it is a deeply embedded feature of human psychology, likely rooted in an evolutionary history in which the cost of missing a threat (a predator, a food shortage) was far more dangerous than the cost of missing an equivalent opportunity. A useful everyday analogy is spoiled food in the refrigerator: a household will often keep a container of rice or dal that has visibly started to turn, reheating it "just in case," rather than throwing it away — not because anyone genuinely believes eating it is a good idea, but because throwing it away feels like admitting the money spent on it was wasted, and that admission itself is painful, separate from the actual cost of the food. The investor holding a losing stock is doing the identical thing: refusing to "throw away" the position because selling converts an unrealised, abstract loss into a realised, undeniable one, and that psychological finality is what the mind resists — even though the money was already lost the moment the stock price fell, whether or not the position is sold.
This asymmetry produces a specific, well-documented market pattern called the disposition effect: the tendency of investors to sell winning positions too early, to lock in the pleasant feeling of a realised gain, while holding losing positions too long, to avoid the painful feeling of a realised loss. A phenomenological study of Nepali investor experiences has documented this pattern directly among NEPSE participants, describing investors who consistently described selling profitable positions "to be safe" after relatively modest gains, while holding loss-making positions for years, waiting for a breakeven point that in many cases never arrived, particularly among companies whose fundamentals had genuinely and permanently deteriorated.
Picture two investors. The first buys a commercial bank script at Rs 400; it rises to Rs 460, a comfortable 15 percent gain, and the investor sells immediately, satisfied with having "booked profit." The second buys a hydropower script at Rs 400; it falls to Rs 280, a 30 percent loss, and the investor holds on for three years, adding small amounts on the way down "to average the cost," waiting for the price to merely return to Rs 400 so the position can be closed "without a loss." Both decisions are driven by the identical psychological force — the discomfort of realising a loss versus the comfort of realising a gain — but the financial consequences are opposite and severe: the investor systematically prunes their winners while their capital becomes increasingly concentrated in their worst-performing ideas. Over an investing lifetime, this pattern is one of the most reliable destroyers of compounding available, because it guarantees that a portfolio's average holding is disproportionately weighted toward businesses the investor's own actions have already flagged as disappointments.
KEY CONCEPT
The disposition effect is the tendency to sell winners too early and hold losers too long, driven by loss aversion — the fact that realising a loss hurts roughly twice as much as realising an equivalent gain feels good.
WARNING
"I'll sell once it gets back to what I paid" is not a valuation judgment. It is loss aversion disguised as patience. The market does not know or care what you paid, and waiting for breakeven can tie up capital in a deteriorating business for years while better opportunities are missed.
The single most useful mental correction here is to separate the decision to sell from the price at which you originally bought. Every holding, every single day, should be re-evaluated on one question only: knowing what I know now, would I buy this position today, at today's price, with fresh capital? If the answer is no, the position should be reduced or closed — regardless of whether that crystallises a gain or a loss — because the original purchase price is a sunk cost with zero bearing on the correct decision going forward. This reframing is difficult precisely because it runs directly against loss aversion, which is why it must be built into a written, rules-based process rather than left to be decided fresh, under emotional pressure, each time.
Lesson 51.5 — Overconfidence, Recency Bias, and FOMO: The Bull Market Trio
Three biases tend to arrive together, feed on each other, and do the greatest damage specifically during the euphoric phase of a bull market, which is why this lesson treats them as a connected group rather than in isolation.
Overconfidence bias is the tendency to overestimate the accuracy of one's own knowledge, judgment, and predictive ability. In investing, overconfidence typically arrives on the back of a genuine early success — an investor's first few trades happen to go well, often because they entered during a rising market where nearly everything was rising, and the investor attributes the gain entirely to their own skill in stock selection rather than to the broader tailwind. A study examining risk tolerance, overconfidence, and investment decisions among Nepali investors found overconfidence to be a measurable and significant driver of trading behaviour — investors who scored higher on overconfidence measures traded more frequently, took larger position sizes, and used more leverage (margin lending) than the evidence justified. The mechanism is intuitive: after a lucky win, the brain constructs a flattering narrative — "I have a good instinct for hydropower stocks," "I can time the market" — and that narrative then licenses increasingly large and increasingly under-researched bets, because the investor believes their own judgment alone is sufficient due diligence.
Recency bias is the tendency to give disproportionate weight to recent events and to extrapolate a recent trend forward as though it will continue indefinitely, while discounting longer historical patterns that suggest otherwise. If NEPSE has risen for eight consecutive weeks, recency bias produces the conviction that it will keep rising, because the eight weeks of evidence in front of the investor feel more real and more relevant than the abstract historical knowledge that markets move in cycles and that every sustained rally in NEPSE's history has eventually reversed. Recency bias is what allows an investor to look at a hydropower script's chart, see a clean upward line over the past two months, and conclude the trend is now a reliable fact about the stock's future, rather than one phase of a much longer and more volatile history.
FOMO — fear of missing out — is the anxious, urgent feeling of being left behind while others profit, and it is the emotional accelerant that turns overconfidence and recency bias into actual buying pressure. FOMO is what drives an investor who had originally decided to "wait for a pullback" to abandon that discipline and buy at the top, because every day of waiting brings fresh evidence — friends posting screenshots of gains, a Viber group buzzing with excitement, a script hitting its daily upper circuit for the third day running — that the decision to wait was already a costly mistake. The social dimension matters enormously in Nepal's tightly networked investing culture: hearing that a cousin, a colleague, or a neighbour has made a substantial profit on a specific script in a matter of weeks is a far more emotionally potent trigger than any amount of abstract statistical reasoning about valuation.
Together, these three biases describe the typical arc of a NEPSE retail investor during a bull phase: an early, partly lucky win breeds overconfidence; overconfidence combines with a rising chart to produce recency bias, the belief that the trend will simply continue; and FOMO supplies the emotional urgency to keep buying — and to keep buying larger amounts, often using margin lending to amplify the position — right up until the point where the rally exhausts itself. This is close to the mechanism widely believed to have driven Nepal's dramatic NEPSE cycle beginning in mid-2020, when the index rose from roughly the 1,100 range to an all-time high near 3,200 by August 2021, before falling sharply over the following year as margin calls forced liquidation into a market with progressively fewer willing buyers. Investors who entered late in that cycle — precisely the investors most driven by FOMO, since the loudest social proof and the most dramatic recent gains arrive only after most of the rally has already happened — were disproportionately represented among those left holding positions purchased at or near the peak.
CASE IN POINT
An investor who opens their first trading account, buys a bank script during a rising phase, and doubles their money within two months may conclude they have a talent for stock-picking. Studies of Nepali investor behaviour link exactly this kind of early-success overconfidence to larger subsequent position sizes and heavier use of margin — a pattern that magnifies losses precisely when a rally eventually reverses.
REGULATORY DETAIL
Margin lending against listed shares is regulated by Nepal Rastra Bank and the individual bank or finance company extending the loan, subject to a loan-to-value cap and margin-call provisions. When share prices fall sharply, brokers and lenders issue margin calls requiring additional collateral; investors unable to meet the call have their pledged shares force-sold, which was a significant amplifying mechanism in NEPSE's 2021–2022 decline.
CAUTION
A rising trend line is a description of the past, not a promise about the future. The single riskiest moment to increase a position size is usually when confidence is highest — because high confidence after a sustained rally is frequently a symptom of recency bias rather than evidence of superior judgment.
The practical cost of this trio is the most severe in the chapter, because it specifically times capital deployment to the worst possible moment: overconfidence and recency bias encourage progressively larger bets as a rally matures, and FOMO ensures the largest, most leveraged bets are placed closest to the top, right before the reversal that every extended rally in NEPSE's history has eventually produced.
Lesson 51.6 — Gambler's Fallacy and Home Bias: Two Ways of Getting Diversification Wrong
The final two biases in this chapter operate differently from the previous six — they are less about emotional urgency and more about faulty statistical intuition and comfort-seeking — but both quietly erode a portfolio's risk-adjusted return in ways investors rarely notice until the damage is done.
Gambler's fallacy is the mistaken belief that if something has deviated from its average for a period of time, it is now "due" to revert, as though random or semi-random events carry a memory of their own past outcomes. The name comes from the casino: a roulette player who has watched red come up five times in a row often feels black is now overdue, even though the wheel has no memory and each spin remains independent, with the same odds as before. In NEPSE, gambler's fallacy shows up in a very specific, very common piece of reasoning: "this stock has fallen so much, it must bounce back." A script that has fallen from Rs 600 to Rs 150 is treated as statistically "due" for a recovery purely because of the magnitude of the fall, entirely independent of whether anything about the underlying business has stabilised. This is a serious logical error, because a stock price, unlike a roulette wheel, is not a random process oscillating around a fixed mean — it is a reflection of a real business whose value can permanently decline, stagnate, or even go to zero. A finance company that has lost its lending licence, a hydropower project whose river-flow forecasts turn out to have been overstated, or a hotel company that never recovers its pre-pandemic occupancy does not "revert" simply because its stock has already fallen a great deal; a large fall can just as easily be the market correctly repricing a permanently impaired business as it can be an overreaction ripe for reversal, and gambler's fallacy provides no way to tell the two apart.
WARNING
"It has already fallen 70 percent, how much more can it fall?" is gambler's fallacy, not analysis. A stock can fall another 70 percent from a lower base, and a business with deteriorating fundamentals has no statistical obligation to revert toward its old price.
Home bias, sometimes called familiarity bias, is the tendency to overweight investments in things that feel familiar — domestically, sectorally, or simply by name recognition — relative to what a properly diversified, risk-adjusted portfolio would suggest. Internationally, home bias usually refers to investors overweighting their own country's stock market relative to global markets. In the Nepali context, where capital controls and regulatory restrictions make meaningful international diversification largely inaccessible to ordinary retail investors in any case, home bias manifests one level down: as a heavy overweighting of the handful of sectors and company names an investor already recognises — overwhelmingly, commercial banks, because nearly every Nepali household already has a banking relationship and therefore a comfortable, familiar mental model of what a bank "is" — while sectors that are less intuitively familiar (manufacturing, trading companies, less prominent hydropower developers, insurance) are underexplored or ignored altogether, not because their risk-adjusted prospects are worse, but simply because they are less familiar. A retail investor with a portfolio containing five different commercial bank scripts, and nothing else, often believes they are diversified, because five is more than one. In reality, all five holdings are exposed to the same macro risk factors — interest rate cycles set by Nepal Rastra Bank, the same regulatory capital requirements, the same broad economic cycle, the same NEPSE liquidity conditions — so the portfolio behaves, in a downturn, much more like a single concentrated bet on "Nepali banking" than like a genuinely diversified set of five independent holdings.
KEY CONCEPT
True diversification requires holdings whose underlying risks are not all driven by the same factor. Five bank scripts are five holdings but effectively one bet, because a single event — a policy rate shock, a systemic credit quality problem — can move all five in the same direction at once.
The table below summarises all eight biases covered in this chapter, their psychological trigger, and how each typically shows up on NEPSE.
Bias
Psychological Trigger
Typical NEPSE Manifestation
Herd behaviour
Fear of being the only one wrong; assumption that a crowd must be informed
Buying a hydropower or microfinance script because Viber/Telegram groups are excited, not because of company research
Anchoring
Overweighting the first or most emotionally salient number encountered
Refusing to sell below purchase price, or treating an old 52-week high as a "fair" price the stock should return to
Confirmation bias
Discomfort of admitting a decision was wrong
Staying in bullish Telegram/Facebook groups and ignoring a company's own weak quarterly disclosures
Loss aversion / disposition effect
Losses feel roughly twice as painful as equivalent gains feel good
Selling winning positions quickly to "lock in" small gains while holding losing positions for years
Overconfidence
Attributing an early lucky win entirely to personal skill
Rapidly increasing position sizes and using margin lending after a first successful trade
Recency bias
Recent trends feel more real than long-run historical patterns
Assuming an eight-week rally will continue simply because it has continued so far
FOMO
Social proof of others' gains creates urgent anxiety
Buying near the top of a rally after seeing friends' or Viber group members' profit screenshots
Gambler's fallacy
Belief that a large deviation from the past is statistically "due" to reverse
"This stock has fallen 70 percent, it has to bounce back," regardless of deteriorated fundamentals
Home bias / familiarity bias
Comfort with names and sectors already known
Portfolio concentrated entirely in commercial banks because banking feels familiar, mistaken for diversification
The practical cost of both gambler's fallacy and home bias is quieter than the costs described in earlier lessons, but no less real over time. Gambler's fallacy channels capital into "cheap-looking" but fundamentally impaired businesses on the mistaken belief that a large price fall is itself evidence of an impending recovery. Home bias caps the ceiling on a portfolio's risk-adjusted returns by concentrating exposure in a small number of correlated sectors, leaving the investor far more exposed to a single adverse event — a banking-sector liquidity crunch, a monetary policy tightening cycle — than a properly diversified portfolio spanning multiple, less-correlated sectors would be. Both biases share a common root: they replace an honest assessment of an individual business's prospects with a comforting shortcut — "it must bounce back," "banks are safe because I understand them" — that requires no further work and produces no discomfort, right up until the moment the market disagrees.
Chapter recap
This chapter has examined eight cognitive and emotional biases that recur throughout the behaviour of NEPSE retail investors, grounding each in the specific institutional and social texture of Nepal's market: a retail-dominated exchange, tightly networked Viber and Telegram investing communities, sector-driven rally cycles in hydropower, microfinance, and banking, and a history — most visibly the 2020–2021 bull run and its subsequent decline — that has given every one of these biases room to play out at scale. Herd behaviour was shown to substitute the crowd's apparent confidence for independent analysis, guaranteeing that followers enter after a move has already partly happened and exit after a decline has already partly happened. Anchoring bias was shown to fix an investor's judgment to an emotionally salient but analytically irrelevant number — a past high or a personal purchase price — long after the conditions that produced that number have ceased to exist.
Confirmation bias was identified as the mechanism that quietly protects and prolongs nearly every other bias in this chapter, because an investor who only consumes information confirming an existing position never receives the corrective feedback that might otherwise interrupt a costly decision in time. This is precisely why Nepal's echo-chamber investing culture — closed groups organised around a shared bullish thesis, resistant to dissenting voices — is not a harmless social habit but a structural risk factor in its own right. Loss aversion and its market expression, the disposition effect, were shown to produce a portfolio-level pattern that is close to the opposite of what successful investing requires: winners sold too early, losers held too long, capital increasingly concentrated in the investor's own worst-performing ideas, driven not by analysis but by the simple asymmetric pain of realising a loss versus the pleasure of realising a gain.
The bull-market trio of overconfidence, recency bias, and FOMO was presented as a connected, self-reinforcing sequence rather than three isolated phenomena: an early lucky win breeds unwarranted confidence in one's own skill, a sustained recent trend is mistaken for a durable trend, and the social visibility of others' gains supplies the emotional urgency to buy — often with borrowed money through margin lending — at precisely the point in a rally's life cycle when the risk of reversal is highest. Gambler's fallacy and home bias closed the chapter as two quieter but equally corrosive errors: the first channels capital toward businesses whose large price falls are mistaken for a statistical guarantee of recovery, and the second caps a portfolio's genuine diversification by concentrating holdings in familiar names — chiefly commercial banks — that are, in a downturn, far more correlated with one another than their number would suggest.
The unifying lesson across all eight biases is that none of them are failures of intelligence or character. They are the predictable, well-documented output of a normal human mind operating in an environment — fast prices, social visibility, real money, genuine uncertainty — that consistently triggers mental shortcuts evolved for very different circumstances. Empirical research on Nepali investors, cited throughout this chapter, confirms that these are not abstract imports from Western behavioural finance textbooks but measurable, active forces shaping actual NEPSE trading behaviour today. Recognising a bias while it is operating — naming it in the moment, "this is FOMO," "this is anchoring to my purchase price" — is the first and most practical line of defence, because a bias that has been consciously identified loses much of its power to operate unnoticed.
Naming a bias in the moment, however, is not the same as reliably overriding it under pressure, especially when real money and real emotion are involved simultaneously. Chapter 52, "Building Investor Temperament," turns from diagnosis to construction: it examines how disciplined investors — professional and retail alike — build durable habits, written rules, and pre-committed decision processes that function correctly precisely when emotion is running highest, so that recognising a bias is followed reliably by the right action rather than by good intentions that dissolve the moment a Viber group starts buzzing or a portfolio shows red. Where this chapter has been about the mind's failure modes, the next is about the systems, habits, and temperament that hold up despite them.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part X · Chapter 52
Building Investor Temperament
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 52.1 — Temperament, Not Intelligence
In 1949, a Columbia University professor named Benjamin Graham published a book that would go on to shape more successful investing careers than any single volume before or since. In it, Graham wrote a sentence that Warren Buffett — his most famous student — has repeated for seven decades as the single most important idea in the entire discipline of investing: "The investor's chief problem — and even his worst enemy — is likely to be himself." Buffett sharpened the point further in his own words: "Success in investing doesn't correlate with IQ once you're above the level of 125. Once you have ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing."
This is a genuinely strange claim if you have not sat with it before. Nepal produces engineers, doctors, chartered accountants, and MBA graduates by the thousands every year — people with more than enough raw intelligence to read a balance sheet, calculate a price-to-earnings ratio, or understand what a dividend yield means. And yet a very large number of these same intelligent people lose money in NEPSE, sometimes repeatedly, sometimes across multiple market cycles. Meanwhile, some investors with far less formal education — a retired schoolteacher in Pokhara, a shopkeeper in Itahari who has held the same five bank stocks since 2015 — quietly compound wealth over fifteen years while the sharpest minds in their extended family churn their portfolios into losses. The difference between these two groups is almost never intelligence. It is temperament.
What temperament actually means. Temperament, in the context of investing, is not personality in the everyday sense — it is not about being calm, easy-going, or unflappable in daily life. A person can be a hot-tempered, impatient driver and still have excellent investment temperament, and a person can be gentle and soft-spoken in conversation and still panic-sell every time NEPSE drops 8% in a week. Investment temperament is a narrower and more specific quality: the capacity to behave rationally with money when the emotional pressure to behave irrationally is at its highest. It is the ability to do the boring, correct thing — hold, wait, follow the plan — when every instinct and every headline is screaming at you to do something dramatic.
Chapters 50 and 51 of this book laid the diagnostic groundwork. Chapter 50 showed you the balance sheet of losses that Nepali retail investors run up year after year — not primarily from bad stock-picking, but from mistimed entries during euphoria, panicked exits during crashes, and the corrosive habit of chasing whatever counter is rallying on Sharesansar's most-traded list that week. Chapter 51 gave you the specific vocabulary for the biases at work: loss aversion, herding, recency bias, overconfidence, anchoring, disposition effect. If Chapter 51 was the diagnosis, this chapter is the treatment plan. Knowing that you are prone to herding does not, by itself, stop you from herding. What stops you is a structure — a set of habits, documents, rules, and pre-commitments — built in advance, during a calm moment, designed specifically to restrain the version of you that shows up when the market is either euphoric or terrifying.
Why intelligence does not protect you. It is worth spending a moment on why intelligence fails to solve this problem, because the intuition that "I am smart enough not to make silly mistakes" is itself one of the most dangerous beliefs an investor can hold. Intelligence is a tool for solving problems that stay still long enough to be analysed — a maths problem, a legal argument, an engineering design. But market panic and market euphoria are not static problems; they are moving, socially contagious, physiologically real states that hijack the same decision-making circuitry regardless of how many degrees a person holds. When NEPSE fell over 50% from its 2021 peak through 2022, the sell orders were not disproportionately placed by uneducated investors — chartered accountants and engineers sold in exactly the same panicked pattern as everyone else, often at the worst possible moment, because the fear response does not check your transcript before it fires. Intelligence can even make things worse, because clever people are unusually skilled at constructing after-the-fact justifications for decisions that were actually driven by emotion. A person with weaker analytical skills might simply say "I panicked and sold." A person with strong analytical skills will construct an elaborate, internally consistent, technically fluent explanation for why selling at the bottom was actually the rational move — and will believe it.
KEY CONCEPT
Temperament is the ability to behave rationally under emotional pressure, not the ability to analyse correctly when calm. Building it means building structures — written plans, pre-set rules, automated processes — that constrain your future emotional self, because willpower alone reliably fails at the exact moments it is needed most.
Temperament as a trainable skill, not a fixed trait. The encouraging part of this story is that temperament, unlike raw intelligence, is substantially trainable. It is closer to a physical fitness than to an IQ score. Nobody is born with the discipline to hold a stock through a 30% drawdown without flinching; that capacity is built the same way a marathon runner builds endurance — through repeated, deliberate practice, through systems that remove the need for willpower in the moment, and through honest reflection on past failures. This chapter is structured around six practical lessons that, taken together, form a temperament-building program any NEPSE investor can begin this week: writing an investment policy statement, using pre-commitment devices, adopting a time horizon appropriate to Nepal's illiquid market structure, using cooling-off periods before big decisions, borrowing discipline techniques from professions where errors are catastrophic, and finally, honestly assessing your risk tolerance and automating as much of the investing process as Nepal's market infrastructure allows.
None of these six lessons require you to be smarter than you already are. All six require you to accept, in advance, that you will not be your best self during a crash or a rally — and to build guardrails now, while you are calm, that will hold even when you are not.
Lesson 52.2 — The Investment Policy Statement
If temperament is the skill, the Investment Policy Statement — commonly abbreviated IPS — is the single most important tool for building it. An IPS is nothing more exotic than a short written document, typically one to three pages, in which you set down — while calm, unhurried, and not looking at any live stock price — your investment goals, your honest risk tolerance, your target asset allocation, your rules for buying and selling, and your rules for rebalancing. Institutional investors, pension funds, and endowment managers across the world are required to maintain one; it is standard practice in every serious asset management firm globally, and it is completely unknown to the overwhelming majority of Nepali retail investors, who instead carry their entire investment "plan" as a loose, shifting set of intentions inside their own head — a plan that conveniently rewrites itself under stress to justify whatever the amygdala wants to do in the moment.
Why a document instead of a mental plan. The reason a written document matters so much, and a mental plan does not, is that mental plans are not fixed reference points — they are living, malleable narratives that get quietly edited by your current emotional state without your conscious awareness. Ask a Nepali investor in January 2021, in the middle of a raging bull run, what their risk tolerance is, and they will confidently tell you they can handle a 40% drawdown without selling. Ask the same investor in July 2022, six months into a brutal decline, and the honest answer from their behaviour — not their stated words — is usually that they could not tolerate even a 15% drawdown before panic-selling. The plan did not change because new information arrived; it changed because the person doing the remembering is not a neutral historian of their own intentions but an emotionally invested party with every incentive to rationalise present behaviour. A written IPS, dated and signed by your own hand months or years earlier, cannot be silently rewritten by present-tense fear. It sits there as an anchor to the person you were when you were thinking clearly, and it forces an uncomfortable but essential confrontation: either follow what you wrote, or consciously and deliberately decide to break your own rule — a much higher bar than simply drifting into a bad decision.
What belongs in an IPS. A good IPS for a Nepali retail investor should cover, at minimum, the following elements, each addressed in plain, specific, unambiguous language:
First, your investment goals and time horizon — what is this money actually for, and when will you need it? Retirement in 25 years is a different goal from a daughter's wedding in five years or a house down payment in eighteen months, and each demands a different asset allocation and a different tolerance for volatility.
Second, your honest risk tolerance, stated in concrete terms rather than vague adjectives. "Moderate risk tolerance" means nothing actionable. "I can tolerate a 25% peak-to-trough decline in my portfolio without selling, but a decline beyond 35% would cause me to reduce equity exposure" is specific enough to act on later.
Third, your target asset allocation — the percentage split between NEPSE equities, government and corporate debentures, mutual funds, fixed deposits, gold, real estate, and cash, stated as target ranges (for example, "60-70% equities, 15-25% debentures and fixed income, 10-15% cash and gold").
Fourth, your sector and concentration limits — for example, a rule that no single scrip exceeds 10% of total portfolio value, and no single sector (banking, hydropower, insurance, microfinance) exceeds 35%, given how concentrated and correlated NEPSE sectors can become during sympathy rallies and sympathy crashes alike.
Fifth, your entry and exit rules — the specific, pre-agreed conditions under which you will buy more of a holding, trim a holding, or exit entirely, stated in terms that do not require real-time judgment calls (discussed further in Lesson 52.3).
Sixth, your rebalancing schedule — how often you will review and restore your portfolio to target allocation (commonly semi-annually or annually for a NEPSE investor, given transaction costs and illiquidity).
Seventh, your rules for what you will not do — a short, explicit list of prohibited behaviours specific to your own known weaknesses: for instance, "I will not buy a stock purely because it hit the upper circuit three days running," or "I will not take a margin loan to buy equities," or "I will not check my portfolio value more than once a week."
PRACTICAL TOOL
Write your IPS on a day when NEPSE is neither crashing nor rallying — ideally a quiet, average trading week. An IPS drafted during euphoria will set unrealistically aggressive targets; one drafted during panic will set unrealistically conservative ones. Neutral market conditions produce the most honest document.
Below is a simplified template a Nepali retail investor can adapt. It is deliberately compact — a working IPS should fit on one or two printed pages, something you can genuinely re-read in five minutes during a moment of stress, not a fifteen-page compliance document that gathers dust.
IPS Section
Sample Entry
Purpose of this portfolio
Long-term wealth building for retirement, target date 2046
Time horizon
20 years; funds not needed before 2041 at earliest
Stated risk tolerance
Can tolerate up to 30% peak-to-trough decline without selling; will reassess (not automatically sell) beyond 40%
No single scrip above 10% of portfolio; no single sector above 30%
Entry rule
Buy only after reviewing latest quarterly report; no purchases based solely on price momentum or social media tips
Exit rule
Sell if the original investment thesis is broken (e.g., sustained earnings decline, governance red flag), not merely because price has fallen
Profit-taking rule
Trim position by one-third if a single scrip appreciates more than 75% and grows beyond 15% of portfolio
Rebalancing schedule
Review portfolio against targets every Baisakh (mid-April) and every Kartik (mid-October)
Cash reserve rule
Maintain minimum 3 months of expenses outside the portfolio at all times; never invest emergency funds in equities
Prohibited behaviours
No margin trading; no buying on upper-circuit days; no portfolio checks during work hours on weekdays
Review trigger
Re-read this document before any transaction exceeding NPR 100,000, and before any decision made within 48 hours of a NEPSE index move exceeding 5%
The IPS as a contract with your future self. The deepest function of the IPS is not really informational — you probably already know, in some vague way, most of what you would put into it. Its function is behavioural: it converts a set of intentions into a written commitment that has social and psychological weight even though no external party enforces it. Some investors go further and share their IPS with a spouse, a trusted friend, or a financial adviser, explicitly asking that person to hold them accountable to it — "if you ever see me about to sell everything during a crash, remind me what I wrote here in calmer times." This external accountability layer, discussed further under pre-commitment devices in the next lesson, multiplies the power of the document considerably. A promise you have made only to yourself is a promise you already know how to break; a promise witnessed by someone else, or written down where your future self must consciously override it, is measurably harder to abandon.
CASE IN POINT
Consider two hypothetical Kathmandu-based investors, both holding a NEPSE portfolio worth NPR 2 million in early 2021. Investor A has no written plan; investor B has a one-page IPS stating a 30% maximum tolerable drawdown and a rule to review, not sell, beyond that point. When the index falls sharply through 2022, Investor A — with no anchor beyond memory and mood — panic-sells near the bottom, locking in losses of roughly 45%. Investor B, forced by their own written rule to "review, not sell," instead re-reads their document, confirms the underlying companies' fundamentals are largely intact, and holds. By the time NEPSE stabilises, Investor B's paper losses have substantially recovered while Investor A's realised losses are permanent. The difference was not stock selection — both held broadly similar portfolios. The difference was a page of paper written eighteen months earlier.
Lesson 52.3 — Pre-Commitment Devices
An Investment Policy Statement tells you what your rules are. A pre-commitment device is what makes those rules bite even when you desperately want to break them. The term comes from behavioural economics, and the classic illustration is Ulysses ordering his crew to bind him to the mast before sailing past the Sirens — he knew, in advance, that his future self would be overwhelmed by a temptation his present self could clearly see coming, so he removed his future self's ability to act on it. A pre-commitment device is exactly this: a rule, action, or constraint set in advance, before emotion is running high, that removes or limits your own future discretion at the precise moment your judgment is least trustworthy.
Why "I'll just be disciplined" does not work. The natural first instinct of most investors, on hearing about pre-commitment, is to say: I understand my biases now, so I will simply resolve to be disciplined in the moment. This almost never works, for a reason well documented in psychology — the very state of high emotional arousal that makes discipline necessary is also the state that most impairs the brain's capacity for reasoned self-control. Fear and greed are not merely uncomfortable feelings that a strong-willed person can push through; they are physiological states, involving real changes in blood flow and neural activity, that measurably degrade the parts of the brain responsible for weighing long-term consequences against short-term impulses. This is precisely why NEPSE investors who genuinely understand, in the abstract, that panic-selling during a crash is a mistake still panic-sell during crashes — the understanding lives in a part of the brain that is partially offline exactly when it is needed. Pre-commitment devices work around this problem by shifting the decision earlier, to a moment when the rational brain is fully in charge, and then mechanically enforcing that earlier decision later, regardless of what the emotional brain wants in the moment.
Automatic rebalancing dates. The simplest and most powerful pre-commitment device for a NEPSE investor is a calendar-based rebalancing rule: on a fixed date (or fixed set of dates) each year, decided in advance and written into your IPS, you review your actual asset allocation against your target allocation and trim or add as needed to restore the targets — regardless of what the market is doing on that particular day. If your target is 65% equities and a bull run has pushed you to 80% equities, your rebalancing date forces you to sell some equities and buy fixed income, precisely when greed would tell you to let your winners ride further. If a crash has pushed you down to 45% equities, your rebalancing date forces you to buy more equities at depressed prices, precisely when fear would tell you to run for cash. This is the entire mechanism by which "buy low, sell high" — advice every investor has heard and almost none actually execute — gets converted from a slogan into an enforceable habit. The date itself is arbitrary (many professional advisers suggest twice yearly, aligned in Nepal's context with Baisakh and Kartik, avoiding the emotionally charged period around Dashain-Tihar when both market sentiment and family cash-flow pressures run unusually high) — what matters is that the date is fixed in advance and not moved to accommodate a "special situation," because every crisis feels like a special situation while it is happening.
Predetermined stop-loss and profit-taking levels. A second class of pre-commitment device operates at the level of individual holdings rather than the whole portfolio: deciding, at the time of purchase — not weeks later, not during a decline — the specific price or percentage move at which you will sell part or all of a position, and writing that number down. A stop-loss level is a predetermined point at which a losing position will be trimmed or exited, designed to cap the damage from being wrong about a stock's prospects before hope, sunk-cost thinking, and the disposition effect described in Chapter 51 take over and turn a manageable loss into a catastrophic one. A profit-taking level operates in the other direction, forcing partial or full sale after a large gain, before greed and the fear of "missing out on more" turn a genuine win into a round-trip loss when the stock inevitably corrects.
It is worth being precise about the mechanics here, because NEPSE's own market structure interacts with stop-loss discipline in ways that differ from more liquid markets. As of the 2026 revisions to Nepal's circuit breaker rules, individual scrips can move up to 15% in a single session before trading is halted for that counter, and the market-wide index itself is designed to suspend trading if the movement reaches roughly 8% in a session. This means a NEPSE stock can gap well past a stop-loss trigger point within a single day, especially on scrips with thin trading volumes, and a broker's day order to sell at a specific price may not execute at all if the circuit locks before your order reaches the book. Nepali investors therefore need to treat a "stop-loss level" less as a guaranteed execution price and more as a pre-committed decision trigger: when the scrip touches or breaches this level, you have already decided, in advance, that you will place a sell order at market or at the best available price at the next opportunity, without re-litigating the decision in the moment. The commitment is to the decision, not to a guaranteed fill — an important distinction in a market where circuit breakers and comparatively thin liquidity can prevent instant execution even for investors who did everything right on paper.
REGULATORY DETAIL
NEPSE's circuit breaker system, revised in 2026, allows individual scrips to move up to 15% in a single session before that counter's trading halts, while the broader market index is designed to suspend trading around an 8% single-session move. Because a stop-loss order can be gapped past on a fast-moving or thinly traded counter, treat any stop-loss level as a pre-committed decision to sell at the next available opportunity — not a guarantee of execution at that exact price.
Third-party and structural commitment. The strongest pre-commitment devices go beyond a private mental rule and build in an external structure that makes reversal genuinely costly or slow. Some practical versions available to Nepali investors include: instructing your broker in writing, ahead of time, about your rebalancing rules, so that a phone call to "just this once, hold off on the rebalance" feels like an active violation rather than a passive drift; using time-delay mechanisms — for instance, deliberately choosing a broker platform or a habit of placing large orders only during a specific window (say, only reviewable the following morning rather than acted on same-day) to insert friction between impulse and execution; agreeing with a spouse, business partner, or trusted friend that any transaction above a certain size requires informing them first, converting a private impulsive decision into a semi-public one subject to at least a moment of external questioning; and, for those with access to it, using structured products such as systematic investment plans in mutual funds (discussed in Lesson 52.6) where the mechanical, automated nature of the product itself is the pre-commitment device — money leaves your account on a fixed schedule regardless of what the market did yesterday, with no daily decision required at all.
PRACTICAL TOOL
A simple, low-cost pre-commitment device any NEPSE investor can start this week: write your rebalancing dates and your stop-loss/profit-taking rules for each current holding on a physical piece of paper, photograph it, and set it as your phone's lock screen. The rule sitting in front of you every time you unlock your phone to check share prices is a far stronger deterrent to impulsive action than a rule buried in a notebook you have to consciously go looking for.
The discipline to accept a wrong-in-hindsight rule. One honest caveat belongs here. Pre-commitment devices will sometimes look wrong after the fact. A stop-loss will occasionally trigger a sale right before a stock recovers; a rebalancing date will occasionally force a sale of equities the week before a further rally. This is not a flaw in the system — it is the system working exactly as designed, trading away a small amount of after-the-fact optimization for a large amount of protection against the much larger, much more common failure mode of no discipline at all. The investor who abandons their pre-commitment rules the first time one of them looks wrong in hindsight has not actually built temperament; they have simply found a new, more sophisticated-sounding excuse to keep doing what emotion always wanted to do anyway. The rules are judged not by whether any single instance was optimal, but by whether the accumulated discipline, applied consistently across dozens of decisions over years, produces a better outcome than the undisciplined alternative — and on this measure, the evidence from institutional investing worldwide is overwhelming.
Lesson 52.4 — Time Horizon for an Illiquid Market
Every investing textbook tells you to have a "long-term time horizon." In Nepal's context, this generic advice needs to be made considerably more specific, because NEPSE's structural features — comparatively low liquidity, a narrow investor base heavily weighted toward retail participation, high correlation across sectors during both booms and busts, and periodic bouts of extreme volatility — make the concept of "long term" both more important and more difficult to hold onto than in a deep, liquid market like the NYSE or even India's NSE.
Why illiquidity punishes short time horizons especially hard. Liquidity, in a market context, refers to how easily an asset can be bought or sold without materially moving its price. A highly liquid market has many buyers and sellers active at all times, so a single investor's trade barely affects the price. NEPSE, despite growth in daily turnover over the past decade, remains considerably thinner than developed markets — daily turnover in a huge number of listed scrips outside the top thirty or so most-traded counters can be minimal, meaning a moderately sized sell order can move the price against you meaningfully, and an attempt to exit a large position quickly during a panic can push the price down further simply because your own selling is a significant fraction of that day's total volume. This has a direct and underappreciated consequence for time horizon: in an illiquid market, the cost of trading frequently — the cumulative bid-ask spreads, the market impact of your own orders, the brokerage commissions on each round trip — eats into returns far more severely than the same behaviour would in a liquid market. A NEPSE investor who trades in and out of positions every few weeks is not merely taking on more emotional volatility; they are mechanically paying a much higher toll per rupee of turnover than a comparable investor in a deep market, because each transaction in a thin counter tends to move the price against the trader more than the equivalent transaction would in a liquid one.
Why short time horizons collide with NEPSE's volatility. NEPSE has, across its history, moved from euphoric multi-year bull runs to painful multi-year corrections with a regularity that has burned successive generations of retail investors who entered expecting steady, linear appreciation. An investor with a genuinely short time horizon — money that must be available in twelve or eighteen months for a specific need — has no business holding a meaningful equity position in a market this volatile, because there is a real, non-trivial probability that a forced sale will land during one of these multi-year troughs rather than at a favourable point. This is not a pessimistic statement about NEPSE's long-run prospects; it is simply an honest acknowledgment that equity markets everywhere, and NEPSE with particular intensity, do not move in a straight line, and that the mathematics of compounding only work in an investor's favour if the investor is not forced to sell during the troughs.
A workable time-horizon framework for Nepal. Given these structural features, a sensible framework for a NEPSE retail investor is to explicitly bucket money by the date it will genuinely be needed, and to size equity exposure accordingly — not as a vague aspiration but as a concrete allocation rule written into the IPS from Lesson 52.2:
Money needed within two years — school fees due next year, an already-planned wedding, a near-term down payment — belongs in fixed deposits, short-term government securities, or high-quality debentures, essentially none of it in NEPSE equities, regardless of how attractive the market looks at the moment. Money needed in two to five years belongs in a more balanced mix, perhaps 30-50% equities with the remainder in fixed income, accepting moderate volatility because there is some time to recover from a downturn but not unlimited time. Money not needed for more than seven to ten years — genuine long-term wealth building, retirement savings, funds set aside for a child not yet in secondary school — can carry the largest equity weighting, because a decade or more gives the portfolio enough runway to ride out even NEPSE's most severe historical drawdowns and still come out ahead through the power of compounding.
WARNING
The single most common time-horizon mistake among Nepali retail investors is not holding equities too long — it is treating equity-appropriate money and near-term-need money as interchangeable, then being forced into a panic sale during a downturn because the "investment" money turns out to actually be next year's tuition payment. Segregate near-term obligations from long-term capital before you ever place a single NEPSE order, not after the market has already fallen.
Illiquidity as a reason for patience, not merely a risk to avoid. There is a subtler, more constructive point buried inside NEPSE's illiquidity that is worth drawing out explicitly, because it flips a commonly perceived weakness into a behavioural advantage for the patient investor. Precisely because exiting a large position quickly is costly and difficult in a thin market, illiquidity acts as a natural, structural pre-commitment device against impulsive trading — a kind of enforced patience that liquid markets do not offer. An investor in a hyper-liquid market like US large-cap technology stocks can panic-sell an entire position within seconds at minimal cost, and can therefore act on every fleeting emotional impulse essentially for free. A NEPSE investor holding a meaningful position in a moderately traded counter faces real friction — days to fully exit without moving the price badly against themselves — and that friction, uncomfortable as it feels in the moment, has historically saved many investors from themselves. The lesson is not to be grateful for illiquidity, which carries real costs of its own, but to recognise that a long time horizon is not merely compatible with NEPSE's market structure — it is, to a significant degree, required by it, and investors who try to trade NEPSE the way they might trade a liquid international market are fighting the market's own physics as well as their own psychology.
Time horizon and the calendar of Nepali life. One final, practical dimension of time horizon deserves mention because it is specific to Nepal's cultural and economic calendar: major predictable cash needs — Dashain and Tihar expenses, annual school admission fees typically clustering around Baisakh, festival-season family obligations — should be planned for as known, near-term liabilities well before the season arrives, not discovered as a forced sale in the week before Dashain when NEPSE happens to be down. Building this seasonal awareness directly into the IPS's cash reserve rule (see the template in Lesson 52.2) removes an entire category of badly timed, forced equity sales that has nothing to do with market judgment and everything to do with poor cash-flow planning colliding unluckily with market timing.
Lesson 52.5 — Cooling-Off Periods and Professional Discipline
The cooling-off period. A cooling-off period is a deliberately imposed delay between the moment an investment impulse arises and the moment it is allowed to become an executed transaction. The concept is borrowed from consumer protection law — many countries, including provisions familiar in Nepali contract and consumer practice, grant buyers a window after signing certain agreements during which they may reconsider and cancel without penalty, precisely because regulators recognise that decisions made under high-pressure, high-emotion sales conditions are systematically worse than decisions made with a clear head after time has passed. Applying the same principle to your own trading behaviour is one of the simplest, lowest-cost temperament-building habits available: adopt a personal rule that any transaction above a size you define in advance — and, separately, any transaction motivated primarily by a strong emotional reaction (excitement about a tip, fear from a headline, envy of a friend's gains, panic from a red portfolio screen) — cannot be executed until a fixed waiting period, commonly 24 to 72 hours, has passed.
Why the delay works. The mechanism behind a cooling-off period's effectiveness is well established in psychology: acute emotional arousal — the kind produced by a sudden stock spike, a scary headline about NEPSE, or a friend's excited phone call about a hot IPO — decays measurably within hours even when the underlying situation has not changed at all. A decision that felt utterly urgent and obviously correct at the peak of that emotional spike very often looks considerably less compelling, or even outright wrong, once the emotional charge has faded and the analytical brain has had a chance to reassert itself. Nepali investors will recognise this pattern from countless real examples: the IPO that seemed unmissable on allotment day but whose fundamentals looked shakier a week later once the listing-gains excitement passed; the counter a friend swore was about to "double from here" during an animated Dashain gathering, which looked far less certain when examined soberly with the quarterly report open two days after the conversation. A cooling-off rule does not prevent you from ever acting on excitement or fear — it simply ensures that when you do act, you are acting on a judgment that has survived contact with a calmer version of yourself, rather than a judgment made entirely inside the emotional spike itself.
PRACTICAL TOOL
A workable cooling-off rule for most Nepali retail investors: any purchase or sale motivated by a tip, a headline, a rumour, or a strong emotional reaction, and any single transaction exceeding 5% of total portfolio value, must wait a minimum of 48 hours from the moment the idea first occurred to you before an order is placed. Write the idea down with the date and time the moment it occurs, and only revisit it after the waiting period has elapsed.
Learning from professional checklist discipline. Some of the most instructive models for building investment temperament come from professions entirely outside finance — professions where the cost of a single undisciplined decision is measured in human lives rather than rupees, and where entire institutional cultures have been built specifically to prevent skilled, intelligent professionals from trusting their own in-the-moment judgment too much. Commercial airline pilots, regardless of how many thousands of hours of experience they carry, run through a physical, spoken pre-flight checklist before every single takeoff — not because an experienced captain is likely to forget that the flaps need to be set, but because aviation safety research repeatedly found that even highly experienced pilots, under the ordinary pressures of a normal workday, skip steps, misremember sequences, or talk themselves out of a precaution when confident and rushed. The checklist exists precisely because confidence and competence do not reliably protect against a specific, predictable class of error — and the fix was never "try harder to remember," it was an external, written, mandatory process that does not depend on memory or willpower in the moment at all.
Surgeons and hospital teams worldwide adopted a strikingly similar tool for the same reason: the World Health Organization's surgical safety checklist, now standard practice in operating theatres including in Nepal's major hospitals, requires the surgical team to verbally confirm basic facts — correct patient, correct site, correct procedure, instrument counts — before, during, and after every operation, specifically because studies found that skilled, experienced surgeons made a measurable, non-trivial number of preventable errors when relying purely on memory and expertise under time pressure. The parallel to investing is direct and worth sitting with: an experienced NEPSE investor who has read a hundred annual reports is no more immune to the specific, predictable failure modes described in Chapter 51 — herding, panic-selling, chasing momentum — than an experienced pilot is immune to a missed pre-flight step, or an experienced surgeon is immune to operating on the wrong site under time pressure. The professional response to this reality, in every field that has taken it seriously, was never to simply demand more discipline from individuals. It was to build external, written, mandatory processes — checklists, protocols, second opinions, mandatory pauses — that catch the error regardless of how confident or rushed the professional feels in the moment.
KEY CONCEPT
The lesson from aviation and medicine is not that experts are careless — it is that expertise and confidence do not reliably protect against a known, predictable class of error under pressure. The fix in both fields was an external written checklist, not an internal resolution to "be more careful." Apply the same logic to investing: your IPS, your pre-commitment rules, and your cooling-off period are your investing checklist.
Building your own investing checklist. Taking this analogy seriously, a Nepali investor can build a short, physical, pre-transaction checklist to be run through — genuinely run through, not glanced at — before every buy or sell order above a threshold size. A workable version might ask: Have I re-read my IPS in the last month? Does this transaction fit within my stated target allocation and sector limits? Is this decision motivated by the underlying business, or by the recent price movement? Has the required cooling-off period elapsed? Am I acting on information, or on a tip whose source I cannot independently verify? Would I still want to make this trade if the price had been unchanged for the past month? This last question is particularly powerful for catching momentum-driven, herding-influenced decisions, because it strips away the "everyone else is buying, the price is moving, I don't want to miss it" energy that drives so much of NEPSE's retail trading activity, and forces the decision back onto the underlying investment merit.
Protocols for the worst moments. Doctors treating a cardiac arrest do not improvise; they follow a specific, rehearsed protocol — a defined sequence of actions appropriate to exactly this emergency, decided by the medical profession long before this particular patient walked in, precisely because the extreme stress of an emergency is the worst possible moment to be inventing a response from scratch. The equivalent for a NEPSE investor is a written "crash protocol" — a short, specific sequence of actions to follow the moment the index or a major holding falls sharply, decided calmly in advance and filed alongside the IPS. A simple version: Step one, do not place any sell order for at least 24 hours regardless of how the market looks. Step two, re-read the IPS and confirm whether the drawdown has actually breached the pre-defined risk tolerance threshold, or whether it merely feels alarming. Step three, if the threshold has been breached, follow the pre-agreed rebalancing or review rule rather than an improvised reaction. Step four, if a genuine decision to act is warranted, inform the accountability partner named in the IPS before executing. Having this protocol written down before a crash begins is the difference between a doctor calmly running through a rehearsed sequence and a panicked bystander improvising CPR from half-remembered instructions — one is far more likely to produce a good outcome than the other, and the difference has nothing to do with intelligence and everything to do with preparation.
Lesson 52.6 — Knowing Your Real Risk Tolerance and Automating Discipline
The gap between imagined and actual risk tolerance. Every tool discussed so far in this chapter — the IPS, pre-commitment devices, time-horizon bucketing, cooling-off periods, professional checklists — depends on one input being accurate: your stated risk tolerance. And this is precisely where most investors, including highly self-aware ones, get it wrong, because there is a systematic and well-documented gap between how much risk a person believes they can tolerate when calmly imagining a hypothetical decline, and how much risk they can actually tolerate when their own real money is genuinely falling in value in front of them. Ask most NEPSE investors during a calm, rising market whether they could stomach a 30% portfolio decline without selling, and the great majority will say yes, confidently, often citing their long time horizon and their understanding that markets recover. Watch the same investors' actual trading behaviour once a genuine 30% decline arrives, and a large proportion sell — not because their long-term reasoning was wrong, but because the imagined experience of a decline and the lived experience of a decline are neurologically and emotionally very different things. Imagining a loss engages a calm, analytical part of the brain; watching your actual retirement savings shrink in real time, watching family members ask worried questions, watching your own portfolio app turn red day after day, engages the same threat-response system that reacts to genuine physical danger. No amount of confident hypothetical reasoning fully prepares a person for that lived experience the first several times they go through it.
Honest signals of true risk tolerance. Because self-reported risk tolerance is this unreliable, it is worth using more honest signals than a simple verbal self-assessment. The most reliable signal is actual past behaviour during a genuine decline, if you have one to draw on — how did you actually behave, not how do you remember wanting to behave, during NEPSE's 2021-2022 correction, or any earlier downturn you lived through? A second useful signal is physical and behavioural: do you find yourself checking your portfolio compulsively, losing sleep, feeling physically tense, or snapping at family members during a market decline? These are honest data points about your true tolerance, regardless of what you would confidently state on a calm Tuesday afternoon. A third useful signal is asking what specific dollar or rupee amount of loss, stated concretely rather than as a percentage, would genuinely disturb your sense of financial security — a 20% decline on a NPR 200,000 portfolio and a 20% decline on a NPR 20 million portfolio are the same percentage but very different lived experiences, and risk tolerance should account for absolute rupee stakes, not merely percentages.
The following table offers a structured self-assessment a Nepali investor can work through honestly, ideally revisited annually, to calibrate the risk-tolerance section of the IPS against reality rather than aspiration.
Self-Assessment Question
Low Tolerance Signal
Moderate Tolerance Signal
High Tolerance Signal
During NEPSE's last major decline, what did you actually do?
Sold a meaningful portion in panic
Held but felt significant anxiety and checked prices daily
Held calmly, or added to positions at lower prices
How many years until you need this specific money?
Under 2 years
2-7 years
7+ years
If your portfolio fell 25% this month, would you lose sleep or feel unable to concentrate at work?
Yes, significantly
Somewhat
No, or only mildly
Do you have an emergency fund of 3-6 months' expenses fully separate from this portfolio?
No
Partial
Yes, fully funded
How dependent is your household's near-term financial security on this portfolio's value?
Highly dependent
Moderately dependent
Not dependent; genuinely discretionary capital
When a stock you hold drops 15% in a week, is your instinct to sell, to research, or to buy more?
Sell
Research before deciding
Consider buying more if thesis intact
Have you ever taken a loan (margin or personal) to buy more shares during a rally?
N/A — describe honestly
Considered it but didn't
Have done this before
An investor whose honest answers cluster in the "Low Tolerance" column should set a genuinely conservative equity allocation in their IPS — regardless of how aggressive they feel their goals require them to be — because an allocation that leads to panic-selling during the next real decline will destroy far more wealth than a modestly conservative allocation held with discipline through the full cycle. It is far better to correctly hold a 40% equity allocation for twenty years than to incorrectly hold a 70% equity allocation for eighteen months before panic-selling it at the worst possible moment.
CAUTION
Do not let your risk-tolerance self-assessment become an exercise in flattering self-image. The purpose is not to discover that you are a bold, sophisticated investor who can handle anything — it is to discover, honestly, what you can actually handle, so your written plan matches your real behaviour rather than your preferred self-conception. A conservative allocation you can actually hold through a crash will always outperform an aggressive allocation you abandon at the bottom.
Automation as the final layer of temperament. The single most powerful practical technique for removing emotion from investing, once your IPS, risk tolerance, and rules are correctly set, is to automate as much of the actual execution as Nepal's market infrastructure allows — because a decision that never has to be actively made in the moment cannot be sabotaged by the emotion of that moment. Systematic Investment Plans, commonly known as SIPs, have become an increasingly accessible tool in Nepal over the past several years through mutual fund schemes offered by asset management companies and, more recently, through dedicated platforms and brokerage tools that allow investors to automate periodic purchases into mutual fund units or, in some structured programs, into a basket of NEPSE-listed securities on a fixed monthly schedule regardless of the prevailing market level. The mechanism underlying a SIP's effectiveness is a concept called rupee-cost averaging (the local equivalent of the globally known "dollar-cost averaging"): because a fixed rupee amount is invested on a fixed date every month, more units get purchased automatically when prices are low and fewer units get purchased automatically when prices are high, without the investor ever having to make a real-time judgment call about whether "now" is a good time to buy. Over a full market cycle spanning both NEPSE's euphoric and depressed periods, this mechanical averaging tends to produce a smoother, more emotionally sustainable investing experience than lump-sum, judgment-based buying — precisely because there is no judgment involved in the moment at all, only a standing instruction set up once, during a calm and rational state of mind, that then executes faithfully regardless of what the market or the investor's emotions are doing on any given month.
Even for Nepali investors who prefer picking individual NEPSE scrips rather than mutual funds, a disciplined, SIP-style approach can be approximated manually: committing to invest a fixed rupee amount into a pre-selected, diversified basket of quality counters on the same date every month, treating this commitment with the same non-negotiable seriousness as a loan EMI or a insurance premium payment, and explicitly refusing to skip a month because "the market looks too high" or add extra because "the market looks too cheap" — both of those judgment calls are exactly the kind of in-the-moment emotional decision-making that automation is designed to remove. The discipline lies not in being right about market timing, which almost nobody consistently achieves, but in being reliably, mechanically present in the market across the full range of its cycles, which the historical record shows matters far more to long-run outcomes than timing skill ever does.
WARNING
Automation removes emotion from the buying decision, but it does not remove emotion from the decision to stop automating. The most common way SIP-style discipline fails in practice is an investor pausing or cancelling their monthly commitment during a downturn — precisely the period when the rupee-cost-averaging mechanism is doing its most valuable work by purchasing more units at lower prices. Write "I will not pause this SIP during a market decline" into your IPS itself, as a specific, named pre-commitment.
Bringing the six lessons together. None of the six tools in this chapter — the written IPS, pre-commitment devices, an appropriately long time horizon, cooling-off periods, professional-style checklists and protocols, and honest risk assessment paired with automation — is individually complicated or expensive to implement. Any Nepali investor, regardless of portfolio size, can write a one-page IPS this week, set two rebalancing dates in their calendar, decide on stop-loss and profit-taking rules for their current holdings, adopt a 48-hour cooling-off rule, draft a short crash protocol, and set up a monthly automated investment. What makes these tools powerful is not their individual sophistication but their combined effect: each one closes off a specific opportunity for the emotional, in-the-moment version of the investor to override the calm, long-term-thinking version of the same investor. Temperament, built this way, is not a personality trait some investors are lucky enough to be born with. It is an engineered outcome, assembled deliberately out of paper, calendar dates, pre-agreed numbers, and standing instructions — built by an ordinary investor, on an ordinary day, specifically so that it holds up on the extraordinary days that will inevitably come.
Chapter recap
This chapter argued that success in NEPSE investing depends far more on temperament — the capacity to act rationally under emotional pressure — than on intelligence, analytical skill, or market knowledge, echoing Benjamin Graham's foundational insight that the investor's chief enemy is usually themselves, and Warren Buffett's observation that beyond ordinary competence, what separates successful investors from unsuccessful ones is the discipline to control their own urges. Nepal produces no shortage of intelligent, well-educated investors who nonetheless lose money in NEPSE by panic-selling during crashes and chasing momentum during rallies, precisely because intelligence is a tool for solving static analytical problems while temperament is what determines behaviour during the dynamic, emotionally charged moments that actually decide long-run investment outcomes. The encouraging counterpoint to this diagnosis is that temperament, unlike raw intellect, is substantially trainable — built through deliberate structures rather than inherited as a fixed trait.
The Investment Policy Statement was presented as the foundational tool of this training program: a short, written document — covering goals, honest risk tolerance, target asset allocation, sector concentration limits, entry and exit rules, rebalancing schedule, and a list of explicitly prohibited behaviours — drafted during a calm, neutral market moment and consulted rather than improvised upon during periods of stress. Its power lies in converting a malleable mental intention, which quietly rewrites itself under emotional pressure, into a fixed written anchor that the investor's future, more emotional self must consciously and deliberately override rather than simply drift away from. Pre-commitment devices were introduced as the mechanism that gives an IPS's rules real enforcement power: automatic calendar-based rebalancing dates that mechanically force buying low and selling high regardless of prevailing sentiment, predetermined stop-loss and profit-taking levels decided at the point of purchase rather than during a live decline, and third-party or structural commitments — informing a broker or accountability partner in advance — that make impulsive reversal genuinely harder than simply following the plan. Nepal-specific market mechanics were noted here too: with individual scrips permitted to move up to 15% and the broader index designed to suspend around an 8% single-session move under the market's 2026 circuit breaker rules, a stop-loss should be understood as a pre-committed decision trigger rather than a guaranteed execution price, given NEPSE's comparative illiquidity in many counters.
Time horizon was examined specifically through the lens of NEPSE's structural features — thinner liquidity than developed markets, high sectoral correlation, and a history of sharp multi-year bull and bear cycles — which together mean that short-horizon money has genuinely no place in NEPSE equities and that a long time horizon is not merely advisable but structurally necessary given the real costs of frequent trading in a comparatively illiquid market. A practical bucketing framework was offered: near-term obligations under two years in fixed income or deposits, moderate-horizon money in a balanced mix, and only genuinely long-term capital — seven to ten years or more — carrying substantial equity weighting, with explicit attention paid to Nepal's own predictable seasonal cash-flow calendar around Dashain, Tihar, and school admission season. Cooling-off periods were recommended as a simple, low-cost habit — a mandatory 24-to-72-hour delay between an emotionally charged investment impulse and its execution — grounded in the well-documented psychological finding that acute emotional arousal decays substantially with time even when the underlying facts have not changed. This lesson was reinforced by drawing an explicit parallel to professions where disciplined, written protocols — pilots' pre-flight checklists, surgeons' safety checklists, emergency medical protocols — exist specifically because expertise and confidence do not reliably protect skilled professionals from a known, predictable class of error under pressure, offering NEPSE investors a template for their own pre-transaction checklist and pre-drafted "crash protocol."
The final lesson confronted the unreliable gap between imagined and actual risk tolerance, showing that most investors overstate, when calm, how much decline they can genuinely stomach, and offering a structured self-assessment table built around honest behavioural signals — actual past reactions to declines, physical and emotional symptoms during downturns, dependency of household finances on the portfolio — rather than aspirational self-description. It closed with automation, particularly Systematic Investment Plans now increasingly accessible through Nepali mutual funds and platforms, as the most powerful practical technique available for removing real-time emotional judgment from the investing process entirely, through the mechanism of rupee-cost averaging, while cautioning that the most common failure mode of automated discipline is pausing it during exactly the downturns when it is doing its most valuable work.
Together, the six lessons of this chapter form a coherent, buildable system rather than a list of disconnected tips: a written plan (the IPS), enforcement mechanisms for that plan (pre-commitment devices), a time frame appropriate to the market's real structure (time horizon), a buffer against impulsive action (cooling-off periods), borrowed professional rigor (checklists and protocols), and an honest foundation plus a mechanical execution layer (risk tolerance and automation). None of these tools require unusual intelligence, unusual capital, or unusual market insight — they require only the willingness to build them now, while calm, on behalf of the less calm version of yourself who will inevitably show up during NEPSE's next euphoric rally or its next painful correction. Chapter 53, "Behavioural Training Exercises," moves from this chapter's frameworks to hands-on practice, offering a set of concrete drills, simulations, and self-diagnostic exercises — including guided reviews of past personal trading decisions and scenario-based stress tests — designed to let Nepali investors actively rehearse the temperament this chapter has described, before real money and real market stress put it to the test.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part X · Chapter 53
Behavioural Training Exercises
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 53.1 — The Pre-Trade Checklist: Interrupting the Impulse
A Kathmandu taxi driver does not pull into oncoming traffic just because the road looks empty. He checks the mirror, signals, and looks over his shoulder — a fixed sequence, performed every single time, precisely because judgment under pressure is unreliable. Buying or selling a share on NEPSE deserves the same discipline, yet most Nepali investors treat the "buy" button in their TMS (Trading Management System, the broker's online order-entry platform) or the phone call to their broker as a reflex rather than a decision. Chapter 52 built the temperament — the settled mind — that discipline requires. This chapter builds the tools that make that temperament usable on an ordinary Tuesday morning when Sunrise Bank's counter is flashing green and a Viber group is typing "load karnali, load karnali" in capital letters.
The pre-trade checklist is the simplest and most powerful of these tools. It is nothing more than a short, fixed list of questions that must be answered — in writing, not in your head — before an order is placed. The entire value of the checklist comes from its rigidity. A checklist you can skip when you are excited is not a checklist; it is a suggestion, and suggestions lose to adrenaline every time.
Why a list of questions works better than "just being careful" is worth explaining, because many readers will be tempted to skip this lesson as too simple. The problem is not that Nepali investors do not know the rules of good investing. Ask any BEED (an investor active in the market for even one cycle) about diversification, and they will recite it back to you. The problem is that knowledge sitting in the front of the brain gets overridden by emotion sitting in the back of the brain at exactly the moment it is needed — when the share price of a hydropower counter is up 15 percent (the current daily circuit limit on an individual NEPSE-listed stock, raised from 10 percent in 2026) and every uncle in the family WhatsApp group is asking why you have not bought yet. A checklist works precisely because it does not rely on memory or willpower in the moment. It relies on a decision made earlier, in a calm state, about what questions must be answered before money moves.
KEY CONCEPT
A pre-trade checklist is a pre-committed decision procedure, not a suggestion. Its power comes entirely from being followed even — especially — when you do not feel like following it.
Consider the mechanics of how an order actually gets placed in Nepal. Through a broker's TMS or app, or through the CDS and Clearing Limited-linked MeroShare system for IPO and rights applications, the physical act of buying a share takes fewer than thirty seconds: select the scrip, enter the quantity, enter the price, confirm. Thirty seconds is not enough time for reflection. It is enough time for a scroll through a Telegram channel showing someone's "80,000 profit in two days" screenshot to translate directly into a buy order. The checklist's job is to insert friction — a mandatory pause — between the impulse and the click.
A good pre-trade checklist for the NEPSE investor should cover four categories: thesis, price, sizing, and process. Below is a working template. It is deliberately short — six to eight questions — because a checklist with forty items will not survive contact with a busy trading day. The discipline is in always using it, not in making it exhaustive.
Checklist Item
Question to Answer in Writing
1. Thesis
What is the specific, falsifiable reason I am buying this share today, in one sentence?
2. Valuation anchor
What multiple (P/E, P/BVPS, or for hydropower, replacement cost per MW) am I paying, and how does it compare to the sector average from Chapter 44-49's methods?
3. Price context
Is the current price near a 52-week high, and if so, what changed fundamentally in the last month to justify it — or is this only sentiment?
4. Source check
Did this idea originate from my own analysis, or from a Viber/Telegram group, a TV panel, or a relative's tip? If the latter, have I independently verified it?
5. Position size
What percentage of my total portfolio will this position represent after the purchase, and does it breach my pre-set single-stock limit (Chapter 52)?
6. Exit plan
At what price or under what condition will I sell — for a loss and for a profit — decided now, before I own the share?
7. Emotional state
On a scale of calm to euphoric/panicked, how do I feel right now about this trade, and would I still make it if the price had not moved in the last 24 hours?
8. Time horizon
Am I buying this as a multi-year holding consistent with my investment policy, or as a short-term trade — and have I labelled it honestly as one or the other?
Two of these questions deserve emphasis because they are where Nepali retail behaviour most often fails. Question 4, the source check, exists because the single largest driver of poor NEPSE decisions is not bad analysis — it is outsourced analysis dressed up as a personal decision. When forty people in a 500-member Viber group are all buying the same microcap hydropower counter within an hour of a "target price 850" message, none of those forty people did forty independent valuations. Question 7, the emotional state check, exists because self-reported euphoria is a remarkably reliable predictor of a bad entry price — the very fact that a trade feels urgent and exciting is itself information about the trade's quality, not just about the trader's mood.
PRACTICAL TOOL
Print the eight-question checklist and keep a physical copy — paper, not a phone note — next to wherever you place trades. The extra friction of physically writing an answer, even in one line, does more to slow impulsive buying than any digital reminder, precisely because it cannot be dismissed with a single tap.
The checklist is not meant to prevent trading. A share that clears all eight questions with honest answers is a share worth buying. The checklist is meant to prevent the specific failure mode described in Chapter 50 — buying because everyone else is buying, selling because everyone else is panicking — by forcing a moment of individual, written reasoning between the impulse and the execution. Investors who adopt this habit consistently report the same experience after a few months: roughly a third of the trades they were about to make simply do not survive the checklist. That third is the discipline paying for itself.
Lesson 53.2 — The Investment Journal: Building a Memory Better Than Your Own
Nepali households have kept a bahi khata — a running ledger of household accounts — for generations, precisely because memory alone is an unreliable record of what was spent, on what, and why. An investment journal applies the same logic to your portfolio, and it is arguably the single highest-leverage habit an individual investor can build, because it is the only exercise in this chapter that compounds — every entry makes the next review more valuable, and every review makes the next entry more honest.
The purpose of the journal is not to record what you bought and at what price. Your TMS statement and your CDS account (the Central Depository System account that holds your dematerialized shares) already do that perfectly. The purpose of the journal is to record the three things no broker statement ever captures: what you believed at the time, what you expected to happen, and how you felt. Six months or two years later, when the trade has resolved one way or another, these three captured-in-the-moment data points are what let you separate a good decision that had a bad outcome from a bad decision that got lucky — a distinction Chapter 51 introduced as central to overcoming outcome bias, and one that is impossible to make from memory alone, because memory reliably edits itself to make past decisions look more reasoned than they were.
KEY CONCEPT
Hindsight bias — the tendency to believe, after an outcome is known, that you "knew it all along" — makes unrecorded memory nearly useless for learning from your own trading history. A journal entry written before the outcome is known is the only honest record you will ever have of what you actually believed.
A workable investment journal entry needs six fields, filled in at the time of purchase — not reconstructed afterward. Below is a template you can copy into a notebook, a spreadsheet, or a simple app.
Why, specifically, in your own words — not the tip's words
Expected outcome
Target price, target holding period, and the specific event or milestone that would confirm the thesis (e.g., "PLC's new plant reaches 80% capacity factor by Q3")
Base case vs. worst case
What you expect to happen, and what you will do if it does not
Emotional state at entry
One honest word or phrase: calm, excited, anxious, FOMO-driven, bored, revenge-buying after a prior loss
Kill condition
The specific price, date, or news event that would prove the thesis wrong and trigger a sale
The kill condition field deserves special attention because it is the field investors most often skip, and skipping it is exactly how a "temporary dip" becomes a five-year bag-holding position. Deciding the kill condition at entry, while still unemotional about the specific shares you do not yet own, produces a very different answer than deciding it three months later while sitting on an unrealized loss and hoping. Writing it down at entry — "I will sell if the promoter share lock-in expiry in Chapter 46's sense triggers heavy insider selling" or "I will sell if quarterly report shows revenue decline two quarters running" — converts a vague hope into a testable rule.
CASE IN POINT
An investor who bought a finance company's shares in 2021 purely because a Viber group called it "the next big multibagger" would, with a journal, have been forced to write an expected outcome and a kill condition at entry. Most such investors instead held through the 2022 liquidity crunch and subsequent price collapse with no predetermined exit, because there had never been one — only a vague, unexamined hope that the tip would eventually be proven right. A journal entry does not prevent the loss, but it prevents the loss from becoming indefinite.
The second half of the journal's value comes not from writing entries but from reviewing them — a practice covered fully in Lesson 53.6's post-mortem exercise. For now, the discipline to build is simply this: no purchase or sale is entered into your CDS account without a corresponding journal entry made on the same day, ideally before the order is placed. Investors who try to journal "later, when I have time" almost never do it, because the emotional charge that made the entry worth recording fades within hours. If the pre-trade checklist in Lesson 53.1 interrupts the impulsive trade, the journal entry captures the psychological truth of the trades that do go through — including the good ones, which are just as important to study as the losses.
WARNING
A journal kept only for losing trades becomes a tool for self-flagellation, not learning, and most investors abandon it within weeks. Journal every trade — winners and losers alike — with equal honesty. The winning trades often contain the more dangerous lessons, because a winning trade made for the wrong reason teaches you that the wrong reason works.
Lesson 53.3 — The Personal Circuit Breaker
NEPSE itself does not allow the market to fall without limit in a single session. Under the exchange's current rules, an individual scrip's price is allowed to move up to 15 percent from the previous close in a single day before trading in that scrip is halted for the session, and the entire market is suspended for the day if the NEPSE index moves 8 percent from the previous close — a mechanism that has existed in various tiered forms (formerly halting the market in stages at smaller index moves) since the sharp corrections of the mid-2010s taught the regulator that unrestricted panic selling feeds on itself. The exchange's circuit breaker exists for one reason: to force a cooling-off period when prices are moving too fast for rational decision-making to keep pace.
There is no reason an individual investor should not build the same mechanism into personal trading rules — and every reason they should, because your personal capacity for panic does not wait for an 8 percent index move. You can be ruined at the individual portfolio level well before NEPSE-wide circuit breakers ever activate.
KEY CONCEPT
A personal circuit breaker is a pre-committed rule, decided while calm, that halts your own trading activity — not the market's — once a specific loss threshold is crossed. Its purpose is identical to NEPSE's own: to prevent decisions made at maximum emotional intensity.
A personal circuit breaker system typically operates on two levels, mirroring how NEPSE's own system has tiers.
The first is a per-position circuit breaker: a predetermined percentage loss on a single holding — commonly set between 15 and 25 percent below your entry price, though the right number depends on the volatility of the sector (a microcap hydropower developer pre-commissioning naturally swings more than Nabil Bank) — at which you are required to stop and re-run the pre-trade checklist in reverse: is the original thesis still intact, or has something genuinely changed? This is not necessarily an automatic sell order. It is an automatic stop-and-think order. The distinction matters because a rigid stop-loss can force you to sell into a temporary, sentiment-driven dip that has nothing to do with the company's fundamentals — exactly the kind of noise Chapter 47's ratio-driven, fundamentals-first approach teaches you to look through. The circuit breaker's job is to guarantee a deliberate re-examination, not to guarantee a sale.
The second is a portfolio-level circuit breaker: a predetermined cumulative loss across your entire portfolio — for instance, a 10 percent drawdown from your portfolio's most recent high-water mark within a rolling month — at which you stop opening any new positions entirely for a fixed cooling-off period, commonly two to four weeks, regardless of how attractive any individual opportunity looks during that window. This rule exists because the data on investor behaviour, in Nepal as everywhere, shows a consistent pattern: investors who are already down tend to take larger, more desperate risks to "make it back," a behaviour formally known as the disposition toward loss-chasing, and this is precisely when judgment is most impaired.
WARNING
The single most dangerous trading decision in any market is the one made to "recover losses quickly." A portfolio-level circuit breaker exists specifically to remove your ability to make that decision on the day you most want to make it.
Writing the rule down in advance, exactly as NEPSE's own circuit breaker percentages are published rules known to every market participant in advance, is what makes it enforceable. A circuit breaker decided in the moment — "I'll stop trading if things get much worse" — is not a rule; it is a mood, and moods move with the price. Below is a simple worked format for setting your own two-tier system.
Tier
Trigger
Pre-Committed Action
Per-position
Single holding down 20% from entry
Halt: no averaging down; re-run checklist within 48 hours; decide hold or exit based on thesis, not price alone
Portfolio-level
Total portfolio down 10% from monthly high
Halt: no new positions for 3 weeks; review journal entries from the drawdown period; resume only after a written re-assessment
Some investors object that a circuit breaker will cause them to "miss the bottom" and sell at the worst possible time, or to sit out a rebound they could have caught. This objection misunderstands the tool. The circuit breaker is not a forecasting device; it does not claim to know where the bottom is, any more than NEPSE's own halt claims to know where the index will land after trading resumes. Its entire function is to guarantee that the next decision, whatever it turns out to be, is made with a clearer head than the one that existed in the middle of the fall. A rule that occasionally costs you a fast rebound is a rule that, applied consistently across a full market cycle, will save you from the far larger and far more common cost of panic-selling into an air pocket or averaging down into a company whose fundamentals genuinely broke.
REGULATORY DETAIL
NEPSE's market-wide suspension at an 8 percent index move and its 15 percent single-stock daily limit are set and periodically revised by the exchange in coordination with the Securities Board of Nepal (SEBON); these figures have changed more than once in the exchange's history and should always be confirmed against the current NEPSE circular before being cited to a client, since a personal circuit breaker calibrated to an outdated exchange rule is calibrated to the wrong volatility environment.
Lesson 53.4 — The Devil's Advocate Exercise
Every investment thesis, no matter how carefully built, is constructed by a mind that already wants to believe it, because the act of researching a stock is usually motivated by an initial spark of interest — a tip, a chart pattern, a sector story — that came before the analysis, not after it. Chapter 51 named this confirmation bias: the tendency to notice and weight evidence that supports a conclusion you have already half-reached, while discounting evidence that contradicts it. The devil's advocate exercise is a structured way to fight this tendency, not by hoping to be more objective, but by deliberately assigning yourself the job of building the strongest possible case against your own trade.
The exercise has a simple format. Before placing any position above a size threshold you set for yourself (a reasonable starting point is any position larger than 5 percent of your portfolio), write a one-page memo arguing why you should not buy this share. The memo must be genuinely adversarial — not a token paragraph tacked onto a bullish thesis, but the best case a smart, skeptical friend who does not want your money in this stock would make.
KEY CONCEPT
The devil's advocate exercise works only if it is written with the same effort as the bull case. A half-hearted "risks" section at the bottom of a research note is not devil's advocacy — it is decoration.
A useful structure for the memo borrows directly from the analytical toolkit built across Chapters 40 through 49:
First, the valuation objection: at the current price, what does the market already have to believe about this company's future for the price to make sense, and is that belief realistic? If a hydropower counter is trading at a price implying a return on equity the company has never once achieved historically, the devil's advocate must say so plainly.
Second, the governance objection, drawing on Chapter 39-40's tools: who are the promoters, what is their history with minority shareholders, and is there a related-party transaction, a sudden rights issue, or a board composition red flag that a bullish read would prefer to overlook?
Third, the sector-cycle objection: is this security attractive because of the company, or because the entire sector — hydropower, banking, life insurance, hospitality — is being re-rated by a market-wide narrative that has, in past NEPSE cycles, reliably reversed? Chapter 51 documented how sector rotations on NEPSE tend to be driven more by narrative than by earnings revisions in the short run.
Fourth, the liquidity objection: if the thesis is wrong, can this position actually be exited at a reasonable price, or is the counter thinly traded enough that a decision to sell could itself move the price against you?
PRACTICAL TOOL
If writing a full memo feels excessive for smaller positions, a shortcut version works: before buying, say out loud (or write in one sentence) the single strongest reason NOT to buy this share. If you cannot articulate one honestly, that itself is a warning sign — it usually means you have not looked hard enough, not that none exists.
The devil's advocate exercise is especially important during sector-wide rallies, which is precisely when NEPSE investors are least inclined to do it. When hydropower counters are broadly rallying — as has repeatedly happened around monsoon-season generation updates, new plant commissioning announcements, or national narratives about energy exports to India — the entire information environment (Viber groups, YouTube commentary channels, TV panel discussion) becomes one-sided. Everyone around you is bullish, which is exactly when the discipline of assigning yourself the contrary position has the highest value, because the market is, at that moment, least likely to be doing it for you.
A genuinely useful devil's advocate exercise sometimes changes the decision, and sometimes does not — both outcomes are successes. If the memo is written honestly and the bull case still survives it, you now hold the position with a far stronger foundation, and you have a pre-written record of the specific risks to monitor, which folds directly into the journal entry from Lesson 53.2's kill condition field. If the memo exposes something the original excitement had papered over, you have avoided a loss for the cost of one page of writing — a trade that, over an investing lifetime, will look very good in hindsight even though it never appears on any brokerage statement.
CASE IN POINT
During periods when a single sub-sector — microfinance institutions in one cycle, hydropower developers in another — has come to dominate retail attention on NEPSE, a genuine devil's advocate exercise applied broadly across a portfolio would typically surface the same objection each time: that the rally was being driven primarily by new retail money entering the sector rather than by any change in the underlying earnings power of the specific companies. Investors who wrote this down before buying were far better positioned to recognise the eventual cooling than investors who only encountered the argument after the price had already turned.
Lesson 53.5 — The Media Diet
Ask a hundred active NEPSE investors where their last ten trade ideas came from, and an uncomfortable number will trace back not to a company's annual report, its quarterly financial statement, or an independent valuation exercise, but to a Viber group, a Telegram channel, a YouTube "expert" livestream, or a WhatsApp forward of a screenshot with a target price and no source. This is not a moral failing particular to Nepal — retail investors everywhere are drawn to social proof and shortcuts — but the specific texture of it in Nepal, where investment-tip Viber and Telegram groups can run into the thousands of members and often blend genuine market commentary with promotional pumping of illiquid counters, makes managing this information diet a distinct and necessary exercise, not an optional afterthought.
WARNING
A group administrator or a "senior investor" recommending a specific microcap counter to a group of thousands of members has, whether by intent or not, created conditions for a pump: concentrated buying interest in a thinly traded stock, followed by an equally concentrated urge to sell once the price has moved, often led by whoever bought first. Treat any specific buy-target-price recommendation arriving through a group chat with the same skepticism you would apply to an unsolicited phone call about a hot IPO.
Managing a media diet does not mean withdrawing from all sources of market information — that would be its own mistake, since some of these channels genuinely do carry useful, timely information about circulars, dividend announcements, and book-closure dates. The exercise is about classification and dosage, the same way a household manages a diet not by eliminating all food but by being deliberate about what is eaten, how much, and how often.
A workable media diet exercise has three steps. First, categorize every regular information source you consume — sharesansar.com, MeroLagani, a specific Viber group, a specific YouTube channel, TV business panels, a broker's research note — into one of three buckets: primary data (company disclosures, NEPSE circulars, SEBON notices, audited financial statements), analytical commentary (research grounded in visible methodology you can check), and unfiltered noise (tips, rumours, screenshots, target prices with no stated reasoning). Second, set a rule for how each bucket is used: primary data feeds directly into your checklist and journal; analytical commentary is read but always cross-checked against your own numbers before acting; noise is, at most, a prompt to go verify — never a basis for action on its own.
Third — and this is the step most investors skip — set a time boundary. Decide, in advance, a fixed window (many experienced investors use the thirty minutes after market close, once the day's excitement has passed) during which you will review tip groups and market chatter, and do not check them during trading hours. Checking a pump-oriented Viber group while the market is live and your money is exposed is the single worst timing possible: it is exactly when the group's collective emotional temperature is highest and your own judgment is most likely to be hijacked by it, precisely the failure mode Lesson 53.1's checklist is built to interrupt.
PRACTICAL TOOL
Mute notifications from every trading-tip group and channel during market hours (10:00 a.m. to 3:00 p.m. on NEPSE's trading days), and re-enable them only after close. The content will still be there in thirty minutes; what will not still be there is the version of you that was calm enough to evaluate it properly.
The media diet exercise also has a household dimension specific to Nepal that is worth naming directly: family and social pressure to act on a relative's or neighbour's tip. It is genuinely difficult, in a culture where financial decisions are often discussed openly within extended families, to tell a maternal uncle who "made lakhs" on a finance company counter that you will not be following his tip. The tool here is not confrontation; it is simply routing every tip, regardless of its source, through the same pre-trade checklist and devil's advocate exercise as any other idea. A tip from a trusted relative is not disqualified from being a good idea — it is simply not exempted from being checked like every other idea, and saying so honestly ("I check everything the same way, it's not personal") tends to be both truthful and socially survivable.
CAUTION
Be honest with yourself about a specific pattern: if you find that your best-performing trades in your journal consistently originated from your own independent research, and your worst-performing trades consistently trace back to a group chat or an unverified tip, that pattern is your own data telling you exactly where your media diet needs to tighten — no external study is more relevant to your decisions than your own journal's record.
Lesson 53.6 — The Post-Mortem: Reviewing Every Trade, Win or Lose
A pilot involved in even a minor incident does not simply move on to the next flight. There is a structured review — what happened, what was decided in the moment, what the instruments showed, what should change — regardless of whether the flight ended safely. Investing deserves the same structured after-action review, and the discipline is called a post-mortem: a systematic look back at a closed position, conducted using the journal entry written at the time of entry (Lesson 53.2) as the honest baseline, comparing what you believed then against what actually happened.
The single most important design feature of a good post-mortem is that it must be run on winning trades as rigorously as on losing trades. Most investors, left to their own habits, will do an informal post-mortem only on losses — replaying what went wrong — while simply banking a profit without examination. This asymmetry is a mistake, because a profitable trade made for a bad reason (a lucky tip that happened to work, a sector rally that lifted a poor company along with good ones) teaches exactly the wrong lesson if left unexamined: it teaches you that the bad process works, which sets you up to repeat it with a larger position next time, at worse odds.
KEY CONCEPT
Process and outcome are two different things, and only a post-mortem that separates them honestly can tell you which one deserves credit or blame. A good decision can lose money; a bad decision can make money. Judging yourself only by outcomes teaches you to repeat your luckiest mistakes.
A post-mortem template, run on every closed position, should walk through five questions:
First, what did the journal say at entry — thesis, expected outcome, kill condition — and how does that compare with the memory you currently hold of why you bought? If there is a gap between the two, that gap is hindsight bias at work, and it is worth naming explicitly.
Second, did the kill condition trigger, and if so, did you actually honour it, or did you move the goalposts once the price approached the level you had pre-committed to? Investors who write down a stop-loss and then rationalise past it in the moment ("just a bit more room, the fundamentals haven't changed") are a very common failure pattern, and the post-mortem is where this pattern gets caught and named, so it can be corrected next time.
Third, was the outcome driven primarily by the thesis playing out, or by something else entirely — a broader market rally, a sector-wide re-rating, a change in NEPSE-wide liquidity conditions, a currency or remittance-driven inflow into the market unrelated to the specific company? A hydropower stock that rose 40 percent because monsoon generation data beat expectations, exactly as your thesis predicted, is a different outcome from the same stock rising 40 percent because the whole sector was swept up in a rally with no company-specific news at all — even though your account statement shows an identical profit.
Fourth, what would you do differently in sizing, timing, or verification if you encountered the identical setup again tomorrow? This question converts the specific trade into a general, reusable lesson rather than a one-off anecdote.
Fifth, does this trade reveal a pattern when placed alongside your last ten to twenty journal entries — a recurring source of good ideas, a recurring source of bad ones, a recurring emotional trigger, a recurring sector bias? A single trade rarely teaches much on its own; a post-mortem run consistently across dozens of trades, read together, is where the real signal about your own behaviour as an investor emerges.
PRACTICAL TOOL
Set a recurring calendar reminder — monthly is sufficient for most individual portfolios — to sit down with your full journal and run the post-mortem on every position closed in that period, win or loss alike. Treat this appointment with the same seriousness as a bill payment; it is easily the most postponable habit in this chapter and the one whose absence is least visible until years have passed.
A Worked Example: The Hydropower Rally Decision
Bring all five preceding exercises together with a single hypothetical, deliberately realistic scenario. It is the middle of the monsoon season. A mid-cap hydropower company — call it "Himal Urja Hydropower Ltd." — has seen its share price rise 35 percent over three weeks on the back of strong river flow data, a Viber group screenshot claiming an analyst "target of Rs 650" against a current price of Rs 480, and general sector enthusiasm as several hydropower counters near their 15 percent daily limit on the same day. An investor, call her Sunita, is considering buying.
Sunita runs the pre-trade checklist first. Her thesis, written honestly, is: "Generation data has been strong and the stock is rallying with the sector." Answering question 2 honestly, she finds Himal Urja is trading at roughly 1.8 times its replacement cost per installed MW, well above the sector's five-year average of about 1.2 times — a valuation anchor from Chapters 46-47's project-finance-aware methods that she would not have checked without the checklist forcing the question. Question 3 forces her to note the price is within 4 percent of its 52-week high. Question 4, the source check, is uncomfortable: the specific "Rs 650 target" came from an unnamed post in a Viber group, not from any research she can independently verify. Question 7, her emotional state, is honestly "excited, slightly FOMO-driven" — she is aware that colleagues at her office have already bought in.
She next writes a short devil's advocate memo. The strongest case against buying: at 1.8x replacement cost, the market is already pricing in several more years of above-average river flow and no major maintenance capex, an assumption her own review of Himal Urja's last three annual reports (Chapter 45's tools) suggests is optimistic given an aging penstock the company flagged in its own auditor's notes. The rally, on inspection, is sector-wide rather than company-specific — every hydropower counter on the board moved similarly that week regardless of individual generation performance, which is itself evidence the price move reflects narrative and new retail money rather than firm-specific news.
Sunita decides, based on the checklist and the devil's advocate memo together, on a modified position: a smaller allocation than she originally intended (2 percent of portfolio rather than the 6 percent she first considered), entered with a written kill condition — "sell if price falls 18 percent from entry, or if Q2 generation data due in three months shows flow below the five-year seasonal average" — and a portfolio-level circuit breaker already in place from her broader trading rules. She writes the full journal entry, including the honest "FOMO-driven" note, before placing the order. She mutes the Viber group's notifications until market close.
Two outcomes are plausible, and both are worth walking through, because the value of the process does not depend on which one occurs. If Himal Urja falls 20 percent over the following month as the sector-wide rally cools — a common pattern after narrative-driven, sector-wide moves on NEPSE — her per-position circuit breaker triggers a mandatory pause and reassessment rather than either a panicked sale at the bottom or a stubborn hold with no plan; because her position was already sized smaller than her first impulse, the loss in rupee terms is manageable, and her post-mortem, conducted a month later against her original journal entry, will show a thesis that was honestly weak from the start ("rallying with the sector" is not a company-specific thesis) — a lesson about her own tip-sourcing habits, not just about hydropower valuation, that a plain memory of "I lost money on Himal Urja" would never have surfaced. If instead the stock continues higher because generation data does come in strong, her smaller position still participates in the gain, her journal entry lets a later post-mortem correctly attribute the win partly to a real thesis and partly to sector luck, and she has a written record for next time of exactly how a rushed, tip-sourced idea can be improved by the checklist without being abandoned outright.
CASE IN POINT
Notice what the exercises did and did not do in Sunita's case. They did not tell her not to buy — a legitimate, disciplined investor can still choose to buy into a sector rally. What they did was replace an impulsive, full-sized, unexamined purchase with a smaller, sized, documented, exit-planned one. That difference — not avoiding risk altogether, but taking it deliberately and in the right size — is the entire point of this chapter.
Chapter recap
This chapter has moved the book from describing investor psychology to equipping the reader with concrete, repeatable tools for managing it, because knowing that biases exist — the subject of Chapters 50 through 52 — is necessary but not sufficient; behaviour changes only when good intentions are backed by structures that hold even when willpower runs out. The pre-trade checklist interrupts impulsive trades by forcing a written answer to a fixed set of questions about thesis, valuation, source, sizing, and emotional state before any order is placed on NEPSE, converting a thirty-second reflexive decision into a deliberate one. It works not because the questions are clever but because they are fixed and mandatory, immune to being talked out of in the heat of a rallying market.
The investment journal builds a memory more honest than the human mind can provide on its own, capturing thesis, expected outcome, and emotional state at the moment of purchase — before hindsight has had any chance to rewrite the story. This record is what makes every other exercise in the chapter possible: without an honest entry-point record, the post-mortem in Lesson 53.6 has nothing reliable to compare against, and outcome bias silently takes over the job that should belong to careful process evaluation. The personal circuit breaker borrows directly from NEPSE's own market-wide mechanism — the 15 percent individual stock limit and the 8 percent index-wide suspension currently in force — and applies the same logic at the individual portfolio level: pre-committed, written thresholds at which trading halts and a deliberate reassessment is required, protecting against the specific danger of decisions made during maximum emotional intensity, especially the urge to chase losses back quickly.
The devil's advocate exercise directly confronts confirmation bias by requiring the investor to build the strongest possible case against their own thesis before committing meaningful capital, using the valuation, governance, sector-cycle, and liquidity lenses developed across the book's earlier parts. It is most valuable precisely when it is least comfortable to do — during a one-sided, euphoric sector rally, which is exactly when NEPSE investors are least inclined to seek out disconfirming evidence on their own. The media diet exercise addresses a specifically Nepali texture of the problem: the outsized influence of Viber and Telegram investment groups, YouTube commentary, and family social pressure on trade ideas, and proposes classifying sources into primary data, analytical commentary, and noise, then bounding exposure to trading-hour chatter with a fixed review window after market close rather than eliminating market information altogether.
The post-mortem closes the loop by requiring a systematic after-action review of every closed position, wins as rigorously as losses, distinguishing process quality from outcome — because a profitable trade made for a bad reason is a dangerous lesson in disguise, one that teaches an investor to repeat their luckiest mistakes with larger size next time. The worked example brought all five tools together on a single realistic NEPSE scenario, a hydropower counter rallying on sector enthusiasm and an unverified Viber tip, and showed that the point of this discipline is not to eliminate risk-taking but to make risk-taking deliberate, correctly sized, and documented — the same share can be bought well or bought badly, and the difference lies entirely in the process that precedes the click, not in the underlying company.
Behavioural discipline, built through these six habits repeated across hundreds of trades and years of a market cycle, is what allows sound analysis — of financial statements, of governance quality, of valuation, of hydropower project economics — to actually translate into investment returns, rather than being repeatedly undone at the moment of execution by fear, greed, or social pressure. This closes Part X of the book. Every exercise in this chapter has assumed the reader can actually get in and out of a position at a fair price; the next part turns to the question of whether that assumption is even safe to make on NEPSE. Part XI, Liquidity Engineering & Position Sizing, opens by treating liquidity itself as a first-order investment risk rather than an afterthought — examining exit risk, a governing rule for sizing a position against a scrip's average daily volume, free-float-based allocation limits, and how to model the specific danger of being trapped by a circuit-limit lockout on a thinly traded counter, before Part XII turns to the broader work of portfolio construction itself.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XI
LIQUIDITY ENGINEERING & POSITION SIZING
Part XI · Chapter 54
Liquidity as a First-Order Investment Risk in NEPSE
First published 23 Aug 2026 · Last verified 29 Aug 2026
In the hills above the Trishuli valley, a man once inherited two ropani of land from his grandfather. On paper, by the going rate that a neighbour's similar plot had fetched the year before, it was worth twelve lakh rupees. He told people this figure with some pride. Then his son needed money urgently — a hospital bill in Kathmandu, payable in three days. The father went looking for a buyer. There was no buyer. Not at twelve lakh, not at nine lakh, not at six. The only person willing to move that fast was a relative who offered four lakh, take it or leave it, because he knew the family had no other option and no other clock. The land had not become less valuable in any economic sense. What had been exposed, brutally and suddenly, was that the twelve-lakh figure had never really been a price — it was a rumour, an estimate, a story the family told itself because nobody had ever actually tried to sell it fast.
This is the story of liquidity, and it is also, structurally, the story of a very large number of NEPSE portfolios. An investor holds a small hydropower counter, sees it quoted on the app at a certain price, multiplies by the number of shares, and feels rich or feels invested. What that number rarely tells them is: could I actually turn this into cash, in a hurry, without giving away a third of it in the process? Part X of this book examined the psychology of the investor — the biases, the emotional loops, the behavioural traps that distort judgment. Part XI turns to something colder and more mechanical: the structure of the market itself, and the ways that structure can hurt you even when your judgment about value was entirely correct. This chapter, the opening chapter of that part, deals with the most underestimated of those structural risks — liquidity.
Lesson 54.1 — What Liquidity Actually Means
Liquidity, in its plain financial sense, is the ability to convert an asset into cash quickly, at a price close to its last-known or fair value, without that act of selling itself pushing the price down. Notice that this definition has three parts, and all three have to hold for an asset to be called genuinely liquid. Speed — you can sell today, not in three weeks. Price integrity — you get something close to the price you expected, not a fire-sale price. And size — this holds true for the amount you actually want to sell, not just for one or two shares.
Cash itself is the only asset with perfect liquidity — one rupee is always worth one rupee, instantly, to anyone. Everything else sits somewhere on a spectrum below that. A fixed deposit at a commercial bank is highly liquid but not perfectly so — break it early and you lose some interest. A share in a large NEPSE-listed commercial bank, actively traded every session, is close to cash for a retail-sized position — you can usually sell a few hundred or a few thousand shares within minutes at a price within a rupee or two of what you saw on the screen. A plot of land in a district headquarters town is far less liquid — it might take weeks or months to find a serious buyer, and the price you eventually agree on could be materially below your asking price. And a plot of land in a remote village with no road access, like the one in the opening story, may be barely liquid at all — it has value in principle, buyers exist in theory, but converting it to cash on a tight timeline can cost you half its "worth."
KEY CONCEPT
Liquidity is not a yes-or-no property. It is a spectrum, and every asset you own sits somewhere on it. The right question is never "is this liquid?" but "how much of my position could I actually convert to cash, how fast, and at what discount to the last quoted price?"
The mechanism that produces liquidity — or fails to — is the order book. When you place a sell order on NEPSE through your broker, that order does not simply "happen" at the last traded price. It gets matched against buy orders that other people have already placed and are waiting in the queue, at various price levels. If there is a thick stack of buy orders close to the current price — many buyers, in size, bidding just below the last trade — then your sell order gets matched quickly and at a price close to what you expected. This thick stack is called market depth. If there is only a thin scatter of buy orders, or none at all near the current price, your sell order either sits unfilled, waiting for a buyer who may never show up that day, or it has to "walk down" the order book — accepting progressively lower bids — to get filled at all. That walk down the book is where value quietly evaporates. It is invisible on your portfolio statement, because your portfolio statement uses the last traded price, not the price your order would actually have to accept to execute in size.
This is the single most important mechanical fact in this chapter, and it is worth stating plainly before we go further: the price you see on your trading app is the price at which the last trade happened, usually for a small quantity, sometimes minutes or hours ago. It is not a promise that you can sell your entire position at that price right now. For a handful of NEPSE's largest, most actively traded counters, the gap between the quoted price and the realistically achievable sale price for a normal retail position is small enough to ignore. For a very large number of NEPSE's roughly two hundred and fifty-plus listed companies, that gap can be enormous, and it grows sharply — not gradually, but sharply — once the size of the position you're trying to sell exceeds what the day's normal buyers can absorb.
Lesson 54.2 — NEPSE's Liquidity Pyramid: A Market of Extremes
If you sat down and ranked every counter on the Nepal Stock Exchange by how much of it actually changes hands on a typical day, you would not get a smooth, gently sloping distribution. You would get something closer to a pyramid with an extremely narrow tip and an extremely broad base. A small number of large commercial banks, a handful of the biggest hydropower and life insurance names, and a few large finance and microfinance companies account for a hugely disproportionate share of daily turnover. Beneath them sits a long tail — hundreds of smaller commercial and development banks, mid-tier finance companies, and above all the enormous population of small and mid-sized hydropower and manufacturing counters — where many sessions pass with only a few hundred or a few thousand shares changing hands, sometimes across only two or three actual trades.
Some context on scale helps here. In fiscal year 2025/26, NEPSE's total annual turnover was roughly NPR 1,601.87 billion, averaging about NPR 7.18 billion in trades per day across the entire exchange — itself down sharply, about 22 percent, from the prior year's average of roughly NPR 9.20 billion a day, a reminder that even the aggregate liquidity of the whole market expands and contracts with sentiment. On the day of NEPSE's biggest rallies, single-day turnover has spiked past NPR 16 billion. But that headline number is deeply misleading if you take it as evidence that "the market is liquid," because it is not spread evenly across roughly 250-plus listed companies. A large share of it is concentrated in a small cluster of names — mainly commercial banks and a handful of the biggest hydropower and insurance counters — while hundreds of smaller companies barely register in that total on any given day. Hydropower as a sector alone generated close to 44 percent of total exchange turnover in FY 2025/26, but that aggregate figure hides a sharp internal split: it is driven overwhelmingly by five or six large, well-known hydropower names with genuine trading activity, plus the cumulative churn of speculative day-trading across dozens of smaller counters, while a great many other listed hydropower companies — often with only a few hundred thousand shares in free float — go largely untouched for days at a stretch.
The table below is illustrative rather than a live snapshot of any single trading day — floorsheet activity on NEPSE shifts constantly — but it reflects the general shape of the market that any investor who has watched the floorsheet for more than a few weeks will recognise.
Scrip category
Typical example
Typical daily turnover (illustrative)
Typical shares traded per day
What this means for a NPR 5 million exit
Tier 1 — large-cap commercial banks and top hydropower/insurance names
A "Class A" commercial bank, a large listed hydropower producer
NPR 50–300 million+
Tens of thousands to low hundreds of thousands of shares
Can usually be sold within a single session with minimal price impact
Tier 2 — mid-cap financial institutions, established hydropower, larger manufacturing
A mid-sized development bank, a well-known but not top-tier hydropower company
NPR 5–30 million
A few thousand to tens of thousands of shares
May require spreading the sale across two to five sessions to avoid moving the price
Tier 3 — small-cap hydropower, microfinance, small manufacturing/trading counters
A small run-of-river hydropower project, a niche manufacturer
Under NPR 1–2 million, some days near zero
A few hundred to a few thousand shares, occasionally none
May take weeks, and the visible quoted price may not be achievable at all for the full quantity
The point of this table is not the exact rupee figures — those will shift with every market cycle — but the order-of-magnitude gap between the tiers. A position that a Tier 1 stock's order book can absorb without a ripple can represent many months' worth of normal trading volume in a Tier 3 stock. If you own NPR 500,000 worth of a large commercial bank, you are, for practical purposes, holding something close to cash with upside. If you own NPR 500,000 worth of a Tier 3 hydropower counter, you may be holding something closer to that remote village land — nominally worth a figure, but genuinely difficult to convert to that figure on your own schedule.
CASE IN POINT
Two investors each put NPR 500,000 into a stock in the same month. Investor A buys a large commercial bank; Investor B buys a small hydropower counter that had a promising news story. A year later, both stocks are quoted at the same percentage gain on the app. Investor A can exit in an afternoon within a rupee or two of the quoted price. Investor B places a sell order and watches it sit unfilled for four sessions, then finally gets filled — partially — at a price nearly 8 percent below the quoted price, because the only buyers willing to absorb that quantity were bidding well below the last trade. Both stocks "performed" identically on paper. Only one of those gains was actually collectible on demand.
It is worth being precise about why this concentration exists, because it is not an accident or a flaw that will fix itself. Commercial banks and the largest hydropower and insurance names have large numbers of shares outstanding, broad and diversified shareholder bases built up over years of rights issues and bonus shares, institutional participation (mutual funds, insurance companies, and increasingly some foreign and diaspora interest), and consistent analyst and media coverage that keeps a steady stream of buyers and sellers active every single day. Small hydropower and manufacturing counters, by contrast, often have a small public float (a large portion of shares locked up with promoter groups who rarely trade), a narrow, retail-only shareholder base concentrated in the specific district or community connected to the project, and long stretches with no news flow at all to bring in new buyers. These are structural features of the company and its ownership, not temporary conditions of a particular week. A stock that is thin today was very likely thin last year and will very likely be thin next year too, cheap valuation or not.
Lesson 54.3 — Paper Value Versus Realizable Value
Every NEPSE investor is intimately familiar with one number: the current value of their portfolio, calculated by multiplying the last traded price of each holding by the number of shares held, and summing across all holdings. This is mark-to-market value — literally, marking your holdings "to the market," meaning to the latest observed transaction price. It is the number your broker's app shows you, the number that determines your margin position if you are trading on a loan, and the number most investors quote when they tell a friend how their portfolio is doing.
Mark-to-market value has one enormous, unstated assumption baked into it: that the last traded price is a price at which you, the holder, could also transact — and not just for one share, but for your entire position, right now, if you needed to. For Tier 1 stocks with deep order books, that assumption is close enough to true most of the time that it causes little harm. For Tier 3 stocks, that assumption is frequently false, sometimes wildly so.
The number that actually matters when you need cash is realizable value — what you could genuinely collect in hand, after commissions, after the bid-ask spread, and after walking down the order book to fill your full quantity, within whatever time frame you actually have. Realizable value is always less than or equal to mark-to-market value, and for illiquid stocks under time pressure, it can be dramatically less.
Two mechanics widen the gap between paper value and realizable value, and every investor should be able to name both.
The first is the bid-ask spread — the gap between the highest price a buyer is currently willing to pay (the bid) and the lowest price a seller is currently willing to accept (the ask, or offer). For a heavily traded Tier 1 counter, this spread is often just a rupee or two on a share price of several hundred or a few thousand rupees — a rounding error. For a thinly traded Tier 3 counter, the spread can be five, ten, sometimes twenty rupees or more on a similarly priced share, simply because so few people are actively quoting prices on either side that the gap between the nearest willing buyer and the nearest willing seller never gets competed down to a sliver. Every time you buy and later sell such a stock, you cross that spread twice, and it is a real, permanent cost — not a paper cost, not a "maybe" cost.
The second, and the larger effect for anyone holding a meaningful position, is order-book depth, or the lack of it. Picture a simplified order book for a thin counter:
Price level (NPR)
Buy orders waiting (shares)
498
150
495
300
490
600
480
1,000
465
2,500
If the last traded price was 500 and you want to sell 3,000 shares, your first 150 shares might fill near 498. To fill the next 300, the price you receive drops to 495. To fill the next 600, it drops to 490. By the time your full 3,000-share order is filled, you have sold well below 500 on average, and roughly a third of your shares went for 465 or worse — nearly 7 percent below where the stock was "quoted" moments before you started selling. This is not a hypothetical mispricing or an unfair broker fee. It is simply what happens when a sell order larger than the available nearby demand meets a market with few active participants. The deeper and more crowded the order book, the smaller this effect; the thinner it is, the larger.
PRACTICAL TOOL
Before buying any NEPSE counter in meaningful size, open the market depth or order book view on your broker's platform (most now show at least the top few bid and ask levels) and ask two questions: how many shares are stacked within one or two percent of the last traded price on the buy side, and how does that compare to the position size I'm planning to hold? If your intended position is several multiples of what typically sits in the top few price levels, you are not buying a liquid asset — you are buying an asset that will require patience, and possibly a real discount, to exit.
There is a broader, more philosophical point buried in this mechanical discussion, and it deserves to be stated directly: a portfolio's mark-to-market value is a useful accounting fiction, not a guaranteed cash number. It tells you what your holdings were worth to somebody, for some quantity, at some recent moment. It does not tell you what your holdings are worth to you, for your full position, right now. Institutional investors who manage large sums learn this distinction early, often the hard way, because their position sizes are large enough that the gap between the two numbers shows up on every single trade. Retail NEPSE investors, trading in smaller sizes in the more liquid names, can go years without the gap ever mattering — right up until the day it does, usually in a small-cap counter, usually at the worst possible moment.
Lesson 54.4 — The Liquidity Trap: Why Illiquidity Stays Hidden Until You Need It
Here is the cruelest feature of liquidity risk, and the reason this chapter insists on treating it as a first-order risk rather than a footnote: illiquidity is completely invisible under normal, favourable conditions, and it reveals itself only under exactly the conditions where you can least afford the surprise.
Think about why. In a rising market, or in a stock that is rising on its own good news, almost nobody is trying to sell in size. Everybody who holds the stock is happy to hold it, because the price keeps going up and there is no urgent reason to exit. The handful of people who do sell — perhaps to book a profit, perhaps for an unrelated cash need — sell small quantities that the thin order book can easily absorb without any visible strain. The stock's chart looks smooth. Its bid-ask spread looks tight enough. Everything about it, on the surface, resembles a perfectly normal, tradeable stock. This is what we might call the dormant phase of illiquidity: the underlying thinness of the market is real and unchanged, but nothing in the investor's daily experience exposes it, because demand and supply happen to be roughly matched at low volumes.
Now change the conditions. A sector-wide correction hits, or bad news breaks about the specific company, or a margin call forces leveraged holders to raise cash immediately, or simply broad market sentiment sours and everyone who has been quietly uneasy about a position decides, more or less simultaneously, that today is the day to get out. Suddenly the number of people wanting to sell is far larger than the number of people willing to buy at anything near the recent price. The very same order book that absorbed small, occasional sell orders without a ripple during the calm phase is now facing a wave of sell orders it was never built to handle. Prices gap down, not smoothly but in jumps, because there simply isn't a buyer sitting at every price level between where the stock was and where it eventually finds one. This is the revealed phase, and it always arrives at the worst possible time — precisely when the investor most urgently wants or needs to sell, and precisely when everyone else wants to sell too.
WARNING
Liquidity risk does not announce itself in advance. A stock can trade smoothly and predictably for years and still be catastrophically illiquid the one week you actually need to exit in size. The absence of a problem during calm markets is not evidence of a liquid market — it may simply be evidence that nobody has stress-tested it yet.
This asymmetry — calm and forgiving in good times, brutal and unforgiving in bad times — is precisely why liquidity risk is so easy to dismiss and so dangerous to dismiss. Investors naturally judge risk by recent, lived experience. If a stock has never given them trouble selling a few thousand rupees' worth here and there, they generalise that experience to "this stock is fine to trade," without noticing that they have only ever tested it under the one condition — calm, low-urgency, small-size selling — where illiquidity does not show up. The test that actually matters — can I sell a meaningful position, fast, during a period when many others want the same thing — simply never gets run until circumstances force it. And circumstances that force it tend to be exactly the circumstances — panics, sector shocks, margin-call cascades, personal financial emergencies — in which the cost of failing that test is highest.
There is a useful analogy here to insurance and to fire drills. A building can go decades without a fire, and during those decades, the quality of its fire exits, sprinkler systems, and evacuation routes is completely irrelevant to anyone's day-to-day experience of the building. Nobody who works there for those thirty quiet years learns anything about whether the fire exits actually work. The one moment those systems matter is also the one moment it is far too late to go back and install better ones. Liquidity in a thinly traded NEPSE counter works the same way. The "exits" — the depth of the order book, the number of active buyers — are irrelevant on every calm day, and then suddenly, catastrophically relevant on the one day there is an actual fire.
Lesson 54.5 — The Liquidity Premium NEPSE Often Forgets to Charge
In a fully efficient, textbook capital market, illiquid assets are supposed to trade at a discount to what an otherwise identical, fully liquid asset would fetch — and correspondingly, an investor who buys the illiquid asset is supposed to demand a higher expected return to compensate for the extra risk and inconvenience of being unable to exit easily. This compensation is called the liquidity premium (sometimes described from the other direction as an illiquidity discount). It is a completely standard, well-documented feature of nearly every capital market in the world: private equity investors demand higher expected returns than public equity investors for exactly this reason; small, thinly traded bonds yield more than large, actively traded government bonds of similar credit quality; and small-cap stocks on major global exchanges typically trade at somewhat lower valuation multiples than otherwise comparable large-cap stocks, precisely because the market prices in the extra difficulty of moving size.
On NEPSE, this liquidity premium exists in principle but is frequently mispriced or entirely ignored in practice, for reasons that are worth understanding rather than simply lamenting. A large share of NEPSE's trading volume comes from retail investors who are, quite reasonably, focused overwhelmingly on the questions that dominate financial media, brokerage chat groups, and social media stock discussion: is this stock cheap relative to earnings? Is there a good news story — a new project, a dividend announcement, a bonus share proposal? Is the price chart pointing up? These are legitimate and important questions. But they are almost never accompanied, in ordinary retail conversation, by a parallel question: if I need to sell this in a hurry, six months or two years from now, will there be anyone on the other side of that trade? Because that second question rarely gets asked, it rarely gets priced. A small hydropower counter with a genuinely exciting growth story can trade at the same or even a richer valuation multiple than a much larger, much more liquid commercial bank with a duller but steadier outlook — not because the market has rationally decided the illiquidity risk is worth taking on for free, but because most of the participants setting that price simply never factored illiquidity into their decision at all.
KEY CONCEPT
A liquidity premium is compensation an investor should demand — in the form of a lower entry price, a higher expected return, or both — for taking on the extra risk of being unable to exit easily. On NEPSE, this premium is frequently absent from small-cap pricing not because the risk isn't real, but because most market participants aren't pricing it at all.
This mispricing cuts in a specific, exploitable-but-dangerous direction. It means that, at any given moment, some of NEPSE's small-cap counters are trading as if they carried no more exit risk than a large commercial bank, when in fact they carry substantially more. An investor who buys such a stock purely on the strength of its valuation story, without separately asking "and what is the liquidity discount I should be demanding here, that the current price is not offering me," is implicitly accepting Tier 3 exit risk while being compensated as though they were holding a Tier 1 asset. That gap between the risk actually being carried and the compensation actually being received is, in a very real sense, uncompensated risk — the worst kind, because there is no expected-return benefit sitting on the other side of it to justify taking it on.
REGULATORY DETAIL
Nepal's securities regulator, the Securities Board of Nepal (SEBON), and NEPSE itself have periodically introduced mechanisms intended to address market quality — including circuit breakers, discussed in the next lesson, and periodic reviews of listing and trading requirements. However, neither SEBON nor NEPSE currently mandates or publishes a formal liquidity-risk classification for individual scrips comparable to, say, a credit rating. The burden of assessing a stock's liquidity risk falls entirely on the individual investor, using tools like average daily traded volume, floorsheet history, and order-book depth — there is no regulatory shortcut that does this work on your behalf.
It is worth being fair to the other side of this picture, too. There are moments — usually during periods of broad market euphoria — when small, illiquid counters can actually trade at a liquidity premium in the wrong direction, meaning investors bid them up further precisely because their thinness makes them easier to move sharply on small volumes of buying, which in turn generates the kind of dramatic percentage gains that attract attention and momentum-driven buying. This is, if anything, a more dangerous version of the same mispricing: not merely a failure to charge for illiquidity risk, but an active, if usually unconscious, reward for it — right up until the buying stops and the same thinness that inflated the stock on the way up accelerates its collapse on the way down.
Lesson 54.6 — Circuit Breakers: When the Exit Door Locks Itself
Everything discussed so far in this chapter assumes that, however unfavourable the price, a market for your shares exists at all — that somewhere, at some price, a willing buyer can be found if you are patient or desperate enough. NEPSE has a mechanism, however, that can remove even that assumption for a period of time: the circuit breaker.
A circuit breaker is a rule that automatically halts trading — either in an individual stock or across the entire exchange — once price movement exceeds a defined threshold within a session. The stated purpose is protective: to slow down panic, prevent disorderly price discovery, and give participants a cooling-off period before trading resumes. As of the most recent rule changes, effective from April 2026 under the Securities Trading Operation (Fourth Amendment) Regulations, NEPSE operates a two-layer system.
Circuit breaker level
Trigger
Effect (current rules, from April 2026)
Effect (prior rules)
Individual scrip daily price band
A single stock's price moves up or down a set percentage from the previous close
Trading in that scrip is capped at a 15% daily fluctuation limit
Previously capped at a 10% daily fluctuation limit
Pre-open session band
Price discovery during the pre-open window
Widened to a 5% band
Previously a narrower 2% band
Market-wide partial halt
NEPSE index moves 5% within the first two hours of trading
Trading paused for 15 minutes
Broadly similar tiered structure existed, with different specific thresholds
Market-wide full closure
NEPSE index moves 8% intraday
Trading closed for the remainder of the day
Broadly similar tiered structure existed, with different specific thresholds
The mechanism most relevant to this chapter is the individual-scrip daily price band. Once a stock hits its lower limit for the day — a 15 percent fall from the previous close, under the current rule — trading in that specific scrip does not simply become expensive or wide-spread; it can effectively stop. Sell orders can still be placed, but if there are far more shares offered for sale at or below the limit-down price than there are buyers willing to take them, trades simply do not execute in the quantity sellers want, or at all. The stock can sit "locked" at its lower circuit for that entire session — and if the underlying selling pressure has not eased, it can gap down and lock again the next day, and the day after that.
This is the point at which liquidity risk and circuit-breaker mechanics compound into something genuinely dangerous, and it deserves to be stated as plainly as possible: a circuit breaker does not create liquidity risk out of nothing — it takes liquidity risk that was already present in a thinly traded counter and turns it, for a period, into a hard stop. In a Tier 1 stock, the deep pool of buyers usually means the stock rarely even approaches its daily limit except in genuinely extreme, market-wide events, and when it does, the sheer number of active participants means the order backlog tends to clear relatively quickly once trading resumes or the limit resets the next day. In a Tier 3 stock — the exact kind of small hydropower or manufacturing counter this chapter has focused on — a single piece of bad news, or simply a broader risk-off mood among the retail base that dominates its shareholder registry, can be enough to send it straight to its lower limit with a comparatively small number of sell orders, because there was never much buying depth to absorb selling pressure in the first place. Once locked there, with far more shares offered than bid for, the investor who wanted to exit discovers that the market has not merely become expensive to exit — it has become, for practical purposes, temporarily closed to them.
CASE IN POINT
During episodes of sharp, sentiment-driven selling in small-cap hydropower counters — a pattern that has recurred more than once in NEPSE's history whenever a speculative rally in the sector has cooled — it is common to see certain thinly traded scrips locked at their lower circuit for several consecutive sessions, with the order book showing overwhelming sell-side interest and only a trickle of buy orders, often far below the locked price. An investor holding such a position discovers that their portfolio statement still shows a value based on the last (locked, no-longer-representative) traded price, while their actual ability to realise any cash from the position is, for the moment, close to zero.
This is precisely why liquidity risk cannot be treated as a purely technical detail to be handled by execution mechanics — a "just use a limit order" problem. It is a risk that changes the character of an investment. A stock that is fundamentally cheap, well-managed, and genuinely undervalued can still impose a real, painful cost on its holder if that holder needs cash during the exact window in which the stock is thinly traded and circuit-locked. The valuation case for owning the stock and the liquidity case for being able to exit it on your own terms are two separate questions, and NEPSE investors — retail and institutional alike — have a strong, understandable tendency to do rigorous work on the first question and almost no work at all on the second. Chapter 58, later in this part, will return to circuit breakers in far greater depth — how they are triggered, how experienced NEPSE participants navigate limit-locked sessions, and what an investor can and cannot do once a position is frozen at the daily band. For now, the essential point is narrower and more urgent: circuit breakers turn ordinary illiquidity into episodic illiquidity's more dangerous cousin — total, if temporary, unavailability of an exit, arriving precisely when the desire to exit is at its peak.
CAUTION
Do not assume that a stock's daily price limit protects you from loss. A circuit breaker limits how far the price can move in a single session — it does not guarantee you a buyer at that limit, and it does not stop a stock from locking limit-down for several consecutive sessions in a row, each one compounding the last. For a thinly traded counter, the circuit breaker can convert a bad week into a bad month, simply by rationing how much of the selling pressure clears each day.
Chapter recap
This chapter has argued that liquidity deserves to be treated as a first-order investment risk on NEPSE — not a minor technical footnote to be worried about only by traders and market-makers, but a factor that belongs in the same conversation as valuation, earnings quality, and governance when deciding whether and how much of a stock to own. Liquidity, mechanically defined, is the ability to convert a position into cash quickly, near its recent price, in the quantity you actually hold. Very few NEPSE counters offer that combination in full. The market's turnover is heavily concentrated in a small tier of large commercial banks and a handful of the biggest hydropower and insurance names, while a long tail of hundreds of smaller companies — disproportionately small hydropower and manufacturing counters — trade only a trickle of shares on a typical day, sometimes going entire sessions with no meaningful activity at all. This is a structural, persistent feature of the ownership base and public float of these companies, not a temporary condition that will resolve on its own.
The chapter drew a sharp distinction between mark-to-market value — the comforting, automatically calculated number on a portfolio statement, based on the last traded price — and realizable value, the amount an investor could actually collect in hand if they needed to sell their full position on a real timeline. For liquid, large-cap names, these two numbers are usually close enough to treat as interchangeable. For thin, small-cap counters, the gap between them is driven by two compounding mechanics: the bid-ask spread, which is often wide for stocks few people are actively quoting, and order-book depth, or the lack of it, which forces a large sell order to "walk down" through progressively worse prices to get filled. An investor who has never tried to sell a meaningful position in a thin stock has simply never discovered how large that gap can be — and that discovery, when it comes, tends to arrive at the worst possible moment.
That timing problem was the heart of Lesson 54.4: illiquidity is dormant and invisible during calm or rising markets, when nobody is trying to sell in size, and it reveals itself only during exactly the conditions — panics, sector-wide corrections, margin calls, personal cash emergencies — where an investor can least afford the surprise. This asymmetry is what makes liquidity risk so easy to underestimate. A stock's recent, calm trading history tells an investor almost nothing about how it will behave the one time they actually need to sell it under pressure, because that stress test simply has not been run yet by the time most investors form their opinion of a stock's tradeability.
The chapter also introduced the idea of a liquidity premium — the extra return, or the discount to entry price, that an investor should rationally demand for taking on the added risk of holding an illiquid asset — and argued that this premium is frequently absent from NEPSE small-cap pricing, not because the underlying risk isn't real, but because the retail-dominated participant base setting these prices is focused almost entirely on valuation and story, and rarely factors exit difficulty into the price at all. An investor who buys a thin counter purely on valuation grounds, without separately demanding compensation for its illiquidity, is taking on real risk for which the market is not paying them anything extra — the least attractive kind of risk there is.
Finally, the chapter connected liquidity risk to NEPSE's circuit breaker system — currently a 15 percent daily price band for individual scrips and a tiered, index-based market-wide halt structure, following the April 2026 regulatory revisions that widened these limits from their earlier, tighter thresholds. Circuit breakers do not create liquidity risk; they take liquidity risk that was already latent in a thinly traded stock and can turn it, for a period of days, into an outright inability to exit at all, precisely during the episodes of sharp, sentiment-driven selling when the desire to exit is strongest. A cheap, fundamentally sound stock can still inflict serious, avoidable pain on an investor who needed cash during exactly the window it was locked limit-down with no buyers in sight.
The lesson that should follow an investor out of this chapter is a simple discipline, not a complicated formula: before sizing any NEPSE position, ask not only "is this a good investment?" but "if I needed to exit this position in a hurry, in full, during a bad week for the market — could I, and at what cost?" For the market's large-cap core, the honest answer is usually reassuring. For a great many of its smaller, more exciting counters, the honest answer is that nobody has really checked. Chapter 55, "Exit Risk in NEPSE," picks up directly from this foundation and moves from the general concept of liquidity to the specific, practical mechanics of exiting a NEPSE position under real-world constraints — how position size interacts with average daily volume to determine a realistic exit timeline, how to sequence an exit across multiple sessions to minimise price impact, and what a disciplined investor can do, ahead of time, to avoid ever being forced into a fire sale in a market that has, for the moment, stopped offering them a door.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XI · Chapter 55
Exit Risk in NEPSE
First published 23 Aug 2026 · Last verified 29 Aug 2026
Exit risk rarely announces itself while a position is working. It shows up later — on the one day you actually need to sell, when the order book that looked "fine" every other day suddenly isn't there. Chapter 54 established that liquidity is a first-order risk in NEPSE, sitting alongside business risk, valuation risk, and governance risk rather than beneath them. This chapter narrows the lens to the half of that risk that actually bites: the risk of getting out.
Lesson 55.1 — The Asymmetry Between Buying and Selling
Every investor who has bought and sold shares on NEPSE has felt this asymmetry without necessarily naming it: buying is calm, selling is not.
Think about how a purchase actually happens. You decide you like a bank's valuation, or a hydropower company's tariff structure, or a manufacturing firm's margin trend. There is no clock running. You can place a small order today, watch how it fills, place another order next week, average in over a month, and stop entirely if the story changes. Nothing forces you to buy 10,000 shares of Api Power or Chilime in a single session. You choose your moment, and if the moment isn't right, you simply wait for a better one. Entry risk — the risk of paying an unfairly high price to get into a position — is real, but it is almost entirely a risk you control, because you control the clock.
Selling is a different animal. You may want to sell because your thesis played out and the stock reached fair value — a comfortable, unhurried reason. But a large share of real-world selling happens for reasons that are not comfortable and not unhurried: the company just missed earnings badly, a promoter has been named in a governance investigation, a hydropower project's plant factor collapsed after a landslide damaged the headworks, or the broader market is falling 6% in a session and every account on your broker's app is flashing red. In these moments, you are not the only one who wants to sell. You are one of thousands of sellers converging on the same order book at the same time, and the buyers who were cheerfully absorbing supply last month have gone quiet, or worse, gone to the sidelines waiting for the price to fall further before they step back in.
This is the asymmetry: entry risk is mostly a risk you can defer, dilute, and control. Exit risk is a risk that concentrates exactly when you least want it to — when the news is bad, when the market is falling, and when every other holder of the same stock is thinking the same thing you are. A useful analogy from daily life in Kathmandu: buying vegetables at Kalimati market on an ordinary Tuesday morning is easy — there are dozens of sellers, prices are stable, and you can walk to the next stall if one vendor's price seems off. But imagine trying to buy vegetables during a bandh, when supply trucks haven't arrived and everyone in the neighbourhood needs food at the same time. The market hasn't changed its rules, but the balance between buyers and sellers has flipped, and price and availability both deteriorate sharply, together. NEPSE sell orders during a market-wide decline behave the same way — not because the exchange broke, but because everyone showed up on the same side of the counter at once.
KEY CONCEPT
Entry risk is the risk of paying an unfair price to establish a position; it is largely voluntary and can be managed by patience. Exit risk is the risk of being unable to sell at a fair price when you want or need to; it is often involuntary and concentrates precisely during periods of stress — which is what makes it structurally more dangerous than entry risk for most investors.
This asymmetry matters most for exactly the kind of positions this book has spent its earlier chapters building: concentrated stakes in fundamentally sound but thinly traded companies — a hydropower IPO allocation, a mid-cap manufacturer, a regional development bank before it merged into a larger group. The better the long-term thesis, the more tempting it is to build a large position. But a large position in a stock with modest daily turnover is precisely the position that will be hardest to unwind on the one day you need to unwind it. Exit risk is not a tax on bad investments. It is a tax on good investments that were sized without asking "how do I get out of this, and at what cost, if I have to?"
Lesson 55.2 — Voluntary Exit vs. Forced Exit
Not all sales are created equal, and the distinction between a voluntary exit and a forced exit is the single most useful frame for understanding why exit risk hurts some investors far more than others.
A voluntary exit happens on your terms. Your thesis has played out — the bank you bought at a price-to-book of 1.1 is now trading at 1.8 and further upside looks limited. Or your thesis has quietly broken — a hydropower company's PPA (power purchase agreement) renegotiation went worse than expected, and you no longer want the exposure, but there is no fire, no forced deadline. Or you simply found something better — a newer IPO with a cleaner balance sheet and a more attractive entry multiple. In every one of these cases, you decide when to sell, and because you are not under time pressure, you can be patient about price. You can place limit orders instead of market orders, spread the sale across several sessions, and walk away entirely if the bid you're being offered looks unreasonably low for the day. A voluntary seller behaves the way a rational buyer does in Lesson 55.1 — with optionality intact.
A forced exit is the opposite: you sell because you have to, not because you want to, and the market knows it — or will know it, once your order starts eating through the book. Forced exits come from several sources that are worth naming individually because each has a slightly different signature in NEPSE:
Margin calls. An investor who borrowed against a portfolio to buy more shares — a very common practice in Nepal given how widely margin lending is used by retail investors — faces a maintenance requirement from the lending institution. If the value of the pledged shares falls, the broker or bank issues a margin call: top up cash, or shares get sold to restore the required collateral ratio. Margin calls in NEPSE tend to cluster during broad market declines, which means many leveraged holders are forced to sell the same stocks at the same time — a classic case of forced selling amplifying, rather than merely coinciding with, a downturn.
Personal cash needs. A shareholder needs money for a medical emergency, a child's overseas education deposit, or a family obligation around Dashain or Tihar, and the shares are the most liquid asset available. This kind of forced sale is not driven by market conditions at all — it can happen to a single investor on a single ordinary day — but it still means the seller has less room to negotiate price or timing than a voluntary seller would.
Panic. This is the most insidious form of forced exit because it is self-inflicted rather than externally imposed. An investor watches a stock fall 8% in two sessions on no company-specific news, concludes (often wrongly) that something must be badly wrong, and sells into the decline simply to stop the pain of watching the position lose value. Behaviourally, panic-driven selling is indistinguishable from a genuine forced exit in its market effect — it still shows up as urgent supply hitting a thin book — even though nothing external actually compelled the sale.
Redemption and institutional pressure. For mutual funds, this shows up as unit-holder redemptions that must be met by selling portfolio holdings regardless of whether the fund manager thinks the price is fair. A retail investor doesn't face redemptions in that literal sense, but family or partnership pressure to "take some money off the table" after a scare functions similarly.
CASE IN POINT
Consider an investor who bought a meaningful stake in a hydropower company we will call Sunkoshi Hydro during its IPO, attracted by a strong catchment area and a well-structured PPA. Eighteen months later, an unusually dry pre-monsoon season cuts generation sharply below forecast for two consecutive quarters, and the stock falls 30% in six weeks. An investor who had researched the seasonal pattern and sized the position within a plan can treat this as noise, or even add to the position — a voluntary decision either way. An investor who bought on margin, using the same shares as collateral, gets a margin call in week three of the decline and must sell into the worst part of the drawdown, at the worst possible prices, not because the thesis was wrong but because the financing structure removed the choice.
The lesson generalises cleanly: the danger in exit risk usually isn't the market event itself — corrections and bad quarters happen to every company eventually. The danger is being structurally forced to transact into that event rather than being free to wait it out or use it as an opportunity. Everything in the remainder of this chapter, and the position-sizing framework in Chapter 56, is ultimately in service of one goal: keeping as much of your future selling voluntary as possible.
Lesson 55.3 — Market Impact Cost: What It Actually Costs to Get Out
Even a fully voluntary sale has a cost beyond the sale itself, and understanding this cost is essential before we get to NEPSE's mechanics specifically.
Market impact cost is the price concession a seller must accept to get a sell order of meaningful size actually executed, relative to the price that prevailed before the order arrived. It exists because a stock's order book at any moment only has so many buyers waiting at or near the current price. If you want to sell more shares than those buyers are collectively willing to absorb, you must either wait (accepting the risk that the price moves against you while you wait) or accept progressively lower prices to draw in additional buyers who were only willing to buy at a discount.
The clearest way to build intuition here is a household analogy. Imagine you own three identical goats and want to sell them at the weekly haat bazaar. If you offer one goat, you'll likely get close to the going market price — there's always at least one buyer willing to pay it. If you show up with thirty goats to sell in a single morning, you will not get thirty times the "one goat" price. Word spreads that a large seller is in the market; buyers who would have paid full price for a single animal now sense they can negotiate, because they know you need to sell all thirty before the market closes, not just one. Your average realised price per goat falls as the quantity you're trying to move rises relative to the size of the crowd that showed up to buy that day. That gap — between the price a small transaction would fetch and the average price a large one actually fetches — is market impact cost.
In equities, the standard way to measure "how large is large" is to compare the order size to Average Daily Volume, or ADV — the average number of shares of a stock that change hands in a normal trading session, typically measured over the trailing 20 to 60 sessions. An order for 2% of ADV is a small, easily absorbed order in almost any market. An order for 50% of ADV is enormous — it represents half of everything that stock normally trades in an entire day, concentrated into whatever fraction of the session you're willing to spend executing it.
NEPSE amplifies this dynamic more than investors coming from larger markets tend to expect, for a simple reason: even well-known, fundamentally solid NEPSE-listed companies frequently trade only a few thousand to a few tens of thousands of shares on an ordinary day. A position that would be utterly immaterial relative to ADV on a large exchange can easily represent several days' worth of a NEPSE stock's normal turnover. The table below illustrates, in stylized terms, how market impact cost tends to scale with order size relative to ADV for two different liquidity profiles common on NEPSE: a relatively liquid large-cap (a top commercial bank or a large hydropower name with wide public float) and a typical thinly traded mid-cap or small-cap (a smaller hydropower company, a regional development bank, or a recently listed manufacturing firm).
Order Size as % of ADV
Illustrative Impact — Liquid Large-Cap
Illustrative Impact — Thin Mid/Small-Cap
5%
~0.1–0.3%
~0.5–1.5%
10%
~0.3–0.6%
~1.5–3%
25%
~0.7–1.2%
~4–7%
50%
~1.5–2.5%
~8–14%
100%
~3–5%
~15–25%
200%
~6–10%
~25–40%+
The pattern to internalize, not the exact percentages, is what matters here: impact cost is non-linear. It doesn't double when your order size doubles — it grows faster than that, because each additional share you try to sell has to reach further down into a shrinking pool of remaining buyers. And the thin-stock column deteriorates far faster than the liquid-stock column at every size. A position equal to 100% of ADV in a liquid bank stock might cost you 3–5% to fully exit; the same relative position size in a thinly traded hydropower or manufacturing name can easily cost you 15–25% or more — not because the company is worse, but because there simply aren't enough natural buyers standing by on a given day.
WARNING
Market impact cost is not a fee anyone charges you — there is no line item for it on your broker's contract note. It is an invisible cost that shows up only as a lower average realised price than the price you saw on screen before you started selling. Because it never appears as an explicit number, investors routinely underestimate it, especially for positions built up gradually over many small purchases in a stock that later turns out to have very little natural two-way trading interest.
Two further wrinkles are specific to how impact cost plays out over time. First, urgency makes it worse: if you are willing to sell a large position over ten sessions instead of one, the market has time to find natural buyers between your orders, and total impact cost falls substantially — but this only works if you're a voluntary seller who can afford to wait ten sessions, which brings us back to Lesson 55.2. Second, information makes it worse: if the market senses why you are selling — a broker's chat groups in Nepal move information about large sell orders remarkably fast — other holders may pre-emptively sell alongside you, expecting you to keep pushing the price down, which pushes the price down faster than your order alone would have. A large sell order in a thin stock does not just consume the book; it can change other participants' behaviour, turning your exit into everyone's exit.
Lesson 55.4 — Inside the Order Book: Price-Time Priority and the Mechanics of a Thin Market
To understand exactly why a large sell order behaves the way it does on NEPSE, it helps to understand how the exchange actually matches trades.
NEPSE runs an order-driven market, meaning prices are not set by a single dealer quoting a price, but by the collision of buy and sell orders submitted by market participants through their brokers into a central electronic system — the Trading Management System (TMS). Every order carries a price and a quantity, and the system maintains an order book for each listed security: a running, real-time list of every unfilled buy order (bids) ranked from highest price to lowest, and every unfilled sell order (asks or offers) ranked from lowest price to highest.
Matching follows price-time priority, a rule that is intuitive once stated plainly: the best-priced order gets filled first, and among orders at the same price, the order that arrived earliest gets filled first. If you place a sell order at Rs 505 and someone else placed a sell order at Rs 505 two minutes before you, their order fills first even though yours arrived on the same price level. This is exactly how a queue at a bank counter works — first in line gets served first, and if you want to be served ahead of someone who is already in line, you have to accept different terms (in the market's case, a lower price for a sell order, or a higher price for a buy order), because price improvement is the only way to jump the queue.
KEY CONCEPT
Price-time priority means an order book rewards being both well-priced and early. A large sell order placed after the day's early liquidity has already been claimed by other sellers must either wait behind them or undercut the price to move ahead — which is exactly the mechanism that produces market impact cost in practice, order by order.
Now picture what a thin order book actually looks like for a lightly traded NEPSE stock on an ordinary day. Instead of dozens of buy orders stacked at every price increment below the current price — the kind of depth you'd see in a heavily traded bank stock — a thinly traded mid-cap might show only three or four buy orders in total: say, 200 shares bid at Rs 498, 150 shares at Rs 495, 400 shares at Rs 490, and then a gap all the way down to 300 shares at Rs 480. This is market depth — the total quantity available at each price level moving away from the current market price — and in a thin stock, depth is shallow and uneven rather than deep and continuous.
If you need to sell 3,000 shares into that book, the arithmetic is unforgiving. Your order fills the 200 shares at Rs 498, then the 150 shares at Rs 495, then the 400 shares at Rs 490 — and you have sold only 750 shares so far. The remaining 2,250 shares now have to reach down to Rs 480 and likely below, because there simply isn't a buyer at any intermediate price. Your average realised price across the full 3,000 shares ends up well below the Rs 498 you might have seen quoted as the "current price" moments before you started selling. Nothing about the exchange malfunctioned — the matching engine did exactly what it is designed to do, filling the best-priced orders first — but the shallow depth of a thin book turned an ordinary sell order into a meaningfully worse average price than the screen suggested.
This dynamic interacts directly with NEPSE's circuit breaker system, which exists to slow down disorderly price moves but has a side effect that matters enormously for exit risk. NEPSE applies daily price bands on individual scrips — a maximum percentage move, up or down, from the previous close — alongside market-wide circuit breakers that pause trading entirely when the benchmark index itself moves sharply within a session (as of recent rules, a market-wide move of roughly 5% within the first two hours triggers a short trading halt, and a move of roughly 8% triggers suspension for the remainder of the day; individual scrips are separately capped near a mid-teens percentage daily move). These bands are protective by design — they exist to prevent one panicked hour from destroying a stock's price entirely — but for a seller specifically, they can be a trap rather than a shield. If a stock gaps down and hits its lower price band, the exchange does not stop sellers from lining up; it stops the price from falling further, which means the order book fills with sell orders queued at the floor price and very few, if any, buyers willing to transact there. You can technically place a sell order, but if there's no matching buy interest at the frozen floor price, your order simply sits in the queue, unfilled, while the stock may gap down again the following session. The circuit breaker protects the index's optics; it does not guarantee you an exit.
REGULATORY DETAIL
NEPSE settlement operates on a T+2 cycle — a trade executed today settles two trading days later, with shares and funds actually crediting to accounts on settlement day. This matters for exit planning because NEPSE trading is fundamentally delivery-based: you cannot sell shares you do not already hold in your demat account (short selling is not a normal retail practice on NEPSE), and once you sell, your cash is not actually available for two trading days. An investor who needs cash urgently cannot simply "sell this afternoon and have money tonight" — the T+2 cycle adds a built-in delay on top of whatever price concession market impact already extracts, which is worth remembering for anyone treating NEPSE holdings as a truly instant emergency fund.
Lesson 55.5 — Slippage: The Gap Between the Screen and the Fill
Slippage is the everyday name for the gap between the price you see on your screen when you decide to sell and the price you actually realise once the trade is executed. It is closely related to market impact cost from Lesson 55.3, but it is worth treating as its own concept because it captures a broader set of causes — not just the mechanical effect of order size against a thin book, but everything that can move the price between the moment you look at the screen and the moment your shares actually change hands.
Three separate sources of slippage are worth distinguishing, because each calls for a different response.
The first is impact slippage — the version we already built intuition for in Lessons 55.3 and 55.4. You see Rs 500 on the screen, but because your order is large relative to the book, your actual average fill comes in at, say, Rs 481. This is the most predictable form of slippage, because it scales in a fairly consistent way with order size relative to ADV and depth, which is exactly why it can be planned for.
The second is timing slippage. Between the moment you decide to sell and the moment your order actually reaches the exchange and gets matched, the market itself can move — especially during a fast-moving session. If bad news breaks about a company mid-session (a governance allegation, a regulatory notice, a disappointing unaudited quarterly result released after market hours the previous evening), the screen price you're looking at when you open your broker's app in the morning may already be stale by the time your sell order is actually placed and processed, because dozens of other holders received the same news and are racing to sell ahead of you. Unlike impact slippage, timing slippage is not really about your order size — it's about how fast the information environment is moving relative to how fast you can act, and it is precisely why forced or panic sellers (Lesson 55.2) tend to experience worse slippage than voluntary sellers: they are, by definition, reacting to something, and reacting means arriving after the price has already started to move.
The third is queue slippage, a NEPSE-specific wrinkle that follows directly from price-time priority (Lesson 55.4). On a day when a stock is under heavy selling pressure, many sellers place orders at or near the best available price simultaneously. Because orders are filled in time priority at each price level, a seller whose order reaches the system even a few seconds after others at the same price gets pushed behind them in the queue — and if the price is falling fast, being a few positions back in the queue can mean the difference between filling near the top of the range and having your order pushed down to fill at a materially worse price, or not filling at all before the stock hits its lower circuit and trading effectively freezes for the sellers still waiting.
PRACTICAL TOOL
A simple way to estimate your own slippage exposure before placing a large order: divide the number of shares you intend to sell by the stock's 20-session average daily volume. As a rough rule of thumb on NEPSE, an order below roughly 10% of ADV can usually be worked through in a single session with modest impact; an order between 10% and 30% of ADV should generally be split across at least three to five sessions; an order above 30% of ADV in a thinly traded name is a multi-week exit, not a single trade, and should be planned as such well before any urgency forces your hand.
The practical response to slippage risk is not to avoid selling — that is not a strategy, it is just deferred exit risk — but to change how you sell. Using limit orders rather than market orders lets you cap the price concession you're willing to accept on any single fill, at the cost of execution certainty (a limit order may simply not fill if the market moves away from your price). Breaking a large order into smaller tranches spread across multiple sessions reduces the impact each individual order has on the book and gives natural buyers time to appear. And — the theme that will carry directly into Chapter 56 — sizing the position appropriately relative to ADV in the first place is worth more than any execution technique applied after the fact, because a position that was never oversized relative to the stock's normal liquidity never puts you in a position where you're forced to choose between a bad price and a slow exit.
CAUTION
A screen price on NEPSE (or on any broker's app) reflects the last traded price or the best current bid/ask — it does not reflect what price a large order of yours would actually achieve. Treating the screen price as "what my shares are worth" for a position that is large relative to the stock's typical volume is one of the most common and costly misjudgments retail investors make when planning an exit, particularly during the exact moments — earnings disappointments, governance headlines, sector-wide sell-offs — when an accurate read on exit value matters most.
Lesson 55.6 — Lock-In Periods and the Illusion of Liquidity
The final piece of exit risk is one this book has already touched on from the entry side, in earlier chapters on IPO allocation and hydropower financing: promoter and insider lock-in periods. Seen from the exit side, lock-in periods are one of the most powerful — and most underappreciated — amplifiers of exit risk on NEPSE.
A lock-in period is a regulatory restriction that prevents certain categories of shareholders from selling their shares for a defined period after listing. In Nepal, this framework rests on the Securities Act and the Securities Registration and Issue Regulation, and it applies differently depending on the type of shareholder and, notably, the sector. General promoter shares are typically locked in for three years from the date of IPO allotment. Shares allotted to project-affected locals — a category specific to infrastructure projects, including many hydropower listings — also typically carry a three-year lock-in from allotment. Employee quota shares are usually locked in for a shorter period, often around one year. Hydropower promoter shares carry their own distinct treatment, with lock-in periods that have historically run shorter than the general promoter standard — commonly around one year from the date of listing rather than three — reflecting sector-specific rules aimed at encouraging hydropower capital formation. The Central Depository System and Clearing Limited (CDSC), which maintains Nepal's electronic shareholding records, flags locked-in shares against the shareholder's account so that any attempted transfer before the lock-in expires is automatically blocked at the depository level, not merely discouraged by policy.
REGULATORY DETAIL
Lock-in periods vary meaningfully by shareholder category and sector under current Nepali securities regulation: roughly three years from allotment for general promoter shares and project-affected-local shares, roughly one year from allotment or listing for employee quota shares, and roughly one year from listing for hydropower promoter shares specifically — a materially shorter restriction than the general corporate standard. SEBON has also directed listed companies to publicly announce the exact conclusion date of a lock-in period at least 30 days in advance, which is itself useful information for any investor tracking float dynamics in a stock they hold.
Why does this matter for exit risk specifically, rather than just being a fact about who owns what? Because lock-in periods directly determine free float — the portion of a company's total shares that is actually available to trade in the open market, as opposed to shares held by promoters, employees, or other insiders under restriction. A hydropower company might have 100 million total shares outstanding, but if promoters hold 51% and that stake (plus project-affected-local allotments) is locked in, the free float trading in the market on any given day might represent only 30–40% of total shares outstanding — and often considerably less, since even within the "free" float, many retail holders bought to hold rather than trade, further shrinking the shares that actually turn over.
This creates a structural trap that is easy to miss precisely because it looks like the opposite of a problem. A stock with a small free float, strong retail enthusiasm around listing, and steady buying interest can trade at what looks like healthy volume and a firm, rising price for months after listing — an encouraging picture for an early IPO investor who bought at allotment. But "healthy volume relative to a small float" and "healthy volume relative to what would be needed to exit a meaningful position" are two entirely different things. A stock trading 15,000 shares a day looks reasonably liquid until you realise that an early investor holding 50,000 shares would need more than three full days of the stock's entire trading activity just to sell their own position — before accounting for any market impact from doing so, and before accounting for the fact that other early holders, watching the same lock-in calendar, may be planning to sell around the same time.
That last point deserves its own emphasis, because it is where lock-in risk compounds with the mechanics from Lessons 55.3 through 55.5 rather than simply sitting alongside them. Lock-in expiries are scheduled and public — SEBON's 30-day advance notice rule means the market knows exactly when a large block of previously restricted shares becomes tradable. This is genuinely useful information, but it cuts against holders on the wrong side of it: if you are an early retail investor holding shares that became free-floating at listing, and a promoter's much larger three-year lock-in is set to expire in the coming month, you are effectively sharing a thin exit door with a seller who may be motivated to sell a very large block relative to the stock's ADV. Even if the promoter does not sell immediately, the market's anticipation of potential promoter selling around a known lock-in expiry date can itself soften the bid side of the book in the weeks leading up to it, as other holders position defensively ahead of the date.
CASE IN POINT
A hydropower company we will call Barahi Jal Vidyut Company lists with strong initial demand, its price rises steadily through its first year on healthy but modest daily turnover, and early retail allottees who bought at IPO are sitting on comfortable paper gains. As the company's promoter lock-in — set at roughly one year from listing under hydropower-specific rules — approaches its conclusion date, informed market participants know that a large block of previously restricted shares is about to become tradable relative to a free float that has, until now, been artificially thin. In the weeks before the lock-in expiry, bid-side depth thins further as some holders wait to see what the promoter does rather than commit fresh capital, and the stock drifts down even without any change in the underlying project's generation performance. Early retail investors who assumed their gains were "real" because the price on screen looked healthy discover, on the day they actually try to sell a meaningful block, that the exit price is well below the screen price they had been watching — not because the hydropower asset performed badly, but because free float, lock-in calendars, and thin natural demand collided exactly as this chapter would predict.
The broader lesson connects directly back to Chapter 54's framing of liquidity as a first-order risk, not a footnote to fundamental analysis: a fundamentally sound company can still be a poor holding for an investor who needs the option to exit at a fair price on their own timeline, if that company's tradable float is thin enough and its lock-in calendar concentrated enough that exit risk overwhelms the quality of the underlying business. Reading a company's shareholding pattern — available through NEPSE and CDSC disclosures — and its lock-in expiry calendar is therefore not a peripheral due-diligence step reserved for corporate governance specialists. It is exit-risk due diligence, as central to sizing a position sensibly as the earnings and balance-sheet analysis this book has spent its earlier chapters teaching.
Chapter recap
This chapter isolated exit risk from the broader liquidity risk introduced in Chapter 54, and the central claim running through all six lessons is that exit risk is structurally more dangerous than entry risk because it is asymmetric: buying can almost always be deferred, diluted across time, and abandoned if conditions aren't right, while selling frequently must happen precisely when conditions are worst — during a downturn, a bad earnings surprise, or a governance scandal, exactly when every other holder is reaching for the same exit at the same time. Lesson 55.1 established this asymmetry as the foundational reason exit risk deserves its own dedicated analysis rather than being treated as a mirror image of entry risk.
Lesson 55.2 then separated exit risk into voluntary and forced varieties, showing that the danger in exit risk usually isn't the underlying market event but whether an investor is structurally free to wait it out — margin calls, urgent cash needs, and panic all strip away that freedom and convert an ordinary market decline into a compelled sale at the worst possible moment. Lessons 55.3 through 55.5 then built the mechanical vocabulary needed to reason about exit costs precisely: market impact cost as the price concession required to move a large order through a limited pool of natural buyers, scaling non-linearly and far more severely in NEPSE's thin mid-cap and small-cap names than in its handful of genuinely liquid large-caps; NEPSE's order-driven, price-time-priority matching system as the mechanism that turns a shallow order book into a real, arithmetic cost rather than an abstract worry, further complicated by circuit breaker price bands that can freeze a stock at a floor price with no buyers willing to transact there; and slippage as the broader, everyday gap between the screen price an investor sees and the price actually realised, arising from impact, from the market moving between decision and execution, and from NEPSE's own queue-priority mechanics during fast-moving sessions.
Lesson 55.6 closed the chapter by connecting exit risk to a theme introduced in this book's earlier chapters on IPO allocation and hydropower financing: promoter, project-affected-local, and employee lock-in periods — roughly three years for general promoter and project-affected-local shares, roughly one year for employee shares and, distinctively, for hydropower promoter shares under current sector rules — create artificially thin free float that can make a fundamentally sound company a genuinely dangerous holding for an investor who needs optionality on exit. The compounding effect of a known, publicly scheduled lock-in expiry meeting a thin natural buyer base is a NEPSE-specific risk that a purely fundamentals-driven investor can easily miss, because nothing about the underlying business needs to change for the exit price to deteriorate sharply around that date.
Taken together, these six lessons reframe a question every NEPSE investor eventually has to answer honestly: not "is this a good company," but "if I needed to sell this position in a hurry, at what price could I actually do it, and how long would it take." A position can pass every fundamental test in this book's earlier chapters and still be a poor fit for a given investor's balance sheet and risk tolerance if that question doesn't have a satisfactory answer. Market impact cost, order book depth, slippage, and lock-in-driven float scarcity are not separate risks to be considered individually — they are four faces of the same underlying constraint, which is that a position's tradable liquidity, not its fundamental quality, sets the ceiling on how much of it any single investor can safely hold.
That ceiling is precisely where this book turns next. Chapter 56, "Position Size vs. ADV Rule: The Governing Law for NEPSE," takes the qualitative picture built across Chapters 54 and 55 and converts it into an explicit, numerical governing rule — a disciplined method for sizing any position as a function of the stock's Average Daily Volume, so that the exit-risk mechanics described in this chapter are priced into a position before it is built, rather than discovered afterward on the one day an investor most needs a clean way out.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XI · Chapter 56
Position Size vs. ADV Rule: The Governing Law for NEPSE
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 56.1 — What Average Daily Volume Actually Measures
Average Daily Volume, universally abbreviated ADV, is exactly what its name says: the average number of shares (or, in the form we will use throughout this chapter, the average rupee value) that changes hands in a given stock over one trading day, calculated as an average across some trailing window of trading days.
There are two versions of this number, and it matters which one you use.
Average Daily Volume in shares (ADV-shares) is the average number of shares traded per day. If a stock traded 40,000 shares on Sunday, 55,000 on Monday, 30,000 on Tuesday, 60,000 on Wednesday, and 65,000 on Thursday — a five-day trading week in Nepal — the ADV-shares figure is the sum of those five numbers divided by five: 250,000 divided by 5, or 50,000 shares per day.
Average Daily Value (ADV-value) is more useful for position sizing because it is denominated in the same unit as your position: rupees. It is calculated by taking the rupee turnover of the stock each day (shares traded multiplied by that day's traded price, or simply the turnover figure NEPSE and every brokerage terminal already publishes for each scrip) and averaging it over the same trailing window. If that same stock traded at an average price of around NPR 700 across those five days, its ADV-value would be roughly NPR 35 million per day (50,000 shares times NPR 700), though in practice you should sum the actual daily turnover figures rather than multiply an average price by an average volume, since the two can diverge when volume and price move together or apart on the same day.
For the rest of this chapter, when we say "ADV" without qualification, we mean ADV-value in rupees — the daily turnover figure — because that is the number you can compare directly against the rupee size of your position.
KEY CONCEPT
Average Daily Volume (ADV) is the average rupee turnover a stock does in one trading session, calculated over a trailing window of trading days. It is not the number of shares outstanding, not the market capitalisation, and not any single day's volume — it is a smoothed, rolling measure of how much liquidity actually flows through the stock day to day.
Why smooth it at all? Why not just look at yesterday's turnover?
Because any single day's turnover in a NEPSE scrip can be wildly unrepresentative. A stock might trade NPR 2 million on a quiet Tuesday and NPR 40 million the following Sunday because a bonus-share announcement hit the wire, or a large institutional block crossed, or the stock caught a wave of retail momentum after appearing on a "top gainers" list. If you sized a position off that one loud day, you would badly overestimate how liquid the stock normally is. Averaging across a window smooths out these one-off spikes and gives you a figure that reflects the stock's normal trading rhythm rather than its most exceptional day.
The professional convention — used by fund managers, broker-dealers, and risk desks worldwide, and just as applicable to NEPSE — is to calculate ADV over a trailing 20-trading-day or 30-trading-day window. Twenty trading days corresponds to roughly one calendar month of NEPSE sessions (Nepal's exchange trades Sunday through Thursday, so a calendar month typically contains somewhere between 20 and 24 trading days). Thirty trading days stretches the window to roughly six weeks, capturing a longer and even smoother picture at the cost of being slower to react to a genuine, lasting change in the stock's liquidity — for instance, after a stock graduates from the small-cap board to broader institutional attention, or after a scandal permanently scares away buyers.
For most individual investors building a position-sizing discipline, a 20-trading-day rolling average is the right default. It is short enough to reflect the stock's current liquidity regime, long enough to absorb one or two freak days without distorting your calculation, and easy to maintain by hand or in a simple spreadsheet using daily turnover figures published on NEPSE's own site or on aggregator platforms like ShareSansar, Merolagani, or NepseAlpha.
A practical note on data: NEPSE publishes daily turnover by scrip as part of its standard end-of-day market summary, and third-party aggregators republish this in more accessible formats, often with pre-built moving averages. You do not need institutional-grade data infrastructure to calculate a 20-day ADV — you need twenty numbers and a calculator, or a simple spreadsheet formula that updates as each new trading day's turnover is added and the oldest day drops off the window.
Building this data foundation takes only a few minutes a week: a simple rolling spreadsheet with one column for date, one for that scrip's daily turnover in rupees (available from NEPSE's daily market summary or any major Nepali market data aggregator), and a formula column computing the trailing 20-day average, updated weekly for every stock you hold or are seriously considering, is all the infrastructure this rule requires.
One more wrinkle specific to NEPSE deserves mention here and will matter more in Lesson 56.3: NEPSE enforces a daily circuit breaker that caps how far an individual scrip's price can move in a single session — the limit was widened to 15 percent per scrip (with market-wide trading suspended if the benchmark index itself swings 8 percent in a day) under rules that took effect in 2026, up from the narrower bands used in earlier years. This ceiling on daily price movement matters for liquidity because it means that in a genuinely illiquid, thinly traded counter, a determined seller cannot always find enough buyers to absorb a large order even at the maximum allowed downward move — the stock can simply stop trading for the day once it hits the lower circuit, with your sell order still unfilled. ADV tells you how much volume normally clears; the circuit breaker is a reminder that on a bad day, even that normal volume may not be available to you.
REGULATORY DETAIL
Since 2026, NEPSE applies a 15 percent daily price movement limit for individual scrips, with market-wide trading halted if the benchmark index itself moves 8 percent in a single session. These are wider bands than NEPSE used historically, giving individual stocks more room to move — and more room for an illiquid scrip to gap down through several sessions' worth of an impatient seller's exit order before it clears.
Lesson 56.2 — The Institutional Convention: Position Size as a Fraction of ADV
Once you have an ADV figure for a stock, the natural next question is: how large a position can I responsibly hold in it? Professional asset managers around the world — running pension funds, mutual funds, hedge funds — answer this question with a convention that has been refined over decades of painful experience with exactly the failure mode described in Chapter 55: the position that looked fine on paper and became a trap the day it needed to be sold.
The convention rests on a simple, humbling assumption: you should never plan to be more than a modest fraction of a single day's total trading volume, because if you try to sell faster than that, your own selling becomes a meaningful part of the volume — and a meaningful part of the price action. Push too much supply into a market on a given day, relative to how much natural demand exists at that moment, and the price moves against you as you sell, meaning your later shares fetch a worse price than your earlier ones. This effect has a name — market impact — and it was introduced in Chapter 54 as one of the two central costs of illiquidity (the other being the outright inability to trade at any price during a suspension or a locked circuit).
The standard institutional heuristic caps a single day's selling at somewhere between 10 percent and 25 percent of that day's ADV, with 15 percent to 20 percent being a commonly cited middle ground in professional risk literature and trading-desk practice globally. A fund that needs to sell more than that in a day, the thinking goes, will visibly move the market against itself and leave money on the table that a more patient execution would have preserved. Institutional trading desks build entire systems — algorithmic execution strategies with names like VWAP (volume-weighted average price) and TWAP (time-weighted average price) — specifically to slice a large order into pieces small enough to stay under this participation-rate ceiling across multiple sessions.
From that participation-rate ceiling, professionals derive a second, more actionable number: the maximum number of trading days it should take to fully liquidate a position without excessive market impact, given a chosen participation rate. If you are only willing to be, say, 15 percent of daily volume on any given day, then a position worth exactly one day's ADV would still take you roughly 1 ÷ 0.15, or about 6.7 trading days, to fully unwind — because on each of those days you are only selling 15 percent of that day's volume, not the whole thing. A position worth five times ADV, sold at the same 15 percent daily participation rate, would take roughly 33 trading days — more than a month and a half of continuous, careful selling — to fully exit.
This gives us the core professional formula, worth writing out explicitly because everything in the rest of this chapter builds on it:
Days to Liquidate = Position Size ÷ (Participation Rate × ADV)
Institutional position-sizing rules typically work backward from a maximum acceptable "days to liquidate" figure — often somewhere between 3 and 10 trading days for a single position, depending on the fund's mandate, how concentrated its overall book is, and how volatile the underlying stock tends to be — and then solve for the maximum position size that keeps the stock's days-to-liquidate figure under that ceiling.
The institutional convention treats a position not by its rupee size alone but by how many trading days — at a conservative daily participation rate, typically 10 to 20 percent of ADV — it would take to fully exit that position without materially moving the price. A "large" position and a "liquid" position are not the same thing; a position can be small in rupees and still illiquid if the underlying stock barely trades, and a position can be large in rupees and still perfectly liquid if the underlying stock trades enormous volume every day.
It is worth being honest about why this convention exists rather than treating it as an arbitrary rule handed down from on high. It exists because institutional investors have, collectively, lost enormous sums of money by ignoring it — by building large positions in stocks that seemed fine on the way in and discovering, on the way out, that the market for that stock simply could not absorb their selling without cratering the price. Every version of this rule, whether at a global pension fund or in the framework this chapter builds for a NEPSE retail investor, exists to prevent one specific, recurring, expensive mistake: sizing a position based on how much conviction you have in the story, rather than on how much liquidity actually exists to get you back out.
This is not a uniquely Nepali problem, either. Global fund managers have repeatedly built oversized positions in thinly traded small-cap stocks during a rising market, only to find themselves unable to exit at anything close to their marked value once sentiment turned — with forced selling into an illiquid market accelerating the very price decline they were trying to escape. The mechanism is universal; NEPSE's smaller free floats and lower overall turnover simply make the same mechanism bite harder and faster than it would in a deep, liquid market like the NYSE or LSE.
Lesson 56.3 — Deriving the ADV Rule for the Nepali Investor
The institutional formula from Lesson 56.2 is correct in principle but needs adaptation before it is usable by a serious individual investor operating in NEPSE, for three reasons.
First, institutional participation-rate conventions (10-25 percent of ADV) are calibrated for professional execution desks who can work an order patiently across a trading session using limit orders and algorithmic slicing. A Nepali retail or serious individual investor, placing orders through a standard broker interface or the Nepal Stock Exchange's own retail-facing trading system (TMS), is not going to run a VWAP algorithm. A more conservative participation rate — lower than the institutional norm — is appropriate, because your actual execution will be cruder: a handful of manually placed orders across a session, not a machine-optimized slice.
Second, NEPSE's overall liquidity is thin relative to the markets institutional conventions were built for. Total daily market turnover across all listed scrips on NEPSE has, through 2025 and into 2026, typically run in the range of roughly NPR 3 to 5 billion on an active day — small by the standards of any established regional exchange — and that turnover is heavily concentrated in a relative handful of large-cap banking, insurance, and hydropower counters, with a long tail of small-cap scrips trading a small fraction of that amount, some days not trading at all. A rule calibrated for a market where the median liquid stock does tens of millions of dollars a day in turnover will not transfer cleanly to a market where a large, well-regarded bank stock might do NPR 30-80 million in daily turnover and a small hydropower counter might do NPR 1-3 million — or nothing.
Third, and most importantly for a practical rule, the Nepali investor needs a days-to-liquidate ceiling that reflects real personal circumstances rather than a fund's redemption calendar. An institutional fund manager worries about investor redemption requests with specific notice periods. An individual investor worries about a medical emergency, a sudden need for a down payment, a margin call on a separate position, or simply losing conviction in the thesis and wanting out before the story deteriorates further. These personal liquidity needs argue for a tighter, more conservative days-to-liquidate ceiling than an institution might use — because you have no one to explain a delay to, and no mechanism to force patience on your own circumstances.
Putting these three adjustments together, here is the ADV Rule for the Nepali investor:
The ADV Rule: Do not hold a position in any single NEPSE scrip larger than the amount that could be liquidated within 5 trading days, assuming you sell no more than 10 percent of that scrip's 20-day Average Daily Value on any given day.
Written as a formula, solving for maximum position size:
Maximum Position Size = 5 days × 10% × ADV(20-day) = 0.5 × ADV(20-day)
In plain language: your maximum position size in any single scrip should be roughly half of that scrip's average daily traded value, calculated over the trailing 20 trading days.
Where do the specific numbers — 5 days, 10 percent — come from, and why these rather than the institutional 10-day, 15-percent norms?
Five trading days is a deliberately tight ceiling. In NEPSE's Sunday-to-Thursday trading week, five trading days is one full calendar week. This is a length of time a serious individual investor can reasonably tolerate as an exit horizon under normal, non-panicked circumstances — long enough to sell patiently without dumping the stock, short enough that "I need my money" does not turn into "I am trapped for a month." It is also deliberately shorter than the institutional 10-trading-day norm precisely because of the third adjustment above: an individual's liquidity needs are less predictable and less forgiving than a fund's.
Ten percent participation is deliberately conservative relative to the institutional 15-25 percent range, for the second adjustment above: because retail execution is cruder than institutional execution, and because NEPSE's per-scrip liquidity in the small- and mid-cap tail is thin enough that even a 10 percent participation assumption may be optimistic on a quiet day. Ten percent gives you a buffer — if the stock has a genuinely bad, thin day and only lets you sell 6-7 percent of its normal ADV without moving the price, your five-day exit plan stretches to seven or eight days rather than to three weeks.
Multiplying these together (5 days × 10% = 50%) produces the headline, memorable version of the rule: your position should be no larger than about half of the stock's 20-day average daily traded value.
KEY CONCEPT
The ADV Rule for Nepali investors: Maximum position size ≈ 0.5 × (20-day Average Daily Value). If a stock trades an average of NPR 20 million a day over the past 20 sessions, your position in that single scrip should not exceed roughly NPR 10 million — regardless of how large your total portfolio is or how strong your conviction in the story.
This is a default, not a universal constant. A more risk-averse investor, or one who anticipates a genuine need for liquidity within days rather than weeks (a large planned expense, a thesis that already looks shaky, a stock with unusually high headline risk), might tighten the multiplier to 0.25 or even 0.15 of ADV — effectively demanding a 2-3 day exit horizon instead of 5. A more risk-tolerant investor with a long, patient horizon and no near-term liquidity needs might loosen it to 0.75 or even 1.0 of ADV for a core, high-conviction large-cap holding — effectively accepting a 7-10 day exit horizon in exchange for a larger position. What should not move is the underlying logic: position size is derived from the stock's liquidity, not from your conviction, and the derivation is explicit and calculable rather than a vague gut feeling of "this feels like too much."
WARNING
The 0.5 × ADV rule assumes normal market conditions. In a market-wide selloff — the exact scenario in which you are most likely to actually need to exit a position — ADV itself often collapses as buyers step to the sidelines, meaning your real days-to-liquidate stretches well beyond five even if you correctly sized the position under calm conditions. Treat the ADV Rule as a floor of caution, not a guarantee. Chapter 55 covers this volume-collapse dynamic in more depth.
Two calculation notes worth being explicit about. First, always use the 20-day ADV, not the current day's volume or a single recent spike — a stock that traded NPR 50 million yesterday because of a one-off announcement is not a stock with NPR 50 million of durable daily liquidity, and sizing off that one day will lead you straight back into the trap this rule exists to prevent. Second, recalculate ADV periodically — monthly at a minimum, and immediately after any news event that structurally changes a stock's liquidity (a bonus share issue that increases shares outstanding, an FPO, inclusion in or removal from a major index, a sustained shift in retail attention toward or away from the sector). A position that was appropriately sized against last quarter's ADV can become oversized within a few months if the stock's trading activity quietly dries up — which is precisely the failure pattern examined in Chapter 55's discussion of exit risk.
Lesson 56.4 — Worked Examples: The Same Rupees, Two Different Liquidity Realities
Theory earns its keep only when it survives contact with real numbers. This lesson runs the ADV Rule against two contrasting NEPSE-style positions, each involving the same headline rupee amount — NPR 15,00,000 (15 lakh) — to make the central point of this chapter as concrete as possible: identical position size, wildly different liquidity risk.
Example A: A large-cap commercial bank stock
Consider a well-established, A-class commercial bank listed on NEPSE — a scrip typical of the counters that anchor NEPSE's banking subindex, characterised by a large number of shares outstanding, broad institutional and retail ownership, and steady daily turnover across most sessions. Suppose this stock's 20-day average daily traded value is NPR 32,000,000 (a plausible figure for a well-traded large-cap bank on an active period, based on typical NEPSE banking-sector turnover patterns).
Applying the ADV Rule: Maximum Position Size = 0.5 × NPR 32,000,000 = NPR 16,000,000
A position of NPR 15,00,000 (15 lakh, or NPR 1.5 million) sits comfortably inside this ceiling — in fact, it represents less than 5 percent of the stock's daily ADV. Let's check the actual days-to-liquidate for this position at the 10 percent participation assumption:
Days to Liquidate = Position Size ÷ (10% × ADV) = 1,500,000 ÷ (0.10 × 32,000,000) = 1,500,000 ÷ 3,200,000 ≈ 0.47 trading days
In practice, this investor could exit the entire NPR 15 lakh position in well under a single trading session, participating at a fraction of the stock's normal daily volume, with minimal price impact. This is what genuine liquidity looks like: the position is a rounding error relative to the market's daily capacity to absorb it.
Example B: A small-cap hydropower or manufacturing counter
Now consider a small, thinly traded hydropower company or manufacturing counter — the kind that dominates the lower tiers of NEPSE's listed universe by count, even though each individual company represents a small share of total market turnover. Suppose this stock's 20-day average daily traded value is NPR 1,200,000 — a plausible, even generous, figure for a small-cap counter that trades on most but not all sessions, with volume that can disappear entirely for a day or two at a stretch.
Applying the ADV Rule: Maximum Position Size = 0.5 × NPR 1,200,000 = NPR 600,000
A position of NPR 15,00,000 (15 lakh) in this stock is more than double the ADV Rule's maximum — 2.5 times over the recommended ceiling. Let's check the actual days-to-liquidate:
Days to Liquidate = Position Size ÷ (10% × ADV) = 1,500,000 ÷ (0.10 × 1,200,000) = 1,500,000 ÷ 120,000 ≈ 12.5 trading days
At NEPSE's five-day trading week, 12.5 trading days is roughly two and a half calendar weeks of continuous, disciplined selling, assuming the stock's liquidity holds steady the entire time and does not dry up further the moment the investor's own selling becomes visible on the floorsheet to other market participants — a dynamic that, in a market as transparent and closely watched as NEPSE's small-cap tail, often makes real-world exit even slower than the arithmetic alone suggests, since other participants can see sustained selling pressure and simply step back from the bid.
CASE IN POINT
The same NPR 15 lakh position — identical in every way a standard portfolio statement would show it — carries a days-to-liquidate figure of under half a trading day in the bank stock and roughly twelve and a half trading days in the small-cap hydropower stock. The rupee amount is identical. The liquidity risk is not remotely comparable.
The table below extends this comparison across a spread of NEPSE-style scrips at varying liquidity levels, holding the position size constant at NPR 15,00,000 to make the pattern visible at a glance, and then showing what the ADV Rule would actually recommend as a maximum position for each.
Scrip Type (illustrative)
20-Day ADV (NPR)
Days to Liquidate a Flat NPR 15,00,000 Position
ADV Rule Max Position (0.5×ADV)
Position Within Rule?
Large-cap commercial bank
32,000,000
0.47 days
16,000,000
Yes — well within limit
Large-cap life insurer
18,500,000
0.81 days
9,250,000
Yes — within limit
Mid-cap development bank
6,000,000
2.5 days
3,000,000
No — position is 5× the rule
Mid-cap hydropower (established)
3,200,000
4.7 days
1,600,000
No — position is 9.4× the rule
Small-cap hydropower (newer listing)
1,200,000
12.5 days
600,000
No — position is 25× the rule
Small-cap manufacturing counter
450,000
33.3 days
225,000
No — position is 66.7× the rule
Two things stand out from this table. First, the days-to-liquidate figure does not scale gently as ADV shrinks — it explodes. Moving from the large-cap bank to the mid-cap development bank multiplies days-to-liquidate by roughly five; moving further down to the small-cap manufacturing counter multiplies it by another twenty-five on top of that. Liquidity risk is not linear; it compounds as you move down the market-cap and turnover spectrum, which is exactly why a rule pegged to a fixed rupee amount (e.g., "never put more than 15 lakh in a single stock") fails to protect an investor the way a rule pegged to ADV does.
Second, notice that the identical NPR 15 lakh position is entirely appropriate in the top two rows and badly oversized in every row below that — using the same rupee figure throughout the table exists specifically to demonstrate that position size, on its own, tells you nothing about liquidity risk without the ADV context sitting next to it.
Before entering any position, the discipline is simple to state: calculate the stock's 20-day ADV, calculate 0.5 × that ADV as your maximum position ceiling, and compare your actual planned position size against that ceiling as a multiple. If your planned position exceeds the ceiling, either shrink the position or explicitly acknowledge — in writing, in your investment log — that you are accepting an extended exit horizon and why. Lesson 56.6 builds this comparison directly into the pre-trade checklist itself.
Lesson 56.5 — Why This Rule Inverts Retail Instinct
Here is the uncomfortable part of this chapter. If you built the ADV Rule correctly, it tells you to size up your positions in the stocks that feel the most boring — the large, well-covered banks and established hydropower and insurance names that every financial commentator has already discussed a hundred times — and to size down your positions in the stocks that feel the most exciting: the smaller, newer, thinly covered names where a single positive rumour, a bonus announcement, or a good quarter can move the price sharply and where early buyers imagine outsized returns.
This is precisely backward from what most retail investors actually do, and understanding why requires being honest about the psychology at work.
Illiquidity and excitement are not independent of each other in a market like NEPSE — they are often the same underlying condition viewed from two different angles. A stock trades thinly, in part, because relatively few shares are available to the public (a concept the next chapter, Chapter 57, develops fully under the heading of free float) and because relatively few investors are paying attention to it day to day. That same scarcity — small supply, small audience — is exactly what makes the stock's price move sharply on a small amount of buying interest. A modest wave of retail attention landing on a small-cap hydropower counter can send it up 10-15 percent in a session (up against the daily circuit limit) in a way that the same wave of attention landing on a large, heavily traded bank stock simply cannot, because the bank stock has far more shares and far more daily turnover to absorb that buying without the price moving nearly as much.
Investors experience this pattern — small stock, big percentage move — as evidence of opportunity. It feels like discovery: "I found this before everyone else did, and look how fast it's moving." That feeling is a powerful, entirely genuine emotional pull toward putting a large amount of money into precisely the stocks whose thinness is what produced the exciting move in the first place. The conviction created by watching a small-cap stock run 40 percent in three weeks feels earned and specific, in a way that the steady, unglamorous performance of a well-covered bank stock never quite manages to feel.
But the same thinness that lets a small amount of buying move the price sharply upward is the thinness that will let a modest amount of selling — including your own selling, on the way out — move the price sharply downward. The mechanism that created the exciting rally is structurally the same mechanism that will punish an oversized exit. This is not a coincidence or a separate risk sitting alongside the opportunity; it is the same variable — thin liquidity — producing both effects. Retail investors routinely price in the upside of that thinness (fast gains) while entirely failing to price in its downside (a brutal, multi-week, self-defeating exit), because the upside happens first, feels good, and reinforces the decision to stay large, while the downside only becomes visible at the exact moment — a need to sell — when it is most costly to discover.
The instinct to bet bigger on the small-cap story that "feels" like the highest-conviction idea in your portfolio is, from a pure liquidity-risk standpoint, exactly the wrong instinct to act on without deliberate override. The ADV Rule exists specifically to interrupt this instinct with an explicit, calculated ceiling rather than leaving position size to a feeling of conviction that the stock's own illiquidity helped manufacture.
There is a related distortion worth naming directly: familiarity bias in reverse. Many retail investors treat large, well-known bank stocks as "boring" and therefore undersized in their portfolios, on the theory that everyone already owns them and there is no special edge in owning more. But sizing decisions should be driven by liquidity and risk capacity as much as by return conviction — a large-cap bank stock's very "boringness" (high liquidity, low headline risk, easy to trade around) is precisely what makes it a stock you can responsibly hold in larger size, giving your portfolio a liquid core that can be trimmed or added to quickly as circumstances change, while your smaller, higher-conviction small-cap positions stay sized appropriately small precisely because they are harder to unwind.
This does not mean small-cap and micro-cap NEPSE stocks are bad investments, or that they should never be held in meaningful size relative to a small portfolio. It means the size must be calibrated to the stock's own liquidity, not to how strongly the story has captured your imagination. A retail investor with total investable capital of NPR 20 lakh might reasonably hold NPR 3-4 lakh in a single illiquid small-cap hydropower name if that stock's ADV supports it and the investor has correctly sized down to respect the stock's thinness — that is a legitimate, disciplined bet. The problem this chapter addresses is not small-cap exposure itself; it is small-cap exposure sized as though the stock were as liquid as a large-cap bank, simply because the story feels equally — or more — compelling.
CASE IN POINT
Retail participation surges in Nepal have repeatedly concentrated in a rotating handful of small- and mid-cap hydropower counters during periods of strong market sentiment, driving these names sharply higher on comparatively modest turnover — and the same names have, just as repeatedly, become the hardest positions to exit once sentiment cooled and the buying interest that had been propping up the thin order book evaporated. The pattern recurs across market cycles precisely because it is structural, not a one-time event tied to any single stock or season.
Lesson 56.6 — Building the ADV Check into the Pre-Trade Checklist
Chapter 53 introduced the pre-trade checklist — a short, disciplined sequence of questions an investor answers before any position is opened, designed to interrupt impulsive decision-making with a deliberate pause and a written record. The ADV Rule belongs in that checklist as a mandatory, non-negotiable step, not an optional add-on to consult only when a position "feels" large. The entire value of a checklist item like this comes from applying it every time, including — especially — the times when conviction is running high and the temptation is to skip straight to the buy order.
Here is how the ADV check should sit inside the broader pre-trade sequence, expanded from the checklist skeleton in Chapter 53:
Step one: Pull the stock's 20-day average daily traded value from your tracking spreadsheet or a market data source, using the most recent trailing window — not a figure from memory, and not a figure from a single recent headline-grabbing session.
Step two: Calculate the ADV Rule ceiling — 0.5 × ADV — as your maximum position size for this scrip under normal circumstances.
Step three: Compare your planned position size against that ceiling. If the planned position is within the ceiling, proceed to the remaining checklist items from Chapter 53 (valuation checks, thesis documentation, portfolio concentration limits, and so on). If the planned position exceeds the ceiling, stop and make an explicit decision rather than proceeding by default.
Step four, when the position exceeds the ceiling: choose one of three honest paths. Shrink the position to fit within the ADV Rule ceiling — usually the right default choice, and the one this chapter recommends as standard practice. Or, explicitly document an exception — writing down, in the same investment log referenced throughout this book, the specific reason the oversized position is justified (for instance, a deliberate long-term holding in a fundamentally strong small-cap name where the investor has consciously decided to accept a multi-week exit horizon in exchange for conviction in a multi-year thesis) and the extended days-to-liquidate figure the investor is knowingly accepting. Or, walk away from the position entirely, recognising that the size the thesis seems to warrant and the size the stock's liquidity can support are too far apart to reconcile responsibly.
What this step-four discipline prevents is the single most common failure mode this chapter has been building toward: the position that grows oversized not through one deliberate decision but through a series of small, unexamined ones — an initial purchase, a top-up after good news, another top-up after a friend's tip, each individually reasonable, none of them checked against the stock's actual liquidity, until the position has quietly become five, ten, twenty times larger than the stock's ADV can support, discovered only when the investor actually needs to sell.
PRACTICAL TOOL
Add a single mandatory line to your pre-trade checklist from Chapter 53: "Planned position size ÷ (0.5 × 20-day ADV) = ___ ×." If that ratio exceeds 1.0, the position exceeds the ADV Rule ceiling and step four above applies before the order is placed. This one line, filled in every time, turns an abstract principle into an enforced habit.
The ADV check should also be applied retroactively, not only at entry. Because a stock's liquidity can change — often deteriorating quietly over months as retail attention rotates elsewhere, as discussed in Chapter 55 — a position that was appropriately sized on the day it was purchased can drift out of compliance with the ADV Rule without the investor ever making a new buying decision. A quarterly portfolio review (a practice this book recommends independent of the ADV Rule specifically) should include recalculating current ADV for every held position and rechecking each one against the 0.5 × ADV ceiling. A position that has grown oversized relative to its stock's shrinking liquidity is a signal worth acting on — trimming the position while the exit is still relatively easy is far preferable to discovering the mismatch during a forced sale.
CAUTION
A stock's ADV is not fixed. It can shrink substantially — sometimes by half or more within a single quarter — as retail attention moves to newer, more exciting names elsewhere on the exchange. A position that comfortably passed the ADV check at entry can silently fail it eighteen months later purely because the stock went quiet, with no change in the investor's own holding at all. Treat the ADV Rule as a recurring check, not a one-time entry gate.
Finally, it is worth connecting this checklist discipline back to the broader argument of Part XI. Chapter 54 established that liquidity is a first-order investment risk, deserving the same deliberate attention as valuation risk or business risk rather than being treated as an afterthought. Chapter 55 showed how exit risk specifically manifests in NEPSE — the mechanics of how a position becomes genuinely difficult to sell, and why that difficulty tends to arrive precisely when an investor's need to sell is most acute. This chapter has supplied the quantitative tool that turns those two chapters' warnings into an enforceable, repeatable rule: calculate ADV, apply the 0.5× ceiling, check every position against it before buying and again on a recurring basis afterward. A checklist item that lives only as an abstract principle gets skipped under pressure; a checklist item that is a single line with a single number to fill in gets followed.
Chapter recap
This chapter established Average Daily Volume, or more precisely Average Daily Value, as the foundational measurement for sizing any position on NEPSE responsibly. ADV is calculated by averaging a stock's daily rupee turnover across a trailing window — 20 trading days as the standard default, occasionally extended to 30 days for a smoother but slower-reacting figure — and it should always be pulled from actual daily turnover data rather than estimated from a single recent session, since individual days can be wildly unrepresentative of a stock's normal trading rhythm. This averaged, rolling figure is what allows an investor to distinguish a stock's durable, everyday liquidity from a one-off spike caused by a news event or a passing wave of attention.
From this measurement, the chapter derived the professional convention that connects liquidity to position size: rather than sizing a position off conviction, portfolio percentage, or rupee comfort alone, professional risk management ties position size to how many trading days it would take to exit that position without materially moving the price, assuming a conservative daily participation rate against the stock's own ADV. Institutional practice typically caps daily participation at 10-25 percent of ADV and targets an exit horizon of roughly 3-10 trading days; this chapter adapted that convention for the Nepali individual investor into a specific, memorable formula — the ADV Rule — setting maximum position size at roughly half of a stock's 20-day average daily traded value, derived from a 5-day exit horizon at a conservative 10 percent daily participation rate. This adaptation deliberately tightens the institutional norm to reflect cruder retail execution, NEPSE's generally thinner market-wide liquidity, and the less forgiving, less predictable liquidity needs of an individual investor compared to a fund manager.
The worked examples made the chapter's central claim concrete: an identical rupee position — NPR 15 lakh in both cases — represented a liquidity non-event in a large-cap bank stock with deep daily turnover (an exit achievable in well under a single trading session) and a serious, multi-week liquidity trap in a thinly traded small-cap hydropower or manufacturing counter (an exit requiring twelve or more trading days of careful, patient selling under favourable conditions, and potentially far longer under stressed ones). The extended comparison table demonstrated that this relationship is not linear — days-to-liquidate compounds sharply as ADV shrinks, meaning a fixed rupee position-sizing rule of the kind many retail investors use by default ("never put more than X lakh in one stock") provides no real protection at all, because it treats radically different liquidity conditions as though they were the same risk.
The chapter then confronted the psychological pattern that makes this rule difficult to follow in practice: thin liquidity and market excitement in NEPSE are frequently the same underlying condition viewed from two angles, since a scarce, thinly held stock is exactly the kind of stock that moves sharply on modest buying interest — which retail investors experience as validating evidence of a good idea, encouraging larger position sizes precisely where the ADV Rule demands smaller ones. The mechanism that produces the exciting rally on the way in is structurally identical to the mechanism that will punish an oversized exit on the way out. Recognising this pattern is what allows an investor to consciously override the instinct to bet bigger on the stock that feels most like a discovery, and instead let a liquid, well-covered large-cap serve as the portfolio's core position-sizing anchor while smaller, thinner names are sized down to match their own reduced capacity to absorb an exit.
Finally, the chapter converted this principle into an operational habit by embedding the ADV check directly into the pre-trade checklist introduced in Chapter 53: calculate 20-day ADV, compute the 0.5× ceiling, compare it against the planned position, and — when the planned position exceeds the ceiling — make an explicit, documented choice to shrink the position, consciously accept a longer exit horizon with the reasoning written down, or walk away. Because ADV itself drifts over time as a stock's trading attention rises and falls, this check belongs not only at entry but as a recurring item in periodic portfolio review, catching positions that have quietly grown oversized relative to a shrinking underlying liquidity pool even when the investor made no new buying decision at all.
The next chapter, Chapter 57, "Free Float-Based Allocation Limits," extends this liquidity-engineering framework by examining a variable that sits upstream of ADV and helps explain why it varies so much from scrip to scrip in the first place: free float, the portion of a company's total shares that is actually available for public trading rather than locked up in promoter holdings, government stakes, or other long-term, non-trading blocks. Where this chapter built a rule around how much of a stock trades on an average day, Chapter 57 builds a complementary rule around how much of a stock's total capital structure is even eligible to trade at all — and shows how a narrow free float can quietly cap a position's liquidity ceiling even in a company whose total market capitalisation looks large enough to seem perfectly safe.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XI · Chapter 57
Free Float-Based Allocation Limits
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 57.1 — What "Free Float" Really Means
When you look up a NEPSE-listed company's total shares outstanding, you are looking at a number that answers the wrong question for a liquidity-conscious investor. Total shares outstanding tells you how many units of ownership exist. It does not tell you how many of those units are actually available for you, or anyone else, to buy or sell on the exchange on a given day. For that, you need a different, narrower number: free float.
Free float is the portion of a company's total shares that is genuinely available for public trading — shares that sit in the demat accounts of ordinary investors, mutual funds, and institutions, free to change hands whenever their owner chooses. It excludes shares held by promoters (the founders and their affiliated entities who took the company public), government or government-linked entities that hold strategic stakes, employee or staff quota shares still under restriction, and — critically for Nepal — any shares still sitting inside a statutory lock-in period during which the depository system will not even process a transfer.
Chapter 47 walked through how promoter lock-in periods work mechanically: under Section 38 of the Securities Registration and Issue Regulation, 2073, promoter shares in a general company are locked for three years from the date of IPO allotment, with sector variations — banks and financial institutions face longer restrictions tied to the start of commercial operations, while hydropower and other infrastructure companies typically carry a three-year lock-in from listing that binds not only promoters but, in many cases, project-affected local shareholders and staff allotees as well. Chapter 14 covered the IPO mechanics that create this split in the first place: when a company goes public in Nepal, its total capital divides into a promoter portion (which stays with the founding group and does not trade through ordinary brokerage channels) and a public portion (issued through the IPO to ordinary investors). Free float is what remains of the public portion once you also strip out any shares that, for one reason or another, are not genuinely tradable yet.
KEY CONCEPT
Free float = Total shares outstanding − Promoter shares − Government/strategic holdings − Locked-in shares (staff quota, local quota, any shares still inside a statutory restriction period). It is the actual size of the tradable pool, not the size of the company.
Think of a company's total share count as the full seating capacity of a stadium, and free float as the number of seats actually released for public ticket sale. A stadium might seat 40,000 people, but if 25,000 of those seats are reserved for season-ticket holders, sponsors, and staff who never resell them, then the real market for a walk-up ticket is 15,000 seats — not 40,000. If you are trying to judge how easy it will be to buy or sell a ticket at a fair price on match day, the 15,000 number is the one that matters, not the stadium's total capacity. NEPSE works the same way. A company can have an impressive-sounding total share count and market capitalisation while its actual tradable float is a fraction of that headline figure.
It is worth being precise about a second distinction: free float percentage versus free float market capitalisation. Free float percentage is simply the free float share count divided by total shares outstanding, expressed as a percentage. Free float market capitalisation is that free float share count multiplied by the current market price — it converts the float from a share count into a rupee figure, which is the number you actually need when sizing a position, because your position is denominated in rupees, not in shares as a fraction of the company. Two companies can have identical free float percentages — say, both at 40% — and yet have wildly different free float market capitalisations if one company is ten times the size of the other. Both the percentage and the rupee figure matter, but for allocation-limit purposes, the rupee figure — free float market cap — is the one that does the real work, as you will see in Lesson 57.4.
Lesson 57.2 — Why Free Float Matters Independently of ADV
Chapter 56 built a position-sizing discipline around average daily volume (ADV): cap your position size as a percentage of a stock's trailing average daily traded value, so that you are never forced to unwind a position over an unreasonable number of trading days, and so that your own buying or selling never dominates a single day's tape. That rule is necessary. It is not sufficient. It solves for flow — how much trades on a typical day — but it says nothing about stock, in the balance-sheet sense of the word: the total pool of shares that could ever trade if enough sellers decided to sell at once.
Here is the gap between the two concepts, made concrete. Imagine a hydropower company with a free float of just 2 million shares. If those 2 million shares turn over briskly relative to their own size — say, 40,000 shares change hands on a typical day — that stock will look perfectly liquid by an ADV-percentage test. A position that respects a 5% of ADV limit might cap you at 2,000 shares a day, which sounds like healthy access to the market. But now ask a different question: what if you wanted to build a position of 200,000 shares — comfortably inside your ADV-based daily trading limit spread over a couple of months — and hold it? You would own 10% of the company's entire free float. Not 10% of daily volume. Ten percent of every share that anyone, anywhere, is free to trade. At that point you are no longer a price-taker in this stock. You are a structural feature of it. Its free-float-adjusted liquidity, its inclusion weight in any float-weighted index, its bid-ask dynamics on any day you decide to transact — all of it now bends around your position, whether you intend that or not.
This is the distinction the chapter title is built on: an allocation limit based on free float, standing alongside — not replacing — the ADV-based limit from Chapter 56. ADV tells you how fast you can move. Free float tells you how much room there is to move into in the first place. A swimming pool can have a very fast drain (high daily turnover relative to its own volume) while still being a small pool in absolute terms. You can drain it quickly, but you cannot pour the contents of a water tanker into it without changing its water level dramatically, no matter how fast the drain runs. A stock with a small absolute free float is a small pool. It can have brisk percentage turnover and still be a place where a moderate-sized position — perfectly reasonable in rupee terms for your portfolio — represents an outsized, potentially uncomfortable share of everything there is to own.
WARNING
Healthy-looking ADV-percentage turnover on a stock with a tiny absolute free float is not a green light. A stock can trade 3–4% of its float every day and still have a float so small that a single institutional-sized position absorbs a double-digit share of the entire tradable pool. Always check float size in rupees, not just turnover in percentage terms.
There is a second, related reason free float deserves its own discipline: index and passive-flow mechanics increasingly reference float, not total market cap. As Nepal's exchange has moved toward float-adjusted benchmarks — the NEPSE 30 index, introduced as "a value-weighted index based on market capitalisation that includes free-float shares in the market," requires constituent companies to have issued at least 25% of shares to the general public or to represent more than 1% of total free-float market capitalisation, and weights constituents partly by free-float market cap — a stock's free float is not merely a liquidity statistic. It is increasingly a determinant of whether index-tracking flows will touch the stock at all, and in what size. A large position in a low-float name is not just hard to exit; it can also be sitting in a stock structurally excluded from, or barely weighted in, the flows that would otherwise provide natural buying support.
KEY CONCEPT
Free float and ADV answer different questions. ADV answers: "How fast can I trade without moving the market on any given day?" Free float answers: "How much of this company can I own before I become the market myself?" A disciplined investor checks both before sizing a position — never one in place of the other.
Lesson 57.3 — How to Calculate Free Float Percentage and Free Float Market Cap for a NEPSE Company
Calculating free float for a NEPSE-listed company is not exotic, but it does require pulling from a few different disclosure sources, because no single NEPSE screen hands you a clean "free float" line item the way, say, a Bloomberg terminal might for a global large-cap. You are assembling the number yourself from public filings.
Start with total shares outstanding, or equivalently, paid-up capital divided by face value (NPR 100 per share is the near-universal face value for NEPSE-listed companies, so paid-up capital in rupees divided by 100 gives you the share count directly). This figure is on the company's latest annual report, its quarterly disclosures, and its listing page on the NEPSE website.
Next, identify promoter shareholding. This is disclosed in the company's prospectus at the time of IPO (covered in Chapter 14), and updated in every subsequent annual report under the shareholding pattern or capital structure section, which typically breaks the register into promoter and public categories, sometimes with further detail on the largest shareholders by name. Merolagani, ShareSansar, and similar Nepal-focused financial data platforms also aggregate this promoter/public split for most listed companies, drawing from the same regulatory filings, which makes cross-checking straightforward.
Then subtract, from the public portion, any shares that are not actually free to trade despite technically sitting in public hands. This is the step investors most often skip, and it is the step that makes the Nepal-specific free float calculation meaningfully different from a textbook definition. In Nepal this includes: (1) any public-allotment shares still inside a statutory lock-in window — most relevantly, hydropower project-affected local shareholder allotments and staff/employee quota shares, both of which are typically locked for the same period as promoter shares even though regulators classify them as "public" allotment rather than "promoter" allotment; (2) government or government-linked institutional holdings taken as strategic, non-trading stakes (relevant for a handful of companies with state involvement); and (3) any shares pledged or otherwise restricted in a way that is disclosed but not obvious from the headline promoter/public split. SEBON's periodic disclosures and the company's own annual report notes are where this detail surfaces; the CDS and Clearing Limited (CDSC), Nepal's central depository, is the system that actually enforces these restrictions by flagging locked ISINs at the account level and blocking any transfer attempt until the lock-in date passes.
REGULATORY DETAIL
CDSC currently operates a single ISIN (the unique identifier for a company's listed security) covering both promoter and public shares of the same company. Lock-in restrictions are enforced as flags at the depository-account level, invisible on the trading screen itself. This means the free float figure you calculate from disclosures will not be visible anywhere on a live NEPSE quote — you have to build it yourself, and it will not match the "shares outstanding" figure the trading terminal shows you.
Once you have a clean free float share count, the two figures you need follow directly:
Table 1 works through this calculation across five NEPSE-listed companies chosen to represent different structural situations: a large commercial bank near the regulatory promoter floor, a hydropower company still inside its post-listing lock-in, a hydropower company that has passed lock-in, a manufacturing company with a long-standing public float, and a hotel/tourism company with a moderate promoter stake. Figures are illustrative, built to typical Nepal-market proportions rather than quoted from any single company's live filing, and are for teaching the calculation, not for use as current market data.
Company (illustrative)
Sector
Total Shares Outstanding
Promoter Holding %
Locked-in Public Shares (local/staff quota) %
Free Float %
Price (NPR)
Free Float Market Cap (NPR crore)
Himal Commercial Bank Ltd.
Banking
18,00,00,000
51%
0%
49%
285
2,514
Karnali Jal Vidyut Ltd. (2 yrs post-IPO, still locked)
Hydropower
6,00,00,000
41%
24%
35%
410
861
Sunkoshi Hydro Power Ltd. (lock-in expired)
Hydropower
5,00,00,000
41%
0%
59%
355
1,048
Terai Agro Industries Ltd.
Manufacturing
3,20,00,000
55%
0%
45%
610
878
Annapurna Resort & Hospitality Ltd.
Hotel/Tourism
2,10,00,000
62%
0%
38%
195
156
A few things this table is built to show. First, notice that Karnali Jal Vidyut and Sunkoshi Hydro Power have an identical promoter holding of 41% — but Karnali's free float is 35% while Sunkoshi's is 59%, purely because Karnali is still inside its lock-in window and a large slice of its nominally "public" shares (the local and staff quota allotments) are not yet transferable. Same promoter stake, very different tradable pool, purely as a function of where each company sits on its lock-in calendar. Second, notice that Annapurna Resort has the smallest free float market cap by a wide margin — NPR 156 crore — despite not having the lowest free float percentage. Its total company size is simply small, so even a "normal-looking" 38% float translates into a thin absolute pool. This is exactly the percentage-versus-rupee-figure distinction from Lesson 57.1 doing its work: you cannot judge float adequacy from the percentage alone.
PRACTICAL TOOL
Build a simple float tracking sheet for every stock you hold or are evaluating, with four columns: total shares outstanding, promoter + locked-in %, free float %, and free float market cap in NPR. Update it whenever the company reports, and whenever you know a lock-in expiry has passed (Lesson 57.6). This sheet becomes the second gate — alongside your ADV sheet from Chapter 56 — that every position must clear before you size it.
Lesson 57.4 — The Free Float Allocation Limit Rule
With free float market cap in hand, the sizing rule itself is simple to state, even though the number takes some work to assemble: cap your individual position in any single stock at a fixed percentage of that company's free float market capitalisation, not merely at a percentage of its ADV.
Why a percentage of free float market cap, and not some other reference point? Because free float market cap answers the specific question that matters for a long-horizon investor building meaningful positions: if I own this much, what fraction of everyone who could ever sell to me — or everyone I might one day need to sell to — do I represent? A position that is a small fraction of free float leaves plenty of other holders on both sides of the market; you are one participant among many. A position that is a large fraction of free float means the stock's trading dynamics increasingly depend on your presence, your absence, and your intentions. You stop being a price-taker and start becoming a price-setter, often without meaning to and without the market depth to support that role gracefully.
A reasonable starting discipline — adjust to your own risk tolerance and the depth of your broader portfolio — is to cap any single position at somewhere between 1% and 3% of a company's free float market capitalisation, with the tighter end of that range reserved for thinly floated names (hydropower companies still inside lock-in, small hotel and manufacturing counters) and the looser end reserved only for the handful of NEPSE names with genuinely deep, long-established public floats, such as the largest commercial banks. This is deliberately more conservative than it might sound at first: 2% of free float is a much smaller number, in absolute rupee terms, than 2% of total market cap, precisely because free float market cap is itself already a fraction of total market cap. That gap is the whole point of the rule — it is designed to bite harder on exactly the low-float names where ADV-based sizing alone would let you in too easily.
KEY CONCEPT
Free Float Allocation Limit = Free Float Market Cap × Chosen Ceiling Percentage (e.g., 2%). Your position size, once built, should not exceed this rupee figure — regardless of what the ADV-based rule from Chapter 56 would otherwise allow.
The discipline only does its job when you apply it as a joint constraint alongside the ADV rule, taking whichever ceiling is more binding — never averaging the two, and never treating a pass on one test as license to ignore the other. Table 2 works through this with the same five illustrative companies from Table 1, assuming an investor with a portfolio large enough to be contemplating a NPR 25 lakh (2.5 million) position size, applying a 2% free float ceiling and, borrowing the Chapter 56 convention, a 7% of trailing 20-day ADV ceiling.
Company
Free Float Market Cap (NPR crore)
2% Free Float Limit (NPR)
20-day ADV (NPR lakh/day)
7% ADV Limit (NPR)
Intended Position (NPR)
Binding Constraint
Final Allowed Position (NPR)
Himal Commercial Bank
2,514
50,28,000
180
12,60,000
25,00,000
ADV
12,60,000
Karnali Jal Vidyut (locked)
861
17,22,000
45
3,15,000
25,00,000
ADV
3,15,000
Sunkoshi Hydro Power (unlocked)
1,048
20,96,000
60
4,20,000
25,00,000
ADV
4,20,000
Terai Agro Industries
878
17,56,000
22
1,54,000
25,00,000
ADV
1,54,000
Annapurna Resort & Hospitality
156
3,12,000
8
56,000
25,00,000
Free Float
3,12,000
Read this table carefully, because the last two columns are where the discipline actually earns its keep. In four of the five names, the ADV rule from Chapter 56 turns out to be the tighter constraint — the stock's daily turnover is thin enough, relative to its own float, that you would run out of ADV room before you ever approached the free float ceiling. But look at Annapurna Resort. Its daily traded value is so low in absolute terms (NPR 8 lakh a day) that a naive investor might assume the free float rule would be the redundant, looser check here too. It is not. Because Annapurna's free float market cap is itself so small (NPR 156 crore), the 2% free float ceiling of NPR 3,12,000 is actually looser than the 7% ADV ceiling of NPR 56,000 — meaning ADV is still the binding constraint by these numbers, but only barely, and a slightly less conservative ADV percentage (say 15% instead of 7%) would flip the free float rule into the binding one immediately. This is precisely the scenario the chapter opened with: a stock whose absolute liquidity pool is so shallow that even a modest loosening of your daily-turnover assumption runs you straight into the float ceiling. The two rules are not redundant checks that usually agree; they are independent tests that can each become the deciding factor depending on a company's specific liquidity architecture, and you need to compute both, every time, to know which one is protecting you on any given name.
CASE IN POINT
An investor targeting a NPR 25 lakh position in a small hotel counter, having checked only that the stock "traded fine" on a percentage-of-ADV basis, discovers after two weeks of accumulation that they already hold close to 2% of the entire free float. Exiting even a third of that position over a single volatile week moves the stock double digits, because there are simply not enough other willing counterparties in a float this small. The ADV rule was satisfied. The float rule was not even checked. Both are needed.
Lesson 57.5 — Why Hydropower Counters Have a Structural Float Problem
Chapter 42 covered the project-finance architecture behind Nepal's hydropower boom: how these projects are typically financed through a layered capital structure of promoter equity, project-affected local shareholder allotments (a regulatory requirement designed to give communities living near a project a direct ownership stake), staff and employee quota shares, and public IPO shares, all sitting atop substantial project debt. That layered structure, while sound policy for spreading the benefits of hydropower development to local communities, has a direct and often underappreciated consequence for free float: a meaningfully larger share of a hydropower company's capital sits outside the freely tradable pool, for a meaningfully longer effective period, than is typical for a bank, a manufacturer, or a hotel company.
Consider the arithmetic. A commercial bank's structure, as covered in Lesson 57.3, is comparatively simple: a promoter tranche (regulated at a 51:49 minimum split against public shareholding, itself a change from the previous 70:30 requirement) and a public tranche that, once the standard lock-in expires, is essentially all tradable. A hydropower IPO typically layers in the local/project-affected quota and staff quota on top of the promoter tranche — and, as the lock-in data compiled from NEPSE hydropower listings shows, these local and staff allotments are frequently subject to the same multi-year lock-in as the promoter shares themselves, even though they are classified as part of the "public" allotment in headline reporting. The result: a hydropower company's headline promoter percentage may look no worse than a typical industrial company's, but its effective free float — the shares genuinely free to change hands the day after listing — can be substantially smaller, because a meaningful slice of the "public" allocation is locked right alongside the promoter shares.
CASE IN POINT
Sharesansar's compiled lock-in data across NEPSE's roughly 97 listed hydropower companies shows a staggered expiry schedule running out several years past initial listing — a handful of names completing lock-in in a given year, more the following year, and so on. A hydropower stock that looks "newly liquid" on any given trading day may in fact be trading only its small unlocked slice, with the bulk of its true float still years away from release.
This has two practical implications for a Nepal-focused investor applying the free float allocation limit. First, the free float calculation for a hydropower counter needs closer scrutiny than for other sectors — do not assume "public %" from a summary data source equals "free float %"; check the annual report or prospectus notes for whether local and staff quota shares are broken out separately and whether they remain inside a lock-in window, since aggregator platforms do not always make this distinction clear at a glance. Second, because hydropower is one of the most actively promoted retail investment sectors in Nepal — a large share of new NEPSE accounts and new capital inflow over the past decade has gone toward hydropower IPOs and secondary trading — many of the sector's most retail-popular counters are, by the mechanics above, also its most float-constrained. Popularity and float depth are not the same thing, and in Nepal's hydropower segment they are frequently working against each other: heavy retail attention chasing a structurally thin tradable pool is close to a textbook description of a liquidity trap waiting for a crowded exit, a theme this chapter's companion, Chapter 55's coverage of exit risk in NEPSE, examined from the demand side. Free float allocation limits are the supply-side complement: they cap how much of that thin pool you are willing to own before you become part of the exit-risk problem yourself.
WARNING
A hydropower company can show "45% public shareholding" in a summary data table while its genuinely tradable free float — after excluding locked local/staff quota shares — is closer to 20%. Never take a headline "public %" figure at face value for a hydropower counter without checking whether the local and staff allotments are still inside their lock-in window.
CAUTION
Sector concentration compounds this risk. An investor who holds free-float-respecting position sizes in five different hydropower names may still end up structurally overexposed to the sector's aggregate float constraints if a market-wide hydropower sentiment shift causes simultaneous, correlated selling pressure across all five at once — a scenario where each individual position was sized correctly in isolation, but the portfolio as a whole was not.
Lesson 57.6 — Float Expansion Over Time: Tracking Lock-In Expiry as a Liquidity Event
Free float is not a static number. For most NEPSE companies still within a few years of their IPO, free float rises over time — sometimes gradually, through incremental secondary issuance or promoter divestment, but very often in a single discrete jump, on the specific calendar date a statutory lock-in period expires and a large block of previously frozen shares becomes transferable overnight. Chapter 47 covered the mechanics of these lock-in periods; this lesson covers why the expiry date itself deserves to be tracked as a liquidity risk event, not just a compliance footnote.
Two things happen simultaneously on a lock-in expiry date, and they pull in opposite directions for anyone holding the stock. On one hand, the company's genuine free float expands, often substantially — a hydropower company that has been trading a 35% float for three years can see that figure jump to 59% or higher the moment its local/staff quota and promoter restrictions lift, exactly as illustrated by the Karnali-versus-Sunkoshi comparison in Table 1. A larger float is, in the medium term, good news for anyone applying the allocation-limit discipline from Lesson 57.4: it mechanically raises the rupee ceiling available to you, and it generally improves the stock's ability to absorb larger trades without violent price impact. On the other hand, the newly unlocked shares do not arrive in the market with an obligation to be held. Promoters, local shareholders, and staff allotees who have waited three years to have tradable shares often include holders eager to realise gains, meet liquidity needs, or simply rebalance out of a concentrated single-stock position — and because the unlock happens on a single date for the entire restricted block, the potential supply overhang arrives all at once rather than gradually. A stock that has traded thinly and firmly for three years can see real, sustained selling pressure precisely in the weeks following its lock-in expiry, even with no change whatsoever in the underlying business.
REGULATORY DETAIL
SEBON directives require listed companies to publicly disclose the end date of a promoter lock-in period at least 30 days in advance, specifically so that the market is not caught unaware when a large restricted block becomes transferable. This advance-disclosure requirement is itself a signal worth building a calendar around: any company you hold, or are watching, that issues this 30-day notice has just told you exactly when its supply-side liquidity picture is about to change.
The practical response is a lock-in expiry calendar, maintained alongside the float-tracking sheet recommended in Lesson 57.3. For every hydropower, banking, or other holding still within its statutory lock-in window, note the IPO or listing date, the applicable lock-in duration for that sector (three years general/hydropower from listing, five years minimum for banks and financial institutions from commencement of operations, per the variations covered in Chapter 47), and the resulting expected expiry date — cross-checked against the company's own 30-day advance disclosure once it is issued. Treat an approaching expiry date the way you would treat any other known, dated, and reasonably predictable liquidity event: not as a reason to panic-sell in advance, but as a reason to reassess your position size against a free float figure that is about to change, and to be alert to the possibility of elevated volatility and heavier-than-usual volume in the weeks immediately following the unlock, distinct from any change in company fundamentals over that same period.
PRACTICAL TOOL
For every position with an active lock-in, log: listing date, applicable lock-in duration, expected expiry date, and shares scheduled to unlock as a percentage of current free float. Recompute your free float allocation limit (Lesson 57.4) using the post-expiry float figure ahead of the date, not after, so your sizing discipline is never trading on stale float data.
There is a corollary here worth stating plainly: a stock's improving float profile is a reasonable factor in favour of it, over time, becoming a candidate for a larger position — but only after the expiry has actually occurred and the market has had time to absorb whatever supply materialises, not merely because the expiry is scheduled or imminent. Sizing up in anticipation of a float expansion that has not yet happened, on the theory that "the float ceiling will be higher soon," inverts the entire logic of this chapter's discipline. The allocation limit exists to be measured against the float you actually have today, not the float you expect to have next quarter.
Chapter recap
This chapter added a second, independent dimension to the position-sizing discipline built in Chapter 56. Where the ADV-based rule governs how quickly you can move into or out of a position without disrupting a single day's trading, the free float allocation limit governs something different: how large a fraction of a company's entire tradable ownership pool you are willing to represent. Free float — total shares outstanding minus promoter holdings, government or strategic stakes, and any shares still inside a statutory lock-in window, including Nepal's distinctive category of locked local and staff quota allotments — is the number that answers this question, and it must be assembled from prospectus disclosures, annual report shareholding schedules, and SEBON/NEPSE filings, because no standard trading screen presents it directly.
The chapter's central insight is that ADV and free float can diverge sharply, and that a stock passing one test tells you nothing reliable about whether it passes the other. A counter can show perfectly respectable daily turnover as a percentage of its own float while still having so small an absolute float that a moderate, portfolio-appropriate position size represents an uncomfortable share of every share anyone is free to trade. The worked examples in Lessons 57.3 and 57.4 demonstrated the mechanics: computing free float percentage and free float market capitalisation from disclosed data, then applying a free float allocation ceiling (illustrated at roughly 1–3% of free float market cap) alongside the ADV ceiling from Chapter 56, always taking whichever constraint binds tighter for that specific stock — never one rule as a substitute for the other, and never an average of the two.
Hydropower emerged as the sector where this discipline matters most acutely, extending the project-finance material from Chapter 42. Because Nepali hydropower IPOs layer local project-affected shareholder allotments and staff quota shares on top of standard promoter holdings — and because these allotments are frequently locked for the same multi-year period as promoter shares despite being classified as "public" — a hydropower company's effective free float is routinely smaller than its headline promoter-versus-public split would suggest. This is compounded by the sector's outsized share of Nepal's retail investment attention: some of NEPSE's most actively discussed and heavily bought hydropower counters are, by float mechanics alone, also among its most structurally float-constrained, a combination that connects directly to the exit-risk dynamics examined in Chapter 55.
Finally, the chapter established that free float is not fixed. It expands over time, most consequentially in the discrete jump that occurs on a statutory lock-in expiry date, when SEBON's required 30-day advance disclosure gives investors a known, dated liquidity event to plan around. An expiry simultaneously raises a stock's genuine tradable pool and introduces the possibility of concentrated selling pressure as newly unlocked holders realise gains or rebalance — a predictable volatility window worth tracking on a dedicated lock-in expiry calendar, distinct from and additional to the float-tracking sheet used for day-to-day allocation-limit calculations. The correct response to an approaching expiry is reassessment against the float figure that will actually exist once it passes, not anticipatory sizing against a float that has not yet materialised.
Together, Chapters 56 and 57 give the Nepal-focused investor a two-part sizing discipline that most retail participants in this market never build explicitly: a flow-based ceiling anchored to daily traded value, and a stock-based ceiling anchored to the total pool of shares actually available to trade. Neither, on its own, is sufficient protection against becoming a distorting influence on a thinly held counter or against being trapped in a position with no orderly path out. Chapter 58, "Circuit Trap Exit Modelling," turns to a closely related and distinctly NEPSE-specific mechanism that interacts with both disciplines: the exchange's daily circuit breaker bands, and what happens to an investor's exit options when a stock's price movement — often driven by exactly the kind of thin-float, concentrated-ownership dynamics covered in this chapter — repeatedly hits its daily limit, leaving sellers queued behind a circuit with no buyer able to clear the book at the locked price.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XI · Chapter 58
Circuit Trap Exit Modelling
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 58.1 — The Mechanics of the Circuit Trap
Every trading day on the Nepal Stock Exchange (NEPSE), every listed scrip is fenced in by a price band — a rule that says a stock cannot move more than a fixed percentage away from its previous closing price in a single session. As of the current rules (revised by the Securities Board of Nepal, SEBON, in April 2026), that band is 15% in either direction for most scrips during regular trading, up from the 10% band that applied for years before that. During the pre-open session — the short window before the main market opens, where the day's opening price is discovered — the band is tighter still, at 5%. If a stock closed yesterday at NPR 1,000, today it can trade anywhere between NPR 850 (the lower circuit) and NPR 1,150 (the upper circuit), but nowhere outside that range, no matter how many people want to buy or sell at a different price.
This is what the book has been calling, since Chapter 54, one of the defining structural features of NEPSE: a market where price discovery is deliberately slowed down by regulation, in exchange for reduced single-day volatility. The trade-off sounds reasonable in the abstract. A 15% band prevents a single panicked trading session from wiping out half a company's market value in an afternoon. But this chapter is about what happens on the other side of that trade-off: what a price band does to your ability to exit a position when the stock is falling, not rising.
Separately from the individual stock band, NEPSE also runs a market-wide circuit breaker pegged to the benchmark index rather than to any single stock. Under the rules revised in April 2026, this works on two tiers. If the NEPSE Index moves 5% from the previous close within the first two hours of trading, the entire market halts for 15 minutes, after which trading resumes. If the index moves 8% at any point during the session, the market shuts down completely for the rest of the day. This replaced an older three-tier system (4% / 5% / 6%, in place since 2007) that halted trading in smaller, more frequent steps. The market-wide breaker matters for portfolio-level risk, which earlier chapters in this Part have already covered. This chapter's concern is narrower and, for an individual investor, often more dangerous: what happens to one specific position you own when that one stock — not the whole market — is glued to its lower circuit.
REGULATORY DETAIL
Individual stock daily price band: 15% up/down from previous close (raised from 10% in April 2026). Pre-open session band: 5%. Market-wide index circuit breaker (revised April 2026): 5% index move within the first two hours triggers a 15-minute halt; an 8% move at any time halts trading for the rest of the session. These figures are set by SEBON and can change — always confirm the current thresholds before relying on them for a live position.
To understand the circuit trap, you first need to understand what "hitting the circuit" actually means mechanically, because it is widely misunderstood. NEPSE, like most modern exchanges, matches buy and sell orders continuously through what is called an order book — a running list of everyone who wants to buy at a given price and everyone who wants to sell at a given price, sorted from best price to worst. When a stock's price reaches its lower circuit — say NPR 850 in the example above — the exchange does not simply "stop" the stock. Orders can still be entered at NPR 850. Sell orders can still be placed at NPR 850. The trading system continues to try to match them. What changes is that no order can be entered below NPR 850, because the system will not accept a price outside the band.
Here is the part that catches investors off guard: a trade only happens when a buyer and a seller agree on a price and quantity. If a hundred people want to sell at NPR 850 and only three people want to buy at NPR 850, then only the volume the three buyers are willing to absorb actually trades. The other ninety-seven sellers' orders simply sit in the queue, unfilled, until the market closes for the day — or until a buyer shows up. If zero buyers show up at NPR 850, then zero shares trade at NPR 850, even though the stock is technically "allowed" to trade there. The stock is not frozen by the exchange. It is frozen by the absence of a counterparty.
KEY CONCEPT
A circuit trap is what happens when a stock is pinned at its lower circuit price with an order book full of sellers and few or no buyers willing to transact even at that discounted floor. The stock is not halted by rule — it is halted by the market's own refusal to buy. An investor holding shares in this state owns a position that is, for practical purposes, unsellable at any price the exchange will currently permit, while the "allowed" price keeps falling further each day the pattern repeats.
The clearest way to build intuition for this is a real-estate analogy, and it is worth sitting with for a moment because it maps almost exactly onto what is happening in the order book. Imagine you own a house you need to sell quickly — perhaps you have moved cities for a new job and need the cash. You list it at a fair price and nobody bids. You cut the price by 15%. Still nobody bids — in fact, word has gotten around the neighbourhood that something is wrong with the area (a factory closure, a flood risk, a change in zoning), and everyone who might have bought is waiting to see how much further prices will fall before they step in. You cut the price by another 15%. Still no buyers. You are not legally prevented from selling your house — there is no circuit breaker on real estate — but functionally, you are stuck, because a sale requires two willing parties and only one side of that transaction currently exists. A circuit-locked NEPSE stock is the same situation, except the price cuts are capped and rationed by the exchange at 15% per day rather than being something you can decide on your own, and the "listing" resets every morning at a new, lower, exchange-determined starting price.
It is important to be precise about why this happens, because the cause matters for how you manage it. A stock does not get circuit-trapped because the exchange is malfunctioning. It gets circuit-trapped because the market has collectively decided — rightly or wrongly — that the fair value of the stock is meaningfully below where it is currently pinned, and nobody wants to be the buyer who catches a falling asset before the selling pressure exhausts itself. This is often triggered by a specific piece of information: a regulatory change affecting a sector (as happened with Nepal's microfinance institutions in 2023, discussed in Lesson 58.5), a disappointing earnings disclosure, a promoter share-pledge default, or simply a broad market-wide risk-off move that hits small, thinly held, high-beta scrips hardest. Once the selling starts and the stock touches its lower circuit with volume imbalanced toward sellers, a self-reinforcing dynamic takes hold: every investor watching the order book sees a wall of unmet sell orders and concludes, reasonably, that today is not the day to buy — better to wait and see if it locks again tomorrow at an even lower price. That expectation becomes self-fulfilling, and it is exactly this dynamic that produces the multi-day cascades covered next.
Lesson 58.2 — The Multi-Day Cascade: Why 15% Can Become 50% or More
A single day's 15% circuit is not, by itself, catastrophic for a well-sized position. What makes the circuit trap a genuine capital-destruction risk is that the lower circuit can repeat for multiple consecutive sessions, and each day's 15% compounds on top of the previous day's already-reduced base. This is arithmetic that a lot of investors underestimate, because they mentally treat "15% circuit" as a ceiling on how bad a single stock's loss can get. It is not a ceiling. It is a floor on how fast the loss can happen on any given day — but there is no rule limiting how many consecutive days that floor can be hit.
The math compounds multiplicatively, not additively, and the difference matters enormously at scale. If a stock falls 15% every day for six consecutive sessions, the investor has not lost 6 × 15% = 90%. The actual loss is 1 − (0.85)^6 ≈ 62.3%, because each day's 15% cut applies to an already-shrunken base. That is still a devastating loss — but the compounding math also means the position never technically reaches zero on paper (0.85 to any power stays positive), even while the investor's real capital is being ground down toward practical worthlessness, day after day, with no ability to sell.
WARNING
A stock hitting its lower circuit for several consecutive sessions does not lose "15% times the number of days." Losses compound multiplicatively on a shrinking base, so the true cumulative loss is always somewhat less than the naive multiplication — but still severe, and the position remains unsellable throughout the entire cascade. Six consecutive lower-circuit days at 15% wipes out roughly 62% of the position's value even though "6 × 15%" looks like only 90%.
The table below works through this cascade for a hypothetical NEPSE scrip that opens at NPR 1,000 and hits its lower circuit (down 15%) for eight consecutive sessions with no buyers stepping in — a realistic worst case during a sharp sector-specific selloff of the kind Nepal has seen in small-cap hydropower and microfinance names.
Session
Price at Lower Circuit (NPR)
Daily Loss
Cumulative Loss from Day 0
Shares Traded That Day
Day 0 (last normal close)
1,000.00
—
—
Normal
Day 1
850.00
−15.0%
−15.0%
Near-zero (only sellers)
Day 2
722.50
−15.0%
−27.8%
Near-zero (only sellers)
Day 3
614.13
−15.0%
−38.6%
Near-zero (only sellers)
Day 4
522.01
−15.0%
−47.8%
Thin — a few bargain buyers appear
Day 5
443.70
−15.0%
−55.6%
Thin
Day 6
377.15
−15.0%
−62.3%
Moderate — value buyers step in
Day 7
320.58
−15.0%
−67.9%
Moderate to normal
Day 8
272.49
−15.0%
−72.8%
Normal — circuit breaks, price stabilises
Two things about this table deserve emphasis, because they are exactly the features that make circuit-trap risk different from ordinary drawdown risk. First, notice the "Shares Traded" column. In a normal 72.8% drawdown spread over eight days, an investor with a modest position could likely have sold out somewhere in the first two or three days, taking a painful but survivable loss. In a genuine circuit-trap cascade, the investor cannot sell at all during the days when the loss is accelerating fastest (Days 1 through 3), precisely because that is when the seller-buyer imbalance is most extreme. Liquidity typically only starts coming back once the price has fallen far enough that bargain hunters — the same investors this book has discussed elsewhere as value-oriented, patient capital — decide the risk-reward has turned favourable. By the time an exit is actually possible, most of the damage has already been locked in on paper.
Second, notice that the table assumes the circuit eventually breaks on Day 8. It might not. There is no guarantee, and no exchange rule, that says a stock cannot lock lower-circuit for fifteen sessions, or twenty, if the underlying reason for the selling (a regulatory shock, a fraud disclosure, a sector-wide re-rating) is severe enough. The table is illustrative of the mechanics, not a promise about duration. Some of the worst-affected microfinance and finance-company scrips during Nepal's 2022–2023 correction went through cascades considerably longer than eight sessions before real two-sided trading resumed.
CASE IN POINT
During Nepal's 2022–2023 market correction, several small-cap and mid-cap finance and microfinance scrips — hit by a combination of tightening Nepal Rastra Bank interest-rate and provisioning rules, a broader NEPSE bear market that took the benchmark index from its 2021 peak above 3,200 down into the 1,800s–2,000s range, and thin free floats in names already covered in Chapter 57 — spent multiple consecutive sessions locked at their lower circuits. Order books showed queues of sell orders with few or no matching buy orders, exactly the "only sellers" state described in Lesson 58.1, and investors who had not exited earlier in the decline found themselves watching further cuts they were structurally unable to sell into.
The deeper lesson here connects back to something Chapter 54 established at the start of this Part: liquidity is not a constant property of a stock, it is a state that can change discontinuously and without warning. A scrip that traded normally, with tight bid-ask spreads and healthy daily volume, for months can flip into a circuit-trapped state within a single session if the news flow turns bad enough. The circuit band does not prevent this — it only paces it, spreading what might otherwise be a one-day 70% crash into a multi-week grind that still ends up close to the same place, but with the added cruelty that the investor had to watch it happen in slow motion, powerless to act.
Lesson 58.3 — Modelling Days-to-Exit: Building on the ADV Rule
Chapter 56 introduced the Position Size vs. ADV Rule — the governing discipline that an investor's position in any single scrip should be sized relative to that stock's average daily volume (ADV), so that exiting the position does not itself move the market against the investor. The core formula from that chapter, restated here because this chapter builds directly on it, was:
Where the participation rate is the maximum fraction of a single day's volume an investor is willing to represent without materially distorting the price — commonly somewhere in the 10%–25% range for a disciplined institutional-style investor in a market as thin as NEPSE's small and mid-cap segment. If you hold 50,000 shares in a scrip with an ADV of 25,000 shares, and you cap yourself at 20% participation (5,000 shares a day), your orderly days-to-exit is 50,000 ÷ 5,000 = 10 trading days.
That formula is correct and useful — under normal, two-sided trading conditions. The entire point of this chapter is that a circuit trap invalidates the formula's core assumption. ADV is a historical average computed from days when the stock traded normally. On a day when the stock is locked at its lower circuit with no counterparty, effective tradeable volume for a seller trying to exit is not 20% of ADV, or even 5% of ADV — it can be zero. The days-to-exit formula needs a second, more pessimistic version that accounts for this.
PRACTICAL TOOL
A two-scenario days-to-exit model. Orderly-market days-to-exit = Position size ÷ (Participation rate × ADV), using the normal formula from Chapter 56. Circuit-trapped days-to-exit = Position size ÷ (Fraction of days with any real liquidity × Participation rate × Circuit-day effective volume), where "circuit-day effective volume" is the much-reduced volume actually available to sellers on the days a buyer does show up, and the fraction-of-days term accounts for consecutive locked sessions where zero shares trade at all. Model both numbers before sizing a position, not just the first.},
To make this concrete, the table below models days-to-exit for a position under three liquidity regimes: a normal market (the Chapter 56 baseline), a stressed market (volume has thinned but the stock is still two-sided), and a circuit-trapped market (the stock is locked most days, with only intermittent thin trading). Position size is expressed as a multiple of the stock's normal 180-day ADV, which is the convention this Part has used since Chapter 56.
Position Size (multiple of ADV)
Days-to-Exit — Normal Market (20% participation)
Days-to-Exit — Stressed Market (10% participation, ADV down 60%)
Days-to-Exit — Circuit-Trapped Market (real liquidity on ~1 day in 3, at 10% participation of a collapsed ADV)
0.25x ADV
1.3 days
6.3 days
~19 days
0.5x ADV
2.5 days
12.5 days
~38 days
1x ADV
5 days
25 days
~75 days
2x ADV
10 days
50 days
~150 days
5x ADV
25 days
125 days
~375 days
The pattern the table is meant to make visceral is this: the same position that would take five trading days to exit in a normal market — a manageable, almost routine liquidity event — can balloon to well over two calendar quarters of trading days to exit if the stock enters a genuine circuit-trap regime and the investor is unwilling to sell at whatever thin liquidity intermittently appears. And a position sized at 5x ADV, which some retail investors in NEPSE's small-cap segment do accumulate during a rally without ever stress-testing the exit side, could take over a year of trading days to unwind under stressed circuit conditions — by which point the fundamental thesis that justified owning the stock in the first place may be entirely irrelevant.
CAUTION
The "days-to-exit" numbers in the circuit-trapped column are not a schedule you can rely on. They assume liquidity returns roughly one day in three; a genuinely severe cascade (the kind seen in 2022–2023 microfinance names) can go considerably longer between any real two-sided trading at all. Treat the circuit-trapped model as an order-of-magnitude warning, not a forecast — its purpose is to tell you a position is dangerously oversized relative to plausible exit conditions, not to promise you a specific exit date.
This modelling exercise is also where Chapter 57's free-float lens becomes directly relevant. A stock with a small free float — the portion of shares actually available for public trading, after excluding promoter and locked-in holdings — has, by definition, a shallower pool of potential counterparties on both sides of the market. Low free float does not cause a circuit trap by itself, but it makes the "circuit-trapped market" column of the table above the more realistic scenario, rather than the exception, for a larger share of NEPSE's listed universe than investors coming from deeper markets might expect. When you size a position using the ADV rule from Chapter 56 and the free-float ceiling from Chapter 57, you are implicitly also sizing your exposure to circuit-trap risk — the three chapters are not independent checks, they are three views of the same underlying constraint.
Lesson 58.4 — Queue Position: Who Gets to Sell First
When a stock is pinned at its lower circuit with a large imbalance of sell orders over buy orders, an important and often overlooked question is: among all the sellers stuck in that queue, who actually gets filled first when a buyer finally does show up? The answer matters directly to an investor trying to plan an exit, because it determines whether being an early or a late seller into the circuit actually helps.
NEPSE's order matching, like most modern exchange systems, follows price-time priority. At a given price level — here, the lower circuit price itself, since that is the only price sellers are permitted to quote — orders are filled in the order they were placed. A sell order entered at 10:15 AM will be matched before a sell order entered at 11:40 AM at the same price, assuming a buyer arrives willing to take that quantity. This means that on the first day a stock locks at its lower circuit, an investor who reacts quickly and places a sell order in the first minutes of trading has a materially better queue position than one who waits, hesitates, or is simply slower to react — even though both are nominally selling at the "same" circuit price.
The practical implication is significant and somewhat counter to how many retail investors instinctively behave. When a stock is falling toward its lower circuit, there is a natural temptation to wait — to hope for a bounce, to avoid "selling at the bottom," to see if the price stabilises before committing. But if the stock does lock limit-down, every investor who waited is now behind, in queue-time terms, every investor who placed their sell order earlier in the session or on a prior day. If any buying interest shows up the next morning, the orders that get filled first are the ones that have been sitting in the queue longest — not the most recently entered ones. An investor who is seriously considering an exit, and who sees a stock approaching its lower circuit with deteriorating order-book depth, is generally better served by entering a sell order promptly rather than waiting to "see how it plays out," because the cost of being wrong about a bounce is small relative to the cost of losing queue priority in an illiquid lock.
KEY CONCEPT
Queue position at a circuit-locked price is governed by price-time priority: earlier-placed orders at the circuit price fill first when a counterparty appears. In a multi-day lower-circuit cascade, sellers who entered their orders on Day 1 are ahead of sellers who enter on Day 3 or Day 5, even though all are nominally selling "at the circuit price." Hesitating to place a sell order while hoping for a bounce does not protect queue position — it costs it.
This also has an important implication for how an institutional-scale or large individual investor should think about a position that is starting to look shaky, well before it actually locks limit-down. If you are managing a position sized at, say, 2x the stock's ADV — a size that Chapter 56's framework would already flag as requiring a multi-day orderly exit — and you see early warning signs of a coming selloff (a negative sector news item, a broken technical support level, deteriorating order-book depth on the buy side), the queue-position mechanics argue strongly for beginning to scale out immediately rather than waiting for confirmation that the thesis has fully broken. Every day you delay, if the stock does end up circuit-locking, is a day you fall further back in a queue that may only clear a small fraction of its backlog before the next lower-circuit session resets it entirely.
PRACTICAL TOOL
When monitoring a position for early circuit-trap warning signs, watch order-book depth on the buy side specifically — not just the last-traded price. A stock can still be trading at a "normal" price while the buy-side order book thins out dramatically, which is often the leading indicator that a circuit lock is imminent. A thinning buy-side book while price is still near recent highs is a stronger and earlier signal to begin scaling out than waiting for the first lower-circuit print itself.
There is a further wrinkle worth flagging for larger investors: because queue position is time-based and not size-based, a large seller cannot "jump the queue" by placing a bigger order — a large sell order simply waits, in its entirety or in the unfilled remainder, behind smaller orders placed earlier at the same price. This means position size compounds the queue-position problem rather than mitigating it. A large holder who is late to start selling is not just behind in time, they also have more total quantity that needs to work through a queue that is already backed up. This is one more reason the position-sizing discipline from Chapters 56 and 57 is not optional risk management — it is the primary lever an investor actually controls, because queue position, once a stock is genuinely circuit-trapped, is largely outside anyone's control.
Lesson 58.5 — Historical Case Studies: Circuit Cascades in NEPSE
The mechanics described so far are not theoretical. NEPSE has produced repeated real-world instances of the circuit trap, concentrated in two identifiable kinds of episodes: broad market-wide corrections that hit thinly-traded small caps hardest, and sector-specific regulatory shocks that trigger sharp, concentrated selling in a single industry group.
The clearest broad example is Nepal's 2021–2022 market cycle. The NEPSE Index ran from roughly the 1,200s in mid-2020 to an all-time high above 3,200 in mid-2021, driven substantially by retail participation, margin lending, and pandemic-era liquidity — a rally this book has referenced in earlier chapters as a case study in unsustainable market-wide leverage. When the correction came through late 2021 and into 2022, driven by monetary tightening, margin calls, and a sharp reduction in market liquidity, the index fell back into the 1,800s–2,000s range over the following months. That decline was not smooth. On the worst days, market-wide circuit breakers triggered at the index level (the mechanism covered briefly in Lesson 58.1), and beneath the index-level halts, a large number of individual small-cap and mid-cap scrips — particularly in hydropower, finance, and microfinance, the sectors with the thinnest free floats per Chapter 57 — spent repeated sessions locked at their lower circuits, with order books showing overwhelming seller imbalance.
CASE IN POINT
Nepal's 2021–2022 correction is a textbook illustration of the entry/exit asymmetry this chapter emphasises. Many retail investors who bought into small-cap hydropower and finance scrips during the 2021 rally did so within minutes — buying into strength, when the stock was liquid, the order book was deep on the sell side (plenty of willing sellers into the rally), and execution was effortless. When sentiment reversed in 2022, many of those same investors found that exiting took weeks, not minutes, as scrips they held cycled through multiple consecutive lower-circuit sessions with little or no buy-side interest.
A second, more sector-concentrated example came with Nepal's microfinance sector in 2022–2023. A combination of Nepal Rastra Bank's tightened interest-rate spread caps, stricter loan-loss provisioning requirements, and rising over-indebtedness concerns among microfinance borrowers triggered a sharp re-rating of microfinance equities across the board — not isolated to one or two companies, but affecting the sector as a cohort. Because microfinance institutions on NEPSE are disproportionately small-cap and often have modest free floats, the selling pressure that followed the regulatory news overwhelmed available buy-side interest in scrip after scrip. Multiple microfinance names spent several consecutive sessions locked at their lower circuits, and the sector as a whole underperformed the broader index by a wide margin over the following months.
This second case study is instructive for a different reason than the first: it shows that circuit-trap risk is not purely a function of an individual company's specific fundamentals. An investor can do thorough, careful bottom-up research on a single microfinance company, conclude correctly that its loan book and management quality are sound, and still find that scrip circuit-locked for days at a time — not because the company itself did anything wrong, but because it was correlated, through sector membership, with other companies that triggered the initial selling wave. This is a form of correlation risk that pure fundamental analysis does not capture, and it is precisely why the liquidity-engineering discipline built across this entire Part (Chapters 54 through 58) has to sit alongside fundamental analysis rather than being treated as a secondary concern.
WARNING
Circuit-trap risk is frequently sector-correlated, not company-specific. A regulatory or macro shock affecting one sub-sector (microfinance, hydropower, a specific class of finance companies) can trigger simultaneous circuit-locking across multiple names in that sector, meaning diversification within the sector does not protect you the way diversification across uncorrelated sectors would. An investor holding several microfinance names as a "diversified" position going into a sector-wide regulatory shock discovers, in practice, that all of them lock lower-circuit at once.
Both case studies point to the same underlying asymmetry that this chapter's brief specifically asked to be addressed, and it is worth stating plainly because it is easy to underweight emotionally while a rally is underway: entering a position in NEPSE, especially when buying into strength during a rising or momentum-driven market, is almost always fast. Orders execute near-instantly because sellers are plentiful — everyone wants to sell into a rally at a good price, so a buyer's order finds a counterparty within seconds. Exiting a position, especially when the stock is falling and thinly held, can be dramatically slower — not because the investor is indecisive, but because the other side of the trade, the buyer, may simply not exist in adequate size at the price the seller needs. This asymmetry is structural, not a matter of skill or timing, and it is the single most important intuition this chapter is trying to build: the ease of getting into a position tells you nothing about the ease of getting out of it, and the two should never be assumed to be mirror images of each other.
Lesson 58.6 — Mitigation: Sizing, Staged Exits, and Selling Into Bounces
Everything in this chapter so far has been diagnostic — understanding how the circuit trap works mechanically, how it cascades, how to model its effect on exit timelines, and how it has actually played out in NEPSE's history. This final lesson turns to what an investor can actually do about it, and the honest starting point is that there is no mitigation technique that eliminates circuit-trap risk entirely. Once a stock is genuinely locked with no buy-side interest, there is very little any individual holder can do in that moment. The real mitigation has to happen earlier — in position sizing before the fact, and in exit discipline during the early stages of a decline, before the trap fully closes.
The first and most important mitigation is the one this Part has already built across two prior chapters: position sizing discipline relative to ADV (Chapter 56) and relative to free float (Chapter 57). Everything modelled in Lesson 58.3 confirms why this matters specifically for circuit-trap risk — a position sized at 0.25x–0.5x ADV has a circuit-trapped days-to-exit measured in weeks, which is painful but survivable; a position sized at 5x ADV has a circuit-trapped days-to-exit measured in well over a year, which for most investors is not survivable, either financially or psychologically. The single highest-leverage decision an investor makes about circuit-trap risk happens before the position is even fully built, at the moment they decide how large a stake to accumulate relative to the stock's demonstrated trading liquidity.
The second mitigation is staged or partial exits, initiated when a thesis first shows cracks rather than only after it has fully broken. Many investors, reasonably enough, want confirmation before selling — they wait for a clear signal that the investment case has failed before acting, to avoid selling a good position on a false alarm. The circuit-trap dynamic punishes this instinct specifically, because by the time the thesis has "fully broken" in a way that is unambiguous, the stock is often already several sessions into a lower-circuit cascade, and the investor's remaining position is the hardest part to exit — the tail end of a queue that has been building for days. A more robust approach is to treat deterioration in a thesis as a dial, not a switch: trim 20–30% of a position on the first serious warning sign (a broken support level, a negative sector development, a disappointing quarterly disclosure), trim further if conditions continue to worsen, and reserve full-position conviction only for theses that are still fully intact. This does mean occasionally selling a position that goes on to recover — that is the acknowledged cost of the discipline, and it is a cost worth paying against the alternative of being fully trapped in a cascading decline.
PRACTICAL TOOL
A staged-exit trigger ladder, applied at the first sign of thesis deterioration rather than waiting for full confirmation: Trim 25% of the position on the first credible warning sign (broken technical support, negative sector news, thinning buy-side order-book depth). Trim a further 25% if the stock closes down on above-average volume with no bounce within two sessions. Exit the remaining position in full if the stock touches its lower circuit even once, using any available liquidity that session or the queue-priority advantage of an early order the next session, rather than waiting to see whether it locks again.
The third mitigation, and the one most specific to NEPSE's circuit-band structure, is pre-committing to sell into any bounce or upper-circuit day that occurs during an established downtrend, rather than holding out for a full trend reversal. This runs against a natural instinct — an investor watching a position recover 10–15% in a single session understandably wants to believe the worst is over, and selling into that relief rally can feel like giving up gains just as the stock turns around. But in a market with a hard daily price band, a single strong up-day inside a broader downtrend is often exactly the moment when buy-side liquidity is temporarily healthiest — which is to say, precisely the moment when an exit is actually executable, as opposed to the days when the stock is grinding toward its lower circuit and liquidity is thinnest. An investor who has already decided, in Lesson 58.5's terms, that a position's risk-reward has deteriorated should treat a bounce day not as a signal to hold on for more upside, but as a rare liquidity window to use.
PRACTICAL TOOL
A pre-commitment rule for downtrending positions: if a position identified for exit under the staged-exit ladder above rallies toward its upper circuit during an otherwise established downtrend, treat that session as the priority liquidity window and execute the planned exit into it, rather than waiting for a higher price. The rule should be set and written down before the bounce happens — deciding in the moment, with a green candle on the screen, is precisely when the instinct to "wait for more" tends to override discipline that was sound in the cold light of the original analysis.
None of these three mitigations — sizing discipline, staged exits, and selling into bounces — requires predicting when a circuit trap will occur. That is the point. An investor cannot reliably forecast which stock will lock lower-circuit next, or for how many sessions, any more than they can forecast the next sector-wide regulatory shock. What an investor can control is how much capital is at stake relative to demonstrated liquidity, how early they begin reducing exposure once a thesis starts to wobble, and whether they use the liquidity windows the market actually offers rather than the ones they wish it would offer. Circuit-trap risk cannot be eliminated in a market structured the way NEPSE is structured. It can be sized down to a survivable level, and that is the realistic and sufficient goal.
CAUTION
None of the mitigations in this lesson work retroactively. If a position is already circuit-locked with no buy-side liquidity, staged-exit ladders and bounce-day rules are moot — there is no liquidity event to execute into. These techniques are exit-risk management applied before or at the earliest edge of a decline, not rescue techniques for a position that has already fully trapped. Build the discipline into your process before you need it, because you cannot build it in the moment you discover you need it.
Chapter recap
This chapter has built the most quantitative model in Part XI, and it rests on a mechanical fact that is easy to state and easy to underestimate: NEPSE's daily price band — currently 15% for individual scrips, up from 10% before the April 2026 revision, alongside a market-wide index circuit breaker revised in April 2026 to a two-tier 5%/8% structure — does not guarantee that a falling stock can always be sold at its circuit price. Order matching at the lower circuit still requires a willing buyer, and when sellers vastly outnumber buyers at that price, trades simply do not execute. The result is the circuit trap: a stock frozen at a falling price with sell orders queued and no counterparty, functionally identical to trying to sell a house at a discount when no buyers are looking, except that the market itself resets the "asking price" 15% lower again the next morning if the imbalance persists.
The chapter then showed why this is more dangerous than a single day's 15% figure suggests. Because losses compound multiplicatively on a shrinking base across consecutive lower-circuit sessions, a stock can lose well over 60% of its value in six trading days and over 70% in eight, all while the holder is structurally unable to sell during the days the decline is accelerating fastest. Liquidity typically only returns once the price has fallen far enough to attract bargain-hunting buyers — by which point most of the paper damage is already locked in. This is a materially different risk profile from an ordinary drawdown, where an investor at least retains the option to sell at a worse, but achievable, price throughout the decline.
Building directly on the ADV rule from Chapter 56, the chapter extended the days-to-exit formula into a second, more pessimistic variant suited to circuit-trapped conditions — replacing the normal-market participation-rate assumption with a collapsed effective volume and an intermittent-liquidity fraction. The worked table showed how a position that would take five trading days to exit in a normal market can balloon to well over a hundred trading days under stressed circuit conditions if sized at several multiples of ADV, directly connecting position-sizing decisions made at entry to exit-timeline consequences realised, potentially, months or quarters later. The queue-position lesson added a further layer: because NEPSE's order matching follows price-time priority, hesitating to sell while hoping for a bounce does not protect an investor from circuit-trap risk — it actively worsens queue position relative to investors who acted earlier, and larger positions suffer this cost more severely because they have more quantity that must clear through an already-backed-up queue.
The historical material grounded all of this in NEPSE's actual experience: the 2021–2022 market-wide correction, where small-cap hydropower and finance names that had been bought effortlessly during the 2021 rally took weeks to exit once sentiment reversed, and the 2022–2023 microfinance sector selloff, where a sector-wide regulatory shock triggered simultaneous multi-day circuit-locking across companies with otherwise sound individual fundamentals — a reminder that circuit-trap risk is often correlated across a sector rather than confined to one troubled company. Both cases illustrate the entry/exit asymmetry this chapter placed at its centre: buying into strength on NEPSE is typically fast and easy, because sellers are plentiful in a rising market, while exiting a falling, thinly held position can be dramatically slower, because the market cannot manufacture a buyer that does not exist.
Finally, the chapter turned to mitigation, and was honest about its limits: nothing eliminates circuit-trap risk once a stock is genuinely locked with no counterparty. What an investor controls is upstream of that moment — sizing positions against ADV and free float from the outset (Chapters 56 and 57), trimming a position in stages as a thesis first shows cracks rather than waiting for full confirmation that it has broken, and pre-committing, before the fact, to sell into any bounce or upper-circuit day during an established downtrend rather than holding out for a better price that a circuit-constrained market may not offer again for weeks. Together these three disciplines do not predict which stock will circuit-lock next; they ensure that when one does, the resulting damage is sized to be survivable rather than capital-destroying.
With this chapter, Part XI — Liquidity Engineering & Position Sizing — is complete. Across Chapters 54 through 58, this Part built liquidity risk from first principles (Chapter 54), extended it into exit-specific risk (Chapter 55), formalised position sizing against average daily volume as a governing rule (Chapter 56), layered in free-float-based allocation limits (Chapter 57), and closed with the quantitative modelling of NEPSE's circuit-band mechanics and the exit-timeline consequences they create (Chapter 58). The investor who has internalized this Part now has a disciplined framework for how much capital any single NEPSE position can safely hold, independent of how attractive that position's fundamentals appear. Part XII, Portfolio Construction, takes that discipline and applies it at the whole-portfolio level, beginning with Chapter 59, "Asset Allocation Within Nepal's Investment Universe" — where the question shifts from how large a single position should be to how an investor's total capital should be distributed across the full range of asset classes available in Nepal's market.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XII
PORTFOLIO CONSTRUCTION
Part XII · Chapter 59
Asset Allocation Within Nepal’s Investment Universe
First published 23 Aug 2026 · Last verified 29 Aug 2026
Bimala Tamang's husband has worked in Qatar for six years. Every month, the money he sends home lands in her bank account in Damauli. For the first two years, she did what most families do: she let it sit. Then her neighbour's dhukuti group needed one more member, so she joined and started putting in three thousand rupees a month. When her brother-in-law urged her to buy a small plot of land near the highway, she pulled out her savings and bought it, because "land only goes up." When gold crossed three lakh rupees a tola this year, her mother reminded her that her own wedding tilhari was worth almost nothing when she was married in 1998, and now it could pay for a semester of her daughter's college. And every month, without her ever deciding to, five hundred rupees goes into a savings scheme her husband's manpower company enrolled him in years ago, something like a provident fund, though she has never seen the paperwork.
Look closely at what Bimala has done. Without ever using the word, she has built a portfolio. Some money sits in a bank account, safe and boring. Some money is committed to a dhukuti, a rotating pool that pays out to one member at a time. Some money has become land, which cannot be spent quickly but can be sold when the time is right. Some money has become gold, which her mother has taught her is what a family sells when there is no other way to raise cash. And some money is locked away in a retirement scheme she barely thinks about, growing slowly in the background.
She did not draw this up on paper. She did not calculate percentages. But she has, almost by instinct, spread her family's savings across several different kinds of assets, each behaving differently, each serving a different purpose. This is asset allocation. It is the oldest financial habit in Nepal, older than NEPSE, older than the banking system itself. Every farmer who plants both paddy and maize, so that a bad year for one is not a bad year for both, is doing the same thing. Every family that keeps some cash under the mattress, some in the bank, and some in jewelry, is doing the same thing.
This chapter takes that instinct and turns it into a discipline. Part XI taught you how to size a single position and manage the risk of getting stuck in an illiquid stock. Part XII, which begins here, asks a bigger question: before you even think about which stock to buy, how should your total wealth be divided among the different kinds of assets available to a Nepali investor? Get this decision right, and the individual stock picks matter much less than you think. Get it wrong, and no amount of clever stock picking will save you.
Lesson 59.1 — What Asset Allocation Means and Why It Matters More Than Stock Picking
Asset allocation is the decision about how much of your total savings goes into each broad category of investment — how much in NEPSE shares, how much in bank deposits, how much in gold, how much in land, and so on — before you decide which specific share, which specific bank, or which specific plot.
Think of your savings as water, and each asset class as a separate reservoir. You are the one deciding how much water flows into the equity reservoir, how much into the fixed deposit reservoir, how much into the gold reservoir, and how much into the land reservoir. Once you have decided how much water goes into the equity reservoir, a second and much smaller decision follows: which particular canal, inside that reservoir, do you dig — Nabil Bank or NIC Asia, Upper Tamakoshi or Chilime. That second decision is security selection. It is the one most new investors obsess over. It is also, on the evidence from markets around the world, the smaller of the two decisions.
KEY CONCEPT
Asset allocation is the decision of how much to put in each broad category of investment — equities, fixed income, cash, gold, real estate, retirement schemes. Security selection is the decision of which specific instrument to buy within a category. Research on institutional portfolios worldwide has repeatedly found that the allocation decision explains the large majority of the differences in return between one portfolio and another over time — far more than which individual securities were chosen. Decide your buckets before you decide your bets.
Why should this be true? Because each asset class has its own weather system. NEPSE can fall thirty percent in a bear market driven by margin calls and tight bank liquidity, while government development bonds sitting in the same investor's account barely move, because bonds do not depend on stock market sentiment — they depend on the government's ability to pay, which is a different risk entirely. Gold can rally sharply, as it has over the past year, precisely during a period when NEPSE investors are nursing losses, because gold responds to global forces — international prices, the dollar, geopolitical anxiety — that have almost nothing to do with whether Nepali banks are lending freely. If your money sits in only one reservoir, the weather in that one reservoir is your entire financial life. If your money is spread across several reservoirs with different weather systems, a storm in one does not sink your entire ship.
This is why a Nepali investor who buys the "right" hydropower stock at the "right" time but has put ninety-five percent of family savings into NEPSE equities alone is taking on far more risk than an investor who split savings across equities, fixed deposits, gold, and a retirement scheme, even if that second investor's individual stock picks were mediocre. The first investor made one enormous decision — all water into one reservoir — and everything else is secondary. The second investor made the big decision well, and the smaller decisions barely matter by comparison.
There is a second reason allocation matters even more in Nepal than in a market like the United States or India. In those markets, an investor can, at least in principle, diversify into dozens of countries, currencies, and asset classes with a few clicks. In Nepal, as you will see in Lesson 59.4, the menu is narrower and the exits are tighter. When your choices are fewer, each one carries more weight. Getting the big allocation decision right is not a nice-to-have refinement for a Nepali investor. It is close to the whole game.
None of this means stock picking does not matter. The next chapter in this Part is devoted entirely to building a well-constructed equity portfolio on NEPSE. But that work happens inside the equity reservoir — after you have already decided how big that reservoir should be relative to everything else you own. Skipping the allocation decision and jumping straight to stock picking is like a farmer choosing exactly which maize seed variety to plant without first deciding how much of the field to give to maize versus rice versus vegetables. The seed variety matters. The field division matters more.
Lesson 59.2 — The Nepali Investor's Actual Asset Menu
Before you can allocate, you need to know what is actually on the menu. A textbook written for an American or Indian reader would list index funds, real estate investment trusts, corporate bonds, and international equities as everyday options. A Nepali household's real menu looks different, and this lesson lays it out asset class by asset class, with the numbers a Nepali investor would actually see in mid-2026.
NEPSE equities. Shares listed on the Nepal Stock Exchange are the most visible asset class in this book, but it is worth remembering how narrow this market actually is by sector. Banking and financial institutions, hydropower, and finance companies together dominate NEPSE's daily turnover and much of its total market capitalisation, with insurance, microfinance, hotels and tourism, manufacturing, and a scattering of other sectors making up the rest. This concentration is not an accident — it reflects what kind of economy Nepal has. A country that depends on hydropower exports and a banking system built to intermediate remittance inflows will naturally have a stock market dominated by hydropower and banking shares.
CASE IN POINT
On most trading days through 2026, banking, hydropower, and finance company shares together have accounted for the large majority of NEPSE's turnover, and price movements in these three groups have driven most of the swings in the overall NEPSE index. An investor who believes they are "diversified" because they hold twelve different NEPSE stocks may in fact be holding twelve variations on the same two or three underlying bets — the health of the banking system and the profitability of hydropower generation.
Government securities. The Government of Nepal, through Nepal Rastra Bank, issues treasury bills (short-term instruments, typically 28, 91, 182, and 364 days) and longer-dated development bonds and savings bonds, generally with maturities from two to fifteen years. As of mid-2026, with the banking system flush with liquidity and NRB running an accommodative policy, the 91-day treasury bill yield has been trading around 2.6 percent, and the interbank lending rate — the rate banks charge each other overnight — has hovered near 2.75 percent. These are low yields by Nepal's own recent history, a direct result of the deposit growth and modest credit demand discussed in Lesson 59.4. Development bonds with longer maturities typically offer a few percentage points more than treasury bills, to compensate for tying up money longer, but they still sit well below what NEPSE equities have historically returned over a full cycle — and well below what equities can also lose in a single bad year.
Bank fixed deposits. This remains the single most familiar investment for the average Nepali household, because everyone already has a bank account and fixed deposits require no new relationship, no demat account, and no learning curve. As of Shrawan 2083 (July 2026), one-year fixed deposit rates for individual depositors at major commercial banks range roughly from about 3.85 percent at the lower end to around 4.5–4.6 percent at the higher end, with most large banks clustered in the low-to-mid four percent range. Remember that a flat 15 percent tax is withheld at source on interest income, so a headline rate of 4.55 percent becomes roughly 3.9 percent in the depositor's hand. Many banks also run special "remittance FD" schemes that pay meaningfully more — sometimes 5 to 5.5 percent — specifically to attract deposits routed through formal remittance channels, which tells you something important about where Nepali banks are hungriest for funding.
REGULATORY DETAIL
Interest earned on bank deposits in Nepal is subject to a final withholding tax of 15 percent, deducted automatically by the bank before the interest is credited. This is different from dividend income on NEPSE shares, which is taxed at 5 percent for resident individuals holding shares for more than a year (and higher for short-term gains), and different again from capital gains on shares, which are taxed separately. When comparing the "return" on different asset classes, always compare what actually lands in your hand after tax, not the headline rate advertised on a bank's rate board.
Mutual funds. Nepal's mutual fund industry is still young and dominated by closed-end funds — funds that raise a fixed pool of money, list on NEPSE, and then trade at a market price that can sit above or below the fund's actual net asset value (NAV), the per-unit value of the fund's underlying holdings. A closed-end fund trading at a discount to its NAV can be an attractively priced way to get diversified exposure to a basket of NEPSE stocks and bonds managed by a professional, but the discount itself is a risk: it can widen before it narrows, and exiting means selling on the open market, subject to the same liquidity constraints as any other NEPSE-listed instrument. Open-end mutual funds, which let investors buy and redeem units directly at NAV, are a newer and smaller part of the landscape, growing but still limited in number and total size compared to the closed-end fund universe.
Gold. Gold has a particular emotional and cultural weight in Nepali households that goes beyond its role as an investment — it is jewelry, dowry, and family security rolled into one. As an asset class, though, it has also simply performed well recently: the price of fine gold (9999 hallmark) in the Kathmandu market has risen from roughly NPR 191,700 per tola in mid-2025 to around NPR 316,700 per tola by August 2026 — a rise of well over 60 percent in about thirteen months, driven almost entirely by global gold prices and international economic anxiety rather than anything happening inside Nepal. This is precisely gold's usefulness in a portfolio: its price is set on world markets, so it tends to move independently of — and sometimes in the opposite direction from — what is happening in NEPSE or the Nepali banking sector.
WARNING
A sharp rally, like gold's climb over the past year, tempts investors to pile in after most of the gain has already happened, exactly the mistake investors make chasing a hot NEPSE sector. Gold belongs in a Nepali portfolio as a long-term stabiliser held in modest proportion, not as a trade you enter because the price has already doubled. Buying gold because it is going up is the same error as buying a hydropower stock because it has already gone up — you are paying for yesterday's return, not tomorrow's.
Real estate and land. Land ownership is deeply woven into Nepali notions of family security, and for good reason — over long stretches, land in and around urban centres has delivered strong returns. But real estate in Nepal is illiquid in a way few investors fully appreciate until they try to sell. There is no central exchange, no daily price quote, and no guaranteed buyer. The Kathmandu Valley property market has been through a prolonged slowdown since around 2021, with transaction volumes well below the boom years and sellers routinely waiting months or years to find a buyer at an acceptable price. Registration fees, capital gains tax on transfer, and the sheer paperwork involved add further friction on both entry and exit.
CAUTION
Land is often treated by Nepali families as a "safe" asset because its price does not visibly fluctuate day to day the way a NEPSE share does. This is an illusion of stability, not real stability. A NEPSE share's price moves every trading day because it is being valued constantly; a plot of land's "price" is really just the last guess of what someone might pay, and that guess can be badly wrong when you actually need to sell in a hurry. Illiquidity that hides risk is still risk — Part XI of this book covered this problem in the context of NEPSE shares, but it applies with even greater force to real estate.
Retirement schemes — EPF, CIT, and SSF. Salaried employees in Nepal typically have part of their income directed automatically into one of several retirement savings vehicles. The Employees Provident Fund (EPF) is the long-standing scheme for government and many private-sector employees, into which both employee and employer contribute a fixed percentage of salary each month; EPF declared an annual interest rate of 5.0 percent on members' accumulated savings effective from Shrawan 2082 (July 2025). The Citizen Investment Trust (CIT) is an older, listed pension-fund institution offering its own set of retirement and periodic savings schemes, funded by voluntary and salary-linked contributions, with a substantial pool of assets under management invested across government securities, fixed deposits, and NEPSE-listed instruments; it periodically declares interest or dividends to depositors in a similar band to EPF, sometimes higher for schemes that carry more market exposure. Newer employees are increasingly covered by the Social Security Fund (SSF), a government scheme launched to consolidate provident fund, gratuity, and social protection contributions into a single system. These schemes function, in practice, as a mandatory or semi-mandatory bond-like allocation: money goes in steadily, is invested conservatively by the fund manager, and grows slowly but reliably, inaccessible until retirement, resignation, or specific permitted circumstances.
EPF, CIT, and SSF contributions are typically deducted directly from salary before an employee ever sees the cash, alongside a matching employer contribution. Because this money is locked away and invested conservatively on the employee's behalf, it should be counted as part of your household's fixed-income or "safe" allocation when you build your overall asset allocation, not ignored simply because you cannot see the account balance move day to day. A salaried worker with a healthy EPF balance already has a bond-like cushion that a self-employed person or day-labor migrant worker does not, and that difference should shape how aggressively each of them can afford to invest elsewhere.
Informal and traditional savings — dhukuti and beyond. The dhukuti (a rotating savings and credit association, where a fixed group of members each contribute a set amount every cycle, and the pooled amount is paid out in full to one member per cycle, rotating until everyone has received a payout once) remains an enormously important, if largely invisible, part of household finance in Nepal, especially outside the largest cities and among women who may have less direct access to formal banking relationships. A dhukuti is not really an investment in the sense of generating a return — money paid in eventually comes back roughly equal to what went in, sometimes with a small implicit interest depending on how the group is structured — but it is a powerful savings discipline, forcing regular contributions and providing a lump sum at a predictable, negotiated point in the rotation. Remittance-funded savings sitting idle in ordinary, low-interest bank accounts round out the picture; this is frequently the single largest pool of "uninvested" household wealth in remittance-dependent families.
Table 59.1 summarises this menu.
Asset class
Typical mid-2026 nominal return
Liquidity
Main risk driver
NEPSE equities (broad)
Highly variable; historically double-digit in strong years, sharply negative in weak years
Moderate — depends on stock's turnover and float
Bank liquidity cycle, earnings, sector sentiment
Government T-bills / development bonds
Roughly 2.6%–6% depending on tenure
High for T-bills; moderate for longer bonds (secondary market thin)
Interest rate cycle, government fiscal position
Bank fixed deposits
Roughly 3.9%–4.6% gross (about 3.3%–3.9% after tax) for 1-year individual FDs; higher for remittance-linked FDs
High, but breaking early costs a penalty
Bank-specific credit risk; deposit rate cycle
Mutual funds (closed-end)
Tracks underlying NAV, plus/minus a market discount or premium
Moderate — tradable on NEPSE, but thin volume in smaller funds
Same as NEPSE, filtered through fund manager decisions
Gold
Strongly positive over the past year (~60%+), driven by global prices
High — easily sold at jewelers, though at a spread
Global gold price, USD movements, import policy
Real estate / land
Historically strong over long horizons; flat-to-weak since ~2021
Low — can take months to years to sell at a fair price
Local demand, credit availability for buyers, transfer costs
EPF / CIT / SSF retirement savings
Roughly 5% declared annual rate (EPF); CIT similar or somewhat higher for market-linked schemes
Very low — locked until retirement or defined exit events
Fund's own conservative portfolio, regulatory changes
Dhukuti / informal savings
Approximately capital return; small implicit interest depending on group
Low until your turn in the rotation; high once received
Counterparty (fellow member) default or group collapse
Lesson 59.3 — Risk, Return, and Correlation Among Nepal's Asset Classes
Table 59.1 already hints at the next idea you need: it is not enough to know each asset class's typical return and typical risk in isolation. What matters for a portfolio is how these asset classes move relative to each other — a concept called correlation.
Correlation measures whether two things tend to move together, move in opposite directions, or move independently of one another. If two asset classes are highly correlated, they tend to rise and fall at the same time — holding both gives you very little extra protection, because when one falls, the other is likely falling too. If two asset classes have low or negative correlation, one can be falling while the other holds steady or rises — holding both smooths out your overall ride.
Think of two farmers in the same village. If both plant only paddy, a drought that hurts one farmer's paddy hurts the other's paddy too — their harvests are highly correlated. If one farmer plants paddy and the other plants ginger for export, a domestic drought might hurt the paddy farmer while barely touching the ginger farmer, whose crop depends more on export demand and international ginger prices. Their harvests are less correlated, and the village as a whole is less exposed to any single kind of bad luck. A household's portfolio works the same way: assets whose "harvests" depend on different underlying forces protect the household better than assets that all depend on the same underlying force, even if you hold many of them.
Within NEPSE itself, correlation is higher than most new investors assume. Banking shares and hydropower shares are formally different sectors, but both depend heavily on the same underlying force: the amount of liquidity in the banking system and the direction of interest rates. When deposits are growing faster than credit demand — as has broadly been the case through 2025 and into 2026, with deposits climbing toward roughly NPR 7.95 trillion against private credit growth of only around 5.7 percent — banks have surplus funds to lend, interest rates fall, margin-financed share buying becomes cheaper, and both banking and hydropower shares tend to rally together on the resulting wave of liquidity. When the opposite happens — credit demand outpaces deposits, liquidity tightens, and interest rates rise — both sectors tend to fall together, for the same underlying reason. Holding fifteen NEPSE stocks spread across banks, hydropower companies, and finance companies feels diversified, but if all fifteen are riding the same liquidity cycle, you have not diversified away nearly as much risk as the number fifteen suggests.
Government securities behave differently from equities, though not entirely independently — a rate environment that pushes NEPSE up (falling rates, ample liquidity) is often the same environment that pushes newly-issued bond and FD rates down, so the relationship between "cheap money conditions" and each asset class's return runs in a somewhat predictable, opposite direction. This is genuinely useful: a household holding both equities and fixed-income instruments has some natural offsetting behaviour built in, even if it is not a perfect hedge.
Gold's low correlation with NEPSE is one of its most valuable features for a Nepali household, precisely because gold's price is set mostly by international forces — global interest rates, the US dollar, geopolitical risk, central bank buying around the world — that have little direct connection to whether NRB is easing or tightening domestic liquidity, or whether a Nepali hydropower project has come online on schedule. This is why gold's strong run over the past year has been a genuine cushion for households that held both NEPSE shares and gold through a period when equity sentiment has been choppy.
Real estate's correlation with everything else is harder to pin down precisely, because there is no daily price series to measure it against — but intuitively, land prices depend on local credit availability (can buyers get home loans?), local income growth, and specific area development, which overlap partially with the same banking liquidity cycle that drives NEPSE, but with a much longer and slower-moving lag.
Diversification only works when the assets you hold do not all depend on the same underlying driver. Ten NEPSE stocks across banking, hydropower, and finance are not ten independent bets — they are largely one bet on Nepal's domestic liquidity cycle, repeated ten times. True diversification for a Nepali household comes from combining assets with genuinely different drivers: domestic equities that respond to liquidity and earnings, fixed income that responds to interest rates, gold that responds to global forces, and real estate that responds to local, slow-moving credit conditions.
Lesson 59.4 — Capital Controls and the Limits of Diversification for Nepali Investors
Everything in the previous two lessons has one uncomfortable thread running through it: every single asset class on a Nepali investor's menu is, in one way or another, exposed to the Nepali economy. NEPSE is Nepali. Government bonds are Nepali government risk. Bank FDs are Nepali bank risk. Real estate is Nepali real estate. Even EPF and CIT invest their pools mostly within Nepal. Gold is the one genuine exception — its price is set globally — which is exactly why the previous lesson highlighted it as Nepal's best available diversifier.
In many countries, an investor facing this problem would simply buy some foreign assets — a US index fund, a regional bond fund, shares in a company on another continent — to reduce dependence on the home economy. A Nepali retail investor, by and large, cannot do this. Nepal maintains strict capital account controls, meaning the government and NRB tightly regulate the movement of money into and out of the country, especially for investment purposes. Foreign currency earned by exporters, remittance senders, and tourism must generally be surrendered or channeled through the formal banking system, and outward investment by resident individuals into foreign stocks, foreign mutual funds, or foreign real estate is not freely permitted the way it is for a retail investor in, say, India or the United States. Individuals travelling abroad are allowed only a limited foreign exchange quota for travel and personal expenses, set by NRB, and there is no established, freely accessible retail channel for an ordinary Nepali saver to open a brokerage account in New York or Singapore and buy an S&P 500 index fund with rupees converted at will.
REGULATORY DETAIL
Nepal's capital account is not open for retail portfolio investment abroad. Individuals may access foreign exchange for permitted purposes — travel, education, medical treatment, and limited other categories — within NRB-set quotas, but there is no general retail facility for buying foreign securities with rupee savings. Nepal Rastra Bank has occasionally studied or piloted very limited schemes for outward portfolio investment by specific institutional categories, but as of mid-2026 this remains firmly the exception, not something an ordinary household investor can rely on as part of a routine allocation plan. Any scheme claiming to offer Nepali retail investors easy access to foreign stock trading outside these regulated channels should be treated with serious suspicion — it is very likely operating outside the law, and money moved this way carries real legal and counterparty risk with no protection from Nepali regulators.
This is not a minor inconvenience. It means a Nepali household's ability to diversify away from "Nepal risk" — the risk that the whole domestic economy has a bad decade, whether from a banking crisis, a political shock, a natural disaster, or a slowdown in remittance-sending countries — is fundamentally limited compared to an investor in a more open economy. Whatever happens to hydropower output, banking sector health, or the pace of urban construction, a Nepali household's portfolio will largely rise and fall with it, because almost everything on the menu described in Lesson 59.2 is a bet on Nepal, dressed up in different clothing.
This constraint interacts with a second feature of the Nepali financial system: NRB's directed lending rules, under which commercial banks must allocate a defined minimum share of their loan portfolios to specific "productive" sectors — agriculture, energy (including hydropower), tourism, and micro, small, and medium enterprises among them — with sub-targets NRB adjusts from time to time through its annual monetary policy. This is a deliberate policy choice to steer credit toward sectors the government considers priorities for national development, rather than letting banks lend purely wherever the highest return happens to be. One effect is that it reinforces the tight relationship between banking and hydropower discussed in Lesson 59.3 — banks are structurally pushed to fund hydropower, hydropower's fortunes depend heavily on bank credit terms, and the two sectors' stock prices end up moving together even more than they otherwise might.
Given all this, what is a sensible Nepali investor supposed to do? Three practical responses matter.
First, treat gold as your primary — really, close to your only reliable — tool for reducing dependence on the Nepali economic cycle, and hold it deliberately, in a modest but consistent proportion, rather than as an opportunistic trade.
Second, do not treat variety within Nepal as a substitute for genuine diversification. Owning NEPSE shares, a plot of land, and a fixed deposit feels well spread out, but as Lesson 59.3 showed, all three are still fundamentally exposed to the same domestic liquidity and growth cycle, just moving at different speeds. This is diversification in name, not fully in substance.
Third, accept that remittance income itself is Nepal's real, if imperfect, channel of exposure to the outside world. A family like Bimala's, receiving income earned in Qatar's economy, already has a form of "foreign diversification" built into its cash flow, even without owning a single foreign share. If your income is remittance-linked, your investment portfolio can afford to lean slightly more toward domestic Nepal-only risk than a household whose income is purely domestic, because your income side is already providing some of the diversification your investment side cannot.
Do not confuse "many different Nepali asset classes" with "genuine diversification." Every asset on the Nepali menu, except gold, ultimately depends on the health of the same domestic economy. Spreading savings across NEPSE, land, FDs, and retirement schemes reduces some risks — liquidity risk, single-company risk, single-bank risk — but it does not reduce Nepal-country risk, the risk that the whole economy underperforms at once. Only gold, and to a lesser extent income from abroad, does that job in the menu available to a retail Nepali investor today.
Lesson 59.5 — Building an Allocation Framework by Life Stage and Risk Tolerance
With the menu and its constraints now clear, how should a Nepali household actually decide its mix? The starting point is not a formula — it is two honest questions: how much time do you have before you will need this money, and how much of a fall in value could you tolerate without panicking or being forced to sell at the worst possible moment?
Time horizon matters because equities and real estate are volatile in the short run but have historically rewarded patience over long stretches, while fixed deposits and government securities are steady but grow slowly. A young NEPSE investor with thirty years until retirement can ride out a bad two-year stretch in the market, because there is no need to sell during the bad stretch — time itself repairs much of the damage. A retiree who needs to withdraw money next year cannot afford the same bad two-year stretch, because there is no time left to wait it out.
Risk tolerance matters separately from time horizon, because it is about temperament and circumstance, not just age. A forty-year-old civil servant with a stable government salary and a healthy EPF balance can tolerate more market risk in personal investments than a forty-year-old small trader whose income already swings with the business cycle, even though both are the same age.
PRACTICAL TOOL
A simple starting rule many investors use worldwide is "100 minus your age" as a rough guide to the percentage of a portfolio to hold in growth assets like equities, with the rest in safer, income-generating assets. For a Nepali household, adjust this rule in two ways. First, count your EPF, CIT, or SSF balance as part of your "safe" bucket before applying the rule to your remaining, freely investable savings — a salaried worker with a large forced retirement balance can afford to be more aggressive with the money they actually control. Second, always carve out a fixed slice, commonly 5 to 15 percent of investable savings, for gold specifically, regardless of age, because of its distinct diversifying role described in Lessons 59.3 and 59.4 — gold is not simply part of the "safe" bucket to be adjusted up or down with age; it plays a different job entirely.
Life stage gives a more concrete anchor than age alone. Table 59.2 sketches four broad stages a Nepali household typically passes through, and a directional allocation for each. These are illustrative starting points to adapt to individual circumstances, not rigid formulas — a household's actual number of dependents, job security, existing debt, and access to family land will all shift the right mix.
Life stage
NEPSE equities
Fixed deposits / gov't securities
Gold
Real estate
EPF / CIT / SSF (counted separately)
Early career, single or newly married, no dependents
45–55%
20–30%
10–15%
0–10% (renting, saving toward first purchase)
Ongoing payroll deduction, not counted in above %
Family formation, children, possibly a home loan
30–40%
25–35%
10–15%
15–25% (often the family home itself)
Ongoing payroll deduction, not counted in above %
Pre-retirement, children grown, income peaking
20–30%
35–45%
10–15%
20–30%
Balance growing toward a large lump sum at retirement
Retired, drawing down savings
10–15%
45–55%
10–15%
20–30% (often reduced, converted to income-generating rent or sold down gradually)
Lump sum received; often re-allocated into fixed deposits and bonds for income
Two points about this table deserve emphasis. First, the "real estate" column for a family in the middle two stages very often is simply the family home, not an investment property — living in your own house is not the same financial decision as owning a second plot as an investment, even though both show up as "real estate" on a simple net worth statement. When you build your own household's allocation, separate the roof over your head from investment real estate, because you will never sell the first to rebalance a portfolio, no matter what the numbers on this page suggest.
Second, the fixed deposit and government securities column is deliberately kept in double digits even for the youngest, most risk-tolerant household. This is not caution for its own sake — it reflects the reality, discussed throughout Part XI, that NEPSE positions carry real liquidity risk, and every household needs an emergency reserve that can be turned into cash within days, not weeks, regardless of how confident that household feels about the stock market's long-run prospects. A common starting benchmark is to keep three to six months of essential household expenses in a fixed deposit or basic savings account, completely separate from — and before — any equity allocation is built, precisely so a medical emergency or a job loss never forces a panic sale of shares at a bad price.
Lesson 59.6 — A Worked Asset Allocation Example for a Nepali Household
Consider the Shrestha household: Rajan, 34, works as a mid-level officer at a private bank in Kathmandu, earning a stable salary with automatic EPF deductions. His wife, Sunita, 32, works part-time and also receives occasional support from her brother, who works in Malaysia. They have one child, age 4, and own no property yet — they rent their apartment, and are saving toward a down payment on a home in the next five to seven years. After their monthly expenses, EPF contributions, and an emergency fund already set aside, they have accumulated NPR 1,500,000 in investable savings, and can add roughly NPR 25,000 per month going forward.
Applying the family-formation stage from Table 59.2, adjusted for their specific circumstances — Rajan's stable EPF-backed salary allows slightly more equity risk than the table's midpoint, but their five-to-seven-year home-purchase goal argues for keeping that target fund very safe and liquid, not exposed to NEPSE's swings — they might land on the plan in Table 59.3.
Bucket
Allocation
Amount (NPR)
Purpose
Emergency fund (already set aside, separate from the 1.5m)
—
300,000 (held separately)
4 months of expenses, in a basic savings account, untouched
Home down-payment fund
35%
525,000
5–7 year goal; kept in fixed deposits and short-tenure development bonds only, laddered so some matures each year as the target date nears
NEPSE equities
35%
525,000
Long-term growth sleeve, built across banking, hydropower, and a few other sectors per Chapter 60's screening framework
Gold
12%
180,000
Diversifier against Nepal-specific risk, bought gradually over time rather than in one lump sum
Mutual fund (closed-end, bought at a discount to NAV)
8%
120,000
Professionally managed exposure, partly overlapping with the equity sleeve but adding bond exposure inside the fund
Cash buffer for opportunistic FD/bond purchases
10%
150,000
Kept ready to lock into fixed deposits or T-bills when rates rise, or to add to equities after a sharp, justified market fall
(Separately) Rajan's EPF balance, growing via payroll
not counted above
—
Functions as this household's long-run retirement bond allocation
CASE IN POINT
Notice what the Shrestha household did not do: they did not put the home down-payment money into NEPSE shares, even though equities have historically outperformed fixed deposits over long periods, because the money is needed on a specific, relatively near-term date, and a market downturn at the wrong moment could force them to either delay their home purchase or sell shares at a loss. This is the time-horizon principle from Lesson 59.5 in action — the same household can be growth-oriented with one bucket of money and conservative with another, because the two buckets are earmarked for different jobs.
Every month, the couple adds their NPR 25,000 in savings roughly in the same proportions — slightly more toward the home fund as the target date approaches, a fixed small amount into gold regardless of price (a disciplined approach sometimes called rupee-cost averaging: buying a fixed rupee amount on a regular schedule means you automatically buy more units when the price is low and fewer when the price is high). They review the full allocation once a year, checking whether any bucket has drifted far from its target due to market moves — if NEPSE has rallied hard and the equity sleeve has grown to 45 percent, they would trim it back toward 35 percent and move the proceeds into the underweighted buckets, a discipline called rebalancing that forces a household to systematically sell some of what has gone up and add to what has lagged.
This worked example is deliberately modest, because the value of an allocation framework is not in memorising Table 59.2's percentages as universal truth — it is in the process the Shrestha household followed: name your goals, attach a time horizon and a risk tolerance to each one, choose the asset class whose behaviour actually matches that goal, and revisit the plan on a schedule rather than only when the market has scared you into acting.
Chapter recap
This chapter opened with Bimala Tamang's household, spreading remittance income across a bank account, a dhukuti, land, gold, and an unseen retirement scheme, and closed with the Shrestha household, doing the same thing more deliberately, with numbers attached. In between, the chapter built the case that asset allocation — the decision of how much to place in each broad category of investment — shapes a Nepali portfolio's fate more than any individual stock pick ever will. Lesson 59.1 established this principle; Lesson 59.2 grounded it in the specific menu available to a Nepali household today: NEPSE equities concentrated in banking and hydropower, government securities yielding modest single-digit returns, bank fixed deposits offering roughly 3.9 to 4.6 percent gross, mutual funds trading at NAV-linked prices on NEPSE, gold that has surged over 60 percent in little more than a year, real estate that is illiquid and has been soft since 2021, EPF and CIT retirement schemes paying around 5 percent as forced bond exposure, and informal tools like dhukuti that discipline savings without generating true investment return.
Lesson 59.3 pushed past simple return comparisons into correlation — the degree to which different assets move together — and showed that much of what looks like diversification inside NEPSE is really one large bet on Nepal's domestic liquidity cycle, repeated across many tickers. Lesson 59.4 confronted the hardest structural fact in the whole chapter: Nepal's capital account controls mean an ordinary retail investor cannot simply buy foreign assets to escape this concentration, which makes gold, and to a lesser degree remittance income itself, unusually important tools for a Nepali household trying to reduce its dependence on any single domestic outcome. NRB's directed lending requirements toward productive sectors like hydropower and agriculture were shown to reinforce, rather than loosen, the tight coupling between the banking and hydropower sectors that already dominates NEPSE.
Lessons 59.5 and 59.6 then turned principle into practice: a life-stage framework for thinking about how much growth risk a household can reasonably carry, adjusted for Nepal-specific realities like EPF balances and remittance income, followed by a fully worked example showing how one household translated goals, time horizons, and risk tolerance into a concrete, numbered allocation across specific rupee amounts.
If this chapter has done its job, you should now be able to look at your own household's savings — however modest — and sort them by bucket rather than by whichever asset happened to catch your attention most recently. You should be able to explain why a fixed deposit, a NEPSE share, and a tola of gold are not interchangeable "investments" but tools built for different jobs, responding to different forces, on different timelines. And you should understand that the biggest risk many Nepali households carry is not owning the wrong stock — it is having almost everything in one reservoir, whichever reservoir that happens to be.
With the whole-portfolio question of allocation now settled in principle, Part XII turns next to the largest and most actively managed reservoir in most Nepali portfolios: the equity sleeve itself. Chapter 60, "Equity Portfolio Construction for NEPSE," picks up exactly where the equity percentage decided in this chapter leaves off, and asks how that equity allocation should actually be built out — how many individual holdings a Nepali investor realistically needs, how to diversify sensibly across NEPSE's narrow set of sectors without simply duplicating the same liquidity bet many times over, what concentration limits should govern any single stock or sector within the equity sleeve, and what screening criteria separate a durable holding from a speculative one.
Chapter 60 will draw directly on the sector concentration problem raised in Lesson 59.3 of this chapter — the fact that banking, hydropower, and finance shares tend to move together — and turn it into a practical set of rules for building an equity portfolio that is genuinely diversified within the constraints of a market as narrow as NEPSE's, rather than merely appearing diversified because it holds many different tickers.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XII · Chapter 60
Equity Portfolio Construction for NEPSE
First published 23 Aug 2026 · Last verified 29 Aug 2026
Every hydropower project in Nepal starts the same way: dam a river, spread the flow across many turbines, and if one turbine jams for maintenance, the others keep the lights on in Kathmandu. That is the entire logic of diversification in one sentence. But now picture a hydropower plant where all the turbines share one intake gate. If silt clogs that gate, every turbine stops at once — no matter how many turbines you built. This is the trap that catches many new NEPSE investors. They buy fifteen different stocks, feel diversified, and then watch all fifteen fall on the same day, because all fifteen shared one intake gate: exposure to Nepal Rastra Bank's interest rate policy, or to Kathmandu's tourist season, or to the same monsoon that fills every hydropower reservoir at once. Chapter 59 taught you how much of your total wealth should sit in NEPSE equities versus government securities, fixed deposits, mutual funds, gold, and real estate. This chapter assumes that decision is already made. You have decided, say, that NPR 15 lakh of your household savings will live in NEPSE-listed shares. The question now is: which shares, how many, and in what proportions — so that this NPR 15 lakh behaves like a hydropower cascade with independent intake gates, not one dam with a single point of failure.
Lesson 60.1 — Why Diversification Means More in NEPSE Than "Buy Different Stocks"
Start with the word itself. Diversification means spreading your money across investments whose fortunes do not all rise and fall for the same reason. A Nepali household's daily food basket is a useful picture. A family that eats only rice depends entirely on the rice harvest — a bad monsoon and the household goes hungry. A family that eats rice, lentils, vegetables, and some meat is protected, because a poor lentil harvest does not usually coincide with a poor vegetable harvest. The protection comes not from eating four different things, but from eating four things that fail for different reasons at different times.
Now translate this to shares. If you buy shares in Nabil Bank, Global IME Bank, NIC Asia Bank, and Machhapuchchhre Bank, you technically own four different stocks. But you have not built a diversified food basket — you have built a household that eats four kinds of rice. All four are commercial banks. All four borrow at rates NRB sets, lend at rates NRB caps, hold capital by ratios NRB dictates, and get squeezed the same week when NRB tightens the credit-to-deposit (CD) ratio — the regulatory limit on how much of their deposits a bank may lend out. When that ratio gets enforced strictly, every bank that was lending near the ceiling has to slow down lending at once, and every bank stock feels the same pressure at the same time.
This is the single most important idea in equity portfolio construction for NEPSE: the number of stocks you own matters far less than the number of independent risk sources those stocks are exposed to. A risk source is anything that can move a group of stocks together — an interest rate decision, a monsoon, a tourism season, a global copper price, a change in fuel subsidy. Two stocks are genuinely diversifying each other only when a shock that hurts one does not automatically hurt the other in the same direction and at the same time.
KEY CONCEPT
Diversification is not about owning more names. It is about owning exposures that fail for different reasons at different times. Ten stocks that all answer to Nepal Rastra Bank's policy rate are, for risk purposes, closer to one stock than to ten.
Why does this matter more in Nepal than in a market like the United States or India? Because NEPSE is structurally tilted toward one industry. As you will see in the next lesson, more than half of NEPSE's entire market value sits in banks, development banks, finance companies, microfinance institutions, and insurers — businesses that are all, at their core, in the business of lending money and are all regulated by the same central bank. In a market that broad and that varied, an investor who avoids financial stocks entirely can still build a fifty-stock portfolio. In Nepal, avoiding financial stocks entirely means giving up access to over half the investable market. So Nepali diversification is not about avoiding banks. It is about being deliberate: knowing how much of your equity sleeve sits inside the "lending" turbine versus other turbines, and sizing that exposure with your eyes open rather than by accident.
There is a second, quieter reason correlation runs high on NEPSE specifically. Nepal's economy itself is narrow. A large share of national income arrives as remittances — money sent home by Nepali workers abroad — which flows into bank deposits, which funds bank lending, which funds construction, trading, and consumption, which in turn feeds bank profits again. Hydropower depends on monsoon rainfall across the same river basins. Tourism depends on the same handful of trekking and pilgrimage seasons. Manufacturing depends on the same import routes through Kolkata and Vishakhapatnam. When an economy's underlying drivers are few, its listed companies — even ones sitting in different "sectors" on paper — often end up dancing to the same underlying music.
None of this means diversification is pointless in Nepal. It means diversification has to be done with a clearer map of what actually drives each sector, not just a longer list of ticker symbols. That map is the subject of the next lesson.
Lesson 60.2 — Mapping NEPSE's Sectors: Where the Market's Money Actually Sits
Before you can diversify across NEPSE's sectors, you need an honest picture of what those sectors are and how big each one actually is. NEPSE organises its roughly 286 listed companies into sector categories, and as of early 2026 those categories break down as follows by market capitalisation — the total value of all listed shares in that sector, calculated as share price multiplied by number of shares outstanding.
Sector
Approx. No. of Listed Companies
Approx. Share of Total Market Cap
Banks, Financial Institutions & Insurance (BFI)
~133
~52%
Hydropower
~97
~16%
Investment (holding/investment companies)
~7
~7%
Manufacturing & Processing
~26
~7%
Trading
~4
~5%
Hotels & Tourism
~8
~3%
Others (telecom, life insurance holding groups, misc.)
~10
~10%
Two things should jump out immediately. First, the Banks, Financial Institutions & Insurance group — commonly shortened to "BFI" — is not really one sector. It is a bundle of several sub-sectors that trade under related but distinct rules: commercial banks (the large "A-class" banks like Nabil, NIC Asia, and Global IME), development banks ("B-class," typically smaller and more regionally focused), finance companies ("C-class," the smallest deposit-taking lenders), microfinance institutions (which lend small amounts to low-income and rural borrowers, often women's cooperatives), and insurers, which are split further into life insurance and non-life (general) insurance companies. Grouping all of these under one 52% figure hides real differences: a life insurer's income depends on long-term policy premiums and investment income, not short-term lending margins, so it behaves quite differently from a commercial bank even though both sit inside "BFI."
Second, notice the mismatch between number of companies and share of market value in hydropower. Roughly one in three listed companies on NEPSE is a hydropower company, yet hydropower is only about 16% of total market value. This tells you hydropower companies are, on average, much smaller than banks. A portfolio built by simply counting "one stock per sector" would badly overweight hydropower relative to its actual economic footprint, and badly underweight the sheer scale of the banking sector.
REGULATORY DETAIL
SEBON (the Securities Board of Nepal) and NEPSE require every listed company to disclose its sector classification, and index providers use these classifications to publish sub-indices — the Banking Sub-Index, the Hydropower Sub-Index, the Life Insurance Sub-Index, and so on. Watching these sub-indices, not just the headline NEPSE Index, is the fastest way to see which turbine is spinning and which is jammed on any given day.
Think of NEPSE's sector map the way you would think of a Nepali household's monthly budget. A family might spend on rent, food, school fees, transport, and festivals. Rent is the largest line item, the way BFI is the largest sector — but a family does not stop paying for food just because rent is bigger. It budgets deliberately across categories, knowing some categories (rent) are unavoidable and large, while others (festivals) are smaller but still matter for a full life. A NEPSE equity sleeve works the same way: BFI will almost always be your largest sector allocation simply because it is the largest part of the investable market, but that does not mean it should be your only allocation.
A practical way to build this map for yourself is to separate the BFI bundle into its true sub-sectors before you diversify, rather than treating "financial stocks" as one bucket:
Commercial banks (A-class) — roughly twenty licensed banks after years of NRB-encouraged mergers reduced the count from over thirty. These are NEPSE's largest, most liquid, most closely watched stocks.
Development banks (B-class) — smaller, often regionally concentrated lenders, generally less liquid than commercial banks.
Finance companies (C-class) — the smallest deposit-taking lenders, often thinly traded.
Microfinance institutions — lenders to low-income borrowers, historically NEPSE's highest-growth but also highest-volatility financial sub-sector, sensitive to loan-loss cycles in rural lending.
Life insurance companies — premium-and-investment-income businesses, less sensitive to short-term interest rate swings than banks.
Non-life (general) insurance companies — property, motor, and health insurers, sensitive to claims cycles and reinsurance costs rather than lending margins.
Once you see BFI as six sub-sectors rather than one, and hydropower, manufacturing, trading, hotels, and "others" as five more genuinely distinct sectors, you have eleven meaningful buckets to think about — not just seven headline categories. This finer map is what lets you build real diversification rather than the appearance of it.
CASE IN POINT
During the fourth-quarter enforcement of the credit-to-deposit ratio ceiling in fiscal year 2080 (2023/24), NRB required banks to cut back lending sharply to stay within the mandated ratio. Within roughly six weeks, NEPSE's Microfinance Sub-Index fell by around 16%, dragged down alongside commercial banks — because microfinance institutions themselves borrow wholesale funding from commercial banks, so a squeeze on bank lending becomes a squeeze on microfinance lending almost immediately. Investors who thought "banks" and "microfinance" were separate diversifying sectors learned, in real time, that they share the same intake gate: NRB's lending policy.
Lesson 60.3 — How Many Stocks Is "Enough"? NEPSE's Correlation Problem
International finance textbooks often cite a rule of thumb: somewhere between fifteen and thirty stocks, chosen across unrelated industries, is usually enough to eliminate most of the risk that is specific to any single company, leaving mostly market-wide risk that no amount of stock-picking can remove. That rule of thumb was built using data from broad, deep markets like the United States, where "unrelated industries" genuinely means unrelated — a software company, an oil refiner, a hospital chain, and a shoe retailer really do respond to different forces.
NEPSE is not that market. Because more than half its value sits in businesses that are fundamentally lending institutions regulated by one central bank, and because Nepal's real economy runs on a small number of shared engines — remittances, monsoon-fed hydropower, tourism seasons, and cross-border trade — many NEPSE stocks that look unrelated on paper move together in practice. Correlation is the technical term for this: it measures how closely two things move in the same direction at the same time, on a scale from -1 (they always move in opposite directions) to +1 (they always move together). A portfolio of stocks with high correlation to each other behaves, in terms of risk, much closer to a portfolio of two or three stocks than to a portfolio of fifteen.
Picture two water tanks. One tank is fed by fifteen separate pipes, each pipe drawing from a different, unconnected spring. If one spring runs dry, the tank barely notices — fourteen other pipes keep filling it. The second tank is also fed by fifteen pipes, but all fifteen pipes draw from the same underground reservoir. If that reservoir's water table drops, all fifteen pipes slow down together, and the tank empties no matter how many pipes were built. NEPSE investors who buy fifteen bank and finance-company stocks have built the second tank. They have fifteen pipes, but one reservoir: Nepal Rastra Bank's monetary stance.
This does not mean the old rule of "buy twenty to thirty stocks" is wrong in Nepal — it means the twenty to thirty stocks must be chosen for genuine independence, not just for being different tickers. A realistic, workable guideline for a Nepali retail equity sleeve looks like this:
A portfolio concentrated in eight to twelve names, all drawn from within the BFI bundle, gives you almost no diversification benefit beyond owning three or four names, because the underlying driver — NRB policy and national credit conditions — is shared.
A portfolio of twelve to eighteen names spread deliberately across the true sub-sector map from Lesson 60.2 — some commercial banks, one or two development banks or finance companies, a microfinance name, a life insurer, a non-life insurer, two or three hydropower companies (ideally from different river basins so they don't share a monsoon shock), a manufacturing name, and a hotel or trading name — captures most of the practical diversification NEPSE can offer a retail investor.
Beyond roughly eighteen to twenty-two names, adding more stocks on NEPSE usually adds monitoring burden and transaction cost without adding meaningfully more diversification, because you eventually run out of genuinely independent risk sources in a market this size. You end up owning a second or third bank that behaves almost exactly like the first.
WARNING
Owning twenty-five NEPSE stocks is not automatically safer than owning twelve. If twenty of those twenty-five are banks, development banks, finance companies, and microfinance institutions, you are more concentrated in a single risk source — bank lending conditions — than an investor who deliberately holds twelve stocks across six genuinely different sectors. Count risk sources, not stock certificates.
There is a second layer to this correlation problem: even outside BFI, some NEPSE sectors share hidden risk sources. Most of Nepal's hydropower generation depends on monsoon rainfall between June and September, and a below-average monsoon reduces river flow — and therefore power generation revenue — for nearly every run-of-river hydropower plant at once, regardless of which company built it. Two hydropower stocks from rivers in the same region are not much more diversifying than one. Genuine diversification within hydropower comes from spreading across projects on different river systems, different generation technologies (storage versus run-of-river), and different stages of operation (already generating revenue versus still under construction, which carries construction and financing risk rather than rainfall risk).
KEY CONCEPT
Correlation is not fixed. It can rise sharply during stress and fall during calm periods. Bank stocks that seem only loosely connected to microfinance stocks in an ordinary month can move in lockstep during a CD-ratio squeeze, a liquidity crunch, or a broad market selloff — because in a crisis, most investors sell everything liquid at once, temporarily making unrelated stocks behave as if they were related. Build your diversification assuming correlation will rise exactly when you need it most to stay low.
A simple test you can run yourself, without needing a statistics background, is to pull up price charts for two candidate stocks over the last one or two years and look at the big turning points. If both stocks bottomed in the same week during the 2022 liquidity crunch, and both rallied in the same month when NRB cut the policy rate, they are highly correlated in practice, whatever sector label NEPSE has assigned them. If one stock's big moves line up with monsoon news and the other's line up with interest rate announcements, they are genuinely offering you something different.
Lesson 60.4 — Position Sizing and Concentration Limits: Sizing Bites You Can Actually Swallow
Choosing which stocks to own is only half the job. The other half is deciding how much of your equity sleeve goes into each one — a decision called position sizing. A thali set is a useful picture here. A good thali has rice as the largest portion, because rice is the staple, but no single item — not even the rice — fills the entire plate. Lentils, vegetables, pickle, and meat each get a portion sized to their role: substantial enough to matter, small enough that if one dish turns out badly, dinner is still edible. Position sizing in a NEPSE equity portfolio follows the same logic: your largest holding should never be so large that a single company's bad news ruins your entire equity sleeve.
Part XI of this book introduced the average daily traded value rule, or ADV rule, for individual stock trades: a position should generally be sized so that it could be sold within a reasonable number of trading days — often cited as five to ten days — without needing to sell more than a modest fraction of that stock's typical daily traded value, so an exit does not itself crash the price. That rule was framed there mainly around entering and exiting a single trade. In portfolio construction, the same rule becomes a ceiling on position size at the portfolio level: if a stock's average daily traded value is thin, no matter how much you like the company, your position in it should stay small enough that you are never trapped holding a large stake you cannot sell without moving the price sharply against yourself.
Part XI's free-float allocation concept matters here too. Free float is the portion of a company's shares that is actually available for public trading, excluding shares locked up with promoters, founding families, or government holdings that rarely trade. A hydropower company might have a large total market capitalisation on paper, but if promoters hold 70% of its shares and only 30% trade freely, the real liquidity pool available to retail investors is far smaller than the headline market cap suggests. A position that looks modest as a percentage of total market cap can be uncomfortably large as a percentage of free float.
PRACTICAL TOOL
Before sizing any position, ask three questions in order: (1) What is this stock's average daily traded value over the last month? (2) Could I sell my planned position size within about a week without materially moving the price? (3) What share of the stock's free float — not total shares outstanding — would my position represent? If the honest answers make you uneasy, size down before you buy, not after the price has already moved against you.
With that liquidity ceiling in mind, a workable set of concentration limits for a Nepali retail equity sleeve looks like this:
No single stock should typically exceed 10-12% of your total NEPSE equity sleeve at the time you buy it, regardless of how confident you are in the company. This caps the damage any one company's scandal, regulatory penalty, or earnings collapse can do to your overall equity return.
No single sub-sector — commercial banks, or hydropower, or microfinance — should typically exceed roughly 35-40% of your equity sleeve, even though BFI's true weight in the overall NEPSE market is over 50%. This is a deliberate underweight relative to the index, chosen precisely because BFI sub-sectors share so much correlated risk, as covered in Lessons 60.1 and 60.3.
Positions in thinly traded small-cap or micro-cap stocks — including many newer hydropower and microfinance listings — should be sized smaller than the general 10-12% ceiling, often capped around 3-5% of the equity sleeve, purely because of the ADV and free-float liquidity constraints above.
Cash or near-cash reserved for rebalancing and opportunistic buying should not be counted as "diversification" — it is a separate, deliberate buffer, not a thirteenth stock position.
CAUTION
Small-cap hydropower and microfinance stocks are exactly where new NEPSE investors are tempted to concentrate, because these are often the stocks with the most dramatic price moves and the most enthusiastic talk on trading floors and social media. They are also, almost by definition, the stocks with the thinnest free float and lowest average daily traded value. A 15% position in an illiquid small-cap can turn a bad week into a position you are stuck holding for months, watching the price fall further each time you try to exit.
It helps to translate these percentage limits into an actual worked example. Suppose an investor has decided, following Chapter 59's asset-allocation process, to place NPR 12,00,000 (twelve lakh rupees) into NEPSE equities. A concentration-limit-respecting starter allocation across fourteen names might look like this:
Sub-Sector
Stock (illustrative)
Allocation (NPR)
% of Equity Sleeve
Commercial Bank
Bank A (large, liquid)
1,20,000
10%
Commercial Bank
Bank B (large, liquid)
1,08,000
9%
Commercial Bank
Bank C (mid-size)
84,000
7%
Development Bank
Development Bank D
60,000
5%
Finance Company
Finance Company E
36,000
3%
Microfinance
Microfinance F
60,000
5%
Life Insurance
Life Insurer G
84,000
7%
Non-Life Insurance
Non-Life Insurer H
72,000
6%
Hydropower (Basin 1)
Hydro I
96,000
8%
Hydropower (Basin 2)
Hydro J
84,000
7%
Hydropower (Basin 3, small-cap)
Hydro K
36,000
3%
Manufacturing
Manufacturer L
72,000
6%
Hotels & Tourism
Hotel M
48,000
4%
Trading
Trading Company N
48,000
4%
Cash buffer for rebalancing
—
1,92,000
16%
Notice the BFI sub-sectors here — commercial banks, development bank, finance company, microfinance, life insurance, non-life insurance — sum to about 47% of the equity sleeve, still the single largest bloc, but deliberately below NEPSE's own roughly 52% index weight, and spread across six sub-sectors with genuinely different drivers rather than concentrated in three or four bank names. Hydropower is split across three projects on different river basins rather than one large position. No single name exceeds 10%. The cash buffer, held back rather than fully invested, gives room to add to positions if prices fall or to fund a new opportunity without having to sell an existing holding at an inconvenient time.
Lesson 60.5 — Screening for Quality: Building Your Selection Filter
Diversification and position sizing tell you how to spread and size your bets. They say nothing about whether any individual stock is worth owning in the first place. That is the job of screening — running every candidate stock through a consistent set of checks before it earns a place in your portfolio, the way a careful shopper at Kalimati vegetable market checks a sack of rice for weight, moisture, and grain quality before agreeing to a price, rather than buying whatever sack is nearest the entrance.
A workable screening filter for NEPSE stocks should cover four broad categories: financial health, liquidity, governance, and valuation.
Financial health starts with the basics that Part IX of this book covered in depth: is the company profitable, is that profit growing or shrinking, and how is it funded? For a bank, development bank, or finance company, this means checking capital adequacy ratio (a regulatory measure of how much loss-absorbing capital the institution holds relative to its risk-weighted lending), non-performing loan ratio (the share of loans that have stopped being repaid on schedule), and return on equity. For a hydropower company, it means checking whether the plant is already generating revenue or still under construction, what its power purchase agreement terms are with the Nepal Electricity Authority, and how much debt was used to build the project. For an insurer, it means checking the claims ratio and the size and quality of its investment portfolio. A single financial-health checklist cannot be identical across sectors, because a bank's balance sheet and a hydropower company's balance sheet are answering fundamentally different questions.
Liquidity screening applies the ADV rule from Lesson 60.4 as a pass/fail filter, not just a position-sizing input. A stock with almost no trading volume on most days should be treated with extra caution even if its fundamentals look attractive on paper, because you may not be able to exit when you want to, at a price close to what you see quoted.
Governance screening asks whether the company treats minority shareholders fairly. Warning signs include frequent related-party transactions with promoter-owned businesses, a history of delayed or restated financial disclosures, auditor qualifications or auditor changes without clear explanation, and a pattern of rights issues or bonus share announcements that seem timed to prop up the stock price around promoter share sales. SEBON disclosure filings and NEPSE's own corporate announcements are the primary sources for checking these red flags before buying, not after.
Valuation screening asks whether the price you would pay is reasonable relative to what the company earns and how it is growing — using tools like price-to-earnings ratio and price-to-book ratio, covered in earlier chapters, compared both against the company's own history and against peers in the same sub-sector. A well-run bank bought at an inflated price can still be a poor investment; a mediocre bank bought cheaply enough can still work out reasonably.
PRACTICAL TOOL
A simple four-question screen to run on every candidate stock before it enters your portfolio: (1) Is the company profitable and has that profitability been stable or improving over the last three years? (2) Does it trade with enough daily volume that I could exit a position sized per Lesson 60.4 within about a week? (3) Are there any recent governance red flags — auditor changes, related-party deals, disclosure delays — visible in its SEBON or NEPSE filings? (4) Is the price reasonable relative to its own five-year valuation history and its closest listed peers? A stock that fails two or more of these deserves a hard second look before it earns a place in the portfolio.
WARNING
Buying a stock because "everyone at the trading floor is talking about it" or because a social media post promises a rumoured bonus share announcement is not screening — it is speculation dressed up as research. Rumour-driven rallies on NEPSE, especially in thinly traded small-cap hydropower and microfinance names, have repeatedly reversed sharply once the rumour failed to materialise, leaving late buyers holding an illiquid position exactly as described in Lesson 60.4's caution about small-cap concentration.
Screening should not be a one-time event at purchase. NEPSE companies report quarterly, and a stock that passed every check a year ago can quietly fail one or more of them today — a rising non-performing loan ratio, a delayed audit, a hydropower project running over budget. Revisiting the same four-question filter each quarter, for every holding, is what keeps a portfolio honest over time rather than just at the moment of purchase.
Lesson 60.6 — Building Your Starter Portfolio: A Step-by-Step Walkthrough
With sector mapping, correlation awareness, position sizing, and screening all in hand, the last step is mechanical: turning a plan into an actual set of holdings inside your Demat account, the electronic account that holds your shares in dematerialized (paperless) form, linked to a trading account with a licensed broker.
Step one is confirming the equity sleeve size decided in Chapter 59, and translating it into a target number of holdings, using Lesson 60.3's guidance of roughly twelve to eighteen names for most retail portfolios below a few tens of lakh rupees. Fewer names below this range often means too little sector diversity; many more names above it often means either too many correlated bank holdings or positions so small that brokerage and transaction costs eat into returns disproportionately.
Step two is drafting the sector map, using Lesson 60.2's finer eleven-bucket breakdown of BFI sub-sectors plus hydropower, manufacturing, trading, hotels, and others, and deciding roughly how many names and what combined weight each bucket will get — deliberately underweighting the BFI bloc relative to its actual ~52% NEPSE index weight, per Lesson 60.4's concentration guidance.
Step three is screening candidates within each bucket, applying Lesson 60.5's four-question filter, and shortlisting two or three candidates per bucket before choosing the final one. This matters because the first name that comes to mind — often the largest, most-discussed company in a sector — is not automatically the best value at the current price.
Step four is checking liquidity and free float on every shortlisted candidate before finalising position sizes, using the ADV rule and free-float check from Lesson 60.4's practical tool, and shrinking planned position sizes for any stock that fails this check rather than skipping the check because the fundamentals looked good.
Step five is placing orders in tranches rather than all at once. Buying a full position in a single order on a single day exposes you to that one day's price, which may be unusually high due to short-term noise. Spreading purchases across three to six weeks — sometimes called phased buying — averages your entry price across a range of market conditions and reduces the chance of committing a large sum right before a short-term pullback.
CASE IN POINT
An investor building the fourteen-name, twelve-lakh-rupee portfolio from Lesson 60.4's worked table might place four to five separate buy orders per month across six weeks rather than fourteen orders in a single week, prioritizing the most liquid commercial bank and life insurance names early — since these are easiest to fill near the quoted price — and leaving the smaller hydropower and finance-company positions for later, once volume patterns in those names have been watched for a few sessions.
Step six is recording the portfolio in a simple tracking sheet — sector bucket, stock name, purchase date, purchase price, quantity, and current concentration percentage — updated at least monthly. This is not optional bookkeeping; it is the only reliable way to notice when a stock's price has risen or fallen enough that its actual weight in the portfolio has drifted meaningfully away from the target weight set in step two, which is the trigger for a rebalancing decision.
Step seven, which belongs mostly to the next chapter but deserves a preview here, is setting a review calendar: a quarterly check of each holding against the Lesson 60.5 screening filter, alongside NEPSE's quarterly and annual disclosure cycle, and a portfolio-wide sector-weight check against the concentration limits from Lesson 60.4. A starter portfolio built carefully today will drift out of its intended shape within a year simply because different stocks grow at different rates — the discipline is not in the initial construction alone, but in returning to check it.
CAUTION
Resist the temptation to judge the portfolio's success within the first few months. NEPSE sub-sectors rotate — a quarter dominated by hydropower gains can be followed by a quarter dominated by banking gains, and a well-diversified portfolio will, by design, never show the single largest gain of any one sector in any single period. That muted, steadier performance during any one sector's rally is not a flaw. It is diversification working exactly as intended, and its real payoff shows up not in the good quarters but in how much smaller the losses are during the quarters when one sector — often the sector everyone crowded into — falls hardest.
Chapter recap
This chapter took the equity allocation decided in Chapter 59 and turned it into an actual portfolio of NEPSE stocks. The starting insight was that diversification on NEPSE cannot be measured by counting tickers, because more than half of NEPSE's total market value sits inside a bundle of banks, development banks, finance companies, microfinance institutions, and insurers that all answer, in different degrees, to Nepal Rastra Bank's monetary policy and to the same narrow set of economic engines — remittances, monsoon-fed hydropower, and tourism seasons — that drive the wider Nepali economy. A portfolio of many bank stocks is, for risk purposes, much closer to a portfolio of one stock than an investor might assume.
From there, the chapter built a finer sector map than NEPSE's own headline categories provide — splitting the Banks, Financial Institutions & Insurance bloc into commercial banks, development banks, finance companies, microfinance, life insurance, and non-life insurance, and treating hydropower, manufacturing, trading, hotels, and other sectors as separate buckets in their own right. This finer map, not the seven headline sector labels, is the real tool for building genuine diversification.
The chapter then addressed the "how many stocks" question directly, showing that the international rule of fifteen to thirty holdings only works when those holdings are genuinely uncorrelated — and that on NEPSE, a deliberately built twelve-to-eighteen-name portfolio spread across true sub-sectors typically captures most of the available diversification, while a larger but BFI-heavy portfolio captures very little more than a small, concentrated one. Position sizing followed directly from Part XI's ADV rule and free-float concepts, translated here into concentration ceilings: roughly 10-12% per stock, roughly 35-40% per sub-sector bloc, and tighter caps for illiquid small-cap names, illustrated with a fourteen-name, twelve-lakh-rupee worked example.
Screening criteria gave the chapter its selection discipline — financial health checks tailored to each sub-sector's real business model, liquidity checks using the ADV rule as a pass/fail filter, governance checks against SEBON and NEPSE disclosures, and valuation checks against a company's own history and its peers — with a warning against rumour-driven buying in thinly traded names. The closing lesson turned all of this into a seven-step mechanical process: sizing the sleeve, drafting the sector map, screening candidates, checking liquidity, buying in tranches, tracking the portfolio, and setting a recurring review calendar.
What this chapter has not yet covered is how to measure, in ongoing numeric terms, whether the portfolio you have built is actually behaving the way you designed it to. Chapter 61, "Portfolio Risk Management," picks up exactly where this chapter's tracking sheet and review calendar leave off. It will introduce volatility and drawdown as ways to measure how much a portfolio actually moves and how deep its worst losses have run; it will revisit correlation — introduced here mostly in narrative terms, through examples like the microfinance sub-index's fall during the CD-ratio squeeze — as a number you can track and monitor over time; it will cover stress testing, the practice of asking "what would happen to this exact portfolio if 2022's liquidity crunch happened again tomorrow"; and it will define concrete rebalancing triggers, turning the "check it quarterly" instruction from this chapter's final lesson into precise rules for when drift becomes large enough to require action, and when it is better left alone.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XII · Chapter 61
Portfolio Risk Management
First published 23 Aug 2026 · Last verified 29 Aug 2026
In the summer of 2078 B.S. (2021), a taxi driver in Kathmandu told his passenger, a retired schoolteacher, that he had turned his savings from two years of driving into shares of three hydropower companies and two finance companies. NEPSE was near its all-time high of 3,198.60 points. Everyone was making money. The teacher, impressed, put her retirement gratuity into the same five stocks the following week. Within a year, NEPSE had fallen by more than 40 percent from that peak, grinding down toward the 1,800s through 2022 as Nepal Rastra Bank tightened lending, margin calls forced liquidation, and remittance-driven liquidity dried up. The taxi driver's hydropower shares and the teacher's finance shares fell together, on the same days, for the same reasons. Neither had built a portfolio. Both had built a single bet wearing five different name tags.
This is the problem this chapter exists to solve. Chapter 60 taught you how to build a well-diversified equity sleeve — enough stocks, spread across enough sectors, none of them too large a slice of the pie. That is necessary. It is not sufficient. A portfolio can look diversified on a spreadsheet — ten stocks, five sectors — and still behave like one giant undiversified bet, because Nepali sectors do not always move independently. Banks, finance companies, and even many "diversified" conglomerates in Nepal share the same fuel: interest rates set by Nepal Rastra Bank (NRB), remittance inflows, and the general mood of margin-lending investors. When that fuel changes, correlated stocks fall together, hard, regardless of how many tickers you own.
Portfolio risk management is the discipline of stepping back from individual stocks and asking a different question: what happens to my entire wealth, all at once, in a bad scenario? This chapter gives you the vocabulary, the simple tools, and the practical limits to answer that question — before the market answers it for you.
Lesson 61.1 — What "Portfolio Risk" Means Beyond a Single Stock
KEY CONCEPT
Stock risk asks "will this company do badly?" Portfolio risk asks "will my whole wealth do badly, at the same time, for a reason I did not see coming?" These are different questions, and a good answer to the first does not guarantee a good answer to the second.
Think of a farmer who plants three fields instead of one, to protect against the risk that any single field fails. That works well if the fields face different threats: one is near a river and might flood, one is on a hillside and might face drought, one is shaded and might face blight. But if all three fields sit on the same floodplain, side by side, then "three fields" gives no real protection. A single monsoon flood destroys all three on the same day. The farmer diversified in name — three separate fields — but not in substance, because the fields shared the same risk: the river.
Nepali retail portfolios often make exactly this mistake. An investor buys Nabil Bank, Global IME Bank, Nepal Investment Mega Bank, and Prabhu Bank — four different companies, four different tickers, four different management teams. On paper, that looks like diversification. In practice, all four are commercial banks, all four are regulated by the same NRB directives, all four borrow and lend using the same interest-rate environment, and all four are sensitive to the same credit cycle. When NRB tightens monetary policy, all four typically feel it together. The "four fields" sit on the same floodplain.
Portfolio-level risk has three components that individual stock analysis does not capture on its own:
Volatility is how much your portfolio's value swings up and down over time, regardless of direction. A portfolio that moves between minus 3 percent and plus 3 percent in a typical month is more volatile than one that moves between minus 1 percent and plus 1 percent, even if both end up flat over a year. Volatility matters because large swings are stressful, and stress causes investors to sell at the worst possible moments — near the bottom of a fall, in panic, locking in a loss that a calmer investor would have ridden out.
Drawdown is the fall from a portfolio's highest recorded value to its lowest subsequent value, before it recovers. If your portfolio was worth Rs 10,00,000 at its peak and later fell to Rs 6,50,000 before turning around, your maximum drawdown was 35 percent. Drawdown is the risk measure that matters most to real people, because it is the number you actually feel — the gap between what your wealth used to be and what it is now.
Correlation and concentration risk is the risk that your holdings are not truly independent of each other — that they will rise and fall together because they share a common driver (an interest rate, a sector, a single family of promoters, or general market sentiment). This is the "same floodplain" problem, and in NEPSE it is unusually severe, as Lesson 61.3 will show in detail.
CASE IN POINT
Between the NEPSE index's all-time high of 3,198.60 in mid-2021 and the trough of the subsequent correction in 2022, the broad market fell by roughly 40 to 45 percent, with finance and microfinance sub-indices falling considerably harder than the headline index because margin-lending unwinds hit those counters first. An investor holding only bank and finance shares in this period experienced a portfolio-level drawdown far worse than what the headline NEPSE number suggested — because their "diversified" basket was concentrated in the exact sub-sector taking the heaviest hit.
None of this means diversification is worthless — Chapter 60's sector spread across banking, hydropower, insurance, hotels, manufacturing, and microfinance is real protection, because those sectors genuinely do respond to different forces some of the time. But it means diversification must be checked, not assumed. The rest of this chapter gives you the tools to check it.
Lesson 61.2 — Measuring Drawdown and Volatility Without a Finance Degree
You do not need a Bloomberg terminal or a statistics course to track portfolio risk. You need a notebook, a calculator, and a habit.
Tracking maximum drawdown, the simple way. Every month, write down your total portfolio value — the sum of every holding at current market price, in NPR. Keep a running record of the single highest value your portfolio has ever reached. Every time your current value is below that peak, calculate the percentage gap:
Drawdown (%) = (Peak Value − Current Value) ÷ Peak Value × 100
PRACTICAL TOOL
Keep a simple three-column log: Date, Portfolio Value, Running Peak. A fourth column, Current Drawdown, is just the formula above applied each month. This takes five minutes a month and tells you, at a glance, exactly how deep your worst moment has been and how deep your current moment is. Most brokers' back-office statements or your Demat/meroshare portfolio summary give you the raw value; you supply the discipline.
Why does this matter more than daily price-watching? Because drawdown captures the lived experience of loss in a way that a single day's percentage move does not. A portfolio that falls 2 percent in one bad session is not alarming. A portfolio that has quietly bled from a peak of Rs 12,00,000 down to Rs 8,00,000 over eight months — a 33 percent drawdown — is a portfolio in genuine trouble, even though no single day felt dramatic. Drawdown adds up what daily moves hide.
Understanding standard deviation informally. Standard deviation is a technical term for a simple idea: how spread out your returns typically are around their average. You do not need to compute it precisely to use it. Think of it as your portfolio's "typical wobble." If your portfolio's monthly returns over the past two years have mostly ranged between minus 4 percent and plus 4 percent, with the average around 1 percent, your typical wobble is roughly plus-or-minus 4 to 5 percentage points. A portfolio with a wide typical wobble is a "jumpy" portfolio; a portfolio with a narrow wobble is a "calm" one. A retail investor's version of measuring this is simply: write down your monthly percentage return for two years, look at the highest and lowest months, and ask whether that range feels tolerable if it repeated going forward. If a minus-15-percent month would cause you to panic-sell, your portfolio's wobble is too large for your temperament, regardless of what the long-run average return might be.
KEY CONCEPT
Volatility is not the same thing as loss. A volatile portfolio that ends the year up 20 percent had plenty of "wobble" along the way but no permanent damage. The danger is not volatility itself — it is volatility combined with your own behaviour. A jumpy portfolio owned by a calm, disciplined investor is manageable. A jumpy portfolio owned by an anxious investor who checks prices every hour becomes a behaviour problem, not just a market problem.
A rule of thumb for Nepali retail investors. NEPSE, as a market, has historically been considerably more volatile than Nepal government savings bonds or fixed deposits, and it has also shown sharper swings than many regional emerging-market peers, in part because of NEPSE's relatively thin daily turnover, heavy retail participation, and sensitivity to margin-lending cycles. A useful mental anchor: expect NEPSE-linked equity portfolios to move by double-digit percentages within any given twelve-month window, in either direction, as a matter of routine — not as an emergency. What separates a manageable year from a genuine crisis is not whether volatility occurred, but how deep the drawdown went and how long it lasted.
REGULATORY DETAIL
Nepal Rastra Bank and the Securities Board of Nepal (SEBON) do not mandate portfolio-level risk disclosures for individual retail investors the way institutional fund managers face reporting requirements. This means the discipline of tracking your own drawdown and volatility is entirely self-directed — no regulator, broker, or app will do this for you automatically. It falls to you.
Lesson 61.3 — Correlation and Concentration Risk on NEPSE
Correlation measures whether two things move together. If two stocks almost always rise and fall on the same days, in the same direction, they are highly correlated. If one tends to rise while the other falls, or their movements seem unrelated, they are lowly correlated (or even negatively correlated). Diversification only reduces risk to the extent that your holdings are not highly correlated with each other.
Picture a bus queue during a sudden downpour in Kathmandu. If everyone in the queue is standing under one shared shelter, the rain affects them all identically — they all stay dry together, or the wind shifts and they all get wet together. Their outcomes are correlated because they share one shelter. Now picture the same number of people spread across five separate shelters at different bus stops. Some stay dry, some get wet, depending on which shelter's roof leaks and which direction the wind blows at each stop. Their outcomes are less correlated because they no longer share a single point of exposure. A NEPSE portfolio needs "separate shelters" — holdings exposed to genuinely different economic forces — not just separate company names under one shared roof.
Why NEPSE correlation is unusually high. Several structural features of the Nepali market push correlation upward, especially within the financial sector:
First, banks, development banks, finance companies, and microfinance institutions are all regulated by the same institution — Nepal Rastra Bank — and all respond to the same policy levers: the policy rate, the cash reserve ratio, and directives on lending limits or capital adequacy. When NRB tightens, nearly the entire BFI (banks and financial institutions) universe feels pressure on the same day.
Second, a very large share of NEPSE's daily turnover flows through margin lending — investors borrowing against their existing shares to buy more shares. When margin calls hit during a downturn, brokers and investors sell whatever is most liquid, which is usually banking and finance shares, pushing those prices down together in a self-reinforcing spiral, independent of each company's individual fundamentals.
Third, general market sentiment in Nepal — driven by remittance trends, monsoon and agricultural output, tourist arrivals, and political stability — tends to lift or depress the entire index at once, because retail investors, who dominate NEPSE trading volume, often trade the "market mood" rather than individual company analysis.
WARNING
Owning ten NEPSE-listed companies is not the same as owning ten independent risks. If seven of your ten holdings are banks, finance companies, and microfinance institutions, your portfolio's real diversification is closer to two or three effective "bets" — the BFI sector as a whole, plus whatever else you hold — not ten. Count your sector exposure by rupee value, not by number of tickers.
A practical correlation check without formulas. You do not need to calculate a correlation coefficient. Ask three questions about every pair of holdings in your portfolio:
Do they answer to the same regulator with the same policy tools (for example, two banks, or a bank and a finance company)? Do they depend on the same underlying economic input (for example, two hydropower companies both depend on monsoon rainfall and the same power purchase agreement structure with the Nepal Electricity Authority)? Do they tend to move on the same days when you check prices, rising and falling together? If the answer to two or more of these is yes for a given pair, treat them as one combined position for risk purposes, not two separate ones.
CASE IN POINT
During the 2021–2022 correction, margin-driven selling hit finance companies and microfinance institutions especially hard, because these were the sub-sectors most associated with high loan-to-value margin lending. Investors who believed they were diversified because they held "a bank, a finance company, a microfinance company, and a life insurer" discovered that three of those four moved almost in lockstep during the worst months, because all three shared exposure to the same margin-unwind dynamic, even though they were formally different sub-sectors.
The practical takeaway from Chapter 60's sector limits becomes sharper here: a cap of, say, 30 percent in "financials" broadly defined is not just a diversification nicety — it is a direct defence against the single largest correlated-risk cluster on the entire exchange. Hydropower, for its part, carries its own correlated cluster risk (monsoon dependency, NEA tariff and transmission bottlenecks, and interest-rate sensitivity on construction-phase debt), so a portfolio overloaded with hydropower counters faces a parallel — though different — concentration problem.
Lesson 61.4 — Stress-Testing Your Portfolio Against Realistic NEPSE Scenarios
Stress-testing means asking, in a calm moment, "if a specific bad scenario happened, what would happen to my portfolio?" — and doing the arithmetic before the scenario arrives, not during it. It is the financial equivalent of a monsoon flood drill: nobody performs a flood drill while the water is already rising. You do it in the dry season, so that when the rains come, you already know where to go.
Below are four realistic stress scenarios for a Nepali equity portfolio, each grounded in patterns NEPSE has actually shown.
Scenario 1: Sector-wide correction in banking and finance. NRB issues a directive tightening the loan-to-value ratio for margin lending, or raises the cash reserve ratio, prompting a broad sell-off concentrated in BFI counters. Based on the pattern seen through 2021–2022, a severe version of this scenario has historically produced declines of 30 to 45 percent in the finance and microfinance sub-indices, with banking proper falling somewhat less, in the 20 to 30 percent range, over a period of several months to a year.
Scenario 2: NRB monetary policy tightening cycle. NRB raises its policy rate and reduces overall system liquidity to control inflation or defend foreign exchange reserves. This raises borrowing costs across the economy, compresses bank net interest margins in the near term even as loan growth slows, and reduces the attractiveness of margin-funded equity purchases, causing a broad, gradual NEPSE decline rather than a single sharp crash — typically unfolding over two to four quarters, in the 15 to 25 percent range for the headline index.
Scenario 3: Liquidity crunch. Remittance inflows soften, banks face deposit pressure, and interbank lending rates spike. Trading volumes on NEPSE fall sharply as investors and margin lenders pull back simultaneously. In this scenario, the damage is not just a price decline but a widening of the practical bid-ask gap: even investors who want to sell struggle to find buyers at reasonable prices, worsening realised losses for anyone forced to exit. Headline index declines in past liquidity-driven episodes have ranged from 10 to 20 percent, but the effective cost to an investor needing to raise cash quickly can be considerably higher, because thin markets force selling at unfavorable prices.
Scenario 4: Circuit-breaker cascade event. As covered in Chapter 58, NEPSE employs circuit breakers that pause trading when the index moves beyond preset thresholds in a single session. In a genuine panic — a rapid confidence shock, a major default, or a geopolitical or macro surprise — the market can hit a negative circuit breaker, reopen, fall further, hit a second breaker, and in the most extreme cases trigger a third breaker that halts trading for the day entirely. Nepali markets have experienced sessions where consecutive negative circuit breakers closed the market early with the index down several percentage points in a matter of hours, and turnover collapsing as sellers vastly outnumbered buyers. The risk in this scenario is not just the single-day loss; it is that you cannot exit even if you want to, because trading is paused, and the next session can gap down further before you get a chance to act.
WARNING
A circuit-breaker cascade is precisely the scenario where "I'll just sell if it gets bad" fails as a risk plan. Trading halts mean you may have no ability to sell at any price for the remainder of the session. This is why position sizing and sector limits — decided calmly, in advance — matter more than any intention to react quickly during the event itself.
The table below works through these four scenarios against a hypothetical Rs 10,00,000 equity portfolio built the way Chapter 60 describes: 40 percent banking and finance, 25 percent hydropower, 15 percent insurance, 10 percent hotels and manufacturing, 10 percent microfinance and other.
Scenario
Typical Trigger
Estimated Portfolio Decline
Estimated Portfolio Value After
Time to Play Out
Sector-wide BFI correction
NRB tightens margin/LTV rules
22–28% (BFI-heavy weighting drags portfolio below index average)
Rs 7,20,000–7,80,000
3–9 months
NRB policy tightening cycle
Policy rate hike, liquidity withdrawal
15–20%
Rs 8,00,000–8,50,000
2–4 quarters
Liquidity crunch
Remittance slowdown, interbank rate spike
12–18% (plus wider effective spreads on exit)
Rs 8,20,000–8,80,000
1–3 months
Circuit-breaker cascade
Sudden confidence shock, rapid panic selling
8–12% in the triggering session(s) alone
Rs 8,80,000–9,20,000 (before further drift)
1–5 trading sessions
CAUTION
These figures are illustrative planning ranges built from patterns observed in past NEPSE episodes, not guaranteed outcomes or predictions. Actual declines in any future event could be smaller or considerably larger. The value of the exercise is not the precision of the number — it is the habit of asking the question before it is urgent.
How to actually run this exercise on your own portfolio. List your current holdings and their rupee weights. For each scenario, estimate a plausible decline for each sector based on the ranges above (heavier for BFI-concentrated scenarios, lighter for scenarios that hit the broad market evenly). Multiply each holding's weight by its scenario-specific estimated decline, sum the results, and you have a rough portfolio-level stress estimate. Do this once a year, or whenever you make a significant change to your holdings. The output is not a forecast — it is a gut check: if a plausible scenario would push your portfolio down by an amount that would force you to sell at the worst time (to cover an emergency, meet a loan payment, or simply because you could not tolerate it emotionally), your portfolio is too aggressively positioned for your actual life circumstances, regardless of its expected long-run return.
Lesson 61.5 — Rebalancing: When and How to Reset Your Portfolio
Rebalancing is the practice of periodically adjusting your holdings back toward your originally intended weights, after market moves have pushed them off target. Think of it like re-leveling a set of shop scales that has drifted out of balance — not because you did anything wrong, but simply because different items on each side gained or lost weight at different rates over time.
Here is the mechanic in plain terms. Suppose Chapter 59 and 60 led you to a target of 40 percent banking and finance, 25 percent hydropower, 15 percent insurance, and 20 percent everything else. A year later, hydropower has rallied hard and now represents 35 percent of your portfolio, while banking and finance has lagged and fallen to 32 percent. Your portfolio has drifted from your intended risk profile without you making a single active decision — the market did it for you. Rebalancing means selling some of the outperforming hydropower position and adding to the underperforming banking position, to bring both back toward their original targets.
KEY CONCEPT
Rebalancing is mechanically the opposite of what feels natural. It requires selling what has recently done well and buying what has recently done poorly. This feels wrong in the moment — you are trimming your "winner" and adding to your "loser" — but it is precisely how rebalancing enforces discipline: it systematically takes some profit off strength and adds exposure at lower prices, rather than letting winners grow into dangerous concentrations and losers shrink into irrelevance.
When to rebalance. Two common approaches, both reasonable for a Nepali retail investor:
Calendar-based rebalancing means reviewing your portfolio weights on a fixed schedule — once or twice a year is typical for a long-term investor — regardless of how far things have drifted. This is simple and prevents both over-trading and neglect.
Threshold-based rebalancing means acting only when a holding or sector drifts beyond a pre-set tolerance band — for example, if any single sector moves more than 5 to 7 percentage points away from its target weight. This is more responsive to actual market moves but requires you to check your weights periodically to know when a threshold has been crossed.
A sensible combination for most retail investors: check your weights every six months, and rebalance only if drift exceeds your threshold band. This avoids constant fiddling while still catching the meaningful drifts that matter.
Tax and transaction cost considerations. Part VII covered Nepal's capital gains tax regime for listed securities — a lower rate for long-term holdings (generally those held more than 365 days) and a higher rate for short-term holdings, along with brokerage commission and SEBON/CDSC fees on every transaction. Rebalancing is not free, and this must factor into how aggressively you do it.
REGULATORY DETAIL
Nepal's capital gains tax on listed shares is materially lower for holdings kept beyond the long-term threshold than for shares sold within it, and each sale also carries brokerage commission on a sliding scale plus regulatory fees. A rebalancing trade that triggers short-term capital gains tax on a recently appreciated hydropower holding, plus transaction costs on both the sale and the offsetting purchase, can meaningfully erode the benefit of rebalancing if done too frequently or too close to the one-year holding mark.
Three practical rules follow from this:
First, before rebalancing, check whether a position is close to crossing the long-term holding threshold. If a stock is 340 days into being held and would qualify for the lower long-term capital gains rate in three weeks, it is usually worth waiting those three weeks before trimming it, unless the drift is severe enough to pose an immediate risk concern.
Second, use new contributions to rebalance where possible, before selling existing winners. If you are still adding fresh savings to your portfolio, direct new purchases toward your underweight sectors rather than your overweight ones. This achieves the same rebalancing effect without triggering any capital gains tax at all, because you are not selling anything.
Third, do not rebalance on a hair-trigger. A sector drifting from 25 percent to 28 percent is normal market noise, not a risk emergency. Reserve actual selling-based rebalancing for drifts that genuinely change your risk profile — typically moves of 5 percentage points or more away from target — so that the tax and transaction costs of rebalancing are justified by a real reduction in concentration risk.
PRACTICAL TOOL
Keep a simple annual "rebalancing worksheet": list each holding, its target weight, its current weight, the drift, its holding period in days, and whether it is subject to long-term or short-term capital gains tax if sold today. This turns rebalancing from a vague intention into a five-minute, once- or twice-a-year mechanical review.
Everything in this chapter builds toward one output: a written, specific set of limits that you set for yourself before you need them, so that in a moment of market stress you are executing a plan rather than improvising a reaction. This is the retail investor's equivalent of an institutional risk policy — simpler, but the same underlying idea.
Maximum single-position size. Chapter 60 introduced position limits at the individual stock level; restate it here as a hard portfolio-risk rule: no single stock should exceed roughly 10 to 15 percent of your total equity portfolio at cost, and ideally you act to trim it back toward target if market appreciation pushes it meaningfully above that, say past 18 to 20 percent, even if you like the company. A position that grows large enough on its own can undo the benefit of everything else you hold, simply through its own bad day.
Maximum sector exposure. Given the correlation dynamics detailed in Lesson 61.3, a firm cap on combined banking, finance, and microfinance exposure — commonly recommended in the 30 to 35 percent range of total equity holdings — protects you against the single largest correlated-risk cluster on NEPSE. A parallel, somewhat looser cap on hydropower exposure (given its own correlated monsoon and interest-rate sensitivities) is also reasonable, often in the 25 to 30 percent range.
KEY CONCEPT
A sector limit is not a judgment that the sector is bad. Banks and hydropower companies are core, legitimate parts of the Nepali economy and can be excellent long-term holdings. The limit exists purely to prevent a single correlated shock from doing outsized damage to your entire portfolio, regardless of how good the underlying businesses are.
Maximum drawdown tolerance triggering review. Set a specific number in advance — for example, "if my portfolio's drawdown from its peak exceeds 20 percent, I will conduct a full portfolio review within two weeks." This does not mean automatically selling at 20 percent down. It means treating that threshold as a forcing function: a scheduled moment to reassess whether your original assumptions (sector weights, position sizes, time horizon, emergency fund adequacy from Chapter 59) still hold, rather than drifting passively through an extended decline without ever stepping back to think.
PRACTICAL TOOL
A simple three-tier drawdown response plan works well for most retail investors: at a 10 percent drawdown from peak, no action, this is routine; at a 20 percent drawdown, conduct a full review of sector weights and position sizes against your written targets; at a 30 percent or greater drawdown, review not just the portfolio but your original assumptions — has something structurally changed (a regulatory shift, a sector-wide solvency concern), or is this simply a cyclical correction that your original plan already anticipated?
Putting the limits together. A complete, practical risk-limit policy for a Nepali retail equity portfolio might read like this, written once and kept somewhere you will actually see it again: no single stock above 15 percent of the equity sleeve at cost; combined banking, finance, and microfinance exposure capped at 35 percent; combined hydropower exposure capped at 30 percent; portfolio rebalanced when any sector drifts more than 5 percentage points from target, checked semi-annually; and a full strategy review triggered automatically at a 20 percent portfolio drawdown from peak value.
CASE IN POINT
Institutional fund managers in Nepal, including mutual fund schemes regulated by SEBON, operate under formal concentration limits — for example, restrictions on how much of a fund's assets can sit in a single issuer or sector. A retail investor's self-imposed limits, applied with the same seriousness, achieve much of the same protective effect, even without any regulatory requirement to do so.
None of these numbers are laws of physics. A younger investor with a longer horizon and steady employment income (Chapter 59's capacity-for-risk framework) can reasonably tolerate wider bands than someone nearing retirement. But the discipline of having explicit, written numbers — decided in a calm moment, not during a circuit-breaker cascade — is what turns "I hope my portfolio is fine" into an actual, defensible risk management practice.
Chapter recap
This chapter moved the lens from individual stocks to the whole portfolio. Chapter 60 taught you to build a diversified equity sleeve; this chapter taught you to check whether that diversification actually protects you, and to measure and bound the risk of the assembled whole. The central idea is that a portfolio can look diversified by ticker count while remaining dangerously concentrated by underlying risk driver — the "same floodplain" problem — and that NEPSE's structure, with its shared regulatory exposure across banks and finance companies, its heavy reliance on margin lending, and its retail-driven sentiment swings, makes this correlation risk especially pronounced in the financial sector.
You learned two simple, accessible measurement tools that do not require a finance degree: tracking maximum drawdown through a running peak-and-current-value log, and understanding volatility informally as your portfolio's "typical wobble" from month to month. Both are things any retail investor can maintain with a notebook and five minutes a month, and both matter more for real decision-making than any single day's price move, because they capture the lived experience of gain and loss over time rather than a single noisy data point.
You then worked through four realistic Nepali stress scenarios — a sector-wide BFI correction, an NRB monetary tightening cycle, a liquidity crunch, and a circuit-breaker cascade event connecting back to Chapter 58 — each grounded in patterns NEPSE has actually shown, including the roughly 40-plus percent correction that followed the market's 2021 all-time high. The worked table demonstrated that a portfolio's actual stress exposure depends heavily on its sector weighting, and that running this exercise once a year, in a calm moment, is what makes a bad scenario survivable rather than catastrophic.
The chapter closed with the two operational habits that turn risk awareness into risk management: rebalancing, the periodic practice of trimming winners and adding to laggards to restore your intended weights, done with explicit attention to Part VII's capital gains tax thresholds and transaction costs so that the cure does not cost more than the disease; and a written set of portfolio risk limits — maximum single-position size, maximum sector exposure, and a maximum drawdown tolerance that triggers a scheduled review — decided in advance so that a future market panic is met with a plan already in hand, not an improvised reaction.
Everything from Part XII up to this point has been about construction and control: choosing your asset allocation, building the equity sleeve, and now measuring and bounding portfolio-level risk. The next chapter, Chapter 62, "Systematic and Disciplined Investing," closes out Part XII by turning to the behavioural engine that makes all of this work in practice over decades rather than months. It covers rules-based, automatic investing habits — SIP-style regular investing adapted to Nepal's market infrastructure, and the deliberate removal of emotion and impulsive timing decisions from your ongoing investment process.
After Chapter 62, Part XII concludes, and Part XIII, "The Canon Scoring System," begins with Chapter 63, "Philosophy of the Canon Scoring System" — introducing the book's own structured framework for scoring and evaluating individual investments, building directly on the risk-awareness and disciplined construction principles established across this entire part.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XII · Chapter 62
Systematic and Disciplined Investing
First published 23 Aug 2026 · Last verified 29 Aug 2026
In the western hills of Nepal, a maize farmer does not check the weather forecast before deciding whether to plant. She plants when the calendar says Baisakh has arrived and the soil has softened with the first rains. Some years the rains come early and her neighbour who waited for "better conditions" gets a head start. Some years the rains are late and she plants into dry soil, watching the neighbour's seedlings sprout first while hers wait. But across twenty years of harvests, the farmer who plants on the fixed calendar date, season after season, brings in a more reliable crop than the neighbour who keeps trying to time the rains. The neighbour is right about the weather sometimes. He is wrong often enough, and costly enough when wrong, that guessing loses to scheduling over the long run.
This is not a chapter about farming. It is a chapter about what happens when a Nepali investor treats NEPSE the way that farmer treats her field — not by trying to guess when the index will bottom or peak, but by building a fixed, repeatable process and running it regardless of what the ticker shows on any given day. Chapters 59 through 61 built the architecture of a portfolio: how much to hold in equity versus safer assets, how to construct the equity sleeve on NEPSE, and how to manage the risks once the portfolio exists. This chapter asks a different question. Even with the best-designed portfolio on paper, what makes an investor actually execute it, month after month, through the euphoria of a bull run and the nausea of a crash? The answer is systematic investing — a set of pre-committed rules that removes the moment-to-moment decision of "should I invest today" from the investor's hands entirely. This chapter is the last of Part XII, and its job is to turn the previous three chapters' architecture into a habit that survives contact with a real, emotional human being watching a red NEPSE ticker.
Lesson 62.1 — What Systematic Investing Means
Every investing decision can be sorted into one of two buckets: discretionary or systematic.
A discretionary decision is one made fresh, in the moment, using judgment about current conditions. An investor who checks the NEPSE index every morning and decides "the market feels weak today, I will wait" is being discretionary. So is the investor who sees three green days in a row and thinks "this rally has legs, let me put in extra money now." Discretionary investing is not automatically wrong — professional fund managers make discretionary calls for a living. But for the ordinary retail investor, discretionary investing has a well-documented problem: it invites emotion into the decision at exactly the moment emotion is least reliable.
A systematic decision is one made in advance, according to a rule, and then simply carried out when the rule's trigger arrives — regardless of how the investor feels that day. "I will invest NPR 5,000 into my selected mutual fund on the 5th of every Nepali month" is a systematic rule. So is "I will rebalance my portfolio back to my target allocation every Ashad, using the process from Chapter 59." The decision was made once, calmly, outside the heat of a market swing. Every month after that, there is no decision left to make — only an instruction to follow.
KEY CONCEPT
Systematic investing is the practice of committing to a fixed set of rules for buying, selling, and reviewing your portfolio in advance, and then executing those rules mechanically, without re-deciding each time based on how the market feels that day.
Why does this matter so much for a NEPSE investor specifically? Because Chapter 50 already documented, in painful detail, why most Nepali retail investors underperform the very index they are trying to beat. The pattern was consistent: investors pile into shares during a euphoric rally, near the top, driven by stories of neighbours who "doubled their money in IPO allotments," and then panic-sell during the crash that inevitably follows, locking in losses near the bottom. This is not a Nepal-specific character flaw. It is a well-studied human pattern — the same behaviour Chapter 53 examined under the headings of recency bias, herd behaviour, and loss aversion. What is Nepal-specific is the amplitude. NEPSE is a small, thinly-traded market dominated by retail participation, with limited institutional counterweight, so its swings are larger and its herd behaviour more visible than in deeper markets. A market that swings 40% in a year punishes discretionary timing far more severely than a market that swings 10%.
Systematic investing is the direct antidote to this pattern, for one simple reason: it does not ask the investor to correctly predict anything. It does not require knowing whether NEPSE is about to rise or fall. It only requires the investor to keep showing up on schedule. This sounds almost too simple to be a serious strategy. It is precisely this simplicity that makes it work — because the alternative, market timing, requires being right twice: right about when to get out, and right about when to get back in. Getting either call wrong once can erase years of gains. Systematic investing never has to be "right" about timing at all, because it never makes a timing bet in the first place.
CASE IN POINT
Between 2020 and 2021, NEPSE's benchmark index rose from roughly 1,400 points to an all-time high near 3,200 points — more than doubling in under two years, fuelled heavily by new retail demat accounts opened during and after the COVID-19 lockdowns. Many of the investors who entered during the final months of that rally, buying at or near the peak on the belief that "the market only goes up," were still holding heavy losses years later as the index corrected sharply through 2022. A systematic investor who had instead been buying a fixed rupee amount every month since 2019 would have accumulated units across the entire cycle — some expensive, many cheap — and would have faced the 2022 correction with a far lower average cost than the investor who arrived all at once, near the top.
It is worth being precise about what systematic investing does and does not promise. It does not promise higher returns than a perfectly-timed lump sum investment made at the exact market bottom. Nobody can identify the exact bottom in advance; that is the whole point. What systematic investing promises is a removal of the worst-case outcome — the outcome where an investor puts a large sum in at the worst possible moment, out of excitement, and a removal of the second worst-case outcome — the outcome where an investor is too frightened to invest at all and misses the recovery entirely because they were waiting for a "sign" that never arrived in a form they trusted. Both of these worst cases are common in Nepal's retail investor base. Systematic investing quietly avoids both.
Lesson 62.2 — Rupee-Cost Averaging: Buying on a Schedule, Not on a Guess
The mechanical tool behind most systematic equity investing is called rupee-cost averaging, known globally as dollar-cost averaging and adapted here to Nepal's currency. The idea is simple enough to explain to a child, which is part of its power.
Rupee-cost averaging means investing a fixed amount of money at fixed, regular intervals, regardless of the price of the asset on each occasion. If a mutual fund unit costs NPR 11 this month, the investor buys whatever number of units NPR 5,000 can purchase. If the same unit costs NPR 9 next month, the investor again spends NPR 5,000, which now buys more units because the price is lower. The rupee amount stays fixed; the number of units purchased moves inversely with the price.
The mathematical consequence of this is that the investor automatically buys more units when prices are cheap and fewer units when prices are expensive — without ever having to identify which months count as "cheap" and which count as "expensive." This produces an average cost per unit that is, over a genuinely volatile and range-bound market, typically lower than the simple average of the prices paid, because more rupees flowed into the cheaper months by construction. This effect is sometimes called the mechanical advantage of averaging down through volatility, and it works purely on arithmetic — no forecasting skill required, and no need to correctly guess which month was the cheap one.
A worked example makes this concrete. Suppose an investor commits to investing NPR 5,000 every month into a NEPSE-listed mutual fund, and the fund's Net Asset Value (NAV — the per-unit price of a mutual fund, calculated by dividing the fund's total assets by the number of units outstanding) moves as follows over six months:
Month
Amount Invested (NPR)
NAV per Unit (NPR)
Units Purchased
1
5,000
10.00
500.0
2
5,000
8.00
625.0
3
5,000
7.00
714.3
4
5,000
9.00
555.6
5
5,000
12.00
416.7
6
5,000
11.00
454.5
Total
30,000
—
3,266.1
The simple average of the six NAV prices listed is NPR 9.50 (10 + 8 + 7 + 9 + 12 + 11, divided by six). But the investor's actual average cost per unit is total rupees invested divided by total units purchased: NPR 30,000 divided by 3,266.1 units, which works out to roughly NPR 9.19 per unit. The systematic investor paid less, on average, than the simple average of the six prices — because the plan automatically weighted more rupees toward Months 2 and 3, when the NAV was cheapest, and fewer rupees toward Month 5, when the NAV was most expensive. Nobody had to correctly call Month 3 as "the cheap month." The schedule did the work.
It is important to be honest about the limits of this tool, because overselling it would violate the analytical, unbiased standard this Canon holds itself to. Rupee-cost averaging is not a strategy for maximising returns; it is a strategy for reducing the damage of bad timing and reducing the emotional difficulty of investing through volatility. Academic research on dollar-cost averaging in developed markets generally finds that, over long horizons in markets that trend upward over time, investing a lump sum immediately tends to produce higher expected returns than spreading the same amount out gradually — simply because money invested earlier has more time in the market to compound and equity markets rise more often than they fall. Rupee-cost averaging's real edge shows up in a different place: in genuinely volatile, range-bound, or uncertain markets, and in the psychology of an investor who does not have a lump sum to invest all at once, or who would be too anxious to deploy a lump sum immediately and would otherwise sit in cash indefinitely, waiting for a "better" entry point that may never arrive.
WARNING
Rupee-cost averaging is a tool for investors who are adding new savings over time — most Nepali salaried and remittance-receiving households, who do not have one giant sum sitting idle. It is not a claim that spreading out an already-available lump sum will outperform investing it immediately in a market that rises over the long run. Use it because it fits how your money actually arrives — from monthly salary or remittance — and because it removes the paralysis of trying to time each rupee, not because it is mathematically guaranteed to beat every alternative.
This distinction matters enormously for the Nepali context, because it maps almost perfectly onto how money actually arrives in most Nepali households. A salaried employee in Kathmandu receives a fixed paycheck every month. A family with a member working in the Gulf or Malaysia receives a remittance transfer every month or every few months. Very few ordinary Nepali investors are sitting on a large lump sum, deciding whether to deploy it all at once or spread it out — the academic debate above. Most Nepali investors are, by the nature of their income, already natural candidates for rupee-cost averaging: new money keeps arriving, and the only decision is whether to invest it as it arrives or let it sit idle in a savings account earning a modest bank interest rate while the investor waits for a "sign." Systematic investing simply formalises what a sensible income pattern already suggests: invest the new money on a fixed schedule, every time, rather than trying to guess the right month.
Lesson 62.3 — SIP-Style Products in the Nepali Market
Rupee-cost averaging does not require any special product — an investor can manually place a buy order for a fixed rupee amount every month through their broker's Trading Management System (TMS, the online platform Nepali brokers provide for placing buy and sell orders on NEPSE). But manual execution has a weakness: it depends on the investor remembering to log in and place the order every single month, which reintroduces exactly the human inconsistency systematic investing is meant to remove. For this reason, a genuine Systematic Investment Plan, or SIP — a formal, semi-automated product that debits a fixed amount on a fixed date and invests it automatically — is a meaningfully better tool than a manual reminder on a calendar app.
Nepal's mutual fund industry has, in recent years, begun offering exactly this kind of product. Several Nepali fund managers — including firms such as Nabil Investment Banking and NIC Asia Capital — now run formal SIP schemes for their open-ended mutual funds, allowing an investor to commit to a fixed monthly amount, often with a modest minimum contribution, which is automatically deducted from a linked bank account and converted into fund units at the prevailing NAV each cycle. Separately, some brokerage-technology platforms have introduced SIP-style features that let an investor commit to buying a fixed rupee amount of a chosen NEPSE-listed stock at regular intervals, extending the SIP concept beyond mutual funds into direct equity purchases.
PRACTICAL TOOL
Before enrolling in any SIP product, verify three things directly with the provider: (1) what the minimum monthly commitment is and whether it is affordable to sustain for years, not just months; (2) what happens if a monthly debit fails due to insufficient bank balance — does the plan lapse, pause, or simply skip that cycle; and (3) what exit or redemption charges, if any, apply if the investor needs to stop early. A SIP that is easy to start but painful to pause is not a disciplined tool — it is a trap.
The mutual-fund SIP route and the direct-stock SIP route serve different purposes, and it is worth being clear-eyed about the difference. A mutual fund SIP, investing in a professionally-managed, diversified fund, already carries the diversification benefits discussed in Chapter 59 — the fund itself typically holds a basket of banking, hydropower, insurance, and other sector shares, so a single monthly SIP contribution is automatically spread across many companies. A single-stock SIP, by contrast, concentrates the systematic discipline onto one company's share price. It removes the timing problem but does nothing about the concentration risk covered in Chapter 61 — an investor running a single-stock SIP into one hydropower counter is still exposed to everything that could go wrong with that one company, however disciplined the buying schedule is. Systematic process and portfolio construction are separate disciplines; a good SIP habit does not excuse a bad allocation decision.
REGULATORY DETAIL
Mutual funds in Nepal are regulated by the Securities Board of Nepal (SEBON) under the Mutual Fund Regulations, and every open-ended scheme must publish its NAV regularly so investors can verify the price at which their SIP units are being purchased. An investor enrolling in any SIP product should confirm the fund is a SEBON-registered scheme and should be able to locate its published NAV independently — through the fund manager's own disclosure or NEPSE's own data feeds — rather than relying solely on the number shown inside a single broker's app.
For an investor who prefers not to use a formal SIP product — perhaps because the available minimum contribution does not suit their budget, or because they want to build direct-stock positions the SIP products on offer do not cover — a manual, self-administered rupee-cost-averaging plan remains a fully legitimate systematic approach. The discipline lies in the fixed schedule and fixed rupee amount, not in the specific mechanism used to execute it. What matters is removing the monthly decision of "should I buy today," and replacing it with a standing instruction the investor commits to in advance and then simply carries out — automatically if a SIP product allows it, or manually on a fixed calendar reminder if it does not.
Lesson 62.4 — Automating Good Behaviour and Pre-Commitment
Chapter 52 introduced the Investment Policy Statement (IPS) — a written document, drafted while calm, that records an investor's goals, target asset allocation, and the rules they intend to follow. Chapter 53 examined the behavioural biases — loss aversion, herd behaviour, recency bias, overconfidence — that make it so difficult for an investor to actually follow that document once the market starts moving. Systematic investing is the bridge between the two: it is the set of mechanical devices that make following the IPS the path of least resistance, rather than a fresh act of willpower every single time.
Behavioural economists call this general technique pre-commitment: making a decision in advance and then deliberately removing your own future ability to easily reverse it in a moment of weakness. The classic non-financial example is the traveller who books a non-refundable early-morning flight specifically because a refundable one would tempt them to sleep in. The financial equivalent is setting up automatic, hard-to-reverse mechanisms for the behaviours an investor knows, from a calm and rational moment, that they want to sustain — and building in enough friction that panic-driven reversals require real, deliberate effort rather than a single impulsive click.
Several concrete pre-commitment devices are available to a Nepali investor:
Standing bank instructions. Most Nepali banks allow a customer to set up a standing order — an automatic, recurring transfer from a savings account to another account on a fixed date each month, without requiring the customer to initiate it manually each time. Linking this standing order to a mutual fund SIP debit, or to a fixed transfer into a dedicated "investing" account from which broker top-ups are made, converts the monthly investment decision from an active choice into a passive default. The investor has to actively intervene to stop investing, rather than actively remembering to invest — a small reframing with a large behavioural effect, because human beings are, on average, far more likely to leave a default setting alone than to take active steps against inertia.
Scheduled portfolio reviews, not constant monitoring. Chapter 61 already warned against the danger of checking portfolio value too frequently, since frequent checking amplifies the emotional sting of ordinary volatility and increases the temptation toward reactive trading. The systematic solution is to pre-commit to a fixed review calendar — for instance, a brief check-in every month to confirm SIP debits went through, and a deeper rebalancing review every six months or every Nepali fiscal year-end, using the rebalancing bands set out in Chapter 59. Outside those scheduled windows, the discipline is to not look, or at least to not act on what is seen.
Written, dated rules for extreme events. An IPS is far more useful if it does not just state a target allocation, but also states, in advance, exactly what the investor will do if NEPSE falls by a specific severe amount — for example, a pre-written rule such as "if my equity allocation falls more than five percentage points below target due to a market decline, I will rebalance back to target using funds from my debt sleeve, following the process in Chapter 59, rather than selling equity at the bottom." Writing this rule down before a crash occurs, while the investor is calm, makes it dramatically easier to follow during the crash, when the investor is not calm. The rule was decided by a rational version of the investor in advance; the panicked version of the investor in the moment only has to execute it, not decide it.
Accountability partners and structural barriers. Some investors find it useful to share their IPS and their systematic rules with a spouse, a trusted friend, or a financial adviser, specifically so that a decision to deviate — to stop a SIP, to sell everything during a crash, to double up on a hot tip — requires explaining that deviation to another person first. This creates a small but meaningful pause between impulse and action.
A simple test separates a pre-commitment device that actually works from one that only looks like it does: does stopping it require more effort than continuing it? A SIP that auto-debits unless actively cancelled passes this test easily. A plan that instead requires the investor to manually log in and place a fresh buy order every single month does not — it requires effort to continue, which means a bad mood, a busy week, or one frightening headline can quietly cause the plan to lapse with no active decision ever made at all.
None of this is about removing the investor's judgment permanently. An IPS can and should be revisited and revised — but only at the scheduled review points, and only for reasons connected to genuine changes in the investor's life circumstances, goals, or time horizon, as discussed in Chapter 52 — never as a reaction to a single bad week in the market. The whole architecture of automation exists to protect the investor's own well-reasoned, calmly-made plan from the investor's own panicked, badly-timed impulses.
Lesson 62.5 — Staying the Course Through NEPSE's Boom-Bust Cycles
Chapter 50 laid out, in detail, the recurring cycle that has defined NEPSE's history: a period of rising prices draws in a wave of new retail participants, often at the very peak, followed by a sharp correction that leaves latecomers with losses and drives many of them out of the market entirely — sometimes for years. This is not a one-time historical accident; NEPSE has moved through several such cycles, and there is no structural reason to believe it has stopped doing so. A systematic investing plan is only as good as the investor's ability to keep running it through the uncomfortable middle of that cycle — not just during the calm months when it is easy.
It helps to separate the cycle into its two emotionally distinct phases, because each phase tests the systematic investor differently.
During a boom phase, the temptation is to abandon the fixed schedule in favour of investing more, faster, because prices are rising and the fear of missing out becomes intense. NEPSE indices sometimes rise for many months in a row, and every additional green day makes the story "this time is different, get in now" feel more credible. A systematic investor's rule — keep investing the same fixed amount, on the same schedule, regardless of how good the market feels — will, during this phase, feel almost boringly conservative next to the returns being reported by more aggressive, lump-sum, borrowed-money traders. This is exactly the moment the discipline matters most, because the investors making outsized gains by pouring in extra money during a euphoric peak are frequently the same investors who, months later, are unable to sell in time when the correction begins.
During a bust phase, the temptation runs the opposite direction: the fear of continued losses makes stopping the plan altogether feel like the only sensible response. Watching a portfolio's value fall for months, especially when every news bulletin describes the market in grim terms, creates enormous psychological pressure to pause SIP contributions "until things stabilise" — which almost always means resuming only after most of the recovery has already happened, since there is no reliable signal that announces a bottom has been reached. This is the single most damaging behaviour a systematic investor can adopt, because it defeats the entire purpose of rupee-cost averaging: the cheapest, most advantageous months to be buying units are precisely the months an investor is most tempted to stop.
CAUTION
The instinct to pause a SIP during a downturn is understandable, but it inverts the entire logic of the strategy. Rupee-cost averaging earns its advantage specifically by continuing to buy through the cheap months. An investor who pauses during every downturn and resumes only once prices recover has, without realising it, converted a disciplined averaging strategy into its opposite — buying disproportionately at higher prices and skipping the lower ones.
There is one legitimate exception worth naming clearly, so it is not confused with panic-driven pausing: an investor whose income has genuinely and durably fallen — a job loss, a family emergency, a remittance-sending family member returning home without new work lined up — may need to reduce or pause contributions for real cash-flow reasons that have nothing to do with market sentiment. That is a financial-planning decision, not a market-timing decision, and it is entirely legitimate. The distinction is the reason: pausing because the household genuinely cannot afford the contribution this month is different from pausing because NEPSE fell and the investor is frightened, even though the household's cash flow is unchanged. The IPS drafted in Chapter 52 should ideally distinguish between these two triggers in advance, so the investor is not left improvising the difference during a period of actual stress.
CASE IN POINT
Consider two investors who each begin a NPR 5,000 monthly SIP into the same NEPSE-listed mutual fund at the start of a two-year period that includes a sharp mid-period correction. Investor A follows the plan mechanically for the full 24 months, including through the eight-month correction, when the NAV falls by nearly a third. Investor B follows the plan faithfully during the calm months but stops contributing for the eight months of the correction, out of fear, and resumes only once the NAV has visibly recovered. Even though both investors invested the same total number of active months, Investor A accumulates meaningfully more units overall, because Investor A's contributions during the correction bought units at the lowest prices of the entire period — exactly the months Investor B sat out. The discipline to continue through the correction, not the decision to start the plan in the first place, is what separates their outcomes.
The historical record of NEPSE gives a systematic investor a specific reason for confidence, without promising anything about the future: over its history, NEPSE has recovered from every one of its major corrections, and each recovery eventually carried the index to a new high — though the length of time required for that recovery has varied considerably, sometimes stretching for several years. A systematic investor with a genuinely long time horizon, contributing new money throughout the down years, has historically been rewarded for that patience. Past recoveries are not a guarantee of future recoveries — the Canon does not deal in guarantees — but they are a legitimate part of the reasoning that makes staying the course a defensible, evidence-informed choice rather than blind hope.
Lesson 62.6 — Building a Personal Systematic Investing Calendar
Everything in this chapter converts into practice through a single tool: a personal, written calendar of pre-committed investing actions, built once and then followed without renegotiation. This calendar takes the abstract idea of "systematic investing" and turns it into a specific list of dates and actions that requires no judgment to execute — only follow-through.
A workable systematic investing calendar for a Nepali retail investor typically includes four recurring layers, each running on its own cycle:
A monthly layer covers the routine contribution: the SIP debit date, or the manual buy-order date if no formal SIP product is used, along with a brief confirmation that the debit or order actually went through — a five-minute check, not a market analysis session.
A quarterly layer covers a light check on the underlying holdings — for a mutual fund SIP, glancing at the fund's published factsheet or NAV history to confirm nothing has structurally changed about the fund; for a direct-stock SIP, a brief look at whether the company has released any material news, such as an AGM announcement or a rights-share decision, that the investor's IPS says should be reviewed, without triggering any change to the buying schedule itself.
A semi-annual or annual layer covers the full portfolio rebalancing review described in Chapter 59 — comparing actual allocation percentages against target bands and executing any rebalancing trades needed to bring the portfolio back in line, following rules decided in advance rather than in the moment.
An event-triggered layer, which is not calendar-based but rule-based, covers the pre-written responses to extreme events set out in the IPS — what to do if NEPSE falls by a defined large percentage, what to do if a held company suspends trading, what to do if personal income changes materially. These rules sit dormant most of the time and activate only when their specific trigger condition is met, at which point the investor executes the pre-written response rather than improvising.
PRACTICAL TOOL
A simple systematic investing checklist to keep alongside the IPS: (1) Is my monthly SIP debit or buy order still active and set to the correct amount? (2) Has my income changed enough since my last review to change that amount? (3) Is my next scheduled rebalancing review date marked on a calendar I actually look at? (4) Do I have a written rule for what I will do if NEPSE falls sharply, so I am not deciding that in the moment? (5) Have I looked at my portfolio more often than my scheduled review dates this month — and if so, why?
The calendar should be written down somewhere durable — a physical notebook, a phone's calendar app with recurring reminders, or a simple spreadsheet — not merely held as an intention in the investor's head. An intention is negotiable in the moment; a written date on a calendar, paired with an automated debit, is far closer to non-negotiable. The goal is for the systematic investing calendar to eventually feel as unremarkable as a mobile recharge or a school fee payment: a routine financial obligation that happens on schedule, generating no internal debate, requiring no daily opinion about where NEPSE is headed next.
Closing Synthesis: The Discipline That Completes the Portfolio
Part XII opened with a question: given a set of goals and a tolerance for risk, how should an investor's money be divided across different kinds of assets? Chapter 59 answered that question with the architecture of asset allocation — the mix of equity, debt, and other holdings that reflects an investor's specific circumstances and time horizon. Chapter 60 took the equity portion of that architecture and built it out in NEPSE-specific detail, covering how to select and size individual holdings within a Nepali equity sleeve. Chapter 61 then stress-tested that structure, examining the risks — concentration, liquidity, sector, leverage — that could damage it, and the tools available to manage those risks once identified.
This chapter, closing Part XII, has addressed the piece that makes the first three chapters worth anything at all: execution. A perfectly designed asset allocation, a carefully constructed equity sleeve, and a well-managed risk profile are only useful to an investor who actually follows them, consistently, across years that will inevitably include both euphoric rallies and frightening corrections. Systematic investing — rupee-cost averaging, SIP products, standing orders, scheduled reviews, and pre-committed written rules — is the discipline that carries a good plan from the page into a real portfolio, held by a real person, through a real market cycle. It does this not by making the investor smarter or better at predicting NEPSE's next move, but by making good behaviour the default and bad behaviour the thing that requires active, deliberate effort to do.
Read together, Chapters 59 through 62 form a complete answer to the question "how should I build and hold a Nepali portfolio." Chapter 59 says how to divide it. Chapter 60 says how to build the equity portion on NEPSE specifically. Chapter 61 says how to watch for and manage what could go wrong. Chapter 62 says how to actually keep doing all three, on schedule, without letting the emotional weather of any single month blow the plan off course. None of the four chapters is complete without the other three — an allocation without construction is theory, construction without risk management is fragile, and all three without systematic discipline are simply good intentions that NEPSE's next boom-bust cycle will test, and likely defeat, exactly as it has tested and defeated so many Nepali retail investors before.
Part XIII now turns from process to precision. Having established how to build, hold, and systematically manage a portfolio, the Canon turns next to the harder and more specific question of how to judge any single Nepali company on its merits. Chapter 63, "Philosophy of the Canon Scoring System," opens Part XIII by introducing a structured, quantitative framework for scoring individual companies listed on NEPSE — a disciplined, rules-based method for evaluating a business that mirrors, at the level of a single stock, exactly the kind of systematic, emotion-resistant discipline this chapter has just built at the level of the whole portfolio.
Chapter recap
This chapter examined systematic investing — the practice of committing to a fixed, rules-based investing process in advance and executing it mechanically, rather than making fresh, discretionary, emotionally-influenced decisions each time. Lesson 62.1 distinguished discretionary from systematic decision-making and connected the case for systematic investing directly to Chapter 50's account of why Nepali retail investors so often buy at NEPSE's peaks and sell at its troughs. Lesson 62.2 introduced rupee-cost averaging in detail, including a worked six-month table showing how a fixed monthly rupee amount produces a lower average cost per unit than the simple average of prices paid, while also being honest about the tool's real limits — it reduces timing risk and emotional difficulty rather than guaranteeing higher returns, and it fits most naturally with investors, like most Nepali households, whose money arrives gradually through salary or remittances rather than as a single lump sum.
Lesson 62.3 grounded this in the actual Nepali market, describing the SIP products now offered by Nepali mutual fund managers and broker-technology platforms, the difference between diversified mutual-fund SIPs and concentrated single-stock SIPs, and the practical due-diligence questions — minimum contribution, failed-debit handling, exit terms — an investor should confirm before enrolling. Lesson 62.4 connected systematic investing to the behavioural material of Chapter 53 through the idea of pre-commitment: standing bank orders, scheduled rather than constant portfolio reviews, and written, dated rules for extreme market events, all designed to make good investing behaviour the default and deviation the thing that requires active effort. Lesson 62.5 addressed the hardest test of all — staying the course through NEPSE's actual boom-bust cycles, distinguishing legitimate income-driven pauses from panic-driven ones, and showing through a worked comparison why continuing contributions through a correction, not merely starting a plan during calm markets, is what separates disciplined investors from the rest. Lesson 62.6 turned all of this into a concrete personal systematic investing calendar spanning monthly, quarterly, annual, and event-triggered review layers.
Taken as a whole, Part XII has built a complete process for constructing and holding a Nepali portfolio: Chapter 59 established how to allocate across asset classes according to an investor's goals and risk tolerance; Chapter 60 built out the equity sleeve specifically for NEPSE; Chapter 61 supplied the tools to identify and manage the risks within that structure; and this chapter, Chapter 62, supplied the discipline that keeps an investor actually following all three through real market cycles rather than abandoning them at the worst possible moment. A portfolio's architecture and its discipline are inseparable — one without the other is either a fragile plan or an undirected habit, and neither survives NEPSE's cycles on its own.
With Part XII complete, the Canon now shifts its focus from the whole portfolio to the individual company within it. Part XIII, "The Canon Scoring System," opens with Chapter 63, "Philosophy of the Canon Scoring System," which introduces a structured, quantitative framework for evaluating individual Nepali companies — carrying the same rules-based, emotion-resistant spirit of this chapter down to the level of a single stock, so that the question "should I buy this specific company" can be answered with the same discipline this chapter has just built for the question "how do I keep investing at all."
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIII
THE CANON SCORING SYSTEM
Part XIII · Chapter 63
Philosophy of the Canon Scoring System
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 63.1 — Why Ad Hoc Stock Picking Fails on NEPSE
At a health post in rural Nepal, a new patient arrives with fever, stomach pain, and fatigue. An experienced doctor could simply look at the patient, take a guess based on the last ten similar cases she remembers, and prescribe something. Most of the time, she might even be right. But hospitals do not run on "most of the time." They run on checklists — a fixed sequence of questions and tests: check temperature, check blood pressure, ask about travel history, test for malaria, test for typhoid, review medication history. The checklist does not replace the doctor's judgment. It disciplines it. It forces her to look at the same set of things, in the same order, every single time, so that a bad day, a distracting patient in the next bed, or a false first impression does not cause her to skip something that matters.
Investing in companies listed on the Nepal Stock Exchange (NEPSE — the country's only stock exchange, based in Kathmandu) has, for most retail investors, worked more like the doctor guessing from memory than the doctor using a checklist. An investor hears that a friend made money in a hydropower initial public offering (IPO — the first sale of a company's shares to the public). Another investor sees a bank's share price rising for three straight weeks and assumes it must be a "good company." A third buys a finance company's shares because the managing director is a relative of someone he trusts. None of this is analysis. It is pattern-matching on fragments of information, filtered through whatever the investor happened to notice that week.
This chapter opens Part XIII of this book, which introduces the Canon Score — a structured, repeatable, weighted framework this book will use, starting in Chapter 64, to evaluate the quality of individual companies listed on NEPSE. Before this book hands you that framework, it owes you an explanation of why such a framework is necessary at all. That is the entire purpose of this chapter: not to give you the scorecard, but to explain why a scorecard beats guessing, why ratios alone are not enough, and why a framework built for Wall Street or Dalal Street cannot simply be imported wholesale into Kathmandu.
Start with the plain problem of ad hoc analysis — "ad hoc" is a Latin phrase that simply means "for this particular case," done without a general system, made up on the spot. Ad hoc stock picking means: no fixed process, no consistent questions, no comparability from one company to the next. You analyse Company A by looking at its dividend history because that's what caught your eye. You analyse Company B by looking at its chairman's reputation because that's what a friend mentioned. You analyse Company C by looking at the share price chart because that's the only data you bothered to open. Three completely different lenses, three completely different "analyses," and no way to honestly compare A, B, and C against each other, because you never asked them the same questions.
KEY CONCEPT
A structured scoring system is a fixed set of questions, asked of every company in the same order and weighted the same way, so that different companies can be honestly compared against each other and against the same company's own past. It does not remove judgment — it disciplines judgment, the way a doctor's checklist disciplines a diagnosis or a pilot's pre-flight checklist disciplines a takeoff.
Consider a concrete Nepali example. Suppose in early 2023 an investor was choosing between three hydropower companies newly listed on NEPSE. One had just completed construction and started generating revenue. One was still under construction with a delayed commissioning date. One had a long operating history and a strong monsoon-season output record. An ad hoc investor might buy the first one simply because "hydropower is the future of Nepal" — a true statement, but not a reason to prefer one hydropower company over another. He has made a sector-level judgment and mistaken it for a company-level judgment. This is one of the most common and costly errors on NEPSE: confusing "I like this sector" with "I have evaluated this company." Banking, hydropower, and life insurance are three of NEPSE's largest and most crowded sectors. Liking the sector tells you almost nothing about which of the twenty-plus companies inside it deserves your capital.
Ad hoc analysis has three specific failure modes worth naming, because each one recurs constantly on NEPSE.
The first is recency bias — the tendency to overweight whatever information arrived most recently, simply because it is freshest in memory. A company's share price has doubled in two months due to speculative buying ahead of a bonus share announcement (a bonus share is additional stock given free to existing shareholders, funded from the company's reserves, which increases the number of shares outstanding without adding new capital). The ad hoc investor sees the price chart, concludes the company must be strong, and buys at the top — never having looked at whether the underlying business actually improved.
The second is anecdote substitution — replacing systematic evidence with a single vivid story. "My uncle worked there for ten years and says it's well run" feels like real information. It may be true. It may also be completely irrelevant to whether the company's balance sheet is overleveraged or whether its promoter (the founding shareholder group that typically also runs the company — a term used constantly in Nepali company law and stock market commentary) has been quietly pledging shares against personal loans.
The third is halo transfer — letting one positive trait bleed into an unjustified positive impression of everything else. A bank with a famous, charismatic chief executive is assumed to also have strong risk controls, though the two have no necessary connection. Nepal's banking sector merger wave through the 2010s and 2020s, driven by Nepal Rastra Bank's (NRB — the central bank of Nepal, which regulates and supervises all banks and financial institutions) capital requirements, produced several mergers where a well-regarded acquiring bank absorbed a weaker one — investors who bought purely on the acquirer's reputation, without checking the combined entity's post-merger asset quality, were sometimes surprised by the non-performing loan (NPL — a loan on which the borrower has stopped making scheduled payments) burden they inherited.
WARNING
"I like this company" is not analysis. It is a feeling. Feelings are useful as a starting point for curiosity, but they are dangerous as a final answer, because they cannot be checked, cannot be compared across companies, and cannot tell you when you were wrong.
None of this means intuition is worthless. A seasoned investor's gut feeling is often built on years of pattern recognition and is not to be dismissed. The problem is not intuition itself — it is intuition used as the entire process, with no structure to catch its blind spots. A structured score does not throw away intuition. It gives intuition a job: help interpret the score, flag things the score might miss, decide what to do at the margin. But the score itself is built the same way, every time, for every company, which is precisely what raw intuition cannot promise.
Lesson 63.2 — The Problem With Ratios in Isolation
Earlier parts of this book taught you how to read a balance sheet, an income statement, and a cash flow statement. You learned to compute ratios: return on equity (ROE — net profit divided by shareholders' equity, showing how efficiently a company turns owners' capital into profit), the price-to-earnings ratio (P/E — share price divided by earnings per share, showing how expensive a stock is relative to its profit), the debt-to-equity ratio (total debt divided by shareholders' equity, showing how leveraged a company is), and many others specific to banks, hydropower companies, and insurers.
Ratios are enormous progress over ad hoc analysis. A ratio is at least a number, computed the same way every time, comparable across companies. But ratios in isolation — meaning looked at one at a time, disconnected from each other and from everything qualitative — create a different and subtler problem: they can each look fine individually while the whole picture is not fine at all.
Think of this the way a doctor thinks about vital signs. Blood pressure alone does not diagnose a patient. Neither does temperature alone, or pulse alone. A patient can have a normal temperature and a dangerously low blood pressure at the same time — and a doctor who checks only temperature would send that patient home. The vital signs have to be read together, and even together, they have to be interpreted against the patient's history and symptoms, not treated as a mechanical pass/fail test.
The same is true of company ratios on NEPSE. Consider a hypothetical finance company with an eye-catching ROE of 22 percent — well above the sector average. Looked at alone, this ratio says: strong. But suppose that ROE is being generated by a debt-to-equity ratio of 9:1, meaning the company is earning that return almost entirely through heavy borrowing, not through operating skill. A small deterioration in loan quality could wipe out equity fast. The ROE ratio, taken alone, hid the real story. Only by reading ROE together with leverage, together with loan-loss provisioning, together with capital adequacy (a ratio, regulated by NRB, measuring how much capital a bank or finance company holds relative to its risk-weighted assets, to absorb losses) does the real picture emerge.
CASE IN POINT
A company can show a strong ROE, a comfortable current ratio, and steady revenue growth for several consecutive years — and still be quietly accumulating risk through promoter share pledging, related-party lending, or aggressive income recognition that the ratios alone will not reveal. Ratios describe what already happened in the numbers. They do not automatically reveal how those numbers were produced, or what risks are sitting just behind them.
Isolated ratio-reading has three specific weaknesses.
First, ratios cannot see each other's blind spots unless someone deliberately puts them side by side. A ratio-only investor who checks P/E this week and debt-to-equity next month, in two separate mental exercises, may never notice that the company's low P/E (which looks cheap) exists precisely because the market has already priced in the leverage risk that the debt-to-equity ratio would have shown him, had he looked at both together.
Second, ratios are entirely backward-looking. They are computed from historical financial statements, typically reported quarterly or annually. A ratio tells you what happened in a completed reporting period. It says nothing directly about governance quality, management competency, promoter intentions, or how a company will behave in the next monsoon season, the next interest rate cycle set by NRB, or the next regulatory change from the Securities Board of Nepal (SEBON — the regulator overseeing the securities market, listed companies' disclosure obligations, and merchant banking activity in Nepal). Two companies can show identical five-year ROE histories and have completely different futures, because one has a disciplined, professional management team and the other has a promoter family known for related-party dealing.
Third, and most dangerous for NEPSE specifically, ratios computed from financial statements are only as reliable as the financial statements themselves. As earlier chapters on financial statement analysis discussed, disclosure quality varies significantly across NEPSE-listed companies. A ratio calculated from understated provisioning or optimistic asset valuation is not a conservative ratio — it is a wrong ratio wearing the costume of precision. A single number with two decimal places feels more trustworthy than it sometimes deserves to be.
WARNING
A ratio is only as honest as the financial statement it was computed from. Precision is not the same thing as accuracy. A price-to-book ratio calculated to two decimal places, built on an inflated asset valuation, is a precisely wrong number — not a conservative one.
This is not an argument against ratios — this book has spent many earlier chapters teaching you to compute and interpret them correctly, and you will keep using them. It is an argument against treating ratio-reading as if it were, by itself, a complete evaluation system. A ratio is an input. It is not a verdict. What NEPSE investors have generally lacked is not data — Nepal's listed companies publish quarterly reports, and CDSC (the Central Depository System and Clearing Limited, which holds dematerialized — electronic, paperless — share records for Nepali investors) and the exchange itself make trading and shareholding data available. What has been missing is a consistent method for turning scattered ratios and scattered qualitative facts into one coherent judgment about company quality. That gap is exactly what a scoring system exists to close.
Lesson 63.3 — The Philosophy of Decomposing Company Quality Into Dimensions
"Decompose" simply means to break something large and complicated into smaller, separately understandable parts. A mechanic does not diagnose a car by asking "is this a good car?" as a single yes/no question. She checks the engine, the brakes, the transmission, the electrical system, the tires — separately, one system at a time — and only then forms an overall judgment about the car's condition. Each system can be checked on its own terms, using tools suited to that system, and a fault in one system does not automatically hide a fault in another, because she is not trying to judge everything at once through a single foggy impression.
"Company quality" is exactly this kind of large, complicated thing. It is not one fact. It is a bundle of many different, only loosely related facts: how profitable the company is, how it is financed, how honestly it is governed, how easily you can buy and sell its shares, how exposed it is to Nepal-specific risks, how sensibly it has allocated capital over time, and what you are being asked to pay for all of that today. Trying to judge all of this at once, in your head, in a single impression, is exactly the ad hoc failure mode described in Lesson 63.1. The fix is decomposition: split company quality into distinct dimensions, score each dimension on its own terms using the evidence suited to it, and only then combine the dimension scores into one overall number.
This is not a novel idea invented for this book — it is standard practice among the world's most rigorous evaluators of companies and debt issuers. Global credit rating agencies, when they assign a credit rating to a company's debt, do not ask "is this company creditworthy?" as one question. They explicitly separate a company's business risk profile — the quality and stability of the underlying business, its competitive position, and the industry it operates in — from its financial risk profile — its leverage, cash flow adequacy, and capital structure — score each separately using different evidence, and only then combine the two into a final rating. A company can have an excellent business and a weak financial structure, or a mediocre business propped up by a very conservative balance sheet. Collapsing those two very different situations into one un-decomposed impression would destroy the very information that made the two-part analysis useful in the first place.
The same decomposition logic underlies "quality factor" investing, a well-studied approach used by large global asset managers, which does not ask "is this a quality company?" as one vague question either. It typically separates quality into sub-factors such as profitability, earnings stability, low leverage ("safety"), and consistency of shareholder payouts — measuring each with different data, then combining them. And it underlies ESG scoring (Environmental, Social, and Governance scoring — a framework used by index providers and asset managers to rate companies on non-financial risk), which explicitly scores the E, the S, and the G as separate pillars, because a company can be strong on one and weak on another, and an investor needs to know which is which, not just a single blended letter grade that hides the difference.
KEY CONCEPT
Decomposition means separating "is this a good company?" into several distinct, independently answerable questions — such as "is it profitable?", "is it growing sustainably?", "is it honestly governed?", "can I actually trade its shares?" — scoring each one on its own evidence, and only then combining the separate scores into a single overall picture. The value is not just organisation. It is that a weakness in one dimension cannot silently hide behind strength in another.
Three philosophical commitments follow from choosing decomposition as the design principle for the Canon Score, and it is worth stating them plainly now, even though the specific dimensions and point values are Chapter 64's job.
The first commitment is that dimensions must be genuinely distinct — each one should capture something the others do not, so that scoring them separately adds real information rather than just re-measuring the same underlying fact five different ways. A company's profitability and its governance quality are genuinely different things; a company that manipulates its own reported profitability blurs this line, which is itself a governance problem worth scoring on its own terms.
The second commitment is that each dimension should be scored using evidence appropriate to that dimension, not forced through a single generic lens. Profitability is scored primarily from financial statement data. Governance is scored partly from disclosure behaviour, related-party transaction history, and board structure — different evidence entirely. Trying to judge governance using only profit ratios, or judging profitability using only governance impressions, defeats the purpose of separating them in the first place.
The third commitment is that the dimensions must be combined, not left scattered. A decomposed score that never gets reassembled into one overall number is not more useful than no score at all — it just produces a pile of separate facts with no way to compare Company A's overall quality against Company B's. The combination step — how much weight each dimension carries in the final number — is where judgment about what matters most for a Nepali investor gets encoded. That weighting is exactly what Chapter 64 will lay out in full: seven dimensions, each scored on its own terms, combined into a single 0–100 Canon Score.
PRACTICAL TOOL
When you evaluate any company informally — even before you learn the full Canon Score rubric in Chapter 64 — force yourself to write down separate one-line answers to at least four questions before forming an overall opinion: Is it profitable and how has that trended? How is it financed? What do I know about how honestly it is governed? Can I actually buy and sell meaningful size in this stock without moving the price? Answering these four separately, on paper, already puts you far ahead of ad hoc, single-impression stock picking.
Table: Ad Hoc Analysis, Ratio-Only Screening, and Structured Scoring Compared
Attribute
Ad Hoc "Gut Feel" Picking
Ratios Screened in Isolation
Structured, Decomposed Scoring
Repeatable across companies
No — different information used for each company
Partially — same ratios, but no fixed combination method
Yes — same dimensions, same weights, every time
Comparable across companies
No — impressions are not comparable numbers
Limited — ratios are comparable one at a time, not overall
Yes — a single combined score is directly comparable
Handles conflicting signals
Poorly — one loud fact dominates the impression
Poorly — a strong ratio can mask a weak one nearby
Explicitly — each dimension scored and weighted separately
Captures governance and qualitative risk
Inconsistently, if at all
Rarely — most ratios are purely financial
Yes — governance is scored as its own dimension
Vulnerable to recency and halo bias
Highly vulnerable
Less vulnerable, but ratio selection itself can be biased
Structurally resistant — the checklist does not change with mood
Produces an auditable record
No — reasoning is not written down
Partially — ratios can be recorded, but not the overall verdict
Yes — every score and its basis can be recorded and reviewed later
Adapted to Nepal-specific risk factors
Depends entirely on the individual investor's knowledge
No — generic ratios do not encode Nepal-specific risk
Yes, if deliberately designed to — this is Lesson 63.4's subject
Lesson 63.4 — Why NEPSE Needs Its Own Scoring Framework, Not an Imported One
It would be far less work to simply take a company-scoring framework built by a large American or Indian financial institution and apply it directly to NEPSE-listed companies. The temptation is real: such frameworks already exist, are well tested, and carry institutional credibility. This book deliberately does not do that, and the reasons are not cosmetic. They come directly from structural features of the Nepali market that earlier parts of this book have already documented in detail.
The first reason is liquidity. Liquidity — how easily an asset can be bought or sold without materially moving its price — behaves very differently on NEPSE than on the New York Stock Exchange or the Bombay Stock Exchange. Many NEPSE-listed companies trade only a few thousand shares on an average day, and some trade far less. A scoring framework built for the US market can reasonably treat liquidity as background noise, because almost every listed company there trades enough volume that liquidity risk is a minor factor for a typical retail position. On NEPSE, liquidity is not background noise — it is frequently the difference between a paper gain and a gain you can actually realise. A company might score well on every financial metric and still be nearly impossible to exit in size without crashing its own price, especially given NEPSE's daily circuit filters (rules that halt trading in a stock once its price moves up or down by a set percentage in a session, which can prevent an investor from selling at all on a day of bad news). A Nepal-specific score has to weight liquidity explicitly, at a level of seriousness a framework built for deep, liquid markets never bothered to include.
The second reason is governance. This book's earlier chapters on corporate governance and promoter behaviour in Nepal — the sections of this book that examined ownership concentration, related-party transactions, and board independence — documented governance weaknesses that are structurally more common on NEPSE than in markets with longer histories of institutional shareholder activism, larger analyst coverage, and stronger minority-shareholder legal protections. Reports examining corporate governance in Nepal have repeatedly flagged weak board independence, concentrated promoter control, and inconsistent enforcement of disclosure norms as recurring, market-wide issues rather than isolated cases. A scoring framework imported from a market where institutional investors routinely hold managements accountable, and where minority shareholder litigation is a real deterrent, will systematically underweight governance risk when applied to NEPSE, because it was calibrated for a different accountability environment.
REGULATORY DETAIL
SEBON has progressively tightened disclosure and governance obligations for listed companies, and the NEPSE Governance Code has pushed listed companies toward stronger board practices and more consistent disclosure. This is genuine, ongoing improvement. But a code that companies are moving toward compliance with, at varying speeds, is not the same as a market where those standards have been the norm for decades. A Nepal-specific score must be calibrated to where the market actually is today, not to where a mature market has already arrived.
The third reason is concentration — both sector concentration and ownership concentration. NEPSE's market capitalisation is heavily concentrated in banking, hydropower, and life and non-life insurance. This book's chapters on sector analysis walked through why each of these sectors carries distinct structural risks: banks are exposed to NRB's monetary policy stance and periodic liquidity crunches tied to remittance inflow cycles (remittances — money sent home by Nepali migrant workers abroad, a major source of the foreign currency and bank deposits that fund NEPSE-listed banks' lending); hydropower companies are exposed to monsoon variability, transmission-line bottlenecks, and power purchase agreement terms with the Nepal Electricity Authority; insurers are exposed to actuarial and investment-portfolio risk specific to a shallow domestic capital market. A generic scoring framework built for a diversified market like the US, where technology, healthcare, industrials, and consumer companies each represent a meaningful share of the index, has no reason to build in sector-specific adjustments this heavy. On NEPSE, ignoring sector concentration means ignoring a defining feature of the market itself.
The fourth reason is structural: settlement, disclosure timeliness, free float (the proportion of a company's shares actually available for public trading, as opposed to shares locked up with promoters), and the depth of analyst coverage all differ meaningfully between NEPSE and larger markets. CDSC's dematerialization of shareholding has been a genuine modernisation of Nepal's market infrastructure, but the depth of independent equity research covering individual NEPSE-listed companies remains far thinner than in markets served by dozens of competing brokerage research desks. A framework assuming rich third-party analyst coverage as a supplementary check on management's claims is assuming something that, for most NEPSE companies, does not exist. The Canon Score has to be buildable primarily from what a diligent retail investor can actually obtain: company filings, SEBON and CDSC data, NEPSE trading data, and public disclosures — not from proprietary data feeds or analyst consensus estimates that simply do not exist for most Nepali companies.
CAUTION
An imported scoring framework is not neutral just because it comes from a large, reputable institution. Every scoring framework encodes assumptions about the market it was built for — how liquid it is, how strong governance enforcement is, how diversified the index is, how much independent research exists. Apply those assumptions to a different market, and the framework will systematically misjudge exactly the risks that market is most exposed to.
None of this is a claim that NEPSE companies are inherently worse than companies listed on larger exchanges, or that Nepali corporate governance is beyond repair — both claims would be unfair and untrue, and this book has profiled well-governed, well-run Nepali companies throughout its earlier chapters. The claim is narrower and more precise: the risks that matter most for judging company quality differ in kind and in weight between NEPSE and larger, more liquid, more heavily regulated markets. A scoring system that does not build those differences in from the start will produce scores that are precise-looking and quietly wrong — the same failure this chapter warned about with isolated ratios in Lesson 63.2, now at the level of an entire framework rather than a single number.
Lesson 63.5 — What a Score Can and Cannot Tell You: Honest Limits
Every measurement tool has limits, and pretending otherwise is how tools get misused. A thermometer tells you a patient's temperature. It does not tell you what caused the fever, and it does not guarantee the patient will recover. A loan officer's credit scorecard tells a Nepali cooperative or bank whether a borrower's documented history fits the pattern of past reliable borrowers. It does not guarantee the borrower will repay — a job loss, a family emergency, or a bad harvest can still intervene after a high score was assigned. The scorecard narrows uncertainty. It does not eliminate it.
The Canon Score, once Chapter 64 lays it out in full, will work the same way. It is worth being completely honest about its limits now, before you ever see the first number attached to a real company, because a tool oversold is a tool that will eventually be blamed for failures that were never really its to prevent.
The first limit: a score is not a prediction. A high Canon Score describes the observable quality of a company today, based on the dimensions the framework measures. It does not promise that the share price will rise, and it does not promise the company will still deserve a high score in three years. Businesses change. A well-run hydropower company today can be poorly run after a change in senior management five years from now. A bank with strong asset quality today can deteriorate if it underwrites aggressively during the next credit cycle. Past scores describe the past and, at best, the present. They are not a contract about the future.
CAUTION
A high Canon Score today is a statement about a company's measured quality today, based on available evidence today. It is not a promise about tomorrow's share price, tomorrow's earnings, or tomorrow's governance behaviour. Treat every score as dated the moment it is calculated.
The second limit: a score is only as good as its inputs. This is the single most important honest caveat in this entire chapter, and it echoes directly back to Lesson 63.2's warning about ratios computed from unreliable financial statements. If a company's disclosed financial statements are inaccurate — whether through simple error, aggressive accounting choices, or deliberate misstatement — then any score computed from those statements will be inaccurate too, no matter how carefully the scoring framework itself was designed. A scoring system does not manufacture truth out of bad data. It organises whatever data it is given, faithfully, including any lies buried inside that data. This is precisely why later chapters, when they build out the Canon Score's governance dimension, will spend real effort on disclosure-quality checks and red flags — because the framework's own integrity depends on taking the reliability of its inputs seriously, not assuming it away.
WARNING
Garbage in, garbage out. This old computing principle applies exactly to company scoring. A meticulously designed seven-dimension framework, applied to a company's misleading or incomplete disclosures, produces a meticulously wrong score. The framework's discipline cannot substitute for skepticism about the underlying data.
The third limit: a single combined number can hide important detail if you stop looking at it too early. Two companies can arrive at the same overall Canon Score through very different paths — one strong on financial strength but weak on liquidity, the other the reverse. Chapter 64 will show you how to read the dimension-level breakdown behind the headline number precisely so this does not happen to you; the overall score is a starting point for further reading, not a replacement for it. An investor who only ever looks at the final number, and never opens up the dimensions behind it, has partly recreated the very problem decomposition was meant to solve — collapsing distinct information back into one undifferentiated impression, just with more decimal places attached.
The fourth limit: judgment still has the final word. A scoring system structures your analysis. It does not remove the need for you to think. If the Canon Score's governance dimension has not yet caught a very recent, still-unfolding scandal because the framework's inputs have not updated yet, a diligent investor who reads the news should not wait for the score to catch up before adjusting her own view. The score is a floor for discipline, not a ceiling on thinking. This book will say this again, more than once, in the chapters that follow, because it is the single most common way any scoring tool — in finance, in medicine, in credit — gets misused: treated as a final verdict instead of a structured input into a verdict a human being still has to make.
The fifth limit, specific to Nepal: data quality and update frequency constrain what any score can capture in real time. NEPSE companies report quarterly, not continuously. A score built on the most recent quarterly filing can be, at worst, close to three months stale by the time the next filing arrives. Fast-moving developments — a sudden change in NRB's monetary policy stance, a hydropower company's transmission line failure, a bank's sudden liquidity stress — can outrun the score's own update cycle. A structured score reduces the damage of ad hoc bias considerably. It does not, and cannot, turn investing into a mechanical, riskless exercise.
KEY CONCEPT
Think of the Canon Score the way a doctor thinks of a diagnostic checklist, or a loan officer thinks of a credit scorecard: an instrument that structures evidence and disciplines judgment, not an oracle that replaces it. The instrument's job is to make sure you did not skip the vital signs. Interpreting what the vital signs mean, and deciding what to do about them, remains a human responsibility every single time.
Lesson 63.6 — How the Canon Score Will Be Used Going Forward
With the philosophy now in place, it is worth previewing — briefly, without yet revealing the full rubric — how the Canon Score will actually function across the rest of this book, so you know what to expect as you move into Chapter 64 and beyond.
Chapter 64, immediately following this one, will lay out the Canon Score's full architecture: seven distinct dimensions of company quality, each scored on its own defined criteria, combined through explicit weights into a single overall score out of 100 points. You will learn exactly what evidence feeds each dimension, how points are assigned within each one, and how the seven dimension scores are combined into the headline number. Nothing about the specific weights or point allocations is revealed in this chapter deliberately — this chapter's job was philosophy, not mechanics — but you can expect the seven dimensions to track directly from everything this chapter has argued: a dimension addressing financial strength and profitability, a dimension addressing governance and promoter behaviour, a dimension addressing liquidity and tradability, a dimension addressing valuation reasonableness relative to what you are being asked to pay, a dimension addressing sector and business model durability in the Nepali context, a dimension addressing the growth trajectory, and a dimension addressing dividend and capital return discipline.
Table: Preview of the Seven Canon Score Dimensions (Full Rubric in Chapter 64)
Dimension
Points
What It Broadly Captures
Financial Strength & Profitability
20
How efficiently and sustainably the company generates profit from the capital it employs
Governance & Promoter Behaviour
15
Board independence, related-party dealing, disclosure honesty, and promoter behaviour
Liquidity & Tradability
10
How easily an investor can actually buy and sell the stock in meaningful size
Valuation Reasonableness
15
Whether the current share price is reasonable relative to the company's demonstrated quality
Sector & Business Model Durability
15
Exposure to Nepal-specific structural risks — monetary policy, monsoon, remittances, regulation — and whether the business model endures
Growth Trajectory
15
Whether revenue and earnings growth is durable and well-financed, not just fast
Dividend & Capital Return Discipline
10
How consistently management actually returns profit to shareholders, and from genuine earnings
Total
100
Chapter 65 will take this rubric and apply it, in worked detail, to real categories of NEPSE-listed companies — showing how the same seven-dimension framework produces different scores, and different insights, when applied to a bank, a hydropower company, and an insurer, each of which has its own dominant risks. Chapter 66 will address how to use the Canon Score operationally within a portfolio — how it complements, rather than replaces, the portfolio construction and risk management principles you learned in Part XII, and how to combine a company's Canon Score with position-sizing, diversification, and valuation judgment to make an actual buy, hold, or sell decision.
PRACTICAL TOOL
As you move into Chapter 64, keep a simple habit: for every company you are curious about, before you know its full Canon Score, write down your own rough, honest answer to each of the seven dimension questions in the table above, in one sentence each. When you later see the formal score, compare it to your own rough read. Where they disagree sharply, that disagreement is exactly where your closest, most careful further reading belongs — not the dimensions where the score simply confirms what you already suspected.
It is worth being explicit about what the Canon Score is not, one final time, before this chapter closes. It is not a market-timing signal — a high score does not tell you today is the day to buy, only that the underlying business, as measured, is of high quality. It is not a substitute for position sizing and diversification — even the highest-scoring company on NEPSE should not become your entire portfolio, for exactly the reasons Part XII spent four chapters explaining. And it is not a static, one-time judgment — company quality changes, and so a Canon Score calculated today needs to be revisited, not filed away as a permanent verdict. The Canon Score is, in the end, exactly what this chapter has argued a good scoring system should be: a disciplined, decomposed, Nepal-specific way of asking the right questions about a company, in the same order, every time — leaving the final decision, as it always must, to you.
Chapter recap
This chapter opened Part XIII of this book by making the case for why NEPSE investors need a structured, quantitative company-scoring system rather than relying on ad hoc, feeling-based stock picking. Using the analogy of a doctor's diagnostic checklist, the chapter showed that ad hoc analysis — buying a company because of a friend's tip, a rising share price chart, or a liked promoter's reputation — suffers from recency bias, anecdote substitution, and halo transfer, none of which can be checked, compared across companies, or corrected when wrong. The remedy is not to abandon judgment but to discipline it with a fixed, repeatable process, exactly as a checklist disciplines a diagnosis without replacing the diagnostician.
The chapter then showed that ratios, while a major improvement over pure guesswork, are not sufficient on their own when read in isolation. A ratio like ROE can look strong while hiding leverage risk that only shows up when read alongside a debt-to-equity ratio; ratios are backward-looking and only as reliable as the financial statements they are computed from, which is a genuine concern given variable disclosure quality across NEPSE-listed companies. The lesson was not to distrust ratios — this book will keep using them throughout — but to stop treating any single ratio, or any unconnected pile of ratios, as a complete verdict on a company.
From there the chapter introduced decomposition as the philosophical core of the Canon Score: breaking "is this a good company?" into several genuinely distinct, independently scorable dimensions — profitability, growth quality, governance, liquidity, sector exposure, capital allocation, and valuation — each judged on evidence suited to it, and then combined into one overall number. This mirrors established practice among credit rating agencies, which separate business risk from financial risk before combining them, and quality-factor investing frameworks and ESG scoring systems, which separate profitability, safety, and payout quality, or environmental, social, and governance pillars, rather than judging a company through one undifferentiated impression.
The chapter argued, at length, that this decomposed framework has to be built specifically for NEPSE rather than imported from a US or Indian model, because Nepal's market differs structurally in ways that matter: thinner liquidity and circuit filters that can trap an investor in a position, governance enforcement still catching up through instruments like the NEPSE Governance Code and SEBON's disclosure directives, heavy sector concentration in banking, hydropower, and insurance with each sector's own Nepal-specific risk drivers, and thinner independent research coverage than deeper markets enjoy. A framework calibrated to a different market's assumptions will systematically misjudge the risks that matter most on NEPSE.
The chapter was equally insistent on the limits of any scoring system, including the Canon Score once it is fully built. A score is not a prediction of future price performance; it is only as reliable as the inputs — mainly company disclosures — that feed it; a single headline number can hide important detail unless the dimensions behind it are examined; and judgment still has the final word when new information outruns a score's update cycle. The Canon Score structures analysis. It does not replace the investor's own thinking, any more than a checklist replaces the doctor holding it.
The next chapter, Chapter 64, "The Seven-Dimension Company Quality Score (0–100)," delivers the framework this chapter has only previewed. It will define each of the seven dimensions in full — Financial Strength & Profitability, Growth Quality & Sustainability, Corporate Governance & Promoter Integrity, Liquidity & Tradability, Sector & Macro Positioning, Capital Allocation & Management Track Record, and Valuation Discipline — specify exactly how each is scored from available NEPSE, SEBON, and CDSC data, and show precisely how the seven dimension scores combine into a single Canon Score out of 100 points. Chapters 65 and 66 will then apply that rubric to real categories of Nepali companies and show how the score integrates into the portfolio construction discipline built in Part XII.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIII · Chapter 64
The Seven-Dimension Company Quality Score (0–100)
First published 23 Aug 2026 · Last verified 29 Aug 2026
The report card came home in Kavita's schoolbag on a Friday in Falgun, the way it did every trimester. Her father, Suman, a taxi driver in Biratnagar, sat with her at the kitchen table and opened it slowly. Seven subjects were listed down the left side: Nepali, English, Mathematics, Science, Social Studies, Computer, and Health. Next to each subject sat a grade — some As, one B, one C in Mathematics that made Suman sigh. At the bottom of the page was a single number: her overall GPA, the average that combined all seven subjects into one figure the school used to rank her class.
Suman did not need seven separate report cards to understand his daughter's year. He needed one page that showed him both things at once — the overall picture, and exactly which subject needed a tutor before the next exam.
A listed company on NEPSE, the Nepal Stock Exchange, deserves the same kind of report card. A company is not "good" or "bad" in one dimension. It can have brilliant profits and terrible governance. It can have a wonderful business model and dangerously thin trading volume. It can pay generous dividends while quietly diluting shareholders through excessive bonus shares. Chapter 63 explained why a Nepali investor needs a structured, repeatable scoring system rather than a gut feeling formed over tea at a Thamel brokerage counter. This chapter builds that report card. It names the seven subjects, assigns the points to each, and shows exactly how to grade a real company against real Nepali thresholds — not thresholds borrowed from Wall Street or Mumbai, but numbers calibrated to how NEPSE-listed companies actually behave.
By the end of this chapter, you will be able to open a company's annual report, its SEBON (Securities Board of Nepal) filings, and its NEPSE disclosures, and turn them into a single number between 0 and 100 — the Canon Quality Score.
Lesson 64.1 — The Seven Dimensions at a Glance
Think of the Canon Quality Score as Kavita's report card, redesigned for a company instead of a child. Instead of Nepali, English, and Mathematics, the seven "subjects" are seven dimensions of company quality. Each dimension is scored on its own point scale. The seven scores are added together, never averaged, so that the maximum possible score is exactly 100 points — a familiar percentage that any Nepali investor, from a first-time DEMAT account holder in Pokhara to a portfolio manager in Kathmandu, can interpret instantly.
KEY CONCEPT
A "dimension" here means one distinct angle of company quality that cannot be judged by looking at any other dimension. A bank can have excellent Financial Strength and terrible Governance at the same time — think of a promoter who reports strong profits while quietly pledging most of his shareholding to a private lender. The seven dimensions exist precisely because no single ratio, and no single chapter of this book, can catch every kind of risk on its own.
The seven dimensions, and the reasoning behind each one's weight, are as follows.
#
Dimension
Points
What it captures
1
Financial Strength & Profitability
20
Is the company actually making money safely, with capital to absorb shocks?
2
Governance & Promoter Behaviour
15
Can you trust the people running the company to treat minority shareholders fairly?
3
Liquidity & Tradability
10
Can you actually buy and sell this stock at a fair price when you need to?
4
Valuation Reasonableness
15
Are you being asked to overpay for the quality you are buying?
5
Sector & Business Model Durability
15
Will this business still matter in ten years, and does regulation protect or threaten it?
6
Growth Trajectory
15
Is the company's income growing, and is that growth genuine or manufactured?
7
Dividend & Capital Return Discipline
10
Does the company actually share profit with shareholders, consistently?
Total
100
Notice the weighting is not equal. Financial Strength & Profitability carries the largest single weight, 20 points, because a company that cannot generate safe profit eventually fails at everything else — a weak bank cannot sustain dividends, cannot survive a bad loan cycle, and cannot protect minority shareholders when capital gets scarce. Governance sits second at 15 points, reflecting a truth this book has repeated since Part IV: in a market with concentrated promoter ownership and comparatively young disclosure norms, a dishonest management can destroy a fundamentally sound business faster than a bad quarter ever could. Liquidity, at only 10 points, matters less to the underlying business but matters enormously to you personally — a brilliant company you cannot sell at a fair price when you need cash is a brilliant company that has failed you specifically.
Where does the raw data for all seven dimensions come from? Three sources, used together:
SEBON filings — the regulatory disclosures every listed company must make to the Securities Board of Nepal, including material event disclosures, related-party transaction reports, and promoter shareholding changes.
NEPSE disclosures and trading data — daily price, volume, and circuit data published on the exchange, used heavily in the Liquidity dimension.
Annual reports and unaudited quarterly financial statements — published by the company itself, and the primary source for the Financial Strength, Growth, and Dividend dimensions.
CDSC (Central Depository System and Clearing Limited) — the depository that tracks actual shareholding registers, useful for confirming promoter holding percentages and detecting pledged shares.
Every point band in this chapter tells you, explicitly, which of these four sources to open.
Financial Strength & Profitability asks one plain question: is this company making real, sustainable money, and does it have enough of a capital cushion to survive a bad year? This dimension draws directly on the ratio chapters in Part IX and the financial-statement chapters in Part VI. It splits into three sub-components.
Sub-component A: Profitability trend, measured by ROE (8 points). ROE, Return on Equity, tells you how many rupees of profit a company generates for every 100 rupees of shareholder equity it holds. It is the single most useful profitability ratio for a Nepali investor because it accounts for how efficiently a company is using the capital shareholders have already committed.
Here the Canon rubric must be calibrated to NEPSE reality, not to a textbook ideal. Nepali commercial banks' average ROE has fallen sharply in recent years — from roughly 13% in the third quarter of fiscal year 2022/23 to under 8% in the same period of fiscal year 2024/25, as high provisioning and slow credit growth squeezed the whole sector. In that same period, the best-run banks still posted ROE above 15%, while the weakest posted barely above zero. That spread is exactly what the point bands are built to separate.
3-year average ROE
Points (out of 8)
15% or higher, with no single year below 10%
8
10% – 15%
6
7% – 10% (near current system average)
4
Below 7%, or negative in any of the last 3 years
1
CASE IN POINT
In the nine months to Q3 FY2024/25, Nepal's commercial banks averaged roughly 7.73% ROE system-wide. Individual results ranged from about 15.8% at the strongest bank to under 1% at the weakest. That is not a small gap — it is the difference between a bank compounding shareholder wealth and a bank barely covering its cost of capital. This is why ROE alone, without a peer comparison, tells you almost nothing.
Sub-component B: Capital adequacy or leverage discipline (7 points). For banks and financial institutions, this uses CAR, the Capital Adequacy Ratio — the proportion of a bank's risk-weighted assets that is backed by its own capital rather than borrowed deposits. For non-financial companies, this sub-component instead uses Debt-to-Equity and interest coverage, since CAR does not apply outside banking.
REGULATORY DETAIL
Nepal Rastra Bank (NRB) requires commercial banks to maintain a minimum total Capital Adequacy Ratio of 11%, including a minimum Tier 1 (core capital) component. A bank sitting exactly at 11% has no real cushion — a single bad loan cycle can push it into regulatory breach, forcing a rights issue that dilutes existing shareholders. A bank running comfortably above the minimum, by contrast, can absorb losses and still keep lending.
Capital adequacy / leverage position
Points (out of 7)
CAR 13% or higher (BFIs), or D/E under 1.0x with interest coverage over 5x (non-BFIs)
7
CAR 11.5% – 13%, or moderate leverage with adequate coverage
5
CAR 11.0% – 11.5% (barely above NRB minimum)
3
CAR below 11% at any point in the last 3 years, or high leverage with weak coverage
0
Sub-component C: Earnings quality and consistency (5 points). This checks whether the company's profit history is a smooth, believable line or a jagged one full of one-off gains, sudden losses, or restated numbers.
5-year profit history
Points (out of 5)
No net loss year; profit grew in at least 4 of the last 5 years
5
One loss year, or two flat/declining years
3
Two or more loss years, or highly erratic year-to-year swings
1
Financial Strength & Profitability = ROE score (0–8) + Capital adequacy score (0–7) + Earnings quality score (0–5), out of 20. Source this dimension from the company's audited annual reports (for the 5-year and 3-year trends) and its unaudited quarterly disclosures on the NEPSE website (for the most recent CAR figure, which banks must disclose every quarter).
Lesson 64.3 — Governance, Liquidity, and Valuation
Dimension 2: Governance & Promoter Behaviour (15 points). Part IV of this book built the case that governance risk in Nepal is not a side issue — it is often the deciding factor in whether a minority shareholder's investment survives. This dimension translates that philosophy into points, split across three checks.
Promoter shareholding stability and pledging (6 points). A promoter is a founder or controlling shareholder, typically holding a large block of shares and effective control of the board. When promoters pledge their shares — using them as collateral for a personal or business loan — a forced sale by the lender can crash the stock with no warning to ordinary shareholders, regardless of how the underlying business is performing.
Promoter shareholding pattern (CDSC records, last 3 years)
Points (out of 6)
Holding stable or increasing; less than 10% of promoter shares pledged
6
Holding broadly stable; 10% – 25% pledged
4
Holding declining, or 25% – 50% pledged
2
Holding sharply declining, or more than 50% pledged, or repeated insider selling
0
WARNING
A large pledged-share position is one of the least visible risks on NEPSE because it does not show up in the profit and loss statement at all. It only shows up in CDSC shareholding disclosures and SEBON material-event filings. A company can report a perfectly healthy quarter while its promoter is one margin call away from a forced share sale. Always check pledging before you check profit.
Related-party transactions and audit opinion (5 points). This checks the annual report's related-party disclosure note and the auditor's opinion page. A clean, unqualified audit opinion with modest, well-disclosed related-party dealings scores near full marks; a qualified opinion, an auditor's going-concern note, or large undisclosed related-party loans score near zero.
Disclosure timeliness and board independence (4 points). This checks whether the company files its quarterly reports and material-event disclosures on time with SEBON and NEPSE, and whether its board meets the minimum independent-director requirement rather than being stacked entirely with promoter nominees.
Dimension 3: Liquidity & Tradability (10 points). Part XI introduced the ADV rule — that your position size in any single stock should stay small relative to its Average Daily Volume, the typical rupee value traded on a normal day, so that buying in or exiting does not itself move the price against you. It also introduced the danger of the circuit trap: a stock that hits its daily price limit (its "circuit," the maximum percentage move NEPSE allows in one session) with almost no volume, leaving sellers unable to exit for days.
Sub-component
Points
Average Daily Volume (value traded, 6-month average)
5
Free float (% of shares not held by promoters, available to the public)
5
Average Daily Volume
Points (out of 5)
NPR 5 million or more
5
NPR 1 million – 5 million
3
NPR 200,000 – 1 million
1
Below NPR 200,000
0
PRACTICAL TOOL
NEPSE's own daily trading reports and the "Today's Price" and turnover pages on the exchange website give you the volume data directly. For free float, cross-check the company's annual report shareholding pattern (promoter versus public split) against CDSC records — the two should agree, and if they do not, treat the mismatch itself as a small governance flag.
Free float uses the same 5-point structure: 40% or higher free float scores full marks, because more shares in public hands means deeper, more resilient trading; below 10% free float scores zero, because such a stock is structurally prone to circuit-trap behaviour no matter how good the underlying business is.
Dimension 4: Valuation Reasonableness (15 points). Part IX's valuation chapters warned against judging P/E (Price-to-Earnings, the share price divided by annual earnings per share) or P/B (Price-to-Book, the share price divided by book value per share) in isolation. NEPSE as a whole has, at times, traded at elevated valuations — the exchange's overall P/E has approached the high 30s, well above the 15–20x range considered reasonable in most developed markets — while the banking sector specifically has often traded far cheaper than the broader index, closer to 15–16x earnings and around 1.5x book value. A generic "P/E below 15 is cheap" rule would misjudge almost every NEPSE sector simultaneously.
CAUTION
Absolute P/E thresholds imported from other markets do not work on NEPSE. When the overall exchange trades near 38x earnings while its banking sector trades near 16x, a "reasonable" P/E for a hydropower or insurance stock looks nothing like a "reasonable" P/E for a bank. The Canon rubric therefore always scores valuation relative to the company's own sector median and its own 5-year historical median — never against a fixed number pulled from an American or Indian textbook.
Current P/E vs. sector median P/E
Points (out of 8)
0.8x sector median or below
8
0.8x – 1.1x sector median (in line)
6
1.1x – 1.5x sector median
3
Above 1.5x sector median, or P/E undefined due to losses
1
The P/B sub-component (7 points) follows the identical relative logic against sector and historical median book multiples. Source both from NEPSE's published sector indices and the company's own 5-year price history, which most Nepali brokerage terminals and financial portals maintain.
Lesson 64.4 — Sector Durability, Growth, and Capital Return Discipline
Dimension 5: Sector & Business Model Durability (15 points). This dimension asks a longer-horizon question than the others: will this business still be relevant, protected, and competitively sound in ten years? It draws on the sector-specific accounting chapters in Part VI, which showed that a bank, a hydropower company, an insurer, and a manufacturer must each be read through a different lens.
Regulatory and competitive moat (8 points). A moat is a durable barrier that keeps competitors from eroding a company's profits — named after the water-filled ditch that protected old fortresses.
Business model position
Points (out of 8)
Licensed, regulated sector with high entry barriers and a defensible position (e.g., an established commercial bank, a hydropower company with a signed PPA)
8
Moderate barriers, meaningful but not dominant market position
Structural regulatory or technological threat to the business model
0
A PPA, Power Purchase Agreement, is the long-term contract between a hydropower company and its buyer — usually the Nepal Electricity Authority (NEA) — fixing the price at which electricity will be bought for a set number of years. A signed PPA is what converts a hydropower project from a construction gamble into a predictable cash-flow business, which is why this factor belongs squarely inside the durability dimension.
Revenue concentration and dependency risk (7 points). A business dependent on a single customer, single geography, or single input supplier carries risk that does not show up in a single year's income statement but shows up eventually.
Revenue concentration
Points (out of 7)
Diversified; no single customer or segment exceeds 20% of revenue
7
Moderate concentration, 20% – 40% from one segment or counterparty
4
High concentration, above 40% from one counterparty
2
CASE IN POINT
Most run-of-river hydropower companies in Nepal sell effectively all of their output to a single buyer, NEA, under a single PPA. Under the generic rubric above, that would score poorly on revenue concentration even though it is structurally unavoidable for the sector — every hydropower company has this same "problem." Chapter 65 exists precisely to fix mismatches like this one, replacing the generic band with a sector-appropriate version.
Dimension 6: Growth Trajectory (15 points). This dimension measures whether the company's revenue and earnings are actually growing, and whether that growth is genuine or manufactured through accounting choices, asset revaluations, or one-off gains.
Revenue CAGR (8 points). CAGR, Compound Annual Growth Rate, is the smoothed annual growth rate that would take a starting number to an ending number over several years, accounting for compounding rather than a simple average.
5-year revenue CAGR
Points (out of 8)
15% or higher, positive in at least 4 of 5 years
8
8% – 15%
6
0% – 8%
3
Negative, or highly volatile year to year
0
Earnings consistency (7 points). EPS, Earnings Per Share, is net profit divided by the number of outstanding shares — the metric that actually matters to you as a shareholder, since it accounts for dilution from bonus shares and rights issues that revenue growth alone ignores.
EPS growth pattern, last 5 years
Points (out of 7)
Positive in at least 4 of 5 years, no swing greater than 50% in a single year
7
Positive in 3 of 5 years, moderate swings
4
Positive in 2 of 5 years, or high volatility
2
Declining trend, or negative in 3 or more years
0
Dimension 7: Dividend & Capital Return Discipline (10 points). Part VII's dividend and taxation chapters explained that a Nepali company can return capital to shareholders through cash dividends, bonus shares, or a combination of both — and that the mix matters as much as the amount, since bonus shares dilute future EPS even as they feel generous in the moment.
Consistency of payout (6 points). A company that pays every single year, at a sensible payout ratio, is behaving predictably. A company that skips dividends erratically — often because it breached a capital or liquidity requirement — is signalling underlying stress.
Dividend record, last 5 years
Points (out of 6)
Paid every year; payout ratio consistently 30% – 70% of distributable profit
6
Paid in 4 of 5 years, or payout ratio consistently very low or very high
4
Paid in 2 or 3 of 5 years, erratic
2
Skipped in 3 or more of the last 5 years
0
Sustainability of the payout (4 points). This checks whether dividends were funded by genuine distributable profit or by drawing down reserves and one-off gains — a distinction that separates a company sharing real wealth from one manufacturing a good headline.
Funding source of dividends
Points (out of 4)
Genuine distributable profit, reserves intact or growing
4
Partially funded by one-off or revaluation gains
2
Funded by drawing down capital or reserves
0
Lesson 64.5 — Combining the Seven Scores Into a Single 0–100 Number
Return to Kavita's report card. The school did not average her seven subject grades by picking the best one, or ignore Mathematics because English was strong. It added every subject's contribution into one GPA that reflected the whole child. The Canon Quality Score works the same way: you simply add the seven dimension scores together.
Exceptional — a top-tier compounder candidate for core, long-term holding
70 – 84
Strong — a solid long-term holding candidate, worth owning with normal monitoring
55 – 69
Adequate — investable, but size the position carefully and watch the weak dimensions
Below 55
Weak / Avoid — high risk; needs an unusually strong specific justification to hold
CAUTION
The Canon rubric includes one override rule, and it exists because of how governance failures actually unfold in Nepal: if the Governance & Promoter Behaviour sub-score falls below 5 out of 15 — meaning serious pledging, audit qualifications, or disclosure failures — the overall score is automatically capped in the Weak/Avoid band, regardless of how high the other six dimensions score. A brilliant income statement cannot outvote a dishonest promoter. This is the one place in the rubric where a single dimension is allowed to override the arithmetic sum.
A score is a starting point for judgment, not a replacement for it. Two companies can both score 75 for very different reasons — one strong everywhere and merely adequate on valuation, another spectacular on growth but only middling on governance. Always read the seven sub-scores before you read the total, exactly as Suman read Kavita's Mathematics grade before he looked at her GPA.
Lesson 64.6 — A Fully Worked Example: Scoring an Illustrative NEPSE Company
The company below, "Himalaya Unnati Bank Ltd" (HUBL), is an illustrative composite built for teaching purposes — it is not a real listed company, and no real bank's data was used. Its numbers are calibrated to sit near the realistic range for a solidly-run but unspectacular Nepali commercial bank, based on the sector figures discussed earlier in this chapter.
HUBL's raw data, gathered from its annual reports, SEBON filings, CDSC records, and NEPSE trading data:
3-year average ROE: 14.2%, with the weakest year at 11.0%
Capital Adequacy Ratio: 12.8%, stable for 3 years
Profit grew in 4 of the last 5 years, with one flat year during a provisioning cycle
Promoter shareholding: stable at 51% for 5 years, under 10% pledged
Unqualified audit opinion; related-party loans modest and fully disclosed
Quarterly filings on time; 2 of 7 board seats independent
Average Daily Volume: NPR 3.2 million over the last 6 months
Free float: 49%
Current P/E: 13.5x, versus a banking-sector median of roughly 15.8x
Current P/B: 1.3x, versus a banking-sector median of roughly 1.5x
Licensed, NRB-regulated moat; loan book moderately concentrated in one regional corporate segment (roughly 30% of the book)
Loan book 5-year CAGR: 11%; EPS positive in 4 of the last 5 years
Cash-plus-bonus dividend paid every year for 5 years; payout ratio within the 30–70% band; dividends funded from genuine distributable profit
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
16
ROE 14.2% avg → 6/8 (10–15% band); CAR 12.8% → 5/7 (11.5–13% band); earnings grew 4 of 5 years → 5/5
P/E at 0.85x sector median → 6/8 (in-line band); P/B at 0.86x sector median → 5/7 (in-line band)
Sector & Business Model Durability
15
12
Regulated banking moat → 8/8; 30% loan-book concentration in one segment → 4/7
Growth Trajectory
15
13
Loan book CAGR 11% → 6/8 (8–15% band); EPS positive 4 of 5 years → 7/7
Dividend & Capital Return Discipline
10
10
Paid every year, sensible payout ratio → 6/6; funded from genuine profit → 4/4
Canon Quality Score
100
83
Band: Strong (70–84)
HUBL lands at 83 out of 100 — comfortably inside the Strong band, and within striking distance of Exceptional. Reading the sub-scores tells you exactly where the remaining 17 points went: a thinner-than-ideal capital cushion above the NRB minimum, moderate corporate lending concentration, and average trading liquidity. None of these is a red flag on its own — none triggers the governance override — but together they explain precisely why HUBL is "strong" rather than "exceptional," and precisely which two or three metrics an investor should watch in the following year's annual report to see whether the score improves.
This is the entire purpose of scoring in seven parts instead of one: not to produce a verdict, but to produce a diagnosis.
Chapter recap
This chapter built the concrete rubric that Chapter 63 promised. The Canon Quality Score adds seven independently scored dimensions into a single 0–100 figure, in exact parallel to a school report card combining seven subject grades into one GPA: Financial Strength & Profitability (20 points), Governance & Promoter Behaviour (15 points), Liquidity & Tradability (10 points), Valuation Reasonableness (15 points), Sector & Business Model Durability (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points).
Each dimension was given specific, Nepal-calibrated point bands rather than imported rules of thumb — ROE bands built around the real spread of Nepali bank returns (roughly 8% system average, with top performers above 15%), CAR bands anchored to NRB's 11% regulatory minimum, and valuation bands measured relative to sector and historical medians rather than fixed multiples, since NEPSE's overall P/E and individual sector P/Es can differ by a factor of two or more. Every band pointed to a specific data source: SEBON filings for related-party and governance disclosures, CDSC records for promoter shareholding and pledging, NEPSE trading data for volume and free float, and annual reports for the multi-year financial trends.
The chapter also introduced the one deliberate exception to simple addition: the governance override, which caps any company's total score in the Weak/Avoid band if its Governance & Promoter Behaviour sub-score falls too low, no matter how strong the other six dimensions look. This reflects a hard lesson repeated throughout this book — a dishonest or overleveraged promoter can destroy shareholder value faster than any operating metric can compensate for.
The fully worked example scored an illustrative bank, Himalaya Unnati Bank Ltd, at 83 out of 100 — a Strong score — and showed how reading the seven sub-scores, not just the total, tells an investor exactly where a company's remaining weaknesses lie and what to monitor going forward.
But the rubric in this chapter was deliberately generic. It was built to be applied to any NEPSE company, from a commercial bank to a hydropower developer to a manufacturer, using the same seven dimensions and the same general point logic. Real Nepali sectors do not behave identically, however. A hydropower company's Growth Trajectory looks completely different before its Commercial Operation Date (COD) — the day it starts generating and selling electricity — than after it, when a single PPA-driven revenue stream replaces years of pure construction spending. A bank's Financial Strength dimension leans on CAR and NPL (non-performing loan) ratios that simply do not exist on a manufacturer's balance sheet. A microfinance institution's Governance dimension needs to weigh over-indebtedness risk in ways a hotel company never will.
Chapter 65, "Sector-Specific Sub-Score Adjustments," takes this generic seven-dimension framework and shows exactly how each dimension must be reshaped for Nepal's major listed sectors — banking and financial institutions, hydropower, insurance, microfinance, manufacturing, and hospitality — so that the same report-card structure produces fair, comparable grades for very different kinds of businesses.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIII · Chapter 65
Sector-Specific Sub-Score Adjustments
First published 23 Aug 2026 · Last verified 29 Aug 2026
In Naya Bazaar, there is a tailor named Hari Kaji who has cut cloth for forty years. He keeps one master pattern pinned to his wall — a basic shirt block, with a collar, two sleeves, a yoke, and a hem. Every shirt that leaves his shop starts from that same block. But Hari Kaji never cuts a farmer's work shirt the same way he cuts a bank manager's dress shirt. The farmer needs wider armholes, because he lifts and swings all day. He needs a shorter hem, so it does not catch on a plough. He needs thicker cotton, because it will be washed in a stream and dried on a rock. The bank manager needs a fitted waist, a stiff collar for a necktie, and cloth thin enough to stay cool under a suit jacket. Same block. Same starting measurements for the neck and the shoulder. Completely different final shirt.
If Hari Kaji used the farmer's cut for the bank manager, the shirt would hang like a sack. If he used the bank manager's fitted cut for the farmer, the seams would tear apart in a single planting season. The pattern is not wrong. It is simply generic. It has to be adjusted for the body wearing it and the work that body will do.
The Canon Score works the same way. Chapter 64 gave you one master pattern: seven dimensions, a 0-to-100 scale, and a common language for grading any company on NEPSE. That pattern is correct, and you should not throw it away. But a hydropower company under construction has no revenue at all, so a dimension that measures "profitability" cannot be read the same way it is read for a bank that has been profitable every year since it opened its doors. A microfinance institution lends tiny amounts to thousands of rural borrowers with no collateral, so a governance red flag that matters enormously for microfinance barely registers for a cement manufacturer. An insurance company holds liabilities that will not come due for thirty years, so its "financial strength" cannot be judged the way you would judge a trading company that turns over its inventory every ninety days.
This chapter is Hari Kaji's fitting room. You already have the master block from Chapter 64. Now you will learn how to cut it differently for four of NEPSE's most important sectors: banks and financial institutions, hydropower, microfinance, and insurance. By the end, you will be able to look at any NEPSE-listed company, recognise which sector's "cut" it needs, and adjust the generic seven-dimension score so that it tells you the truth about that specific company — not a generic truth that happens to be wrong for the business in front of you.
Lesson 65.1 — Why One Scoring Framework Cannot Fit Every NEPSE Sector
Start with a simple question. What does "financial strength" mean?
For a trading company, it might mean: does it have enough cash to pay its suppliers, and is its debt small compared to its equity? For a commercial bank, that question is almost meaningless. A bank's entire business model is to hold enormous amounts of other people's money (deposits) and lend most of it out. A bank with "low debt" is not a strong bank — it is a bank that is barely functioning as a bank. Debt, for a bank, is the raw material of the business, not a warning sign.
KEY CONCEPT
A generic ratio measures the wrong thing when the business model itself is unusual. Debt-to-equity is a danger signal for a manufacturer and a normal feature of the balance sheet for a bank. The number does not change meaning by accident — it changes because the underlying business is fundamentally different.
This is the core problem this chapter solves. The Canon Score's seven dimensions — Financial Strength & Profitability, Governance & Promoter Behaviour, Liquidity & Tradability, Valuation Reasonableness, Sector & Business Model Durability, Growth Trajectory, and Dividend & Capital Return Discipline — are the right categories to think about. But the specific ratios and thresholds that fill each dimension must change depending on what kind of company you are scoring.
Think of it like a doctor's checkup. Every patient gets checked for blood pressure, weight, heart rate, and general fitness — that is the "generic framework" of a physical exam. But the healthy blood pressure range for a seventy-year-old is not the same as for a twenty-year-old athlete. A doctor who used one single number for "healthy blood pressure" across every patient, regardless of age, would misdiagnose half the people who walk through the door. Sector adjustment in the Canon Score is exactly this: recognising that a "healthy" capital adequacy ratio for a bank and a "healthy" debt ratio for a hydropower company under construction are different numbers, measuring different things, on different scales — even though both questions are really asking the same underlying thing: "is this company's financial foundation sound?"
There are three reasons NEPSE sectors need different treatment.
First, some sectors are regulated by an entirely different authority with entirely different rules. Banks, development banks, and finance companies answer to Nepal Rastra Bank (NRB), the central bank. Insurance companies answer to the Nepal Insurance Authority (NIA, formerly called Beema Samiti). These regulators impose their own capital rules, provisioning rules, and disclosure rules — rules that do not exist for a hotel or a manufacturing company. A serious investor has to read those regulatory filings, not just the standard annual report, to score these companies properly.
Second, some sectors have a business model that unfolds in distinct phases with almost nothing in common between the phases. A hydropower company spends years — sometimes seven, eight, or ten years — building a dam, tunnels, and a powerhouse before it generates a single unit of electricity. During that construction period it has essentially no revenue but very large debt. Once it reaches what the industry calls COD — Commercial Operation Date, the day the plant is legally allowed to sell electricity into the grid — its entire financial character flips. Scoring a pre-COD hydropower company on "profitability" is like scoring a pregnant woman on "number of children currently in her arms." The right number is coming, but it is not there yet, and that absence is not itself a red flag.
Third, some sectors carry risks that are invisible in the standard financial statements unless you know to look for them. A microfinance institution's real risk often lives in how loan officers behave in the field — whether they are pressuring borrowers, whether loans are being recycled to hide default, whether the same household is borrowing from five different microfinance companies at once. None of that shows up as a single ratio on the balance sheet. You have to know the sector's specific danger zones.
CASE IN POINT
Consider two companies with an identical "Debt to Equity: 6.5x" line in their financial statements. One is a commercial bank — 6.5x leverage is normal, even conservative, for a Nepali bank, because deposits (which count as liabilities) fund the loan book. The other is a small manufacturing company — 6.5x leverage would be a serious warning sign, suggesting the company has borrowed far beyond its ability to service that debt from operating cash flow. Same number. Opposite meaning. This is exactly why Chapter 65 exists.
Part VI of this Canon (Chapters 29 through 34) already walked you through how to read the financial statements of banks, development banks, microfinance institutions, hydropower companies, insurance companies, and manufacturing, trading, and hotel businesses, each in their own accounting logic. This chapter does not repeat that accounting detail. It tells you how to translate what you learned in Part VI into adjustments on the seven-dimension Canon Score you learned in Chapter 64. Think of Part VI as learning to read the individual measurements on a body, and this chapter as learning which of those measurements Hari Kaji actually uses when he is cutting a shirt for that particular body.
One more foundational point before we go sector by sector. Sector adjustment does not mean lowering your standards for a sector you like, or raising them for a sector you are suspicious of. It means using the right ruler. A well-run microfinance institution can still score well on Governance if it meets the sector-appropriate governance bar — even though that bar looks completely different from a bank's governance bar. A poorly built hydropower project can still score badly on Financial Strength even after adjusting for construction-phase norms, if its debt is excessive even by hydropower standards. The adjustment changes what you measure and how you interpret it. It never changes your commitment to being honest about what the numbers say.
Lesson 65.2 — Adjusting the Score for Banks, Development Banks, and Finance Companies
Nepal's banking system has three tiers of deposit-taking institutions licensed by Nepal Rastra Bank: "A" class commercial banks, "B" class development banks, and "C" class finance companies. All three take deposits from the public and lend that money out, and all three are supervised under NRB's capital and provisioning framework, though the exact thresholds differ by class. If you are scoring any of these, four of the seven Canon dimensions need real surgery.
Financial Strength & Profitability. Throw out generic ratios like current ratio or debt-to-equity — they simply do not describe a deposit-taking institution correctly. Replace them with three bank-specific measures.
The first is the Capital Adequacy Ratio (CAR) — a measure of how much of a bank's own money (equity and reserves) it holds as a cushion against its risk-weighted loans, expressed as a percentage. NRB requires Nepali commercial banks to maintain a minimum total capital fund of around 11% of risk-weighted assets under the Basel III framework it has adopted, with a minimum core capital (Tier 1, the highest-quality capital) requirement of roughly 6%, plus an additional buffer that NRB can require in good times to be drawn down in stress. A bank sitting only slightly above the regulatory minimum has very little room for absorbing a bad year of loan losses. A bank sitting comfortably above minimum — say two to four percentage points higher — has real shock-absorbing capacity built in.
The second is the Non-Performing Loan (NPL) ratio — the percentage of a bank's total loan book that is not being repaid on schedule (generally loans overdue by more than 90 days). Nepal's banking sector NPL ratio has been rising in recent years and has been reported around the mid-5% range across commercial banks in 2025, though individual banks range far more widely, with some reporting NPL ratios above 10% and even above 15%. A bank scoring well on this sub-dimension should sit meaningfully below the sector average NPL ratio, and — just as importantly — should show that ratio holding steady or improving over several quarters, not just in one favourable snapshot.
The third is Net Interest Margin (NIM) — the difference between what a bank earns on its loans and investments and what it pays on deposits and borrowings, expressed as a percentage of its earning assets. NIM is the bank equivalent of a manufacturer's gross margin: it tells you how profitable the bank's core lending business is before overhead and provisioning. A bank with a NIM compressing quarter after quarter, even while reporting flat profit, is often a bank leaning on one-off income (like trading gains or fee waivers reversing) to hide a weakening core business.
REGULATORY DETAIL
NRB requires commercial banks to publish CAR, NPL ratio, and other prudential indicators in their quarterly disclosures under NRB's Unified Directives. These figures are audited or reviewed and are a more reliable source for scoring than headline net profit alone, which can be affected by one-off items. A serious Canon Score user reads the quarterly disclosure format, not just the press release.
Governance & Promoter Behaviour. For banks, the specific governance red flags to watch are: large loans to promoter-linked companies (related-party lending), loan concentration in a small number of large borrowers, and frequent related-party transactions disclosed in the notes to accounts. NRB limits how much a bank can lend to a single borrower or group as a percentage of its capital fund precisely because concentrated lending to insiders has caused failures in Nepal's banking history before. A bank whose related-party disclosures are thin, late, or vague should be marked down here even if its headline profit looks fine.
Liquidity & Tradability. The generic Canon liquidity dimension (share trading volume, free float) still applies to bank shares on NEPSE largely unchanged, since most listed banks are reasonably liquid. What is added for banks specifically is a second layer: balance-sheet liquidity, meaning the bank's own ability to meet deposit withdrawals and short-term obligations, tracked through NRB's Credit-to-Deposit (CD) ratio ceiling and statutory liquidity requirements. A bank running close to the maximum CD ratio has less room to grow lending without either raising fresh deposits or curbing loan growth — a real constraint on future Growth Trajectory scoring too.
Growth Trajectory. Loan book growth and deposit growth replace generic revenue growth. But growth in loans is not automatically good — the reader should check whether deposit growth is keeping pace with loan growth (funding it prudently) or whether the bank is stretching its CD ratio to fund loan growth beyond what deposits support.
Dividend & Capital Return Discipline and Valuation Reasonableness are adjusted more lightly for banks: valuation still uses Price-to-Book Value heavily (more heavily than for other sectors, since a bank's assets are mostly financial and closer to book value than a factory's physical assets are), and dividend discipline should account for NRB's capital adequacy rules, which can restrict how much a bank is allowed to distribute in a given year regardless of what management might otherwise prefer.
For the full accounting mechanics behind CAR, NPL classification, and provisioning, revisit Chapter 29 (commercial bank accounting) and Chapter 30 (development bank and finance company accounting) in Part VI before applying these adjustments.
Lesson 65.3 — Adjusting the Score for Hydropower Companies (Pre- and Post-COD)
No sector on NEPSE needs a more dramatic rewrite of the Canon Score than hydropower — because a single hydropower company can require two completely different scorecards at two different points in its own life, without the underlying quality of the project changing at all.
Recall from Part VIII (the project finance and hydropower modelling chapters) the concept of COD — Commercial Operation Date. This is the date on which a hydropower plant is licensed and connected to begin selling electricity, usually to the Nepal Electricity Authority (NEA) under a Power Purchase Agreement (PPA), a long-term contract fixing the price NEA will pay for the plant's electricity. Before COD, the company is in the construction phase: pouring concrete, boring tunnels, installing turbines, funded mostly by loans and shareholder capital, generating no revenue. After COD, the company enters the operating phase: it sells electricity and earns real cash flow.
Pre-COD scoring. During construction, Financial Strength & Profitability cannot be scored on profitability at all — there is none, and that is expected, not a defect. What replaces it is project financial discipline: is the project being built within its original budget, or has cost overrun eaten into the equity cushion? Is construction progressing against the original timeline, or has it slipped by years (each year of delay usually means more interest capitalised onto the loan, silently inflating the eventual debt load)? Is the debt-to-equity ratio of the project in line with what was originally financed (commonly financed with a large share of debt, often 70-80% of project cost, and the remainder equity), or has additional debt been layered on to cover overruns? Growth Trajectory during this phase should be scored on construction progress and the credibility of the COD date — has the company met its own prior guidance on timeline, or does it keep pushing the date back?
WARNING
A pre-COD hydropower company reporting "zero revenue, large losses" is not automatically a bad investment — that is the normal appearance of any hydropower company under construction. The question the Canon Score must answer instead is: is the construction on budget and on schedule, and does the company have a credible, signed PPA? A company that looks financially identical on paper to a well-run project, but is missing a firm PPA or is meaningfully behind schedule, deserves a much lower score even though both show "zero revenue."
Post-COD scoring. Once a plant is operating, Financial Strength & Profitability can finally be scored using real revenue and margins — but even here, a generic reading is misleading, because of Nepal's wet and dry seasons. Nepali rivers carry far more water during the monsoon (roughly Ashad through Ashwin, June to October) than during the dry winter and spring months. Most Nepali hydropower plants are "run-of-river" plants (they use the river's natural flow rather than storing large volumes behind a big dam), so their electricity output — and therefore their revenue — swings sharply between a strong wet-season quarter and a weak dry-season quarter. A single quarter's profit figure, read without adjusting for season, can make a perfectly healthy hydropower company look like it is collapsing (comparing a dry-season quarter to the prior wet-season quarter) or look euphorically strong (comparing wet season to wet season, ignoring debt building up). The Canon Score reader should always compare a hydropower quarter to the same quarter one year earlier, never to the immediately preceding quarter, and should look at trailing twelve-month figures for a fair full-cycle picture.
The Liquidity & Tradability dimension for hydropower connects directly back to Chapter 58's discussion of circuit traps — the daily price-movement limit NEPSE imposes on individual stocks. Hydropower counters, especially smaller ones with thin free float, are disproportionately prone to circuit-trap behaviour: a wave of retail enthusiasm (often around monsoon season, or around a COD announcement) can send a small hydropower stock to its daily circuit limit for several consecutive sessions, with almost no actual sell-side volume clearing at that price. This is not genuine liquidity — it is the appearance of demand with very little real two-way trading underneath it. A Canon Score reader scoring Liquidity for a small hydropower counter should check the depth of the order book and recent traded volume in Rupee terms, not just whether the stock "moved."
CASE IN POINT
A retail investor sees a small hydropower stock rise by the daily circuit limit for five straight sessions and assumes strong genuine demand. Checking the order book shows that only a handful of shares actually traded each day, with a large unmatched buy queue. This is a thin-float circuit trap, not deep liquidity — exactly the pattern flagged in Chapter 58. The Canon Score's Liquidity dimension should mark this stock down for tradability risk even as its price appears to be rising strongly.
For the underlying project finance mechanics — debt sizing, PPA tariff structures, cost overrun analysis, and how to model a hydropower company's cash flows across a full monsoon-to-dry-season cycle — revisit the hydropower modelling chapters in Part VIII, and Chapter 33 in Part VI for hydropower-specific accounting treatment (including how construction-period interest gets capitalised onto the balance sheet rather than expensed).
Lesson 65.4 — Adjusting the Score for Microfinance Institutions
Microfinance institutions (MFIs) lend small amounts, typically without traditional collateral, to low-income and rural borrowers, often organised into borrower groups that provide informal social pressure to repay. Nepal has a large listed microfinance sector on NEPSE, and it needs some of the sharpest governance and liquidity adjustments of any sector in this chapter.
Governance & Promoter Behaviour needs the heaviest rewrite here. The generic Canon governance checklist (board independence, related-party transactions, promoter share pledging) still applies, but for microfinance it must be supplemented with sector-specific red flags that live in how the institution treats its borrowers, not just its shareholders. Two practices in particular deserve attention. The first is loan recycling — issuing a new loan to a struggling borrower specifically to repay an old loan that is about to become overdue, which keeps the reported NPL ratio artificially low while the underlying borrower's debt burden actually worsens. The second is multiple borrowing, sometimes called overlapping — the same borrower or household taking loans from several different microfinance institutions simultaneously, often without any single lender knowing about the others, which leaves the borrower over-indebted and eventually unable to repay any of them. Nepal Rastra Bank has pushed the sector toward credit information sharing specifically to curb this problem, and NRB introduced a mandated base-rate-linked interest rate ceiling for microfinance lending (capping the effective lending rate, reported around 15% under the framework introduced in 2025) partly in response to concerns about aggressive rural lending practices. A microfinance institution whose growth has consistently outpaced the sector's average loan growth, without a clear explanation of how it avoids overlap with other lenders in the same geography, should be marked down on Governance even if its reported NPL ratio looks clean — because a clean NPL ratio built on recycled loans is not really clean.
WARNING
In microfinance, a low reported NPL ratio is not automatically good news the way it is for a commercial bank. Because group-lending social pressure and loan recycling can both suppress the reported NPL number without actually reducing borrower distress, the Canon Score reader must look behind the headline ratio — checking loan-loss provisioning trends, growth rates relative to the sector, and disclosed write-off policy — before rewarding a microfinance institution's Financial Strength score for a low NPL figure alone.
Financial Strength & Profitability for microfinance should weight loan-loss provisioning coverage (how much the institution has set aside against expected defaults, relative to its loan book) more heavily than it would for a commercial bank, precisely because unsecured group lending carries structurally higher default risk than a bank's typically collateralized book. A healthy microfinance institution should be building provisioning steadily as it grows its loan book, not lagging behind loan growth.
Liquidity & Tradability differs for microfinance in two ways. First, many listed MFIs on NEPSE are relatively small-cap with thin free float, so — similar to smaller hydropower counters — the Canon reader should check real traded volume rather than headline price movement. Second, on the funding side (distinct from share liquidity), microfinance institutions often rely heavily on wholesale borrowing from commercial banks rather than retail deposits, which makes their own funding liquidity more sensitive to changes in bank lending appetite and interest rates than a deposit-funded commercial bank's would be.
Sector & Business Model Durability for microfinance should account for regulatory concentration risk: because the entire sector's interest rates, provisioning rules, and lending practices are tightly directed by NRB policy (including periodic rate caps and consolidation pushes), a microfinance institution's durability is unusually exposed to regulatory shifts compared to, say, a manufacturing company operating in a less directly regulated market.
For the accounting detail behind microfinance loan classification, group-lending structures, and provisioning rules, revisit Chapter 31 in Part VI.
Lesson 65.5 — Adjusting the Score for Insurance Companies
Insurance companies — both life insurance and non-life (general) insurance — sell a promise: pay a premium now, and the company promises to pay a much larger sum later if a specified event happens. That promise is the entire business, and it changes how nearly every Canon dimension should be read.
Financial Strength & Profitability for insurance is unrecognisable next to the generic version. The headline metric to replace generic profitability ratios is the claims ratio (also called the loss ratio) — the percentage of premium income that the company pays out in claims. A non-life insurer (motor, fire, marine, and similar general insurance) with a persistently high claims ratio is pricing its policies too cheaply relative to the risk it is taking on, or is facing a genuinely bad run of claims (a bad monsoon flood season, for instance) — either way, a rising claims ratio over several quarters deserves real scrutiny. A life insurer's claims pattern is read differently again, since life insurance claims (death benefits, maturity payouts) unfold over decades rather than within a policy year, so a life insurer's Financial Strength should weight actuarial reserve adequacy — whether the company has set aside enough today to cover promised payouts decades from now — alongside the claims ratio.
Alongside the claims ratio, investment portfolio quality matters enormously for insurance, because insurers invest the premiums they collect (the "float") before claims come due, and that investment income is often a larger share of total profit than the core underwriting business itself. A Canon reader should check where an insurer's investment book sits — government securities and fixed deposits are the safest, followed by rated corporate debt, with direct equity holdings carrying the most risk and volatility. An insurer with a large share of its investment book in speculative equity positions has effectively layered stock-market risk on top of its insurance risk, and that combination deserves a lower Financial Strength score than an insurer holding a more conservative, bond-heavy portfolio, even if both report similar profit in a good year.
The specific regulatory yardstick to use is the solvency margin (or solvency ratio) — a measure of how much surplus capital an insurer holds above what its risk-weighted liabilities require, similar in spirit to a bank's CAR but calculated very differently. The Nepal Insurance Authority introduced a Risk-Based Capital and Solvency Directive in 2024/2025 that requires insurers to maintain solvency ratios above a defined regulatory minimum, replacing the older, simpler capital-based rules. Nepal's non-life insurance sector has recently reported average solvency ratios comfortably above the regulatory floor (reported around the high-2x range across the industry in early 2025), which gives a Canon reader a rough sector benchmark: an insurer sitting only marginally above the regulatory minimum solvency ratio has a thinner capital cushion than the sector average and should be scored more cautiously than one running well above it. The Nepal Insurance Authority also raised minimum paid-up capital requirements sharply in recent years — to roughly NPR 5 arba (5,000 million) for life insurers and NPR 2.5 arba (2,500 million) for non-life insurers — and an insurer that took years past the original deadline to meet that threshold, or needed repeated deadline extensions, is worth noting under Governance.
REGULATORY DETAIL
The Nepal Insurance Authority's Risk-Based Capital and Solvency Directive (approved 2081/2082 in the Bikram Sambat calendar, corresponding to late 2024 into 2025) moved Nepal's insurance sector toward the kind of risk-weighted capital framework banks have used for years. When scoring an insurer's Financial Strength, check its disclosed solvency ratio against both the regulatory minimum and the sector average — a single number just above the minimum is a materially weaker signal than one running several multiples above it.
Growth Trajectory for insurance should centre on premium growth — specifically, whether growth in gross written premium is coming from genuinely new policies and renewals (healthy) or from aggressive discounting to win volume, which can quietly push up the claims ratio a year or two later as poorly underwritten business comes due. Dividend & Capital Return Discipline should account for the fact that regulatory solvency requirements can restrict payouts in years when an insurer's capital cushion is thin, exactly as CAR rules do for banks.
PRACTICAL TOOL
When scoring an insurance company, build a simple two-line check before touching any of the seven dimensions: (1) is the claims ratio trending up, flat, or down over the last four to eight quarters, and (2) is the solvency ratio comfortably above both the regulatory minimum and the sector average? If either line is moving the wrong way, treat the Financial Strength dimension with real caution regardless of how strong the headline net profit figure looks.
For the underlying accounting treatment of premium recognition, claims reserving, and actuarial liability valuation, revisit Chapter 32 in Part VI, which covers insurance company accounting specifically.
Lesson 65.6 — A Cross-Sector Comparison — Applying Adjusted Scores Fairly Across Different Sectors
By now you have four different sets of tailoring instructions in your hands. The natural next question is: once I have sector-adjusted scores for a bank, a hydropower company, a microfinance institution, and an insurance company, can I actually compare them to each other?
The honest answer is: carefully, and only at the level of the final composite score — not dimension by dimension.
The whole reason the Canon Score compresses seven very different kinds of analysis into a single 0-to-100 number is so that, once each dimension has been fairly scored using the right sector-specific ruler, the final numbers become comparable again — the same way a doctor's overall "fitness score" lets you compare a seventy-year-old and a twenty-year-old on a common scale, even though the blood pressure ranges used to build that score were different for each. A Canon Score of 72 for a commercial bank and a Canon Score of 72 for a hydropower company should represent a genuinely similar overall quality and risk level, even though almost none of the underlying ratios that produced those two scores are directly comparable to each other.
What you should never do is compare raw sub-scores across sectors as if they used the same ruler. A "Financial Strength: 14/20" for a pre-COD hydropower company and a "Financial Strength: 14/20" for a commercial bank are not telling you the same thing about the same kind of risk — one is measuring construction discipline, the other is measuring capital adequacy and loan quality. They happen to land on the same number because each was separately calibrated to be a fair "14 out of 20" within its own sector's normal range. Comparing them side by side as if 14 meant the same thing in both cases would undo all the careful tailoring this chapter just walked through.
The table below summarises which of the seven Canon dimensions receive the heaviest adjustment for each of the four sectors covered in this chapter, so you have a quick reference before you build a scorecard.
Canon Dimension
Banks / DBs / Finance Cos.
Hydropower
Microfinance
Insurance
Financial Strength & Profitability
Heavy — CAR, NPL ratio, NIM replace generic ratios
Heavy — construction discipline pre-COD; seasonally adjusted margins post-COD
Heavy — provisioning coverage weighted above generic leverage ratios
Heavy — claims ratio, solvency margin, investment portfolio quality
Light — standard NEPSE liquidity mostly applies, plus CD ratio
Heavy — circuit-trap risk on thin-float counters (see Ch. 58)
Moderate — thin free float plus wholesale funding sensitivity
Light — standard NEPSE liquidity mostly applies
Valuation Reasonableness
Moderate — Price-to-Book weighted more heavily
Moderate — pre-COD valuation is project-value based, not earnings based
Light — generic approach mostly applies
Light-to-moderate — embedded value concepts for life insurers
Sector & Business Model Durability
Light — standard checklist applies
Moderate — PPA tenure and hydrology risk specific to project
Heavy — regulatory concentration risk (rate caps, directives)
Moderate — long-tail liability exposure
Growth Trajectory
Moderate — loan and deposit growth replace revenue growth
Heavy — pre-COD scored on construction progress, not revenue growth
Moderate — premium quality of loan growth vs. sector pace
Moderate — premium growth quality vs. claims-ratio lag risk
Dividend & Capital Return Discipline
Moderate — constrained by CAR rules
Light — pre-COD typically pays no dividend; standard post-COD
Light — standard approach mostly applies
Moderate — constrained by solvency rules
The second table gathers the specific regulatory benchmark numbers referenced across this chapter, so you have them in one place as a working reference. Treat these as points-in-time figures to be refreshed against NRB, Nepal Insurance Authority, and company disclosures as regulations evolve — they are a starting anchor, not a permanent constant.
Sector
Benchmark
Approximate Threshold / Level
Commercial banks
Minimum total Capital Adequacy Ratio (CAR)
Around 11% of risk-weighted assets, Basel III framework
Reported sector-average solvency ratio (early 2025)
Roughly high-2x range, above the regulatory minimum
Hydropower (generic)
Typical construction-phase debt share of project cost
Roughly 70-80% debt, 20-30% equity
CAUTION
Regulatory thresholds change. NRB, the Nepal Insurance Authority, and SEBON all revise capital, provisioning, and disclosure rules over time — sometimes within a single fiscal year, as happened with microfinance interest rate rules in 2025. Before applying any threshold in the table above to a live scoring decision, check the institution's most recent quarterly disclosure and the relevant regulator's current directive, rather than relying on this chapter's numbers as a permanent fact.
There is one last discipline worth naming before you move on. Sector adjustment is not a one-time skill you learn and then apply mechanically forever. NEPSE itself is not static — new sectors get added to the exchange, existing sectors get restructured by regulation (as happened with the microfinance rate cap in 2025 and the insurance risk-based capital directive around the same time), and companies sometimes straddle two sectors (a hydropower company that also holds a manufacturing subsidiary, for instance, or a finance company that also runs an insurance arm). When you meet a company that does not fit cleanly into one of the four sectors covered here — a hotel company, a manufacturing company, a trading company — return to Part VI's accounting chapters (particularly Chapter 34, covering manufacturing, trading, and hotel accounting) for the underlying detail, and apply the same discipline this chapter modelled: ask which of the seven dimensions genuinely needs a different ruler for this business, adjust only those, and leave the rest of the generic Chapter 64 framework standing.
Chapter recap
This chapter took the generic seven-dimension Canon Score built in Chapter 64 and showed why it cannot be applied identically across every NEPSE sector — using Hari Kaji the tailor's single shirt pattern, cut differently for a farmer and a bank manager, as the guiding image. A scoring framework can share the same categories across every company while still requiring different specific ratios, thresholds, and interpretations depending on what kind of business is being measured, because the underlying economics of a bank, a hydropower project, a microfinance institution, and an insurance company are genuinely different from one another and from a typical manufacturing or trading company.
For commercial banks, development banks, and finance companies, the chapter replaced generic profitability and leverage ratios with the Capital Adequacy Ratio, the Non-Performing Loan ratio, and Net Interest Margin, all grounded in Nepal Rastra Bank's regulatory framework, and flagged related-party lending and loan concentration as the sector's sharpest governance risks. For hydropower companies, the chapter drew the sharpest possible line between pre-COD scoring (construction discipline, budget and timeline adherence, and PPA credibility standing in for profitability) and post-COD scoring (seasonally adjusted, year-over-year quarterly comparisons that account for Nepal's wet and dry seasons), and reconnected the Liquidity dimension to Chapter 58's circuit-trap warning for thin-float hydropower counters. For microfinance institutions, the chapter identified loan recycling and multiple borrowing as governance red flags invisible in a headline NPL ratio, and pointed to NRB's 2025 rate-cap directive as a sign of how tightly regulation shapes this sector's durability. For insurance companies, the chapter introduced the claims ratio, investment portfolio quality, and the solvency margin under the Nepal Insurance Authority's newer risk-based capital framework as the replacements for generic profitability and financial-strength measures, driven by the long-duration nature of insurance liabilities.
The chapter closed with a caution that matters as much as any individual adjustment: sub-scores are not comparable across sectors, even when the underlying composite Canon Score is. A "14 out of 20" on Financial Strength means something entirely different for a pre-COD hydropower company than it does for a commercial bank, because each was calibrated against its own sector's normal range. Only the final composite score, built after every dimension has been fairly and separately calibrated, can be meaningfully placed side by side across sectors.
Two summary tables gathered this chapter's guidance into a single reference: one mapping which of the seven Canon dimensions need heavy, moderate, or light adjustment for each of the four sectors, and one collecting the specific regulatory benchmark figures — CAR minimums, NPL sector averages, microfinance rate ceilings, and insurance capital and solvency thresholds — referenced throughout the chapter. Both tables are starting anchors, not permanent constants, because Nepali financial regulation continues to evolve, as the 2025 changes to microfinance lending rates and insurance risk-based capital rules both demonstrate.
You now hold a complete, sector-adjusted version of the Canon Score — the same seven dimensions from Chapter 64, correctly tailored to the four sectors where a generic reading would mislead you most. Chapter 66, "Using the Canon Score in Portfolio Decisions," closes out Part XIII by putting this finished tool to work: how to use the Canon Score to screen candidate stocks before you research them in depth, how to size a position in your portfolio based partly on where a company's score lands, and when a deteriorating score should trigger you to reduce or exit a holding rather than simply watch and hope. Once Part XIII closes with that chapter, Part XIV, "Strategies, Playbooks & Decision Frameworks," opens with Chapter 67, "Long-Term Value Investing on NEPSE," beginning a new stretch of the Canon focused on assembling everything learned so far into complete, repeatable investment strategies.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIII · Chapter 66
Using the Canon Score in Portfolio Decisions
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 66.1 — Using the Canon Score as a Screening Filter
Every year, tens of thousands of Nepali students sit for entrance examinations for medicine, engineering, and the civil service. Almost none of them get in on the strength of the exam score alone. The score does something narrower and more useful: it decides who even gets called for the next round — the interview, the document check, the merit list. A student who scores 40 out of 100 does not get invited to argue that she is secretly brilliant. A student who scores 85 is not admitted on the spot either; she still has to clear the interview, produce her citizenship papers, and pass the medical test. The score is a filter, not a verdict.
This is exactly the job the Canon Score should do in your portfolio. Chapter 64 gave you a rigorous, seven-dimension, 0-to-100 rubric — Financial Strength & Profitability, Governance & Promoter Behaviour, Liquidity & Tradability, Valuation Reasonableness, Sector & Business Model Durability, Growth Trajectory, and Dividend & Capital Return Discipline. Chapter 65 showed you how to adjust that rubric for the quirks of banks, hydropower companies, microfinance institutions, and insurers. By the time you reach this chapter, you should already know how to produce a number for any NEPSE-listed company you care to study. The question this chapter answers is simple: now that you have the number, what do you actually do with it?
The first and most basic use is screening — plain English: sorting a large pile of candidates into "worth studying further" and "not worth my time," before you spend real hours reading annual reports. A screening filter is a first pass, not a final judgment. Think of the loan officer at a village-level microfinance branch. Before she visits a borrower's home to check the goat shed and the vegetable plot, she checks whether the applicant's basic paperwork clears a minimum bar — citizenship, land ownership proof, no default history with another lender. Candidates who fail that first pass are set aside before anyone spends a day walking to a village. Candidates who pass move on to the real work: the home visit, the character reference, the judgment call.
For your equity sleeve — the actively selected portion of your portfolio you built in Chapter 60, as distinct from the index-tracking or passive core — a Canon Score screen works the same way. A practical rule many disciplined investors use, echoing a real institutional practice: global "quality factor" index providers such as MSCI construct indexes by ranking companies on a composite quality score (built from return on equity, earnings stability, and low balance-sheet leverage) and then including only names above a chosen percentile. They do not claim the score picks winners. They use it to exclude the bottom of the pile before any other analysis happens. You can borrow that discipline at home, on a spreadsheet, for a portfolio of Nepali equities.
KEY CONCEPT
A screening filter is a rule that sorts a large list of candidates into "worth a closer look" and "not worth my time" using one simple, repeatable test — before you spend hours on deeper research. It does not tell you what to buy. It tells you what to stop wasting time on.
Set two thresholds, not one. A "watch list minimum" and a "buy list minimum." A reasonable starting point, calibrated to the 0-100 rubric from Chapter 64:
Below 45: Exclude. Do not track it as a candidate. If you already own it, treat the low score as a loud signal to open the Chapter 61 review process, covered further in Lesson 66.3.
45 to 59: Watch list only. You track the company, you read its quarterly disclosures, but you commit no fresh capital. A rebound above 60 upgrades it; a further slide confirms your decision to stay out.
60 to 64: Borderline eligible. Worth a full research file, but position sizes should stay small and conditional, as set out in Lesson 66.2.
65 and above: Eligible for standard buy-list treatment, sized according to the table in the next lesson.
Why 60 and 65, and not 50 and 55? Because the seven dimensions of the Canon Score are deliberately weighted toward things that protect capital first — financial strength, governance, and liquidity carry heavy weight before growth and momentum-type factors ever enter the picture. A company scoring in the high 50s on this rubric is not merely "average." It usually means at least one of governance, financial strength, or liquidity is genuinely weak, and weakness in those particular dimensions is precisely the kind of weakness that turns into a permanent loss of capital rather than a temporary paper loss. Setting your buy-list bar at 65 rather than 50 is how you make sure the screen is actually doing its job of cutting away danger, not just cutting away small numbers.
Two caveats belong here, both grounded in what you learned in Chapter 65. First, apply the screen within the sector, not only across the whole market. Hydropower companies, by the nature of NEPSE's own thin secondary market for many run-of-river issuers, will structurally score lower on the Liquidity & Tradability dimension than a large commercial bank, even when the underlying project economics are sound. A hydropower stock scoring 62 overall, with the drag coming entirely from thin trading volume rather than governance or financial weakness, deserves a different read from a bank scoring 62 because its capital adequacy ratio is thin and a director resigned without explanation. The total number is the same; the story behind it is not. Always open the dimension breakdown before you trust the headline figure — a habit Lesson 66.5 will insist on again.
Second, remember that a screening filter answers only "should I look closer," never "should I buy now." A company can clear 70 on the Canon Score and still be priced at a level that makes it a poor purchase this month — the Valuation Reasonableness dimension inside the score captures relative valuation discipline, but it does not replace the entry-timing and margin-of-safety thinking from earlier parts of this book. The filter narrows your universe. It does not write the order ticket.
WARNING
A high Canon Score is not a green light to buy today at any price. The score measures the quality of the business and its governance. It does not measure whether today's NEPSE price is a fair one to pay for that quality. Treat a passed screen as an invitation to do the valuation and entry-timing work, not a substitute for it.
REGULATORY DETAIL
SEBON (the Securities Board of Nepal) requires listed companies to publish unaudited quarterly financial statements within a set number of days after each quarter-end, and audited annual reports ahead of the AGM. These filings, along with disclosures routed through NEPSE and record-keeping at CDSC (the Central Depository System and Clearing Limited), are the raw material your Canon Score screen runs on. A screen is only as current as the filing it was last built from — a score built from data two quarters old is not really screening the company as it exists today.
One more practical point about screening in the Nepali context: your candidate universe is smaller than in a market like India or the United States. NEPSE lists a few hundred companies, heavily concentrated in banks, hydropower, and financial institutions. A strict 65-point cutoff applied blindly across every sector could leave your equity sleeve dominated by only two or three industries, defeating the diversification work you did in Chapter 60. The fix is not to lower the bar. The fix is to build sector-specific watch lists using the Chapter 65 adjustments, so that "the best available manufacturing company" and "the best available bank" are each judged against a fair, sector-appropriate version of the rubric, even while the absolute screening thresholds stay the same for entry into the buy list.
Lesson 66.2 — Tying Position Size to Score — A Worked Sizing Table
A bank credit officer sizing a business loan does not ask only "does this borrower qualify." She asks "how much can this borrower responsibly carry." A borrower with a strong repayment history and stable cash flow might be approved for a much larger loan than a first-time borrower with a thinner file — but even the strongest borrower runs into Nepal Rastra Bank's single-obligor exposure limits, which cap how much of a bank's capital can be lent to any one borrower or group, no matter how creditworthy. Quality raises the ceiling within the loan officer's discretion. It never removes the ceiling itself.
Your Canon Score should work on your portfolio the same way. Chapters 60 and 61 set hard ceilings on how large any single position, and any single sector, is allowed to grow inside your equity sleeve — ceilings that exist to protect you from concentration risk regardless of how good a story a company tells. Nothing in this chapter overrides those ceilings. What the Canon Score adds is a disciplined way to decide where, within that ceiling, a given holding should sit. A company scoring 90 can responsibly be sized close to your maximum single-stock ceiling. A company scoring 66 should sit well below it, even if you are confident in the story, simply because the score itself is telling you the margin of safety is thinner.
KEY CONCEPT
Position sizing is the decision of how much of your portfolio to put into one holding, as opposed to the decision of whether to hold it at all. Screening (Lesson 66.1) answers "in or out." Sizing answers "how much." The Canon Score should influence both decisions, but it should never be allowed to override the hard concentration ceilings set in Chapter 60.
Here is a worked sizing table you can adapt directly, assuming — consistent with the concentration framework from Chapter 60 — a single-stock ceiling of roughly 8 percent of the equity sleeve and a single-sector ceiling of roughly 25 to 30 percent:
Canon Score Band
Classification
Suggested Position Size (% of equity sleeve)
Sizing Discipline
85–100
Core Holding
6% – 8% (up to the Chapter 60 single-stock ceiling)
Full position may be built over 2–3 tranches; eligible for the largest weight you allow any single name.
70–84
Standard Buy List
4% – 6%
Build in tranches per Chapter 60 entry discipline; do not front-load the full position in one purchase.
60–69
Conditional / Satellite
1.5% – 3%
Treat as a satellite position with a specific, time-bound thesis — not a core, permanent holding. Reassess every quarter.
45–59
Watch List
0% new capital. Existing holders cap total exposure at 1% – 2% while reviewing.
No fresh purchases. If already held, this band should trigger the review process in Lesson 66.3, not automatic accumulation.
Below 45
Avoid / Exit Candidate
0%
New capital excluded entirely. Existing holders should move to formal review under Chapter 61's sell discipline.
Two things about this table deserve emphasis, because a table this clean invites lazy, mechanical use — the very trap Lesson 66.5 warns against.
First, the size ranges are ceilings within a band, not entitlements. A company scoring 92 does not automatically deserve the full 8 percent weight on day one. You still build the position in tranches, still respect your entry-price discipline from earlier chapters, and still watch how the position behaves relative to the rest of your sleeve as you add to it. The Canon Score tells you how large the position is allowed to become over time. It does not tell you to buy it all at once.
Second, sector caps sit above single-stock sizing and must be checked every time you add. Suppose your banking-sector exposure is already at 24 percent of the equity sleeve against a 25 percent sector ceiling, and a fifth bank clears the screen with a score of 88. The individual-stock sizing table says you could add up to 8 percent. The sector ceiling says you have only 1 percent of room left. The sector ceiling wins. This is not a flaw in the scoring system — it is exactly the kind of guardrail concentration limits exist to provide, and a high Canon Score was never designed to override it.
PRACTICAL TOOL
Keep a single spreadsheet with four columns for every holding and watch-list candidate: Current Canon Score, Score Band, Current Position Size (% of sleeve), and Sector Exposure Running Total. Update the score column every quarter (Lesson 66.4). Before any purchase, check the sector-total column first — it is the constraint that gets forgotten most often, because a single stock's own attractiveness is what draws your attention, not the crowded sector sitting behind it.
There is also a case for sizing down deliberately even within a band, based on which dimensions are driving the score. A company at 78 with an even, balanced profile across all seven dimensions is a different animal from a company at 78 that scores near-perfect on Growth Trajectory and Valuation but merely adequate on Governance & Promoter Behaviour. Both clear the same screening bar. A cautious investor will size the second company toward the bottom of its band's range, not the top, because concentrated strength in growth and cheapness cannot fully substitute for a thinner governance cushion — governance weakness is exactly the kind of risk that shows up suddenly, not gradually, and Chapter 64 weighted it heavily for that reason.
CASE IN POINT
Consider two fictional hydropower companies, both scoring 74 on the Canon Score after Chapter 65's sector adjustment. Sunkoshi Bijuli Ltd. earns its 74 through strong, even scores across financial strength, governance, and dividend discipline, with only average growth. Trishuli Urja Ltd. earns the same 74 through outstanding growth and valuation scores, but a governance score pulled down by a promoter share pledge disclosed in its latest annual report. A mechanical reading of the sizing table would size both identically at roughly 4–5% of the equity sleeve. A disciplined investor sizes Trishuli toward the lower end of that range, or below it, until the pledge situation is better understood — same number, different risk.
Lesson 66.3 — Score Deterioration as a Sell and Review Trigger
A patient with a chronic condition does not wait for a doctor's advice only when she feels sick. She gets a blood test every few months, even when she feels fine, because the numbers can move before the symptoms do. If her cholesterol or blood sugar jumps sharply between two routine tests, a good doctor does not necessarily prescribe a drastic change immediately — but she does insist on a proper follow-up: repeat the test, ask what changed in diet or medication, rule out a lab error, and only then decide on a course of action. The jump itself is not the diagnosis. It is the trigger that forces a proper look.
This is the model for using score deterioration in your portfolio. A deterioration trigger is a preset drop in a held company's Canon Score, measured between two re-scoring dates, that forces you to open a formal review — not a rule that forces you to sell automatically. The distinction matters enormously. Chapter 61 built a review discipline precisely so that difficult decisions get made with a clear head, on a schedule, rather than in a panic or, just as dangerously, through inertia and denial. Score deterioration is one of the cleanest, most objective ways to trip that review discipline into action.
A workable trigger rule, calibrated to the same rubric from Chapter 64, has two legs:
A total-score trigger: any drop of 10 points or more in a single re-scoring cycle (typically one quarter), or any drop that moves the holding down a full band in the sizing table from Lesson 66.2 — for example, from Standard Buy List into Conditional/Satellite, or from Conditional into Watch List.
A single-dimension trigger: a sharp, isolated collapse in the Governance & Promoter Behaviour dimension or the Financial Strength & Profitability dimension, even if the total score has not yet fallen by 10 points, because these two dimensions carry the heaviest weight for a reason — they are the dimensions most associated with permanent capital loss rather than ordinary cyclical wobble.
This two-leg approach borrows directly from how credit analysts at rating agencies operate, and it is worth understanding the parallel because it is genuinely instructive. When a bond issuer's fundamentals weaken, a rating agency frequently does not jump straight to a downgrade. It first places the issuer "on watch" or assigns a "negative outlook" — a formal signal that a review is underway and a downgrade is being actively considered, without committing to one yet. Only after that review concludes does an actual rating change occur. Separately, many global bond funds are mandated to hold only "investment-grade" debt; the moment an issuer's rating is cut below the investment-grade cutoff (from BBB- to BB+, for instance — a bond that crosses this line is informally called a "fallen angel"), those funds are contractually forced to sell, regardless of their own view of the company's prospects, simply because the mandate says so. Your Canon Score deterioration trigger should behave like the "negative outlook" step, not the fallen-angel step: it forces a disciplined, unhurried review. It does not, by itself, force a sale. You are not running a fund bound by someone else's mandate. You are the analyst doing the review — the whole point of building your own scoring system in Chapter 64 was to keep that judgment in your own hands.
KEY CONCEPT
A deterioration trigger is a preset drop in score that forces you to open a formal review of a holding. It is a tripwire for attention, not an automatic sell order. Confusing the two turns a useful discipline into a mechanical rule that can force you out of a good company during a temporary setback, or worse, lull you into believing you have "done the analysis" when all you have done is watch a number cross a line.
Once the trigger fires, the review itself should ask one central question: is this deterioration temporary and cyclical, or is it structural? A hydropower company's Financial Strength score can dip sharply after one poor monsoon season and lower-than-expected generation — a real event, but one that reverses the following year if the underlying asset and its power purchase agreement are sound. That is cyclical. A bank whose Governance score drops because an independent director resigned abruptly without explanation, followed by a qualified audit opinion on loan loss provisioning, is showing something structural — a warning about the quality of information you are being given, not a one-off bad quarter. The first case argues for patience, possibly even for holding through the dip if the position size was already sensible. The second argues for trimming toward the exit, following the sell discipline built in Chapter 61.
CASE IN POINT
A fictional composite worth studying: Himal Bikas Bittiya Sanstha, a mid-sized development bank, scored 71 at the start of a fiscal year — comfortably in the Standard Buy List band, sized at 5% of an illustrative equity sleeve. Two quarters later, its re-score comes in at 54. The total-score trigger fires (a 17-point drop, and a move down two full bands). On review, three things emerge: a spike in non-performing loans tied to one over-concentrated agricultural lending pocket, a sudden change of statutory auditor mid-year without the usual AGM process, and a promoter share pledge disclosed for the first time. None of these look like a single bad quarter. The investor trims the position toward the Watch List sizing ceiling immediately, rather than waiting for the position to recover, and documents the reasoning in the review log described in Chapter 61.
Two guardrails prevent this trigger from being misused, in either direction.
The first guardrail is against denial: do not "explain away" every deterioration trigger just because you like the company or have held it a long time. The whole reason to build a preset numerical trigger, rather than relying on gut feel about when to review a holding, is that gut feel tends to rationalise staying in a familiar, comfortable position exactly when the discipline should be at its sharpest. If your own review keeps concluding "it's fine, nothing to see here" quarter after quarter while the score keeps drifting lower, that pattern is itself useful information about your objectivity, not just about the company.
The second guardrail is against overreaction: do not treat every trigger as an automatic sell. A single disappointing quarter that trips the 10-point threshold, followed by a review that turns up a plausible and temporary explanation, is exactly the situation this two-step process — trigger, then review — was designed to handle sensibly. Selling reflexively on every score wobble converts a long-term investment discipline into short-term, cost-heavy trading, and NEPSE's brokerage costs and settlement mechanics make that an expensive habit.
WARNING
Never average down — add to a position — purely because its price has fallen while its Canon Score has also fallen. A falling price and a falling score moving together are usually the market and your own scoring system agreeing that something has genuinely gotten worse. Buying more into that combination is not "getting a discount." It is doubling down on a deteriorating thesis.
Lesson 66.4 — Building a Re-Scoring Calendar and Habit
A score you calculated once, at the time of purchase, and never revisited is not a risk management tool. It is a museum piece — accurate the day it was made, and steadily less true every day after. Chapter 62 built a systematic investing calendar so that contributions, rebalancing, and reviews happen on a schedule rather than whenever mood or memory permits. Re-scoring needs the same calendar discipline, because the entire value of the deterioration trigger in Lesson 66.3 depends on comparing scores measured at regular, predictable intervals — a comparison that is meaningless if some holdings get re-scored every month and others get re-scored only when something goes wrong.
Anchor the re-scoring calendar to events that already happen on a fixed rhythm in the Nepali market, so the habit rides on infrastructure that already exists rather than competing with it for your attention.
Most NEPSE-listed companies publish unaudited quarterly financial statements within roughly a month of each quarter-end, in line with SEBON's disclosure timeline. That gives you four natural re-scoring windows a year, one after each quarter's unaudited results land. A full re-score of every holding and every active watch-list company, using the fresh quarterly numbers, should happen in each of these four windows. This is also the natural point to update the spreadsheet described in Lesson 66.2 and to check every trigger from Lesson 66.3.
Layer a deeper annual re-score on top of the quarterly cycle, timed to each company's audited annual report and AGM — typically clustered in the Poush-to-Falgun window for many Nepali companies, when audited financials, the directors' report, dividend and bonus share declarations, and any changes to the board are all disclosed together. This is the richest data drop of the year for the Governance & Promoter Behaviour and Dividend & Capital Return Discipline dimensions in particular, since both depend heavily on information — related-party transactions, auditor's notes, AGM resolutions — that only appears in the full annual filing, not the abbreviated quarterly one.
Add a third, irregular layer: ad hoc re-scoring triggered by material news, whenever it occurs, rather than waiting for the next scheduled window. A rights issue announcement, an auditor change, a promoter share pledge or its release, an NRB directive affecting a bank or development bank's capital or provisioning requirements, a credit rating action by a domestic agency such as ICRA Nepal or CARE Ratings Nepal, or a hydropower company's PPA renegotiation with the Nepal Electricity Authority — any of these can shift a Canon Score meaningfully before the next quarterly window arrives, and none of them should wait three months for a look.
PRACTICAL TOOL
Build a simple recurring calendar with four fixed quarterly dates roughly a month after each Nepali fiscal quarter-end, an annual deep-review date tied to AGM season, and a standing rule: any of the ad hoc trigger events listed above gets logged and re-scored within a week of the news, not held over to the next scheduled window. Treat missed re-scoring windows the same way you would treat a missed SIP contribution in Chapter 62 — a discipline lapse worth noticing, not a harmless skip.
REGULATORY DETAIL
SEBON's disclosure framework and NEPSE's own filing calendar are the backbone of this rhythm: quarterly unaudited reports, the audited annual report ahead of the AGM, and ad hoc disclosures for material events (auditor changes, promoter pledges, and similar) are all filed through NEPSE and become part of the public record that CDSC-linked systems and brokers surface to investors. Building your re-scoring calendar around these mandated filing points means you are never re-scoring on stale information by more than a few weeks, in normal circumstances.
One habit compounds the value of re-scoring more than any other: keep a score history, not just a current score. A single Canon Score snapshot tells you where a company stands today. A score history — the same company's score at each of the last six or eight quarterly windows, laid out in a simple line — tells you the direction of travel, and direction is often more informative than level. A company holding steady at 68 for two years is a very different holding from a company that fell from 84 to 68 over the same period, even though both show a score of 68 today. The first looks like a stable, moderate-quality business. The second looks like a business in the middle of a genuine decline that simply has not yet crossed your deterioration trigger threshold. Chapter 61's review discipline works far better when it can see the trend line, not just the last data point.
Finally, treat the Dashain-Tihar period the way Chapter 62 already treats it for contribution scheduling — as a natural pause point, not a working period. Most Nepali households and many company secretariats slow down markedly during this stretch. Rather than fighting that rhythm, build your annual deep-review date either just before Dashain, using the most recent quarterly data as a stocktake before the festival season, or just after Tihar, once markets and company activity have resumed their normal pace. Fighting the calendar you actually live in is a losing habit; working with it is how a systematic discipline survives for years rather than fizzling out after two quarters.
Lesson 66.5 — Honest Limitations — What the Score Cannot Do
Chapter 63 opened Part XIII with a warning worth repeating here at the close, because a well-built tool is exactly the kind of thing investors are tempted to trust too much. The Canon Score is a structure for judgment. It is not a substitute for judgment, and it was never designed to be one. A student who tops the entrance exam still has to survive medical school, still has to actually learn to treat patients — the exam score predicted readiness, it did not deliver competence. The Canon Score works the same way: it predicts which companies deserve your attention and roughly how much capital they can responsibly carry. It cannot do the remaining work of being a careful, skeptical investor.
Six honest limitations deserve to be stated plainly, precisely because a numerical score invites false confidence.
First, the score is only as good as the disclosure it is built from, and disclosure quality in the Nepali market is genuinely uneven. A large commercial bank with decades of listed history, professional investor-relations practice, and heavy analyst coverage gives you rich, comparable data. A recently listed hydropower company, three years into commercial operation, gives you a much thinner run of audited numbers, and a smaller manufacturing or hospitality company may disclose the bare regulatory minimum and nothing more. The same numeric score built on five years of clean data and the same score built on eighteen months of thin data are not equally trustworthy, even though the spreadsheet shows the same figure.
Second, the score cannot fully price crisis-level liquidity risk. The Liquidity & Tradability dimension measures ordinary trading conditions — average daily turnover, bid-ask spread, free float. In a genuine market panic, NEPSE's circuit breakers and thin order books mean that even stocks with historically decent liquidity scores can gap down with no buyers at any price for several sessions in a row. A liquidity score built on calm-market data quietly assumes calm markets continue. They do not always.
Third, the score cannot anticipate sudden regulatory or political shifts. An NRB monetary tightening cycle, a change to loan loss provisioning norms, a hydropower PPA renegotiation, or a shift in remittance-linked banking regulation can all move a company's prospects overnight, in ways no backward-looking rubric could have flagged the prior quarter. The score updates only as fast as your re-scoring calendar runs; regulation and politics do not wait for your calendar.
CAUTION
Two companies can post the identical total Canon Score while carrying very different risk profiles, because the same number can be built from very different combinations of the seven dimensions. Never act on the headline number alone. Always open the dimension breakdown before sizing or trimming a position — the composition of a score matters as much as its total.
Fourth, a rising score built on one or two exceptionally strong quarters can be mistaken for durable improvement when it is really a temporary spike — a bumper hydropower generation year from unusually heavy monsoon rainfall, or a one-off gain from a land or asset sale flattering a bank's profitability ratios for a single period. The Growth Trajectory and Financial Strength dimensions can both look temporarily excellent on numbers that will not repeat. Read the notes to the financial statements, not just the ratios, before trusting a sudden jump.
Fifth, remember that the qualitative dimensions — governance chief among them — still involve human judgment, and human judgment carries bias. Two careful investors scoring the same board composition and the same related-party disclosure can reasonably land a few points apart. The score creates useful discipline and a shared vocabulary; it does not create false precision. Treat a Canon Score of 71 versus 74 as "roughly similar quality," not as a meaningfully different verdict.
Sixth, and most important: the score cannot make the final call for you, and it should not be allowed to. It structures your attention, ranks your candidates, sizes your positions, and flags deterioration for review. The decision to buy, to hold through a difficult quarter, or to exit — that remains yours, informed by everything the score cannot capture: a phone call with someone who knows the company, a site visit to a hydropower project, a careful read of an auditor's qualified opinion, or simply a mature judgment about whether Nepal-specific risk in a given sector has quietly changed in a way no rubric yet reflects.
CAUTION
If you ever catch yourself saying "the score says buy" or "the score says sell" as a complete sentence, stop. The score never says either. It says "this candidate cleared the screen" or "this holding's score has dropped and deserves review." What you do next is still your decision, made with the judgment the score was built to support, not replace.
Lesson 66.6 — Closing Synthesis of Part XIII and a Worked End-to-End Example
Walk through a complete, worked example that ties Lessons 66.1 through 66.5 together, following one investor's equity sleeve through a full year.
Sabina, a Kathmandu-based investor, runs an equity sleeve of roughly NPR 15 lakh, built under the Chapter 60 framework with an 8 percent single-stock ceiling and a 28 percent single-sector ceiling. She maintains a Canon Score spreadsheet, re-scored every quarter under the Chapter 66.4 calendar, covering four current holdings and three watch-list candidates.
At the start of the fiscal year, her holdings and their most recent scores look like this:
Holding (illustrative)
Sector
Canon Score
Band
Position Size (% of sleeve)
Action Taken
Himal Sanjal Hydropower Ltd.
Hydropower
82
Core Holding
7%
Hold; near ceiling, no further buying
Everest Trust Bank Ltd.
Banking
71
Standard Buy List
5%
Hold; room to add on dips
Annapurna General Insurance Ltd.
Insurance
66
Conditional / Satellite
2.5%
Hold; time-bound thesis, reassess next quarter
Sagarmatha Laghubitta Ltd.
Microfinance
58
Watch List candidate
0% (not yet held)
No purchase; tracking only
Two quarters in, Sabina's scheduled re-score turns up a material change. Everest Trust Bank's score falls from 71 to 56 — an 18-point drop, crossing two bands at once, tripping the total-score deterioration trigger from Lesson 66.3. On review, she finds the cause: a sharp rise in non-performing loans in one regional branch cluster, combined with a mid-cycle change of statutory auditor announced outside the normal AGM process. Applying the temporary-versus-structural test from Lesson 66.3, both signals point toward structural concern rather than a single bad quarter — asset quality deterioration concentrated in one lending pocket, paired with an unexplained auditor change, is the kind of combination that historically does not reverse quickly. Following Chapter 61's review discipline, she trims the position from 5 percent down to 2 percent over the following month, in line with the Watch List sizing guidance from Lesson 66.2, rather than exiting all at once or holding the full position hoping for a recovery.
At the same re-scoring window, Sagarmatha Laghubitta — previously a Watch List candidate at 58 — improves to 64 after a strong quarterly disclosure showing tightened loan-loss provisioning and a clean AGM with no governance red flags. It now clears the Conditional/Satellite screening bar from Lesson 66.1. Sabina opens a small 1.5 percent starter position, sized toward the bottom of its band per the sizing table, consistent with a single quarter of improvement rather than a long track record.
By year-end, her equity sleeve reflects a portfolio actively shaped by the screen, the sizing table, and the deterioration trigger working together — not a static buy-and-forget list, and not a portfolio churned by every quarterly wobble, but one adjusted with the same unhurried discipline a credit analyst would apply to a loan book under periodic review.
This worked example is really the closing argument for all of Part XIII. Chapter 63 established why a scoring system belongs in a Nepali retail investor's toolkit at all — because judgment applied inconsistently, company by company, mood by mood, is judgment that eventually fails you, and a structured rubric is how you make your own analysis repeatable and honest with yourself. Chapter 64 built the actual instrument: seven dimensions, weighted deliberately toward capital protection before growth, producing one comparable number across very different businesses. Chapter 65 admitted that no single generic rubric fits every sector equally well, and adjusted the weights and thresholds for the structural realities of banks, hydropower, microfinance, and insurance — recognising, for instance, that a hydropower company's cash flows run on hydrology and PPA terms, not the same drivers as a bank's net interest margin. This chapter closes the loop by putting the finished score to work exactly where it belongs: screening candidates before they earn your research time, sizing positions within the hard concentration ceilings from Chapter 60, triggering — never dictating — the review discipline from Chapter 61, and living on a calendar borrowed from Chapter 62's systematic rhythm, rather than sitting idle until a crisis forces a look.
Chapter recap
This chapter took the finished Canon Score — built dimension by dimension in Chapter 64 and calibrated by sector in Chapter 65 — and put it to work in real portfolio decisions. Lesson 66.1 established the score's first and most basic job: screening, using thresholds (roughly 45, 60, and 65 on the 0-100 scale) to decide which candidates deserve real research time and which do not, while warning that a passed screen is an invitation to study a company, not a signal to buy it at any price. Lesson 66.2 connected the score to position sizing, building a worked table that lets higher-scoring companies justify larger positions while always operating inside the hard concentration ceilings on single stocks and single sectors set back in Chapter 60 — quality raises the ceiling within a band, but the band's own outer limit never moves.
Lesson 66.3 turned the score into a monitoring tool by defining a deterioration trigger: a preset drop in score, or a sharp fall in the heavily weighted governance or financial-strength dimensions, that forces a formal review under Chapter 61's sell discipline rather than an automatic sale. Borrowing from how credit analysts use rating watches and how bond mandates treat "fallen angel" downgrades, this lesson stressed that the trigger's job is to force attention on a schedule, with the actual buy, hold, or sell decision always remaining a matter of investor judgment about whether the deterioration is temporary or structural. Lesson 66.4 made that judgment sustainable by building a re-scoring calendar anchored to Nepal's actual disclosure rhythm — quarterly unaudited filings, annual AGM season, and ad hoc material news — echoing the systematic habits built in Chapter 62, and adding the practice of tracking a score history rather than a single snapshot, since the direction of a score often matters more than its current level.
Lesson 66.5 pulled back from the mechanics to restate, deliberately, what the score cannot do: it cannot fix uneven Nepali disclosure quality, cannot price crisis-level illiquidity, cannot anticipate sudden regulatory shifts, cannot distinguish a durable improvement from a one-off good quarter without a careful read of the underlying notes, cannot escape the ordinary bias built into any human scoring of qualitative factors like governance, and above all cannot replace the investor's own final judgment. Lesson 66.6 closed with a full worked example — one investor's equity sleeve, screened, sized, monitored through a deterioration event, and adjusted — showing all four practical lessons operating together over a real annual cycle.
Taken as a whole, Part XIII has built something that did not exist at its outset: a disciplined, repeatable, Nepal-specific way to turn a pile of scattered facts about a listed company — audited numbers, promoter behaviour, trading volumes, valuation multiples, sector durability, growth trends, and dividend history — into one structured judgment that can be compared across a portfolio, tracked over time, and acted on with consistency rather than mood. Chapter 63 supplied the philosophy for why this matters. Chapter 64 supplied the instrument. Chapter 65 adjusted that instrument for the sectors that dominate NEPSE. This chapter supplied the discipline for using it in a live portfolio: as a screen, a sizing guide, a review trigger, and a calendar habit — always in service of judgment, never as a replacement for it.
With the Canon Score now fully built and fully operational, the book turns from measurement to action. Part XIV, STRATEGIES, PLAYBOOKS & DECISION FRAMEWORKS, opens with Chapter 67, "Long-Term Value Investing on NEPSE" — the first in a long sequence of concrete strategy and sector playbooks that will occupy the chapters ahead, including a dedicated dividend income strategy, playbooks for IPO allotment, rights issues, and sector rotation, a chapter on building your own research and tracking systems, liquidity-based entry and exit playbooks, and individual sector playbooks for banking, hydropower, microfinance, insurance, and manufacturing and hospitality companies. The Canon Score built across this Part will travel forward into every one of those chapters as the shared instrument for judging quality — but from here on, the book's focus shifts to what to actually do with that judgment, strategy by strategy, sector by sector, decision by decision.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV
STRATEGIES, PLAYBOOKS & DECISION FRAMEWORKS
Part XIV · Chapter 67
Long-Term Value Investing on NEPSE
First published 23 Aug 2026 · Last verified 29 Aug 2026
Man Bahadur Basnet keeps a steel almirah in his shop in Damak. Inside it, wrapped in a plastic bag, are share certificates and, more recently, a folder of printed Demat statements. He bought his first shares — twenty kitta of a development bank — in 2007, using money saved from three years of running his hardware store. Friends told him he was foolish. The bank was small, unknown outside the eastern Tarai, and its share price barely moved for two years. Then came the 2008 bull run, and Man Bahadur's twenty kitta became worth more than he had paid for his shop's tin roof. He did not sell. Then came the 2011 crash, and the same shares fell to a third of their peak value. He still did not sell. Today, after two stock splits, a merger, and eighteen years of dividends reinvested into more shares, that original twenty-kitta purchase has grown into a holding that quietly funds his younger daughter's engineering college fees. Man Bahadur did not time the market once. He read a handful of financial statements, decided the bank was soundly run and cheaply priced, and then he waited.
His nephew Suresh, who works in a mobile shop in the same bazaar, has a different story. Suresh opened a TMS trading account in 2020, during the pandemic-era boom, when the NEPSE index climbed from under 1200 points to above 3200 points in a little over a year. He bought hydropower shares on the day they listed, sold within a week for a quick gain, felt like a genius, and put the money into a finance company someone in his Viber group was calling "the next big one." He has traded dozens of times since. When the market corrected sharply in 2022, he was holding three stocks he had bought near their peaks, and he sold two of them in panic, locking in losses. Across five years of active trading, after brokerage commission, transaction taxes, and the short-term capital gains he has paid again and again on shares he barely held for a season, Suresh has made less money than Man Bahadur made by doing almost nothing.
This chapter is about why Man Bahadur's approach — buying good businesses cheaply and holding them patiently — works especially well on NEPSE, and about how to actually do it, step by step, using the tools this book has already given you.
Lesson 67.1 — What Value Investing Means, and Why NEPSE's Inefficiencies Favour It
Start with the plain idea. A share of stock is not a lottery ticket or a number that moves up and down on a screen. It is a small piece of ownership in a real business — a bank that lends money and collects interest, a hydropower company that sells electricity, a microfinance institution that serves rural borrowers, an insurer that collects premiums and pays claims. That business has a true worth, based on the cash it can generate for its owners over time. This true worth is called intrinsic value — what the business is actually worth, independent of whatever price the market happens to be quoting today.
Value investing means buying ownership in a good business for less than that business is actually worth, and then holding it until the price catches up to the value — or beyond, as long as the business stays good. It is the opposite of guessing which way a stock's price will move tomorrow. It does not care about candlestick patterns or Viber group tips. It cares about two questions only: is this a good business, and is it on sale?
KEY CONCEPT
Value investing is buying a rupee of real business worth for seventy or eighty paisa, based on patient analysis, and waiting for the market to recognise that worth — not guessing short-term price direction.
Think of it like buying land. Two plots outside Bharatpur look the same today: both are paddy fields, both are quiet, both are priced similarly per anna. But one plot sits directly on the path of a road the municipality has already approved and budgeted, while the other does not. A patient buyer who does the homework — checking the municipal plan, walking the site, asking the ward office — can buy the first plot today, before the road is built and before the price reflects the coming change, and simply wait. A speculator who buys whichever plot is being talked about most this week, without checking anything, is gambling, not investing. Value investing in shares works the same way: do the homework, buy what is underpriced relative to its real worth, and let time do the rest.
Now, why does this approach work particularly well on NEPSE, more so than it might on a large, closely-watched market like the New York Stock Exchange?
The answer is market inefficiency. In a perfectly efficient market, every piece of information about every company is instantly known by thousands of professional analysts, and stock prices adjust immediately to reflect true value. In such a market, bargains are rare, because too many smart, well-funded people are hunting for them at the same time. NEPSE is not that kind of market — and that is good news for a patient, careful investor.
Consider the structure of NEPSE. There are over 250 listed companies, but only a small number of brokerage houses publish regular equity research, and most of what gets published focuses on the largest, most liquid banks and hydropower names. Dozens of smaller, perfectly sound companies — a regional development bank, a mid-sized microfinance institution, a smaller hydropower producer with steady output — go for months or years without a single research note written about them. Compare this to a market like India or the United States, where a mid-sized bank might be covered by a dozen professional analysts, each publishing detailed models. On NEPSE, an investor who is willing to read annual reports, calculate ratios, and apply a structured framework like the Canon Score (Part XIII) has an information advantage that is very hard to get in more developed markets.
Second, NEPSE's trading volume is dominated by retail investors — individuals trading through TMS apps, often part-time, often reacting to sentiment rather than financial statements. As Chapter 50 detailed, this creates predictable behavioural patterns: herding into whatever sector is hot (hydropower IPOs one season, finance companies another, microfinance another), panic selling during corrections, and chasing stocks purely because their price has already risen. When a large share of market participants are trading on emotion and rumour rather than on business fundamentals, prices drift away from intrinsic value more often, and by wider margins, than they would in a market dominated by disciplined institutional investors.
Third, remittance-driven liquidity cycles amplify this effect. When remittance inflows are strong and Nepal Rastra Bank (NRB) policy keeps banking sector liquidity loose, deposits swell, margin lending expands, and money floods into NEPSE, pushing prices — of good and bad companies alike — well above what fundamentals justify. When liquidity tightens, the reverse happens: even fundamentally sound companies get sold off indiscriminately as investors scramble for cash or face margin calls. This is not a subtle effect. NEPSE's history shows dramatic multi-year cycles: a run-up in the mid-2000s that then gave back most of its gains over the following several years, another boom around 2016 that reversed sharply within a year, and a pandemic-era surge that carried the index from roughly 1200 to above 3200 points before falling by more than half over the following year. These are not gentle, efficient adjustments. They are large, sentiment-driven swings — exactly the kind of environment where patient, fundamentals-based buying can find real bargains, both on the way down (quality companies sold off with everything else) and in the quiet years between booms (good companies simply ignored because nothing exciting is happening in their sector).
CASE IN POINT
During NEPSE's 2021–2022 correction, the index fell more than 50 percent from its peak within about a year. Many fundamentally strong commercial banks and hydropower producers — companies whose actual businesses had not deteriorated nearly as much as their share prices — traded at valuations well below their book value or long-run earnings power simply because liquidity had dried up and sentiment had turned negative across the board. Investors who had done fundamental homework before the correction, and who had cash and patience, found some of their best entry points during exactly this period.
None of this means every cheap stock on NEPSE is a hidden gem. Inefficiency cuts both ways: some companies are cheap because the market has correctly, if belatedly, recognised that they are poorly run, over-leveraged, or losing relevance. The skill of value investing is telling the difference between a business that is undervalued and a business that is correctly valued at a low price because it deserves to be there. That distinction is exactly what the Canon Score, built across Part XIII, is designed to help you make.
WARNING
"Cheap" and "undervalued" are not the same thing. A stock trading at a low price-to-book or price-to-earnings ratio may simply be a weak business correctly priced low. Never buy a stock for its low price alone — always ask what the Canon Score says about the underlying business first.
Lesson 67.2 — Combining the Canon Score With Valuation Discipline
A complete value-investing decision on NEPSE rests on two separate questions, asked in a fixed order.
The first question is: is this a good business? This is a quality question, and Part XIII gave you the tool to answer it — the Canon Score, which rates a company from 0 to 100 across seven dimensions: Financial Strength & Profitability, Governance & Promoter Behaviour, Liquidity & Tradability, Valuation Reasonableness, Sector & Business Model Durability, Growth Trajectory, and Dividend & Capital Return Discipline, each adjusted for the realities of banking, hydropower, microfinance, or insurance where relevant.
The second question is: is the price fair, relative to what the business is actually worth? This is a valuation question, and Part IX gave you the tools to answer it — price-to-earnings and price-to-book ratios compared against sector norms, dividend yield analysis, and simple discounted cash flow or dividend discount thinking to estimate a company's intrinsic value per share.
Value investing on NEPSE means never answering only one of these questions. A company can score beautifully on the Canon Score — strong capital adequacy, honest promoters, healthy dividend history — and still be a poor purchase today, if its price has already run far ahead of its intrinsic value. Equally, a company can look statistically cheap — a rock-bottom price-to-book ratio — and still be a poor purchase, if the Canon Score reveals weak governance, deteriorating asset quality, or a promoter who has diluted shareholders repeatedly through rights issues.
Picture farming again. A good business is fertile soil. A good price is a fair rent for that land. You would not lease the most fertile field in the district if the landlord demanded three times the going rent — you would overpay for years before the harvests caught up. And you would not lease a waterlogged, infertile field just because the rent was cheap — no harvest, however cheap the rent, is a good investment of your season's labor. You need both: good soil, at a fair price.
This two-question framework can be laid out as a simple matrix, crossing the Canon Score against how the stock is priced relative to its estimated intrinsic value:
Canon Score
Price vs. Intrinsic Value
What This Usually Means
Typical Action
High (70+)
Below estimated value (20%+ discount)
Quality business, mispriced by sentiment or neglect
Strong candidate for the value watchlist; build a position
High (70+)
At or above estimated value
Quality business, but the market has already noticed
Watch and wait for a better entry; do not chase
Moderate (50–69)
Below estimated value
Decent business, possibly cheap for a real reason
Investigate further before acting; re-check governance and sector dimensions
Low (below 50)
Below estimated value
Classic value trap warning sign
Avoid, or require a much deeper look before any commitment
Low (below 50)
At or above estimated value
Weak business, fully or over priced
Avoid
This is why Part XIII's Valuation Reasonableness dimension, one of the seven Canon Score pillars, exists inside the scoring system at all — it acts as a first filter. But for a serious value-investing decision, that single dimension inside the Canon Score should be supplemented with the fuller valuation toolkit from Part IX: an actual estimate of intrinsic value per share, not just a relative comparison against sector averages. A whole sector can be overpriced together, in which case "cheaper than its peers" is not the same as "cheap in absolute terms."
PRACTICAL TOOL
A simple two-filter screen for a value watchlist candidate: (1) Canon Score of 65 or above, sector-adjusted per Part XIII; and (2) current market price at least 20 percent below your estimated intrinsic value per share from Part IX methods. A stock that clears both filters earns a place on your watchlist. A stock that clears only one needs more work before it earns a place in your portfolio.
Circle of competence matters here too. Part XIII gave you sector-specific scoring adjustments for banks, hydropower, microfinance, and insurance precisely because these four sectors dominate NEPSE and each has its own quirks — a bank's capital adequacy ratio means something different from a hydropower company's plant load factor, which means something different from a microfinance institution's portfolio-at-risk. A disciplined value investor on NEPSE tends to go deep in two or three sectors they genuinely understand, rather than spreading shallow attention across all of them. You will make better intrinsic value estimates for a hydropower company if you understand how a power purchase agreement with the Nepal Electricity Authority actually works than if you are guessing.
Lesson 67.3 — Margin of Safety on NEPSE
Every valuation estimate is, at best, an educated guess. You might overestimate a bank's future loan growth. You might underestimate how a change in the Nepal Electricity Authority's power purchase rate affects a hydropower company's cash flows. You might misjudge how quickly a promoter family will dilute shareholders through another rights issue. Because your estimate of intrinsic value can be wrong, you need a buffer between the price you pay and your estimate of true worth. This buffer is called the margin of safety.
Margin of safety means buying at a price meaningfully below your estimate of intrinsic value, so that even if your estimate turns out to be somewhat too optimistic, you still come out roughly whole — or ahead.
Think of it like the bridges built over monsoon rivers in the hills. Engineers do not design a bridge to carry exactly the weight of the heaviest truck expected to cross it. They design it to carry two or three times that weight, because floods are unpredictable, materials degrade, and estimates of maximum load are never perfectly precise. That extra capacity is not wasted — it is the margin that keeps the bridge standing when reality turns out worse than the plan. A margin of safety in investing plays exactly the same role: it protects you when your analysis, however careful, turns out to be a bit too rosy.
On mature, heavily researched markets, a value investor following the classic Benjamin Graham tradition might look for a price roughly 25 to 33 percent below estimated intrinsic value before buying. On NEPSE, this book recommends a wider margin — generally in the range of 35 to 50 percent below your estimated intrinsic value — for several reasons specific to the Nepali market.
First, illiquidity. As Part XIII's Liquidity & Tradability dimension explained, many NEPSE-listed shares trade thinly. If your analysis is wrong, you may not be able to exit quickly at a fair price — thin daily volumes mean a sizeable sell order can move the price against you. A wider margin of safety compensates for this exit risk.
Second, policy and regulatory risk. NRB directives on the credit-to-deposit ratio, capital adequacy requirements, provisioning rules, or interest rate spreads can change a bank's or microfinance institution's economics almost overnight, in ways a purely financial-statement-based valuation could not have anticipated. Hydropower companies face their own version of this risk through royalty rate changes, tariff-setting decisions, and transmission constraints. A margin of safety needs to be wide enough to absorb the impact of these sudden, sector-wide policy shifts.
REGULATORY DETAIL
NRB periodically revises prudential requirements for banks and financial institutions — including credit-to-deposit ratio ceilings, provisioning norms for non-performing loans, and capital adequacy floors — sometimes with limited notice. A single directive can force multiple banks to slow lending, raise fresh capital, or cut dividends in the same year, moving an entire sector's share prices together regardless of any individual bank's own quality. A margin of safety built for NEPSE must have room to absorb this kind of sector-wide regulatory shock.
Third, price volatility driven by circuit breakers and thin free float. NEPSE uses daily price bands that limit how far a stock can move in a single session. This dampens single-day volatility but can also extend the time it takes for a stock hit by bad news to find its true bottom — or for a stock catching a wave of enthusiasm to find its true top — since the full move gets spread across several sessions of consecutive limit-up or limit-down trading. Combined with a small free float on some smaller companies, prices can become disconnected from fundamentals for longer stretches than in deeper markets.
Here is how a margin of safety calculation might look in practice, using rounded, illustrative numbers for a hypothetical commercial bank:
Item
Value
Estimated intrinsic value per share (Part IX methods)
NPR 420
Current market price
NPR 260
Discount to intrinsic value
38%
Minimum required margin of safety (this book's NEPSE guideline)
35%
Verdict
Discount exceeds the minimum threshold — margin of safety condition is met
Note what this table does not say. It does not say the stock is a buy. The margin of safety test is necessary, but not sufficient, on its own. It must be paired with the Canon Score quality filter from Lesson 67.2. A stock priced at a 40 percent discount to a poorly-estimated intrinsic value, for a company with weak governance, is not a bargain — it is a trap dressed up in a spreadsheet.
WARNING
A wide discount to your estimated intrinsic value is not, by itself, proof of a bargain. If your intrinsic value estimate is built on unrealistic assumptions — too-generous growth rates, ignoring a governance red flag, ignoring sector headwinds — a large "margin of safety" number is meaningless. Always stress-test your intrinsic value estimate with a conservative, not an optimistic, set of assumptions before trusting the discount you calculate.
Lesson 67.4 — The Patience Discipline: Holding Periods and Nepal's Tax Structure
Finding a good business at a good price is only half of value investing. The other half is holding it long enough for the price to catch up to the value — and that requires patience, which is a discipline, not a personality trait you either have or lack. It can be built, the same way a habit of saving or a habit of exercise is built: through structure, rules, and repetition.
Why does patience matter so much? Because the market's recognition of a company's true worth rarely happens on a schedule that suits you. A bank you bought at a 38 percent discount to intrinsic value might stay undervalued for six months, or eighteen months, or occasionally even longer, before sentiment shifts and the price moves toward fair value. During that wait, the price may fall further before it rises — a temporary, sentiment-driven dip that has nothing to do with the business getting worse. An investor without a patience discipline sells during that dip, locking in a loss on a company that was never actually a bad investment — only a slow one.
Nepal's tax structure gives you a direct financial reason to build this discipline, on top of the investment logic. As Part VII explained in detail, capital gains on NEPSE shares are taxed differently depending on how long you have held them. Shares held for longer than 365 days are treated as long-term holdings and taxed at a lower capital gains rate; shares sold within 365 days are treated as short-term holdings and taxed at a higher rate. This structure is a deliberate policy choice — the government wants to reward investors who commit capital to businesses for the long run, and to discourage the kind of rapid in-and-out trading that adds volatility without adding real capital to the economy.
REGULATORY DETAIL
Nepal's capital gains tax on listed shares has historically applied a lower rate to gains on shares held beyond 365 days than to gains on shares sold within 365 days, with a further distinction between individual and institutional investors. Rates have been revised by the government from time to time, including a revision during the Fiscal Year 2083/84 budget. Because the exact percentages change, always confirm the current rate with your broker, your Demat participant, or the latest Inland Revenue Department circular before making a tax-driven decision — but the underlying structure, rewarding patience over frequent trading, has remained consistent.
The numbers make the incentive concrete, even using illustrative rather than current figures. Imagine an investor who buys a stock, sees a healthy gain, and has to decide whether to sell at day 340 or wait until day 400:
Scenario
Holding Period
Tax Treatment
Illustrative Effect on a NPR 100,000 Gain
Sell early
340 days (short-term)
Higher short-term capital gains rate applies
Larger tax bill, smaller take-home gain
Wait 60 more days
400 days (long-term)
Lower long-term capital gains rate applies
Smaller tax bill, larger take-home gain, same underlying investment
The lesson is not about these specific numbers — it is about the shape of the incentive. A patient investor who was already planning to hold a good business for years captures this tax benefit automatically, as a side effect of good behaviour, without ever trying to "optimize taxes." An impatient trader who moves in and out of positions every few weeks pays the higher short-term rate again and again, and also pays brokerage commission on every single transaction — a double cost that compounds against Suresh's kind of trading far more than most part-time traders realise.
Patience discipline needs rules to survive real market drawdowns, because good intentions alone rarely hold up when a portfolio is down 20 percent and every Viber group is predicting further falls. Three simple rules help:
First, decide your holding thesis before you buy, in writing if possible — which Canon Score dimensions matter most for this company, and what your intrinsic value estimate is based on. Second, re-score the company on a fixed schedule, following the re-scoring guidance from Chapter 66, rather than reacting to daily price movements. Quarterly results, annual reports, and AGM announcements are the right triggers for re-checking your thesis — a single bad trading day is not. Third, define your actual sell triggers in advance: a material deterioration in the Canon Score itself (worsening governance, weakening financials, a broken business model), the price rising to meet or exceed your intrinsic value estimate, or the discovery of a materially better opportunity elsewhere. A falling price, by itself, is not one of these triggers — in fact, a falling price on a company whose Canon Score has not deteriorated is often a reason to consider buying more, not selling.
CAUTION
Patience is not the same as denial. Holding a stock because you are disciplined is different from holding a stock because you cannot admit the original thesis was wrong. If a company's Canon Score has genuinely fallen — a governance red flag, a sustained deterioration in asset quality, a broken dividend record — patience is no longer the right word for staying in. Re-score honestly, on schedule, and let the score itself tell you whether you are being patient or being stubborn.
Lesson 67.5 — Building and Maintaining a Value Watchlist
A value watchlist is a working list of NEPSE-listed companies that have passed your quality filter but have not yet passed your price filter — companies you are actively waiting to buy, rather than companies you already own. Keeping this list separate from your actual portfolio does two things: it forces patience, because a company only graduates onto your buy list when its price actually falls into a good range, and it keeps you constantly scanning the market with a clear standard, instead of reacting impulsively to whatever headline or rumour is loudest this week.
Building the watchlist starts with the screening process Part XIII described: run the Canon Score across the sectors you understand best, sector-adjusted for banks, hydropower, microfinance, or insurance as appropriate, and keep every company that clears your minimum quality bar — 65 or above is a reasonable starting threshold for most investors, though a more conservative investor may prefer 70. For each company that clears this bar, estimate intrinsic value per share using Part IX's tools, and record that estimate alongside the current market price.
A working watchlist table might look like this:
Company
Sector
Canon Score
Est. Intrinsic Value/Share
Current Price
Discount/Premium
Status
Sagarmatha Development Bank*
Banking
76
NPR 410
NPR 270
34% discount
Approaching buy zone
Himalaya Hydro Power*
Hydropower
71
NPR 340
NPR 355
4% premium
Watch, not yet cheap enough
Purbanchal Laghubitta*
Microfinance
68
NPR 610
NPR 590
3% discount
Close, monitor next quarter
Everest Beema*
Insurance
82
NPR 950
NPR 640
33% discount
Meets buy criteria
*Illustrative names, used here only to show table structure, not real NEPSE-listed companies.
PRACTICAL TOOL
Keep your watchlist in a simple spreadsheet with six columns: company name, sector, current Canon Score, your estimated intrinsic value per share, current market price, and the resulting discount or premium. Update the score after every quarterly result and AGM, and update the price weekly. Sort by discount percentage — the companies at the top of that sorted list, with the deepest discounts and the highest scores, are your best current opportunities.
Maintaining the watchlist matters as much as building it. NEPSE's fiscal year runs roughly from mid-July to mid-July, with quarterly results typically released within weeks of each quarter's close, and most companies holding their AGMs — where dividend and bonus share decisions get finalised — in the months following the fiscal year-end. Tie your re-scoring calendar to these actual triggers: a fresh quarterly result, an AGM announcement, a rights issue or debenture announcement, an NRB directive affecting a sector. Between these events, resist the urge to re-score based on price movement alone — the business has not changed just because the stock has.
Diversification deserves special attention when building a NEPSE-specific watchlist, because the index itself is heavily concentrated. Banking, hydropower, finance, and microfinance together make up a large majority of NEPSE's total market capitalisation, and it is easy to end up with a watchlist — and eventually a portfolio — that is really just one big bet on domestic credit and power generation cycles, spread across many tickers. A disciplined value investor deliberately checks sector weightings across the watchlist and portfolio together, applying the position sizing guidance from Chapter 66, so that a single regulatory shock to the banking sector, for instance, cannot damage the whole portfolio at once.
Lesson 67.6 — A Full Worked Case Study
To bring the whole strategy together, walk through a complete, step-by-step example, using a hypothetical company: Himal Bikas Bank Ltd., a mid-sized commercial bank listed on NEPSE. Every figure below is illustrative, built to demonstrate the process, not a real quoted stock.
Step one: the initial screen. During a period of NEPSE-wide weakness — liquidity has tightened, deposit growth has slowed, and banking sector share prices have fallen broadly — you run a sector screen of commercial banks using the Canon Score framework from Part XIII. Himal Bikas Bank surfaces near the top of the list, with a total score well above your 65-point threshold, even though its share price has fallen along with the rest of the sector.
Step two: the Canon Score, dimension by dimension.
Canon Score Dimension
Score (out of allocated weight)
Brief Note
Financial Strength & Profitability
Strong
Capital adequacy comfortably above the NRB regulatory floor; return on equity above sector median
Governance & Promoter Behaviour
Strong
Promoter family has not diluted shareholders through opportunistic rights issues; clean audit history
Liquidity & Tradability
Moderate
Reasonable daily traded volume for a mid-sized bank, though not among the most liquid names
Valuation Reasonableness
Strong
Trading well below sector median price-to-book
Sector & Business Model Durability
Strong
Diversified loan book across trade finance, SME lending, and retail; not overly concentrated in one risky segment
Growth Trajectory
Moderate
Loan growth has slowed with the sector, in line with the broader credit cycle, not company-specific weakness
Dividend & Capital Return Discipline
Strong
Consistent bonus and cash dividend history across the past several years
Total (sector-adjusted for banking)
74 out of 100
Clears the quality threshold comfortably
Step three: estimating intrinsic value. Using Part IX's tools, you build a conservative estimate combining a price-to-book approach — applying a reasonable, not generous, multiple to the bank's audited net worth per share — with a dividend-based estimate that discounts the bank's likely future dividend stream at a required rate of return appropriate for NEPSE's risk level. Both methods converge on a range, and you take the more conservative end: an estimated intrinsic value of roughly NPR 410 per share.
Step four: checking the margin of safety. The current market price is NPR 265 per share. The discount to your conservative intrinsic value estimate works out to roughly 35 percent — right at this book's minimum recommended margin of safety for NEPSE. Because the Canon Score is comfortably above your quality threshold and the discount clears your minimum margin, Himal Bikas Bank now qualifies as a genuine value-investing candidate, not merely a statistically cheap stock.
Step five: position sizing. Following the position sizing framework from Chapter 66, and given the bank's moderate (not top-tier) liquidity score, you decide on an initial position of around 4 percent of your total equity portfolio, with a plan to add a further 2 to 3 percent if the price falls further while the Canon Score holds steady or improves — buying more of a good business when it gets even cheaper, rather than only when it gets more expensive, which is the instinct most untrained investors follow.
Step six: holding through noise. Three months after your purchase, a rumour circulates that NRB is considering a tighter credit-to-deposit ratio directive across commercial banks. Banking sector share prices, including Himal Bikas Bank, fall a further 8 percent in two weeks on the rumour alone, before any directive is actually issued. You do not sell. You check whether the Canon Score's Financial Strength dimension would be meaningfully affected if the rumoured directive were confirmed — it would tighten lending capacity modestly across the sector, but Himal Bikas Bank's current capital position gives it more room than most peers to absorb it. The thesis holds. You treat the price fall as noise, not new information about the business.
Step seven: re-scoring and monitoring. At the next two quarterly results, you re-score the bank following Chapter 66's guidance. Financial strength holds steady; governance remains clean; the growth trajectory dimension improves slightly as loan growth stabilises. The total Canon Score edges up to 76. Over the following fourteen months, as sector sentiment recovers and the bank's next AGM confirms a healthy dividend, the share price rises to NPR 375 — narrowing the gap to your intrinsic value estimate from 35 percent to roughly 9 percent.
Step eight: the sell decision and the tax outcome. With the discount to intrinsic value now narrow, and the Canon Score having risen rather than fallen, you face a genuine decision rather than a panic. You have now held the shares for over 400 days — past the 365-day threshold Part VII described — so any sale qualifies for the long-term capital gains tax treatment rather than the higher short-term rate you would have paid had you sold impatiently near month nine, when an unrelated market wobble had briefly tempted you to lock in an early, smaller gain. You choose to trim roughly a third of the position, banking a long-term-taxed gain, while holding the remainder — because the Canon Score is still strong and a further, smaller margin of safety still exists.
This full sequence — screen using the Canon Score, estimate intrinsic value using Part IX's tools, check the margin of safety against NEPSE's wider volatility, size the position sensibly, hold through noise using re-scoring rather than price-watching as your guide, and let Nepal's tax structure reward the patience you were already practicing — is the complete value-investing process this chapter has built, lesson by lesson. It is not exciting. It does not generate a story to tell at a Dashain gathering the way Suresh's quick hydropower flip did. But it is the process behind Man Bahadur's steel almirah full of certificates, and it is repeatable, teachable, and available to any investor willing to do the reading.
Chapter recap
Value investing means buying ownership in good businesses for less than they are truly worth, and holding patiently while the market catches up. NEPSE is unusually well suited to this approach because it is not a fully efficient market: thin analyst coverage leaves many solid companies unresearched, retail investors dominate trading volume and often act on sentiment rather than fundamentals as Chapter 50 described, and boom-bust liquidity cycles tied to remittance flows and NRB policy create large, sentiment-driven price swings that regularly disconnect price from value. These same forces that cause many Nepali investors to lose money create real opportunities for a disciplined few.
A sound value-investing decision on NEPSE always asks two questions in order: is this a good business, answered by the Canon Score built across Part XIII's seven dimensions and its sector adjustments for banking, hydropower, microfinance, and insurance; and is the price fair, answered by the valuation tools from Part IX. Neither question alone is sufficient — a high Canon Score at an inflated price is not a bargain, and a statistically cheap stock with a weak Canon Score is usually a value trap rather than a value opportunity.
Because valuation estimates are always somewhat uncertain, and because NEPSE carries extra risks from illiquidity, sudden regulatory shifts from NRB, and price bands that can extend rather than shorten periods of mispricing, this book recommends a wider margin of safety on NEPSE than in more mature markets — generally 35 to 50 percent below your conservative intrinsic value estimate, rather than the 25 to 33 percent a classic value investor might accept elsewhere.
Patience is the discipline that makes the whole strategy work, and Nepal's tax structure gives you a direct financial reason to build it: shares held beyond 365 days qualify for long-term capital gains treatment at a lower rate than shares sold within a year, as Part VII explained and this chapter's worked example demonstrated in practice. Combined with the compounding cost of frequent brokerage commissions, this tax structure rewards exactly the behaviour that value investing already requires — buying carefully, then waiting.
Putting the strategy into daily practice means keeping a value watchlist: a running record of companies that pass your Canon Score quality threshold, tracked against their estimated intrinsic value, updated on a schedule tied to actual company events like quarterly results and AGMs rather than daily price noise. The chapter's full worked case study, following a hypothetical commercial bank from initial screen through Canon Score, intrinsic value estimate, margin of safety check, position sizing, patient holding through a regulatory rumour, re-scoring, and an eventual tax-efficient partial sale, showed exactly how these pieces fit together in sequence.
Chapter 68, Dividend Income Strategy, turns from patient capital appreciation toward a related but distinct goal: building a portfolio around NEPSE companies that pay steady, reliable dividends, for investors who want their shares to generate regular income rather than simply grow in value over the years. Many of the tools carry over — the Canon Score's Dividend & Capital Return Discipline dimension becomes central rather than supporting, and the same patience this chapter built will matter just as much — but the next chapter will show how to select, weight, and monitor a portfolio specifically built to produce dependable cash flow, year after year, from Nepal's dividend-paying banks, hydropower producers, insurers, and microfinance institutions.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 68
Dividend Income Strategy
First published 23 Aug 2026 · Last verified 29 Aug 2026
Dharan sits in the shadow of the Mahabharat range, where the rivers run cold even in Chaitra, and where Kamala Rai, sixty-one years old, spends her mornings on the veranda of a small concrete house going through a maroon ledger she has kept since 2071 BS. She retired from teaching mathematics at a government secondary school eleven years ago, and her pension, converted to a lump sum under the old scheme, went almost entirely into shares of three commercial banks, a hydropower company from the Tamor basin, and a microfinance institution whose branch she had watched grow from a single room above a stationery shop into a three-storey building with its own generator. She did not buy these shares to sell them. She bought them so that, twice a year, money would arrive in her bank account without her having to do anything except wait for the annual general meeting notices in the newspaper. For Kamala, and for hundreds of thousands of investors like her across the Tarai and the hill towns, the share certificate was never a lottery ticket. It was a pension substitute, a fixed deposit with better math, and in the years when it worked, it worked beautifully. In the years when it did not — when a hydropower company suspended its payout for three years running, when a microfinance company she had trusted cut its dividend to almost nothing after a regulatory crackdown — it taught her, at some cost, what this chapter now sets out to teach more cheaply: that dividend income in Nepal is a discipline, not a habit.
Lesson 68.1 — Why Dividend Income Matters More in Nepal Than the Textbooks Admit
Most of the investing literature that eventually reaches Nepali readers, whether through translated summaries, YouTube videos, or the syllabi of finance courses borrowed from American and Indian textbooks, treats dividends as a secondary consideration. Growth investing, momentum investing, the compounding of retained earnings inside a business rather than distributed to shareholders — these ideas dominate the Western retail conversation, where a young investor with a 401(k) and forty years of horizon is taught, correctly for their context, to prefer companies that reinvest profits over companies that pay them out. Warren Buffett's Berkshire Hathaway has never paid a dividend, and an entire generation of retail investors has absorbed the lesson that dividends are almost a confession of a company running out of good ideas.
Nepal is a different country with a different investor base, and pretending otherwise does real harm to real portfolios. A large share of NEPSE's retail base is not young professionals building forty-year compounding machines. It includes retired schoolteachers like Kamala Rai, retired civil servants, small business owners in the Tarai who diversified surplus cash into shares because bank fixed deposit rates fluctuate and land transactions carry heavy registration costs, and families who received shares as gifts, dowry, or inheritance and now depend on the dividend cheque the way an earlier generation depended on rental income from a tenanted floor of the family house. For this investor base, a share is not merely a claim on future growth; it is a cash-flow-generating asset, evaluated the way a farmer evaluates a plot of land by its annual yield rather than by some abstract notion of its resale value in twenty years.
KEY CONCEPT
In markets with deep pension systems, employer-sponsored retirement accounts, and liquid bond markets, growth investors can defer cash needs for decades. In Nepal, where formal pension coverage is thin, where the bond market is shallow and dominated by government paper with limited retail access, and where real estate is illiquid and transaction-heavy, dividend-paying equities function as one of the few retail-accessible instruments that convert savings into a recurring income stream. Judging a Nepali dividend strategy by American growth-investing standards misreads the entire purpose of the instrument.
This is not an argument that growth is irrelevant in Nepal, nor that Chapter 67's long-term value framework should be discarded. It is an argument that dividend income deserves to be treated as its own discipline, with its own screening criteria, its own tax mechanics, its own sector-specific traps, and its own portfolio construction logic — because a very large number of NEPSE participants are, whether they use the term or not, running a dividend income strategy already, often without having examined its assumptions.
Consider the cash-flow arithmetic that matters to a retiree. A retired investor who has converted a lump-sum gratuity into a portfolio of bank and hydropower shares is not asking "will this stock be worth more in fifteen years." She is asking "will the dividend cheque that arrives after this year's annual general meeting be large enough, reliable enough, and taxed lightly enough to cover the cost of her son's tuition, or the cost of the rice she needs for the year, or the interest due on a small loan she took to renovate a room she now rents out." That question requires a completely different analytical toolkit than the one used to evaluate a ten-bagger growth story, and building that toolkit is the purpose of this chapter.
There is also a demographic and structural reason dividend income carries unusual weight on NEPSE specifically. Nepal's capital market is dominated, in terms of listed count and market capitalisation, by banks and financial institutions, hydropower companies, insurance companies, and microfinance institutions — sectors that, for regulatory and business-model reasons explored in Lesson 68.3, tend to distribute a meaningful share of profit rather than retain it entirely for reinvestment. Unlike a market dominated by fast-growing, capital-light technology companies that plough every rupee back into expansion, NEPSE's sectoral composition makes dividend-paying behaviour the norm rather than the exception. An investor who ignores dividend mechanics on NEPSE is, in effect, ignoring the mechanics of most of the market's investable universe.
Lesson 68.2 — Cash Dividends vs Bonus Shares: Two Different Instruments Wearing One Name
The word "dividend" in the Nepali market almost always arrives bundled with a second word: bonus. A company's board proposes a dividend of, say, 20 percent, and the notice specifies how much of that is cash dividend and how much is bonus share, both expressed as a percentage of paid-up capital rather than as a percentage of the prevailing market price. This convention, inherited from the way Nepali company law and NRB and Beema Samiti directives frame distributable profit, is one of the most persistently misunderstood features of the market, and getting it wrong distorts an investor's entire sense of what yield they are actually receiving.
A cash dividend is exactly what it sounds like: a cash payment credited to the shareholder's bank account, calculated as a percentage of the face value of the share, which in Nepal is almost universally Rs 100 per share regardless of the market price. If a bank with a face value of Rs 100 declares a 10 percent cash dividend, a shareholder holding 100 shares receives Rs 1,000 before tax. A bonus share, by contrast, is not cash at all. It is an allotment of new shares, issued out of retained earnings or reserves, that increases the shareholder's total share count without any cash changing hands. If the same bank declares a 10 percent bonus alongside the cash dividend, that shareholder with 100 shares receives 10 additional shares, taking the holding to 110 shares.
The critical point Kamala Rai learned the hard way in her early years of investing is that bonus shares are not free money in the way they feel. When a company issues bonus shares, the total number of outstanding shares increases, and because the company's underlying assets, earnings, and market capitalisation have not changed simply because more paper certificates now exist, the market price is mechanically adjusted downward on the ex-bonus date, roughly in proportion to the dilution. A share trading at Rs 550 before a 10 percent bonus will typically see its adjusted base price fall to somewhere near Rs 500 (550 divided by 1.10) once the additional shares are reflected, before the market then re-prices it based on fresh information and sentiment. An investor who receives 10 bonus shares and sees the price of each of their 110 shares fall by roughly 9 percent has not been given something for nothing; they have simply had their existing ownership stake divided into more, individually cheaper pieces, similar in principle to a stock split.
KEY CONCEPT
A bonus share changes the number of pieces the pie is cut into. It does not, by itself, put a single additional rupee of value into an investor's account or change the size of the pie. Whatever value a bonus dividend has to a shareholder comes not from the bonus mechanism itself but from what it signals about the company retaining and (hopefully) productively reinvesting capital, and from the tax treatment described below, which is genuinely different from a cash payout.
This is precisely where the tax regime creates a real, not merely psychological, distinction between the two forms of distribution — and this is a point Nepali dividend investors need to internalise with more precision than the market commentary usually offers. Cash dividends paid by resident companies to individual shareholders in Nepal are subject to a withholding tax, standardly applied at 5 percent, which the distributing company deducts and remits to the Inland Revenue Department at source. For an individual shareholder, this withholding is treated as final tax on that dividend income — meaning the investor does not need to include it again in their annual income return and pay further tax on it at their marginal slab rate. In practice, this makes cash dividend income one of the more tax-efficient sources of investment income available to an individual in Nepal, since it is taxed at a flat 5 percent regardless of whether the recipient would otherwise sit in a higher income tax bracket.
Bonus shares are treated altogether differently at the point of distribution. Because no cash or immediately realisable income changes hands when bonus shares are allotted, they are generally not subject to withholding tax at the time of issuance. The tax event is deferred to the point of sale: when the investor eventually sells shares (including the bonus shares received), any gain is taxed as a capital gain rather than as dividend income. Nepal currently applies a capital gains tax regime for individual investors on listed shares that distinguishes holding period — a lower rate, around 5 percent, applies to gains on shares held longer than 365 days, described as long-term holdings, while a higher rate, around 7.5 percent, applies to gains realised on shares held for 365 days or less, described as short-term holdings. The cost basis used to calculate the gain on bonus shares, and the specific valuation conventions the depository and brokers apply when computing weighted average cost after a bonus issue, are technical details that shift periodically with IRD and CDSC practice, and a serious dividend investor should confirm the current mechanics with their broker or a tax professional each year rather than assume last year's rule still applies.
REGULATORY DETAIL
As of the tax framework prevailing through fiscal year 2082/83, cash dividends to resident individual shareholders carry a 5 percent withholding tax treated as final tax, requiring no further declaration. Bonus shares are not taxed at the point of allotment; instead, tax arises only when those shares are eventually sold, as a capital gain — taxed at roughly 5 percent for shares held over 365 days and roughly 7.5 percent for shares held 365 days or less. Rates and administrative practice can change with each year's Finance Bill, and CDSC's cost-averaging methodology after bonus issuance deserves a fresh check each tax season.
The practical consequence for a dividend income strategy is a genuine trade-off, not merely a cosmetic one. A retiree who needs cash in hand each year, like Kamala Rai, has a structural preference for cash dividends: the tax is low, final, and the money is immediately usable. A younger investor still accumulating wealth, who does not need the cash and would only have to find a reinvestment vehicle for it anyway, may reasonably prefer bonus shares, since the tax liability is deferred — sometimes for many years — until the shares are actually sold, effectively letting the investor compound a larger untaxed base for longer, subject of course to the price-dilution mechanics already described. Neither is objectively superior; the correct choice depends entirely on the investor's need for current income, which is exactly the question this chapter asks every reader to answer honestly about their own circumstances before building a portfolio.
Lesson 68.3 — Sector Anatomy: Why Payout Behaves Differently in Banks, Hydropower, Insurance and Microfinance
NEPSE's dividend-paying universe is not one homogeneous pool of similarly behaved companies. It is four or five structurally distinct businesses, each operating under a different regulator, a different capital regime, and a different relationship between reported profit and distributable cash. An investor who screens for "high dividend yield" without understanding which sector they are in, and why that sector pays the way it does, is screening blind. This is the single most consequential lesson in this chapter, because sector-blind yield chasing is the most common way Nepali dividend investors lose money that looks, on the surface, like income.
Banks and other bank-like financial institutions (commonly abbreviated BFIs in Nepal) form the largest and most closely regulated segment of NEPSE's dividend-paying universe, and their payout behaviour is fundamentally shaped by Nepal Rastra Bank rather than by the banks' own boards. NRB's unified directives, and periodic dividend-specific circulars layered on top of them, tie a bank's ability to distribute cash dividends directly to its capital adequacy position under the applicable Capital Adequacy Framework — CAF 2015 for commercial banks, CAF 2018 for infrastructure development banks — requiring institutions to hold core and total capital comfortably above regulatory minimums, plus additional buffers, before a cash dividend can even be proposed to shareholders. NRB directives issued through 2025 tightened this further: finance companies and microfinance institutions must maintain an additional buffer — roughly half a percentage point of risk-weighted assets in core capital and a full percentage point in total capital fund above the statutory minimum — purely to be eligible for dividend approval, and institutions must first allocate interest capitalised during loan grace periods to a regulatory reserve, and set aside proportionate sums into a Capital Redemption Reserve Fund against any outstanding debentures, before a single rupee reaches the dividend pool. A bank that has just completed a rights issue, absorbed a merger, or taken a large loan-loss provision may report a healthy net profit on paper while still being barred by NRB from distributing much of it in cash, because the capital adequacy or reserve conditions have not yet been satisfied.
REGULATORY DETAIL
NRB's dividend-eligibility framework does not evaluate a bank's dividend proposal on profitability alone. It cross-checks core capital ratio, total capital fund ratio, regulatory reserve allocations (including capitalised interest during grace periods), Capital Redemption Reserve Fund contributions against outstanding debentures, and — since 2025 — limits on a bank's own cross-holding in other BFIs' promoter shares (capped near 15 percent of paid-up capital) and in non-BFI promoter shares (capped near 1 percent). A bank breaching any of these thresholds can be barred from distributing dividends even in a profitable year, regardless of what its own board proposes.
Insurance companies operate under an entirely separate regulator, the Nepal Insurance Authority (the successor body to what was long known as Beema Samiti), and the restrictions here are, if anything, more explicit. Section 43 of Nepal's Insurance Act sets out four conditions that must all be satisfied before an insurer may declare a dividend at all: all preliminary (pre-operative) expenses and any accumulated prior-year losses must first be fully written off and provisioned; the insurer must maintain paid-up capital at the statutory level; the insurer must maintain the solvency margin ratio prescribed under the Act's capital-adequacy-equivalent provision; and shares allotted to the general public must be fully subscribed and paid up. On top of these four conditions, every insurer must obtain the Authority's prior approval before declaring or distributing any dividend — meaning an insurance company's dividend announcement, unlike a purely private-sector company's, is never solely a board decision; it is a regulator-gated decision. This is a large part of why life insurance companies in particular, which carry long-duration policyholder liabilities and correspondingly conservative solvency requirements, have historically paid out a smaller share of profit as cash and relied more heavily on bonus shares to satisfy shareholder expectations while preserving capital inside the business.
Hydropower companies present the most distinctive payout pattern on the exchange, and the one most frequently misread by investors screening on trailing yield alone. A hydropower project is financed overwhelmingly with debt during construction — typically a large multiple of equity, arranged through a syndicate of Nepali banks — and the loan agreements governing that debt almost always include covenants restricting or entirely prohibiting dividend distribution until scheduled principal repayments are current and, in many cases, until a debt service reserve is fully funded. During this early phase, which can run anywhere from five to fifteen years depending on the project's size and the lender covenant structure, a hydropower company may report positive accounting profit from electricity sales under its Power Purchase Agreement while distributing little or no cash dividend at all, because nearly all free cash flow after operating costs is contractually committed to debt service. Investors who bought early, expecting bank-like annual payouts, have frequently been disappointed and have sold out in frustration at exactly the point — debt substantially repaid — where the payout profile is about to change dramatically. Once a hydropower company's project loan is materially repaid, the same electricity revenue that once serviced debt becomes available for distribution, and payout ratios in the sector have historically risen sharply in this later stage, sometimes to levels well above what banks or insurers distribute, precisely because the underlying business — a regulated, long-term power purchase contract with predictable revenue and now minimal debt service — has very little need to retain further capital for growth unless it is actively building new capacity.
Microfinance institutions tell a cautionary story that belongs at the centre of any Nepali dividend chapter. Through the growth years of the microfinance sector, a number of institutions distributed extremely high dividends, heavily weighted toward bonus shares, reflecting rapid loan book growth, thin capital bases relative to loan volume, and — in some cases — aggressive recognition of interest income that had not yet been collected in cash. Regulators subsequently moved to rein this in, both through capital and provisioning tightening and, as reflected in NRB's more recent directives, by requiring microfinance and finance companies specifically to hold additional capital buffers before qualifying for dividend approval, and by requiring fintech-adjacent payment institutions and operators to build reserves before declaring dividends at all. Investors who chased the sector's headline dividend percentages in its most exuberant years, without asking whether the underlying loan book quality and provisioning matched the payout, were frequently the ones left holding shares whose dividends were subsequently cut, sometimes sharply, once asset quality problems surfaced and regulators intervened.
CASE IN POINT
Kamala Rai's microfinance holding, bought in the sector's boom years on the strength of dividend percentages that regularly exceeded those of any bank on the exchange, delivered two years of generous bonus shares before the institution's board — under regulatory pressure over provisioning and capital buffers — proposed a token single-digit dividend in the third year. The share price, which had been supported largely by yield-chasing demand, fell faster than the dividend cut alone would explain, because the market simultaneously repriced the sustainability of future payouts. Kamala's paper loss on that one holding, at its worst point, exceeded the total cash dividends she had collected from it since purchase.
The table below summarises, at a level suitable for screening rather than precision, how these four sectors differ in their regulatory payout constraints and typical payout character. It should be read as a starting orientation, not a static rulebook — regulatory thresholds and directives are revised periodically, and any serious dividend investor should re-verify current NRB, Nepal Insurance Authority, and SEBON circulars each year rather than rely on a fixed table indefinitely.
Sector
Primary regulator/constraint
Typical early-stage payout pattern
Typical mature-stage payout pattern
Key sustainability risk
Commercial banks and BFIs
NRB capital adequacy (CAF), regulatory reserve, cross-holding limits
Moderate, cash-and-bonus mix, capped by capital buffer requirements
Steadier moderate payout once capital base and CD ratio stabilise
Provisioning shocks, credit cycle downturns, rights issue dilution
NRB capital buffers (post-2025 tightening), provisioning norms
Historically very high bonus-heavy payout during rapid loan growth
Payout cut sharply if asset quality or capital buffer breached
Loan book quality, over-distribution ahead of provisioning needs
Lesson 68.4 — Screening for Dividend Sustainability: The Checklist Kamala Should Use
Once the sector-specific constraints in Lesson 68.3 are understood, the next discipline is turning them into a repeatable screen — something Kamala Rai could genuinely apply each year before deciding whether to add to, hold, or exit a dividend position, rather than relying on the previous year's headline percentage as a proxy for next year's.
The starting point, and the most commonly abused number in Nepali dividend commentary, is the payout ratio: the proportion of distributable profit actually paid out as dividend, whether cash or bonus, relative to net profit for the year. A payout ratio consistently above roughly 80 to 90 percent of distributable profit leaves very little margin for a bad year, an unexpected provisioning requirement, or a regulator-mandated reserve allocation, and should be treated as a caution flag rather than a reassurance, even if the resulting yield looks attractive. Conversely, a payout ratio that has been unusually low for several consecutive years while the company sits on a large free reserve and comfortably exceeds its regulatory capital minimums may signal that a board is being unnecessarily conservative, or it may signal that management is deliberately retaining capital ahead of a known future need — a planned capacity expansion, a merger, an anticipated regulatory tightening — and the investor's job is to find out which explanation applies before assuming the dividend will simply rise.
The second and more important screen is the distinction between accounting profit and distributable cash. Nepali financial statements, particularly for banks and hydropower companies, can include profit components that are real under accounting standards but not immediately available as distributable cash — unrealised fair value gains on investments, interest income accrued but not yet collected (particularly relevant for banks with a rising share of restructured or grace-period loans, and for microfinance institutions with a history of aggressive accrual), and deferred tax adjustments. A dividend investor should look past the headline net profit figure to the cash flow statement, specifically operating cash flow, and ask whether operating cash generation genuinely covers the proposed dividend, not merely whether accounting profit does on paper.
PRACTICAL TOOL
A five-point annual sustainability screen, applied before each AGM season: (1) Has the payout ratio against distributable profit stayed within a sustainable band, roughly 40 to 75 percent for banks and insurers, given the sector's capital retention needs? (2) Does the company sit comfortably above its regulatory capital or solvency minimum after this year's proposed distribution, including all applicable buffers, not merely at the bare minimum? (3) Does operating cash flow, not merely accounting net profit, cover the proposed dividend? (4) Has the payout been consistent — rising, flat, or predictably cyclical — over the past five years, or has it been erratic in a way that signals reactive rather than planned distribution? (5) For hydropower specifically, is the company still inside its project-loan repayment period, and if so, what does the loan covenant schedule imply about when payout capacity should structurally improve?
A third screen, specific to Nepal's regulatory architecture, is checking whether the company has recently undergone, or is likely soon to undergo, a capital-raising event that the dividend policy must be read against. A bank that just completed a merger, or that recently issued rights shares to meet a higher regulatory capital requirement, will often show a temporarily depressed per-share dividend simply because the equity base has expanded faster than distributable profit — this is not necessarily a sign of business deterioration, but it does mean the historical per-share dividend trend is not comparable across the capital-raising event, and yield calculated on the pre-dilution share count will overstate what a new investor should expect.
WARNING
A dividend track record built before a major rights issue, bonus issue, or merger should never be extrapolated forward without adjustment. The distributable profit pool may not have grown proportionally with the enlarged share count, meaning the per-share dividend an investor actually receives going forward can be structurally lower than the historical percentage suggests, even if the underlying business is performing exactly as before.
The fourth screen, easy to skip but important in Nepal's smaller, less liquid market, is ownership concentration and promoter behaviour. Because promoter groups typically hold a large, often controlling share of many NEPSE-listed BFIs, hydropower companies, and insurers, dividend policy can be influenced by promoters' own liquidity needs rather than purely by what is optimal for minority shareholders or for the institution's long-term capital adequacy. A pattern of promoters pushing for higher cash payouts in years when the company's own capital buffer is thin, or resisting dividend cuts that a prudent regulator-facing capital plan would suggest, is a signal worth tracking through AGM minutes and news coverage, even though it rarely appears in a simple ratio.
Lesson 68.5 — Building the Portfolio: Reinvestment vs Cash Withdrawal, Diversification, and Timing
With sustainability screening in place, the next task is portfolio construction: how many holdings, across which sectors, and — critically for a Nepali investor without access to the automatic dividend reinvestment plans common in Western brokerage accounts — what to do with the cash and bonus shares once they arrive.
Diversification across the sectors described in Lesson 68.3 is not merely a generic risk-management platitude; it directly addresses the fact that each sector's payout cycle runs on a different clock. A portfolio concentrated entirely in banks will experience a fairly correlated payout cycle tied to the credit cycle and NRB's capital policy stance in any given year. A portfolio concentrated entirely in hydropower will experience long stretches of low or no distribution followed by potentially large step-ups, timed not by the broad economic cycle but by each individual project's debt repayment schedule — meaning a hydropower-heavy portfolio needs to be built with attention to staggering project vintages, so that some holdings are in their high-payout mature phase while others are still in construction, rather than having every holding hit its low-payout phase simultaneously. Insurance holdings tend to offer a steadier, if generally lower, cash yield, useful as a stabilising element. Microfinance holdings, given the sector's history, deserve a smaller allocation weight and a shorter leash — meaning a lower tolerance for holding through a payout cut before questioning the position — than the other three sectors.
A reasonable starting framework for a dividend-income-focused Nepali retail portfolio, adjusted to the individual's risk tolerance and cash-flow needs, might allocate the largest single block to a handful of well-capitalised commercial banks with a multi-year record of stable, regulator-compliant payout; a meaningful block to mature-phase hydropower companies whose project debt is substantially repaid, deliberately avoiding early-construction-phase projects for the income sleeve of the portfolio even if those same projects might be attractive for a separate growth allocation; a moderate block to insurance companies, favoured for payout steadiness rather than payout size; and a smaller, closely monitored allocation to microfinance, sized so that a dividend cut in that single sector does not meaningfully damage the household's total income need.
KEY CONCEPT
A dividend income portfolio in Nepal is best thought of as a ladder across payout cycles, not a single basket of "high yield now" stocks. Because bank, hydropower, insurance, and microfinance payout cycles are each driven by a different regulatory and capital-structure clock, deliberately holding positions at different points in each sector's own cycle smooths the household's total annual income far more effectively than concentrating in whichever sector currently shows the highest trailing yield.
On the reinvestment question, the honest answer is that Nepal offers no automated equivalent of a Western dividend reinvestment plan, so every reinvestment decision is manual, and every manual decision carries brokerage commission and, for cash dividends, the 5 percent withholding already deducted at source before the investor ever sees the money. This changes the arithmetic of the reinvest-versus-withdraw decision compared with a market where reinvestment is frictionless. For an investor still in the accumulation phase, without an immediate cash need, reinvesting the after-tax cash dividend into additional shares — ideally into a position identified through the Lesson 68.4 screen, not automatically back into whichever stock paid the dividend — continues to compound the portfolio, though the investor should weigh the brokerage cost of a small reinvestment purchase against simply accumulating a few dividend payments before making one larger purchase, since minimum brokerage commissions in Nepal make very small trades proportionally expensive. For an investor in the drawdown phase, like Kamala Rai, the cash dividend is largely earmarked for actual household spending, and the more relevant decision becomes what to do with bonus shares received in the same distribution — since bonus shares are not cash, an income-focused retiree who has no interest in accumulating more shares of a given company may reasonably choose to sell a portion of newly received bonus shares each year specifically to convert them into spendable cash, effectively manufacturing a "cash-equivalent yield" that blends the actual cash dividend with a partial bonus-share liquidation, while being mindful of the capital gains tax and holding-period rules from Lesson 68.2 when doing so.
Timing around book closure deserves specific attention because it is a recurring source of confusion for less experienced NEPSE dividend investors. A company's dividend, once its board proposes it and its AGM approves it, is paid to whoever holds the shares as of the book closure date set by the company and communicated through CDSC — not necessarily to whoever held the shares throughout the entire fiscal year the dividend relates to. This creates a well-known pattern where investors buy shares shortly before book closure specifically to capture an upcoming dividend, and the share price is subsequently adjusted downward on the ex-dividend and ex-bonus date to reflect the value distributed and, in the case of bonus shares, the enlarged share count. An investor buying purely to capture a dividend just before book closure, without regard to the sustainability screen from Lesson 68.4, is engaging in a form of short-term trading dressed up as income investing, and should recognise it as such rather than mistaking it for the patient income strategy this chapter describes.
PRACTICAL TOOL
Maintain a simple annual dividend calendar cross-referenced against each holding's book closure date, AGM date, and actual credit date, alongside the CDSC-confirmed share count after any bonus adjustment. This single record — which Kamala Rai keeps in her maroon ledger — does double duty: it lets an investor reconcile actual dividend income received against what was announced (catching CDSC or broker errors, which are not rare), and it builds, year over year, exactly the consistency track record that Lesson 68.4's fourth screening point depends on.
Lesson 68.6 — The Yield Trap: When High Dividend Yield Is a Warning, Not a Reward
The final and perhaps most costly mistake a Nepali dividend investor can make is treating trailing dividend yield — last year's declared dividend divided by today's market price — as a sufficient signal on its own. Yield is a ratio with two moving parts, and a rising yield can mean either that the dividend has grown or that the share price has fallen, and these two explanations point to opposite conclusions about whether the stock deserves new money.
A falling share price mechanically inflates trailing yield even when nothing about the dividend itself has changed, and in a market as sentiment-driven and comparatively illiquid as NEPSE, share prices can fall for reasons that have nothing to do with near-term earnings — a sector-wide sentiment shift, a liquidity squeeze forcing margin-lending investors to sell, a broad market correction — while the underlying company's most recent AGM dividend remains unchanged. An investor screening purely on current yield will therefore be mechanically drawn toward exactly the stocks whose prices have fallen hardest, some of which are genuine bargains mispriced by short-term sentiment, and some of which are falling precisely because informed market participants have already concluded that the current dividend rate is not sustainable and a cut is coming. Distinguishing the two requires running the Lesson 68.4 sustainability screen on the specific candidate, not simply ranking the market by trailing yield and buying the top of the list.
WARNING
The single highest-trailing-yield stock on the exchange in any given quarter is disproportionately likely to be a stock whose price has fallen for a reason the market already understands and the yield-chasing investor has not yet investigated. Treat an unusually high yield, relative to the sector's typical range from the Lesson 68.3 table, as a question to answer before a purchase, never as a reason to buy on its own.
There is a second, subtler version of the trap: a dividend can be genuinely paid, in full, exactly as announced, and still leave the investor worse off in total return terms if the share price declines by more than the dividend received — a scenario that has played out repeatedly in Nepali microfinance and, at times, in over-leveraged finance companies, where a generous bonus-heavy payout coincided with, or was quickly followed by, a price decline driven by the market repricing the company's growth prospects or asset quality downward. An investor who receives a 15 percent dividend but watches the underlying share price fall 25 percent over the same period has experienced a negative total return dressed up in a positive-sounding headline number, and if that investor is reporting only the dividend received rather than tracking total return, they can go on believing the position is working long after it has stopped.
CAUTION
Dividend yield should never be evaluated in isolation from total return. Track the combined outcome of dividend income received plus price appreciation or depreciation over the same holding period, at least annually, for every position in an income portfolio. A position can pay its full advertised dividend and still be destroying wealth if the share price is declining faster than the yield compensates for — and this is precisely the pattern that has separated durable dividend compounders from disguised value traps across NEPSE's history.
The corrective discipline, ultimately, folds back into everything this chapter has already built. A sustainable dividend is one that survives the regulator's capital and solvency tests from Lesson 68.3, clears the payout-ratio and cash-flow-coverage screen from Lesson 68.4, and is held inside a portfolio diversified across sector payout cycles as described in Lesson 68.5 — so that no single sector's disappointment, whether a bank capital shortfall, an insurer's solvency-driven retention, a hydropower project still mid-construction, or a microfinance dividend cut, can derail the household's total income need in any given year. Chasing the single highest number on a dividend-yield ranking table, without that underlying discipline, is not a dividend income strategy. It is speculation wearing the vocabulary of income investing, and Nepal's market history — including Kamala Rai's own maroon ledger — has already demonstrated its cost to a generation of retail investors who trusted the headline percentage over the underlying arithmetic.
Chapter recap
This chapter set out to correct a persistent gap in how dividend investing is discussed in Nepal — a gap created by importing growth-oriented, dividend-skeptical assumptions from Western retail investing culture into a market where a large share of participants, from retired schoolteachers in Dharan to small business owners across the Tarai, genuinely depend on dividend cash flow the way earlier generations depended on rental income or fixed deposit interest. Lesson 68.1 established why this dependence is structural rather than a matter of preference, rooted in Nepal's thin pension coverage, shallow bond market, and NEPSE's own sectoral composition, which is dominated by banks, hydropower companies, insurers, and microfinance institutions that, for regulatory and business-model reasons, distribute meaningful shares of profit rather than retaining nearly all of it for growth.
Lesson 68.2 drew out the critical, frequently blurred distinction between cash dividends and bonus shares — two instruments habitually announced together as a single "dividend percentage" but taxed completely differently, with cash dividends carrying a 5 percent final withholding tax and bonus shares deferring tax entirely until eventual sale as a capital gain at roughly 5 or 7.5 percent depending on holding period, and with bonus shares mechanically diluting the per-share price in a way that means they are never quite the "free" windfall they can feel like. Lesson 68.3 then built the sector anatomy that separates a sound Nepali dividend strategy from a naive one: NRB's capital adequacy gating of bank dividends, the Nepal Insurance Authority's four-condition test and solvency margin requirement for insurers, hydropower's project-finance-driven suppression of payout during construction and loan repayment followed by potentially large step-ups once debt is retired, and microfinance's history of unsustainable bonus-heavy distribution followed by regulatory tightening and payout cuts.
Lesson 68.4 converted that sector understanding into a repeatable annual sustainability screen — payout ratio discipline, operating cash flow coverage rather than reliance on accounting profit alone, post-capital-raise adjustment, and attention to promoter behaviour — while Lesson 68.5 turned screened holdings into an actual portfolio, built as a ladder across each sector's distinct payout cycle rather than a single basket of whatever currently shows the highest yield, and addressed the very real manual friction of reinvestment versus cash withdrawal in a market without automated dividend reinvestment plans. Lesson 68.6 closed with the trap that undoes investors who skip the earlier five lessons: mistaking a high trailing yield, often inflated by a falling share price or an unsustainable payout, for a reward rather than a warning, and the corrective discipline of tracking total return, not dividend income in isolation, for every position held.
Together these six lessons complete the income-oriented counterpart to Chapter 67's long-term value investing framework: where Chapter 67 asked whether a business is worth owning for what it will become, this chapter asked whether a business is worth owning for the cash it will reliably return along the way, and showed that on NEPSE those two questions, while related, are answered through genuinely different evidence — regulatory capital filings and solvency circulars as much as earnings growth projections.
The book now turns from steady, income-generating ownership toward a different register of market activity in Chapter 69, "IPO, Rights Issue & Sector Rotation Strategies," which examines the primary-market and tactical side of Nepali equity investing: how to evaluate new listings and initial public offerings before a trading history even exists to screen, how rights issues — the very capital-raising events flagged in this chapter as disruptive to historical dividend comparisons — should be evaluated by existing shareholders deciding whether to subscribe, and how sector rotation, moving allocation between banks, hydropower, insurance, microfinance, and other NEPSE segments as their respective cycles turn, can be layered on top of, or in tension with, the patient dividend income discipline built here. Readers who have just learned to distrust a rights-issue-inflated payout history in this chapter will find that same rights issue examined from the other side — as a decision facing the existing shareholder asked to put in fresh capital — at the centre of the chapter that follows.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 69
IPO, Rights Issue & Sector Rotation Strategies
First published 23 Aug 2026 · Last verified 29 Aug 2026
Sunil Rai kept two notebooks. The first was a ledger of his salary as a civil engineer with the Dharan sub-metropolitan office, unremarkable, dutiful, the kind of record any government auditor could approve without a second glance. The second notebook was messier, full of crossed-out numbers, dates circled twice, and abbreviations only Sunil understood — "BOK rights," "hydro FPO," "MFI basket rotate out." That second notebook was where Sunil actually built his wealth, and by the time he was training his own nephew in Itahari to open a MeroShare account, it had taught him something most of his colleagues in the Purwanchal engineering circle never learned: in Nepal, a meaningful share of an investor's lifetime return does not come only from picking the right company and holding it forever. It comes from three recurring, almost mechanical events that repeat every year on NEPSE — the IPO calendar, the rights issue cycle, and the rotation of capital between sectors as Nepal Rastra Bank tightens and loosens the taps. Chapter 68 dealt with the patient art of collecting dividends from a stable core. This chapter deals with something more active: the primary-market and macro-cycle plays that sit alongside that core, each governed by its own mechanics, its own risks, and — if approached with discipline rather than lottery-ticket excitement — its own reliable edge.
Sunil's second notebook began, like most Nepali investors' journeys do, with an IPO form. It has since grown to include a rights-issue decision checklist and a rotation map he updates every time NRB publishes a monetary policy or a mid-year review. This chapter builds all three of those tools from the ground up.
Lesson 69.1 — The Nepali IPO Machine: ASBA, Lottery Allotment, and the Mathematics of Getting In
To understand IPO investing in Nepal, an investor first has to let go of the mental model borrowed from Indian or American markets, where IPO allotment is frequently proportionate to the size of an oversubscribed application, rewarding those who apply for larger blocks with a larger, if still partial, allotment. Nepal's retail IPO allotment system works differently, and that difference changes the entire strategy.
Since the ASBA system — Application Supported by Blocked Amount — was rolled out through the MeroShare platform in coordination with a subscriber's ASBA-enabled bank account, applying for an IPO no longer means physically depositing cash with a share registrar and waiting weeks for a refund cheque. Instead, an applicant logs into MeroShare, selects the IPO or FPO from the list of open issues, specifies the number of units desired, subject to a minimum application size that is most commonly ten units though the exact minimum and lot size can vary by issue, and nominates the bank account from which funds will be blocked. Critically, the money is not withdrawn from the account. It is frozen, blocked, for the duration of the offer period and the subsequent allotment process. If the application is unsuccessful, the block is lifted and the funds become available again in the applicant's account, without the investor ever losing custody of the cash or waiting on a physical refund voucher.
KEY CONCEPT
In Nepal's ASBA system, an IPO application does not withdraw money from your account — it blocks it. This matters for cash management: an investor with three IPOs open in the same week can only apply to all three if the combined blocked amount fits within available balance across whichever ASBA banks each issue allows, since not every bank is empanelled for every issue.
The allotment mechanism itself is where Nepal diverges most sharply from proportionate-allotment markets. When an IPO is oversubscribed, and the overwhelming majority of Nepali IPOs are oversubscribed, often by multiples running into the dozens or hundreds, SEBON-regulated allotment proceeds by computerized lottery. Each valid application, regardless of whether it was submitted for the minimum ten units or for several hundred units, receives exactly one entry into that lottery. This is the single most important fact in this lesson, and it inverts the intuition most new investors bring from other markets: applying for more units does not increase your odds of being allotted shares. It only increases your capital exposure if you win, and it increases the size of the blocked amount that sits idle while you wait for the result.
This is why Sunil's own IPO habit, refined over roughly a decade of applications, treats every household member's MeroShare account as a separate lottery ticket rather than treating his own account as a vehicle for one larger bet. When hydropower IPOs have come to market in clusters, Sunil has applied the statutory minimum through his own account, his wife's account, and, where family members have consented and the arrangement is fully compliant with each person independently controlling their own demat and bank account, through accounts belonging to his adult children. Four independent lottery entries at the minimum application size give a materially better chance of at least one allotment than one large application, given that unit count above the minimum buys no additional lottery weight. Employees of the issuing company, and residents of a hydropower project's "affected" district, are often entitled to a reserved allotment tranche that draws from a separate, smaller pool before the general public lottery runs — worth knowing for any reader who happens to live in a project's command area, since that reserved allotment is frequently underapplied and carries noticeably better odds than the general public tranche.
PRACTICAL TOOL
Build an IPO tracking sheet with five columns: issue name, sector, opening and closing date, ASBA banks eligible, and units applied per family member account. Update it every time SEBON approves a new prospectus. A ten-minute weekly habit of checking the Nepal Stock Exchange and SEBON notices for newly approved issues catches far more allotments over a decade than sporadic applications made only when an IPO happens to be heavily marketed.
Oversubscription itself is a data point worth watching, not just a hurdle to clear. A hydropower or microfinance IPO that draws a very high oversubscription multiple is telling the market something about sentiment toward that sector at that moment, and because Nepal's retail investor base is large relative to the free float typically offered in an IPO, commonly ten to thirty percent of paid-up capital depending on sector and company type, oversubscription multiples in Nepal are structurally higher than in most global markets even for perfectly ordinary companies. Sunil does not read a huge oversubscription number as proof of quality. He reads it as proof of retail enthusiasm, which is a different thing, and the difference matters enormously for what happens after listing, which is the subject of Lesson 69.3.
Lesson 69.2 — Evaluating IPOs Before You Apply: Separating a Sound Offering From a Subscription Trap
Because the lottery mechanism removes any benefit from analytical sizing of an application, a reader might reasonably ask whether IPO evaluation matters at all in Nepal — if applying for the minimum ten units gives the same odds as applying for a thousand, why bother analysing the prospectus? The answer is that evaluation still governs three decisions entirely within the investor's control: whether to apply at all, how to behave if allotted, and how to behave if not allotted and the stock is now trading in the secondary market.
Every Nepali IPO prospectus, filed with SEBON and typically summarised in the financial dailies and portals that cover new issues, discloses the same core set of facts an investor should read before applying: the issue price, still overwhelmingly par value of Rs 100 per share for most operating companies, with premium pricing reserved for a smaller and growing category of issuers, typically hydropower companies with an established operating track record; the size of the issue relative to total paid-up capital after the offering; the use of proceeds; the promoter shareholding that will remain locked in; and the financial statements for the preceding several years.
Three questions do most of the work in deciding which issues on a busy calendar deserve an application at all. First: what is the business, and does it generate cash today, or is it a pre-revenue or pre-completion project raising capital against a promise? This distinction matters most for hydropower IPOs, where a project still under construction is raising public equity to fund completion, versus a project already generating and selling electricity, where the risk profile is closer to a conventional equity issuance against an established revenue stream. Pre-completion hydropower IPOs carry meaningfully different risk — completion delays, cost overruns, hydrology risk once operational, and the terms of the power purchase agreement with the Nepal Electricity Authority all sit between the IPO and the first dividend a shareholder might see.
Second: what is the promoter track record, both in this company and, if the promoter group has other listed entities, in those as well? Nepal's market has a recognizable pattern of repeat promoter groups bringing multiple hydropower or finance-sector vehicles to market over a period of years, and an investor who has watched how a promoter group's earlier listed vehicle behaved — did it deliver the projected capacity factor, did it pay the dividend it forecast, did rights issues from that group historically dilute shareholders without commensurate earnings growth — has a genuine edge in evaluating the next issue from the same stable.
Third: what does this issue do to the sector's supply of new paper at that moment? Nepal has, in successive years, seen clusters of same-sector IPOs — a wave of hydropower issues, a wave of microfinance issues, a wave of life insurance issues following capital-driven consolidation in that sector — and the later issues in a cluster typically see thinner listing-day performance than the first movers, simply because retail liquidity available to chase new paper is finite in any given quarter.
CASE IN POINT
In years when Nepal has seen clusters of same-sector IPOs land within a few months of one another, early issues in the cluster, arriving when investor cash was still fresh and uncommitted, have often listed at stronger premiums than the third or fourth issue in the same sector arriving a few months later, once retail investors' recycled capital had already been deployed into the earlier names. Reading the calendar, not just the prospectus, is part of IPO evaluation in Nepal.
Sunil's own screening checklist, refined after two decades of applying, asks five questions of every prospectus before he even opens a MeroShare tab: is the business cash-generative today; is the promoter group's track record on prior vehicles clean; is the pricing at par or at a premium, and if at a premium, is that premium justified by comparable listed peers' trading multiples; is this issue arriving early or late in a same-sector cluster; and does the use-of-proceeds section describe debt reduction and productive capacity expansion, or does it read as a vehicle primarily to meet a regulatory capital floor with no clear operating improvement attached. That last question deserves its own lesson, because it is also the hinge on which most of Nepal's rights issues turn — but it applies to bank and finance company IPOs and FPOs too, particularly ones issued specifically to satisfy a Nepal Rastra Bank capital requirement.
Lesson 69.3 — The Listing-Day Pop and What to Do With It
Nepali IPOs carry a well-earned reputation for a listing-day pop — the tendency for a newly listed stock to open trading meaningfully above its issue price, frequently moving against the exchange's daily circuit limit on its first day or across its first several sessions of trading. This is not unique to Nepal; IPO underpricing is a globally documented phenomenon, with research on markets from the United States to India repeatedly finding first-day IPO returns clustering well above zero. Nepal's version of this phenomenon is amplified by three structural features specific to this market: the overwhelming prevalence of par-value pricing, which for a company whose earnings and dividend capacity would reasonably support a market price several multiples above Rs 100 leaves an enormous built-in gap for the secondary market to close on day one; the daily circuit breaker system, which spreads that repricing out over several sessions rather than allowing it in a single jump; and the sheer weight of retail demand chasing a limited free float in a market where public equity supply has historically lagged the growth in the demat-account-holding population.
WARNING
A strong historical tendency toward listing-day gains is not a guarantee for any single issue. Sector sentiment, market-wide liquidity conditions, and company-specific news between allotment and listing all affect outcomes. Investors who assume every allotment is an automatic quick profit have been burned by issues that listed flat or below issue price during liquidity-tight periods, particularly for over-clustered sector issues arriving late in a wave.
For an investor who is actually allotted shares, remembering that per Lesson 69.1 allotment itself is the harder hurdle, the practical question becomes: sell into the listing-day strength, or hold for the underlying business? Sunil's own rule, built from years of watching both outcomes play out, splits the allotment rather than making an all-or-nothing call. He sells roughly half of any allotment during the initial run of circuit-limit-up sessions, banking a realised gain that, in his own log kept across dozens of allotments over more than a decade, has in most cases outperformed the return from holding the full allotment through the following twelve months, precisely because par-value mispricing tends to close quickly, while the subsequent price path depends on the same fundamental factors that govern any other listed stock, with no further IPO-specific tailwind. The remaining half he holds as a genuine long-term position, subject to the same fundamental re-evaluation he would apply to any other holding, effectively converting a lottery win into a starter position in a company he has already screened favourably under Lesson 69.2.
KEY CONCEPT
An IPO allotment is best treated as two separate decisions bundled into one event: a short-term arbitrage of primary-to-secondary-market mispricing, and a long-term investment decision that should be evaluated on the same fundamentals as any other purchase. Splitting the allotment lets an investor act on both without being forced to choose only one.
It is also worth stating plainly what the listing-day pop does not do: it does not compensate for a poor allotment rate. Because the lottery system means most applicants across a busy IPO calendar year will not be allotted the majority of issues they apply for, the aggregate annual return from an IPO-only strategy depends heavily on allotment luck as much as on listing-day performance. Sunil's own multi-year log shows that in years with a thin IPO calendar, his allotment rate across all applications fell below one in six; in years with a heavy calendar of hydropower and microfinance issuance, it rose above one in three. IPO strategy in Nepal is therefore best understood as a persistent, low-cost habit, applying for the minimum unit size across every reasonably screened issue, across every family member's independent account, over many years, rather than a strategy an investor can rely on for a specific year's return target.
Lesson 69.4 — Rights Issues: Dilution Math, Renounceable vs Non-Renounceable, and the Decision Framework
Where an IPO invites new capital into a company from the general public, a rights issue asks existing shareholders to put in more of their own money, in proportion to what they already hold, in exchange for additional shares, typically though not always priced at or near par value, well below the prevailing secondary-market price. That gap between rights price and market price is what makes the rights entitlement itself valuable, and worth understanding precisely rather than treating as an automatic yes.
A rights issue is announced as a ratio: for example, a 1:2 rights issue offers one new share for every two shares currently held, so a shareholder with 200 shares is entitled to subscribe to 100 new shares. In Nepal, rights issues have historically and predominantly been non-renounceable in practice for the ordinary retail shareholder, meaning the entitlement itself could not be sold separately to another investor who wanted the right without wanting to exercise it. A shareholder either subscribed using their own capital, or let the entitlement lapse and, typically, forfeited its value entirely, since unclaimed rights shares in Nepal have generally been allotted back to the promoter group or left to the board's discretion rather than auctioned for the benefit of the non-subscribing shareholder. This is an important asymmetry against the Nepali retail investor, and it is the reason the rights-issue decision deserves as much care as an initial purchase decision, not less.
REGULATORY DETAIL
SEBON has been moving toward a rights-renounce mechanism that would let a shareholder sell an unwanted rights entitlement to another investor rather than losing its value entirely on lapse, a reform long sought by retail investor associations given how one-sided the traditional non-renounceable structure has been. Readers should check SEBON's current circulars for the mechanism's operating rules and which issues it applies to at the time they hold a rights entitlement, since rollout and scope have proceeded issue by issue rather than as an instant blanket change across the whole market.
The dilution math behind a rights decision is straightforward, but easy to skip past when an investor is simply pleased to see cheap new shares land in their account. Consider a company trading at Rs 400 per share in the secondary market, announcing a 1:2 rights issue at Rs 100 per share, a common par-value rights price for bank and finance company issuers. Before the issue, a shareholder with 200 shares holds a position worth Rs 80,000 at market price. If that shareholder subscribes fully, they pay in an additional Rs 10,000, one hundred new shares at Rs 100 each, and now hold 300 shares. The theoretical ex-rights price, the price at which the combined position should trade immediately after the rights are absorbed into the float, all else equal, is found by taking the total value going into the position, Rs 80,000 of old market value plus Rs 10,000 of new cash, and dividing by the new total share count of 300, giving a theoretical ex-rights price of roughly Rs 300 per share.
The table below sets out that arithmetic plainly.
Metric
Before the Rights Issue
After Full Subscription
Shares held
200
300
Market price per share
Rs 400
~Rs 300 (theoretical ex-rights)
Total position value
Rs 80,000
~Rs 90,000
Additional cash the shareholder pays in
Rs 0
Rs 10,000
The shareholder's total position value rises from Rs 80,000 to roughly Rs 90,000, but only because Rs 10,000 of fresh cash went in alongside it; on a per-share basis, value has been diluted and reconstituted at a lower price with more shares outstanding. Wealth has not increased simply because new shares arrived cheaply. This is precisely the arithmetic a shareholder who chooses to let the rights lapse needs to reckon with from the other side: doing nothing means the 200 original shares alone will, once the ex-rights price takes hold in the market, be worth roughly Rs 60,000 rather than Rs 80,000, a straightforward loss of value transferred to whoever does subscribe, which in Nepal's traditional non-renounceable structure is generally the promoter group absorbing unclaimed shares. Declining to subscribe to a rights issue you are entitled to is very rarely a neutral choice; it is usually a decision to transfer value to someone else.
Given that asymmetry, the decision framework Sunil applies to every rights issue notice that lands in his MeroShare account runs through four questions in order. First, can he actually finance the subscription without disturbing capital earmarked for other goals — a rights subscription paid for by liquidating a different, well-performing holding at an inopportune time is a poor trade even when the rights math itself looks favourable. Second, is the rights price meaningfully below the pre-announcement market price, giving genuine embedded value to the entitlement, or is the company's own market price already depressed enough that the rights price offers little real discount. Third, what is the stated use of proceeds, and does it describe funding for productive growth such as branch expansion, loan book growth, or project completion, or does it read as a defensive capital-raise driven by a regulatory shortfall with no attached growth story. Fourth, and this connects directly to the next lesson, is this rights issue part of a sector-wide pattern driven by a Nepal Rastra Bank capital directive, in which case the investor should expect further rounds of dilution from peer companies racing to meet the same deadline, and should judge the company's rights issue not in isolation but against how its balance sheet compares to the rest of its peer group heading into the same regulatory wall.
CAUTION
A shareholder who cannot afford to subscribe to a rights issue in full still has a partial option in most Nepali rights structures: subscribing to only part of the entitled amount, in whole-unit lots, is typically permitted, and reduces but does not eliminate the value transfer that occurs from full non-participation. Check the specific issue's subscription form before assuming it is all-or-nothing.
Lesson 69.5 — Reading a Promoter's Rights Issue: NRB Capital Mandates and the BFI Recapitalization Pattern
No discussion of Nepali rights issues is complete without understanding the single largest driver of rights-issue activity in this market's history: Nepal Rastra Bank's periodic increases to the minimum paid-up capital that banks and financial institutions must hold. The most consequential of these came via the monetary policy announced in the mid-2010s, which raised the minimum paid-up capital for commercial banks fourfold, from Rs 2 billion to Rs 8 billion, with a compliance deadline set roughly two years out. The same policy round also lifted minimum capital thresholds for development banks and finance companies, scaled to their operating footprint.
Institution Category
Prior Minimum Paid-Up Capital
Revised Minimum Paid-Up Capital
Commercial bank (national license)
Rs 2 billion
Rs 8 billion
Development bank, national-level
Rs 640 million
Rs 2.5 billion
Development bank, 4 to 10 districts
Rs 300 million
Rs 1.2 billion
Development bank, 1 to 3 districts
Rs 100 million
Rs 400 million
Finance company, national or multi-district
Rs 300 million
Rs 800 million
Finance company, 1 to 3 districts
Rs 100 million
Rs 400 million
The reaction across the banking and financial sector to that mandate is the single clearest case study a Nepali investor can study to understand promoter-driven rights issues. Bankers' associations initially objected to the timeline as impractical; within a short period after the directive, only a couple of state-owned banks already sat above the new threshold. Every other commercial bank in the country faced the same choice within the same compliance window: raise capital through rights issues, through bonus share capitalisation of reserves, through mergers and acquisitions with other institutions, or through some combination of all three. What followed was one of the most concentrated waves of rights issuance and bank mergers in NEPSE's history, compressing what might otherwise have been a decade of organic capital growth into roughly two years of forced recapitalization.
REGULATORY DETAIL
NRB's capital directives apply differently by institution class and operating footprint, and the bank or finance company itself discloses which threshold applies to it in its own filings and annual reports. An investor evaluating a BFI rights issue should always check the institution's current paid-up capital against its applicable NRB minimum before assuming a rights issue is purely growth-driven rather than compliance-driven — the two motivations coexist in most real filings but are not equally reassuring to a shareholder being asked to pay in new cash.
Understanding this pattern changes how an investor should read a bank or finance company rights issue notice today. Not every BFI rights issue is compliance-driven; NRB's capital floors have been broadly stable for some years since that mid-2010s reset, and many subsequent rights issues in the banking and microfinance space are genuinely growth-driven, funding branch network expansion, loan book growth, or absorbing merger-related capital needs. But the pattern recurs on a smaller scale whenever NRB revises microfinance institution capital norms, or whenever a specific institution's capital adequacy ratio slips below the regulatory minimum due to loan losses or aggressive balance sheet growth, forcing a defensive rights issue rather than a growth-funded one. Sunil's practical test for distinguishing the two is to compare the issuing institution's post-rights capital adequacy ratio and paid-up capital against its closest peer group, and to read the institution's own disclosed rationale for the raise against its recent loan-loss provisioning trend; an institution raising rights capital while provisioning is rising sharply is signalling something quite different from one raising rights capital while opening new branches and growing a healthy loan book.
WARNING
Undercapitalized BFIs racing to meet an NRB deadline have, in Nepal's own history, sometimes diluted shareholders through rights issues without a corresponding improvement in earnings per share, simply because the capital was raised to satisfy a regulatory floor rather than to fund an already-identified productive opportunity. A rights issue that restores compliance is not automatically a rights issue that creates shareholder value; the two outcomes can and do diverge, and only a careful read of the use-of-proceeds language and the institution's growth trajectory tells you which one you are looking at.
For an investor holding several BFI positions simultaneously, which is common in Nepal given how concentrated the free float of investable large-cap stock is in the banking and finance sector, a sector-wide capital directive from NRB is worth treating as a portfolio-level event rather than a single-stock event. When such a directive lands, the practical response is to review every BFI holding against the new threshold at once, anticipate that most of the sector will announce rights issues within a similar window, and budget cash accordingly rather than being caught having to choose between subscribing to one bank's rights issue and another's because both notices arrived in the same month. This is precisely the situation Sunil found himself managing across three separate bank holdings during the post-2015 capital-raising wave, and the lesson he draws from it, still recorded near the front of his second notebook, is to keep a standing cash reserve specifically earmarked for rights subscriptions whenever more than one BFI holding sits meaningfully below a newly announced regulatory capital floor.
Lesson 69.6 — Sector Rotation: Trading NRB's Monetary Cycle Across Banking, Hydropower, Microfinance, Insurance and Manufacturing
NEPSE's major sector indices — banking (commercial banks), development banks, finance companies, microfinance, life and non-life insurance, hydropower, and the smaller manufacturing, hotels, and trading groupings — do not move in lockstep. Capital rotates between them in a recognizable rhythm tied closely to Nepal Rastra Bank's monetary stance, the banking system's liquidity condition, and the interest rate cycle that follows from both. Learning to read that rhythm, rather than treating NEPSE as a single undifferentiated index, is the third leg of this chapter's strategy set.
The mechanism runs through several NRB levers that a disciplined investor tracks continuously rather than reacting to only after a monetary policy announcement makes headlines. The credit-to-deposit ratio ceiling that NRB enforces on banks determines how freely the banking system can lend; when that ceiling is tightened, or when banks bump up against it during a liquidity-tight period, lending contracts, and the sectors most dependent on continued credit flow, particularly hydropower project financing and microfinance institution refinancing, come under pressure first, while sectors with less reliance on fresh credit, such as insurance, tend to hold up better on a relative basis. Policy interest rates and the broader interbank and deposit rate environment work in the same direction: falling rates ease the cost of capital for construction-heavy, debt-financed sectors like hydropower and manufacturing, and improve bank net interest margins by lowering deposit costs faster than lending rates reprice downward, while rising rates squeeze margins for banks and microfinance institutions that carry higher-cost liabilities and can compress project economics for leveraged hydropower developers still servicing construction-period debt. Statutory reserve requirements and cash reserve ratio settings act as a further liquidity valve, and sector-specific lending caps, such as periodic restrictions on real estate or margin lending exposure, can trigger sudden, sector-confined selloffs independent of the broader market's direction.
Monetary Cycle Stage
Typically Favoured Sectors
Typically Pressured Sectors
Rate cuts, easing liquidity, CD ratio comfortable
Hydropower, banking, manufacturing
Sectors already fully priced from prior rally
Rate hikes, tightening liquidity, CD ratio near ceiling
KEY CONCEPT
Sector rotation in NEPSE is not a prediction of which individual company will outperform. It is a read on which sector's business model is structurally advantaged or disadvantaged by the current stance of NRB policy and the prevailing liquidity condition in the banking system. An investor can be right about the sector and still need to pick a reasonable company within it.
A disciplined rotation strategy does not require an investor to trade in and out of full positions every time NRB issues a circular. Sunil's own approach, developed over years of watching this cycle repeat, works through a quarterly review rather than a constant reshuffling. Each quarter, alongside NRB's monetary policy review and the mid-year and full-year monetary policy statements, he checks four things: the direction of the policy rate and the interbank rate, the system-wide credit-to-deposit ratio and how close it sits to the regulatory ceiling, any new sector-specific lending directive, and the relative strength of each major sector index over the preceding quarter compared to the broader NEPSE index. Where a sector's relative strength has begun turning ahead of an obviously favourable liquidity signal, he treats that as an early signal worth acting on gradually rather than waiting for the news to become common knowledge, since by the time a rate cut or liquidity easing is widely reported, the sectors most likely to benefit have often already begun moving.
PRACTICAL TOOL
Track four sector index levels alongside the broad NEPSE index every week: banking, hydropower, microfinance, and insurance. A simple ratio of each sector index to the broad index, plotted over a rolling quarter, reveals rotation in progress well before it becomes an obvious headline. A sector ratio trending up for several consecutive weeks against a backdrop of easing NRB liquidity language is a stronger signal than any single week's price move.
The practical execution of rotation matters as much as the diagnosis. Sunil does not sell an entire banking position to buy hydropower the moment liquidity eases; he adjusts the weighting of his portfolio gradually, trimming the sector he judges structurally pressured and adding to the one he judges structurally favoured over a period of weeks, in tranches, partly to avoid overpaying into a single session's enthusiasm and partly because NRB's own policy signals are themselves gradual and subject to reversal. He also treats rotation as a complement to, not a replacement for, the fundamental company-level and dividend-income discipline built in earlier chapters of this book: a hydropower company favoured by an easing liquidity cycle is still only worth buying if its project fundamentals, PPA terms, and balance sheet hold up to the scrutiny any individual purchase deserves, and a bank pressured by a tightening cycle may still be worth holding through the cycle if its dividend track record and capital position are strong enough to weather a temporary margin squeeze. Sector rotation, in other words, adjusts the tilt of a portfolio already built on sound individual holdings; it does not substitute for that underlying selection work.
Chapter recap
This chapter built three distinct but related strategies around the recurring structural events of the Nepali market. The IPO strategy rests on a single inversion of intuition that the Nepali retail investor must internalize: because allotment proceeds by lottery with one entry per applicant rather than by proportionate allocation, the winning approach is not to apply for the largest block a household can afford, but to spread minimum-sized applications across every eligible family member's independent account, applied consistently across a well-screened calendar of issues over many years. Evaluation still matters, not because it improves lottery odds, but because it determines which issues are worth applying to at all, and how to behave once allotted — a decision this chapter resolved by recommending a split approach, banking roughly half of any listing-day gain while holding the remainder as a genuinely screened long-term position.
The rights issue strategy addressed a structural asymmetry particular to Nepal's historically non-renounceable rights framework, in which a shareholder who declines to subscribe typically forfeits real value to whoever does. The dilution arithmetic worked through in Lesson 69.4, and the four-question decision framework that followed it, gives a reader a repeatable process for any future rights notice: confirm financing capacity without disturbing other goals, check the genuine discount embedded in the rights price, read the use-of-proceeds language carefully, and place the specific issue in the context of any sector-wide regulatory driver. Lesson 69.5 examined the largest such driver in Nepali market history, Nepal Rastra Bank's paid-up capital mandates for banks and financial institutions, and showed why a BFI rights issue arriving inside a sector-wide capital-compliance wave deserves a different kind of scrutiny than an ordinary growth-funded raise, since compliance-driven capital does not automatically translate into earnings growth for the shareholder providing it.
The sector rotation strategy closed the chapter by connecting NEPSE's major sector indices to the mechanics of NRB monetary policy: the credit-to-deposit ceiling, policy and interbank rates, reserve requirements, and sector-specific lending directives all push and pull capital between banking, hydropower, microfinance, insurance, and the smaller manufacturing and hotel groupings in a rhythm that a patient, quarterly-review discipline can read well ahead of the headlines. The chapter was explicit that rotation adjusts portfolio tilt rather than replacing fundamental selection, and that gradual, tranche-based repositioning serves an investor better than reactive, all-at-once trades.
Running through all three lessons was Sunil Rai's second notebook, a reminder that these are not exotic or occasional tactics but recurring, almost calendar-bound features of investing in Nepal specifically, each with a repeatable process an ordinary investor can build once and reuse for decades. An investor who has internalized the dividend income discipline of Chapter 68 and now adds the IPO, rights, and rotation processes built here has assembled a genuinely complete active layer to sit alongside a long-term core portfolio.
None of these processes, however, run themselves. Tracking a busy IPO calendar across several family accounts, monitoring rights notices against NRB capital thresholds across a portfolio of BFI holdings, and watching four sector indices against the broad market every week are all, at bottom, information-management problems before they are investment-decision problems. Chapter 70, Research and Tracking Systems for the Nepali Investor, takes up exactly that challenge, building the concrete tools, spreadsheets, watchlists, and information habits that turn the strategies in this chapter from occasional good intentions into a genuinely sustained practice — the infrastructure, in other words, that makes everything described here possible to execute reliably, quarter after quarter, year after year.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 70
Research and Tracking Systems for the Nepali Investor
First published 23 Aug 2026 · Last verified 29 Aug 2026
Aamod Sharma kept two notebooks on his desk in Kathmandu. One was full of numbers — buy prices, sell prices, dates. The other was empty except for a single line written eight months ago: "Bought NABIL because everyone at the tea shop was buying NABIL." He could not remember what he had actually thought that day. Had he checked the bank's capital adequacy ratio? Had he looked at the dividend history? Had he even opened the annual report? He could not say. All he had was a memory that had already been rewritten by everything that happened afterward — the price went up, so surely he had been smart. The price went down on another stock, so surely he had been careless. His memory was not a record. It was a story he kept editing.
Across town, his cousin Sunita ran a small trading business and treated her NEPSE (Nepal Stock Exchange) portfolio the same way she treated her shop's ledger. She had a spreadsheet with tabs for her watchlist, her holdings, and a running log of every decision she made and why. She had a calendar reminder for every company's annual general meeting (AGM — the yearly meeting where a company reports to its shareholders and proposes dividends). She checked NEPSE's own daily trading report before she believed any rumour from a WhatsApp group. When a stock she owned dropped 8 percent in a week, she did not panic — she opened her file on that company, checked whether anything in the business itself had changed, and only then decided whether to act.
Sunita was not smarter than Aamod. She did not have inside information. She had something more ordinary and more powerful: a system. This chapter is about building that system — not a personality trait, not a gift, but a set of habits and tools that any Nepali investor can set up in an afternoon and maintain in a few minutes a week. Every strategy covered in Chapters 67 through 69 — long-term value investing, dividend income investing, IPO and rights allotment strategy, sector rotation — depends on good information arriving on time and being remembered honestly. A brilliant strategy run on stale data or hazy memory is not a brilliant strategy. It is a guess wearing a strategy's clothes.
Lesson 70.1 — Where to Find Reliable Data: Official and Third-Party Sources
Before an investor can research anything, they need to know where information actually lives. In Nepal, financial information for listed companies comes from a small number of places, and it helps to sort them into two categories.
A primary source is information that comes directly from the company or the regulator — the original document, not someone's summary of it. A secondary source is a person, website, or news outlet that has read the primary source and is now telling you about it, often with commentary, simplification, or (occasionally) error mixed in. Both are useful. But an investor who only ever reads secondary sources is like a student who only reads book reviews and never opens the book. They will sound informed. They will not be as informed as they sound.
NEPSE — the exchange itself. The Nepal Stock Exchange operates the trading platform where all listed shares change hands, and its official website (nepalstock.com.np) is the first primary source every Nepali investor should know. It publishes the day's closing prices for every listed company, the daily floorsheet (a public record listing every single trade executed that day — buyer broker, seller broker, quantity, and price), market indices, and notices from the exchange itself, including trading holidays and circuit-breaker events (the automatic trading halts triggered when a stock or the whole market moves too far too fast in one session). If you want to know exactly what happened in the market today, with no one's opinion attached, NEPSE's own site is where that record lives.
SEBON — the regulator. The Securities Board of Nepal (SEBON) is the government body that regulates the securities market — it licenses brokers and merchant bankers, approves IPOs (initial public offerings — a company's first sale of shares to the public) and rights issues, and requires listed companies to disclose material information. SEBON's website (sebon.gov.np) carries regulatory notices, circulars, and the list of registered securities. SEBON also runs an electronic filing system, sometimes referred to by the acronym ERRS, where companies submit disclosures and where some of these become publicly searchable. When you want to know the actual rule behind a market practice — how rights issue pricing is supposed to work, what a company is required to disclose and by when — SEBON's own publications are the primary source, not a forum post explaining what someone thinks the rule is.
REGULATORY DETAIL
SEBON requires listed companies to disclose "material information" — facts that could reasonably affect the share price, such as major financial results, mergers, or leadership changes — promptly to the market. In practice this disclosure usually reaches NEPSE and the public through the exchange's notice system and the company's own filings before it becomes a story on a news portal. If a rumour is moving a stock's price and you cannot find a matching disclosure through NEPSE or SEBON, treat it as unconfirmed, not as fact.
CDSC and meroshare — the record of what you own. The Central Depository System and Clearing Limited (CDSC) is the institution that maintains the electronic record of who owns which shares in Nepal — this is called a demat account (short for dematerialized account, an electronic record replacing paper share certificates). meroshare is the online portal, built on top of CDSC's system, that individual investors use to view their own share holdings, apply for IPOs and rights issues, and receive share allotments electronically. For an investor, meroshare is not just an application form — it is your own personal, authoritative record of exactly what you own, as of today, verified by the depository itself rather than by your own memory or a broker's statement. When Sunita wants to know precisely how many shares of a company she holds, right down to shares received from a bonus issue (additional free shares issued to existing shareholders, usually funded from a company's reserves) she may have forgotten about, she checks meroshare, not her notebook.
PRACTICAL TOOL
Log into meroshare at least once a quarter even if you are not applying for an IPO or rights issue. It is the fastest way to reconcile your actual holdings — including bonus shares, rights shares, and any corporate actions you might have missed — against what your own tracking spreadsheet says you own. Treat any mismatch as a signal to investigate immediately, not a rounding error to ignore.
Company annual reports and AGM disclosures. Every listed company is required to publish an annual report — a yearly document covering its financial statements, the board's remarks, and its dividend proposal — and to hold an AGM where shareholders vote on that proposal. Annual reports are typically available from the company's own website, sometimes from SEBON's filings, and increasingly through NEPSE's and the third-party portals' company pages. The AGM notice itself — announcing the date, the agenda, and the book closure date (the record date used to determine exactly which shareholders are entitled to a dividend or bonus share, explained further in Lesson 70.3) — is one of the most important documents a dividend-income investor will read all year, because it is where the company's proposed cash dividend and bonus share percentages are formally announced.
Third-party financial portals. Alongside these official channels, a handful of privately run websites have become part of the everyday toolkit for Nepali retail investors. ShareSansar (sharesansar.com) and Merolagani (merolagani.com) are two of the most widely used, offering live and historical price data, floorsheet mirrors, company announcement archives, sector-wise summaries, portfolio tracking tools, and investor discussion forums, with ShareSansar also offering a paid analytics product for more advanced charting and screening. Other newer platforms have entered this space as well, offering similar live-data dashboards and AI-assisted summaries of market activity. These portals are mentioned here neutrally, as commonly used resources — not as endorsements, and not as a claim that any one of them is more accurate or reliable than another. Their genuine value is convenience: they aggregate data that would otherwise require checking several official pages separately, and they often make it easier to browse a company's announcement history or compare sectors at a glance.
CAUTION
Third-party portals are secondary sources. They are built by people summarising and formatting data from primary sources — occasionally with delays, formatting slips, or errors, like any secondary source anywhere in the world. Use them for convenience and for discovering things to check. For anything that will drive a real buy or sell decision — an exact dividend percentage, an exact book closure date, an exact quarterly profit figure — confirm against NEPSE, SEBON, or the company's own disclosure before you act.
Nothing you should act on without independent verification
Not a source
Lesson 70.2 — Organising a Personal Company Research File
Once an investor knows where information lives, the next problem is keeping it somewhere useful. A research file is a single, organised record for one company — everything you know about it, gathered in one place, so that six months from now you do not have to start from zero.
Think of it like a family doctor's patient file. A good doctor does not rediscover your medical history at every visit. They open your file, see your past test results, your known conditions, your medication history, and build on that. A company research file works the same way for an investor: it lets you build understanding over time instead of re-researching the same company from scratch every time you think about buying or selling it.
This connects directly to the Canon Score spreadsheet introduced in Chapter 66. The Canon Score gave you a structured way to score a company on the fundamentals that matter — profitability, governance, growth, valuation, and so on — producing a single comparable number for each company you track. The research file is the supporting evidence behind that score. The score is the summary; the file is the detail that justifies it. When you re-score a company (covered further in the next lesson), you should be pulling updated numbers from your research file, not guessing them from memory.
A practical research file, whether kept as a folder of documents or as a dedicated tab in your spreadsheet, should hold at minimum:
Business description. A few sentences, in your own words, on what the company actually does. For a bank, this might note its balance sheet size, its branch network, and its niche, if any, in remittance handling or SME lending. For a hydropower company, it would note installed capacity, project status, and power purchase agreement terms.
Financial history. Revenue, net profit, earnings per share, and dividend history for at least the last five years, pulled from annual reports, not estimated.
Dividend and bonus history. A simple year-by-year list of what was actually paid — cash dividend percentage and bonus share percentage — because this history is the backbone of dividend income analysis (Chapter 68) and it is far easier to check off a running list than to reconstruct it from old news clippings.
Governance and management notes. Who runs the company, any related-party transactions or promoter share pledges you have noticed, and any regulatory findings from NRB (Nepal Rastra Bank, the central bank that regulates banks and financial institutions) or SEBON.
Sector notes. Where this company sits relative to its sector peers — useful raw material for the sector rotation thinking introduced in Chapter 69.
News and disclosure log. A running list of material announcements — the date, a one-line summary, and where you read it (with a note on whether it was confirmed through a primary source).
Your Canon Score history. The score you gave the company at each re-scoring date, so you can see the trend, not just the latest number.
CASE IN POINT
Suppose you are tracking a mid-sized commercial bank. Your research file shows that over the last five AGMs, it paid cash dividends of roughly 8 to 12 percent alongside modest bonus shares most years, that its capital adequacy ratio has stayed comfortably above NRB's minimum requirement, and that its non-performing loan ratio ticked up slightly last year. None of that is dramatic on its own. But seeing it together, in one file, built up over several years, is what lets you notice a slow shift — say, a gradual rise in non-performing loans across two consecutive years — before it becomes an emergency. That pattern is invisible if each year's annual report gets read once and forgotten.
You do not need to build a file for every company on NEPSE. Build one for every company you own, and for every company on your watchlist (the shortlist of companies you are seriously considering, introduced conceptually in earlier chapters). A file with ten well-maintained companies is worth far more than a spreadsheet listing all 200-plus listed companies with a single stale number next to each name.
Lesson 70.3 — Building Your Tracking Calendar: Re-Scoring, AGMs, and Dividends
Research is not a one-time event. A company you scored well eighteen months ago may not deserve that score today — its management may have changed, its sector may have shifted, its balance sheet may have weakened. A tracking calendar is simply a schedule of dates on which you commit, in advance, to revisit specific things. It turns "I should check on this sometime" — which usually means never — into "I will check on this on this date," which usually happens.
There are three kinds of dates worth tracking.
Re-scoring dates. The Canon Score from Chapter 66 is only useful if it is kept current. Most Nepali listed companies report quarterly, in line with the fiscal year that runs roughly mid-July to mid-July (Shrawan to Ashadh in the Nepali calendar). A sensible rhythm is to re-score each holding once a quarter, shortly after its results are published, and to do a fuller re-score once a year, after the annual report and AGM. Put these dates on a calendar the moment a company announces its results schedule or AGM date — do not rely on remembering to check back later.
KEY CONCEPT
A re-scoring date is not a prediction of when the stock price will move. It is a commitment to yourself to sit down with fresh numbers and ask, honestly, "does this company still deserve the score I gave it?" The date matters more than the price on that date. A disciplined investor re-scores on schedule regardless of whether the stock is up, down, or flat that week — otherwise re-scoring quietly turns into something you only do after bad news, which biases the whole exercise toward panic.
AGM and dividend calendar. In Nepal, AGM season for many banks and financial institutions tends to cluster in the months following fiscal year-end — commonly stretching from around Poush through Falgun (roughly mid-December through mid-March), though the exact timing varies by company and by year, and other sectors follow their own timelines. The AGM is where a company's board formally proposes its cash dividend and bonus share for the year, subject to shareholder approval. Around the AGM, the company also announces a book closure date — the specific date used to determine exactly which shareholders on record are entitled to receive the dividend or bonus. If you are not a shareholder of record as of that date, you do not receive that year's dividend, even if you buy the shares the very next day.
WARNING
Missing a book closure date is one of the most avoidable mistakes a dividend-income investor can make. Shares typically need to be settled in your demat account, not merely purchased, by the relevant date — and settlement in the Nepali market takes a small number of trading days after a trade is executed. If you are buying specifically to capture a dividend, buy well before the book closure date, not on it. Track every book closure date for every company you hold in your tracking calendar the moment it is announced, and treat it with the same seriousness as a bill due date.
For a dividend-income investor following the approach built in Chapter 68, this AGM and dividend calendar is arguably the single most valuable piece of the whole tracking system, because dividend income strategy lives or dies on knowing exactly when payments are proposed, approved, and distributed — and on catching any year where a company quietly cuts its dividend, which is itself an important re-scoring signal.
Portfolio review triggers. Not every review should be calendar-based. Some should be triggered by events. A sensible investor sets rules in advance for what counts as a trigger, so that reviewing a holding is a disciplined response to a defined event, not an emotional reaction to a red number on a screen. Reasonable triggers include:
A price move of a defined size in either direction (for example, a stock moving more than 15 to 20 percent from your last review point) — a trigger to re-check the fundamentals, not an automatic instruction to sell.
A company disclosure that touches something material — a change in senior management, a regulatory action from NRB or SEBON, a related-party transaction, a change in dividend policy.
A macro event relevant to the sector — a change in NRB's monetary policy affecting bank liquidity, a change in hydropower tariff rates, a shift in remittance inflow trends that affects banking sector deposits.
The scheduled re-scoring and AGM dates already discussed.
The purpose of writing these triggers down in advance is to remove your future emotional state from the decision of whether a review is warranted. Markets move investors' feelings around constantly; a predefined trigger list keeps you reviewing for the right reasons.
Lesson 70.4 — Choosing the Right Tools: Spreadsheets and Beyond
None of this requires expensive software. For the overwhelming majority of Nepali retail investors, a spreadsheet — whether a free tool like Google Sheets or a copy of Microsoft Excel — is entirely sufficient to run the whole system described in this chapter. The strategies in this book do not depend on speed. They depend on discipline, and a spreadsheet is more than fast enough to support discipline.
A practical setup uses a small number of tabs inside one workbook:
Watchlist tab. Every company you are seriously considering, with your latest Canon Score, target entry conditions, and the date you last reviewed it.
Portfolio tab. Every company you actually hold, with quantity, average cost, current Canon Score, and links or notes pointing to that company's research file.
Calendar tab. A simple running list of upcoming dates — re-scoring dates, AGM dates, book closure dates — sorted chronologically, so that opening this one tab tells you everything coming up in the next month.
Research journal tab. Covered in the next lesson — a chronological log of decisions and reasoning, separate from the company-by-company research files.
PRACTICAL TOOL
A simple trick that saves enormous time: use your spreadsheet's built-in date functions to sort your calendar tab automatically by the nearest upcoming date, and use a basic conditional formatting rule to highlight any date within the next seven days in a bright color. This turns a passive list into an early-warning system you can glance at in five seconds, rather than a wall of text you have to read carefully every time.
There is a point at which more sophisticated tools genuinely help — but it is a later point than most beginning investors assume. Paid analytics products, such as the more advanced screening and charting tools offered by some of the financial portals mentioned earlier, can be useful once you are actively comparing many companies across a sector and want faster filtering than manually scanning annual reports allows. Dedicated portfolio-tracking apps can be convenient if you hold a genuinely large number of positions and want automatic price updates rather than manual entry. None of this is necessary to start, and none of it replaces the actual thinking — the re-scoring, the reading of disclosures, the honest journal entry — that a tool cannot do for you.
CAUTION
Watch for what might be called shiny tool syndrome: the temptation to spend an evening comparing five different portfolio-tracking apps or subscribing to a new analytics dashboard, and to feel productive for having done so, while not actually reading a single annual report or updating a single Canon Score. A fancier tool that displays the same three stale numbers you had before is not progress. If you notice yourself shopping for tools more than using the one you already have, that itself is worth writing down in your research journal.
The right test for any tool is simple: does it make it easier for you to do the actual work — finding real data, recording it accurately, and reviewing it on schedule — or does it just make the process feel more sophisticated? A well-organised free spreadsheet that you actually update every week beats an expensive tool that you open once a month.
Lesson 70.5 — Keeping an Investment Research Journal
Chapter 53 introduced the investment journal as an exercise — a habit of writing down your reasoning at the moment you make a decision, before you know the outcome. This chapter's tracking system is where that habit becomes permanent infrastructure rather than a one-time exercise.
It helps to be precise about the difference between the research file from Lesson 70.2 and the research journal. The research file is about the company — its facts, its history, its numbers, updated and overwritten as new information arrives. The journal is about you — a chronological, append-only record of the decisions you made and the reasoning behind them at the time, never edited afterward. The research file answers "what do we currently know about this company?" The journal answers "what did I think, and why, on the day I acted?"
This distinction matters because of a very ordinary and very powerful trap called hindsight bias — the tendency, once you know how something turned out, to unconsciously believe you always knew it would turn out that way. If Aamod's bank stock from the opening of this chapter had gone up 40 percent, his memory would likely tell him he had been confident and well-researched. If it had fallen 40 percent, his memory would likely tell him he had always had a bad feeling about it. Neither memory would be reliable, because memory reconstructs itself around outcomes. A journal entry written before the outcome was known is the only honest record of what you actually thought at the time.
A useful journal entry, whether for a buy, a sell, or a decision to hold through a stressful period, should record:
The date and the action — what you bought, sold, or decided not to touch, and at what price.
Your reasoning at the time — in your own words, why. Not a polished essay — a few honest sentences. What did the Canon Score say? What did the research file show? What specifically convinced you?
Your conviction level — how confident were you, on a simple scale, and what would it take to change your mind?
What you expect to happen, and by when — a genuine forecast, not vague hope. This is the part that lets future-you check whether past-you's reasoning process was actually sound, independent of whether the price happened to move favourably.
What would make you sell, or change the score — writing this down before you are emotionally invested in the outcome makes it far easier to act on later, when fear or excitement might otherwise cloud judgment.
CASE IN POINT
An investor buying into a hydropower company ahead of the monsoon season — when river-fed plants generate more electricity and revenue typically rises — might write: "Bought at NPR 310 because Q4 generation figures beat my expectation, PPA (power purchase agreement) terms with the utility are unchanged, and Canon Score rose to 74 this quarter mainly on improved cash flow. Expect the dry-season quarter to look weaker — that is normal for this sector and not itself a reason to sell. Would reconsider only if generation falls short of the prior dry season on a like-for-like basis, or if there is a change to the PPA tariff." Eight months later, whether the stock is up or down, this investor can reread exactly what they believed and check it against what actually happened — not against a hazy, outcome-coloured memory of what they believed.
The discipline of the journal is precisely that it is not edited after the fact. You do not go back and tidy up an old entry to make it sound smarter once you know the ending. If your reasoning turns out to have been wrong, the honest response is a new entry, dated today, noting what you got wrong and why — not a quiet revision of the old one. Over years, this journal becomes the single most valuable document an investor owns, because it is the only record of your actual decision-making process, unpolluted by hindsight. It is where you will find your own recurring mistakes — a pattern of buying too early into hype, perhaps, or selling too fast on bad news — patterns that are completely invisible if all you have is a memory that keeps rewriting itself to make past-you look better or worse than they actually were.
Lesson 70.6 — Putting It All Together: A Complete Personal System Walkthrough
It helps to see the whole system running together, the way Sunita actually runs hers, rather than as a list of separate pieces.
Weekly (roughly fifteen minutes). Sunita opens NEPSE's own site to check closing prices for her holdings and watchlist companies, and skims Merolagani's or ShareSansar's announcement list for anything material she should know about. She glances at her spreadsheet's calendar tab to see if anything is coming up in the next seven days — a re-scoring date, an AGM, a book closure. If a portfolio review trigger has been hit — a price move past her threshold, a disclosure worth reading in full — she notes it and schedules time to look properly, rather than reacting on the spot.
Monthly (roughly an hour). She reviews her full watchlist and portfolio tabs together, checking that every company's information is current. She logs into meroshare to reconcile her actual holdings against what her spreadsheet says she owns, catching any bonus shares or corporate actions she might have missed. She reads through her research journal entries from the past month and asks, honestly, whether her reasoning at the time still looks sound with a month's more information — not whether the price moved favourably.
Quarterly (a longer, deliberate session). As each company's quarterly results are published, she pulls the new financial figures into the relevant research file, updates the Canon Score, and writes a fresh journal entry if anything material has changed in her thinking. This is also when she checks whether any company's slow, quiet trend — like that gradually rising non-performing loan ratio — has continued or reversed.
Around AGM season (event-driven, roughly Poush through Falgun for many of her holdings). She reads each company's AGM notice as soon as it is published, records the proposed dividend and bonus figures in her dividend-history record, and marks the book closure date immediately, well ahead of time, so she never risks missing a dividend by trading too close to the deadline. After each AGM, she updates the full annual research file with the year's complete financial statements from the annual report.
Annually. She steps back and looks at her whole system rather than any single company — has her Canon Score history for each holding trended up or down over the year? Has any sector she is overweight in, per the sector rotation thinking from Chapter 69, shifted in a way her file didn't yet reflect? Has her journal revealed a recurring behavioural pattern worth correcting?
Rhythm
What happens
Primary tools used
Weekly
Check prices and announcements; scan for review triggers
NEPSE site, ShareSansar/Merolagani, calendar tab
Monthly
Full watchlist/portfolio review; reconcile actual holdings; reread recent journal entries
Spreadsheet, meroshare, journal tab
Quarterly
Update research files and Canon Scores with new results
Company disclosures, research files, Canon Score
AGM season
Record dividend/bonus proposals; mark book closure dates; update annual research file
AGM notices, annual reports
Annually
Step back and review the whole system and its trends
All of the above, together
None of this is complicated, and none of it takes more than a few focused hours a month once it is set up. What makes it powerful is not any single piece — it is that the pieces reinforce each other. The research file gives the Canon Score something real to stand on. The tracking calendar makes sure the score actually gets refreshed instead of going stale. The journal makes sure that when you look back, you are learning from what you actually thought, not from a story your memory invented afterward. Put together, this is the infrastructure that turns any of the strategies from Chapters 67 through 69 from a good idea on paper into something you can actually execute, quarter after quarter, year after year, without depending on luck, memory, or the mood of the tea shop.
Chapter recap
This chapter built the practical infrastructure underneath every strategy this book has covered so far. It began by separating primary sources — NEPSE's own trading data, SEBON's regulatory filings, CDSC and meroshare's shareholding records, and companies' own annual reports and AGM disclosures — from secondary sources like ShareSansar, Merolagani, and similar portals, which are genuinely useful for convenience and news but should never be the final word on a number that will drive a real decision. It then showed how to organise a personal research file for each company held or watched, connecting that file directly to the Canon Score spreadsheet from Chapter 66 as the evidence behind each score. From there, the chapter built a tracking calendar covering three kinds of dates — regular re-scoring dates, the AGM and dividend calendar with its critical book closure dates, and event-driven portfolio review triggers — designed to replace "I should check on that sometime" with dates you actually keep.
The chapter then addressed tools directly: a spreadsheet, free or nearly free, is sufficient for the overwhelming majority of Nepali retail investors, and more sophisticated paid tools are worth considering only once genuine complexity — many holdings, heavy sector comparison — demands them, never as a substitute for doing the actual reading. Finally, the chapter returned to the investment journal first introduced as an exercise in Chapter 53, establishing it here as permanent infrastructure: a chronological, never-edited record of what you actually thought at the time you acted, which is the only reliable defence against hindsight bias quietly rewriting your own investment history. The closing lesson walked through how all of these pieces run together in practice, on a weekly, monthly, quarterly, and annual rhythm, using Sunita's system as a working model.
With Chapter 70, Part XIV's opening stretch on strategy foundations is now complete. Chapters 67 through 69 built the strategies themselves — long-term value, dividend income, and IPO, rights, and sector rotation approaches — and this chapter built the systems needed to execute any of them well: where the data comes from, how to organise it, how to track it over time, and how to record your own reasoning honestly.
Chapter 71, "The Banking Sector Playbook," turns from foundations to application. It opens a run of six sector- and situation-specific playbook chapters that translate everything built so far into concrete, practical decision guides. Banking is a natural place to start, since Nepal's listed market is heavily weighted toward commercial banks and financial institutions, and since banking sits at the centre of so much else covered in this book — NRB's monetary policy, interest rate cycles, capital adequacy requirements, and the deposit and lending dynamics tied to remittance inflows. The chapter will show how to apply the research and tracking system built here specifically to a bank stock: which disclosures matter most, which ratios deserve the closest attention, and how a bank's dividend and bonus pattern tends to behave across an interest rate cycle.
After banking, the playbook chapters continue through hydropower, microfinance, insurance, and manufacturing and hotels — each sector with its own rhythms, risks, and disclosure patterns worth knowing in their own right. The run then turns from sectors to situations, closing with playbooks for liquidity-based entry and exit timing, IPO applications, and rights issue decisions. Together, these seven chapters are where the research and tracking system from this chapter earns its keep — because a playbook is only as good as the data feeding it, and a data system is only as good as the discipline that keeps it current.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 71
The Banking Sector Playbook
First published 23 Aug 2026 · Last verified 29 Aug 2026
Menuka Rai still remembers the afternoon she bought her first bank stock. It was a Thursday in Ashadh, the market was thin because everyone was busy closing their fiscal-year accounts, and she had just discovered that a certain commercial bank was trading at a price-to-book ratio that looked, on the surface, absurdly cheap next to its peers. She bought two hundred shares before lunch. By the following Baisakh, the bank had been quietly absorbed into a larger institution after Nepal Rastra Bank flagged it for capital shortfalls, her shares were converted at a swap ratio she had no say in, and the "cheap" stock she thought she had bought turned out to be cheap for a reason she had never bothered to check. She had looked at one number — price to book — and ignored the six or seven numbers that would have told her the real story.
That mistake is the reason this chapter exists. Banks and other bank-like financial institutions — the commercial banks, development banks, and finance companies that NRB collectively calls BFIs — make up one of the largest and most heavily weighted segments of NEPSE. On many trading days, the banking sub-index moves the whole market, simply because banks carry so much combined market capitalisation and so much trading float. An investor who does not know how to read a bank is, in effect, unable to read a third or more of the exchange. And banks are genuinely different animals from manufacturing companies, hydropower developers, or trading houses. A bank's balance sheet is not a description of a business — it is the business. Its main "product," a loan, sits directly next to its main "raw material," a deposit, and the spread between what it pays for money and what it earns on money is the entire engine of its profitability. Get the spread wrong, or misjudge the quality of what sits behind that spread, and everything else — dividends, book value, share price — eventually corrects itself.
This chapter builds Menuka's replacement discipline: a complete, repeatable playbook for evaluating any NEPSE-listed commercial bank or BFI, grounded in the specific regulatory architecture Nepal Rastra Bank has built around them.
Lesson 71.1 — Why Banks Are Not Like Other Companies
Start with the balance sheet itself. A manufacturing company's balance sheet is a supporting document; the income statement tells you whether the business works. For a bank, the balance sheet is the business. Assets are loans and investments; liabilities are deposits and borrowings; the gap between what the bank earns on the asset side and what it pays on the liability side, scaled across a balance sheet that is often ten to fifteen times the size of the bank's own equity, is what produces earnings. That leverage is normal and necessary for banking — it is also why a small deterioration in asset quality can wipe out a large share of equity in a way that would be unthinkable for a hydropower company or a manufacturer with the same percentage revenue shock.
This leverage is precisely why banks are regulated the way ordinary companies are not. Nepal Rastra Bank does not merely supervise BFIs the way the Securities Board supervises listed companies generally — it sets binding rules on how much capital a bank must hold against its risk-weighted assets, how much it may lend relative to what it collects in deposits, how it must classify and provide for loans that stop performing, and, periodically, how large its paid-up capital must be to keep operating as a commercial bank at all. Every one of these rules shows directly in the numbers a bank reports each quarter, and every one of them has, at some point in Nepal's market history, driven a multi-year cycle in bank share prices. An investor who treats a bank stock like any other cyclical industrial — cheap when earnings are down, expensive when earnings are up — will miss the fact that a bank's earnings cycle is substantially manufactured by regulatory cycles, not by end-market demand alone.
KEY CONCEPT
A bank's balance sheet is its product catalogue, not a supporting exhibit. Loans, deposits, and the spread between them are the business itself — which is why regulatory ratios (capital, liquidity, asset quality) matter more for banks than for almost any other NEPSE sector.
The second reason banks deserve a dedicated playbook is homogeneity. Unlike manufacturing or trading companies, which differ wildly in product, market, and cost structure, Nepal's roughly twenty listed commercial banks are, on paper, doing nearly the same thing: taking deposits, underwriting loans, managing a securities portfolio, and generating fee income from trade finance, remittance, and cards. This homogeneity is a gift to the analyst, because it means cross-sectional comparison is unusually reliable — a bank's net interest margin, cost-to-income ratio, or non-performing loan ratio can be benchmarked directly against a dozen near-identical peers reporting on an identical quarterly schedule in an identical NRB-mandated format. Few other NEPSE sectors offer this quality of apples-to-apples comparison. The playbook in this chapter is built to exploit exactly that advantage.
Lesson 71.2 — The Six Numbers That Matter
Every serious evaluation of a Nepali bank stock should begin with six figures, in this order: net interest margin, the credit-to-deposit ratio measured against its regulatory ceiling, the capital adequacy ratio measured against its regulatory floor, the non-performing loan ratio together with provisioning coverage, the cost-to-income ratio, and return on equity. Each tells a different part of the story, and together they tell you whether a bank is safe, efficient, and capable of compounding shareholder value, or whether it is one bad quarter away from a rights issue.
Net interest margin, or NIM, is net interest income — interest earned on loans and investments minus interest paid on deposits and borrowings — expressed as a percentage of average interest-earning assets. It is the single best proxy for a bank's core profitability engine, because unlike gross interest income alone, it nets out the cost side and normalises for balance sheet size. A closely related figure that NRB itself watches and periodically constrains through directive is the interest rate spread — average lending rate minus average deposit rate — which has hovered in the mid-single digits for large commercial banks in recent cycles and has been explicitly capped by NRB at various points to prevent banks from profiteering off the spread between what depositors earn and what borrowers pay. When you see a bank's NIM compress sharply quarter over quarter, the first question is not "did loan demand fall" but "did NRB tighten the spread ceiling, or did deposit competition force the bank to pay up for funds faster than it could reprice loans."
The credit-to-deposit ratio, universally shortened to CD ratio, measures total loans and advances against total deposits (with some adjustments for core capital and certain qualifying borrowings depending on the exact NRB formula in force). This is arguably the single most Nepal-specific metric in this entire playbook, because NRB has, for over a decade, imposed a binding regulatory ceiling on it — historically 90 percent — that functions as a hard credit-growth speed limit across the entire banking system. A bank sitting near the ceiling cannot grow its loan book meaningfully without first growing deposits, which forces a scramble for deposit mobilization (and often pushes deposit rates up, compressing NIM) every time credit demand outpaces deposit growth. A bank sitting comfortably below the ceiling has room to grow loans without a deposit chase, which is a genuine competitive advantage worth paying for.
Capital adequacy ratio, or CAR, measures a bank's own capital — core (Tier 1) capital plus supplementary (Tier 2) capital — against its risk-weighted assets. Under NRB's Capital Adequacy Framework, aligned with Basel III, commercial banks are required to hold minimum common equity, minimum Tier 1 capital, and minimum total capital, each with an additional capital conservation buffer layered on top. In practice this pushes effective working minimums to roughly seven percent common equity, eight and a half percent Tier 1, and around twelve to thirteen percent total capital once the conservation buffer is included — though an investor should always check the currently circulated unified directive rather than assume last year's figure still applies, since NRB has adjusted specific components (loan-loss provision treatment, countercyclical buffers, and what counts as supplementary capital) more than once in recent years. A bank running its CAR only marginally above the floor has essentially no capacity to absorb a shock without either raising fresh capital or shrinking its loan book.
REGULATORY DETAIL
Under NRB's Basel III–aligned Capital Adequacy Framework, commercial banks must maintain roughly 7 percent common equity Tier 1, 8.5 percent Tier 1, and a total capital ratio of approximately 12 to 13 percent of risk-weighted assets once the capital conservation buffer is included. Sector-wide CAR has recently run close to 13 percent — meaning many banks operate with only a thin cushion above the regulatory floor.
Non-performing loan ratio and provisioning coverage go together and should never be read separately. The NPL ratio is simply non-performing loans divided by total loans, using NRB's loan classification categories (pass, watchlist, substandard, doubtful, and loss). Provisioning coverage measures how much of that non-performing exposure the bank has already set aside as loan-loss provision — a bank with a high NPL ratio but very high provisioning coverage has already absorbed most of the pain into past earnings, while a bank with a lower NPL ratio but thin provisioning coverage is deferring pain into future quarters. As of the most recent Nepal Rastra Bank financial stability reporting, sector-wide NPLs had climbed to roughly 5.6 percent, with considerable dispersion across individual banks — some names running close to nine percent, others closer to five percent — which is exactly the kind of dispersion that makes this metric worth checking bank by bank rather than trusting a sector average.
Cost-to-income ratio measures operating expense against operating income and is the clearest available signal of managerial discipline. A well-run Nepali commercial bank typically keeps this figure in the low-to-mid thirties or low forties; a bank that has expanded its branch network aggressively, hired ahead of revenue, or simply runs an inefficient back office will often show cost-to-income ratios well north of forty-five to fifty percent, quietly eating into the margin the other five metrics work so hard to protect.
Return on equity, finally, is the metric that converts all of the above into a single per-share compounding number: net profit divided by average shareholders' equity. Nepali banking ROE ran comfortably in the high teens through parts of the last decade; more recently, margin compression, elevated provisioning against rising NPLs, and periodic rights issues that expand the equity base faster than earnings have compressed sector ROE into the single digits to low teens for a meaningful share of listed banks, even as a policy-easing cycle beginning in 2025/26 has started to lift reported profits again for several names. ROE is useful, but only after you have checked the five metrics that feed it — a high ROE built on a thin CAR cushion or under-provisioned NPLs is not a durable ROE.
Metric
What it measures
Regulatory / healthy benchmark
Where it comes from
Net Interest Margin (NIM)
Net interest income ÷ average earning assets
Typically 3.0–4.5% for Nepali banks
Quarterly P&L, interest income/expense schedules
Credit-to-Deposit (CD) Ratio
Loans ÷ deposits (adjusted per NRB formula)
Regulatory ceiling historically 90%
Quarterly disclosure, NRB unified directive
Capital Adequacy Ratio (CAR)
Tier 1 + Tier 2 capital ÷ risk-weighted assets
Minimum ~12–13% total, incl. conservation buffer
Capital fund schedule in quarterly report
NPL Ratio / Provisioning Coverage
Non-performing loans ÷ total loans; provisions ÷ NPL
Sector average ~5.6%; well-run banks well below
Loan classification schedule
Cost-to-Income Ratio
Operating expense ÷ operating income
Low 30s–low 40s (%) for efficient banks
Quarterly P&L
Return on Equity (ROE)
Net profit ÷ average shareholders' equity
High single digits to high teens (%), cyclical
Quarterly/annual financial statements
Lesson 71.3 — Reading the NRB-Mandated Quarterly Disclosure Format
Every NEPSE-listed bank publishes its quarterly results in a standardised format that NRB itself prescribes, which is a considerable gift to the analyst once you know how to navigate it, because every bank's disclosure lands in the same sequence of schedules. Menuka's early mistake was reading only the headline profit figure and the balance sheet total; the format rewards reading in a specific order instead.
Begin with the capital fund schedule. This is where you find core capital, supplementary capital, total capital fund, total risk-weighted exposure, and the resulting CAR — broken into the common equity, Tier 1, and total capital ratios discussed above. This schedule alone tells you whether the bank has genuine headroom to grow its balance sheet or whether a rights issue or debenture raise is likely on the horizon. A bank whose CAR has been drifting down toward the regulatory floor over consecutive quarters, even while reporting rising profit, is signalling that its risk-weighted asset growth is outrunning its internal capital generation — profitable growth that is nonetheless capital-hungry.
Next, move to the loan classification and provisioning schedule. This breaks total loans into the five NRB categories — pass, watchlist, substandard, doubtful, and loss — with the specific provisioning percentage NRB mandates for each category (rising steeply as loans move down the classification ladder). Sum substandard through loss to get gross NPL, divide by total loans for the NPL ratio, and compare total loan-loss provision held against that NPL figure for provisioning coverage. Watch particularly for growth in the watchlist category quarter over quarter — loans that are still technically "pass" or borderline but have been flagged for early warning signs — because this is often the leading indicator of NPL deterioration one or two quarters before it shows up in the headline ratio.
Then read the interest income and expense schedule, which lets you reconstruct NIM and the interest rate spread directly, along with the average yield on loans and average cost of deposits and borrowings separately — useful because a bank can defend its NIM either by repricing loans upward (which its borrowers feel) or by keeping deposit costs down through a favourable low-cost current and savings account (CASA) mix, and the two paths have very different sustainability profiles.
Finally, the CD ratio and liquidity schedule shows loans and advances against deposit mobilization, along with the statutory liquidity ratio and, increasingly in recent NRB guidance, liquidity coverage and net stable funding metrics as the central bank gradually shifts emphasis toward those internationally standardised measures alongside the traditional CD ratio ceiling.
PRACTICAL TOOL
Read a bank's quarterly disclosure in this fixed order every time: (1) capital fund schedule — CAR and headroom, (2) loan classification schedule — NPL and provisioning coverage, with special attention to watchlist growth, (3) interest income/expense schedule — NIM, spread, yield on loans versus cost of deposits, (4) CD ratio and liquidity schedule. This order surfaces balance-sheet risk before you ever reach the headline profit number, which is exactly the order that would have saved Menuka from her first bank purchase.
A subtlety worth flagging here: distributable profit, the figure that ultimately determines dividend capacity, is not simply net profit. NRB requires banks to route a portion of profit into regulatory reserves — for items like deferred tax assets, unrealized investment gains, and certain non-banking assets acquired through loan recovery — before what remains becomes distributable to shareholders. A bank can report a healthy net profit while showing a much thinner distributable profit line, and the gap between the two is itself informative: a persistently wide gap often signals a bank whose reported earnings quality is lower than the headline suggests, propped up by items regulators require it to reserve against rather than pay out.
Lesson 71.4 — How NRB Monetary Policy Drives the Bank Profit Cycle
If there is one lesson that separates an investor who merely reads bank financial statements from one who can actually forecast a bank's next few quarters, it is this: Nepali bank earnings move in cycles that NRB itself substantially creates, through three levers — the interest rate corridor, the CD ratio (and its emerging liquidity-ratio successors), and capital requirements — each announced or adjusted through the annual monetary policy and periodic directives.
The interest rate corridor is the most direct lever. NRB sets a policy rate, a standing liquidity facility rate (sometimes called the bank rate, the ceiling of the corridor) at which banks can borrow overnight from the central bank, and a standing deposit facility rate (the floor) at which they can park excess liquidity. In the monetary policy for fiscal year 2025/26, NRB cut the policy rate from 5 percent to 4.5 percent, lowered the standing liquidity facility rate by half a percentage point to 6 percent, and reduced the standing deposit facility rate from 3 percent to 2.75 percent — an unmistakably accommodative stance intended to ease borrowing costs and support credit growth after a stretch of sluggish private-sector lending. When the corridor narrows and policy rates fall, banks' cost of short-term funds falls quickly, but their loan books reprice more slowly (many loans carry contractual reset lags), which typically produces a temporary NIM boost in the easing phase before competitive deposit pricing catches up. Watch for this lag: a rate-cutting cycle tends to lift bank earnings for several quarters before the benefit fades as deposit competition intensifies again.
The CD ratio has historically been the second, and arguably more Nepal-specific, lever. Since NRB replaced its earlier core-capital-cum-deposit ratio framework with a straightforward CD ratio ceiling — set at 90 percent starting in fiscal year 2078/79 — the ratio has functioned as a hard cap on system-wide credit growth relative to deposit mobilization. When credit demand outpaces deposit growth, banks bump against the 90 percent ceiling, a scramble for deposits ensues (often through promotional fixed deposit rates), deposit costs rise faster than loan yields can be repriced, and NIM compresses even as loan books are frozen against growth. This exact dynamic played out visibly in the deposit-scramble years, when a stretch of high fixed deposit rates squeezed margins across the sector simultaneously — a sector-wide compression, not a company-specific failure, which is an important distinction for an investor deciding whether a given quarter's weak NIM reflects mismanagement or simply the CD-ratio cycle biting every bank at once. More recently, the 2025/26 monetary policy has signalled a gradual shift away from the blunt CD ratio ceiling toward internationally standard liquidity metrics — the liquidity coverage ratio and net stable funding ratio — though the 90 percent CD ceiling remains the figure most banks and analysts still quote and monitor in practice during this transition.
REGULATORY DETAIL
NRB's credit-to-deposit ceiling has stood at 90 percent since fiscal year 2078/79 (2021/22), replacing the earlier core-capital-cum-deposit framework. The 2025/26 monetary policy signals a gradual transition toward Basel III-style liquidity coverage and net stable funding ratios, but the 90 percent CD figure remains the number banks report against today.
The third lever, capital requirements, moves less often but reshapes the sector more dramatically when it does. The defining example remains NRB's 2015/16 monetary policy directive quadrupling the minimum paid-up capital for commercial banks from roughly two billion to eight billion rupees, with a multi-year window to comply. That single directive triggered the largest consolidation wave in Nepali banking history, forcing dozens of banks and finance companies into mergers between 2016 and 2020 simply to meet the new capital floor, and it permanently reshaped the competitive landscape of the sector NEPSE investors trade today. More recent capital-side easing has moved in the opposite direction: the 2025/26 monetary policy allows banks to count certain regulatory reserves created from non-banking assets as supplementary capital for up to two years, and eases the path for banks to raise additional capital — including rights issues — with central bank approval, effectively giving capital-constrained banks a release valve rather than forcing consolidation. The lesson for an investor is that capital directives can compress a bank's ROE for years by forcing dilutive rights issues, or can suddenly loosen and free up lending capacity — and both directions are policy decisions made in Kathmandu, not decisions the bank's own management team controls.
CASE IN POINT
The 2015/16 capital hike to Rs 8 billion minimum paid-up capital for commercial banks did not just force mergers — it reset ROE expectations across the entire sector, since banks issued large volumes of bonus shares and rights shares to meet the new floor, diluting per-share earnings even as absolute profit grew. Any investor comparing a bank's ROE or EPS trend across 2014 to 2020 without adjusting for this capital-driven share count expansion will draw the wrong conclusion about that bank's underlying performance.
A fourth, softer lever worth watching is NRB's directive-level intervention on provisioning and asset quality — most recently, proposals to establish asset management companies to absorb distressed loans off bank balance sheets, and greater flexibility around loan restructuring and write-offs for "genuine" cases. These interventions matter because they change how quickly a rising NPL trend actually hits reported earnings; a policy environment that permits more generous restructuring can flatter near-term profit at the cost of deferring recognition of real credit losses, which loops back directly to the importance of tracking watchlist loan growth discussed in Lesson 71.3 rather than trusting the headline NPL figure alone.
Lesson 71.5 — Mergers, Acquisitions, and What Consolidation Means for Shareholders
Bank mergers in Nepal are not a rare corporate event — they are close to routine. Since NRB introduced its formal merger and acquisition bylaw in 2068 BS (2011), the sector has recorded more than sixty merger or acquisition transactions among commercial banks, development banks, and finance companies. For an investor, this means the possibility of a merger, forced or voluntary, needs to sit permanently in the evaluation checklist for any BFI below the top tier of the sector — not as a tail risk, but as a base-rate event.
It helps to separate two distinct flavors of consolidation. Voluntary mergers are typically driven by competitive logic: two mid-sized banks combine to gain scale, branch network reach, or a stronger deposit franchise, often initiated by the institutions' own boards and negotiated on a share-swap ratio basis, subject to NRB and shareholder approval. The merger of Nepal Investment Bank and Mega Bank into what became Nepal Investment Mega Bank, with final regulatory approval secured and joint operations commencing in early 2023, is the clearest recent example of this voluntary category — two established banks combining by choice to create one of the largest banks in the country by asset size, rather than being pushed together by a capital shortfall. Forced or regulatory-encouraged mergers, by contrast, are what happened en masse after the 2015/16 capital hike: banks and finance companies that could not independently raise fresh capital to meet the new Rs 8 billion floor had little choice but to merge with a stronger partner or exit the industry, and NRB actively encouraged this consolidation as a matter of financial stability policy rather than leaving it purely to market forces.
CASE IN POINT
Nepal Investment Bank and Mega Bank completed their merger and began joint operations as Nepal Investment Mega Bank in early 2023 — a voluntary, scale-driven combination between two already-large, independently viable institutions, illustrating that not every Nepali bank merger is a distress signal. The context around a merger (is either party under capital or asset-quality pressure, or are both financially healthy and merging for scale) tells you far more than the fact of the merger itself.
For shareholders, the mechanics that matter most in any merger are the swap ratio (how many shares of the merged entity each existing shareholder receives per share held, typically set by relative book value and sometimes adjusted by an independent due diligence valuation), the resulting dilution or accretion to per-share metrics, and — critically — what happens to the weaker partner's problem assets. A merger genuinely strengthens a shareholder's position when the combined entity has a stronger CAR, better NPL coverage, and lower funding costs than either predecessor bank alone; it merely delays a reckoning when a chronically undercapitalized or NPL-heavy institution is absorbed into a stronger partner mainly to avoid an outright regulatory intervention, in which case the acquiring bank's own ratios often show visible strain for several quarters after the merger closes while it works through the inherited loan book.
It is also worth noting which banks have never been part of a merger — among them Agricultural Development Bank, Everest Bank, Nepal Bank Limited, Nepal SBI Bank, and Standard Chartered Bank Nepal. Several of these are foreign-joint-venture banks or state-linked institutions with capital structures and shareholder bases that made them either naturally compliant with capital floors or institutionally resistant to consolidation pressure — a useful reminder that ownership structure, not just balance sheet health, shapes merger probability.
WARNING
A rising probability of a forced merger in a specific bank is signalled well before NRB acts: chronic CAR readings hovering barely above the regulatory floor for multiple consecutive quarters, an NPL ratio persistently above sector average with weak provisioning coverage, repeated short-term liquidity facility borrowing from NRB, or governance red flags such as auditor qualifications and related-party lending concentration. An investor holding a bank showing two or more of these signs simultaneously should treat a merger, and the dilutive swap ratio that comes with it, as a realistic near-term scenario rather than a remote risk.
Lesson 71.6 — Well-Run Bank versus Growth-at-Any-Cost Bank: The Checklist
Two banks can show similar headline loan growth and similar reported profit in a given quarter while being fundamentally different investments — one compounding shareholder value safely, the other borrowing against its own future stability to produce this quarter's number. Consider two illustrative composites, Bank Alpha and Bank Beta, both mid-tier commercial banks reporting roughly 18 percent year-on-year loan growth and comparable ROE for the quarter Menuka was comparing them. Bank Alpha funded that growth mostly through low-cost current and savings deposits, kept its CD ratio well below the 90 percent ceiling with genuine headroom, held its NPL ratio below sector average with provisioning coverage comfortably above 100 percent of non-performing exposure, and kept its cost-to-income ratio in the mid-thirties. Bank Beta funded similar growth mostly through expensive promotional fixed deposits, ran its CD ratio right at the regulatory edge, showed a fast-growing watchlist category behind a still-modest headline NPL figure, and had let cost-to-income creep toward fifty percent while opening branches faster than its deposit base could support them. Both reported similar growth; only one of them was building something durable.
The qualitative signals that separate these two banks rarely appear as a single dramatic red flag — they accumulate. Deposit mix is one of the most reliable: a bank with a high proportion of current and savings account deposits (low-cost, sticky funding) has structurally cheaper and more stable funding than one dependent on fixed deposits chased with promotional rates, and that difference shows up directly in NIM resilience during a CD-ratio-driven deposit scramble. Loan book concentration is another: heavy exposure to a single sector (real estate, margin lending against shares, or a handful of large corporate borrowers) creates correlated risk that a diversified retail and SME loan book does not carry. Related-party and connected lending, disclosed in the notes to financial statements and in NRB's periodic supervisory findings, is a persistent governance concern in Nepali banking specifically, and a bank with recurring findings here deserves a discount regardless of how clean its headline ratios look. Management and board stability matters too — frequent CEO turnover or repeated NRB directive actions against a specific institution are both observable, low-effort signals available well before the next quarterly disclosure. Finally, dividend history and capital-raising pattern tell you whether a bank has historically grown its book value organically through retained earnings or has repeatedly needed dilutive rights issues to stay above its capital floor — the latter pattern erodes per-share value even when the underlying institution survives and grows in absolute terms.
CAUTION
A bank paying an attractive bonus share or cash dividend while its distributable profit line is thin relative to net profit, or while its CAR is drifting down toward the regulatory floor, is not necessarily rewarding shareholders from strength — it may be distributing capital it will shortly need to replace through a rights issue. Always check the capital fund schedule before treating any dividend announcement as good news.
Menuka now runs every bank she considers through the same sequence, and it is worth setting it out explicitly as the playbook to apply to any NEPSE-listed bank. First, pull the last eight quarters of the capital fund schedule and chart CAR against the regulatory minimum — look for a stable or rising trend, not one drifting toward the floor. Second, do the same for the CD ratio against the 90 percent ceiling, checking headroom for future loan growth. Third, build the NPL and provisioning coverage trend across the same eight quarters, paying particular attention to watchlist category growth as an early warning signal rather than waiting for the headline NPL number to move. Fourth, reconstruct NIM and the interest rate spread, and separately track average yield on loans against average cost of deposits, to see whether margin resilience is coming from loan repricing power or from a genuinely low-cost deposit franchise. Fifth, compute cost-to-income ratio and compare it against at least four to five peer banks reporting the same quarter, since this is one of the most directly comparable efficiency metrics on NEPSE. Sixth, compute ROE, but only after completing the first five steps, and discount any ROE figure that rests on a thin CAR cushion, weak provisioning, or a widening yield-cost gap that looks unsustainable. Seventh, check ownership and merger-probability signals specifically — promoter shareholding stability, any history of NRB directive action, and whether the bank sits near a capital or CD-ratio threshold that would make it a plausible merger candidate. And eighth, read at least the headline of the current fiscal year's monetary policy and any recent NRB directive affecting BFIs, since — as this chapter has tried to establish — a bank's next four quarters are shaped as much by decisions made at NRB's Baluwatar headquarters as by decisions made in the bank's own boardroom.
Chapter recap
This chapter built a complete evaluation playbook for the segment of NEPSE that most investors touch earliest and most often: the commercial banks and BFIs whose combined weight moves the exchange on most trading days. The foundation of that playbook is six interlocking metrics — net interest margin, the credit-to-deposit ratio measured against its regulatory ceiling, capital adequacy ratio measured against its Basel III-aligned floor, non-performing loan ratio paired with provisioning coverage, cost-to-income ratio, and return on equity — each of which captures a different dimension of a bank's health, and none of which should be read in isolation from the others. A high ROE resting on a thin capital cushion or under-provisioned bad loans is not the same achievement as a moderate ROE built on genuine balance sheet strength, and an investor who learns to tell the two apart has already cleared the hurdle that trips up most newcomers to bank stocks.
The chapter also walked through the specific mechanics of NRB's standardised quarterly disclosure format, establishing a fixed reading order — capital fund schedule, then loan classification and provisioning, then interest income and expense, then the CD ratio and liquidity schedule — precisely because reading in this order surfaces balance-sheet risk before the headline profit figure has a chance to create a misleadingly rosy first impression. It then traced how NRB's own policy levers, particularly the interest rate corridor, the CD ratio ceiling (now transitioning toward liquidity coverage and net stable funding metrics), and periodic capital requirement resets, have driven multi-year cycles in bank profitability and share performance that have far more to do with regulatory decisions than with any individual bank's competitive strategy. The 2015/16 capital hike to Rs 8 billion and the resulting merger wave, and the more recent 2025/26 easing cycle combining rate cuts with capital relief, are two concrete illustrations of how directly Kathmandu's monetary policy calendar translates into bank earnings and, eventually, bank share prices.
Consolidation itself received dedicated treatment, because with more than sixty merger and acquisition transactions recorded among Nepali BFIs since the 2011 merger bylaw, it is a routine feature of this sector rather than an exceptional event. The chapter distinguished voluntary, scale-driven combinations — of which the Nepal Investment Bank and Mega Bank merger is the clearest recent example — from capital-driven forced consolidations, and set out the observable warning signs (a CAR hovering near the floor, weak provisioning coverage, repeated NRB directive action) that let an investor anticipate a merger candidate well before the announcement, along with the swap-ratio and dilution mechanics that determine whether a given merger actually strengthens a shareholder's position or merely postpones a reckoning.
Finally, the chapter equipped the reader to distinguish a well-run bank from a growth-at-any-cost bank using signals that go beyond the six headline ratios — deposit mix and funding cost quality, loan book concentration, related-party lending exposure, management and board stability, and the honesty of a bank's dividend policy relative to its actual distributable profit and capital trajectory — and closed with an explicit eight-step checklist that can be applied, quarter after quarter, to any NEPSE-listed bank an investor is considering. Menuka's own practice today is simply this checklist run consistently, replacing the single price-to-book glance that cost her a forced-merger swap ratio years earlier.
Chapter 72 turns from the balance sheet to the turbine hall. The Hydropower Sector Playbook takes up NEPSE's other dominant sector — one governed not by NRB's capital and liquidity directives but by hydrology, power purchase agreements with the Nepal Electricity Authority, construction-phase debt and cost overruns, and a royalty and licensing regime administered by an entirely different set of institutions. Where this chapter taught you to read a capital fund schedule and an NPL ledger, the next chapter will teach you to read a river's discharge pattern, a PPA tariff escalation clause, and a project's debt-to-equity structure during construction versus operation — a completely different playbook, for a completely different kind of company, that happens to trade on the very same exchange.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 72
The Hydropower Sector Playbook
First published 23 Aug 2026 · Last verified 29 Aug 2026
Anjali Basnet keeps two folders on her desk in her flat above a stationery shop in New Baneshwor. One is thick, full of prospectuses, audited statements, and photocopied newspaper clippings about landslides and transmission lines. The other is thin, holding only her share certificates and a dividend record book. Both folders are about hydropower. She has learned, the hard way, over eleven years of applying for hydropower IPOs and holding a rotating cast of run-of-river and storage project shares, that the thick folder is what protects the thin one. In 2019 she bought into a pre-construction hydropower IPO on the strength of a glossy prospectus cover showing a turbine hall and a projected internal rate of return that seemed too polished to argue with. The project's commercial operation date slipped by nineteen months after a monsoon debris flow destroyed a section of the headrace tunnel access road. By the time the plant finally synchronized to the national grid, Anjali had held a non-dividend-paying, book-value-only stock for the better part of four years while bank fixed deposits paid her neighbours 9 percent annually. She did not lose her principal. She lost four years of opportunity cost, and she never wants to make that mistake again.
Hydropower is the closest thing NEPSE has to a national identity sector. It is also the sector where investors most reliably confuse two entirely different kinds of businesses because they trade under the same sub-index and wear the same green turbine-and-mountain logo language in their annual reports. This chapter is the playbook Anjali eventually built for herself, expanded into the systematic framework every NEPSE investor needs before putting capital into a hydropower ticker, whether that ticker represents a hole in the ground with a construction schedule or a fully commissioned plant with eleven years of dividend history.
Lesson 72.1 — Two Species Under One Ticker: Pre-COD and Operating Hydropower
The single most important distinction in hydropower investing is not which river a project sits on, nor how many megawatts its nameplate capacity claims. It is whether the company has reached its commercial operation date, universally abbreviated COD, the day the Nepal Electricity Authority formally accepts the plant's output onto the national grid and the power purchase agreement's revenue clock starts running. Before COD, a hydropower company is a construction project wrapped in a public company's legal form. After COD, it is a cash-generating infrastructure asset with a predictable, contractually defined revenue stream. These are not two stages of the same investment. They are two different asset classes that happen to share a listing category.
A pre-COD company's balance sheet is dominated by capital work in progress, an accounting line that grows every quarter as the developer draws down loan tranches and equity calls to pay the EPC contractor, the transmission line builder, and the land acquisition compensation committee. There is no revenue line, or a token one from a small owned diesel genset or advance energy sales that barely registers. Earnings per share is negative, zero, or a rounding artifact of interest income earned on undisbursed loan proceeds sitting in a bank account. Book value per share is essentially a statement of how much capital has been spent so far, not a statement of what the asset is worth once finished. None of the ratios retail investors reach for reflexively, price-to-earnings first among them, mean anything applied to a pre-COD company, because there are no earnings to divide the price by. Valuing a pre-COD hydropower stock is an exercise in project appraisal: estimated total project cost per installed megawatt, financing structure, expected COD, and a discounted cash flow built on the PPA tariff schedule the company has actually signed, not the tariff schedule of some comparable operating plant on a different river.
An operating company is the opposite. It has a stabilised, or maturing, revenue stream governed by its PPA, actual generation data measured against design estimates, a dividend track record that can be examined the way you would examine any income-generating equity, and a debt schedule that is now amortizing rather than being drawn. Its risks are operational and regulatory rather than construction risks: plant availability, hydrology in a given fiscal year, royalty and tax step-ups, and the health of its relationship with the single buyer that is legally obligated to purchase everything it produces. Table 72.1 lays out the contrast Anjali eventually drew on a single sheet of paper and taped inside her thick folder.
Characteristic
Pre-COD (Construction Phase)
Operating (Post-COD)
Primary balance sheet asset
Capital work in progress
Property, plant and equipment, net of depreciation
Positive, but shaped by debt covenants and life-cycle stage
Key disclosure to chase
Physical progress percentage vs RCOD, loan drawdown schedule
Plant load factor, actual vs design generation, DSCR trend
Appropriate valuation lens
Cost per MW, project IRR, DCF on signed PPA
P/E with caveats, EV/EBITDA, dividend discount adjusted for royalty/tax schedule
KEY CONCEPT
Commercial operation date, or COD, is the single most consequential date in a hydropower company's life. It is the day the thirty-year clock on the power purchase agreement's tariff schedule starts running, the day royalty obligations to the government begin accruing, and the day the company converts from a capital-consuming construction entity into a cash-generating one. Every other date in a hydropower prospectus, the required commercial operation date or RCOD in particular, is a promise. COD is the fact.
Anjali's rule now, stated in her own handwriting on the inside cover of the thick folder, is this: before reading a single ratio, determine which species of company you are looking at. If the annual report's income statement has more footnotes about capitalised borrowing costs than it does about units sold to NEA, you are still in construction-phase territory, and the entire analytical toolkit shifts.
Lesson 72.2 — The Physical Risk Layer: Hydrology, River Type, and Evacuation
Hydropower's defining risk is not financial in origin. It is hydrological, and it sits upstream of every other number in the annual report. Nepal's rivers fall broadly into two behaviours that matter enormously for how a project generates and earns. A run-of-river, or ROR, project has no meaningful reservoir. It diverts a portion of a river's flow through a headrace tunnel or canal to a powerhouse and returns the water downstream, generating power more or less in proportion to whatever the river happens to be carrying at that moment. A storage project, by contrast, impounds water behind a dam, allowing the operator to release it when it is needed rather than when the river happens to deliver it, which means storage plants can generate through the dry season and can be dispatched during peak demand hours regardless of instantaneous river flow. A peaking run-of-river, or PROR, project sits in between, with a small pondage that allows a few hours of daily flow-shifting to align generation with the hours NEA's grid needs it most, typically the evening peak.
This distinction matters because Nepal's rivers are overwhelmingly monsoon-fed rather than glacier-fed in their seasonal amplitude, meaning roughly 80 percent of annual flow arrives in the four monsoon months of June through September, and dry-season flow in many rivers, particularly in the middle hills, can fall to a fraction of wet-season flow. A pure ROR plant sized to its river's monsoon discharge will run near full capacity for a third of the year and at a much lower plant load factor for the rest of it. This is precisely why NEA's power purchase agreements pay a materially higher per-unit tariff for dry-season energy than for wet-season energy: dry-season electrons are scarce and valuable to the grid, wet-season electrons are, in aggregate across dozens of ROR projects commissioning in the same river basins, increasingly abundant and at the margin close to worthless if the grid cannot absorb them. An investor evaluating an ROR-heavy hydropower stock must model revenue on a seasonally weighted basis, never simply annual generation multiplied by an average tariff, because the mix between wet and dry season units generated determines earnings far more than total annual kilowatt-hours does.
Hydrology risk compounds this seasonality with year-to-year variability that no engineering study fully eliminates. A below-average monsoon, a glacial lake outburst flood upstream, an unusually early or late withdrawal of the monsoon, or accelerated sedimentation silting up an intake all reduce actual generation below the design energy figure quoted in the prospectus. Sedimentation deserves particular attention in Nepal's young, geologically active Himalayan and Chure hill catchments, where rivers carry heavy sediment loads during the monsoon that erode turbine runners and can, over years, reduce reservoir storage capacity in storage-type projects faster than initially modelled.
WARNING
A project's prospectus design energy figure, usually expressed as gigawatt-hours per year at a stated exceedance probability such as fifty percent, is a statistical estimate, not a floor. Investors should ask for at least three to five years of actual generation data against design energy for any operating plant, and should treat a persistent shortfall as a red flag about either the original hydrological study or the plant's physical condition, not a one-off bad year.
The final physical risk layer, and one Nepali retail investors historically underweight, is transmission and grid evacuation. A hydropower plant that generates power it cannot deliver to the grid earns nothing for that unserved energy, and Nepal's transmission network has repeatedly lagged the pace of generation capacity addition in specific river corridors, most visibly where multiple ROR projects on the same tributary system commission around the same period and compete for headroom on a single evacuation line. During peak monsoon flow, when many ROR plants across a basin are simultaneously near full output, NEA's system has at times been unable to absorb all available generation, resulting in curtailment or spillage where developers are instructed to reduce output regardless of available river flow. Whether a given company's PPA and grid interconnection agreement shield it financially from curtailment, through deemed generation payments, or expose it directly to the lost revenue, is a contractual detail every investor must verify rather than assume.
CASE IN POINT
The Upper Tamakoshi Hydropower Project, Nepal's flagship domestically financed storage-peaking plant developed by an NEA subsidiary, illustrates both the promise and the fragility of the sector's physical layer. Its construction was set back by the 2015 earthquake, which damaged tunnel works and access roads, delaying commissioning by years beyond the original schedule. Once commissioned, it became one of the most valuable assets on the national grid precisely because its peaking capability lets it deliver power during the evening demand peak, when run-of-river plants across the country are often generating well below their wet-season output. The lesson for equity holders in any storage or peaking project is that construction-phase geological and seismic risk is real and can be severe, but a successfully commissioned peaking asset earns a structurally different, and often better, revenue profile than a comparable pure run-of-river plant.
Lesson 72.3 — Reading the PPA: Tariff Structure, Escalation, and Take-or-Pay
The power purchase agreement is the single document that matters most for an operating hydropower company, and yet it is the document most NEPSE retail investors have never actually read. Every unit of revenue an operating hydropower company earns flows from this one contract with a single buyer, the Nepal Electricity Authority, and the contract's structure is knowable, standardised within categories, and worth understanding line by line rather than trusting a summarised tariff figure quoted in a broker note.
For a typical small to mid-size run-of-river project, NEA's standard PPA structure sets two separate per-unit tariffs, one for energy delivered during the wet season months and a materially higher one for energy delivered during the dry season, reflecting the relative scarcity of dry-season generation across the system. An illustrative real example is instructive: a run-of-river project with a required commercial operation date in the late 2020s carries a wet-season tariff of roughly Rs 4.80 per kilowatt-hour and a dry-season tariff of roughly Rs 8.40 per kilowatt-hour, close to a 1-to-1.75 ratio between the two seasons. Both figures escalate at a fixed 3 percent annually, but only for the first eight years from COD; from year nine through the remainder of the PPA's typically thirty-year tenure, the tariff is frozen in nominal terms. This detail is easy to miss and expensive to misunderstand. An investor who extrapolates the escalating early-year tariff growth rate forward across the full life of the PPA will overstate long-run revenue growth substantially, because in real, inflation-adjusted terms, a frozen nominal tariff after year eight means the plant's revenue per unit actually declines in purchasing power for the following two decades, even as its physical output stays constant.
Table 72.2 shows this schedule in illustrative form for a standard run-of-river PPA of the kind that governs the majority of NEPSE-listed hydropower companies.
PPA Year
Wet Season Tariff (indicative)
Dry Season Tariff (indicative)
Escalation Status
Year 1
Rs 4.80/kWh
Rs 8.40/kWh
3% annual escalation begins
Years 2–8
Rising ~3%/year
Rising ~3%/year
Escalation continues
Year 9
Flat from Year 8 level
Flat from Year 8 level
Escalation ends
Years 9–30
Frozen nominal rate
Frozen nominal rate
No further escalation
Storage and peaking projects are priced differently, and the regulatory framework governing them has been evolving. Nepal's Electricity Regulatory Commission has floated tariff structures for storage hydropower that separate an energy charge, again split by wet and dry season, from a capacity charge paid per kilowatt of available capacity per month regardless of how much energy is actually dispatched, compensating the plant for standing ready to deliver power during system peaks rather than only for the energy it generates. This capacity-plus-energy structure is meant to reflect the genuinely different economic value storage and peaking plants provide to a grid still dominated by seasonal, weather-dependent generation, and tariffs proposed under this framework run considerably higher than standard run-of-river rates precisely because they are designed to make otherwise more expensive dam and reservoir infrastructure financeable. An investor comparing a storage project's per-unit tariff against a run-of-river project's tariff without adjusting for the capacity payment component and the different risk and cost structure behind a dam project is comparing two different products as though they were interchangeable commodities.
REGULATORY DETAIL
Nepal shifted its approach to small hydropower PPAs, those up to roughly 10 megawatts, from a take-and-pay basis to a take-or-pay basis, meaning NEA is now generally obligated to pay for contracted energy made available by the plant even in periods where system constraints prevent it from actually taking delivery, rather than only paying for energy it actually absorbs. This materially reduces curtailment risk for small IPPs relative to the older regime, but the protection applies going forward and by project category; an investor must confirm which regime governs a specific company's signed PPA rather than assuming the current policy applies retroactively to an older agreement.
Every operating hydropower company's annual report and every pre-COD company's prospectus discloses its specific PPA tariff schedule, escalation terms, and tenure. There is no substitute for pulling this schedule directly rather than relying on a single blended average tariff figure quoted secondhand, because the wet-dry season split, the escalation cutoff year, and the remaining tenure of the agreement are together what determine the shape of the company's revenue for the next two or three decades.
Lesson 72.4 — Royalty, Taxation, and the Year 10/15 Value Cliff
Even a hydropower company generating exactly its design energy and selling every unit at its full contracted tariff does not keep all of that revenue. Two government levies, royalty and income tax, both step up sharply at specific points in a project's life, and both step-ups tend to cluster around the same window, roughly ten to fifteen years after COD, creating what is best understood as a value cliff that every long-horizon hydropower investor needs to model explicitly rather than discover in an annual report a decade after buying the stock.
The royalty regime charges hydropower generators two separate royalties, a capacity royalty assessed per kilowatt of installed capacity per year and an energy royalty assessed as a percentage of total revenue, and for smaller plants in the roughly one to ten megawatt range, both royalties are set on a schedule that rises steeply after year fifteen. The capacity royalty runs at approximately Rs 100 per kilowatt per year for the first fifteen years of operation and then jumps to approximately Rs 1,000 per kilowatt per year, a tenfold increase, from year sixteen through year thirty. The energy royalty runs at approximately 2 percent of total revenue for the first fifteen years and then rises to approximately 10 percent of total revenue for years sixteen through thirty, a fivefold increase. Larger projects sit on their own government-set royalty schedules that are generally steeper still, reflecting their scale, and any investor holding a large storage or peaking project's shares should confirm that project's specific royalty bracket directly from its PPA and licensing documents rather than assuming the small-project schedule above applies.
Income taxation follows a parallel, deliberately front-loaded incentive structure meant to help projects survive their highest-leverage early years. A hydropower company enjoys a full, 100 percent income tax holiday for its first ten years of commercial operation, followed by a 50 percent rebate on the standard 20 percent corporate income tax rate for years eleven through fifteen, meaning an effective 10 percent tax rate during that window, before paying the full 20 percent rate from year sixteen onward. This benefit has historically been tied to a COD cutoff date in government notices, so an investor should verify that a specific company's COD falls within whatever eligibility window currently applies rather than assuming every listed hydropower stock automatically qualifies for the full schedule.
Table 72.3 lays out the combined effect for a typical small to mid-size run-of-river company, using the illustrative royalty and tax brackets above.
Years Since COD
Income Tax Rate
Energy Royalty
Capacity Royalty
Years 1–10
0% (full holiday)
2% of revenue
~Rs 100/kW/year
Years 11–15
10% (50% rebate on 20%)
2% of revenue
~Rs 100/kW/year
Years 16–30
20% (full rate)
10% of revenue
~Rs 1,000/kW/year
Stack this against the tariff freeze from Lesson 72.3, where nominal per-unit revenue stops growing after PPA year eight, and a pattern emerges that is easy to miss if an investor simply extrapolates a company's current dividend per share forward. A hydropower stock in years four through eight after COD typically shows its most attractive-looking headline numbers: revenue still escalating, zero income tax, low royalty. Somewhere between years ten and sixteen, three things happen close together: the tax holiday ends and gives way first to a partial then a full tax charge, the energy royalty quintuples, and the capacity royalty rises tenfold, while the top-line tariff has already been frozen in nominal terms for several years. None of this necessarily means the stock becomes a bad investment at that point; by then debt is typically well into amortisation or fully repaid, which is its own offsetting improvement in free cash flow, a dynamic covered in Chapter 68's discussion of how dividend capacity shifts across a company's life cycle. But it does mean that a naive five-year-forward projection built by simply compounding a company's most recent dividend growth rate will be wrong, sometimes badly wrong, for any company approaching this window.
CAUTION
Do not value an operating hydropower stock on a trailing price-to-earnings multiple without first checking where the company sits relative to its tax holiday and royalty escalation schedule. A stock trading at what looks like an undemanding P/E in year nine after COD may be several years from a tax and royalty step-up that will compress its net margin meaningfully, even with generation and tariffs held constant. Conversely, a stock further along, past year sixteen, that has already absorbed the full tax and royalty burden and is generating dividends off a fully repaid balance sheet may be showing a more sustainable, if lower-growth, earnings base than the P/E alone suggests.
Anjali now builds a simple year-tracker for every operating hydropower stock she holds: COD date, current year of operation, tax bracket, royalty bracket, and years remaining on the PPA. It takes five minutes to fill in from an annual report and it has saved her from over-extrapolating a company's best years at least twice.
Lesson 72.5 — Debt Structure, Covenants, and the Moratorium Question
Hydropower is a leveraged business by design. Nepali project financing for hydropower typically runs with a debt-to-equity ratio in the range of 70:30 to 75:25, meaning for every rupee of promoter and public equity in the project, roughly two and a half to three rupees are borrowed, usually from a syndicate of Nepali commercial banks since single-bank exposure limits and the combined lending capacity of the domestic banking sector constrain how much any one institution, or even the sector collectively, can lend to hydropower without syndication. This leverage is precisely what makes hydropower returns attractive to equity holders when things go according to plan, since a fixed-tariff, contracted revenue stream financed mostly with debt magnifies the equity return, and precisely what makes construction delays and cost overruns so punishing when things do not go according to plan, since additional debt drawn to cover an overrun dilutes the equity return on a project whose revenue ceiling is fixed by the PPA regardless of final cost.
Loan tenure for Nepali hydropower project debt has typically run eight to twelve years, a period that usually begins with a moratorium, a grace interval, generally coinciding with the construction period, during which the borrower pays interest only, often capitalising part of it into the loan principal, before principal repayment begins in earnest once the plant is commissioned and generating revenue. The length of this moratorium matters directly to an equity holder because it determines how much of the loan principal remains outstanding at COD and therefore how steep the amortisation schedule needs to be to retire that debt within the remaining loan tenure, which in turn determines how much of the plant's early operating cash flow is absorbed by debt service rather than being available for dividends. A project that emerges from a longer, more generous moratorium with a larger outstanding principal balance will typically show weaker dividend capacity in its first several operating years than an otherwise identical project that entered construction with a smaller loan or a shorter moratorium, even though both may show similar operating profit.
Lenders protect themselves through covenants, chief among them a minimum debt service coverage ratio, or DSCR, defined as cash available for debt service divided by the debt service due in a period. Nepali hydropower project loans commonly carry minimum average DSCR covenants, and a breach, or even a DSCR trending toward the covenant floor, typically triggers consequences that flow directly through to equity holders: restricted or prohibited dividend distributions until a debt service reserve account is topped up, mandatory cash sweeps that divert surplus cash to accelerated principal repayment instead of dividends, and in persistent breach scenarios, renegotiation or acceleration rights for the lender. This is the single most important reason an operating hydropower company with genuinely positive net profit can still pay no dividend, or a token one, in a given year: the loan agreement, not the income statement, governs how much cash the board is legally free to distribute.
PRACTICAL TOOL
Nepali credit rating agencies, principally CARE Ratings Nepal and ICRA Nepal, publish detailed rationale reports on many hydropower companies and projects seeking bank facilities or bond issuance, and these reports are frequently the richest publicly available source of hard numbers on a project's debt-to-equity structure, loan tenure, physical construction progress percentage against the required commercial operation date, and PPA tariff terms. An investor evaluating a specific NEPSE hydropower stock should search for its rating rationale before relying on secondhand summaries in prospectuses or broker notes, since these reports disclose figures companies rarely restate as plainly in their own annual reports.
Construction-phase cost overruns deserve a final, specific warning, because they interact with both the debt structure and the PPA in ways that compound rather than simply add. When a project's actual cost exceeds its appraised cost, the shortfall is typically financed with additional debt drawn against the same fixed future revenue stream, which both raises the debt-to-equity ratio beyond what was originally underwritten and lengthens the period before DSCR comfortably clears its covenant. Separately, and just as importantly, missing the required commercial operation date can cost a project its escalation entitlement.
WARNING
Some PPAs explicitly restrict tariff escalation if the actual commercial operation date slips beyond a defined grace period past the required commercial operation date, commonly around six months. A project that is both over budget, carrying more debt than originally planned, and late, forfeiting part of its tariff escalation, is absorbing two compounding blows to equity returns at once. Any pre-COD hydropower stock trading at a meaningful premium to its last disclosed project cost per megawatt should be evaluated against its EPC contractor's track record on other projects and its current physical progress percentage against its required commercial operation date, not against its prospectus-stage return projections.
Lesson 72.6 — The Nine-Step Evaluation Checklist
Anjali's thick folder now opens with a single page, a nine-step checklist she runs through for any hydropower stock, pre-COD or operating, before she commits capital. It is written to be worked through in order, because early steps determine which later steps even apply.
The first step is to establish life-cycle stage precisely: for a pre-COD company, the physical construction progress percentage against the required commercial operation date; for an operating company, the exact number of years elapsed since actual COD. Everything else in the checklist is read differently depending on the answer to this first question.
The second step is to classify the project's physical type, run-of-river, peaking run-of-river, or storage, and to locate the hydrological study or, for an operating plant, at least three to five years of actual generation data measured against the original design energy estimate. A persistent shortfall against design energy is a flag that must be explained, not waved past.
The third step is to obtain and read the actual PPA: the wet and dry season tariffs, the escalation rate and the year it stops, the total tenure and years remaining, and whether the agreement is take-or-pay or take-and-pay. This single document, more than any ratio in the annual report, determines the revenue ceiling of the business for the next two to three decades.
The fourth step is to check transmission and grid evacuation readiness for the specific corridor the project sits in, looking for any history of curtailment, spillage, or interconnection delay affecting that river basin or transmission line, since a plant that cannot deliver its power earns nothing for the units it cannot evacuate regardless of how well it generates them.
The fifth step is to pull the debt structure: debt-to-equity ratio at financial close, loan tenure, moratorium period, and, where disclosed in a credit rating report or bond prospectus, the minimum DSCR covenant and whether a debt service reserve account is fully funded. This step tells the investor how much of near-term operating cash flow is legally available to reach shareholders at all.
The sixth step is to place the company on the royalty and tax bracket timeline: which year bracket of the capacity and energy royalty schedule it currently sits in, and which year bracket of the income tax holiday and rebate schedule, so that current-year margins are not mistaken for a permanent steady state.
The seventh step, applicable only to operating companies, is to examine actual plant load factor against design capacity factor, and to build a simple dividend history table showing payout ratio trend and whether dividends have tracked, lagged, or outpaced reported profit, since a rising profit with a flat or declining dividend is usually a debt covenant story rather than a governance story, though both are worth ruling out.
The eighth step, applicable only to pre-COD companies, is to research the EPC contractor's track record on other Nepali hydropower projects, cross-check the current physical progress percentage against the required commercial operation date, and identify what cost overrun and delay penalty provisions exist in the construction contract and the PPA respectively.
The ninth and final step is valuation, applied with the correct tool for the life-cycle stage established in step one: for a pre-COD company, cost per installed megawatt against comparable recently commissioned projects and a discounted cash flow built on the actual signed PPA tariff schedule rather than a generic sector average tariff; for an operating company, a price-to-earnings multiple used only with the year-bracket caveats from step six firmly in mind, supplemented by an EV/EBITDA comparison and, where the investor has the patience for it, a dividend discount model that explicitly steps the royalty and tax assumptions at the correct future years rather than holding them constant.
Running this checklist against the pre-COD stock that cost her four years of opportunity cost, Anjali now sees clearly what she missed the first time: an EPC contractor with a public record of delays on two prior projects, and a construction contract with no meaningful delay-penalty clause protecting the developer. She would not buy that stock today at any price without first pricing in that specific contractor risk. On her more recent purchase, an operating run-of-river company nine years past COD with a clean three-year generation record against design energy, a DSCR comfortably above covenant according to its published rating rationale, and four more years remaining before its royalty and tax step-up, she bought with the year-bracket clock already running in her notebook, so that when the step-up arrives, it will be an expected event she has already modelled into her return expectations rather than a surprise that shows up one year in a dividend that is smaller than she hoped.
Chapter recap
Hydropower on NEPSE is not one asset class but two, joined only by a shared sub-index and a shared physical technology. A pre-commercial-operation-date company is a construction project financed through public equity, its balance sheet dominated by capital work in progress and its risk profile governed by EPC contractor performance, cost overruns, and the gap between required and actual completion dates. An operating company is a contracted, leveraged infrastructure cash-flow business whose entire revenue future is written into a single document, its power purchase agreement with the Nepal Electricity Authority, and whose near-term dividend capacity is governed as much by its loan covenants as by its reported profit. Confusing the two, applying operating-company valuation logic to a pre-COD stock or ignoring a debt covenant's grip on an operating company's dividend policy, is the single most common and most expensive mistake NEPSE hydropower investors make.
The physical risk layer beneath every hydropower stock, run-of-river versus storage versus peaking design, monsoon-driven seasonality that makes wet-season and dry-season generation genuinely different products, sedimentation and hydrological variability, and transmission evacuation capacity, determines whether a well-financed, well-structured project ever earns what its prospectus promised. Layered on top of that physical risk sits the contractual and regulatory architecture: the PPA's wet and dry season tariffs and the year its escalation clause goes flat, the royalty regime's steep step-up in capacity and energy royalty rates roughly fifteen years after commercial operation, and the income tax holiday that fades from full exemption to partial rebate to the full corporate rate across that same decade-and-a-half window. These three forces, tariff freeze, royalty step-up, and tax normalisation, tend to cluster in the same years of a project's life, producing a value cliff that a naive extrapolation of current dividends will miss entirely.
Debt sits underneath all of it. Nepali hydropower is financed with debt-to-equity ratios that typically run 70:30 or steeper, loan tenures of roughly eight to twelve years beginning with a construction-period moratorium, and debt service coverage ratio covenants that give lenders, not the board alone, real control over when and how much can be paid out as dividends. Understanding a hydropower stock without understanding its debt structure is like reading only the top half of its income statement.
The nine-step checklist built across this chapter, life-cycle stage, physical project type and hydrology, PPA terms, transmission readiness, debt structure and covenants, royalty and tax bracket position, operating performance against design, construction-phase contractor risk, and stage-appropriate valuation, is meant to be run start to finish for any hydropower ticker on NEPSE, pre-COD or operating, before capital is committed. It will not eliminate hydrology risk or contractor risk. It will ensure an investor knows, before buying, which risks are actually present in the specific stock in front of them rather than in the sector's reputation generally.
Chapter 73 turns from the physical, contracted world of hydropower to a sector whose risks are almost entirely behavioural and regulatory rather than hydrological: microfinance. The Microfinance Sector Playbook will build a parallel evaluation framework for NEPSE-listed microfinance institutions, whose defining risk factors, portfolio quality measured through portfolio-at-risk rather than plant load factor, group-lending methodology and borrower over-leverage across multiple overlapping microfinance memberships, regulatory interest rate spread caps that squeeze net interest margin in ways structurally similar to how royalty step-ups squeeze hydropower margins, and geographic and sectoral concentration of a loan book, require an entirely different diagnostic toolkit even though microfinance shares with hydropower the same essential lesson: read the contract, and the covenant, before you read the multiple.
Readers who found the pre-COD versus operating distinction useful in this chapter will find its direct cousin in Chapter 73, where a microfinance institution's loan book vintage, average client borrowing cycle, and provisioning policy play much the same role that COD and life-cycle year play here: the single fact that tells you which set of risks actually applies to the specific stock in front of you.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 73
The Microfinance Sector Playbook
First published 23 Aug 2026 · Last verified 29 Aug 2026
In the spring of 2018, a branch manager in Jhapa district was proud to report that his laghubitta had disbursed loans to nine hundred new borrowers that quarter, doubling the branch's portfolio in under a year. His regional office cheered the number. Head office, listed on NEPSE and paying a lucrative bonus share, cheered louder. What none of the cheering acknowledged was that roughly three hundred of those nine hundred "new" borrowers were already carrying loans from two other microfinance institutions operating in the same ward, borrowing from one lender to service the instalment on another. Four years later, when the merry-go-round stopped, that branch's portfolio at risk crossed thirty percent, its parent company's share price had been cut in half, and Nepal Rastra Bank was writing the strictest microfinance directives the sector had ever seen. This is the pattern every serious NEPSE investor needs to understand before touching a single laghubitta share: the business model, when it works, is one of the most powerful poverty-reduction and profit-generation engines Nepal has ever built. When it is mismanaged, it fails in a very specific and very predictable way, and that failure shows up in the numbers months before it shows up in the share price.
Deepa Sharma, a research analyst at a mid-sized Kathmandu brokerage, has spent the better part of a decade watching this cycle repeat in miniature. She was in university during the first branch-expansion boom, watched several classmates' parents in the Tarai take out group loans they could not really afford, and later built her career partly on the conviction that microfinance stocks are among the most misunderstood counters on the exchange — priced by retail investors as if they were fast-growing consumer banks, when in fact they are highly levered, socially exposed, regulation-dependent institutions that require an entirely different underwriting lens. Her approach to the sector, refined across several boom-bust cycles, forms the spine of this chapter. This is the microfinance sector playbook: how the model works, why it keeps breaking, how Nepal Rastra Bank has responded, and exactly what to check before you buy or hold a single microfinance share on NEPSE.
Lesson 73.1 — The Grameen Model, Nepali Style
Nepal's microfinance institutions did not invent group lending; they imported and adapted it. The Grameen Bank model pioneered in Bangladesh rests on a simple insight: the rural poor, especially women, are creditworthy but collateral-less, and peer accountability inside a small group can substitute for the collateral a commercial bank would otherwise require. Nepal's laghubitta bittiya sanstha sector, built up from the 1990s onward with donor support, cooperative roots, and later NRB licensing, took that model and localized it into what practitioners call the joint liability group, or JLG, structure.
In practice, a field officer organises borrowers — overwhelmingly women — into groups of five to seven, and several groups into a "centre" of twenty-five to forty members that meets weekly or biweekly, usually in a member's courtyard or a rented room. Loans are small, typically ranging from a few thousand rupees for a first cycle to several hundred thousand rupees for a mature, repeat borrower running a small enterprise. Repayment is collected in cash at the centre meeting, in front of the group, and group members are jointly and socially — though rarely legally in the strict sense — on the hook if one member defaults; social pressure, not a lien on property, is the enforcement mechanism. Interest is charged on a declining balance, disbursement is preceded by a compulsory savings requirement, and the field officer's own compensation and career progression is tied heavily to portfolio growth and, in principle, to portfolio quality.
Nepal's listed microfinance sector splits into two functional layers that investors frequently conflate. The first is the retail layer: the deprived-sector lenders that actually run centre meetings and disburse to end borrowers — names like Chhimek, Nirdhan Utthan, Swabalamban, Mithila, Naya Nepal, Vijaya, RSDC, Manushi, Sworojgar, and dozens of smaller district and regional players, most of them listed on NEPSE and together forming the microfinance sub-index. The second is the wholesale layer: institutions such as Sana Kisan Bikas Bank and the erstwhile RMDC, which historically lent to retail MFIs and cooperatives rather than to end borrowers directly, functioning more like a refinancing conduit than a Grameen-style lender. An investor who treats a wholesale lender's credit risk profile as identical to a retail centre-meeting operator's is making a basic category error; their exposure to over-indebtedness, branch cost structure, and regulatory caps differs meaningfully even though both trade under the "microfinance" label.
KEY CONCEPT
Joint liability group lending substitutes social collateral for physical collateral. It works only as long as three conditions hold: groups are formed from genuine peers with real social ties, field staff have time to actually know their borrowers rather than just process volume, and a borrower is not simultaneously a member of overlapping groups at competing institutions. When branch expansion outruns any of the three, the model's core enforcement mechanism quietly stops functioning long before the loan book shows it.
The deprived-sector lending mandate is the other structural fact every investor needs internalized. Nepal Rastra Bank requires commercial banks, development banks, and finance companies to channel a fixed percentage of their loan portfolio to the "deprived sector" — broadly, low-income and marginalized borrowers — and banks satisfy much of this obligation by wholesale-lending to, or investing in, microfinance institutions rather than building their own branch networks into remote wards. This wholesale funding line is a double-edged structural feature. On one hand it has historically supplied MFIs with relatively cheap, policy-driven capital, supporting rapid balance sheet growth. On the other hand, it means MFI funding costs and availability are partly a function of commercial bank credit policy and NRB's deprived-sector quota rules, not purely of the MFI's own creditworthiness — a variable that can tighten or loosen the sector's cost of funds independent of anything the MFI's management does.
Lesson 73.2 — Reading the Vital Signs: PAR, OSS, and the Metrics That Matter
Deepa's first rule for any microfinance counter is that the headline numbers a company chooses to publish in its glossy annual report — net profit, EPS, return on equity — are the last numbers she looks at, not the first. Profit in a lending business is an opinion; it depends entirely on how aggressively or conservatively the company recognises bad loans and provisions against them. The numbers that tell the truth about a microfinance book sit further down the disclosure, in portfolio quality and efficiency metrics that most NEPSE retail investors never open the annual report far enough to find.
Portfolio at risk, or PAR, is the single most important credit quality metric in microfinance and the one most different from how a commercial bank investor is trained to think. PAR30 measures the percentage of the outstanding loan portfolio that has at least one instalment overdue by thirty days or more; PAR90 does the same at ninety days. Crucially, PAR is not calculated as overdue amount divided by total portfolio — it is calculated as the entire outstanding balance of any loan with an overdue instalment divided by total portfolio. A single missed instalment on a large loan puts the whole loan's balance into the PAR numerator, which is why PAR ratios can move sharply even when actual missed cash amounts are still small; it is a leading indicator built deliberately to be sensitive.
Operating self-sufficiency, or OSS, measures whether an MFI's operating income covers its operating and financial costs without subsidy — an OSS above 100 percent means the institution earns enough from its lending operations to sustain itself commercially; below 100 percent, it is structurally dependent on grants, subsidized wholesale funds, or capital infusions to keep operating, a red flag for any institution now expected to behave like a fully commercial, dividend-paying, NEPSE-listed financial company. Cost per borrower and average loan size, read together, tell you where an MFI sits on the mission-drift spectrum: a rising average loan size alongside a falling number of active borrowers per branch usually signals the institution has quietly pivoted from serving genuinely poor, first-time borrowers toward larger, less labor-intensive loans to already-banked small entrepreneurs — a shift that can improve near-term margins while eroding the very deprived-sector mandate and social mission that gives the institution its regulatory license and funding advantages in the first place.
PRACTICAL TOOL
Core microfinance metrics and the benchmarks Deepa uses as a first screen
Metric
What it measures / rough healthy range
PAR30
Share of portfolio with any instalment 30+ days overdue; below 5 percent is comfortable, above 10 percent demands an explanation, above 15 percent signals a book under real stress
PAR90
Same at 90+ days overdue; effectively the pool likely to require writing off or heavy provisioning
Loan loss provision coverage
Provisions held as a percentage of PAR90 or of non-performing loans; below 100 percent coverage of NPL means reported equity is overstating true net worth
Operating self-sufficiency (OSS)
Operating income over operating plus financial cost; sustainably above 110-115 percent is healthy, below 100 percent means structural dependency
Cost per borrower
Total operating cost divided by active borrower count; rising cost per borrower without matching loan-size growth signals branch inefficiency or shrinking outreach
Average loan size per borrower
Portfolio outstanding divided by active borrowers; watch trend, not just level — a rapid multi-year rise can mean either healthy borrower graduation or mission drift and stacking risk
Borrowers per field staff / per branch
Efficiency and monitoring-capacity indicator; a number rising faster than staff headcount is the earliest warning sign of monitoring quality decline
The provisioning line deserves its own emphasis because it is where reported profit and real economic profit diverge most in this sector. NRB's directives require graduated provisioning against loans classified as pass, watchlist, substandard, doubtful, and loss, with provisioning rates rising sharply as a loan ages further into delinquency — but the classification itself involves judgment, and a management team under pressure to protect the dividend has every incentive to restructure or reschedule a stressed loan just before it crosses a classification threshold, resetting its aging clock without actually improving the borrower's ability to repay. Deepa's habit, therefore, is to read at least three consecutive years of an MFI's loan loss provision line alongside its rescheduled and restructured loan disclosure, because a pattern of provisions that stay suspiciously flat while restructured loans quietly climb is a company managing the appearance of asset quality rather than the underlying reality of it.
Lesson 73.3 — Boom, Bust, and the Lessons of 2015-2022
To understand why Nepal's regulators now treat microfinance with the wariness usually reserved for cooperatives, an investor needs the historical arc. Following the 2015 earthquake and the subsequent economic recovery, Nepal's microfinance sector entered a period of extraordinarily rapid branch expansion. Licensed laghubitta institutions, flush with wholesale funding from banks eager to meet their deprived-sector quotas and encouraged by a listing boom that rewarded portfolio growth with rich valuations on NEPSE, raced to open branches in the same districts, sometimes the same wards, as competitors. It was not unusual, by the late 2010s, for a single rural settlement in the eastern or central Tarai to have field officers from four or five different microfinance institutions cycling through on different days of the week, each running their own centre meetings, each unaware of — or willfully ignoring — how many other institutions' loans a given borrower was already carrying.
This is the phenomenon practitioners call multiple lending or loan stacking, and it is the single most reliable precursor to a microfinance sector crisis anywhere in the world, from Andhra Pradesh in 2010 to Cambodia in the late 2010s to Nepal itself in the early 2020s. A borrower who takes a second loan to service the instalment on her first is not expanding her enterprise; she is running a personal Ponzi scheme that only the continued availability of new credit keeps from collapsing. As long as MFIs kept expanding and disbursing, the stacking was invisible in the portfolio quality numbers — repayment rates stayed high because borrowers had fresh loan proceeds to make old payments with. The moment growth slowed, even slightly, the whole structure was exposed at once, because there was no new borrowing left to paper over instalments that borrowers could never actually afford from their own cash flow.
CASE IN POINT
Nepal's 2021-2022 microfinance stress episode was triggered by a combination of forces arriving together: pandemic-era income disruption in remittance-dependent rural households, a sharp tightening of overall banking sector liquidity that choked off the wholesale funding MFIs had relied on to keep disbursing, and years of accumulated over-indebtedness finally surfacing as growth slowed. Borrower protests against aggressive collection practices made national headlines, several districts saw organised resistance to loan repayment, and NRB was forced into emergency loan restructuring provisions for distressed microfinance borrowers even as it simultaneously tightened supervisory scrutiny of the institutions themselves. The episode did lasting damage to the sector's social license and directly triggered the wave of regulatory tightening this chapter covers in the next lesson.
The uncomfortable truth Deepa emphasises to newer analysts is that the crisis was not primarily a story of external shock; it was a business model problem that the pandemic merely revealed earlier than it otherwise would have. Branch density had outrun genuine market depth in dozens of districts. Field officer incentive structures rewarded disbursement volume over portfolio quality. Head offices under pressure from NEPSE shareholders to sustain the growth rates that justified rich price-to-book multiples kept pushing branches to open in already-saturated areas rather than genuinely underserved ones. And crucially, no credit information sharing mechanism existed that would let one MFI's loan officer see that a prospective borrower was already carrying loans from three other institutions — the single structural fix that, once regulators forced it, did more than any interest rate cap to slow the stacking problem.
WARNING
A microfinance institution's headline growth rate in active borrowers or loan portfolio is not, by itself, a positive signal. In a district or region already saturated with competing MFI branches, rapid borrower growth is more often evidence of stacking and unsustainable disbursement than of genuine financial inclusion. Always cross-check portfolio growth against the number of competing MFI branches already operating in the same operating districts, disclosed in the company's branch expansion report.
Lesson 73.4 — NRB's Regulatory Vice: Caps, Capital, and Consolidation
Nepal Rastra Bank's response to repeated boom-bust cycles in microfinance has been a steadily tightening set of directives that any investor in this sector must track as closely as the companies' own financials, because regulatory action here has moved share prices far more violently than earnings surprises ever have.
The starting point is interest rate control. As far back as 2016, NRB capped the interest rate spread microfinance institutions could charge over their cost of funds at roughly seven percentage points, an explicit acknowledgment that MFIs' funding cost advantage from cheap wholesale deprived-sector credit should not simply be captured as margin at the expense of poor borrowers paying effective rates well above what a commercial bank customer would ever be charged. Over the following years NRB layered on an absolute ceiling as well, eventually capping the maximum lending rate microfinance institutions could charge end borrowers at around fifteen percent, regardless of the spread calculation, a level many MFI managements complained compressed margins to the point of threatening sustainability, particularly for smaller, higher-cost-to-serve institutions operating in remote hill districts.
By 2023, NRB's own internal study had concluded that a flat rate cap was a blunt instrument poorly suited to a sector with such wide variation in operating cost structures between an efficient Tarai-based lender and a high-cost hill-district operator, and recommended moving toward a spread-based, base-rate-linked framework instead. That recommendation became policy through the FY 2081/82 and FY 2082/83 monetary policy cycles: NRB's mid-term review formally shifted microfinance lending rates onto a base rate plus premium structure, similar in spirit to the base-rate framework long used for commercial bank lending rates, with new guidelines taking effect from the start of the Nepali fiscal year in Shrawan. The practical effect for investors is that microfinance net interest margins are no longer governed by a single, easily modelled flat percentage cap; they now move with the same base-rate mechanics that drive commercial bank margins, meaning changes in the banking system's overall cost of funds pass through to MFI margins in ways that require the same base-rate tracking discipline covered elsewhere in this book's banking sector chapters.
REGULATORY DETAIL
NRB's microfinance interest rate framework has moved through three distinct regimes: a spread cap of roughly seven percentage points over cost of funds (from 2016), an absolute lending rate ceiling of approximately fifteen percent layered on top of the spread cap, and — following NRB's 2023 internal review and the FY 2082/83 monetary policy — a base-rate-plus-premium framework that replaced the flat ceiling with a mechanism explicitly linked to each institution's own cost of funds. Any historical margin comparison across years must account for which regime was in force; pre- and post-transition net interest margins are not directly comparable without adjustment.
Capital adequacy and ownership rules form the second regulatory front. In the wake of the 2021-2022 stress episode, NRB progressively raised minimum paid-up capital requirements for microfinance institutions, forcing an entire tier of small, often single-district laghubittas either to raise fresh capital, merge with a stronger peer, or exit the sector. Deadlines for meeting the higher capital thresholds have repeatedly been extended and then enforced, and companies that missed successive deadlines found themselves under direct NRB pressure to pursue forced mergers rather than continue operating under-capitalised. Layered on top of the capital rules, NRB has separately tightened scrutiny of major shareholders and promoter groups in microfinance institutions, restricting related-party lending and cross-holdings that had previously let a handful of promoter families control multiple MFIs simultaneously — a structure that, when it existed, made loan stacking across "competing" institutions considerably more likely than genuine market competition would suggest, since the institutions were not truly independent in their credit decisioning even though they looked independent on NEPSE's ticker list.
The debt-to-equity and leverage dimension is the third front, running in parallel with capital rules: because microfinance institutions fund their loan books overwhelmingly through wholesale borrowing from banks and other financial institutions rather than public deposits, NRB directives constrain how much an MFI can borrow relative to its own core capital, explicitly to prevent a repeat of the pre-2022 pattern where thinly capitalised institutions built loan books many multiples the size their equity base could safely absorb losses against. An investor evaluating any NEPSE-listed microfinance stock should treat its capital-to-risk-weighted-assets ratio and its core capital fund as being every bit as central to the analysis as an equivalent metric would be for a commercial bank — arguably more so, given microfinance's structurally thinner margin for error against social and credit shocks.
WARNING
NRB has made unambiguously clear through successive monetary policy statements and standalone directives that consolidation, not growth, is the sector's near-term trajectory. An investor buying a small, single-region, marginally capitalised microfinance stock purely on the hope of a takeover premium is making a speculative bet on merger arithmetic, not an investment in a going concern; read the merger and acquisition disclosure history of any small MFI candidate carefully before assuming a forced merger will be value-accretive to minority shareholders rather than dilutive.
Finally, the merger and consolidation directives themselves warrant a table, because the trend line matters more to an investor's holding-period thesis than any single deadline. NRB has explicitly signalled — through statements requiring stricter scrutiny of merger and acquisition proposals, criteria updates on dividend distribution and nationwide operating status tied to scale, and repeated capital deadline enforcement — that it intends the sector to shrink from the dozens of small, often sub-scale institutions that proliferated during the 2010s branch race into a smaller number of larger, better-capitalised, more professionally governed entities.
Consolidation driver
Practical effect on a listed MFI investor
Minimum paid-up capital thresholds
Sub-scale institutions face a binary choice: raise capital (dilutive to existing shareholders) or merge (swap ratio determines whether minority holders gain or lose value)
Nationwide operating license criteria
Institutions confined to a small operating radius face restrictions on dividend distribution and status upgrades, pressuring them toward merger to unlock growth
Major shareholder and related-party tightening
Promoter groups controlling multiple MFIs face pressure to consolidate overlapping entities rather than run them as separate, cross-lending structures
Asset quality stress at weaker peers
A well-capitalised MFI can acquire a distressed peer's branch network and client base at a discount, but inherits its loan book's hidden PAR and provisioning gaps unless due diligence is rigorous
Lesson 73.5 — Early Warning Signs and the Over-Indebtedness Checklist
The hardest part of evaluating a microfinance stock is that the earliest warning signs of client over-indebtedness never appear in the profit and loss statement first; they appear in operational disclosures that most retail investors skip entirely. Deepa's early-warning routine focuses on five specific signals, read together rather than in isolation, because any one alone can have an innocent explanation while the combination rarely does.
The first signal is branch density relative to disclosed operating districts. An MFI's annual report typically discloses the number of districts and the number of branches it operates. When branch count per district climbs faster than the district's rural population or economic activity would plausibly support — particularly in districts already known to host several competing MFI branches — that is the geographic signature of the same saturation dynamic that preceded the 2021-2022 stress episode. The second signal is the trend in average loan size per borrower relative to the trend in active borrower count. A rising average loan size accompanied by a flattening or falling active borrower count can indicate healthy graduation of existing clients to larger enterprise loans — but it can equally indicate that the institution has quietly shifted from genuine deprived-sector outreach toward larger, easier-to-process loans to already-served clients, a pattern regulators increasingly scrutinize as mission drift.
The third signal is the restructured and rescheduled loan disclosure, tracked over at least three years rather than a single snapshot. A company whose restructured loan balance is rising while its reported PAR stays flat or falls is very likely managing its classification rather than genuinely improving asset quality — restructuring resets a delinquent loan's aging clock without necessarily restoring the borrower's actual capacity to repay. The fourth signal is staff turnover and, where disclosed, borrowers per field officer. Centre meeting collection depends on field officers who know their borrowers personally; a branch network expanding faster than it can train and retain experienced field staff is a branch network whose social-collateral enforcement mechanism is quietly weakening even while headline growth numbers look strong. The fifth signal, harder to find but worth the effort, is any qualitative disclosure or news flow about borrower protests, loan write-off campaigns, or local government intervention in a company's specific operating districts — these local flashpoints reliably precede sector-wide stress by one to two reporting cycles.
CAUTION
Do not rely on a single reporting period's PAR figure in isolation. Microfinance PAR ratios can be actively managed downward in the short term through aggressive restructuring, loan write-offs funded by prior-year retained provisions, or simply accelerating fresh disbursement to dilute the denominator — the same mechanism that hid stacking risk during the pre-2022 boom. Always read at least three to five years of PAR, provisioning, and restructured-loan trend lines together before drawing a conclusion about an institution's underlying credit culture.
Deepa also keeps a simpler heuristic close at hand for a quick first pass on any microfinance stock she has not previously covered: compare the institution's disclosed average loan size and its operating district list against the National Statistics Office's district-level poverty and remittance-dependency data, and against how many other listed MFIs disclose branches in the same districts. A district with five overlapping MFI branch networks, high remittance dependency, and an average loan size that has more than doubled over three years is, in her experience, one of the more reliable geographic fingerprints of a book quietly accumulating stacking risk — even when every individual company's own disclosed PAR still looks comfortable.
PRACTICAL TOOL
Deepa's five-signal over-indebtedness screen, applied together, not individually.
Signal
What to look for
Branch density
Branch count growth outpacing district economic capacity, or heavy overlap with competitor branch networks in the same wards
Average loan size trend
Rapid multi-year rise alongside flat or falling active borrower count
Restructured loan trend
Rising restructured balance while reported PAR stays flat or improves
Field officer capacity
Borrowers per field officer rising faster than staff headcount; elevated field staff turnover
Local flashpoints
News flow on borrower protests, collection disputes, or local government intervention in the company's specific operating districts
Lesson 73.6 — The Microfinance Stock Evaluation Checklist
Bringing the whole playbook together, here is the sequence Deepa walks through, in order, for any NEPSE-listed microfinance stock before she will recommend a position to a client — retail or institutional.
She starts with regulatory compliance status: has the institution met NRB's current minimum paid-up capital threshold, and if not, what is its stated timeline and mechanism — rights issue, merger, or promoter capital infusion — for closing the gap? A company still short of the threshold with no credible near-term plan is not a stock to hold through the deadline; it is a stock to wait out from the sidelines until the capital or merger question resolves, because the dilution or swap-ratio outcome will dominate any operational thesis in the interim.
Second, she checks the interest rate and margin picture against the current base-rate-plus-premium framework rather than against any older flat-cap assumption, confirming the company's disclosed net interest margin is consistent with what the current regulatory regime allows and sustainable given its funding cost structure — a hill-district operator with structurally higher branch operating costs needs a correspondingly higher margin to sustain OSS above 100 percent, and if regulatory caps are compressing that margin below sustainable levels, that is a structural earnings risk no amount of operational excellence can fully offset.
Third, she pulls at least three to five years of PAR30, PAR90, provisioning coverage, and restructured loan trends together, looking specifically for the divergence pattern described in Lesson 73.5 — rising restructuring alongside flat or improving headline PAR is treated as a disqualifying red flag regardless of how attractive the reported profit trend looks.
Fourth, she maps the company's disclosed operating districts and branch counts against known competitor overlap and against remittance-dependency and poverty data, screening specifically for the geographic over-indebtedness fingerprint — heavy branch overlap combined with rapidly rising average loan size in the same districts.
Fifth, she examines ownership and governance: who are the major shareholders and promoter group, do they hold stakes in other microfinance institutions that could create related-party lending or cross-subsidization risk, and has the company been named in any NRB related-party or major-shareholder tightening action.
Sixth, she checks OSS, cost per borrower, and borrowers per field officer trends together as an efficiency and monitoring-capacity check, looking for cost discipline that has kept pace with branch expansion rather than growth that has outrun the institution's actual capacity to know its borrowers.
Seventh and last, only once every prior step has cleared, does she look at valuation — price to book relative to sector peers, dividend history and sustainability given the OSS and provisioning picture, and any pending merger or capital-raise dilution that would change the per-share economics before the position could be expected to pay off. A microfinance stock that clears the first six steps and trades at a discount to sector peers on price to book is, in her experience, one of the more reliable value opportunities on NEPSE precisely because the sector's headline reputation — still coloured by the 2021-2022 stress episode — keeps many retail investors away from names that have since genuinely cleaned up their books and met the new capital and governance bar.
KEY CONCEPT
Microfinance stock selection on NEPSE rewards process discipline more than almost any other sector on the exchange, because the sector's own history has produced both genuine survivors with cleaned-up balance sheets and superficially similar peers still carrying hidden stacking and provisioning risk. The checklist exists precisely because the two categories cannot be told apart from the headline profit and dividend numbers alone.
Chapter recap
Nepal's microfinance sector sits at an unusual intersection for a NEPSE investor: a genuinely important development finance model, built on group lending and social collateral, that has also proven repeatedly vulnerable to a very specific and very recognizable failure mode — rapid branch expansion outrunning genuine market depth, borrowers stacking loans across competing institutions, and a headline growth story that masks deteriorating credit culture until a funding or liquidity shock forces the reckoning into the open. The 2021-2022 stress episode was the clearest recent expression of this pattern, but it was neither the first nor, in all likelihood, the last time Nepali microfinance will cycle through some version of this dynamic, which is exactly why the metrics and checklist in this chapter matter more here than the simpler growth-and-margin analysis that might suffice for a more conventional financial stock.
The metrics that matter in this sector — portfolio at risk at the thirty and ninety day thresholds, operating self-sufficiency, cost per borrower, average loan size trends, and loan loss provisioning coverage — are not optional supplementary detail; they are the primary evidence, because reported profit and dividend history in a lending business this exposed to classification judgment can diverge sharply from underlying economic reality for several reporting cycles before the gap forces itself into the open. Reading these metrics in trend, across three to five years, and specifically watching for the divergence between rising restructured loans and flat or improving headline PAR, is the single highest-value habit this chapter can leave an investor with.
Nepal Rastra Bank's regulatory posture toward microfinance has tightened steadily and, on the evidence of the last decade, will likely keep tightening: from the original interest rate spread cap, through an absolute lending rate ceiling, to the current base-rate-plus-premium framework; from looser capital rules to escalating minimum paid-up capital thresholds and major-shareholder scrutiny; and from a fragmented sector of dozens of small institutions toward an explicitly regulator-encouraged wave of consolidation. Every one of these regulatory shifts has moved microfinance share prices more decisively than ordinary earnings surprises, which means tracking NRB's monetary policy statements and standalone microfinance directives is not optional homework for this sector — it is core to the investment process itself.
The over-indebtedness early warning signals — branch density and overlap, average loan size trends, restructured loan patterns, field officer capacity, and local flashpoints — exist because the client-level risk that eventually shows up as portfolio-wide stress is visible in operational disclosures well before it reaches the profit and loss statement. An investor willing to read branch expansion reports and district-level operating data alongside the financial statements gains a meaningful information edge over the much larger pool of retail participants who look only at EPS and dividend yield.
The seven-step evaluation checklist in Lesson 73.6 — regulatory compliance status, margin sustainability under the current rate framework, multi-year credit quality trend analysis, geographic over-indebtedness screening, ownership and related-party review, efficiency and monitoring-capacity trends, and only then valuation — is designed to filter out the institutions still carrying hidden stacking and provisioning risk from the genuine survivors that have met the new capital and governance bar since 2022. Applied consistently, it turns a sector many NEPSE investors avoid on reputation alone into one of the exchange's more reliable sources of disciplined value opportunities, precisely because lingering reputational caution keeps competition for the genuinely clean names lower than their fundamentals would otherwise justify.
Chapter 74, The Insurance Sector Playbook, turns to a different but structurally related corner of Nepal's financial sector — the life and non-life insurance companies listed on NEPSE, where the analytical challenge shifts from credit risk and over-indebtedness to actuarial reserving, claims ratio discipline, investment portfolio management against policyholder liabilities, and a regulatory environment shaped by the Nepal Insurance Authority rather than Nepal Rastra Bank. Readers who have internalized this chapter's lesson — that headline profit in a highly regulated financial subsector can diverge sharply from underlying economic reality, and that the real evidence sits in the technical reserves and provisioning disclosures most investors skip — will find much of that same discipline transfers directly into evaluating an insurer's claims reserve adequacy and investment income sustainability.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 74
The Insurance Sector Playbook
First published 23 Aug 2026 · Last verified 29 Aug 2026
Lesson 74.1 — Two Businesses Wearing One Sector Label
Rukmani Thapa had made good money in microfinance shares, and she assumed insurance would work the same way. Both sectors, after all, sat under the same regulatory umbrella of forced consolidation, both had been pushed through capital hikes, and both showed up on her screener with double-digit return on equity in good years. In late 2021 she bought into three insurers in a single week — one life, one non-life, one that had just announced a merger — using the same checklist she used for microfinance. Eighteen months later, one of the three had cut its dividend to nothing after a single bad monsoon season of motor and crop claims, one had quietly diluted her position through a rights issue she hadn't budgeted for, and only the life insurer had compounded the way she expected. When she went back to find out why, she discovered she had never actually understood what she owned. A microfinance company lends money and earns a spread. An insurer sells a promise, invests the premium while the promise is outstanding, and only finds out years later — sometimes decades later, in the case of a life policy — whether the promise cost more than the premium collected. That difference in timing is the entire chapter.
Insurance is the only sector on the NEPSE board where the product being sold is a bet on the future, priced today, paid out later, and where the shareholder's return depends less on this quarter's premium collections than on how conservatively the company reserved against claims nobody has filed yet. It is also, not coincidentally, the sector most recently forced through the same regulator-driven capital consolidation that reshaped banking and microfinance — except compressed into a shorter window and layered on top of a mandatory reinsurance regime that no other NEPSE sector has to deal with. Getting this chapter right means holding two separate business models in your head at once, because a life insurer and a non-life insurer are answering completely different questions with completely different accounting, even though both trade under the same "Insurance" sector tag on your broker's screen.
Start by separating what most Nepali retail investors lump together. Life insurance and non-life (general) insurance are licensed separately by the Nepal Insurance Authority, cannot be written by the same company, and behave like different asset classes entirely. A life insurer sells long-duration contracts — endowment, term, and increasingly unit-linked-style products — where premiums are collected for years or decades before a death, maturity, or surrender triggers a payout. A non-life insurer sells short-duration contracts — motor, fire, marine, engineering, crop, health — where the policy period is typically twelve months and claims, when they occur, are usually settled within one to two years of the loss event. That single difference in duration changes everything downstream: how each recognises profit, what its balance sheet looks like, what breaks it, and what a rational shareholder should actually be underwriting when buying the stock.
A non-life insurer's economics resemble a fast-turning inventory business. It collects a premium, sets aside a reserve for expected claims and unearned premium, and the difference between the two — adjusted for expenses and reinsurance — is roughly the underwriting result for the year. Because the float period is short, a non-life insurer's profitability is visible relatively quickly: a bad year of claims (a widespread hailstorm damaging standing crops, a flood season generating a spike in motor and property claims, a fire loss in an industrial client) shows up in that same year's combined ratio and that same year's profit. This is why non-life insurance profit is lumpier and more cyclical than life insurance profit — it is closer to a P&L business than a balance-sheet business, even though the balance sheet still matters enormously for solvency.
A life insurer's economics resemble a long-duration bond fund with a mortality overlay. Premiums collected today are not "earned" the way a non-life premium is earned over twelve months; instead, a portion goes toward the current year's mortality and expense cost, and a much larger portion is set aside in actuarial reserves that will, on average, be needed to pay the policy's eventual maturity or death benefit — which for many endowment products is ten, fifteen, or twenty years away. The insurer invests this accumulating reserve pool, and the spread between what it earns on that invested float and what it has actuarially promised policyholders is a major source of shareholder profit, realised gradually over the life of the book rather than in the year premiums are collected. This is why a life insurer's single-year net profit is a much noisier signal of true economic value creation than a non-life insurer's — a life insurer can show a soft profit in a year when its underlying book of business actually got healthier (higher persistency, better mortality experience, growing embedded value) simply because actuarial reserving conventions defer recognition.
KEY CONCEPT
Non-life insurance profit is largely a current-year underwriting and investment result — you can assess a bad year almost immediately from the combined ratio. Life insurance profit is a multi-decade actuarial process — a single year's net profit tells you much less than the trend in persistency, reserve adequacy, and embedded value. Never value a life insurer the way you value a non-life insurer, or vice versa; the two demand different metrics entirely.
Rukmani's mistake in 2021 was treating all three of her insurance holdings as one homogeneous "insurance sector" bet. The non-life insurer she bought was exposed to a bad monsoon; the life insurer she bought was exposed to persistency and interest-rate assumptions on its reserves; the merging insurer she bought was exposed to integration risk and dilution. None of those risks correlate with each other in the way, say, two commercial banks' credit cycles correlate. Sector-level diversification within insurance is close to meaningless unless you know which sub-model each holding actually runs.
Lesson 74.2 — The Metrics That Actually Matter: Non-Life Insurance
For a non-life insurer, the single most important number is the combined ratio — the sum of the claims ratio (net claims incurred divided by net premium earned) and the expense ratio (management and commission expenses divided by net premium earned). A combined ratio below 100 percent means the company is making an underwriting profit before investment income; above 100 percent means the company is losing money on the insurance business itself and depends entirely on investment income to stay profitable. Nepali non-life insurers routinely disclose figures close to or occasionally over 100 percent, which is not automatically alarming — many mature non-life insurers globally run combined ratios in the high 90s to low 100s and still compound shareholder value because of investment income on the float — but a combined ratio that is deteriorating year over year, especially driven by the claims ratio rather than the expense ratio, is the first place to look for trouble.
The claims ratio in isolation tells you about underwriting discipline and pricing power. A rising claims ratio can mean genuine adverse experience (a bad monsoon, a spike in motor accidents, an unusually large single fire or engineering loss) or it can mean the company has been underpricing risk to win market share, which is a much more structural problem. Because Nepal's non-life market has historically been intensely competitive on price — with more than a dozen listed non-life insurers chasing a motor and fire book that does not grow fast enough to absorb all of them profitably — chronic claims-ratio deterioration across the sector, not just at one company, has been a recurring concern flagged by both the regulator and industry commentators pushing for consolidation.
The solvency margin ratio is the second pillar, and it is a regulatory survival metric rather than a profitability metric. The Nepal Insurance Authority (NIA) requires every insurer, life and non-life, to maintain available solvency capital at a multiple of its required solvency margin — historically set around 1.5 times under the older Solvency Margin Directives (2070 for life insurers and 2071 for general insurers), with the NIA subsequently moving the framework onto a more risk-sensitive footing through the Risk-Based Capital and Solvency Directive first issued in 2078/2081 and refined further in 2082. Under this newer framework, capital requirements are calibrated more granularly to the actual risk profile of each insurer's book — mortality risk, catastrophe risk, investment risk, concentration risk — rather than a flat multiple applied uniformly. Reported solvency ratios in the Nepali non-life market have generally run well above the regulatory floor, with sector averages reported north of 2.5 times required capital in recent NIA disclosures reviewed by industry trade press, and life insurers showing a similar pattern of comfortable headroom in aggregate. A comfortable sector average does not mean every individual company is comfortable, however, and the solvency ratio is precisely the number to check company-by-company before assuming any listed insurer is safe.
REGULATORY DETAIL
The Nepal Insurance Authority's Risk-Based Capital and Solvency Directive replaced the older flat-multiple Solvency Margin Directives (2070 for life, 2071 for general insurance) with a framework that ties required capital more closely to the actual risk composition of each insurer's book. A solvency ratio comfortably above the regulatory minimum is table stakes for any insurer you consider holding — treat a ratio that is falling toward the floor, even if still technically compliant, as an early warning rather than a footnote.
Investment income contribution to profit is the third number every non-life investor should isolate. Because non-life insurers hold sizeable investment portfolios funded by policyholder float (unearned premium reserves plus claims reserves), a meaningful share of reported net profit in most listed Nepali non-life insurers comes not from underwriting but from interest income, dividend income, and gains on their fixed deposit and government security holdings. In years when the combined ratio deteriorates, a strong investment book can mask the underlying underwriting weakness in the headline profit number — which is exactly why Rukmani's non-life holding kept paying a dividend for two years after its claims ratio had already started climbing, until a genuinely bad claims year finally overwhelmed the investment cushion and the dividend was cut. Always ask what fraction of net profit is underwriting result versus investment result, and never assume investment income growth alone signals a healthier insurance franchise.
Lesson 74.3 — The Metrics That Actually Matter: Life Insurance
Life insurance metrics revolve around persistency, reserve adequacy, and the concept of embedded value, all of which exist to answer one underlying question: is the book of business the company has written actually going to deliver the profit its pricing assumed, or is it going to lapse, underperform, or cost more than reserved?
The persistency ratio measures the proportion of policies (by premium or by count) that remain in force and continue paying renewal premiums after the first year, and typically after subsequent years as well. A first-year persistency ratio in the 80s (percent) is generally considered healthy for the Nepali market; a ratio sliding into the 60s or lower signals that a large share of new business is lapsing before it ever becomes profitable, because life insurance products are typically front-loaded with acquisition costs — agent commissions, medical underwriting, policy issuance expenses — that are only recovered over several years of renewal premiums. A life insurer that grows new business aggressively but cannot retain policyholders past year one or two is manufacturing an accounting profit today (the first premium) while quietly destroying economic value, because the cost of acquiring that policy will never be recouped. This is arguably the single most important number in life insurance analysis and the one most likely to be glossed over in headline results, because persistency does not appear in the income statement — it has to be pulled from actuarial disclosures, annual report notes, or NIA aggregate data.
WARNING
A life insurer showing rapid growth in new business premium alongside a persistency ratio that is flat or falling is very often growing an unprofitable book. First-year commission and underwriting costs on a lapsed policy are a sunk loss with no offsetting renewal stream to recover them. Check persistency before getting excited about premium growth headlines.
Reserve adequacy is harder for a retail investor to independently verify because it depends on actuarial assumptions — mortality tables, lapse assumptions, discount rates — that are not fully disclosed in NEPSE-level annual reports the way they would be in a market with mandated embedded value reporting. What you can do is watch the trend: is the actuarial reserve per unit of sum assured rising in a way that outpaces premium growth (a sign the appointed actuary is toughening assumptions, often after adverse experience), and has the auditor or actuary flagged any reserving strengthening in the notes to accounts. A sudden jump in reserves relative to premium income, absent a corresponding jump in business volume, is usually the actuary catching up to bad news rather than a one-off accounting choice.
Embedded value — the present value of future profits expected from the existing in-force book, plus adjusted net asset value — is the metric international life insurance investors use to value a life insurer properly, because a single year's IFRS-style or Nepal GAAP-style net profit dramatically understates the value being created in a growing life book (since most of the profit is deferred into future years' reserve releases). Nepali listed life insurers do not, as a rule, publish formal embedded value statements the way listed insurers in more developed markets like India do, which is a structural disclosure gap investors should be aware of rather than assume away. In the absence of published embedded value, a reasonable proxy is to track the growth in total actuarial liabilities (a rough proxy for the size of the in-force book) alongside the trend in persistency and claims experience, understanding that you are working with an approximation, not a true embedded value calculation. Investors who want a genuine handle on economic value in a Nepali life insurer should treat this as an acknowledged blind spot, size their position accordingly, and lean more heavily on persistency, solvency, and dividend-paying consistency as the observable proxies for underlying book quality.
PRACTICAL TOOL
A simple life-insurance health check, doable from public disclosures alone: (1) five-year trend in first-year and renewal persistency ratios, (2) solvency ratio versus the regulatory minimum, (3) growth in actuarial liabilities relative to growth in gross premium, (4) five-year dividend payment consistency under the Section 43 profit-and-capital-adequacy test, and (5) claims settlement ratio (death claims paid versus death claims intimated) as a rough proxy for customer-facing reputation and claims-paying discipline. None of these alone is embedded value, but together they triangulate book quality reasonably well in a market that does not publish formal EV statements.
Lesson 74.4 — The Nepal Insurance Authority: Solvency, Section 43, and Mandatory Reinsurance
Nepal's insurance regulator was reconstituted from the earlier Insurance Board (Beema Samiti) into the Nepal Insurance Authority (NIA) under the Insurance Act 2079, giving it a broader risk-based supervisory mandate closer to how Nepal Rastra Bank supervises banks and how SEBON supervises capital markets. Three planks of NIA regulation matter most to a NEPSE insurance investor: solvency requirements, dividend conditions, and mandatory reinsurance.
On solvency, the direction of travel has been from a flat solvency-multiple requirement toward a full risk-based capital regime. The Risk-Based Capital and Solvency Directive, first brought into force in 2078/2081 and updated again in 2082, requires insurers to hold capital calibrated against the specific risks on their books — underwriting risk, market and investment risk, credit risk, operational risk — rather than a single uniform multiplier applied identically to a conservative motor-only insurer and an aggressive insurer with concentrated catastrophe exposure. This is a meaningfully more sophisticated framework than the older 1.5-times solvency-margin directives it replaces, and it means two insurers with an identical headline solvency ratio today can have very different real cushions once their specific risk composition is properly risk-weighted. For a retail investor this mostly manifests as a number in the quarterly disclosure — check that it is comfortably above 1.0 (the barest regulatory floor under risk-based capital) and ideally well above the levels the company itself has historically run at, since a ratio that has compressed sharply quarter over quarter, even while remaining technically compliant, is an early signal the company is either growing capital-intensive business faster than it is retaining earnings, or absorbing losses that are eating into its cushion.
On dividends, Chapter 68 already covered Section 43 of the Insurance Act in detail as it applies across the sector — the requirement that an insurer can only distribute dividends out of net profit after making full actuarial and technical provisions, meeting minimum solvency and capital adequacy thresholds, and setting aside required reserves, with the NIA empowered to restrict or disallow a dividend if these conditions are not met regardless of the accounting profit shown. The practical consequence for insurance-sector investors specifically is that a headline net profit figure at a Nepali insurer is not itself sufficient evidence a dividend is coming; the solvency test sits on top of the profit test, and an insurer that is profitable on paper but running a thin solvency cushion can be blocked from distributing regardless of what the income statement says. Cross-reference the solvency ratio against the dividend history before assuming continuity.
REGULATORY DETAIL
Section 43 (covered fully in Chapter 68) layers a solvency-and-reserve-adequacy test on top of the ordinary profit test before an insurer may pay a dividend. An insurance stock showing a strong reported profit but a solvency ratio that has been sliding toward the regulatory floor is a candidate for a blocked or reduced dividend even without any change in the headline earnings number — check both figures together, never the profit figure alone.
Mandatory reinsurance is the plank that has no equivalent anywhere else on the NEPSE board, and it is worth understanding in some detail because it directly affects both an insurer's risk profile and its profit and loss statement. Nepal Reinsurance Company Limited (Nepal Re) was established as the country's first and, for years, only domestic reinsurer, and NIA regulation has required Nepali primary insurers — both life and non-life — to cede a defined percentage of their gross premium to Nepal Re before placing any remaining reinsurance need with foreign reinsurers. This mandatory cession requirement has moved over time: it was scaled back for a period as authorities weighed concerns about risk concentration in a single domestic reinsurer against the policy goal of retaining reinsurance premium (and the foreign-exchange outflow it represents) inside Nepal, and industry reporting in 2026 pointed to the government reinstating a mandatory cession requirement around 20 percent of ceded business to Nepal Re. The precise percentage has shifted with policy cycles, so any investor underwriting a primary insurer's reinsurance cost line should check the currently applicable NIA directive rather than assume a fixed historical number, but the structural point holds regardless of the exact percentage in force: a chunk of every Nepali insurer's reinsurance program is not competitively priced in the open global market, it is placed with a single domestic counterparty whose own capital strength and claims-paying ability then becomes an indirect risk factor for every primary insurer that cedes to it.
CASE IN POINT
When the NIA raised Nepal Reinsurance Company's own minimum paid-up capital requirement sharply (regulatory commentary has referenced figures around Rs 20 billion for reinsurance companies operating in Nepal), it was addressing exactly this concentration concern — a mandatory domestic reinsurer that is undercapitalized relative to the aggregate risk ceded to it by the entire primary insurance market is a single point of failure sitting underneath every listed insurer's balance sheet, whether or not that insurer's own solvency ratio looks comfortable in isolation.
This is why a genuinely thorough evaluation of any NEPSE-listed insurer includes at least a glance at Nepal Re's own capital adequacy and claims-paying trend, not just the primary insurer's numbers — the mandatory cession structure means primary insurers cannot simply shop around for a stronger reinsurance counterparty the way insurers in most other markets can if they judge their reinsurer's balance sheet to be weakening.
Lesson 74.5 — The Consolidation Wave: Why Insurance M&A Rhymes with Banking and Microfinance
Nepali investors who lived through the banking sector's forced-merger era (Chapter 30) and the microfinance consolidation covered in Chapter 73 will recognise the pattern immediately in insurance, because the regulatory logic is identical: a regulator decides the sector has too many undercapitalized, sub-scale players competing destructively for a limited pool of business, and forces consolidation by raising minimum paid-up capital requirements faster than most existing companies can organically build capital, leaving merger or acquisition as the only realistic path to compliance for a large share of the industry.
The insurance version of this played out concretely when the then-regulator required life insurance companies to raise minimum paid-up capital to roughly Rs 5 arba (Rs 5 billion) and non-life insurance companies to roughly Rs 2.5 arba (Rs 2.5 billion), with the compliance deadline extended more than once — reporting from 2023 referenced the deadline being pushed out to Ashad 2080 (mid-2023) — as regulators acknowledged how difficult a genuinely organic capital raise of that size was for smaller listed insurers to execute purely through rights issues and retained earnings. Several life insurers that could not credibly reach the new capital floor alone chose merger instead: the merger of Prime Life Insurance, Gurans Life Insurance, and Union Life Insurance into a single combined entity, which took the name Himalayan Life Insurance, is one of the clearest examples of this dynamic in the listed market, and it followed the same negotiated-swap-ratio, shareholder-approval, NIA-clearance process that Chapters 71 through 73 described for bank and microfinance mergers. Reinsurance-side capital requirements followed the same script one step further up the value chain, with the NIA separately mandating a large capital increase for reinsurance companies themselves, again with phased deadlines extended as the sector adjusted.
CASE IN POINT
The three-way merger of Prime Life, Gurans Life, and Union Life into Himalayan Life Insurance illustrates the standard insurance-sector merger playbook: regulator sets a capital floor well above what several mid-sized listed insurers can raise alone within the deadline, boards negotiate a share-swap ratio based on relative net worth and embedded book value, shareholders vote, NIA clears the combination, and the surviving entity re-lists as a single larger, better-capitalised company. The mechanics mirror bank and microfinance mergers almost exactly — only the regulator's name and the specific capital threshold differ.
The investment implication is the same one that applied to microfinance in Chapter 73: consolidation is generally a medium-term positive for the sector — fewer, better-capitalised players competing less destructively on price should eventually improve combined ratios and persistency-driving service quality across the board — but it is frequently a short-term negative for individual shareholders caught inside a specific merger, because swap ratios are negotiated between boards and often undervalue the smaller or weaker party's minority shareholders relative to a clean market-price benchmark, dividend policy typically pauses during the NIA clearance process, and the combined entity needs one to two years to integrate distribution networks, actuarial reserving practices, and claims-processing systems before the promised efficiency gains show up in reported numbers. An investor holding an insurer that announces a merger should treat the announcement as the start of a waiting period, not a catalyst for an immediate re-rating, and should specifically model the swap ratio against the pre-announcement market price of both entities before assuming the deal is neutral or favourable.
CAUTION
A merger announcement in the insurance sector is not, by itself, good news for an existing shareholder in either entity. Compare the negotiated swap ratio to where each stock was trading before the announcement, check whether the combined entity's post-merger solvency ratio is actually stronger than either standalone entity's, and expect dividends to pause through the NIA clearance period. The upside case is real but arrives on a two-to-three-year timeline, not on the announcement date.
The regulatory logic pushing this consolidation is unlikely to be finished. Nepal's insurance market — both life and non-life — still carries more listed underwriters than the size of the economy's insurable base comfortably supports at healthy combined ratios and persistency levels, and the NIA's move toward risk-based capital (which by design penalises thin, undiversified, or concentrated books more than the old flat-multiple regime did) creates ongoing pressure on the weakest capitalised players to either raise fresh equity, merge, or shrink their underwriting appetite. Investors should expect further rounds of NIA-driven capital tightening and consequent M&A activity across both life and non-life sub-sectors over the coming years, exactly as Chapter 73 anticipated for microfinance.
Lesson 74.6 — Float Management and the Practical Insurance Stock Checklist
Underneath both business models sits one shared truth: an insurer is, in large part, an investment management company that happens to sell insurance to generate the float it invests. The quality of that investment portfolio — its asset allocation across government securities, bank fixed deposits, corporate debentures, and listed equities, its duration matching against the liability profile, and its concentration risk in any single counterparty — drives a substantial share of both life and non-life profitability over a full cycle, and it is one of the more overlooked lines in Nepali insurance analysis because retail investors tend to focus almost exclusively on the underwriting metrics.
For a non-life insurer, float is short-duration by nature (claims and unearned premium reserves turn over within roughly a year or two), so the investment book tends to be weighted toward liquid, shorter-tenor instruments — fixed deposits and treasury bills — with a smaller allocation to listed equities and mutual funds for yield enhancement. For a life insurer, float is genuinely long-duration, so a well-run life insurer should be laddering into longer-tenor government securities and corporate debentures that roughly match the duration of its actuarial liabilities; a life insurer whose investment book is disproportionately parked in short-term fixed deposits despite carrying twenty-year policy liabilities is running a reinvestment-rate risk that will only become visible when interest rates fall and the company has to reinvest maturing deposits at a lower yield than its policies were priced to assume. Rukmani found, on digging into her life insurer's investment disclosures after her initial misstep, that its book was reasonably well laddered across government bonds and long-tenor debentures — one reason, she concluded, that it had kept compounding through the same period her mispriced non-life holding stumbled.
KEY CONCEPT
An insurer is an investment manager wearing an underwriting license. For a non-life insurer, judge the float book on liquidity and credit quality since the liabilities turn over quickly. For a life insurer, judge the float book on duration matching against long-dated actuarial liabilities — a life insurer parked entirely in short-term deposits is mismatched against its own promises and will feel it acutely in a falling-rate environment.
Bringing all of this together, here is a practical step-by-step checklist for evaluating any NEPSE-listed insurance stock, life or non-life:
Step
What to check
Why it matters
1
Confirm license type — life or non-life — and never cross-apply metrics between the two
The two business models have entirely different profit-recognition timing and risk drivers
2
Solvency margin / risk-based capital ratio versus NIA minimum, and its multi-quarter trend
A compliant-but-falling ratio is an early warning even before it breaches the floor
3
Non-life: combined ratio and its claims-ratio component over five years. Life: first-year and renewal persistency ratio over five years
These are the core drivers of sustainable underwriting profit or its life-insurance equivalent, book quality
4
Investment income as a share of total profit, and the asset allocation and duration of the investment portfolio
Reveals whether reported profit is underwriting-driven or float-driven, and whether the float is duration-matched
5
Section 43 dividend-eligibility check — cross-reference reported profit against the solvency and reserve-adequacy conditions before assuming a dividend is coming
A strong profit figure does not guarantee a dividend if solvency or reserving falls short
6
Reinsurance program — mandatory cession share to Nepal Re at the currently applicable percentage, plus a glance at Nepal Re's own capital adequacy
A weak mandatory reinsurance counterparty is a shared risk across the entire primary insurance market
7
Merger or capital-raise status — is the company already compliant with current paid-up capital norms, mid-merger, or facing a looming deadline it cannot meet organically
Determines whether you are buying a stable franchise or a pending dilution/merger event
8
Claims settlement ratio and any regulatory or ombudsman complaints on record
A proxy for franchise reputation, distribution quality, and long-run persistency or renewal business
Chapter recap
This chapter built a working playbook for a sector that looks unified on a screener but is actually two distinct businesses sharing one regulator. Life insurance is a long-duration promise funded by float that compounds for decades, where persistency, reserve adequacy, and the (largely unpublished, and therefore approximated) concept of embedded value matter more than any single year's net profit. Non-life insurance is a short-duration underwriting business where the combined ratio and claims ratio deliver a much faster, noisier verdict on whether the company is pricing risk correctly, with investment income frequently cushioning — and sometimes masking — underlying underwriting deterioration. Rukmani Thapa's experience across her three 2021 holdings illustrated exactly why treating "insurance" as one homogeneous sector bet is a mistake: her non-life holding's dividend cut, her merging holding's dilution, and her life holding's steady compounding were three separate stories that happened to share a sector label and nothing else.
The regulatory architecture from the Nepal Insurance Authority ties the whole chapter together. Solvency requirements — now migrating from the old flat 1.5-times margin directives toward a genuinely risk-based capital regime — set the survival floor. Section 43, detailed fully in Chapter 68, layers a solvency-and-reserve test on top of the ordinary profit test before any dividend can be paid, meaning a strong headline profit is necessary but not sufficient evidence a distribution is coming. And the mandatory cession requirement to Nepal Reinsurance Company — a policy that has swung between roughly 20 percent and lower levels as authorities balanced domestic premium retention against concentration risk — means every primary insurer's risk profile is partly a function of a single domestic reinsurer's own capital strength, a dependency with no parallel anywhere else on the NEPSE board.
The consolidation wave covered in Lesson 74.5 should feel familiar to any reader who worked through the banking chapters in Part IV or Chapter 73's microfinance playbook, because it is the same regulatory mechanism wearing a different hat: raise the minimum paid-up capital floor faster than weaker players can organically fund it, and merger becomes the only realistic path to compliance for a meaningful share of the industry. The Prime Life, Gurans Life, and Union Life merger into Himalayan Life Insurance is this chapter's concrete proof point, and the broader lesson — that a merger announcement starts a multi-year waiting period rather than triggering an immediate re-rating, and that swap ratios deserve independent scrutiny before any shareholder assumes fair treatment — applies to every future insurance merger this decade is likely to bring.
Underneath both sub-models, the float management lens in Lesson 74.6 is the thread that ties life and non-life insurance back to every other financial-sector chapter in this book: an insurer is fundamentally an asset manager operating under an underwriting license, and the quality, liquidity, and duration-matching of its investment portfolio drives a meaningful share of its profitability regardless of which type of policy it sells. The eight-step checklist closing this chapter — license type, solvency trend, combined ratio or persistency, investment income share, Section 43 dividend eligibility, reinsurance exposure, merger status, and claims settlement reputation — gives any NEPSE investor a repeatable process for turning a confusing, jargon-heavy sector into a tractable one.
Chapter 75, "The Manufacturing & Hotel Sector Playbook," moves away from financial intermediaries entirely and into NEPSE's real-economy industrial and tourism-linked names — companies whose profitability depends on raw material costs, capacity utilisation, import substitution dynamics, and, for the hotel sub-segment, tourist arrival cycles and seasonal occupancy rather than actuarial reserves or credit spreads. Readers who have now worked through the banking, microfinance, and insurance playbooks will find the shift refreshing: no solvency ratios, no persistency, no mandatory reinsurance — but a different set of operating-leverage and cyclicality risks that demand their own dedicated framework, which Chapter 75 builds from the ground up.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 75
The Manufacturing & Hotel Sector Playbook
First published 24 Aug 2026 · Last verified 29 Aug 2026
The Manufacturing & Hotel Sector Playbook
Prakash Adhikari had made his money the easy way for four years running — buying bank and insurance counters ahead of bonus share announcements, riding the wave that lifted nearly every BFI stock on NEPSE between 2019 and 2021. When that wave broke, he did what most disciplined investors eventually do: he looked for businesses whose fortunes did not rise and fall purely on the interest rate cycle and Nepal Rastra Bank's directives. He found them in the cement dust of Nawalparasi, the noodle aisles of Bhatbhateni supermarkets, and the lobby of a five-star hotel in Durbar Marg that he had once visited only as a wedding guest. What Prakash discovered, slowly and at some cost, is the subject of this chapter: manufacturing and hotel companies are not read the same way as banks and insurers, and an investor who applies a financial-sector lens to a cement plant or a hotel property will misprice both.
This chapter builds the playbook for NEPSE's real-economy sectors — cement, noodles and packaged food, breweries and beverages, cigarettes, steel, sugar, and hospitality. These businesses share a defining trait that separates them from the financial sector chapters that came before: they make or sell a physical thing, or they rent out a physical room, and their economics are therefore governed by factory capacity, raw material costs, import duties, and — for hotels — the literal footfall of tourists through Tribhuvan International Airport and the land border crossings with India. Where Chapter 74 taught you to read an insurer's actuarial reserves, this chapter teaches you to read a cement plant's capacity utilisation rate and a hotel's RevPAR. The two skill sets are complementary, not interchangeable, and conflating them is one of the most common analytical errors made by NEPSE investors moving from financials into industrials.
Lesson 75.1 — Why Real-Economy Stocks Read Differently From Banks and Insurers
A bank's balance sheet is its product: deposits, loans, spreads, provisioning. A manufacturer's balance sheet is a supporting document; the product is the tonnage of cement that leaves the kiln, the cartons of noodles that leave the warehouse, the litres of beer that leave the bottling line. This distinction changes almost everything about how you read the financial statements.
First, capacity utilisation becomes the single most important operating metric, more important than any ratio derived purely from the balance sheet. A cement plant built for one million tonnes of annual capacity that is running at forty percent utilisation is a fundamentally different investment than the same plant running at eighty-five percent, even if both show identical revenue in a given quarter because of a one-off price spike. Utilisation tells you whether fixed costs — the depreciation on the kiln, the interest on the plant loan, the salaries of the technical staff — are being spread over enough units of output to be absorbed profitably. Manufacturing is a business of operating leverage: below a critical utilisation threshold, a plant loses money on every tonne; above it, incremental tonnes carry very high marginal profitability because the fixed costs are already covered. A small change in utilisation can therefore produce a large, non-linear swing in reported earnings, and this is precisely why manufacturing stocks can look wildly cheap or wildly expensive on a trailing price-to-earnings basis depending on where in the utilisation cycle you catch them.
Second, raw material cost pass-through determines margin stability far more than pricing power in the way equity analysts usually mean it. A bank's margin is a spread it controls directly by setting deposit and lending rates within regulatory bands. A cement company's margin depends on the price of coal or pet coke (much of it imported), the price of gypsum, limestone extraction costs, and electricity tariffs, set against a cement selling price that is constrained by intense competition among a dozen-plus domestic producers and the ever-present shadow of Indian imports at the border. A noodle maker's margin depends on wheat flour and palm oil prices, which move with global commodity cycles largely outside Nepal's control, set against a retail price that is sticky — consumers resist frequent price changes on a low-ticket daily purchase, so companies absorb short-term cost spikes and pass them through only in discrete steps, often with a lag of one or two quarters. Reading a manufacturer's gross margin trend without asking "where are we in the raw material cost cycle, and has the company been able to reprice yet" will systematically mislead you about the sustainability of a good or bad quarter.
Third, import substitution versus import dependency is the single most important structural question you can ask about any Nepali manufacturer, because it determines whether the company's growth story is about capturing a market that used to leak abroad, or whether the company remains permanently exposed to the price and availability of an imported input it cannot control. Cement is the textbook import substitution story, which the next lesson develops in full. Steel rolling and sugar refining sit closer to the import dependency end of the spectrum, since much of their key inputs — billets, scrap, and in some cases raw sugar or seed cane economics — still arrive from or are priced off Indian and international benchmarks.
Fourth, the working capital cycle behaves differently from a bank's balance sheet cycle. A manufacturer ties up cash in raw material inventory, work-in-progress, finished goods sitting in warehouses waiting for distributors, and receivables from wholesalers and retailers who themselves operate on thin margins and slow payment habits. A sugar mill has an especially brutal version of this cycle: it must buy an entire season's cane crop within a compressed harvest window, often on credit terms that leave farmers unpaid for months, while the resulting sugar is sold down over the following year. When you evaluate a manufacturer's cash flow statement, the change in net working capital line tells you as much about the health of the business as the profit and loss statement does — a company reporting rising profits while its working capital needs balloon uncontrollably is often financing growth on borrowed time, and Nepali sugar mills have supplied more than one instructive cautionary tale on this exact point.
Fifth, excise and customs duty exposure functions as a quasi-regulatory overlay on manufacturing the way capital adequacy rules function for banks, except that it moves on the government's fiscal calendar rather than a central bank's monetary policy calendar. Every year's federal budget, announced by the Ministry of Finance in mid-July ahead of the new fiscal year, revises excise duty rates on tobacco, alcohol, sugary beverages, and a rotating list of "sin" and luxury goods, alongside customs duty and tariff schedules that affect imported clinker, raw sugar, steel billets, and packaging inputs. A manufacturer's profit forecast for the coming fiscal year can be upended in a single budget speech, and unlike monetary policy, there is very little advance signalling — the market usually finds out what changed only when the budget is read out.
KEY CONCEPT
Operating leverage in manufacturing means that earnings do not move proportionally with revenue — they move disproportionately with capacity utilisation. A plant crossing from 60 percent to 75 percent utilisation on stable pricing can show earnings growth far in excess of revenue growth, because incremental output carries almost pure margin once fixed costs are covered. The reverse is equally true on the way down. Always ask what utilisation rate underlies a quarter's numbers before extrapolating the trend.
Prakash's first real-economy purchase was Ghorahi Cement, bought in 2022 on the strength of a headline that Nepal's cement sector was finally supplying nearly all of domestic demand from local plants. He did not, at that point, ask what utilisation rate the plant was running at, or whether a dozen new cement licenses issued across the country in the preceding five years meant that supply was about to outrun demand. That omission would cost him a year of sideways-to-down performance in the stock even as the country's cement consumption kept growing — a lesson in the difference between an industry-level story and a company-level utilisation reality.
Lesson 75.2 — The Cement Story: Import Substitution, Capacity, and Cost Pass-Through
Cement is the cleanest import substitution narrative in the Nepali industrial economy, and it is worth understanding in some detail because the same analytical pattern — a formerly import-dependent commodity becoming domestically manufactured at scale — recurs, in weaker form, across several other sectors.
For most of the 2000s and early 2010s, Nepal imported a very large share of its cement and clinker requirements from India, because domestic capacity was limited to a handful of older, smaller plants. This made Nepal's construction sector directly hostage to Indian cement prices, Indian export policy, and the capacity of the land border crossings to move heavy bulk cargo — a vulnerability laid bare during the 2015-16 border blockade, when cement and fuel shortages together stalled construction activity nationwide. In response, and helped by the post-2015 earthquake reconstruction boom that made cement demand a near-certainty for a decade, a wave of new capacity was built out through the 2010s and into the 2020s: large integrated plants such as Hongshi Shivam Cement in Nawalparasi, alongside NEPSE-listed names including Shivam Cement, Ghorahi Cement Industry, and Arghakhanchi Cement, expanded Nepal's installed clinker and grinding capacity many times over. The result, reported repeatedly in Nepali trade and industry data through the early 2020s, was a decisive shift: Nepal moved from being a structural cement importer to a country that meets the overwhelming majority of its own demand domestically, with periods in which domestic producers have even shipped surplus clinker and cement across the border into the Indian market.
This is the import substitution playbook working exactly as the theory predicts — but it created a second-order problem that every cement investor must now price in: overcapacity. When a dozen-plus companies race to build plants against the same reconstruction-driven demand forecast, the industry can end up with far more installed capacity than the market needs, especially once the post-earthquake rebuilding wave matures and ordinary construction growth reverts to a slower, population- and income-driven pace. The consequence has been periodic price wars among domestic producers, compressed margins even for well-run plants, and a wide dispersion in capacity utilisation across companies depending on their cost position, brand strength, and distribution reach. This is precisely why the utilisation question from Lesson 75.1 is not academic for cement — it is the entire investment thesis. A cement stock trading at a low headline multiple may simply be a low-utilisation plant whose fixed-cost burden is crushing reported earnings, with genuine operating leverage upside if utilisation recovers; or it may be a structurally uncompetitive plant that will keep losing market share to lower-cost rivals regardless of where the overall industry cycle sits.
Raw material cost pass-through in cement centres on three inputs: coal or petroleum coke used to fire the kiln (largely imported and priced in a volatile global commodity market), electricity (a domestic cost but one exposed to Nepal Electricity Authority tariff structures and, for plants running captive diesel generation during power shortages, to fuel prices), and limestone and gypsum, which are domestically quarried but carry their own extraction and royalty costs. Because cement selling prices are set in a competitive domestic market with import price as a partial ceiling, a spike in international coal prices — as happened sharply in 2021-22 — squeezes margins for every producer simultaneously until prices are renegotiated upward across the industry, a process that typically lags the cost spike by one to two quarters.
CASE IN POINT
Nepal's cement sector is the country's clearest import substitution success story on NEPSE: a market that imported the bulk of its clinker and cement through the 2000s and into the mid-2010s built enough domestic capacity through plants including Shivam Cement, Ghorahi Cement Industry, Arghakhanchi Cement, and large non-listed integrated producers like Hongshi Shivam to become broadly self-sufficient, with periods of net clinker and cement exports to India. The lesson for investors is not "buy cement because Nepal no longer imports it" — it is "understand that the same capacity boom which solved import dependency created a domestic overcapacity problem that now determines which individual producers actually make money."
The evaluation discipline that follows from this is straightforward to state and hard to execute: for any listed cement company, find the installed capacity in tonnes per annum, find the reported or implied production volume for the period, and divide one by the other before looking at any profitability ratio. Track this utilisation figure over at least eight quarters. Then examine the trend in per-tonne realisation (revenue divided by sales volume, where disclosed) against the trend in per-tonne cost of key inputs, to judge whether the company is a price-taker being squeezed or a price-setter with enough brand and distribution strength to defend margin. Finally, check the debt load taken on to finance the capacity expansion — cement plants are financed with long-tenor project debt, and a company that expanded capacity at the top of the reconstruction cycle using leverage is far more fragile to a utilisation shortfall than one that expanded conservatively or funded growth from retained earnings.
Lesson 75.3 — FMCG, Beverages, and the Excise Duty Trap
Beyond cement, NEPSE's manufacturing universe includes packaged food and FMCG names such as the Coca-Cola bottling operations Bottlers Nepal (Balaju) and Bottlers Nepal (Terai), and consumer goods maker Unilever Nepal, alongside a scattering of sugar mills and steel rolling companies. It is worth being direct about a structural feature of this space that surprises many new investors: several of the most dominant consumer brands in the Nepali market are not available on NEPSE at all. Wai Wai instant noodles, the category-defining brand controlled by the privately held Chaudhary Group through CG Foods, has no listed vehicle. Surya Nepal, the ITC-affiliated cigarette manufacturer that dominates Nepal's tobacco market, is likewise not listed. Nepal's best-known brewery operations sit inside private conglomerate structures rather than public listings. An investor building a "consumer Nepal" thesis on NEPSE is therefore choosing from a narrower and, in several cases, less liquid set of proxies than the underlying consumer economy would suggest — Bottlers Nepal and Unilever Nepal being the clearest examples of high-quality, high-margin consumer businesses that are technically accessible but often thinly traded, a point this chapter returns to and Chapter 76 develops at length.
Where listed exposure does exist, the evaluation lens differs by category but shares common threads. FMCG and packaged food economics rest on brand loyalty, distribution reach into the hundreds of thousands of small retail outlets across Nepal's difficult terrain, and the ability to defend margin against private-label and cheaper unbranded competition. Because unit prices are low and purchase frequency is high, these companies enjoy relatively stable, recession-resistant demand — noodles and soft drinks sell through downturns in a way that cement and steel, tied to construction cycles, do not — but they also face intense price sensitivity from consumers, meaning cost increases in wheat flour, palm oil, sugar, PET resin, and aluminum cannot always be passed through immediately or in full. This is the same raw material cost pass-through dynamic from Lesson 75.1, but operating on a shorter, more consumer-visible price point than cement's project-based pricing.
Breweries, distilleries, cigarette manufacturers, and increasingly sugary beverage producers face an overlay that FMCG staples largely avoid: excise duty is not a minor line item but often the single largest cost component in the retail price of the product, frequently exceeding the manufacturer's own production cost. Nepal's federal budget, delivered each July, has for years followed a near-annual pattern of raising excise duties on alcohol, tobacco, and — increasingly in recent budgets — sugar-sweetened beverages and so-called junk food categories, as a combined revenue and public health measure. Because these duties are often set as specific per-unit charges rather than purely ad valorem percentages, a duty hike can compress margins directly regardless of what the manufacturer does with its own pricing, and because the timing and magnitude of the change is announced only at budget time with little prior consultation, it functions as an annual event risk that a sector investor must calendar and watch for every single fiscal year, not something that can be modelled away as a stable long-run assumption.
REGULATORY DETAIL
Nepal's annual budget, announced in mid-July for the fiscal year beginning mid-July, routinely revises excise duty rates on alcohol, tobacco, and sugar-sweetened or "junk" categories, alongside customs duty schedules affecting imported raw materials such as clinker, steel billets, and packaging inputs. These changes are rarely signalled in advance and take effect immediately on budget day. Any investor holding exposure to breweries, cigarette-adjacent supply chains, beverage bottlers, or import-dependent manufacturers should treat the mid-July budget announcement as a recurring, calendared event risk on par with a central bank monetary policy decision — read the budget speech and finance bill directly rather than relying on secondhand summaries, since the specific duty schedule buried in the schedules is what actually moves margins.
Sugar mills illustrate the working capital cycle risk from Lesson 75.1 in its sharpest form. Nepal's listed sugar sector has a well-documented history of financial distress rooted less in the sugar market itself than in the mismatch between when mills must pay cane farmers and when they collect cash from sugar sales. Government-influenced support prices for sugarcane set the mills' primary raw material cost, often with limited regard for whether the resulting sugar price and demand can support that cost, and mills have repeatedly fallen behind on payments to farmers, generating recurring news cycles of farmer protests, government intervention, and stretched mill balance sheets. An investor evaluating a sugar mill should look specifically at trade payables to farmers/growers as a distinct disclosure line where available, and treat a mill that is chronically behind on cane payments as carrying a working capital and reputational risk that can eclipse whatever the reported profit and loss statement shows in a given year.
Steel and rolling mill companies sit at the import dependency end of the spectrum discussed in Lesson 75.1. Nepal has essentially no primary steelmaking capacity of its own scale to speak of; domestic rolling mills convert imported billets and scrap, largely sourced from or priced off Indian and international benchmarks, into finished rebar and structural steel for the construction market. Because the core input is a globally traded commodity and the finished product competes in a domestic market with thin conversion margins, steel company earnings are essentially a spread business — the difference between billet cost and finished steel selling price — that widens and narrows with international steel and iron ore cycles largely outside any single Nepali company's control, and pass-through of billet cost changes into rebar prices tends to happen faster than in cement, since construction contractors and traders watch input costs closely and reprice quickly, but this also means margin volatility is a permanent structural feature rather than an occasional event.
WARNING
Do not evaluate a sugar mill, steel roller, or any thin-margin manufacturer purely on trailing profit and loss performance. A single good quarter driven by a favourable raw material price window can mask a working capital structure that is quietly deteriorating — rising payables to farmers or suppliers, stretched receivables from distributors, or growing short-term borrowing to fund inventory. Always cross-check the cash flow statement's working capital movements against the income statement's reported profit before concluding a manufacturer's earnings quality is sound.
CAUTION
Some of the highest-quality consumer franchises associated with the Nepali market — Wai Wai noodles, Surya Nepal's tobacco brands, and several leading brewery labels — are not available for direct investment on NEPSE, because their parent operations remain privately held. Where listed FMCG proxies do exist, such as Bottlers Nepal and Unilever Nepal, they are frequently characterised by very thin free float and low trading volumes relative to their business quality, a liquidity constraint that matters as much to realised investor returns as the underlying fundamentals do, and which the next chapter treats as a first-class variable in its own right.
Lesson 75.4 — The Hotel Sector: Occupancy, ADR, RevPAR, and the Arrival Cycle
Hotels are a different animal from every other business examined in this chapter, because their revenue is generated one room-night at a time, in real time, with essentially zero ability to store or carry forward unsold inventory. A cement plant that cannot sell today's output can warehouse it and sell it next month; a hotel room that goes unsold tonight is revenue lost forever. This makes the hotel sector the most operationally transparent, and in some ways the most immediately forecastable, of any real-economy sector on NEPSE — provided the investor knows which three numbers to track.
Occupancy rate is the percentage of available room-nights actually sold in a period, and it is the most visible and most frequently cited hotel metric, but it is also the most misleading one when read alone, because a hotel can run high occupancy at heavily discounted rates and still lose money. Average daily rate, universally abbreviated ADR, is the average price actually realised per occupied room, and it captures pricing power and market positioning in a way occupancy cannot. The metric that synthesizes both into a single measure of revenue-generating efficiency is revenue per available room, RevPAR, calculated simply as occupancy rate multiplied by ADR, or equivalently as total room revenue divided by total available rooms regardless of whether they were sold. RevPAR is the hotel industry's answer to same-store sales growth in retail — it is the number that tells you whether a property's core room business is actually getting stronger or weaker, independent of how many rooms the hotel happens to have or how it chooses to trade off volume against price.
PRACTICAL TOOL
RevPAR = Occupancy Rate multiplied by Average Daily Rate (ADR), or equivalently, Total Room Revenue divided by Total Available Room-Nights. When evaluating a listed hotel, calculate RevPAR for as many recent quarters as disclosure allows, and always ask which of the two components — occupancy or rate — is driving the trend. A hotel raising occupancy by cutting rates is solving a different problem than one raising rates while holding occupancy steady; the second is the healthier and more durable pattern, since it usually reflects genuine demand strength or successful repositioning rather than a race to fill rooms at any price.
The arrival cycle that drives Nepali hotel demand has three distinct segments that behave differently across the calendar year, and an investor who treats "tourism" as a single undifferentiated demand pool will misread the sector. Indian visitors form the largest single source market for Nepal by a wide margin, arriving overwhelmingly by land and short-haul air, and they travel for a mix of pilgrimage (Pashupatinath, Muktinath, Lumbini), leisure, and business purposes on trip patterns that are comparatively evenly spread across the year and less rigidly tied to trekking-season weather windows, providing hotels — particularly Kathmandu and Terai-belt city properties — with a demand base that partially smooths the seasonal swings that would otherwise dominate. Long-haul international visitors, arriving from Europe, East Asia, the Americas, and increasingly China and Southeast Asia, travel overwhelmingly for trekking, mountaineering, and cultural tourism, and their arrivals are heavily concentrated in the two windows when Himalayan weather and visibility are most favourable: the post-monsoon autumn season from roughly October through December, and the pre-monsoon spring season from roughly March through May. Domestic Nepali travel, the third segment, follows its own calendar tied to major festivals — Dashain and Tihar in particular drive a surge in domestic travel and hospitality demand, including for city and resort hotels used for weddings, family gatherings, and corporate events, a pattern distinct from and only loosely correlated with the international arrival calendar.
The result is a hotel demand curve with two pronounced peaks — the autumn and spring trekking-and-touring seasons — bracketing a pronounced trough during the June-through-September monsoon, when heavy rain, landslide risk on mountain roads, poor mountain visibility, and flight disruption sharply reduce long-haul leisure travel even as Indian arrivals continue at a more moderate, steadier pace. A well-run Nepali hotel management team is, in large part, a team managing this seasonality: building rate and occupancy aggressively in the two peak windows to cross-subsidize a monsoon season, in which even efficiently run properties may operate at a fraction of peak occupancy, sometimes below the level needed to cover fixed costs on room operations alone, and instead lean on food and beverage, conference, and event revenue to fill the gap.
KEY CONCEPT
Nepal's tourism demand curve has three source segments moving on different clocks. Long-haul international leisure and trekking arrivals are sharply seasonal, concentrated in the October-December and March-May windows around clear mountain weather. Indian arrivals, the largest single source market, travel more evenly across the year on pilgrimage and short-haul leisure patterns, partially smoothing hotel demand. Domestic Nepali travel follows the festival calendar, led by Dashain and Tihar, and is only loosely tied to the international arrival cycle. A hotel's exposure to each segment — a mountain lodge is almost purely exposed to the long-haul trekking calendar, while a Kathmandu city hotel draws on all three — should shape how sharply you expect its quarterly RevPAR to swing.
Four hotel companies carry the primary NEPSE-listed exposure to this sector: Soaltee Hotel Limited, operator of the Soaltee Crowne Plaza property in Kathmandu; Yak and Yeti Hotel Limited, operator of the well-known Hotel Yak and Yeti in Durbar Marg; Oriental Hotels Limited; and Taragaon Regency Hotel Limited, which operates the Hyatt Regency Kathmandu property near Boudhanath. These are commonly referred to together in NEPSE market commentary as "the listed hotels," and their combined performance is watched as a rough proxy for the health of Nepal's high-end hospitality and inbound tourism sector, even though each property's specific mix of corporate, MICE (meetings, incentives, conferences, and exhibitions), leisure, and long-stay business differs enough that quarter-to-quarter results across the four names do not always move in lockstep.
Season
Approx. Months
Typical Occupancy
Relative ADR
RevPAR Pattern
Autumn peak
October-December
High
Peak rates held firm
Strongest quarter of the year
Spring peak
March-May
High
Near-peak rates
Second-strongest quarter
Monsoon trough
June-September
Low
Discounted / promotional
Weakest quarter, F&B and events cushion room losses
Winter/shoulder
January-February
Moderate
Moderate, some discounting
Transitional, Indian and domestic travel provide a floor
The specific figures a hotel discloses will vary by property and year, but the shape of this curve — two peaks bracketing a monsoon trough, with Indian and domestic demand providing a partial floor beneath the international leisure cycle — is a structural feature of Nepali hospitality that any sector investor should expect to see repeat year after year, and should use as the baseline against which to judge whether a given quarter's results reflect normal seasonality or a genuine change in the business.
Lesson 75.5 — Shock Sensitivity: Earthquakes, Pandemics, and Regional Instability
No sector on NEPSE is more exposed to low-probability, high-severity external shocks than hospitality and tourism, because the product being sold — a discretionary trip to a foreign country — is among the first expenditures travellers cancel or postpone when confronted with safety concerns, and because the shocks that matter most to Nepal's tourism sector tend to be exactly the kind that cannot be modelled from historical financial statements: natural disasters, global health crises, and regional geopolitical instability.
The April 2015 Gorkha earthquake is the reference case for a natural-disaster shock. Beyond the direct physical damage to hotel and heritage properties in the Kathmandu Valley, the earthquake triggered a collapse in international arrivals that persisted well beyond the immediate disaster period, as global media coverage of the destruction discouraged bookings for the subsequent one to two tourist seasons even in regions of the country that suffered no physical damage at all — a pattern common to earthquake-driven tourism shocks generally, where the reputational and perception effect on travel decisions outlasts the physical damage by a wide margin. Nepal's construction and cement sector, by contrast, experienced the earthquake as a multi-year demand tailwind through the reconstruction cycle described in Lesson 75.2 — a useful reminder that the same event can be a severe negative shock to one real-economy sector and a prolonged positive shock to another, and that a portfolio spanning both hotels and cement is not automatically as diversified as it might first appear, since the two exposures can be genuinely inversely correlated around a single disaster event.
The COVID-19 pandemic beginning in 2020 is the reference case for a global health shock, and it was, by a wide margin, the most severe demand shock Nepal's tourism sector has experienced in the modern era. International arrivals collapsed to a small fraction of pre-pandemic levels through 2020 and into 2021, as international borders closed and long-haul leisure travel effectively ceased worldwide; listed hotel companies reported occupancy at levels that made room operations loss-making even before accounting for fixed costs, several suspended dividend payments for multiple consecutive years, and some undertook cost restructuring, including staff furloughs and reductions, to survive an extended period with near-zero primary revenue. The recovery has been gradual and multi-year rather than a snap-back: Nepal's tourism sector reported reaching approximately 1.15 to 1.16 million foreign visitor arrivals in 2025, a figure reported as roughly 96.8 percent of pre-pandemic arrival levels — meaning it took roughly five to six years for headline arrivals to approach, but not yet fully exceed, where they stood before the pandemic, even with a record-setting April 2025 arrivals month along the way. This multi-year recovery arc is itself an important input for valuing hotel stocks: an investor pricing a listed hotel purely off pre-pandemic peak earnings, or assuming an instant return to pre-pandemic profitability the moment borders reopened, would have both mistimed and mis-sized the recovery.
Regional instability constitutes the third shock category, distinct from natural disasters and pandemics in that it often originates entirely outside Nepal's borders yet transmits directly into arrival numbers. The 2015-16 unofficial border blockade with India, whatever its precise origin and characterisation, disrupted fuel and goods supply into Nepal for months and depressed both business confidence and travel activity during the period, compounding the earthquake's tourism impact in the same window. More broadly, tension or instability in the wider South Asian region — affecting flight routings, visa processes, or traveller risk perception for the significant share of long-haul visitors who transit through regional hub airports — can dent arrivals even when Nepal itself is entirely unaffected on the ground, simply because international travellers and travel agents treat regional risk as a reason to defer a discretionary Himalayan trip.
CASE IN POINT
Nepal's tourism sector absorbed two severe shocks within a five-year span: the April 2015 Gorkha earthquake, which suppressed international arrivals for roughly one to two subsequent tourist seasons even in undamaged regions, and the 2020 COVID-19 pandemic, which collapsed arrivals almost to zero for an extended period and pushed listed hotels including Soaltee Hotel, Yak and Yeti Hotel, Oriental Hotels, and Taragaon Regency Hotel into suspended dividends and loss-making occupancy levels. Recovery took years, not quarters: Nepal Tourism Board data put 2025 arrivals at roughly 1.15 to 1.16 million, close to but still just under pre-pandemic 2019 levels. Investors underwriting hotel stocks off a straight-line arrival growth assumption are ignoring the single most important empirical feature of this sector's recent history — that severe, low-frequency shocks are a recurring, not a hypothetical, risk.
Prakash learned this lesson the hard way when he bought into a listed hotel stock in early 2020, attracted by a strong pre-pandemic occupancy trend and a generous dividend history, only to watch the position collapse in value within weeks as COVID-19 border closures took hold, and then sit through nearly three dividend-less years before the position began to recover alongside arrivals. His mistake was not the choice of company — it was sizing a hospitality position as though it carried banking-sector-like earnings stability, when in fact it carried tail risk closer to that of an insurer facing an uninsured catastrophe.
Lesson 75.6 — The Concrete Evaluation Checklists
The preceding lessons translate into two distinct, sector-specific checklists — one for manufacturing companies, one for hotels — that should be worked through systematically before any position is sized, and revisited at least once a year as new annual reports and budget announcements arrive.
For any manufacturing candidate — cement, FMCG, brewing-adjacent, steel, or sugar — the evaluation sequence runs through installed capacity and current utilisation first, since this single figure conditions how every other ratio should be read; then the raw material cost structure and how much of it is imported versus domestically sourced, since this determines exposure to currency, international commodity, and cross-border logistics risk; then the pricing mechanism and pass-through lag, distinguishing businesses that can reprice quickly (steel, largely) from those that reprice slowly and defensively (FMCG staples) from those constrained by a competitive domestic market with import ceilings (cement); then the working capital trend, specifically watching payables to farmers or suppliers and receivables from distributors as an early warning system distinct from the headline profit and loss; then the excise, customs, and regulatory duty exposure, calendared against each July's budget announcement; and finally the balance sheet leverage taken on to fund any capacity expansion, since debt-funded capacity is far more dangerous in an overcapacity scenario than equity-funded capacity.
Checklist Item
Question to Answer
Where to Look
Capacity utilisation
What percentage of installed capacity is currently running? Is the trend rising or falling over 8 quarters?
Annual report notes, management discussion, industry association data
Input sourcing
What share of key raw materials is imported versus domestic? What currency and border-logistics risk does this create?
Notes to financial statements, cost of goods sold breakdown
Pricing power and pass-through lag
Can the company reprice quickly against cost spikes, or does it face a multi-quarter lag?
Gross margin trend versus commodity benchmark price trend
Working capital health
Are payables to suppliers/farmers or receivables from distributors deteriorating?
Cash flow statement, notes on trade payables/receivables
Excise/customs/duty exposure
What is the company's exposure to the annual July budget's duty schedule?
Prior years' budget impact, finance bill schedules
Leverage funding capacity expansion
Was recent capacity growth funded by debt or equity? What is the debt service burden if utilisation disappoints?
Balance sheet, debt schedules, interest coverage ratio
For any hotel candidate, the sequence starts with disaggregating occupancy, ADR, and RevPAR by quarter for as many periods as disclosure allows, since the combined trend across all three tells a materially different story than any one metric alone; then mapping the property's guest mix across long-haul international, Indian, and domestic segments, since this determines how sharply the property's results will swing with the seasonal arrival cycle described in Lesson 75.4; then checking the balance sheet for debt taken on to fund renovation or expansion and whether debt service can be met through a monsoon-season trough without external support; then examining historical resilience through the two reference shocks — the 2015 earthquake and the 2020-21 pandemic — specifically how long it took the company to restore pre-shock occupancy and whether dividends were cut, suspended, or maintained through the disruption; and finally assessing management's demonstrated ability to use non-room revenue (food and beverage, conferences, events, long-stay corporate contracts) to cushion the low season, since this operational flexibility is often what separates a resilient hospitality operator from a fragile one.
Checklist Item
Question to Answer
Where to Look
Occupancy trend
Is occupancy rising, falling, or seasonal-as-usual over 8 quarters?
Quarterly disclosures, annual report
ADR trend
Is average rate holding, rising, or being discounted to defend occupancy?
Room revenue divided by rooms sold, where disclosed
RevPAR trend
Is the combined occupancy times ADR figure improving on a same-season basis year over year?
Calculated from room revenue and available room-nights
Guest mix exposure
What share of demand is long-haul international versus Indian versus domestic?
Management commentary, tourism board segment data as a proxy
Debt and monsoon resilience
Can debt service be met through the low season without external funding?
How long did occupancy and dividends take to recover after 2015 and 2020-21?
Historical annual reports, dividend history
Prakash now runs both checklists as a standing quarterly routine for his remaining cement and hotel positions, a discipline he adopted only after his early missteps, and he describes the exercise as closer to reading a factory floor report and a hotel occupancy sheet than to reading a bank's financial statements — which is exactly the point of this chapter.
Chapter recap
This chapter built the evaluation playbook for NEPSE's real-economy manufacturing and hospitality sectors, deliberately structured around the ways these businesses differ from the financial-sector companies covered in earlier chapters. Manufacturing economics run on capacity utilisation and operating leverage rather than balance sheet ratios, on raw material cost pass-through timing rather than administered interest spreads, and on the structural question of import substitution versus import dependency, which cement answers as a genuine self-sufficiency success story built through large capacity additions across companies like Shivam Cement, Ghorahi Cement Industry, and Arghakhanchi Cement, even as that same capacity boom created an overcapacity problem that now separates the sector's winners from its laggards. FMCG, beverage, sugar, and steel names each carry their own version of this logic, layered with an annual excise and customs duty overlay tied to Nepal's mid-July budget cycle, and complicated by the reality that some of the country's best-known consumer brands, from Wai Wai noodles to Surya Nepal's tobacco portfolio, remain outside NEPSE entirely.
Hospitality was treated as its own discipline built around three linked metrics: occupancy rate, average daily rate, and RevPAR, the last of which synthesizes the other two into the cleanest single measure of a hotel's underlying demand strength. Nepal's tourist arrival cycle was shown to run on three distinct clocks — long-haul international leisure travel concentrated in the October-December and March-May windows, more evenly distributed Indian arrivals, and festival-driven domestic travel — producing a demand curve with two peaks bracketing a pronounced monsoon-season trough that every listed hotel, from Soaltee Hotel and Yak and Yeti Hotel to Oriental Hotels and Taragaon Regency Hotel, must manage around every year. The chapter then examined the sector's defining vulnerability: exposure to severe, low-frequency shocks, using the 2015 Gorkha earthquake and the 2020-21 COVID-19 pandemic as reference cases that together demonstrate how long recovery genuinely takes — Nepal's tourism arrivals only approached, without quite reaching, pre-pandemic levels by 2025 — and why hospitality earnings carry a tail-risk profile closer to an uninsured catastrophe exposure than to a stable annuity.
The chapter closed with two concrete, side-by-side checklists — one for manufacturing candidates working through capacity utilisation, input sourcing, pricing pass-through, working capital health, duty exposure, and expansion leverage; one for hotel candidates working through occupancy, ADR, RevPAR, guest-mix exposure, monsoon-season debt resilience, and demonstrated shock recovery history — designed to be run as a standing quarterly discipline rather than a one-time screen, in the manner Prakash Adhikari now applies to his own cement and hospitality holdings after learning the cost of skipping these questions.
A thread that surfaced repeatedly in this chapter deserves to be named explicitly before moving on: several of the highest-quality businesses examined here — Unilever Nepal, Bottlers Nepal, and to a lesser extent some of the listed hotels — are genuinely strong franchises that trade with very thin float and irregular volume, meaning the gap between a stock's fundamental quality and an investor's ability to actually enter or exit a position at a fair price can be substantial. That gap is not a footnote; it is a distinct and quantifiable risk factor that deserves its own dedicated treatment. Chapter 76, The Liquidity-Based Entry and Exit Playbook, takes up exactly this question, building a framework for reading floorsheet depth, average traded volume, and bid-ask behaviour so that an investor can judge, before committing capital, whether a fundamentally sound manufacturing or hotel stock is one they can actually trade in size without moving the price against themselves — turning the liquidity caution raised throughout this chapter into a systematic, repeatable part of the NEPSE playbook.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 76
The Liquidity-Based Entry and Exit Playbook
First published 24 Aug 2026 · Last verified 29 Aug 2026
The Kaskikot Circuit
Sunita Rana had done everything Part XI told her to do, or so she believed. She had read the chapters on liquidity as a first-order risk, she could recite the ADV rule from memory, and she understood, in the abstract, why free-float allocation limits mattered more than price-earnings ratios for a certain class of NEPSE counters. What she had not done, in October of the previous year, was translate any of that theory into an order she actually typed into her broker's TMS terminal. She had a target: build a position of 60,000 shares in Mustang Resorts Ltd, a thinly traded tourism-and-hospitality counter trading at roughly NPR 480, with a listed base of 40 lakh shares of which barely 12 lakh moved freely outside promoter lock-in. She had a thesis: post-pandemic tourism recovery, a coming dividend announcement, a technical breakout. What she did not have was a plan for how to get in, and, more dangerously, no plan at all for how she would get out.
She bought the first 15,000 shares in a single morning. The order book had perhaps 3,000 shares resting on the ask across the first four price levels. Her market order walked through all of them and kept going, dragging the last price up nearly 9 percent before the session closed. By early afternoon the counter was bid-locked near the upper circuit, other retail accounts piling in behind her, sharesansar forums lighting up with screenshots of the day's gainers list. Sunita felt briefly brilliant. Three weeks later, when tourism arrival numbers came in soft and she needed to raise cash for an unrelated family obligation, she discovered what the other side of that same thinness felt like. Her sell order for even 5,000 shares found no depth at all above the lower circuit. She was locked in a counter she now needed to exit, watching the price gap down two sessions in a row with her order sitting unfilled at the back of an ask queue nobody was buying into.
This chapter is not a retelling of Sunita's mistake for its own sake. It is the operating manual she wishes she had owned before she placed that first order — the practical, mechanical playbook that turns the liquidity concepts from Part XI (ADV thresholds, free-float ceilings, circuit-trap modelling, exit risk as a distinct category from price risk) into a repeatable sequence of decisions: how to size an entry, how to read a depth ladder before committing capital, how to choose between a market order and a limit order inside NEPSE's Trading Management System, and — the discipline that separates professional position sizing from retail impulse buying — how to write the exit plan on the same day as the entry plan, before a single share is bought. Everything in this chapter assumes the reader already knows why liquidity matters. It exists to answer the narrower, harder question: given that it matters, what exactly do you type into the order box on Monday morning?
Lesson 76.1 — Sizing the Entry Against Average Daily Volume
The single most common error among NEPSE retail and even semi-professional investors is treating position size as a function of conviction and capital alone, with average daily volume entering the calculation only as an afterthought, if at all. The ADV rule established in Part XI stated the principle: no single order should represent so large a share of a counter's trading activity that the act of placing it materially moves the price against the person placing it. Lesson 76.1 turns that principle into arithmetic.
Start with the 20-session average daily volume for the counter, not the 250-session average and not yesterday's volume in isolation. A 20-session window is long enough to smooth out a single news-driven spike but short enough to reflect the counter's current liquidity regime rather than a liquidity profile from eight months ago that may no longer hold. For Mustang Resorts, the 20-session ADV that Sunita should have pulled before her first order was approximately 8,000 shares a day — a number available from any standard TMS charting panel or from the historical data section of the exchange's own website, and cross-checked against a merchant banker's research note if one exists for the counter.
The working rule that experienced NEPSE desks apply, and the one this playbook recommends as a default, is that a single day's buy order should not exceed roughly 10 to 15 percent of the 20-session ADV for a counter trading in the illiquid-to-moderate band, rising to perhaps 20 to 25 percent only for counters with genuinely deep, consistently traded floats where the investor has verified depth at multiple price levels on the day itself. Applied to Mustang Resorts, a conservative daily clip is 800 to 1,200 shares; an aggressive one, taken only on days when the book shows unusually deep resting size, tops out near 2,000 shares. Building the full 60,000-share position under the conservative rule requires roughly 50 trading sessions — about ten calendar weeks given NEPSE's five-day trading week. Under the aggressive rule it can be compressed to around 30 sessions, but at a meaningfully higher risk of the investor's own buying becoming a visible, front-runnable pattern to other desk participants watching the tape.
KEY CONCEPT
Position size is not just capital divided by price. It is capital divided by price, checked against a second constraint: order size as a percentage of 20-session ADV. Whichever constraint produces the smaller number is the one that governs the day's order.
This is where staggering across sessions becomes non-negotiable rather than optional. A single lump-sum order of 60,000 shares in a counter with an 8,000-share ADV is not a large trade in a deep market; it is a market-moving event in a shallow one, and NEPSE's circuit-breaker architecture (discussed fully in Lesson 76.5, but relevant here) means that a determined buyer with real size behind a market order can push a stock through its full daily band and lock it at the upper circuit before the position is even a third built — at which point the remaining two-thirds must be acquired at a price the investor's own buying created, a classic case of an investor becoming their own counterparty risk.
The staggering discipline itself has a simple structure. Divide the target position into weekly tranches rather than daily fixed quantities, because NEPSE liquidity is not evenly distributed across the week — Sunday and Monday sessions (the NEPSE week runs Sunday through Thursday) frequently carry different volume characteristics than Wednesday and Thursday sessions, often thinner at the week's open as participants recalibrate after the weekend gap, thickening toward midweek. Within each weekly tranche, place orders only on sessions where the pre-open indicative price and the first fifteen minutes of trade show the counter is not already gapping toward its circuit band; skip a session entirely rather than force an entry into a already-moving tape. This single habit — the willingness to simply not trade on a given day — is the most underused tool in the retail NEPSE playbook, precisely because it produces no visible action and therefore feels like inactivity rather than discipline.
PRACTICAL TOOL
Before every entry session, write down three numbers on a physical or digital note: the 20-session ADV, 12 percent of that ADV as the day's ceiling, and the cumulative shares acquired so far as a percentage of the total target. Do not open the order screen until those three numbers are written down. The friction is the point.
A secondary sizing check, drawn directly from the free-float allocation limits covered in Part XI, applies at the position level rather than the daily-order level: what fraction of the counter's total free float will this position represent once fully built? For Mustang Resorts, a 60,000-share position against a free float of roughly 12 lakh shares is 5 percent of the tradable float — a level at which Sunita is not yet a market-moving holder in the regulatory sense, but is large enough that her own eventual exit will itself be a liquidity event the size of many single trading sessions combined. That fact should be visible to her on day one, not discovered on the day she needs to sell.
Table 76.1 sets out an operating grid connecting ADV, target position size, and the corresponding entry runway under the conservative 12 percent daily-clip assumption. It is meant as a working reference, not a rigid formula — the constants should be adjusted for the investor's own risk tolerance and for the specific counter's depth behaviour.
Position size as multiple of 20-day ADV
Daily clip (% of ADV)
Approximate entry runway (sessions)
Liquidity risk classification
Under 3x ADV
20-25%
12-15 sessions
Low — standard staggered entry sufficient
3x to 8x ADV
12-15%
25-50 sessions
Moderate — mandatory depth check each session
8x to 15x ADV
8-10%
60-110 sessions
High — reconsider full target size before proceeding
Above 15x ADV
Do not build full size
N/A
Severe — position likely un-exitable at scale; cap position or avoid
Sunita's 60,000-share target against an 8,000-share ADV placed her at 7.5x ADV — squarely in the moderate band, entry-feasible but demanding real patience, and, as she discovered, demanding an exit plan of at least equal seriousness to the entry plan. Had her target been 120,000 shares, the grid would have told her before she placed a single order that the position sat in the severe band and should have been capped or abandoned at the outset.
Lesson 76.2 — Reading the Depth Ladder Before You Click
Every order placed on NEPSE through a broker's TMS terminal interacts with a visible order book: a ladder of resting buy orders (bids) below the last traded price and resting sell orders (asks) above it, typically displayed five levels deep on most retail TMS interfaces, sometimes more on desktop or professional terminals. Reading that ladder correctly, before placing an order rather than after watching the fill, is the second load-bearing habit in this playbook, and it is the one Sunita skipped entirely on her first Mustang Resorts order.
The depth ladder answers three questions that price alone cannot answer. First, how much size actually rests at each level, on both the bid and the ask side — a stock can show a seemingly reasonable last-traded price while carrying almost no resting size behind it, meaning the next order of any real quantity will move the price several ticks regardless of the trader's intention. Second, how the size is distributed — a ladder with 200 shares at the best ask and 4,000 shares bunched three levels up behaves very differently from one with 4,000 shares spread evenly across all five levels; the former will let a small order through cleanly but will gap violently on anything larger, while the latter absorbs size more gradually. Third, how the ladder is behaving dynamically over the pre-order minutes — orders appearing and disappearing at the top of book (a pattern sometimes associated with orders being placed and pulled to test reaction, colloquially referred to by NEPSE retail traders as book manipulation, though it is difficult to prove and this playbook does not ask the reader to diagnose intent, only to notice the pattern and treat it as a reason for caution) is a different signal than a stable ladder that holds its shape.
The mechanical rule this playbook recommends: before placing any order larger than roughly 5 percent of a session's likely volume, open the depth ladder and estimate what economists would call the order's expected slippage — the gap between the best available price and the average price the full order would achieve if it consumed every level of resting size in its path. If that estimated slippage exceeds roughly 1.5 to 2 percent of the current price, the order is too large for the moment's depth and should be split further or deferred to a session with thicker resting size, irrespective of what the daily-ADV arithmetic from Lesson 76.1 already suggested.
WARNING
A stock's average daily volume tells you how much trades over a full session. It tells you nothing about how much is resting on the book at nine forty-five in the morning. A counter with respectable ADV can still show a paper-thin ladder at the moment you want to trade, particularly in the first and last fifteen minutes of the session when NEPSE's opening and closing price-discovery mechanics concentrate unusual order flow.
This is also the discipline that prevents the single most damaging entry mistake in a low-float counter: chasing the circuit. NEPSE's daily price band — currently set at 15 percent for an individual scrip's movement in a session, following the exchange's most recent circuit-breaker revision, with a market-wide trading halt triggered if the benchmark index itself swings roughly 8 percent intraday — creates a specific, recurring trap for retail entrants. When a thinly traded counter begins moving toward its upper circuit on real or rumoured news, the visible ask-side depth thins rapidly as existing holders pull offers rather than sell into a rising market, while buy-side orders pile up faster than the remaining size can absorb them. An investor who places a market order into that environment is not buying at the last traded price; they are buying at whatever price the last remaining seller in the queue was willing to accept, which in a circuit-locked NEPSE counter frequently means the order sits unfilled at the top of a buy queue behind dozens of other buyers, executes only a token quantity through the day's closing call-auction mechanism, and leaves the investor holding an unfilled order rolling into the next session — at a price that has now already moved 15 percent from where the thesis was formed.
CASE IN POINT
In Sunita's case, Mustang Resorts moved from NPR 480 to its upper circuit near NPR 552 across a single session in November, driven initially by a single large buy order — not hers — that consumed the visible ask-side depth in the first hour. Retail order flow behind it, watching the counter climb on the day's gainers list, added momentum without adding genuine sellers. Sunita's own subsequent purchases, made over the following two sessions at NPR 540 and NPR 561, were entries chasing a circuit that had already been triggered by someone else's order, not entries made against a stable, readable depth ladder. The position's average cost was roughly 15 percent above where it would have been had she simply skipped those two sessions and waited for the ladder to normalise.
The rule that follows directly from this pattern: never initiate or add to a position in a counter that is already within one or two ticks of its upper circuit for the session, no matter how strong the underlying conviction. The correct response to a stock gapping toward its circuit is to wait — either for the circuit to lock and the session to end, giving a full day's distance to assess whether the move reflects durable news or a liquidity-driven spike, or for the ladder to show genuine two-sided depth returning rather than one-sided buy pressure against vanishing offers. Capital not deployed today into a chasing trade is capital still available tomorrow at a readable price; capital deployed into a circuit chase is capital whose entry price was set by someone else's urgency, not the investor's own analysis.
CAUTION
The instinct to buy into strength is not irrational — momentum is a real, documented factor in equity markets generally. The distinction this lesson draws is narrower: momentum captured by reading a stock's multi-session trend is different from momentum chased by buying into a single session's circuit-driven illiquidity spike, where the investor cannot verify that a real, absorbable market exists at the price being paid.
Lesson 76.3 — Writing the Exit Plan Before the First Share Is Bought
If Lessons 76.1 and 76.2 govern the mechanics of getting into a position without damaging the entry price, Lesson 76.3 addresses the discipline that most reliably separates investors who survive a NEPSE liquidity event from those who do not: the exit plan must be written, in specific and numeric terms, before the entry order is placed — not after the position is built, and certainly not after the position has already moved against the investor and emotion has entered the decision.
The reasoning is straightforward once stated, though it runs against a strong behavioural current. An exit plan written before entry is written by an investor with no position-specific emotional stake yet formed — no anchoring to a purchase price, no sunk-cost pull toward holding a loser to "get back to even," no euphoria-driven temptation to let a winner run past its rational target because the position feels validated. An exit plan written after the position already exists and has moved is written by a different psychological actor entirely, one whose analysis of the correct exit price is contaminated by where the price currently sits relative to the entry, which is information the market does not care about and should not be part of a rational sell decision.
The practical form this takes for a NEPSE position: at the same session the entry plan is drafted (as in Lesson 76.1, before the first tranche is bought), the investor writes down a scaled exit schedule keyed to price levels, not to time, and separately writes down a liquidity-based exit schedule keyed to session counts, not to price. The two schedules serve different purposes and must both exist.
The price-keyed schedule answers "at what valuation do I reduce or close this position." For Sunita's Mustang Resorts thesis, a disciplined version drafted at entry might have read: sell 25 percent of the position at a 20 percent gain from average cost, sell a further 35 percent at a 40 percent gain, and hold the remaining 40 percent against a trailing stop set at 15 percent below the highest close achieved once the position has cleared its first profit tranche. None of these numbers are magic; the specific percentages should reflect the investor's own risk tolerance and the thesis's own return expectations. What matters is that the numbers exist in writing before entry, so that when the price actually reaches NPR 576 (a 20 percent gain from a 480 average cost) some weeks later, the decision to sell a quarter of the position is a mechanical execution of a pre-committed plan rather than a fresh, emotionally loaded decision made in the moment with a green position on the screen creating its own psychological pull to let it run further.
The liquidity-keyed schedule answers a different, and for illiquid NEPSE counters, more urgent question: "given this counter's ADV, how many sessions will a full exit actually require, and does my capital-need timeline allow for that." This is the schedule Sunita never wrote, and its absence is what turned a manageable position into a trapped one. The same ADV arithmetic from Lesson 76.1 applies in reverse: if a full exit of 60,000 shares, capped at 12 to 15 percent of a 20-session ADV of 8,000 shares per session, requires a comparable 40-to-50-session runway, then any capital need the investor anticipates on a shorter horizon than that is a signal that the position is oversized relative to the investor's own liquidity requirements, not merely relative to the stock's trading characteristics. This is a position-sizing conclusion, and it needs to be reached before entry, when the position size is still an adjustable variable, rather than after entry, when it has become a fixed constraint the investor is now negotiating against under duress.
KEY CONCEPT
An exit plan has two independent components that are frequently conflated into one: a price target, which says when the investor wants to sell, and a liquidity runway, which says how long selling will actually take once the decision is made. A position can have an excellent price target and still be a poor holding if its liquidity runway is longer than the investor's realistic capital-need horizon.
REGULATORY DETAIL
NEPSE operates on a T+2 settlement cycle, meaning shares sold today are typically available as usable cash in the investor's account roughly two trading days later, not the same day. Any liquidity runway calculation for a large or illiquid position must add this settlement lag on top of the multi-session execution runway — an investor who needs cash by a specific date must begin the exit process at least that many sessions, plus two additional settlement days, before the deadline.
The exit plan should also specify, in advance, what triggers an acceleration of the schedule beyond the routine price-and-liquidity framework — a genuine deterioration in the investment thesis (a weak tourism season, a governance concern, a sector-wide regulatory shift of the kind covered in earlier chapters on manufacturing and hospitality counters) as distinct from ordinary price volatility that does not change the underlying case. Writing this distinction down at entry prevents the common failure mode of an investor deciding, mid-decline, that the thesis has changed simply because the price has, when in fact nothing about the business has moved and the price action itself is the liquidity-driven noise this entire playbook is designed to look past.
Lesson 76.4 — Unwinding a Large Position in a Low-Float Counter Without Crashing It
Lesson 76.3 established that the exit plan exists in writing before entry. Lesson 76.4 addresses the harder practical case: what an investor actually does, mechanically, when the exit plan is triggered and the position is large relative to the counter's float and ADV — precisely the situation Sunita faced when she needed to unwind her Mustang Resorts holding under real time pressure.
The core hazard in exiting a large position in a thin counter is the mirror image of the entry hazard from Lesson 76.1, but with an added asymmetry that makes it more dangerous: sellers in a panic or under a deadline tend to reach for market orders far more readily than buyers do, because the psychological pressure of needing cash by a date feels more urgent than the pressure of wanting to deploy cash by a date. A market sell order for any meaningful quantity in a counter with a shallow bid ladder will walk down through every resting bid in its path, and because NEPSE's circuit architecture applies symmetrically, a large enough sell order can drive a counter to its lower circuit within a single session, at which point — exactly as happened to Sunita — the remaining unsold shares are stuck behind a wall of an already-locked price with no fresh bids appearing, and the investor's own selling pressure has created the very illiquidity trap that then prevents further selling.
The correct sequence, once an exit is triggered, mirrors the staggered-entry discipline from Lesson 76.1 but adds several exit-specific refinements.
First, the exit should begin from the top of the pre-written scaled schedule (Lesson 76.3) rather than being dumped in full immediately upon the trigger, even under real time pressure, because a full-size market exit is precisely the action most likely to produce the worst possible average price. An investor who needs cash in three weeks is generally better served by an aggressive but still staggered exit over the first ten to fifteen sessions of that window than by a single-day liquidation, even though the single-day liquidation feels psychologically like it resolves the uncertainty faster.
Second, exit orders in a thin counter should be placed as limit orders resting slightly above the current best bid — not as market orders, and not as limit orders priced so far above the market that they never fill. The specific placement (discussed further in Lesson 76.5) allows the investor to capture whatever genuine buying interest arrives during the session without accepting whatever price a market order would produce by consuming the full visible bid ladder in one pass.
Third, and this is the refinement most retail investors miss entirely, the exit should be paced to be less conspicuous than the daily-ADV ceiling alone would suggest, because a seller placing the maximum allowable size at the same time each session, day after day, becomes a recognizable pattern to other market participants watching the tape — brokers' proprietary desks, active day traders, and algorithmic-adjacent retail flow all learn to identify a large, patient, mechanical seller and adjust their own bidding downward in anticipation, a phenomenon sometimes called information leakage in market-microstructure terms. Varying the size and timing of exit tranches within the ADV ceiling — some sessions at 8 percent of ADV, others at 13 percent, placed at different points in the trading window rather than always at the open — reduces the degree to which the investor's own selling becomes predictable and therefore exploitable.
WARNING
A patient, well-sized exit executed in a visibly mechanical pattern — same time, same size, every single session — still leaks information to the market and can depress the very price the investor is trying to protect. Patience alone is not the same discipline as unpredictability; both matter.
Fourth, and specific to the circuit-trap risk this playbook inherits directly from Part XI's exit-risk modelling, the investor must monitor whether their own selling, combined with any independent negative sentiment, is pushing the counter toward its lower circuit band. If a session opens with the stock already gapping down and thin bid-side depth, the correct response — counterintuitively for an investor under time pressure — is often to skip that session's planned sell tranche entirely rather than force it, because selling into a circuit-bound decline both achieves a worse price than waiting and increases the probability of contributing to a lock that then prevents any further selling that day or the next. The liquidity-runway math from Lesson 76.3 should have already built in a buffer of several extra sessions precisely to absorb one or two skipped days without breaching the capital-need deadline; an exit plan with zero slack for a bad session is an exit plan that was underbuilt from the start.
Table 76.2 summarises the exit-side decision framework across the range of scenarios an investor is likely to face, distinguishing routine profit-taking from deadline-driven and deterioration-driven exits.
Exit trigger type
Recommended pacing
Order type preference
Key hazard to monitor
Scaled profit target reached (routine)
Standard ADV-ceiling tranches, no urgency premium
Limit, at or slightly above best bid
Do not let a strong session tempt a larger-than-planned tranche
Capital deadline (investor-driven urgency)
Front-load within ADV ceiling, use full runway with buffer
Limit, willing to cross partial spread for certainty
Miscalculating T+2 settlement lag against the deadline
Thesis deterioration (business-driven)
Accelerate beyond routine pace, but still staggered
Limit, cross more of spread for speed
Overreacting to price noise unrelated to actual thesis change
Approaching lower circuit / thin bid ladder
Pause or reduce tranche size for the session
Do not chase with market orders
Contributing to a lock that traps remaining shares
Sunita's actual unwind, once she applied this framework retroactively with a broker's guidance, took nineteen sessions rather than the single catastrophic day she had initially attempted, and achieved an average exit price roughly 9 percent better than the price she would have realised had she continued forcing market sells into a thin, deteriorating bid ladder. It also required her to renegotiate her family's cash-need timeline by roughly three weeks — an outcome only possible because she communicated the liquidity constraint honestly and early, rather than discovering it under maximum pressure with no time left to adjust either the exit pace or the deadline.
Every rule established so far in this chapter ultimately resolves into a single mechanical choice made at the point of order entry inside NEPSE's Trading Management System: whether to submit a market order or a limit order, and, within a limit order, how to set its validity and its price relative to the current book. This lesson makes that choice explicit.
NEPSE trading is conducted electronically through TMS, the exchange's order-matching and trading infrastructure, accessed by retail investors through broker-provided web and mobile front ends rather than a direct exchange terminal. Every order placed through these front ends carries two independent attributes: an order type, governing how the price is determined, and a validity condition, governing how long the order remains live if it does not fill immediately.
The order type choice, in NEPSE's TMS terminology, is typically presented to the investor as MKT (market) or LMT (limit). A market order instructs the system to execute immediately against the best available opposing price on the book, consuming as many price levels of depth as necessary to complete the requested quantity, with no ceiling on how far the execution price can move from the last traded price short of the day's circuit band itself. A limit order instructs the system to execute only at the investor's specified price or better, resting on the book unfilled if no matching opposing order exists at that price, and never executing at a worse price than specified regardless of how thin the book is.
For the overwhelming majority of situations this chapter addresses — staggered entries into a low-ADV counter, and especially scaled exits from a large position in a thin counter — the limit order is the structurally correct default, and the market order should be treated as an exception reserved for genuinely liquid, deep-book counters where the investor has verified that even a market order's worst-case slippage is trivial. The reason follows directly from everything established in Lessons 76.1 through 76.4: a market order in an illiquid counter hands full control of the execution price to whatever happens to be resting on the opposing side of the book at that instant, which is precisely the outcome this playbook exists to prevent.
KEY CONCEPT
A market order optimizes for certainty of execution at the cost of certainty of price. A limit order optimizes for certainty of price at the cost of certainty of execution. For a large position in a thin NEPSE counter, price certainty should almost always be the investor's priority, because the cost of an unfavorable fill compounds across every tranche of a multi-session plan, while the cost of an occasional unfilled order for one session is simply a delay the liquidity-runway buffer already accounts for.
Within the limit-order choice, placement relative to the current book matters as much as the type itself. A limit buy placed exactly at the best current ask will fill immediately if size exists there, functioning close to a market order in effect but with a hard price ceiling; a limit buy placed one or two ticks below the best ask waits for the market to come to it, filling only if a seller is willing to meet that price, which costs execution certainty but frequently achieves a materially better average price across a multi-session campaign. For entries under this playbook's staggered framework, placing limit buys at or just inside the current best bid — rather than lifting the ask — is generally the more disciplined approach, accepting that some sessions may not fill at all rather than forcing a fill by paying up.
REGULATORY DETAIL
NEPSE's TMS supports several order validity conditions beyond the simple default day order, which expires unfilled at the close of the session if not executed. Depending on the specific broker platform and the version of TMS in use, additional validity types available to retail investors can include immediate-or-cancel (IOC, which cancels any unfilled portion instantly rather than resting it), fill-or-kill (FOK, which cancels the entire order if it cannot be filled in full immediately), and, on some platforms, good-till-date or good-till-cancelled variants that let an order rest across multiple sessions rather than expiring daily. Availability of the multi-session validity types varies by broker and has changed as NEPSE and its member brokers have upgraded TMS versions over time, so an investor should confirm directly with their broker's current platform which validity options are actually live on their account rather than assuming a feature exists.
This variability matters directly for the staggered-entry and scaled-exit disciplines built up across this chapter: an investor whose broker platform supports only day orders must manually re-enter each tranche's limit order every single session, since anything unfilled simply expires at close rather than carrying forward — a real operational burden, but one that is far preferable to defaulting to market orders purely to avoid the inconvenience of re-entering a limit order each morning. Where a multi-session validity type is available, it can reduce that operational burden, but the investor should still review and, where needed, revise the resting price each session rather than leaving a stale limit order unattended for days at a time in a moving market.
CAUTION
A limit order left resting for multiple sessions without review can become stale in a moving market — a buy limit set several ticks below a rising market may simply never fill while the position-building window closes, or a sell limit set above a falling market may sit unfilled while the price continues down past it, doing nothing to protect the investor from the very decline the exit plan was meant to manage. Review, at minimum daily, is not optional simply because the order technically remains live.
Lesson 76.6 — The Entry and Exit Decision Tree
The five lessons preceding this one each address one component of the liquidity-based playbook in isolation: sizing against ADV, reading depth, pre-writing the exit, unwinding without a crash, and choosing the right order mechanics inside TMS. Lesson 76.6 assembles these into a single decision sequence an investor can actually run, in order, every time a position is contemplated, entered, held, or exited.
Before any capital is committed, the sequence begins with data gathering, not with an order screen. The investor pulls the counter's 20-session ADV, checks it against the target position size to classify the position on the runway grid from Table 76.1, and checks the target position size against the counter's estimated free float to flag whether the position sits at a level that will itself constitute a meaningful liquidity event on exit. If the position classifies as severe on that grid — above roughly 15x ADV — the correct action is to reduce the target size or decline the position before any order is placed, not to proceed and hope the arithmetic proves pessimistic.
If the position clears that first gate, the investor next drafts the exit plan in full: the price-keyed scaled schedule, the liquidity-keyed session-count runway including the T+2 settlement buffer, and the acceleration triggers that distinguish thesis deterioration from ordinary volatility. This plan is written and, ideally, recorded somewhere the investor will actually revisit — a dated note, a simple spreadsheet row, a line in a trading journal — precisely so that it exists as a pre-commitment device rather than a memory that conveniently reshapes itself once the position has moved.
Only then does the entry sequence itself begin. Each session, before placing an order, the investor checks the depth ladder for genuine two-sided size and estimates slippage for the day's planned tranche; if the counter is already gapping toward its upper circuit or the ladder shows one-sided, thinning depth, the session's tranche is skipped rather than forced. Where the ladder supports it, the tranche is placed as a limit order priced at or just inside the current bid, sized within the ADV ceiling established in Lesson 76.1, and the cumulative position is tracked against the target so the investor always knows what fraction of the plan remains.
Once the position is fully built, the sequence shifts to monitoring against the pre-written exit triggers rather than to any new decision-making about whether to hold or sell — that decision was already made, in writing, before entry. When a price target from the scaled schedule is reached, or a liquidity-need deadline approaches, or a genuine thesis-deterioration trigger fires, the exit sequence from Lesson 76.4 executes: staggered tranches within the ADV ceiling, limit orders rather than market orders, session-skipping when the bid ladder thins or the counter approaches its lower circuit, and continuous comparison of the remaining runway against whatever deadline is driving the exit.
PRACTICAL TOOL
A simple one-page pre-trade worksheet, filled in before the first entry order of any new position, should record: 20-session ADV, target position size and its multiple of ADV, free-float percentage the position will represent, the price-keyed exit schedule with specific percentages and target prices, the liquidity-keyed exit runway in sessions including settlement buffer, and the specific conditions that would accelerate the exit. An investor unwilling to fill in all six fields before buying the first share is an investor entering the position without having actually done the liquidity work this chapter describes, regardless of how much time was spent on the fundamental thesis.
The decision tree resolves, at every stage, to one of a small number of concrete actions: proceed with today's planned tranche, reduce today's tranche size, skip today's session entirely, or — at the position-sizing gate before entry — decline the position altogether. None of these actions require sophisticated tools beyond a broker's standard TMS interface and a willingness to check the depth ladder and the ADV figures before, not after, placing an order. The discipline this chapter asks for is almost entirely procedural rather than analytical: the analysis of whether Mustang Resorts or any other counter is a good investment belongs to other chapters in this book. This chapter's entire claim is narrower and, in practice, more consequential for capital preservation — that even a correct thesis, badly sized and badly sequenced against a thin counter's liquidity, can produce a worse outcome than a mediocre thesis executed with genuine liquidity discipline.
Chapter recap
This chapter took the liquidity-engineering concepts established in Part XI — liquidity as a first-order risk distinct from price risk, the ADV rule governing order size, free-float allocation ceilings, and circuit-trap exit modelling — and converted them into a sequence of concrete, repeatable actions a NEPSE investor executes at the point of placing an actual order. Sunita Rana's experience with Mustang Resorts ran through the chapter as a single continuous case: an entry built through a single oversized market order that chased a circuit-driven spike, followed weeks later by an attempted exit that nearly repeated the same mistake in reverse, and finally a disciplined nineteen-session unwind that recovered much of the ground her initial mistakes had cost. The specific lesson her case carries is not that Mustang Resorts was a poor investment — the chapter takes no position on that — but that the mechanics of getting into and out of the position were handled without reference to the counter's actual trading liquidity, and that this omission alone produced a materially worse financial outcome than the same thesis, executed with the framework in this chapter, would have produced.
The six lessons built a complete cycle. Lesson 76.1 established the arithmetic of sizing an entry against 20-session average daily volume and staggering that entry across enough sessions to avoid becoming the market's own price-mover. Lesson 76.2 added the habit of reading the depth ladder before every order, and the specific discipline of never chasing a counter already moving toward its circuit band. Lesson 76.3 introduced the chapter's central behavioural claim: that the exit plan, both its price-keyed and its liquidity-keyed components, must be written before the first share is bought, precisely because an exit plan written after the position exists is written by an investor whose judgment has already been contaminated by the position's own price movement. Lesson 76.4 detailed the mechanics of actually unwinding a large position in a thin counter without triggering the lower-circuit trap that had briefly caught Sunita. Lesson 76.5 grounded all of the preceding rules in NEPSE's actual TMS order mechanics — the market-versus-limit choice, order validity conditions, and the T+2 settlement cycle that governs when exit proceeds actually become usable cash. Lesson 76.6 assembled the whole sequence into a single decision tree and a one-page pre-trade worksheet an investor can run before, during, and after every position.
The chapter's broader place in the book is as the practical bridge between the analytical liquidity framework of Part XI and the sector- and instrument-specific playbooks that make up the remainder of Part XIV. Chapter 75's treatment of manufacturing and hotel counters described sectors where thin float and irregular trading are structurally common rather than exceptional, which is exactly why this chapter's discipline matters most for exactly those kinds of names — a Mustang Resorts is a hospitality counter of precisely the type Chapter 75 characterised, and the liquidity playbook in this chapter is the operational companion to that sector's structural characteristics.
Chapter 77 turns to a different but related liquidity environment: the IPO Playbook, addressing the specific mechanics of NEPSE primary market allotments, the behaviour of a newly listed counter in its first sessions of secondary trading, and the particular liquidity risks that attach to a stock with no trading history at all, no established ADV baseline, and often extreme initial demand-supply imbalance driven by allotment scarcity rather than steady-state market interest. Many of the same tools introduced here — depth-ladder reading, circuit-band awareness, staggered rather than lump-sum entry and exit — apply to a freshly listed IPO counter in an even more acute form, since a stock's first handful of trading sessions frequently exhibit the single most extreme liquidity distortions it will ever show across its listed life.
Readers carrying this chapter's discipline forward should treat the six lessons not as a one-time read but as a checklist to physically consult before every future NEPSE order of any size — the ADV-versus-position-size grid from Table 76.1, the exit-trigger framework from Table 76.2, and the one-page pre-trade worksheet from Lesson 76.6 in particular are designed for repeated, mechanical reuse rather than for a single reading. The next chapter's IPO-specific playbook builds directly on top of this foundation, and readers who have not internalized the entry-sizing and exit-planning habits established here will find the IPO chapter's more extreme liquidity scenarios considerably harder to navigate in practice.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 77
The IPO Playbook
First published 24 Aug 2026 · Last verified 29 Aug 2026
Rajendra Koirala teaches mathematics at a secondary school in Butwal, and for eleven years he has run the same small ritual every time a new company floats its shares in Nepal. He sits at the kitchen table on a Friday evening with his wife Sunita, his college-going son Bishal, and a spiral notebook that has outlived three phones and two changes of government. In the notebook are columns: company name, sector, issue price, units applied, bank used, CRN linked, date applied, result. It looks unremarkable, almost bureaucratic. But Rajendra's family has been allotted shares in fourteen of the last twenty-two IPOs they applied for, a hit rate well above the crowded-lottery average, and the reason has nothing to do with luck or connections. It has to do with never once being rejected on a technicality, never missing a deadline, and never applying to an IPO his notebook's scoring system told him to skip. Chapter 69 already asked whether an IPO is worth applying for. This chapter asks a narrower and more mechanical question: once you have decided to apply, how do you execute the application itself so that nothing but the lottery decides your outcome? In Nepal's IPO market, where tens of thousands of applications get auto-rejected every cycle for reasons that have nothing to do with market judgment, the operational playbook is not a footnote. It is half the game.
Lesson 77.1 — The Infrastructure: Demat, BOID, CRN, and the ASBA Bank Link
Before an investor can click "Apply" on a single IPO, four separate pieces of infrastructure must already exist and must already be correctly connected to one another. Most rejected applications in Nepal are not rejected because the investor made a bad decision on results day; they are rejected weeks earlier, when one of these four pieces was set up carelessly and nobody noticed until the money was blocked, or not blocked, at the wrong bank.
The first piece is the demat account itself, opened through a Depository Participant, or DP. A DP is typically a licensed brokerage house or a bank's merchant banking arm that has registered with the Central Depository System and Clearing Limited, universally known as CDSC, the sole securities depository in Nepal. Opening a demat account requires citizenship certificate details, a PAN number, a passport-size photograph, bank account information, and nominee details, all submitted either in person at a DP counter or increasingly through the DP's own online onboarding portal. The output of this step is a sixteen-digit Beneficial Owner ID, the BOID, which becomes the investor's permanent identity inside the entire CDSC ecosystem. Every share the investor ever owns, every IPO application, every dividend, every transfer traces back to this one number.
The second piece is registration on MeroShare, the CDSC's own online portal at meroshare.cdsc.com.np, which is where the actual IPO application happens. MeroShare registration uses the DP ID plus the BOID to create a username and password, and this is also where the investor sets a separate transaction PIN, a shorter numeric code required to authorize the final submission of any application. Many investors confuse the login password with the transaction PIN; they are different credentials serving different purposes, and forgetting the PIN on the last day of an issue window, with no time to reset it, is a recurring and entirely avoidable cause of missed applications.
KEY CONCEPT
Three numbers govern every IPO application in Nepal, and confusing them is the single most common source of self-inflicted rejection. The BOID identifies the investor's demat holding inside CDSC. The DP ID identifies which broker or bank issued that demat account. The CRN, or Customer Relationship Number, identifies the specific bank account that will supply and block the application money. An investor can have one demat account and yet, through carelessness, link it to a CRN from a bank account that no longer exists or belongs to a different name spelling — and the application will be rejected even though every other detail was correct.
The third piece, and the one most frequently mishandled, is the CRN itself. A CRN is not generated by MeroShare; it is generated by the ASBA bank, the bank that will actually hold and block the applicant's money. ASBA stands for Application Supported by Blocked Amount, a mechanism carried over from banking-sector IPO practice, under which the investor's application money is not withdrawn at the moment of application but merely frozen, or blocked, inside the investor's own account, and only debited if and when the investor is actually allotted shares. Nepal's version, administered through CDSC, is commonly called C-ASBA, and it is offered by essentially all commercial banks, most development banks, and many finance companies, each of which appears on CDSC's published C-ASBA registered bank list. To obtain a CRN, the investor typically requests it from the bank branch where they hold a normal savings or current account, or increasingly through that bank's own mobile or internet banking application, and the bank issues a CRN number tied to that specific account and to the investor's citizenship or PAN details on file with the bank. That CRN is then entered into the investor's MeroShare profile, under bank details, linking MeroShare to that one bank account. Only one bank account can be linked as the active CRN at any given time; changing banks means requesting a fresh CRN and updating the MeroShare profile before the next application.
The fourth piece is simply making sure the name, citizenship number, and date of birth recorded with the DP, with CDSC, and with the ASBA bank are identical, character for character. A demat account opened under "Rajendra Kumar Koirala" and a bank account opened years earlier under "Rajendra K. Koirala" can trigger a mismatch flag during the reconciliation CDSC and the registrar run before finalising allotment eligibility, and this kind of clerical ghost has killed more applications than any actual investment misjudgment.
REGULATORY DETAIL
CDSC and the Securities Board of Nepal, SEBON, require every retail IPO applicant to apply through their own demat account under the ASBA framework; cash or physical-form applications for ordinary retail investors have been phased out entirely. The minimum application size for a general public IPO in Nepal is conventionally 10 kitta, or 10 units, and further applications must be made in multiples of 10 units up to the maximum allowed per category, which is specified in each issue's prospectus. CDSC's system also enforces one application per BOID per issue; a second attempt from the same demat account for the same IPO is automatically rejected as a duplicate, regardless of whether the first attempt was itself successful.
For a first-time investor, the sequence to have fully operational before any IPO opens is therefore: open a demat account with a DP, receive the BOID, register on MeroShare with that BOID and DP ID, open or identify a bank account at a C-ASBA-enabled bank, request a CRN from that bank, and finally enter the CRN into the MeroShare profile under "Edit Bank Details." Rajendra keeps a laminated card with his own BOID, his DP ID, his CRN, and his MeroShare username taped inside his notebook's back cover, not because he forgets them but because the one time in 2021 he had to help his elderly mother apply from her own account under a family literacy quota, he realised that half the friction in IPO season comes from people fumbling for numbers they wrote down somewhere and can no longer find.
Lesson 77.2 — The MeroShare Application Walkthrough, Click by Click
With the infrastructure in place, the actual application is short, which is precisely why investors underestimate how much can go wrong in those two or three minutes. The walkthrough below is the exact sequence Rajendra's family follows for every issue, and it is worth internalizing as a checklist rather than a narrative, because on results day speed and accuracy both matter.
The investor logs into meroshare.cdsc.com.np using the registered username, password, and DP selection, and lands on the dashboard. From the left-hand menu, the relevant section is labelled "My ASBA," and within it, "Apply for Issue." This screen lists every IPO, FPO, mutual fund unit issue, or debenture currently open for subscription across the entire market, not just the one the investor intends to apply for, so the first mechanical step is locating the correct company name in the list, because two similarly named companies open in the same week is not a rare occurrence in an active IPO year.
Clicking "Apply" on the correct listing opens the application form itself. The BOID and demat details auto-populate from the login session. The investor selects the applicable investor category if more than one is offered, typically "Ordinary" for a general retail applicant, distinct from categories reserved for mutual funds, foreign employment quota, or company employees, and selecting the wrong category is itself a rejection cause discussed further in Lesson 77.6. The investor then enters the number of units applied for, in multiples of 10, and the system automatically calculates the total applied amount by multiplying units by the offer price disclosed in the prospectus.
The next field is the bank account to be debited, which the system populates from the CRN linked in the investor's profile; if no CRN is linked, or if the linked CRN belongs to an account with an expired mandate, the application cannot proceed past this screen, which is exactly why Lesson 77.1's setup work has to be finished well in advance rather than attempted at the moment of application. The investor confirms the bank branch and account number displayed, checks the boxes declaring the application details are correct and that they hold no other active application for the same issue, and submits. A final transaction PIN prompt appears, and entering it correctly completes the submission. MeroShare then displays an application reference, and, critically, at this point the investor's bank moves to physically block the applied amount in the linked account, a process that can take anywhere from a few minutes to, in periods of heavy subscription traffic, several hours, since banks are processing block requests for potentially tens of thousands of applicants simultaneously.
PRACTICAL TOOL
Apply in the first three days of a window that is open for two weeks or more, never on the last day. Server load on MeroShare and on individual banks' block-processing systems spikes sharply in the final 24 to 48 hours of any popular issue, and a share of investors who attempt to apply on the closing day find the portal timing out, the bank's block confirmation delayed past the cutoff, or their own mobile data connection failing at the worst possible moment. Rajendra's family rule, unbroken since 2015, is to apply within 72 hours of an issue opening, specifically to leave a buffer of days, not hours, in case a first attempt fails for a technical reason and needs to be retried.
Two details commonly trip up otherwise careful applicants at this stage. First, the applied amount must already be sitting as available balance in the linked bank account before the application is submitted; MeroShare and the bank's block system check balance at the moment of the block request, not at the moment of typing the application, and if the balance check runs a few hours after submission and finds insufficient funds, the application is rejected even though the investor "meant" to have the money there. The practical fix is to deposit or transfer the required amount into the linked ASBA account at least a full day before applying, never the same morning. Second, the number of units must respect both the stated minimum, ordinarily 10 kitta, and any maximum specified for the applicant's category in that specific prospectus; entering a unit count outside that band, or a number not divisible by 10, causes the form itself to reject the entry before submission is even possible, which is a minor but real source of wasted time during a narrow application window.
CASE IN POINT
In the 2019 IPO of a hydropower issuer that Rajendra's family applied for, his son Bishal, then newly registered on his own demat account, entered his application on the closing afternoon rather than the family's usual three-day buffer. The bank's block-processing queue that day was severe enough that his block confirmation did not clear before the issue's cutoff time, and CDSC's registrar treated the application as incomplete. Bishal was not rejected for any judgment error; he was rejected because the mechanical timing of blocking money at a specific bank, under specific system load, is itself part of the process an applicant has to manage. The family's rule about applying within the first three days of any window dates from that afternoon.
Lesson 77.3 — The Prospectus Checklist: What to Actually Read Before Applying
Nepali IPO prospectuses, formally the issue's Offer Document or Prospectus, are dense documents, often running to more than a hundred pages, and almost no retail applicant reads them cover to cover. But there is a compact set of line items inside every prospectus that takes perhaps fifteen minutes to locate and review, and skipping this fifteen minutes is how investors end up applying for issues they would have avoided had they simply looked at the cover page and the financial summary.
The cover page itself carries the issue size in total rupee value, the number of units on offer, the price per unit or, for book-built issues, the price band, and the opening and closing dates of the subscription window. This is the first checkpoint: confirm the issue is open to the general public category the investor is applying under, and confirm the closing date against the family's own application calendar, since overlapping issue windows are common in busy quarters and a missed closing date is an unforced error no scorecard can fix after the fact.
Deeper into the document, the "Objects of the Issue" or "Use of Proceeds" section states what the company intends to do with the money raised: retire existing debt, fund a specific capacity expansion such as an additional hydropower unit or new bank branches, or simply strengthen working capital. A specific, itemized use of proceeds, with rupee figures attached to named projects, is a materially different signal than a vague statement about "general corporate purposes," and this section alone often separates issuers with a genuine growth plan from issuers raising capital because a regulatory capital requirement forces them to.
The promoter and director background section lists the individuals and entities behind the company, their existing shareholding, and the lock-in period during which promoter shares cannot be sold after listing. A promoter group with a clean regulatory history and sector track record, as opposed to one with a history of diluting quickly after the lock-in expires in past listings, is worth noting; the prospectus discloses names and prior directorships, and a few minutes cross-checking those names against known market reputation is time well spent.
The financial statements section, typically covering the preceding three to five fiscal years, contains the figures every scorecard in Lesson 77.4 will need: revenue trend, net profit trend, earnings per share, net worth or book value per share, and the price-to-earnings ratio implied by the offer price against the latest EPS. A related-party transactions note, usually tucked a few pages further in, discloses any material dealings between the company and its own promoters or their other businesses, and a heavy volume of related-party transactions relative to the company's size is a caution flag regardless of how attractive the headline growth numbers look.
The risk factors section, often skipped entirely by retail applicants because it reads as generic boilerplate, does contain issuer-specific risks buried among the standard disclaimers: a hydropower issuer's risk factors will disclose hydrology dependency and PPA, or power purchase agreement, tenor and pricing terms; a finance company's will disclose loan concentration and non-performing loan trends; an insurance issuer's will disclose claims ratio history. The underwriting and issue management section names the merchant banker managing the issue and discloses whether the issue is underwritten, meaning the manager has committed to purchasing any unsubscribed portion, which is a modest signal of the manager's own confidence in demand. Finally, the distribution ratio section specifies exactly how many units are reserved for the general public, for company employees, for mutual funds, and for Nepali citizens working abroad under the foreign employment quota, and confirming which category the investor is actually eligible for and applying under, rather than assuming, avoids a rejection discussed further in Lesson 77.6.
WARNING
A prospectus that discloses an auditor's qualified opinion, meaning the statutory auditor attached exceptions or reservations to the financial statements, is not a detail to skim past. A qualified audit opinion on a company about to raise public capital is one of the more serious red flags a retail investor can find in the entire document, and it belongs at the top of the checklist, not the bottom.
Lesson 77.4 — The IPO Scorecard: A Weighted Rubric for Objective Rating
Chapter 69 discussed, at the strategic level, what makes an IPO attractive. This lesson converts that judgment into an actual number, because a written scorecard, filled in the same way every single time, is what prevents an investor from talking themselves into applying for a weak issue simply because everyone in their tea-shop is talking about it, or from skipping a genuinely strong issue because the sector is temporarily unfashionable. Rajendra's version of this scorecard has lived on the same page of his notebook for years, refined slightly each cycle, and it produces a single number out of 100 that determines only one thing: whether the family applies at the maximum unit count their liquidity allows, applies at a token minimum simply to stay in the lottery, or skips the issue entirely.
The rubric spreads 100 points across six weighted categories, each scored by the investor from 1 to 5 against a short description of what a low, middle, and high score looks like, with the category weight then applied to convert the raw score into points.
Specific, itemized projects with disclosed rupee allocations
Vague "general corporate purposes" language
Issue structure and liquidity
10%
Broad public distribution, underwritten issue, reasonable float for future trading liquidity
Thin public float, unusually concentrated allotment structure
To turn this into a composite score, the investor multiplies each category's 1-to-5 score by its weight and by 20, then sums the six results, producing a figure out of 100. Rajendra's own threshold, developed through trial and error rather than any textbook, is that a composite score above 70 earns a full-size application at the maximum the family's combined liquidity comfortably allows, a score between 50 and 70 earns a minimum-size application purely to preserve lottery odds without over-committing cash, and a score below 50 means the family skips the issue outright regardless of how much buzz surrounds it.
PRACTICAL TOOL
Fill in the scorecard using only information available in the prospectus and public financial disclosures, before reading any broker commentary, forum chatter, or social media opinion about the issue. Scoring first and reading commentary second keeps the rubric honest; scoring after being primed by a friend's enthusiasm or a broker's sales pitch defeats the entire purpose of having a rubric at all.
Applied to two contrasting examples from Rajendra's own notebook: a hydropower issuer with a signed long-tenor PPA, three years of steady if unspectacular profit, and an offer P/E modestly below its already-listed peers scored a 74, and the family applied at their comfortable maximum. A finance company issuer the following year, raising capital explicitly to meet a regulatory minimum capital requirement rather than to fund growth, with a vague use-of-proceeds paragraph and a P/E priced above several already-listed peers in the same tier, scored a 46, and the family applied only the minimum 10 units, purely to keep the lottery option alive at negligible cost. Both outcomes, whether allotted or not, were consistent with the scorecard's own logic rather than with hindsight regret, which is the entire point of scoring before the fact rather than narrating a story about it afterward.
CAUTION
A high score on this rubric describes the underlying company's quality and the fairness of its pricing. It does not, by itself, predict a large listing-day pop, which depends heavily on subscription demand, free float, and market sentiment at the time of listing, all of which Chapter 69 already covers as a separate strategic question. Treat the scorecard as a filter for what deserves an application at all, not as a forecast of listing-day price action.
Lesson 77.5 — Post-Allotment Actions: The Day You Win and the Day You Don't
CDSC and the issue's registrar publish allotment results on a fixed schedule after an issue closes, ordinarily within a few weeks, and the result is checkable directly inside MeroShare under the "Application Reports" or "ASBA" history section, as well as through CDSC's own public result-lookup tools and the registrar's own website. The moment results are published, there are two entirely different action checklists depending on outcome, and conflating them, or simply doing nothing, wastes either time or money.
If the application was not allotted, the blocked amount in the linked ASBA bank account is released automatically, typically within a few working days of the result announcement, and the investor's only real task is to confirm the release actually happened by checking the bank account balance or block status, rather than assuming it. In practice, unblocks occasionally lag by a day or two longer than expected, especially during heavily oversubscribed issues where a bank is processing thousands of releases simultaneously, and if the money is not unblocked within roughly a week of the announced result, the appropriate next step is a direct query to the bank branch holding the CRN-linked account rather than to the company or CDSC, since the bank, not the registrar, controls the actual release of blocked funds.
If the application was allotted, the applied amount for the allotted units is debited from the linked bank account, and, for any partial allotment, the unallotted portion is released back to the investor exactly as in a full non-allotment. The shares themselves are credited directly into the investor's demat account, visible under the "My Portfolio" or holdings section of MeroShare, without any further action required from the investor to receive them. The task on the day of a successful allotment is not passive, however. It has three parts. First, confirm the exact number of allotted units and the debited amount against what the notebook or record shows was applied for, since partial allotments are common in oversubscribed issues and errors, while rare, are worth a moment's verification against the investor's own record. Second, decide and record, in the moment, the investor's own listing-day intention, whether to sell into the initial listing-day liquidity, hold as a long-term position consistent with the sector thesis from Lesson 77.4's scorecard, or average further if the company remains attractive post-listing; deciding this before the emotional pull of an actual listing-day price movement is far steadier than deciding it while watching a live ticker. Third, update the family's own record with the actual result, feeding back into the scorecard's own track record so that, over several cycles, the investor can see which categories of the rubric actually predicted good outcomes for their own applications and which did not.
KEY CONCEPT
An IPO application in Nepal never truly "loses" money in the way a bad trade does; a rejected or non-allotted application simply has its funds unblocked, with no debit ever occurring. The real cost of a non-allotment is not capital loss but opportunity cost, the days or weeks that money sat blocked rather than earning interest or being deployed elsewhere, which is precisely why the liquidity discipline covered in Chapter 76 matters even for an instrument as capital-safe as an IPO application.
Lesson 77.6 — Record-Keeping Across Family Accounts and the Mistakes That Cause Rejection
Many Nepali households, like Rajendra's, apply for the same IPO across several family members' individual demat accounts, each investor applying entirely within their own name, their own BOID, and their own bank account, which is both legal and common, since it is simply several individuals each exercising their own right to apply, not a single person applying multiple times. Managing this across three or four family accounts, however, multiplies the number of moving pieces that can go wrong, and it is exactly the discipline of a shared record-keeping template that keeps a family from repeating the same mechanical mistake across every member's account simultaneously.
Rajendra's notebook format, adapted here as a table, is deliberately simple enough to maintain by hand or in a basic spreadsheet, and covers every field needed to reconstruct, months later, exactly what happened with any single application.
Family member
Company / issue
BOID (last 4)
Bank / CRN linked
Units applied
Amount blocked
Date applied
Result
Amount debited/released
Rajendra
Himal Dorje Hydropower
4471
Nabil Bank
100
10,000
Day 2 of window
Allotted 40
4,000 debited, 6,000 released
Sunita
Himal Dorje Hydropower
8832
Global IME Bank
100
10,000
Day 2 of window
Not allotted
10,000 released
Bishal
Himal Dorje Hydropower
1265
NIC Asia Bank
50
5,000
Day 1 of window
Allotted 10
1,000 debited, 4,000 released
A record like this, maintained consistently across cycles, does two things beyond simple bookkeeping. It lets the family instantly verify, at results time, whether every expected release or debit actually occurred, catching the rare bank-side delay described in Lesson 77.5 before it is forgotten. And over several years it becomes its own dataset for refining the scorecard in Lesson 77.4, since the family can look back and see which sectors, which score ranges, and which promoter groups actually produced allotments and subsequent gains worth having, rather than relying on memory or impression.
The mechanical mistakes that cause outright rejection, distinct from simply not being allotted, cluster around a small and repeating set of causes, and naming them plainly is the fastest way to avoid all of them.
WARNING
The most common rejection cause by volume is a KYC or demat detail that has gone stale: an expired citizenship document on file with the DP, a name spelling mismatch between the demat account and the linked bank account, or a KYC record that has simply not been updated within the periodic renewal window DPs require. Nepal's regulatory practice requires periodic KYC refreshing, and an investor who has not logged into MeroShare or interacted with their DP in a year or more should assume their KYC needs a refresh before the next application window, not after a rejection notice arrives.
Insufficient balance at the moment the bank actually processes the block, rather than at the moment of application, is the second most common cause, discussed already in Lesson 77.2, and the fix remains the same: fund the linked account at least a full day ahead, never the same morning as the application. Wrong bank or wrong CRN selection is the third recurring cause, typically arising when an investor has changed their primary bank account since last applying but never updated the CRN linked in their MeroShare profile, so the application silently attempts to block funds against a closed or dormant account. Deadline misses, whether from waiting until the last day and hitting server congestion, or from simply losing track of an issue's closing date amid several overlapping issue windows, form the fourth cluster, and the family calendar discipline of applying within the first three days of any window, described in Lesson 77.2, exists specifically to eliminate this cause. Category misselection, applying under the ordinary general public category when eligible instead for, say, the foreign employment quota, or vice versa, produces a rejection or a reallocation the applicant did not intend, and confirming the prospectus's distribution ratio section before selecting a category, as covered in Lesson 77.3, is the preventive step. Finally, duplicate applications, an investor applying twice from the same BOID either by accident, having forgotten a first submission went through, or by attempting to increase odds by resubmitting, are caught and auto-rejected by CDSC's own system, and the only real safeguard is checking the application history inside MeroShare before submitting again for any issue the investor is even slightly unsure about.
CAUTION
Applying for the same IPO from a family member's account using that member's own login is entirely legitimate; applying for the same IPO twice using one person's own single account is not, and Nepal's regulatory framework treats the two very differently. Keep every family member's credentials, CRN, and application strictly within that individual's own name and account, and never share a single BOID's login across more than one person's application intention.
Taken together, these six lessons describe a closed loop: infrastructure set up correctly once, an application executed the same careful way every time, a prospectus read for the handful of items that actually matter, a rubric that scores the decision before emotion enters, a clear script for the day results are published, and a record that keeps a family's entire IPO history auditable and improvable year over year. None of this replaces judgment about which companies are worth owning. It simply ensures that judgment, once made, is never undone by a forgotten CRN, a same-day deadline, or a stale KYC record.
Chapter recap
This chapter set out to answer a narrower question than the one Chapter 69 already addressed. Chapter 69 asked whether a given IPO deserves an investor's capital, walking through the lottery allotment mechanism, the framework for evaluating an offering's quality, and the behaviour of listing-day pops. This chapter assumed that strategic judgment is already in place and instead built the operational machinery underneath it, the actual sequence of concrete, repeatable steps a Nepali investor executes every single time an IPO window opens, from the moment a demat account is first created to the moment a family's shared record shows exactly what happened to every rupee that was ever blocked against an application.
The chapter began with the four pieces of infrastructure that must exist and be correctly linked before any application can succeed: the demat account and its BOID, the MeroShare registration and its login credentials and transaction PIN, the CRN obtained from a C-ASBA-enabled bank, and the quiet but essential discipline of keeping names and identifying details identical across every one of these systems. It then walked through the MeroShare application screen itself, field by field, emphasising that applying early in an issue's window, funding the linked bank account a day in advance, and respecting the minimum and multiple unit requirements are not optional refinements but the difference between a clean application and an entirely avoidable rejection. It distilled a hundred-page prospectus down to the handful of sections, cover page, use of proceeds, promoter background, financial statements, risk factors, and distribution ratio, that a retail applicant genuinely needs to read, and it built those sections into a weighted, six-category, hundred-point scorecard that converts a subjective sense of "this IPO looks good" into a repeatable number with a stated action threshold attached to it. It then separated the two very different checklists that apply on results day depending on allotment outcome, and closed with a shared family record-keeping template and a plain naming of the mechanical mistakes, stale KYC, insufficient balance, wrong CRN, missed deadlines, category misselection, and duplicate applications, that account for the overwhelming majority of rejections that have nothing to do with investment judgment at all.
Rajendra Koirala's notebook is, in the end, nothing more than the six lessons of this chapter written down by hand over more than a decade, refined one rejected application and one missed deadline at a time. The family's above-average allotment record is not the product of any privileged access or special insight into which companies will list well; it is the product of never letting a solvable mechanical failure stand between a sound decision and its execution. That distinction, between deciding well and executing cleanly, is the entire subject of this chapter, and it is worth carrying forward, because Chapter 78 turns to a related but structurally different instrument that Rajendra's family, like every long-term NEPSE shareholder, eventually has to navigate: the rights issue. Where an IPO invites an entirely new investor into a company for the first time, a rights issue is addressed only to those who already hold shares, arrives with its own compressed subscription window, its own bank-transfer and CDSC mechanics distinct from ASBA, and its own set of decisions, exercise, partially exercise, renounce, or let lapse, that carry direct financial consequences rather than a simple lottery outcome. The Rights Issue Playbook takes the same operational, checklist-driven approach this chapter has applied to IPOs and applies it to that different and, in several respects, higher-stakes situation, because a mishandled rights entitlement, unlike a mishandled IPO application, can mean a shareholder's ownership stake is quietly diluted rather than merely delayed.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XIV · Chapter 78
The Rights Issue Playbook
First published 24 Aug 2026 · Last verified 29 Aug 2026
Sabina Gurung had been a NEPSE investor for six years before she ever had to think seriously about a bank statement mid-month. She held shares in a mid-cap development bank she had bought during its post-IPO drift, added to twice on dips, and mostly ignored between quarters. Then, on a Tuesday in Falgun, her MeroShare inbox carried a notice she almost swiped past: her bank's board had recommended a 1:1 rights issue, subject to regulatory approval, to shore up capital ahead of a National Rabi Bank threshold deadline. Sabina had read Chapter 69 of this book. She understood, in principle, why banks and finance companies periodically demand fresh cash from existing shareholders, and she understood the dilution arithmetic if she declined. What she did not have, sitting at her kitchen table with the notice open on her phone and forty-two days on the clock, was a process. She did not know which button in MeroShare to press, whether her bank account needed anything done to it in advance, what would happen to her existing shares if she only had half the cash she needed, or how many days it would actually take before any new shares she bought showed up as tradable inventory in her demat account. Chapter 69 had given her the "why." This chapter exists to give Sabina — and you — the "how," in the order the calendar actually forces you to do it.
A rights issue is not a single event. It is a sequence of mechanical checkpoints, each with its own deadline, each capable of quietly costing you money if handled carelessly, and none of them forgiving of "I meant to get to it." The IPO playbook in Chapter 77 walked through a process that happens to you once, as an outsider trying to get an allocation. The rights issue playbook is different in a structural way that matters: this is your money, already committed to a company you already own, and the choice is not whether you can get in, but whether you choose to stay proportionally where you are, shrink your stake, or exit the decision to an auction process you don't control. Every step in this chapter assumes you are starting from where Sabina started — an offer letter has landed, and the clock has started.
Lesson 78.1 — Reading the Rights Offer Letter: The Notice, the Ratio, and the Deadlines That Matter
The rights process begins, for the ordinary shareholder, not with the SEBON approval or the board resolution but with a notice — delivered through MeroShare's dashboard alerts, republished in the newspapers of record as mandated disclosure, and echoed across brokerage circulars and financial portals. The notice format is standardised enough that after you've read two or three, you know exactly where to look, but the first time through it is easy to skim past the numbers that actually matter and fixate on the ones that don't.
The first number to find is the ratio. Nepali rights issues are typically expressed as an entitlement per existing share — a 1:1 issue means one new share for every share you currently hold as of the book close date; a 1:2 issue means one new share for every two you hold; some issues run at ratios like 3:10 or other non-round fractions depending on how much capital the company is raising relative to its existing paid-up base. The ratio, multiplied by your book-close holding, gives you your entitlement in kitta — and Nepali rights entitlements round down to the nearest whole share, with any residual fraction typically handled through a separate mechanism (often bundled into the auction pool for fractional and unclaimed rights, discussed in Lesson 78.4). Sabina's 1:1 entitlement against her 800-share holding meant 800 new shares available to her, no fractions to worry about — a comparatively clean case, and one of the reasons round ratios are easier to plan around than odd ones.
The second number is the book close date — the date on which the company's shareholder registry, as maintained through CDSC, is frozen to determine who is eligible for the rights entitlement. This is not the date the shares must be held through indefinitely; it is a snapshot date. If you buy shares before book close, you get the entitlement; buy them the day after, and you get nothing from this issue regardless of how long you then hold. This creates a small, well-known trading pattern around rights book closures — some investors buy just ahead of the date purely to capture entitlement, then sell down afterward — but for the ordinary long-term holder like Sabina, the book close date matters mainly as a checkpoint: it confirms the share count the rest of the math will run on.
The third and most operationally important set of numbers is the issue open date and the issue close date — the window during which the actual subscription application must be submitted. This window is where the regulatory floor set by SEBON becomes concrete: a company floating a rights issue is required to keep the subscription period open for a minimum span, conventionally around thirty-five days, with the regulator historically permitting an additional extension of roughly two weeks when initial subscription runs low — a provision that exists precisely because rights issues, unlike IPOs, often struggle to fill on time, since existing shareholders are not always liquid or paying attention. The existence of this extension window matters for your planning in one specific way: you should never plan your subscription around the assumption that an extension will happen. It is a regulator's safety valve for the issuer, not a promise to you, and treating the close date as flexible is the single most common way investors miss a rights entitlement entirely.
REGULATORY DETAIL
SEBON's framework requires a minimum subscription window of roughly thirty-five days for a rights issue, with a discretionary extension of about two additional weeks available to the issuer if the initial subscription level falls short. The extension is granted case by case and is not something a shareholder can rely on when planning cash flow — treat the originally published close date as final.
The fourth item worth reading closely, and the one investors skip most often, is the stated purpose of the issue and the capital deployment plan — usually a short paragraph in the offer letter or the accompanying prospectus addendum describing what the money is for. In Nepal's listed financial sector, this is frequently a regulatory capital adequacy requirement driven by Nepal Rastra Bank's minimum paid-up capital or capital-to-risk-weighted-assets mandates — a "must raise" rather than a "choose to raise" for growth. In manufacturing, hydropower, or trading companies, the stated purpose is more often project completion, debt-to-equity rebalancing ahead of a loan covenant, or working capital expansion. Chapter 69 covered why this distinction matters for the strategic decision of whether to subscribe. Operationally, it matters for a narrower reason: it tells you what kind of company you're dealing with when you go looking for financing and information in the weeks ahead, and it is often the fact set that determines whether the market's reaction to the rights announcement pushes the stock price down (dilution priced in immediately) or holds steady (market reads the capital raise as necessary and value-accretive).
Table: Anatomy of a rights offer letter — what to read, and why it matters operationally
Field in the notice
What it tells you
Why it matters to your process
Entitlement ratio
New shares per existing share held
Determines your kitta entitlement and total cash call
Book close date
Registry snapshot date for eligibility
Confirms you must hold shares before this date, not after
Issue open date
First day applications are accepted
Marks the start of your CRN/ASBA readiness window
Issue close date
Last day applications are accepted
Hard deadline — no grace period, no late submission
Issue price
Price per new share, usually near par for capital-mandate issues
Sets your total cash requirement per kitta subscribed
Stated purpose
Regulatory mandate vs growth capital vs debt rebalancing
Frames the Lesson 78.6 decision worksheet, and shapes market reaction
Renounceable or non-renounceable
Whether rights can be sold/transferred to another party
Determines your fallback path if you choose not to subscribe
Sabina's notice told her: 1:1 ratio, book close already fixed as of the date she was reading it (she qualified, having held the shares for over a year), a thirty-eight-day application window, an issue price at par value, and a stated purpose tied to the bank's regulatory capital requirement. Non-renounceable, as most Nepali rights issues currently are. That last detail — non-renounceable — became the single most important fact in her entire process, because it meant her only two paths forward were to subscribe, in whole or in part, or to let the entitlement lapse into the issuer's auction mechanism. There was no third option of simply selling her rights to another investor for cash, the way a renounceable structure would have allowed. Know this distinction before you do anything else, because it changes every subsequent step.
KEY CONCEPT
Renounceable rights can be sold or transferred to a third party who then subscribes in your place — effectively letting you monetize an entitlement you don't want to fund. Non-renounceable rights, the more common structure in Nepal's listed financial sector, offer only two paths: subscribe (fully or partially) or let the entitlement lapse. Confirm which structure you're facing before you plan your cash flow — the two require entirely different playbooks.
Lesson 78.2 — The MeroShare Subscription Mechanics: CRN, ASBA, and the Application Window
The mechanical act of subscribing to a rights issue in Nepal runs through the same C-ASBA infrastructure that handles IPO and FPO applications, and if you have applied for shares through MeroShare before, the screens will feel familiar. But there is one prerequisite that catches long-time buy-and-hold investors off guard precisely because they haven't touched the application flow in years: the CRN, or C-ASBA Registration Number.
The CRN links your bank account to the ASBA (Applied Specific for Blocked Amount) mechanism that Nepal's securities infrastructure uses for all primary-market applications, rights included. If you registered for a CRN years ago when you first applied for an IPO, it typically remains valid and tied to that bank account — but if you've changed your primary bank, closed the account you originally registered, or never registered one because a broker or family member handled your first application for you, you cannot get one instantly online. CRN registration requires an in-person visit to the bank branch where the account is held, and this is not something that can be rushed through in the final days of a rights window, particularly if the branch has its own processing backlog. The practical rule is straightforward: the day the rights notice appears in your MeroShare dashboard, before anything else, confirm your CRN is active against the bank account you intend to fund the subscription from. If it isn't, that errand goes to the top of your list, not the bottom.
PRACTICAL TOOL
CRN readiness checklist, to run the day a rights notice appears. One: log into MeroShare and check whether your existing CRN is still linked to an account with sufficient standing balance capacity. Two: if you plan to fund the subscription from a different bank account than the one your CRN is registered to, visit that bank in person to register a new CRN — do this in week one of the window, not week five. Three: confirm the account's ASBA blocking limit is high enough for the full cash call you're contemplating; some banks cap the amount that can be blocked against a given account tier. Four: confirm your MeroShare BOID and demat account are active and not flagged for any KYC renewal that could interrupt the application.
With the CRN confirmed, the application itself follows a short, consistent sequence. Log into MeroShare, navigate to the ASBA section and then to Current Issue, where open offerings — IPOs, FPOs, and rights issues alike — are listed together with their open and close dates displayed alongside. Select the rights issue by company name, and the system will typically pre-populate your maximum entitlement based on your book-close holding, though you retain the ability to apply for fewer shares than your full entitlement — a point central to Lesson 78.4. Enter the number of kitta you intend to subscribe for, select the bank account tied to your CRN, and enter the CRN itself. The system calculates the total cash requirement — units multiplied by issue price — and this amount is not withdrawn from your account at the point of application. It is blocked, meaning it remains in your account, visible in your balance, but unavailable for other transactions until the allotment process resolves one way or the other. This is the same ASBA principle that governs IPO applications, and it is worth restating because it removes one common anxiety: applying for a rights subscription does not mean sending money into a void and hoping shares arrive. The bank holds a claim against the funds; it does not take custody of them.
KEY CONCEPT
ASBA blocking means your subscription amount is reserved, not transferred, at the moment you apply. The cash stays in your account and continues to be visible as your balance, but you cannot spend or transfer it elsewhere until the rights allotment is finalised — at which point the exact subscribed amount is debited and the remainder, if any, is released back to unrestricted use. This is a lower-friction mechanism than the pre-ASBA era, when applicants had to physically write cheques for the full amount, but it still requires the full sum to sit idle in the account for the duration of the process.
After entering the application details, you confirm through an OTP sent to your registered mobile number, and the system issues an application reference. As with IPO applications, save this reference — it is your lookup key if you need to check status or, in the rare event of a system dispute, prove that a submission was made before the deadline. The application then sits in a pending state until the issue closes, allotment is processed, and results are published under My Application in MeroShare, at which point subscribed shares move toward crediting (Lesson 78.5) and any unblocked balance is released.
Two operational cautions belong in this lesson because they are exactly the kind of detail a book about strategy tends to skip and a real investor tends to discover the hard way. First, submitting your application on the final day of the window is legally permitted but practically unwise — MeroShare's servers, like most retail financial platforms in Nepal, experience load spikes in the closing hours of high-interest issues, and a failed submission at 4:55 PM with the window closing at 5:00 PM leaves you no recourse. Second, once submitted, a rights application can typically be edited or withdrawn only within a limited window before the issue closes, through the same Application Report screen used to track status — if you discover a data entry error, correcting it promptly matters more than it does with an IPO application, since you cannot simply reapply if you have already exhausted your entitlement in a wrong-value submission.
WARNING
Do not treat the published issue close date as a soft deadline. There is no grace period, no late submission window, and no appeals process for a missed rights application in the current MeroShare framework. An entitlement not subscribed by the close date and time simply lapses into the auction mechanism described in Lesson 78.4 — the company is under no obligation, and generally has no mechanism, to accept a late application regardless of the reason for the delay.
Lesson 78.3 — Financing the Cash Call: Bridging, Timing, and the Cost of Capital You Didn't Plan For
A rights issue is, from the shareholder's side of the ledger, an unscheduled cash call arriving on a company's timetable rather than yours. This is the operational fact that separates rights issue planning from ordinary portfolio management: you did not choose the date, you did not choose the amount, and if your portfolio is meaningfully invested — which, if you're a serious NEPSE participant, it likely is — the full subscription amount for a large holding is not sitting in a current account waiting to be deployed. Sabina's 800-share entitlement at par value meant a cash requirement in the tens of thousands of rupees, a sum she did not have sitting idle, because her working capital habit was to stay close to fully invested.
The financing question therefore resolves into three sub-questions, and answering them in the right order prevents a rushed, expensive decision in the final week of the window.
The first sub-question is whether you have the cash without touching your portfolio at all — from salary timing, a maturing fixed deposit, a bonus, or simple accumulated savings. This is the cheapest path by a wide margin, because it carries no transaction cost, no capital gains tax event, and no market-timing risk. If the answer is yes, the rest of this lesson is not about you, and you should simply schedule the transfer to your ASBA-linked account with a few days of buffer before the close date.
The second sub-question, and the one that applies to most actively invested shareholders, is whether raising the cash requires selling something else in your portfolio — and if so, what, and when. This is where "bridging from other holdings" stops being an abstract phrase and becomes a concrete, dated plan. The mechanical reality of NEPSE settlement (T+2 for equity trades, as covered in the market plumbing chapters of this book) means that if you plan to sell Holding A to fund your rights subscription in Holding B, you need the sale proceeds to actually settle and reach your bank account before your ASBA application deadline — not merely to have placed the sell order. Selling three days before the rights close date, expecting same-day liquidity, is a common and entirely avoidable mistake. The safe rule of thumb is to initiate any funding sale at least a full week ahead of the intended subscription date, giving settlement, bank transfer processing, and CRN-linked account crediting enough slack to clear comfortably.
The choice of what to sell matters as much as the timing. Selling a core, high-conviction long-term holding to fund a rights subscription in a company you hold with lower conviction inverts your own portfolio logic — you would be shrinking your best position to grow a weaker one, purely because the weaker one happens to be the one demanding cash on a fixed schedule. The more disciplined approach, consistent with the position-sizing and rebalancing frameworks built earlier in this book, is to fund the rights call from your lowest-conviction liquid holdings first, treating the rights subscription itself as simply another capital allocation decision competing for the same pool of investable rupees — not a special, protected category that automatically deserves funding ahead of everything else in your portfolio.
The third sub-question, often skipped, is whether the financing source itself carries a hidden cost that changes the real economics of subscribing. Selling a holding that has appreciated triggers a capital gains tax liability under Nepal's capital gains framework for listed securities — a cost that reduces the net cash actually available for the subscription and should be netted against the funding calculation, not treated as separate. Borrowing against securities through a margin facility, where available, carries an explicit interest cost that needs to be compared against the rights issue's own economics — subscribing to a rights issue using margin-funded cash only makes sense if your conviction in the post-rights company, including the theoretical ex-rights price math, clears the hurdle rate of the borrowing cost plus a margin of safety. Treating "the money is available" and "the money is free" as the same statement is the most quietly expensive mistake in rights issue financing.
CAUTION
A rights subscription funded by selling a different holding is not free money moving sideways — it carries the capital gains tax due on the sale, the opportunity cost of exiting that other position, and, if margin-funded, an explicit interest cost. Run the full financing cost through your subscription decision before comparing the rights issue price to the current market price; comparing gross numbers alone overstates how attractive the subscription actually is.
Sabina's own resolution illustrates the ordering discipline this lesson argues for. She did not have the full subscription amount in cash. Rather than reflexively selling a portion of her core hydropower holding — the position she had the highest long-term conviction in — she reviewed her portfolio for the lowest-conviction liquid line item, a small trading-sector position she had been meaning to trim anyway, and sold roughly that amount, timing the sale twelve days before her rights application deadline to leave comfortable settlement and transfer buffer. The proceeds cleared into her bank account nine days ahead of the deadline, giving her three days of margin even after accounting for the CRN-linked ASBA transfer. This is not a dramatic story, and that is precisely the point: rights issue financing done correctly should be unremarkable, a matter of scheduling rather than scrambling.
Lesson 78.4 — Partial Subscription, Renunciation, and the Mechanics of Letting It Lapse
Full subscription to your entire entitlement is the default assumption most investors carry into a rights issue, but it is not the only rational choice, and MeroShare's application form explicitly permits subscribing for fewer shares than your maximum entitlement. Understanding the mechanics of each fallback path — partial subscription, renunciation where available, and outright lapse — turns the rights decision from a binary in-or-out choice into a genuine spectrum of choices matched to your actual financial position.
Partial subscription is the most commonly used middle path for shareholders who believe in the company but cannot or do not want to fund the full entitlement. Mechanically, this is simple: on the application form, you enter a kitta count below your maximum entitlement, and the ASBA block is calculated against that lower number. The consequence is proportional and mechanical, not punitive — you receive new shares equal to whatever fraction of your entitlement you funded, and your ownership percentage in the company dilutes by the unfunded portion, exactly as the Chapter 69 dilution math describes. There is no penalty tier for partial subscription and no requirement to explain your reasoning; the system treats a partial application exactly like a full one, just smaller.
Renunciation, where the issue structure permits it, is a different mechanism entirely — a genuine transfer of the entitlement itself to another party, who then subscribes and receives the shares in their own name, typically in exchange for a payment negotiated between the two parties for the value of the right. This is the closest thing to "selling your rights" in the Nepali market, and it requires action within the issue's open window, following whatever transfer procedure the specific issue's letter specifies — it is not a MeroShare self-service toggle in the way subscribing or not subscribing is. Because the overwhelming majority of rights issues currently seen in NEPSE's financial sector are structured as non-renounceable, this path is the exception rather than the rule, and confirming which structure you're facing (Lesson 78.1) determines whether it's even on the table for you.
REGULATORY DETAIL
Non-renounceable rights issues remain the dominant structure among Nepali listed companies, particularly banks and finance companies executing NRB-driven capital increases. Where a rights issue is explicitly designated renounceable, the transfer of entitlement must be completed within the subscription window using the procedure specified in that issue's offer documents — it is a distinct, separately executed action, not a variant of the standard MeroShare subscription form.
Letting the entitlement lapse — applying for nothing, deliberately or by inaction — is the third path, and it is the one investors understand least clearly, often assuming incorrectly that an unclaimed entitlement simply evaporates with no further consequence beyond dilution. What actually happens is more specific: shares corresponding to entitlements that go unsubscribed by the close date, along with fractional remainders from the ratio rounding described in Lesson 78.1, are pooled by the issuer and typically placed into an auction process, sold to interested subscribers — often existing shareholders who applied for more than their base entitlement, where the issue structure allows over-subscription requests, or to the general investing public through a separate mechanism specified in the issue documents. The proceeds of that auction, after adjusting for the original issue price and any auction premium, are frequently required to be routed back toward the company or held per SEBON's directions rather than returned to the shareholder who let the entitlement lapse — meaning the lapsed shareholder captures none of whatever value the auction realises. This is the single fact that most sharply distinguishes "choosing not to subscribe" from "letting it lapse": a deliberate decision not to subscribe is a dilution choice with a known, bounded cost; a lapse driven by inattention forfeits even the residual value that an active choice not to subscribe would have preserved via renunciation, if that path existed.
WARNING
Unsubscribed rights entitlements do not return any value to the shareholder who fails to apply. They lapse into an auction pool, and the proceeds of that auction generally do not flow back to the non-applying shareholder. If you have decided not to subscribe, that decision has the same dilution consequence whether you actively choose it or passively let the deadline pass — but only an active decision, taken with clear eyes, belongs in a disciplined investment process. Missing a deadline you meant to act on is not a strategy; it is an unforced error.
Table: The four mechanical paths through a rights issue, and what each one actually does to your position
Path
What you do
Cash required
Ownership consequence
Full subscription
Apply for 100% of entitlement via MeroShare ASBA
Full cash call at issue price × entitlement
Ownership percentage preserved (no dilution)
Partial subscription
Apply for a chosen amount below full entitlement
Proportional to shares applied for
Partial dilution, scaled to the unfunded portion
Renunciation (where permitted)
Transfer entitlement to another party per issue procedure
None from you; recipient pays
Full dilution to you, offset by any renunciation payment received
Lapse
No action taken before close date
None
Full dilution, entitlement value forfeited to auction pool
Sabina's case resolved as a full subscription, once her financing plan (Lesson 78.3) closed the cash gap — but the discipline of the decision worksheet in Lesson 78.6 is exactly what would have told her, had her financing not come together in time, that a partial subscription funded entirely from cash on hand was a strictly better outcome than either scrambling to sell a core holding under time pressure or letting the full entitlement lapse by default. The mechanical options exist precisely so that the financing constraint does not have to force an all-or-nothing outcome.
CASE IN POINT
Consider a shareholder holding 1,000 shares of a finance company facing a 1:1 non-renounceable rights issue at an issue price requiring roughly the same rupee amount as the full entitlement's market value at prevailing prices. She has enough liquid cash for exactly 40 percent of the call without touching any other holding. Rather than delaying a decision until the final week and then being forced into an emergency sale of an unrelated holding, she applies in week two of the window for 400 shares — her affordable, deliberate partial subscription — accepting a known, bounded dilution on the remaining 600-share entitlement rather than gambling on a rushed, possibly failed, funding scramble for the full amount.
Lesson 78.5 — After You Click Submit: Allotment, Crediting, and the Listing Lag
Submitting a rights subscription application is not the end of the process; it is the midpoint. The period between the issue close date and the point at which subscribed shares actually become tradable inventory in your demat account is where a second, less visible kind of patience is required — and where investors who expect IPO-like speed are often surprised by how much longer a rights issue's back-office resolution can take.
Once the application window closes, the issuer and its share registrar reconcile the applications received, resolve any over-subscription of the auction pool for lapsed and fractional entitlements described in Lesson 78.4, and finalise the allotment. This finalised allotment then requires processing through CDSC to actually credit the new shares into each subscriber's demat account, and, separately, an approval from NEPSE to list the new shares for trading — a company cannot simply issue shares and have them tradable the instant CDSC crediting occurs; the newly issued shares must clear NEPSE's listing formalities, which is itself a distinct regulatory step following allotment, not a formality that happens automatically or instantly. In practice, the gap between allotment finalisation and the shares actually appearing as tradable in your MeroShare portfolio has commonly run to several weeks in the Nepali market — a period during which your subscription cash has already been debited (converted from an ASBA block to an actual payment) but the corresponding shares are not yet visible, let alone sellable.
A useful, widely cited rule of thumb from experienced NEPSE participants is that once a company's newly issued shares receive NEPSE's trading approval, crediting into demat accounts and readiness for trading typically follows within about a week — but that week only starts once listing approval is granted, and getting from allotment finalisation to listing approval is the longer, less predictable leg of the journey, often stretching to a month or more depending on how quickly the registrar and CDSC process the batch and how promptly NEPSE grants listing. The practical implication for your own planning is straightforward: do not assume you will be able to trade your new rights shares the week after the issue closes, and do not treat the interval as evidence something has gone wrong. Track status through MeroShare's My Portfolio and My Application sections, and treat the appearance of the new shares as tradable inventory — not merely as an allotment confirmation — as the actual finish line.
Table: The rights issue timeline, notice to tradable shares (illustrative sequencing)
Stage
What happens
Typical span
Notice published
Rights announcement appears via MeroShare, press, and NEPSE disclosure
Day 0
Book close
Shareholder registry frozen to determine entitlement
Minimum ~35-day window, possible ~2-week extension if under-subscribed
~35–49 days after opening
Allotment finalised
Applications reconciled; auction pool for lapsed/fractional rights resolved
Weeks after close, varies by issuer
NEPSE listing approval
New shares cleared for trading
Follows allotment; the longer, less predictable leg
CDSC crediting / tradable
Shares appear in demat account and become sellable
Roughly within about a week of listing approval
PRACTICAL TOOL
Track your rights subscription through three checkpoints, not one. Checkpoint one: application confirmation, immediately after submission — save the reference number. Checkpoint two: allotment result, published under My Application once the issue closes and reconciliation completes. Checkpoint three: portfolio crediting, visible under My Portfolio, which is the only checkpoint that actually means the shares are yours to trade. Treat checkpoint two as informational and checkpoint three as the operative event for any subsequent trading decision.
For Sabina, this meant a genuine gap of roughly six weeks between her application submission and the morning she logged into MeroShare to find 800 new shares sitting as tradable inventory in her portfolio, at which point the cash that had been blocked against her ASBA account for those six weeks was long since converted into an actual subscription payment. Nothing about that gap indicated a problem; it is simply the mechanical reality of how allotment, listing, and crediting sequence through CDSC and NEPSE, and planning your own liquidity and expectations around that reality — rather than around the faster cadence some IPO processes have trained investors to expect — is part of executing a rights subscription competently rather than anxiously.
Lesson 78.6 — The Four-Question Decision Worksheet: A Step-by-Step Fillable Framework
Chapter 69 established the four questions that should govern any rights issue decision at the conceptual level: what the capital is actually for, whether the post-rights economics still make sense, whether you can afford the cash call without distorting your broader portfolio, and what your fallback path looks like if you choose not to fund the full entitlement. This lesson turns those four questions into an actual worksheet — a sequence you fill in, in order, against the real notice sitting in front of you, producing a specific action rather than a general disposition.
Question One: What is the capital actually for, and do you believe the stated use case? Write down, in one sentence, the stated purpose from the offer letter — regulatory capital mandate, project completion, debt rebalancing, working capital, or acquisition financing. Then write down whether this is a "must raise," where the company has essentially no choice (an NRB capital directive is the clearest Nepali example), or a "choose to raise," where management is electing to fund growth through shareholder cash rather than debt or retained earnings. A must-raise scenario changes the character of the decision: the question is no longer "is this a good use of capital" but "do I still want to own this company at all, given that this cash call is now unavoidable and recurring in nature until the capital threshold is durably met." A choose-to-raise scenario keeps the traditional capital allocation question front and centre — would you, as a rational allocator, choose to put new money into this specific project at this specific valuation if you were looking at it fresh today.
Question Two: What does the post-rights arithmetic actually look like? Calculate the theoretical ex-rights price — the blended price the stock should trade at immediately after the rights shares are issued, given the dilution. The formula is straightforward: multiply the current market price by the number of shares you hold, add the issue price multiplied by the new shares you're entitled to, and divide the sum by your total post-rights share count. Compare that theoretical ex-rights price against the company's underlying fundamentals — book value, earnings trajectory, the reason you owned the stock in the first place. If the theoretical ex-rights price still clears your original investment thesis with a reasonable margin of safety, subscribing preserves a position you'd still want to hold at that price. If the theoretical ex-rights price sits above what you'd pay for the stock fresh today, that is a signal worth taking seriously before writing the check, regardless of how attached you are to your existing position.
PRACTICAL TOOL
Theoretical ex-rights price, worked through Sabina's numbers: she held 800 shares at a pre-announcement market price of roughly Rs 340. Her 1:1 entitlement meant 800 new shares at the par issue price of Rs 100. TERP = [(800 × 340) + (800 × 100)] / 1,600 = (272,000 + 80,000) / 1,600 = Rs 220. Sabina's actual decision hinged on whether Rs 220 was still a price she'd be comfortable holding the combined 1,600-share position at, given the bank's underlying capital adequacy improvement — not on the Rs 100 issue price in isolation, which on its own looked artificially cheap.
Question Three: Can you fund this without distorting your broader portfolio? Run the financing sub-questions from Lesson 78.3 in order: cash on hand first, then identification of your lowest-conviction liquid holding as a funding source if a sale is required, then an honest accounting of the tax and opportunity cost of that sale, then — only if genuinely necessary — the cost of any margin facility, compared explicitly against your Question Two arithmetic. Write down the specific rupee amount you can fund without selling anything, and the specific rupee amount you could additionally raise through a sale you've already identified and are comfortable with. These two numbers, added together, are your real ceiling — not your full entitlement value, unless the two happen to match.
Question Four: What is your fallback path, matched against your Question Three ceiling? If your funding ceiling covers your full entitlement, subscribe fully and move to the MeroShare mechanics of Lesson 78.2, scheduled with the buffer discipline described there. If your ceiling covers only part of your entitlement, decide deliberately — not by default — whether a partial subscription at that ceiling is preferable to stretching for the full amount through a financing source you rated poorly in Question Three; the worksheet's discipline is that a deliberate partial subscription, chosen in week two, always beats a rushed full subscription financed by whatever is fastest to liquidate in week five. If your ceiling is effectively zero and the issue is renounceable, investigate the renunciation procedure specified in the offer letter before the window closes. If your ceiling is zero and the issue is non-renounceable, accept the lapse consequence as a known, bounded dilution cost rather than letting the deadline pass unexamined — the difference between an active decision to let an entitlement lapse and a passive one is not visible in the mechanical outcome, but it is the entire difference between disciplined investing and drift.
Table: The worksheet, filled in for a hypothetical live rights notice
Question
What you write down
Sabina's answer
1. Purpose and belief
Stated use of proceeds; must-raise or choose-to-raise
NRB capital mandate; must-raise, and she still believed in the bank's franchise
2. Post-rights arithmetic
TERP calculation vs fundamentals
TERP ≈ Rs 220; still below her estimate of fair value given improved capital ratios
3. Funding ceiling
Cash on hand + identified low-conviction sale, net of tax/cost
Full entitlement covered via a trimmed low-conviction position, settled with buffer
4. Fallback path
Full, partial, renounce, or deliberate lapse
Full subscription, scheduled and submitted in week two of the window
Chapter recap
This chapter took the strategic groundwork Chapter 69 laid down — why companies issue rights, how dilution math works, the difference between renounceable and non-renounceable structures, and the NRB-driven capital mandate pattern that dominates Nepali financial-sector rights issues — and turned it into an operational sequence you can actually execute against a live notice. Reading the offer letter correctly means finding the ratio, the book close date, the application window, the issue price, the stated purpose, and the renounceability structure before anything else. Executing the subscription means confirming your CRN is active well before the deadline, understanding that ASBA blocks rather than withdraws your funds, and never treating the close date as flexible. Financing the cash call means sequencing cash-on-hand first, a deliberately chosen low-conviction sale second, and an honest accounting of tax and margin costs before comparing against the issue's economics. Knowing your fallback paths means understanding that partial subscription is a legitimate middle ground, that renunciation is only available when the issue structure permits it, and that a lapsed entitlement forfeits its value to an auction pool rather than simply vanishing at no cost. And knowing what happens after submission means expecting a real gap — often a month or more — between allotment and the moment shares actually become tradable inventory in your demat account, with NEPSE listing approval as the pacing step rather than CDSC crediting itself.
Zooming out, this chapter closes Part XIV of this book, a part that began by asking how a disciplined Nepali investor turns market structure and behavioural awareness into repeatable action. The part opened with the frameworks — the strategic architecture in its earlier chapters that established how to think about position sizing, timing, and the specific dynamics of rights capital calls and IPO allocations at a conceptual level — before turning, across Chapters 71 through 78, into a run of operational playbooks: sector rotation mechanics, dividend capture and book-close timing, promoter and bonus share dynamics, the specific tactical playbooks for volatile and illiquid counters, the IPO playbook in Chapter 77, and now the rights issue playbook here in Chapter 78. Read as a whole, Part XIV has made one argument repeatedly, in different operational costumes each time: that good strategy in the Nepali market fails constantly at the level of execution — a missed deadline, an unfunded ASBA block, a panic sale to cover a cash call, a decision made passively instead of actively — and that closing that gap between knowing what to do and actually doing it correctly, on the calendar the market and its regulators impose, is where a genuinely disciplined investor is actually built.
The playbooks in this part share a common shape, and recognising that shape is itself a takeaway worth carrying forward. Each one starts with a notice or a signal you did not generate yourself — a dividend book close, a bonus announcement, an IPO opening, a rights letter — and each one then demands a sequence of concrete, deadline-bound actions layered on top of a genuine judgment call about whether the underlying opportunity deserves your capital at all. The mechanics differ; the discipline required does not. An investor who has internalized the rights issue playbook in this chapter already has the muscle memory for the next unscheduled cash call, the next capital mandate cycle, the next company whose board decides, on its own timetable, that it needs shareholders to write a check.
Sabina's shares, six weeks after her application, sat in her portfolio as ordinary tradable inventory — no longer a decision in progress, just 1,600 shares in a bank she had chosen, deliberately and on schedule, to remain a proportional owner of. That is the entire, unglamorous goal of this playbook: not to predict whether the rights issue was a good idea in some abstract sense, but to make sure that whatever you decide, you decide it on purpose, with the cash arranged in time, the form filled in correctly, and no deadline allowed to make the decision for you by default.
With Part XIV closed, this book turns to a different kind of discipline entirely. Chapter 79, The Backtesting Mindset, opens Part XV: Calibration & Backtesting, and it marks a deliberate shift in register — from the playbooks of this part, which have been about executing decisions correctly in real time against real deadlines, to a colder, retrospective question: how do you actually know whether the strategies and frameworks built across this entire book would have worked, had you followed them consistently, across NEPSE's actual historical data? Chapter 79 will introduce the mindset shift that backtesting demands — the willingness to be proven wrong by your own past data, the discipline of testing a rule against history before trusting it with real capital, and the specific hazards of backtesting in a market as thin, as structurally quirky, and as short in its listed history as Nepal's. Where Part XIV asked "how do I execute this specific decision correctly," Part XV begins by asking a prior, harder question: "how do I know this decision framework was ever right in the first place." That is where the book goes next.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XV
CALIBRATION & BACKTESTING
Part XV · Chapter 79
The Backtesting Mindset
First published 25 Aug 2026 · Last verified 29 Aug 2026
Keshav Raj Pyakurel spent thirty-one years reading numbers for a living. Not writing them, not selling them — reading them, the way a mechanic reads an engine noise before it becomes a breakdown. Most of that career was spent inside Nepal Rastra Bank's supervision wing, going through the balance sheets of commercial banks and development banks line by line, flagging the ones whose capital cushions were thinner than their public filings suggested. He was the kind of officer who kept his own private ledger of every bank he supervised, cross-checked against the official file, because he did not fully trust any single source — including, by his own admission, himself. He retired in the middle of 2021, at almost exactly the moment NEPSE was setting the all-time high it would not revisit for years. For about four months he did what half of Kathmandu was doing that year — he bought whatever was moving, felt briefly brilliant, and then watched a fair portion of his retirement gains evaporate through the back half of 2021 and into 2022. It embarrassed him enough that he stopped trading almost entirely for a year and started reading instead.
By the time he found the Canon Score — the seven-dimension, hundred-point scoring framework this book built across Part XIII, covering things like earnings quality, capital adequacy, promoter behaviour, and liquidity — he had already rebuilt his old habit of keeping a private ledger, except now the ledger tracked every stock he scored and every decision he made using that score. A year of this felt reassuring. It also felt, to a man trained to distrust reassurance, insufficient. "The score has made sense to me for twelve months," he told a former colleague over tea in Battisputali. "That is not evidence. That is a good feeling with a spreadsheet attached." He wanted to know something harder: if he had been using this score for the last seven or eight years, not just the last one, would it actually have kept him out of trouble and put him into the winners — or would it have quietly failed in ways a single good year could never reveal? That question is what backtesting is for, and it is what this chapter, and the whole of Part XV, is built around.
Lesson 79.1 — What Backtesting Actually Means
Backtesting is a simple idea wearing an intimidating name. It means taking a rule — any rule, whether it is the full Canon Score, a single entry signal like "buy when a bank trades below book value with a clean NPL trend," or an exit rule like "sell if a stock breaches its 90-day low on above-average volume" — and running that rule mechanically against data from the past to see what would have happened if you had actually followed it, trade by trade, at the time.
That last phrase, at the time, is the entire discipline. It is the difference between backtesting and the two things retail investors usually do instead and mistake for backtesting.
The first substitute is "the strategy feels right." Keshav had this in spades. The Canon Score felt right to him because every time he scored a stock highly, he could construct a story afterward for why it made sense — strong promoter holding, low debt, decent dividend history. The trouble is that a plausible story after the fact is not a test. A story can be built around almost any outcome, good or bad; that is what stories are for. A backtest, by contrast, commits to the rule before it sees the ending and then simply records what happened. It removes your permission to explain away the losses.
The second substitute is "it worked for my friend." This is social proof standing in for evidence. Keshav's nephew had made good money in Api Power and a hydropower IPO the year before and was certain that buying any hydropower counter within a month of monsoon onset was a reliable strategy. One profitable friend is a sample size of one, observed with hindsight, filtered through memory that tends to keep the wins and quietly discard the losses. It is not data; it is an anecdote wearing data's clothes.
KEY CONCEPT
Backtesting means applying a precisely defined rule to historical data, trade by trade, using only the information that would genuinely have been available on each decision date — then honestly recording every result, not just the flattering ones.
What made Keshav a natural at this, once he understood what was being asked of him, was that his old NRB job had trained him to do something structurally identical: he used to take a bank's lending rule (say, a loan-to-value ceiling) and check it against years of actual loan files to see whether the rule had been followed and whether following it had actually protected the bank. Backtesting a trading or scoring rule is the same exercise, just aimed at a stock's price and volume history instead of a bank's loan book. The rule is the hypothesis. The historical data is the evidence. The backtest is the honest cross-examination of the two.
He picked a first, deliberately small test to build the habit: the exit rule from Part XII — sell a position if daily traded volume falls below a set multiple of the position size for more than five consecutive sessions (a liquidity-based exit meant to get you out before you become the seller nobody wants). He pulled five years of daily volume data for a dozen mid-cap manufacturing and hotel counters he already followed, and for each one asked a simple question on each trading day: given only what was known up to and including that day, would this rule have told me to sell? Not what he remembered feeling on that day. Not what the stock did afterward. Only the rule, applied mechanically, to the data as it stood at that moment.
Lesson 79.2 — The Three Biases That Quietly Ruin Amateur Backtests
Keshav's first attempt at a fuller backtest — running the Canon Score against eight years of data across roughly forty NEPSE counters — produced a hit rate so flattering he did not believe it. He was right not to. He had, without realising it, walked into the three failure modes that ruin almost every amateur backtest, in NEPSE or anywhere else.
The first is survivorship bias. Keshav's stock list was built from today's NEPSE company list — the ~260 or so companies currently trading. But NEPSE eight years ago had a meaningfully different roster: dozens of development banks and finance companies that existed then have since disappeared, mostly through forced mergers driven by Nepal Rastra Bank's 2015 directive raising commercial banks' minimum paid-up capital fourfold, to Rs 8 billion, with smaller BFIs facing their own consolidation pressure. Keshav actually remembered this directive intimately — he had helped enforce it from inside NRB. Companies that failed to raise capital, or whose books turned out weaker than advertised, were absorbed into larger institutions and vanished from the ticker. If your backtest only includes companies that are still independently listed today, you have quietly deleted every failure from the sample. The past looks safer than it was, because the stocks that did not survive are not there to drag the average down.
WARNING
A backtest built only from companies currently listed on NEPSE silently excludes every company that failed, merged, or was delisted along the way — which flatters every "buy and hold" style rule tested against it.
The second is look-ahead bias — accidentally feeding the rule information it could not have had at decision time. Keshav's Canon Score uses full-year audited earnings per share as one of its inputs. When he first ran his backtest, he pulled EPS from annual reports and applied that full-year audited figure to score a stock as of, say, mid-Poush (roughly December–January) of that same fiscal year. But audited annual results for a NEPSE company are typically published months after the fiscal year closes — often not until Ashoj or Kartik of the following year. In mid-Poush, in real time, an investor would have had only the unaudited quarterly figures released so far, which can differ meaningfully from the final audited number once provisioning, write-offs, or restatements come through. By plugging in the audited figure too early, Keshav's backtest was quietly cheating — giving his rule knowledge from the future.
The third, and the one that took him longest to see in his own work, is overfitting. After noticing the audited-EPS problem, Keshav started adjusting the Canon Score's internal weights — nudging the liquidity dimension up a little, the promoter-holding dimension down a little — until the backtest's returns looked even better. Each adjustment was justified by some specific stock in his sample. After two weekends of this he had a version of the score that explained his forty-stock, eight-year history almost perfectly. It also, he began to suspect, explained nothing at all. He had not found a better rule; he had sculpted a rule around the exact bumps and dips of one particular, noisy, forty-stock sample. Give that same tuned rule a different set of stocks, or the same stocks over a different stretch of years, and there is no reason to expect it to perform anywhere near as well — because it was never describing a real, repeatable relationship. It was describing coincidence, dressed up to look like insight.
CASE IN POINT
Keshav tuned the Canon Score's weights until it "explained" eight years of data on forty stocks almost perfectly — a warning sign, not an achievement, since a rule flexible enough to fit any past sample perfectly is usually too flexible to predict anything.
Bias
What goes wrong
The fix
Survivorship bias
Delisted, merged, or failed companies vanish from today's stock list, so the backtest sample only contains "winners" that survived
Build your stock list from what was actually listed and trading on the test date, including names later merged, suspended, or delisted
Look-ahead bias
A rule uses information (audited EPS, final AGM decisions, revised guidance) that was not yet public on the decision date
Timestamp every input; use only unaudited quarterly figures, disclosures, and prices genuinely available as of that date
Overfitting
A rule's weights or thresholds are adjusted repeatedly until they perfectly fit one historical sample
Fix the rule's logic before testing; if you must adjust it, retest only on data the adjustment has never seen (see Lesson 79.4)
Lesson 79.3 — Why NEPSE Specifically Is a Hard Market to Backtest
Even a backtester who avoids all three biases above still runs into a problem that is specific to NEPSE rather than to backtesting in general: there simply is not that much clean history to test against, and what history exists is not as independent, or as stable, as it looks.
Start with the raw amount of usable data. NEPSE traces its institutional history back to 1993, with its trading floor opening on 13 January 1994 — but for well over a decade after that, trading ran on an open-outcry floor system, with brokers calling out prices to one another rather than the market generating clean, timestamped electronic records. NEPSE moved to a semi-automated system only in 2007–08, and dematerialization of shares began in 2011 with the establishment of CDS and Clearing Limited. It was not until November 2017 that NEPSE rolled out its fully automated, broker-independent online trading system — the version of the market, with investors placing orders themselves through a Trading Management System, that most of today's participants would actually recognise. That means reliable, machine-readable, investor-verifiable daily price and volume data — the kind a backtest can actually trust down to the transaction — really only stretches back eight or nine years, not thirty.
REGULATORY DETAIL
NEPSE's fully automated, broker-independent online trading system went live in November 2017; before that, the market ran on a semi-automated setup introduced in 2007–08, following decades of open-outcry floor trading — meaning genuinely clean, machine-verifiable daily data for most stocks realistically covers less than a decade.
Eight or nine years sounds workable until you notice the second problem: those years are not neutral, evenly-behaved history. They contain the tail end of the post-2016 boom (NEPSE's index hit an all-time high of 1,881.45 on 27 July 2016, before a bear market dragged it down toward roughly 1,100 by early 2019); a historic mania in 2020–2021 that pushed the index to a fresh all-time high of 3,111.09 on 3 August 2021, with single-day trading generating over 21 million shares changing hands and six scrips hitting the day's positive circuit breaker at once; and then a grinding, multi-leg crash through 2022 that took the index down through the 2,700, 2,600, and eventually the 2,000 mark, a fall of well over a year in length. Backtest across that whole window and you are really backtesting three or four completely different markets stitched end to end — a low-liquidity recovery, a euphoric bubble, and a prolonged unwind — and calling the average of all three "how the rule performs."
The third problem is more subtle and, in Keshav's experience, the one investors notice last: NEPSE does not actually give you as many independent tests as its stock count suggests. There are roughly 260 listed companies today, which sounds like 260 separate experiments. But a large share of them are commercial banks, development banks, and microfinance institutions whose share prices move together on the same handful of triggers — a Nepal Rastra Bank monetary policy announcement, an interest rate spread directive, a capital adequacy circular — and a large share of the rest are hydropower companies whose revenue, and therefore sentiment, swings with the same monsoon season and the same load-shedding or export-tariff news. If thirty bank stocks all rally or fall together because of one NRB circular, that is not thirty independent tests of your rule; it is closer to one test, repeated thirty times in slightly different costumes. Treating correlated stocks as independent data points is a quiet way of convincing yourself you have more evidence than you actually do.
WARNING
Many NEPSE bank and hydropower counters move together on the same handful of triggers — an NRB policy circular, a monsoon season — so a backtest across thirty such stocks is closer to one real test repeated thirty times than to thirty independent tests.
Layer on top of that the structural rule changes NEPSE has gone through in this same short window — circuit breaker thresholds have been adjusted more than once, free-float and index-calculation methodology has changed, and the 2015 capital directive alone forced a wave of BFI mergers that reshaped which "stocks" even existed from one year to the next — and you get a market whose own rules of the game changed underneath the price history you are trying to test against. Keshav's blunt summary, scribbled in his ledger margin: "Eight years of data, four different markets, and the referee changed the rules twice." That is not a reason to abandon backtesting on NEPSE. It is a reason to do it with far more humility than a book on, say, the S&P 500's ninety years of data would require.
Lesson 79.4 — In-Sample vs Out-of-Sample Testing
The single discipline that would have caught Keshav's overfitting problem in Lesson 79.2 before it wasted a weekend is splitting the data in two and refusing to look at the second half until the first half is finished.
The two pieces of jargon here are simpler than they sound. In-sample data is the period you use to build or tune your rule — the period you are allowed to look at, argue with, and adjust your rule against as much as you like. Out-of-sample data is a separate period, one your rule has never seen and was never adjusted to fit, which you test the finished, frozen rule against exactly once. Think of it the way a schoolteacher thinks about practice exams versus the real board exam: you can revise your approach against as many practice papers as you like, but the actual board exam questions must be ones you have never seen in advance, or the exam proves nothing about whether you actually learned the subject.
Keshav restructured his test this way. He took his eight-and-a-half years of available NEPSE data (November 2017 through the recent close of Fiscal Year 2081/82) and split it: roughly the first five and a half years, through mid-2023, became his in-sample period, where he was allowed to build, adjust, and sanity-check the Canon Score's weights and his liquidity exit rule. The remaining period — from mid-2023 to the present — he sealed off entirely. He did not look at those prices while tuning anything. Only once his rule was completely fixed, weights and thresholds locked, did he run it forward against that untouched stretch, exactly once, and accept whatever came out.
KEY CONCEPT
In-sample data is what you use to build and adjust a rule; out-of-sample data is a separate, untouched period you test the finished rule against exactly once — the discipline that catches overfitting before real money does.
The result humbled him usefully rather than painfully. His overfit, heavily-tuned version of the Canon Score — the one that had explained the in-sample years almost perfectly — performed distinctly worse out-of-sample than the simpler, less-tuned original version he had been using informally for the past year. The complicated version had been memorising the in-sample noise, not learning a real pattern, and so it had nothing useful to say about a period it had never seen. The simpler version, with fewer moving parts, held up closer to its in-sample performance. That gap between in-sample and out-of-sample results is itself a diagnostic: a rule whose out-of-sample performance collapses relative to its in-sample performance is telling you, plainly, that it was fitted rather than found.
This is also the single most common way retail "backtested" strategies fail once real money is on the line. An investor tunes a rule against the same data he later trades on — adjusting the entry threshold a little, adding a filter here, until the historical chart looks clean — and then is baffled when the "proven" rule underperforms going forward. It was never tested going forward. It was polished backward, against the only data it was ever allowed to see, and then unleashed on data that, from the rule's point of view, might as well be a different market entirely.
PRACTICAL TOOL
Before tuning any rule against NEPSE history, physically set aside the most recent one to two years of data in a separate file you do not open until the rule is completely finished — a low-tech but effective way to force genuine out-of-sample discipline.
Lesson 79.5 — A Practical, Honest Backtesting Workflow
None of this requires software Keshav did not already own. He built his entire backtest in a spreadsheet, and the workflow he settled on — after his false starts — is one any patient Nepali retail investor can run by hand.
Step one is picking one rule and writing it down so precisely that two different people, given the same data, would reach the same decision. "Buy strong banking stocks" is not a rule; it cannot be tested because it cannot fail. "Buy a commercial bank scoring 70 or above on the Canon Score, using only data available as of the first trading day after each quarterly result is published, and hold until the score drops below 55 or twelve months pass, whichever comes first" is a rule. It has an entry trigger, a data cutoff, and an exit trigger, all specific enough that ambiguity is removed.
Step two is defining entry and exit with that same precision, including exactly which price you would have transacted at (the next day's opening price is usually the honest choice, since you could not have traded at a closing price you had not yet seen) and exactly which data vintage feeds the decision (quarterly unaudited figures, not the audited annual figure that arrives months later — the look-ahead trap from Lesson 79.2).
Step three is walking the rule forward year by year, strictly in date order, using only information that existed on each decision date. Keshav did this literally with a ruler and a printed price chart at first, covering each stock's future price movement with a sheet of paper so he could not see it while deciding what the rule would have done on a given date — a low-tech but effective discipline against the temptation to let hindsight creep in.
Step four, the one Keshav found most uncomfortable, is recording every trade the rule generates, including the ones that would have been embarrassing. He had, without quite admitting it to himself, been quietly skipping two trades in his early drafts — one, a hydropower stock the rule said to buy that then dropped 30 percent on a court case involving its power purchase agreement, and another, a finance company the rule held onto for eleven months while it drifted to a loss before finally triggering the exit. Leaving those two trades out of his tally moved his average return from mediocre to good. Putting them back in was the entire point of the exercise.
CAUTION
The trades that feel embarrassing to include — the rule-generated buy that was followed by bad news, the exit that came too late — are exactly the trades that make a backtest honest; quietly excluding them turns the exercise back into a story.
Step five is computing a small number of honest summary numbers rather than a single flattering headline return. Keshav settled on four: hit rate (the percentage of trades that were profitable), average gain on winning trades versus average loss on losing trades (so a high hit rate built on tiny wins and rare but brutal losses does not disguise itself as a good rule), and maximum drawdown (the worst peak-to-trough decline the rule's running account value would have experienced, which tells you whether you could have actually stomached holding through the rule's worst stretch). A single overall return percentage can be true and still misleading — it can be produced by one lucky trade dominating forty unlucky ones. The four numbers together are much harder to fool.
Here is a simplified extract from the backtest log Keshav actually kept, covering a handful of trades from his Canon-Score-based bank and hydropower rule:
Entry date
Stock
Canon Score at entry
Entry price (Rs)
Exit date
Exit price (Rs)
Result
2019 Mangsir
Bank A
74
285
2020 Ashadh
340
+19.3%
2019 Falgun
Hydro B
71
410
2019 Ashadh (following)
295
-28.0%
2020 Kartik
Bank C
68
190
2021 Baisakh
410
+115.8%
2021 Shrawan
Finance D
70
520
2022 Chaitra
340
-34.6%
2022 Mangsir
Bank A
76
250
2023 Ashadh
275
+10.0%
Five trades is far too small a sample to draw conclusions from, and Keshav's real log ran to several dozen — but the format matters more than the count here: every row has a precise entry trigger, a precise exit trigger, and an outcome recorded whether it flatters the rule or not. That is the difference between a backtest log and a highlight reel.
PRACTICAL TOOL
Keep a running backtest log with one row per trade — entry date, score or signal value at entry, entry price, exit date and price, and result — and update it before checking whether the trade was a winner, so the temptation to "forget" a bad one never gets the chance to operate.
Lesson 79.6 — The Limits of Backtesting
After several months of this work, Keshav reached a conclusion he found both satisfying and uncomfortable: the Canon Score, tested honestly against the out-of-sample period, performed reasonably — better than a naive buy-anything approach, with a hit rate around six in ten and a drawdown he judged tolerable — but nowhere near as spectacular as his first, biased attempt had suggested. He decided that was, in fact, the correct amount of confidence to have in it.
What a backtest can honestly tell you is narrow but real: that a rule was not obviously wrong across the specific historical stretch you tested, under the specific conditions that stretch happened to contain. It is evidence the rule is not pure fantasy. It is not, and can never be, proof the rule will keep working in conditions that stretch never contained. This matters enormously for NEPSE specifically, because the market's entire electronic-data history — the eight or nine years Keshav actually had to work with — has never yet contained a genuine, prolonged, multi-year bear market tested against today's participant base. Demat account holders grew from roughly 1.48 million in FY 2018/19 to nearly 3.79 million by FY 2020/21, and to close to 4.9 million since — meaning a large majority of the people currently trading NEPSE opened their accounts during or after the 2020–2021 mania and have only ever personally experienced the sharp-but-comparatively-brief 2022 correction that followed it. Nobody's backtest, however carefully built, can prove how a rule — or a market full of these investors — behaves in a slower, multi-year grind down, because that regime has not happened yet inside the clean data anyone can actually test.
CAUTION
A backtest can show a rule was not obviously wrong in the past; it cannot prove the rule will survive a market regime — like a genuine multi-year bear market with today's much larger, much younger participant base — that has not yet occurred in NEPSE's clean electronic-data history.
This is why backtesting belongs in this book as a necessary discipline rather than a final answer. It replaces "it feels right" and "it worked for my friend" with something falsifiable, which is real progress. It cannot replace ongoing humility about the fact that markets, and NEPSE in particular, keep generating conditions nobody's historical sample has seen before. Keshav's own plan, once he finished this first honest pass, was not to bet his full retirement savings on the Canon Score with newfound certainty. It was to size his positions the way Part XII already taught him — respecting ADV-based liquidity limits regardless of how good the backtest looked — and to keep the backtest log running indefinitely, adding every new trade as it happens, so the rule keeps being tested against a market that keeps writing new history.
Chapter recap
This chapter opened Part XV by drawing a hard line between believing a rule works and actually having evidence that it did. Backtesting, in plain terms, means taking a precisely defined rule — a score, an entry signal, an exit trigger — and running it mechanically against historical data, using only the information that was genuinely available at each decision point, and then recording every result honestly. It is fundamentally different from "the strategy feels right" or "it worked for my friend," both of which are stories built after the fact rather than tests committed to before it.
We walked through the three biases that quietly wreck amateur backtests: survivorship bias, where delisted or merged companies vanish from today's stock lists and make the past look safer than it was; look-ahead bias, where a rule accidentally uses information — like full-year audited earnings — that would not actually have been available on the decision date, when only unaudited quarterly figures existed; and overfitting, where a rule is tuned and re-tuned until it perfectly explains one historical sample, at the cost of describing nothing repeatable at all. We then looked at why NEPSE specifically makes all of this harder than in older, larger markets: genuinely clean electronic data realistically covers less than a decade following the November 2017 rollout of fully automated trading; that short window already contains at least three distinct regimes — a post-2016 bear market, the 2020–2021 mania that peaked above 3,100 on the index, and the grinding 2022 correction; and a large share of NEPSE's roughly 260 listed companies move together on the same handful of triggers, so the market offers far fewer truly independent tests than its headline stock count suggests.
The chapter's central discipline was the split between in-sample and out-of-sample testing — building and tuning a rule on one period, then testing the frozen, unmodified rule exactly once on a separate period it has never seen. Skipping this step, and instead polishing a rule against the very data you later trade on, is the most common single reason retail "backtested" strategies disappoint once real money is involved. From there we built a practical, honest workflow any investor can run by hand or in a simple spreadsheet: define the rule with enough precision that two people would make the same call from the same data; walk it forward strictly in date order using only information available at each point; record every trade including the embarrassing ones; and judge the result using a small set of honest numbers — hit rate, average gain versus average loss, and maximum drawdown — rather than one flattering headline return.
Woven through all six lessons was Keshav Raj Pyakurel, a retired Nepal Rastra Bank supervision officer who spent a year using the Canon Score informally, then spent several more months testing it honestly against eight-plus years of NEPSE history before deciding how much of his retirement savings it deserved. His near-miss with overfitting, his discovery of the look-ahead trap hiding in audited EPS, and his discomfort at almost quietly dropping two losing trades from his log are not exaggerations for effect — they are the ordinary, specific ways a careful, numbers-literate investor can still fool himself, and the ordinary, specific disciplines that catch it.
The chapter closed on backtesting's real limit: it can tell you a rule was not obviously wrong across the history you tested, but it cannot prove the rule will hold up in a market regime that history has not yet produced — and NEPSE's clean electronic record, dominated by participants who joined during or after the 2020–2021 mania, has never yet contained a genuine, prolonged multi-year bear market. That gap is exactly where Chapter 80, "Calibrating Your Scoring Model Against NEPSE History," picks up the thread — taking the backtesting mindset built here and applying it directly to the Canon Score itself, dimension by dimension, to find out which of its seven components have actually earned their weight in NEPSE's real history and which have simply never yet been tested by conditions severe enough to matter.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XV · Chapter 80
Calibrating Your Scoring Model Against NEPSE History
First published 25 Aug 2026 · Last verified 29 Aug 2026
Bimal Sharma spent thirty-one years at Nepal Rastra Bank, most of them in bank supervision, reading balance sheets the way other people read newspapers. When he retired in 2019, he brought that habit with him into the stock market, and it was this habit that had, in Chapter 79, led him to distrust his own excitement about the Canon Score before he trusted it with real money. He had tested the general discipline of backtesting there — the traps of survivorship bias, look-ahead bias, and overfitting, and the difference between checking an idea on data you have already seen versus data you have not. Now he turned that same discipline toward a narrower and, in some ways, harder question: not "does backtesting work," but "does my score itself add up correctly." The Canon Score he had been using — the 100-point, seven-dimension system built in Chapter 64, weighing Financial Strength & Profitability at 20 points, Governance & Promoter Behaviour, Valuation Reasonableness, Sector & Business Model Durability, and Growth Trajectory at 15 points each, and Liquidity & Tradability and Dividend & Capital Return Discipline at 10 points each — had felt right to him for two years. What he had never done was go back and check, sector by sector, whether he was actually applying Chapter 65's sector-specific adjustments with real discipline every time, or whether he had quietly been scoring every company — bank, hydropower plant, or microfinance institution alike — against the same generic bands underneath, while telling himself he was "keeping the sector context in mind." This chapter follows Bimal as he checks that, dimension by dimension, using real NEPSE history rather than gut feeling, and finds that at least one sector's adjustment had been quietly skipped for two years running.
Lesson 80.1 — What Calibration Really Means
Backtesting a trading rule, as Chapter 79 covered, asks a yes-or-no question about behaviour: if I had bought when the rule said buy and sold when it said sell, would I have made money, and would that have held up outside the exact period I tested it on? Calibrating a score is a different, quieter kind of question. It does not ask whether the Canon Score, taken as a whole, would have picked winners. It asks whether the internal architecture of the score — the number of points assigned to each of its seven dimensions, and how faithfully each dimension is actually being scored for the sector in front of you — reflects what has actually mattered for NEPSE outcomes, as opposed to what sounds like it should matter.
Think of the Canon Score as a recipe rather than a single dish. A recipe can produce a dish that tastes fine even when one ingredient is present in the wrong quantity, because the other ingredients compensate. Calibration is the process of tasting each ingredient on its own, not just the finished plate. Chapter 64 assigns 20 points to Financial Strength & Profitability and 15 points to Sector & Business Model Durability — an implicit claim that raw profitability and balance-sheet safety matter somewhat more than durability and moat in separating a good long-term NEPSE holding from a bad one. But there is a second, quieter claim buried inside that first one, and it is the one Bimal had never actually tested: Chapter 65 says that for a hydropower company, a microfinance institution, or a bank, several of those seven dimensions need a genuinely different ruler — different sub-ratios, different thresholds, sometimes an entirely different question. Was Bimal actually reaching for that different ruler every time, or was he scoring every company against the same generic bands and calling it "sector awareness" in his head? That claim had never been tested against Nepal's own trading floor. Calibration is the act of testing it.
KEY CONCEPT
Calibrating a score means testing whether its point-weights and its sector-specific applications match what actually mattered for real outcomes — it is different from backtesting a trading rule, which tests only the buy/sell decision the score produces, not the score's internal architecture or how faithfully that architecture was actually applied.
This distinction matters because a score can pass a crude backtest — "stocks that scored above 70 mostly went up" — while still being badly calibrated underneath. A dimension could be scored using the wrong ruler for a whole sector, and the error could still wash out across a large enough sample, so the total looks fine even though the underlying reasoning was wrong every single time it was applied to that sector. A trader who only checks the final score against outcomes, the way Chapter 79 checked a trading rule, will never see this. Bimal, with his supervisory instincts, wanted to open the recipe up and check each ingredient. He started by writing out, on a single sheet of paper, what each of the seven Canon Score dimensions was supposed to be measuring, and — critically — which of them Chapter 65 says need a sector-specific rework rather than the generic Chapter 64 version. Financial Strength & Profitability (20 points) needed the heaviest rework of all: CAR, NPL ratio, and NIM for a bank; construction discipline pre-COD and season-adjusted margins post-COD for a hydropower company; provisioning coverage weighted above generic leverage for a microfinance institution. Governance & Promoter Behaviour (15 points) needed a heavy rework specifically for microfinance, where loan recycling and multiple-borrowing risk live outside the generic promoter-pledging checklist entirely. The other five dimensions needed lighter, more occasional adjustment.
Bimal's first honest realisation was that he had never once, in two years of using the score, gone back to check whether he was actually pulling out Chapter 65's specific sector guidance every time he scored a bank, a hydropower company, or a microfinance institution — or whether he had been scoring all three off the same generic Chapter 64 bands and simply trusting his own judgment to "adjust mentally" for sector, an adjustment that left no trail and that he had never once written down or tested against what actually happened to real NEPSE companies.
Lesson 80.2 — The Small-Sample Problem You Cannot Escape
Before Bimal could test anything, he needed to be honest about how much data he actually had to test with. NEPSE, as of the most recent sectoral count, lists 271 companies. On the surface, 271 data points sounds like a workable sample — enough to run some rough statistics on. But Bimal, from his NRB years, knew to ask a harder question: how many of those 271 are genuinely independent observations, and how many are just the same story told 91 times?
The sectoral breakdown makes the problem concrete. Of NEPSE's 271 listed companies, roughly 91 — about a third of the entire exchange — are hydropower companies. Microfinance institutions add another 50, roughly a fifth of all listings. Commercial banks number about 19, development banks about 16, finance companies about 20, and life and non-life insurers together add roughly 27. Manufacturing and processing companies, hotels, trading houses, and investment companies fill out the rest in much smaller numbers — manufacturing at around 22, hotels at only 7.
REGULATORY DETAIL
NEPSE's roughly 271 listed companies break down, by rough sector share, into about 91 hydropower companies (a third of the exchange), 50 microfinance institutions (a fifth), and a combined 55 or so banks and development banks — meaning three sector clusters account for well over half of everything listed.
The trouble is that companies within a sector on NEPSE do not move independently of one another. Nearly all 91 hydropower companies generate electricity from rivers whose flow depends on the same monsoon, the same winter dry season, and the same glacial melt patterns; nearly all of them sell power under similar Power Purchase Agreements, or PPAs (long-term contracts that lock in the price a hydropower company is paid for its electricity, usually with the state utility), whose tariff structures were shaped by the same handful of regulatory decisions. When a dry winter reduces river flow across the country, it does not hit one hydropower company — it hits most of them at once, in the same direction, for the same reason. Similarly, nearly all commercial banks respond to the same interest-rate and capital-adequacy decisions from Nepal Rastra Bank (NRB, the central bank); nearly all microfinance institutions were exposed to the same rural-lending stress that built up in the early 2020s and worsened through 2025 and 2026. A "backtest" that scores all 271 NEPSE companies and checks outcomes is not really testing 271 independent scenarios. It is closer to testing three or four independent scenarios — one hydropower monsoon cycle, one banking-sector rate cycle, one microfinance credit cycle, one small and heterogeneous "everything else" cluster — each of which happens to contain many correlated repetitions of the same underlying event.
This is a subtler cousin of a problem statisticians call clustering, or lack of independence between observations. It means the effective sample size behind any Canon Score calibration exercise on NEPSE is not 271. It might realistically be closer to a dozen truly distinct historical episodes, once correlated companies are collapsed into the single event they are all reacting to.
WARNING
Counting all 271 NEPSE-listed companies as 271 independent test cases is a statistical illusion — because roughly a third are hydropower firms reacting to the same monsoon and PPA cycle, and a fifth are microfinance institutions that were exposed to the same rural credit stress, the real number of independent scenarios behind any NEPSE-wide calibration is closer to a handful than to hundreds.
Bimal's conclusion from this was not that calibration was pointless — it was that any conclusion drawn from it had to be held loosely, and stated with appropriate humility. If he found that Chapter 65's microfinance-specific Governance and Financial Strength questions had correctly separated strong from weak microfinance institutions during the 2021-2026 stress period, that was one genuine data point about one genuine historical episode, not proof that his newly-added process would catch every future sector shock Nepal's capital market might produce. He wrote this caveat at the top of his calibration notes in block letters, precisely because he knew that after a few hours of satisfying pattern-matching, it would be tempting to forget it: one good match on one sector cluster is a clue, not a law.
Lesson 80.3 — Three Real Situations, Scored Blind
With that caution in place, Bimal built a practical method. He selected three real, verifiable NEPSE situations from the past decade — one from banking, one from microfinance, one from hydropower — chosen specifically because their outcomes were already public and could not be argued with. For each, he tried to reconstruct, as honestly as he could, what the Canon Score would have said before the outcome was known, using only information that would have been available at the time. Then he compared that pre-outcome score against what actually happened. This is the same in-sample discipline Chapter 79 described — except here the "rule" being tested was not a buy signal but the internal weighting of the score itself.
The first case was the 2013 merger that created NIC Asia Bank, formed when Nepal Industrial and Commercial Bank combined with Bank of Asia — the first-ever merger between two commercial banks in Nepali banking history. At the time of the merger, an investor scoring the combined entity would have been weighing genuinely uncertain integration risk against two banks with decent underlying fundamentals and management teams with a credible track record. Reconstructing the pre-merger picture using Chapter 65's bank-specific reading of Financial Strength (CAR and NIM in place of generic ratios) and Governance (related-party lending and loan concentration in place of a generic checklist), Bimal estimated the Canon Score would have landed in the low-to-mid seventies — solid marks on Financial Strength & Profitability and Governance & Promoter Behaviour for both underlying banks, a modest penalty on Sector & Business Model Durability for the genuine, temporary integration uncertainty a first-of-its-kind bank merger carried, and unremarkable marks elsewhere. The actual outcome: the combined bank went on to become Nepal's largest by customer base and balance sheet size, was recognised internationally, and sustained years of profitable growth. On this case, the score's direction was right, and specifically because Bimal had actually reached for Chapter 65's bank-specific rulers rather than scoring both banks on generic terms.
The second and third cases came from the microfinance sector, and this is where Bimal's confidence started to wobble. Nepal's microfinance institutions (MFIs — regulated lenders that provide small, mostly rural loans, often without traditional collateral) went through a well-documented stress period beginning around 2021-22 and worsening substantially by 2025-26, as rural loan demand cooled, over-indebtedness among borrowers became visible, and the sector's average non-performing loan ratio — the NPL ratio, meaning the share of loans not being repaid on schedule — climbed sharply, reaching roughly 11.35 percent sector-wide in one recent quarter, up from under 7 percent a year earlier. Eighteen microfinance companies crossed the 10 percent NPL threshold, with the worst performers — institutions in the Infinity and Dhaulagiri clusters, among others — reporting NPL ratios above 20 percent, in some cases approaching a quarter of their entire loan book. At the other end of the same sector, Chhimek Microfinance held its NPL ratio at roughly 2.3 percent throughout the same period, the lowest in the sector by a wide margin.
CASE IN POINT
During Nepal's 2021-2026 microfinance stress episode, the weakest institutions reported non-performing loan ratios above 20 percent while Chhimek Microfinance held its ratio near 2.3 percent — the same sector, the same rural credit downturn, and a roughly tenfold difference in outcome.
Bimal tried to reconstruct pre-stress Canon Scores for a representative strong MFI (using Chhimek's public disclosures as a stand-in) and a representative weak one, using only what would have been visible before 2021 — loan book growth rates, published capital ratios, branch expansion pace, and management commentary in annual reports. The uncomfortable finding was that the two pre-stress scores came out close to each other, both somewhere in the mid-to-high sixties. Both institutions showed reasonable Financial Strength & Profitability on paper — decent growth, positive earnings, expanding branch networks — and neither showed obvious red flags on Governance & Promoter Behaviour, scored the way Bimal had always scored it: promoter shareholding stability, related-party transactions, disclosure timeliness. But that was exactly the problem. Chapter 65 is explicit that a microfinance institution's Governance dimension needs to be supplemented with two sector-specific questions the generic checklist never asks — whether the institution is recycling loans to disguise a rising NPL ratio, and whether its borrowers show signs of overlapping debt with other lenders in the same geography — and Bimal had never actually asked either question of either institution before 2021. He had also never applied Chapter 65's instruction to weight loan-loss provisioning coverage more heavily than generic leverage within Financial Strength & Profitability for a lender whose loan book is largely unsecured. The specific portfolio-quality signals that later proved decisive — loan concentration in overlapping rural districts, aggressive growth in loan officer headcount relative to oversight capacity, early upticks in loan rescheduling — were sitting exactly where Chapter 65 said to look for them. Bimal simply had not been looking there. The score, as he had actually been applying it, would not have told him which of these two institutions was headed for an NPL crisis and which was headed for the sector's best asset quality. That is a real calibration failure, not a hypothetical one, and Bimal wrote it down exactly that way rather than explaining it away.
The table below summarises all three test cases as Bimal recorded them.
Situation
Pre-outcome Canon Score (approx.)
Dimension driving the score
Actual outcome
Score's verdict, in hindsight
NIC Asia Bank, 2013 merger (NIC Bank + Bank of Asia)
Low-to-mid 70s
Governance and Financial Strength scored well using Chapter 65's bank-specific rulers; Durability lightly penalised for merger uncertainty
Became Nepal's largest bank by customers and balance sheet; sustained profitable growth for a decade
Correct call, for the right reasons
Chhimek Microfinance (pre-2021, stand-in for a well-run MFI)
Mid-to-high 60s
Financial Strength (growth, earnings) scored well on generic terms; Chapter 65's loan-recycling, multiple-borrowing, and provisioning-coverage checks never actually applied
NPL ratio held near 2.3 percent through the 2021-2026 stress period, best in sector
Right outcome, but the score could not explain why in advance
Same generic scoring as above — Chapter 65's MFI-specific questions never asked here either
NPL ratio rose above 20 percent by 2025-26, among the weakest in the sector
Wrong outcome — score failed to separate two institutions that turned out very differently
PRACTICAL TOOL
To test a scoring model's calibration without needing a spreadsheet full of statistics, pick three to five real, already-resolved situations from different NEPSE sectors, reconstruct the score using only information available before the outcome, and compare against what actually happened — writing down honestly where the score would have been wrong, not just where it would have been right.
The fourth situation Bimal examined came from hydropower, and it exposed a different kind of miscalibration — not a dimension too weak to catch real danger, but a dimension too loud, reacting to the calendar rather than to anything company-specific. He picked a representative, already-operating (post-COD, in Chapter 65's terms) run-of-river hydropower company during a dry-season quarter, when reduced river flow cut generation sharply for several months, and scored its Financial Strength & Profitability the way he always had: off the single most recent reported quarter. That single quarter showed weak revenue, thin margins, and a return on equity that looked, in isolation, like a company in real trouble. Because nearly every run-of-river hydropower company on NEPSE was reporting a similarly weak dry-season quarter at the same time, the Canon Score would have marked down almost the entire sector at once — exactly the correlated, sector-wide reaction Lesson 80.2 warned about — even though the company's underlying PPA tariff was fixed by long-term contract and unaffected by the dry season, and nothing about its competitive position had changed at all. Chapter 65 says explicitly that a hydropower company's post-COD numbers must be read against the same quarter a year earlier, or on a trailing-twelve-month basis, precisely to strip out this wet-dry seasonality — and Bimal had never actually built that comparison into his own process. Once the following monsoon arrived on a normal schedule, generation and earnings recovered, and the single-quarter score penalty proved to have measured nothing durable — only which month it happened to be. This is the mirror image of the microfinance case: not a dimension whose generic version was too thin to catch a real problem, but a dimension being scored on the wrong window of time, for a sector where Chapter 65 had already said, in writing, which window to use instead.
Lesson 80.4 — Common Calibration Failure Patterns
Looking across all four situations together, Bimal could name three distinct failure patterns, and it was useful to him to give each one a name so he would recognise it faster the next time.
The first pattern is a dimension scored on the wrong window of time or the wrong ruler, so that it sounds important in theory but barely discriminates in practice. The hydropower case is the clearest example from Bimal's own test. Financial Strength & Profitability is intuitively the right dimension to carry real weight — of course whether a company is making safe, sustainable money matters — but scored off a single raw quarter for a sector like hydropower, where nearly every company's generation swings together on the same wet-dry cycle, the dimension does not help separate a good company from a bad one; it mostly just measures which month it is. A dimension that moves in lockstep across almost every company in a sector, simply because it was measured on the wrong time window, is not adding information a careful investor did not already have; it is adding noise dressed up as signal, even though the dimension itself — and its 20-point weight — is exactly the right one, once measured correctly.
WARNING
A scoring dimension that swings the same way, at the same time, across nearly every company in a sector is not measuring company-specific quality — it is measuring the sector's shared weather. Before concluding a dimension is badly weighted, check whether it is simply being measured on the wrong time window for that sector.
The second pattern is the reverse: a dimension whose generic Chapter 64 version is scored correctly on its own terms, but which Chapter 65 says needs specific sector-specific questions added that never get asked in practice. Governance & Promoter Behaviour's generic checklist — promoter pledging, related-party transactions, disclosure timeliness — is a perfectly reasonable general-purpose measure. But in the microfinance case, the specific danger signals that mattered — loan recycling, multiple-borrowing and overlap risk, growth-versus-oversight ratios — are sector-specific additions Chapter 65 explicitly calls for, and Bimal realised he had been treating that guidance as an optional footnote rather than something he actually applied with real weight when scoring microfinance names. The calibration exercise made the cost of that neglect concrete: a footnote-level adjustment, never actually opened, could not have caught a tenfold difference in eventual NPL outcomes between two similarly-scored institutions.
KEY CONCEPT
A dimension can fail calibration in two opposite directions — by carrying the right weight but being measured on the wrong time window for a seasonal sector (too loud, reacting to the calendar), or by carrying the right weight in principle while its sector-specific questions are never actually asked (too quiet where it matters most) — and both failures require a different fix.
The third pattern is the most dangerous, because it feels like careful, honest work while actually being its opposite: quietly reshaping the sector-specific questions and thresholds, case by case, after already knowing how each test case turned out, until the score would have gotten every single historical case right. This is overfitting wearing the costume of diligence — the same trap Chapter 79 named when it warned against tuning a trading rule until it perfectly matches the very data used to build it. If Bimal had simply invented an ever-more-specific set of MFI red flags and hydropower seasonal corrections, hand-tailored until his four test cases scored perfectly, he would have produced a process that explained the past flawlessly and would very likely fail the next situation Nepal's market produced, because it had been shaped to fit noise specific to these four cases rather than the genuine, durable guidance Chapter 65 already provides in general terms. The tell-tale sign of this trap, Bimal noted, is a calibration session that ends with the investor feeling triumphant rather than sober — real calibration, done honestly, should leave you a little uneasy, aware of how thin your evidence base still is.
CAUTION
If a round of calibration ends with every single historical test case scoring perfectly, that is a warning sign of overfitting, not proof of a well-built score — genuine calibration usually leaves at least one case only partly explained.
Lesson 80.5 — Adjusting Responsibly: A Mandatory Checklist, a Written Log, and a Fresh Test
Having named the failure patterns, Bimal resisted an urge that surprised him: the temptation to "fix" the problem by simply moving points between Chapter 64's seven dimensions. He caught himself starting to sketch exactly that — trim Liquidity, add the difference to Financial Strength — before realising it would have been the wrong lesson entirely. Chapter 64's 100-point architecture had not failed either test case. The NIC Asia merger, the Chhimek comparison, and the hydropower quarter had all been mis-scored not because the seven dimensions carried the wrong number of points, but because Bimal had not been applying Chapter 65's sector-specific version of those dimensions with any real discipline. Moving points around would have quietly changed a book-wide standard — the same 20/15/10/15/15/15/10 architecture every other chapter in this Canon, including every case study, relies on — to paper over what was actually a gap in his own process. So he made no changes to Chapter 64's weights at all. Instead, he made two process changes, each tied to a specific piece of evidence, and each written down in a running log with the date, the reasoning, and the evidence that triggered it — the same kind of paper trail an NRB bank examiner would have insisted on before approving any change to a supervisory rating model.
The first change: for any hydropower company already past its Commercial Operation Date, Bimal committed to never scoring Financial Strength & Profitability off a single quarter's raw figures again. Every ROE and earnings-quality reading for a post-COD hydropower stock would now be computed either as a trailing-twelve-month figure or compared against the same quarter one year earlier, exactly as Chapter 65 instructs, before any point value was assigned. Reason logged: the dry-monsoon test case showed a single-quarter reading swinging the score by ten to fifteen points for reasons that had nothing to do with the company's underlying quality, purely because the seasonal comparison Chapter 65 already specifies had not actually been built into his process.
The second change: for any microfinance institution, Bimal committed to two mandatory questions before finalising Governance & Promoter Behaviour — has the institution's loan growth or NPL trend shown signs consistent with loan recycling, and is there evidence of borrower overlap with other lenders in the same operating district — and to explicitly weighting loan-loss provisioning coverage, not generic leverage, within Financial Strength & Profitability's capital-adequacy sub-component. Reason logged: the Chhimek-versus-weak-MFI comparison showed that these exact signals, already named in Chapter 65, were the single clearest predictor of which institution would go on to suffer severe NPL stress — and neither had actually been checked before 2021.
The third change, the smallest and most structural: Bimal built a one-page checklist, one row per sector, that he now pulls out and physically checks off before finalising any Canon Score — a forcing function to make sure Chapter 65's guidance gets applied every time, rather than trusted to memory. Reason logged: both failures traced back to the same root cause — sector-specific guidance that existed in the book but was never actually consulted at the moment of scoring.
Scoring ROE and earnings quality off the most recent single reported quarter
Compare the same quarter year-on-year, or use trailing-twelve-month figures, to strip out wet/dry seasonality
Added a mandatory TTM/YoY check before finalising this dimension for any hydropower company
Microfinance, Governance & Promoter Behaviour
Generic checklist only — promoter pledging, related-party transactions, disclosure timeliness
Also check for loan recycling and multiple-borrowing/overlap risk specific to MFIs
Added two mandatory MFI-specific questions before finalising this dimension
Microfinance, Financial Strength & Profitability
Generic leverage-style capital-adequacy sub-check
Weight loan-loss provisioning coverage more heavily than generic leverage, given unsecured group lending's structurally higher default risk
Replaced the generic leverage sub-check with a provisioning-coverage-weighted version for any MFI
Chapter 64's 100-point architecture is untouched by any of this — no dimension gained or lost a single point. What changed was whether Chapter 65's already-written sector-specific instructions were actually being opened and applied at the moment of scoring, every time, rather than approximated from memory or skipped under time pressure.
PRACTICAL TOOL
Keep a running, dated log of every process change made to how a scoring model is applied: what changed, which sector it affects, and which specific piece of evidence triggered it. A process change with no logged evidence behind it is indistinguishable, a year later, from a guess — and a scoring model's weights are not the only thing that can drift out of calibration; how faithfully its existing rules are actually applied can drift too.
The final and most important part of Bimal's method was what Chapter 79 called out-of-sample testing, applied here to a scoring process instead of a trading rule. Having changed his process based on the NIC Asia, Chhimek, weak-MFI, and hydropower cases, he did not declare victory. Instead he pulled a fifth situation he had deliberately set aside and had not looked at while making the changes — a small development bank that had gone through a rocky capital-raising period earlier in the decade — and scored it fresh using the new, checklist-enforced process. Only after checking that the new process still produced sensible, defensible judgments about a case that had played no role in shaping it did he consider the round of calibration provisionally complete. This is the same discipline as testing a trading rule on a different time period than the one used to build it: the evidence used to change the process and the evidence used to check the change must never be the same evidence, or the check proves nothing except that the process now agrees with itself.
Lesson 80.6 — What a Calibrated Score Can and Cannot Promise
After this exercise, Bimal's Canon Score was, in a real sense, better than it had been — not because it now guaranteed correct calls, and not because a single point had moved between dimensions, but because its application now reflected actual evidence about what has mattered for NEPSE outcomes rather than sector guidance that existed on paper but was not consistently opened in practice. That is a genuine and valuable improvement, and it is worth being precise about exactly what kind of improvement it is.
A calibrated score can promise that its application is no longer arbitrary — every sector-specific check it now relies on traces back to a specific, checkable piece of NEPSE history, and that history is written down in a checklist, not just remembered. It can promise a more honest starting point for the next stock an investor considers, one less likely to be silently misled by a dimension scored on the wrong time window, or blind to a sector-specific danger that Chapter 65 already named but that never actually got checked. It can promise that when the score turns out to be wrong about a future company, the investor will have a clearer basis for asking which dimension failed and why, rather than throwing out the whole system in frustration.
A calibrated score cannot promise that it will keep working forever without further attention. Nepal's capital market is still young and still thin — 271 listed companies is a modest universe by any regional standard, its sectors are lopsided and correlated in the ways Lesson 80.2 described, and the specific historical episodes available to calibrate against will keep being a small, imperfect sample for years to come. New kinds of companies will list, new regulatory regimes will reshape entire sectors overnight the way past NRB merger waves reshaped banking, and dimensions that look well-calibrated today may prove hollow the next time Nepal's market produces a genuinely new kind of stress. Calibration, done honestly, is not a task an investor finishes once. It is a practice an investor returns to, the same way Bimal, in his NRB years, never considered a bank's risk rating a permanently settled question — only a current best estimate, due for review the next time meaningful new evidence arrived.
Chapter recap
This chapter asked a narrower and more technical question than Chapter 79's general introduction to backtesting: not whether the discipline of testing an idea against history is worthwhile, but whether Chapter 64's seven real dimensions — Financial Strength & Profitability at 20 points, Governance & Promoter Behaviour, Valuation Reasonableness, Sector & Business Model Durability, and Growth Trajectory at 15 points each, and Liquidity & Tradability and Dividend & Capital Return Discipline at 10 points each — were actually being applied with Chapter 65's sector-specific rulers in practice, dimension by dimension, sector by sector, or merely being nodded at in principle. Calibrating a score this way is different from backtesting a trading rule: it opens up the recipe and tastes each ingredient separately, rather than only checking whether the finished dish came out well.
NEPSE's own structure makes this harder than it first appears. With roughly 271 listed companies clustered heavily into hydropower, microfinance, and banking, a calibration exercise that treats every company as an independent test case is fooling itself — most of those companies move together, reacting to the same monsoon cycle, the same NRB policy shift, or the same rural credit downturn, which means the real number of independent scenarios behind any NEPSE-wide test is a small handful, not hundreds. Every conclusion drawn from calibration has to be held with that humility built in.
Using real, verifiable NEPSE history — the 2013 merger that created NIC Asia Bank, the sharp divergence between strong and weak microfinance institutions during the 2021-2026 NPL stress episode, and a hydropower company's temporary, seasonally-driven score swing during a dry monsoon quarter — Bimal Sharma tested the Canon Score's pre-outcome verdicts against what actually happened. He found the score correct on the bank merger, right for an incomplete reason on the strongest microfinance institution, and outright wrong in failing to separate that strong institution from a weak peer that later suffered severe loan stress. That last finding was a genuine calibration failure, not a hypothetical one, and naming it honestly — rather than explaining it away — was the point of the exercise.
From these failures, the chapter drew out two opposite calibration problems worth watching for in any scoring system: a dimension scored on the wrong time window for a seasonal sector, so it sounds important but barely discriminates because it moves in lockstep across the entire sector at once (Financial Strength & Profitability, scored off a single raw quarter, in hydropower), and a dimension whose sector-specific questions exist in writing but are never actually asked at the moment of scoring (Governance & Promoter Behaviour's loan-recycling and overlap checks, and Financial Strength's provisioning-coverage weighting, in microfinance). It also named the trap that lurks behind both: quietly reshaping the scoring process after seeing outcomes until it explains history perfectly, which is overfitting dressed as diligence, not honest calibration. The responsible alternative demonstrated here left Chapter 64's 100-point architecture completely untouched — no dimension gained or lost a single point — and instead built a logged, evidence-triggered checklist that forces Chapter 65's sector-specific guidance to actually be opened and applied every time: a mandatory trailing-twelve-month or year-on-year comparison for post-COD hydropower companies, and two mandatory microfinance-specific questions for Governance plus a provisioning-weighted read of Financial Strength for lenders. That revised process was then tested against a fresh case that played no role in shaping it, echoing Chapter 79's insistence on keeping in-sample evidence and out-of-sample checks strictly separate.
A well-calibrated Canon Score, the chapter closed by arguing, becomes a more honest reflection of what has actually mattered for NEPSE outcomes so far — not a guarantee of future accuracy, and not a task that is ever finally finished, given how young and thin Nepal's capital market still is. That same unfinished quality applies just as much to the other half of any investing system: knowing when to sell. Chapter 81, "Testing and Refining Exit Rules," turns this exact discipline — real historical test cases, honest identification of failure patterns, small logged adjustments, and out-of-sample re-checking — away from the entry score covered here and onto the rules that govern when a Nepali investor should exit a position: stop-losses, profit-taking triggers, and the harder judgment calls about when a thesis has genuinely broken versus when it is merely being tested by short-term noise.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XV · Chapter 81
Testing and Refining Exit Rules
First published 25 Aug 2026 · Last verified 29 Aug 2026
Rupak Basnet had a rule. He could tell you the rule in one breath: sell if a stock falls 15 percent from what he paid for it. He had said this rule out loud to friends at the tea shop below his electronics store in Pokhara for two years, and it had made him sound disciplined, the kind of investor who does not fall in love with a stock. What Rupak did not know, because he had never checked, was that his rule had already failed him once, in the exact year he was proudest of having one. This chapter is about closing that gap: the gap between a rule that sounds disciplined and a rule that has actually been tested against real NEPSE price history, including the ugly stretches where the rule and the market disagreed about what price a trade could happen at.
Part XII built the plumbing for this: the 20-session average daily volume, or ADV, rule for sizing positions so that a single investor's own buying and selling does not move a thin stock's price, and the free-float limits that cap how much of a low-float company any one investor should hold, precisely because low-float stocks are the ones that get stuck. Chapter 76 built the habit of deciding an exit plan before entry, and reading the depth ladder — the live list of buy and sell orders waiting at each price level — so an investor knows in advance whether an exit is likely to find a buyer or not. Chapter 79 laid out the general discipline of backtesting: testing a rule against past data honestly, watching out for biases that make a rule look better than it is, and holding back some data as an out-of-sample test the rule was never tuned against. Chapter 80 applied that discipline to the entry side, calibrating the weights inside the Canon Score. This chapter turns the same discipline toward the other end of the trade: the exit. An entry score can be excellent and the trade can still lose money, if the exit rule attached to it was never actually tested.
Lesson 81.1 — Why Exits Need Their Own Backtest, Not a Borrowed One
Most retail investors in Nepal, and most retail investors anywhere, put far more thought into when to buy a stock than when to sell it. This is understandable. Buying is exciting; it is the moment of a decision, backed by a story about a company's earnings, a sector tailwind, a friend's tip, or a score like the Canon Score built earlier in this book. Selling is different. Selling usually means admitting one of two things: either the story is not playing out the way it was supposed to, or the story played out and now it is time to stop being greedy. Both admissions are uncomfortable, and discomfort is exactly the condition under which people reach for vague language instead of precise rules.
Ask most NEPSE investors what their exit rule is and you will hear something like: I will sell if it feels like it is turning, or I will get out if the fundamentals change, or I am in this for the long term so I do not really have a stop-loss. None of these are rules in the sense this book uses the word. A rule, for the purposes of testing, is an instruction specific enough that it produces the same decision no matter who is applying it. "If it feels like it is turning" fails this test immediately, because two investors looking at the same chart on the same day will feel differently about it, and the same investor will feel differently about it depending on whether they had a good or bad day. A feeling cannot be backtested, because a feeling has no fixed definition to apply consistently to a hundred past trades.
This matters more than it sounds like it should, because the entry side of a trade and the exit side of a trade are graded on completely different curves. The Canon Score, calibrated in Chapter 80, is trying to answer one question: is this a reasonable stock to buy right now, given liquidity, price behaviour, and the other factors folded into the score. It says nothing about what happens after the buy. A stock can score well on entry, behave exactly as expected for two months, and then the position can still lose a third of its value, purely because the investor's plan for getting out was never precise enough to execute, or was precise but untested against the specific way NEPSE stocks actually behave when they fall. Good entries and bad exits combine into bad outcomes constantly. Testing only the entry side and calling the job done is like a driving instructor who only ever teaches acceleration.
KEY CONCEPT
An exit rule is not a feeling or an intention; it is a specific, mechanical trigger that produces the same decision for any two people looking at the same price data. If a rule cannot be applied identically by two different people, it cannot be backtested, and if it cannot be backtested, nobody actually knows whether it works.
Rupak's own history is the clearest illustration available. He had an exit habit, in the loose sense — sell around 15 percent down, unless he felt the stock still had a story — that he had never once gone back and tested against his own trading history. He assumed it worked because he could recite it. The rest of this chapter follows him through the process of writing that habit down as an actual testable rule, running it against real NEPSE-style price sequences from the 2021-2022 correction, discovering it had already failed him once in a way he had half-forgotten, and refining it into something narrower and more honest.
Lesson 81.2 — Writing an Exit Rule Precisely Enough to Test
A testable exit rule needs three things: a trigger condition stated in numbers or unambiguous mechanical terms, a clock or price series it is checked against, and an action that follows automatically once the trigger condition is met. If any of the three is missing or fuzzy, the rule is not a rule yet, it is an intention.
Here are two examples of rules that pass the test.
Rule A, a fixed percentage stop-loss: exit the position at the close of any session in which the closing price is 15 percent or more below the purchase price. There is nothing to argue about here. Given a purchase price and a sequence of daily closes, any two people, or a spreadsheet, will identify the exact same session as the trigger session every time.
Rule B, a moving-average trend exit: exit the position at the close of the second consecutive session in which the closing price is below the 50-day simple moving average, where the 50-day simple moving average is the average of the most recent 50 closing prices, recalculated every session. Again, given the same 50 sessions of closing prices, two people will always agree on where the average sits and which sessions closed below it.
Compare those with the rules retail investors actually carry around in their heads. "I will sell if the volume looks weak and the story stops making sense" fails on every count: weak compared to what, over what number of sessions, and whose judgment about the story counts. "I will hold long term but get out if it really falls apart" has no numeric trigger and no defined clock; it just relocates the decision to some future moment of gut feeling, which is exactly what a testable rule exists to remove. A rule that cannot be written as an instruction to a very literal-minded assistant, one who has access only to a price and volume table and nothing else, is not ready to be backtested, and arguably is not ready to be traded on either.
WARNING
If your exit plan uses words like feels, probably, or if things get bad, it is not a rule yet — it is a placeholder for a decision you have postponed. Write the exact number and the exact trigger before you ever put capital behind it.
Precision has a second benefit beyond testability: it removes the exit decision from the emotional moment itself. When a stock is actually falling and the investor's own money is draining away in real time, that is the worst possible moment to be inventing criteria on the spot. A rule decided in advance, in a calm moment, and already tested against history, is a rule the investor can execute without negotiating with themselves at the worst possible time.
Lesson 81.3 — The NEPSE Wrinkle: When the Exit You Wanted Is Not the Exit You Get
Everything in Lesson 81.2 assumes that once a rule fires, the investor can actually sell at or near the trigger price. On most developed exchanges, for a reasonably liquid stock, that assumption is close enough to true to build a simple backtest on. On NEPSE, for a meaningful share of the market, that assumption is dangerously wrong, and the reason is the circuit breaker system.
A circuit breaker, in plain terms, is a rule that limits how far a stock's price is allowed to move in a single trading session, expressed as a percentage band around the previous closing price. If a stock closed the prior session at 500 rupees and the daily band is 15 percent, the stock can trade anywhere between 425 and 575 in the next session, but not beyond either edge, no matter how many people want to buy or sell at a more extreme price. Hit the lower edge of that band, and NEPSE terminology calls it the lower circuit; hit the upper edge, the upper circuit. The mechanism exists for a good reason, to slow down panic and prevent single-session chaos, but it has a side effect that matters enormously for exit planning: a stock can sit frozen at its lower circuit price, with far more sellers queued up than buyers willing to appear, for several consecutive sessions in a row. During that stretch, an investor who wants out is not choosing whether to sell. They are watching a locked door.
REGULATORY DETAIL
For most of NEPSE's recent history the daily circuit band on individual stocks was 10 percent up or down from the previous close. Nepal's market regulator widened that band to 15 percent in a rules update in April 2026, alongside a simplified market-wide mechanism: a 5 percent index move triggers a 15-minute trading pause during the first two hours of the session, and an 8 percent move closes the market for the rest of the day. An investor building or testing an exit rule needs to know which band was in force during the period their historical data covers, because a rule tested under a 10 percent band will behave differently once run forward under a 15 percent band.
The mechanical detail matters because it changes the arithmetic of a stop-loss. Under a 10 percent daily band, a stock cannot fall more than 10 percent in one session even if every single order on the sell side is a market order desperate to get out. That sounds protective, until the stock keeps closing at its lower limit day after day because there is still no buyer willing to step in anywhere near that price. A five-session run of consecutive lower-circuit closes, each one 10 percent below the last, takes a stock down roughly 41 percent from where it started, and for every session of that run, an investor's sell order sits in the queue unfilled, because a lower circuit session with no genuine two-sided trading is a session where almost nobody's exit order actually executes.
This is not a hypothetical risk invented for this book. During the correction that followed NEPSE's 2021 peak, when the index fell from roughly 3,200 points to below 1,700 over about two years, the descent did not happen as one smooth line; it happened as a staircase of sharp drops and partial bear-market rallies, and thinner stocks were disproportionately the ones that got stuck at the bottom of each step. A separate, well-documented episode in 2023 saw a cluster of microfinance company shares, a sector already known on NEPSE for thin free float and low daily turnover, hit consecutive lower circuits during a regulatory scare specific to that sector. Investors holding those shares were not choosing to hold through the decline out of conviction; they were locked in place while the price kept resetting lower each morning, unable to find a buyer at any price near the frozen quote.
CASE IN POINT
In 2023, several NEPSE-listed microfinance companies hit consecutive lower-circuit sessions during a sector-wide regulatory scare, leaving shareholders unable to exit at any price near the frozen quote for days at a stretch. This is the exact mechanism a NEPSE exit-rule backtest has to model: not "did the rule identify the right day to sell," but "could the rule's sell order have actually been filled on that day, or any day soon after."
This is the wrinkle that makes NEPSE exit-rule testing different from generic exit advice found in international investing books. Those books, quite reasonably, assume that once a stop-loss triggers, the position gets sold that day or the next, give or take a small amount of slippage — the gap between the price you wanted and the price you actually got. On NEPSE, for a low-free-float, low-ADV stock in the middle of a broad correction, that gap between wanted and got can stretch into weeks and tens of percentage points, and a backtest that ignores this will produce numbers that look far better than what an investor would have actually experienced. From here forward, every exit-rule test in this chapter tracks two separate numbers for every trade: the paper outcome, meaning what the rule's trigger price says the exit should have been, and the realistic-execution outcome, meaning the price at which the position could plausibly have actually been sold once thin liquidity and circuit-limited sessions are accounted for.
Lesson 81.4 — Running the Backtest: Walking Forward Through Real Price Sequences
A backtest for an exit rule is, mechanically, simpler than the entry-side backtesting covered in Chapters 79 and 80, but it demands more discipline about one specific thing: honesty about what could actually be sold, on what day, at what price. The process runs like this.
Start with a real historical price and volume sequence for the stock in question, going back far enough to include at least one meaningfully stressful period, not just a calm uptrend. Pick a plausible entry point and price, exactly as an investor would have faced it at the time, using only information that was available on that date. Then walk forward one session at a time, applying the exit rule mechanically at each new session using only the data available up to and including that session — never using tomorrow's price to decide today's action, which is the same look-ahead bias problem flagged in Chapter 79's discussion of backtesting pitfalls. At each session, check whether the rule's trigger condition is met. If it is, do not simply record the trigger price as the outcome. Instead check the depth ladder and volume conditions for that session and the sessions immediately after: was this a normal trading session with genuine two-sided volume, or was it a circuit-limited session with the price frozen at its band edge and little to no real matched volume? If normal, record a realistic exit close to the trigger price, adjusted for typical slippage. If circuit-limited, carry the position forward, session by session, until a session appears with genuine two-sided volume at a price the position could actually have been sold into, and record that price as the realistic exit.
This produces two numbers for every simulated trade: the paper return, which is what a naive backtest would report by simply applying the rule's percentage to the trigger price, and the realistic return, which is what an investor holding that exact position would actually have experienced. The gap between the two numbers is not noise to be averaged away. It is the single most important output of the entire exercise, because it tells you how much to trust the paper number for any given rule and any given category of stock.
PRACTICAL TOOL
Keep a simple walk-forward log with one row per candidate trade: entry date, entry price, trigger date (the session the rule's condition was first met), paper exit price, number of subsequent sessions the stock spent frozen at a circuit limit before a real fill was possible, realistic exit price, paper return, and realistic return. Running the same rule across ten or twenty historical entry points and averaging both columns separately tells you far more than a single dramatic example ever will.
The final piece of an honest exit-rule backtest is comparing more than one candidate rule against the identical historical sequences, not testing each rule against whichever period happens to flatter it. A tighter stop-loss and a looser one, a price-based rule and a time-based or trend-based rule, should all be walked through the same stretch of NEPSE history, including the same correction periods, so that their outcomes are directly comparable. Lesson 81.5 does exactly that, using a reconstructed price sequence modelled on the pattern NEPSE stocks actually followed during the 2021-2022 correction and the 2023 circuit-trap episode described above.
Lesson 81.5 — Two Rules, One Price Sequence: Where Each One Breaks
Rupak's original rule was a fixed percentage stop-loss: exit at the close of any session where the price sits 15 percent or more below the purchase price. Call this Rule A. As a natural alternative, consider a trend-following exit built on the 50-day moving average introduced in Lesson 81.2: exit at the close of the second consecutive session below the 50-day average. Call this Rule B. Rule A reacts fast to a sharp drop and does not care about the broader trend; Rule B is slower to react to a sharp drop but tends to keep an investor in a stock through ordinary short-term wobbles, since a single bad day rarely drags the 50-day average down with it.
To compare them fairly, both rules were walked forward, mechanically and identically, through four reconstructed NEPSE-style price sequences, each modelled on a real pattern this market has shown: a grinding decline in a liquid, high-free-float stock; a sideways, choppy stretch with no clear trend; a sharp correction in a thin, low-free-float stock that ends in a multi-session lower-circuit trap, modelled directly on the pattern described in Lesson 81.3; and a sharp but short-lived dip followed by a V-shaped recovery. The reconstructed sequences are composites, not a single real ticker's exact tape, but every price move, circuit band, and freeze length in them is drawn from patterns NEPSE has actually produced.
The clearest and most consequential case is the third one, so it is worth walking through in detail before looking at the summary table. Picture Rupak buying a small, thinly traded finance-sector stock, call it a composite ticker TRSL, at 850 rupees per share in early November 2021, near the broad market's peak, sized correctly under the ADV rule from Part XII so his own order would not move the thin market by itself. His Rule A trigger sits at 722.50 rupees, 15 percent below his purchase price. The reconstructed sequence, built to match how the 2021-2022 correction actually behaved in low-float names, unfolds like this:
Date
Session Close (Rs)
Move from Prior Close
Session Type
2021-11-01
850
Purchase
Normal trading
2021-11-15
810
-4.7%
Normal trading
2021-11-16
729
-10.0%
Lower circuit (10% band, still above trigger)
2021-11-17
656
-10.0%
Lower circuit, Rule A trigger fires (below 722.50)
2021-11-18
656
0.0%
Frozen at lower circuit, no real buy-side volume
2021-11-21
656
0.0%
Frozen at lower circuit, no real buy-side volume
2021-11-22
590
-10.0%
Lower circuit continues
2021-11-23
531
-10.0%
Lower circuit continues
2021-11-24
480
-9.6%
First session with genuine two-sided volume, realistic fill
Rule A's paper outcome says Rupak should have exited at 656, an 22.8 percent loss, once the position is actually marked at the trigger session's close rather than the round 15 percent figure, since the stock gapped past the exact trigger price on the way down. But 656 was a circuit-frozen close with no real buyers behind it. The realistic exit, once actual sellable volume reappears, is 480, a 43.5 percent loss from the 850 purchase price, nearly double what the rule's own percentage promised. This is the honest failure moment Rupak had never gone back to check: his 15 percent rule had already let him down once, not because the rule was badly designed on paper, but because it was never built with a circuit-trap scenario in mind, and he had simply not looked closely enough afterward to notice how large the gap between paper and reality had actually been.
Rule B behaves differently on the same sequence. The 50-day moving average lags a sharp drop like this substantially, since it is averaging in many earlier, higher prices; it does not cross below the falling price and stay there for two sessions until later in the decline, by which point the stock is already deep into the circuit-locked stretch. Rule B's own trigger, in this scenario, does not even fire until the stock is already frozen, meaning Rule B offers no earlier warning than Rule A here, and produces a similar realistic exit near 480, simply arriving at the same bad outcome by a different and slower path.
The fourth scenario shows the opposite failure. In a sharp but short-lived dip followed by a swift recovery, a liquid stock drops 16 percent over a week on broad market jitters, briefly clearing Rule A's 15 percent trigger, and then rallies back to a new high over the following month, once whatever caused the dip passes. Rule A exits near the bottom of the dip and misses the entire recovery, turning a temporary paper loss into a locked-in one and forfeiting the subsequent gain. Rule B, because the 50-day average has not had time to turn down meaningfully over a one-week dip, never triggers at all, and the position rides through to the recovery intact.
The pattern across all four rows is the trade-off this lesson is built to show concretely. Rule A protects capital fastest in an ordinary decline but sacrifices upside whenever the drop turns out to be temporary, and it does not protect against a circuit trap at all, since the trigger fires but the fill does not follow. Rule B avoids some whipsaw losses and rides out temporary dips well, but its slowness becomes actively dangerous in the exact scenario where speed matters most, a fast, illiquid collapse, because by the time its trigger condition is finally met the stock may already be frozen. Neither rule, in its raw form, handles the low-free-float circuit-trap case acceptably. That is the specific problem Lesson 81.6 is built to fix.
CAUTION
Do not tune a rule's threshold to make it look perfect against this one reconstructed crash sequence. A rule sharpened until it wins on a single historical episode is very likely just memorising that episode, and Chapter 79's warning about overfitting applies just as much to exit rules as it does to entry scores. Test any adjustment against several different historical stretches, not just the one that hurt the most.
Lesson 81.6 — Refining the Rule: Small Steps, Documented Reasons, and a Liquidity Buffer
Once a backtest has shown where a rule breaks, the temptation is to overhaul it completely, swapping 15 percent for some other round number pulled out of the air, or abandoning fixed stops for trend rules or vice versa. Resist that. The discipline that made Chapter 80's calibration of the Canon Score trustworthy applies here too: adjust thresholds in small, deliberate steps, test each step against the same historical sequences used before, and write down the specific reason for each change so that a future version of yourself, or another investor reading your notes, can see why the number is what it is rather than treating it as an arbitrary preference.
The single most important refinement to come out of this chapter's backtest is not a different percentage. It is recognising that a single exit rule applied identically to every stock on NEPSE is itself the mistake, because the free-float and ADV rules from Part XII already told us these stocks are not identical in one crucial respect: how easily a seller can find a buyer when the market turns. A rule tuned as a compromise across both liquid and illiquid names will always be too loose for the illiquid ones, since that is where the circuit-trap risk actually lives.
The practical fix is a liquidity buffer: a stricter, earlier trigger applied specifically to positions in stocks that fall below a free-float or ADV threshold already defined back in Part XII, chapters 54 through 58. For a stock comfortably above that threshold, Rupak's original 15 percent stop, refined only slightly, remains reasonable, because such a stock is far less likely to freeze at consecutive circuit limits with no buyers appearing for days. For a stock below the threshold, the same 15 percent trigger is not conservative at all, it is close to useless, since the backtest showed the realistic exit landing more than 40 percent below purchase price precisely because the position could not be sold anywhere near the intended level. For those names, the rule should trigger earlier, for example at 8 to 10 percent below purchase rather than 15, precisely to get the sell order into the queue before the stock has enough downward momentum to start gapping through consecutive circuit limits, and it should be paired with the depth-ladder reading habit from Chapter 76, checking whether there is still real buy-side depth at each price level before assuming a trigger will actually fill.
KEY CONCEPT
A liquidity buffer means giving low-free-float, low-ADV stocks a tighter, earlier exit trigger than liquid ones, specifically because these are the names most likely to gap straight through an ordinary stop-loss level during a circuit-limited stretch, turning what looks like a 15 percent stop-loss on paper into a 40-percent-plus realised loss.
Rupak's refined rule, after this backtesting exercise, reads like this: for stocks above the Part XII free-float and ADV thresholds, exit at the close of any session where price is 15 percent or more below purchase price, treating this as validated by the backtest's grinding-decline scenario. For stocks below those thresholds, exit at the close of any session where price is 10 percent or more below purchase price, and additionally begin exiting a portion of the position, not waiting for the full trigger, the moment the depth ladder shows buy-side volume thinning meaningfully below its 20-session average, since that thinning is an early warning that a circuit trap may be forming before the price trigger itself is even reached. Each of these numbers came from a specific comparison against the reconstructed 2021-2022 and 2023-style sequences, not from a gut feeling about round numbers, and each is written down with the reasoning attached, so the next review of this rule, whenever the market regime shifts, has something concrete to test against rather than a vague memory of what once felt right.
This is also where the refinement process has to stop rather than keep tightening indefinitely. An exit trigger set too tight, say at 5 percent for every stock regardless of liquidity, would have converted the sideways-chop scenario in Lesson 81.5 into a string of small, repeated losses from whipsaw, since ordinary daily noise in a NEPSE stock routinely exceeds 5 percent without indicating a real trend change. The right threshold is not the tightest possible one; it is the one that the backtest across several different historical conditions, not just the worst one, shows to perform reasonably across all of them.
Chapter recap
This chapter took the backtesting discipline built in Chapter 79 and applied to entry scoring in Chapter 80, and pointed it at the other half of every trade: the exit. The starting problem was that most retail exit plans are not rules at all, but vague intentions dressed up as discipline, things like selling when it feels like it is turning, which cannot be tested because they cannot be applied identically twice. A real exit rule needs an exact, numeric trigger, whether that is a fixed percentage stop-loss like Rupak's original 15 percent rule or a trend-based rule like the 50-day moving-average exit, stated precisely enough that any two people looking at the same price history would make the identical decision on the identical day.
The specifically NEPSE part of this chapter was the circuit breaker system: the daily percentage band, long set at 10 percent and widened to 15 percent in 2026, along with the market-wide index halt mechanism, and the very real risk that a falling stock, especially a thin, low-free-float one, can sit frozen at its lower circuit price for several consecutive sessions with no buyers appearing at any price near the frozen quote. This is not a theoretical risk; it echoes real patterns from NEPSE's 2021-2022 correction and the 2023 microfinance circuit-trap episode. Because of this, an honest backtest of any NEPSE exit rule has to track two separate numbers for every simulated trade: the paper outcome the rule's trigger price implies, and the realistic-execution outcome that accounts for circuit-limited sessions where no genuine sale was actually possible. Rupak's own worked example showed exactly this gap in action: a rule that promised roughly a 15 percent loss delivered a realised loss of over 43 percent once a five-session circuit-trap stretch was properly modelled, a failure his own informal version of the rule had already suffered once, unnoticed, years earlier.
The worked comparison between a fixed percentage stop and a trend-following moving-average exit showed that neither rule is simply better in every condition. The fixed stop protects capital fastest in an ordinary decline but forfeits recoveries after temporary dips and offers no real protection once a stock gaps into a circuit trap. The trend-following rule avoids some whipsaw losses in sideways markets and rides out short dips well, but its natural lag becomes dangerous in exactly the fast, illiquid collapse where speed matters most. Neither rule, used identically across every NEPSE stock regardless of its liquidity profile, is good enough, which is why the refinement in Lesson 81.6 tied the exit trigger itself back to the free-float and ADV thresholds from Part XII: a looser, later trigger for liquid names, a tighter, earlier trigger with a liquidity buffer for thin ones, each adjustment made in a small step, tested against several historical stretches rather than just the worst one, and documented with its reasoning attached rather than left as an unexamined habit.
The broader lesson underneath all of this is that both halves of a trading system, the entry score from Chapter 80 and the exit rule from this chapter, are only trustworthy for as long as the market conditions they were tested against keep resembling the market conditions they will actually be used in. NEPSE in 2026 is not NEPSE in 2021: the circuit band itself has changed, sector composition shifts, and the free-float and liquidity profile of individual stocks moves as companies grow, merge, or get diluted through further share issuance. A rule calibrated carefully against the 2021-2022 correction is not guaranteed to be calibrated correctly for whatever the next correction looks like.
That is precisely the subject of Chapter 82, Measuring and Managing Model Drift. A well-backtested Canon Score and a carefully refined exit rule, of the kind built across this chapter and the two before it, are not finished products to be set once and trusted forever. Markets change regime, sometimes gradually and sometimes suddenly, and a scoring system or exit rule that quietly stops matching current conditions will not announce its own failure, it will simply start producing worse results without an obvious cause. The next chapter builds the ongoing monitoring habits needed to catch that decay early: what to measure, how often, and what a meaningful warning sign looks like as distinct from ordinary short-term noise, so that calibration becomes a continuing practice rather than a one-time achievement.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XV · Chapter 82
Measuring and Managing Model Drift
First published 25 Aug 2026 · Last verified 29 Aug 2026
Rukmini Thapa had not touched the Canon Score's weightings in fourteen months. That was, in itself, unusual for her. She had spent thirty-one years at Nepal Rastra Bank reviewing bank balance sheets, and the habit of a supervisor is to tinker — to tighten a ratio here, flag an exception there, never quite leave a rule alone. But when she retired and turned to building her own scoring system for NEPSE stocks, she had made herself a promise: calibrate it properly once, using the discipline she had learned two chapters ago, and then leave it alone unless she had real evidence, not a hunch, that it needed to change.
It was July 2026, and she was looking at a chart that made her deeply uncomfortable. The chart tracked something simple: every quarter for the last three years, she had ranked NEPSE stocks by their Canon Score and checked whether the top-scoring group actually went on to outperform the bottom-scoring group over the following ninety days. For most of that stretch, the gap between the two groups — what she'd started calling the "spread" — had hovered in a healthy, reassuring band. In the most recent two quarters, it had nearly vanished. Her score was still producing numbers. It had simply stopped telling her anything useful.
This chapter is about what Rukmini was looking at, and what she did next. It is the closing chapter of Part XV, and it exists because of an uncomfortable truth the last three chapters have been building toward: a properly backtested score and a properly calibrated set of exit rules are not permanent achievements. They are perishable. They decay. The market you calibrated against in 2021 is not the market you are trading in 2026, and a tool that does not get checked against that fact will quietly go stale while still looking, on the surface, exactly as confident as the day you built it.
Lesson 82.1 — What "Model Drift" Actually Means
Start with the plainest possible definition. Model drift is what happens when a rule that used to work well keeps running exactly as designed, while the world it was designed for changes underneath it — so the rule's outputs slowly stop matching reality, even though nothing about the rule itself has "broken."
Think of a tailor who takes your measurements once and cuts every shirt from that pattern for the next ten years. The pattern was not wrong the day it was made — it fit you perfectly then. But you do not stay the same size forever. Somewhere along the way, the shirts stop fitting, not because the tailor made a mistake, but because you changed and the pattern didn't. Nobody notices the exact day it stopped fitting; it happens gradually, one shirt at a time, until eventually every shirt in the closet feels slightly wrong and you can't say when that started.
A backtested investing rule is a pattern cut to fit a particular market at a particular time. The Canon Score, as calibrated in Chapter 80, was cut to fit NEPSE roughly as it behaved across 2018 to 2021 data — a market with a certain mix of banks, hydropower companies, microfinance institutions, and manufacturing firms; a certain size and temperament of retail investor base; a certain regulatory backdrop. The exit rules calibrated in Chapter 81 were cut to fit NEPSE's circuit-breaker mechanics and liquidity patterns as they existed when that testing was done. Neither of those things is fixed in stone. NEPSE in 2026 is not the same market, and pretending the pattern still fits because it fit once is exactly the kind of complacency this book has tried to train out of you since Part XV began.
KEY CONCEPT
Model drift is not a sign your rule was wrong when you built it. It is a sign the market has moved and your rule hasn't moved with it. The two require completely different responses — one calls for humility about your original work, the other calls for an honest, unemotional update.
It helps to separate drift from two things it is often confused with. It is not the same as a single bad quarter, which can happen to a perfectly sound rule simply through ordinary variance — more on that distinction in Lesson 82.5. And it is not the same as the rule being poorly built in the first place, which is a calibration failure Chapter 80 already dealt with. Drift specifically describes decay over time in a rule that was sound when built. The tailor's pattern was cut correctly; you grew.
The uncomfortable part, and the reason this chapter exists at all, is that drift is invisible from the inside of any single decision. Rukmini's Canon Score, on any given day, still produced a number between 0 and 100 for any stock she typed in. It still felt authoritative. Nothing about using it that day would have told her the number meant less than it used to. Drift only becomes visible when you deliberately step back and measure performance over time — which is precisely the discipline the rest of this chapter builds.
Lesson 82.2 — Four Ways NEPSE Changes Under Your Feet
Drift does not happen for mysterious reasons. It happens because specific, identifiable things about a market change. On NEPSE, four sources of change matter most, and a Canon Score user should be able to name all four without hesitation.
The first is a regulatory shock — a rule change from Nepal Rastra Bank (NRB, the central bank) or the Securities Board of Nepal (SEBON, the capital markets regulator) that changes what "good" looks like almost overnight. The textbook example, and one worth knowing in real detail because it will recur in this book's case studies, is NRB's paid-up capital mandate for commercial banks. In 2015, NRB directed commercial banks to raise their minimum paid-up capital — the base equity cushion a bank must hold before it can call itself a "class A" commercial bank — roughly fourfold, from Rs 2 billion to Rs 8 billion, with a deadline of mid-2017 (the end of the Nepali fiscal year 2073/74). Banks that could not raise that capital organically were pushed into mergers, rights issues, and bonus-share issuances at a scale NEPSE had never seen. The number of listed commercial banks fell sharply as weaker institutions were absorbed into stronger ones, and every surviving bank's balance sheet looked structurally different on the other side of that deadline than it had before it.
REGULATORY DETAIL
NRB's 2015 directive lifting commercial bank paid-up capital requirements from Rs 2 billion to Rs 8 billion, with compliance due by mid-2017, triggered one of the largest waves of bank mergers and forced capital-raising in NEPSE's history. Any scoring dimension built on "does this bank clear a capital threshold" measured something meaningfully different before and after that window.
Now imagine a capital-adequacy dimension in a scoring model built to reward banks that comfortably cleared a capital bar. Before the mandate, that dimension usefully separated the strong banks from the weak ones — the spread between them was wide. After every surviving bank was forced to clear the same much higher bar, nearly all of them cluster near the top of that dimension. The dimension hasn't become wrong; it has become uninformative, because the thing that used to distinguish banks from each other no longer distinguishes much of anything. This is exactly the kind of drift Rukmini eventually found, and we will return to it directly in Lesson 82.3.
The second source is sector composition shift — a change in what a "typical" NEPSE-listed company even looks like. Hydropower listings have expanded dramatically in recent years; by 2025, hydropower companies made up the largest single block of newly listed and pipeline companies on NEPSE, with dozens of hydropower IPOs approved by SEBON and a multi-billion-rupee pipeline of further hydropower issuances still to come. A scoring model built when banks and manufacturing firms dominated the index will have been implicitly calibrated on the financial patterns of those sectors — steady earnings, established capital structures, long trading histories. Hydropower companies behave differently: many are pre-revenue or early-revenue at listing, dependent on monsoon-driven river flows for output, carry different debt structures tied to project financing, and often list with thin early trading volumes. A model that quietly assumed "most stocks look like a bank" will misjudge a growing share of the market it is now being asked to score.
CASE IN POINT
Hydropower listings and pipeline IPOs became the largest single sector by count of new NEPSE companies by 2025. A Canon Score built mostly against bank and manufacturing data from 2018-2021 is now being asked to judge a market with a meaningfully different sector mix than the one it learned from.
The third source is participant base change — who is actually doing the buying and selling. NEPSE's investor base has grown enormously; demat accounts (the electronic share-holding accounts required to trade) numbered in the low millions a few years ago and had climbed toward roughly eight million by 2026, swelled by remittance-fed household savings and a post-pandemic wave of first-time retail participation. A market with a much larger base of newer, smaller, more sentiment-driven retail traders can behave differently at the margin than one dominated by a smaller pool of more experienced participants — prices can move faster on rumour, IPO listings can pop and fade more sharply, and a scoring dimension that weights "market sentiment" or "momentum" was calibrated against the crowd psychology of a smaller, different crowd.
The fourth source is the plainest: calendar time itself. A rule calibrated on 2018-2021 data is, by definition, being asked to perform in a market it has never seen — different government, different remittance flows, different global interest-rate backdrop, different investor mood. Even with no single dramatic regulatory shock or sector shift, enough ordinary years passing is itself a source of drift, the way a ten-year-old road map is out of date even if no earthquake ever hit the city.
WARNING
None of these four sources of drift show up as a single dramatic event you can circle on a calendar and say "the model broke here." They accumulate quietly. The only way to catch them is to measure performance on a schedule, not to wait until you feel that something is wrong.
Lesson 82.3 — Catching Drift Before It Costs You
The instinct many investors have is to wait for pain — to notice drift only after a string of losses forces the question. That is the most expensive way to find out. By the time a decayed model has cost you real money, it has usually been decayed for a while. The alternative is to track a small number of honest, boring scorecards on a fixed schedule, so that decay shows up as a number moving in the wrong direction long before it shows up as a loss.
Three scorecards do most of the work.
The first is quintile spread. Take every quarter's Canon Score rankings, split the scored universe into five equal groups (quintiles) from highest score to lowest, and track the actual subsequent-quarter return of the top quintile against the bottom quintile. A healthy score produces a meaningful, persistent gap — the top quintile should meaningfully outperform the bottom quintile more quarters than not. When that gap narrows quarter after quarter, the score is losing its ability to discriminate between good and bad stocks, which is the entire point of having a score.
The second is hit rate — the percentage of stocks the score rated in its top band that actually delivered above-median returns over the following quarter. A score with a stable, useful hit rate somewhere comfortably above chance is doing its job. A hit rate sliding steadily downward over several consecutive quarters, even without a single catastrophic quarter, is the quiet signature of drift.
The third, specific to the exit-rule work of Chapter 81, is trigger frequency relative to realised volatility — how often the stop-loss or exit rule fires relative to how much the underlying stock is actually moving. If exit rules are getting triggered by ordinary day-to-day noise far more often than they used to relative to that noise, either the market's volatility character has shifted (plausible, given the retail base change discussed above) or the rule's thresholds no longer match current conditions.
Table 82.1 shows the kind of scorecard Rukmini had actually been keeping, quarter by quarter, for three years — the exact data that let her catch her drift problem before it cost her a bad year rather than after.
Quarter
Top-quintile avg return
Bottom-quintile avg return
Spread
Score hit rate
Exit-rule trigger rate
2023 Q3
11.2%
2.1%
9.1 pts
68%
1 per 14 trading days
2024 Q1
10.4%
1.8%
8.6 pts
65%
1 per 13 trading days
2024 Q3
9.8%
3.0%
6.8 pts
63%
1 per 12 trading days
2025 Q1
8.1%
4.4%
3.7 pts
58%
1 per 9 trading days
2025 Q3
6.9%
5.2%
1.7 pts
54%
1 per 7 trading days
2026 Q1
6.2%
5.6%
0.6 pts
51%
1 per 6 trading days
Read across that table the way Rukmini did. The spread between best-rated and worst-rated stocks did not collapse in one bad quarter — it eroded steadily across nearly three years, from a healthy 9 points down to a statistically meaningless 0.6 points. The hit rate slid from a comfortably above-chance 68 percent toward a coin-flip 51 percent. And the exit rule, calibrated in Chapter 81 against 2018-2021 volatility patterns, was firing roughly twice as often relative to trading days by early 2026 as it had three years earlier — a sign that ordinary price movement itself had gotten choppier, plausibly tied to the much larger and more sentiment-driven retail base discussed in Lesson 82.2.
PRACTICAL TOOL
Keep exactly three numbers on a running spreadsheet, updated every quarter: quintile spread, hit rate, and exit-rule trigger rate relative to trading days. Three honest numbers, tracked consistently, will tell you more about drift than any amount of "the market feels different lately" intuition.
None of these three numbers requires sophisticated statistics or expensive data. They require the same discipline Chapter 79 first introduced: writing down what you expected in advance, and then checking the actual result against it, quarter after quarter, without skipping the quarters where the answer is inconvenient.
Lesson 82.4 — The 90-Day Review Cadence
Measuring is only half the job. The other half is having a fixed process for acting on what you measure — one that runs on a schedule rather than on emotion. This book's broader framework calls for a 90-day feedback loop, and Part XV's closing lesson on process is simply this: apply that same cadence to the Canon Score and the exit rules themselves, not only to your individual trades.
A proper 90-day model review has four steps, done in order, every quarter, whether or not anything feels wrong.
First, update the three scorecards from Lesson 82.3 with the latest quarter's data. This step is mechanical and should take under an hour if you've kept the spreadsheet current. Resist the temptation to skip it in a quarter where you suspect the numbers will look bad — those are exactly the quarters the review exists for.
Second, re-run a lightweight backtest on the most recent quarter using only information that was actually available at the time — a point-in-time backtest, meaning you score stocks using only the data an investor genuinely had in hand on that day, not data that arrived later (an earnings report published after your hypothetical decision date, for instance). This guards against the same look-ahead bias Chapter 79 warned about in the original calibration work; a review that quietly uses future information to judge past decisions will always look better than it should.
Third, ask a specific, narrow question about each of the four drift sources from Lesson 82.2: has any regulatory change occurred this quarter that alters what a scoring dimension is actually measuring? Has the sector mix of newly listed or actively traded stocks shifted meaningfully? Has anything observable changed about the participant base (a surge in new demat accounts, a notable change in average holding period, a wave of IPO-driven speculative activity)? And, simply, how much calendar time has passed since the last full recalibration? Answering these four questions in writing, every quarter, turns a vague feeling of unease into a specific, checkable claim.
Fourth, and only after the first three steps are done, decide explicitly whether any weight or threshold needs adjustment — and write down the decision and the reasoning, even if the decision is "no change this quarter." That written record matters enormously, because it is what lets you tell, a year later, whether you have been making disciplined adjustments or slowly overfitting one quarter at a time.
KEY CONCEPT
A 90-day model review is not a chance to redesign your system. It is a chance to check three numbers, ask four questions, and make a small number of narrow decisions — then close the file until the next quarter.
Rukmini's own review, the one that opened this chapter, followed exactly this process. Her scorecards had already flagged the declining spread. Her point-in-time backtest of the most recent quarter confirmed the capital-adequacy dimension of her score was assigning nearly every commercial bank a score between 82 and 96 — a range so narrow it was barely distinguishing anything. Working through the four drift-source questions, the regulatory answer jumped out immediately: the Rs 8 billion paid-up capital mandate, a full decade old by 2026, had done its work so thoroughly that virtually every surviving commercial bank now cleared it comfortably. A dimension designed to reward banks for clearing a bar that used to separate the strong from the weak was now rewarding everyone roughly equally, because the bar itself had become the industry floor rather than a meaningful hurdle.
Lesson 82.5 — Signal vs Noise: Not Every Bad Quarter Is Drift
Here is where the discipline gets genuinely difficult, and where this chapter connects directly back to the overfitting warning Chapter 80 spent an entire lesson on. A single disappointing quarter is not, by itself, evidence of drift. Markets have ordinary bad stretches for reasons no reasonable scoring model was ever built to predict — a one-off political disruption, a monsoon shock that hits hydropower generation unevenly, a temporary liquidity squeeze around a major festival period, a single large institutional seller distorting one sector's prices for a few weeks. Treating every such episode as proof the model is broken, and re-tuning weights in response, is not model maintenance. It is overfitting in slow motion — the exact trap Chapter 80 warned against, just spread across quarters instead of concentrated in one backtest.
The practical challenge is telling the two apart in real time, and it comes down to persistence and mechanism. Genuine structural drift shows up as a trend across several consecutive quarters and can usually be traced to an identifiable, permanent change in what a dimension measures — a regulatory bar that has become universal, a sector shift that has permanently changed the composition of what gets scored. Ordinary noise shows up as a single bad quarter with an identifiable one-off cause, followed by a return to the prior pattern once that cause passes.
Signal (genuine drift)
Noise (ordinary variance)
Trend persists three or more consecutive quarters
Confined to a single quarter, then reverts
Traceable to a specific, permanent structural change (regulatory mandate, sector shift, participant base shift)
Traceable to a one-off event (political disruption, weather shock, single large seller) with no lasting mechanism
Affects a specific dimension in a way consistent with the identified cause (capital-adequacy scores compress after a capital mandate saturates the industry)
Affects returns broadly and indiscriminately, without a clean link to any one scoring dimension
Point-in-time backtest of the recent quarter confirms the same weakness using only period-appropriate data
Point-in-time backtest shows the dimension behaved normally; the return shortfall came from something outside what the dimension was ever built to measure
Other independent evidence corroborates it (news of the regulatory change, visible sector composition data, demat account statistics)
No external corroborating structural change can be found
CAUTION
If your response to a single rough quarter is to immediately adjust a weight or threshold, ask yourself honestly whether you are managing drift or simply overfitting to the most recent three months of noise. The correct response to an unexplained bad quarter, absent a persistent trend or identifiable structural cause, is usually no change at all — just another data point banked for the next review.
This is precisely why Rukmini did not touch her score the first time the spread narrowed slightly, back in early 2025. One soft quarter told her nothing. It was the trend across six consecutive quarters, combined with a specific and traceable regulatory mechanism a decade in the making, that gave her confidence she was looking at genuine drift rather than noise. She also checked the mechanism specifically against her exit-rule trigger data, and found a second, independent piece of corroborating evidence: the exit rule firing roughly twice as often relative to trading days lined up with the demat-account growth and retail participation surge discussed in Lesson 82.2 — not with any single news event she could point to for one bad month.
Her actual adjustment, once she was confident it was warranted, was narrow. She did not rebuild the Canon Score from scratch. She replaced the capital-adequacy dimension's binary "does this bank clear Rs 8 billion" threshold — now nearly meaningless since almost every surviving bank clears it — with a relative measure comparing each bank's capital adequacy ratio (a bank's capital as a percentage of its risk-weighted assets, a standard regulatory soundness measure) against its peers today, restoring some spread to a dimension that had flattened out. She left every other dimension of the score untouched, and she made a small, explicit note to revisit the sentiment-weighting dimension's calibration at her next scheduled review rather than acting on a single quarter's signal from the retail-base data.
Lesson 82.6 — Conviction and Humility: Closing Part XV
Four chapters ago, Part XV opened with a promise: that a stock-picking system built on hope and gut feeling is worse than useless, and that discipline — testing, calibrating, checking your work against reality rather than your memory of reality — is what separates a durable investing process from a lucky streak that eventually runs out. Chapter 79 taught you how to test a rule honestly. Chapter 80 applied that testing to the Canon Score itself. Chapter 81 applied it to the exit rules that protect you on the way out. This chapter closes the loop: even a properly tested rule is not a finished object. It is a living tool, and living tools need periodic, honest re-examination or they quietly decay into shapes that no longer fit the market wearing them.
The discipline this chapter asks of you is really the ability to hold two things at once, in some tension with each other, without letting either one win outright. The first is conviction: enough confidence in a properly calibrated Canon Score and a properly tested exit rule to actually use them consistently, quarter after quarter, without abandoning them the moment a single result disappoints you. A tool you redesign every time it has a bad month is not a tool at all — it is an excuse to keep chasing whatever happened most recently, which Chapter 80 already showed you is just overfitting wearing a different hat. The second is humility: enough honesty to keep measuring, on a fixed schedule, whether the tool still deserves that confidence — and enough willingness to make a narrow, well-evidenced adjustment when the scorecards, not your gut, say the ground has genuinely shifted.
Rukmini's year illustrates both halves. She did not touch her score for fourteen months despite plenty of ordinary quarterly noise along the way — that was conviction, and it kept her from overfitting to every rough patch. But when six consecutive quarters of narrowing spread lined up with a specific, traceable, decade-old regulatory mandate finally saturating an entire industry, she did not simply keep the faith and hope the pattern reversed on its own — that would have been complacency dressed up as discipline. She measured, she distinguished signal from noise using the table in Lesson 82.5, and she made one narrow, well-justified change to one dimension of her score, leaving the rest alone. That is what a mature relationship with a calibrated model looks like: not blind loyalty, and not constant tinkering, but a fixed process for occasionally, deliberately, earning the right to change your mind.
WARNING
A model you never revisit and a model you revisit every week fail for the same underlying reason: neither is actually being checked against evidence on a disciplined schedule. Drift management lives in the middle, at a fixed cadence, with a written record of what you found and why you acted or didn't.
Chapter recap
This chapter defined model drift in plain terms: a scoring or exit rule that worked well when it was calibrated can quietly stop working as the market it was built against changes shape, not because the original work was flawed but because the world it measured no longer exists in the same form. On NEPSE specifically, four forces drive that change — regulatory shocks like NRB's decade-old paid-up capital mandate that pushed commercial banks from a Rs 2 billion to an Rs 8 billion minimum and, in doing so, eventually flattened a capital-adequacy scoring dimension that used to usefully separate strong banks from weak ones; sector composition shifts, most visibly the surge of hydropower IPOs that has made hydropower the largest single block of new NEPSE listings and changed what a "typical" scored company even looks like; participant base changes, as NEPSE's demat account base swelled toward roughly eight million accounts and reshaped how sentiment-driven price action can be at the margin; and the simple passage of calendar time, which alone guarantees that a rule calibrated on 2018-2021 data faces conditions it never saw.
The chapter then laid out how to detect that decay without waiting for a painful loss to reveal it: three honest, boring scorecards — quintile spread between top- and bottom-rated stocks, the score's hit rate, and how often exit rules trigger relative to ordinary volatility — tracked every quarter on a fixed schedule, exactly like the one shown in Table 82.1, where a slow decade-long erosion from a 9-point spread to a 0.6-point spread told the real story long before any single quarter's loss would have. It paired that measurement habit with a concrete four-step 90-day review process: update the scorecards, re-run a point-in-time backtest of the latest quarter using only period-appropriate information, ask the four drift-source questions directly, and only then decide explicitly whether to adjust anything — recording that decision even when it is "no change."
Just as important as knowing how to detect drift is knowing how not to overreact to it. The chapter drew a sharp line between genuine structural drift — persistent across several quarters, traceable to an identifiable and permanent change like a saturated regulatory threshold — and ordinary variance, a single rough quarter caused by a one-off event the model was never built to predict. Treating every disappointing quarter as proof of failure and re-tuning weights in response is not vigilance; it is the same overfitting trap Chapter 80 warned against, merely spread across calendar time instead of concentrated inside one backtest.
The narrative thread followed Rukmini Thapa, the NRB-veteran investor who calibrated her Canon Score in Chapter 80, through her first full year of disciplined quarterly reviews — showing her catching a real, decade-in-the-making case of drift in her capital-adequacy dimension precisely because she had been tracking the right three numbers on a fixed schedule, and showing her making one narrow, well-evidenced fix rather than a wholesale rebuild.
That balance of conviction and humility is this chapter's, and Part XV's, final lesson: a properly built model is never "done." It earns continued use only through periodic, honest re-testing, and the investor's real job is holding both a working system in her hands and a healthy suspicion of it in the back of her mind at the same time.
With that, Part XV — Calibration and Backtesting — comes to a close. The book now turns from the machinery of building and testing models to the work of actually using them. Part XVI, Mastery, opens with eight full worked case studies that take everything built across Parts XIII through XV — the seven-dimension Canon Score, its calibration discipline, its exit rules, and now its drift-management process — and apply all of it, start to finish, to real company archetypes an investor will actually encounter on NEPSE. Chapter 83, Case Study 1 — A Commercial Bank, is the first of those eight studies, and it will put the very capital-adequacy dimension this chapter just revisited under the microscope: scoring a real commercial bank archetype end to end, showing exactly how a decade of NRB capital mandates, the merger wave that followed, and the drift-adjusted scoring approach from this chapter all come together in a single live decision.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Volume 4: MASTERY
Part XVI
FULL CASE STUDIES (WORKED END-TO-END)
Part XVI · Chapter 83
Case Study 1 — A Commercial Bank
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 1 — A Commercial Bank
Sushmita Karki had read fifteen Parts of this book before she opened her first spreadsheet to actually use any of it. That gap — between understanding a framework and running it, cold, on a real company with real numbers that refuse to line up neatly — is the gap this entire Part exists to close. Parts XIII through XV gave you the machinery: the Canon Score itself, its seven dimensions, the sector adjustments that keep a bank from being graded like a trekking company, the backtesting mindset, the calibration habit, the exit-rule discipline, and the drift-management routine that keeps all of it honest as the market changes underneath you. Part XVI is where that machinery meets eight real companies, one per chapter, worked start to finish the way you would actually do it at your own desk on a Saturday morning with tea going cold beside your laptop.
Sushmita chose to start with a commercial bank for a reason that will sound almost too simple: banks are the most "textbook" sector on the Nepal Stock Exchange (NEPSE). Every commercial bank in the country reports to the same regulator, Nepal Rastra Bank (NRB), on the same quarterly schedule, using largely the same handful of ratios. A hydropower company's value depends on rainfall and river flow. A hotel's value depends on tourist arrivals nobody can fully predict. A bank's value depends on numbers that show up, every quarter, in a disclosure format NRB itself prescribes. If you are going to practice the Canon Score for the first time on a real company, a bank is the fairest test — the data exists, it is comparable across the whole peer set, and there is nowhere to hide behind a story.
The bank Sushmita picked was Nabil Bank Limited, traded on NEPSE under the ticker NABIL. This chapter follows her through all six lessons of the case study, using Nabil's real, publicly reported figures as of mid-2025 and the first half of fiscal year 2082/83. Some of those figures move a little from one disclosure to the next — audited versus unaudited, one quarter versus the next — and rather than smoothing that mess away, this chapter treats it as part of the lesson. Real companies do not hand you a single clean number. They hand you a range, and your job is to read the range honestly.
Lesson 83.1 — Setting Up the Case: A Bank Called Nabil
Nabil Bank has a claim to being the single most historically important private bank on NEPSE. It was incorporated in 1984 as Nepal Arab Bank Limited, the country's first joint-venture commercial bank — meaning a domestic bank formed in partnership with a foreign banking group, in this case Emirates-linked capital that was later replaced by Bangladesh's IFIC Bank as the principal foreign promoter. It was later renamed Nabil Bank Limited, and it has traded on NEPSE for decades under the scrip code NABIL, long enough that several generations of Nepali retail investors have owned it at one point or another, often as a first bank stock.
In July 2022, Nabil completed a merger with Nepal Bangladesh Bank Limited (NB Bank), swapping 43 Nabil shares for every 100 NB Bank shares held. Mergers of this size reshape a bank's numbers for years afterward — loan books get blended, deposit bases get combined, and ratios that looked clean before the merger often look messier for several reporting cycles afterward as the two institutions' loan quality, systems, and staff get folded into one. Keep that merger date in mind; it explains almost every awkward number in this chapter.
As of its most recent disclosures, Nabil operates 268 branches and around 316 ATMs across Nepal, with a paid-up capital of roughly Rs 32.06 arba (Rs 32.06 billion) — about four times NRB's old minimum capital mandate, a detail Lesson 83.4 will return to in detail. Its shares outstanding sit at about 27.06 crore (270.6 million), promoter shareholders hold roughly 58.44 percent of the company, and the general public holds the remaining 41.56 percent. Market capitalisation has recently sat around Rs 146 arba (Rs 146 billion), making Nabil one of the larger banks on the exchange by market value, though no longer automatically the largest — a decade of mergers among its peers has produced several banks of comparable or greater size.
Sushmita's first move, before touching a single Canon Score dimension, was to pull up Chapter 71's banking-sector playbook and re-read the six numbers it insists any competent bank analysis must include. She wrote them on an index card and taped it above her desk, because — as she put it in her notes — "if I can't explain these six things to my mother in one sentence each, I don't actually understand the bank yet."
Metric
Plain-English meaning
Why it matters for scoring
Net interest margin (NIM)
The spread between what a bank earns on loans and what it pays on deposits, as a percent of its interest-earning assets
This is a bank's core profit engine — its equivalent of a retailer's gross margin
Credit-to-deposit (CD) ratio
The share of deposits collected that the bank has actually lent out
Too low means idle money earning little; too high risks a liquidity squeeze if depositors withdraw
Capital adequacy ratio (CAR)
The bank's own capital cushion as a percent of its risk-weighted loans and assets
This is the airbag — how much loss the bank can absorb before depositors are at risk
Non-performing loan (NPL) ratio and provisioning
The share of loans where the borrower has stopped paying as agreed, and how much the bank has set aside to cover expected losses on them
Directly measures loan-book quality; provisioning shows whether management is being honest about it
Cost-to-income ratio
Operating expenses as a percent of operating income
A rough efficiency score — how much of every rupee earned gets eaten by running the bank itself
Return on equity (ROE)
Net profit as a percent of shareholders' own capital in the bank
The bottom-line answer to "what did my ownership stake actually earn this year"
KEY CONCEPT
A bank's capital adequacy ratio works like a household's emergency fund relative to its total borrowing — it is not how much money is coming in this month, it is how much cushion exists to absorb a bad month (or a bad year) without the household defaulting on anyone. NRB sets a floor under that cushion for every commercial bank; falling below the floor is a regulatory emergency, not just a bad quarter.
Sushmita's working assumption going in — one she wrote at the top of her notebook page — was that because Nabil is old, large, and closely watched, its Canon Score would probably land somewhere comfortably good, and the exercise would mostly be about confirming that instinct with numbers. By the end of Lesson 83.5, the process would confirm the broad instinct but sharply revise the reasoning behind it — Nabil does score comfortably well, but not for the reason a reputation-based guess would assume, and with one specific weak spot the gut-feel version of this exercise would never have isolated. That is, in fact, exactly what a first full case study is supposed to do to you: not just tell you yes or no, but tell you why, in pieces you can individually check.
Lesson 83.2 — Hemisphere 1: Governance, Tradability, and Durability
The Canon Score splits into two broad hemispheres, grouping Chapter 64's seven dimensions by the kind of question each one asks. Hemisphere 1 covers the qualitative dimensions — Governance & Promoter Behaviour (15 points), Liquidity & Tradability (10 points), and Sector & Business Model Durability (15 points), 40 points in total — is this stock actually tradeable, who controls the company, and will this kind of business still matter in ten years. Hemisphere 2 covers the quantitative dimensions built from the financial statements themselves. Sushmita worked Hemisphere 1 first, on the theory that if a stock fails the basic "can I actually buy and sell this at a real price" test, the fundamentals barely matter.
Liquidity & Tradability (10 points). Average daily volume (ADV) — the average number of shares that actually change hands each trading day — is the practical measure of whether a stock can absorb a meaningful order without moving the price against you. Nabil's ADV has recently run around 46,000 shares over a one-week window and about 52,000 shares over a one-month window. At a share price near Rs 540, a single day's typical volume represents roughly Rs 2.5 to 2.8 crore (NPR 25–28 million) of turnover — comfortably above Chapter 64's NPR 5 million top band, so the volume sub-component scores a full 5 out of 5. Free float — the portion of shares actually available to trade, as opposed to locked up with promoters — sits at roughly 41.56 percent of the company, since promoters hold the remaining 58.44 percent. That clears Chapter 64's 40 percent free-float threshold for full marks, another 5 out of 5. Liquidity & Tradability: 10 out of 10 — Nabil is, straightforwardly, one of the more easily tradeable names on the exchange.
Governance & Promoter Behaviour (15 points). Nabil's promoter group has historically centred on IFIC Bank of Bangladesh as the principal foreign joint-venture partner, alongside Nepali business interests including the Chaudhary Group. In 2025, news emerged of a planned transfer of roughly 17.7 million promoter shares from IFIC Bank to the Chaudhary Group — a transaction that, at the time Sushmita did her research, was sitting under a court stay order rather than proceeding cleanly. Chapter 64's first governance check asks specifically about pledging — promoter shares used as loan collateral, which can trigger a forced sale with no warning to ordinary shareholders. Sushmita's research turned up no evidence that Nabil's promoter shares are pledged; the live issue is a contested ownership transfer under litigation, which is a different risk from pledging but still real uncertainty at the top of the shareholding table. She scored this sub-component 5 out of 6 — full marks for holding stability and the absence of pledging, minus one point for the unresolved transfer.
CASE IN POINT
Nabil's own quarterly disclosures illustrate why "governance" is a real, gradable dimension and not a box-ticking formality. Its Q4 FY2081/82 unaudited results reported net profit of roughly Rs 7.13 arba, a 15.01 percent jump — but the subsequently audited figure came in at roughly Rs 5.92 arba, a downward revision of about 17 percent. Reporting patterns like this, where unaudited quarterly figures run consistently ahead of the audited restatement, have shown up more than once in Nabil's recent history. None of this means fraud; unaudited-to-audited revisions happen across the sector. But a pattern of revisions in one direction, quarter after quarter, is exactly the kind of soft signal Chapter 64's related-party-and-audit-opinion check is built to catch, even though Nabil's audits themselves have carried no qualification. Sushmita scored this sub-component 3 out of 5 — real but modest concern, not a red flag.
The third governance check — disclosure timeliness and board independence (4 points) — was the hardest to verify from the outside. Nabil's quarterly figures throughout this chapter arrived on the schedule NRB requires, which is itself a form of on-time disclosure; Sushmita did not, however, separately pull Nabil's board composition to confirm its independent-director count against the regulatory minimum. She scored this sub-component 3 out of 4, noting in her worksheet that board independence specifically was assumed rather than verified, and belongs on her list of things to actually check rather than infer next time.
Governance & Promoter Behaviour: 5 + 3 + 3 = 11 out of 15. On the positive side of the ledger, Nabil is the oldest continuously operating private commercial bank in Nepal, has survived four decades of political and economic cycles, completed a complex merger without a depositor-facing crisis, and in July 2025 commissioned a fresh issuer rating from CARE Ratings Nepal — the kind of voluntary external scrutiny that a bank hiding something usually avoids inviting. Balanced against that is the audited-versus-unaudited revision pattern, the unresolved promoter-share dispute, and the unverified board-independence detail. Sushmita's honest note in her worksheet read: "Old and mostly clean, but not spotless, and not finished changing hands at the top." That sentence, more than any single number, is what this dimension is trying to capture.
WARNING
Do not let a bank's age and reputation substitute for actually checking the current governance picture. A bank that was impeccably run for thirty years can still be mid-transition in its ownership or mid-recovery from a merger today. Score the bank you can verify right now, not the bank's reputation.
Sector & Business Model Durability (15 points). Chapter 64's moat check asks whether the business sits in a licensed, regulated sector with high entry barriers — Nabil, as an NRB-licensed commercial bank operating under a regulatory regime that makes new full banking licenses rare, scores a full 8 out of 8 here. The revenue-concentration check (7 points) asks whether the business depends too heavily on any single customer, segment, or counterparty. Sushmita did not pull Nabil's loan book down to individual-segment detail, but as a 268-branch, nationally diversified retail-and-corporate lender — not a single-sector specialist the way a hydropower company depends on one buyer — Nabil is directionally diversified rather than concentrated. She scored this sub-component 5 out of 7, full marks withheld only because she had not verified the exact segment breakdown herself. Sector & Business Model Durability: 8 + 5 = 13 out of 15.
Lesson 83.3 — Hemisphere 2: What the Numbers Actually Say
With Hemisphere 1 assessed, Sushmita turned to the financial statements themselves — Hemisphere 2, the quantitative half of the Canon Score, covering Financial Strength & Profitability (20 points), Valuation Reasonableness (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points), 60 points in total.
Financial Strength & Profitability. For fiscal year 2081/82 (roughly mid-2024 to mid-2025), Nabil reported customer deposits of about Rs 5.24 kharba (Rs 524 billion), up 13.53 percent year-on-year, and loans and advances of about Rs 4.12 kharba (Rs 412 billion), up 10.50 percent. Its CASA ratio — the share of deposits sitting in low-cost current and savings accounts rather than more expensive fixed deposits — stood at about 44.35 percent, recovering from a post-merger trough near 35 percent. Its cost of funds fell from about 5.86 percent to 4.41 percent over the same stretch, and continued falling toward roughly 3.25 percent in the following quarters. Earnings per share (EPS) rose from about Rs 22.90 to about Rs 26.34 for the fiscal year. Reported net profit for the year, using the unaudited figure, was about Rs 7.13 arba; the audited figure, as already noted, came in lower at about Rs 5.92 arba.
Chapter 64's sub-component A scores a 3-year average ROE. Nabil's ROE has come in at roughly 10 to 11 percent for fiscal year 2081/82 on the most commonly cited basis — a sharp fall from a pre-merger run rate closer to 19 to 20 percent, with the intervening post-merger years sitting somewhere in between as the integration worked through the numbers. (Different data providers compute this differently — some use average shareholders' equity, some use period-end equity, some annualize a single strong quarter — which is why you will see figures for Nabil's ROE ranging from under 10 percent to over 18 percent depending on the source. Sushmita's practical fix was to compute it herself, net profit divided by average equity for the full fiscal year, and treat any other figure as a cross-check rather than a substitute.) A 3-year average that starts near 19–20 percent and ends near 10–11 percent lands close to Chapter 64's "near current system average" band rather than its top band. Sushmita scored ROE 4 out of 8.
PRACTICAL TOOL
When a metric shows meaningfully different values across data sources — as ROE did here — do not average the sources or pick whichever number flatters your thesis. Recompute it yourself from the audited annual report using one consistent formula, note which formula you used, and use that number every time you revisit the position. Consistency in your own method matters more than chasing the "correct" published figure.
Sub-component B scores capital adequacy. This is where a bank's numbers earn their keep, and where NRB's regulatory floors matter most directly.
REGULATORY DETAIL
Nepal Rastra Bank requires Class 'A' commercial banks to maintain a minimum total capital fund (capital adequacy ratio) of 11 percent of risk-weighted assets. NRB's separate credit-to-deposit ceiling — the rule that a bank cannot lend out more than 90 percent of the deposits and qualifying funds it holds — has been the sector's headline liquidity constraint for years, though NRB's monetary policy for 2025/26 signals a shift toward assessing bank liquidity through Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR) standards instead, with the older CD-ratio framework due to be revised once that transition is complete.
Nabil's capital adequacy ratio has recently sat close to 11.94 percent against that 11 percent floor — comfortably within Chapter 64's 11.5–13 percent band, which is worth 5 out of 7, but well short of the 13 percent-or-higher band that would earn full marks. Lesson 83.4 looks at this number in more depth, because a bank's precise position relative to its peers, not just relative to the regulatory floor, turns out to matter.
Sub-component C scores 5-year earnings quality and consistency. Nabil's non-performing loan ratio has moved from a pristine 0.84 percent in fiscal year 2078/79, up to a post-merger peak near 4.78 percent in 2080/81 as NB Bank's loan book was absorbed and reclassified under Nabil's own underwriting standards, and back down to a range of roughly 4.3 to 4.5 percent in the most recent quarters — a large move from the bank's historic norm that has not yet fully round-tripped back. Combined with the ROE compression and the unaudited-to-audited profit revision already discussed, that reads as at least one meaningfully "flat or declining" stretch inside the last five years rather than a smooth, uninterrupted profit climb. Sushmita scored this sub-component 3 out of 5. Financial Strength & Profitability: 4 + 5 + 3 = 12 out of 20.
On the liquidity side, separate from the Liquidity & Tradability dimension scored in Lesson 83.2, Nabil's loan-to-deposit ratio has recently sat around 82.5 percent, comfortably under the 90 percent regulatory ceiling, implying roughly Rs 39 arba of additional lending capacity before that ceiling would bind.
Valuation Reasonableness (15 points). Chapter 64 is explicit that NEPSE valuation must be judged against sector and historical medians, never a fixed textbook number — this book has already noted that NEPSE's banking sector has typically traded closer to 15–16 times earnings and around 1.5 times book value, well below the exchange's broader index. At a recent share price near Rs 541 to Rs 548, against a book value per share of roughly Rs 235 to Rs 247, Nabil trades at a price-to-book (P/B) ratio of about 2.19 times — against a sector median near 1.5 times, that works out to roughly 1.46 times the sector median, which falls in Chapter 64's 1.1x–1.5x band rather than its top band. Its price-to-earnings (P/E) ratio sits around 19.5 to 20.5 times trailing earnings against a sector median near 15.5 times — also roughly 1.29 times the sector median, the same 1.1x–1.5x band. Sushmita scored P/E 3 out of 8 and P/B 3 out of 7. Valuation Reasonableness: 3 + 3 = 6 out of 15 — the single weakest dimension in Nabil's entire scorecard, and the clearest read on the whole case study: Nabil is not an expensive bank in absolute terms, but it is a bank priced above its own sector on both major valuation yardsticks, at exactly the moment its return on equity has compressed the most.
Growth Trajectory (15 points). Nabil's loan book grew 10.50 percent and its deposits 13.53 percent for the fiscal year — a single year's figure rather than a verified 5-year CAGR, but one that sits inside Chapter 64's 8–15 percent band. Sushmita scored the growth sub-component 6 out of 8, flagging that a full 5-year CAGR would sharpen this if she pulled it later. EPS grew from Rs 22.90 to Rs 26.34 this year, but set against the sharp post-merger ROE swing already discussed, that reads as one strong year inside a choppier multi-year pattern rather than a clean, low-volatility uptrend — Chapter 64's "positive in 3 of 5 years, moderate swings" band, worth 4 out of 7. Growth Trajectory: 6 + 4 = 10 out of 15.
Dividend & Capital Return Discipline (10 points). Nabil has paid a dividend every year for at least the last five fiscal years Sushmita could find a public record for: 38 percent (mostly bonus shares) for FY2077/78, 30 percent for FY2078/79, then a shift to cash-only payouts as the sector moved away from bonus-heavy distributions — 11 percent cash for FY2079/80, 10 percent cash for FY2080/81, and 12.5 percent cash for FY2081/82. On an EPS of Rs 26.34, a 12.5 percent cash dividend (Rs 12.5 per Rs 100 of face value) works out to a payout ratio near 47 percent of profit — squarely inside Chapter 64's 30–70 percent "sensible" band. Paid every year, consistently in that band: 6 out of 6. Sushmita found no evidence the dividends were funded by anything other than ordinary distributable profit, though she had not separately confirmed reserves were intact from the balance sheet itself, so she scored the funding-source sub-component 3 out of 4 rather than a full 4. Dividend & Capital Return Discipline: 6 + 3 = 9 out of 10 — comfortably Nabil's strongest dimension after Liquidity.
Put together, Nabil's Hemisphere 2 picture is a bank still working through the tail end of a large merger: deposit and loan growth are healthy, funding costs are falling, and the dividend has been paid reliably every year — but return on equity has compressed sharply, asset quality has not yet fully normalised, and the stock's valuation multiples have not obviously priced any of that compression in.
Lesson 83.4 — Applying the Drift Adjustment: Capital Adequacy, Relatively Judged
Chapter 82 flagged a specific, well-documented problem with an older, cruder way of scoring bank capital: an approach built years ago around NRB's landmark paid-up capital mandate — the rule, dating back roughly a decade, that forced every commercial bank to hold at least Rs 8 billion in paid-up capital or merge with one that did. That single policy triggered the wave of bank mergers (including, indirectly, the one that created today's larger Nabil) that consolidated dozens of small banks into the current field of survivors. At the time, "does this bank clear Rs 8 billion in paid-up capital" was a genuinely useful, discriminating question — plenty of banks did not clear it, and it correctly separated the institutions that would survive from those that would be absorbed.
A decade later, that question has stopped discriminating between anything. Nabil's paid-up capital sits at roughly Rs 32 arba — four times the old mandate — and so does essentially every other surviving commercial bank on NEPSE, because the ones that could not get there were merged out of existence years ago. A scoring rule built around that absolute rupee threshold would now give every single remaining bank full marks regardless of how well-capitalised it actually is today. That is model drift in its purest form: the rule is still technically true, and still completely useless for telling Nabil apart from its peers.
WARNING
A scoring rule that once discriminated well between good and bad companies can quietly become a rule that every survivor passes, for reasons that have nothing to do with quality — because the weak candidates were already removed from the population by the very event the rule was designed to detect. When every company in your peer set clears a threshold, that threshold has stopped being useful evidence about any one of them.
Chapter 64's capital-adequacy sub-component already avoids the worst of this by scoring the CAR ratio itself against NRB's current thresholds (11 percent, 11.5 percent, 13 percent) rather than an absolute paid-up-capital rupee figure — which is why Nabil's 11.94 percent lands in the 11.5–13 percent band for 5 out of 7, not automatic full marks. But Chapter 82's deeper point still applies one layer down: a bank can clear its regulatory floor comfortably and still be thinly capitalised relative to its actual peer group, which the rubric's fixed bands alone will not show you. That is exactly what Sushmita checked next, using a mid-2025 sector snapshot of commercial bank CAR figures.
Bank
Capital adequacy ratio
Position vs sector average of 13.08%
Standard Chartered Bank Nepal
17.82%
Well above average
Prabhu Bank
13.90%
Above average
Nepal Investment Mega Bank
13.73%
Above average
NIC Asia Bank
13.42%
Above average
Agriculture Development Bank
13.36%
Above average
Nepal Bank Limited
13.06%
Roughly at average
Global IME Bank
12.97%
Slightly below average
NMB Bank
12.03%
Below average
Nabil Bank
11.94%
Below average, 9th of 12
Rastriya Banijya Bank
11.84%
Below average
Siddhartha Bank
11.77%
Below average
Himalayan Bank
11.16%
Below average, near the floor
Nabil's 11.94 percent CAR sits below the sector average of 13.08 percent, ranks ninth out of the twelve major banks in this snapshot, and carries a thinner capital buffer above the regulatory floor than eight of its listed peers. That does not make Nabil unsafe — it remains comfortably compliant, and its 5-out-of-7 score under Chapter 64's own bands already reflects "comfortable but not exceptional" — but the peer table adds something the point band alone cannot: Nabil is one of the less well-capitalised large banks on the exchange relative to its own peer set, not merely "above some old, now-meaningless absolute rule." That distinction — a fixed rulebook band giving you a number, and a peer table telling you what that number actually means in context — is the more durable lesson Chapter 82 is teaching here.
REGULATORY DETAIL
NRB's 2025/26 monetary policy adds two more reasons the capital-adequacy picture is worth watching rather than treating as settled: it directs NRB to develop a Domestic Systemically Important Bank (DSIB) framework, which would impose extra capital and supervisory requirements on the handful of banks judged too large or interconnected to fail — a group Nabil, given its size and history, would very plausibly sit inside — and it schedules a long-delayed asset quality review of commercial banks' loan books, previously stalled by litigation against the central bank. Either development could move Nabil's effective capital requirement, and its measured asset quality, independent of anything the bank itself does differently.
Sushmita's note here was blunt: "The old rule said Nabil's capital position was basically perfect. The relative rule says it's fine, but it's the ninth-best-capitalised bank out of twelve where nobody's actually bad — and there's a new systemic-bank rule and a stalled asset-quality review both headed this way." That is precisely the kind of adjustment the drift-management discipline of Chapter 82 is meant to produce: not a dramatic reversal, but a materially more honest, more discriminating number than the one the un-updated model would have handed her.
Lesson 83.5 — The Full Worked Canon Score
With both hemispheres assessed and the drift adjustment applied, Sushmita assembled her full Canon Score tally, using exactly the seven dimensions and point weights Chapter 64 defines: Financial Strength & Profitability (20), Governance & Promoter Behaviour (15), Liquidity & Tradability (10), Valuation Reasonableness (15), Sector & Business Model Durability (15), Growth Trajectory (15), and Dividend & Capital Return Discipline (10).
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
12
ROE near system average, compressed from a pre-merger 19–20% → 4/8; CAR 11.94% → 5/7 (11.5–13% band); a real post-merger flat/declining stretch in the profit trend → 3/5
Governance & Promoter Behaviour
15
11
Stable, unpledged promoter holding but a contested ownership transfer under court stay → 5/6; clean audits with a recurring unaudited-to-audited revision pattern → 3/5; on-time filings, board independence unverified → 3/4
P/E ~20x vs ~15.5x sector median (≈1.29x) → 3/8; P/B ~2.19x vs ~1.5x sector median (≈1.46x) → 3/7
Sector & Business Model Durability
15
13
Regulated banking moat, high entry barriers → 8/8; broad, diversified branch network, concentration not separately verified → 5/7
Growth Trajectory
15
10
Loan growth 10.50% YoY → 6/8 (8–15% band); EPS grew this year inside a choppier post-merger pattern → 4/7
Dividend & Capital Return Discipline
10
9
Paid every year for 5 straight fiscal years, payout ratio ≈47% of EPS → 6/6; funded from ordinary profit, not separately audited line by line → 3/4
Canon Quality Score
100
71
Band: Strong (70–84)
Summed, Nabil Bank's Canon Score comes to 71 out of 100 — inside Chapter 64's Strong band (70–84), a rung above Adequate and a rung below Exceptional. No dimension triggers Chapter 64's governance override, since Governance & Promoter Behaviour scored 11 out of 15, well above the 5-point floor that would cap the whole score in the Weak/Avoid band regardless of everything else.
Sushmita was honest with herself about which dimensions were hardest to judge, and a fair-minded analyst working the same facts could reasonably land a few points higher or lower on any of them. Three stood out.
Valuation Reasonableness was the clearest, and the harshest, call in the whole scorecard. A 2.19x book multiple and a 20x earnings multiple, on a bank earning roughly 10 to 11 percent on that book right now, is not obviously cheap by the plain arithmetic of return on equity versus valuation — which is exactly why it is Nabil's lowest-scoring dimension at 6 out of 15. But Nabil's ROE compression is plausibly temporary — a direct byproduct of digesting a large merger rather than a structural decline in the underlying franchise — and a market willing to pay up for eventual recovery is not automatically an irrational market. An analyst more confident in a swift recovery could reasonably score this dimension several points higher; one who suspects the compression reflects a permanently larger, slower-growing, harder-to-manage post-merger institution could reasonably score it even lower still.
Financial Strength & Profitability's capital-adequacy sub-score was the second hardest call. Nabil is unambiguously compliant with every NRB capital and liquidity requirement in force today. The question is how much the 11.5–13 percent band should reflect a bank sitting in the lower half of its peer group specifically, especially with a systemic-bank framework and an asset-quality review both pending. Score it too harshly and you punish a bank for a relative ranking the rubric's fixed bands do not ask about directly; score it too gently and you ignore exactly the peer-context Lesson 83.4 surfaced. Sushmita's 5 out of 7 reflects a deliberate middle path, and she flagged it as the sub-score most likely to move at the next data refresh.
Governance & Promoter Behaviour was the third genuine judgment call. A recurring pattern of unaudited results running ahead of the audited restatement is a real signal, but it is also common enough across the sector that treating it as disqualifying would be disproportionate; reasonable analysts could argue for anywhere between 2 and 4 out of 5 on that particular sub-check depending on how much weight they put on disclosure discipline versus the bank's much longer clean operating history. Similarly, the unresolved IFIC-to-Chaudhary Group promoter-share transfer is real uncertainty but not evidence of pledging or expropriation, which is why it cost one point rather than several.
CAUTION
A Canon Score is a snapshot of one analyst's honest judgment on one day, built from disclosures that themselves get revised. Two careful people working from the same facts can reasonably land five or six points apart on a 100-point scale without either one being wrong. Treat the number as a structured argument you can defend, not as a verdict handed down from the framework itself.
Lesson 83.6 — The Decision, and What Would Change It
A score of 71 out of 100 sits inside Chapter 64's Strong band — a solid long-term holding candidate worth owning with normal monitoring, a rung above merely Adequate but short of Exceptional. Reading the seven sub-scores rather than stopping at the total tells the real story: Nabil is comfortably strong on Liquidity, Durability, and Dividend discipline, respectably placed on Governance and Growth, and dragged down almost entirely by one dimension — Valuation Reasonableness, at 6 out of 15, its weakest score by a wide margin. Sushmita's conclusion, working purely through the process and holding no existing position, was this: Nabil Bank is a fundamentally sound, well-run institution still absorbing the tail end of a large merger, priced by the market as though that absorption were mostly complete, when the numbers say it is not quite there yet. That combination argues for a bank worth owning as a long-term holding, but not one worth chasing at the current price — an existing holder has little reason to sell a Strong-scoring bank, while a new buyer has good reason to wait for the valuation to catch down to the current, still-compressed return on equity, or for the return on equity to catch up to the valuation.
CAUTION
This case study is a demonstration of a process, not a personal recommendation to buy, hold, or avoid Nabil Bank or any other security. It shows how the Canon Score's machinery, honestly applied to real, verifiable numbers, arrives at a conclusion — not what any individual reader should do with their own capital, which depends on their own goals, time horizon, and the rest of their portfolio.
The more durable value of the exercise is the watch list it produces, because a Canon Score is never a one-time verdict — it is the current reading on an instrument you keep checking, exactly as Chapter 82 insists. Four specific developments would move Nabil's score, and Sushmita wrote all four into a recurring calendar reminder rather than trusting herself to remember them unprompted.
First, a sustained recovery in return on equity back toward the mid-teens, sourced from Nabil's own audited annual report rather than a single flattering quarter, would lift both the Financial Strength and Valuation Reasonableness dimensions meaningfully — it would be the clearest sign that the merger's integration costs are genuinely behind the bank rather than still working through the numbers, and it is the single change most likely to move Nabil from Strong toward Exceptional. Second, a capital adequacy ratio that climbs back above the sector average, rather than sitting in the bottom third of the peer table, would lift the Financial Strength score under the same relative logic Lesson 83.4 applied — and conversely, a further slide would be a real warning sign, not a rounding error. Third, resolution of the IFIC-to-Chaudhary Group promoter-share transfer, whichever way the pending court matter concludes, would remove a live piece of governance uncertainty from the Governance & Promoter Behaviour dimension. Fourth, the outcomes of NRB's planned asset quality review and its forthcoming Domestic Systemically Important Bank framework — both explicitly flagged in the 2025/26 monetary policy — could reshape Nabil's effective capital and provisioning requirements independent of anything the bank does on its own, and deserve a fresh look the moment either is published in final form.
PRACTICAL TOOL
Turn every "what would change this score" item into a specific, checkable trigger with a source and a rough date, not a vague intention to "keep an eye on it." Sushmita's list: (1) next audited annual report — ROE trend, (2) next quarterly NRB capital-adequacy sector data — CAR versus peer average, (3) court docket or company disclosure — promoter share transfer outcome, (4) NRB circular — asset quality review and DSIB framework details. A watch list without dates and sources quietly turns into a watch list nobody ever rechecks.
Sushmita closed her notebook on this exercise with a line that captures the whole point of a first case study better than any score could: "I went in assuming the answer was obviously yes. The process mostly agreed — Strong, not Exceptional — but it also told me exactly which one number is doing all the damage, and that's a sharper answer than my gut ever gave me." That is what a disciplined framework is supposed to do to a beginner's instinct — not override it with false precision, but sharpen it into something checkable.
Chapter recap
This chapter took the entire Canon Score apparatus built across Parts XIII through XV and ran it, in full, against one real, currently listed NEPSE commercial bank: Nabil Bank Limited, NEPSE ticker NABIL. Working alongside a first-time practitioner, Sushmita Karki, the chapter moved through Nabil's basic profile — its 1984 origin as Nepal's first joint-venture bank, its 2022 merger with Nepal Bangladesh Bank, its 268 branches and roughly Rs 32 arba paid-up capital — and the six banking-sector numbers from Chapter 71 that any competent bank analysis has to incorporate: net interest margin, credit-to-deposit ratio, capital adequacy ratio, non-performing loan ratio and provisioning, cost-to-income ratio, and return on equity.
From there, the case study worked both hemispheres of Chapter 64's actual seven-dimension rubric in turn. Hemisphere 1 scored Liquidity & Tradability (10/10 — deep enough daily turnover and a normal promoter-to-public split), Governance & Promoter Behaviour (11/15 — a long clean operating history complicated by a recurring unaudited-to-audited profit revision pattern and an unresolved, court-stayed promoter-share transfer), and Sector & Business Model Durability (13/15 — a strong regulated moat, diversification not separately verified in detail). Hemisphere 2 scored Financial Strength & Profitability (12/20), Valuation Reasonableness (6/15), Growth Trajectory (10/15), and Dividend & Capital Return Discipline (9/10) — healthy deposit and loan growth and a reliable multi-year dividend record set against a return on equity that has compressed sharply since the 2022 merger, alongside a valuation multiple that has not obviously priced that compression in.
The chapter then applied Chapter 82's drift-adjustment discipline directly and concretely: rather than leaning on the now-saturated Rs 8 billion paid-up-capital mandate that every surviving bank clears by a wide margin and that plays no part in Chapter 64's actual rubric, it checked Nabil's capital-adequacy ratio against its current NEPSE peer set — finding Nabil compliant with NRB's 11 percent floor and correctly placed in Chapter 64's 11.5–13 percent band, but sitting below the 13.08 percent sector average, ninth of twelve major banks. That peer-table context, layered on top of a fixed regulatory-threshold score, is the clearest illustration in this book so far of why model drift management is not an abstract concern but a routine habit that adds real information a rulebook alone will miss.
Tallied across all seven dimensions, Nabil's full Canon Score came to 71 out of 100 — Strong, per Chapter 64's own score bands, with Valuation Reasonableness singled out honestly as the one dimension dragging an otherwise strong scorecard down, and Financial Strength's capital-adequacy sub-score and Governance's audit-revision sub-score flagged as the two closest judgment calls where a reasonable analyst working the same facts could land a few points differently. The chapter closed by turning that score into a decision framed with appropriate humility — a demonstration of process, not personalized advice — and into a concrete, dated watch list tied to Chapter 82's ongoing-monitoring discipline: Nabil's next audited ROE figure, its next quarterly capital-adequacy standing against peers, the resolution of its promoter-share dispute, and the outcomes of NRB's pending asset quality review and Domestic Systemically Important Bank framework.
Chapter 84 turns to Case Study 2 — A Hydropower Plant, applying this same complete framework to a company whose economics could hardly be more different from a bank's. Where a commercial bank's numbers arrive on a predictable quarterly schedule under a single regulator's uniform template, a hydropower company's fortunes turn on monsoon rainfall, river flow, power purchase agreement terms, and a construction and commissioning history that, unlike a bank's loan book, cannot be smoothed by refinancing. The next chapter will show how the same seven Canon Score dimensions, the same qualitative-versus-quantitative hemisphere split, and the same drift-awareness discipline apply to a business built on rivers instead of deposits — and where the sector-specific adjustments of Chapter 65 diverge most sharply from the banking playbook this chapter just finished applying end to end.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 84
Case Study 2 — A Hydropower Plant
First published 26 Aug 2026 · Last verified 29 Aug 2026
Sabina Thapa opened her notebook to a fresh page and, out of habit, wrote the date at the top before crossing it out and writing today's date instead: August 25, 2026. She almost didn't notice the coincidence until she typed the ticker she had chosen for this case study — CHCL, Chilime Hydropower Company Limited — into her broker's terminal and the company profile line read: commissioned August 25, 2003. Twenty-three years to the day. She sat back in her chair in Kathmandu and allowed herself a small smile. It felt like a sign, though she knew better than to let a coincidence do any analytical work for her. In Chapter 83 she had taken the Canon Score — the book's 100-point, seven-dimension framework for scoring a NEPSE-listed company — and applied it start to finish to a commercial bank. That case had taught her to read a balance sheet full of loans and deposits, to weigh capital adequacy against loan growth, to treat thousands of small, similar transactions as the unit of analysis. Now she was staring at something almost the opposite: one river, one plant, one buyer, and a business whose entire personality was shaped by monsoon clouds and glacial snowmelt rather than by interest rate cycles and credit committees.
That contrast is the entire point of this chapter. A hydropower company is not a smaller, simpler version of a bank. It is a different species of business, and the same Canon Score framework has to be applied with different instincts and different sector adjustments — exactly the kind of sector-specific calibration this book asked you to build in Chapter 65, and exactly the kind of humility about the score's limits this book asked you to practice in Chapter 80. Sabina's task in this chapter is to run Chilime Hydropower Company through the full Canon Score, dimension by dimension, using real, current numbers, and to show her working — including the places where a hydropower company genuinely resists being reduced to a single tidy number.
Data vintage
Scored on 25 August 2026 using Chilime’s publicly reported figures to that date, including FY2081/82 results. The plant was damaged by the Bhotekoshi flood of 26 August 2026, one day later, and nothing in this chapter reflects that event. Upper Tamakoshi comparison figures in Lesson 84.6 were verified against NEPSE data providers in August 2026. Hydropower and financial-sector figures move with each quarterly disclosure, so re-derive every number from current filings before acting on it.
Lesson 84.1 — Setting Up the Case: One River, One Buyer, One Number
Chilime Hydropower Company Limited, traded on the Nepal Stock Exchange under the symbol CHCL, owns and operates a 22.1 megawatt (MW) run-of-river hydropower plant on the Chilime Khola in Rasuwa district, roughly 133 kilometers north of Kathmandu near the Nepal-Tibet border. A megawatt (MW) is a unit of generating capacity — think of it as the maximum rate at which a plant can produce electricity at full throttle, the way a car's horsepower rating describes its maximum output rather than what it actually uses on an ordinary drive. The Chilime plant uses two Pelton turbines (a type of water turbine well suited to Nepal's steep, fast-flowing rivers) and has been generating power since its commissioning on August 25, 2003. It typically produces around 150 gigawatt-hours (GWh) of electricity a year — a gigawatt-hour is simply a thousand megawatt-hours, a measure of energy actually delivered over time, as opposed to MW, which measures capacity at an instant.
Nepal Electricity Authority (NEA), the state-owned electric utility that is effectively the only legal buyer of wholesale electricity in Nepal, owns 51 percent of Chilime Hydropower Company, with the remaining 49 percent held by the public — including a 10 percent slice specifically reserved for local residents of Rasuwa, the district where the plant sits. Chilime sells essentially all of its output to NEA under a long-term Power Purchase Agreement, or PPA — a contract that locks in the price NEA pays per unit of electricity for a period of years, in exchange for the hydropower company committing to deliver its output reliably. Since its original IPO, Chilime has also become an investor in two much larger plants nearby: Rasuwagadhi Hydropower (111 MW) and Sanjen Hydropower (14.8 MW), both of which began commercial generation in 2024 after years of delay, including damage sustained during the 2015 earthquake. This matters for the case study, because it means the listed company Sabina is scoring is not quite a single, isolated asset — it is a legacy plant plus a growing ownership stake in newer capacity, which changes the concentration-risk conversation this chapter will return to.
Chapter 72 drew a hard line between two fundamentally different hydropower asset classes: pre-COD and operating. COD stands for commercial operation date — the day a plant is certified ready and legally allowed to start selling power under its PPA. A pre-COD hydropower company is still building; its risks are construction risk, financing risk, and the risk that the project never reaches COD at all, or reaches it years late and over budget. An operating company like Chilime, twenty-three years past COD, carries almost none of that risk. Its plant is built, tested, and has a two-decade operating history. This is the single most important qualitative fact Sabina had to establish before she touched a single number on the Canon Score.
KEY CONCEPT
Pre-COD and operating hydropower are different asset classes, not different points on the same risk scale. A pre-COD project can be wiped out by a landslide during construction or a financing shortfall before a single unit of power is ever sold. An operating project like Chilime has already survived that phase — its remaining risks are about how much water flows through an existing turbine, not whether the turbine gets built at all.
That single distinction reframes everything else. A bank's revenue comes from thousands of individual loans, deposits, and fee-generating relationships, spread across sectors and borrowers — genuinely diversified, but also genuinely uncertain, since any of those loans could sour and interest rates could move against the bank at any time. Chilime's revenue, by contrast, comes from one contractually fixed tariff schedule paid by one counterparty, NEA, for as long as the PPA runs. In a good monsoon year, the plant generates close to its physical maximum and NEA pays a known price for every unit. In a bad year, the water simply isn't there, but the price per unit doesn't change — the risk is in volume, not price. Sabina wrote in her notebook: "A bank's risk is who owes me money and whether they'll pay it back. A hydropower plant's risk is whether the sky and the glaciers cooperate — the buyer isn't going anywhere, and the price is already agreed." That is a much narrower, more predictable form of revenue risk than a bank carries — but it comes bundled with something a bank almost never has: the entire business sitting inside a single physical structure, on a single river, that cannot be moved, duplicated, or hedged if something goes badly wrong with that one location.
Lesson 84.2 — Hemisphere 1: Governance, Tradability, and Durability
Sabina followed Sushmita's method from Chapter 83 exactly: split Chapter 64's seven dimensions into the two hemispheres Lesson 83.2 introduced. Hemisphere 1 covers the qualitative dimensions — Liquidity & Tradability (10 points), Governance & Promoter Behaviour (15 points), and Sector & Business Model Durability (15 points), 40 points in total. Hemisphere 2 covers the three dimensions built from the financial statements. She worked Hemisphere 1 first, same as before: if a stock cannot actually be traded and its owners cannot be trusted, the numbers barely matter.
Liquidity & Tradability (10 points). Chapter 65's hydropower guidance is explicit that this dimension needs real scrutiny for thin-float counters, reconnecting it to Chapter 58's circuit-trap warning: a stock can look "liquid" by rising to its daily circuit limit for several sessions while almost no real volume actually clears, leaving sellers unable to exit. On the day Sabina pulled CHCL's data, the session traded just under 19,000 units at a price near Rs 478 — a single day's print rather than the 6-month average Chapter 64 actually asks for, a limitation she noted honestly rather than pretending one day's volume settles the question. Taken as a rough proxy, that print implies roughly Rs 90 lakh (about NPR 9 million) of same-day turnover, comfortably above Chapter 64's NPR 5 million top band, worth a provisional 5 out of 5 on the volume sub-component. Just as importantly, CHCL's 52-week range of roughly Rs 450 to Rs 525 — a band of about 16 percent — moves in both directions over the year rather than sitting pinned at one circuit limit, which is exactly the kind of two-way price action Chapter 65 says to check for before trusting a stock's apparent liquidity. Free float is a tighter fit: with NEA holding a fixed 51 percent stake and 10 percent reserved for local Rasuwa shareholders who tend to hold rather than trade, only about 39 percent of Chilime's roughly 94.8 million shares are genuinely free-floating — just under Chapter 64's 40 percent threshold for full marks, landing instead in the next band down, worth 3 out of 5. Liquidity & Tradability: 5 + 3 = 8 out of 10.
CASE IN POINT
A retail investor who only checks "did the price move" can mistake a circuit-trap climb for genuine demand. Chilime does not show that pattern — its price has moved within a real, two-sided 16 percent band over the past year rather than grinding up or down at the daily limit — but a Canon Score user should check for it explicitly on every hydropower counter, not assume its absence.
Governance & Promoter Behaviour (15 points). Chapter 65 flags this as a lighter adjustment for hydropower than for banks or microfinance — the standard checklist mostly applies — but Chilime has one structural wrinkle worth naming plainly: its majority shareholder, NEA, is also its sole customer under the PPA. Promoter shareholding stability and pledging (6 points) scores cleanly here — NEA's 51 percent stake has been stable for decades with no evidence of pledging, and the 10 percent locally reserved tranche is a stabilising, not a destabilizing, feature — a full 6 out of 6. Related-party transactions and audit opinion (5 points) is where the buyer-owner overlap actually belongs: a wholly independent hydropower company negotiates its PPA tariff at arm's length, while a NEA-majority-owned company negotiating with NEA itself has less obvious separation between the two sides of that contract, and Nepali hydropower more broadly carries a well-documented history of related-party risk around engineering, procurement, and construction (EPC) contractors. Chilime's own equity-stake projects, Rasuwagadhi and Sanjen, both ran years behind schedule, partly due to 2015 earthquake damage — a disclosed delay rather than a concealed one, but still worth a discount. Sabina scored this sub-component 3 out of 5. Disclosure timeliness and board independence (4 points) — Chilime has filed quarterly results on a regular cadence for two decades, including its most recent quarters, which supports on-time disclosure, but Sabina did not separately verify board independence against the regulatory minimum, so she scored this sub-component 3 out of 4 rather than a full mark. Governance & Promoter Behaviour: 6 + 3 + 3 = 12 out of 15.
WARNING
Related-party EPC arrangements are one of the most common sources of value leakage in Nepali hydropower. A construction contract awarded to a promoter-affiliated firm, without competitive tendering, can quietly transfer value away from minority shareholders years before it ever shows up as a headline problem. Always ask who built the plant and who owns the company that built it — and, for a state-linked hydropower company like Chilime, always ask who is on both sides of the power purchase agreement itself.
Sector & Business Model Durability (15 points). Chapter 64's own worked example names "a hydropower company with a signed PPA" as the textbook case for full marks on the moat sub-component (8 points), and Chilime fits it exactly: a licensed river reach that cannot be duplicated, a signed, long-standing PPA with NEA, and twenty-three years of operating history behind it. Moat: 8 out of 8. Revenue concentration (7 points) is more awkward than it looks. Chapter 64 itself flags, in its own case-in-point callout, that virtually every Nepali run-of-river hydropower company sells effectively all of its output to the single buyer, NEA — and says Chapter 65 exists to fix mismatches like this one. In practice, Chapter 65's sector table only marks this dimension "Moderate" for hydropower, without spelling out a specific numeric fix for the single-buyer structure it promised to address. Sabina's honest judgment call was to treat single-buyer concentration as a sector-wide feature rather than a Chilime-specific red flag — every hydropower peer has the same buyer — while still not awarding full marks, since it remains a genuine, structural dependency on one counterparty and one national grid operator's solvency. She scored it 4 out of 7, noting the partial diversification benefit of now holding stakes across three separate river systems (the original Chilime plant, Rasuwagadhi, and Sanjen) rather than one. Sector & Business Model Durability: 8 + 4 = 12 out of 15.
CAUTION
When a chapter's own rubric promises a fix that a later chapter does not fully deliver, the honest move is not to invent one on the spot — it is to say so, apply the closest reasonable reading of what guidance does exist, and write down why. Chapter 65's cross-sector table marks hydropower revenue concentration as only a "Moderate" adjustment; it does not hand you a replacement point band the way it does for, say, microfinance governance. Score it as fairly as you can and flag the gap rather than pretending the framework resolved something it did not.
Lesson 84.3 — Hemisphere 2: What the Numbers Actually Say
With Hemisphere 1 assessed, Sabina turned to the quantitative half of the Canon Score: Financial Strength & Profitability (20 points), Valuation Reasonableness (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points), 60 points in total — read, throughout, using Chapter 65's instruction to compare same-quarter figures year over year rather than sequential quarters, since a run-of-river plant's output swings sharply between the monsoon and dry seasons for reasons that have nothing to do with the underlying business getting better or worse.
Financial Strength & Profitability. Chapter 64's sub-component A scores a 3-year average ROE. Chilime's most recently reported ROE sits at roughly 6.7 percent, close to its 10-year median of about 7.2 percent, and its 3-year average — pulled down by a trough near 3.4 percent a couple of years ago before a partial recovery — lands below the 7 percent line Chapter 64 uses as its lowest band cutoff. Read literally, on Chapter 64's generic bands, that scores 1 out of 8. Lesson 84.4 returns to this number, because Sabina was not convinced the literal score told the whole story on its own. Sub-component B, capital adequacy or leverage discipline (7 points), uses the non-bank version of this test for a company like Chilime: Debt-to-Equity and interest coverage rather than CAR. At the parent level, Chilime's own balance sheet is close to unlevered — a net margin above two-thirds of revenue paired with an equity-to-assets ratio near one suggests debt-to-equity comfortably under 1.0x and very high interest coverage, which clears Chapter 64's top band for full marks. But this is a parent-level reading only: the real project debt sits inside the Rasuwagadhi and Sanjen investments, which are equity-accounted stakes rather than fully consolidated subsidiaries, so Chilime's own D/E ratio simply does not capture that leverage the way a full consolidation would. Sabina scored the literal, parent-level number at 7 out of 7, flagging the equity-stake caveat explicitly rather than silently assuming it away. Sub-component C, earnings quality and consistency (5 points), reflects a genuinely mixed recent record: quarterly profit and EPS have moved in both directions year over year across the last few fiscal years — a real decline in one recent year alongside modest growth in another — without any outright loss year. That fits Chapter 64's "one loss year, or two flat/declining years" band, worth 3 out of 5. Financial Strength & Profitability: 1 + 7 + 3 = 11 out of 20.
PRACTICAL TOOL
When scoring leverage for any hydropower holding company, check whether debt sits at the parent level or inside equity-accounted project investments. A parent with a clean, low-leverage balance sheet can still carry real risk through minority stakes in heavily project-financed plants — sitting outside its own D/E ratio entirely. Score what the ratio actually measures, and say plainly what it does not.
Valuation Reasonableness (15 points). Chilime trades around Rs 478 per share against recent per-share earnings near Rs 7.6 and a book value per share near Rs 125 — a price-to-earnings ratio near 63 times and a price-to-book ratio near 3.8 times. Nepal's hydropower sector has, at different points, been reported anywhere from a sector-average P/E in the high teens to broadly "trading rich" alongside most non-bank NEPSE sectors, well above banking's roughly 15-16x — a real range Sabina could not narrow to one clean number, so she treated the wider end as the more conservative comparison. Even using the more generous high-teens anchor, CHCL's ~63x P/E works out to roughly three times the sector median, deep in Chapter 64's "above 1.5x sector median" band — 1 out of 8. Its P/B of about 3.8x, checked against NEPSE's whole-exchange average of roughly 2.8x (a hydropower-specific P/B median was not something Sabina could pin down cleanly, a sourcing gap worth naming rather than hiding), works out to roughly 1.4 times that broader benchmark — Chapter 64's 1.1x-1.5x band, worth 3 out of 7. Valuation Reasonableness: 1 + 3 = 4 out of 15 — Chilime's weakest dimension by a wide margin, and a real, current finding rather than a comfortable one: on both major yardsticks, the stock is priced well above what its own earnings and book value would suggest, however the comparison is drawn.
CAUTION
Chapter 64 warns against importing a fixed "cheap" P/E from another market. It does not warn you that sometimes the sector-median benchmark itself is genuinely unsettled — different sources reporting different hydropower sector averages at different points in time. When that happens, use the more conservative comparison, state the range you found, and let the reader see exactly how much of the conclusion depends on which anchor is right.
Growth Trajectory (15 points). Revenue CAGR (8 points) at the original 22.1 MW plant has been modest — recent quarterly power-sales growth in the low single digits year over year — consistent with a mature, physically capacity-constrained run-of-river asset with little organic room left to grow on its own. The real growth story sits in the 2024-commissioned Rasuwagadhi and Sanjen stakes, whose earnings contribution is only just beginning to show up in consolidated results and has not yet had time to appear in a multi-year revenue trend. Sabina scored this sub-component 3 out of 8, in the "0%-8%" band, while flagging the new capacity as the genuine forward catalyst this trailing figure cannot yet see. Earnings consistency (7 points) reads more concerning on the numbers she could actually check: EPS has fallen year over year in more than one recent quarter, a real decline rather than a seasonal illusion, since the comparisons were same-quarter-to-same-quarter rather than sequential. Without a full, verified 5-year EPS series in hand, Sabina scored this conservatively at 2 out of 7 rather than the lowest band, flagging that a complete year-by-year record might move this figure in either direction. Growth Trajectory: 3 + 2 = 5 out of 15 — Chilime's second-weakest dimension, and one the original capacity-expansion story in Lesson 84.1 does not fully redeem, at least not yet in the trailing numbers.
Dividend & Capital Return Discipline (10 points). Chilime has paid a distribution every year for as long as Sabina could find record of: 8 percent bonus shares plus 4 percent cash for fiscal year 2081/82, down from a 12 percent distribution the year before, and as high as 15 percent in earlier years. The cash component alone, against recent EPS near Rs 7.6, works out to a payout ratio in the neighbourhood of 50 percent of earnings — squarely inside Chapter 64's 30-70 percent sensible band, paid every single year: 6 out of 6. The declining trend looks, on inspection, like conservative capital allocation rather than distress — retained cash has visibly gone into funding the Rasuwagadhi and Sanjen stakes rather than disappearing, and Sabina found no sign the payout was propped up by drawing down reserves or a one-off gain: 4 out of 4. Dividend & Capital Return Discipline: 6 + 4 = 10 out of 10 — Chilime's strongest dimension, and its clearest sign of a management team sharing profit with shareholders on a predictable schedule even while investing for growth.
Put together, Chilime's Hemisphere 2 picture is a mature, disciplined operator with an almost debt-free parent balance sheet and an unbroken dividend record, but a stock priced well above what its own earnings and book value currently support, and a growth story that exists mainly in recently commissioned capacity that has not yet worked its way into the trailing numbers.
Lesson 84.4 — Reading Return on Equity Like a Utility, Not a Bank
Chapter 64's ROE bands were built and calibrated around Nepali commercial banks — a 15 percent-plus top band, a 7-to-10 percent "near system average" band, and so on. Applying that same ruler to Chilime literally, the way Lesson 84.3 just did, gives a 3-year average ROE below 7 percent a score of 1 out of 8 — the lowest band the rubric has. Sabina's instinct was that this felt too harsh, for a reason Chapter 65 explains in the abstract but does not spell out specifically for hydropower ROE: a capital-intensive utility with a huge asset base and a large, mostly depreciated plant sitting on its books will structurally show a lower return on equity than a bank or a trading company, even when it is a genuinely well-run business earning a very healthy margin on every rupee of revenue.
Rather than quietly bump the number up, Sabina did what Sushmita had done with Nabil's capital adequacy ratio in Lesson 83.4: she kept the literal score and built a peer table to see what it actually meant in context.
Company
Return on equity
Position vs Chilime's 6.7%
Chilime Hydropower (CHCL)
~6.7%
—
Sanima Mai Hydropower
~4.7%
Below Chilime
Utilities / independent power producer sector median
~3.6%
Below Chilime
Upper Tamakoshi Hydropower
~-2.1%
Well below Chilime (financing costs from project debt overwhelming operating profit)
Read against Chapter 64's bank-calibrated bands alone, 6.7 percent looks weak. Read against its own sector — a sector where the disclosed median sits near 3.6 percent, where a well-regarded peer like Sanima Mai sits below Chilime, and where a heavily project-financed peer like Upper Tamakoshi is posting a negative return on equity because financing costs are consuming operating profit before it ever reaches shareholders — Chilime's 6.7 percent looks like one of the stronger returns among genuine hydropower peers, not a weak one. That is exactly the pattern Chapter 65 warns about in the abstract: a generic ratio measuring the wrong thing when the business model itself is unusual. Sabina left the literal 1-out-of-8 score standing in her tally, on the same discipline Sushmita modelled in Chapter 83 — the peer table changes what the number means, not the number itself — but she wrote a clear note next to it: "Do not read this sub-score as 'Chilime is a weak business.' Read it as 'the bank-built ruler under-measures every hydropower company, and Chilime still comes out ahead of its own peers on it.'"
CAUTION
A peer table does not exist to let you inflate a score you feel is unfairly low. It exists to tell you what a low or high number actually means once you know the right comparison group. Chilime's ROE sub-score stayed exactly where the literal rubric put it — the context changed how alarmed that number should make you, not the arithmetic itself.
Lesson 84.5 — The Full Worked Canon Score
With both hemispheres assessed and the ROE context applied without changing the arithmetic, Sabina assembled her full tally, using exactly the seven dimensions and point weights Chapter 64 defines.
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
11
3-yr avg ROE below 7% on bank-calibrated bands, though above the hydropower peer median → 1/8; near-unlevered parent balance sheet (equity stakes carry the real project debt) → 7/7; mixed but no-loss-year profit record → 3/5
Governance & Promoter Behaviour
15
12
Stable, unpledged 51% NEA holding → 6/6; NEA is both majority owner and sole PPA counterparty, plus disclosed subsidiary construction delays → 3/5; on-time filings, board independence unverified → 3/4
Liquidity & Tradability
10
8
Single-day print ~Rs 9 million turnover, no circuit-trap pattern in the 52-week range → 5/5; free float ~39%, just under the 40% threshold → 3/5
Valuation Reasonableness
15
4
P/E ~63x vs a high-teens-to-low-20s sector anchor (≈3x sector median) → 1/8; P/B ~3.8x vs NEPSE's ~2.8x whole-exchange average (≈1.4x) → 3/7
Sector & Business Model Durability
15
12
Licensed river reach plus signed PPA, Chapter 64's own textbook full-marks case → 8/8; 100% single-buyer (NEA) concentration, sector-standard rather than company-specific, partly offset by three separate river assets → 4/7
Growth Trajectory
15
5
Legacy-plant revenue growth low single digits; Rasuwagadhi/Sanjen contribution not yet visible in trailing figures → 3/8; EPS down year over year in more than one recent quarter → 2/7
Dividend & Capital Return Discipline
10
10
Paid every year, cash-component payout ≈50% of EPS → 6/6; funded from genuine profit, redeployed rather than reserve-drawn → 4/4
Canon Quality Score
100
62
Band: Adequate (55–69)
Summed, Chilime's Canon Score comes to 62 out of 100 — inside Chapter 64's Adequate band (55-69), a rung above Weak/Avoid and two rungs below Exceptional. No dimension triggers Chapter 64's governance override, since Governance & Promoter Behaviour scored 12 out of 15, comfortably above the 5-point floor that would cap the whole score regardless of everything else.
Sabina was honest with herself about which sub-scores were the hardest calls, and a careful analyst working the same facts could reasonably land a few points differently on any of them. Three stood out. Valuation Reasonableness depended on which hydropower sector-median P/E she trusted, and different sources genuinely disagreed by a wide margin — she used the more conservative anchor and said so, but a reader with a firmer, more current sector figure could reasonably shift this dimension a point or two in either direction. Sector & Business Model Durability's revenue-concentration sub-score was a genuine judgment call precisely because Chapter 65 promised a hydropower-specific fix for the single-buyer problem and did not fully deliver one in its own text — Sabina's 4 out of 7 reflects a reasoned middle path, not a number the rubric handed her cleanly. And Growth Trajectory's earnings-consistency sub-score was scored conservatively, at 2 out of 7, precisely because she did not have a complete, verified 5-year EPS series in hand — a fuller record could move this dimension in either direction once someone pulls it.
CAUTION
A Canon Score is a snapshot of one analyst's honest judgment on one day, built from disclosures that themselves get revised, from sector benchmarks that themselves get reported differently by different sources, and — as Lesson 84.2 showed honestly — sometimes from a sector-adjustment chapter that promises more precision than it actually delivers. Treat the number as a structured, defensible argument, not as a verdict handed down from the framework itself.
Lesson 84.6 — Scale Is Not Quality: Chilime Against Nepal’s Largest Plant
Sabina closed her Chilime worksheet with a question she had been avoiding. Chilime is a
22.1 MW plant. Nepal’s largest is the 456 MW Upper Tamakoshi Hydroelectric Project in
Dolakha, listed on NEPSE as UPPER — more than twenty times the installed capacity, a
national-pride project financed entirely with domestic money, sponsored by the state utility
itself. If size and prestige translate into investment quality, Upper Tamakoshi should score
well above Chilime. Chapter 91 works that company end to end. It scores 45 out of 100.
Seventeen points below the small plant, and in Chapter 64’s Weak/Avoid band
rather than Adequate. That gap is worth sitting with, because it is the single clearest
demonstration in this book of why the Canon Score refuses to collapse into one impression of
“good company.”
Dimension
Chilime (22.1 MW)
Upper Tamakoshi (456 MW)
Financial Strength & Profitability
11 / 20
5 / 20
Governance & Promoter Behaviour
12 / 15
11 / 15
Liquidity & Tradability
8 / 10
7 / 10
Valuation Reasonableness
4 / 15
4 / 15
Sector & Business Model Durability
12 / 15
12 / 15
Growth Trajectory
5 / 15
5 / 15
Dividend & Capital Return Discipline
10 / 10
1 / 10
Canon Score
62 / 100
45 / 100
Read the table across rather than down. On four of the seven dimensions the two companies
score identically or near-identically: both are single-asset operators selling to one
buyer, so Durability is 12 for each; both face the same hard growth ceiling once commissioned,
so Growth is 5 for each; both carry the hydropower sector’s sentiment premium, so
Valuation is 4 for each. Governance and Liquidity differ by a single point. The structural
business is, in scoring terms, almost the same business at both scales.
The entire seventeen-point gap comes from two dimensions, and both describe the same
underlying fact: how the plant was paid for.
KEY CONCEPT
A completed hydropower plant’s score is set less by how much electricity it makes
than by what it still owes. Chilime is an old, largely deleveraged plant returning cash to
shareholders. Upper Tamakoshi is a young plant servicing a debt load inflated by a decade of
construction delay. Same rivers, same buyer, same regulatory clock — opposite balance
sheets.
Dividend Discipline: 10 versus 1
This is the widest single gap in the comparison, and the most revealing. Chilime has paid
every year, funded from genuine operating profit, with a cash component near half of earnings
per share. Upper Tamakoshi has not distributed a single cash or bonus dividend to public
shareholders in the five years since the plant was commissioned — verified
against NEPSE data providers in August 2026, all of which return an empty dividend
history for the ticker. Its only shareholder action has been the opposite of a payout:
a 1:1 rights issue in FY2080/81 that asked shareholders for more money. A retail investor who bought UPPER expecting a utility’s
income stream has, so far, received nothing at all — and Chapter 64 scores what a company
has actually done, not what its size suggests it ought to be able to do.
Financial Strength: 11 versus 5
Upper Tamakoshi posted losses for four consecutive fiscal years after commissioning, because
interest on its construction debt consumed operating profit before it reached shareholders.
Chapter 32 traces the arithmetic: roughly NPR 24 billion of capitalised interest sat on top of
a roughly NPR 52 billion construction cost, against an originally approved budget near NPR 35
billion. The plant generates the electricity its engineers promised. It is the financing, not
the engineering, that the score is punishing.
CASE IN POINT
Both plants have now been taken offline by water, two years and one river apart. A
landslide during the heavy rains of 27–28 September 2024 destroyed Upper
Tamakoshi’s control room, desanders and culvert, killing four people and halting
generation for 88 days; partial output resumed at 120 MW that December, and full 456 MW
capacity only in late June 2025. Reported damage ran to roughly NPR 2 billion against an
insurance claim near NPR 1.8 billion, with lost revenue around NPR 40 million per day. The
outage drove a net loss of roughly NPR 2.57 billion in FY2024/25 on revenue of about NPR
6.92 billion. Chilime, on a different river in a different district, was damaged by the
Bhotekoshi flood of 26 August 2026. Neither event was a failure of management. Both are the
single-asset concentration risk this chapter has described from its first page, arriving on
a schedule nobody sets.
There is a mechanical detail in Upper Tamakoshi’s Power Purchase Agreement worth
carrying to any hydropower analysis: the plant is designed for a discharge of 66 cubic metres
per second and is contractually required to shut down when river flow exceeds 250. During the
September 2024 event, flow at the dam site reached roughly 461. A run-of-river plant can be
stopped not only by too little water but by far too much of it, and the PPA itself specifies
the threshold. That is a modellable risk, and most retail models never model it.
PRACTICAL TOOL
Before scoring any Nepali hydropower company, write down three numbers from the PPA and
the notes to accounts: design discharge, the flow at which the plant must shut down, and
capitalised interest as a share of total project cost. The first two tell you how often the
asset stops earning. The third tells you whether shareholders will see the money when it
does earn.
The lesson generalises past hydropower. A larger company is not a safer one; a
nationally important asset is not automatically a good holding; and a plant that produces
twenty times the electricity can be a materially worse investment than a small one that has
paid its debts and shares its profits. The full Upper Tamakoshi analysis — the
earthquake, the decade of delay, the interest-during-construction mechanism, and each of the
seven sub-scores — is Chapter 91.
Lesson 84.7 — The Decision, and What Would Change It
A score of 62 out of 100 sits inside Chapter 64's Adequate band — investable, per the book's own definition of that band, but sized carefully and watched closely on its weak dimensions, a rung below Strong and two rungs below Exceptional. Reading the seven sub-scores rather than stopping at the total tells the real story: Chilime is comfortably strong on Dividend discipline and Sector Durability, respectably placed on Governance and Liquidity, and dragged down by two dimensions working together — Valuation Reasonableness at 4 out of 15 and Growth Trajectory at 5 out of 15 — with Financial Strength held back mainly by an ROE sub-score that the Lesson 84.4 peer table suggests is measuring the whole sector's structural profile as much as anything Chilime-specific. Sabina's honest conclusion, working purely through the process: Chilime is a mature, well-run, dividend-disciplined hydropower company trading at a price that has run well ahead of both its current earnings and its trailing growth, with real new capacity in Rasuwagadhi and Sanjen that has not yet shown up in the numbers that matter for scoring. That combination argues for patience rather than either enthusiasm or alarm — a name worth watching for the new capacity's earnings to actually appear in trailing figures, or for the price to settle toward what the current fundamentals support, rather than a name to chase at the present valuation or to write off entirely.
CAUTION
This case study is a demonstration of a process, not a personal recommendation to buy, hold, or avoid Chilime Hydropower Company or any other security. It shows how the Canon Score's machinery, honestly applied to real, verifiable numbers, arrives at a conclusion — not what any individual reader should do with their own capital.
What would move this score? Sabina named four concrete, watchable developments. First, and most directly within her control to check, the next few quarters' consolidated results actually showing Rasuwagadhi and Sanjen's earnings contribution flowing through — this would be the clearest test of whether Growth Trajectory's low score is a genuine trailing-data lag or something more persistent, and it is the single change most likely to move Chilime materially up the band. Second, either a price correction toward the sector's actual valuation anchor or several years of EPS growth catching up to the current price would resolve Valuation Reasonableness's weak score one way or the other. Third, the royalty step-up dates already calendared for Rasuwagadhi and Sanjen in the late 2030s, and a PPA renewal whenever Chilime's own agreement approaches its end, remain known, schedulable events worth a standing calendar entry rather than a surprise. Fourth, a genuinely bad drought year — an unusually weak monsoon — would test generation and near-term cash flow directly, though a single weak year would not by itself change the plant's decades-long average flow profile.
PRACTICAL TOOL
Build a simple calendar of known, dated events for any hydropower holding: PPA expiry dates, royalty step-up anniversaries (year 15 from each plant's COD), and the first few reporting periods after a major new asset's commissioning, when its earnings contribution should start to show up in consolidated numbers. These are not predictions — they are known future checkpoints worth revisiting the score against.
Sabina closed her notebook feeling that this case study had taught her something Chapter 83 could not have: that applying the same seven-dimension framework honestly sometimes means admitting a sector-adjustment chapter did not fully deliver what it promised, building your own peer context where the rubric's literal bands mismeasure a business model, and still landing on a number you can defend line by line. A score is only as trustworthy as the analyst's honesty about where it came from.
Chapter recap
This chapter applied the Canon Score's full seven-dimension framework, exactly as Chapter 64 defines it and Chapter 65 adjusts it for hydropower, to Chilime Hydropower Company Limited, a NEPSE-listed, NEA-majority-owned operating hydropower company running a 22.1 MW plant on the Chilime Khola in Rasuwa district since August 2003, now expanded through equity stakes in the larger Rasuwagadhi and Sanjen plants commissioned in 2024. Hemisphere 1 scored Liquidity & Tradability (8/10 — real same-day turnover and no circuit-trap pattern, offset by free float just under the 40% threshold), Governance & Promoter Behaviour (12/15 — a stable, unpledged majority holding complicated by NEA sitting on both sides of the power purchase agreement, plus disclosed subsidiary construction delays), and Sector & Business Model Durability (12/15 — a textbook licensed-river-plus-PPA moat, offset by single-buyer revenue concentration that Chapter 65 flagged for a sector-specific fix it did not fully deliver). Hemisphere 2 scored Financial Strength & Profitability (11/20), Valuation Reasonableness (4/15), Growth Trajectory (5/15), and Dividend & Capital Return Discipline (10/10) — an almost debt-free parent balance sheet and an unbroken, sensibly funded dividend record, set against a stock priced well above its trailing earnings and book value, and a growth story concentrated in newly commissioned capacity that has not yet appeared in the numbers.
The chapter's central methodological lesson, in Lesson 84.4, mirrored Chapter 83's drift-adjustment discipline rather than inventing a new mechanic: Chilime's return on equity, scored 1 out of 8 on Chapter 64's bank-calibrated bands, looked weak in isolation but landed above its own hydropower and independent-power-producer peers once checked against a real peer table — evidence that a generic ratio can measure the wrong thing for an unusual business model, exactly as Chapter 65 warns in the abstract, without ever changing the literal arithmetic score itself.
Tallied across all seven dimensions, Chilime's full Canon Score came to 62 out of 100 — Adequate, per Chapter 64's own score bands, with Valuation Reasonableness and Growth Trajectory singled out honestly as the two dimensions dragging an otherwise disciplined scorecard down, and the revenue-concentration and sector-P/E-benchmark sub-scores flagged as the closest judgment calls where a reasonable analyst working the same facts could land a few points differently. The chapter closed with a decision framed as patience rather than conviction in either direction, and a concrete, dated watch list: whether Rasuwagadhi and Sanjen's earnings actually show up in consolidated results, whether price or earnings close the current valuation gap, the already-calendared royalty step-up and eventual PPA renewal dates, and the ever-present risk of a genuinely bad monsoon year.
Chapter 85 turns to a third and structurally distinct case study: a microfinance institution, or MFI, a lender specialising in small, often collateral-light loans to low-income and rural borrowers who typically sit outside the reach of commercial banks. Readers will find yet another set of sector-specific wrinkles waiting there — interest rate caps unique to microfinance regulation, loan-recycling and multiple-borrowing governance risks invisible in a headline NPL ratio, and provisioning-coverage-weighted financial strength standing in for the ratios a commercial bank or hydropower case study would use. Sabina Thapa, having now worked a bank and a hydropower plant end to end, will carry the same framework and the same hard-won honesty about a rubric's own limits into that third case, continuing this Part's project of showing that mastery of the Canon Score is built one worked case at a time, not memorised once and applied blindly forever after.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 85
Case Study 3 — A Microfinance Institution
First published 26 Aug 2026 · Last verified 29 Aug 2026
Anjana Koirala opened her notebook to a fresh page and wrote two words at the top: "small loans." She had spent Chapter 83 inside the balance sheet of a commercial bank, learning to read capital adequacy ratios and net interest margins the way a mechanic reads an engine's vital signs. Chapter 84 had taken her up a hillside penstock, learning to price a hydropower company's water year by water year, discounting a river the way you would discount a bond. Now she was looking at a business built on loans of twenty, forty, sixty thousand rupees each, made to women running vegetable stalls, buffalo sheds, and tailoring shops in villages she had never visited. Her bank case study had had a market capitalisation measured in tens of arba (one arba equals one billion rupees, roughly the scale at which Nepal habitually counts its largest listed companies). Her hydropower case study had had a single, physical, inspectable asset: a dam, a tunnel, a powerhouse. This company had neither a single site to visit nor a single number that fully captured its risk. It had, instead, hundreds of thousands of tiny, unsecured promises, scattered across dozens of districts, each one no bigger than a mid-sized grocery bill back home in Kathmandu.
That, she reminded herself, was the whole point of doing this as her third case study. The Canon Score — the 100-point, seven-dimension scoring framework this book has built chapter by chapter — is only as good as an investor's willingness to apply it honestly to businesses that do not look like each other. A bank borrows short and lends long against collateral and a regulatory capital cushion. A hydropower company sells a single physical commodity, electricity, under a long-term contract, and lives or dies by rainfall and interest rates. A microfinance institution (an MFI — a company whose entire business is making small, typically collateral-free loans to low-income borrowers, often organised in groups) does something different again: it manufactures trust, at scale, in places the formal banking system finds too expensive to reach. Getting the Canon Score right for an MFI meant testing whether the framework's discipline survived contact with a genuinely different kind of risk. This chapter follows Anjana through that test, using a real, currently NEPSE-listed microfinance institution: Chhimek Laghubitta Bittiya Sanstha Limited, trading under the ticker CBBL.
Data vintage
Scored using Chhimek Laghubitta’s publicly reported figures for its most recent disclosed fiscal year as at mid-2026, and NRB’s microfinance pricing framework as amended effective Shrawan 2082 (mid-July 2025). Hydropower and financial-sector figures move with each quarterly disclosure, so re-derive every number from current filings before acting on it.
Lesson 85.1 — Setting Up the Case: A Different Kind of Bank
Chhimek Laghubitta was established in 2058 BS (2001 AD) as one of Nepal's earliest Grameen-model microfinance replicators — meaning it borrowed its founding method from the Grameen Bank of Bangladesh, the institution credited with popularizing group-based microlending to poor, largely rural women. Over two and a half decades it grew from a single-district pilot into one of Nepal's largest laghubitta ("small finance," the Nepali term used for microfinance institutions) networks, with a branch footprint spanning well over a hundred offices across the country and a borrower base numbering in the hundreds of thousands, concentrated among women in rural and semi-urban households who use small loans to finance livestock, retail trade, agriculture, and cottage-industry work. It is one of the older, larger, and more closely watched names in a sector that, unusually for Nepal, has an enormous number of separately listed companies: roughly fifty microfinance institutions trade on NEPSE, a level of sector fragmentation with no real parallel among Nepali banks or hydropower companies.
Anjana's first task, before she touched a single number, was to understand why an MFI needs its own scoring lens rather than a copy of the bank playbook from Chapter 65. Three structural facts drove this.
The first is the joint-liability-group (JLG) lending model itself. Rather than lending to individuals against collateral, as a commercial bank does, most Nepali MFIs organise borrowers into small groups — typically five to seven women — who meet regularly, often weekly, and who each guarantee the others' loans informally through peer pressure and shared reputation rather than through a mortgage or a fixed deposit.
KEY CONCEPT
Joint liability group (JLG) lending replaces collateral with social collateral: a small circle of borrowers who know each other's businesses and families, and who each have a stake in the others repaying, because default by one member can affect the group's — and sometimes the whole centre's — access to future credit.
This is a powerful mechanism when it works, because it turns a village's own social fabric into an underwriting tool a bank could never replicate. But it is also a mechanism that can fail all at once: when a whole village's cash flow is hit by the same shock — a bad monsoon, a remittance slowdown, a local market collapse — the very thing that made repayment reliable (shared local conditions) turns into a common point of failure. A bank's thousands of individually underwritten mortgage borrowers are far less correlated with each other than a microfinance centre's thirty group members farming the same watershed.
The second structural fact is the dual mandate. An MFI is chartered, in part, on a social mission — expanding financial access to households the formal banking system has historically ignored — alongside its obligation, as a NEPSE-listed company, to deliver a commercial return to shareholders. These two mandates usually pull in the same direction, but not always: a purely profit-maximising lender might raise loan sizes and interest rates faster than borrower repayment capacity can bear, while a purely mission-driven one might resist raising loan-loss provisions during a stress cycle for fear of looking like it is retreating from its social purpose. An investor reading Chhimek's numbers has to keep both mandates in view.
The third is regulatory. Nepal Rastra Bank (NRB, the central bank), which regulates banks, hydropower financing, and microfinance institutions all under one roof but with very different rulebooks for each, has historically applied tools to microfinance that it does not apply to commercial banks at all.
REGULATORY DETAIL
For years, NRB capped the effective interest rate microfinance institutions could charge borrowers at a flat ceiling (around 15 percent all-in), and separately capped the interest rate spread — the gap between an MFI's cost of funds and what it charges borrowers — at a fixed number of percentage points. Effective from Shrawan 2082 (mid-July 2025), NRB moved away from that flat ceiling toward a base-rate-plus-premium framework, similar in spirit to how commercial banks already price loans: each MFI publishes its own base rate, reflecting its actual cost of funds and overheads, and then adds a regulator-bounded premium on top, rather than being handed one uniform number for the whole sector.
This single regulatory change matters enormously for an MFI's future profitability, because it determines how much room an institution has to reprice its loan book as its own funding costs move — a lever a commercial bank takes almost for granted but that microfinance institutions in Nepal have only recently been given in a more flexible form.
Anjana's note to herself at the end of this lesson: everything from here on has to be read through these three lenses — social collateral that can fail correlated, not independent; a dual mandate that can quietly trade off against itself in a downturn; and a regulatory regime for pricing that has just changed shape under the sector's feet.
Lesson 85.2 — Hemisphere 1: Liquidity, Governance, and Durability
Anjana followed the same method Sushmita and Sabina had used before her: split Chapter 64's seven dimensions into Hemisphere 1 (Liquidity & Tradability, Governance & Promoter Behaviour, and Sector & Business Model Durability, 40 points total) and Hemisphere 2 (the four dimensions built from the financial statements). She worked Hemisphere 1 first.
Liquidity & Tradability (10 points). Chhimek's 30-day average trading volume runs close to 9,700 shares a day; at a recent price near Rs 930, that works out to roughly Rs 90 lakh (about NPR 9 million) of daily turnover — comfortably above Chapter 64's NPR 5 million top band, worth 5 out of 5. Its 52-week range of roughly Rs 882 to Rs 1,093 is a genuine two-way band of about 24 percent, not a stock pinned at a circuit limit. Free float is stronger than Anjana expected going in: with promoters holding 51 percent and the public holding the remaining 49 percent, Chhimek clears Chapter 64's 40 percent free-float threshold comfortably, worth another 5 out of 5. Liquidity & Tradability: 5 + 5 = 10 out of 10 — a genuinely liquid microfinance name, not the thin, hard-to-trade stock the sector's reputation might suggest.
WARNING
Do not assume every microfinance name is thinly traded just because the sector as a whole has a reputation for it. Chhimek's own numbers clear Chapter 64's liquidity bands comfortably — check the actual float and volume for the specific company in front of you, rather than inheriting the sector's general reputation wholesale.
Governance & Promoter Behaviour (15 points). Chapter 65 flags this as the sector's heaviest-rewrite dimension, supplementing the generic checklist with two microfinance-specific red flags: loan recycling (issuing a fresh loan to a struggling borrower specifically to repay an old one about to go overdue, which keeps reported NPL artificially low while the borrower's real debt burden worsens) and multiple borrowing, or overlap, across competing lenders in the same village. Promoter shareholding and pledging (6 points) scores cleanly: the 51 percent promoter stake shows no evidence of pledging or recent decline, worth a full 6 out of 6. The recycling-and-overlap check (5 points) is where Chapter 65's own warning matters most: a clean NPL ratio alone is not proof of clean lending, since recycling can suppress the headline number without fixing the underlying borrower distress. But Chhimek's non-performing loan ratio held near 2.3 percent through the entire 2021-2026 sector stress period — the lowest in the sector by a wide margin, against a sector average that climbed to roughly 11.35 percent and eighteen listed peers crossing 10 percent NPL — and a multi-year, full-cycle result is a materially harder thing to fake through recycling than one clean quarter would be. Anjana scored this sub-component 4 out of 5, crediting the durability of the result while still not awarding an automatic full mark for a metric Chapter 65 explicitly warns can mislead. Disclosure timeliness and board independence (4 points) — Chhimek files regular quarterly results and is one of the more closely watched names in its sector, supporting on-time disclosure, but board independence was not separately verified, so Anjana scored this sub-component 3 out of 4. Governance & Promoter Behaviour: 6 + 4 + 3 = 13 out of 15.
CASE IN POINT
Nepal's 2021-2026 microfinance stress episode is the clearest natural experiment available for governance quality in this sector: the institutions that had kept branch and staff growth roughly in line with their internal audit and supervision capacity came through with far smaller portfolio-quality deterioration than the ones that expanded fastest beforehand. A single clean quarter tells you almost nothing; a clean multi-year result through an actual sector-wide stress cycle tells you a great deal.
Sector & Business Model Durability (15 points). Chapter 65 flags this as a heavy adjustment for microfinance too, for a different reason than governance: regulatory concentration risk. Because interest rates, provisioning rules, and lending practice across the entire sector are tightly directed by NRB policy — including the periodic rate caps and the 2025 shift to a base-rate-plus-premium regime described in Lesson 85.1 — a microfinance institution's durability is unusually exposed to regulatory shifts compared with a less directly regulated business. The moat sub-component (8 points) reflects a licensed, NRB-regulated activity with real entry barriers (branch buildout capital, a lending license, years of borrower-trust-building), but the sector is also unusually fragmented — roughly fifty separately listed microfinance institutions compete on NEPSE, a level of fragmentation with no real parallel among Nepali banks or hydropower companies — which caps this at a moderate rather than dominant position: 5 out of 8. The revenue-concentration sub-component (7 points), read here as geographic and borrower-livelihood diversification rather than customer concentration, favours Chhimek clearly: its decades-long, multi-district branch network across the Tarai, hills, and mountain belts is real diversification against the correlated local shocks Lesson 85.1 described, a genuine and unglamorous edge over smaller, more regionally concentrated laghubitta peers. Anjana scored this 6 out of 7. Sector & Business Model Durability: 5 + 6 = 11 out of 15.
Lesson 85.3 — Hemisphere 2: What the Numbers Actually Say
With Hemisphere 1 assessed, Anjana turned to the quantitative half of the Canon Score: Financial Strength & Profitability (20 points), Valuation Reasonableness (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points).
Financial Strength & Profitability. Chapter 65 says this dimension needs real surgery for microfinance: loan-loss provisioning coverage and portfolio quality should weigh more heavily than they would for a commercial bank, since unsecured group lending carries structurally higher default risk than a typical bank's collateralized book. Chhimek's net profit came to roughly Rs 100 crore for the most recent fiscal year, down 5.83 percent from the year before, against equity of roughly Rs 9 arba (net worth per share near Rs 249, against 3.62 crore shares outstanding) — a return on equity near 11 percent, in Chapter 64's 10-15 percent band, worth 6 out of 8 on the ROE sub-component. Capital adequacy or leverage discipline (7 points) was the hardest sub-component to pin down precisely: Anjana could not find a single disclosed capital-adequacy figure for Chhimek the way NRB requires banks to publish one, so she used net worth per share (roughly 2.5 times the Rs 100 face value, reflecting two decades of retained earnings) as a rough proxy for a well-capitalised institution, scoring this conservatively at 5 out of 7 and flagging the data gap rather than pretending a precise ratio existed. Earnings quality and consistency (5 points) is where the recent asset-quality evidence matters most: profit dipped in one recent year, but with the sector's provisioning-and-margin story in mind — a rate-cap transition compressing spreads sector-wide, not a credit-quality collapse — and set against Chhimek's NPL ratio holding near 2.3 percent through the entire multi-year sector stress cycle (versus a sector average that climbed to roughly 11.35 percent), Anjana scored this generously at 4 out of 5 rather than the default "one flat year" band, on the reasoning Chapter 65 specifically asks for: weight provisioning-quality evidence more heavily than a bank's smoother profit-history norm would suggest. Financial Strength & Profitability: 6 + 5 + 4 = 15 out of 20.
PRACTICAL TOOL
When an MFI's own disclosures don't hand you a clean capital-adequacy ratio the way a bank's quarterly filing does, net worth per share relative to face value is a rough, honest proxy for capital cushion built from retained earnings — say so plainly rather than presenting an estimate as a precise regulatory figure.
Valuation Reasonableness. Chhimek trades around Rs 930 per share against trailing earnings near Rs 34-36 per share and book value near Rs 250 per share — a price-to-earnings ratio near 26-28 times and a price-to-book ratio near 3.7-3.9 times. Nepal's microfinance sector has recently carried one of the richest average valuations on the whole exchange — a sector-average P/E cited around 47 times, second-highest among NEPSE's eleven sectors — which makes Chhimek's own P/E, at roughly 0.6 times that sector average, land in Chapter 64's cheapest band: 0.8x sector median or below, worth a full 8 out of 8. That is a genuinely interesting, current finding: the microfinance sector as a whole trades rich, but the market is pricing Chhimek — arguably the cleanest name in the sector through its worst stress cycle in years — at a real discount to its own peers. P/B is a harder comparison to make cleanly: a microfinance-specific sector P/B median was not something Anjana could pin down with confidence, so she checked Chhimek's 3.7-3.9x against NEPSE's whole-exchange average of roughly 2.8x, a rougher but honestly-labelled anchor, landing at about 1.4 times that broader benchmark — Chapter 64's 1.1x-1.5x band, worth 3 out of 7. Valuation Reasonableness: 8 + 3 = 11 out of 15.
CASE IN POINT
A stock trading cheap relative to its own sector is not automatically a bargain — sometimes the whole sector is cheap for a good reason. Here the direction cuts the other way: microfinance broadly trades rich, and Chhimek trades at a real discount to that already-rich sector despite carrying the best asset-quality record through the sector's own stress cycle. That gap between price and demonstrated quality is worth sitting with, not just noting in passing.
Growth Trajectory. Loan book growth has slowed deliberately across the sector since the 2021-22 stress episode, and Chhimek's own recent figures show the same pattern — profit essentially flat-to-declining in the most recent full fiscal year before a partial recovery in more recent quarters. Anjana scored the revenue/loan CAGR sub-component 3 out of 8, in Chapter 64's 0-8 percent band, reading the deceleration as a discipline choice consistent with the sector's post-stress caution rather than a demand collapse. EPS consistency showed a similar mixed-but-recovering pattern — one down year, followed by EPS climbing back above its prior level in the most recent quarter — fitting Chapter 64's "positive in 3 of 5 years, moderate swings" band, worth 4 out of 7. Growth Trajectory: 3 + 4 = 7 out of 15 — Chhimek's weakest dimension, and a real one: deliberate post-stress deceleration is a defensible choice, but it is still a growth story that has genuinely slowed, not merely a market misperception to correct.
Dividend & Capital Return Discipline. For the most recent fiscal year, Chhimek distributed a 12.5 percent bonus share issue plus a 12.5 percent cash dividend. The cash component alone, against EPS near Rs 34, works out to a payout ratio near 37 percent of earnings — squarely inside Chapter 64's 30-70 percent sensible band. Combined with two decades of retained earnings visible in its net worth per share, this reads as a company with a consistent distribution record, worth 6 out of 6 for consistency. Anjana found no evidence the distribution was funded by anything other than genuine profit or that reserves were being drawn down, worth 4 out of 4. Dividend & Capital Return Discipline: 6 + 4 = 10 out of 10 — tied with Liquidity as Chhimek's strongest dimension.
Lesson 85.4 — Reading a Clean NPL Ratio Honestly, and What Chapter 80 Confirms
Before assembling the full score, Anjana paused on the single most important piece of evidence in this whole case study: Chhimek's non-performing loan ratio holding near 2.3 percent, the best in its sector, through a multi-year stress cycle that pushed the sector average above 11 percent and put eighteen listed peers over the 10 percent threshold. This is exactly the kind of sector-specific evidence Chapter 65 says to weight heavily for microfinance — and exactly the kind of number Chapter 65 also warns not to trust blindly, since loan recycling can manufacture a clean-looking NPL ratio that does not reflect real borrower health.
Two things convinced Anjana this particular number deserved real trust rather than suspicion. First, duration: recycling can flatter a single quarter, but sustaining the sector's best asset-quality figure across a multi-year cycle that genuinely broke many of its peers is a far harder thing to fake. Second, corroboration: Chhimek's governance profile — the multi-district diversification from Lesson 85.2, the absence of any disclosed pledging or ownership instability — is consistent with an institution that built supervisory capacity alongside its branch network, rather than one manufacturing a clean number while its underlying book quietly deteriorated. Neither point makes recycling impossible to rule out with total certainty from the outside — Anjana was honest that a retail investor working from public disclosures alone cannot fully verify this the way an on-site auditor could — but together they were enough to treat the 2.3 percent figure as real evidence rather than a number to distrust reflexively.
This is also the right place to connect Chhimek's case to something else in this book. Chapter 80, on calibrating the scoring model, follows an NRB veteran named Bimal Sharma discovering — through his own, independently chosen test cases — that the costliest gap in how he had actually been applying the Canon Score was exactly this one: failing to ask Chapter 65's two microfinance-specific Governance questions (whether loan growth or NPL trends show signs of recycling, and whether borrowers show evidence of overlapping debt with other lenders in the same district) before finalising a score. Bimal's fix was a mandatory checklist forcing those two questions to actually be asked every time — a process change, not any change to Chapter 64's point weights. Anjana had not read Chapter 80 before starting this case study, and arrived at the same place from a different direction: she asked exactly those two questions of Chhimek's NPL record before trusting it, rather than crediting a clean number on sight. Two analysts, two different real companies, landing on the same sector-specific check Chapter 65 had already written down — that is what a well-calibrated framework is supposed to produce.
CASE IN POINT
A framework earns trust when independent analysts, working different companies, keep landing on the same sector-specific checks for the same reasons — not because they copied each other, but because the underlying evidence keeps pointing the same way. Chapter 80's Bimal and this chapter's Anjana never compared notes; Chapter 65's microfinance guidance is what did the work in both places.
Lesson 85.5 — The Full Worked Canon Score
With both hemispheres assessed, Anjana assembled her full tally using exactly the seven dimensions and point weights Chapter 64 defines.
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
15
ROE ~11% (3-yr basis) → 6/8; capital cushion estimated via net worth per share, no disclosed CAR figure → 5/7; one down year against an exceptional multi-year NPL record → 4/5
Governance & Promoter Behaviour
15
13
Stable 51% promoter holding, unpledged → 6/6; NPL held near 2.3% through a multi-year sector stress cycle, credited but not blindly → 4/5; regular filings, board independence unverified → 3/4
Liquidity & Tradability
10
10
~Rs 9m/day turnover → 5/5; free float 49% → 5/5
Valuation Reasonableness
15
11
P/E ~27x vs ~47x sector average (≈0.6x) → 8/8; P/B ~3.8x vs NEPSE's ~2.8x whole-exchange average (≈1.4x) → 3/7
Paid every year, cash payout ≈37% of EPS → 6/6; funded from genuine profit → 4/4
Canon Quality Score
100
77
Band: Strong (70–84)
Summed, Chhimek's Canon Score comes to 77 out of 100 — inside Chapter 64's Strong band, a rung above Adequate and within real reach of Exceptional. No dimension triggers the governance override, since Governance & Promoter Behaviour scored 13 out of 15, comfortably above the 5-point floor.
Anjana was honest about the closest calls. The capital-adequacy sub-score was scored conservatively specifically because she could not find a disclosed figure — a reader with access to Chhimek's full regulatory filings could reasonably move this up or down once a real number is in hand. The NPL-driven governance and earnings-quality credit was a genuine judgment call, not something the rubric handed her cleanly — she leaned on duration and corroboration to justify trusting the number, but a more skeptical analyst could reasonably score both sub-components a point or two lower. And the sector-fragmentation discount inside Sector & Business Model Durability reflects a real, structural feature of Nepali microfinance — fifty-odd separately listed competitors — that a reader focused only on Chhimek's own individual strength might be tempted to score higher.
CAUTION
A Canon Score is a snapshot of one analyst's honest judgment on one day, built from disclosures that themselves have real gaps — as the missing capital-adequacy figure here shows — and, on occasion, from a book whose own chapters do not fully agree with each other. Treat the number as a structured, defensible argument, not a verdict handed down from a framework that is above being checked.
Lesson 85.6 — The Decision, and What to Watch
A score of 77 out of 100 sits inside Chapter 64's Strong band — a solid long-term holding candidate worth owning with normal monitoring. Reading the seven sub-scores rather than stopping at the total tells the real story: Chhimek is comfortably strong on Liquidity, Dividend discipline, and Valuation, respectable on Governance and Financial Strength, and dragged down by two related weaknesses — Growth Trajectory at 7 out of 15 and, more moderately, Sector & Business Model Durability at 11 out of 15 — both rooted in the same underlying reality: a mature institution in a fragmented, post-stress sector that has deliberately chosen discipline over speed. Anjana's honest conclusion: Chhimek looks like one of the stronger names in a sector the broader market is still pricing with real caution, trading at a discount to its own rich sector despite the best demonstrated asset quality through a genuine multi-year stress test. That combination argues for a real, properly sized position rather than either enthusiasm or avoidance — a name whose price has not yet caught up to what its loan-book discipline has already proven.
CAUTION
This case study is a demonstration of a process, not a personal recommendation to buy, hold, or avoid Chhimek Laghubitta or any other security. It shows how the Canon Score's machinery, honestly applied to real, verifiable numbers, arrives at a conclusion — not what any individual reader should do with their own capital.
Chapter 82's drift-monitoring discipline applies here as much as it did to the bank and the hydropower plant. Three items matter most for a microfinance holding. First, the quarterly NPL and provisioning trend, read against the sector average rather than in isolation — a modest uptick that merely tracks the whole sector's cycle is a different signal than one that outpaces peers facing the same regulatory and macro environment. Second, any further change to NRB's base-rate-plus-premium regime, since it is still new enough that its full effect on sector-wide portfolio yields has not fully shown up in the numbers yet. Third, whether loan growth resumes at a pace that outruns the institution's own supervisory capacity — the same growth-versus-oversight discipline that separated strong from weak microfinance institutions during the 2021-2026 stress cycle in the first place.
WARNING
A single quarter's NPL uptick, especially right after harvest season or during a remittance slowdown, is not automatically a reason to sell — but a multi-quarter deterioration that outpaces the sector median is exactly the kind of drift Chapter 82 asks investors to treat as a trigger to re-score, not merely to note and forget.
Anjana closed her notebook on this third case study having learned something the bank and hydropower cases had not fully prepared her for: that a clean-looking number like a 2.3 percent NPL ratio is only trustworthy once you have actually asked the sector-specific questions Chapter 65 insists on, rather than crediting it on sight — and that this book's own Chapter 80 had arrived at the same requirement independently, from a completely different direction. A framework is only as trustworthy as an analyst's willingness to keep checking its cleanest-looking numbers against the questions that could unmask them, not just against the company being scored.
Chapter recap
This chapter applied the Canon Score's real seven-dimension framework, as Chapter 64 defines it and Chapter 65 adjusts it for microfinance, to Chhimek Laghubitta Bittiya Sanstha (CBBL), one of Nepal's largest and longest-operating microfinance institutions. Hemisphere 1 scored Liquidity & Tradability (10/10 — real daily turnover and a genuinely available 49% free float, contrary to the sector's thin-liquidity reputation), Governance & Promoter Behaviour (13/15 — a stable, unpledged promoter holding and a non-performing loan ratio that held near 2.3% through a multi-year sector stress cycle, credited carefully rather than automatically per Chapter 65's own warning about recycling), and Sector & Business Model Durability (11/15 — genuine multi-district diversification offset by a highly fragmented, roughly fifty-competitor sector). Hemisphere 2 scored Financial Strength & Profitability (15/20), Valuation Reasonableness (11/15 — cheap relative to an expensive sector on P/E, more ordinary on P/B), Growth Trajectory (7/15 — deliberately slowed since the sector's 2021-22 stress episode), and Dividend & Capital Return Discipline (10/10).
The chapter's central methodological finding, in Lesson 85.4, connected Chhimek's case to this book's own Chapter 80: Bimal Sharma's calibration exercise there independently identified the same two Chapter 65 microfinance questions — checking for loan recycling and for borrower overlap across lenders — as the single costliest gap in how the Canon Score gets applied to lending institutions, and fixed it with a mandatory checklist rather than any change to Chapter 64's point weights. Anjana, working Chhimek's numbers with no knowledge of Bimal's exercise, asked exactly those same questions before trusting Chhimek's exceptional NPL record, landing on the same real requirement from a different direction — a stronger form of validation than either chapter simply asserting that Chapter 65's guidance matters.
Tallied across all seven dimensions, Chhimek's full Canon Score came to 77 out of 100 — Strong, per Chapter 64's own bands — with Growth Trajectory and Sector Durability flagged honestly as the two dimensions holding the score back, and the missing capital-adequacy disclosure and the NPL-trust judgment call named as the closest calls a different analyst could reasonably score differently. The chapter closed by tying the decision to Chapter 82's drift-monitoring discipline: watch the quarterly NPL trend against the sector, watch how the still-new base-rate-plus-premium regime settles in, and watch whether loan growth resumes faster than supervisory capacity can keep up — the same growth-versus-oversight question that separated Nepal's strong microfinance institutions from its weak ones during the 2021-2026 stress cycle.
Chapter 86, Case Study 4 — A Manufacturing Company, takes this same worked-example discipline into a fourth, again fundamentally different terrain: a real, NEPSE-listed manufacturer, where the central risks shift again — to raw material input costs, capacity utilisation, import dependence and customs duty exposure, and a competitive landscape shaped by both domestic rivals and cross-border imports. Readers who have followed Anjana through all three case studies so far should expect Chapter 86 to ask the same foundational question this chapter asked of microfinance: which of the Canon Score's seven dimensions need a sector-specific lens, grounded in real, verifiable numbers rather than an invented shortcut.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 86
Case Study 4 — A Manufacturing Company
First published 26 Aug 2026 · Last verified 29 Aug 2026
Kabita has now sat through three full Canon Score workups in this Part — a commercial bank in Chapter 83, a hydropower developer in Chapter 84, a microfinance institution in Chapter 85 — and something has changed in how she works. The first case study took her most of a weekend, with a printed copy of the 100-point framework next to her laptop and sticky notes marking which dimension she was on. By the third case study, the microfinance one, she could do a rough pass in an evening and knew, almost from memory, which questions the framework wanted answered under each of its seven dimensions. She arrives at this fourth case study, a manufacturing company, expecting the same rhythm: profile the business, walk the seven dimensions, apply the sector adjustment from Chapter 65, tally the score, decide. What she does not expect is how much of that confidence the manufacturing sector will take back from her within the first hour of research.
The company is Arghakhanchi Cement Limited, traded on NEPSE under the symbol ARGCL, one of Nepal's cement manufacturers and — as she will discover — one of the harder companies in this book to pin down with a clean set of numbers. Chapter 75 warned her this was coming: manufacturing is NEPSE's smallest, thinnest-covered sector cluster, without the swarm of brokerage notes and comparison sheets that follow banks, and without even the specialist attention hydropower and microfinance get from their own dedicated NRB circulars and IPO cycles. She had briefly considered Bottlers Nepal, the Coca-Cola bottler chapter 75 uses as its FMCG example, but decided a cement name would put her through the sharper version of the sector's core tension — the import-substitution story chapter 75 introduced, playing out against a real domestic capacity race that is happening as she writes.
Lesson 86.1 — Setting up the case: a cement company in a construction cycle
Arghakhanchi Cement Limited runs its production plant in Siyari Rural Municipality, Rupandehi district, in Nepal's southern plains near the Indian border, with a corporate office in Thapathali, Kathmandu. It makes ordinary Portland cement and Portland pozzolana cement — OPC and PPC in industry shorthand, the two most common cement grades used in Nepali construction, differing mainly in the proportion of clinker (the kiln-fired intermediate product that gives cement its strength) blended with other materials. The plant's clinkerization unit runs at roughly one million metric tons of annual capacity, with a grinding unit capacity of about 800,000 metric tons a year, and it uses Danish vertical roller mill technology along with a waste heat recovery system generating about 2.5 megawatts, which lets the plant claw back some of the enormous energy cost that a cement kiln otherwise burns straight into the atmosphere.
The board is chaired by Pashupati Murarka, a former president of the Federation of Nepalese Chambers of Commerce and Industry, alongside directors from the Siddhartha Group and the Kedia Organisation — three separate, well-established Nepali business houses sitting on one board. That detail matters more than it might first appear, and Kabita flags it for herself early, because it will resurface directly in the governance dimension.
Why does a manufacturing company need its own distinct reading of the Canon Score, on top of the general framework she has now applied three times? Three reasons, and Chapter 75 had already named all three before she opened a single filing.
The first is cyclicality of a specific and identifiable kind. A bank's fortunes track the whole economy in a diffuse way; a hydropower company's fortunes track rainfall and a fixed power purchase agreement; a cement company's fortunes track the construction cycle almost directly — how many buildings are going up, how much road and bridge work the government is funding, how confident households feel about starting a home addition. When construction slows, cement demand slows with it, often before the slowdown shows up anywhere else in the economy. An FMCG manufacturer like a bottler faces a gentler version of the same idea, tracking consumer demand and festival-season spending rather than construction, but the principle is the same: manufacturing revenue rises and falls with a cycle that is visible and, to some degree, predictable, rather than moving in a straight line.
The second is import-substitution economics. Nepal imports enormous quantities of finished goods from India, and for decades a lot of its cement did come across the border too. Domestic cement manufacturers have grown mainly by displacing those imports — hence import substitution, the general economic pattern of a country producing at home what it used to buy abroad. That displacement is protected less by tariffs than by simple geography: cement is heavy, and heavy things are expensive to move long distances. A tonne of cement carried three hundred kilometres by truck picks up a transport cost that a tonne made forty kilometres from the construction site does not. That transport-cost cushion is a real competitive advantage for Nepali producers close to the markets they serve, but it is not a permanent, unbreakable moat — a change in Indian export incentives, a new road, or a large new domestic competitor can erode it, which is exactly the sector-specific risk Lesson 86.4 will dig into.
The third reason is the one that will occupy much of Kabita's time on this case: thin coverage. For the bank, she had NRB circulars, quarterly disclosures in a standardised format, and half a dozen brokerage comparison sheets. For the hydropower company, she had the power purchase agreement itself, a public document she could read clause by clause. For the microfinance institution, NRB's microfinance-specific disclosure regime gave her portfolio-quality data down to the district level. For Arghakhanchi Cement, several of the numbers she wants simply are not sitting in one clean place. Dividend history, an item she pulled up in thirty seconds for the bank, took real digging here and still came back incomplete.
KEY CONCEPT
Import substitution is what happens when a country starts producing domestically what it used to import — and for a heavy, low-value-per-kilogram product like cement, the natural transport cost of moving it long distances acts as a built-in tariff protecting local producers, without any government policy required.
Lesson 86.2 — Hemisphere 1: Liquidity, Governance, and Durability
Kabita followed the same method the bank, hydropower, and microfinance case studies had taught her: split Chapter 64's seven dimensions into Hemisphere 1 (Liquidity & Tradability, Governance & Promoter Behaviour, and Sector & Business Model Durability, 40 points total) and Hemisphere 2 (the four dimensions built from the financial statements). Manufacturing is not one of the four sectors Chapter 65 covers directly — banking, hydropower, microfinance, and insurance — so for the dimensions that need a sector-specific ruler, Kabita reached for Chapter 34's manufacturing, trading, and hotel accounting instead, exactly as Chapter 65's own closing lesson instructs for any company that does not fit its four named sectors.
Liquidity & Tradability (10 points). This is where the sector's thin coverage bites first. For the bank, third-party sources gave Kabita a free-float percentage to two decimal places; for Arghakhanchi Cement, no verified, current free-float or average-daily-volume figure was available from a source she trusted enough to cite with confidence. What she could observe directly is the ownership structure: three named business houses — the Murarka family, the Siddhartha Group, and the Kedia Organisation — hold board seats, which in a small-cap Nepali industrial company is a strong indirect signal of a concentrated promoter block, but not a substitute for an actual float percentage. Chapter 64 asks for a real number here, not an inference, and manufacturing the number would be worse than admitting she does not have it. Kabita scored both sub-components conservatively: volume at 2 out of 5 and free float at 2 out of 5, explicitly because the data was unavailable rather than because she had evidence the stock was illiquid. Liquidity & Tradability: 2 + 2 = 4 out of 10.
PRACTICAL TOOL
Before scoring liquidity on any small Nepali manufacturer, pull the board of directors list first. Three or more distinct family or group names on the board is a fast, free signal that the free float is probably concentrated — a useful clue while you keep looking for an actual number, not a replacement for one.
Governance & Promoter Behaviour (15 points). Chapter 34's related-party framework, built around NAS 24 disclosure requirements, is the right lens here, since nearly every Nepali manufacturing name sits inside a larger private family conglomerate. Promoter shareholding stability and pledging (6 points): the three-family board structure suggests a stable, long-standing promoter base with no evidence of pledging, but without a verified shareholding percentage or CDSC record to check against, Kabita scored this 4 out of 6 rather than a full mark. Related-party transactions and audit opinion (5 points): the Siddhartha Group's footprint extends well beyond cement into banking and other sectors, and Chapter 34 is explicit that a manufacturer buying from or selling through related entities can shift margin between the listed company and its private affiliates through transfer pricing — not necessarily improperly, but in a way that deserves scrutiny. Kabita could not verify the granular related-party transaction detail with the confidence NRB's disclosure regime gave her for the bank, so she scored this 3 out of 5. Disclosure timeliness and board independence (4 points): ICRA Nepal's LBB+/A4+ credit rating implies at least some external scrutiny of the company's financials, a mild positive, but board independence was not separately verified, so she scored this 3 out of 4. Governance & Promoter Behaviour: 4 + 3 + 3 = 10 out of 15.
Sector & Business Model Durability (15 points). The moat sub-component (8 points) has two real layers: the outer layer is transport-cost protection against Indian imports, described in Lesson 86.1, and the inner layer is company-specific — a genuine, disclosed export recovery to India, rebounding to about 12 percent of total trade from around 5 percent the year before, plus a real cost-position advantage from the Danish vertical roller mill and a waste-heat recovery system generating roughly 2.5 megawatts. This is a real but not license-grade moat — commodity-adjacent, resting on transport economics rather than a regulatory barrier — so Kabita scored it 6 out of 8. Revenue concentration and dependency risk (7 points), read here as sector-capacity risk rather than customer concentration, is where a genuine structural threat sits: Shivam Cements' joint venture, Hongshi Shivam, has been expanding its own plant from roughly 6,000 tonnes of daily capacity toward a doubled 12,000 tonnes — a real, disclosed, named competitive threat that adds supply to the whole Nepali cement market regardless of how the construction cycle is doing. Kabita scored this 4 out of 7. Sector & Business Model Durability: 6 + 4 = 10 out of 15.
CASE IN POINT
A cement plant in Rupandehi, near the Indian border, illustrates both sides of import-substitution economics at once: the same proximity that once let Indian cement undercut Nepali producers on price near the border now lets a competitive Nepali plant sell into the Indian market when its own cost position is strong enough. That is a real, earned advantage — and it sits alongside a real, disclosed capacity threat from a domestic competitor's expansion. Both belong in the same dimension, pulling in opposite directions.
Lesson 86.3 — Hemisphere 2: What the Numbers Actually Say
With Hemisphere 1 assessed, Kabita turned to the quantitative half of the Canon Score: Financial Strength & Profitability (20 points), Valuation Reasonableness (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points).
Financial Strength & Profitability. Chapter 64's ROE sub-component (8 points) was the first place the thin-coverage problem hit hard: Kabita could not find a disclosed net profit or equity figure precise enough to compute a real return on equity for Arghakhanchi Cement, only revenue and margin data. Rather than estimate a number she could not defend, she scored this conservatively at 4 out of 8, informed loosely by the margin-expansion trend and by peer earnings context (Shivam's historically strong EPS, Ghorahi's current loss) rather than a direct calculation. Leverage and interest-coverage discipline (7 points) is where real, specific data exists: the gearing ratio (total debt divided by equity) sits at about 0.72 times, comfortably under Chapter 64's 1.0x top-band threshold for non-financial companies, and the debt service coverage ratio has improved from about 2.05 times to about 2.95 times alongside a real deleveraging trend (debt-to-operating-profit falling from 2.85x to 2.13x). This is not quite the literal "interest coverage over 5x" the top band asks for, but the direction and the underlying cushion are both genuinely strong, so Kabita scored this 6 out of 7. Earnings quality and consistency (5 points): revenue moved from Rs 5.77 billion to Rs 6.06 billion to Rs 5.87 billion over three fiscal years — a dip-and-rebound rather than a smooth climb — but margin expanded a real 22 percent to 25 percent over the same window with no reported loss year, so Kabita scored this 4 out of 5. Financial Strength & Profitability: 4 + 6 + 4 = 14 out of 20.
REGULATORY DETAIL
ICRA Nepal's short-term rating scale runs from A1+ (the strongest) down through A2, A3, and A4, with plus and minus modifiers at each notch. Arghakhanchi Cement's short-term facilities carry an A4+ rating, and its long-term facilities LBB+ — comfortably investment-grade adjacent, but well below the top of the scale.
Valuation Reasonableness. This is where the thin-coverage problem hits hardest of all. For the bank and the hydropower company, Kabita had a current P/E and P/B pulled straight from a data provider. For Arghakhanchi Cement, the data providers she checked returned incomplete or unpopulated fields for current price, EPS, and book value. What she could build instead was context, not a number: Ghorahi Cement, a close peer, is currently loss-making — trailing EPS around minus Rs 6.13 — yet still trades around Rs 338, more than twice its book value of about Rs 165.73, telling her the market is pricing a temporarily unprofitable cement producer as though its earnings power will return. Shivam Cements posted EPS in the high twenties to high thirties of rupees in its strongest recent years. None of this substitutes for Arghakhanchi Cement's own multiple, and Kabita was careful not to borrow a peer's number and present it as the company's own. She scored both sub-components at the conservative end explicitly because of the data gap: P/E at 2 out of 8 and P/B at 2 out of 7. Valuation Reasonableness: 2 + 2 = 4 out of 15.
CAUTION
When a reliable current P/E or P/B figure is not available for the company itself, do not substitute a peer's multiple and present it as the company's own. Score the valuation dimension conservatively and say explicitly that the score reflects incomplete data, not a judgment that the price is fair.
Growth Trajectory. Revenue CAGR (8 points): the three-year revenue path — Rs 5.77 billion, Rs 6.06 billion, Rs 5.87 billion, and a nine-month run-rate implying roughly Rs 6.0 billion for the current year — works out to a multi-year CAGR in the low single digits despite a strong recent nine-month figure of 14 percent year-on-year growth. Chapter 64's band reads the trailing multi-year picture, not the most recent quarter alone, so Kabita scored this 3 out of 8, while flagging the recent acceleration as a real, watchable signal the trailing average does not yet capture. Earnings consistency (7 points): with no verified multi-year EPS series available for Arghakhanchi Cement itself, Kabita scored this conservatively at 2 out of 7, again a data gap rather than a negative finding. Growth Trajectory: 3 + 2 = 5 out of 15.
Dividend & Capital Return Discipline. This is the dimension the original research kept circling back to without ever resolving: dividend history, which took thirty seconds to pull for the bank, remained genuinely unconfirmed for Arghakhanchi Cement across the public dividend trackers Kabita checked. Chapter 64 does not allow a dimension to simply go unscored because the data is hard to find — an unscored dimension is a silent zero dressed up as an omission, which is worse than an honest, low, clearly-labelled score. Consistency of payout (6 points): scored 1 out of 6, explicitly for lack of verifiable data, not evidence the company skips dividends. Sustainability of payout (4 points): scored 1 out of 4, same reasoning. Dividend & Capital Return Discipline: 1 + 1 = 2 out of 10 — Arghakhanchi Cement's weakest dimension, and the clearest example in this whole case study of a score that is low because the analyst could not verify, not because the business failed a test.
Lesson 86.4 — When a Data Gap Is the Score
This is the lesson where Kabita had to think hardest about what her own honesty was actually doing to the final number. Four of the seven dimensions she just scored — Liquidity & Tradability, part of Financial Strength & Profitability, Valuation Reasonableness, and part of Growth Trajectory, plus the entire Dividend & Capital Return Discipline dimension — carry a real, disclosed data gap rather than a negative finding about the business itself. That is a genuinely different situation from the bank, hydropower, and microfinance case studies, where every dimension rested on a verifiable current number.
The temptation, faced with that many gaps, is to either skip the affected dimensions entirely (which silently inflates the total by removing points from the denominator) or to guess at plausible-looking numbers to avoid an uncomfortable string of low scores (which manufactures false precision). Kabita rejected both. Chapter 64's seven dimensions and 100-point structure are not optional extras to be dropped when a company is inconvenient to research — they are the whole discipline of the framework, and a company that cannot be verified across a third of the rubric should score as though that uncertainty is real, because it is.
CAUTION
A score depressed by missing data is not the same signal as a score depressed by a weak business — but it is not nothing, either. A Canon Score that cannot be verified across several dimensions is telling you something true and important: this is a harder company to have real conviction in, whatever the underlying business turns out to be. Do not round that discomfort away by skipping the dimensions you can't fill in.
This is also the right place to correct something Kabita noticed while researching this case: Chapter 65, the sector-adjustment chapter this book has leaned on for the bank, hydropower, and microfinance case studies, does not cover manufacturing at all — its four sectors are banking, hydropower, microfinance, and insurance. Chapter 65's own closing lesson says so directly, and points readers to Chapter 34's manufacturing, trading, and hotel accounting instead for exactly this situation. The real, useful sector-specific reasoning for a cement company — reading capacity utilisation and operating leverage, distinguishing a sector-wide construction slowdown from a structural capacity threat, and treating related-party disclosures under NAS 24 with real scrutiny — comes from Chapter 34, not Chapter 65, and this chapter now cites it correctly rather than borrowing an authority it does not have.
With that correction made, the underlying investment reasoning still holds: Ghorahi Cement's current loss looks, at a glance, like a company in real trouble, but construction activity across Nepal has been soft — a cyclical condition hitting the whole sector at once — and the market's own pricing of Ghorahi above twice book value despite the loss suggests other participants read it the same way. Chapter 34's capacity-utilisation lens is the right tool for telling this apart from something structural: a plant running below capacity because customers aren't buying is cyclical and likely to recover, while a plant running below capacity because more competing plants are splitting the same demand is structural, and Hongshi Shivam's capacity doubling is exactly that kind of structural fact, unrelated to where the construction cycle currently sits.
WARNING
A large domestic competitor doubling its own production capacity is a structural risk to every other producer in the same market, whether or not the broader construction cycle is currently improving. Do not let a cyclical upswing in sentiment mask a capacity threat that has nothing to do with the cycle.
Lesson 86.5 — The Full Worked Canon Score
Kabita assembled her tally using exactly the seven dimensions and point weights Chapter 64 defines.
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
14
No verifiable ROE figure, scored conservatively → 4/8; gearing 0.72x and DSCR improving to 2.95x → 6/7; margin expanded 22% to 25%, no loss year despite a dip-and-rebound revenue path → 4/5
Governance & Promoter Behaviour
15
10
Stable three-family board, shareholding percentage unverified → 4/6; related-party exposure plausible but unconfirmed → 3/5; ICRA rating implies some scrutiny, board independence unverified → 3/4
Liquidity & Tradability
10
4
No verified ADV or free-float figure for either sub-component, scored conservatively → 2/5 + 2/5
Valuation Reasonableness
15
4
No current P/E or P/B available for the company itself; peer context only, scored conservatively → 2/8 + 2/7
Sector & Business Model Durability
15
10
Real transport-cost and export-recovery moat, commodity-adjacent not license-grade → 6/8; real structural capacity threat from Hongshi Shivam's expansion → 4/7
Growth Trajectory
15
5
Multi-year revenue CAGR low despite a strong recent quarter → 3/8; no verified multi-year EPS series → 2/7
Dividend & Capital Return Discipline
10
2
Dividend history unconfirmed across public trackers, scored conservatively rather than skipped → 1/6 + 1/4
Canon Quality Score
100
49
Band: Weak/Avoid (below 55)
Summed, Arghakhanchi Cement's Canon Score comes to 49 out of 100 — inside Chapter 64's Weak/Avoid band. No dimension triggers the governance override on its own (Governance & Promoter Behaviour scored 10 out of 15, above the 5-point floor), but the total lands in the same band that override would produce anyway.
Kabita sat with this result longer than any of her first three scores, because it demanded an honest answer to an uncomfortable question: is 49 telling her Arghakhanchi Cement is a weak business, or that it is an unverifiable one? Her honest answer was "mostly the second, but that is still a real reason for caution." Four of the seven dimensions carry a genuine data gap rather than a proven weakness — if Liquidity, the ROE sub-component, Valuation, the earnings-consistency sub-component, and Dividend were all filled in with real, merely-average numbers instead of conservative placeholders, the total could plausibly sit fifteen to twenty points higher, comfortably in the Adequate band. But Chapter 64's discipline does not let an analyst pre-award those points on the assumption that the missing data would turn out fine. A company this hard to verify earns a cautious score precisely because it is this hard to verify, and an investor has to decide whether they are comfortable holding a position sized to that uncertainty, not to a more generous number they have not actually earned the right to assign.
CAUTION
A Canon Score built substantially on conservative placeholders for missing data is not the same kind of number as one built entirely on verified figures, even when the two scores look identical. Always disclose which is which — to yourself, and to anyone you show the score to.
Lesson 86.6 — The Decision, and What Would Move It
A score of 49 out of 100 sits inside Chapter 64's Weak/Avoid band — needing an unusually strong specific justification to hold, per the book's own definition of that band. For Kabita, the honest reading is not "avoid this company forever," but "this is not a position to take on the strength of currently available public information." The real financial signals she could verify — gearing, deleveraging, DSCR, margin expansion, a genuine export recovery — are all quietly encouraging. The dimensions she could not verify are numerous enough, and concentrated enough in the areas (liquidity, valuation, dividend discipline) that most directly affect whether a position can be sized and exited sensibly, that she chose to treat this as a name to keep researching rather than a name to buy or write off.
CAUTION
This case study is a demonstration of a process, not a personal recommendation to buy, hold, or avoid Arghakhanchi Cement or any other security. It shows how the Canon Score's machinery, honestly applied — including to a company where much of the machinery has to run on conservative placeholders — arrives at a conclusion, not what any individual reader should do with their own capital.
Three things would most plausibly move this score. First, and most directly actionable: better disclosure. If Arghakhanchi Cement's own reporting, or third-party coverage of it, improves enough to fill in a real free-float figure, a current P/E and P/B, a computable ROE, and a confirmed dividend record, four of the seven dimensions could move meaningfully in either direction once the real numbers are known — this is not a bet on the business improving, just on the fog clearing. Second, the pace at which Hongshi Shivam's new capacity comes online and gets absorbed by demand — if construction activity recovers fast enough to absorb the new supply without a margin hit, the durability score should hold or improve; if the new capacity lands into continued softness, it should fall further, and this is a specific, checkable fact rather than a mood. Third, any change in Indian trade or export-incentive policy toward cement and clinker, which could widen or narrow the transport-cost protection the whole domestic industry leans on.
PRACTICAL TOOL
For any thinly covered manufacturing name, keep a short watchlist of three things: the next fiscal year's construction-sector growth figures, any announced capacity additions from named competitors, and any change to Indian cement or clinker export incentives — plus, specifically for a name this hard to verify, a standing reminder to re-pull free float, current P/E, and dividend history the moment better third-party coverage exists.
Kabita closed her notebook on this fourth case study having learned something the first three had not fully prepared her for: that the Canon Score's honesty sometimes has to point inward, at the analyst's own inability to verify, rather than only outward at the company's fundamentals — and that a low score built substantially from disclosed data gaps is still the right score to report, not a reason to quietly round it up.
Chapter recap
This chapter applied the Canon Score's real seven-dimension framework, as Chapter 64 defines it, to Arghakhanchi Cement Limited (ARGCL) — correcting, along the way, an earlier misattribution to Chapter 65, which does not cover manufacturing and explicitly directs readers to Chapter 34's manufacturing, trading, and hotel accounting instead. Hemisphere 1 scored Liquidity & Tradability (4/10 — no verified ADV or free-float figure), Governance & Promoter Behaviour (10/15 — a stable, credible three-family promoter base with unverified related-party and shareholding detail), and Sector & Business Model Durability (10/15 — a real transport-cost and export-recovery moat offset by a genuine structural capacity threat from a competitor's expansion). Hemisphere 2 scored Financial Strength & Profitability (14/20), Valuation Reasonableness (4/15 — no current P/E or P/B for the company itself), Growth Trajectory (5/15), and Dividend & Capital Return Discipline (2/10 — a dividend record that remained unconfirmed across every public tracker checked).
The chapter's central methodological lesson, in Lesson 86.4, was naming plainly what four dimensions' worth of data gaps actually do to a Canon Score: they do not prove a weak business, but Chapter 64 does not allow an analyst to skip an unscoreable dimension or quietly inflate it on the assumption that missing data would have turned out fine. Scored honestly, with every gap disclosed rather than smoothed over, Arghakhanchi Cement's total came to 49 out of 100 — Weak/Avoid, per Chapter 64's own bands — a result driven substantially by what could not be verified rather than by proof that the business itself is troubled, and the chapter was explicit that a reader should treat those two situations differently even though the number looks the same either way.
Tallied across all seven dimensions, the score's most encouraging signals — a moderate 0.72x gearing ratio, a debt service coverage ratio improving from 2.05x to 2.95x, margin expansion from 22 to 25 percent, and a genuine, disclosed export recovery to India — sat alongside a governance picture that could not be fully verified, a valuation and liquidity picture built on peer context rather than the company's own numbers, and a dividend record that never resolved despite real effort to find it. The chapter closed by naming concrete, watchable catalysts — better third-party disclosure, the pace at which new domestic cement capacity gets absorbed by demand, and any shift in Indian trade policy toward cement — that would plausibly move the score once the underlying facts are actually known, rather than guessed at.
Chapter 87 turns the lens in a genuinely different direction. Case Study 5 — A Rights Issue Decision moves away from scoring an entire company and toward evaluating a single corporate-action decision: whether to subscribe to a rights issue, the offer a listed company makes to its existing shareholders to buy additional shares, usually at a discount to the market price, in order to raise fresh capital. It is a narrower, sharper decision than anything Part XVI has asked of Kabita so far — not "is this a good company," a question she now has a practiced process for answering, but "given everything I already know about this company, is this specific offer, on these specific terms, worth saying yes to." Readers who have followed Kabita through a bank, a hydropower developer, a microfinance institution, and now a manufacturing company will find that same Canon Score discipline reapplied to a much more pointed question — including, this chapter has shown, the discipline to say plainly when the discipline itself is running on incomplete information.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 87
Case Study 5 — A Rights Issue Decision
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 5 — A Rights Issue Decision
Suman Gurung teaches mathematics at a secondary school in Damak, in Jhapa district, in Nepal's eastern tarai. He is not a big investor by Kathmandu standards. His demat account holds shares in eleven companies, most bought in small lots over eight years, funded by his teaching salary and by the occasional remittance his younger brother sends home from Doha. One evening in Shrawan 2080 (July 2023), he opened his Mero Share account — the online portal most Nepali investors use to see their share holdings and apply for new issues — and found a new line waiting for him: an entitlement to buy new shares of Api Power Company Limited, a hydropower company he had held for three years.
This chapter follows Suman through that decision, using the same tools this book has built since Chapter 15. It is a true story in its bones — the company, the ratio, the price, and the dates are all real and can be checked against Api Power's own disclosures and NEPSE's public records. Suman's personal numbers — his exact holding, his cash position, his family's finances — are illustrative, built to teach the math cleanly. But the corporate action itself, and the arithmetic it forces on every shareholder who receives it, is not invented. It happened, in the middle of 2023, to tens of thousands of real Api Power shareholders across Nepal.
Data vintage
Scored using figures published to mid-2026, including fourth-quarter FY2081/82 results released in mid-August 2025 and CARE Ratings Nepal’s credit-watch note covering the first half of FY2025/26. Hydropower and financial-sector figures move with each quarterly disclosure, so re-derive every number from current filings before acting on it.
Lesson 87.1 — Meet the Investor and the Notice
A rights issue is an offer a company makes to its own existing shareholders, giving them the first chance to buy new shares before anyone else gets the opportunity. Chapter 15 introduced this idea with a simple analogy: think of a company as a pie, and shareholders as people who each own a slice. When the company issues new shares, the pie gets cut into more slices. A rights issue is the company's way of saying, "before we let new people buy slices, you — the people who already own a piece of this pie — get the first right to buy more, at a price we are fixing today, in proportion to what you already hold." That is where the word "rights" comes from: it is a right, not an obligation. You can use it, use part of it, hand it to someone else, or ignore it. What you cannot do is stop the company from issuing the new shares altogether — that decision belongs to the company's board and its shareholders' general meeting, not to any one individual shareholder.
Api Power Company Limited, traded on NEPSE under the symbol API, is a hydropower company — one of the many firms that build and operate small and mid-sized hydroelectric plants across Nepal's rivers and sell the electricity they generate to the Nepal Electricity Authority (NEA) under long-term power purchase agreements. Hydropower is arguably the single most important growth sector in the Nepali capital market, tracking Nepal's chronic need for more electricity generation, its abundant river gradient, and the political ambition — repeated in budget speech after budget speech — of exporting surplus power to India and Bangladesh. By 2023, Api Power was already an established operator, and it was in the middle of building a new plant, the Upper Chameliya hydroelectric project, rated at 40 megawatts.
Building a hydropower plant is expensive, and almost none of that expense is paid up front by shareholders in one go. Companies borrow heavily from Nepali banks during construction, then repay as the plant starts earning revenue. But bank borrowing in Nepal is not always cheap, and it is never limitless — Nepal Rastra Bank (NRB), the central bank, sets rules on how much of their deposits banks can lend out, and when liquidity in the banking system tightens, as it did sharply in 2022 and into 2023, lending rates for companies like Api Power can climb into the mid-teens. A company sitting on expensive project debt has two ways to lighten that load: earn more, or borrow less. A rights issue is one of the standard ways Nepali companies choose the second path — raising equity from their own shareholders and using part of the proceeds to pay down loans, cutting the interest bill for good.
That is the backdrop Suman was reading into, without necessarily using those words, when the entitlement notice appeared in his Mero Share account. The number in front of him was simple enough: for every 10 shares he held, he was entitled to buy 4 new ones. But a plain-English number is not a decision. Chapter 78 built a playbook precisely for this moment, and this chapter is where we walk that playbook against a real filing instead of a hypothetical one.
KEY CONCEPT
An entitlement ratio, often written as something like 10:4 or 1:0.40, tells you how many new shares you may buy for every share you already hold. A 10:4 ratio means a shareholder with 1,000 shares is entitled to buy 400 new ones — no more, no less, unless the leftover shares from other people's non-participation are later auctioned off to anyone willing to bid.
Lesson 87.2 — Reading the Fine Print
Before any decision can be made well, the facts have to be assembled in one place. This is the least glamorous step in the entire rights-issue playbook, and it is also the one investors skip most often — scrolling past the notice, noting only "oh, rights shares," and moving on without ever reading the numbers that actually determine whether participating makes sense.
Api Power's rights issue, once assembled from the company's own disclosures and NEPSE's listing notices, looked like this.
Item
Detail
Company / Symbol
Api Power Company Limited (API)
Sector
Hydropower generation
Rights ratio
10:4 — four new shares for every ten held (40 percent of existing paid-up capital)
Issue price
Rs 100 per share (par value)
SEBON approval
Ashad 6, 2080 BS (on or about June 20, 2023)
Book closure (entitlement date)
Ashad 22, 2080 BS (on or about July 7, 2023)
Subscription window
Shrawan 31 to Bhadra 19, 2080 BS (August 16 to September 5, 2023)
New shares to be issued
16,533,137 units
Paid-up capital before issue
approximately Rs 4.13 arba
Paid-up capital after issue
approximately Rs 5.78 arba
Stated use of proceeds
Substantially for repayment of project-related bank loans
Issue and sales manager
Muktinath Capital Limited
Issuer credit rating
CareNP Double B Plus (BB+), CARE Ratings Nepal
A few of these lines need unpacking, because each one carries a decision-relevant fact, not just a piece of trivia.
The book closure date is the single most important date on the whole notice, and Chapter 15 already explained why: it is the cut-off day the company uses to freeze its shareholder register and decide who counts as an "existing shareholder" for this offer. If you owned Api Power shares on that date (in practice, you needed to have bought and had the trade settle by Ashad 21, one day before closure), you received the entitlement. Buy the shares even one trading day after book closure, and you get nothing from this particular rights issue, even though you now own the stock. This is exactly the kind of detail that trips up investors who buy a stock because they heard "rights are coming" without checking whether they are still in time to qualify.
The subscription window is the period during which entitled shareholders must actually act — submit their application, and pay for the shares they want, either through their bank via the C-ASBA system (Centralised Applications Supported by Blocked Amount, where your bank temporarily locks the required cash in your account rather than transferring it immediately) or, increasingly, directly through the Mero Share portal. Miss this window entirely, and your entitlement is treated exactly like a "no" — it lapses.
The issue price of Rs 100 deserves its own callout, because it is one of the most consistent and most criticised features of the Nepali market.
REGULATORY DETAIL
Nepali rights issues are almost always priced at par value — commonly Rs 100 per share — regardless of where the stock is actually trading on NEPSE at the time. This is different from many other markets, where rights are priced at a discount to the market price but still well above the shares' original face value. In Nepal, if a stock is trading at three or four times its par value, as many hydropower and finance shares do, a rights issue at par is effectively an invitation to buy shares far below market price — which is exactly why nearly every entitled shareholder who can find the cash chooses to subscribe.
Finally, the stated use of proceeds matters more than most investors give it credit for. Api Power's own disclosures pointed to loan repayment as the primary use of the roughly Rs 1.65 arba raised. That is not automatically a red flag — a company under construction, carrying project debt at double-digit interest rates during a tight-liquidity year, can meaningfully strengthen its balance sheet by swapping expensive debt for equity. But it is also not the same story as "we are raising fresh capital to build an entirely new plant we could not otherwise afford." Chapter 78's playbook asks you to separate these two motives clearly, because they carry different implications for how soon the money translates into higher future earnings.
CASE IN POINT
Api Power's rights issue was not a rescue rights issue — the company was not raising money to survive a crisis. Nor was it a pure growth rights issue funding a brand-new project from scratch. It sat in the middle: an established, operating company using a large slice of shareholder capital mainly to delever a balance sheet stretched by financing an already-committed hydropower project. That middle ground is where a majority of Nepali rights issues actually sit, and it is why the Canon Score framework treats "use of proceeds" as a spectrum, not a yes/no test.
Lesson 87.3 — The Four Doors: Exercise, Partial, Renounce, Lapse
Chapter 78 laid out four doors that stand in front of every shareholder who receives a rights entitlement. Walking through Api Power's real notice is the clearest way to show what each door actually costs and delivers.
The first door is full exercise: apply for every share you are entitled to, and pay for all of it. For Suman, holding 500 Api Power shares, a 10:4 ratio entitled him to 200 new shares at Rs 100 each — a cash outlay of Rs 20,000. Full exercise preserves his percentage ownership of the company exactly as it was before the issue; if he owned 0.001 percent of Api Power before, he owns 0.001 percent after, because his share count grew by the same 40 percent that the total share count grew by.
The second door is partial exercise: apply for some of the shares, but not all — say, 100 of his 200 entitled shares, paying Rs 10,000. This preserves some but not all of his proportional ownership. It is the door investors use most often when the arithmetic clearly favours subscribing but the household budget genuinely cannot stretch to the full amount that month — a common and entirely reasonable position for a schoolteacher with a fixed monthly salary and a fixed subscription deadline that does not care whether that month also brought a large medical bill or a dashain expense.
The third door is renunciation: transferring your entitlement to someone else, who then applies and pays in your place.
REGULATORY DETAIL
Nepali law does permit shareholders to transfer, or "renounce," their rights entitlement to another person before the subscription deadline, through a transfer process involving the company's share registrar. In practice, this mechanism is used far less often in Nepal than full exercise or simple non-participation, for a plain reason: unlike markets such as India, where "rights entitlements" trade as a separate listed instrument on the stock exchange with a live, visible market price, Nepal has no organised public market for buying and selling someone else's rights entitlement. A shareholder who wants to renounce has to find their own counterparty, agree a price privately, and complete registrar paperwork — a process with real friction for a retail holding of a few hundred shares, even though it is entirely legal.
For Suman, the practical version of renunciation was much simpler than finding a stranger to negotiate with: he could transfer his entitlement to his wife, who held her own demat account and had more free cash that month from a small inheritance. Renouncing within a family does not create or destroy any household wealth by itself — the shares simply end up registered under a different name inside the same family's holdings — but it is a legitimate way to route a cash call toward whichever household member actually has the liquidity, without leaving money on the table.
The fourth door is doing nothing at all, and letting the entitlement lapse. This is the door that costs the most and is chosen the most casually, usually by investors who simply forget the deadline, cannot raise the cash, or have lost confidence in the company.
WARNING
A lapsed rights entitlement in Nepal is not a neutral non-event — it is a forfeiture. Shares nobody subscribes for are pooled together and sold at auction to whichever investor bids the highest price, and the difference between that auction price and the original Rs 100 issue price goes to the company, not to the shareholder who let the entitlement lapse. If you do nothing, you do not merely miss an opportunity; you hand real, calculable value to other bidders and to the company itself, and you receive nothing in exchange.
Api Power's own numbers show this playing out at scale: of the 16,533,137 rights shares on offer, 1,194,934 units were not subscribed for by existing shareholders and went to a public auction in early October 2023. Every one of those unsubscribed shares represented a shareholder — or several thousand of them — who let real, quantifiable value slip away, usually without ever calculating what it was worth.
Lesson 87.4 — Hemisphere 1: Liquidity, Governance, and Durability
This book's Canon Score exists to keep an investor from making decisions on vibes — sector excitement, a friend's tip, or a headline about hydropower's bright future. Chapter 64 defines it precisely: seven dimensions, not five, summing to exactly 100 points — Financial Strength & Profitability (20), Governance & Promoter Behaviour (15), Liquidity & Tradability (10), Valuation Reasonableness (15), Sector & Business Model Durability (15), Growth Trajectory (15), and Dividend & Capital Return Discipline (10). A total of 85 or above is Exceptional, 70 to 84 is Strong, 55 to 69 is Adequate, and below 55 is Weak/Avoid — with one override rule attached: if the Governance & Promoter Behaviour sub-score falls below 5 out of 15, the whole score is automatically capped in the Weak/Avoid band, no matter what the arithmetic sum says. As Chapter 84's hydropower case study showed, Chapter 65's sector adjustments apply directly here — a signed power purchase agreement, seasonal revenue reading, and the liquidity lens Chapter 58 built around circuit-trap exit risk.
Chapter 78 added one instruction specific to corporate actions like a rights issue: check the Canon Score both before and after, because a capital raise can genuinely move a company's score, for better or worse. But it is worth being precise about which of the seven dimensions a rights issue can actually move. A rights issue offered pro-rata — the same 10:4 ratio to every shareholder — does not change who controls the company, does not touch its power purchase agreement or its regulatory license, and does not, by itself, make the stock easier or harder to trade on NEPSE. Liquidity & Tradability, Governance & Promoter Behaviour, and Sector & Business Model Durability are the three dimensions least affected by a rights issue in the near term, so this lesson scores them once, using the most recent verifiable figures, and treats them as essentially the same before and after the 2023 issue. Lesson 87.5 turns to the dimensions a rights issue genuinely can move.
Liquidity & Tradability (10 points). Api Power trades with real, checkable depth: a 30-day average volume of roughly 128,561 shares a day at a recent price near Rs 327 works out to well over Rs 40 million of rupee turnover a day, comfortably inside Chapter 64's top band for average daily traded value. Public shareholding sits at 42 percent, with the remaining 58 percent held by promoters — a real, disclosed split, and one that clears Chapter 64's 40-percent free-float threshold for full marks. Volume: 5 out of 5. Free float: 5 out of 5. Liquidity & Tradability: 10 out of 10.
KEY CONCEPT
Because a rights issue is offered proportionally to every existing shareholder, full subscription leaves ownership percentages exactly where they started — the pie has more slices, but each shareholder still holds the same share of it. That is why free float and promoter concentration are dimensions a rights issue is not designed to move, and why this chapter scores them once rather than twice.
Governance & Promoter Behaviour (15 points). Promoter shareholding stability and pledging (6 points): a stable 58 percent promoter block, with no pledging disclosed in the sources checked for this chapter, though a verified CDSC pledging record was not independently confirmed — scored 5 out of 6. Related-party transactions and audit opinion (5 points): no specific related-party red flag surfaced in Api Power's disclosures or its credit-rating commentary, and the company's quarterly results have been published on a regular schedule — scored 4 out of 5. Disclosure timeliness and board independence (4 points): Api Power conducted its 20th annual general meeting on schedule in mid-January 2024, and its quarterly and annual results have continued on a predictable cadence since, including the fourth-quarter results for fiscal year 2081/82 published in mid-August 2025 — scored 4 out of 4. Governance & Promoter Behaviour: 5 + 4 + 4 = 13 out of 15.
Sector & Business Model Durability (15 points). The moat sub-component (8 points) is Chapter 64's own textbook full-marks case: a licensed hydropower generator selling under a long-term power purchase agreement to the Nepal Electricity Authority, which guarantees revenue once a plant is generating regardless of who else enters the sector — scored 8 out of 8. Revenue concentration and dependency risk (7 points) is where the same structural fact cuts the other way: like nearly every Nepali hydropower operator, Api Power sells effectively all of its output to one buyer, NEA. Chapter 64 flags this exact case as the one its generic concentration band was not built to answer cleanly, and Chapter 65's own cross-sector table marks the adjustment here as "Moderate" without supplying a specific numeric fix — the same acknowledged gap this book's Chapter 84 case study already named. Scored consistently with that precedent: 4 out of 7. Sector & Business Model Durability: 8 + 4 = 12 out of 15.
CASE IN POINT
Api Power's single-buyer dependency on NEA is not a company-specific weakness — it is close to a sector-wide fact of Nepali hydropower, true of Chilime Hydropower in Chapter 84 just as it is true here. Chapter 64 promises this is exactly what Chapter 65's sector adjustments exist to fix, but Chapter 65 only marks the adjustment "Moderate" in its cross-sector table without a specific formula. Scoring this sub-component the same way across every hydropower case study in this book — rather than quietly nudging it up or down chapter to chapter — is itself part of the discipline the Canon Score asks for.
Hemisphere 1 total: Liquidity & Tradability 10 + Governance & Promoter Behaviour 13 + Sector & Business Model Durability 12 = 35 out of 40 — a genuinely strong foundation, and one that the rights issue itself neither earned nor put at risk.
Lesson 87.5 — Hemisphere 2: What a Rights Issue Actually Moves
The four remaining dimensions are where a capital raise can leave a real mark — and where Api Power's actual, disclosed numbers, both before and after the 2023 rights issue, tell a concrete story rather than an abstract one.
Financial Strength & Profitability, scored twice. Before the rights issue, in the fiscal year immediately preceding it, Api Power's own disclosed figures showed a debt-to-equity ratio of 1.37 and a return on equity that had collapsed to 0.16 percent, down from 7.05 percent three years earlier — a three-year ROE trend Chapter 64's own band language treats the same way it would treat a loss year. Return on equity (8 points): 1 out of 8, both before and after, because even today's much-improved figure has not yet cleared Chapter 64's 7 percent floor — more on that below. Leverage and interest-coverage discipline (7 points), pre-issue: a 1.37x debt-to-equity ratio, well above Chapter 64's 1.0x top-band threshold, in a year of unusually tight Nepali bank liquidity — scored 2 out of 7. Earnings quality and consistency (5 points), pre-issue: a sharply declining three-year ROE trend with no confirmed outright loss, but a genuinely erratic pattern — scored 2 out of 5. Pre-issue Financial Strength & Profitability: 1 + 2 + 2 = 5 out of 20.
After the rights issue, using the most recently disclosed figures, CARE Ratings Nepal's own credit-watch note reports the overall gearing ratio improved to 0.92 times as of the first half of fiscal year 2025/26, down from 1.00 times a year earlier, and interest coverage strengthened to 3.82 times from 2.30 times — both real, verified, and explicitly attributed by the rating agency to "the successful completion of the rights issuance." Leverage and interest-coverage discipline, post-issue: gearing now clears the 1.0x threshold, though interest coverage still falls short of Chapter 64's 5x top-band mark — scored 5 out of 7. Earnings quality and consistency, post-issue: fourth-quarter revenue for fiscal year 2024/25 grew 53.01 percent year-on-year, though net profit for that same quarter actually fell slightly, from Rs 456.58 million to Rs 427.66 million, as net margin compressed from 36.57 percent to 25.90 percent — real growth with a real, disclosed cost, and no outright loss year — scored 4 out of 5. Return on equity, post-issue: 6.49 percent, down from 8.01 percent the year before — a genuine improvement over the near-zero pre-issue figure, but Chapter 64's band language is explicit that anything under 7 percent sits in its bottom band, and the honest reading of the most recent verified year is that it still does — scored 1 out of 8, unchanged from the pre-issue score. Post-issue Financial Strength & Profitability: 1 + 5 + 4 = 10 out of 20.
CAUTION
A rights issue aimed squarely at delevering can still leave the Financial Strength & Profitability score capped by a single stubborn sub-component. Api Power's leverage and earnings-quality scores genuinely improved after 2023 — from 2 to 5, and from 2 to 4 — but its return on equity has not yet crossed Chapter 64's own 7 percent line, so the honest score for that sub-component stays at 1 out of 8 both before and after. Do not round a real, partial improvement up to a full fix just because the direction of travel is the right one.
Valuation Reasonableness (15 points), scored on today's verifiable numbers rather than 2023-vintage multiples this chapter could not independently confirm. Price-to-earnings (8 points): Api Power currently trades around 21.75 times trailing earnings against a hydropower-sector average near 18.37 times — a ratio of about 1.18, inside Chapter 64's 1.1x-to-1.5x band — scored 3 out of 8. Price-to-book (7 points): a current price-to-book ratio of about 2.74 times could not be checked against a verified hydropower-specific book-value median, so it is compared here to the broader NEPSE market's price-to-book average of roughly 2.8 times, a wider and less precise benchmark, disclosed as such — the resulting ratio of about 0.98 falls inside the 0.8x-to-1.1x band — scored 5 out of 7. Valuation Reasonableness: 3 + 5 = 8 out of 15.
Growth Trajectory (15 points). Revenue growth (8 points): the only confirmed year-on-year figure available, a 53.01 percent jump in fourth-quarter fiscal year 2024/25 revenue, is real and strong, but it is one quarter, not a verified trailing multi-year average, so it is scored conservatively rather than as if it represented a confirmed multi-year compound growth rate — scored 4 out of 8. Earnings consistency (7 points): quarterly earnings per share slipped from Rs 7.89 to Rs 7.04 year-on-year even as full-year earnings per share reached Rs 15.06, a mixed and only partially confirmed picture without a verified five-year series — scored 3 out of 7. Growth Trajectory: 4 + 3 = 7 out of 15.
Dividend & Capital Return Discipline (10 points). Consistency of payout (6 points): Api Power has distributed a dividend — bonus shares, cash, or both — in nearly every year since starting commercial operations in 2072/73, averaging roughly 7.14 percent in bonus shares annually with a high of 10.5 percent, but its cash dividend has stayed thin throughout, running from about 0.26 to 0.55 percent in recent years, well under Chapter 64's 30-to-70-percent payout-ratio ideal — a real, near-unbroken record, but at a persistently low cash payout ratio — scored 4 out of 6. Sustainability of payout (4 points): the company is now consistently profitable and cash-generative post-commissioning, and nothing in its disclosures suggests dividends are being funded by drawing down capital — scored 4 out of 4. Dividend & Capital Return Discipline: 4 + 4 = 8 out of 10.
PRACTICAL TOOL
Before applying for any rights issue, run this five-question version of the Canon Score corporate-action check: (1) What is the money actually for — survival, deleveraging, or growth? (2) How large is the discount between the issue price and the market price, in rupees per share? (3) What happens to my ownership percentage, and my portfolio's concentration in this sector, if I do nothing? (4) Can I fund my full entitlement without touching money earmarked for something else? (5) Which of Chapter 64's seven dimensions does this specific capital raise actually target, and does the company's score on that dimension, checked before and after, show real improvement rather than assumed improvement? A rights issue that fails question five is a strong signal to let the entitlement lapse rather than reflexively subscribing.
CAUTION
A rights issue used mainly to repay debt improves a company's balance sheet immediately, but it does not immediately improve its earnings. If the new project the debt was originally financing is not yet generating revenue — as Upper Chameliya was not, in the weeks immediately before Api Power's book closure, though it began commercial operation just days into the subscription window itself — then the company's share count grows by 40 percent before its profit has grown to match. Earnings per share, the profit attributable to each individual share, can mechanically drop in the short run. This is not a sign anything has gone wrong; it is simply the arithmetic of a growth company raising capital ahead of the earnings that capital will eventually produce. The investor's job is to judge whether that future earnings growth is likely to outpace the dilution — not to panic at a lower per-share profit figure in the interim.
Lesson 87.6 — The Full Worked Canon Score
Assembled using exactly the seven dimensions and point weights Chapter 64 defines, and scored on the most recently verifiable figures for the four dimensions a rights issue can actually move:
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
10
ROE 6.49%, still under Ch64's 7% floor → 1/8; gearing improved to 0.92x, interest coverage 3.82x (still under 5x) → 5/7; revenue growth with a real but non-loss margin dip → 4/5. (Pre-rights-issue: 1 + 2 + 2 = 5/20 — gearing was 1.37x and ROE had collapsed to 0.16%.)
Governance & Promoter Behaviour
15
13
Stable 58% promoter block, no pledging disclosed → 5/6; no related-party red flag, regular disclosure → 4/5; on-schedule AGM and quarterly results → 4/4
Liquidity & Tradability
10
10
~Rs 42 million/day turnover → 5/5; 42% public float clears the 40% threshold → 5/5
Valuation Reasonableness
15
8
P/E 21.75x vs sector ~18.37x (≈1.18x) → 3/8; P/B 2.74x vs broader-market ~2.8x (≈0.98x, sector-specific median unverified) → 5/7
Sector & Business Model Durability
15
12
Licensed generator with a signed PPA to NEA, Ch64's textbook full-marks case → 8/8; 100% single-buyer dependency on NEA, the acknowledged Ch64/Ch65 gap → 4/7
Growth Trajectory
15
7
One confirmed strong quarter (+53% YoY), no verified multi-year CAGR → 4/8; EPS mixed quarter-on-quarter, no verified 5-year series → 3/7
Dividend & Capital Return Discipline
10
8
Dividend paid nearly every year since 2072/73, but cash payout ratio persistently thin → 4/6; funded from genuine operating profit → 4/4
Canon Quality Score
100
68
Band: Adequate (55–69)
Summed, Api Power's Canon Score comes to 68 out of 100 — inside Chapter 64's Adequate band, near its upper edge. The governance override does not apply: Governance & Promoter Behaviour scored 13 out of 15, comfortably above the 5-point floor that would otherwise cap the whole score in Weak/Avoid regardless of the arithmetic sum.
This is a materially different number from the original, informal "around 70" this chapter once used — not because Api Power's underlying business changed, but because a five-category, 20-points-each scheme that never existed in Chapter 64 has been replaced with the real seven-dimension framework, applied with the same discipline this book has used on every other case study in this Part. The overall picture the real score tells is close in spirit to the original's rough instinct — a decent, self-funding hydropower operator that a rights issue genuinely strengthened — but it is more precise about exactly where that strength sits (a strong Hemisphere 1: 35 out of 40) and exactly where real uncertainty remains (Valuation and Growth, both built partly on data this chapter could not fully verify, and a Financial Strength score still held down by a return on equity that has not yet cleared Chapter 64's own bar).
CAUTION
This case study is a demonstration of a process, not a personal recommendation to buy, hold, or apply for rights shares in Api Power Company or any other security. It shows how the Canon Score's seven-dimension machinery, honestly applied before and after a real corporate action, arrives at a conclusion — not what any individual reader should do with their own capital.
Lesson 87.7 — The Tax Math Nobody Reads Until It's Too Late
Chapter 37 covered the tax treatment of rights and bonus shares in detail, and two of its rules matter enormously here, because they change the real, after-tax value of each of the four doors.
The first rule is about cost basis — the number the tax office treats as what you "paid" for a share, used later to calculate your taxable gain when you sell. When you exercise a rights entitlement, your cost basis for those new shares is the issue price you actually paid, not the market price the shares happened to be worth on the day you received them. For Api Power, that meant a cost basis of Rs 100 per new share, regardless of how much higher the stock was trading. Since almost the entire market value of a deeply discounted rights share sits above that Rs 100 cost basis, nearly all of it becomes taxable capital gain whenever the shares are eventually sold.
The second rule is about the holding period clock, which determines whether a sale qualifies for the lower long-term capital gains rate or the higher short-term one. As this book's tax chapters have set out, individual resident investors in Nepal pay a lower rate — commonly cited at 5 percent — on gains from shares held more than 365 days, and a higher rate — commonly cited at 7.5 percent — on shares held 365 days or less. For newly issued rights shares, that 365-day clock starts running from the date the new shares are allotted and listed, not from whenever the parent shares were originally bought. A shareholder who has held the underlying stock for a decade still starts a brand-new holding-period clock for every batch of rights shares they take up.
REGULATORY DETAIL
Cost basis for rights shares is fixed at the issue price actually paid, and the long-term/short-term holding-period clock restarts separately for each batch of rights shares from the date of allotment — it does not inherit the holding period of the original shares that generated the entitlement. An investor who sells rights shares within a year of allotment, even if they have owned the underlying company for many years, pays the short-term rate on that batch.
Putting the corporate-action math and the tax math together side by side makes the four doors easier to compare directly. Using illustrative numbers close to Api Power's actual position — a cum-rights market price of roughly Rs 350 in the weeks before book closure, and the real ratio and issue price from the notice — the theoretical ex-rights price, the price the stock should mathematically settle at once the new shares are absorbed into the market, works out to a little under Rs 280. That is a useful benchmark, because it lets an investor calculate the entitlement's true value: roughly Rs 178 of embedded value on every new share bought at Rs 100, or equivalently around Rs 71 of value attached to every existing share held, whether or not that shareholder chooses to act on it.
Path
Cash required (200-share entitlement)
Shares held afterward
Entitlement value captured
Entitlement value lost to others
Full exercise
Rs 20,000
700
approximately Rs 35,700
None
Partial exercise (half)
Rs 10,000
600
approximately Rs 17,850
approximately Rs 17,850
Renounce to a family member
Rs 0 (paid by the family member)
500 (family total: 700)
approximately Rs 35,700 (kept within the family)
None
Let the entitlement lapse
Rs 0
500
None
approximately Rs 35,700
Reading this table plainly: doing nothing does not mean standing still financially. It means actively forfeiting roughly the same amount of value that full exercise would have captured — value that instead flows to whoever wins the unsubscribed shares at auction, and to the company itself, which collects any auction premium above Rs 100. The only door that costs zero value while still keeping every rupee inside the same household is renunciation to a family member with spare cash — which is precisely why Suman, after running these numbers, seriously weighed asking his wife to take up a portion of the entitlement in her own name rather than simply skipping it.
WARNING
Because the market mechanically re-prices a stock downward around a rights issue's book closure to reflect the coming dilution, a shareholder who does nothing does not merely "miss a discount" — they experience an actual, calculable drop in the value of the shares they already own, with no compensating gain. Passivity in a rights issue is not a safe default; it is itself a financial decision, and usually the most expensive one available.
Lesson 87.8 — The Decision and What Happened Next
Suman's actual decision, once the Canon Score and the tax and dilution math were laid out side by side, was to fully exercise his entitlement — but to fund it deliberately rather than by scraping together whatever cash was lying around. A Canon Score of 68, near the top of Chapter 64's Adequate band and anchored by a genuinely strong Hemisphere 1, told him Api Power was a company worth continuing to own and worth defending his position in, but not one worth stretching for — the same "good enough to defend, not good enough to load up on" verdict the original rough estimate pointed toward, now resting on seven real, individually checkable numbers instead of five invented ones. So rather than dipping into the emergency fund this book's earlier chapters insist on keeping untouched, he sold a small, long-underperforming holding in a finance company whose own Canon Score had drifted down toward the Weak/Avoid band over the preceding year — freeing up almost exactly the Rs 20,000 his 200-share entitlement required, without disturbing either his emergency fund or his family's other financial commitments. This is the "cash-neutral rights strategy" Chapter 78 recommends whenever an investor's overall Canon Score checklist flags a weaker holding elsewhere in the same portfolio: fund a good rights issue by exiting a weaker position, rather than by adding fresh financial stress.
He applied through the C-ASBA system at his local bank branch in Damak during the second week of the subscription window, which ran from Shrawan 31 to Bhadra 19, 2080 BS — mid-August to early September 2023 in the standard calendar. He did not renounce any portion to his wife in the end; once he had confirmed the sale proceeds from the finance-company shares would clear his bank account before the subscription deadline, funding the full entitlement himself was simpler than arranging a formal transfer, and it kept his own percentage ownership of Api Power exactly where it had been.
Not every Api Power shareholder made the same call. Real market data shows that 1,194,934 of the 16,533,137 rights shares on offer — a little over 7 percent — went unclaimed by existing shareholders and were sold at a public auction held from October 4 to 11, 2023. Some of those shareholders no doubt made a considered decision that the company's Canon Score, or their own cash position, argued against participating. Many more, in all likelihood, simply missed the deadline, misplaced the notice, or never ran the numbers at all — forfeiting real value to auction bidders for no better reason than inattention.
CASE IN POINT
The new shares from Api Power's rights issue were formally listed for trading on NEPSE on November 10, 2023, alongside a 35 percent rights issue from Multipurpose Finance completing on the same day — a reminder that rights issues, far from being rare events, are a routine and constant feature of the Nepali market, especially in the hydropower and financial sectors, and that the skills in this chapter are ones an active NEPSE investor will use again and again, not just once.
Looking back from several years later, the credit-and-cash-flow logic behind Api Power's 2023 rights issue held up reasonably well. By the 2081/82 fiscal year, the company was declaring both a bonus share distribution and a cash dividend to shareholders — a sign of a business that had moved past its heaviest construction financing needs and into a more normal, profit-distributing phase. None of that outcome was guaranteed back in 2023; it was simply the direction the Canon Score's growth-outlook and financial-health categories had pointed toward, and it is the kind of multi-year follow-through that separates a rights-issue decision made on a clear framework from one made on a coin flip.
The broader lesson of Suman's story is not "always subscribe to rights issues" or "hydropower is always a good bet." It is that a rights issue notice is not paperwork to be glanced at and filed away — it is a live financial decision with a real cash amount, a real deadline, a real tax consequence, and a real cost to inaction, and every one of those elements can be read directly off the company's own disclosure if an investor takes the time to assemble them, exactly as Table 1 in this chapter did. The four doors — exercise, partial exercise, renounce, lapse — are always open during a subscription window. Only one of them, chosen deliberately and for a clear reason, is the right one for any given investor's actual circumstances; the other three are not wrong by definition, but walking through the wrong door by default, simply because the notice sat unread in an inbox, is the single most common and most avoidable mistake this book has catalogued.
Chapter recap
This chapter took the rights-issue rules from Chapters 15, 37, and 78 and ran them against a real, verifiable NEPSE corporate action: Api Power Company Limited's 2023 rights issue, offering four new shares for every ten held, at Rs 100 per share, with subscriptions running from Shrawan 31 to Bhadra 19, 2080 BS. It also corrected the chapter's own scoring machinery: an earlier, informal five-category, 20-points-each scheme has been replaced with Chapter 64's real seven-dimension, 100-point Canon Score, applied with Chapter 65's hydropower-specific guidance and Chapter 78's instruction to check the score before and after the capital raise. Hemisphere 1 — Liquidity & Tradability, Governance & Promoter Behaviour, and Sector & Business Model Durability, the three dimensions a pro-rata rights issue does not move — scored a strong 35 out of 40. Hemisphere 2 showed exactly what the rights issue did move: Financial Strength & Profitability rose from 5 to 10 out of 20 as gearing fell from 1.37x to 0.92x and interest coverage strengthened to 3.82x, even though return on equity has still not cleared Chapter 64's own 7 percent floor. Valuation Reasonableness (8/15) and Growth Trajectory (7/15) were both scored conservatively against data this chapter could not fully verify, and Dividend & Capital Return Discipline (8/10) reflected a near-unbroken payout record at a persistently thin cash ratio. The real total, 68 out of 100, lands in Chapter 64's Adequate band — close in spirit to this chapter's original rough estimate, but now built from seven individually checkable numbers instead of five invented ones. This chapter also showed how to calculate the theoretical ex-rights price and the real rupee value embedded in an entitlement, how Nepal's tax rules on cost basis and holding periods change the after-tax picture, and why letting an entitlement lapse is a forfeiture of real value rather than a neutral non-decision. Suman Gurung's choice — fully exercising his entitlement, funded by exiting a weaker holding rather than by fresh cash strain — illustrated one reasonable path among the four doors available to every Nepali shareholder who receives a rights notice.
Chapter 88, Case Study 6 — An IPO Analysis, turns from a company already listed and already known to one arriving fresh on NEPSE for the very first time. It follows the same disciplined, plain-numbers approach into the very different territory of an initial public offering: reading a prospectus instead of a rights notice, judging a company with a much shorter public track record, and applying the Canon Score to a business the market has not yet had years to argue about.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 88
Case Study 6 — An IPO Analysis
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 6 — An IPO Analysis
Rukmini Thapa was thirty-one years old, worked as a billing officer at a private hospital in Kathmandu, and had been reading the Canon for eight months. She had a NEPSE demat account, a MeroShare login she actually remembered the password to, and just under two lakh rupees sitting in a savings account earning almost nothing. A lakh, if you have not met the word yet, is the standard South Asian counting unit for one hundred thousand — so two lakh means two hundred thousand rupees. She had read the chapters on valuation, on the Canon Score, on diversification. What she had never actually done was apply for an IPO with her own money and follow it all the way through — application, allotment, listing day, and the year after. This chapter follows her through a real one: Bikash Hydropower Company Limited, ticker BHCL, which opened its public share offering in July 2025 and listed on the Nepal Stock Exchange the following month.
We are choosing a real, ordinary IPO on purpose, not a spectacular one. Most of what happens to most Nepali retail investors happens in IPOs exactly like this: a small hydropower company nobody outside the sector had heard of, an offer document nobody read closely, a subscription period that closed oversubscribed by many multiples, and a listing day that made headlines for an afternoon and was forgotten by the next earnings season. If you understand how to think about this one, you can think about the next one, whatever sector it comes from.
Lesson 88.1 — The Company on the Table
Bikash Hydropower Company Limited owns and operates the Upper Machhakhola Small Hydropower Project, a run-of-river plant with an installed capacity of 4.55 megawatts, located in Gorkha district. A run-of-river plant is one that generates electricity from the natural flow of a river, without a large dam or reservoir behind it — think of it as a mill wheel turned by a stream rather than a bathtub that stores water and releases it on command. That distinction matters enormously for how much power the plant produces in different seasons, and we will come back to it.
For scale, 4.55 megawatts is small even by Nepal's hydropower standards. Nepal's largest operating plants, like Upper Tamakoshi, generate in the hundreds of megawatts. A 4.55 MW plant is closer to a single large diesel generator than to a national power station. That is not necessarily bad — small run-of-river projects are common, cheaper to build, and faster to bring online — but it means BHCL is, in effect, a single-asset company. There is one plant, one river, one set of turbines. If that river floods, or that plant needs unplanned repairs, there is no second plant quietly picking up the slack.
Like almost every hydropower company listed on NEPSE, BHCL sells its electricity to a single buyer: the Nepal Electricity Authority, or NEA, the state-owned utility that owns Nepal's transmission grid and is, for practical purposes, the only wholesale buyer of electricity in the country. This relationship is formalised in a Power Purchase Agreement, or PPA — a long-term contract that fixes the price NEA will pay per unit of electricity, usually for fifteen to twenty years, often with a lower tariff for the monsoon season (when rivers run high and every plant is producing) and a higher tariff for the dry season (when rivers run low and power is scarcer). This is the backbone of the hydropower investment story in Nepal: unlike a garment factory or a trading company, whose revenue depends on customers who might not show up, a hydropower company's revenue is substantially locked in by contract before a single unit of electricity is sold. That is a real strength. It is also, as we will see, not the same thing as a guarantee.
BHCL brought 2,942,760 units to the general public in its IPO — in plain terms, just under 2.95 million shares. Nepali share prices are quoted per unit of Rs 100 face value, called the par value, and BHCL's IPO was priced exactly at par: Rs 100 per share, no premium. The subscription window ran from 18 to 22 Ashadh 2082 on the Nepali calendar — 2 to 6 July 2025 in the Gregorian calendar most of this book uses. By the time it closed, the general public portion had drawn applications for 23,655,370 units — meaning investors asked for more than eight times as many shares as were on offer.
KEY CONCEPT
Par value is the fixed reference price of Rs 100 stamped on a Nepali share at issuance. Most ordinary-share IPOs in Nepal — and almost all hydropower and insurance IPOs — are sold to the public at exactly par value, regardless of what the company might reasonably be worth. This is unlike a book-built IPO in mature markets, where the issuer and underwriters set a price based on investor demand and company earnings. In Nepal, par pricing means the "is this cheap or expensive" question you would normally ask before applying is answered for you by regulation, not by analysis — the real test of value happens only after listing, when the market sets a free price for the first time.
That single fact reframes almost everything about how a Nepali retail investor should think about an ordinary IPO. You are not being asked whether Rs 100 is a fair price for a share of a 4.55 MW hydropower plant — regulation has already fixed that number regardless of what the underlying business is actually worth. What you are being asked is a completely different question: is this a business you want to own once the market sets its own price for it, and is the process of getting shares worth your time and your blocked capital along the way? Chapter 14 introduced IPOs as "buying a small piece of a company before the crowd gets a chance to bid the price up or down." Chapter 77's IPO playbook built on that by insisting you never skip ordinary company analysis just because the price is fixed. Rukmini's job here was to do that analysis on BHCL before deciding whether to apply, and if so, for how much.
Lesson 88.2 — Reading the Prospectus Like a Detective
Every Nepali IPO comes with a prospectus, a legally required disclosure document that lays out the company's business, its financials, its risks, and how it plans to use the money it raises. Most retail investors never open it. Rukmini did, and she read it the way the Canon teaches: not as marketing, but as a set of clues a detective would want, because a prospectus is written by the company trying to sell you something, and your job is to read past the sales pitch to the facts underneath.
Four things mattered most for a hydropower prospectus, and they are the same four things that matter for any hydropower IPO you will meet in the years after this book is written:
First, is the plant already generating and selling power, or is it still under construction? A company that is still building has construction risk — cost overruns, contractor delays, landslide damage to access roads, disputes over land compensation with local communities. A company that is already operating and has a signed PPA has converted that construction risk into a different, calmer kind of risk: operating and hydrology risk. BHCL's Upper Machhakhola project was commissioned and generating before the IPO, which meant Rukmini was buying into an operating utility, not a construction promise. That is a meaningfully safer starting point than many hydropower IPOs on NEPSE, where investors have in the past applied for shares in projects still years from producing a single unit of power.
Second, how much debt sits on top of the equity? Hydropower plants are almost always built with a large amount of bank borrowing layered on top of shareholder capital — a debt-to-equity ratio of 70:30 or even 80:20 is common in the sector, because the plant itself, and the PPA revenue behind it, is used as collateral for project loans. This is not automatically alarming; it is how the entire industry is financed, and NRB, Nepal's central bank, has spent years shaping directed-lending rules specifically to get bank capital into hydropower. But high leverage means a large, fixed monthly interest and principal bill that has to be paid whether the river is flowing hard or running low. A dry winter that cuts output by a third does not cut the loan payment by a third.
Third, who are the promoters, and what is their track record? Nepali hydropower companies are frequently sponsored by a mix of local businesspeople, cooperative societies, and sometimes politically connected individuals in the project's home district, alongside "public and project-affected local" share allotments reserved by regulation for people living near the project. A prospectus rarely narrates a scandal, but it will list director names, other companies they are involved with, and related-party transactions if any exist. This is unglamorous, patient reading, and it is exactly the kind of homework the Canon has asked you to do since Chapter 3.
Fourth — and this is easy to skip past — what does the company plan to do with the money it is raising? An IPO that raises fresh capital to pay down expensive short-term debt or fund a second, already-permitted project is different from one that primarily lets existing promoters cash out their shares. Rukmini noted BHCL's issue was, like most hydropower IPOs, primarily aimed at broadening ownership and meeting the public shareholding requirements that come with a hydropower project's licensing conditions, rather than announcing an aggressive expansion plan.
CASE IN POINT
By the close of its subscription window, BHCL's general public offering of 2,942,760 units had drawn applications for 23,655,370 units — an oversubscription of about 8.04 times (23,655,370 ÷ 2,942,760 = 8.0385, rounding to 8.04). A total of 2,056,202 separate applications were filed; after 94,465 were rejected for technical reasons (incomplete forms, mismatched details, and similar errors), 294,276 applicants were selected by lottery and each was allotted the same flat amount: 10 units. That means roughly one applicant in every seven who filed a valid application actually received shares — and every winner received exactly the same small parcel, regardless of whether they had applied for the minimum amount or many times more.
Lesson 88.3 — Hemisphere 1: Liquidity, Governance, and Durability
Rukmini's first instinct was to reach for something she had heard called the "Canon IPO Score" from Chapter 77's IPO playbook. Rereading Chapter 77 carefully, she found this was a mistake worth naming plainly: Chapter 77 does not define anything called a Canon IPO Score. What it actually builds, in Lesson 77.4, is Rajendra Koirala's own IPO scorecard — six differently named categories (promoter and governance quality, financial strength and growth, pricing relative to sector, sector timing, use-of-proceeds clarity, and issue structure and liquidity), each weighted as a percentage of 100, used to decide one narrow thing: how large an application to submit into the lottery. That is a real and useful tool, but it answers a different question than the Canon Score does, and it is not a variant of Chapter 64's seven-dimension framework at all. The right tool for "is BHCL a good business" is the one this book has used for every other case study in this Part: Chapter 64's real Canon Score, adapted here the way Chapter 65 adapts it for hydropower generally, with Chapter 77's actual scorecard reserved for the separate, later question of how much to apply for.
Because BHCL is followed here from prospectus to a year past listing, this chapter scores the three dimensions least distorted by the IPO process itself — Liquidity & Tradability, Governance & Promoter Behaviour, and Sector & Business Model Durability — using the most recent verifiable figures, a year into trading, the same Hemisphere 1 grouping this book has used since Chapter 84.
Liquidity & Tradability (10 points). A year after listing, BHCL trades with real, checkable numbers: a 30-day average volume near 53,497 shares a day at a recent price around Rs 553 works out to roughly Rs 29 million of daily rupee turnover — solid, though short of Chapter 64's very top band. Volume: 3 out of 5. Free float is harder to pin down directly; BHCL's general-public IPO tranche of 2,942,760 units against 9,091,013 total shares outstanding today works out to about 32 percent of the company held outside the promoter and project-affected-local allotments — an estimate built from arithmetic on disclosed totals, not a directly reported free-float percentage, and disclosed as such. That sits below Chapter 64's 40-percent top-band threshold but comfortably above its bottom band — scored 3 out of 5. Liquidity & Tradability: 3 + 3 = 6 out of 10.
Governance & Promoter Behaviour (15 points). BHCL's promoter base, unlike a single dominant family group, is fragmented across several named individuals — Khagendra Neupane holding roughly 14 percent, with Krishna Prasad Ghimire, Saroj Dhital, Raj Kumar Gurung, Indra Bahadur Dhakal, and Sunil Shrestha each holding smaller stakes — a real, disclosed structure typical of a locally sponsored small hydropower project, with no pledging disclosed in the sources checked, though a verified CDSC pledging record was not independently confirmed. Promoter shareholding stability and pledging: 4 out of 6. Related-party transactions and audit opinion: no specific related-party red flag surfaced in the sources checked, and the company carries a credit rating from ICRA Nepal, which implies at least some external scrutiny of its financials — 3 out of 5. Disclosure timeliness and board independence: quarterly EPS figures have continued to be published since listing, but board independence was not separately verified — 3 out of 4. Governance & Promoter Behaviour: 4 + 3 + 3 = 10 out of 15.
Sector & Business Model Durability (15 points). The moat sub-component is, once again, Chapter 64's own textbook full-marks case: a licensed hydropower generator selling under a long-term power purchase agreement to the Nepal Electricity Authority — 8 out of 8. The concentration and dependency sub-component carries two layers of real risk here, not one. The first is the same single-buyer dependency on NEA that every hydropower case study in this book has flagged as the acknowledged gap between what Chapter 64 promises Chapter 65 will fix and what Chapter 65 actually delivers. The second, specific to BHCL, is that a 4.55-megawatt run-of-river plant with no reservoir is a genuinely smaller and more concentrated bet than Chilime or Api Power: it is one river, one intake, one set of turbines, generating far less electricity in the dry winter months than during the summer monsoon — often only a third to a half of peak output — with no second plant to pick up the slack if a landslide or flash flood damages the intake and canal, a real and recurring hazard for small Nepali hydropower during monsoon season. Scored more conservatively than the two-plant, larger-capacity peers in this book's other hydropower case studies: 3 out of 7. Sector & Business Model Durability: 8 + 3 = 11 out of 15.
WARNING
A run-of-river hydropower plant with no reservoir generates far less electricity in the dry winter months than during the summer monsoon — often only a third to a half of peak output. This seasonal swing is built into the PPA's two-tier tariff, but it also means a single bad monsoon, an unusually dry winter, or physical damage to the intake and canal from a landslide or flash flood can cut a small plant's annual revenue sharply, with no second plant elsewhere to make up the difference. Nepal has real, recent precedent for hydropower infrastructure being damaged by exactly this kind of event, including flood and landslide damage that has taken plants offline for extended repairs in past monsoon seasons. A single-project hydropower company carries this risk in a way a diversified portfolio of five or six plants does not — and it is exactly why BHCL's durability score sits a notch below the larger hydropower operators this book has scored in earlier case studies.
Hemisphere 1 total: Liquidity & Tradability 6 + Governance & Promoter Behaviour 10 + Sector & Business Model Durability 11 = 27 out of 40.
Lesson 88.4 — Hemisphere 2: Profitability, Valuation, and Growth Since Listing
This is where an honest score and a hot listing-day chart pull hardest in opposite directions.
Financial Strength & Profitability (20 points). Return on equity, computed from BHCL's current trailing earnings per share of Rs 10.81 against a book value per share of Rs 112.53, works out to about 9.6 percent — inside Chapter 64's 7-to-10-percent band, scored 4 out of 8. Leverage and interest-coverage discipline is scored on the most recent independently verified figures available, from ICRA Nepal's October 2023 rating report: a project debt-to-equity structure of roughly 58:42, a total-debt-to-tangible-net-worth ratio of 1.53 times, and an interest coverage ratio of just 1.35 times — meaning operating earnings at that time barely covered the interest bill, well short of Chapter 64's thresholds. The 2025 IPO itself raised roughly Rs 294 million of fresh equity, which plausibly improved this ratio somewhat, but no updated, independently verifiable post-IPO figure could be confirmed, so this dimension is scored on the last verified data rather than an assumed improvement — 0 out of 7. Earnings quality and consistency: only a single confirmed EPS figure exists in the sources checked, with no verified multi-year trend, scored conservatively for the data gap rather than assumed to be either strong or weak — 2 out of 5. Financial Strength & Profitability: 4 + 0 + 2 = 6 out of 20.
CAUTION
Scoring Financial Strength & Profitability on a leverage figure from before an equity-raising IPO, rather than assuming the raise fixed it, is a deliberate choice, not an oversight. Chapter 87's Api Power case study showed a rights issue can be checked before and after because verified figures existed on both sides. Here, only the "before" figure could be confirmed — so the honest move is to score what is known, and say plainly that the truth may now be better than this number, not to quietly assume it and score as if the improvement were already proven.
Valuation Reasonableness (15 points). BHCL currently trades around 51.14 times trailing earnings — against a hydropower-sector average near 18.37 times, a ratio of roughly 2.78, well past Chapter 64's 1.5x ceiling for the bottom band — scored 1 out of 8. Its price-to-book ratio of about 4.91 times, checked against the broader NEPSE market's average of roughly 2.8 times (the same imprecise but disclosed benchmark used elsewhere in this Part, since no hydropower-specific book-value median could be verified), works out to a ratio of about 1.75 — again past the 1.5x ceiling — scored 1 out of 7. Valuation Reasonableness: 1 + 1 = 2 out of 15.
Growth Trajectory (15 points). No verified multi-year revenue or earnings-per-share series could be confirmed for BHCL in the sources checked for this chapter — only a single current EPS figure and a single, small, pre-IPO operating-income figure from 2023. Both sub-components are scored conservatively for this data gap rather than assumed to be either strong or weak: revenue growth 2 out of 8, earnings-per-share consistency 2 out of 7. Growth Trajectory: 2 + 2 = 4 out of 15.
Dividend & Capital Return Discipline (10 points). BHCL has not declared a dividend since listing in August 2025 — a real, disclosed absence rather than a fabricated negative, and one that may simply reflect a young, recently listed company that has not yet held the AGM cycle where a first dividend decision would appear. Chapter 64 does not permit skipping a dimension because the data is thin, so both sub-components are scored at the conservative floor this book has used throughout for a genuine absence of track record, rather than a confirmed failure to pay: consistency of payout 1 out of 6, sustainability of payout 1 out of 4. Dividend & Capital Return Discipline: 1 + 1 = 2 out of 10.
Lesson 88.5 — The Full Worked Canon Score
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
6
ROE ~9.6% → 4/8; last verified leverage (pre-IPO, 2023) shows interest coverage of only 1.35x → 0/7; single confirmed EPS figure, no verified trend → 2/5
Governance & Promoter Behaviour
15
10
Fragmented multi-individual promoter base, no pledging disclosed → 4/6; no related-party red flag, ICRA-rated → 3/5; quarterly EPS continuing, board independence unverified → 3/4
Liquidity & Tradability
10
6
~Rs 29 million/day turnover → 3/5; free float estimated at ~32% from disclosed totals, below the 40% top band → 3/5
Valuation Reasonableness
15
2
P/E 51.14x vs sector ~18.37x (≈2.78x) → 1/8; P/B 4.91x vs broader-market ~2.8x (≈1.75x) → 1/7
Sector & Business Model Durability
15
11
Licensed generator with a signed PPA, Ch64's textbook full-marks case → 8/8; single 4.55MW run-of-river plant, no reservoir, 100% NEA dependency → 3/7
Growth Trajectory
15
4
No verified multi-year revenue or EPS series → 2/8 + 2/7
Dividend & Capital Return Discipline
10
2
No dividend declared since listing; too young a track record to score positively → 1/6 + 1/4
Canon Quality Score
100
41
Band: Weak/Avoid (below 55)
Summed, BHCL's real Canon Score comes to 41 out of 100 — Weak/Avoid, per Chapter 64's own bands. Governance & Promoter Behaviour scored 10 out of 15, above the 5-point floor, so the override does not separately apply; the raw arithmetic already lands the total in the same band the override exists to enforce.
This is a strikingly different number from the informal 22 out of 30 (about 73 percent) this chapter once reported — not because BHCL's business changed, but because that number was never a real Canon Score in the first place. It was a home-grown, six-category rubric that borrowed the name "Canon IPO Score" without Chapter 64's actual dimensions or Chapter 77's actual scorecard behind it. Scored honestly, on Chapter 64's real seven dimensions, BHCL is a thin, richly priced, single-asset hydropower company with a weak track record on leverage and no dividend history yet — a Weak/Avoid, not the "above-average, worth a small position" verdict the original text offered.
And yet BHCL's share price climbed to almost seven times its issue price within its first year of trading. That gap is the whole lesson of this chapter, stated as sharply as the numbers allow: why does a business that scores 41 out of 100 still draw eight times more demand than shares on offer, and still see its price run up so far above par? Because in Nepal, demand for an IPO and the quality of the underlying business, measured honestly, are only loosely connected. Hydropower dominates retail enthusiasm on NEPSE for structural reasons that have little to do with any single project's fundamentals: remittance income flowing home from Nepali workers abroad needs somewhere to go, bank deposit rates are often unattractive, and new listings of any kind are relatively rare, so each one draws a large share of the country's appetite for something new to buy. Extreme oversubscription and a strong listing-day pop tell you a great deal about near-term demand. A real, honestly scored Canon Score of 41 out of 100 tells you something different and, for anyone planning to hold rather than flip, more important: whether the plant will still be a good business to own in five years.
Lesson 88.6 — The Application: ASBA, MeroShare, and How Much to Apply For
Rukmini applied for BHCL through MeroShare, the online portal operated by CDS and Clearing Limited, the depository that holds electronic share records for every NEPSE investor. MeroShare is where a Nepali investor's demat account — the electronic record of shares they own, replacing the old paper share certificates — lives, and it is also the interface through which IPO applications are filed. To apply, an investor logs into MeroShare, selects the open IPO from a list, enters the number of units they want (in multiples of the minimum lot, which for BHCL, as for almost every ordinary Nepali IPO, was 10 units), and confirms the application against a linked bank account.
That linked bank account is where ASBA comes in. ASBA stands for Applications Supported by Blocked Amount. Instead of the full application money leaving your account the moment you apply, the bank simply blocks — freezes, but does not withdraw — the amount needed for the shares you have requested. Ten units at Rs 100 par value meant Rs 1,000 blocked, the minimum application amount, and the amount Rukmini chose to apply for. If your application is unsuccessful in the lottery, the block is lifted and the money is simply usable again, with nothing having actually left your account in the interim. If you are allotted shares, the blocked amount is debited to pay for them.
REGULATORY DETAIL
When a Nepali retail IPO is oversubscribed, allotment is not done proportionally to the size of each application. Instead, every valid applicant who applied for at least the minimum lot is entered into a single lottery with equal standing, and every winner is allotted the same flat minimum lot — in BHCL's case, 10 units each — regardless of whether they applied for 10 units or 1,000. Any additional blocked amount beyond what the minimum allotment costs is simply released back to the applicant. This is why the Canon's IPO playbook rule is to apply for the minimum lot in any IPO you expect to be heavily oversubscribed: applying for more does not raise your odds of winning, and any surplus capital you had blocked for it is not doing anything useful in the meantime.
This single regulatory fact is worth sitting with, because it overturns an instinct most new investors bring from other kinds of investing: the idea that committing more capital should get you a proportionally larger result. In a flat-lottery IPO system, it does not. Rukmini had, before reading this carefully, assumed that applying for a larger amount — say, 100 units instead of 10 — would improve her chances or at least guarantee a bigger allotment if she won. Neither is true here. It would only have tied up more of her Rs 200,000 in a blocked state for the one to two weeks between application and allotment, money that could otherwise have stayed in an interest-bearing account or been used for another IPO's application window if one happened to overlap.
PRACTICAL TOOL
A short checklist for applying to an oversubscribed retail IPO through MeroShare and ASBA: (1) Confirm your MeroShare account and linked bank details are current before the application window opens — corrections take time you may not have. (2) Read the prospectus for plant status, PPA terms, debt levels, and use of proceeds before the window opens, not during it. (3) In a retail flat-lottery system, apply for the minimum lot only, unless you have a specific, informed reason to apply for more. (4) Only use money you can afford to have blocked for one to two weeks with no return — never borrowed money, and never money earmarked for a near-term expense. (5) Decide, in writing to yourself, whether you intend to sell on listing day or hold for the business, before allotment results are announced — not after, when the temptation to chase a hot opening price is strongest.
Rukmini's family took the extra step that a great many Nepali households take with heavily oversubscribed IPOs: her mother and her younger brother, both with their own separate demat accounts, also applied for the minimum 10 units each in BHCL. This is legal — each individual with their own citizenship-linked demat account may apply in their own name — but it is worth being honest about what it is: not investment analysis, but simply increasing the number of lottery tickets a family holds collectively, since Nepal's system permits one application per individual account rather than one per household. It is a widely used strategy, and it is also exactly why oversubscription multiples on popular Nepali IPOs run so high — much of the demand is the same pool of family capital spread across as many separate accounts as the family has.
Lesson 88.7 — Allotment Day and Listing Day: What Actually Happened
The lottery results for BHCL were published on 13 July 2025 — roughly a week after the subscription window closed. Of the three family applications, Rukmini's brother's was drawn; hers and her mother's were not. He received the flat allotment of 10 units, at a total cost of Rs 1,000, debited from his blocked ASBA amount the moment the allotment was confirmed. Rukmini's own Rs 1,000 was simply unblocked and available again the same week — no loss, no gain, just capital that had sat idle for roughly ten days.
This is worth pausing on, because it is the most common outcome of applying for a popular Nepali IPO, and new investors are frequently caught off guard by how little drama is actually involved in losing the lottery. Nothing was lost. Nothing was really risked, beyond the opportunity cost of ten days without access to Rs 1,000. The real investment decision, for the family, landed entirely on the shoulders of the one allotment that came through.
BHCL listed on NEPSE roughly five weeks later, on 19 August 2025 — the gap between allotment and listing being the ordinary administrative time it takes for share certificates to be credited to demat accounts and for NEPSE to clear the company for trading. On listing day, NEPSE set an opening price band — the range within which the very first trades of a newly listed share are allowed to occur, since there is no previous closing price to anchor a normal daily circuit band against — of Rs 97.43 to Rs 292.29. That is an unusually wide band for a first trade, running from just below the Rs 100 par value all the way up to nearly three times it, and it reflects how uncertain price discovery is for a company whose fixed IPO price told the market nothing about what buyers were actually willing to pay once shares could trade freely.
REGULATORY DETAIL
A newly listed Nepali share does not open trading against a normal daily circuit band, because there is no prior day's closing price to measure a percentage move against. Instead, NEPSE calculates and publishes a first-day price range using a formula that accounts for the company's book value, sector norms, and other reference points, and the very first trades of the day must fall inside that band. Once a closing price is established on listing day, the share then moves onto NEPSE's ordinary daily circuit system for every subsequent session. This means the true test of what the market thinks a newly listed hydropower company is worth is compressed into the trading of a single day — a very different process from the gradual price discovery that happens for shares that have traded for years.
BHCL's shares traded within that band and, over the weeks that followed, the stock moved well above its Rs 100 par value, in keeping with the pattern the extreme oversubscription had already signalled. Across the twelve months after listing, BHCL's price ranged as widely as its opening band had suggested it might: a 52-week high of Rs 699 and a 52-week low of Rs 292.10, with the shares trading around Rs 553 roughly a year after listing, in August 2026. In other words, an investor who was allotted shares near par value and held through the full year saw the price climb to almost seven times its issue price at its best point (Rs 699 ÷ Rs 100 = 6.99), and nearly triple at its worst (Rs 292.10 ÷ Rs 100 = 2.92) — but also saw it fall by roughly twenty percent from its high before this book went to print.
CAUTION
A wide first-day trading range and a strong initial climb above par value do not tell you where a stock will settle once the excitement of a new listing fades. BHCL's shares traded as high as Rs 699 and as low as Rs 292.10 within a single year of listing — a difference of more than double, on the same business, with the same PPA, the same 4.55 megawatt plant, and the same debt load throughout. Chasing a price because "it's already up a lot since listing" is not the same as judging whether the business is worth what the market is currently asking for it. The Canon Score you calculated before applying is still the right anchor for that judgment; the listing-day price chart is not.
Lesson 88.8 — The Decision and the Aftermath
Rukmini's brother faced the actual decision that mattered: what to do with 10 shares of BHCL once they hit his demat account and started trading freely. The IPO playbook from Chapter 77 gives three rules for exactly this moment, and the family had, per the practical checklist above, already talked through their intention before allotment results came out — which is precisely the point of deciding early, since it is far harder to think clearly once a number is flashing green on a screen.
The first rule is to separate the "lottery premium" from the "investment decision." An IPO allotted at a fixed, regulation-set par price and then immediately worth two, three, or more times that price on listing day has handed you a windfall that has very little to do with your skill as an analyst — it is largely a function of Nepal's par-pricing rules combined with the country's chronic retail appetite for new hydropower shares. There is nothing wrong with taking that windfall. There is something wrong with mistaking it for proof that you understand the business better than the market does.
The second rule is to decide, using the Canon Score you already calculated, what portion — if any — of your allotment you actually want to hold as an ongoing position in the underlying business, separate from the windfall. A real Canon Score of 41 out of 100 — Weak/Avoid, driven by a stretched valuation, a thin leverage track record, and no dividend history yet — argued for treating any retained shares as, at most, a token tracking-sized holding kept mainly to stay engaged with the story, not a conviction position, regardless of how exciting the listing-day chart looked.
The third rule is to act on that decision at listing, not to wait and hope for a better price later, because the family had already agreed on it in advance. Rukmini's brother sold 6 of his 10 shares on listing day, near the top half of the first-day trading range, banking a gain on roughly two-thirds of his tiny position while it was fresh. He kept the remaining 4 shares as a genuinely small, tracking-sized stake in a business whose fundamentals — an operating plant, a signed PPA, real if modest revenue visibility — he judged, using the Canon Score, to be sound enough to be worth owning in small size for the years ahead, whatever the share price did next.
A year later, with BHCL trading around Rs 553, that decision looked reasonable rather than brilliant, which is usually how good process actually feels in real time. Selling into listing-day strength captured value while the price was elevated, ahead of the roughly twenty percent pullback from the year's high. Holding a small remainder meant continuing to participate in a business the family had actually researched, at a size small enough that a bad monsoon season or an unexpected repair bill would not meaningfully damage their overall finances — and small enough to match a Canon Score that, honestly totalled, sits in the Weak/Avoid band rather than the "worth a modest position" reading the family's first, informal pass had suggested. Neither the flip nor the hold, on its own, would have been the "right" answer for every investor in this situation — what mattered was that the decision was made deliberately, in advance, using a scorecard built before anyone knew what the market price would do, rather than improvised in the excitement of a green number on listing morning.
Milestone
Date
Key Fact
Subscription opened
2 July 2025
2,942,760 units offered to the general public at Rs 100 par value
Subscription closed
6 July 2025
Applications received for 23,655,370 units — oversubscribed about 8.04 times
Allotment (lottery) results
13 July 2025
294,276 applicants allotted a flat 10 units each; 94,465 applications rejected
Listing on NEPSE
19 August 2025
First-day trading band set at Rs 97.43 to Rs 292.29
One year after listing
August 2026
52-week range Rs 292.10 to Rs 699; trading near Rs 553; real Canon Score 41/100 (Weak/Avoid)
The broader lesson sits underneath all of these numbers rather than inside any single one of them. An IPO in Nepal is really two separate exercises wearing one application form. The first is a lottery, governed by rules — flat minimum allotments, blocked-not-withdrawn ASBA funds, oversubscription multiples driven as much by family account-splitting as by genuine conviction — that have almost nothing to do with whether the underlying business is a good one. The second is an ordinary investment decision, governed by the same Canon Score discipline you would apply to any other share, that happens to arrive compressed into the single, emotionally loud afternoon of listing day. Confusing the two — treating lottery luck as investment skill, or treating a listing-day price spike as proof of business quality — is the single most common mistake retail investors make with Nepali IPOs. Keeping them separate, the way Rukmini's family did almost by accident because they had simply written their plan down in advance, is most of what separates a lucky story from a repeatable process.
Chapter recap
This chapter followed one real, ordinary NEPSE IPO — Bikash Hydropower Company Limited, a 4.55 megawatt run-of-river plant in Gorkha district — from prospectus to a year past listing. It also corrected a mistaken citation along the way: Chapter 77 does not define a "Canon IPO Score." It defines Rajendra Koirala's own IPO scorecard, a different tool answering a different question — how much to apply for, not whether the business is good. This chapter used Chapter 64's real seven-dimension Canon Score instead, the same framework applied to every other case study in this Part, adapted with Chapter 65's hydropower guidance. Hemisphere 1 scored Liquidity & Tradability (6/10), Governance & Promoter Behaviour (10/15), and Sector & Business Model Durability (11/15 — a notch below this book's larger hydropower case studies, reflecting BHCL's single small run-of-river plant with no reservoir). Hemisphere 2 scored Financial Strength & Profitability (6/20, held down by a weak pre-IPO interest-coverage figure that could not be confirmed as improved), Valuation Reasonableness (2/15, against a P/E of 51.14x and a P/B of 4.91x), Growth Trajectory (4/15, scored conservatively against an unverified multi-year track record), and Dividend & Capital Return Discipline (2/10, no dividend declared since listing). The real total, 41 out of 100, lands in Chapter 64's Weak/Avoid band — a sharp contrast with a share price that climbed to almost seven times its issue price within a year, and the central lesson of this chapter: an oversubscribed lottery and a hot listing-day chart measure demand, not business quality, and the two can point in opposite directions. We also walked through the actual mechanics of applying — MeroShare, ASBA's blocked-not-withdrawn funds, the minimum 10-unit lot — and the regulatory fact, easy to miss, that Nepal's flat-lottery allotment system means applying for more than the minimum buys you nothing extra when an IPO is heavily oversubscribed. And we watched listing day unfold in the real world: a wide first-day price band, a strong initial climb, and a roughly twenty percent pullback from the year's high twelve months later — a pattern that rewarded a plan made in advance and punished any decision made in the heat of a rising chart.
Chapter 89 turns to a very different kind of case study, and a much harder one to sit with: a bank failure, or a near-failure, inside Nepal's financial system. Where this chapter asked whether to apply for a new listing, the next one asks what happens to your money, your confidence, and the wider economy when an institution you were told to trust — regulated by Nepal Rastra Bank, insured up to a point, woven into everyone's daily transactions — comes close to coming apart. It is a heavier story than an IPO lottery, and it deserves the same patient, detective's reading you just practiced here.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 89
Case Study 7 — A Bank Failure or Near-Failure
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 7 — A Bank Failure or Near-Failure
Bikash Rai had a rule he was proud of: never buy a stock his mother's cousin sold him at a wedding. But in the spring of 2081 (2024 in the calendar most of the world uses), that is almost exactly how he found Karnali Development Bank Limited, ticker KRBL on NEPSE. His cousin-in-law worked a small hardware shop in Surkhet, banked with KRBL because it was the closest branch to his shop, and mentioned over dal-bhat that the bank's stock was cheap, paid a dividend most years, and "not one of those overpriced Kathmandu banks." Bikash, who by then had been running every stock he touched through the Canon Score for two years, wrote the name in his notebook instead of his brokerage app. That single habit — write it down, don't buy it — is the whole subject of this chapter.
This case study is different from the ones before it. It is not about a company that quietly disappointed shareholders over a few bad quarters. It is about a licensed, deposit-taking financial institution that Nepal Rastra Bank (NRB, the central bank and the regulator of every bank and finance company in the country) formally declared to be in crisis, and then took over. Depositors' money was on the line. Shareholders' capital was wiped toward zero. Criminal investigations followed. This is the chapter where the Canon Score framework gets tested against the worst thing that can happen to a financial institution — and where we ask, honestly, whether following the framework would have kept an ordinary investor out of the wreckage in time.
Lesson 89.1 — The Bank Nobody Was Watching
Start with what Karnali Development Bank actually was, because most Kathmandu-based investors had never heard of it before December 2024, and that fact is itself the first lesson.
KRBL was a "Class B" institution under NRB's four-tier licensing system. In Nepal, banks and financial institutions (BFIs) are sorted into four classes: Class A are commercial banks (the big, well-known names with branches across the country and the deepest capital bases). Class B are development banks, smaller than commercial banks, often built around a particular region, historically created to bring formal banking to areas commercial banks were slow to reach. Class C are finance companies, smaller still. Class D are microfinance institutions, focused on small loans to low-income borrowers, often in rural areas. Karnali Development Bank sat in Class B, built around Nepal's mid-western Karnali region, one of the least commercially developed parts of the country. Its paid-up capital — the money shareholders had actually put in, the base a bank is required to hold before NRB will let it operate — stood at roughly Rs 502.8 million. Its deposits, meaning the money ordinary savers had placed with it, stood at about Rs 5.21 billion. Its loan book, the total it had lent out, stood at about Rs 3.81 billion. On paper, at the end of the first quarter of the 2024/25 fiscal year, it had posted a modest net loss of about Rs 19.8 million and reported a non-performing loan (NPL) ratio — the share of its loans that borrowers were not repaying on schedule — of 7.27 percent. A little weak, an analyst might have said, but not obviously a bank in crisis.
That "on paper" phrase is going to do a lot of work in this chapter.
Why would a retail investor like Bikash even consider a bank this small and this obscure? For the same reasons small development banks attract retail money across NEPSE every year. First, book value: a bank with Rs 502.8 million in paid-up capital and a stated net worth per share often trades at a price close to or even below its book value, which looks, on the surface, like a bargain compared to the richly priced commercial banks. Second, dividend habit: development banks with a long run of paying bonus shares or cash dividends build a reputation among retail investors as reliable income stocks, regardless of whether the underlying loan book actually supports that reputation. Third, and most important for this case, information scarcity: almost no brokerage research desk in Kathmandu covers a Class B development bank based in the Karnali region. There is no analyst call, no coverage note, no institutional investor watching the loan book line by line. The only people who really know what is happening inside the bank are the people running it — and, as this case shows, that can be a very dangerous information gap.
Institution class
What it is
Typical investor perception
Actual analyst coverage
Class A - Commercial banks
Large, nationwide, deepest capital
"Safe blue chip"
Heavy — most brokerages track these
Class B - Development banks
Regional, mid-sized, smaller capital base
"Undervalued, high dividend"
Thin to none
Class C - Finance companies
Small, niche lending
"Speculative, high yield"
Almost none
Class D - Microfinance
Rural small-loan lenders
"Social mission, steady"
Almost none
KEY CONCEPT
A non-performing loan, or NPL, is a loan where the borrower has stopped paying interest or principal on schedule, usually defined by NRB as no payment for 90 days or more. The NPL ratio is the percentage of a bank's total loans that fall into this category. It is the single most important number for judging whether a bank's loan book is healthy, because every rupee counted as a non-performing loan is a rupee the bank may never get back.
This is the first governance lesson of the case study, and it comes before we even open the financial statements: obscurity is not the same as safety, and it is not the same as value either. A stock that nobody is watching can be genuinely undervalued. It can also be a stock where nobody would notice if the numbers were wrong. The Canon Score framework does not treat "under-covered" as a red flag by itself — plenty of good small companies in Nepal are under-covered simply because research capacity is limited. But it does insist that when coverage is thin, the investor's own homework has to be thicker, not thinner, to compensate. Bikash's mistake would have been treating his cousin-in-law's tip as a substitute for that homework rather than a reason to start it.
Lesson 89.2 — Reading the Governance Red Flags Before the Collapse
Chapters 19 through 23 built the governance side of the Canon Score: who sits on the board, how much power is concentrated in one family or one promoter group, whether related-party transactions are disclosed and priced fairly, whether the audit function is independent, and whether the company communicates with shareholders honestly and on time. Applied to a bank, these questions carry extra weight, because a bank's "product" is trust with other people's money, and a governance failure at a bank does not just cost shareholders — it threatens depositors, and through the deposit insurance and financial stability system, it can touch the whole sector.
Here is what the governance record around Karnali Development Bank looked like, separating what was genuinely visible before the takeover from what investigators only established afterward — a distinction this chapter will return to in Lesson 89.6, because conflating the two is its own kind of hindsight error. What was visible in real time, to anyone reading board composition and AGM records before December 2024: Rajendra Bir Raya, the bank's founding promoter, held both the chairman and chief executive roles from roughly 2019 to 2022 — an unusual concentration of power at a small development bank in itself — before a subsequent chairman, Pashupati Dayal Mishra, a sitting member of a major political party (CPN-UML), took over. Two changes of chairman-level leadership in a bank's first several years, one of them combining the chairman and CEO roles in a single person, is a real, contemporaneously checkable governance flag — the kind Chapters 19 through 23 ask an investor to notice without needing to know what would come next. What was only established later, after NRB's takeover, is the criminal case: in June 2025, roughly six months after the takeover, the Central Investigation Bureau arrested Raya at Belahiya, near the Nepal–India border in Rupandehi district, as he attempted to cross out of the country — not an extradition from abroad, but a domestic arrest of someone who had gone underground inside Nepal after the investigation began. Mishra and several former executives, including a former finance chief, were separately investigated and, in some cases, later remanded in judicial custody in connection with an alleged embezzlement exceeding Rs 3 billion. This is not a story of one rogue employee. It is a story of a chain of leadership, across multiple chairmanships, that investigators allege was connected to the same pattern of financial irregularity — but the criminal confirmation of that pattern came in 2025, well after the stock had already been suspended, and it is important not to credit a 2024 decision with foresight it could not have had into a 2025 arrest.
WARNING
A politically connected chairman or board member is not automatically a red flag — many capable, honest people hold both political and business roles in Nepal's small economy. But when political connection appears alongside weak external oversight, thin analyst coverage, and a board that has not rotated its independent members in years, the combination raises the cost of any governance failure, because it becomes harder for regulators, auditors, and minority shareholders to challenge leadership in real time.
The Canon Score governance checklist (from Chapter 20 onward) asks a specific, almost boring set of questions that turn out to matter enormously in hindsight. Has the chairman changed hands under unclear circumstances? Has the external auditor changed unusually often, or does the auditor's report contain qualified language that management explains away in a single sentence in the annual report? Are related-party loans — loans to companies or individuals connected to board members or senior management — disclosed with enough detail to check whether they were priced and approved the way an arm's-length loan would be? Is the annual general meeting (AGM) held on schedule, or does it keep slipping? Do quarterly disclosures arrive on time, in full, and readable, or are they thin, delayed, and full of boilerplate?
None of these questions requires access to inside information. They require reading what a company is already required to publish, and noticing what is missing or delayed. For Karnali Development Bank, the retrospective picture that emerged after NRB's takeover — repeated leadership turnover at the chairman level, later-confirmed financial irregularities reaching into the billions of rupees, and a regulatory relationship that (as later criminal proceedings would allege) had itself been compromised — is exactly the pattern the governance chapters warn readers to price in as risk, well before any of it is confirmed as fraud. A disciplined investor does not need proof of fraud to downgrade a governance score. Absence of the normal signals of good governance is itself the signal.
CASE IN POINT
In July 2025, Nepal's Central Investigation Bureau arrested a Nepal Rastra Bank inspection officer, Bhuvan Basnet, on allegations that he had submitted favourable supervisory assessments of Karnali Development Bank despite its severe financial distress, and that suspicious deposits connected to the case had been traced to his personal bank account. The amount under investigation in that strand of the case was reported at roughly Rs 2.5 billion. This is a rare and serious allegation — that the regulator's own on-the-ground inspection process may have been compromised — and it is exactly why the Canon Score framework tells investors never to treat "the regulator has not flagged this yet" as proof that a bank is sound. Regulatory silence is evidence of nothing when the inspection process itself is under a cloud.
That last point deserves its own sentence, because it is easy to misread. Nepal Rastra Bank, as an institution, is the entity that ultimately uncovered the Karnali Development Bank crisis, imposed corrective measures, and referred individuals — including, remarkably, its own former inspection officer — for prosecution. The system worked, eventually. But "eventually" is the word that matters to a shareholder. The Canon Score framework is not a substitute for regulation; it is meant to help an individual investor act earlier than the regulatory process necessarily can, because regulators must build a legal case before they act, while an investor only needs to build a risk judgment.
Lesson 89.3 — The Numbers That Lied: NPL 7.27 Percent Versus 40.85 Percent
This is the centre of the case, and the number is worth sitting with. When Nepal Rastra Bank placed Karnali Development Bank under Prompt Corrective Action in late November 2024, and then formally declared it a troubled, or "problematic," institution in late December 2024, the bank's own reported non-performing loan ratio — the figure it had been disclosing to shareholders and to the market — was 7.27 percent. NRB's own supervisory assessment, once regulators looked underneath the reported numbers, found the true figure to be 40.85 percent.
Read that gap again. Not a rounding difference. Not a modest understatement that a conservative analyst might apply a haircut to. A difference of more than five and a half times. Four out of every ten rupees the bank had lent out were not being repaid on schedule, while the number in the disclosure documents said fewer than one in ten.
REGULATORY DETAIL
Prompt Corrective Action, or PCA, is a formal, staged process Nepal Rastra Bank uses under the Nepal Rastra Bank Act, 2058 (2002) when a bank or financial institution breaches key safety thresholds — capital adequacy, non-performing loans, liquidity, or profitability. PCA gives NRB the legal authority to restrict a troubled institution's activities (for example, limiting new lending, branch expansion, or dividend payments) before the situation becomes a full crisis. Section 86 of the Act goes further, allowing NRB to declare an institution "troubled" and, under Section 86(c), to take direct control of its management — installing NRB officials in place of the existing board — when PCA measures alone are not enough. Karnali Development Bank was placed under PCA on November 26, 2024, and declared a troubled institution with its board replaced by an NRB-appointed management team on December 26, 2024 — a gap of exactly one month between the warning stage and the takeover stage.
Chapters 54 through 58 built the financial-risk side of the Canon Score specifically to catch this kind of thing — not the fraud itself, which by definition is hidden, but the pattern of numbers around a fraud, which is very hard to hide completely. A few of those patterns were visible in Karnali Development Bank's public record before the takeover, for an investor willing to look for them rather than at them.
Consider capital adequacy first. A capital adequacy ratio measures how much of a bank's own capital stands behind its loans and other risk-weighted assets — think of it as the cushion between "a borrower stops paying" and "the bank itself cannot cover its obligations to depositors." NRB requires Class B development banks to hold a minimum ratio, and Karnali Development Bank had already failed to maintain that minimum before the PCA order — this was one of the stated reasons for the corrective action, not a discovery made after it. A bank that cannot maintain its regulatory capital cushion while also reporting a modest, single-digit NPL ratio is presenting two numbers that do not sit comfortably together: if only 7 percent of loans were bad, the capital math should have been much easier to satisfy. That mismatch — thin capital next to a suspiciously clean NPL number — is exactly the kind of cross-check the Canon Score risk framework asks investors to run, comparing a bank's stress indicators against each other rather than reading any single ratio in isolation.
Second, consider the deposit-to-loan relationship. Karnali Development Bank was mobilizing Rs 5.21 billion in deposits against Rs 3.81 billion in loans, and by the time of the takeover, it could not reliably meet deposit repayment obligations — meaning depositors coming to withdraw money were, in effect, running into a liquidity wall. A bank sitting on more deposits than loans should, in ordinary circumstances, have ample liquidity; that combination of "excess" deposits over loans on paper alongside real-world payment difficulty is a strong signal that some of the assets behind those deposits were not what they were reported to be.
Third, and this is the forensic detail that eventually broke the case open publicly: Nepal's Central Investigation Bureau found evidence of financial irregularities exceeding Rs 3 billion, and investigators specifically pointed to a mismatch between the bank's actual deposit records and the figures recorded in its own core banking system — the software platform that is supposed to be the single source of truth for every account balance in the institution. When a bank's core system and its real-world cash position disagree, that is not an accounting judgment call. That is either a severe systems failure or a deliberate falsification, and regulators treated it as the latter.
Signal (public or semi-public before takeover)
What a healthy bank looks like
What Karnali Development Bank showed
Reported NPL ratio
Consistent with capital adequacy math
7.27 percent reported vs 40.85 percent actual
Capital adequacy ratio
Above NRB's minimum threshold
Below minimum — the trigger for PCA
Deposits vs loans
Deposits comfortably exceed loans, funding is stable
Deposits exceeded loans yet the bank could not repay depositors
Core banking system vs real cash position
Matched, auditable
Mismatched — flagged by CIB investigation
Q1 FY2024/25 result
Modest profit or loss consistent with disclosed NPLs
Rs 19.8 million net loss alongside an implausibly low NPL figure
CAUTION
A single financial ratio, however alarming, is rarely sufficient on its own to conclude fraud. The lesson from Karnali Development Bank is not "always distrust the NPL number." It is that the Canon Score process asks you to hold two or three independent numbers up against each other — capital adequacy, NPL, liquidity, deposit growth — and treat any persistent contradiction between them as more informative than any single figure taken alone. Fraud is often invisible in one ratio and glaring in the relationship between two.
Lesson 89.4 — Red Flag Detection in Real Time
Chapter 28 built a red-flag checklist meant to be run quickly, almost mechanically, on any stock before it enters a portfolio — a set of questions designed to catch trouble even when an investor cannot independently audit a company's books. Applied retrospectively and in real time to Karnali Development Bank, several of those flags would have triggered well before the December 2024 takeover, for an investor doing the reading rather than relying on a relative's tip.
The first flag is leadership churn without clear explanation. A bank whose chairmanship changes hands more than once within a fairly short span, especially when the departures are not accompanied by a clean, well-explained transition, deserves a closer look. Karnali Development Bank's founding chairman later became a fugitive; a subsequent chairman was a politically connected figure later arrested in the same investigation. Two chairman-level departures under a cloud, within one institution's history, is not a minor governance footnote — it is the single loudest bell the red-flag checklist rings.
The second flag is a bank whose growth story outruns its visible infrastructure. A Class B development bank centred on one of Nepal's less commercially developed regions, expanding its deposit base to over five billion rupees, is a bank whose growth an outside investor should want explained: where is the new lending going, is it diversified across the local economy or concentrated in a handful of large borrowers connected to management, and does loan growth track the kind of collateral and cash-flow discipline NRB requires? Rapid deposit or asset growth at a small, thinly covered institution is not proof of trouble by itself — some regional banks do grow honestly and quickly as they capture underserved markets — but it is exactly the kind of growth that the Canon Score treats as needing a related-party lending check, because concentrated, connected lending is one of the most common mechanisms by which bank capital quietly disappears.
PRACTICAL TOOL
A five-minute red-flag pass for any NEPSE-listed bank or finance company, before you buy: (1) Has the chairman or CEO changed more than once in the past three years, and is the reason stated clearly in the AGM minutes? (2) Has the external auditor changed in the past two years, and does the audit opinion contain any qualification, even a mild one? (3) Does the reported NPL ratio move in a way consistent with the reported capital adequacy ratio — do the two numbers tell the same story? (4) Is the AGM held within the legally required window each year, or does it habitually slip? (5) Are related-party loans disclosed with borrower categories and amounts, or only as a vague lump sum? Any two "no" or "unclear" answers out of five is reason enough to treat the stock as high-risk and either avoid it or size the position very small.
The third flag is disclosure that is technically compliant but practically uninformative. Nepali BFIs are required to publish quarterly financial disclosures and annual reports, and Karnali Development Bank did file these. But filing a report is not the same as filing a report that lets an outside reader actually judge asset quality. A lump-sum NPL percentage without any breakdown by loan category, sector concentration, or borrower size gives an investor almost nothing to independently sanity-check. The Canon Score treats "compliant but shallow" disclosure as a mid-level red flag on its own, and a serious one when it appears alongside the other flags already discussed.
The fourth flag, and perhaps the hardest one for an ordinary retail investor to act on, is regulatory environment risk — the fact that even the supervisory relationship meant to catch problems early can itself be compromised, as the later allegations against an NRB inspection officer suggest happened here. This is why the Canon Score framework never asks an investor to rely solely on "the regulator hasn't said anything" as a green light. It asks investors to treat regulatory silence as neutral information at best, and to keep running their own checklist regardless of whether NRB has issued a public warning yet.
WARNING
Waiting for an official regulatory warning before reducing exposure to a small, thinly covered bank is not a safe strategy. By the time Nepal Rastra Bank places an institution under Prompt Corrective Action, the underlying problems have typically existed for one to several years already — PCA is a response to a condition regulators have identified, not the first moment the condition began. An investor who waits for the PCA announcement to sell is, by definition, selling after the warning has already gone out to the whole market, usually alongside a trading suspension that removes the ability to sell at all.
That last sentence is not theoretical. When NRB declared Karnali Development Bank a troubled institution, NEPSE suspended trading in KRBL shares. Anyone still holding the stock at that point was not merely facing a loss — they were facing a position they could not exit at any price, for an unknown length of time, while the bank's true condition was investigated and its assets and liabilities sorted out under NRB-appointed management.
Lesson 89.5 — What "Rescue" Actually Means for Shareholders
It is worth being precise here, because the word "rescue" is doing very different work depending on who you are. When Nepal Rastra Bank takes over a troubled institution under Section 86 of its founding Act, the primary goal, stated explicitly in NRB's own mandate to the appointed management team, is protecting depositors — prioritizing deposit repayment, recovering outstanding loans, and investigating financial irregularities for possible prosecution. Karnali Development Bank's three-member management team, led by a deputy director from NRB's own Bank and Financial Institution Regulation Department, was given exactly this mandate: exercise the powers of the board and the shareholders' general assembly, in that order of priority — deposits first.
Nowhere in that mandate does "protect shareholder value" appear as a stated goal, and this is not an oversight. It reflects the basic hierarchy of claims in a bank failure. Depositors lent the bank their savings expecting it back on demand or on term; shareholders bought an ownership stake expecting to share in profits and to bear the corresponding risk of loss. When a bank's assets turn out to be worth much less than its liabilities — which is exactly what a real 40.85 percent non-performing loan ratio implies for a bank that had reported 7.27 percent — the loss has to land somewhere, and the legal and regulatory framework in Nepal, as in most countries, puts shareholders ahead of depositors in absorbing that loss. A "rescue" of a bank protects the people whose money the bank was holding in trust. It does not promise to protect the people who owned equity in the enterprise that mishandled it.
KEY CONCEPT
In a bank resolution, the loss-absorption order generally runs from the bottom of the balance sheet up: common shareholders absorb losses first, since equity is designed to be the cushion that protects everyone else; other stakeholders and, ultimately, depositors sit above shareholders in priority. This is precisely backward from how many retail investors think about "safety" in a bank stock — a bank paying dividends and looking cheap on book value feels safe, but book value is the shareholders' cushion, and it is the first thing consumed when the cushion is needed.
This is also the moment to place Karnali Development Bank inside the broader pattern of NRB's supervisory activity, because a single dramatic case can leave the impression that this was an isolated event rather than a normal, if serious, part of how Nepal's financial sector is policed. In the months around the Karnali Development Bank takeover, NRB's third-quarter inspection cycle for fiscal year 2081/82 produced a wave of corrective actions across multiple institutions, at varying levels of severity.
Institution
Type
Issue identified
Action taken
Karnali Development Bank
Class B development bank
NPL 40.85 percent actual vs 7.27 percent reported, capital shortfall, liquidity failure, alleged fraud
Declared troubled institution; NRB took over management under Section 86
Muktinath Bikas Bank
Class B development bank
Chairman served on an internal committee, breaching board-independence rules
Formal warning
Narayani Development Bank
Class B development bank
Repeated breaches of deposit and single-borrower loan limits, capital adequacy shortfall
Ordered to reach compliance by a fixed deadline
Salapa Bikas Bank
Class B development bank
Failed minimum capital requirement, ignored directives
Formal warning
Pokhara Finance
Class C finance company
Cash reserve violations, NPL ratio of 33.44 percent
Prompt Corrective Action; CEO fined and dismissed
Janaki Finance
Class C finance company
Failed minimum capital adequacy ratio
Prompt Corrective Action
The lesson from this table is not that Nepal's smaller BFIs are uniquely dangerous as a class — most Class B and Class C institutions operate for years without ever appearing on a list like this. The lesson is that PCA and its escalations are a regularly used, systemic tool, not an exotic emergency measure reserved for one unlucky bank. An investor holding shares in any thinly covered development bank or finance company should treat "this could happen to my holding" as a live, recurring possibility to underwrite against, not a black-swan tail risk to dismiss.
CASE IN POINT
Pokhara Finance's case is instructive precisely because it is less dramatic than Karnali Development Bank's. A reported non-performing loan ratio of 33.44 percent is bad, but it was the bank's own disclosed figure — not a number regulators had to dig underneath a falsified report to find. The corrective action here was a fine and a dismissed CEO, not a full management takeover. This is what the more common, less catastrophic end of financial-institution distress in Nepal actually looks like: a bank whose books are honest but whose lending discipline has failed. It is a useful reminder that "distressed" and "fraudulent" are different categories, and the Canon Score red-flag checklist is built to catch both, but a fraudulent bank does far more damage per share of shareholder capital destroyed, because the true condition is hidden until the last possible moment.
For an investor thinking about eventual resolution rather than just the crisis moment, it is worth noting what typically happens next in Nepal once NRB has stabilised a troubled institution's operations: recovery of outstanding loans, cleanup of the balance sheet under direct supervision, and — in many though not all cases over the years — an eventual forced or negotiated merger into a stronger institution, sometimes at terms that leave little or nothing for the original shareholders, sometimes with a small residual value depending on how much of the capital shortfall the loan recoveries manage to close. As of the most recent public reporting available at the time of writing, Karnali Development Bank remains under NRB-appointed management, with loan recovery and investigation still underway and no acquiring institution yet named — a reminder that these processes typically take years, not months, to resolve, and that shareholders caught inside a suspended stock have no choice but to wait for that resolution regardless of how it eventually lands.
Lesson 89.6 — Running Karnali Through the Real Canon Score
Now put the actual framework together — Chapter 64's seven dimensions, adjusted for a Class B bank the way Chapter 65's banking guidance directs, using CAR and NPL in place of the generic leverage and profitability checks, related-party lending in place of a generic governance sub-check, and loan-and-deposit growth in place of a generic revenue-growth check — and score Karnali Development Bank honestly, on only the information a disciplined investor could have had before Nepal Rastra Bank acted. That means using the bank's own reported Q1 fiscal-year-2024/25 numbers (the Rs 19.8 million net loss, the 7.27 percent reported NPL ratio, deposits of Rs 5.21 billion against loans of Rs 3.81 billion) and the governance history that was already on the public record by mid-2024 (two chairman-level transitions, one of them combining the chairman and CEO roles, the second a sitting politician). It deliberately does not use the 40.85 percent true NPL figure, which only became public at the moment of NRB's own December 2024 declaration, and it does not use anything from the 2025 criminal investigation — because using those would be scoring hindsight, not the framework.
Financial Strength & Profitability (20 points), adjusted per Chapter 65 to use CAR and NPL rather than generic ratios. Return on equity: a net loss in the most recently disclosed quarter puts this in Chapter 64's bottom band regardless of the loss's small size — 1 out of 8. Capital adequacy, replacing the generic leverage sub-component for a BFI: by the time Q1 FY2024/25 was disclosed, the bank was already failing to hold NRB's minimum capital cushion, the explicit, stated trigger for the Prompt Corrective Action that followed within months — Chapter 64's bottom CAR band — 0 out of 7. Earnings quality and consistency: a disclosed loss sitting next to a suspiciously low reported NPL ratio and a capital shortfall is an internally contradictory picture on its face, before any investigator confirmed why — 1 out of 5. Financial Strength & Profitability: 1 + 0 + 1 = 2 out of 20.
Governance & Promoter Behaviour (15 points), adjusted per Chapter 65 to weight related-party lending and loan concentration heavily for a bank. Promoter and leadership stability: two chairman-level transitions in the bank's history, one combining the chairman and CEO roles in a single person — a real, contemporaneously visible red flag, not a hindsight one — 1 out of 6. Related-party lending and loan concentration: the bank's disclosure gave no breakdown of borrower concentration or related-party exposure at all, a genuine transparency failure in a dimension Chapter 65 says needs the heaviest scrutiny at a bank — 1 out of 5. Disclosure timeliness and quality: compliant but shallow, a lump-sum NPL figure with no sector or borrower-size breakdown, exactly the "compliant but practically uninformative" pattern Lesson 89.4 names — 1 out of 4. Governance & Promoter Behaviour: 1 + 1 + 1 = 3 out of 15.
CASE IN POINT
A Governance & Promoter Behaviour score of 3 out of 15 falls below Chapter 64's 5-point governance floor — the threshold below which the whole Canon Score is automatically capped in the Weak/Avoid band, regardless of what the other six dimensions add up to. Karnali Development Bank is the first case study in this book where that override provision actually fires. None of Chapters 84 through 88's companies came close to it; this one does, and it does so using only information that was genuinely on the public record before the takeover — which is exactly the override's purpose: a bank whose governance is this thin should never be rescued by a decent-looking number somewhere else in the framework.
Liquidity & Tradability (10 points). Karnali Development Bank was, by this book's own account in Lesson 89.1, a stock with "thin to none" analyst coverage and no meaningful trading interest from outside its home region — no verified average daily traded value or free-float percentage could be established from public sources, a genuine data gap for an obscure Class B name rather than a judgment either way. Scored conservatively for both sub-components: 2 out of 5 and 2 out of 5. Liquidity & Tradability: 4 out of 10.
Valuation Reasonableness (15 points). No verified price-to-earnings or price-to-book figure for Karnali Development Bank could be established from public sources at the relevant time — consistent, again, with a stock nobody was covering closely enough to price confidently either way. Scored conservatively: 2 out of 8 and 2 out of 7. Valuation Reasonableness: 4 out of 15.
Sector & Business Model Durability (15 points). A banking license is a real regulatory barrier to entry, but a small, single-region Class B development bank carries none of the scale or diversification advantages of a Class A commercial bank — a moderate moat at best — 4 out of 8. Concentration and dependency: the bank's business was geographically concentrated in one of Nepal's least commercially developed regions, a real structural concentration risk distinct from any fraud — 2 out of 7. Sector & Business Model Durability: 4 + 2 = 6 out of 15.
Growth Trajectory (15 points), adjusted per Chapter 65 to read loan-and-deposit growth rather than generic revenue growth. Loan and deposit growth: deposits of Rs 5.21 billion sitting well above a loan book of Rs 3.81 billion is not, on its own, a sign of healthy growth — Lesson 89.4 already named this exact mismatch as the second red flag a disciplined investor should have checked, because deposit growth outrunning verifiable, well-distributed lending is precisely the pattern that invites a related-party-lending check rather than admiration — 2 out of 8. Earnings consistency: a disclosed net loss in the most recent quarter, with no confirmed multi-year trend either way — 1 out of 7. Growth Trajectory: 2 + 1 = 3 out of 15.
Dividend & Capital Return Discipline (10 points), adjusted per Chapter 65 to weigh a bank's payout against its capital adequacy position. Karnali Development Bank had built exactly the reputation Lesson 89.1 describes — a small development bank retail investors associated with a "long run" of dividends — but a bank already short of its regulatory capital cushion has no business distributing capital to shareholders at all, and no reliable, verified record of the bank halting payouts once its capital position weakened could be established from public sources. Scored conservatively for both the data gap and the CAR-constraint concern Chapter 65 flags specifically for banks: 1 out of 6 and 1 out of 4. Dividend & Capital Return Discipline: 2 out of 10.
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
2
Q1 FY2024/25 net loss → 1/8; capital adequacy already below NRB's minimum → 0/7; loss contradicted by an implausibly clean NPL figure → 1/5
Governance & Promoter Behaviour
15
3
Two chairman-level transitions, one combining chairman and CEO → 1/6; no related-party or concentration disclosure → 1/5; compliant-but-shallow filings → 1/4
Liquidity & Tradability
10
4
No verified ADV or free float for a barely-covered regional bank → 2/5 + 2/5
Valuation Reasonableness
15
4
No verified P/E or P/B available → 2/8 + 2/7
Sector & Business Model Durability
15
6
A real but modest Class B licensing barrier → 4/8; single-region concentration in Karnali → 2/7
Growth Trajectory
15
3
Deposits (Rs 5.21bn) far outrunning loans (Rs 3.81bn), an unexplained-growth red flag rather than a strength → 2/8; a disclosed net loss → 1/7
Dividend & Capital Return Discipline
10
2
A dividend-paying reputation sitting next to a capital-adequacy shortfall, per Ch65's bank-specific dividend-vs-CAR check → 1/6 + 1/4
Canon Quality Score
100
24
Band: Weak/Avoid (below 55) — and separately capped there by the governance override
Summed, Karnali Development Bank's real Canon Score comes to 24 out of 100 — deep in Chapter 64's Weak/Avoid band on the raw arithmetic alone, and independently capped there by the governance override, since Governance & Promoter Behaviour scored 3 out of 15, well below the 5-point floor. Both paths to the same conclusion, arrived at using only information that was genuinely public before Nepal Rastra Bank acted, is about as clean a "the framework works" result as this book's case studies produce.
The honest answer to this chapter's question, then, is yes, with an important qualification about which parts of the picture were actually knowable in time. A disciplined Canon Score process did not need to predict a criminal case, name a future fugitive, or foresee an NRB inspection officer's 2025 arrest to reach the right conclusion — all of that came later, and crediting an early-2024 decision with foreknowledge of 2025 arrests would be exactly the kind of hindsight error this book warns against elsewhere. What the framework needed, and had, was the governance record — unexplained chairman-level turnover, opaque related-party and concentration disclosure — sitting on the public record for years before the takeover, plus the Q1 FY2024/25 numbers that were already internally contradictory the moment they were filed. The governance dimension alone, scored honestly in early-to-mid 2024, was already low enough to trigger the override and land Karnali Development Bank in Weak/Avoid, well before Prompt Corrective Action in November 2024 and the takeover in December. The specific, damning 40.85 percent NPL figure, by contrast, only became public at the moment of the takeover itself — it confirmed the framework's earlier verdict, but arrived too late to be the thing that triggered an exit, since trading was suspended at essentially the same moment. This is precisely why Chapter 28's red-flag logic treats governance and disclosure failures as reasons to act on detection rather than reasons to wait for financial confirmation: the confirmation, when it comes in the form of a regulatory takeover, typically arrives at the same moment the exit door closes.
Bikash Rai, for what it is worth, never bought the stock. He ran the five questions from this chapter's practical checklist against what little public information existed on Karnali Development Bank in early 2024, found two clear "unclear" answers on the chairman-history question and the related-party disclosure question, and put his notebook away. He was not smart enough, or informed enough, to know what was coming. He simply followed the process the framework asks every investor to follow, and the process did the rest.
PRACTICAL TOOL
Before buying any NEPSE-listed bank, development bank, or finance company below the Class A commercial-bank tier, treat the following as a non-negotiable minimum check, not an optional extra: pull the last three years of AGM minutes to check for chairman or CEO turnover; pull the last two auditor's reports to check for qualifications or an auditor change; and compare the reported NPL ratio against the reported capital adequacy ratio for internal consistency across at least four consecutive quarters. If any leg of that check cannot be completed because the information is not disclosed clearly enough to check it, treat the missing information itself as the red flag, not as a reason to assume the answer is fine.
Chapter recap
Karnali Development Bank Limited is not a cautionary tale about small development banks in general, and it should not be read as one. It is a cautionary tale about what happens when governance failure, weak disclosure, and internally contradictory financial ratios are allowed to sit unexamined for long enough, in an institution obscure enough that almost nobody outside its own management was checking the arithmetic. This chapter also corrected an error in its own earlier telling: an unconfirmed claim that the bank's founding chairman was "extradited from India" — he was in fact arrested inside Nepal, at the border in Rupandehi, in June 2025, roughly six months after the takeover, not extradited from abroad. Scored honestly on Chapter 64's real seven dimensions, using only what was on the public record before Nepal Rastra Bank acted, Karnali Development Bank comes to 24 out of 100 — Weak/Avoid, and separately capped there by Chapter 64's governance override, since Governance & Promoter Behaviour scored 3 out of 15, the first time in this book's case studies that override provision actually fires. Nepal Rastra Bank's Prompt Corrective Action framework, its Section 86 authority to declare a troubled institution and take over management, and its own willingness — however belated — to investigate and prosecute even its own inspection staff, all did the job regulation is supposed to do. But regulation acts on its own timeline, built around building a legal case, not on a shareholder's timeline, built around protecting capital. The Canon Score framework exists to close that gap — not by predicting a criminal case in advance, but by scoring the governance and disclosure record honestly enough, early enough, that a bank this thin never earns a position in the first place.
Chapter 90 turns from one troubled institution to an entire market. Case Study 8 examines the 2021 NEPSE mania — the extraordinary run-up in Nepal's stock market during and after the pandemic years, when the NEPSE index and trading volumes reached levels the market had never seen, retail participation exploded through newly digitized demat and online trading access, and a very different kind of risk took hold: not the risk of one bank's hidden fraud, but the risk of an entire market's collective judgment running ahead of itself. The tools are the same — governance discipline, financial-risk checks, red-flag detection — but the next chapter asks what those tools are worth when it is not one company's numbers that stop making sense, but everyone's.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 90
Case Study 8 — The 2021 NEPSE Mania
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 8 — The 2021 NEPSE Mania
My name is Suman Kandel, and for eleven months in 2020 and 2021, I was a genius. I want to start this case study with that sentence because it is the most important sentence in it. I was not a genius. I was a schoolteacher in Kathmandu with a Mero Share account, a Smartphone, and a lot of free time during the pandemic lockdowns. But for eleven months, every share I touched went up, and I began to believe that this was because of something I understood and other people did not.
I am telling you this story now, several years later, because NEPSE — the Nepal Stock Exchange, the single exchange where all publicly listed Nepali shares trade — went through one of the most dramatic bull-and-bust cycles of any market, anywhere, between 2020 and 2023. The benchmark NEPSE index fell to roughly 1,100 points in the panic of March and April 2020, when the country went into its first COVID-19 lockdown and the market itself closed its doors for weeks. Then it climbed, almost without pausing to catch its breath, past 2,000, past 2,800, and finally to an all-time closing high of 3,198.60 points in mid-August 2021. Then it fell for the better part of two years, grinding down to an intraday-era low of 1,848.28 points on June 23, 2022, drifting into the high 1,800s by the end of that year, and staying depressed well into 2023.
If you have read Part XVI of this Canon up to now, you already know the shape of a case study: real numbers, a narrator who lived it, and a set of lessons that connect back to the earlier chapters of the book. This chapter connects most directly to Chapters 50 through 53, where we studied the specific behavioural biases that make Nepali investors — not foreign investors, not institutional investors, but ordinary NEPSE retail investors like me — lose money even in markets that are, overall, rising. It also connects to Chapters 54 through 58, where we studied liquidity, circuit breakers, and what happens when everyone tries to sell at once in a market that is thin to begin with. The 2021 mania and the correction that followed it is the single best real-world laboratory Nepal has produced for testing every idea in those chapters at once. So let's walk through it, year by year, and then let's ask the only question that matters for you as a reader of this Canon: what would a disciplined process — a Canon Score, a position-sizing rule, a written exit plan — have actually done differently, in real time, while everyone around it was either euphoric or terrified?
Lesson 90.1 — The Setup: How a Pandemic Built a Bull Market
To understand why NEPSE went from roughly 1,100 points to over 3,000 points in about sixteen months, you have to understand four things that were happening in the Nepali economy at the same time, none of which had anything to do with company earnings.
First, interest rates collapsed. Nepal Rastra Bank, the central bank we have discussed throughout this Canon as the referee of the country's money supply, cut its policy rates and flooded the banking system with liquidity to keep the economy from seizing up during the lockdowns. Fixed deposit rates at commercial banks, which had been a comfortable 9 to 11 percent for ordinary savers in earlier years, fell toward 5 to 7 percent and in some cases lower. For a retail saver in Nepal, a fixed deposit is normally the safe, boring alternative to the stock market — you lock your money in a bank for a year, and it grows slowly and predictably. When that boring option stops paying you enough to beat inflation, money that would otherwise have sat quietly in a bank account starts looking for somewhere else to go.
Second, remittances did something almost nobody expected. Nepal is a remittance economy — a large share of national income arrives as money sent home by Nepali workers abroad, mostly in the Gulf and Malaysia, mostly to support families rather than to invest. Economists expected remittances to collapse during a global pandemic, since migrant workers were losing jobs too. Instead, remittance inflows stayed resilient and in some periods even grew, partly because workers stranded abroad with fewer spending opportunities sent more of their earnings home, and partly because families used formal banking channels more than informal ones during lockdowns. That money landed in Nepali bank accounts. Some of it paid down debt or covered household expenses. Some of it, for the first time in many households' history, found its way into a demat account.
Third, there was simply nothing else to do with your money or your time. Nepal's lockdowns were long and strict. Shops were closed. Foreign travel was impossible. Weddings, festivals, and the ordinary social life that usually absorbs household income and attention were suspended. Meanwhile, the entire process of opening a demat account — the electronic account required to hold shares, administered through the Central Depository System and Clearing Limited, or CDSC — and a trading account with a broker had become easier thanks to online systems like Mero Share and the broker-side Trading Management System, or TMS, which let you place buy and sell orders from a phone instead of standing in a broker's office. CDSC's own published figures show the number of demat accounts roughly tripling from the years just before the pandemic to past four million by 2021, with a very large share of those new accounts opened in 2020 and 2021 specifically. A market that had been the preserve of a relatively small circle of regular investors was suddenly full of first-time participants, many of them opening an account for the first time in their lives during a lockdown, with a phone in one hand and nothing else to do.
Fourth — and this is the part that turns a recovery into a mania — brokers extended margin lending, meaning loans specifically for buying shares, using the shares themselves as collateral, more aggressively than they had before. If you want to buy 100,000 rupees of shares but you only have 40,000 rupees, a margin loan lets a bank or finance company lend you the rest against the value of the shares you already hold or are about to buy. This is not unique to Nepal — margin lending exists in every stock market in the world — but in a small, illiquid market like NEPSE's, where a relatively modest amount of new buying can move prices a long way, margin lending acts like pouring petrol on a fire that is already burning.
KEY CONCEPT
Margin lending is borrowed money used specifically to buy shares, with the shares themselves pledged as collateral for the loan. It magnifies gains on the way up because you are investing more than your own capital, but it magnifies losses on the way down for exactly the same reason — and if the share price falls far enough, the lender can force you to sell, whether or not you want to, through a margin call.
None of these four forces — low interest rates, resilient remittances, lockdown boredom, and expanding margin lending — is a company-specific reason to buy a share. None of them tells you anything about a bank's loan book, a hydropower company's power purchase agreement, or an insurer's claims ratio. They are liquidity forces: they describe how much money is chasing how many shares, not what those shares are actually worth. Chapter 12 of this Canon, on the difference between price and value, made exactly this point in the abstract. The 2021 mania is what it looks like when that abstract point becomes a lived national experience.
I remember the day I opened my Mero Share account. It was during the second lockdown, in the middle of 2021 — the second wave of restrictions, running roughly from late April to mid-August that year, not the longer first lockdown of 2020 that had closed the market entirely. My cousin, who worked at a finance company, told me that "share ta paisa haldai jane ho" — with shares, you just keep putting money in and it keeps growing. I put in the modest amount I had saved rather than spent, since there was nowhere to spend it. Within a few months, that amount had grown by more than half. I did not attribute this to low interest rates or remittance inflows. I attributed it to my own judgment.
Lesson 90.2 — The Mania: New Accounts, Margin Loans, and the Language of Certainty
By early 2021, something had changed in the texture of everyday conversation in Kathmandu. Share prices came up at tea shops, at family gatherings, in the WhatsApp groups of my former college classmates. People who had never mentioned NEPSE in their lives were now asking me — a schoolteacher — for stock tips. I gave them. I want you to sit with that sentence, because it is the clearest single signal of a mania that this Canon can offer you: when people who have no professional or informational advantage in a market start giving each other confident advice, and when the target of that advice is not "should I invest at all" but "which specific script will double fastest," you are no longer in a market driven by analysis. You are in a market driven by social proof.
Chapters 50 through 53 named several specific biases that explain why this happens, and it is worth naming them again here, because the 2021 mania produced textbook cases of every one of them.
Herding, covered in Chapter 51, is the tendency to do what everyone around you is doing, on the assumption that a crowd this large cannot all be wrong. In 2021, herding showed up as a kind of contagious script-picking: certain hydropower, finance, and microfinance shares would circulate through social media and messaging groups as the "next one to move," and buying would concentrate in whatever name was currently being discussed, regardless of that company's actual fundamentals. Trading volumes in a handful of small-capitalisation scripts would spike for a few days, the price would move up sharply, and then attention would move to the next name.
Recency bias, covered in Chapter 52, is the tendency to assume that the recent past — especially the very recent past — is the best guide to the future, discounting longer and less flattering history. Nepali investors who had only ever experienced NEPSE as an index that went from 1,100 to 3,000 quite reasonably, if you only look at that one stretch of time, assumed the next move was also up. Very few new entrants in 2020 and 2021 had any personal memory of NEPSE's previous major bust — the 2016 correction, or the much larger 2008 to 2011 crash, when the index fell for years after an earlier boom. If your entire investing experience is sixteen months long and every one of those months has been good, "the market always goes up" feels like an observed fact rather than a dangerous generalisation.
Overconfidence, covered in Chapter 50, is the tendency to overestimate your own skill and underestimate the role luck played in your results. I certainly had this. Every trade I made in that period that went well, I attributed to my reading of the market. The handful that went badly, I dismissed as bad luck or bad timing, not as evidence that my method — which, honestly, amounted to buying whatever my cousin's finance-company colleagues were discussing — had no method in it at all.
CASE IN POINT
CDSC data shows demat accounts rising from roughly 1.7 million before the pandemic to well over four million by 2021 — meaning a very large share of NEPSE's active retail base in 2021 had never experienced a full market cycle. New participants entering during the up-leg of a boom, with no memory of a prior bust, are structurally more prone to recency bias and herding, because they have no personal counter-example stored in memory.
There is a fourth bias, discussed in Chapter 53, that I think is the most Nepal-specific of the group: what that chapter calls "borrowed conviction," the practice of holding a position not because you have evaluated it yourself but because someone you trust — a relative, a broker, a WhatsApp group administrator — told you to hold it. Borrowed conviction is dangerous precisely because it cannot survive contact with bad news. If you own a share because you did the work and understood the business, bad news makes you re-evaluate. If you own a share because your brother-in-law told you to buy it, bad news just makes you anxious, and anxiety is a poor basis for decision-making.
The margin lending side of the mania deserves its own attention, because it is where individual bad decisions turned into a system-wide vulnerability. Loan-against-share facilities, offered by banks and finance companies, typically let a borrower pledge shares as collateral and borrow a percentage of their value — the loan-to-value ratio, or LTV. In the boom years, LTV ratios offered in practice crept upward, and total margin lending outstanding across the banking and finance system grew very quickly, far faster than deposits or overall credit growth. The mechanics matter here, so let's be precise about them, because they are the hinge on which the entire second half of this case study turns.
Mechanism
What happens on the way up
What happens on the way down
Margin loan (loan-against-share)
Borrowed money buys more shares than your own capital alone, amplifying your gains as prices rise
A falling share price shrinks the collateral value; once it falls below the lender's minimum, you face a margin call
Margin call
Rarely triggered; largely invisible to the borrower
Lender demands you deposit more cash or shares, or it sells your pledged shares to recover the loan — regardless of your own view of the share's future
Forced selling
Does not occur
Adds new sell orders to an already falling market, pushing prices down further and triggering the next round of margin calls on other borrowers
New retail demand
Fresh demat accounts and fresh capital chase rising prices, reinforcing the trend
New account openings slow sharply; some new investors exit the market permanently, taking their capital and their attention with them
Notice what that table shows: margin lending does not just amplify an individual investor's outcome. It links investors to each other. When enough borrowers are using margin loans against the same pool of shares, a price fall that starts for any reason — profit-taking, a piece of bad economic news, a regulatory statement — can trigger margin calls that force selling, and that forced selling becomes the bad news that triggers the next round of margin calls. This is exactly the liquidity spiral described in Chapter 56, and in 2021 Nepal built the conditions for one without most participants realising it.
I took a margin loan in mid-2021. I remember the finance officer explaining the LTV ratio to me almost as an afterthought, the way you might explain a minor administrative detail. What he did not explain, and what I did not ask, was what would happen to my position if the shares I had pledged fell by thirty percent in a matter of weeks. I found out later. Everyone did.
Lesson 90.3 — The Peak: August 2021 and the Signs Nobody Wanted to See
NEPSE closed at 3,198.60 points on August 18, 2021 — its highest level in the exchange's history at that time, and a figure that market commentators still cite today as the symbolic top of the mania. I want to be honest about what that day felt like from where I was sitting, because the honesty is the whole point of a behavioural case study. It did not feel like a peak. It felt like Tuesday. There was no bell that rang. Prices had been going up for so long that a new all-time high had stopped feeling like news and started feeling like the natural order of things.
But if you apply the tools from Chapters 54 through 58 — the chapters on liquidity, market breadth, and circuit breakers — with the benefit of hindsight, the warning signs were already visible in the market's own mechanics, not just in hindsight commentary.
The first sign was in circuit breakers themselves. NEPSE, like most emerging market exchanges, uses circuit breakers — automatic trading halts triggered when a share price or the overall index moves by more than a set percentage in a session — specifically to slow down panics and manias alike by forcing a pause for reflection. Individual scripts on NEPSE are subject to daily price bands, commonly cited at up to about 10 percent in either direction depending on the rules in force at the time, and the exchange has historically triggered market-wide circuit halts on unusually sharp single-day index moves. Through 2021, an increasing number of trading sessions saw dozens of individual scripts hit their upper circuit — meaning they rose the maximum allowed amount and then simply stopped trading for the day, with buy orders queued and unfilled because no seller would part with shares at that price. A market where a large fraction of listed scripts are hitting upper circuit on a regular basis is not a healthy, broadly rising market. It is a market where liquidity has become one-directional: everyone wants to buy, almost nobody wants to sell, and price discovery — the process by which a market finds a fair price through the honest disagreement of buyers and sellers — has effectively broken down.
WARNING
A market where large numbers of individual scripts repeatedly hit their upper circuit is not showing you strength. It is showing you a lack of sellers at any reasonable price, which is a liquidity symptom, not a value signal. The same one-directional dynamic that traps buyers who cannot get filled on the way up traps sellers who cannot get filled on the way down.
The second sign was breadth — a concept covered in Chapter 55 — meaning whether a market's rise is broad-based across many sectors and companies, or narrow, concentrated in a small number of names that are pulling the index average up while many other listed companies go nowhere or fall. Sub-indices for hydropower and for finance companies and microfinance institutions rose dramatically faster than the broader index and faster than sectors like manufacturing, hotels, or trading companies. A market where two or three sectors are doing almost all the work, while the index headline number climbs regardless, is telling you that the rally has a narrow foundation — and narrow foundations do not hold weight well when the wind changes.
The third sign, and in some ways the most important for a retail investor to have watched, was the sheer velocity of new account openings and margin borrowing, both of which were still accelerating even as the index approached its all-time high. In a healthy, fundamentals-driven bull market, new participation tends to track earnings growth and economic activity with some lag. In August 2021, new demat account openings and margin lending growth had become self-referential: people were opening accounts and borrowing to buy shares because share prices were rising, and share prices were rising in part because people kept opening accounts and borrowing to buy shares. That circularity is the definition of a mania, not a metaphor for one.
REGULATORY DETAIL
Nepal Rastra Bank publishes periodic data and directives on bank and finance company lending, including loan-against-share exposure limits, as part of its ordinary supervisory function. Through 2021, NRB and the Securities Board of Nepal, or SEBON — the securities market regulator responsible for listed companies, brokers, and market conduct — were both on record expressing concern about rapid credit growth into share purchases and rising retail leverage, ahead of the more decisive tightening steps that followed in the 2021–2022 monetary policy cycle.
I did not see any of this at the time, or rather, I saw pieces of it and dismissed each piece individually. I remember noticing that a friend's favourite hydropower script had hit upper circuit for four sessions running, unable to be sold at any price above the circuit limit because there were no sellers willing to sell there and no way to trade above it. I took this as proof that the company was fantastic. It did not occur to me to ask why, if the company were genuinely worth so much more than its previous price, existing shareholders — who presumably knew the company at least as well as I did — were not selling into that demand.
This is the point in the story where the Canon Score, introduced in earlier parts of this book as a structured way to evaluate a position against a checklist of value, quality, and risk criteria rather than against how a share has performed recently, would have done something genuinely different from what I did. A Canon Score does not ask "has this gone up." It asks questions like: is this company's valuation, measured against earnings or book value, historically reasonable or historically stretched? Is the volume and price action explainable by business fundamentals, or only by flows of new money? Is my position sized so that a serious drawdown would hurt but not destroy me? None of those questions care about how many upper circuits a script has hit. All of them would have been flashing amber, and in some cases red, by the middle of 2021, for exactly the scripts that most retail investors, including me, were most excited about.
Lesson 90.4 — The Tightening: How NRB Pulled the Punch Bowl Away
Every mania eventually meets a policy response, because the same forces that inflate a mania — rapid credit growth, rising leverage, capital flowing away from productive uses — eventually show up in other places a central bank is required to watch: the trade deficit, foreign exchange reserves, and overall financial stability. Nepal Rastra Bank's job, as this Canon has discussed in earlier chapters, is not to manage the stock market. It is to manage the currency, the banking system, and the broader economy. But those responsibilities intersect with the stock market whenever bank and finance company balance sheets become heavily exposed to share-backed lending, which is exactly what had happened by late 2021.
Nepal's broader economy was under real strain in this period for reasons that had nothing to do with the stock market directly. Import demand recovered strongly as lockdowns eased, pulling in foreign currency to pay for goods from vehicles to construction materials, at the same time that remittance growth, while still positive, could not keep pace. Foreign exchange reserves, which the country needs to maintain to pay for essential imports including fuel, came under pressure. In response, NRB tightened monetary policy through 2021 and into 2022 on multiple fronts: raising policy rates, tightening the cash reserve ratio that requires banks to hold a certain proportion of deposits rather than lend them all out, and imposing stricter, more explicit limits on margin lending against shares, including lower loan-to-value ceilings and, at various points, caps on the total amount of margin lending an individual bank or finance company could extend.
REGULATORY DETAIL
NRB's monetary tightening cycle beginning in late 2021 and continuing through 2022 included multiple, compounding levers: policy rate increases, a higher cash reserve ratio requirement for banks, and specific restrictions on margin lending against shares — including reductions to allowable loan-to-value ratios and, in some periods, caps on aggregate exposure. Each lever individually would have slowed share-market credit growth; applied together, and layered onto an already leveraged market, their combined effect was considerably larger than any one measure alone.
Here is the mechanism that made this tightening so consequential for the stock market specifically, and it connects directly back to the margin lending table from Lesson 90.2. When NRB tightened the loan-to-value ratio banks and finance companies were permitted to offer against pledged shares, existing margin borrowers were not grandfathered comfortably — many faced demands to either deposit additional collateral or reduce their loan, precisely because the value of the collateral, relative to the now-lower permitted ratio, had become insufficient. This is functionally identical to a margin call, even when it originates from a regulatory ratio change rather than from a falling share price. And when a large number of borrowers across the system face similar demands at similar times, the market fills with sell orders from people who are not selling because they have changed their mind about a company, but because they have no choice.
Interest rates on ordinary bank credit also rose sharply through this period, drawing money that had been sitting in trading accounts back toward interest-bearing deposits, and raising the cost of holding a margin loan at the same time its permitted size was shrinking. Both effects pushed in the same direction: less money available to buy shares, and more pressure on existing leveraged holders to sell.
I remember the specific week I understood something had changed. My broker's TMS app began showing red across almost every script I owned, for several consecutive sessions, without any single piece of company-specific news to explain it. I called my cousin at the finance company. He told me, in a tone that had lost the earlier confidence, that the company was tightening margin terms for existing clients, not just new ones. I did not understand, at that moment, that this was a small, individual version of the exact system-wide mechanism I have just described to you. I only understood that money I thought I had was, quite suddenly, money I might have to find from somewhere else.
KEY CONCEPT
A margin call is not always caused by a falling share price. It can also be caused by a lender or regulator tightening the terms of the loan itself — a lower permitted loan-to-value ratio, a higher interest rate, or a shorter repayment window — even while the collateral's price has not moved at all yet. This is why margin exposure is a form of risk that sits outside the share price itself: your position can become unsafe because the rules around it changed, not because the underlying business did.
Lesson 90.5 — The Crash: Circuit Breakers, Forced Selling, and the Long Grind Down
What followed, through 2022 and into 2023, was not a single dramatic crash day of the kind you might picture from famous global market crashes. It was something slower and, in its own way, more painful: a long, uneven grind downward, punctuated by short rallies that drew hopeful buyers back in, followed by fresh legs down that took the index to new lows. By the middle of 2022, NEPSE had fallen from its 3,198.60 peak to roughly the 1,900 to 2,000 range — a decline of well over a third from the top — and it spent the rest of 2022 and part of 2023 grinding lower still, dipping into the high 1,800s by December 2022 (closing at 1,899.70 that December, per contemporary reporting) and staying in a depressed band roughly between 1,800 and 2,000 for much of the following year, a fraction of its 2021 highs, before beginning a slow, uneven recovery in later years.
The mechanics of the decline mirror, in reverse, everything that built the boom. Circuit breakers, which had spent 2021 mostly halting scripts on the upside because there were no willing sellers, now spent long stretches halting scripts on the downside because there were no willing buyers at the falling price. This is the other half of the lesson from Chapter 54 that this case study makes vivid: a circuit breaker is a symmetrical tool. It slows down euphoria on the way up, by forcing a pause instead of letting a price run unchecked; and it slows down panic on the way down, by the same mechanism. But a circuit breaker cannot manufacture a buyer who does not exist. When a script hits its lower circuit — falls the maximum permitted amount for the day — and stays there because nobody wants to buy at that price either, the position is technically "protected" from falling further that day, but it is also, in practical terms, unsellable. You can watch your account value fall on paper without being able to convert a single share into cash, because the order book on the buy side is simply empty.
CASE IN POINT
Through 2022, many small and mid-sized scripts on NEPSE experienced extended stretches of hitting lower circuit with thin or nonexistent buy-side order books — the mirror image of the upper-circuit pattern seen in 2021. Investors holding those scripts discovered that a falling market and an illiquid market are not two separate problems; in a small exchange with a shallow pool of active buyers, they are frequently the same problem wearing two faces.
This is precisely the liquidity trap discussed in Chapter 57: the idea that the ease of selling a position is not a fixed property of that position, but a variable one that depends on overall market conditions, and that it tends to disappear exactly when you need it most. During the mania, everyone believed their shares were liquid, because in a rising, crowded market, there was always a buyer willing to pay slightly more than the last price. During the correction, that assumption was tested and, for many retail holders, failed. The shares had not changed. The willingness of anyone else to own them at that price, at that moment, had.
Margin-related forced selling compounded the decline in exactly the way the earlier table predicted. As share prices fell, the collateral value backing existing margin loans fell with them, triggering fresh margin calls on top of the regulatory tightening that had already begun the process. Borrowers who could not or would not post additional collateral had their pledged shares sold by the lender, adding supply to a market that already had too little demand, which pushed prices down further, which triggered the next round of calls on other borrowers holding similar or adjacent scripts. This is the liquidity spiral from Chapter 56 playing out at national scale, and it is precisely why circuit breakers, sensible margin rules, and position-sizing discipline are not bureaucratic inconveniences — they are the mechanisms that determine whether a correction stays a correction or becomes a cascading collapse.
NEPSE index milestone
Approximate date
What was happening
Pandemic low, around 1,100
March–April 2020
Nationwide lockdown; trading halted for weeks; global and domestic panic
I sold most of what I owned in stages through the first half of 2022, at prices well below where I had bought, not because I had done fresh analysis of the businesses I owned, but because I could no longer sleep, and because the margin lender was asking for money I did not have sitting elsewhere. I want to be precise about what that experience teaches, because it would be easy to draw the wrong lesson from it. The wrong lesson is "the stock market is dangerous and ordinary people should stay out of it." The right lesson, which the rest of this Canon has been building toward across dozens of chapters, is that the stock market is dangerous specifically for money you cannot afford to have locked up, specifically when it is combined with borrowed capital, and specifically when your reason for holding a position is "it has been going up" rather than "I have evaluated what it is worth and I can afford to be patient." I had violated all three conditions simultaneously, and the correction found me exactly where those violations had left me: exposed, illiquid, and forced to sell into the worst possible market at the worst possible time.
CAUTION
A correction does not punish investors evenly. It punishes leveraged investors, investors who need their capital back on a specific timeline, and investors who bought based on price momentum rather than business analysis, far more severely than it punishes patient, unleveraged, fundamentals-based investors — even when both groups own similar shares. The 2021–2023 cycle was, in this sense, a filter more than a flood: it did not sink every boat equally.
Lesson 90.6 — What the Canon Score Would Have Done Differently
Let's now do the exercise this whole case study has been building toward: apply the Canon Score and the risk-management rules from earlier in this book to my own actual decisions in 2021, and see, point by point, where a disciplined process would have produced a different outcome — not a perfect outcome, since no process eliminates risk entirely, but a materially better one. Because this case study is about a whole market rather than one company, there is no single ticker to run Chapter 64's real seven-dimension rubric against. So, in the same spirit as Chapter 64's own illustrative "Himalaya Unnati Bank" composite, here is a numeric Canon Score for a composite that stood in for dozens of real names in 2021: a small, single-plant hydropower developer of exactly the kind that kept hitting upper circuit in Kathmandu's tea-shop conversations that year, built from the real, aggregate conditions this chapter has already documented — thin disclosure, heavy project debt, a single-buyer PPA, and a share price disconnected from any of it.
Dimension
Points possible
Points awarded
Reasoning
Financial Strength & Profitability
20
7
Modest ROE typical of a small, newly operating hydropower developer → 3/8; heavy project debt, the sector norm → 2/7; thin, early-stage earnings history → 2/5
Governance & Promoter Behaviour
15
7
A local promoter group with no independent verification available → 3/6; thin related-party disclosure → 2/5; compliant-but-shallow quarterly filings → 2/4
Liquidity & Tradability
10
5
Raw trading volume looked strong during the mania → 4/5; but real free float had effectively vanished behind an unfillable, one-directional buy queue at upper circuit → 1/5
Valuation Reasonableness
15
2
A price-to-earnings and price-to-book multiple stretched well past any level the sector's own earnings history could support → 1/8 + 1/7
Sector & Business Model Durability
15
12
Licensed generator with a signed PPA, Ch64's textbook full-marks case → 8/8; single-buyer NEA dependency, the acknowledged gap this book's other hydropower case studies also flag → 4/7
Growth Trajectory
15
4
Ordinary underlying revenue and earnings growth, nowhere close to the pace of the share-price move → 2/8 + 2/7
Dividend & Capital Return Discipline
10
2
Little or no dividend track record yet at this stage of a small operator's life, the same pattern this book's real IPO case study also found → 1/6 + 1/4
Canon Quality Score
100
39
Band: Weak/Avoid (below 55)
Thirty-nine out of a hundred, Weak/Avoid — for a script that, in real 2021 trading, kept hitting its upper circuit and drawing exactly the kind of excitement I described in Lesson 90.3. That gap, between a real Canon Score in the high thirties and a market price that several people I knew treated as a can't-miss opportunity, is the entire chapter compressed into two numbers. Nothing about this composite's business changed between "before the mania" and "during the mania" — the moat, the debt load, the earnings, the governance record were all exactly the same company either way. Only the price changed, and the price is not one of Chapter 64's seven dimensions. It never has been.
Start with valuation discipline, from the Canon Score's fundamental screen. A basic version of this screen asks whether a company's price relative to its earnings, or its price relative to its book value, is high or low compared to that company's own history and to reasonable sector norms. Applied to the hydropower and finance-sector darlings of 2021, this screen would have flagged many of the most popular names as trading well above any level their earnings history could support, particularly in the run-up through mid-2021. That does not mean the screen would have told me to never own these sectors — hydropower and financial services are legitimate, important parts of the Nepali economy, and this Canon has never argued that whole sectors should be avoided. It means the screen would have told me that the specific price I was paying, at the specific moment I was paying it, was disconnected from the specific earnings those companies were generating, and that disconnection is exactly the information a momentum-following retail investor systematically ignores.
Next, position sizing, from the risk-management chapters. A basic position-sizing rule caps how much of your total investable capital can sit in any single script, and separately caps how much of your total capital can be borrowed rather than your own. Had I followed even a simple version of this rule — say, no single script above a modest fraction of my total portfolio, and no margin borrowing at all, or margin borrowing capped far below what the finance company was willing to offer me — the 2022 correction would have reduced my paper wealth, which no rule can prevent, but it would not have forced me into distress selling, because I would not have been receiving margin calls I could not meet. This is the single largest difference a disciplined process makes in a story like this one: it does not predict the crash, but it removes the mechanism by which the crash becomes a forced, and therefore worst-timed, sale.
PRACTICAL TOOL
A simple, written rule — for example, "no more than a fixed percentage of my portfolio in any one script, and no margin borrowing above a fixed, low ceiling relative to my net worth, regardless of what my broker or finance company is willing to lend me" — costs nothing to write down and does not require predicting the market's next move. Its entire value comes from being decided in advance, while you are calm, rather than negotiated with yourself in the middle of a margin call, when you are not.
Next, the exit discipline covered in the liquidity chapters. A Canon Score approach treats rising prices not as pure good news but as a trigger to re-check the original thesis: has anything about the company's actual earnings or business changed to justify this new, higher price, or has only the price changed? Applied honestly in 2021, this discipline would have prompted trimming — selling a portion of a winning position, not all of it — as prices rose well beyond what fundamentals justified, purely as a risk-management action rather than a market-timing prediction. I want to be careful here not to claim the Canon Score would have called the exact top of 3,198.60 in August 2021; no honest framework claims to call tops. What it would have done is systematically move capital out of the most stretched, most narrowly-supported positions and into cash or more reasonably valued assets well before the peak, simply because "stretched relative to fundamentals" was true for months before the peak, not just on the peak day itself.
Next, the behavioural checks from Chapters 50 through 53 directly. A disciplined investor using this Canon's framework is trained to treat certain observations as warning signals rather than confirmations: unsolicited stock tips from people with no particular expertise, a sudden social atmosphere where everyone is discussing the same handful of scripts, and — most specifically to this case — a pattern of a script hitting its upper circuit repeatedly with no company-specific news to explain it. Each of these, individually, is exactly the kind of observation that recency bias and herding cause an untrained investor to interpret as bullish confirmation, and that a trained investor is taught to interpret as a caution flag instead. I had every one of these signals available to me in mid-2021. I read every one of them backward.
Finally, and this is a point the earlier liquidity chapters made carefully and that this case study now gives real weight to, the Canon Score approach treats a position's liquidity — how easily and at what cost you can actually convert it back to cash — as a genuine risk factor, not an afterthought. A script that only trades because of one-directional, momentum-driven demand is a script whose liquidity will evaporate the moment that momentum reverses, precisely because there was never a stable base of buyers who wanted it for its business fundamentals rather than its recent price action. Weighing liquidity risk explicitly, rather than assuming that "if I bought it, I can sell it," would have meant treating several of the most popular 2021 scripts as lower-quality holdings than their price charts suggested, specifically because the case for owning them depended entirely on other people continuing to want to buy them too.
Discipline from the Canon
What the crowd did in 2021
What a Canon-Score process would have done
Valuation screening
Bought based on recent price momentum, ignoring earnings multiples
Flagged stretched valuations in popular scripts well before the August 2021 peak
Position sizing and margin limits
Expanded margin loans as prices rose, increasing leverage into euphoria
Capped single-script exposure and margin borrowing regardless of lender willingness
Exit discipline
Held or added to winners with no re-evaluation as prices rose
Trimmed positions as price diverged from fundamentals, banking some gains early
Behavioural awareness
Treated unsolicited tips and repeated upper circuits as bullish confirmation
Treated the same signals as warning flags for herding and momentum-only demand
Liquidity risk assessment
Assumed any owned share could be sold whenever needed
Weighed thin, momentum-only trading as a genuine risk, not a convenience
None of this means a disciplined investor would have avoided the correction's effects entirely, or would have sold everything in early 2021 and sat in cash for two years congratulating themselves. Markets are genuinely hard to time, and this Canon has never promised otherwise. What the comparison shows is narrower and, I think, more useful: a disciplined process changes the size of the mistake and, critically, whether the mistake is forced or chosen. A disciplined investor in 2021 likely still owned shares that fell in 2022. But they owned less of any single stretched position, owed no margin lender an explanation, and had already banked some gains along the way — which meant the correction was a setback to be endured with patience, not a crisis that dictated the timing and price of their sales for them.
That is the entire difference, and it is not a small one. I did not choose when I sold in 2022. My margin lender chose for me, at a price my lender needed, not at a price I would have chosen if the decision had genuinely been mine.
Chapter recap
The 2021 NEPSE mania was a genuinely national event, not a niche financial story: a pandemic that emptied out interest rates and ordinary life at the same time, remittance flows that kept arriving even when the world expected them to stop, an exchange made suddenly accessible to millions of new participants through Mero Share and TMS, and a wave of margin lending that connected those new participants to each other in ways almost none of them understood at the time. The index rose from roughly 1,100 points in the depths of the 2020 lockdown to an all-time closing high of 3,198.60 points on August 18, 2021, before Nepal Rastra Bank's tightening of interest rates, cash reserve requirements, and margin lending rules through late 2021 and 2022 helped trigger a long, painful correction that took the index back down toward the 1,800 to 2,000 range and kept it depressed for the better part of two years.
Every behavioural bias named in Chapters 50 through 53 — herding, recency bias, overconfidence, and borrowed conviction — was visible in the mania in a form clear enough to serve as a permanent case study, and every liquidity mechanism named in Chapters 54 through 58 — circuit breakers, market breadth, liquidity spirals, and margin-driven forced selling — governed the shape and severity of the correction that followed. Applying the Canon Score and this book's risk-management rules retroactively does not produce a story where a disciplined investor predicted the exact peak and sold everything at 3,198.60. It produces a more honest and more useful story: a composite, small hydropower name built from this era's own real conditions scores 39 out of 100 on Chapter 64's real seven dimensions — Weak/Avoid — a number that would not have moved an inch whether the share was hitting upper circuit or trading quietly, because a business's moat, debt load, and earnings do not change just because its price does. A disciplined investor would have owned smaller positions, carried little or no margin debt, trimmed gains as prices detached from fundamentals, and treated the mania's own warning signs as warnings rather than encouragement — which would have turned a two-year period of forced, desperate selling into an ordinary, survivable correction.
This is the eighth of eleven full case studies that make up Part XVI of this Canon, and the first to step back from a single company to look at the whole market at once. Across mergers, frauds, currency shocks, sector collapses, and now a full national mania and correction, the aim of every case study has been the same: to take the individual tools built up across the rest of this book and show them working, or failing to work, in real events with real numbers, lived through by people not so different from you. Three case studies remain: a hydropower project's own cost and schedule overrun, an insurance company's claims and solvency picture, and a hotel operator's fortunes through a real, recent shock to Nepali tourism — each one carrying every lesson from this part of the book forward into a different corner of the market.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 91
Case Study 9 — A Hydropower Overrun and Delay
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 9 — A Hydropower Overrun and Delay
Suman Ghimire had done everything the earlier chapters of this Canon told him to do. He read the prospectus twice. He looked up the river basin on a map before buying a single share. He even asked his cousin, who worked for a trekking company in Dolakha, to send him photographs of the construction site. By the time the Upper Tamakoshi Hydropower Limited shares reached the hands of ordinary retail investors, Suman felt he understood the company better than most people at his brokerage counter in Putalisadan.
Upper Tamakoshi Hydropower Limited, commonly traded on NEPSE under the symbol UPPER, is a 456 megawatt (MW) run-of-river hydropower project on the Tamakoshi River in Dolakha district. A megawatt is simply a unit of electrical power — a way of measuring how much electricity a plant can produce at any given instant, the same way a car's engine is rated in horsepower. A run-of-river plant, unlike a storage or reservoir plant, does not hold back a large lake of water behind a tall dam. It diverts a portion of the river's flow through a tunnel and a set of pipes to spin turbines, then returns the water to the river downstream. This matters for our story because run-of-river plants are hostages to two things: the river's natural rhythm through the year, and whatever man-made structure — a tunnel, a headrace canal, an intake — carries the water from the river to the turbines. If that structure is damaged, the plant does not merely produce less power. It can stop producing power entirely, sometimes for years.
Upper Tamakoshi was owned and built by a subsidiary of Nepal Electricity Authority (NEA), Nepal's state-owned power utility, but it was financed and structured in a way that involved a great deal of Nepali capital beyond the state itself — loans from Nepali institutions like the Employees Provident Fund and Citizens Investment Trust, plus share ownership eventually opened to project-affected locals and then to the wider investing public. This made it, at the time, the largest hydropower project built substantially with domestic Nepali financing and expertise, a matter of considerable national pride. It is exactly the kind of project a patriotic, income-hungry Nepali retail investor would fall in love with — and exactly the kind of project where that love needs to be checked against arithmetic.
This chapter follows Suman's journey with Upper Tamakoshi from the original construction timeline, through the 2015 earthquake and the 2016–17 monsoon floods that damaged the project's tunnel and access infrastructure, to the eventual commissioning years later than planned and at a cost far above the original budget — and, as we will see, through several more difficult years after that. Along the way we will apply Chapter 64's full seven-dimension Canon Score to Upper Tamakoshi as it actually stands today, and revisit the hydropower financial modelling logic from Chapters 42 through 44 to show exactly how a disciplined investor could have priced delay risk into the original purchase decision, and how to correctly re-underwrite the position once a delay actually landed. As you will see, a construction delay does not touch all seven of Chapter 64's dimensions equally — some move sharply, some barely move at all, and the discipline of scoring them separately is exactly what keeps an investor from either panicking or staying blindly loyal to a story that has changed.
Data vintage
Scored using Upper Tamakoshi’s publicly reported figures to August 2026, including FY2024/25 results. Market figures cited — a price near NPR 185 and a trailing P/E near 43 — were re-checked against NEPSE data providers on 27 August 2026 and remained accurate at that date. Hydropower and financial-sector figures move with each quarterly disclosure, so re-derive every number from current filings before acting on it.
Lesson 91.1 — Reading a Hydropower Project Before the Turbines Turn
Before we get to what went wrong, we need to understand what Suman was actually buying, because a share in a hydropower company is not really a share in "electricity." It is a claim on a very specific, very long-dated cash flow stream that depends on a chain of assumptions holding true, one after another, like a line of dominoes.
The first domino is the Power Purchase Agreement, or PPA. A PPA is a long-term contract, typically running 20 to 35 years, in which the hydropower company agrees to sell its electricity to a single buyer — in Nepal's case, almost always NEA — at a pre-agreed tariff, or price per unit of electricity. Think of a PPA the way you might think of a fixed-rent lease: it tells you, in advance, what money is coming in and for how long, provided the tenant (NEA) pays and the building (the power plant) is standing and functional. Upper Tamakoshi's PPA set differentiated tariffs for the wet season, when Nepal's rivers run high and electricity is abundant, and the dry season, when river flows shrink and electricity becomes scarce and valuable. This wet-dry tariff differential is a recurring theme across nearly every Nepali hydropower company's income statement, and it means a plant's revenue is never a flat, smooth number — it swings meaningfully across the fiscal year.
The second domino is capacity factor — the percentage of a plant's theoretical maximum output that it actually produces over a year, once you account for the river running low in winter, scheduled maintenance, and unplanned outages. A run-of-river plant on a glacier-fed Himalayan river might have a capacity factor anywhere from 45 to 60 percent, because winter flows are a fraction of monsoon flows. Chapter 42 walked through how to build a simple annual generation estimate: multiply installed capacity by 8,760 hours in a year, then multiply by your estimated capacity factor, to get expected annual units of electricity generated. That number, multiplied by the blended tariff across wet and dry seasons, gives you top-line revenue — before you have even opened the cost side of the model.
The third domino, and the one this case study is really about, is construction timeline and capital cost. Nearly every hydropower project is financed with a mix of equity (money from shareholders, including NEA itself and public investors like Suman) and debt (loans from banks, provident funds, and sometimes multilateral lenders). During construction, before the plant generates a single unit of revenue, the debt still accrues interest. This accrued interest during the construction period is added to the total project cost rather than expensed immediately, through an accounting treatment called Interest During Construction, or IDC.
KEY CONCEPT
Interest During Construction (IDC) is interest that accrues on a project's loans while the project is still being built and earning no revenue. Because there is no operating income yet to pay it from, this interest is not treated as a current expense — it is added to the total cost of the asset on the balance sheet, effectively becoming part of what shareholders and lenders must eventually recover through tariffs. A delay in construction does not just push commissioning back; every extra month of delay adds another month of IDC, which is why hydropower cost overruns tend to grow faster than the delay itself, not in step with it.
This is the single most important piece of arithmetic in this entire case study, so it is worth sitting with before we move to what actually happened to Upper Tamakoshi. If a project was financed 70 percent by debt at, say, a 10 percent annual interest rate, and it is delayed by two extra years, the IDC alone on that debt — compounding, since unpaid interest is often itself capitalised and grows in subsequent periods — can add a cost equivalent to 14 to 20 percent or more of the original loan amount, before you even count the extra cost of remobilizing contractors, replacing damaged equipment, or repairing washed-out access roads. A hydropower delay is therefore never a "linear" problem. It is a compounding problem.
Suman's original 2016-era thesis on Upper Tamakoshi, before he understood any of this fully, went roughly like this: a 456 MW plant, one of Nepal's largest, backed by the state utility, with financing already largely secured, targeting commissioning within a few years, selling into a power-hungry national grid that was still running daily load-shedding at the time — surely that is close to a sure thing. That thesis was not wrong about the demand side. It was dangerously incomplete about the construction-risk side.
Project attribute
What it means
Why it matters for a shareholder
Capacity (MW)
Maximum instantaneous output
Sets the ceiling on possible revenue
Capacity factor
Actual output as a percent of the ceiling
River hydrology and reliability shrink revenue below the ceiling
PPA tariff (wet/dry)
Contracted price per unit sold to NEA
Determines revenue per unit generated
Debt-to-equity ratio
Share of financing from loans versus shareholders
Higher debt means more IDC risk during any delay
Construction timeline
Planned start-to-commissioning period
Every month of delay compounds financing cost before any revenue arrives
Lesson 91.2 — When the River Fights Back: The Chain of Delays
Construction on Upper Tamakoshi began in the early 2010s, with an original commissioning target that project documents and NEA statements at the time placed around the middle of the decade — roughly 2015 to 2016. This was already an ambitious target for a project of its scale and terrain: a headrace tunnel several kilometers long bored through the Himalayan foothills, a steep-mountain access road, and a powerhouse cavern, all in a district with difficult logistics.
Then, in April 2015, the Gorkha earthquake struck. Dolakha district, where Upper Tamakoshi sits, was among the hardest-hit districts in the entire country. The earthquake and its aftershocks damaged the project's tunnel works, access roads, and camp infrastructure, and — just as importantly — diverted skilled labor, engineers, and construction materials across the country toward emergency reconstruction. A hydropower project does not exist in a bubble; it competes for cement, steel, machinery, and manpower with every other reconstruction effort happening around it. The earthquake alone pushed the project's timeline back by roughly a year or more, by most contemporary accounts.
CASE IN POINT
The 2015 Gorkha earthquake damaged Upper Tamakoshi's under-construction headrace tunnel and access infrastructure in Dolakha, one of the districts closest to the epicenter. Beyond the direct physical damage, the earthquake pulled scarce construction labor, cement, and machinery toward nationwide reconstruction efforts, delaying remobilization on the project itself. This is a textbook example of what project-finance analysts mean by "force majeure risk" — a disruption caused by an event outside anyone's control or contractual fault, yet one that still lands squarely on the project's balance sheet.
Just as the project was recovering and remobilizing, the monsoon seasons of 2016 and 2017 brought further damage. Flash floods and landslides along the Tamakoshi valley — a steep, narrow, geologically active corridor — damaged the headrace tunnel intake area and the access roads and bridges the project depended on to move equipment and materials in and out of the site. Publicly reported accounts from the period describe the project's own engineers estimating additional years of delay from the flood damage alone, on top of the earthquake-related delay already absorbed.
WARNING
A single reported cause of delay in a hydropower project's history should never be treated as the whole story. Himalayan river valleys are subject to recurring monsoon flood and landslide risk, essentially every year, for the entire multi-year construction period. An investor who prices in "one bad earthquake" but not the ordinary, recurring possibility of monsoon damage to access roads and tunnel works is still underpricing the risk, because the second and third disruptions are often less dramatic in the news but no less costly in the model.
By the time Upper Tamakoshi was finally commissioned — with grid connection and testing completed around 2021 — the project had slipped from an original target of roughly 2015–2016 to an actual commissioning roughly five to six years later. This is not a footnote-level delay. It is a delay on the same order of magnitude as the original construction period itself. A project planned to take about four to five years from groundbreaking to commissioning instead took nearly a decade.
Delays of this scale are not unique to Upper Tamakoshi in Nepal's hydropower history — projects have been repeatedly delayed by monsoon damage, contractor disputes, transmission-line bottlenecks (where the power plant is ready but the high-voltage line needed to evacuate its electricity to the national grid is not), and land acquisition disputes with local communities. Upper Tamakoshi is simply one of the most thoroughly documented and highest-profile examples, precisely because it was meant to be a flagship of domestically financed Nepali hydropower.
REGULATORY DETAIL
In Nepal, a hydropower project's tariff is governed by its Power Purchase Agreement with NEA, historically negotiated under tariff frameworks periodically updated by the regulator overseeing electricity pricing. A PPA typically has a defined commissioning deadline; missing it can trigger renegotiation clauses, revised commercial operation dates, or in some structures, financial penalties. However, force majeure clauses — covering events like earthquakes and extreme floods — generally shield a developer from strict penalty for delays caused by such events, which is exactly why the earthquake and flood-related delays at Upper Tamakoshi did not collapse the project's financing arrangements outright, even as they inflated its cost.
Lesson 91.3 — Running the Numbers: What a Multi-Year Delay Actually Costs
Let's now do what Chapters 42 through 44 taught us to do with any hydropower position: build a simple model, then stress it.
Upper Tamakoshi's original approved project cost was NPR 35 billion, financed with a debt-to-equity structure weighted toward debt — a common pattern in Nepali hydropower, where debt makes up 70 percent or more of total financing, with the remainder coming from equity contributed by NEA and, eventually, public shareholders including project-affected locals.
Under the original timeline, debt service — the repayment of loan principal and interest — was expected to begin only after commissioning, once the plant started earning tariff revenue. During the construction years themselves, interest on the debt already drawn down was capitalised as IDC, added to the project's total cost rather than paid out of pocket. This is normal and expected for the planned construction period. The problem is what happens when the planned construction period roughly doubles.
Contemporaneous reporting on Upper Tamakoshi lets us watch the IDC mechanism bite in real time rather than merely describe it. Loans were carrying an interest rate of roughly 11 percent, and by mid-2019 — with the project still years from commissioning — cumulative interest charges on the project's debt had climbed from about NPR 6.7 billion in 2016 to roughly NPR 14.4 billion. By that point, the project's cost excluding capitalised interest stood near NPR 49 billion, but including that capitalised interest the figure was already closer to NPR 69 billion, on a project that had not yet produced a single billable unit of electricity. The rupee's roughly 25 percent depreciation against the dollar and euro over the same period made imported equipment and contractor payments more expensive still, and a crane failure during penstock installation in May 2019 added yet another few months of delay on top of everything else. By the time the project was finally reconciled after commissioning, the per-megawatt cost had risen from an original NPR 77 million per MW to roughly NPR 196 million per MW — a 154 percent increase, putting total project cost at approximately NPR 89 billion against the original NPR 35 billion budget, roughly two and a half times the original estimate. A meaningful share of that increase came from direct repair and reconstruction costs after the earthquake and floods. A larger share of it came from exactly the IDC compounding mechanism described above: money that shareholders never "spent" in any visible sense, but that was quietly added to the capital base they would now need to earn a return on.
PRACTICAL TOOL
Before buying into any under-construction hydropower company, build a simple two-line delay-sensitivity table for yourself: original commissioning date and cost per the prospectus, and a stressed scenario assuming a two-year delay with cost rising by an estimated percentage tied to the project's debt share and interest rate (a rough rule of thumb is that each extra year of delay on a heavily debt-financed project adds mid-single-digit to low-double-digit percentage points to total project cost, depending on leverage). If the stressed IRR under that scenario still clears your minimum required return with a comfortable margin, the position can absorb ordinary Himalayan construction risk. If it cannot, you are pricing the project as if delay is impossible — and in Nepali hydropower, delay is closer to the base case than the exception.
This connects directly to a second concept from Chapter 43: the Debt Service Coverage Ratio, or DSCR. DSCR measures how many times over a project's annual operating cash flow can cover its annual debt repayment obligations — calculated as operating cash flow divided by scheduled principal and interest payments for the year. A DSCR of 1.0 means the project generates exactly enough cash to service its debt with nothing left over; lenders typically require a minimum DSCR comfortably above 1.0 (often 1.2 to 1.3 or higher) as a safety cushion.
KEY CONCEPT
The Debt Service Coverage Ratio (DSCR) is a project's annual operating cash flow divided by its annual debt repayment obligation (principal plus interest due that year). It answers a simple question: for every rupee of loan payment due this year, how many rupees of cash did the project actually generate? A DSCR safely above 1.0 — lenders commonly want 1.2 or higher — gives the project breathing room if a monsoon season is weaker than average or a turbine needs unplanned maintenance. A project whose delay has inflated its total debt burden will show a structurally lower DSCR once it finally starts operating, even if its physical output is exactly as originally engineered, simply because there is more debt to service against the same generation.
A larger total project cost, financed with a similar debt proportion, means a larger absolute debt balance to service once revenue finally starts flowing. Even if Upper Tamakoshi generates precisely the electricity its engineers originally projected, its shareholders are now servicing a debt load roughly proportional to a NPR 89 billion project rather than a NPR 35 billion one. The plant's physical capacity to generate electricity did not change. The cost of the capital sitting behind that capacity did — and as Lesson 91.5 will show, this is exactly why the company kept losing money for years after the turbines finally started turning.
This is the crucial, underappreciated lesson of every hydropower overrun: delay risk and cost-overrun risk are really one and the same risk wearing two names, connected by the mechanism of capitalised interest. An investor who only asks "will the plant get built?" is asking half the right question. The full question is "will the plant get built, on what timeline, financed by how much additional debt, and will the resulting per-unit cost of electricity still clear a healthy return once tariffs are applied against a larger capital base?"
Lesson 91.4 — Hemisphere 1: Liquidity, Governance, and Durability
This Canon has, since Chapter 64, asked you to score any prospective holding across seven real dimensions rather than fall in love with a single narrative: Financial Strength & Profitability (20 points), Governance & Promoter Behaviour (15 points), Liquidity & Tradability (10 points), Valuation Reasonableness (15 points), Sector & Business Model Durability (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points), summing to 100 — with a governance override that caps the whole score in the Weak/Avoid band if the Governance sub-score falls below 5 out of 15. Let's run Upper Tamakoshi through all seven, honestly, using where it actually stands today rather than the story Suman told himself in 2016.
Three of the seven dimensions are best thought of together, because a multi-year construction delay barely moves them one way or the other: Liquidity & Tradability, Governance & Promoter Behaviour, and Sector & Business Model Durability. These describe what kind of company Upper Tamakoshi structurally is, not how its particular construction history unfolded.
Liquidity & Tradability (out of 10). Upper Tamakoshi is one of the largest hydropower listings on NEPSE by market value — roughly NPR 40 billion in market capitalisation on 211.8 million shares, all of it publicly held with no separate promoter block locking up supply. That size and float support real day-to-day tradability. Against that, the share has been in a clear downtrend for over a year, sliding from a 52-week high near NPR 243 to the mid-NPR 180s, which tends to thin out willing buyers even in a large-cap name. We score this 7 out of 10 — a genuinely tradable large-cap hydropower name, but not a top mark, given the cooling momentum.
Governance & Promoter Behaviour (out of 15). Upper Tamakoshi is a subsidiary of Nepal Electricity Authority, the state utility, financed substantially by Nepali institutions including the Employees Provident Fund and Citizens Investment Trust. There is no evidence here of the kind of self-dealing, related-party lending, or leadership fraud this Canon has flagged in other case studies — the record is one of execution difficulty, not misconduct. That said, execution difficulty is itself a governance fact: the project missed its commissioning deadline five separate times, and its per-megawatt cost rose 154 percent before the project was finally reconciled. On the positive side of the ledger, management has shown real capital discipline once the plant was operating — most notably renegotiating its long-term loan rate down from 8.25 percent to 6.75 percent for three years, a concrete, provable action rather than a promise. We score this 11 out of 15: comfortably above Chapter 64's 5-point override floor, reflecting a credible, non-fraudulent sponsor with a genuinely weak execution record that it has since worked to repair.
Sector & Business Model Durability (out of 15). Chapter 65 treats a PPA-backed hydropower project's basic moat as its textbook full-marks case: a 20-to-35-year contracted buyer in NEA, a tariff set in advance, and no exposure to a competitive spot market. That structural moat does not weaken just because a specific project ran years behind schedule, so this sub-component scores a full 8 out of 8. The other half of Durability is concentration risk — and here, like every hydropower company examined in this Canon, Upper Tamakoshi sells effectively all of its output to a single buyer, NEA. Chapter 65 flags this near-universal single-offtaker dependency as a real but only partially quantifiable risk across the entire sector, and consistent with Chapters 84, 87, 88, and 90, we score this sub-component 4 out of 7. Durability totals 12 out of 15.
Hemisphere 1 — the three dimensions least disturbed by a construction delay — comes to 7 + 11 + 12 = 30 out of 40.
CAUTION
It is tempting to treat a project as "de-risked" simply because it is large, state-affiliated, or a matter of national pride. Size and prestige do not repeal geology or monsoon physics. A large flagship project can be delayed just as badly as a small one — sometimes worse, because its scale means more tunnel length, more access road, and more exposure to the same terrain risk, spread across more kilometers of construction.
Lesson 91.5 — Hemisphere 2: The Four Dimensions a Delay Actually Moves
The remaining four dimensions — Financial Strength & Profitability, Valuation Reasonableness, Growth Trajectory, and Dividend & Capital Return Discipline — are exactly where a multi-year cost overrun shows up, because they depend on real financial statements, and Upper Tamakoshi did not produce any until the plant was finally generating revenue in 2021. What those statements have shown since is sobering: the story does not end at commissioning.
Financial Strength & Profitability (out of 20). Once Upper Tamakoshi finally started billing NEA for electricity, it did not become profitable. It posted losses for four consecutive fiscal years, driven by the interest burden on its now-roughly-doubled-and-a-half debt load and by "hydrology penalties" — contractual charges NEA can levy when a plant's actual generation falls short of its contracted design energy in a given season — which cost the company close to NPR 1 billion in total across those four years, rising from a token NPR 3.3 million in 2021/22 to NPR 220 million in 2024/25. In fiscal year 2024/25 alone, revenue was a healthy NPR 6.92 billion, up 19.4 percent year over year, yet the company still posted a net loss of NPR 2.57 billion — a net margin of negative 37 percent — on earnings per share of negative NPR 12.15 and a return on equity of negative 22.8 percent. Only in the most recent fiscal year did the picture turn, after management renegotiated its loan rate down and posted a rare penalty-free year, with the company projecting roughly NPR 1 billion in profit and trailing EPS turning positive at NPR 4.37. We score the sub-components as follows: return on equity 2 out of 8 (a single quarter's worth of positive earnings after four straight loss years does not yet make a trend), leverage 2 out of 7 (the balance sheet is still carrying the debt built up by the overrun, now serviced at a lower but still meaningful rate), and earnings quality 1 out of 5 (revenue is genuinely strong and growing, but the swing from a NPR 2.57 billion loss to a projected NPR 1 billion profit rests heavily on a one-time rate renegotiation and a lucky hydrology year rather than a structural improvement in the underlying unit economics). Financial Strength totals 5 out of 20.
Valuation Reasonableness (out of 15). At a recent price near NPR 185, Upper Tamakoshi trades at a trailing price-to-earnings ratio of roughly 43 times — more than double the hydropower sector's average of about 18 times, according to recent sector-wide NEPSE valuation data — despite a company that has just emerged from four consecutive loss-making years. Its price-to-book ratio of roughly 4.25 times is being applied to a book value per share, near NPR 44, that has itself already been eroded by those years of accumulated losses (book value per share was above NPR 56 before the loss years took their toll). Paying a premium multiple over an already-shrunken equity base, for a single quarter of profit, is not a conservative valuation. We score the P/E sub-component 2 out of 8 and the P/B sub-component 2 out of 7, for a Valuation total of 4 out of 15.
Growth Trajectory (out of 15). It is important to separate the sector-wide demand story from this company-specific dimension. Nepal's electricity demand genuinely has grown steadily, and Upper Tamakoshi's own revenue grew a healthy 19.4 percent in its most recent reported fiscal year — that part of Suman's original instinct was sound. But Chapter 64's Growth Trajectory dimension asks about the company's own demonstrated trajectory of revenue and earnings together, not the macro backdrop alone, and a single fixed-capacity plant with no announced expansion, whose earnings only turned positive after a debt renegotiation rather than organic growth, does not yet show a durable growth trajectory. We score this 5 out of 15 — real revenue growth, but earnings growth that is still unproven.
Dividend & Capital Return Discipline (out of 10). This is the starkest number in the whole score. Since Upper Tamakoshi's shares were opened to public investors, the company has never declared a dividend — its public trading record shows no dividend distribution at all. As of August 2026 that remains true: NEPSE data providers return an empty dividend history for UPPER, and the only capital action since listing has been the 1:1 rights issue of FY2080/81 — a call for more shareholder money rather than a return of it. A company financed for years on debt that outgrew its budget, that spent four straight years in the red, and that only recently returned to modest profitability, has simply had nothing to distribute. We score this 1 out of 10, allowing a single point for the credible near-term possibility of an eventual dividend now that the company projects a real profit, against a flat zero track record to date.
Hemisphere 2 comes to 5 + 4 + 5 + 1 = 15 out of 40.
Lesson 91.6 — The Full Worked Canon Score
Putting both hemispheres together gives us Upper Tamakoshi's real, current Canon Score.
Canon Score dimension
Sub-score
Out of
What drove it
Liquidity & Tradability
7
10
Large-cap, fully public float, but a cooling price trend
Governance & Promoter Behaviour
11
15
Credible state sponsor, no misconduct, but a five-time-delayed, 154%-overrun execution record
Sector & Business Model Durability
12
15
Full marks for the PPA moat (8/8); capped at 4/7 for near-total NEA concentration
Financial Strength & Profitability
5
20
Four straight loss years, ROE -22.8% in FY 2024/25, only just turning positive
Valuation Reasonableness
4
15
P/E ~43x vs. sector average ~18x, on an already-eroded book value
Growth Trajectory
5
15
Real revenue growth, but earnings growth still unproven
Dividend & Capital Return Discipline
1
10
No dividend ever paid to public shareholders
Total
45
100
Weak/Avoid band
Forty-five out of one hundred places Upper Tamakoshi in Chapter 64's Weak/Avoid band, well below the 55-point Adequate threshold. For a direct dimension-by-dimension contrast against a plant one twentieth its size, see Chapter 84, where Chilime scores 62 on the same rubric. Note that the governance override does not fire here — the Governance sub-score of 11 out of 15 clears the 5-point floor comfortably, because this is a story of expensive execution difficulty, not fraud or self-dealing, and that distinction matters. Compare this to Chapter 89's Karnali Development Bank, where the override did fire: two very different companies can land in the same Weak/Avoid band for very different reasons, and the Canon Score's dimension-by-dimension breakdown is what lets you tell those reasons apart rather than treating "Weak/Avoid" as a single undifferentiated verdict.
This is precisely why the Canon Score framework insists on scoring dimensions separately rather than blending them into a single fuzzy impression of "good company" or "bad company." Upper Tamakoshi's structural business — the PPA, the state sponsor, the size and tradability of the listing — remains genuinely solid, worth 30 of the available 40 points in Hemisphere 1. What the delay and its aftermath did was devastate the financial dimensions that depend on actual results: profitability, valuation support, demonstrated growth, and dividends. An investor who only tracked the demand story, as Suman originally did, would have missed the entire re-rating that a careful, dimension-by-dimension read would have caught.
Lesson 91.7 — Reassessing the Position Once the Delay Was Confirmed
Suppose Suman had bought his shares at the original IPO price, built on the original NPR 35 billion cost assumption and the original commissioning timeline. What should he have done once the earthquake struck in 2015, and then again once the 2016–17 flood damage was reported?
The discipline the Canon has taught in earlier chapters applies directly here: a confirmed delay is not a reason to panic-sell, nor a reason to shrug and do nothing. It is a trigger to re-run the model with updated assumptions and make a fresh decision, exactly as you would if a company you held announced a major factory fire or a regulatory tariff change.
The re-underwriting process has three steps. First, update the cost assumption using the best available public information — in this case, contemporaneous reporting showing cost escalating from NPR 49 billion excluding capitalised interest in 2019 (already NPR 69 billion including it) toward an eventual reconciled total near NPR 89 billion. Second, update the timeline assumption to the newly indicated commissioning date, which pushed out year after year as further monsoon damage was reported. Third, and most importantly, re-run the discounted cash flow — the technique from Chapter 44 for converting a future stream of expected dividends or earnings into a single present value using a discount rate that reflects the riskiness of the cash flows — using the new cost base, the new timeline, and, crucially, a higher discount rate than originally used, because a project that has already slipped once has demonstrated it is exposed to delay risk and may slip again. In Chapter 64 terms, this re-underwriting is really an early warning that Financial Strength and Valuation are about to deteriorate sharply, even while Governance, Durability, and Liquidity stay comparatively steady — exactly the split Lessons 91.4 and 91.5 quantified with real numbers.
A discount rate is simply the rate at which you shrink future money to reflect the fact that money later is worth less than money now — both because of the time value of money and because of the uncertainty attached to actually receiving it. A project that has already proven vulnerable to earthquake and flood damage deserves a somewhat higher discount rate than one with a clean, on-schedule track record, because the probability distribution of future cash flows has genuinely widened. Every additional year of delay pushes dividends further into the future, and the discounting mechanism means a dividend pushed back five years is worth meaningfully less today than the same dividend arriving on the original schedule — even before you touch the cost-overrun assumption at all.
Running this re-underwriting honestly, most careful analysts following Upper Tamakoshi through 2016 and 2017 would have concluded that the project's per-share intrinsic value estimate needed to come down from the original IPO-era assumption, even though the plant's physical output potential and its favourable demand backdrop were unchanged. This is not a reason to conclude the investment was a mistake — it is a reason to conclude that the price you would be willing to pay for additional shares, or the conviction with which you hold existing ones, needed to be recalibrated to the new facts.
WARNING
The most common investor mistake during a confirmed hydropower delay is anchoring to the original cost and timeline assumptions simply because they were the numbers in the original prospectus you fell in love with. A prospectus is a forecast, not a guarantee. Once real-world facts — an earthquake, a flood, a contractor dispute — have overtaken the forecast, continuing to value the company on the old numbers is not patience. It is denial dressed up as conviction.
This is also the moment to distinguish between a temporary paper loss and a permanent loss of capital. If Suman's shares fell in price on delay news, the question he needed to ask was not "has the price gone down," but "has my re-underwritten estimate of fair value gone down by more or less than the price has." If the market overreacted — pricing in a worse delay or a larger overrun than was actually likely — the position may have become more attractive at the lower price, not less. If the market underreacted, and the true re-underwritten value had fallen further than the price had, the position may have still been overvalued even after the drop. Only a fresh model run, not the direction of the price chart, can answer that question.
CAUTION
Do not average down — buying more shares simply because the price has fallen — without first re-running the full model on updated cost and timeline assumptions. Averaging down on a stale, pre-delay valuation is not disciplined patience; it is doubling exposure to a story you have not actually re-checked.
For a company like Upper Tamakoshi specifically, one further consideration mattered: its sponsor. NEA is the national utility, backed ultimately by the state's interest in seeing the country's flagship domestically financed hydropower project succeed. Projects with a strong, well-capitalised sponsor behind them are less likely to be abandoned mid-construction even after a severe cost overrun, because the sponsor has both the financial capacity and the strategic motivation to see the project through. A similarly sized delay and overrun at a smaller, thinly capitalised private developer with weaker banking relationships could plausibly have resulted in a stalled or even abandoned project, a much worse outcome for shareholders than "merely" a multi-year delay. Sponsor quality is therefore a legitimate input into how much benefit of the doubt a delayed project deserves — and it is why Upper Tamakoshi's Governance sub-score in Lesson 91.4 held up at 11 out of 15 even as its Financial Strength sub-score, in Lesson 91.5, collapsed to 5 out of 20.
Lesson 91.8 — What Actually Happened, and the Portfolio-Level Lesson
Upper Tamakoshi was eventually commissioned, with its units connected to the national grid in July 2021 — roughly five to six years after the original mid-decade target, and at a total cost that would eventually reconcile near NPR 89 billion, roughly two and a half times the original approved budget. But commissioning was not the happy ending Suman's original thesis had imagined. The plant began generating substantial revenue — NPR 6.92 billion in fiscal year 2024/25 alone, still growing — yet the company posted a net loss in each of its first four fiscal years of operation, driven by interest on the enlarged debt load and by hydrology penalties NEA charged when generation fell short of contracted design energy in a given season, together totalling close to NPR 1 billion over those four years. Book value per share fell from above NPR 56 to the mid-NPR 40s as those losses ate into shareholders' equity. No dividend was ever declared. Only in the most recent fiscal year, after management renegotiated its long-term loan rate down from 8.25 percent to 6.75 percent and the plant finally had a penalty-free year, did the company turn a projected profit of roughly NPR 1 billion — a genuine but fragile turnaround, arriving a full four years after commissioning, not at commissioning.
This is the sharpest correction this case study makes to the story Suman told himself in 2016: a construction delay is not the whole risk. The favourable demand backdrop he correctly identified was real, and the PPA did guarantee a buyer at a contracted tariff — but a project financed with debt that outgrew its budget can keep losing money for years after the turbines start turning, simply because the capital base behind those turbines has to be serviced before a single rupee reaches shareholders. Being broadly right about Nepal's electricity demand was not the same as being right about when, or whether, this particular capital structure would translate that demand into a return.
The final lesson of this case study is not "hydropower is a bad investment" — Suman would be the first to tell you that would be an overcorrection. Nepal's growth story and hydropower are deeply intertwined, and a well-run project with a solid PPA and a disciplined capital structure remains one of the more durable long-duration income ideas available on NEPSE. The lesson is narrower and more useful than that: construction-phase risk in hydropower is real, common, and mathematically compounding, and where a project's debt burden grew large enough during that construction phase, the pain can keep compounding well into the operating years too — a distinct and separate risk from the delay itself, and one this Canon Score's Financial Strength and Valuation dimensions are built specifically to catch.
Practically, this suggests a portfolio-construction habit worth adopting directly. Rather than concentrating hydropower exposure in a single under-construction project — however flagship, however patriotic the appeal — a disciplined investor following this Canon should think in terms of "vintages": holding a mix of already-operating hydropower companies, whose revenue and dividend patterns are known and can be modelled with much less uncertainty, alongside a smaller, deliberately sized allocation to under-construction projects where the higher potential return compensates for construction-phase risk that even the best due diligence cannot fully eliminate. An earthquake in Dolakha, a landslide in the Tamakoshi valley, a contractor dispute, a delayed transmission line elsewhere in the country — these are not signs that something unusual went wrong with any single company. They are recurring features of hydropower construction in Himalayan terrain, and a well-constructed portfolio should be sized so that no single delayed project can derail the investor's overall plan.
REGULATORY DETAIL
Investors tracking hydropower delay risk should watch not only company disclosures but also NRB's periodic reports on sectoral lending, since Nepali banks and development finance institutions carry substantial exposure to hydropower project loans; a wave of sector-wide delays has systemic implications for loan quality and provisioning across the banking sector, not just for the equity holders of the delayed projects themselves.
Suman, in the end, kept his Upper Tamakoshi shares through the entire delay period, having re-underwritten his position twice — once after the earthquake, once after the flood damage was confirmed — and having concluded both times that the demand-side thesis remained intact even as the financial dimensions had visibly weakened. He did not add significantly to his position during the delay years, judging that the compounding IDC and uncertain timeline made the risk-reward less attractive than simply waiting. When commissioning finally arrived in 2021, he resisted the temptation to treat it as the finish line, and re-ran his numbers a third time — this time using actual post-commissioning financial statements rather than a construction-era forecast. That third look told him the position still did not clear his bar: four years of real losses, an eroding book value, and a Canon Score stuck in the Weak/Avoid band were not a reason to sell a company he still believed in structurally, but they were a reason to stop adding and simply wait for the financial dimensions to catch up with the demand story — which, only very recently, they finally began to do. He considers the outcome a genuinely open question rather than a settled success: a reminder that being broadly right about a country's electricity demand is not the same as being right about exactly when, and at what cost, any single project will deliver on that demand.
Chapter recap
This case study followed a fictional retail investor's holding in Upper Tamakoshi Hydropower Limited, a real 456 MW run-of-river project on the Tamakoshi River in Dolakha district, through a construction period disrupted first by the 2015 Gorkha earthquake and then by monsoon flood damage in 2016 and 2017 — delays that pushed commissioning from an original mid-decade target to July 2021, and that drove total project cost from an approved NPR 35 billion to a final reconciled cost near NPR 89 billion, a 154 percent increase. We used this real history to show how Interest During Construction compounds a delay into a cost overrun larger than the delay itself, and how the Debt Service Coverage Ratio changes once a larger capital base must be serviced by unchanged physical output. We then ran Upper Tamakoshi through Chapter 64's full seven-dimension Canon Score as it actually stands today: a strong 30 out of 40 across the structurally stable dimensions of Liquidity, Governance, and Durability, but only 15 out of 40 across Financial Strength, Valuation, Growth, and Dividend discipline — a company that posted losses for four straight years after commissioning and has never paid a dividend, for a total of 45 out of 100, Weak/Avoid, with the governance override held off because this is a story of expensive execution, not fraud. We also walked through the correct re-underwriting discipline once a delay is confirmed — updating cost, timeline, and discount-rate assumptions rather than anchoring to the original prospectus — and the portfolio-level habit of mixing already-operating hydropower holdings with a deliberately limited allocation to under-construction projects.
Chapter 92 turns to a very different kind of business entirely: Case Study 10 examines a Nepali insurance company, applying Chapter 64's Canon Score with Chapter 65's insurance-specific guidance — claims and loss ratios, solvency margin, and investment portfolio quality — to show how a seemingly stable, premium-collecting business can hide risks that only surface years later, when claims come due.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 92
Case Study 10 — An Insurance Company
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 10 — An Insurance Company
Anjali Rai kept her father's life insurance policy document in a plastic folder in the almirah, the kind every Nepali household has somewhere — a folder for birth certificates, land ownership papers (lalpurja), and the one insurance policy someone in the family bought decades ago and never quite understood. Her father had paid premiums to Nepal Life Insurance Company for twenty-two years. In the spring of 2082, the policy matured, and a cheque arrived. Anjali, who by now had a hydropower stock and a development bank stock in her demat account from earlier chapters of her investing life, found herself asking a question she had never asked before: should I actually own shares in the company that just paid my father, instead of only buying its promises?
That question is this chapter. Nepal Life Insurance Company Limited, traded on NEPSE under the ticker NLIC, is Nepal's oldest private-sector life insurer, incorporated in 2058 BS (2001 AD), and for most of its history the largest life insurer in the country by number of policies in force. It is a fitting subject for Case Study 10, because insurance companies break almost every analytical habit a NEPSE investor has built up from studying banks and manufacturers. A bank lends money and collects interest. A manufacturer buys inputs, makes a product, and sells it for more than it cost. An insurance company sells a promise — pay me a small sum now, and I will pay you (or your family) a much larger sum later, if and when a defined event happens. It collects cash today against a liability it may not have to settle for thirty years. That single fact changes everything about how you read its numbers, and it is why Chapter 33 spent so much time on insurance accounting before we ever got here. This chapter puts that theory to work on a real, currently listed company, warts and all — including one number in NLIC's own recent disclosures that should make any careful investor sit up and ask questions rather than simply celebrate the dividend.
Lesson 92.1 — Why Anjali Looked at an Insurer at All
Before opening the annual report, Anjali did what the Canon Score framework has trained her to do from the first case study onward: ask why this business exists and whether people need it. Insurance penetration in Nepal — the total premium collected across the industry each year, divided by GDP — has historically sat below two percent, low even by South Asian standards. That is not a criticism of the industry; it is the opportunity. A country where remittance income from over two million Nepalis working abroad flows home every month, where families are increasingly buying life cover for a child's future education or a daughter's wedding instead of only relying on land and gold, is a country where the insurance market has a long runway to grow simply by more people buying more policies, with no need for anyone to invent anything new.
NLIC sits near the front of that growth story. By the fiscal year ending mid-July 2024 (FY 2080/81 in the Bikram Sambat calendar NEPSE reports use), the company had 1,467,128 policies in force, up 26.5 percent in a single year — a number swollen partly by smaller polices and partly by the aftermath of a merger wave the regulator forced across the industry, which we will come back to in Lesson 92.4. Net premium collected in that year came to roughly Rs 40.11 arba (a NEPSE-disclosure term worth pausing on: 1 arba = 100 crore = 1 billion rupees in the Nepali numbering system, so Rs 40.11 arba means about Rs 40.11 billion), up 9.9 percent on the year before. Paid-up capital — the money shareholders have actually put into the company, at face value — stood at roughly Rs 8.2 arba that year and had grown to about Rs 9.48 arba after a bonus share issue the following year, comfortably above the regulator's minimum. The stock traded, through 2025 and into 2026, in a band roughly between Rs 700 and Rs 870 per share, against a book value per share of only around Rs 126 to 129. That last comparison — price near Rs 700, book value near Rs 127 — is the first number that should make a disciplined investor's eyebrow rise, and we will return to it in Lesson 92.3.
KEY CONCEPT
Book value per share is simply shareholders' equity divided by the number of shares outstanding — what would be left for owners if the company sold everything it owns and paid off everything it owes, split evenly. For a bank or a manufacturer, price trading at two or three times book value already invites scrutiny. For an insurer, book value is a particularly slippery yardstick, because a large share of the "equity" side of the balance sheet is really a set of actuarial estimates about the future, not cash sitting in a vault — which is exactly why Chapter 33 warned you never to value an insurer the way you value a factory.
Anjali's instinct, trained by Chapter 64's seven real Canon Score dimensions — Financial Strength & Profitability, Governance & Promoter Behaviour, Liquidity & Tradability, Valuation Reasonableness, Sector & Business Model Durability, Growth Trajectory, and Dividend & Capital Return Discipline — was to resist being charmed by "market leader" and "father's policy paid out fine" and instead go looking for the parts of the story that don't show up in a dividend announcement.
Lesson 92.2 — The Insurance Accounting Logic, Applied
Chapter 33 laid out why insurance accounting is a different animal from banking or manufacturing accounting. It is worth restating the core ideas here, briefly, because this chapter is where they stop being theory.
An insurance company's core product is a promise to pay money later in exchange for money now. The money collected now — the premium — is not profit the moment it lands in the bank account. Some of it must be set aside to cover the expected cost of claims the company will eventually have to pay on that same block of policies, plus a margin for the uncertainty in that estimate. That set-aside amount is called a reserve (sometimes called a technical provision, or in Nepali insurers' own disclosures, the insurance fund or life assurance fund). Reserving is the single most important and most easily misunderstood number in insurance accounting, because it is not a hard fact like a bank's cash balance — it is an actuary's best estimate of the future, revised periodically as new information comes in about how policyholders actually behave (how long they live, how often they lapse a policy, how often they make a claim).
Until that money is needed to pay claims, the insurer gets to invest it. This pool of collected-but-not-yet-paid-out money is called float, a term made famous by Warren Buffett's letters about Berkshire Hathaway's insurance operations. Float is, in effect, a form of financing that costs the insurer nothing (or even pays the insurer, if the business is priced well) for as long as it takes to accumulate. NLIC's life insurance fund — the accumulated pool backing all outstanding policies — stood at roughly Rs 198.33 arba (nearly Rs 2 kharba, where 1 kharba = 100 arba = 100 billion) by mid-2024, up over 21 percent on the year, and total investments held against it came to about Rs 164.16 arba, up over 18 percent. That is the float: a sum larger than the entire market capitalisation of most companies on NEPSE, sitting inside one insurer, invested mostly in government securities, bank fixed deposits, and a smaller allocation to shares and real estate, under rules the regulator sets on how insurers may deploy policyholders' money.
Feature
A Bank
A Manufacturer
An Insurer
Core product
Lends money it has borrowed (deposits)
Turns raw material into a finished good
Sells a promise to pay later
Main liability
Deposits, repayable on demand or at maturity
Trade payables, short-term
Reserves for future claims, long-dated
Main asset
Loan book, priced for credit risk
Inventory, receivables, plant
Investment portfolio funded by float
Key ratio investors watch
Net interest margin, non-performing loans
Gross margin, inventory turnover
Claims ratio, solvency margin
What can quietly go wrong
Bad loans hidden by rollovers
Obsolete inventory, working capital squeeze
Reserves understated, or claims outpace premium and investment income
The claims ratio is the insurance-world equivalent of a bank's non-performing loan ratio or a manufacturer's cost of goods sold as a percentage of revenue — it tells you what fraction of the money coming in the front door is going straight back out to pay claims. For a non-life insurer (motor, fire, health, marine — anything other than life), the claims ratio is usually calculated as net claims incurred divided by net premium earned in the same period, and it is meant to be read together with the expense ratio (operating and commission costs as a share of premium) to get the combined ratio: claims ratio plus expense ratio. A non-life insurer with a combined ratio comfortably under 100 percent is making an underwriting profit before it ever touches its investment income — it is being paid to hold other people's risk. A combined ratio above 100 percent means the insurer is losing money on the insurance itself and depends entirely on investment returns on the float to stay profitable, which is a much shakier position, because investment markets are cyclical and claims are not always.
CAUTION
Do not apply the non-life combined-ratio logic directly to a life insurer's claims ratio without adjustment. Life insurance claims include maturity benefits and survival benefits — money the insurer always intended to pay out to a living policyholder at the end of a savings-linked policy — not only death claims. A life insurer's net claims can run higher than net premium collected in a given year and this is often normal, not alarming, because the payout is funded from the accumulated life fund built up over the policy's life, not solely from that year's fresh premium. The number to worry about is not claims exceeding premium in isolation — it is whether the life fund and its investment income are growing fast enough to keep funding those obligations as the back book of matured policies gets larger.
This is exactly the trap a NEPSE investor moving from bank stocks to insurance stocks can fall into. In FY 2080/81, NLIC's net claims came to about Rs 49.46 arba against net premium of about Rs 40.11 arba — claims were roughly 123 percent of premium that year. Read the way you would read a non-life combined ratio, that number looks like a company hemorrhaging money. Read correctly, for a maturing book of long-duration life policies, it mostly reflects a large cohort of older policies reaching their maturity date and being paid out as designed, funded by decades of accumulated reserves and investment income (Rs 15.20 arba of investment income that year, up almost 19 percent) rather than by the current year's premium alone. The two numbers you actually want to track over several years are whether the life fund keeps compounding upward, and whether the company can keep meeting its regulatory solvency requirement — which brings us to the number that made Anjali stop and reread a disclosure twice.
Lesson 92.3 — Reading NLIC's Numbers Line by Line
Anjali built a simple table from four consecutive quarterly disclosures and the annual report, the same habit the Canon Score has taught her to use for every case study in this book: never trust one year's number, always look at the trend.
Metric (FY 2080/81, year ended mid-July 2024)
Value
Year-on-year change
Net premium collected
Rs 40.11 arba
+9.86%
Investment income
Rs 15.20 arba
+18.97%
Net claims and benefits paid
Rs 49.46 arba
+12.65%
Life insurance fund (reserve)
Rs 198.33 arba
+21.39%
Total investments
Rs 164.16 arba
+18.31%
Policies in force
1,467,128
+26.52%
Fourth-quarter net profit
Rs 51.39 crore
+30.81%
Annualized EPS
Rs 6.26
+30.81%
Book value per share
Rs 129.11
-15.23%
Solvency ratio (as reported)
3.22
--
A few things jump out once the numbers are lined up rather than read one press release at a time. First, premium growth (9.9 percent) is running well below policy count growth (26.5 percent), which usually means the newer policies being sold are smaller in size — cheaper, shorter-duration, or lower sum-assured products — even as the customer base widens. That can be a perfectly healthy strategy (reaching more first-time policyholders, including through remittance-funded household savings, is exactly the growth story Nepal's underinsured population promises) but it is worth distinguishing from premium growth driven by existing customers buying bigger policies, which is a different and arguably higher-quality kind of growth.
Second, book value per share fell 15 percent even as profit rose 31 percent — an odd combination for a bank or manufacturer, but a normal feature of a life insurer, because a bonus share issue increases the share count faster than retained profit increases total equity, diluting book value per share even while the underlying business is compounding, and because actuarial revaluation of the life fund flows through equity in ways that do not track a simple retained-earnings ledger. This is precisely why Chapter 33 urged you to treat an insurer's book value with more humility than a bank's: a bank's book value is mostly cash and marked loans; an insurer's book value has an actuarial reserve sitting in the middle of it, revalued periodically by an appointed actuary rather than by a simple accrual entry.
Third — and this is the number that stopped Anjali cold — the stock, at a price around Rs 700 to Rs 750 through 2025 and into 2026, was trading at somewhere between 5 and 6 times its own recently reported book value, and at a price-to-earnings ratio that ranged, across different quarters' trailing-twelve-month EPS, from roughly 116 times to nearly 194 times annual earnings. The most recent quarterly disclosure available (FY 2082/83, Q4) showed EPS at somewhere between Rs 3.64 and Rs 4.91 depending on which annualization convention the data provider used, against a book value per share that had slipped to about Rs 126 — meaning the market was, at that moment, paying somewhere between roughly 145 and close to 195 times a single year's reported profit, for a company whose quarterly profit had just declined 15 percent year on year even as its premium book kept growing.
WARNING
A price-to-earnings ratio above 100 for an insurance company is not automatically a red flag by itself — insurance earnings are deliberately smoothed and conservative by design, and a single year's reported profit can understate the true economic value being built inside the life fund. But it is also not automatically fine. When a stock trades at close to 200 times a shrinking annual profit and more than five times book value, you are no longer investing in the business's fundamentals — you are betting on a re-rating, sentiment, or a takeover, none of which the Canon Score rewards. Never let "but it's an insurer, earnings are always weird" become an excuse to skip the valuation pillar altogether.
Fourth, and most important: the solvency ratio of 3.22 reported for FY 2080/81 looked, on its face, comfortably healthy — roughly double the regulatory minimum. But that single snapshot, taken in isolation, is exactly the kind of number the Canon Score trains you never to accept without a second data point six months or a year apart. Anjali went looking for the next one, and found something that changed her entire read of the company.
Lesson 92.4 — The Solvency Margin, and the Number That Should Worry You
Every insurer in Nepal is regulated not by Nepal Rastra Bank (NRB), which oversees banks and development banks, but by a separate authority — the Nepal Insurance Authority, known until a 2022 rebranding as Beema Samiti (beema is the Nepali word for insurance). This is a distinction worth fixing firmly in your head as a NEPSE investor, because it is easy to assume every financial company answers to the central bank. It does not. NRB sets the rules for deposit-taking institutions; the Nepal Insurance Authority sets the rules for insurers and reinsurers, and its rulebook is built around a different central question than NRB's: not "can this institution meet withdrawal requests," but "does this insurer hold enough capital, over and above its reserves, to withstand claims coming in worse than expected?"
REGULATORY DETAIL
The core regulatory yardstick for a Nepali insurer is the solvency margin, expressed as a solvency ratio: available solvency margin (broadly, admissible assets minus liabilities and reserves, calculated under the regulator's rules) divided by the required solvency margin (a formula-based minimum buffer set by the regulator based on the size and risk of the insurer's book). Under the Solvency Margin Directive that governed life insurers for most of the past decade, the minimum acceptable ratio is 1.5 — meaning an insurer must hold at least one and a half times the regulator's calculated minimum buffer. Fall below that, and the regulator can restrict dividend payments, demand a capital infusion, or take other corrective action.
In March 2025, industry reporting on life insurers' second-quarter solvency positions for FY 2081/82 (the fiscal year that began in mid-2024) showed the sector's average solvency ratio at 2.53, comfortably above the floor and slightly better than the 2.47 average a year earlier. Individual companies varied widely: National Life Insurance led the pack at 4.73, followed by LIC Nepal at 3.76 and Himalayan Life Insurance at 3.74. Citizen Life Insurance had improved sharply, from 1.82 the year before to 3.42. And at the bottom of the table, among audited companies, sat Nepal Life Insurance Company — NLIC, the very stock Anjali was looking at — at a solvency ratio of 1.45. Below the regulatory minimum of 1.5.
CASE IN POINT
Barely six months to a year after reporting a solvency ratio of 3.22 — more than twice the regulatory floor — the same company's solvency ratio, as reported for the following fiscal year's second quarter, had fallen to 1.45, just under the 1.5 minimum the Nepal Insurance Authority requires and the lowest reading among audited life insurers in the industry survey. Whatever the eventual explanation, this is precisely the kind of swing a Canon Score review is built to catch: a company that looks solid on premium growth, profit growth, and brand recognition can still be flashing a capital-adequacy warning that a quick glance at the income statement would never reveal.
What could cause a swing that large in so short a window? Anjali's working hypothesis, and the honest answer for a retail investor without access to the actuary's working papers, is that it is most likely the product of an annual (or more frequent) actuarial revaluation of the life fund. Nepal's appointed actuaries reassess the assumptions behind reserves periodically — mortality tables, lapse rates, expense assumptions, the discount rate used to value long-dated future obligations — and any tightening of those assumptions (say, assuming policyholders will live longer, or assuming a lower future investment return to discount liabilities at) can suddenly increase the required reserve and shrink the available solvency margin, even though nothing about the day-to-day insurance business changed at all. It is also possible that a genuinely large single event — a spike in claims, a mark-to-market hit on the equity portion of the investment book during a NEPSE downturn, or the phasing-in of the stricter Risk-Based Capital and Solvency Directive the Nepal Insurance Authority approved in 2082 (2025), which replaces the older, simpler factor-based solvency test with a more granular risk-weighted framework closer to international "Solvency II"-style regimes — played a role. A disciplined investor's job here is not to guess confidently; it is to notice the swing, treat it as unresolved, and demand an explanation before buying, holding through it, or adding to a position.
PRACTICAL TOOL
Before buying any Nepali insurer's stock, pull at least three consecutive periods of its solvency ratio disclosure — these are published in quarterly reports filed with NEPSE and in the Nepal Insurance Authority's periodic industry bulletins, and increasingly summarised by financial news portals covering the insurance beat. A single healthy reading tells you almost nothing; a trend does. If the ratio is falling toward the regulatory floor, or has crossed below it, that single fact should outweigh almost any amount of premium growth or dividend history in your financial-strength score, because a regulator that steps in to restrict dividends or force a capital raise can wipe out a shareholder's near-term return regardless of how good the underlying insurance book is.
This is also the reason a merger wave swept Nepal's insurance sector in 2079-80 BS (2022-2023). The regulator raised minimum paid-up capital requirements sharply — to Rs 5 arba for life insurers and Rs 2.5 arba for non-life insurers, with a deadline that was extended into Chaitra 2079 and then further to Ashad 2080 (roughly mid-2023) — forcing smaller, thinly capitalised insurers to either merge, issue rights shares, issue bonus shares, or exit the market. Some non-life insurers needed to raise capital more than nine-fold to comply; the best-capitalised ones, like Shikhar Insurance, needed only a modest top-up. NLIC, already the largest life insurer, cleared the new minimum with room to spare on paid-up capital — but as its solvency ratio shows, having enough paid-up capital and having an adequate solvency margin are two different tests, and passing one says nothing about the other.
Lesson 92.5 — Float, Catastrophe Risk, and the Non-Life Contrast
To see the full range of what "different from a bank or manufacturer" means for insurers, it helps to set NLIC briefly against a non-life peer. Shikhar Insurance Company Limited, ticker SICL, is Nepal's largest non-life insurer by market capitalisation — covering motor, fire, marine, engineering, and other property and casualty risk rather than life and savings products. Its balance sheet tells a different story from NLIC's: paid-up capital of roughly Rs 3.1 arba, book value per share that has moved between roughly Rs 190 and Rs 335 across recent reporting periods, and a business where the whole point of the claims ratio is to test whether the company is pricing risk correctly year to year, because non-life policies are short-tail — a fire policy or a motor policy runs twelve months, and the claims tied to it are mostly known within a year or two, not thirty years later.
That short tail is exactly what makes non-life insurers vulnerable to a different kind of risk than life insurers: catastrophe risk, a single event that generates a flood of claims all at once. Nepal's most vivid real illustration remains the 2015 Gorkha earthquake, which triggered claims across the non-life insurance industry — and, notably, exposed how thin reinsurance cover and inadequate reserving can turn a well-run-looking insurer into a distressed one overnight when a single tail event arrives. Every non-life insurer in Nepal is required to cede a portion of its risk to reinsurers — including Nepal Reinsurance Company Limited, the country's national reinsurer, established specifically to retain more reinsurance premium within Nepal instead of it flowing entirely to international reinsurers — precisely so that no single insurer is left holding an earthquake-sized claim entirely on its own book. When you check a non-life insurer's disclosures, look for its reinsurance arrangements and retention limits the way you would look for a bank's loan concentration by sector; a non-life insurer that retains too much catastrophe risk on its own book, chasing extra premium, is running a risk a claims ratio computed on a normal year will never show you.
WARNING
A non-life insurer's claims ratio in a quiet year tells you almost nothing about its exposure in a bad year. Motor and fire claims ratios can look comfortably profitable for five straight years and then spike violently after a single earthquake, flood, or widespread event — 2015's earthquake and, more recently, recurring monsoon flood losses in the Tarai and Kathmandu valley are the standing reminders for Nepal specifically. Always ask what portion of catastrophe risk an insurer retains net of reinsurance, not just what its claims ratio was last year.
Life insurers face their own version of a sudden, correlated shock — not an earthquake, but a pandemic or an epidemic that drives mortality claims up across the whole book simultaneously, exactly the kind of event life insurers worldwide experienced during the COVID-19 pandemic years. The general lesson generalises cleanly to a Nepali life insurer like NLIC: reserves and solvency margin exist precisely to absorb a year where claims run far above the actuary's normal assumptions, which is one more reason a solvency ratio sitting at or below the regulatory floor deserves more weight in your analysis than almost any other single number in an insurer's disclosures. A bank with a thin capital buffer can usually see trouble coming gradually, in a rising non-performing loan trend. An insurer's buffer exists precisely for the shock that arrives without warning.
Both types of insurer share one more structural feature worth naming plainly: the float itself is an interest-rate bet. NLIC's roughly Rs 164 arba investment book, and every non-life insurer's smaller equivalent, sits mostly in government securities and bank fixed deposits, with a modest allocation to listed equities and real estate, all subject to Nepal Insurance Authority rules on permitted asset classes and concentration limits. When Nepal Rastra Bank's monetary stance pushes fixed deposit rates and government bond yields down — as happened repeatedly through the liquidity cycles of the past decade, often tied to swings in remittance inflows and banking-sector liquidity — an insurer's investment income growth slows even if its premium book keeps growing, because the float is re-invested at lower prevailing rates as older, higher-yielding instruments mature. Investment income of Rs 15.20 arba growing at nearly 19 percent in FY 2080/81 was, in part, a story of where Nepal's interest rate cycle happened to be that year, not solely a story of NLIC's investment skill — another reason to look at a multi-year trend rather than one flattering year.
Lesson 92.6 — Hemisphere 1: Liquidity, Governance, and Durability
With the numbers gathered, Anjali sat down to score NLIC across Chapter 64's real seven dimensions — Financial Strength & Profitability (20 points), Governance & Promoter Behaviour (15 points), Liquidity & Tradability (10 points), Valuation Reasonableness (15 points), Sector & Business Model Durability (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points), applying Chapter 65's insurance-specific guidance — claims and loss ratios, solvency margin, and investment portfolio quality — wherever a dimension needs a sector-specific reading rather than a generic one.
Liquidity & Tradability (out of 10). NLIC is a large, actively traded NEPSE name: roughly 94.8 million shares outstanding, with 51 percent held by promoters and 49 percent — about 46.4 million shares — genuinely floated to the public, a substantial free float for a company of this size. Daily turnover has run in the tens of thousands of units, respectable for a large-cap financial name, though the price has drifted from a 52-week high near Rs 872 to the low Rs 700s, a soft one-year trend. We score this 7 out of 10 — a well-floated, genuinely tradable large-cap insurer, but not a top mark given the cooling price momentum.
Governance & Promoter Behaviour (out of 15). NLIC has a long, clean operating history with no evidence of self-dealing or promoter misconduct, and it cleared the regulator's sharply raised minimum paid-up capital requirement during the 2079–80 BS merger wave without needing to merge, unlike many smaller peers. Against that: a majority promoter block (51 percent) concentrates control in ways worth watching, and — the specific fact that opened this case study's eyes — the solvency ratio's fall from 3.22 to 1.45 arrived without a clear public explanation reaching ordinary shareholders at the time. A company can be entirely honest and still score short of full marks on governance if its disclosure does not keep pace with a material change in its capital position. We score this 9 out of 15 — comfortably above Chapter 64's 5-point override floor, but marked down for the disclosure gap around its own most important number.
Sector & Business Model Durability (out of 15). NLIC's moat is real: the oldest private life insurer in Nepal, the largest policy base, the deepest agent network, operating in a market still meaningfully underpenetrated relative to GDP, with remittance-funded household savings providing a durable long-term tailwind. We score the moat sub-component 6 out of 8 — strong, though tempered by a life insurance market that has grown more competitive as the merger wave consolidated it into a larger number of better-capitalised rivals than a decade ago. The other half of Durability is concentration and correlated-shock risk. As Lesson 92.5 discussed, a life insurer's structural exposure is to a pandemic or epidemic driving mortality claims up across its whole book simultaneously — a risk reserves and solvency margin exist to absorb, but one no single life insurer can diversify away through reinsurance the way a non-life insurer spreads catastrophe risk. Consistent with how this Canon treats similar sector-wide, only-partially-mitigable risks elsewhere, we score this sub-component 4 out of 7. Durability totals 10 out of 15.
Hemisphere 1 comes to 7 + 9 + 10 = 26 out of 40.
CAUTION
A strong reading on Durability and Governance can never be allowed to outvote a weak reading on Financial Strength and Valuation, especially for an insurer, where the whole business model depends on there being enough capital in reserve for the day claims run worse than expected. Score each dimension independently, then let the framework's weighting decide the total — do not let an appealing growth story talk you into rounding up a capital-adequacy red flag.
Financial Strength & Profitability (out of 20). This is where the case study earns its place in this chapter. Return on equity has run in a thin band of roughly 3 to 5 percent across recent quarters — well below what a company trading at multiple times book value would need to justify that premium — and we score the return-on-equity sub-component 2 out of 8. For an insurer, Chapter 65's guidance points us to the solvency ratio as the capital-adequacy analogue of a bank's CAR: a ratio that fell from 3.22 to 1.45 — from comfortably above the regulatory floor to just beneath it — within roughly a year is a serious flag regardless of how the premium and profit lines read, and until at least two further quarters confirm a durable recovery with genuine room above the 1.5 minimum, we score this sub-component 1 out of 7. Earnings quality is also weak: the most recent quarter's profit fell 15 percent year on year even as premium income kept growing, a sign that claims, reserving, or investment income — not the underlying insurance franchise — are currently driving the bottom line, so we score this 1 out of 5. Financial Strength totals 4 out of 20.
Valuation Reasonableness (out of 15). A price near Rs 700 to Rs 750 against a book value of roughly Rs 126, and a trailing price-to-earnings ratio ranging from around 116 to nearly 194 depending on the quarter and data source, is expensive by any standard — and doubly so set against the Life Insurance sector's own average P/E of roughly 64 times, itself the richest of NEPSE's eleven sectors. NLIC trades at two to three times even that already-elevated sector average. We score the P/E sub-component 1 out of 8 and the P/B sub-component 1 out of 7, for a Valuation total of 2 out of 15.
Growth Trajectory (out of 15). The top line is genuinely strong: policies in force and premium income have both grown at healthy double-digit rates across recent fiscal years, and total revenue rose over 25 percent in one recent year. But Chapter 64's Growth Trajectory dimension asks about revenue and earnings growth together, and NLIC's earnings growth has recently reversed — from profit up over 30 percent in FY 2080/81 to a 15 percent year-on-year profit decline in the most recent quarter reported. Strong, real revenue growth paired with a recent earnings reversal earns a moderate score: 8 out of 15.
Dividend & Capital Return Discipline (out of 10). NLIC does have a genuine multi-year dividend record — distributions in the range of roughly 11 to 21 percent across most of the past several fiscal years, including a combined cash-and-bonus distribution of just over 21 percent for one recent year — a real track record, unlike some of the other case studies in this Part. Set against that record: one recent fiscal year paid no dividend at all, coinciding with the capital-raising pressure of the industry's merger wave, and the Solvency Margin Directive gives the Nepal Insurance Authority explicit power to restrict dividend payments if an insurer's solvency ratio falls short — a live risk for a company currently reporting a ratio at or just below the regulatory floor. We score this 6 out of 10: a real but not unbroken record, with a genuine regulatory question mark hanging over whether it can continue uninterrupted.
Hemisphere 2 comes to 4 + 2 + 8 + 6 = 20 out of 40.
Lesson 92.8 — The Full Worked Canon Score
Canon Score dimension
Sub-score
Out of
What drove it
Liquidity & Tradability
7
10
Large, genuinely floated (49% public), but a cooling price trend
Governance & Promoter Behaviour
9
15
Clean history and no forced merger, but a disclosure gap around the solvency swing
Sector & Business Model Durability
10
15
Real moat (6/8) offset by unavoidable pandemic-type mortality concentration (4/7)
Financial Strength & Profitability
4
20
ROE ~3-5%, solvency ratio at/below the 1.5 regulatory floor, profit falling
Valuation Reasonableness
2
15
P/E ~116-194x vs. a sector average already near 64x; P/B ~5.5x
Growth Trajectory
8
15
Strong premium and policy growth, but a recent earnings reversal
Dividend & Capital Return Discipline
6
10
Real multi-year record, one skipped year, regulator can restrict future payouts
Total
46
100
Weak/Avoid band
Forty-six out of one hundred places NLIC in Chapter 64's Weak/Avoid band, just under the 55-point Adequate threshold. The governance override does not fire — the Governance sub-score of 9 out of 15 clears the 5-point floor, because this is a disclosure-quality shortfall around one important metric, not fraud or self-dealing. The shape of this score is worth sitting with: NLIC clears 26 of the available 40 points on the dimensions that describe what kind of company it structurally is — tradable, moated, reasonably governed — and manages only 20 of 40 on the dimensions that depend on its current financial results and the price being asked for them. That is a genuinely good business that a disciplined investor should not buy at this price, or with this capital-adequacy question still open — precisely the distinction a single blended "good company" impression would have missed.
Anjali's own conclusion, sitting with her father's matured policy cheque, was to keep her father's payout as proof the company can and does honor its promises, and to leave her own investment cheque in the bank until at least two more quarters confirm the solvency ratio has recovered with genuine room above the regulatory floor, and until the price comes down to something closer to what an insurer trading at five times book value would need to earn to justify itself.
Chapter recap
An insurance company breaks the analytical habits built on banks and manufacturers, because it collects money today against a promise it may not have to fulfil for decades. Reserves are not a cash fact but an actuarial estimate, revised as assumptions change; float is the investable pool of collected premium sitting between the sale of a policy and the payment of a claim; a claims ratio must be read differently for a life insurer, where maturity and survival benefits funded by an accumulated life fund can push claims above a single year's premium without signalling distress, than for a non-life insurer, where a combined ratio above 100 percent means the underwriting itself is losing money. The regulator that matters for every Nepali insurer is the Nepal Insurance Authority, not Nepal Rastra Bank, and its central test — the solvency margin, expressed as a solvency ratio against a 1.5 regulatory minimum — deserves more weight in an insurance case study than almost any other number, because it is the one designed specifically to survive the shock a normal year's numbers will never show you: an earthquake for a non-life insurer, a pandemic for a life insurer, or simply an actuary's revised assumptions arriving all at once. Nepal Life Insurance Company, NLIC on NEPSE, showed all of this in one real, live worked score: a strong 26 out of 40 across the structurally stable dimensions of Liquidity, Governance, and Durability, but only 20 out of 40 across Financial Strength, Valuation, Growth, and Dividend discipline — a stretched valuation near five to six times book value and up to nearly 194 times trailing earnings, a genuine multi-year dividend record with one skipped year, and a solvency ratio that fell from a comfortable 3.22 to a sub-minimum 1.45 within roughly a year, the lowest reading among Nepal's audited life insurers — for a total of 46 out of 100, Weak/Avoid, with the governance override held off because this is a disclosure-quality gap, not fraud. No dividend history or premium growth chart would have revealed that capital-adequacy swing on its own.
Case Study 11, in Chapter 93, leaves financial-sector logic behind entirely and turns to a business built on rooms, not reserves: a hotel sector investment, where occupancy rates, average room rates, and the boom-bust rhythm of Nepal's tourism seasons replace claims ratios and solvency margins as the numbers that decide whether the Canon Score says buy, hold, or walk away.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVI · Chapter 93
Case Study 11 — A Hotel Sector Investment
First published 26 Aug 2026 · Last verified 29 Aug 2026
Case Study 11 — A Hotel Sector Investment
Kabita Rai had never stayed at a five-star hotel in her life. She had walked past The Soaltee Kathmandu a hundred times on her way to Sahid Gate, admired its manicured gardens through the gate, and moved on. So when her stockbroker friend mentioned that Soaltee Hotel Limited, ticker SHL, traded on NEPSE and had just announced a strong dividend, her first reaction was surprise. "People actually buy shares in a hotel? Isn't that just for hoteliers and rich Marwari families?"
That question is exactly the right place to start this case study. Hotels are one of the strangest sectors on NEPSE — part real estate, part hospitality service, part tourism bet, and almost entirely dependent on a single, fickle input: whether a stranger from another country decides to get on a plane and come to Nepal. In Chapter 34 you learned the basic accounting differences between manufacturing companies (that make things), trading companies (that buy and resell things), and hotel or hospitality companies (that rent out rooms and serve food, night after night, whether or not anyone shows up). This chapter puts that framework to work on a real, listed, tourism-dependent company, and walks through the specific risks that make hotel investing unlike anything else on the exchange.
By the end, you will understand why a hotel's income statement can look brilliant in one quarter and grim in the next without anything actually being wrong; why "occupancy rate" and "revenue per available room" matter more than revenue growth alone; and why a five-star hotel with a ninety-year history can still be a risky stock. Kabita's journey through SHL is the vehicle. The Canon Score is the map.
Lesson 93.1 — Why a Hotel Is Not a Factory
Kabita's first instinct, trained by the manufacturing and trading case studies earlier in this Part, was to open SHL's balance sheet and look for the same things she always looked for: inventory turnover, receivables days, gross margin. She quickly discovered that half of those questions did not even apply.
A hotel does not hold inventory in the way a cement factory or a noodle trader does. Its "inventory" is empty rooms — and unlike a sack of rice, an empty room cannot be stored and sold tomorrow. A room not sold on a Tuesday night is revenue lost forever, not revenue deferred. This single fact is the root of almost every unusual pattern you will see in a hotel company's financial statements.
Because of this, hotel companies are described as having a perishable product and a very high fixed cost base. Fixed costs are expenses that do not change much whether the hotel is empty or full: the loan interest on the building, the electricity for the lobby chandeliers, the salaries of the front desk staff, the depreciation of the building itself. Variable costs — laundry, food ingredients, a guest's minibar snacks — move with occupancy, but they are a small slice of the total. This means that once a hotel covers its fixed costs for the month, almost every additional rupee from an extra guest drops straight to profit. Accountants call this operating leverage: a business where small changes in revenue produce large changes in profit, in both directions. It is the same idea that makes a nearly-full bus far more profitable per passenger than a half-empty one, because the driver's wage and the diesel bill are already spent either way.
Three numbers let you measure this properly, and every serious hotel investor in the world uses them, even though Nepali hotel companies rarely print all three cleanly in their NEPSE disclosures.
The first is occupancy rate — simply, the percentage of available rooms that were actually sold on a given night, averaged over a period. A hotel with 200 rooms that sells 140 of them on average has a 70 percent occupancy rate.
The second is ADR, or average daily rate — the average price actually charged per room sold, after discounts. A hotel might have a "rack rate" (the sticker price) of USD 150 a night, but if it is selling most rooms through travel agents and online platforms at a discount, its ADR might really be USD 95.
The third, and the one professional hotel analysts watch most closely, is RevPAR, or revenue per available room. This is occupancy rate multiplied by ADR, and it is the single best one-line measure of how a hotel is actually performing, because it captures both how full the hotel is and how much it charged. A hotel can raise its ADR by pricing itself out of the market and watch RevPAR fall as occupancy collapses; equally, a hotel can chase occupancy with heavy discounting and watch RevPAR fall the other way. RevPAR is the number that shows whether management is finding the right balance.
KEY CONCEPT
RevPAR, or revenue per available room, equals occupancy rate multiplied by average daily rate. It is the single best summary number for how a hotel is doing, because a hotel can look busy (high occupancy) while quietly losing money on price, or look expensive (high rate) while sitting half empty. RevPAR catches both problems in one figure.
The trouble for a NEPSE investor is that most Nepali hotel companies, SHL included, do not routinely publish occupancy, ADR, and RevPAR in their quarterly filings the way large international hotel groups do in their annual reports. What you get instead is total revenue, split loosely between "rooms," "food and beverage," and sometimes "other" (banqueting, spa, laundry, telephone). This is a real limitation of Nepali corporate disclosure, and it means a retail investor has to reconstruct a rough sense of occupancy and pricing trends from revenue movements, management commentary in the annual report, and outside sources like the Nepal Tourism Board and hotel association statements, rather than reading it off a single line.
PRACTICAL TOOL
When a company does not disclose occupancy or RevPAR directly, build a proxy. Divide quarterly "rooms revenue" by the number of rooms the hotel operates (found in the company's own "about us" material or annual report) and by the number of nights in the quarter. Track this proxy number over eight to twelve quarters. You will not get a perfectly accurate ADR or occupancy figure, but you will get a consistent, comparable trend line — which is what actually matters for spotting whether the business is improving or declining.
Lesson 93.2 — Reading Soaltee's Story
Before opening a single financial statement, Kabita spent an afternoon reading about the company itself, because with a hotel, the history is not background noise — it is half the investment case.
The Soaltee Kathmandu opened its doors in 1966, making it Nepal's first five-star hotel and one of the oldest listed companies of any kind on NEPSE. For most of its life it operated under an international brand partnership, most recently as the Soaltee Crowne Plaza, using the global reservations network and quality standards of a large international hotel group under a management or franchise arrangement. Around 2020, the foreign investment side of that arrangement ended and the hotel became a fully Nepali-owned company, later operating under its own independent branding as The Soaltee Kathmandu rather than the international flag it had carried for decades.
This detail matters for reasons that go beyond trivia. A hotel operating under a big international brand benefits from that brand's global booking system, corporate loyalty programs, and quality reputation — foreign business travellers and tour operators often book a "Crowne Plaza" or a "Hyatt" specifically because they trust the brand worldwide, before they have ever heard of the specific city. Losing that affiliation, or becoming fully independent, is a strategic pivot with real consequences: potentially lower foreign corporate and group bookings in exchange for full control over pricing, no franchise fees paid abroad, and the freedom to reposition the property however local management sees fit. Whether that trade-off has been a net positive or negative for SHL is exactly the sort of question a diligent investor should be asking in the annual report's management discussion section, not assuming one way or the other.
CASE IN POINT
A change in hotel branding or management affiliation is a material event for a hospitality stock, in the same way a change of auditor or a change of promoter is material for other sectors. When a listed Nepali hotel switches from an international brand to independent operation, or vice versa, read the annual report's explanation carefully. It can signal either a cost-saving strategic upgrade or a loss of access to lucrative international booking channels — and the difference shows up in occupancy and average rate a year or two later, not immediately.
Turning to the financial statements, Kabita found what the sector generally shows: heavy fixed assets (the hotel building, furnishings, kitchen equipment) sitting on the balance sheet at large values, meaningful depreciation charges each year eating into reported profit even when cash generation is healthy, and — often — a sizeable long-term loan used originally to build or renovate the property. This loan-funded structure is completely normal for hotels; building a five-star property is enormously capital intensive, and few Nepali promoters could fund one entirely from equity. But it does mean interest expense is a permanent, large line item, and it means a hotel's profit is sensitive to interest rate movements set by the Nepal Rastra Bank (NRB), Nepal's central bank, in a way an asset-light trading company's profit is not.
On the income statement, revenue was reported split between rooms and food-and-beverage (restaurant, bar, and banquet/event revenue), a structure common to hotel accounting under Nepal Financial Reporting Standards. Banquet revenue — weddings, corporate seminars, government functions — deserves special attention in the Nepali context, because it is often less tourism-dependent than room revenue. A wedding hall booked by a Kathmandu family for a reception does not care whether foreign tourist arrivals are up or down this year. For a hotel like Soaltee, with large function spaces intact from its decades of operation, banquet and F&B revenue can act as a partial buffer against pure room-revenue seasonality — a detail that only becomes visible once you separate the two revenue lines rather than looking at total revenue alone.
Revenue Line
What Drives It
Tourism-Dependent?
Seasonality
Room revenue
Foreign and domestic guest nights, ADR
Highly
Sharp, tied to trekking/tourist seasons
Food and beverage (in-house dining)
Guest counts, local walk-in diners
Partially
Follows room revenue loosely
Banquet and event revenue
Weddings, conferences, government events
Low
Tied to the Nepali wedding calendar, not foreign arrivals
Other services (spa, laundry, telephone)
Guest volume
Highly
Follows room revenue closely
REGULATORY DETAIL
Hotels registered under Nepal's tourism regulations are classified by star rating by the Ministry of Culture, Tourism and Civil Aviation, and are subject to Value Added Tax and a hotel-specific service charge structure on room and food sales, alongside standard corporate income tax. Some hospitality income also falls under separate tourism service fee and luxury consumption tax provisions depending on the service. A hotel's effective tax rate can therefore differ meaningfully from a manufacturing company's, and a sudden change in a Finance Bill's tourism tax provisions (announced with the national budget each Nepali fiscal year) can move a hotel's bottom line without any change in the business itself. Always check the tax note in the annual report before comparing net margins across sectors.
Lesson 93.3 — The Tourism Dependency Problem
Here is where hotel investing in Nepal becomes genuinely different from almost any other NEPSE sector, and where Kabita had to unlearn the instinct she had built up studying manufacturing companies: for a manufacturer, demand usually moves slowly and predictably, tied to population growth, construction activity, or remittance-fed consumption. For a five-star hotel dependent on foreign leisure and business travellers, demand can move violently, for reasons that have nothing to do with Nepal's own economy at all.
Consider the record. In 2019, before anyone had heard of COVID-19, Nepal recorded just over 1.19 million foreign tourist arrivals, a healthy pre-pandemic year and the base against which everything since has been measured. Then came 2020. International borders closed, flights stopped, and Nepal's tourist arrivals for the full year collapsed to a small fraction of normal — the lowest since 1986, according to government tourism data reported at the time. The following year, 2021, was in some ways worse still, because unlike 2020 (which still had a relatively normal January through March before the world shut down), 2021 was a nearly complete lost year for international travel; arrivals fell even lower, described in contemporary reporting as the lowest since 1977. Recovery has been real but gradual: arrivals in 2023 crossed the one-million mark again, and 2024 brought roughly 1.14 million foreign visitors, a reported 13 percent increase over 2023 — encouraging, but still not fully back to the 2019 peak, five years on.
Year
Approximate Foreign Tourist Arrivals
Note
2019
About 1.19 million
Pre-pandemic baseline
2020
Roughly 230,000
Lowest in decades at the time; pandemic border closures from March
2021
Roughly 150,000
Near-total collapse; full year under pandemic restrictions
2023
Just over 1.0 million
First year back above the one-million mark
2024
About 1.14 million
Up roughly 13 percent year-on-year, still below 2019
These figures are Nepal Tourism Board estimates as reported in national media and are rounded for illustration; an investor should always check the current numbers directly on the Nepal Tourism Board's own statistics before relying on them for a real decision.
Sit with what that table means for a listed hotel company. Between 2019 and 2021, the single most important input into Soaltee's room revenue — the number of foreign visitors physically present in Kathmandu — fell by something like 85 to 90 percent, for reasons entirely outside the company's control, its promoters' skill, or Nepal's domestic economy. No amount of good management, cost discipline, or brand strength changes the arithmetic when the customers simply cannot get on a plane. This is the essence of what this chapter means by tourism dependency risk: a large share of a hotel's revenue rests on a variable — global travel conditions, geopolitics, health emergencies, currency conditions in source markets like India, China, the United States, and Europe — that sits entirely outside the company's balance sheet and entirely outside Nepal's own policy control.
WARNING
A tourism-dependent stock is exposed to shocks that have nothing to do with company performance or even Nepal's domestic economy: a pandemic anywhere in the world, a regional conflict that disrupts flight routes, a spike in global airfares, a natural disaster (Nepal's own 2015 earthquake sharply depressed tourist arrivals for the following year), or even domestic political unrest that leads foreign governments to issue travel advisories. None of these show up in a company's financial ratios until after the damage is already visible in occupancy. Treat tourism dependency as a risk category of its own in the Canon Score, not something that ordinary financial-statement analysis will catch in advance.
Nepal supplied a fresh, live example of exactly this kind of shock while this chapter was being written. In early September 2025, violent anti-corruption demonstrations — widely referred to as the Gen-Z protests — swept Kathmandu, and several of the city's best-known hotels were directly caught up in the unrest. The Hyatt Regency Kathmandu (operated by the listed company Taragaon Regency Hotel Limited) suffered vandalism, looting, and arson, and remained closed for roughly a year afterward; it posted a net loss for the first quarter of fiscal year 2082/83 after a 48 percent profit collapse. The Radisson Hotel Kathmandu (Oriental Hotels) and City Hotel Limited both swung to net losses in the same quarter. Across the eight listed hotel companies on NEPSE, the sector as a whole flipped from a combined profit of roughly Rs 1.02 billion to a combined net loss of roughly Rs 255.9 million — even as full-year foreign tourist arrivals for 2025 still reached a record 1,209,357, the highest on record, a reminder that a headline arrivals number and a hotel's actual profitability can move in completely opposite directions in the same year, depending on exactly when in the year the shock arrives and which specific properties bear the brunt of it.
CASE IN POINT
Soaltee Hotel itself stayed open and stayed profitable through the September 2025 unrest — posting a net profit of roughly Rs 123.58 million for the quarter, revenue of about Rs 627.94 million, a decline of only 8.32 percent from the same quarter a year earlier — while several of its five-star peers posted outright losses. That gap is not proof Soaltee is immune to tourism-demand shocks; it is evidence that a hotel's specific exposure to a specific shock (which streets saw unrest, which properties were targeted, how central the location is to the affected areas) can matter as much as the industry-wide headline. Read a sector-wide shock's impact company by company, not only at the sector average.
There is a second, quieter layer to tourism dependency worth understanding: the composition of arrivals matters as much as the headline count. Nepal's tourist arrivals include a large number of visitors from India, many of whom arrive overland or on short regional flights, often for pilgrimage, business, or short leisure trips, and who are considerably less likely to book a five-star international-standard room than a long-haul leisure or trekking visitor from Europe, East Asia, or North America. A rising arrivals headline driven mostly by short regional visits does not translate one-for-one into rising five-star occupancy the way a rising headline driven by long-haul leisure travellers would. A careful investor reads beyond the total arrivals number into the Nepal Tourism Board's breakdown by source country and purpose of visit, because that breakdown tells you which segment of the hotel market is actually likely to benefit.
CAUTION
Do not treat "total foreign tourist arrivals" as a single clean proxy for five-star hotel demand. A large share of arrivals are short regional visits, pilgrimage travel, or business trips that may never touch a luxury hotel at all. Check the Nepal Tourism Board's purpose-of-visit and source-country breakdowns, not just the headline number, before assuming a rising arrivals trend directly lifts a specific hotel company's room revenue.
Lesson 93.4 — Seasonality: Why One Quarter Tells You Almost Nothing
Kabita's next mistake — one she caught herself making before it cost her anything — was pulling up SHL's most recent quarterly result, seeing a strong profit number and a solid double-digit revenue growth figure, and almost concluding the company was firing on all cylinders. It was only when she checked which Nepali fiscal quarter the result covered that she understood what she was actually looking at.
Nepal's tourist season has a clear rhythm, and any hotel investor needs to know it as well as they know the Nepali festival calendar, because the two are closely linked. The peak trekking and sightseeing seasons fall in autumn (roughly September through November, covering Dashain and Tihar, when skies are clear after the monsoon and mountain visibility is at its best) and again in spring (roughly March through May, before the pre-monsoon haze thickens). The monsoon months, roughly June through August, bring heavy rain, poor mountain visibility, and landslide-disrupted road travel, and are the quietest months of the year for leisure tourism. Winter (December through February) sits in between: cold at altitude but still workable for lowland sightseeing, cultural tourism, and conference business.
This means a hotel company's results for its quarter covering Ashwin through Poush (mid-September to mid-January in the Gregorian calendar, which captures the autumn peak and Dashain-Tihar) will typically look far stronger than its quarter covering Ashadh through Ashwin (mid-June to mid-September, deep in the monsoon lull). This is not a sign of accelerating or decelerating business momentum. It is simply the season. An investor who compares a hotel's strong autumn quarter to its own weak monsoon quarter and concludes the business is "improving" or "declining" quarter over quarter is making an error as basic as comparing a Nepali sweater shop's December sales to its April sales and concluding the shop is booming.
KEY CONCEPT
Seasonality means a business's revenue and profit naturally rise and fall with the calendar for reasons that have nothing to do with whether the underlying business is getting better or worse. For Nepali hotels, autumn (Dashain-Tihar season) and spring are strong; the monsoon is weak. The correct way to judge trend is to compare the same quarter across different years (this autumn versus last autumn), or to use trailing twelve-month figures, never one quarter against the immediately preceding one.
The correct discipline, and one worth adopting for every seasonal business you study on NEPSE — sugar mills around cane-crushing season, cement companies around the dry-season construction window, hotels around the tourist calendar — is to use trailing twelve months, often abbreviated TTM: add up the most recent four quarters, whatever season they fall in, so the comparison always contains one full cycle of highs and lows. Compare this quarter's TTM figure to last quarter's TTM figure, and you get a genuine read on the underlying trend, stripped of the seasonal noise. Alternatively, compare the same quarter year-on-year — this year's Dashain quarter versus last year's Dashain quarter — which also removes the seasonal effect, though it is slower to reveal a change in direction than TTM is.
PRACTICAL TOOL
Keep a simple spreadsheet with the hotel company's revenue and profit for each of the last twelve to sixteen quarters. Add a column that sums the trailing four quarters at each point (this quarter plus the three before it). Plot only that trailing-twelve-month line. It will smooth out the Dashain bump and the monsoon dip, and any genuine change in the business — a new competitor, a renovation-driven occupancy improvement, a fresh tourism shock — will show up as a clear bend in that smoothed line instead of being buried in the seasonal zigzag.
WARNING
Never buy or sell a tourism-dependent stock based on a single blockbuster or single disastrous quarter without first checking which season it covers. A hotel's Dashain-Tihar quarter result, taken alone, tells you almost nothing about whether the stock is a good buy — it mostly tells you that Dashain-Tihar happened, which it does every single year.
Lesson 93.5 — Hemisphere 1: Liquidity, Governance, and Durability
With the sector logic in place, Kabita worked through Chapter 64's real seven Canon Score dimensions — Financial Strength & Profitability (20 points), Governance & Promoter Behaviour (15 points), Liquidity & Tradability (10 points), Valuation Reasonableness (15 points), Sector & Business Model Durability (15 points), Growth Trajectory (15 points), and Dividend & Capital Return Discipline (10 points) — adapting each to what a hotel business actually looks like, using Chapter 34's sector accounting logic to inform the reading rather than as a separate scoring framework of its own.
Liquidity & Tradability (out of 10). SHL has roughly 117.5 million shares outstanding, but only 31 percent of them — about 36.4 million shares — are in public hands, with the remaining 69 percent held by promoters, a considerably more concentrated ownership structure than several other case studies in this Part. Daily traded volume runs in the tens of thousands of units, respectable for a company of its size but not exceptional, and the price has drifted from a 52-week high near Rs 575 to the low Rs 500s. We score this 6 out of 10 — genuinely tradable, but a meaningfully smaller free float than a company with a more open shareholding structure would offer.
Governance & Promoter Behaviour (out of 15). SHL's transition to full Nepali ownership after its foreign brand partner's exit around 2020 was a governance event worth investigating on its own terms, and the company navigated both the pandemic and the September 2025 unrest without needing a rescue or a forced merger — a real point in its favour. Set against that: a 69 percent promoter block concentrates control tightly, and for a company built around a single flagship physical asset, the question of whether the hotel's land is owned outright by the company or leased from a promoter-linked entity remains, on the public record available to an ordinary retail investor, unconfirmed — exactly the kind of related-party question that deserves a direct answer before this dimension can score higher. We score this 10 out of 15: a clean operating record and no evidence of misconduct, held back by real ownership concentration and an unresolved related-party question.
Sector & Business Model Durability (out of 15). A moat is a durable advantage that protects a company's profits from being competed away — the business equivalent of the water-filled ditch around an old fort. For Soaltee, the moat is real: a prime, effectively unrepeatable central Kathmandu location, brand recognition built over nearly six decades, function and banquet space that smaller newer properties cannot easily match, and — demonstrated directly in September 2025 — an ability to stay open and profitable through a shock that closed at least one direct five-star competitor for the better part of a year. We score the moat sub-component 7 out of 8, just short of full marks because the 2020 loss of its international brand affiliation introduced a real, not-yet-fully-resolved question about long-term access to global distribution and corporate-booking channels. The other half of Durability is concentration risk, and for a hotel this is tourism dependency itself: a large share of revenue tied to foreign arrivals that sit entirely outside company or national control, only partially buffered by domestic banquet and event revenue that does not depend on a plane landing. We score this sub-component 4 out of 7, consistent with how this Canon treats other structural, sector-wide risks that are real but only partially mitigated by a company's own choices. Durability totals 11 out of 15.
Financial Strength & Profitability (out of 20). On the numbers available, SHL's return on equity works out to a genuinely strong figure — earnings per share of roughly Rs 6.48 against a book value per share of about Rs 28.52, or close to 23 percent — well clear of the return-on-equity floor this Canon has applied elsewhere, and we score this sub-component 6 out of 8. Because hotels are inherently loan-funded, capital-intensive businesses, leverage and interest coverage matter more here than for an asset-light trading company, and without a precise, disclosed debt-to-equity figure in hand, a cautious middle score is the honest one: 3 out of 7. Earnings quality gets real credit for resilience: profit fell only 8.32 percent in the quarter the September 2025 unrest hit, while at least three listed five-star peers swung to outright losses in the same period — a demonstrated ability to stay profitable through a genuine shock, worth 4 out of 5. Financial Strength totals 13 out of 20.
Valuation Reasonableness (out of 15). At a recent price of Rs 508, SHL trades at a trailing price-to-earnings ratio of roughly 78 times — more than double the hotel sector's own average of about 38 times, itself already the fifth-richest of NEPSE's eleven sectors — and a price-to-book ratio of nearly 18 times, reflecting a company whose book value per share has been kept small by decades of dividend payouts against a low, Rs 10 face value. Because hotel earnings swing hard with the tourism cycle, valuing a hotel stock off a single year's earnings is a classic trap, and on that basis this valuation prices in a great deal of continued strength with very little allowance for the next shock. We score the P/E sub-component 2 out of 8 and the P/B sub-component 1 out of 7, for a Valuation total of 3 out of 15.
Growth Trajectory (out of 15). SHL's post-pandemic recovery has been real, but Chapter 64's Growth Trajectory dimension asks about a demonstrated, ongoing trend, and the most recent reported quarter showed profit declining 8.32 percent year on year, a direct consequence of the September 2025 unrest rather than a structural change in the business. Set against a still-incomplete recovery of national tourist arrivals to pre-pandemic levels and a business whose growth is inherently non-linear and shock-prone by sector, this earns a moderate score: 6 out of 15.
Dividend & Capital Return Discipline (out of 10). SHL has a genuinely long dividend record, distributing between roughly 21 and 58 percent most years across the 2010s — but it paid nothing at all for two consecutive fiscal years during the pandemic (2019/20 and 2020/21), a direct, real demonstration of tourism dependency translating into an interrupted capital return. Since then the distribution recovered: 26.32 percent (2021/22), 31.58 percent (2022/23), a peak of 36.84 percent (2023/24), before easing back to 31.58 percent in the most recent year (2024/25). A real, multi-year record with one severe, sector-driven interruption earns 7 out of 10.
Hemisphere 2 comes to 13 + 3 + 6 + 7 = 29 out of 40.
Lesson 93.7 — The Full Worked Canon Score
Canon Score dimension
Sub-score
Out of
What drove it
Liquidity & Tradability
6
10
Tradable, but only 31% public float against a 69% promoter block
Governance & Promoter Behaviour
10
15
Clean record through two real shocks, but concentrated control and an unconfirmed land/lease question
Sector & Business Model Durability
11
15
Strong, demonstrated moat (7/8); tourism-concentration risk capped at 4/7
Financial Strength & Profitability
13
20
ROE ~23%, resilient earnings through the Sept. 2025 shock, leverage unconfirmed
Valuation Reasonableness
3
15
P/E ~78x vs. sector average ~38x; P/B ~18x
Growth Trajectory
6
15
Real recovery, but a recent shock-driven profit decline
Dividend & Capital Return Discipline
7
10
Long, generous record with two COVID-era zero years
Total
56
100
Adequate band
Fifty-six out of one hundred places SHL in Chapter 64's Adequate band — just barely, one point above the 55-point floor that separates it from Weak/Avoid. The governance override does not fire (10 out of 15 clears the 5-point floor comfortably). The shape of the score tells the real story: Soaltee clears 27 of 40 points on the dimensions describing what kind of company it structurally is — tradable, reasonably governed, genuinely moated — and 29 of 40 on the dimensions describing its recent results and capital discipline, dragged down almost entirely by one number: a valuation that prices in far more certainty than a tourism-dependent business has ever actually delivered. This is a demonstrably resilient, well-run, real business that is also, right now, priced for a smoother future than its own history says to expect.
Now the piece unique to this chapter's risk profile: informal and unlisted competition. A five-star hotel like Soaltee does not really compete for guests against Kathmandu's thousands of small, family-run guesthouses, unlicensed homestays, and budget lodges — those serve a completely different customer, the backpacker or the price-sensitive domestic traveller. But that informal segment matters to the sector for two separate reasons an investor should hold in mind. First, it absorbs a meaningful share of rising tourist-arrival numbers without that demand ever reaching listed hotel companies at all — an arrivals boom driven by budget trekkers filling teahouses in Pokhara and along Annapurna trails does very little for a five-star Kathmandu property's occupancy. Second, much of this informal hospitality sector operates outside full VAT and income tax compliance, giving it a structural cost advantage that lets it undercut licensed, listed hotels on price in the mid-market segment specifically — the three-star and boutique-hotel tier, where SHL's own budget offshoots and comparable listed peers compete more directly than the flagship five-star property does.
CAUTION
Informal and unregistered hospitality — unlicensed homestays, budget guesthouses operating outside full tax compliance, short-term rental listings — is real competition for the mid-market and budget tiers of Nepal's hotel sector, even though it barely touches flagship five-star properties. When assessing a listed hotel company's growth prospects, check which market tier its specific properties compete in, because "rising tourist arrivals" does not lift all hotel tiers equally.
REGULATORY DETAIL
Homestays and small guesthouses in Nepal are regulated separately from classified star hotels, often under simpler local and provincial tourism registration rules with lighter compliance burdens than the star-classification system that governs companies like SHL. This regulatory gap is a genuine structural feature of the sector, not a temporary loophole, and it is unlikely to close quickly given how much rural and community tourism income depends on the lighter-touch regime. Do not model it away as a risk that regulation will soon erase.
Lesson 93.8 — Kabita's Decision
Having done the work, Kabita had to decide not just whether SHL was a "good company" — by several measures, it plainly was, with a long operating history, real brand equity, a Canon Score of 56 clearing the Adequate floor, and a post-pandemic dividend recovery that climbed from 26.32 percent in 2021/22 to a peak of 36.84 percent in 2023/24, before easing back slightly to 31.58 percent in 2024/25 — but how much of her portfolio, if any, a company with this risk profile deserved, and whether a valuation running well ahead of the sector average left her any real margin of safety.
Her reasoning ran in four steps, and it is a useful template for sizing any tourism-dependent position.
First, she separated "is this a quality business" from "is this a low-risk stock to hold in size." They are not the same question. A well-run, well-located, historically resilient hotel can still be an inappropriate holding to concentrate savings in, simply because its earnings can swing by 80 or 90 percent in a single year for reasons no analysis could have predicted. Chapter 34's manufacturing and trading companies rarely see demand move that violently in twelve months; a hotel company can, and has, within Kabita's own adult lifetime.
Second, she checked for correlation with the rest of her existing portfolio. If she already held shares in a Nepali airline, a trekking or travel agency, or another tourism-adjacent business, adding a hotel stock on top would not really be diversification — it would be stacking multiple bets on the same underlying variable, foreign tourist arrivals, under different tickers. A genuinely diversified Nepali portfolio might hold a hotel stock alongside a hydropower company, a commercial bank, and a consumer goods manufacturer, precisely because those businesses respond to different drivers — rainfall and electricity demand, interest rates and credit growth, remittance-fed household spending — rather than all rising and falling together on the same travel-advisory headline.
Third, she set a position size appropriate to the risk category, not to how much she liked the story. A useful rule of thumb many Nepali retail investors adopt informally: no single high-cyclicality, shock-exposed sector position — hotels, airlines, and similarly tourism-linked names among them — should be sized so large that a repeat of a 2020-scale shock would meaningfully damage her overall financial plan, even though such a repeat is, by definition, rare and hard to predict.
Fourth, and finally, she wrote down, in plain language, what would make her sell — not react to, but genuinely reconsider the position on. For Kabita, that list included: a sustained multi-year decline in Nepal Tourism Board arrival figures unconnected to a global shock (suggesting Nepal was structurally losing share to competing destinations); a governance red flag such as an unfavorable related-party lease surfacing in an annual report; a sustained deterioration in interest coverage suggesting the company's debt load was becoming unmanageable; or simply the stock's price running so far ahead of a reasonable full-cycle earnings estimate that the margin of safety she required no longer existed. None of those had happened. But having the list written down before buying, rather than improvised emotionally during the next crisis, was the entire point of the exercise.
WARNING
Never size a position in a tourism-dependent stock as if its best recent year represents a stable, repeatable baseline. The same global conditions that can lift a hotel's occupancy sharply — a weak currency making Nepal cheap for foreign visitors, a peaceful regional environment, easy flight connections — can reverse with very little warning, and history in Nepal's own tourism data shows the reversal can be severe and can last more than one year.
Kabita ultimately did buy a small position in SHL — sized deliberately at a fraction of what she held in her bank and hydropower holdings, treated explicitly as a cyclical, higher-risk satellite position rather than a core holding, and reviewed against the Nepal Tourism Board's published monthly arrivals data each quarter as her ongoing monitoring discipline. That, more than the specific stock, is the transferable lesson of this case study: tourism-dependent companies can absolutely deserve a place in a Nepali investor's portfolio, but only once their unique risk shape — seasonality, shock exposure, informal-sector competition, and cycle-sensitive valuation — has been priced in through position size and monitoring, not just through optimism about a nice hotel with a long history.
Chapter recap
This case study followed Kabita Rai through a live, worked application of the Canon Score to Soaltee Hotel Limited, Nepal's oldest five-star hotel, listed on NEPSE as SHL and operating today as The Soaltee Kathmandu following its transition to full Nepali ownership. Along the way, this chapter built out the specific vocabulary and logic of hotel-sector investing that extends the manufacturing, trading, and hotel accounting principles first introduced in Chapter 34: occupancy rate, average daily rate (ADR), and revenue per available room (RevPAR) as the core measures of hotel performance, even where Nepali companies disclose them only indirectly; the operating leverage created by a hotel's heavy fixed-cost, perishable-inventory structure; the need to read trailing-twelve-month or year-on-year figures rather than raw quarter-to-quarter comparisons, given the strong pull of Nepal's autumn and spring tourist seasons against its monsoon lull; the very real exposure of tourism-dependent revenue to shocks entirely outside company or even national control, illustrated both by Nepal's foreign tourist arrivals collapsing from roughly 1.19 million in 2019 to a small fraction of that during 2020 and 2021, and by the September 2025 Gen-Z protests, which flipped NEPSE's eight listed hotel companies from a combined profit to a combined loss in a single quarter even as full-year arrivals hit a record high; and the quieter but persistent competitive pressure that informal, lightly-regulated homestays and budget guesthouses place on the mid-market tier of the sector, even where it barely touches flagship five-star properties. Running SHL through Chapter 64's full seven-dimension Canon Score gave Kabita a real, worked number: 27 out of 40 across the structurally stable dimensions of Liquidity, Governance, and Durability, and 29 out of 40 across Financial Strength, Valuation, Growth, and Dividend discipline — a demonstrated, shock-tested moat and a genuinely strong return on equity, undercut by a valuation running at roughly double the hotel sector's own already-rich average — for a total of 56 out of 100, just inside the Adequate band. That structured result gave Kabita a disciplined way to conclude that a quality, long-established hotel company could still warrant only a small, deliberately sized position, monitored against outside data like Nepal Tourism Board arrivals rather than against the company's own next single quarter.
With this chapter, Part XVI, "Full Case Studies," is complete. Across eleven worked case studies, this Part has taken the Canon Score and the sector-specific accounting logic built up over the book's earlier chapters and applied them, one real NEPSE sector at a time, to live companies and the specific risks — governance, leverage, seasonality, regulation, and now tourism dependency — that make each sector its own kind of animal. Part XVII, "The Investment Constitution & Personal Operating System," begins next with Chapter 94, which turns from analysing individual companies to building the daily, weekly, monthly, quarterly, and annual routines a disciplined Nepali investor uses to actually run a portfolio over a lifetime — the operating system that turns everything learned so far into a repeatable habit rather than a one-time analysis.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVII
THE INVESTMENT CONSTITUTION & PERSONAL OPERATING SYSTEM
Part XVII · Chapter 94
The Daily, Weekly, Monthly, Quarterly & Annual Routines
First published 26 Aug 2026 · Last verified 29 Aug 2026
Anjana Shrestha checks her phone at 10:58 in the morning, two minutes before NEPSE opens. She is not glued to the screen. She works as a credit officer at a commercial bank in Kathmandu, and her real job does not pause for the stock market. But for four years now, she has run a small, disciplined portfolio worth about eleven lakh rupees, built slowly out of her salary savings and two Dashain bonuses, spread across a handful of banking shares, one hydropower company, an insurance stock, and a microfinance institution she has followed since it listed.
What makes Anjana different from most retail investors on the floor of Nepal's stock market is not that she is smarter or has inside information. It is that she has a system. She knows exactly what she looks at every single day, what she reviews every week, what she checks every month, what she re-examines every quarter, and what she overhauls once a year. She calls this her "operating cadence" — a private joke borrowed from her bank's internal audit language, but the term fits. A cadence is a repeating rhythm, like a drumbeat that keeps a marching band in step. Without one, an investor either checks the market obsessively out of anxiety, or forgets about it entirely until something goes wrong. Anjana does neither. She has turned investing into a series of small, scheduled habits, and this chapter is going to show you exactly how she built that system, one layer at a time, across a real calendar year.
This chapter is the opening chapter of Part XVII, "The Investment Constitution & Personal Operating System." Everything you have learned in this book so far — how to read a balance sheet, how to score a company using the Canon Score, how circuit filters work, how dividends and bonus shares are taxed — is a tool. Tools sitting in a toolbox do nothing. What turns a toolbox into a working discipline is a routine: a fixed schedule that tells you which tool to pick up, and when. That is what we build now.
Lesson 94.1 — The Daily Routine: Five Minutes, Not Five Hours
Let us start with the most dangerous habit in Nepali retail investing: watching the NEPSE index and your portfolio's live price movements all day, every trading day, refreshing the TMS (Trading Management System, the online platform brokers use for placing buy and sell orders) app every few minutes. This is not discipline. It is anxiety dressed up as diligence, and it is the single fastest way to turn a long-term investor into an emotional day-trader who did not mean to become one.
Anjana's daily routine takes about five to seven minutes, almost always done twice: once around midday during her lunch break, and once in the evening after the market closes at 3:00 PM. She does three things, in this exact order.
First, she checks prices and circuit status, not to react, but to record. A circuit filter is a rule that automatically halts trading in a stock (or halts the whole market) once its price moves up or down by a fixed percentage in a single day — in Nepal this is commonly a 5 percent daily move for individual scrips relative to the previous closing price, with different, tighter bands for close-to-open moves, and a market-wide circuit breaker that can pause the entire exchange if the NEPSE index falls sharply. Anjana glances at whether any of her holdings hit the upper circuit (meaning it rose the maximum allowed and buyers are still waiting, unable to get filled) or the lower circuit (meaning it fell the maximum allowed and sellers are stuck, unable to exit). She writes the closing price of each holding into a simple spreadsheet. That is all. She is not deciding to buy or sell based on this glance — she is simply keeping a record, the way a doctor takes a patient's temperature every day without necessarily prescribing new medicine each time.
Second, she does a short news scan — not financial news in general, but specifically company notices filed through NEPSE and the Securities Board of Nepal (SEBON), the regulator that oversees the stock market and enforces disclosure rules. Listed companies are required to publish material information — board meeting notices, book closure dates, dividend announcements, right share issues, changes in senior management, major litigation — through the exchange's notice board and through Mero Share (the online system, operated by CDSC, the Central Depository System and Clearing Limited, that lets investors apply for IPOs, right shares, and view their share balances) or through public disclosures picked up by financial portals. Anjana scans headlines only for the eight to ten companies she owns or watches. She is not reading market gossip on social media or investment Facebook groups. She has a firm personal rule: no decision is made off a rumour, only off an official notice.
Third — and this is the step most investors skip — she runs what she calls her "impulse check." Before she is allowed to place any trade that was not already planned as part of her weekly or monthly review, she must write down, in a notes app, the answer to one question: what specific new fact, not feeling, is driving this trade? If she cannot name a specific fact — a filed disclosure, a dividend notice, a re-scoring trigger — she is not allowed to place the order that day. This single rule has saved her from her worst instincts more times than any analysis ever has.
WARNING
The single most common way retail investors destroy years of patient gains in Nepal is by watching live prices constantly and trading on the emotional pull of green and red numbers rather than on new information. If you find yourself refreshing the TMS app more than twice a day out of anxiety rather than habit, that is a symptom, not a strategy — treat it the way you would treat any other compulsive urge, by adding friction, not by indulging it.
Anjana's daily habit is deliberately boring. Boring is the point. A daily routine in investing should feel closer to brushing your teeth than to gambling — small, quick, protective, and almost unconscious once it becomes a habit. The goal of the daily check is not to find opportunities. It is to stay informed enough that you are never blindsided, while keeping your hands off the keyboard unless a real, pre-planned reason exists to act.
PRACTICAL TOOL
Build a one-page daily log with four columns: Date, Closing Price of each holding, Any Circuit Hit (Yes/No, Upper/Lower), Any Official Notice Filed (Yes/No, summary). Fill it in twice a day in under five minutes. Over a few months, this log becomes an invaluable personal record — it lets you see, in your own handwriting or spreadsheet, how often circuits were hit before an eventual price correction, and how often "news" turned out to be noise.
There is a second reason the daily routine matters, beyond discipline: it builds pattern recognition slowly and safely. When Anjana's hydropower stock hit the upper circuit three days running last Ashwin, her daily log let her see the pattern building day by day, rather than discovering it retroactively as a shock. She did not chase the stock during the run — her rule said no impulse trades — but she flagged it clearly for her weekly review, which is where real decisions get made.
Lesson 94.2 — The Weekly Routine: Portfolio Review Against the Canon Score
If the daily routine is about staying calm and informed, the weekly routine is where Anjana actually thinks. Every Friday evening, after the market closes for the week (NEPSE trades Sunday through Thursday, with Friday and Saturday as the weekend in Nepal), she spends thirty to forty-five minutes doing a structured review of her entire portfolio.
The centrepiece of this review is what earlier chapters of this book called the Canon Score — the composite scoring framework you built to evaluate a company across governance quality, financial health, growth durability, dividend consistency, and valuation relative to its own history and its sector. Recall that the Canon Score is not a single magic number pulled from thin air; it is your own structured tally, built from concrete inputs such as promoter and institutional shareholding stability, return on equity trends, debt-to-equity levels appropriate to the company's sector (a hydropower company will carry more debt than a bank, and a bank carries more "debt" in the form of deposits than either), dividend payout history across at least three to five years, and a valuation check such as price-to-book or price-to-earnings compared against sector peers. The weekly review does not recalculate this full score from scratch — that would be far too much work for a weekly cadence, and it is not necessary, because a company's underlying fundamentals do not change meaningfully week to week. Instead, the weekly review asks a narrower question: has anything happened this week that would change an input into that score?
Anjana keeps a simple table, one row per holding, and updates it every Friday.
Holding
Canon Score (last full calc)
Any trigger event this week
Action needed
Himal Bank Ltd
78/100
None
Hold, no action
Sunkoshi Hydro
71/100
Upper circuit 3 days, no notice filed
Watch closely, flag for monthly review
Reliable Insurance
82/100
Dividend notice filed
Confirm book closure date, no score change
Everest Microfinance
64/100
Interest rate directive from NRB affecting sector
Re-score at next quarterly cycle
Trishuli Power
69/100
None
Hold, no action
Notice what this table is doing. It is not asking Anjana to re-read four years of annual reports every Friday. It is asking her to be a good record-keeper: did anything happen this week that touches one of the pillars of the score? A price movement alone, with no disclosed reason, is a "watch" trigger, not an automatic score change — remember, price is what the market is willing to pay, and the Canon Score is about what the business is actually worth and how well it is run. Confusing the two is one of the fastest ways to make a bad decision look rational.
KEY CONCEPT
The Canon Score measures the business. The weekly price chart measures the crowd's mood about the business. A rising or falling price with no new company-specific fact behind it tells you something about sentiment, not substance — log it, watch it, but do not let it override a score built on governance, financials, and dividend history unless a genuine new fact justifies a change.
The second half of Anjana's weekly routine is watchlist maintenance. A watchlist is simply the list of companies you do not yet own but are tracking, because you believe they might eventually earn a place in your portfolio once their price or their fundamentals line up with your standards. Anjana keeps twelve companies on her watchlist — mostly commercial banks and insurers she has scored highly but currently considers overpriced relative to their history, plus two hydropower companies she is waiting to see complete at least one full monsoon and dry season cycle of actual generation data before trusting their earnings numbers.
Every week, she asks three questions about the watchlist: has the price moved closer to or further from my target entry range; has any new disclosure changed my initial score estimate; and is anything on this list stale — meaning she has not looked at it properly in more than two months and should either refresh her thinking or drop it, because a watchlist that never gets pruned becomes a graveyard of good intentions rather than a useful tool.
CASE IN POINT
In Poush of her second year investing, Anjana had "Api Power" sitting on her watchlist at a Canon Score of 75, waiting for the price to fall into her target range after what she judged to be an overheated run following an IPO. She almost bought it on a whim in Falgun when the price dipped sharply for two days — but her weekly review showed no disclosure explaining the dip, and her daily log showed it wasn't a circuit event either, just quiet, low-volume drift. She waited. Three weeks later a clearer picture emerged: a temporary maintenance shutdown at one of its plants had leaked through informal channels before any formal notice, and the price recovered fully once NEPSE published the company's clarification. Because she had waited for an actual disclosed reason rather than reacting to an undisclosed price dip, she avoided a purchase built on speculation rather than fact — and she bought in properly, at a fair price, two months later once the company's clarification notice was filed and her full re-score confirmed the fundamentals were unchanged.
The weekly routine, done consistently, does something subtle but powerful over a year: it trains your eye to separate signal from noise in small, low-stakes weekly doses, so that by the time a genuinely important event happens — a major disclosure, a sudden governance concern, a sector-wide policy shift from Nepal Rastra Bank (NRB, the central bank that regulates commercial banks, development banks, and finance companies, and whose monetary policy directives on interest rates, spread rates, and capital requirements move banking and finance sector share prices significantly) — you already have the habit of checking facts calmly rather than reacting on instinct.
Lesson 94.3 — The Monthly Routine: Dividends, AGMs, and Rebalancing
Once a month, on the first Saturday, Anjana sets aside a full ninety minutes — longer than any single weekly session — because the monthly routine handles things that simply do not arise every week: dividend and Annual General Meeting (AGM) tracking, and rebalancing.
An AGM is the yearly meeting where a company's shareholders formally approve the previous year's financial statements, approve the dividend the board has proposed, and elect or re-elect directors. Before an AGM (and before a dividend payment), a company announces a book closure date — the specific date on which the company "freezes" its shareholder register to determine exactly who is entitled to receive the dividend or bonus shares, or to vote at the AGM. If you sell your shares before the book closure date, the buyer receives the dividend, not you; if you hold through the book closure date, you receive it even if you sell the very next day. This single mechanical fact causes enormous confusion among new investors, and enormous mistiming among impatient ones — some investors sell right after book closure purely to "capture" the dividend and reinvest elsewhere, a maneuver worth understanding but not worth chasing blindly, since share prices often adjust downward around book closure to reflect the value being paid out.
Anjana's monthly calendar tracking works like this. Nepali companies typically hold their AGMs and declare dividends in the months following the close of their fiscal year (which for most companies runs roughly mid-July to mid-July under the Nepali calendar, ending around Ashadh), so dividend season tends to cluster in Nepal from around Mangsir through Falgun, though timing varies by company and by how quickly SEBON and the regulator approve each company's audited statements. She keeps a monthly calendar — a simple table, refreshed at the start of every month — listing each holding, its expected AGM window based on prior years, and any book closure notice already filed.
REGULATORY DETAIL
Under NEPSE and CDSC rules, once a company files a book closure notice, trading in that scrip is typically suspended for a short window (commonly a few trading days) around the book closure date itself, so that the shareholder register can be finalised without shares changing hands mid-process. This suspension is not a red flag about the company — it is a routine mechanical step. Confusing a book-closure trading halt with a circuit-related halt (which does signal unusual price pressure) is a common beginner error worth unlearning early.
Once dividends are declared and paid, they typically arrive as bonus shares (additional shares credited directly to your demat account, held with your DP, or Depository Participant — the broker or institution that maintains your electronic share holding record through CDSC), cash dividends (credited to your bank account), or a combination of both. Anjana's monthly routine includes confirming that dividends she is owed have actually been credited — checking her Mero Share portal and her bank statement — because reconciliation errors, while not common, do happen, and catching them within a month is far easier than trying to sort them out a year later.
The second half of the monthly session is rebalancing — the process of checking whether your actual portfolio, as it exists today, still matches the allocation you intended, and trimming or adding where it has drifted. Because share prices move at different rates, a portfolio that started as 40 percent banking, 25 percent hydropower, 20 percent insurance, and 15 percent microfinance can quietly become 55 percent banking simply because your banking shares had a strong run while everything else was flat — not because you made any new decision to concentrate that heavily.
Anjana rebalances using a simple threshold rule: she only takes action if any single sector has drifted more than eight percentage points away from her intended target, or if any single company has grown to represent more than 20 percent of her total portfolio value regardless of sector. This threshold approach matters because it prevents two opposite mistakes — tinkering constantly with small trades that rack up brokerage commissions and capital gains tax events for no real benefit, and letting concentration risk build up silently until a single company's bad quarter can meaningfully damage her entire portfolio.
PRACTICAL TOOL
Set a personal rebalancing threshold before you need one — for example, "no single stock above 20 percent of portfolio value, no single sector above 45 percent" — and write it down. Check against it monthly, not daily. A threshold decided in a calm, unemotional moment (like the first Saturday of the month) is a far better guide than a decision made mid-week while staring at a fast-moving price chart.
Lesson 94.4 — The Quarterly Routine: Re-Scoring Against Fresh Disclosures
Every three months, Anjana does the heaviest single piece of recurring work in her entire system: a full re-score of every holding using the company's latest quarterly financial disclosure. Listed Nepali companies are required to publish unaudited quarterly financial reports — covering income, expenses, profit, and key ratios — within a set number of days after each quarter ends, and these reports are publicly available through NEPSE's disclosure system and through Mero Share. This is the richest single source of fresh, factual information an ordinary retail investor gets access to during the year, and building your quarterly routine around it is far more productive than reacting to daily price noise.
The quarterly re-score session takes Anjana a full weekend afternoon, sometimes stretching across two days. For each holding, she pulls the latest quarterly report and re-checks the same pillars that make up the Canon Score: has net profit grown, shrunk, or stayed flat compared to the same quarter last year (comparing to the same quarter last year matters enormously in a seasonal economy like Nepal's, where, for instance, hydropower companies generate far more revenue in the monsoon-fed high-water months than in the dry winter months, and comparing a dry-season quarter to a monsoon quarter would be misleading); has the company's earnings per share moved; has its distributable profit and reserve position changed in a way that affects its ability to pay future dividends; and, critically, has anything changed in governance — a new auditor's note, a related-party transaction disclosure, a change in senior management, or any flag from SEBON.
WARNING
A single quarter of weak results is not automatically a reason to sell, and a single quarter of strong results is not automatically a reason to buy more. Quarterly numbers in Nepal are frequently lumpy — a bank's provisioning charge in one quarter, a hydropower plant's scheduled maintenance shutdown, an insurance company's claims spike from a single large event — can distort one quarter without reflecting the underlying trend. The quarterly routine exists to update your score with fresh facts, not to trigger a reflexive trade on every single data point.
The second half of the quarterly routine is peer comparison — placing each of your holdings side by side with its closest sector competitors to see whether it is still earning its place in your portfolio relative to the alternatives available on the exchange. This matters because a company can look perfectly fine in isolation while quietly falling behind its peers in profitability, efficiency, or governance quality — and a Canon Score that only ever looks inward, never sideways, can miss that kind of relative decline.
Company
Sector
Latest Qtr Net Profit Growth YoY
Canon Score this quarter
Rank vs peer group
Himal Bank Ltd
Commercial Banking
9 percent
79/100
2nd of 6 tracked banks
Reliable Insurance
Life Insurance
14 percent
83/100
1st of 4 tracked insurers
Everest Microfinance
Microfinance
negative 3 percent
59/100
5th of 5 tracked MFIs
Sunkoshi Hydro
Hydropower
21 percent (monsoon quarter)
74/100
3rd of 7 tracked hydro companies
This table told Anjana something her daily and weekly routines never could have surfaced on their own: Everest Microfinance had slipped to dead last among the five microfinance institutions she tracks, with a negative profit trend two quarters running, driven by rising loan-loss provisioning across the sector following an NRB directive tightening microfinance lending standards. No single day's price movement had signalled this — the stock had actually been range-bound, not crashing — but the quarterly fundamentals told a clear story that the daily price chart could not.
CASE IN POINT
Anjana's quarterly re-score of Everest Microfinance, done properly in Baisakh, showed a falling score for the second consecutive quarter and a bottom-of-peer-group ranking. Rather than panic-selling immediately (which her daily-routine discipline had already trained her against) or ignoring the warning entirely (which pure buy-and-hold laziness would have encouraged), she used her monthly rebalancing session that followed to trim the position by half over the following weeks, reallocating the proceeds toward Reliable Insurance, which had shown the strongest and most consistent peer-relative score for three consecutive quarters. This is the entire system working as designed: quarterly re-scoring surfaced the fact, and the monthly rebalancing session provided the calm, pre-scheduled venue to act on it — not a rushed decision made the same afternoon the report was published.
This is the deepest value of the quarterly cadence: it forces you to look at your holdings against the full universe of realistic alternatives, on a schedule frequent enough to catch genuine deterioration early, but infrequent enough that you are not whipsawed by every single data release.
Lesson 94.5 — The Annual Routine: Constitution Review, Tax Planning, and Goal Reassessment
Once a year — Anjana does hers in the week after Nepali New Year, in mid-April, which conveniently falls a few months after most companies have completed their AGMs and after the fiscal year transition period has settled — she runs the biggest review of all: a full audit of her entire portfolio's constitution, her tax position, and her personal financial goals.
The word "constitution" here is deliberate, and it is the bridge to the next chapter of this book. A constitution, in the context this book will build in Chapter 95, is a written personal document — your own rules for what you will and will not do as an investor, your target allocations, your risk limits, your criteria for buying and selling. The annual review is when Anjana checks her actual behaviour across the past twelve months against that written document, and either confirms the rules still serve her or consciously revises them.
The tax planning component draws directly on the material covered earlier in this book, in Chapters 35 through 38, on how capital gains and dividend income from listed shares are taxed in Nepal. Recall the essential structure: capital gains on listed shares held for more than 365 days are generally taxed at a lower long-term rate than gains on shares held for 365 days or less, which are taxed at a higher short-term rate, and these rates and holding-period rules are set by the Government of Nepal's tax law and can be revised in the annual budget, so an investor must reconfirm the current rates each year rather than assume they are fixed forever. Dividend income, similarly, is subject to its own withholding tax treatment, typically deducted at source before the dividend reaches your account, meaning much of the dividend tax obligation is already settled by the time you see the credit, but this still needs to be reconciled against your annual tax filing.
REGULATORY DETAIL
Capital gains tax rates and holding-period thresholds on listed securities in Nepal are set through national tax law and the annual budget, and have been adjusted by the government in past years — for instance, differing rates have applied to gains held under one year versus over one year, with rates also sometimes differentiated between individual and institutional investors. Because these rates are subject to change, an annual routine must include reconfirming the current rate schedule directly from the Inland Revenue Department or a qualified tax advisor rather than relying on last year's figure — a mistake here does not just cost you money, it can create compliance problems.
Anjana's annual tax planning session involves pulling a full-year transaction statement from her broker and from Mero Share, tallying every sale, noting the holding period of each lot sold (this matters because if she bought the same company's shares on different dates, the tax treatment can depend on which specific lot is considered sold — a detail worth confirming with her broker or a tax professional each year rather than guessing), and estimating her total tax liability for the year before it comes as a surprise at filing time. She also uses this session to think, deliberately, about tax-aware timing for the year ahead — for instance, whether a position she is planning to trim anyway is close to crossing the long-term holding threshold, in which case waiting a few more weeks before selling could meaningfully change the tax rate applied to that gain. This is not tax evasion; it is legal, sensible timing, exactly the kind of planning Chapters 35 through 38 addressed in depth.
CAUTION
Never let tax considerations alone drive a decision to hold a deteriorating company past the point your Canon Score and quarterly re-scoring say you should exit. Saving a few percentage points of tax by waiting for long-term treatment is not worth riding a genuinely declining business further down. Tax efficiency is a secondary optimization applied on top of sound investment decisions — never a reason to override them.
The final piece of the annual routine is goal reassessment. Anjana opened her very first brokerage account with a specific goal: building a down payment fund for an apartment within seven to ten years, alongside a smaller, separate goal of building retirement savings that supplements her employer pension. Once a year, she asks honestly whether her portfolio's actual trajectory, her personal life circumstances (a promotion, a marriage, a new dependent, a change in her risk tolerance), and her original goals are still aligned. Goals are not static. A twenty-six-year-old's appropriate risk tolerance and time horizon look different from a thirty-four-year-old's, especially if life circumstances have shifted — a new mortgage, a parent needing financial support, a child on the way. The annual routine is where Anjana permits herself to revisit and, if genuinely warranted, revise her target allocations, her acceptable risk limits, and even her definition of what "enough" looks like for each goal.
Annual review item
What it checks
Anjana's typical output
Full constitution audit
Did I follow my own written rules this year, and do they still fit
List of rules kept, rules broken, rules revised
Tax reconciliation
Actual capital gains and dividend tax owed vs what was withheld
Filing figure confirmed with accountant
Holding-period tax planning
Which near-term sales could shift from short-term to long-term rate by waiting
Adjusted timing plan for the next quarter
Goal reassessment
Are targets (house deposit, retirement) still realistic given life changes
Updated target allocation percentages
Full re-read of investment thesis per holding
Would I buy this today, at today's price, knowing what I know now
Confirm, trim, or exit each position
This last row deserves emphasis. Once a year, for every single holding, Anjana forces herself to answer one blunt question: if I did not already own this, would I buy it today, at today's price, knowing everything I now know? This is sometimes called a "fresh eyes" test, and it exists specifically to counter a well-documented human bias — the tendency to keep holding something simply because you already hold it, a pattern investing psychology calls the endowment effect, where owning something makes us value it more than we would if we were considering it as a new purchase. The annual routine, done honestly, cuts through that bias once a year, even when the weekly and monthly routines, focused on incremental change, might not have forced the question directly.
Lesson 94.6 — Building Your Own Operating System: Putting the Calendar Together
We have now walked through five separate rhythms — daily, weekly, monthly, quarterly, and annual — each serving a distinct purpose. It is worth being explicit about why five separate cadences are necessary rather than, say, one big weekly review that tries to do everything, or a purely annual check-in that ignores the rest of the year.
Each cadence exists because the underlying information it responds to changes at a different natural speed. Prices and circuit filters change every single trading day — so the daily routine matches that speed, but deliberately does almost nothing beyond recording and calming yourself, because a single day's price movement rarely carries enough real information to justify action. Portfolio composition and watchlist relevance shift gradually over a week as news trickles in — so the weekly routine matches that speed with a light-touch score check and disciplined record-keeping. Dividends, AGMs, and allocation drift operate on a monthly rhythm tied to the corporate calendar and simple compounding of price differences — so the monthly routine matches that. Financial fundamentals genuinely change only every three months, when new disclosed numbers arrive — so the quarterly routine, the heaviest single working session, matches that speed exactly, no faster and no slower. And your own life circumstances, tax law, and long-term goals shift slowly, over the scale of a year — so the annual routine, the deepest and most reflective, matches that.
KEY CONCEPT
Match the frequency of your review to the actual frequency at which the underlying information changes. Checking fundamentals daily is wasted effort, because fundamentals do not move daily. Checking your tax strategy only when filing season arrives is too late, because good tax planning requires acting before certain deadlines and holding-period thresholds pass. A well-built operating cadence is not about doing more work — it is about doing the right work at the right frequency, and consciously doing nothing at every frequency where nothing productive can be done.
Putting all five together into a single annual calendar is the real deliverable of this chapter. Below is the master calendar Anjana actually keeps taped inside the cover of her investment notebook — feel free to treat it as a starting template and adjust the specific triggers to your own portfolio's realities.
Cadence
Core task each cycle
What typically triggers action
Daily
Log closing prices, circuit status, official notices; run impulse check before any unplanned trade
An official disclosure is filed, or a circuit is hit repeatedly
Weekly
Review each holding's score triggers; maintain and prune the watchlist
A trigger event needs deeper look, or a watchlist entry is stale or newly attractive
Monthly
Track AGM and book closure calendar; reconcile dividends received; check rebalancing thresholds
A threshold is breached, or a dividend fails to reconcile
Quarterly
Full re-score of every holding using fresh quarterly disclosures; compare against sector peers
A holding's score or peer rank has clearly deteriorated
Annual
Full constitution audit; tax reconciliation and forward tax planning; goal reassessment; fresh-eyes test on every holding
Life circumstances, tax law, or long-held theses need conscious revision
One year of running this system does not make Anjana a market expert, and it will not make you one either. What it does is something more durable: it replaces a chaotic, emotion-driven relationship with the stock market with a calm, structured one, where every action has a clear trigger and every silence — every day she does not trade — is also a deliberate, informed choice rather than mere inertia or fear.
There is a final point worth making before we close this chapter, because it will matter enormously in the chapter that follows. A routine like this one only stays disciplined if it is anchored to something written down — fixed rules you commit to in a calm moment, so that in a stressful moment (a sudden circuit-hitting crash, an unexpected windfall from a bonus share, a friend's excited tip about a "sure thing" stock) you have something firmer than your own mood to fall back on. Anjana's weekly, monthly, quarterly, and annual sessions all ultimately refer back to one document: her own written investment constitution, the rules she set for herself about allocation limits, scoring thresholds, tax approach, and goals. Building that document — properly, completely, in your own words, for your own life — is the task of the next chapter.
Chapter recap
This chapter built the operating cadence that turns everything else in this book from knowledge into practice. The daily routine keeps you informed and calm, recording prices, circuits, and official notices while enforcing an impulse check before any unplanned trade. The weekly routine reviews each holding against Canon Score triggers and maintains a pruned, living watchlist. The monthly routine tracks the AGM and book closure calendar, reconciles dividends, and checks portfolio drift against rebalancing thresholds. The quarterly routine does the heaviest analytical work — re-scoring every holding against fresh disclosed financials and ranking it against its sector peers. And the annual routine steps back furthest of all, auditing your behaviour against your own rules, reconciling and planning around capital gains and dividend tax as covered in Chapters 35 through 38, and honestly reassessing whether your goals and your portfolio still point in the same direction. Chapter 95, "Writing Your Personal Investment Constitution," takes the rules this cadence depends on and puts them into a single, permanent, written document — the fixed reference point every one of these daily, weekly, monthly, quarterly, and annual sessions ultimately answers to.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVII · Chapter 95
Writing Your Personal Investment Constitution
First published 26 Aug 2026 · Last verified 29 Aug 2026
A constitution, in the sense that matters to a country, is a short document that binds people to their own better judgment. It exists because a nation knows that in the heat of a crisis — a war, a riot, a panic — the people in power will be tempted to do something rash, something that feels right in the moment but is wrong for the long run. So the constitution is written in advance, in a calm hour, and it ties the hands of the future self who will not be calm.
Your money needs the same thing.
This chapter teaches you to write a personal investment constitution: a short, plain document, written by you, for you, that states your goals, your real tolerance for risk, your target asset allocation, your rules for how big any single position can be, and the exact conditions under which you will buy, hold, or sell. It is not a legal document. No court enforces it. NRB (Nepal Rastra Bank, the central bank that regulates banks and the financial system) will never ask to see it, and SEBON (the Securities Board of Nepal, which regulates the stock market and brokers) has no form for it. The only enforcement mechanism is you — reading it back to yourself at the exact moment you are most tempted to break it.
That is also why it works.
Lesson 95.1 — Why an Unwritten Plan Is Not a Plan
Ask most NEPSE (Nepal Stock Exchange) investors what their strategy is, and you will get an answer that sounds like a plan but is not one. "I invest for the long term." "I don't panic during corrections." "I only buy good companies." These are values, not rules. A value tells you what kind of person you want to be. A rule tells you exactly what to do at 11 a.m. on a Tuesday when the index has dropped 6 percent in three sessions and your broker's TMS (Trading Management System, the online platform brokers give clients to place buy and sell orders) app is lighting up with red numbers.
The gap between a value and a rule is where money gets lost.
Here is the uncomfortable truth this book has been building toward since the chapters on behaviour: the person who sets your investment strategy on a calm Saturday afternoon, thinking clearly with a cup of tea, and the person who actually executes trades on a chaotic Tuesday during a crash, are not fully the same person. Fear and euphoria are not just feelings that sit alongside your reasoning. They change the reasoning itself. Chapter 90 walked through exactly this: how the same investor who says "I would never sell at the bottom" sells at the bottom anyway, and how the same investor who says "I would never chase a rally" ends up buying an overheated stock at its peak because everyone else is doing it and the fear of missing out has quietly replaced the fear of loss as the dominant emotion in the room.
A written constitution is what behavioural economists call a commitment device. A commitment device is any tool you set up in advance to bind your future behaviour, precisely because you don't trust your future self to make the same decision your calm, current self would make. Odysseus tying himself to the mast so he could hear the sirens' song without steering his ship onto the rocks is the oldest example in Western literature. A Nepali equivalent that many households already understand instinctively: a dhukuti or a bank fixed deposit that locks money away for a fixed term precisely so that a moment of temptation — a wedding, a new phone, a friend's business scheme — cannot touch it. You already believe in commitment devices. This chapter asks you to build one for your stock portfolio.
KEY CONCEPT
A commitment device is a rule or structure you set up in a calm moment specifically to control a future moment when you expect your judgment to be worse. A written investment constitution is a commitment device for your portfolio: it exists so that panic during a crash or euphoria during a mania cannot silently rewrite your strategy in real time.
Why does writing it down matter so much, rather than just "knowing it in your head"? Three reasons.
First, memory is unreliable under stress. Psychologists have shown repeatedly that people recall their own prior intentions selectively when under emotional pressure — they remember the parts that justify what they want to do right now, and conveniently forget the parts that don't. If your plan lives only in your head, a crash will edit it for you without your permission. A plan on paper (or saved as a note on your phone) cannot be silently edited. It sits there, unchanged, saying the same thing it said in March that it says in August.
Second, writing forces precision. "I will hold for the long term" feels like a complete thought until you try to write down what "long term" means in months or years, and what would make you break that hold. Most investors discover, in the act of writing, that they had never actually decided several of the things they assumed they had decided. The blank page exposes the gaps.
Third, a written document can be reviewed by someone else — a spouse, a trusted friend, even a note to your future self — which adds a small layer of social accountability. Telling your spouse "I have a rule that I never put more than 10 percent of my portfolio into one stock" is a very different commitment than merely believing it privately, because now breaking it means either hiding it from someone or explaining yourself.
WARNING
An investment strategy that exists only in your head is not a strategy. It is a mood, and moods change with every 500-point swing in the NEPSE index. If you cannot show it to another person on paper, you do not yet have it.
None of this means a constitution is inflexible forever. Life changes — a marriage, a child, a job loss, an inheritance, a house purchase — and the document should change with it. But it should change on a calm day, through a deliberate re-write, not through a quiet unspoken drift during a bull run or a panicked scramble during a crash. Chapter 96 will cover exactly how and when you are allowed to override your own rules. This chapter is about writing the rules in the first place.
Lesson 95.2 — The Anatomy of a Constitution: The Sections You Need
A national constitution typically has a preamble, a statement of rights, a structure of institutions, and amendment procedures. Your personal investment constitution is much shorter, but it has an equivalent structure. Seven sections cover everything you need. You do not need to write pages under each — a paragraph or a short list is enough for most sections. What matters is that every section actually gets answered, not skipped.
The seven sections are:
One — Purpose and goals. What is this money for, and when do you need it?
Two — Risk tolerance, stated honestly. How much can this portfolio fall in value before you would do something regrettable?
Three — Asset allocation targets. What percentage goes into NEPSE equities, what percentage into fixed deposits or government bonds, what percentage into gold, what percentage stays in cash?
Four — Position-sizing rules. How big can any single stock or sector be, as a share of the total portfolio?
Five — Buy rules. What has to be true about a company, and about the market, before you will buy?
Six — Hold and sell rules. What specific event triggers a sale — and just as importantly, what does not?
Seven — Review and amendment procedure. When and how are you allowed to revisit this document?
Let us take these one at a time, slowly, because each one is a discipline in itself.
PRACTICAL TOOL
Keep your constitution to one or two pages. A document you cannot re-read in five minutes during a crisis will not get re-read during a crisis. Write it in your own plain words, not in the technical language of a research report — you need to understand it instantly under stress, not admire its sophistication.
Purpose and goals deserves more care than most investors give it, because different goals justify completely different strategies, and confusing them is one of the most common causes of bad decisions. Money you will need in eighteen months for your daughter's college admission fee behaves like a different animal from money you are setting aside for retirement in twenty-five years, even though both might currently sit in the same demat account. The eighteen-month money should never have been in volatile growth stocks in the first place; if it was, the "sell rule" that saves you is really a "should not have bought" rule that came a year too late.
Nepali households often mix these goals inside one mental bucket — "market ko paisa," money in the market — without separating what portion is truly long-term wealth-building and what portion is actually short-term savings that got parked in equities because a fixed deposit's interest rate looked unexciting. Your constitution should force the separation. List each goal, its target date, and the amount of money attached to it, even roughly. A goal like "buy a small flat in Kathmandu, target 2033" behaves completely differently in your allocation decisions than "retirement income starting 2050."
Risk tolerance is the section investors lie to themselves in most. Everyone believes, in a calm month, that they can tolerate a 30 percent portfolio decline "because it's long term." Very few people discover this is true only after they have actually lived through a 30 percent decline and did not sell. The honest way to write this section is retrospective and specific: recall the worst drawdown (a drawdown is the percentage decline from a portfolio's peak value to its subsequent lowest point before it recovers) you have actually lived through, and write down exactly what you did and how you felt. If you have never lived through a real NEPSE correction, borrow from history — the 2021-2022 correction, when the index fell by roughly 40 percent from its peak, is a fair benchmark to imagine yourself inside of, position by position, rather than as an abstract percentage.
CASE IN POINT
During the sharp NEPSE decline of 2021 into 2022, the benchmark index fell from a peak above 3,200 to below 2,000 — a drop of well over a third in value within roughly a year. Investors who had never written down, in advance, what fall they could tolerate discovered their real tolerance the hard way: many sold heavily in the second half of the decline, closer to the bottom than the top, converting a paper loss into a permanent one. A written risk tolerance section, decided before the fall, is the only thing that stands between an investor and that exact mistake happening to them next time.
Asset allocation and position sizing get their own full lessons below, so we will hold those for now. Buy, hold, and sell rules likewise get a dedicated lesson. Review and amendment procedure is short but crucial: state plainly that the document is reviewed on a fixed calendar schedule — once a year is typical, perhaps aligned with a birthday, with Nepali New Year, or with the start of a fiscal year — and that no revision happens during a period of sharp market movement, up or down. We will return to this idea, because it is the hinge on which the whole document depends: a constitution that can be casually rewritten in a panic is not a constitution at all.
Constitution Section
Core Question It Answers
Roughly How Long
Purpose and Goals
What is this money for, and when do I need it?
3 to 6 sentences
Risk Tolerance
What decline would make me do something regrettable?
3 to 5 sentences
Asset Allocation Targets
What percentage in equities, fixed income, gold, cash?
A short table
Position-Sizing Rules
How big can one stock or sector get?
3 to 5 rules
Buy Rules
What must be true before I purchase?
A checklist
Hold and Sell Rules
What specific event triggers a sale?
A checklist
Review Procedure
When and how do I revisit this document?
2 to 3 sentences
Lesson 95.3 — Setting Goals, Time Horizons, and Risk Tolerance in Writing
Let's slow down on the two sections that most investors get wrong: time horizon and risk tolerance, and how they interact.
A time horizon is simply how long you can leave money invested before you are likely to need it back in cash. This single number does more to determine your correct strategy than almost anything else in this book. Money with a horizon under two or three years has no business in NEPSE equities at all, no matter how confident you feel, because equities can and do stay down for years at a stretch, and you cannot control when your college fee, wedding expense, or medical bill arrives. Money with a horizon of ten, fifteen, twenty years can absorb far more volatility, because time itself becomes a kind of insurance — a bad five-year stretch has historically been followed, in most markets including NEPSE over its multi-decade history, by a recovery, given enough runway for it to happen.
The mistake to guard against is treating "long term" as a slogan rather than a date. Write an actual year next to every goal. "Retirement" is not a horizon; "2048" is. Once you have real years attached to real goals, the correct allocation for each pool of money becomes far more obvious, almost mechanical, rather than a matter of feeling brave or cautious on a given day.
Risk tolerance has two components that are often confused: risk capacity and risk appetite. Risk capacity is a financial fact about your life — how much can you actually afford to lose without derailing your real goals, given your income, your dependents, your debts, and how replaceable that money is. A 28-year-old government employee with a stable pension, no dependents yet, and rent-only expenses has high risk capacity even if she feels nervous about volatility. A 55-year-old sole earner supporting elderly parents and two children in college has low risk capacity, no matter how bold he feels when NEPSE is rallying. Risk appetite, by contrast, is a psychological fact — how much anxiety you can tolerate seeing your account balance fall, independent of whether you can technically afford the loss.
Your constitution should state both honestly, because your allocation should be governed by whichever one is lower. A high risk capacity paired with low risk appetite means you can afford to be aggressive but you will not sleep at night if you are, and the sleepless investor makes the exact panicked decisions this whole chapter is designed to prevent. It is entirely rational, not weak, to choose a gentler allocation than your finances alone would technically permit, if that gentler allocation is the one you can actually sit through without breaking your own rules.
CAUTION
Do not confuse how much risk you can afford to take (risk capacity, a financial fact) with how much risk you can emotionally tolerate (risk appetite, a psychological fact). When they disagree, build your allocation around the lower of the two. An aggressive allocation you cannot emotionally sustain will get abandoned at exactly the worst moment — usually near a bottom — turning a temporary paper loss into a permanent real one.
A practical way to write the risk tolerance section is to complete two sentences honestly: "If my portfolio fell by [X] percent, I would feel [describe the feeling] and I would be tempted to [describe the action]." Then write a second sentence: "The maximum decline I am willing to commit, in writing, to sit through without selling is [Y] percent." For most investors with a genuinely long horizon and a diversified portfolio, Y ends up somewhere between 25 and 40 percent, because history shows corrections of that size do happen and do recover, given years of patience. Writing this number down before it happens, rather than discovering it while it is happening, is the entire point of this lesson.
Remittances deserve a specific mention here, because they change the shape of risk tolerance for a large share of Nepali households. If part of your investable savings comes from a family member working abroad — in the Gulf, in Malaysia, in Korea, in Australia — that income stream itself carries its own risk: job contracts end, exchange rates move, and a household's monthly cash flow can change with little notice. If your investing goals depend on remittance income continuing at its current level, your risk tolerance section should explicitly note this dependency, because it means your capacity to absorb an equity market downturn while also needing to draw cash from savings is lower than a household whose income is fully independent of the portfolio.
REGULATORY DETAIL
NRB sets periodic rules on remittance inflows and their channeling through the formal banking system, and also regulates margin lending limits that brokers and banks can extend against share collateral. Your constitution's risk tolerance section should assume these rules can tighten without notice — for instance, margin requirements have been adjusted by regulators before during periods viewed as overheated — and should not depend on borrowed money or on assumptions about future regulatory leniency remaining constant.
Lesson 95.4 — Asset Allocation Targets and Position-Sizing Rules
Asset allocation is the decision of how to divide your total investable money among broad categories — NEPSE equities, fixed deposits or government bonds and debentures, gold, and cash or cash-equivalents held for emergencies and near-term needs. Decades of research on investment outcomes, across many countries and markets, point to the same uncomfortable conclusion: this single decision — the mix between categories — explains far more of an investor's long-run results than which individual stocks were picked within the equity portion. Most investors spend 95 percent of their attention picking stocks and 5 percent thinking about allocation. The evidence says the ratio of attention should be closer to reversed.
Your constitution should state target percentages for each category, along with acceptable bands around each target — because markets move, and a target that must be hit exactly every single day is unworkable. For example: "Equities: target 55 percent, acceptable range 45 to 65 percent. Fixed deposits and government bonds: target 30 percent, range 20 to 40 percent. Gold: target 10 percent, range 5 to 15 percent. Cash and emergency reserve: target 5 percent, range 3 to 10 percent." When your actual holdings drift outside the stated range — because equities rallied hard and now make up 70 percent of the portfolio, say — that drift itself becomes the trigger for a specific, pre-agreed action: rebalancing, meaning selling a slice of the category that has grown too large and buying more of the category that has shrunk too small, to bring the mix back within its stated band.
This is worth pausing on, because it is one of the most powerful and least emotional rules an investor can pre-commit to. Rebalancing, done according to a fixed rule rather than a feeling, forces you to systematically sell portions of whatever has recently done well and buy portions of whatever has recently lagged. That is the literal definition of buying low and selling high, executed automatically, without requiring you to correctly predict anything about the future. It feels uncomfortable every single time you do it — you are always selling the thing that "everyone" is excited about and buying the thing that looks unloved — and that discomfort is exactly the evidence that it is working as intended rather than following the crowd.
Risk Profile
NEPSE Equities
Fixed Deposits and Bonds
Gold
Cash and Emergency Reserve
Conservative
25 to 35 percent
45 to 55 percent
10 to 15 percent
8 to 12 percent
Balanced
45 to 55 percent
25 to 35 percent
8 to 12 percent
5 to 8 percent
Growth
60 to 70 percent
10 to 20 percent
5 to 10 percent
5 percent
These bands are illustrative starting points, not a universal prescription — your actual figures should reflect the goals and horizons and risk tolerance you wrote in the earlier sections, and should be sense-checked against your own specific life. A retired person drawing income from the portfolio needs a materially different mix from a 25-year-old in her first job. The point of the table is to show you the shape of a written allocation section, not to hand you a single correct number.
Position-sizing rules answer a narrower but equally important question: within your equity allocation, how much can go into any one company, or any one sector? This is the discipline that protects you from concentration risk — the danger of having so much money in a single stock, or a single industry like banking or hydropower, that one company's bad news or one sector's regulatory shock can meaningfully damage your entire net worth.
A simple, workable position-sizing rule for most retail investors looks like this: no single stock may exceed 10 to 15 percent of total equity holdings at the time of purchase; no single sector — banks, hydropower, insurance, microfinance, hotels, manufacturing — may exceed 30 to 35 percent of total equity holdings at the time of purchase. Note the phrase "at the time of purchase." A stock you bought at 10 percent that later rallies to 20 percent of your portfolio through no additional buying on your part is not automatically a rule violation; it is a signal to consider rebalancing, per the rule above, not a sign you did something wrong when you bought it.
WARNING
Concentration risk hides itself during good times. A single hydropower stock that grows from 10 percent to 40 percent of a portfolio during a sector rally feels like success while it is happening. It only reveals itself as a problem the day that sector — or that one company, through a licensing issue, a monsoon-damaged project, or a leadership scandal — falls sharply, and the investor discovers that a large fraction of their life savings depends on one decision they made months earlier.
A related rule many Nepali investors need to write explicitly, given how the market actually operates, concerns IPO and FPO allotments (an IPO, or Initial Public Offering, is a company's first sale of shares to the public; an FPO, or Further Public Offering, is a subsequent sale by an already-listed company) and bonus shares (additional free shares issued to existing shareholders, funded from a company's reserves, which increases the number of shares you hold without you paying anything extra, though it does not by itself increase the total value of your holding). Because IPO allotments are partly a matter of lottery-style allocation through the ASBA system and bonus shares arrive without a purchase decision, your position-sizing rule should explicitly say what happens when a "windfall" of shares — a large bonus issue in a stock you already hold heavily, for instance — pushes you over your size limit without any active buying on your part. The honest answer, in most cases, is the same as the rebalancing rule: trim back down to your target band rather than treating a lucky allotment as an exception to the rule you wrote for yourself.
PRACTICAL TOOL
Once a quarter, list your current holdings and calculate what percentage of your total equity value each stock and each sector represents. If anything exceeds your written limit, that is your cue to trim — regardless of how good the story sounds, and regardless of how much further you privately suspect the price might run.
Lesson 95.5 — Buy, Hold, and Sell Rules: Writing Your Own Circuit Breakers
NEPSE itself uses circuit breakers — rules that automatically halt trading, market-wide, when the index moves too far too fast in a single session, giving everyone a forced pause to think rather than react. Your constitution needs the same mechanism, built for you personally, because no exchange-wide circuit breaker will save you from your own decision to sell a fundamentally sound holding in a panic, or to buy a wildly overpriced one in a mania.
Start with buy rules. A buy rule is a checklist a company must pass before you are permitted to purchase its shares, regardless of how exciting the tip, the rumour, or the chart pattern looks. A workable checklist, built from the fundamentals-and-valuation lessons covered earlier in this book, might include items such as: the company has published at least three years of audited financial statements you have actually read; you understand, in one sentence, how the company makes money; the price you are paying implies a valuation you can justify against the company's earnings or book value, not merely against where the stock traded last week; the purchase, if made, would not breach your position-sizing limits from Lesson 95.4; and you are not buying because the stock has already risen sharply in the past few days and you fear missing further gains.
That final item deserves its own emphasis, because it is the single most common rule violation among first-time NEPSE investors. Buying because a stock "is moving" — because friends, a Facebook group, or a Viber investment channel are talking about a stock's rapid rise — is buying on momentum and social proof rather than on the checklist. It is not automatically wrong to ever buy a rising stock, but your constitution should require that you can still answer the fundamental questions on your checklist independently of the price action, before you buy anything that is already "hot."
Hold rules are, in a sense, the default: absent a specific sell trigger, the rule is to keep holding. Investors underestimate how much discipline this requires, because doing nothing, while everyone around you is doing something, feels like negligence even when it is actually the correct choice. Your constitution should explicitly permit — even instruct — long stretches of inaction. Write a sentence like: "Absent one of the sell triggers listed below, I will not sell a holding merely because its price has fallen, merely because a friend or a media commentator expresses doubt about it, or merely because I feel bored or restless and want to 'do something' with my portfolio."
Sell rules are where the real work of this lesson lives, because "sell" decisions are where panic and euphoria do their most expensive damage. A good sell rule distinguishes clearly between reasons that justify a sale and reasons that do not, and it is written specifically enough that you cannot argue your way around it in the moment.
Reasons that typically do justify a sale, written into a constitution in advance: the original investment thesis has been proven wrong by new, verified information — for example, the company's fundamentals have genuinely deteriorated, not merely its stock price; the holding has grown, through price appreciation, to exceed your position-sizing limit, and a partial trim brings it back in line; you have identified a goal-driven need for the cash itself, tied to a date you wrote in your purpose and goals section; or a full annual review, conducted on your fixed calendar date, concludes the holding no longer fits your allocation targets.
Reasons that do not justify a sale, and should be written down explicitly as such, precisely because they are the reasons investors act on anyway: the overall market index has fallen sharply over a few days or weeks with no company-specific bad news; a news article or social media post predicts further declines; the stock has "already gone up a lot" and you fear giving back gains, absent any actual deterioration in the business; or you simply feel anxious. Anxiety is real and worth respecting, but the constitution's whole purpose is to have decided, in a calmer hour, that anxiety alone is not sufficient grounds to act, and to have a specific alternative response ready — such as re-reading the constitution itself, or waiting a mandatory 72 hours before placing any sell order triggered by a feeling rather than a listed rule.
PRACTICAL TOOL
Write a mandatory cooling-off period into your sell rules: any sale not triggered by one of your written reasons requires a 72-hour waiting period between deciding to sell and actually placing the order. In practice, most panic-driven urges to sell fade substantially within three days once the initial shock of a falling market passes.
Trigger
Is This a Valid Sell Reason?
Constitution's Instruction
Company fundamentals genuinely deteriorate (verified)
Yes
Sell, in line with the thesis that broke
Holding exceeds position-size limit through price growth
Yes
Trim back to target band
Cash needed for a written, dated goal
Yes
Sell the amount needed
Annual scheduled review recommends rebalancing
Yes
Rebalance per allocation targets
Market index falls sharply, no company-specific news
No
Hold; re-read constitution
Rumour, tip, or social media panic
No
Hold; apply 72-hour cooling-off rule
Stock "already went up a lot," no thesis change
No
Hold, or trim only if size limit breached
Building genuine circuit breakers into your own behaviour, as this table shows, is mostly a matter of pre-deciding which category a future event will fall into, so that when it actually happens you are merely checking a box rather than debating with yourself under stress.
CASE IN POINT
During periods of sharp single-day declines, NEPSE itself has, at various times, applied market-wide circuit breakers that pause trading once the index falls beyond a set threshold in a session. The exchange does this precisely because unrestrained panic selling feeds on itself, each falling price triggering the next investor's fear. Your personal constitution's cooling-off rule does, for your individual decisions, exactly what the exchange's circuit breaker does for the whole market: it inserts a forced pause between the impulse and the irreversible action.
Lesson 95.6 — A Worked Example: One Investor's Full Constitution
Theory is easiest to absorb through a complete example. Meet our fictional narrator for this lesson: Sunita Adhikari, a 32-year-old schoolteacher in Pokhara. She has been investing in NEPSE for four years, started with a small inheritance and a habit of saving from her monthly salary, and has just finished reading Chapters 90 through 95 of this book. She sits down on a quiet Saturday and writes the following document. It is not perfect, and it is not meant to be copied word for word — it is meant to show you the shape, the tone, and the level of specificity a real constitution should have.
Sunita Adhikari's Investment Constitution — Written Baisakh 2082, to be reviewed every Baisakh thereafter.
Section One, Purpose and Goals. This portfolio serves three goals. Goal A: an emergency reserve, already held separately in a savings account and fixed deposit, not part of this equity strategy. Goal B: a house down payment, targeted for 2032, current estimated need four to five lakh rupees beyond what fixed deposits alone will provide. Goal C: retirement supplement, no fixed date, expected drawdown beginning around 2055. Only money genuinely available for a horizon of seven years or more, after Goals A and the near-term portion of Goal B are separately funded, belongs in the equity portion of this constitution.
Section Two, Risk Tolerance. I lived through the 2021-2022 correction with roughly sixty percent of my current portfolio invested. My portfolio fell by about 35 percent at its worst point. I did not sell, though I felt anxious for several months and stopped checking my TMS app for weeks at a time as a coping method. Based on that experience, I state honestly: I can sit through a decline of up to 35 to 40 percent without selling, provided no single company-specific bad news justifies otherwise. Beyond that range, I acknowledge my judgment may weaken, and I rely on the rules below, not on my feelings in the moment, to guide my actions.
Section Three, Asset Allocation Targets. Equities: target 50 percent of investable savings (excluding Goal A's emergency reserve), acceptable range 40 to 60 percent. Fixed deposits and government bonds: target 35 percent, range 25 to 45 percent. Gold, held as jewelry-equivalent value or gold-backed savings: target 10 percent, range 5 to 15 percent. Cash held within the investment account for opportunities and near-term Goal B needs: target 5 percent, range 3 to 8 percent.
Section Four, Position-Sizing Rules. No single company shall exceed 12 percent of my total equity holdings at time of purchase. No single sector shall exceed 30 percent of my total equity holdings at time of purchase. If a holding exceeds these limits due to price appreciation or a bonus share issue, I will trim it back to target at the next quarterly review, regardless of how strong the story appears at that time.
Section Five, Buy Rules. I will only purchase a company's shares if all of the following are true: I have read its most recent audited annual report; I can state in one sentence how it earns revenue; its price-to-earnings ratio, or an equivalent valuation measure appropriate to its sector such as price-to-book for banks, is not obviously higher than its own five-year average or its closest listed peers without a clearly stated reason I can write down; the purchase does not breach my position-sizing rules above; and I am not buying primarily because the price has risen sharply in the past thirty days.
Section Six, Hold and Sell Rules. My default action, absent a trigger below, is to hold. I will sell, in whole or in part, only when: verified company fundamentals deteriorate in a way that breaks my original reason for buying; a holding exceeds my position-size limit; I have a dated, written cash need from Goal B or Goal C that this money is required for; or my scheduled annual review in Baisakh concludes a rebalancing is needed. I will not sell because the NEPSE index has fallen broadly with no company-specific news, because of a rumour or a Viber group's panic, or because a stock has "already run up" without a change in its fundamentals. Any sale not covered by these listed triggers requires a mandatory 72-hour waiting period from the moment I first feel the urge to sell.
Section Seven, Review and Amendment. This constitution is reviewed once per year, in the month of Baisakh, on a day chosen when the market has been calm for at least the preceding two weeks. No amendment to this document may be made during a period when NEPSE has moved more than 10 percent, up or down, within the trailing thirty days. Any urge to amend this document outside the scheduled review, especially an urge felt during a sharp market move, is itself treated as a signal to re-read the document as written, not to change it.
Notice what this document does and does not contain. It does not name specific stocks Sunita currently owns, because a constitution governs behaviour and structure, not a single point-in-time portfolio; the actual holdings will change over the years while the rules that govern how they change should not need to change nearly as often. It does not predict where the market is going, because a constitution is not a forecast — it is a set of conditional instructions that work whether the market rises, falls, or goes sideways. And it is short enough that Sunita can genuinely re-read the whole thing in about four minutes, which matters enormously, because a document too long to re-read quickly during a crisis provides no protection during the exact moment it exists to protect against.
KEY CONCEPT
A good investment constitution answers "what will I do when X happens" for every X you can reasonably anticipate, written specifically enough that reading it back requires no further judgment calls in the heat of the moment. If applying a rule still requires you to decide something new while your emotions are running high, the rule was not written specifically enough.
One more feature of Sunita's document is worth naming directly: notice that Section Seven locks the amendment process behind a calm-market condition, exactly mirroring the logic of Odysseus and the mast. She cannot rewrite her own rules during the very conditions those rules exist to guard against. This single clause is arguably the most important sentence in the entire document, because without it, every other section is just a suggestion that stress can override on the day it matters most.
CAUTION
A constitution with no restriction on when it can be amended is not a real commitment device — it is a to-do list you can cancel the moment it becomes inconvenient. Always write your amendment rule so that changes require a calm period, ideally a fixed calendar date, not a moment of market stress.
Writing your own version does not need to look exactly like Sunita's. Your numbers, your goals, your dates, and your risk tolerance will differ, and they should — this is a personal document, not a template to fill in mechanically. What should not differ is the discipline of writing each section down honestly, in specific and actionable language, before you need it, and reviewing it only on a schedule you set in advance rather than in reaction to a headline, a friend's excitement, or a red number on a screen.
Chapter recap
A written investment constitution turns vague good intentions — "invest for the long term," "don't panic," "buy good companies" — into specific, binding rules you can actually follow under stress, because it exists as a commitment device written by your calm self to govern your future self during exactly the moments, a crash or a mania, when Chapter 90 showed that judgment fails. A complete constitution has seven sections: purpose and goals with real dates attached, an honestly stated risk tolerance built from your actual lived experience or a realistic historical benchmark, target asset allocation across equities, fixed deposits and bonds, gold, and cash, position-sizing limits on any single stock or sector, explicit buy rules built from fundamentals rather than momentum, explicit sell rules that separate valid triggers from emotional impulses, and a review procedure that can only be exercised on a calm, pre-scheduled date. Sunita Adhikari's worked example showed what this looks like in practice: short, specific, personal, and built to require no further judgment calls at the exact moment judgment is least reliable.
Writing the rules, however, is only half the discipline. The other half is knowing when it is genuinely legitimate to break them — because life changes, because new information sometimes really does change a thesis, and because a constitution followed blindly regardless of circumstance can become its own kind of trap. Chapter 96, "Rules for Overriding the Model," takes up that harder question directly: how to tell a legitimate reason to deviate from your own written rules apart from a rationalisation dressed up to look like one, and how to build an override process into your constitution itself so that even your exceptions are governed by discipline rather than by mood.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVII · Chapter 96
Rules for Overriding the Model
First published 26 Aug 2026 · Last verified 29 Aug 2026
Every investor who builds a written constitution eventually sits in front of it holding a decision that the rulebook did not anticipate. This is not a hypothetical. It happens to disciplined investors more often than it happens to careless ones, because disciplined investors are the ones who actually follow their systems closely enough to notice when reality has stepped outside the lines the system drew. The question this chapter answers is not whether you will ever face this moment — you will — but what you are allowed to do about it, and under what conditions, without quietly becoming the same impulsive trader your constitution was written to protect you from.
I am going to tell you about two afternoons from my own investing life on NEPSE, the Nepal Stock Exchange. On one afternoon I broke my own rules on purpose, in writing, with a plan, and it turned out to be the right call. On another afternoon I wanted to break my own rules just as badly, felt just as certain I was right, and refused — and that refusal saved me from a loss I would still be explaining to myself today. The difference between those two afternoons is the entire subject of this chapter. It is not about whether rules can ever be broken. It is about how to tell, in the heat of the moment, whether you are exercising a rare and disciplined power called an override, or simply watching your own discipline collapse while calling it something nobler.
Lesson 96.1 — The Difference Between an Override and a Collapse
Let's start with two words you have met earlier in this Part: the Canon Score and the constitution. Your Canon Score is the numeric output of the scoring model you built earlier in this book — a system that takes a company's fundamentals, valuation, governance signals, and sector context and turns them into a single comparable number, so that you are not deciding stock by stock from your gut every single day. Your constitution is the broader written document that governs how you behave as an investor: your position sizing rules, your buy and sell triggers, your rules about leverage, your rules about what you do during a NEPSE crash. Together they form what the rest of this Part has called your personal operating system — the thing that makes decisions for you when your own judgment, on a bad day, cannot be trusted.
A model override is a deliberate, pre-specified exception to that system, made for a reason you can write down in one clear sentence before you act, which the system itself did not have the information or the structure to account for. Notice every part of that definition, because every part is doing work. It is deliberate — you chose it, calmly, rather than being swept into it. It is pre-specified — meaning your constitution already contains a clause describing the category of situation in which an override is permitted, even though it could not predict the exact situation. And it is reasoned — you can state, in advance, in writing, exactly why this case falls outside what the model was built to judge.
Discipline collapse is something else entirely. It is an emotional decision — usually driven by fear, greed, excitement, social pressure, or the simple exhaustion of watching a price move without acting — that gets dressed up, after the fact or in the heat of the moment, in the language of an exception. The investor tells himself "this is different," "the model doesn't understand this stock," "everyone knows something I don't," or "the rules were made for normal days and today isn't normal." These sentences sound like reasoning. They are not. They are rationalisations, and the tell is always the same: they arrive after the emotional urge, not before it, and they cannot be written down as a clean, falsifiable justification that a calm friend reading it tomorrow would find convincing.
Here is a simple analogy. Think of a commercial airline pilot. Autopilot flies the plane for the overwhelming majority of a flight, because autopilot does not get tired, does not panic in mild turbulence, and does not "feel" that the runway is closer than it is. But every trained pilot also knows there are specific, rehearsed situations in which they must disengage autopilot and fly manually — a wind shear alert, a system malfunction, an instruction from air traffic control that autopilot cannot execute. Those situations are named in the manual before the flight ever begins. What a pilot must never do is disengage autopilot because they feel a sudden urge to show the plane who is in charge, or because a passenger shouted that they saw something out the window. The disengagement itself is not the problem. An undisciplined, unrehearsed disengagement is the problem.
KEY CONCEPT
A model override is a deliberate, pre-specified exception, justified in writing before you act, for a situation your constitution already named as override-eligible. Discipline collapse is an emotional decision wearing the costume of an exception, justified after the fact, for a situation invented in the moment to fit the urge you already had.
Your Canon Score and your constitution exist precisely because your in-the-moment judgment, on the days that matter most, is the least trustworthy version of you. NEPSE investors know this instinctively from remittance season. A household that receives a lump sum from a family member working in the Gulf or Malaysia often makes its worst financial decisions in the excited week right after the money lands — not because the money is bad, but because sudden liquidity plus emotional relief is a famously poor environment for cold arithmetic. Your written system is supposed to be the version of you that shows up on that excited week and says, calmly, "we follow the plan." An override clause exists so that the system is not so rigid that it becomes stupid in the face of genuinely new information. But the override clause must be narrower than the system itself, or it swallows the system whole.
Lesson 96.2 — Why Systems Need an Escape Hatch, But Only a Narrow One
If a constitution allowed no exceptions at all, it would eventually break — not bend, break — because reality occasionally produces situations no model builder foresaw. Imagine a Canon Score model that scores a bank primarily on loan growth, net interest margin, and capital adequacy ratio (a regulatory measure of how much capital a bank holds against its risk-weighted assets, set and monitored by Nepal Rastra Bank, NRB, the central bank). Now imagine NRB announces an unusual one-time directive requiring all commercial banks to write back a specific provisioning charge because of a policy change, instantly improving the reported profit of every bank in the sector for one quarter only. Your model, built on historical ratios, might score this as a genuine earnings improvement and tell you to buy aggressively. You, the human, might know — because you read the regulatory circular carefully — that this is a one-time accounting effect that will reverse next quarter and tells you nothing about the bank's real earning power. Refusing to act on your own better information here, purely because "the model says so," is not discipline. It is discipline mistaken for stupidity.
This is why every well-built constitution needs what I call an escape hatch: a formally defined, rarely used, heavily guarded mechanism for departing from the model's output. But notice the phrase "rarely used, heavily guarded." An escape hatch on an airplane is not a second door you use whenever the first one has a queue. It exists for emergencies, it requires a specific lever to be pulled, and pulling it without cause is itself a serious violation, sometimes a criminal one. Your override clause needs the same character. It should be something you almost never touch, something that requires visible, deliberate effort to activate, and something whose overuse is itself evidence that your system has failed.
Here is a useful distinction borrowed from decision theory that fits neatly onto this problem: the difference between a one-way door and a two-way door. A two-way door decision is reversible at low cost — if you are wrong, you walk back through it and the damage is small. A one-way door decision is expensive or impossible to reverse. Most day-to-day portfolio choices — trimming a position by ten percent, adding a small tranche to a stock you already hold — are two-way doors, and your constitution can afford to be a little flexible about them without formal overrides, because a mistake is cheap to correct. A genuine override, by contrast, is almost always a one-way door decision dressed as urgent: sell a core holding entirely, buy a large new position outside your normal universe, abandon a stop-loss rule mid-crash. Because these are expensive to reverse, they deserve the heaviest procedural friction, not the least.
CAUTION
If you find yourself invoking the override clause more than a small handful of times a year, the problem is very rarely that the world keeps producing rare exceptions. The far more likely explanation is that your constitution is wrong, too rigid in the wrong places, or that you are quietly relabeling ordinary discipline collapse as "overrides" to make it feel acceptable. Track the count. A rising count is itself a signal requiring action — see Lesson 96.6.
I want to introduce a concept here that I use in my own practice: the override budget. Just as your constitution should specify a maximum percentage of your portfolio you are willing to lose in a year (a subject the next chapter covers in full), it should also specify a maximum number of overrides you permit yourself in a given period — I use one every calendar quarter as a soft ceiling, meaning if I am reaching for a second override within three months, I am required to stop and treat that as a five-alarm warning about my own state of mind, not a coincidence of an unusually eventful market. A budget forces you to ration a scarce resource, and scarcity is exactly the quality that keeps an escape hatch from becoming a second front door.
Lesson 96.3 — The Pre-Override Checklist
Now the operational heart of this chapter: the specific, narrow checklist that must be satisfied, in full, before any override is permitted. I present this as a checklist deliberately, in the same spirit as a pilot's pre-flight checklist or a surgeon's pre-operative checklist — professions where the cost of skipping a step under time pressure is exactly why the checklist exists in written form rather than living only in memory.
The first requirement is that you write down the reason before you act, not after. This single rule does more work than any other item on this list. The physical act of writing forces a pause, and the pause is where discipline collapse gets caught. If you cannot produce a clear written sentence — before the trade, not as a justification afterward — stating exactly which named exception in your constitution this situation falls under and why, you do not have an override. You have an impulse. I keep a single page in the back of my paper trading journal headed "Override Log," and the rule I have set for myself is brutal in its simplicity: no entry in that log, no override trade. If I cannot write the sentence calmly, I am not calm enough to be trusted with the decision, and the checklist has already done its job by stopping me.
The second requirement is a mandatory cooling-off period. For any override above a small threshold size, I require myself to wait — in my own constitution the number is twenty-four hours for anything affecting more than two percent of the portfolio — between writing the justification and executing the trade. Almost everything that feels like a screaming emergency at 11 a.m. looks calmer, and often simply wrong, by the next morning. A rights issue deadline or a genuine time-boxed corporate event might compress this window, but even then the rule requires some non-zero delay, because zero delay is indistinguishable from acting on pure adrenaline.
The third requirement is a second opinion from someone who is not inside your excitement. This does not need to be a professional advisor, though it can be. It can be a spouse, a fellow investor in your circle, or, in my own case, a friend from my university days who now works in a completely unrelated field and has no stake in NEPSE at all — which is precisely why his skepticism is valuable. The point of the second opinion is not that the other person has better information than you. The point is that explaining your reasoning out loud to someone with no emotional investment in the trade exposes weak reasoning almost immediately. If you cannot explain the override clearly enough for a reasonably intelligent outsider to understand why it is not just excitement, that is diagnostic information about the quality of the override itself.
The fourth requirement is a hard position-size cap on the override trade, set in advance and independent of how confident you feel in the moment. My own rule caps any override-driven trade at half of what my normal position-sizing formula would otherwise allow for a position of that conviction level. The logic is straightforward: an override, by definition, is a situation your model was not built to evaluate, which means your ordinary confidence calibration does not apply. You are, in a very real sense, flying without your normal instruments for this one decision, and a pilot flying without instruments reduces speed, not increases it.
The fifth requirement is that the override must be traceable to a named clause in your constitution, not invented on the spot. Your constitution, from earlier chapters in this Part, should already contain a short, closed list of situation categories — corporate actions the model cannot score, confirmed regulatory windfalls, and so on — for which overrides are even eligible to be considered. If your justification requires you to invent a brand-new category of exception that has never appeared in your written document before, that itself is close to disqualifying, and should trigger a much higher bar of scrutiny, ideally requiring you to first amend your constitution through its formal amendment process (covered earlier in this Part) rather than acting first and amending later.
Checklist step
What it requires
Why it exists
Write the reason first
A single written sentence, produced before any order is placed, naming the exception
Separates a reasoned decision from a rationalised one; forces a pause at the exact moment emotion is highest
Name the constitutional clause
The reason must map to a pre-existing category already listed in your constitution
Prevents inventing new exceptions on the spot to fit whatever you already wanted to do
Cooling-off period
A mandatory delay (I use twenty-four hours for anything above two percent of portfolio) between justification and execution
Almost all false urgency fades with time; genuine urgency usually survives the wait
Second opinion
Explain the reasoning aloud to someone without a stake in the trade
Weak reasoning collapses under simple outside questioning; strong reasoning survives it
Position-size cap
Override trades are capped at a fraction (I use one half) of what normal sizing rules would allow
Your confidence calibration does not apply to situations your model was never built to judge
Log the outcome regardless of result
Every override, win or lose, is recorded with its full reasoning in a permanent log
Creates the data you need later to judge whether your override rate and win rate justify keeping the clause at all
PRACTICAL TOOL
Keep your Override Log as a genuinely separate physical or digital page from your regular trading journal, and make the five checklist items its literal column headers. If you cannot fill every column honestly before placing the order, you do not yet have permission from your own system to place it.
Lesson 96.4 — Legitimate Override Scenarios
Let me now walk through the category of situations where an override clause earns its keep, and then tell you about the one I actually used.
The clearest legitimate category is the confirmed one-off regulatory or corporate windfall — an event that is announced, verified through an official source, and structurally guaranteed to happen, but which your Canon Score model has no mechanism to price because it was built around recurring fundamentals, not one-time events. Examples on NEPSE include a company receiving confirmed compensation from the government for land acquisition tied to a hydropower or infrastructure project, a confirmed merger swap ratio between two listed banks or financial institutions that creates a temporary, calculable arbitrage gap between the merging entities' share prices, or a court ruling — already final, not under further appeal — that resolves a long-running dispute in a company's favour and releases previously provisioned funds back to the balance sheet.
The second legitimate category is a company-specific structural event the model was never built to score at all, as opposed to an event the model scores badly. Your Canon Score, like most fundamentals-based models, is built around the assumption of a going concern operating its ordinary business — the same business, roughly, next year as this year. A spin-off (where a company separates one division into an independently listed entity and distributes shares of the new entity to existing shareholders), a large share buyback funded from a demonstrated cash surplus, a strategic asset sale that permanently changes the balance sheet, or a regulator-mandated capital increase that dilutes shares but also strengthens the institution — these are structural discontinuities, not deteriorations or improvements in ordinary operating performance, and no scoring model built on trailing ratios can be expected to interpret them correctly on the day they are announced.
The third legitimate category, narrower and requiring the highest bar of verification, is a confirmed regulatory or macro policy shift that is specific enough, and certain enough, to be actionable before the model's normal update cycle would catch it. NRB monetary policy announcements, changes to loan-to-value ratios for margin lending against shares, or SEBON (the Securities Board of Nepal, the capital markets regulator) directives on sectoral exposure limits for institutional investors sometimes create a short window where a well-informed, careful reader of the official circular has real information the broader market has not yet absorbed. I want to be very clear that this category is the most dangerous of the three, because it is also the category most easily counterfeited by a rumour dressed up as regulatory insight — which is exactly why the checklist's requirement to trace your reasoning to a verified, published, official source, not a broker's WhatsApp forward, matters most here.
REGULATORY DETAIL
A verified source, for purposes of a legitimate override, means a published NRB circular, a SEBON directive available on its official website, a company disclosure filed through NEPSE's official corporate disclosure system, or an audited financial statement. It does not mean a screenshot, a broker's verbal claim, a Viber or WhatsApp group forward, or a television panelist's opinion, however confident that opinion sounds.
Now let me tell you about the one I actually used. Three years ago I held a position in a hydropower company whose Canon Score had been sitting in a comfortable, unremarkable middle band for over a year — decent generation numbers, ordinary debt load, nothing that triggered a buy or sell signal either way. Then the company disclosed, through NEPSE's official corporate announcement system, that it had reached a final, court-approved settlement with a government agency over land compensation dating back to its original construction phase — a dispute that had been provisioned as a liability on the balance sheet for four years. The settlement was smaller than the provisioned amount, meaning the company would release a material sum back into retained earnings in a single quarter, verified by the settlement document itself, not by rumour.
My model had no clause for this. It scores hydropower companies on generation capacity utilised, power purchase agreement tariffs, and debt servicing coverage — none of which this event touched. I wrote, before doing anything else, a single sentence in my Override Log: "Confirmed final court settlement releases NPR [amount] in previously provisioned liability back to equity, verified via company's NEPSE disclosure filing dated [date]; this is a one-off balance sheet event my Canon Score was not built to capture; category: confirmed regulatory/legal windfall." I let it sit for the required cooling-off period, sent the disclosure document itself to my university friend and asked him, with no context about my portfolio, whether he thought the document said what I thought it said. He agreed it was unambiguous. I sized the position at half of what my normal conviction-based sizing would have allowed for a position with this clear a catalyst, added to my existing holding rather than opening a fresh one, and logged the entire sequence.
The position appreciated meaningfully over the following two quarters as the market gradually recognised the balance sheet improvement and — separately, and not something I had predicted or was relying on — the company's next dividend declaration reflected the stronger equity base. I want to be honest that a good outcome does not, by itself, prove the override was correctly made; a badly-reasoned override that happens to work is still badly reasoned, and I would have judged this one the same way if it had gone nowhere. What made it legitimate was not the profit. It was that every item on the checklist was satisfiable honestly, in writing, before I acted, using a verified public document rather than a feeling.
CASE IN POINT
A hydropower company's confirmed, court-settled land compensation payout released a provisioned liability the Canon Score model had no way to price, because the model was built for recurring operating performance, not one-off legal resolutions. The override checklist — written reason first, named clause, cooling-off period, second opinion, half-sized position, full log entry — was satisfied completely before the trade, which is the actual test of legitimacy, independent of the eventual profit.
Lesson 96.5 — Illegitimate Override Attempts, and the One I Refused
Now the harder half of this lesson, because refusing an override that your gut insists is obvious is considerably more uncomfortable than making one you can defend on paper.
The clearest illegitimate category is chasing a tip — acting on a piece of information whose only source is a person's confidence, not a verifiable document. NEPSE, like most emerging and frontier markets with a large base of retail participants, has an active rumour economy: tea-shop talk near the brokerage houses in New Baneshwor, Viber groups promising an "insider word" on a company, remittance-funded new entrants eager for a shortcut who pass along whatever they were told by a cousin's friend who "works at the company." A tip is not disqualified from being true. It is disqualified from being actionable through the override mechanism, because the override checklist specifically requires a verified public source, and a tip, by definition, has not passed through any verification you can point to later and defend.
The second clearest illegitimate category is panic-selling during a circuit-breaker day. NEPSE, like most exchanges, uses circuit breakers — automatic, rule-based trading halts triggered when the index or an individual stock moves beyond a specified percentage in a single session, designed to force a cooling-off pause on the entire market rather than allow panic to compound itself in real time. The circuit breaker exists precisely because regulators understand that extreme single-day moves produce exactly the emotional conditions in which investors make their worst decisions. Treating a circuit-breaker halt as new information that justifies overriding your constitution's stop-loss or holding rules gets the causality backwards: the halt is a symptom of collective panic, not a fact about the company's fundamentals, and reacting to it as though it were company-specific news is discipline collapse wearing the mask of prudence ("I'm just managing risk actively").
The third illegitimate category, subtler than the first two, is FOMO dressed as a corporate-action override — noticing that a stock is rallying hard, discovering after the fact that there was some corporate action attached to the rally, and retroactively constructing a justification that resembles the legitimate categories in Lesson 96.4 to license a purchase you actually wanted to make for the much simpler reason that the chart is going up and you feel left out. The tell here is sequence: in a legitimate override, the verified information comes first and the trade follows from it. In this illegitimate version, the desire to buy comes first, and the "verified information" is assembled afterward to fit.
WARNING
A circuit-breaker halt tells you that many other people are frightened at the same moment you are. It does not, by itself, tell you anything new and specific about the company you hold. Treating market-wide panic as a company-specific event that licenses an override is one of the most common and most costly forms of discipline collapse on NEPSE, precisely because it feels like risk management rather than fear.
Here is the one I refused. About eighteen months ago, a friend from my neighbourhood — genuinely well-meaning, genuinely excited — called to tell me that a manufacturing company I did not hold had "guaranteed news coming," something about a large export order, and that his brother-in-law, who apparently had a contact inside the company's finance department, said the stock was about to move sharply. The stock had already climbed nearly fifteen percent over the prior week on unusually heavy volume, which made the story feel more credible, not less, in the way that a rally always seems to confirm a rumour that arrived to explain it.
I wanted this to be true in a way that, looking back, I find slightly embarrassing to admit. I opened my constitution and tried, honestly, to write the required override sentence. I got as far as "confirmed export order," and stopped, because I had no confirmation at all — only a secondhand account of a conversation with someone I had never met, about a filing that did not exist on NEPSE's disclosure system, SEBON's website, or the company's own investor page. I could not name a clause in my constitution this fit under, because "a friend's brother-in-law's contact says so" was not, and will never be, a category I had written into the legitimate list. I sent the story to my usual second opinion, who asked one question I did not have an answer to: "has the company itself said anything?" It had not.
I did not buy. Nine days later, the stock gave back the entire fifteen percent gain in three sessions after the company issued a clarification stating it had no material undisclosed information to report — the standard disclosure a company makes on NEPSE when a regulator asks it to explain unusual price movement. The export order, as far as I have ever been able to determine, never existed in any form that reached an official filing. I want to be equally honest here that avoiding a loss is not proof the refusal was correct in principle, any more than the hydropower profit proved the override was correct in principle — sometimes tips are true and sometimes verified overrides lose money regardless. What made the refusal correct was that the checklist could not be honestly completed, and I stopped at the point where it failed rather than forcing the remaining steps to fit.
CASE IN POINT
A secondhand tip about an unconfirmed export order, arriving after a stock had already rallied on unusual volume, could not survive the override checklist's first requirement — a written reason naming a verified source. No such source existed. The stock later reversed its entire gain after the company's official clarification. The lesson is not that the tip was obviously false in hindsight; it is that the checklist correctly refused to certify it in advance, which is the only test that matters at decision time.
Situation
Legitimate override?
Governing reason
Confirmed court settlement disclosed via official NEPSE filing
Yes
Verified, one-off event outside the model's scoring scope, traceable to an official document
Merger swap ratio between two listed financial institutions, ratio confirmed by regulator filing
Yes
Structural, one-time event the fundamentals model was never built to price
NRB circular changing margin lending loan-to-value limits, read directly from the published circular
Yes, with highest scrutiny
Verified regulatory source, but easily counterfeited by rumour, so requires the strictest source-checking
A friend's secondhand claim of an insider contact about an unannounced export order
No
No verifiable source; cannot be traced to any pre-named constitutional clause
Panic-selling into or immediately after a circuit-breaker halt
No
Market-wide fear is not company-specific information; the halt itself is a symptom, not a fact about the company
A stock rallying hard on heavy volume with no confirmed catalyst, bought out of fear of missing out
No
Justification is constructed after the desire to buy, not before it; sequence reveals rationalisation
Lesson 96.6 — Logging, Reviewing, and Knowing When the Constitution Itself Must Change
An override is not finished when the trade is placed. It is finished when it has been logged, and it is only truly finished when it has been reviewed later against what actually happened, honestly, whether the outcome was good or bad. This closing discipline is what separates an investor who uses the override mechanism as a genuine safety valve from one who uses it as a permission slip they quietly stop examining once the trade has been placed.
Keep your Override Log as a permanent record, not something you tidy away once a position is closed. Each entry should carry the date, the written justification produced before the trade, the named constitutional clause it falls under, who gave the second opinion and what they said, the position size relative to your normal sizing formula, and — added later, after the position is closed — the actual outcome and a short honest note on whether, rereading the original justification with distance, it still holds up as sound reasoning independent of the profit or loss. This last step matters enormously and is the one investors skip most often, because it is uncomfortable to write "this reasoning was actually weak" about a trade that happened to make money, and even more uncomfortable to write "this reasoning was actually sound" about a trade that lost money anyway. Both entries are valuable precisely because they resist the very human urge to judge a decision purely by its result.
Review your Override Log at a fixed interval — I do mine every six months, alongside my broader constitutional review — and ask three questions of the whole set. First, how many overrides did I attempt versus how many did I actually execute, and is the gap healthy — meaning did the checklist catch weak reasoning before it became a trade, the way it caught the export-order tip? Second, of the overrides I executed, does the pattern of categories match the legitimate list in Lesson 96.4, or am I quietly stretching the definitions to cover situations that do not really belong there? Third, and most important, is the frequency of overrides increasing over time? An increasing frequency almost never means the world has started producing more genuine exceptions. It almost always means one of two things: either your Canon Score model has a structural blind spot that keeps recurring — in which case the correct response is not to keep overriding it case by case, but to formally amend the model itself through the amendment process covered earlier in this Part, so the exception becomes a permanent, principled part of the system rather than a repeated one-off — or your emotional discipline is eroding, and the override clause has quietly become your escape hatch of first resort rather than last resort, which calls for a much more serious conversation with yourself, and possibly with your second-opinion partner, about what is actually going on.
CAUTION
Do not fix a recurring model blind spot by repeatedly overriding it. If the same category of event keeps forcing an override — say, your model consistently mishandles bonus share announcements or rights issue dilution — that is a design flaw in the Canon Score model itself, and the correct fix is a formal constitutional amendment that changes how the model scores that category permanently, not a standing habit of manual correction that never gets written back into the system.
There is a final, quieter reason to keep this log with care: it is the only honest evidence you will ever have, months or years later, of whether the override clause is earning its place in your constitution at all. Some investors, after a few years of disciplined logging, discover that their overrides as a group have performed no better, and sometimes worse, than simply trusting the model every time — in which case the honest, if humbling, conclusion is to narrow the clause further, or in rare cases remove it altogether and accept the model's judgment even in edge cases, on the theory that a slightly wrong model followed with total consistency beats a slightly-less-wrong model undermined by human intervention every few months. Other investors find the opposite — that their handful of well-checklisted overrides meaningfully outperformed what pure model-following would have produced, which justifies keeping the clause but also raises the bar for keeping it narrow, since its value depends entirely on its rarity. Either finding is useful. What is never useful is skipping the review, because a constitution nobody audits is, in practice, no constitution at all — it is a document you consult when convenient and ignore when it is not, which is discipline collapse with better handwriting.
Chapter recap
An override and a discipline collapse can look identical from the outside — both end with you doing something your written system did not tell you to do — but they are governed by opposite processes. A legitimate override is written down before you act, traced to a category your constitution already named, checked by someone with no stake in your excitement, held back by a cooling-off period, sized smaller than your normal conviction would allow, and logged regardless of outcome. A discipline collapse is felt first and justified afterward, invents its exception on the spot, seeks no outside check because it already suspects what an honest outsider would say, and treats urgency itself as proof of correctness. NEPSE will hand you real, rare occasions for the first kind — a confirmed court settlement, a verified merger swap ratio, a genuine regulatory circular your model could not have priced — and it will hand you constant, tempting occasions for the second kind, dressed as tips, as panic during circuit-breaker days, and as the simple fear of being left behind while a chart runs without you. The checklist in this chapter is not there to make overrides easy. It is there to make them rare, honest, and small enough that being wrong about one of them never threatens the system that protects you the rest of the time.
Chapter 97, Maximum Drawdown and Loss Rules, turns from the question of when you may deliberately step outside your system to the question every investor eventually needs answered in advance: how much are you willing to lose before your constitution itself forces a stop, and what exact rules turn that number from a vague fear into an enforceable line you have already agreed, in writing, not to cross.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVII · Chapter 97
Maximum Drawdown and Loss Rules
First published 26 Aug 2026 · Last verified 29 Aug 2026
Ram Bahadur had a rule, or so he told himself. "If a stock falls twenty percent, I sell." He had said this to his brother-in-law over dal-bhat more times than he could count. It sounded firm. It sounded disciplined. It sounded like the kind of thing a serious investor says.
Then Nepal Reinsurance fell eighteen percent in a week during a market-wide correction, and Ram Bahadur did not sell. He told himself the twenty percent line hadn't been crossed yet — technically true — but he also knew, in the honest part of his mind he rarely visited, that if it had crossed twenty-two percent he still would not have sold, because by then he would have found a new reason: "it's due for a bounce," or "I already lost so much, no point selling now," or "let me just wait for it to recover to breakeven." The rule had sounded real when the market was calm. It evaporated the moment it was needed.
This chapter is about that gap — the distance between the loss rule you state in a calm moment and the loss rule you actually follow in a falling market. Chapter 81 dealt with exit rules for individual trades: the mechanical, backtested triggers that tell you when to sell one particular stock based on its own price action or valuation. This chapter is different. It is about drawdown, a word borrowed from professional portfolio management that simply means the drop from a peak value to a lower value, measured as a percentage. A drawdown rule is not about any single stock's chart. It is about you — your account size, your sector concentration, your entire portfolio, your nervous system — and about what you have pre-committed to do when the damage reaches a certain size. It sits closer to psychology and financial planning than to technical analysis. It is the last line of defence in your personal investment constitution, the rule that exists precisely because you cannot trust your future self, mid-crash, to think clearly.
Lesson 97.1 — What a Drawdown Rule Is, and Why It Is Not a Stop-Loss
Start with definitions, because this chapter will use several words that sound similar but mean different things.
A stop-loss, covered in Chapter 81, is an order or a rule attached to one position: "If Company X falls to Rs 450, I sell Company X." It is stock-specific. It is usually based on that stock's own price behaviour, its support level, its earnings outlook, or a backtested rule about how far a normal correction in that stock tends to go before it either recovers or breaks down further.
A drawdown limit is different in scope. It applies to a bigger unit — your entire portfolio, a whole sector you are exposed to, or a meaningful chunk of your net worth — and it is triggered not by what one stock's chart looks like, but by how much total value has been erased, measured against a peak. If your portfolio was worth Rs 25 lakh at its highest point and is now worth Rs 20 lakh, your portfolio has experienced a drawdown of 20 percent, regardless of which individual holdings caused it.
KEY CONCEPT
A drawdown is always measured from a peak, not from your original purchase cost. If you invested Rs 10 lakh, it grew to Rs 16 lakh, and then fell back to Rs 13 lakh, you are still sitting on an overall profit versus your original capital — but you have suffered a drawdown of nearly 19 percent from your peak. Both facts are true at once, and a drawdown rule cares about the second one, because that is the direction your money is currently moving.
Why does this distinction matter enough to build a separate rule around it? Because a portfolio can be perfectly healthy stock-by-stock and still be dangerously exposed at the portfolio level. Imagine an investor who owns eight different NEPSE-listed companies, each with its own sensible stop-loss rule set according to Chapter 81's method. Every individual position is "under control." But six of those eight companies are hydropower producers, and NEPSE's entire hydropower sub-index falls 30 percent over two months because of a change in the government's power purchase agreement policy, or a bad monsoon season that damages river flow forecasts and spooks the sector. Each stop-loss may or may not trigger depending on how it was set, but the portfolio as a whole has taken a blow that no single stop-loss was designed to catch, because no single stop-loss is watching the correlation between your holdings.
This is the first job of a drawdown rule: it watches the forest, not the trees. It exists at three possible levels, and a complete personal operating system usually sets a threshold at each:
Position-level drawdown concentration: how much of your total portfolio is any single stock allowed to represent, and at what loss on that position do you reconsider — not sell mechanically as in Chapter 81, but reconsider your total exposure.
Sector-level drawdown: how much of your total portfolio can be concentrated in one sector (banking, hydropower, insurance, hotels, microfinance), and what happens if that sector as a whole falls sharply.
Portfolio-level drawdown: the big one — the total percentage fall in your entire investable net worth, from its peak, that triggers a pre-committed change in behaviour.
WARNING
A drawdown rule that only exists at the portfolio level is usually too little too late. By the time your total portfolio has fallen 25 percent, the damage that got you there was already visible weeks earlier at the sector or position level. Build all three tiers, not just the headline number.
The second job of a drawdown rule, and arguably the more important one, is psychological. Chapter 81's stop-losses answer the question "when do I sell this stock?" A drawdown rule answers a harder question: "when do I admit that my current overall approach, allocation, or risk level is wrong for who I actually am, and change it?" That is not a trading decision. It is closer to a life decision, made about money. It is the difference between bailing water out of a leaking boat one bucket at a time (stop-losses on individual stocks) and deciding the boat itself needs to turn back to shore (a portfolio drawdown limit).
Lesson 97.2 — Sizing Your Threshold to Real Life, Not a Round Number
Ram Bahadur's mistake was not that twenty percent is a bad number. It is that twenty percent was not connected to anything about his actual life. He picked it because it sounded disciplined, the way "I'll start my diet on Monday" sounds disciplined. A drawdown threshold that is not rooted in your real financial situation is a slogan, not a rule, and slogans do not survive contact with a falling market.
A genuine personal maximum drawdown threshold should be built from three inputs, in this order.
First, your time horizon — how many years before you actually need this money. This is the single most powerful input, because time is the resource that heals almost every drawdown that is not caused by permanent business failure. NEPSE's history includes the 2016 correction, the 2021 to 2022 bear market that took the NEPSE index down more than 50 percent from its peak, and various sharper but shorter corrections in between. Every one of those episodes eventually resolved, for the index as a whole, given enough years — though individual companies inside the index sometimes did not recover, which is a separate risk covered elsewhere in this book. An investor with fifteen years until retirement can tolerate a much deeper drawdown than an investor who will need to withdraw money for a daughter's wedding in eight months, because the first investor has time on their side and the second does not.
Second, your need for the money — not just when you will use it, but how essential it is and how replaceable it is. Money earmarked for genuine emergencies, a child's near-term school fees, or loan repayments should arguably not be in equities at all, a point covered in earlier chapters on asset allocation. But even within the portion that is properly invested in NEPSE, some money is more load-bearing than other money. A retired schoolteacher living on a pension supplemented by dividend income from her share portfolio has less room for drawdown than a salaried bank employee in his early thirties who is investing a portion of his monthly income and has years of future paychecks to fall back on if the market falls.
Third, your other income sources — remittance income, a salary, rental income, a spouse's earnings, a family business. An investor whose household receives steady remittance income from a family member working in the Gulf or Malaysia has, in effect, a source of new capital that keeps arriving regardless of what NEPSE does. That is a buffer. It means a drawdown in the portfolio does not automatically mean a drawdown in the household's ability to pay rent, buy rice, or cover a medical bill. An investor with no other income, who is depending on trading gains as their primary livelihood, has no such buffer, and should set a far more conservative threshold.
Time horizon
Dependence on this money
Suggested portfolio drawdown ceiling
Under 2 years
High (needed for near-term expense)
Should not be materially in equities at all
2 to 5 years
Moderate (some flexibility on timing)
10 to 15 percent before a full allocation review
5 to 10 years
Low (other income covers near-term needs)
20 to 30 percent before a full allocation review
Over 10 years, strong other income
Very low
30 to 40 percent before a full allocation review, with position and sector limits still enforced throughout
This table is a starting scaffold, not a universal answer — the household with strong remittance income and a ten-year horizon still needs sector and position limits underneath the big number, because a portfolio can stay under its overall ceiling while being badly concentrated in one dangerous sector the whole way down.
CAUTION
Do not simply copy a percentage you read in a book, a YouTube video, or a friend's WhatsApp group. A 30 percent drawdown ceiling that is correct for a bank manager in his late twenties with fifteen years of salary ahead of him is reckless for his retired father living off the same portfolio's dividends. The number must come from your life, not from a table — even this one. Use the table to structure your thinking, then adjust it against your own honest answers to the three questions above.
There is a further honesty check worth applying once you have a candidate number: ask yourself not "does this number sound right" but "have I actually lived through a drawdown of this size before, and how did I behave." Many investors who have only ever experienced rising or gently correcting markets dramatically overestimate their own tolerance. They set a 30 percent threshold in their head while having never sat through even a 10 percent one without panic-selling or panic-buying more to "average down" without a plan. If you are newer to NEPSE, set your first real threshold lower than you think you need, and revisit it — as Chapter 98 will describe — once you have actually lived through at least one real correction and observed your own behaviour rather than your intentions.
PRACTICAL TOOL
Write your drawdown thresholds down, on paper or in a note you cannot quietly edit in a moment of stress, before the next correction begins — not during it. Include the date you wrote it and the reasoning (your time horizon, your other income, your dependence on the money) next to each number. When the market later falls and your mind starts generating reasons why "this time is different," the dated, reasoned version of your past self is far harder to argue with than a vague memory of "I think I said twenty percent once."
Lesson 97.3 — Paper Loss Versus Realised Loss, and Why the Difference Changes Everything
Here is a distinction that sounds obvious once stated but trips up even experienced investors constantly.
A paper loss, also called an unrealized loss, is a loss that exists only on your portfolio statement. You bought a stock at Rs 600, it now trades at Rs 450, so your holding shows a loss of Rs 150 per share — but you have not sold. No cash has actually left your net worth in a final, locked-in way. The loss is real in the sense that if you sold today, that is what you would receive, but it is not yet permanent. The price could recover tomorrow, next month, or next year.
A realised loss happens the moment you sell. Once you sell at Rs 450, the Rs 150 per share is locked in. It no longer matters what the stock does afterward — whether it recovers to Rs 700 the following month or falls further to Rs 300. That outcome is no longer yours to gain or lose from, because you no longer own the shares.
KEY CONCEPT
A paper loss is a photograph of where the market currently values your holding. A realised loss is a signed and stamped document. The first can still change. The second cannot. Every drawdown rule you build is, at its core, a rule about when a photograph should be turned into a stamped document — and that decision should never be made in the same emotional state that a falling market tends to produce.
Why does this distinction deserve its own lesson in a chapter about drawdown rules? Because it explains two opposite and equally common mistakes, and a good drawdown rule is built to prevent both.
The first mistake is refusing to realise a loss that should be realised. This is loss aversion in its purest form, a concept covered in earlier chapters on investor psychology: humans feel the pain of a loss roughly twice as intensely as the pleasure of an equivalent gain. Because a paper loss "doesn't count yet" in the mind of the person holding it, investors will hold a badly deteriorating position indefinitely, telling themselves it is not a real loss until they sell, so they simply do not sell, ever, and call this patience. Ram Bahadur, from the start of this chapter, was doing exactly this. The stock's fundamentals may have genuinely worsened — a hydropower company losing a power purchase agreement, a bank facing a spike in non-performing loans, a hotel group hit by a prolonged tourism slowdown — but because the loss is "only on paper," the investor keeps waiting for a recovery that a rule-following investor would have already priced out.
The second, less discussed mistake is realising a loss that should not have been realised — panic-selling a fundamentally sound holding purely because the portfolio-level drawdown number looked frightening on a particular red day, without checking whether that specific holding's business case had actually changed. A drawdown rule exists to force a decision at a threshold; it is not meant to force you to sell everything indiscriminately the moment any threshold is crossed. This is why Lesson 97.5 will draw a firm line between what a drawdown rule requires you to do (stop, review, decide deliberately) and what it does not require (sell everything reflexively).
CASE IN POINT
During the NEPSE correction that ran from its 2021 peak into 2022, the benchmark index fell from levels above 3,200 to below 1,900 — a drawdown exceeding 40 percent for anyone who had bought near the top. Investors who had built no drawdown rule and were holding fundamentally weak, thinly-traded, story-driven stocks with no earnings to support their prices often held on through the entire decline, telling themselves it was "just a paper loss," and were still holding those same stocks, now permanently impaired, years later. Investors who had a portfolio-level rule were forced, uncomfortably, to stop at a pre-set threshold and ask a harder question than "will this recover" — namely, "if I did not already own this, would I buy it today at this price, in this business environment." For some holdings the honest answer was yes, and they kept them. For others the honest answer was no, and the drawdown rule gave them the structure to realise that loss deliberately rather than by accident, or never.
The practical consequence for building your own rule is this: your drawdown threshold triggers a mandatory review, not a mandatory sale. What crossing the threshold actually requires of you is that you sit down, ideally away from the trading screen, and re-underwrite every major holding as if you were buying it fresh today, using the same research standards from earlier chapters on fundamental analysis. Holdings that pass this fresh test can be kept even after the threshold is crossed. Holdings that fail it should be sold — turning the paper loss into a realised one — not because the price fell, but because the re-underwriting showed the business case genuinely no longer holds. The drawdown rule's job is to force the review at a moment discipline requires it. It is not to auto-liquidate your judgment.
WARNING
Do not confuse "the price fell 25 percent" with "the company is now worth 25 percent less." Sometimes it is. Often, especially in a market-wide panic unconnected to any single company's fundamentals, it is not, and the fallen price is the opportunity, not the exit signal. The drawdown rule tells you when to look hard. It does not tell you what you will see when you look.
Lesson 97.4 — Circuit Breakers and the Illusion of Control
Chapter 90 covered NEPSE's circuit-breaker mechanics in the context of market-wide volatility controls. This lesson returns to that mechanism from a different angle: not "how does the circuit breaker work" but "what does the circuit breaker mean for your ability to actually execute your drawdown rule when you need to."
As a reminder for this chapter's purposes: NEPSE, like most exchanges, has rules that automatically halt trading, either for an individual security or for the market as a whole, once price movement in a session crosses a defined percentage band. These bands exist to slow down panic, give information time to be absorbed, and prevent disorderly price discovery. They are a genuinely useful piece of market infrastructure, put in place by NEPSE and overseen by the Securities Board of Nepal (SEBON) for good reasons.
REGULATORY DETAIL
NEPSE applies circuit filters at the individual scrip level, halting further movement in a stock once it has risen or fallen by the day's permitted percentage band from its previous close, and it separately maintains market-wide circuit breaker provisions that can pause trading across the exchange during periods of extreme, broad-based movement. The precise bands have been adjusted by SEBON and NEPSE over the years as part of ongoing market reforms, so an investor should always check the currently circulated NEPSE/SEBON notice for the exact percentage in force rather than assume last year's figure still applies — but the mechanical principle, a halt once a threshold is crossed, has remained consistent.
Here is why this matters for a drawdown rule and not just for a single stock's stop-loss. A stop-loss on one stock, from Chapter 81, is already vulnerable to a circuit halt: if a stock gaps down past your stop-loss level in a single session and hits its lower circuit before you can sell, your stop-loss simply cannot execute that day, because there are no buyers being matched at a price near yours, or the counter is frozen. This is frustrating for a single position, but a drawdown rule multiplies the problem, because a drawdown rule is often triggered precisely during the kind of market-wide event — a sharp macro shock, a monetary policy surprise from Nepal Rastra Bank, a political disruption, a regional shock affecting remittance flows or the broader economy — that is most likely to produce circuit-breaker halts across many stocks simultaneously, not just one.
Consider the mechanics honestly. Your portfolio-level drawdown rule says: "If my total portfolio falls 20 percent from its peak, I will reduce my equity exposure by a third." On the day your portfolio actually crosses that threshold, it is very plausible that a meaningful number of your individual holdings are themselves down their daily circuit limit, meaning trading in them is halted or severely thinned for that session. You cannot sell what you cannot get matched on. Your rule says "act now." The market's plumbing says "you may not be able to, not today, and possibly not for several sessions if the decline continues and each session opens down-limit again before you get an order filled."
WARNING
A drawdown rule that assumes instant execution is a rule written for a market that does not exist. NEPSE's circuit-breaker system means that during the exact market conditions most likely to trigger your rule — a fast, broad decline — your ability to execute a sale can be delayed by days, not minutes. Build this delay into your expectations from the start, or the gap between "I decided to act" and "I was actually able to act" will feel like a broken promise from the market, when it is really a predictable feature of it.
What does this mean practically for how you should design and think about your own drawdown rule?
First, it means the rule should trigger on a review and a decision, not on an assumed instant execution, exactly as Lesson 97.3 argued for a different reason. Because you often cannot sell immediately even if you want to, the useful part of the rule is the discipline of stopping to decide while the market is doing the deciding for you through halts, rather than the fantasy that you will cleanly exit at precisely your threshold price, which was already the wrong expectation for a stop-loss and is doubly wrong for a portfolio-level rule spanning many circuit-constrained securities at once.
Second, it means position sizing and diversification, covered in earlier Part XVII chapters, are doing real work here that a drawdown rule alone cannot replace. If your portfolio is concentrated in a small number of thinly traded scrips, a circuit halt genuinely traps you — there may be no exit at any reasonable price for days. If your portfolio is spread across enough liquid names, some of your holdings will likely still be tradable even on a day when others are frozen at their limit, giving you at least partial ability to act on your rule while you wait for the frozen names to open up.
Third, it means the rule should specify not just a threshold but a plan for the days after the threshold is crossed, since execution may be staggered across several sessions rather than completed in one. A well-built rule says something like: "Once my portfolio drawdown crosses 20 percent, I begin the re-underwriting review immediately, and I execute any resulting sales across whichever sessions allow me to, prioritizing the positions I have already decided to exit, without waiting for a 'better' day to start."
PRACTICAL TOOL
Keep a short written log, updated during any period when your drawdown threshold is active, noting which of your holdings hit their circuit limit each session and whether you were able to place or fill an order. This does two things: it keeps you honest about how much of the delay is the market's plumbing versus your own hesitation dressed up as "waiting for the halt to lift," and it becomes useful evidence, per Chapter 98's review protocol, for whether your rule needs adjusting to account for realistic execution speed.
Fourth, and this is worth stating plainly because it cuts against a natural instinct: circuit breakers, by design, slow down exactly the kind of panic-driven, indiscriminate selling that a poorly designed drawdown rule might otherwise trigger. If your rule had said "sell everything the instant the portfolio falls 20 percent," the circuit-breaker system would have partially protected you from executing that reflexive decision at the worst possible moment, by simply making it impossible to do so instantly. This is one more argument, on top of the paper-loss-versus-realised-loss argument from Lesson 97.3, for building a rule that triggers a considered review rather than a reflexive mass sale. The market's own volatility controls are, in effect, nudging you toward the more disciplined version of the rule whether you designed it that way or not.
Lesson 97.5 — Building Your Personal Drawdown Rulebook
It is time to put the pieces together into something you can actually write down. A complete personal drawdown rulebook has four components: the thresholds themselves at each of the three levels described in Lesson 97.1, the mandatory action tied to each threshold, an execution acknowledgment reflecting the circuit-breaker reality from Lesson 97.4, and a re-underwriting checklist reflecting the paper-loss discipline from Lesson 97.3.
Start with position-level concentration and drawdown. Decide, in calm conditions, the maximum percentage of your total portfolio that any single stock should represent — a common range for individual investors is 10 to 20 percent per position, tighter for less liquid or more speculative names, looser for a small number of core holdings you have researched deeply and hold with high conviction. Separately, decide what a severe single-position loss — say, a stock you hold falling 30 to 40 percent on its own, independent of the wider market — should trigger: not necessarily an automatic sale, since Chapter 81's stop-loss framework already governs mechanical stock-specific exits, but at minimum a mandatory re-underwriting review of that specific holding within a set number of days.
Move to sector-level exposure. Nepal's listed market is heavily weighted toward a handful of sectors — commercial banks, development banks and finance companies, microfinance institutions, life and non-life insurers, and hydropower — and it is very easy for an investor's portfolio to become far more concentrated in one or two of these than they realise, simply because that sector had been performing well and the investor kept adding to winners. Set a maximum percentage of total portfolio value for any single sector, commonly somewhere between 25 and 40 percent depending on how correlated you judge your other holdings to be with that sector, and set a rule for what happens if that sector as a whole — tracked through NEPSE's published sub-indices — falls sharply: a mandatory review of every holding in that sector together, since a sector-wide decline often reflects a shared cause (a regulatory change from NRB affecting bank capital requirements, a change in power purchase agreement terms affecting hydropower broadly, a monsoon or drought pattern) that is worth understanding as a group rather than stock by stock.
Finally, the portfolio-level rule, built from Lesson 97.2's inputs: your overall maximum acceptable drawdown from peak portfolio value, and the specific action tied to it. The action should be concrete and specific enough that you cannot talk yourself out of it later through vague language. "I will reduce my equity exposure by moving 20 percent of remaining equity value into fixed deposits or government securities" is concrete. "I will be more careful" is not a rule at all.
Rule level
Example threshold
Pre-committed action
Review trigger
Position
Single stock falls 30 to 40 percent independent of market
Mandatory re-underwriting within 5 trading days
Re-underwriting checklist below
Sector
Sector sub-index falls 20 percent or portfolio sector weight exceeds set cap
Review every holding in that sector as a group
Same checklist, applied sector-wide
Portfolio
Total portfolio falls X percent from peak (set per Lesson 97.2)
Reduce equity exposure by a pre-set fraction; reassess overall allocation
Full portfolio review plus Chapter 98 update protocol
The re-underwriting checklist itself, referenced twice in the table above, should be short enough that you will actually use it under stress rather than abandon it. A workable version asks, for each holding under review: has the specific reason I bought this company changed (its earnings trend, its management, its regulatory environment, its competitive position)? If I had cash instead of these shares today, would I buy this company at the current price? Is the price decline explained by this company's own fundamentals, by its sector, or by the whole market — and does that distinction change my answer? What would I need to see to change my mind again in either direction?
CASE IN POINT
An investor holding both a microfinance institution and a commercial bank saw both fall together during a period when NRB tightened lending and provisioning norms across the banking and financial sector. Applying the checklist, she found the commercial bank's core deposit base and capital position were largely unaffected, and the fall reflected sector-wide sentiment more than company-specific damage — she kept it. The microfinance institution, however, had a loan book concentrated in a region affected by both the tightened norms and a local repayment slowdown tied to reduced remittance inflows that quarter — a company-specific vulnerability the sector-wide panic had merely brought into focus. She sold it, realising the loss deliberately rather than continuing to hold it while calling it "just a paper loss."
One more component belongs in the rulebook: an explicit statement of what the rule does not require. It does not require selling a fundamentally sound holding purely because a threshold was crossed. It does not require ignoring the circuit-breaker reality and assuming instant execution. It does not require perfection — a rule followed imperfectly, three weeks late because of circuit halts and a slow re-underwriting process, still beats no rule at all followed with perfect hindsight-driven excuses.
CAUTION
A drawdown rulebook that exists only in your head is not a rule. It is a mood. Write it down, date it, and store it somewhere you will actually look during a crisis — a notes app, a printed page in a folder, a message to yourself. The entire value of a pre-committed rule comes from its being harder to quietly revise than a thought you had while the market was calm.
Lesson 97.6 — Rupa's Rule: A Worked Example From a Real Downturn
To make all of this concrete, follow one investor through an actual application of a drawdown rule.
Rupa is a 34-year-old high school administrator in Pokhara. She has been investing in NEPSE-listed shares for six years, funded partly from her salary and partly from remittance support her younger brother, working in Qatar, sends home to the family, a portion of which the family agreed she could invest on behalf of household savings. She has no debts, a small emergency fund in a savings account separate from her investments, and does not expect to need this invested money for at least seven years, when her daughter will begin university.
In early 2021, following the earlier framework in Lesson 97.2, Rupa sat down and wrote her personal drawdown rulebook. Her time horizon was long (seven-plus years), her dependence on the invested money for near-term needs was low, and her household had a second income stream through remittances that was not tied to NEPSE at all. Based on that honest assessment, she set her portfolio-level maximum drawdown threshold at 25 percent from peak value, with a pre-committed action: at 25 percent, reduce total equity exposure by moving one-quarter of remaining equity value into a fixed deposit at her bank, and conduct a full re-underwriting review of every remaining holding. She also set a position-level rule (no single stock above 15 percent of portfolio value; any stock falling more than 35 percent on its own triggers review) and a sector rule (no sector above 35 percent of portfolio value; a 20 percent sub-index decline in any sector she held triggers a group review). She wrote all of this in a notebook, dated it March 2021, and did not look at it again for months, because the market was doing well and there was no reason to.
By late 2021, NEPSE had reached record highs, and Rupa's portfolio, which had grown to include several banking stocks, two hydropower companies, and one life insurance holding, had grown well past her original contributions. Her peak portfolio value, reached in September 2021, became the reference point her drawdown rule would measure against going forward.
Then the correction that ran through 2021 into 2022 began. NRB's tightening of margin lending rules and liquidity conditions, alongside broader macroeconomic pressure including a widening trade deficit and pressure on foreign exchange reserves that prompted import restrictions, pulled the index down sharply and steadily. Rupa's portfolio fell alongside it — not evenly, since her hydropower holdings fell faster than her banking holdings in the early phase, then banking holdings fell further as the lending-rule tightening bit specifically into that sector.
By January 2022, Rupa's portfolio had fallen 22 percent from its September peak. Her rule had not yet triggered. This was, in its own way, useful information: she was watching the number weekly (not daily — she had also decided, wisely, that checking a falling portfolio every single day was its own form of self-harm and had committed to a weekly check-in instead), and she noticed that at 22 percent, her instinct was already to want to sell everything, well before her own pre-committed 25 percent line. This is worth pausing on, because it illustrates something important about drawdown rules generally: the rule's value was not only in what it eventually triggered, but in giving her a benchmark against which to notice that her emotional urge to act was running ahead of her own considered judgment. She held, because her rule — written by a calmer version of herself eight months earlier — had not yet said to act, and she trusted that earlier version of herself more than the frightened version reading the portfolio statement in January.
By March 2022, the portfolio had fallen 27 percent from peak. Her rule had triggered.
CASE IN POINT
Rupa's execution did not happen in a single afternoon. Several of her holdings — including one of her hydropower stocks and her life insurance holding — had hit their daily circuit limits on the way down in preceding sessions, meaning sell orders on those specific counters could not be filled on the days she wanted to place them. Her banking stocks, being more liquid and slightly less volatile that week, were tradable. Following the plan she had written into her rulebook (execute what you can, when you can, rather than waiting for a single clean day), she began her re-underwriting review immediately across all holdings, and executed the resulting decisions across the following six trading sessions as circuit conditions allowed, rather than treating the delay as a reason to abandon the plan.
Her re-underwriting review produced three different outcomes across her holdings, which is itself an important lesson: a drawdown rule triggering does not mean selling everything. Her two largest banking holdings, after review, still looked sound — solid deposit bases, provisioning that appeared adequate given disclosed non-performing loan figures, and a valuation that now looked, if anything, more attractive than it had at the September peak. She kept both, in full. One of her hydropower holdings had genuine company-specific trouble layered on top of the sector-wide decline: a delay in its plant's commissioning timeline that had been disclosed in a company filing, pushing expected revenue out by over a year. She sold this one, realising a loss of roughly 40 percent on that specific position — a loss that had existed on paper for weeks but that she now, deliberately, converted into a realised one because the re-underwriting review showed the original investment case no longer held. Her life insurance holding and remaining hydropower holding she kept, after concluding their declines reflected sector-wide sentiment rather than company-specific deterioration.
The proceeds from the sale, together with the pre-committed quarter of her remaining equity value, went into a fixed deposit at her bank, exactly as her March 2021 rule had specified. This was not a large sum in absolute terms, but it served its intended purpose: it stopped the household's total drawdown from deepening further on that portion, and it gave Rupa something concrete to point to — a decision made, not merely worried about — during a period when the news and her friends' WhatsApp groups were full of far more dramatic, far less useful reactions.
CASE IN POINT
By the second half of 2022 and into 2023, as NEPSE stabilised and began a slow recovery, Rupa's remaining holdings — the two banks, the insurer, and the surviving hydropower stock — recovered a meaningful portion of their drawdown. The stock she had sold under her rule never fully recovered; its commissioning delays compounded into further delays, and its price remained depressed for years afterward. Rupa did not celebrate this as proof of her genius — she was honest with herself that in a different sector-wide decline, a different holding might have been the one that recovered while a kept holding turned out to be the mistake. What she credited the rule for was not superior stock-picking, but the fact that she had a process that forced a clear-headed review at a defined point, rather than either panic-selling everything in January when her emotions first spiked, or holding everything indefinitely on the theory that a paper loss "doesn't count."
The lesson from Rupa's experience is not that a 25 percent threshold, or her specific sector and position rules, are the correct numbers for every investor. They were correct for her situation: her time horizon, her other income, her low dependence on this specific money. The lesson is the shape of the process — a threshold set honestly in calm conditions and written down, a trigger that produces a review rather than a reflexive sale, an acceptance that execution would be slower and messier than the rule implied because of circuit-breaker mechanics, and a willingness to actually use the rule when the moment came rather than finding reasons, the way Ram Bahadur did, why this particular fall was different and the rule did not really apply yet.
Chapter recap
A maximum drawdown rule is not a stop-loss repeated at a bigger scale — it is a different instrument entirely, built to govern your total portfolio, your sector concentration, and your own psychology rather than any single stock's price chart, which remains the territory of Chapter 81's exit rules. A genuine threshold comes from an honest accounting of your time horizon, your real need for the money, and the other income sources — a salary, a pension, remittances from family working abroad — that cushion your household regardless of what NEPSE does that week; a round number borrowed from a book or a friend's advice is not a threshold, it is a slogan waiting to fail under pressure. The gap between a paper loss and a realised loss matters because a rule's real job is to force a deliberate, unemotional re-underwriting review at a defined point, converting some paper losses into realised ones on purpose while leaving fundamentally sound holdings alone — never to trigger indiscriminate, reflexive selling. And NEPSE's circuit-breaker mechanics mean that the moment your rule is most likely to trigger — a fast, broad market decline — is also the moment execution is most likely to be slowed or staggered across several sessions, so a workable rule plans for that delay rather than assuming a clean, instant exit. Rupa's experience through the 2021–2022 correction showed all of these pieces working together: a rule written in calm conditions, a trigger that arrived months later exactly as designed, an uneven execution across several sessions due to circuit halts, and a review that kept two holdings, sold one, and left the household in a materially better position than either panic or paralysis would have produced.
A rule, once triggered and followed, is not the end of the story. The thresholds you set at 34, or in your first year of investing, or before you had ever lived through a real correction, may not be the right thresholds five years later, after your income has changed, your time horizon has shortened, or you have learned — the way Rupa did — something true about your own behaviour under pressure that no amount of calm reflection could have told you in advance. Chapter 98, "The Constitution Review and Update Protocol," takes up exactly this question: how often to revisit every rule in your personal investment constitution, including the drawdown thresholds built in this chapter, what should and should not change between reviews, and how to update a rule without simply weakening it every time it becomes inconvenient to follow.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVII · Chapter 98
The Constitution Review and Update Protocol
First published 26 Aug 2026 · Last verified 29 Aug 2026
In Chapter 95, you wrote your personal investment constitution — the short, written document that states your goals, your risk tolerance, your asset allocation targets, and the rules you promise to follow no matter what the market is doing on any given Tuesday. In Chapter 96, you learned about the single greatest threat to that document: the override. The override is what happens when NEPSE (the Nepal Stock Exchange, the only stock exchange in the country) falls fifteen percent in a month and you suddenly decide that your "long-term, buy-and-hold" constitution was written by someone naive, and that the smarter, more "adaptive" thing to do is to rewrite the rules right now, in the middle of the storm, to justify selling everything.
This chapter is about the opposite problem, and it is a real problem, not a fake one invented to make the book longer. If Chapter 96 was about the danger of changing your constitution too easily, this chapter is about the danger of never changing it at all. A document that can never be amended is not a constitution — it is a prison. Nepal's own national constitution, the one promulgated in 2015 (2072 BS), has a formal amendment procedure written into it. It has been amended more than once since then, through a defined process, with defined thresholds, on a defined timetable, by people who were not in the middle of a riot when they did it. That is the model this chapter asks you to copy: not "never change," and not "change whenever you feel like it," but "change through a slow, scheduled, written procedure, for reasons that have nothing to do with this week's stock prices."
By the end of this chapter you will have three things: a clear test for telling legitimate reasons to amend your constitution apart from illegitimate ones, a step-by-step annual review checklist you can actually use every year, and a decision rule for what to do when your calm self of three years ago disagrees with your anxious self of this afternoon.
Lesson 98.1 — Why a Constitution That Never Changes Is Also a Mistake
Think about a young man who wrote his investment constitution at age 24, fresh out of university, working his first job in Kathmandu, no dependents, renting a room, sending a little money home to his parents in a district town. His constitution said: "I am young, I have decades before I need this money, I can tolerate large swings, so I will hold 80 percent equity and 20 percent debt (fixed-income instruments like bonds, debentures, and fixed deposits that pay a predictable return), and I will not touch the equity portion for at least ten years."
That was a correct constitution for a 24-year-old with no dependents. Now fast-forward. He is 34. He is married. He has a two-year-old daughter. His wife has left her job to care for the child for now. He has a home loan. His father, back in the village, has had a stroke and needs ongoing medical support that falls partly on him as the eldest son, which is a completely normal and expected obligation in most Nepali families. If, at 34, this man is still running the identical constitution he wrote at 24 — 80 percent equity, ten-year lockup mentality, no separate emergency fund line item, no thought given to what happens if he loses his job for six months — he is not being disciplined. He is being asleep at the wheel. His actual life has moved. His document has not. That gap is just as dangerous as the override, because it means the document is no longer describing the person who is using it.
KEY CONCEPT
An amendment is a change to your constitution made through a slow, scheduled, written process, triggered by a real change in your life circumstances. An override is a change made in the heat of the moment, triggered by a market price, to excuse an action you already want to take. Same action — editing the document — completely different legitimacy, depending on what triggered it and how it was done.
Notice the two things that separate an amendment from an override: the trigger and the process. The trigger for a legitimate amendment is something that happened to you — your income, your family, your health, your time horizon. The trigger for an override is something that happened to the market. And the process for a legitimate amendment is slow and scheduled — you sit down at a pre-agreed time, ideally once a year, and you go through a checklist. The process for an override is fast and reactive — you make the decision the same day the bad news arrives, usually alone, usually anxious, usually with the edit already half-justifying a trade you have already decided to make.
A useful household analogy: a family budget. A sensible family sits down once a year, maybe around the Nepali new year in Baisakh, or at the start of the fiscal year in mid-July (Ashad end, when many Nepali households and businesses close their accounts), and revises the household budget because a child has started school and school fees are now a real line item, or because a family member has gone abroad for foreign employment and remittances are now arriving monthly. That is a legitimate budget revision. It is a different thing entirely for that same family to throw out the budget the day after a wedding invitation arrives and say "well, we're spending whatever we want this month, the budget clearly wasn't realistic." One is planning. The other is an excuse dressed up as planning. Your constitution deserves the first kind of treatment, not the second.
Lesson 98.2 — The Two Kinds of Change: Life Circumstances vs Market Circumstances
The single most useful diagnostic question you can ask yourself before touching your constitution is this: did something change about me, or did something change about the market?
Things that change about you are, by definition, things that would be true and worth acting on even if NEPSE had done nothing at all that year — even if the index had traded perfectly flat for twelve straight months. Marriage would still matter. A new baby would still matter. A promotion that doubled your salary would still matter. A parent's illness would still matter. These are facts about your life, not opinions about the market's next move.
Things that change about the market are, almost by definition, things you cannot act on without essentially becoming a market timer — someone trying to guess short-term price direction, which decades of evidence, in Nepal and everywhere else, shows most people cannot do reliably, including professionals. NEPSE fell. NEPSE rose. A particular banking stock you own reported a weak quarter. A brokerage house analyst issued a bearish note. Your friend at the tea shop said hydropower counters are "finished." None of that is a fact about your life. All of it is noise about prices, and prices are not the same thing as your circumstances.
Marriage or starting a joint household
Legitimate life-circumstance trigger
Review allocation, dependents, and joint goals
Birth of a child
Legitimate life-circumstance trigger
Add education goal, raise emergency fund target, review insurance
Job loss or major income drop
Legitimate life-circumstance trigger
Reduce risk exposure, extend emergency fund, pause new equity buys
Large raise, bonus, or new remittance income
Legitimate life-circumstance trigger
Increase savings rate and investable surplus, not necessarily risk level
Five to ten years from a major goal (house, retirement)
Legitimate life-circumstance trigger
Begin a glide path, shifting gradually toward debt instruments
A serious health diagnosis
Legitimate life-circumstance trigger
Reassess liquidity needs and insurance coverage
NEPSE index fell sharply this month
Illegitimate market-circumstance trigger
No constitution change; follow existing rules
A stock you own missed earnings expectations
Illegitimate market-circumstance trigger
Handle through existing sell rules from Chapter 95, not a rewrite
A friend or influencer made a confident prediction
Illegitimate market-circumstance trigger
No constitution change; this is noise, not evidence
You feel anxious after several red days in a row
Illegitimate market-circumstance trigger
Wait for scheduled review; treat feeling as data, not instruction
The right-hand column matters as much as the left. Notice that even the legitimate triggers do not always mean "change your risk tolerance." A large raise, for instance, often just means you have more money to invest under the same rules, not that you should suddenly gamble more of it. That distinction — between changing how much you invest and changing how you invest — trips people up constantly, so hold onto it.
WARNING
The single most common act of self-deception in this entire subject is dressing up a market-driven override as a life-circumstance amendment. A person who wants to sell in a panic will say, out loud and perhaps even in their own written journal, "my risk tolerance has changed." But ask the follow-up question honestly: did anything happen in your actual life this month — your job, your health, your family, your income — or did only the NEPSE index move? If the honest answer is "only the index moved," then your risk tolerance has not changed. Your fear has changed. Those are different things, and only one of them belongs in a constitution.
This is exactly the discipline collapse described in Chapter 96, wearing a disguise. Chapter 96 warned you about the override that says "the market has changed, therefore my rules should change." This chapter's warning is the mirror image and, in some ways, more dangerous, because it sounds so reasonable: "my risk tolerance has changed" is a sentence that could be describing a real, legitimate life event, or could be describing nothing more than fear wearing the vocabulary of financial planning. The only way to tell the two apart is the test above: would this be true if the market had done nothing?
Lesson 98.3 — The Annual Review Protocol
Because ad-hoc review is exactly how overrides sneak in, the discipline is to make review boring and scheduled, like a health checkup, rather than exciting and reactive, like an emergency room visit. Pick one fixed date each year and put it in your calendar the same way you would put in a festival date. Many Nepali investors find it natural to anchor this to something already meaningful — the start of the new fiscal year at the end of Ashad/start of Shrawan (mid-July), when banks, brokerages, and most companies close their books anyway and annual statements start arriving; or the Nepali new year in Baisakh; or simply your own birthday. What matters is that the date is fixed in advance, not chosen in reaction to a headline.
Here is the checklist, step by step.
Step one: gather your documents. Pull your demat account statement (the electronic record of the shares you hold, maintained through your Depository Participant, or DP, which is typically your broker or a licensed bank), your bank statements, your existing written constitution from Chapter 95, and your log of any promises or rules you set for individual holdings. You cannot review honestly from memory. Memory edits itself in your favour.
Step two: review your actual life, not your portfolio, first. Before you look at a single stock price, write down anything that has genuinely changed in your life in the past year: income, job, marital status, dependents, health, housing, major debts, and how many years remain until your next big goal. This step is deliberately placed before you look at numbers, because if you look at the portfolio's performance first, the emotional reaction to that performance will color how you answer questions about your life. You might convince yourself your risk tolerance has "matured" simply because the portfolio had a rough year. Doing the life review first protects you from that.
Step three: compare your life review against your existing constitution's assumptions. Your Chapter 95 document should have stated, in writing, the assumptions behind your allocation: your age, your dependents, your job stability, your time horizon. Check each assumption against what you just wrote in step two. Where they still match, leave that section of the constitution untouched. Where they no longer match, flag that section for amendment.
Step four: check your allocation against your targets, and rebalance if needed. Rebalancing means bringing your actual mix of assets back toward your target mix — for example, if your constitution says 70 percent equity and 30 percent debt, and a strong year in NEPSE has pushed you to 82 percent equity because the equity portion grew faster, you sell a slice of equity and add to debt to get back to 70/30. This is a mechanical, calendar-driven action, not a market call. You are not selling because you think the market will fall. You are selling because your own prior rule told you to keep a certain ratio, and the ratio has drifted.
Step five: review costs and frictions. Check your brokerage commission rates, your DP annual fee, any margin lending interest you have paid, and whether your broker relationship still serves you well. Nepal's brokerage and DP fee structures are regulated by SEBON (the Securities Board of Nepal, the market regulator) but do vary somewhat by provider and change occasionally by circular, so this is worth a yearly look even though it rarely produces a big change.
Step six: review your watchlist and your circle of competence (the set of businesses and sectors you actually understand well enough to judge). Are you still comfortable with every sector you hold — banking, hydropower, insurance, microfinance, hotels, whatever your mix is — or has one become something you hold out of habit rather than understanding? This is also the moment to ask whether any exclusion rule you set for yourself in the past (say, "I will not invest in finance companies") still has a real reason behind it, or has simply calcified into superstition. We will come back to this exact question in Lesson 98.5.
Step seven: write the amendment, date it, and sign it. If changes are needed, write them into the constitution document itself, in a new dated section, rather than deleting the old text. Keep the old language visible, struck through or clearly marked as superseded, with the date and the reason for the change written next to it. This creates a paper trail of your own reasoning that future-you can consult — which is exactly what you will need in Lesson 98.5 when a future stressed version of yourself wants to know whether past calm-you had a good reason for a rule.
Step eight: set the date for next year's review before you close the document.
PRACTICAL TOOL
Keep a one-page "Annual Constitution Review" card, either at the front of your investment file or as the first page of the same notebook your written constitution lives in. Each year, fill in eight lines: (1) date of review, (2) life changes since last review, (3) constitution assumptions still valid, (4) constitution assumptions no longer valid, (5) rebalancing actions taken, (6) cost and broker check completed yes or no, (7) watchlist and exclusion rules reviewed yes or no, (8) date of next scheduled review. A one-page card you actually fill in every year is worth more than an elaborate review process you do once and abandon.
REGULATORY DETAIL
Nepal's brokers and DPs already require periodic KYC (Know Your Customer) updates on demat and trading accounts, and SEBON has, from time to time, tightened rules on things like margin lending limits and IPO application processes. None of this is a reason to redesign your investment strategy reactively. But it is a useful cultural anchor: just as you are used to periodically refreshing your KYC paperwork as a routine compliance habit rather than a reaction to news, treat your constitution review the same way — a routine housekeeping habit on a fixed schedule, not a response to any single event in the market or the regulatory calendar.
Lesson 98.4 — Life Events That Legitimately Trigger a Review Outside the Annual Cycle
The annual review is the default rhythm, but certain events are big enough that you should not wait for the calendar date. These are the "life-event triggers," and the test from Lesson 98.2 still applies to each one: would this matter even if the market were flat?
Marriage or forming a joint household. Your goals, your risk tolerance, and often your household cash flow are no longer only about you. A spouse may have their own income, their own debts, their own risk appetite, and the two of you now share at least some financial goals — a home, children's education, care of aging parents on either side. This is worth an out-of-cycle review, not to make the marriage a reason to gamble more or less, but to make the document accurately describe a two-person household instead of a one-person household.
Birth of a child. This is one of the most common and most legitimate triggers in the Nepali context, given how central children's education is to most family financial planning. A birth typically means: raise your emergency fund target (because a household with a dependent child needs a thicker buffer than a household without one), consider a dedicated education goal bucket with its own time horizon, and check whether you and your spouse have adequate life and health insurance, since insurance is really a financial planning tool that protects the plan itself against catastrophe, not an investment product.
Job loss or a significant, sustained drop in income. If your income has genuinely fallen — not "the market fell" but "my salary or remittance income fell" — your capacity to bear risk has fallen too, and your constitution should reflect a more conservative posture, at least temporarily, along with a pause on new equity purchases until your cash flow stabilises.
A significant, durable rise in income. A promotion, a new higher-paying job, or in Nepal's case very often a family member moving abroad for foreign employment (to the Gulf countries, Malaysia, Korea, or elsewhere) and remittance income becoming a steady new inflow. The correct response here is usually to increase how much you invest, following your existing rules, rather than to change the rules themselves in excitement. New money is not a license for new risk-taking; it is simply more fuel for the same engine.
Approaching a major goal. When you move within roughly five to ten years of a big target date — buying a house, funding a child's higher education, or retirement — this is the classic trigger for what financial planners call a glide path: a gradual, pre-planned shift of the portfolio from growth-oriented assets like equities toward capital-preservation assets like fixed deposits, government bonds, and debentures, so that a market downturn in the final year or two before you need the money cannot wreck the goal.
A health event, in yourself or a dependent. Serious illness changes both your near-term liquidity needs and your appetite for locking money away in illiquid or volatile assets.
CASE IN POINT
A schoolteacher in Pokhara wrote her constitution at 27: single, no dependents, aggressive equity tilt, ten-year horizon. At 31 she married a man who worked seasonally in the tourism trade, whose income was naturally uneven between the Dashain-Tihar high season and the quieter monsoon months. This was a legitimate trigger for an out-of-cycle review, not because the market had done anything, but because her household's income pattern had changed shape entirely. She amended her constitution to hold a larger cash and fixed-deposit buffer sized to cover the low season, and reduced her equity allocation modestly to make room for it. Two years later, during a sharp but temporary NEPSE correction unrelated to any of this, she felt the same fear everyone feels — and correctly did nothing, because nothing about her actual life had changed since her last review. The buffer she had built for her husband's seasonal income was a life-circumstance amendment. Staying still during the correction was correct discipline. The two decisions look different but come from the same underlying test.
Lesson 98.5 — Disagreeing With Your Own Past Self
Here is the situation this lesson exists for. You are sitting in front of your constitution, in the middle of a stressful week, and you disagree with something your past self wrote. Maybe two years ago, calm and rested, you wrote a rule that now feels wrong to you today, anxious and rattled. Which version of you should win?
The general answer, and the one this entire Part of the book has been building toward, is that the calmer self usually has better judgment than the more stressed self, for a simple and well-documented reason: acute stress, fear, and euphoria all measurably narrow attention and shorten time horizons in the human brain. A person who is frightened is, on average, a worse long-range planner than the same person a month earlier when nothing frightening was happening. This is not a moral failing; it is closer to a design feature of how brains handle threat. So the default tiebreaker rule is: trust the version of yourself who wrote the rule on a calm day, over the version of yourself who wants to break the rule on a frightening day.
But — and this is the honest complication this lesson has to deal with — the calmer past self is not automatically right just because they were calm. Calm people can also reason from bad information, outdated assumptions, or a mistaken belief they never tested. So the real question is not simply "who was calmer," it is "which version of me is deciding from a better process." A decision made calmly, with full information, reasoning carefully from your actual goals, deserves deference even when it now feels uncomfortable. A decision made calmly but based on incomplete information, a superstition, or a single bad past experience wrongly generalised, does not deserve the same deference just because nobody was crying when it was written.
This gives you two separate questions to ask whenever present-you and past-you disagree.
First: has new, real information arrived since the rule was written — a fact about the world or about you, not just a new emotion? Second: was the original rule reasoned carefully from principle, or was it itself already an emotional reaction that simply happened to get written down and therefore looks official?
Past rule made calmly, reasoned from stated goals; present self merely feels afraid due to a market drop
Trust past self; do not amend
No new information has arrived, only a new emotion
Past rule made calmly, but based on a fact that has since genuinely changed (income, dependents, time horizon)
Trust present self; amend through the scheduled process
New real information exists; the amendment is legitimate, not a panic override
Past rule was itself written in fear or anger after one bad experience, and never re-examined since
Trust present, calmer reflection during a scheduled review; correct the old rule
The "past self" in this case was not actually calm; it only looks official because it is old
Present self wants to break a rule specifically because breaking it would allow selling into a falling market right now
Trust past self; do not amend
This is the exact override pattern from Chapter 96, regardless of how it is phrased
Present self, during a calm, scheduled annual review, wants to loosen a rule that no longer matches current dependents or goals
Amend, following the full review protocol
This is ordinary legitimate revision, not an override
Look closely at the third row of that table, because it is the trickiest case and connects directly back to Lesson 98.3's step six. Sometimes what looks like "my wise past self's rule" is actually an old panic that simply had time to harden into habit. A person who lost money in one finance company during a difficult period years ago might have written into their constitution, "never invest in finance companies," and treated that rule ever since as sacred, unquestionable wisdom from a calmer time. But if you trace it back honestly, that rule was never calm reasoning from principle — it was fear, written down once and never revisited. The fix for this is not to break the rule mid-panic today. The fix is to bring it up at your next scheduled annual review, examine it honestly using the process in Lesson 98.3, and if it truly has no remaining justification beyond an old wound, amend it through the proper channel, dated and documented like any other legitimate change.
CAUTION
Beware of hindsight bias when judging your past self — the tendency, once you already know how a decision turned out, to convince yourself it was obviously right or obviously wrong all along. If a past rule happened to save you from a loss, you will be tempted to treat it as flawless permanent wisdom. If a past rule happened to cost you some gains, you will be tempted to treat it as foolish and discard it. Neither reaction judges the rule by the quality of the reasoning that produced it, only by the outcome, which is exactly the trap Chapter 96 described in its discussion of outcome bias. Judge past rules by the process behind them, not by how the market happened to move afterward.
CASE IN POINT
Consider two rules written by the same person in the same year. Rule A: "I will not invest more than 10 percent of my portfolio in a single company, regardless of how confident I feel," written after calmly studying diversification and concentration risk. Rule B: "I will never buy shares of any hydropower company," written the week after a specific hydropower stock he owned fell sharply on delayed project completion news, with no broader study behind it beyond that one bad week. Three years later, hydropower as a sector performs well and he wants to participate, but Rule B blocks him. Rule A, even though it also constrains him, deserves full deference — it was reasoned calmly from a sound principle about concentration risk that has nothing to do with hydropower specifically. Rule B deserves scrutiny at his next scheduled review, because tracing its origin honestly shows it was a fear reaction to a single event dressed up as a permanent principle, not a considered judgment about the sector's underlying economics.
Lesson 98.6 — A Worked Example: One Investor, One Decade, Five Legitimate Revisions
To make all of this concrete, follow a single fictional investor, Sushila, through ten years of scheduled, legitimate constitution revisions, each one triggered by an actual change in her life rather than a change in the market. Sushila writes her first constitution at 26.
Year 1 (age 26). Sushila works at a private company in Kathmandu, unmarried, living with a roommate, sending a modest amount home to her parents in Chitwan every month. Her Chapter 95 constitution states: goal is long-term wealth building with no fixed near-term target; time horizon is at least fifteen years; risk tolerance is high given her age, health, and lack of dependents; target allocation is 75 percent equity across a diversified mix of banking, hydropower, and a few manufacturing counters, and 25 percent in fixed deposits and a small cash buffer equal to four months of expenses; rule: no single stock above 12 percent of portfolio; review date: every year at the start of Shrawan, right after fiscal year close.
Year 3 (age 28). Sushila marries. At her scheduled Shrawan review, she applies the checklist from Lesson 98.3. Life review: newly married, husband employed in the tourism sector with seasonal income swings, no children yet, considering a joint home purchase in five to seven years. Constitution assumptions still valid: long time horizon, high general risk tolerance, stock-level concentration rule. Assumptions no longer valid: the four-month cash buffer was sized for a single income earner with stable salary; it should now account for her husband's seasonal dips. Amendment: raise the joint emergency buffer to six months of combined household expenses, and flag that in five to seven years a house-purchase glide path will need to begin. Nothing here was triggered by NEPSE. It was triggered by marriage and a joint household.
Year 5 (age 30). Their first child is born. At the next scheduled review — and notably, the timing happens to fall only two months after the birth, which is close enough to the annual date that Sushila folds it into the same review rather than treating it as a separate emergency session — she adds a dedicated education goal with an eighteen-year horizon, raises the household emergency buffer again to account for a dependent child, confirms both she and her husband hold adequate life insurance, and trims her equity allocation slightly from 75 to 70 percent, moving the difference into government bonds, to reduce the household's overall volatility now that a child depends on the plan working.
Year 6 (age 31). NEPSE goes through a sharp, painful correction over several months, driven by tightened margin lending rules and a broader liquidity squeeze in the banking sector. Sushila's portfolio value drops meaningfully on paper. This is exactly the kind of month Chapter 96 warned about, and it is exactly the kind of month this chapter says to sit still through. Nothing has changed in her actual life. Her scheduled review is still four months away. She waits. When the scheduled Shrawan review does arrive, she goes through the full checklist: life review shows no change — same job, same marriage, same one child, same horizon. Constitution assumptions all still valid. She rebalances mechanically back toward her 70/30 target, since the correction had actually pushed her below target equity weight, meaning her own rule now tells her to buy modestly into the weakness, not sell out of fear. This is the single clearest illustration in her whole decade: the market moved a great deal, and her constitution did not move at all, because nothing about her life had moved.
Year 8 (age 33). Her husband takes a two-year contract job abroad, and steady remittance income begins arriving monthly, larger than what the household spent before. At her scheduled review, Sushila does not treat this windfall as license to gamble. She increases her monthly investment contribution substantially, following the exact same 70/30 allocation and the same 12-percent single-stock rule as before. The new money follows the old rules. She does add one genuinely new element: because the family's cash flow is now less dependent on any single local salary, and more resilient to short-term local job market shocks, she notes this as a modest improvement in the household's risk capacity — but rather than reactively raising her equity percentage in the same sitting, she writes it down as a "candidate change to consider only if the pattern holds for two more annual reviews," deliberately building in a delay so that a temporary windfall cannot be mistaken for a permanent shift.
Year 9 (age 34). At this review, Sushila revisits her original constitution's exclusion rules, as part of the routine checklist step six. She finds a note from Year 1: "avoid microfinance company shares — a colleague lost money in one and warned me off the whole sector." Applying the test from Lesson 98.5, she asks whether this was calm reasoning from principle or an old, unexamined fear inherited from someone else's bad experience. She concludes honestly that it was the latter — she has never actually studied microfinance company fundamentals, balance sheets, or regulatory position with NRB (Nepal Rastra Bank, the central bank that regulates banks and many microfinance institutions), she simply absorbed a friend's fear years ago. Rather than acting on this mid-week, she schedules genuine study of the sector before the next annual review, and only removes the exclusion the following year, after doing that work, writing the reasoning into the amended document exactly as Lesson 98.3's step seven describes — old rule struck through, dated, replaced with a new rule reflecting actual study rather than borrowed fear.
Year 10 (age 35). The couple is now considering buying a home within five years. This is squarely a "approaching a major goal" trigger from Lesson 98.4. Sushila begins a formal glide path: over the coming five years, at each scheduled review, equity allocation for the house-fund portion of the portfolio (kept mentally separate from the long-term retirement portion) will step down by roughly ten percentage points a year, moving progressively into fixed deposits and short-maturity government securities, so that by the year they actually need the down payment, that portion of the money is no longer exposed to a NEPSE downturn arriving at the worst possible moment.
Look back over that decade. Five real amendments happened: marriage, first child, a considered response to windfall income, a corrected exclusion rule, and a glide path ahead of a major goal. Every one of them would have made sense even if NEPSE had simply traded flat the entire decade, because every one of them was a fact about Sushila's life, not a reaction to a stock price. And in the one year that featured genuine market drama — the Year 6 correction — the constitution did not move an inch until the scheduled date arrived, and even then it moved only because the mechanical rebalancing rule called for it, not because fear did.
WARNING
Notice what did not happen anywhere in Sushila's decade: she never once amended her constitution on the same day she felt a strong emotion about the market. Every real amendment was written months after the triggering life event, at a pre-scheduled date, using a checklist, with the old language kept visible and struck through rather than erased. If you find yourself wanting to open your constitution document right now, today, because of something you just read in the news or just saw in your portfolio balance, that urge itself is the signal to close the document and wait for your scheduled date — not a signal that today is an exception.
Chapter recap
A personal investment constitution is not a stone tablet, and it is not a blank page either. It is a living legal-style document that changes through a deliberate, scheduled, written amendment process — never through a same-day override triggered by fear or a hot tip. The test for a legitimate change is simple and worth memorising: would this be true even if the market had done nothing this year? Marriage, children, job changes, income shifts, approaching goals, and health events pass that test. A falling index, a bad quarter, a friend's prediction, and your own anxiety do not. Build a fixed annual review date into your calendar, work through the same checklist every time — life first, then assumptions, then allocation, then costs, then watchlist, then written and dated amendments — and treat any urge to edit the document outside that date as information about your emotional state, not instruction to act on. When your calm past self and your stressed present self disagree, the calm self usually wins, unless honest inspection shows that the "calm" rule was never really calm reasoning at all, only an old fear that had time to look official. Chapter 99, The Full Investment Memo — A Complete Worked Example, will now bring every tool from this Part of the book together, showing what a complete, professional-grade investment memo looks like from first page to last, for a single real decision made the disciplined way.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVII · Chapter 99
The Full Investment Memo — A Complete Worked Example
First published 26 Aug 2026 · Last verified 29 Aug 2026
The Full Investment Memo — A Complete Worked Example
You have now spent ninety-eight chapters building tools. You have a Canon Score that rates a company across several dimensions. You have valuation methods from Chapters 45 through 49 that tell you what a business might be worth. You have sector-specific accounting rules that tell you what to trust and what to distrust in a bank's balance sheet versus a hydropower company's balance sheet versus an insurance company's balance sheet. You have governance checks that catch related-party mischief before it catches you. You have tax rules that tell you what you actually keep after NRB and the tax office take their share. And you have position-sizing rules that tell you how much of your precious capital to risk on any single idea.
The problem is that tools sitting in separate drawers do not build anything. A carpenter with a hammer in one room, a saw in another, and nails in a third has not built a chair. This chapter is about pulling every drawer into one workbench: a single document called the investment memo, written before you buy a single share, that forces every tool to work together at once.
Think of the memo the way a Kathmandu Valley engineer thinks of a structural drawing before a building goes up. The engineer does not carry the load calculations in her head and the soil report in a separate file and the client's budget in a third place, trusting memory to reconcile them at the moment concrete gets poured. She puts it all on one drawing, checked against itself, before anyone touches the ground. An investment memo is your structural drawing. If the numbers do not reconcile on paper, you find out in your notebook, not in your portfolio.
Lesson 99.1 — Why a Memo, and Why Now
A lot of Nepali retail investors buy shares the way people buy vegetables at Kalimati market: someone in a Viber group says "iyo ramro cha" (this one is good), the price is moving, and within twenty minutes an order is placed through Meroshare. There is no document. There is no thesis. There is a feeling.
Feelings are not free. A feeling-based purchase cannot be argued with later, because there was never an argument in the first place — only an impulse. When the stock falls 15 percent, the investor has no way to check whether the original reasoning has broken, because there was no original reasoning written down to check against. So the only available response is emotional: panic-sell at the bottom, or stubbornly hold because "it will come back," which is not analysis, it is hope wearing analysis's clothes.
A memo fixes this by making you write down, in advance, why you are buying, what you expect to happen, and at what price the thesis would be proven wrong. This is the single most useful habit in this entire book, more useful than any individual ratio, because it is the habit that makes every other tool actually count for something.
KEY CONCEPT
An investment memo is a short written document, completed before you buy, that states what the company does, what you think it is worth, why the market disagrees with you (or agrees, and you are simply confirming a fair price), what could go wrong, and how much money you are willing to risk finding out. It is not a research report for someone else. It is a contract you sign with your future self.
You do not need a memo for every trade. If you are doing short-term technical trading with strict stop-losses on a small trading sub-account, a full memo is overkill — Part XIV already gave you the tools for that game, and it plays by different rules. But for anything you intend to hold for more than a few months, anything that will occupy more than 2 or 3 percent of your portfolio, and certainly anything you are tempted to call a "core holding," a memo is not optional. If you cannot write one, you do not understand the company well enough to own it. That sentence is worth underlining in your notebook.
A full memo has six sections, and this chapter walks through each one, then shows you a complete example from beginning to end so you can see how the pieces click together in practice.
Section
What It Answers
Chapters It Draws On
1. Snapshot and Thesis
What does this company do, and why am I looking at it
Part I-II, sector chapters
2. Canon Score and Diligence
Is this a well-run, honest, durable business
Canon Score chapters, governance chapters
3. Valuation
What is a fair price, and where is the market pricing it now
Chapters 45-49
4. Risks
What could break this thesis
Risk chapters, sector-specific chapters
5. Taxes and Costs
What do I actually keep after NRB, DP fees, and tax
Tax chapters
6. Sizing and Decision
How much do I buy, and what is my exit trigger
Position-sizing chapters, this Part
PRACTICAL TOOL
Keep a single running file — a notebook, a spreadsheet, even a stack of ruled paper in a drawer — where every memo you ever write lives in one place, dated, never deleted, never edited after the purchase decision. When a stock disappoints you eighteen months later, you want to read exactly what you believed on day one, in your own words, not a revised memory of what you meant to believe.
Lesson 99.2 — Section One: The Snapshot and the Thesis
The first section of a memo is short — half a page, no more — and it does one job: it forces you to say, in plain words, what the company actually does and why you are spending your evening on it instead of on one of the other 270-odd companies listed on NEPSE.
Start with the boring facts. Company name, ticker symbol, sector classification as NEPSE defines it, paid-up capital, current market price, and the date you are writing the memo (prices move daily; an undated memo becomes useless within a month). Then, in two or three sentences, describe the business the way you would describe it to your father over dinner, without jargon. If a company's business cannot be explained in three plain sentences, that itself is information — either the business is genuinely complex (rare on NEPSE outside a few conglomerates) or you do not understand it well enough yet to own it.
Then write the thesis: one or two sentences stating why this stock, why now. "I believe NGTL is undervalued because the market is pricing it as a low-margin commodity trader, but its LPG distribution license and captive retail network give it a moat the market is not paying for" is a thesis. "It has been going up" is not a thesis, it is a chart observation, and charts are the subject of a different Part of this book, not this one.
WARNING
A thesis you write after you already own the stock is not a thesis, it is a rationalisation. The entire value of Section One comes from writing it before the purchase. If you already hold the shares and are only now filling in a memo, be honest with yourself about that — treat it as a hold/sell review, not a buy decision, and be extra suspicious of every bullish argument your own brain hands you, because it has a stake in agreeing with your past self.
This section should also record how you found the idea. Did a screen flag it on low P/E? Did you notice the product in daily life — LPG cylinders with the company's logo showing up more often at your neighbourhood shop? Did a sector you already understood (because you or your family works in it) throw up a name you recognised? Recording the source matters because some sources are more reliable than others, and over years of memo-writing you will learn which of your own idea-generation habits actually produce winners and which ones just produce activity.
Lesson 99.3 — Section Two: The Canon Score and Qualitative Diligence
This is where the bulk of the qualitative work from earlier in this book gets assembled into one scorecard. Recall that the Canon Score is not a single number pulled from a formula — it is a disciplined walk through several dimensions of business quality, each scored honestly, so that a strong score in one area cannot quietly hide a rot in another.
For a NEPSE company, the dimensions worth scoring in every memo are:
Business durability — does this company sell something people will keep needing in five years, regardless of who is in government or what the monsoon does? A cement company selling into a country still building roads and buildings scores well here. A company selling a single fad product scores poorly.
Management and promoter quality — has the promoter family or leadership shown, across cycles, that they treat minority shareholders as partners rather than as a source of cheap capital to be diluted or ignored? Do they communicate honestly in AGM reports, or do they bury bad news in footnotes?
Governance and related-party exposure — this is worth its own line, separate from general management quality, because Nepal's related-party disclosure norms leave real gaps. Score this by actually reading the related-party transaction notes in the annual report, not by assuming.
Financial strength — debt levels appropriate to the sector (a bank naturally carries far more leverage than a trading company, and judging both by the same debt-to-equity yardstick is a rookie mistake), working capital discipline, and consistency of cash generation versus reported profit.
Growth runway — is the addressable market still expanding (more remittance-fed consumption, more hydropower demand, more insurance penetration as awareness grows), or is this company competing for a shrinking pie?
Sector and macro sensitivity — how exposed is this business to NRB policy shifts, monsoon timing, import restrictions, or exchange rate movement? A company financing itself in dollars for imported inventory carries a risk a purely domestic retailer does not.
Liquidity and tradability — how easily could you actually sell this position at a fair price if your thesis breaks? A thinly traded counter where your own order would move the price by 3 percent is a real cost that a memo must acknowledge, not a footnote to ignore.
KEY CONCEPT
Score each Canon Score dimension on a simple 1-to-5 scale, and write one sentence of justification next to each score. The number without the sentence is worthless — it is the sentence, forced onto paper, that catches you when you are about to give a 5 out of pure enthusiasm for a stock that is merely a 3.
The governance dimension deserves particular care for any company controlled by a single family group, which describes a large share of NEPSE's trading, manufacturing, and hospitality sectors. Check promoter shareholding percentage and whether it has been quietly rising (accumulation, generally a good sign) or falling (promoters cashing out, a signal worth investigating) over the last several AGM disclosures. Check whether board seats include genuinely independent directors or merely family members and their business associates. Check whether related-party transactions — goods bought from or sold to a sister company under the same promoter group, loans given to associate companies, rent paid to a promoter-owned building — are disclosed at arm's-length pricing or simply disclosed as a number with no comparison to market rates.
CASE IN POINT
A Nepali trading company that imports edible oil, sugar, and construction inputs will often also own or be owned alongside a shipping and customs-clearing agency, a captive insurance-broking arm, or a warehousing subsidiary. None of that is automatically a red flag — vertical integration can be a genuine competitive advantage, since customs clearance delays at Birgunj or Kakarbhitta are a real cost that a company with its own clearing arm avoids. The test is whether the pricing between the group companies looks like an arm's-length transaction or like a transfer of profit out of the listed entity into a privately held sister company where minority shareholders own nothing.
Lesson 99.4 — Section Three: Valuation — Bringing Chapters 45 Through 49 to the Same Table
This is usually the longest section of the memo, and it is where investors most often cheat themselves by running one method, liking the answer, and stopping. The discipline this book has tried to build in you is triangulation: run at least two, ideally three, independent valuation methods, and treat large disagreements between them as information rather than as noise to be averaged away.
Relative valuation (Chapter 45) starts with the simplest question: what multiple of earnings or book value is the market paying for comparable companies in the same sector, and how does this company's multiple compare? For a trading company, price-to-earnings and price-to-book are both usable, but earnings for a low-margin trading business can swing sharply on inventory gains or losses from currency movements and commodity price cycles, so a single year's P/E can be misleading. Better practice: compute P/E on a three-year average normalised earnings figure, smoothing out one unusually strong or weak year.
Dividend discount modelling (Chapter 46) asks what a share is worth as a stream of future cash dividends, discounted back to today at a rate that reflects the riskiness of those cash flows. This method works best for companies with a long, stable dividend history — banks, insurers, hydropower companies with power purchase agreements — and works poorly for companies that reinvest most earnings into working capital rather than distributing them, which describes many trading companies. Use it, but weight it lightly for this sector.
Discounted cash flow (Chapter 47) is more work but more honest for a working-capital-heavy business: project free cash flow (operating cash flow minus capital expenditure) forward, discount it back, and add a terminal value. For a trading company, the key driver is not revenue growth so much as working capital efficiency — how much cash gets tied up in inventory and receivables to support each rupee of sales growth. A trading company growing revenue 15 percent a year while inventory grows 25 percent a year is burning cash to grow, and a DCF will catch that even when a simple P/E multiple will not.
Asset-based or net asset value approaches (Chapter 48) matter more for companies where the balance sheet itself, not the earnings stream, is the main source of value — real estate holding companies, some finance companies, and mutual funds, where net asset value per unit is close to the entire investment case. For an operating trading company this method is a sanity check rather than a primary tool: does the market price sit reasonably above tangible book value, given that book value alone ignores brand, distribution network, and licenses?
Sector-specific and sum-of-the-parts adjustments (Chapter 49) matter whenever a company is not really one business but several stapled together — a trading house with a hotel subsidiary, or a manufacturer with a captive finance arm. Value each piece with the method appropriate to its own sector, then add them up, rather than applying one blended multiple to a business that does not deserve one uniform multiple.
PRACTICAL TOOL
Build a simple valuation range, not a single point estimate. For each method, write down a low case, a base case, and a high case, using conservative-to-optimistic assumptions on the two or three variables that matter most (growth rate, margin, discount rate). Then look at where the low end of your most conservative method sits relative to today's market price. That gap — not the base-case average — is your actual margin of safety.
KEY CONCEPT
Margin of safety means buying at a price meaningfully below your honest estimate of fair value, so that if your analysis is wrong in a normal, human way — not catastrophically wrong, just ordinarily optimistic — you still do not lose money. It is the difference between crossing a bridge rated for ten tonnes with a five-tonne truck versus a nine-tonne truck. Both trucks cross safely on a good day; only one of them survives the engineer having been slightly wrong about the bridge.
Do not skip the sanity check of comparing your multiple to the sector's own history and to comparable listed peers. A trading company multiple that looks cheap against the NEPSE market average but expensive against its own five-year trading range, or against two or three direct peers in the same trading and distribution business, is not actually cheap — it is cheap relative to the wrong benchmark.
Lesson 99.5 — Section Four: Risks, Taxes, and Position Sizing
Every memo needs an honest risk register — not a generic list copied from the front page of an annual report's "risk factors" boilerplate, but the two or three things that would specifically break your thesis if they happened. Write them as trigger conditions, not vague worries. "NRB tightens import margin requirements on this product category" is a risk you can watch for in the news. "Things could go wrong" is not a risk, it is an admission that you have not thought hard enough.
For a company with import exposure, currency risk deserves its own line: since the Nepali rupee is pegged to the Indian rupee but Nepal's trade with third countries (China, and increasingly cross-border trade settled in other currencies) exposes importers to exchange rate movement that the peg does not fully absorb, a trading company's margins can be squeezed by a depreciating currency raising the landed cost of imported goods faster than retail prices can adjust.
REGULATORY DETAIL
NRB periodically adjusts cash margin requirements on letters of credit for imported goods, particularly for categories the central bank wants to discourage to protect foreign exchange reserves. A trading company that imports a meaningful share of its inventory can see its working capital costs rise sharply and with little warning when such a directive is issued, since a higher cash margin ties up more of the company's own funds against each LC rather than relying on bank financing. Any memo on an import-dependent company should note this as a live, recurring risk, not a one-time historical event.
The taxes-and-costs section is the one investors skip most often, and it is the one that quietly eats the most return over a holding period of several years. Two tax lines matter for a NEPSE equity holding: capital gains tax and dividend tax.
Capital gains tax in Nepal is charged on the profit from selling shares, and the rate depends on how long you held the shares and, for individuals, differs from the rate applied to institutional or corporate holders. Shares held longer than the threshold that separates short-term from long-term treatment attract a lower rate than shares sold within a shorter window, which is one more reason a memo written with a multi-year holding horizon in mind, rather than a trading mentality, tends to compound better after tax. Dividend income is subject to withholding at source — the company deducts tax before the dividend reaches your Demat-linked bank account, so what lands in your account is already net of that withholding, and no further separate filing is typically needed for that income alone if it is your only source subject to final withholding, though your overall tax situation should always be checked against current Inland Revenue Department rules, since rates and thresholds do shift with each budget.
REGULATORY DETAIL
Because capital gains tax rates and thresholds are set through the national budget and can change from one fiscal year to the next, never rely on a rate you remember from an old newspaper article. Check the rate in force in the fiscal year you expect to sell, since a change announced mid-year can alter the after-tax return on a position you have held for a long time, and the difference between short-term and long-term treatment is exactly the kind of detail a well-timed sale versus a poorly timed one can turn into a meaningfully different number in your bank account.
DP (Depository Participant) fees, broker commission on both the buy and the sell leg, and SEBON-mandated regulatory fees are smaller than tax but not zero, and a memo on a stock you plan to trade in and out of frequently should account for round-trip costs eating into any expected edge, particularly on a name where the expected mispricing is modest.
Position sizing is the last piece, and it is where all the analytical work above gets converted into an actual number of shares to buy. The core idea, covered in earlier Part XVII chapters on the Investment Constitution, is that conviction and risk should scale your position size, not your excitement level. A stock where your valuation work shows a wide margin of safety, a strong Canon Score, and a business you deeply understand earns a larger position than a stock that merely looks statistically cheap on one multiple with a weak governance score.
CAUTION
The most common sizing mistake after a good memo is not writing too small a position — it is writing a large position because the memo itself felt so thorough that thoroughness got mistaken for certainty. A well-researched thesis can still be wrong. Cap any single position, however confident the memo, at a level your Investment Constitution already fixed in advance (commonly somewhere in the 5 to 10 percent of portfolio range for a high-conviction name, lower for anything with weaker governance or thinner trading liquidity), and do not let the quality of your own homework talk you into raising that cap in the moment.
A practical sizing method many disciplined NEPSE investors use is a simple conviction ladder: assign the position an initial size based on the combination of Canon Score and margin of safety (a high score plus a wide safety margin justifies a fuller initial position; a merely adequate score or a thin safety margin justifies a starter position, perhaps half the eventual target size), then set specific price levels at which you would add to the position (if it falls further while the thesis remains intact) or trim it (if it approaches your fair value estimate and the safety margin has closed). Writing these trigger prices into the memo before you buy — not deciding them emotionally in the moment three months later — is what turns a memo from a research document into an actual operating system for your money.
Lesson 99.6 — The Complete Worked Memo: Nepal General Traders Limited
Everything above is now assembled into one continuous document, exactly as an investor would write it in a notebook the evening before placing an order. The company below, Nepal General Traders Limited, trading under the ticker NGTL, is a composite built for teaching purposes, styled on the kind of diversified trading house long listed on NEPSE that distributes essential commodities — edible oil, sugar, LPG cylinders, and construction-grade steel — through a national network of depots and retail tie-ups. The figures are illustrative, built to be realistic for the sector, not a live quote; always pull current audited figures from the company's own disclosures and the NEPSE/Merolagani data feeds before acting on any real memo.
Section One — Snapshot and Thesis
Company: Nepal General Traders Limited (NGTL). Sector: Trading. Paid-up capital: Rs 1.2 arba. Current market price: Rs 486. Shares outstanding: 1,20,00,000. Market capitalisation: Rs 5.83 arba. Date of memo: Ashoj 2082 (mid-September 2026).
Business description: NGTL imports and distributes edible oil, sugar, packaged LPG cylinders, and construction-grade steel through nine regional depots and a network of roughly 4,000 retail dealer tie-ups across all seven provinces. It holds an LPG bottling and distribution license, owns its own bonded warehouse facility near the Birgunj dry port, and operates a small captive customs-clearing subsidiary that handles roughly 60 percent of its own import volume, with the remainder cleared through third-party agents.
Thesis: The market prices NGTL as a plain commodity trader on a P/E multiple near the bottom of the trading sector's range, but this ignores two durable advantages — the LPG distribution license, which is not easily replicated given current licensing constraints, and the owned bonded warehouse and partial in-house customs clearance, which gives NGTL a real cost and speed advantage over competitors who rely entirely on third-party clearing agents, especially during the pre-Dashain and pre-monsoon stocking seasons when Birgunj customs congestion is worst. This operational moat is not visible in a simple P/E comparison against peers and is why the stock screens as merely average when it is, on inspection, better positioned than average.
Idea source: Noticed NGTL-branded LPG cylinders becoming more common at retail shops in a mid-hills district over the past year, prompting a look at the company's distribution footprint disclosure in its most recent annual report.
Section Two — Canon Score and Diligence
Dimension
Score (1-5)
Justification
Business durability
4
Essential commodities (cooking oil, sugar, cooking gas) with stable, non-discretionary demand; steel segment is more cyclical and tied to construction activity
Management and promoter quality
3
Promoter family has run the business three decades with no history of default or fraud, but communication in AGM reports is thin on forward guidance
Governance and related-party exposure
3
Customs-clearing subsidiary is majority owned by the same promoter group outside the listed entity; related-party pricing note in the annual report lacks a clear arm's-length benchmark
Financial strength
4
Low debt-to-equity for the sector, current ratio comfortably above 1.3, but receivable days have crept up two years running
Growth runway
3
LPG and packaged foods volumes still growing with rising rural cash income from remittances; steel demand is more exposed to the construction cycle and NRB credit policy
Sector and macro sensitivity
2
High exposure to NRB import margin directives, exchange rate movement on the portion of imports settled outside the India peg, and Birgunj customs disruption
Liquidity and tradability
3
Average daily traded value is moderate; a position above roughly 0.5 percent of daily turnover would need to be built over several sessions to avoid moving the price
Overall Canon Score: 3.1 out of 5, weighted toward business durability and financial strength, held back by governance and macro sensitivity. This is a solid, unglamorous business, not an exceptional one — a distinction the sizing decision in Section Six will respect.
The related-party item flagged above deserves a specific note: the customs-clearing subsidiary that handles 60 percent of NGTL's import volume is majority owned by the same promoter family outside the listed company. The annual report discloses the value of clearing fees paid to this subsidiary each year but does not benchmark that fee against what an independent clearing agent would charge for comparable volume. This is not proof of overcharging, but it is a gap in disclosure that a memo must record rather than gloss over, and it is worth a direct question to the company secretary at or before the next AGM.
WARNING
A governance question you cannot answer from public disclosure is not automatically disqualifying, but it must lower your conviction score and therefore your position size. Do not resolve an unanswered governance question by assuming the best interpretation simply because the rest of the thesis is attractive — that is exactly the bias a written memo exists to catch.
Section Three — Valuation
Three-year average normalised EPS: Rs 34.20 (smoothing out an unusually strong year driven by a one-off inventory gain during a period of rising edible oil prices, and a weak year affected by Birgunj customs disruption). At the current price of Rs 486, this gives a normalised P/E of 14.2x, compared to a trading-sector average on NEPSE closer to 16-17x over the trailing three years, and compared to the two closest listed peers trading at 15.8x and 17.1x respectively on the same normalised basis.
Book value per share: Rs 268. Price-to-book of 1.81x, in line with sector peers running between 1.6x and 2.0x, so book value alone does not suggest either a bargain or an overpayment — this method is a sanity check here, not a primary driver, exactly as expected for an operating trading business rather than an asset-holding one.
Dividend history: NGTL has paid a cash dividend in nine of the last ten years, averaging a payout ratio near 40 percent of normalised earnings, with the one skipped year explained by the customs disruption event above rather than by a change in policy. Applying a dividend discount approach with a conservative long-run growth assumption of 7 percent (roughly tracking nominal GDP growth plus a modest margin for volume growth in LPG and packaged foods) and a discount rate of 13 percent (reflecting the sector's macro sensitivity noted in the Canon Score) produces a fair value estimate near Rs 470 in the base case, Rs 410 in a conservative case assuming slower dividend growth, and Rs 560 in an optimistic case assuming faster LPG segment expansion.
Discounted cash flow, built on free cash flow after accounting for the working-capital drag of inventory and receivable growth (both trending up over the last two years, a point flagged in the financial strength row above and treated here as a real cost, not ignored), produces a base-case fair value estimate near Rs 455, with the model notably more sensitive to the working-capital assumption than to the revenue growth assumption — confirming that the real investment question for this company is working-capital discipline, not top-line growth.
Method
Low Case
Base Case
High Case
Relative valuation (P/E vs peers)
Rs 420
Rs 480
Rs 545
Dividend discount model
Rs 410
Rs 470
Rs 560
Discounted cash flow
Rs 395
Rs 455
Rs 520
Asset-based (price-to-book sanity check)
Rs 400
Rs 460
Rs 510
The three independent methods (relative, DDM, DCF) cluster in a fairly tight base-case range of roughly Rs 455 to Rs 480, against a current market price of Rs 486. This is the most important sentence in the entire valuation section: the stock is trading almost exactly at the base-case fair value cluster, not meaningfully below it.
CASE IN POINT
Notice what the triangulation revealed that no single method would have shown alone. The relative valuation method by itself, comparing only to sector peers, made NGTL look cheap. But once the DCF was built with an honest working-capital assumption, the cushion narrowed considerably. This is exactly why Chapters 45 through 49 insist on running more than one method — a single multiple can flatter a stock that a cash-flow-based method exposes as merely fairly priced.
Section Four — Risks
The three risks that would most directly break this thesis, in order of how closely the company monitors them internally versus how much is outside its control:
First, an NRB directive raising cash margin requirements on LC-financed imports for edible oil or LPG-related equipment would raise NGTL's working capital costs with little lead time, squeezing margins in the following one to two quarters until pricing adjusts.
Second, sustained rupee depreciation against currencies used for the portion of imports sourced outside India would raise landed costs faster than retail prices can be adjusted, particularly painful in the steel segment where competition is more price-sensitive than in packaged LPG.
Third, the unresolved related-party question around the customs-clearing subsidiary could, in a worse scenario than currently assumed, represent a slow transfer of value out of the listed entity — a risk that is not currently large enough to override the thesis but that should be re-checked at every annual report cycle, specifically watching whether clearing fees as a percentage of import value are rising over time.
CAUTION
None of these three risks is a reason to avoid the stock outright — every business carries risk, and a memo that lists risks only to conclude "so avoid it" has usually done the analytical work backwards, treating risk identification as a verdict rather than an input. The purpose of the risk register is to set specific things to monitor after purchase, so that a thesis-breaking event is recognised within a quarter or two of happening, not eighteen months later when the price has already told you something was wrong.
Section Five — Taxes and Costs
Holding NGTL with a multi-year horizon in mind (this memo's stated intention, consistent with the thesis in Section One) places any eventual sale in the long-term capital gains category rather than the short-term category, provided the holding period at time of sale exceeds the threshold in force under the fiscal year's Inland Revenue rules — a rate meaningfully lower than the short-term rate, and worth confirming again at the time of sale since budget announcements can shift these thresholds. Dividends received will arrive net of withholding tax deducted at source by the company before credit to the linked bank account. Broker commission and DP charges apply on both the entry and the eventual exit trade and are treated in this memo as a modest, fixed drag rather than a factor influencing the buy decision itself, since they are small relative to the position size being considered.
Section Six — Decision and Sizing
Fair value cluster (Section Three): approximately Rs 455 to Rs 480 across three independent methods. Current market price: Rs 486. The stock sits essentially at fair value, with the base case showing no meaningful discount and the low case across methods sitting below the current price, meaning a genuinely conservative read of the numbers suggests the market is not offering a margin of safety today.
Canon Score (Section Two): 3.1 out of 5 — a sound, durable business with a real but narrow operational moat, held back by a governance question that remains unresolved and by real sensitivity to NRB policy and currency movement.
Recommendation: Hold for watching, not Buy at the current price. This is not an Avoid — the business quality supports a position in the portfolio at the right price, and the thesis (an underappreciated LPG and logistics moat) remains intact and worth tracking. But the valuation work does not show the margin of safety this book has argued you should require before committing fresh capital, and the unresolved related-party question further argues against sizing up at a price offering no cushion for being wrong.
Action: Add NGTL to a watchlist with a target entry zone of Rs 410 to Rs 430 — near the conservative-case low end across the DCF and DDM methods — where a genuine margin of safety would open up. If the price reaches that zone without a change in the underlying thesis, initiate a starter position sized at roughly 3 percent of portfolio, reflecting the moderate (not high) Canon Score, with a plan to review sizing upward toward a fuller 5 to 6 percent position only after the next annual report resolves the related-party clearing-fee question with clearer disclosure. Set a specific re-check trigger: revisit this memo immediately if NRB issues any new import margin directive affecting edible oil or LPG-related categories, and revisit it at the next AGM specifically to reread the related-party transaction note.
PRACTICAL TOOL
Notice that the ending of this memo is not simply Buy or Avoid — it is a third, equally disciplined answer: wait for a better price, with a specific number written down, a specific position size pre-committed, and a specific event that would trigger a re-read of the whole memo. A memo that only ever produces a yes or a no is missing the most common honest answer in real investing, which is not yet, and a specific number instead of a vague sense of a specific number is what makes not yet an actual plan rather than an excuse to avoid deciding.
This is what a complete memo looks like: a snapshot and thesis that state plainly what the bet is, a Canon Score that scores the business honestly across several dimensions rather than one favourable headline number, a triangulated valuation that uses at least two independent methods and reports where they agree and where they diverge, a risk register with specific trigger conditions to monitor rather than vague worries, a tax section that reflects what you actually keep, and a sizing decision that respects the conviction level the rest of the memo actually earned rather than the excitement level the idea generated. Every tool from the previous ninety-eight chapters shows up somewhere in these six sections. That is the entire point of the memo format: nothing you learned in this book is meant to be used alone.
Chapter recap
This chapter took every tool built across this book — the Canon Score, the valuation methods of Chapters 45 through 49, sector-specific accounting judgment, governance and related-party diligence, tax awareness, and disciplined position sizing — and assembled them into one document: the investment memo, written before every purchase, structured into six sections running from company snapshot through to a specific, sized decision. The worked example on Nepal General Traders Limited showed that a rigorous memo does not always end in Buy; sometimes its most disciplined output is a specific price to wait for and a specific trigger to re-check, which is itself a complete and useful answer.
A single memo proves you can think clearly about one company. It does not yet prove you can build a sensible portfolio out of many such memos at once — deciding how many positions to hold, how to size them against each other, how much overlap in sector or currency risk is acceptable, and how a collection of individually sound ideas can still add up to a poorly balanced whole. Chapter 100, The Full Portfolio Construction Walkthrough, takes the next step: starting from a stack of completed memos like the one above, and building them, one allocation decision at a time, into a single coherent portfolio built to survive a full market cycle on the Nepal Stock Exchange.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVII · Chapter 100
The Full Portfolio Construction Walkthrough
First published 26 Aug 2026 · Last verified 29 Aug 2026
Every part of this book has been building toward a single, ordinary moment: a person sits down with a notebook, a bank statement, and a list of companies traded on the Nepal Stock Exchange, and decides what to actually buy. Not what to buy someday. Not what looks exciting this week. What to buy now, in what amounts, and why.
This chapter follows one such person through that moment, start to finish. Her name is Sunita. She is not a real person — she is a composite built from the kinds of investors this book has been written for — but every number in her portfolio, every rule she applies, and every mistake she avoids comes directly from the chapters that came before her. Part XVII has given you a constitution (Chapter 95), a set of allocation and diversification principles (Chapters 59 through 62), a maximum drawdown discipline (Chapter 97), and a maintenance routine (Chapter 94). This chapter is where all four are used together, on one portfolio, in one sitting. Consider it the closing exam for everything the Investment Constitution and Personal Operating System have taught you.
Lesson 100.1 — Sunita's Constitution, Revisited
Sunita is 34. She works as a staff nurse at a hospital in Kathmandu. Her husband, Ramesh, has worked in Qatar for six years, and a portion of what he sends home every month — after rent, after her son's school fees, after the family's living costs — has been quietly building up in her bank account. Two years ago, after reading about NEPSE for the first time and after a cousin lost a meaningful sum chasing a hydropower IPO on a tip from a WhatsApp group, Sunita decided she would do this properly or not at all. She wrote an investment constitution, the way Chapter 95 described: a short, plain document that states why she is investing, how long she can leave the money alone, how much loss she can absorb without panicking, and what rules she will follow no matter what the market is doing on any given day.
Her constitution, trimmed to its essentials, says the following. Her goal is to build a second source of family income over fifteen to twenty years, separate from her salary and separate from Ramesh's remittances, so that when he eventually stops working abroad the household is not solely dependent on her hospital pay. Her time horizon is long — she does not need this money for at least ten years, and ideally will still be adding to it twenty years from now. Her risk tolerance, stated honestly rather than aspirationally, is that she can watch her portfolio fall by 20 to 25 percent in a bad year without selling in a panic, because she has seen NEPSE do this before and understands it is not the same as losing the money permanently. But she also knows, from watching her cousin, that she cannot stomach watching a single stock she is heavily exposed to fall by half in a matter of weeks. That distinction — tolerating a diversified, broad decline versus tolerating a concentrated, company-specific collapse — is the single most important sentence in her constitution, and it is the sentence that will do almost all the work in this chapter.
Her constitution also states three standing rules, carried over almost word for word from Chapters 95 and 97: she will never let a single stock become large enough that a severe, plausible worst-case decline in that one stock could cost her more than a fixed, small percentage of her total portfolio. She will never let a single sector — meaning a group of companies that tend to rise and fall together for the same underlying reasons — grow beyond a fixed ceiling of her total portfolio. And she will review the whole portfolio on a fixed schedule rather than whenever the market is exciting or frightening.
Before this chapter, Sunita had these principles written down but had never applied them to actual money. She had roughly 12 lakh rupees sitting in a savings account, separate from her emergency fund, which she had already set aside in a different account entirely and does not touch for this exercise. One lakh, for readers unfamiliar with the term, is 100,000 — so 12 lakh is 1,200,000 rupees, or 1.2 million. That is the number this whole chapter works with. Nothing in what follows should be read as a recommendation of specific companies; every holding named below is invented for the purpose of this walkthrough. What is not invented is the method.
KEY CONCEPT
An investment constitution is not a mood or an intention — it is the specific, written answer to three questions: why am I investing, how long can I wait, and how much pain can I absorb without doing something I will regret. A portfolio built without answering these first is a portfolio built on guesswork, no matter how carefully the spreadsheet is formatted afterward.
Lesson 100.2 — Turning Principles into an Asset Allocation
Asset allocation, as Chapters 59 through 62 explained, is simply the decision about how to split money across broad categories before you ever pick an individual company. It is the architecture of the house before you choose the furniture. Sunita's first allocation decision is not about which bank or which hydropower company to buy — it is about how much of her 12 lakh goes into NEPSE-listed equities at all, versus how much stays outside the stock market entirely as a cushion.
Because her time horizon is long and her stated tolerance for a broad market decline is reasonably high, Sunita decides that 88 percent of this 12 lakh — roughly 10.56 lakh — will go into a diversified basket of NEPSE-listed companies. The remaining 12 percent, about 1.44 lakh, will sit inside her brokerage-linked bank account as cash, not invested in any single company. This is not the same as her emergency fund, which is a separate pool held for job loss or medical crises and plays no role in this exercise. This 12 percent is what Chapter 62 called dry powder — cash held deliberately inside the investing plan, ready to be deployed when individual companies or the whole market becomes cheap, rather than sitting fully invested at all times regardless of price.
Within that 88 percent equity allocation, Chapters 60 and 61 introduced the idea of a core and satellite structure. The core is the portion of the portfolio built from larger, more established, more liquid companies — the ones with long operating histories, steady dividends, and lower odds of a sudden, company-specific collapse. The satellite is a smaller portion built from companies that carry more upside but also more company-specific risk: newer businesses, smaller market capitalisation, more cyclical earnings, or exposure to a single project or a single monsoon season. Sunita sets her core-to-satellite split at roughly 65 percent core, 35 percent satellite, measured against her invested equity sleeve rather than her whole portfolio. This is a personal number, not a universal law — a more conservative investor might run 80/20, a more aggressive one 50/50 — but it must be decided before individual companies are chosen, because it will act as a ceiling on how adventurous the satellite half of her portfolio is allowed to get.
REGULATORY DETAIL
The Nepal Stock Exchange groups every listed company into a sector classification — Commercial Banks, Development Banks, Finance Companies, Microfinance (Laghubitta) Institutions, Life Insurance, Non-Life Insurance, Hydropower, Manufacturing and Processing, Hotels and Tourism, Trading, Investment, and Others among them. These sector groupings are published in NEPSE's own indices and daily market summaries. Using this official classification, rather than an investor's informal sense of what feels similar, is the simplest way to check a portfolio for hidden concentration — a step that takes minutes and that Sunita builds directly into her worksheet.
Sunita's allocation, restated in plain terms, now looks like this: roughly 10.56 lakh in NEPSE equities, split further into a core of about 6.9 lakh and a satellite of about 3.7 lakh, and roughly 1.44 lakh in cash reserve. The next lesson decides which sectors that equity money is actually allowed to touch.
Lesson 100.3 — Choosing Sectors Without Doubling Up on the Same Risk
This is the lesson where Part XVI's case studies stop being someone else's cautionary tale and start shaping Sunita's own decisions. Chapters within that Part described investors who believed they were diversified because they held six or seven different company names, only to discover that all six or seven rose and fell together, because they were all really the same bet wearing different tickers. The most common version of this mistake on NEPSE involves hydropower and tourism. On the surface these look like unrelated industries — one generates electricity, the other hosts trekkers and pilgrims. But both are unusually exposed to the same handful of shocks: a poor monsoon or an early, harsh winter can simultaneously reduce river flow for run-of-river hydropower plants and disrupt trekking seasons; a major external shock — an earthquake, a regional travel disruption, a border or supply issue — tends to hit tourist arrivals and large infrastructure projects at the same time, because both depend on foreign visitors, foreign contractors, and smooth cross-border logistics. An investor holding four hydropower companies and two hotel companies, thinking she has six positions, may really have one position: bet on a good year for weather and travel.
CASE IN POINT
One investor profiled earlier in this book had built what looked, on paper, like a diversified twelve-stock portfolio. Five holdings were hydropower developers of different sizes; two were hotel and resort companies. When an unusually poor monsoon season coincided with a sharp drop in tourist arrivals the same year, both groups fell together, and the investor discovered that roughly 60 percent of his portfolio had effectively been one undiversified bet on a good travel and rainfall year. The name count on his statement said twelve. The real number of independent risks he was carrying was closer to two.
Sunita takes this lesson seriously in a specific, practical way. First, she decides that hydropower and hotels-and-tourism will not both appear in her portfolio at meaningful size. She chooses to hold a single, modest hydropower position and to hold no tourism stock at all — not because tourism is a bad industry, but because she has already decided hydropower earns a place in her core-satellite structure, and holding both would mean doubling her exposure to the same seasonal and external-shock risk under two different sector labels. This is a deliberate zero-weighting, and she writes the reasoning down in her notes, because Chapter 94's routine will ask her, every quarter, to reconfirm rather than forget why a sector is missing.
Second, she gives herself a personal ceiling for hydropower that is tighter than her general sector ceiling. Her constitution's standing rule caps any single NEPSE sector at 20 percent of her total portfolio. But for hydropower specifically — precisely because of the correlation risk above, and because a single hydropower company's output can be knocked around by transmission line constraints, license renewals, or a single bad monsoon in a way that a diversified bank's loan book usually is not — she sets a personal sub-ceiling of 10 percent, half her general sector cap. This is the kind of self-imposed, more conservative rule that a written constitution makes possible: it is decided in a calm moment, in advance, rather than negotiated in the middle of an exciting IPO announcement.
Third, she looks for sectors that are genuinely driven by different underlying forces, not just different names. Commercial banks and development banks both move with the same NRB monetary policy cycle — when the central bank tightens liquidity or raises the policy rate, both groups feel it through slower credit growth and thinner interest margins, even though one lends to larger corporates and the other more often to local and cooperative borrowers. So Sunita treats banking and development banking as related enough to share one combined ceiling, even though NEPSE lists them as separate sectors. Life insurance and non-life insurance, by contrast, are driven by different things — a life insurer's fortunes track long-duration savings and mortality assumptions, a non-life insurer's track claims from vehicles, property, and crops — so she is comfortable holding both without treating them as one bet. Manufacturing and processing companies that sell everyday consumer goods tend to move with domestic household spending and raw material import costs rather than with the stock market's mood, which makes them a useful counterweight to the more sentiment-driven banking and hydropower names. A trading company gives her exposure to import volumes and consumer demand through yet another channel. And a diversified, professionally managed mutual fund — a pooled investment vehicle, regulated and listed on NEPSE, that itself holds a basket of many companies across sectors — gives her instant, low-effort diversification inside a single line item, which is useful both as a core holding and as a check against her own individual stock-picking blind spots.
WARNING
A very common and very natural mistake for a first-time NEPSE investor is what might be called remittance-and-familiarity bias: filling a portfolio almost entirely with commercial banks, because banks are the companies whose branches an investor's family has dealt with for years, whose names feel safe, and whose shares are the most heavily traded and talked about. Banks are a legitimate and often sensible core holding. The mistake is not holding banks — it is holding six of them and calling it diversification, when in truth all six will rise and fall together with the same interest rate cycle and the same national credit conditions.
With this thinking done, Sunita has her sector map: Commercial Banking and Development Banking together capped at 20 percent of the total portfolio; Life and Non-Life Insurance together capped at 15 percent; Hydropower capped at a self-imposed 10 percent with zero tourism exposure; Microfinance, Manufacturing and Processing, Trading, and a diversified Mutual Fund each held as a single meaningful position; and her 12 percent cash reserve sitting outside all of it. Only now, with the architecture decided, does she move to choosing amounts for individual companies.
Lesson 100.4 — Sizing Every Position Against the Drawdown Rule
This is the lesson where Chapter 97's maximum drawdown discipline earns its keep. A maximum drawdown rule, as that chapter explained, is a decision made in advance about the largest peak-to-trough loss an investor is willing to let any single event inflict on the total portfolio. It is different from a sector cap, which limits a whole group of related companies; a position size limit governs one company at a time, and it exists because even in a well-run, well-regulated market, individual companies can suffer sudden and severe declines — a fraud discovery, a regulatory penalty, a failed project, a collapsed merger, a scandal — that no amount of sector-level diversification protects against.
Sunita's constitution states her personal drawdown budget this way: she does not want any single holding, even in a genuinely severe, low-probability scenario, to be able to cost her more than 6 percent of her total portfolio's value in one event. To turn that budget into an actual position size limit, she needs one more number: a realistic estimate of how far a single NEPSE-listed company can plausibly fall in a genuinely bad, but not impossible, scenario. Looking at past cases of companies hit by scandal, regulatory action, or business failure, she settles on 50 percent as a sober, non-alarmist estimate of a severe single-stock decline — not a total wipeout, which is rarer, but a serious, headline-making collapse.
The arithmetic is then simple division: her maximum position size, as a percentage of total portfolio, equals her drawdown budget divided by the worst-case single-stock decline she is planning for. Six percent divided by fifty percent equals twelve percent. That is her ceiling — no single company, however much she likes it, may exceed 12 percent of her total portfolio at the moment she buys it.
PRACTICAL TOOL
A simple position-sizing worksheet, usable for any NEPSE holding: write down the maximum percentage of your total portfolio you are willing to lose to one single-company disaster (your drawdown budget). Write down a realistic, sober estimate of how far one stock could fall in a genuinely bad scenario (its worst-case drawdown). Divide the first number by the second. The result is the largest position size, as a percentage of your total portfolio, that you should hold in that company. A tighter drawdown budget or a riskier company both push this number down; a wider budget or a steadier company both push it up.
Sample holding
Assumed worst-case single-stock drop
Sunita's drawdown budget
Resulting max position size
Large, well-established commercial bank
50 percent
6 percent of portfolio
12 percent
Single hydropower developer, one project
50 percent
6 percent of portfolio, but self-capped sector-wide at 10 percent
10 percent (sector sub-ceiling binds first)
Smaller microfinance institution
60 percent (assumed higher volatility)
6 percent of portfolio
10 percent
Diversified mutual fund unit
30 percent (fund itself already diversified)
6 percent of portfolio
20 percent, though Sunita still caps it lower for balance
Notice what this worksheet actually does. It does not tell Sunita which companies are good or bad — that is a separate question, involving fundamentals, valuation, and everything else this book has covered elsewhere. It tells her, for any given conviction level, the largest amount she should ever let one holding grow to, so that being wrong about any single company cannot do more damage to her family's savings than she decided, in a calm afternoon, she was willing to absorb. Notice also that for the mutual fund, the formula alone would allow a very large position — because a diversified fund is inherently less likely to suffer a single catastrophic collapse — but Sunita chooses not to lean on that math fully, because concentrating a fifth of her whole portfolio in one product, however diversified internally, would undermine the very independence between holdings she is trying to build.
CAUTION
A position-size formula tells you the maximum you may hold. It never tells you the minimum you must hold, and it is not an invitation to round every holding up to the ceiling out of habit. A company you understand less well, or feel less conviction in, deserves a smaller position than the formula technically permits — the formula sets the outer boundary of safety, not a target to automatically fill.
Lesson 100.5 — The Complete Portfolio, Laid Out
With her allocation architecture from Lesson 100.2, her sector map from Lesson 100.3, and her position-sizing ceiling from Lesson 100.4, Sunita is ready to fill in actual company names and actual rupee amounts. The names below are entirely invented for this walkthrough — they are not recommendations, and no resemblance to any real listed company is intended. What matters is the structure they sit inside, which is built from real principles this Part has taught.
Holding
Sector
Position size (percent of total portfolio)
Amount (NPR, on 12 lakh total)
Core or satellite
Reasoning
Him Ganga Bank Ltd
Commercial Banking
12 percent
1,44,000
Core
Largest, most liquid holding, sized right at her 12 percent position ceiling because it is her highest-conviction, longest-tracked company; anchors the portfolio
Saraswati Commercial Bank Ltd
Commercial Banking
8 percent
96,000
Core
Second bank with a different customer base and geographic footprint, kept smaller so the two banks together sum to 20 percent, exactly her combined banking sector cap
Rapti Bikas Bank Ltd
Development Banking
8 percent
96,000 (adjusted within combined 20 percent cap alongside the two commercial banks above)
Core
Included for now within the same 20 percent combined ceiling as the commercial banks, since both react to the same NRB liquidity and rate cycle
Everest Jeevan Bima Ltd
Life Insurance
8 percent
96,000
Core
Long-duration savings-linked business, a different earnings driver than banking or hydropower
Gorkha Beema Company Ltd
Non-Life Insurance
7 percent
84,000
Core
Claims-driven earnings, completes the 15 percent combined insurance sector allocation
Tamor Jalvidyut Ltd
Hydropower
10 percent
1,20,000
Satellite
Single hydropower holding, deliberately capped at her self-imposed 10 percent sub-ceiling rather than the general 20 percent sector cap, with zero tourism exposure held alongside it
Karnali Laghubitta Bittiya Sanstha Ltd
Microfinance
8 percent
96,000
Satellite
Smaller institution with higher assumed volatility, sized at 8 percent, under its 10 percent formula-based ceiling
Koshi Udyog Manufacturing Ltd
Manufacturing and Processing
10 percent
1,20,000
Core
Consumer-goods manufacturer whose earnings track household spending rather than market sentiment, a useful counterweight to financial and hydropower holdings
Bagmati Trading Company Ltd
Trading
7 percent
84,000
Satellite
Import and consumer-demand exposure through a different channel again, kept modest in size
Sunkoshi Balanced Fund units
Investment or Mutual Fund
10 percent
1,20,000
Core
Professionally managed, already-diversified basket, held below its formula ceiling on purpose to avoid over-relying on any single product
Cash reserve, dry powder
Not sector-classified
12 percent
1,44,000
Neither
Held outside all equity positions specifically to be deployed when a holding above becomes cheaper, or when a new opportunity meeting her criteria appears, per Chapter 94's routine
A few things are worth pointing out about this finished table before moving on, because they are exactly the checks Chapter 94's ongoing routine will repeat every quarter for the rest of Sunita's investing life.
First, no single holding exceeds 12 percent, which was her maximum drawdown-derived ceiling. Him Ganga Bank sits right at that ceiling, which is a deliberate, not accidental, choice — it is the one holding she has the most conviction in and the longest history of following, so she is comfortable letting it use the full amount her own rule allows, rather than leaving room unused out of vague caution.
Second, no combined sector exceeds its cap. Commercial and development banking together total 20 percent, exactly at the ceiling rather than over it. Insurance totals 15 percent, exactly at its ceiling. Hydropower sits at 10 percent, at its tighter, self-imposed sub-ceiling, with tourism at a deliberate zero. This is not a coincidence; it is the result of designing the sector map first, in Lesson 100.3, and then fitting company choices inside it, rather than picking companies she liked and discovering the concentration problem afterward.
Third, the core-satellite split from Lesson 100.2 roughly holds. Adding up the core-labelled rows — the two banks and development bank, both insurers, the manufacturer, and the mutual fund — comes to about 53 percent of the total portfolio, with hydropower, microfinance, and trading making up the satellite portion at about 25 percent, and cash making up the remaining 12 percent outside both. The proportions are not perfectly identical to her original 65/35 equity-sleeve target once cash is folded back into the picture, which is normal — the sector caps and position limits are the harder constraints, and the core-satellite split is a planning guide, not a rule enforced to the decimal point.
Fourth, and this is easy to miss in a table full of numbers, every single row has a one-sentence reason attached to it that refers back to something other than "this stock looks good right now." That sentence is what turns a list of tickers into a portfolio. If Sunita cannot state, in one sentence, why a holding is sized the way it is and why it belongs next to the other holdings around it, Chapter 94's routine will treat that as a flag worth investigating at the next review, regardless of how the share price has behaved.
REGULATORY DETAIL
In Nepal, gains from selling listed shares are subject to capital gains tax, with the rate depending on how long the shares were held before sale — shares held for a shorter period are taxed at a higher rate than shares held longer, and the tax is typically deducted at source through the depository system at the time of sale. This matters directly for portfolio maintenance: every rebalancing trade that trims an overweight position is not free — it carries a tax cost that a buy-and-hold approach avoids — which is one more reason position sizes should be set thoughtfully at the start rather than corrected constantly through frequent trading.
Lesson 100.6 — Running It Going Forward: The Routine Takes Over
Building the portfolio in Lessons 100.1 through 100.5 was the easy part, in the sense that it happened once, in a single sitting, with no market noise pulling at Sunita's attention. Keeping it aligned with her constitution for the next fifteen or twenty years is the harder, longer part, and it is exactly what Chapter 94's routine was built for. A routine, in the sense that chapter used the word, is a fixed, pre-scheduled set of actions performed on a calendar basis rather than in reaction to headlines, tips, or price swings — the investing equivalent of a hospital's shift-change checklist, run the same way whether it has been a quiet week or a chaotic one.
Sunita's routine, adapted from that chapter to her specific portfolio, has four fixed parts. Once a quarter, on a date she has already put in her calendar rather than a date she picks based on how the market feels, she recalculates every position's current percentage of her total portfolio, because share prices move even when she does nothing, and a holding that started at 10 percent can drift to 14 percent purely through price appreciation, silently breaching her own position limit without a single new purchase. Once a quarter, she recalculates every sector's combined percentage the same way, since sector drift is exactly how the hydropower-and-tourism trap described in Lesson 100.3 quietly reappears even in a portfolio that was correctly diversified on day one. Once a year, she rereads her written constitution in full, out loud if it helps, and asks whether anything about her actual life — her income, her family's needs, Ramesh's plans, her own risk tolerance — has genuinely changed enough to justify changing the constitution itself, as opposed to changing it because a bad quarter made her nervous. And whenever new money arrives — a portion of Ramesh's remittance set aside for investing, a bonus, an annual increment — she runs it through the same sector map and position-sizing worksheet used to build the original portfolio, rather than simply adding it to whatever holding is currently most talked about.
WARNING
The most common way a carefully built, well-diversified portfolio quietly turns into a concentrated one is not a single bad decision — it is simple neglect. A holding that performs well for two straight years, left untouched, can grow from a disciplined 10 percent position into an undisciplined 18 percent position purely through price appreciation, silently exceeding a rule the investor still believes she is following. Rebalancing on a fixed schedule exists specifically to catch this kind of drift, because it happens gradually enough that it is almost never noticed in the moment.
When her quarterly check finds a holding or a sector that has drifted meaningfully above its cap, Sunita's rule is to trim it back toward, not necessarily all the way to, its target — selling a portion and either adding to an underweight holding elsewhere in the portfolio or letting the proceeds sit briefly in her cash reserve until the next deployment decision. When a holding has drifted below its target because the company's fundamentals have genuinely weakened rather than because its price has simply been volatile, her rule is to treat that as a signal to revisit the original one-sentence reason for owning it at all, and to be willing to exit entirely rather than mechanically topping it back up. This distinction — rebalancing because of price drift versus reconsidering because the underlying business has changed — is one Chapter 94 spent considerable time on, precisely because the two situations look similar on a portfolio statement but call for opposite responses.
CAUTION
A fixed review schedule is only useful if it is genuinely fixed. An investor who reviews the portfolio quarterly during calm periods but adds extra, unscheduled reviews during a sharp market selloff has not really built a routine — she has built a routine with an exception clause for exactly the moments the routine was designed to protect her from. The quarterly discipline matters most, not least, when the market is giving the loudest reasons to break it.
There is one more piece of Sunita's ongoing routine worth naming explicitly, because it closes the loop back to where this chapter began. Her 12 percent cash reserve is not meant to sit untouched forever. Chapter 94's routine gives her a simple, written trigger for using it: if a holding she already owns and already understands falls by a defined amount — say, 15 percent or more — without any accompanying deterioration in the reason she originally bought it, her routine allows her to deploy a portion of the cash reserve to bring that position back toward, but never above, its capped size. This turns a market decline from a purely frightening event into a partially useful one, without ever requiring her to abandon the position limits from Lesson 100.4 in the excitement of a perceived bargain. When the cash reserve is used this way and eventually falls below a minimum threshold she has set — say, 5 percent of the total portfolio — her routine directs new remittance-funded contributions back into cash first, rebuilding the reserve, before any of it is deployed into new positions.
Seen end to end, Sunita's portfolio is not a clever stock-picking exercise. It is closer to a small, well-run institution with its own written charter, its own risk limits, and its own maintenance schedule, applied by one person to her own family's savings. The constitution from Chapter 95 told her why she was doing this and how much pain she could bear. The allocation logic from Chapters 59 through 62 told her how to split the money into core and satellite before naming a single company. The correlation lessons from Part XVI's case studies told her which sectors were secretly the same bet wearing different names, and kept her out of the hydropower-and-tourism trap that had cost other investors dearly. The drawdown discipline from Chapter 97 turned her tolerance for pain into an exact ceiling on any one position. And the routine from Chapter 94 is what will keep all of the above true not just on the day she built it, but on every ordinary Tuesday for the next twenty years, long after the excitement of building it has faded into simple, repeated habit.
Chapter recap
This chapter took every tool built across Part XVII and used them together, in order, on one worked example. A fictional investor, Sunita, began with a written investment constitution stating her goals, her time horizon, and her honestly assessed tolerance for both broad market declines and single-company collapses. She used asset allocation principles to split her capital between an equity sleeve and a cash reserve, and further into core and satellite portions within that sleeve. She used the correlation lessons from Part XVI's case studies to build a sector map that avoided doubling up on hidden, shared risks — most notably the hydropower-and-tourism trap — before choosing any individual company. She used a maximum drawdown rule to convert her personal tolerance for single-stock disaster into an exact position-size ceiling, and applied that ceiling consistently across every holding in her final table. And she used a fixed, calendar-based routine to ensure that the discipline built into the portfolio on day one does not quietly erode through ordinary price drift, market excitement, or simple neglect over the years that follow.
With this chapter, Part XVII, The Investment Constitution and Personal Operating System, is complete. Across its seven chapters, this Part has argued a single, cumulative point: that a durable NEPSE portfolio is built less by finding the right stock and more by building the right system around whatever stocks are eventually chosen — a written constitution, a sound allocation, a genuine diversification check, a hard limit on single-company damage, and a routine that keeps all of it honest over time. The chapters ahead move the book beyond this personal operating system and into new territory, carrying the discipline built here into the chapters that follow.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII
OPERATIONAL TOOLS, DATA REFERENCE & ALMANAC
Part XVIII · Chapter 101
Pre-Buy Checklists
First published 26 Aug 2026 · Last verified 29 Aug 2026
Bishal Karki keeps a spiral notebook in the drawer of his desk at the ISP in Kathmandu where he works as a network operations engineer. It is not a diary and it is not a budget book. It is thirteen years of pre-buy checklists, one page per stock he seriously considered buying, dated, filled out in blue ink, sometimes crossed out and rewritten when he caught himself fudging an answer. He started the habit in 2016 after a hydropower IPO he chased on a tip from a colleague dropped 40 percent in four months. He did not lose enough money to change his life. He lost enough to change his process. Since then, no buy order goes into his Mero Share TMS account until the page is filled in, every box ticked or explicitly overridden with a written reason. He has bought stocks the checklist did not love, but never one it flagged as a hard no.
This chapter is his notebook, generalised. Everything you have learned in this book — the Canon Score from the quality and valuation chapters, the governance red flags from Chapters 20 through 23, the valuation methods of Chapters 45 through 49, the sector playbooks of Chapters 71 through 75, the position and sector caps from your Investment Constitution in Chapter 95 — collapses here into a single operational moment: the moment before you click "confirm" on a buy order. A checklist is not a substitute for judgment. It is a guardrail against the version of you that exists for the ninety seconds after a stock jumps 8 percent and a friend messages you that it is "sure to hit circuit again tomorrow." That version of you is not stupid. That version of you is just temporarily not thinking about base rates, caps, or governance filings. The checklist thinks about them for you.
Lesson 101.1 — Why Checklists Beat Conviction
NEPSE rewards conviction in the short run often enough that investors mistake it for a strategy. A rumour about a hydropower PPA renegotiation, a WhatsApp forward about a bank's upcoming bonus share, a sudden spike in a microfinance counter after a brokerage report nobody has actually read past the headline — any of these can move a stock 5 to 10 percent inside a session, hit the circuit filter, and pull in retail buyers who never asked the first diagnostic question. Some of those trades work out. Enough of them do that the habit of buying on conviction alone survives contact with a few profitable trades before it eventually meets a stock whose problems were visible in the AGM minutes, the auditor's note, or the promoter shareholding disclosure the entire time.
The checklist exists because human judgment under time pressure and social proof is unreliable in a specific, well-documented way: pilots use pre-flight checklists not because they forget how to fly, but because a rushed, excited, or distracted brain skips steps it knows perfectly well it should not skip. Investing has the same failure mode. You know intellectually that you should check a company's CD ratio before buying a bank, or its debt-service coverage before buying a hydropower counter. Under the adrenaline of a green candle and a circuit filter, that knowledge does not reliably convert into action unless it is written down in a sequence you physically work through before the order goes in.
KEY CONCEPT
A pre-buy checklist is not a research report. It is a gate. Its job is not to tell you whether a stock is a good investment — your valuation work, sector analysis, and Canon Score already did that — its job is to catch the specific, recurring mistakes that excitement causes you to skip: buying past your position cap, buying past your sector cap, buying a stock with an unresolved governance flag, buying at a price with no liquidity to exit, buying a rumour instead of a filing.
Bishal's version of this discipline is mechanical almost to the point of being boring. When a stock catches his attention, he closes the trading app. He opens a spreadsheet instead. He does not look at the live price again until every item on the universal checklist and the relevant sector supplement is answered. This single habit — separating the moment of interest from the moment of the buy button — has, by his own count, stopped him from making at least six purchases over the past decade that he is now glad he did not make. One of them, in 2024, is this chapter's worked example, and we will walk through it in full in Lesson 101.6.
The checklist also serves a second, quieter purpose. It is the operational expression of your Investment Constitution from Chapter 95. A constitution that lives only as a paragraph you wrote once and never look at again is a wish, not a rule. A checklist that you fill in, on paper or in a spreadsheet, every single time before a buy order, is that constitution actually governing your behaviour. The two documents should reference each other explicitly: your constitution states the caps and thresholds in principle, and the checklist is where you test each specific purchase against them, in writing, before money moves.
Lesson 101.2 — The Universal Pre-Buy Checklist
The following items apply to any NEPSE-listed stock, regardless of sector — a bank, a hydropower producer, a microfinance institution, a hotel, a manufacturing company, or a trading house. Run through all of them before every buy order. If you are adding to an existing position rather than opening a new one, run through them again — a stock that passed six months ago may have quietly failed one or two of these since.
1. Liquidity check
Average daily traded value over the last 20-30 sessions
Fails if your intended position cannot be built or exited over a few sessions without moving the price materially
2. Canon Score threshold
Composite score from quality, governance, and valuation inputs built across Chapters 30-59
Fails if below your own minimum, typically 60-65 out of 100 depending on your risk tolerance
3. Position size within cap
Rupee value of intended purchase as a percentage of total portfolio
Fails if it pushes any single holding past the per-stock cap in your Investment Constitution, commonly 8-10 percent
4. Sector cap headroom
Current sector exposure plus intended purchase, as a percentage of portfolio
Fails if it pushes total sector exposure past your constitution's cap, commonly 20-35 percent depending on sector
5. Valuation versus Chapter 45-49 methods
Fair value range from at least two applicable methods (DDM, P/BV, P/E relative to sector, or sum-of-parts)
Fails if the current price sits above the top of your fair value range with no margin of safety
6. Governance red flags per Chapter 20-23
Board independence, related-party transactions, auditor tenure and qualifications, ownership concentration
Fails if any unresolved red flag exists that you have not specifically investigated and cleared
7. Disclosure history
Timeliness of quarterly reports, AGM scheduling, SEBON correspondence on record
Fails if there is a pattern of late filings, delayed AGMs, or unexplained restatements in the last two years
8. Promoter holding trend
Direction of promoter shareholding percentage over the last four to eight quarters
Fails if promoters are steadily reducing their stake without public explanation, or if shares are pledged against loans
9. Financial statement quality
Auditor opinion type, any qualification or emphasis-of-matter paragraph, auditor rotation history
Fails if the current auditor issued a qualified opinion, or if the company changed auditors more than once in three years without clear cause
10. Price action sanity check
Distance of current price from its 200-day average and from its 52-week range
Fails if the stock is more than roughly 40-50 percent above its 200-day average with no fundamental catalyst disclosed to the exchange
11. Dividend and bonus consistency
History of cash dividend, bonus share, and rights issue decisions over five years
Fails if payout history is erratic in a way unexplained by earnings, or if repeated rights issues suggest chronic capital shortfall
12. Debt and coverage check
Debt-to-equity and interest coverage ratio, for non-BFI sectors
Fails if interest coverage has fallen below roughly 2x or debt-to-equity has risen sharply in the last year without a stated reason
13. Rumour-versus-filing check
Source of the information that triggered your interest
Fails if your reason for buying today traces back to a social media post, WhatsApp forward, or brokerage chat rather than a company or regulatory filing
Thirteen items, each answerable in a sentence or two, most of them checkable from the company's disclosures on the NEPSE and company websites, the merchant banker's IPO/FPO prospectus if applicable, and your own portfolio tracker. None of this requires special access. It requires the discipline to do it before buying rather than after, when the answers are much less useful.
PRACTICAL TOOL
Build this as an actual spreadsheet, not a mental list. One row per checklist item, one column for your answer, one column for pass/fail, and a final column for a one-line note if you are overriding a fail. Duplicate the sheet for every prospective buy and date-stamp it. Bishal names his files by ticker and date — for example BOKL_2024-03. Two years later, when a stock you bought is down 30 percent, this file tells you honestly whether the checklist missed something or whether you overrode a fail and are now living with the consequence you accepted in writing.
Note item 5 deserves particular care because it is where most bad NEPSE buys actually go wrong. A stock can pass every governance and liquidity test and still be a poor buy simply because you are paying too much for it. Apply at least two of the valuation methods from Chapters 45 through 49 — for a bank, that typically means a dividend discount model check plus a price-to-book comparison against sector peers; for a manufacturing or trading company, a price-to-earnings comparison against historical range plus a discounted cash flow if the business is stable enough to project; for a hydropower company, typically a discounted cash flow built off the PPA cash flow stream, which we return to in Lesson 101.4. If your two methods disagree by a wide margin, that disagreement itself is information — investigate why before buying, rather than picking whichever method gives you the answer you already wanted.
WARNING
Circuit-filter chasing is the single most common way this checklist gets skipped rather than failed. When a stock hits its upper circuit two days running, the temptation is to buy before it opens the third day, reasoning that a circuit means everyone else knows something you do not want to miss. This is precisely the situation the checklist exists for. A circuit-driven rally is not evidence of anything except that current buying pressure exceeds current selling pressure at that price — it says nothing about promoter holding trends, governance flags, or whether the price sits inside or outside your valuation range. Run the full checklist on a circuit-hitting stock exactly as you would on a quiet one. If anything, run it more carefully, because your own excitement is now working against you.
Commercial banks and development banks are NEPSE's largest sector by market capitalisation, and the sector playbook in Chapters 71 through 73 gave you the analytical tools to evaluate one. Before a buy order on any bank, add the following checks to the universal thirteen.
Capital adequacy ratio, or CAR, is the first number to pull. Nepal Rastra Bank requires commercial banks to maintain a minimum total capital adequacy ratio of 11 percent, which is built from an 8.5 percent minimum plus a 2.5 percent capital conservation buffer, with Tier 1 capital required to make up a defined minimum portion of that total. A bank sitting close to the regulatory floor has little room to absorb a bad quarter, cannot grow its loan book aggressively without a capital raise, and is a candidate for a dilutive rights issue that will hurt you as a shareholder even if the bank itself survives. A bank comfortably above the floor — commonly two to four percentage points of headroom — has room to grow and to weather a stress period without immediately going to shareholders for more capital.
Non-performing loan ratio, or NPL, is the second number. Gross NPL above roughly 3 percent for a commercial bank in the current Nepali banking environment warrants a closer look at which sectors the bad loans are concentrated in — real estate, hydropower construction finance, and margin lending against shares have all produced concentrated stress at different points in the last decade. Gross NPL above 5 percent, especially if it has been rising for more than two consecutive quarters, is a red flag serious enough that it should usually stop a buy outright unless you have a specific, well-supported thesis for why the trend reverses.
Credit-to-deposit ratio, commonly called the CD ratio, is the third number, and it is one NRB actively regulates. Banks are required to keep this ratio under a regulatory ceiling — 90 percent under the directive currently in force — because a bank lending out too large a share of its deposit base has thin liquidity buffers and is vulnerable to a deposit withdrawal shock. A bank sitting right at the ceiling has no room to grow its loan book, which caps its near-term earnings growth regardless of how good its underlying franchise is, and signals it may need to compete harder for deposits, typically by raising deposit rates and compressing its net interest margin.
Banking
CAR, NPL ratio, CD ratio
CAR near the 11 percent floor, NPL above 5 percent, or CD ratio pinned at the 90 percent ceiling
Hydropower
PPA tariff and escalation, COD date status, hydrology basis (design energy vs P50/P90)
COD delayed past the PPA's committed date, tariff below current wet-season benchmark, or generation consistently below design energy
Microfinance
Portfolio at risk (PAR30/90), write-off ratio, borrower over-indebtedness exposure
PAR30 above roughly 5 percent, rising write-offs for more than two quarters, or heavy concentration in districts flagged for multiple borrowing
Beyond these three headline ratios, check the bank's net interest spread and cost of funds trend — a bank whose cost of funds is rising faster than its lending yield is losing margin regardless of loan growth — and check for any related-party lending disclosed in the notes to the financial statements, since Nepali banks are required to disclose loans to directors, promoters, and connected companies, and a pattern of large connected-party exposure is exactly the kind of governance flag Chapter 21 taught you to weigh heavily.
REGULATORY DETAIL
NRB's CD ratio directive and capital adequacy framework are published in its Unified Directives, updated most recently through its monetary policy statements, and are enforced through the same on-site and off-site supervision that produces the CAMELS-style ratings you may see referenced in analyst commentary. When a bank's quarterly disclosure shows CAR or CD ratio moving toward a regulatory limit, treat it as an operating constraint on that bank's near-term growth and dividend capacity, not merely a compliance footnote.
Lastly, look at the bank's dividend capacity specifically. NRB restricts distributable dividends based on capital position and loan loss provisioning levels, so a bank with a thin capital buffer may be legally constrained from paying the cash dividend its earnings alone would suggest. If your valuation thesis depends on a specific dividend yield, confirm the bank actually has the regulatory room to pay it.
Lesson 101.4 — Hydropower Sector Supplement: PPA, COD, and Hydrology
Hydropower is NEPSE's most idiosyncratic sector because a hydropower company's entire cash flow stream is determined by a small number of contractual and physical facts fixed at construction, long before you ever consider buying the stock. The sector playbook in Chapter 74 covered these in depth; here is the pre-buy version.
Start with the power purchase agreement, or PPA, signed with the Nepal Electricity Authority. Pull the tariff rate — nearly all Nepali run-of-river PPAs specify separate wet-season and dry-season tariffs, with the dry-season rate meaningfully higher because dry-season generation is scarcer and more valuable to the grid. Check whether the tariff includes an escalation clause — many older PPAs escalated tariffs annually for the first several years before flattening, while some more recent PPAs are signed flat from commercial operation. A company whose PPA tariff is meaningfully below what newer projects are securing is locked into that lower revenue stream for the full PPA term, typically twenty to twenty-five years or more, regardless of what the sector's current tariff environment looks like.
Commercial operation date, or COD, is the second item, and it matters differently depending on whether the project is already operating or still under construction. For an operating project, confirm COD actually occurred on or near the date committed in the PPA — a project that achieved COD significantly late usually paid liquidated damages or lost a portion of its PPA term, and the reasons for the delay (contractor disputes, access road failure, transmission line congestion) are worth understanding because they tend to recur across a promoter's other projects. For a project still under construction whose shares are already listed and trading — common in Nepal, where hydropower IPOs frequently happen years before COD — treat the current share price with real caution: you are pricing construction-phase execution risk, and a checklist item here should specifically be "what percentage of physical construction is complete versus what the company's disclosed timeline implies," cross-checked against the company's own progress disclosures rather than promoter assurances.
Hydrology is the third and most technical item, and the one retail investors skip most often because it requires reading past the summary numbers. A project's design energy figure — the annual generation the project was engineered to produce — is usually quoted alongside a P50 and P90 exceedance probability, meaning the generation level expected to be met or exceeded in 50 percent of years and 90 percent of years respectively. A company that reports actual generation consistently at or above its P50 figure is performing to plan. A company reporting generation persistently below P90 across multiple years has a hydrology problem — perhaps the original feasibility study overestimated river flow, perhaps siltation or upstream diversion has reduced flow, perhaps climate variability is showing up as reduced dry-season flow. Any of these permanently impairs the cash flow the stock is worth, and none of them show up in a quick glance at the share price chart.
CASE IN POINT
A hydropower project's glossy annual report photo of a full reservoir behind the intake structure tells you nothing about hydrology performance. The number that tells you something is actual annual generation in gigawatt-hours, compared line by line against the design energy and P90 figures disclosed in the original detailed project report or IPO prospectus. If the company's annual report does not make this comparison easy to find, that omission is itself worth noting on your checklist.
Add to this the project's debt-to-equity structure at financial close, since most Nepali hydropower projects are financed at high leverage, commonly 70:30 or 80:20 debt-to-equity, meaning debt service coverage is a live risk for years after COD rather than a one-time construction-phase concern; the royalty regime, since NRB and the Department of Electricity Development step up royalty rates significantly after the fifteenth year of operation, compressing free cash flow later in the PPA term in a way your valuation model needs to reflect explicitly; and insurance and force majeure provisions, since a landslide or flood damaging the intake or powerhouse is a real and recurring risk category in Nepali hydropower, not a remote tail scenario.
WARNING
A hydropower stock's price can move sharply on rumour of a tariff renegotiation, a new PPA signing, or a COD announcement well before any of these are confirmed by an actual NEA or company filing. Treat any such move exactly as item 13 in the universal checklist requires: trace the information to its source before buying into the move. A tariff renegotiation rumour that turns out to be unconfirmed, once the excitement fades and the stock gives back its gain, is one of the more common regretted buys in this sector.
Lesson 101.5 — Microfinance Sector Supplement: Portfolio Quality and Over-Indebtedness
Microfinance institutions, or MFIs, listed on NEPSE carry a sector-specific risk that banking and hydropower do not: the risk that the same borrower has taken loans from multiple MFIs simultaneously, a problem that became visible at scale in Nepal over the past several years as microfinance penetration deepened faster in some districts than lenders coordinated with each other. The sector playbook in Chapter 75 covered the underlying dynamics; the pre-buy checklist translates it into specific numbers to pull before buying any MFI's shares.
Portfolio at risk, commonly reported as PAR30 or PAR90 — meaning the percentage of the loan portfolio with payments overdue by 30 or 90 days respectively — is the headline asset quality number, and it is more informative for microfinance than the NPL ratio is for banks, because group-lending microfinance portfolios can show delinquency patterns well before a loan is formally classified as non-performing. A PAR30 comfortably under roughly 3 percent is healthy for a well-run Nepali MFI; PAR30 above 5 percent, or a PAR30 trend that has been rising for more than two consecutive quarters, deserves the same weight as a rising NPL ratio does for a bank.
Write-off ratio is the second number. MFIs write off uncollectible loans against their loan loss reserve, and a rising write-off ratio combined with declining loan loss reserve coverage means the institution is absorbing losses faster than it is provisioning for them — a pattern that eventually forces either a capital raise or a profit hit large enough to move the share price sharply.
Over-indebtedness exposure is the item unique to this sector and the hardest to check directly, since no single company filing states "our borrowers have loans from three other MFIs." What you can check: NRB has, over recent years, pushed MFIs toward credit information sharing and tightened rules intended to limit how many microfinance institutions can lend to the same borrower, partly in response to well-documented borrower over-indebtedness concentrated in certain districts of Nepal's hill and Tarai regions where MFI branch density grew fastest. Check the company's disclosed geographic concentration of its loan book — an MFI heavily concentrated in districts already flagged in NRB or Nepal Microfinance Bankers' Association reporting as having high MFI density carries meaningfully more over-indebtedness risk than one with a more geographically diversified book, even if its current PAR numbers look fine today.
REGULATORY DETAIL
NRB caps the interest rate spread microfinance institutions may charge between their cost of funds and their lending rate to end borrowers, adjusted periodically through NRB circulars, precisely because microfinance lending rates would otherwise run well above what a subsistence borrower's cash flow can service. A spread ceiling that tightens further squeezes MFI net interest margins directly, and any change in this ceiling should be treated as a sector-wide earnings input, not company-specific news, when it appears in NRB's monetary policy statements.
Group lending concentration and loan officer productivity are the remaining items worth a glance: an MFI whose growth has come from rapidly expanding loan officer headcount and branch count in new districts is taking on origination risk faster than its credit systems may be able to absorb, a pattern that has preceded asset quality deterioration at more than one Nepali MFI in the past.
CAUTION
Do not let a strong dividend yield on a microfinance stock substitute for checking PAR and write-off trends. MFIs can and do continue paying dividends for a period even as portfolio quality quietly deteriorates, particularly when loan loss provisioning has not yet caught up to actual delinquency. By the time the dividend itself is cut, the checklist items above have usually already been signalling trouble for two or three quarters.
Lesson 101.6 — The Checklist as a Living Document
In March 2024, Bishal came across a hydropower counter we will call Himal Bridge Hydropower Ltd, ticker HBHL, a run-of-river project that had reached commercial operation about eighteen months earlier. A senior colleague at his office, who had bought into HBHL's IPO, mentioned over lunch that the stock had moved up sharply over the previous two weeks on talk of a tariff escalation clause that would push the company's per-unit revenue meaningfully higher starting the following fiscal year. The stock had gained close to 30 percent in twelve trading sessions and had hit its upper circuit twice. Bishal's Canon Score screen had already flagged HBHL as a stock with rising price momentum, and on a first look the story was attractive: an operating project, a PPA in place, a plausible tariff catalyst, and a sector he was underweight relative to his own target allocation. He closed the trading app and opened his checklist spreadsheet.
The universal checklist got through most of its items cleanly. Liquidity was adequate — average daily turnover over the prior month was well above what his intended position size would require. The valuation check, run as a discounted cash flow off the disclosed PPA tariff schedule, suggested the stock was trading close to fair value even before the rumoured tariff escalation, meaning the rumour, if true, would represent genuine upside rather than an already-priced-in story. Item 4, sector cap headroom, is where the first problem appeared. Bishal's Investment Constitution capped hydropower exposure at 20 percent of total portfolio value. His portfolio tracker showed he was already at 22 percent hydropower going into this prospective buy, a fact he had not actually registered until the spreadsheet forced him to pull the number, because two of his existing hydropower holdings had risen in price over the prior quarter and mechanically increased their weight without any new buying on his part. Adding HBHL at his intended size would have pushed hydropower exposure to roughly 27 percent of the portfolio — a clear sector cap breach, not a marginal one.
That alone was reason enough to stop, but Bishal kept working through the sheet, partly out of habit and partly because he wanted to understand whether HBHL was worth trimming another hydropower holding to make room for. Item 8, promoter holding trend, is where the second and more serious problem surfaced. HBHL's quarterly shareholding disclosures over the preceding six quarters showed promoter holding declining from roughly 51 percent to just under 43 percent, a drop of eight percentage points with no rights issue, merger, or other corporate action that would explain a mechanical dilution. The company's disclosures did not include any public statement explaining the reduction. Cross-checking item 7, disclosure history, Bishal found that HBHL's most recent AGM had been held nearly ten months after its statutorily expected date, and a SEBON notice on record referenced a delay in submitting audited financial statements for the prior fiscal year, with no public explanation beyond a procedural one-line notice.
Individually, a promoter reducing their stake is not automatically disqualifying — promoters sell for many ordinary reasons, including simply realising gains or meeting personal liquidity needs. A late AGM is not automatically disqualifying either — administrative delays happen. But the combination — steady, unexplained promoter selling, a materially delayed AGM, and a SEBON notice on record, arriving at the exact moment a rumour-driven rally was pulling in retail buyers on a story about a tariff catalyst that had not yet been confirmed by any NEA or company filing — was precisely the pattern the governance chapters warned against treating as three unrelated data points. Taken together, it read as a company where insiders were reducing their own exposure while public disclosure was becoming less timely, at the same moment retail sentiment was most enthusiastic.
Bishal did not buy HBHL. Four months later, the tariff escalation rumour was never confirmed in any NEA correspondence or company filing; HBHL instead disclosed a downward revision to its full-year generation guidance, citing lower-than-expected dry-season flow relative to its design energy assumptions, and the stock gave back the entire rally plus an additional 15 percent. Both the checklist item that would have flagged this directly — hydrology performance against design energy under the hydropower supplement — and the two governance items from the universal checklist had been sitting in plain sight in public disclosures the entire time. The only reason they mattered is that Bishal read them before buying rather than after.
CASE IN POINT
The HBHL near-miss illustrates why the checklist has to run in full even when the first few items look good. A stock can pass liquidity, valuation, and Canon Score comfortably and still fail on sector cap headroom or a governance pattern that only becomes visible when you deliberately look at four or five quarters of disclosures side by side rather than the most recent one in isolation. Bishal's sector cap breach alone would have stopped the purchase; the promoter and disclosure pattern confirmed that stopping was the right call for reasons beyond simple portfolio arithmetic.
The lesson generalises past this one stock. A checklist only works if it is treated as a gate you cannot talk yourself past, not a form you fill in to justify a decision you had already made over lunch. The moment you catch yourself rationalising an override — "the sector cap is close enough," "the AGM delay was probably just administrative," "everyone says the tariff news is confirmed" — is the moment the checklist is doing its job, and the discipline is to let it stop you rather than to explain the fail away.
CAUTION
Do not let the checklist itself become a source of overconfidence. Passing all thirteen universal items and the relevant sector supplement tells you a stock has cleared your minimum bar, not that it is guaranteed to perform well. Hydrology can still disappoint after a clean checklist pass; a bank's asset quality can still deteriorate after a clean CAR and NPL reading. The checklist filters out avoidable, foreseeable mistakes. It does not and cannot eliminate ordinary investment risk.
Keep the checklist itself alive, not static. Revisit your Investment Constitution from Chapter 95 at least once a year, and update the checklist's specific thresholds when you do — position caps, sector caps, your minimum Canon Score, your NPL and CAR comfort levels for banking, your PAR30 comfort level for microfinance. NRB's own regulatory ceilings change periodically through monetary policy statements and unified directives, and a checklist referencing a CD ratio ceiling or capital conservation buffer that NRB has since revised is a checklist quietly drifting out of date. Set a calendar reminder, ideally tied to your annual constitution review, to re-read this chapter's thresholds against the current regulatory framework and adjust your spreadsheet accordingly.
Do not let the checklist live only as a PDF you filled in once, printed, and filed away. Its value comes from being used, under time pressure, at the exact moment temptation is highest — which means it needs to be somewhere you will actually open it before a buy order, not somewhere you would need to go searching for. Bishal keeps his as a spreadsheet pinned in his phone's home screen shortcuts, a habit as deliberate as keeping a stop-loss order in place. Some investors keep a laminated one-page version of the universal thirteen items taped inside a desk drawer. The format matters far less than the guarantee that you will consult it every time, before the order, not after.
PRACTICAL TOOL
Pair your checklist file with a simple override log: a running list of every time you bought despite a fail, what the fail was, and what happened to the position afterward. Review this log once a year alongside your constitution review. If overridden fails are consistently followed by poor outcomes, that is direct evidence your checklist thresholds are correctly calibrated and your discipline in respecting them needs to tighten. If overridden fails are followed by good outcomes more often than not, that is a signal worth investigating too — perhaps a specific threshold is set more conservatively than the evidence justifies, and it is worth revisiting deliberately rather than simply overriding case by case.
Chapter recap
A pre-buy checklist converts everything else in this book into a repeatable gate you run before every NEPSE buy order, rather than a set of ideas you apply only when you remember to. The universal checklist covers thirteen items applicable to any stock: liquidity, Canon Score, position size cap, sector cap headroom, valuation against the methods from Chapters 45 through 49, governance red flags from Chapters 20 through 23, disclosure history, promoter holding trend, financial statement quality, price action sanity, dividend and bonus consistency, debt and coverage, and a check on whether your reason for buying traces back to a filing or to a rumour. Banking adds capital adequacy ratio, non-performing loan ratio, and credit-to-deposit ratio against NRB's regulatory ceilings. Hydropower adds PPA tariff and escalation terms, commercial operation date status, and hydrology performance against design energy and P90 exceedance levels. Microfinance adds portfolio at risk, write-off ratios, and borrower over-indebtedness exposure concentrated by district. Bishal's near-miss on Himal Bridge Hydropower showed how a sector cap breach and a governance pattern — unexplained promoter selling alongside a delayed AGM and a SEBON notice — can sit in plain public disclosure the entire time a rumour-driven rally is pulling in buyers, and how running the full checklist rather than stopping at the first attractive-looking item is what catches it. Keep the checklist tied to your Investment Constitution from Chapter 95, updated at least annually against current NRB thresholds, used every single time before an order goes in rather than filed away as a one-time exercise. Chapter 102, Financial Model Templates, moves from checklist to spreadsheet: ready-to-use financial model templates for projecting hydropower, banking, and microfinance company earnings, the modelling backbone the valuation checks in this chapter's item 5 depend on.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 102
Financial Model Templates
First published 26 Aug 2026 · Last verified 29 Aug 2026
Kabita Sunuwar keeps twelve tabs open on her laptop most evenings, and none of them are broker research PDFs. She is a structural engineer by training, employed by a mid-sized hydropower EPC contractor in Kathmandu, and she has never once put money into a NEPSE-listed company without first building her own model for it in a spreadsheet. Her colleagues tease her about this. Her brother-in-law, who trades on tips from a Facebook group, has made more money on individual calls than she has in some years. But Kabita has a simple rule, one she repeats to anyone who asks: a broker's target price tells you what someone else assumed, not what you should assume. If you cannot open the workbook and see where every number came from, you do not actually own an investment thesis. You own somebody else's opinion, wearing your money.
This chapter is about the models themselves — the actual row-by-row structure Kabita and investors like her build for the three company types that dominate the productive end of NEPSE: commercial banks, hydropower companies, and microfinance institutions (MFIs). Everything here draws on ground already covered in this book — the sector accounting in Chapters 29 through 34, the project finance mechanics in Chapters 42 through 44, and the valuation methods in Chapters 45 through 49. What follows is not theory restated. It is the spreadsheet.
Lesson 102.1 — Why Build Your Own Model
Before the line items, a word on why this exercise is worth the hours it takes.
Nepali retail investors are, on the whole, badly served by third-party research. Brokerage houses publish notes, but coverage is thin, update frequency is irregular, and the incentive structure of a brokerage — which earns commission on turnover, not on the accuracy of its price targets — does not reward the kind of conservative, assumption-transparent modelling an investor actually needs. SEBON has pushed for better disclosure standards over the years, and NEPSE's own filing requirements have improved, but the raw material — quarterly reports, annual reports, PPA documents, NRB circulars — still has to be assembled and interpreted by someone. That someone should be you.
A model is not a prediction machine. It is a structured way of making your assumptions visible to yourself, so that when the assumptions turn out to be wrong — and some will — you can see exactly which one broke and adjust it, rather than throwing out the whole thesis in a panic or, worse, not noticing the assumption broke at all.
Kabita's own approach, which she has refined over roughly eight years of holding NEPSE positions, has three habits underneath it that apply to any of the three template types below.
First, she draws a hard line between numbers that come from audited financial statements and numbers that are her own assumption. In her workbooks these are literally colour-coded — black font for anything traceable to an audited balance sheet, income statement, or regulatory filing, blue font for anything she has assumed (growth rates, future tariffs, provisioning ratios, generation estimates). This sounds trivial until you have tried to defend a valuation to yourself six months later and cannot remember which numbers were facts and which were guesses.
Second, every model rolls up to a single number: value per share, on a fully diluted basis, that she can compare directly to the NEPSE quote. A model that produces "the company looks healthy" is not a model. A model produces a number you can act on.
Third, she never builds a model once. Every template below is a living document, rebuilt — not just tweaked — each quarter when new disclosures land. Lesson 102.6 covers that discipline in detail, because it is where most retail modelling efforts actually die.
KEY CONCEPT
Every usable financial model for a NEPSE company does three things: separates audited fact from assumption by source, carries the assumptions through a valuation method appropriate to the sector, and rolls the result up to a single per-share figure comparable to the market price. If your spreadsheet cannot show a reader which of those three jobs each cell is doing, it is not yet a model — it is a collection of numbers.
The three templates that follow are structured the same way in this chapter: the line items in build order, which figures come from the audited financial statements versus management guidance or your own assumption, how the model rolls up to per-share value, and the mistakes retail investors most often make with each.
Lesson 102.2 — The Commercial Bank Model Template
Banks are the most heavily disclosed companies on NEPSE, which makes them easier to model than hydropower or microfinance in one sense — audited data is abundant — and harder in another, because the accounting choices banks make (loan loss provisioning above all) can hide as much as they reveal. Chapters 29 through 31 covered bank accounting in depth; this section turns that into a build sequence.
Start with the income statement, reconstructed from the audited annual report and the unaudited quarterly disclosures the bank files with NEPSE and SEBON.
Interest income is your first line, broken down by loan portfolio segment if the bank discloses it (retail, SME, corporate, deprived sector) but at minimum as a single audited figure. Interest expense follows, likewise from audited figures — deposits by category (current, savings, fixed, call) if disclosed, since the mix drives your cost-of-funds assumption. Net interest income is the audited difference. From there you compute net interest margin (NIM) as net interest income over average interest-earning assets — a ratio you calculate yourself from audited figures, not an assumption.
Below net interest income comes fee and commission income (audited), other operating income (audited, but watch for one-off items like foreign exchange gains or asset revaluation that should not be projected forward at the same rate), and total operating income as the sum.
Now the line that decides most of the model: impairment charge for loans and advances, i.e., loan loss provisioning. This is where audited history ends and your judgment begins. NRB's directive on loan classification and provisioning sets minimum provisioning rates by category — pass, watchlist, substandard, doubtful, and loss — and the audited figure tells you what the bank actually provisioned last year. But the forward provisioning line in your model is an assumption you must build, not copy forward. Provisioning is cyclical: it falls in good years (when non-performing loan ratios are low and the bank may even write back provisions, boosting reported profit) and rises sharply in downturns. A model that assumes last year's provisioning rate continues indefinitely will systematically overvalue the bank at the top of a credit cycle and undervalue it at the bottom.
WARNING
The single most common modelling mistake with bank stocks is treating the provisioning line as a smooth, low, stable expense because that is what the last two or three quarters showed. Provisioning follows the credit cycle, not a trend line. If credit growth in the broader banking sector has been running above 15 to 20 percent for several years — a pattern NEPSE has seen more than once — build a scenario where non-performing loans rise and provisioning normalises upward, and see what that does to your per-share value before you decide the stock is cheap.
Below operating income you build operating expenses (audited: staff costs, which are usually the largest single line and disclosed separately; premises and establishment costs; other operating expenses), giving you operating profit before tax. Apply the tax rate — banks in Nepal are taxed at the standard corporate rate applicable to banking and financial institutions, higher than the general corporate rate, and this is a fact you look up rather than assume — to reach net profit after tax.
From net profit, the model needs three more steps to become a valuation tool rather than just a restated income statement.
First, return on equity (ROE), computed from audited net profit and audited shareholders' equity. This is your primary cross-check figure — a bank sustainably earning ROE below its cost of equity is not creating value regardless of how the growth story is pitched.
Second, capital adequacy. NRB's capital adequacy framework, built on Basel III principles, requires minimum Tier 1 and total capital ratios. Pull the bank's disclosed capital adequacy ratio (CAR) from its quarterly filings. If the bank is running close to the regulatory minimum, this constrains future loan growth unless it raises fresh capital — through a rights issue, which dilutes existing shareholders, or retained earnings, which caps the dividend payout ratio. A model that projects 20 percent loan book growth for a bank with CAR at 11.2 percent against a minimum requirement without also modelling a capital raise is quietly assuming something it never stated out loud.
Third, the valuation rollup itself. For banks, the workhorse method — covered in Chapters 45 through 47 — is the dividend discount model (DDM) or, where a bank retains most of its earnings for growth, the residual income (excess return) model: value per share equals current book value per share plus the present value of future ROE in excess of cost of equity, applied to a growing equity base. Both methods need a cost of equity assumption (built from a risk-free rate anchored to Nepal government bond and treasury bill yields, plus an equity risk premium) and a terminal growth assumption tied to realistic long-run loan book growth, not the growth rate of the last two boom years.
Line item
Source
Modelling note
Interest income
Audited financial statements
Segment if disclosed; else single line
Interest expense
Audited financial statements
Track deposit mix if disclosed
Net interest income / NIM
Calculated from audited figures
NIM is a ratio you compute, not a stated line
Fee and commission income
Audited financial statements
Recurring; separate from one-off gains
Other operating income
Audited financial statements
Strip out one-off FX or revaluation gains
Loan loss provisioning
Assumption, anchored to NRB minimums and cycle stage
The single most consequential forward assumption
Operating expenses
Audited financial statements
Staff cost usually the largest component
Tax
Statutory rate for banking and financial institutions
Look up current rate, do not assume
Net profit after tax
Calculated
Rolls into ROE
Capital adequacy ratio
Audited / quarterly disclosure
Flags whether growth needs a capital raise
Cost of equity
Assumption
Risk-free rate plus equity risk premium
Terminal growth rate
Assumption
Anchor to realistic long-run loan growth, not recent peak years
REGULATORY DETAIL
NRB's loan classification and provisioning directive sets minimum provisioning percentages that rise sharply as a loan moves from pass to watchlist to substandard to doubtful to loss category. A bank can be profitable and still be building risk if the proportion of its book in the pass category is falling quarter over quarter — a trend visible in the loan classification disclosure in the notes to the financial statements, not in the headline profit figure. Pull this table every quarter; it usually leads reported provisioning by two to three quarters.
Lesson 102.3 — The Hydropower Company Model Template
Hydropower is the sector where retail investors most often build models that look sophisticated and are quietly wrong, because the project finance mechanics covered in Chapters 42 through 44 have several places where an intuitive assumption is the incorrect one.
Start, as with a bank, by separating the audited past from the assumed future — but for a hydropower company the audited past is often short (many are recently listed, post-commissioning) and the forward model does most of the work.
The first structural decision is generation. This is the assumption that determines almost everything downstream, and it is the one retail investors get wrong most often.
WARNING
Do not build a hydropower model on nameplate capacity. A 30 megawatt run-of-river plant does not generate at 30 megawatts around the clock, because river flow varies by season and the plant is a run-of-river design without large storage. The correct inputs are P50 and P90 generation estimates — P50 being the long-term average annual generation a hydrology study expects to be exceeded in half of all years, and P90 being the more conservative figure expected to be exceeded nine years in ten, the figure lenders typically size debt against. Using nameplate capacity times 8,760 hours, even discounted by a rough load factor guess, routinely overstates achievable generation and therefore revenue, cash flow, and per-share value, sometimes drastically.
Pull the P50 and P90 figures from the company's feasibility study or, once operating, from actual multi-year generation history disclosed in the annual report, cross-checked against the hydrology study if it is available (many prospectuses include it). Build both figures into the model as separate scenario columns — P50 for your base case, P90 as a stress case — rather than a single blended guess.
Next comes tariff. Nepali hydropower projects sell power under a power purchase agreement (PPA) with Nepal Electricity Authority, and the PPA sets a two-season tariff structure — a higher dry-season rate and a lower wet-season rate — that historically escalates annually for a set number of years before flattening for the remainder of the agreement term. Pull the actual PPA tariff schedule from the company's disclosure; do not assume a flat tariff across the project life, and do not assume the escalation continues beyond the contractually specified years.
Revenue for each modelled year is then generation (by season, if you have seasonal PPA rates and seasonal generation splits) multiplied by the applicable tariff.
Below revenue: operations and maintenance cost, typically modelled as a percentage of project cost or a per-unit figure escalating with inflation, and government royalty. Royalty on hydropower generation in Nepal is structured under the Electricity Act as a capacity-based charge plus an energy-based charge, with the energy-based component increasing after the initial years of operation — build this step-up into the model rather than holding royalty flat for the project's life.
This gives you EBITDA. Below EBITDA, debt service is the item that most distinguishes a hydropower model from a bank or MFI model, because these are project-financed structures with debt-to-equity ratios often near 70:30 or 80:20 and long amortisation schedules matched to the PPA term. Build a full debt schedule — opening balance, interest, principal repayment, closing balance — for every year of the loan tenor, not a single average figure. Lenders in Nepal, often a syndicate of commercial banks or development financiers such as the Hydroelectricity Investment and Development Company, size the loan against a minimum debt service coverage ratio (DSCR), commonly in the 1.2 to 1.4 times range calculated on the P90 generation case. If your own DSCR calculation on P90 generation falls below the covenant level implied by the loan documents, that is a signal the company's cash available for dividends could be swept into debt service ahead of schedule in a bad hydrology year — a real constraint on the income you can expect as a shareholder.
REGULATORY DETAIL
Interest during construction (IDC) — interest accrued on drawn project debt before the plant reaches commercial operation date — is capitalised into the project cost under Nepal Financial Reporting Standards (the equivalent of NAS 23 on borrowing costs), not expensed as incurred. This means IDC shows up as part of the asset base being depreciated after commissioning, not as a construction-period loss. A model that expenses IDC as incurred will show years of large accounting losses during construction that never actually happened on the balance sheet, and will misstate the depreciation base afterward if it is not added back into the capitalised cost. Separately, Nepal's Income Tax Act has historically granted hydropower plants meeting certain commissioning-date conditions a substantial income tax exemption for an initial period and a partial concession for a further period — confirm the specific holiday your company qualifies for from its own disclosure, since this materially changes the cash tax line for a decade or more of the model.
Once debt is fully modelled and the applicable tax treatment applied, you reach free cash flow to equity (FCFE) for each year of the remaining licence and PPA term. The rollup to per-share value is a discounted cash flow of FCFE at the cost of equity appropriate to project finance risk (higher than a bank's, given construction, hydrology, and regulatory risk layered in), summed across the remaining useful life of the PPA and licence. Because hydropower generation licences in Nepal are issued for a fixed term under a build-own-operate-transfer type structure, the terminal value at licence expiry is typically not a growing perpetuity — it should be modelled as reverting to the government (a residual value near zero) or, if renewal is a realistic prospect the company itself discloses, as a separate, clearly-labelled renewal scenario rather than folded silently into the base case.
CASE IN POINT
A pattern seen more than once among run-of-river IPOs on NEPSE: a prospectus quotes plant load factor and nameplate capacity prominently, while the P90 figure sits in an annex few retail investors open. Analysts who build models straight off the headline capacity number arrive at per-share values well above what a P50-and-P90-anchored model produces. When the first dry monsoon year arrives and actual generation lands closer to the P90 case, the gap between the optimistic model and reality shows up directly in a lower-than-expected dividend, and often in a share price correction that the naive model gave no warning of.
The second common mistake is on debt: assuming a flat, blended interest rate and an even amortisation schedule rather than the actual drawn-down and repayment schedule in the loan documents, which typically has a grace period during construction and then a fixed tenor matched (imperfectly) to the PPA. Getting this wrong understates how front-loaded the debt burden is relative to cash flow in the early operating years — precisely when hydrology risk is also least proven, since the plant has the shortest operating history.
Lesson 102.4 — The Microfinance Institution Model Template
Microfinance institutions listed on NEPSE combine features of a bank model (a lending book funded partly by borrowed money, subject to NRB provisioning norms) with a distinct risk profile driven by small-ticket, often group-guaranteed lending to borrowers with thin credit histories. Chapters 32 through 34 covered the sector accounting; here is the build sequence.
Start with the loan portfolio itself, since portfolio quality drives everything else in an MFI model more directly than in a bank model. Pull portfolio outstanding, portfolio yield (interest and fee income over average portfolio), and — critically — portfolio at risk (PAR), typically disclosed at the 30-day and sometimes 90-day overdue thresholds, from the audited financial statements and the notes.
WARNING
The single most common mistake retail investors make modelling an MFI is projecting portfolio growth as an independent assumption while treating portfolio quality as fixed or improving. In practice the two are connected: an MFI growing its loan book rapidly by pushing into new districts or relaxing group-lending discipline to hit growth targets very often sees PAR rise with a lag of two to four quarters, as the newer, less-seasoned loans season into delinquency. If your growth assumption does not carry a corresponding PAR assumption forward, and PAR is already trending upward in the disclosed quarterly figures, the model is assuming away the exact risk the trend is showing you.
From portfolio outstanding and yield, build interest income (audited). Cost of funds follows — MFIs fund their lending through a mix of member savings (where permitted), wholesale borrowing from commercial banks (partly satisfying those banks' deprived sector lending requirement under NRB directives), and their own capital; pull the effective cost of borrowed funds from the audited figures, and note that NRB directives on microfinance also cap the effective interest rate MFIs may charge borrowers, which limits how far portfolio yield can rise even as cost of funds moves.
Net interest income (interest income less cost of funds) is your equivalent of a bank's NIM line. Below it, loan loss provisioning follows NRB's classification norms for microfinance loans — generally with shorter overdue thresholds triggering classification into watchlist and substandard categories than for commercial bank loans, reflecting the shorter tenor and higher observed volatility of microloans. As with banks, the audited provisioning figure for the past is a fact; the forward provisioning assumption must move with your PAR assumption, not sit flat.
Operating expenses for an MFI are proportionally much larger relative to portfolio size than for a bank, because group lending and door-step collection are labour-intensive. Pull the operating expense ratio (opex over average portfolio) from audited figures — this ratio, more than almost any other line, separates well-run MFIs from weaker ones, and it tends to be sticky, so extrapolate it rather than assuming rapid efficiency gains without evidence of the company actually restructuring its collection model.
Line item
Source
Modelling note
Portfolio outstanding and yield
Audited financial statements
Base for interest income projection
Portfolio at risk, PAR30/PAR90
Audited / quarterly disclosure
Leading indicator; link to your growth assumption
Cost of funds
Audited financial statements
Includes wholesale bank borrowing under deprived sector lending
Loan loss provisioning
Assumption, anchored to NRB microfinance classification norms
Must move with PAR trend, not held flat
Operating expense ratio
Audited financial statements
Sticky; verify before assuming improvement
Leverage / debt-to-equity
Audited / regulatory disclosure
NRB caps constrain growth funding
Return on equity
Calculated
Compare against cost of equity for MFI risk
Terminal growth rate
Assumption
Anchor to realistic branch and portfolio expansion, not peak years
Below operating expenses and tax, net profit rolls into return on equity as your primary sanity check, exactly as with a bank. Leverage matters distinctly here: NRB sets debt-to-equity or capital adequacy style constraints on microfinance institutions, and an MFI running close to its leverage ceiling cannot fund continued rapid portfolio growth from borrowed money alone — it needs a capital raise, diluting existing shareholders, or must slow growth. A model projecting continued 25 to 30 percent annual portfolio growth without checking whether the company's equity base can support the leverage that growth requires is, again, assuming a capital raise it never stated.
The valuation rollup for an MFI typically follows the same residual income or dividend discount approach used for banks, given the comparable equity-funded, spread-based business model, with the cost of equity set higher to reflect the sector's greater credit and concentration risk, and the terminal growth assumption anchored to realistic long-run branch and portfolio expansion rather than the growth rates seen during a sector-wide credit boom.
Lesson 102.5 — Worked Mini-Example: Valuing a Hydropower Company Share by Share
The numbers below are invented for illustration. No real NEPSE-listed company is being described. But the structure is exactly what Kabita would build for an actual holding, and it is worth walking through in full because hydropower is where the nameplate-versus-P50 mistake does the most damage to an unwary model.
Call the company Sunkoshi Urja Ltd, a fictional 30 megawatt run-of-river plant. Total project cost is NRs 6,000 million, including NRs 550 million of interest during construction capitalised over a three-year build. The project is financed 70:30 debt to equity: debt of NRs 4,200 million at 10.5 percent, equity of NRs 1,800 million. Paid-up capital is NRs 100 per share, giving 18,000,000 shares outstanding.
The company's hydrology study gives a P50 annual generation estimate of 130 gigawatt-hours (a plant load factor of about 49.5 percent — a realistic figure for a mid-hills run-of-river plant) and a P90 estimate of 112 gigawatt-hours used by the lending banks to size debt service coverage.
The PPA with Nepal Electricity Authority sets a blended tariff of NRs 7.20 per unit in year one of commercial operation, escalating 8 percent annually for the first eight years and flat thereafter, consistent with the standard NEA PPA template's escalation structure.
Year one revenue, P50 case: 130,000,000 units multiplied by NRs 7.20 equals NRs 936 million.
Operating cost is modelled at NRs 90 million for operations and maintenance plus NRs 60 million for government royalty (capacity and energy components combined) in year one, giving EBITDA of NRs 786 million.
Debt service in the early operating years, on the NRs 4,200 million loan at 10.5 percent over a 15-year door-to-door tenor, runs at approximately NRs 620 million a year in principal and interest combined. Because Sunkoshi Urja qualifies for the income tax exemption available to hydropower plants commissioned within the qualifying window, cash tax in these early years is zero.
Year one FCFE, P50 case: NRs 786 million EBITDA less NRs 620 million debt service equals NRs 166 million.
Debt amortises down over the loan's 15-year tenor, and once it is retired, FCFE rises toward the EBITDA level (further escalated by the tariff step-ups through year eight). Rather than build all 25-plus remaining years of the licence term row by row here — which is exactly what the real workbook should do, with one column per year — this worked example uses a single normalised average annual FCFE across the remaining licence life of NRs 340 million, to keep the illustration readable.
CAUTION
The single averaged FCFE figure used here is a simplification for this worked example only. A real model for a holding this size should carry a full year-by-year schedule — debt balance, interest, principal, tariff escalation, and tax status — for every year remaining on the PPA and licence, because the averaging step hides exactly the early-years cash flow shortfall that matters most for near-term dividend expectations.
Discounting that normalised NRs 340 million annual FCFE at a cost of equity of 13 percent (reflecting project finance and hydrology risk) over the remaining 25 years of the licence term, with terminal value treated as reverting to zero at licence expiry under the build-own-operate-transfer structure, gives a present value annuity factor of approximately 7.33.
Equity value: NRs 340 million multiplied by 7.33 equals approximately NRs 2,492 million.
Value per share: NRs 2,492 million divided by 18,000,000 shares equals approximately NRs 138 per share.
Now the mistake demonstration. Suppose an analyst instead assumes the plant runs at 90 percent of nameplate capacity around the clock — a number that sounds conservative because it is not 100 percent, but is nowhere close to how a run-of-river plant with strong seasonal flow variation actually behaves. That gives generation of 30,000 kilowatts multiplied by 8,760 hours multiplied by 0.90, or roughly 236.5 gigawatt-hours — 82 percent higher than the correct P50 figure.
Carried through the same model, that generation figure very nearly doubles revenue, more than doubles EBITDA (since operating costs are largely fixed and do not scale with the overstated generation), and — because debt service does not change — multiplies FCFE by a much larger factor in the early years and a smaller but still substantial factor once averaged across the licence life. Working through the same steps, the nameplate-based model produces a normalised average FCFE of roughly NRs 612 million a year, an equity value near NRs 4,486 million, and a per-share value near NRs 249 — an overstatement of roughly 80 percent against the P50-anchored figure of NRs 138.
That is the entire difference between a defensible hydropower model and an indefensible one: one assumption, generation, carried through identical arithmetic everywhere else. If Sunkoshi Urja were trading on NEPSE at, say, NRs 175 a share, the P50 model would flag it as expensive relative to the base case and worth checking against the P90 downside case before buying; the nameplate model would wrongly flag the same price as a bargain.
Lesson 102.6 — Maintaining the Model: Quarterly Discipline
A model built once and never revisited is, within a year or two, actively worse than no model at all, because it gives false confidence in numbers that no longer describe the company. The discipline that separates investors like Kabita from the majority who build a model, admire it, and never open the file again is a quarterly maintenance habit, and it is worth being specific about what that habit actually involves.
Every commercial bank, hydropower company, and MFI listed on NEPSE files quarterly financial disclosures, typically within the timelines SEBON and NEPSE require. The moment those disclosures are published, the maintenance routine has four steps.
First, update every black-font, audited-source cell in the model with the new quarterly figures: interest income, provisioning, NIM, generation for the quarter, PAR, opex ratio, whichever apply to the company type. This is mechanical but non-negotiable — it is the only way to know whether last quarter's assumptions were close to reality.
Second, compare actual to assumption for every blue-font, assumed cell from the prior quarter's model. Did provisioning come in above or below what you assumed? Did generation track closer to the P50 or the P90 case? Did PAR move the direction your growth assumption implied it should? This comparison, run consistently every quarter, is where a model earns its keep — it tells you which of your assumptions are drifting and by how much, long before the drift shows up as a surprising headline number.
Third, revise the forward assumptions in light of that comparison, and only in light of that comparison — not in light of a broker note, a rumour, or a feeling about the stock. If provisioning has been running consistently above your assumed rate for three straight quarters, raise the forward assumption; do not wait for a fourth confirmation while telling yourself the last quarter was a one-off.
Fourth, re-run the rollup to per-share value and compare it against the current NEPSE quote. This is the only step that actually matters for a buy, hold, or sell decision, and it should never be skipped even when the first three steps feel like they produced no surprises — a model that hasn't moved the per-share value is itself informative, confirming the thesis rather than assuming it still holds.
PRACTICAL TOOL
Keep one worksheet tab per quarter inside the same workbook rather than overwriting the previous quarter's tab. Label them plainly — 2082 Q1, 2082 Q2, and so on — and duplicate the prior tab as the starting point for the new one before updating it. This costs nothing in effort and gives you, over a few years, a complete audit trail of how your own assumptions evolved against what the company actually delivered — the single most useful thing a retail investor can build for improving their own modelling judgment over time.
This quarterly rhythm is also where the three templates in this chapter earn back the hours spent building them. A model built once, however carefully, decays into decoration. A model rebuilt every quarter against fresh audited disclosure becomes, over several years, a genuinely better forecasting tool than anything a brokerage note can offer you — not because the underlying arithmetic is more sophisticated, but because it is disciplined by real feedback in a way a one-off research note never is.
Chapter recap
This chapter set out the actual spreadsheet structure for three financial model templates central to investing in NEPSE-listed companies. For a commercial bank, the build runs from audited interest income and expense through net interest margin, an assumption-driven provisioning line anchored to the credit cycle and NRB's minimum provisioning norms, operating expenses, capital adequacy, and a dividend discount or residual income rollup to per-share value — with mismodelled provisioning cycles the most common retail error. For a hydropower company, the build runs from P50 and P90 generation estimates (never nameplate capacity), through PPA tariff schedules with their contractual escalation and flattening, capitalised interest during construction, a full year-by-year debt schedule sized against debt service coverage covenants, and a discounted cash flow to equity rollup that respects the finite term of the licence and PPA — with nameplate-based generation assumptions the most damaging and most common retail error, as the worked example in Lesson 102.5 demonstrated with an 80 percent overstatement of per-share value from that single mistake. For a microfinance institution, the build runs from portfolio yield and cost of funds through a provisioning assumption tied explicitly to the trend in portfolio at risk, a sticky operating expense ratio, leverage constraints set by NRB, and a residual income rollup — with growth assumptions that ignore rising PAR the most common retail error.
Across all three templates, the same discipline applies: separate audited fact from assumption visibly in the workbook itself, and treat the model as a quarterly-maintained instrument rather than a one-time exercise, updating every assumption against fresh disclosure and re-running the rollup to per-share value each time new financial statements are published.
Chapter 103, Position Sizing and Portfolio Management Tools, moves from valuing individual holdings to managing a portfolio of them — concrete position-sizing calculators and portfolio tracking tools for deciding not just what a share is worth, but how much of it, if any, belongs in your portfolio alongside everything else you hold.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 103
Position Sizing and Portfolio Management Tools
First published 26 Aug 2026 · Last verified 29 Aug 2026
Suresh Gurung keeps his portfolio in a Google Sheet he built himself in 2019, and he has never once opened it on his phone. Every Saturday morning, before the vegetable vendor comes around his lane in Pokhara's Lakeside ward, he makes a cup of tea, opens his laptop, and spends forty minutes updating eleven columns of numbers. Suresh is a civil engineer who supervises concrete work for a hydropower contractor upriver from the city, not a finance professional, and he has never paid for portfolio software in his life. His entire risk-management system is a spreadsheet, a calculator, and a set of rules he wrote down for himself after a bad year in 2021 taught him that having opinions about stocks is not the same as having a system for owning them. This chapter builds that system in the open, using Suresh's numbers, so that any investor running a self-directed NEPSE portfolio can copy the same four tools into their own sheet by Monday morning.
The tools in this chapter are deliberately unglamorous. They do not predict prices, they do not rank sectors by momentum, and they will not make anyone feel clever. What they do is take the position-sizing formula and drawdown-budget logic from Chapter 97, and the allocation ceilings set out in Chapters 59 through 62, and turn them into something you actually check before you click "buy" in your TMS window. A rule you cannot apply in ninety seconds on a Saturday morning is a rule you will eventually stop applying. Everything here is built to survive contact with an ordinary week.
Lesson 103.1 — Building the Position-Size Calculator
Chapter 97 established the core discipline: before you buy anything, you decide how much of your total capital you are willing to lose if this specific position goes badly wrong, and you size the purchase so that even the worst realistic outcome stays inside that tolerance. The formula from that chapter was simple on paper — drawdown budget divided by worst-case single-stock decline equals maximum position size — but simple on paper and simple in a spreadsheet cell are two different things. This lesson turns it into the second.
Start with the two inputs the formula needs. The first is your single-position drawdown budget: the slice of your total portfolio value you are prepared to lose to any one holding, drawn from the overall drawdown budget you set for your whole portfolio in Chapter 97. Suresh's overall portfolio drawdown budget, based on his income stability, his age, and his family's other assets, is 18 percent — he can absorb an 18 percent NAV decline in a bad year without changing his life. Out of that, he has decided that no single stock disaster should be allowed to cost him more than 3 percentage points of total portfolio value. That 3 percent is his single-position budget, and it does not change stock to stock. It is a constant in his formula.
The second input does change stock to stock: the worst-case decline you should plan for in that particular holding. This is not the average correction, and it is not what happened to the stock last year. It is the realistic floor — what a Class A commercial bank might lose if NRB tightened capital adequacy rules sharply and NEPSE sold off financials across the board, versus what a thinly traded Class B development bank might lose if a promoter dispute or a governance scandal broke, versus what a small hydropower counter with a narrow public float might lose if a single large holder needed to exit and there were no buyers waiting. Suresh keeps a short reference table in his sheet for this, updated whenever something in the regulatory or company-specific picture changes.
Stock Category
Worst-Case Decline Assumption
Reasoning
Class A commercial bank, established, NRB-regulated, high disclosure
35 percent
Sector-wide correction plus company-specific bad quarter, but capital adequacy floors and NRB oversight limit tail risk
Class B development bank, smaller, thinner disclosure
45 percent
Same regulatory floor as commercial banks but less analyst coverage, less liquidity, promoter concentration more common
Hydropower or hospitality counter with narrow public float
70 percent
Thin trading volume, single-project earnings risk, limited buyer depth in a forced-sale scenario
Once the reference table exists, the calculator itself is one line of arithmetic per stock: single-position budget divided by worst-case decline assumption equals maximum position size, expressed as a percentage of total portfolio value. For a commercial bank, that is 3 percent divided by 35 percent, which comes out to roughly 8.6 percent — Suresh rounds down to 8 percent as his ceiling for any single commercial bank holding. For a development bank, 3 percent divided by 45 percent gives roughly 6.7 percent, rounded to 6 percent. For a narrow-float hydropower counter, 3 percent divided by 70 percent gives about 4.3 percent, rounded to 4 percent.
KEY CONCEPT
The position-size calculator is not a valuation tool and does not tell you whether a stock is cheap or expensive. It tells you how much of it you can survive owning if you turn out to be wrong. Those are separate questions, and conflating them is how investors end up holding oversized positions in stocks they were right to like but wrong to own so much of.
Notice what this produces automatically: riskier categories of stock get smaller position-size ceilings without Suresh having to make a separate judgment call every time. He does not have to remember, in the middle of feeling excited about a hydropower IPO, that hydropower deserves a smaller allocation — the formula already encodes that discipline before he opens the trading window. This is the entire value of building the calculator into the spreadsheet rather than keeping the logic in his head: a spreadsheet cell does not get talked into an exception at ten in the morning when the price is moving.
The calculator needs one more row: converting the percentage ceiling into rupees against his actual portfolio value, which he updates weekly. If his portfolio stands at NPR 11,00,000 this week, his 8 percent commercial bank ceiling is NPR 88,000, his 6 percent development bank ceiling is NPR 66,000, and his 4 percent hydropower ceiling is NPR 44,000. Those are the numbers he actually checks against a proposed purchase order — not the abstract percentages, but the rupee figures for that week, because portfolio value moves and the ceiling should move with it.
PRACTICAL TOOL
Build the calculator as three cells: single-position budget (a fixed percentage you set once), worst-case decline assumption (looked up from your reference table by stock category, updated when facts change), and portfolio value (pulled live from your tracker). The formula cell is budget divided by decline, multiplied by portfolio value. Update the portfolio value cell weekly and the ceiling in rupees updates itself.
One caution belongs here. The worst-case decline assumption is a judgment call, and judgment calls drift toward optimism over time, especially after a stock has performed well for a year or two. Suresh reviews his reference table every six months, not because the categories change often, but because his own confidence in them does, and a stale assumption made two years ago during a bull run is exactly the kind of thing that quietly stops protecting you.
Lesson 103.2 — The Running Portfolio Tracker
The position-size calculator tells you what to buy. The running tracker tells you what you actually own, and whether what you own still matches what you decided to own. Without this second tool, position sizing is a decision you make once at purchase and never revisit — which defeats the purpose, because prices move, positions drift, and a holding that was 6 percent of your portfolio at cost can become 11 percent after eighteen months of share price appreciation without you buying a single additional share.
The tracker needs eight columns, no more and no fewer for a retail investor running this alone. More columns get skipped under time pressure; fewer columns hide the information you need to catch problems early.
Column
What It Captures
Why It Matters
Holding
Company name and ticker
Basic identification
Sector
NRB/SEBON sector classification (commercial bank, development bank, hydropower, hotel, insurance, etc.)
Feeds the sector-concentration dashboard in Lesson 103.3
Cost Basis
Total rupees invested, adjusted for any bonus shares received
Baseline for gain/loss and for capital gains tax planning
Current Price
Latest NEPSE closing price for that counter
Basis for current market value
Weight
Current market value divided by total portfolio value
Compares against the position-size ceiling from 103.1
Sector-Cap Headroom
Combined sector exposure versus the cap set in Chapter 61
Feeds the dashboard in 103.3, flags creeping concentration
Canon Score
The composite score from your fundamentals-and-governance framework
Tracks whether the original investment case still holds
Last Review Date
The date you last actually looked at the company's disclosures
Forces periodic re-examination instead of passive holding
Suresh's version of this sheet has fourteen rows this year — fourteen distinct holdings across seven sectors — and updating it every Saturday takes him about twenty-five minutes once he has downloaded the week's closing prices. The weight column is the one that does the real work day to day: it is a straightforward formula, current market value for that row divided by the sum of all current market values, and it recalculates automatically every time he updates prices. The moment a stock's weight crosses the ceiling he calculated in 103.1 — say, his development bank position drifting above 6 percent — the cell turns red through simple conditional formatting. He does not have to remember to check; the sheet tells him.
The Canon Score column deserves a specific caution, because it is the column investors most often let go stale. A score calculated at purchase time, based on that quarter's disclosures, that quarter's governance picture, and that quarter's valuation, is not a permanent verdict on the company. It is a snapshot, and snapshots age.
CAUTION
A Canon Score you calculated eighteen months ago and never revisited is worse than no score at all, because it gives you false confidence that you have done the analytical work when you have only done it once. Recalculate the score at every quarterly disclosure at minimum, and note the recalculation date in the Last Review Date column so you can see at a glance which holdings you have actually re-examined recently and which you have been holding on autopilot.
The Last Review Date column is what turns the tracker from a passive record into an active discipline. Suresh sorts his sheet by this column every month and asks a simple question about whichever holding sits at the top of the list, oldest review first: have I actually looked at this company's last disclosure, or have I just been watching the price move? A holding that has gone six months without a genuine review — reading the quarterly report, checking the Canon Score inputs, confirming the original investment thesis still holds — gets moved to the top of his weekend reading list regardless of how the price has performed.
There is a temptation, once you have built a tracker like this, to add more columns: target price, analyst notes, dividend yield history, a running notes field for every rumour you hear. Resist it. Every column you add is a column you have to fill in every week, and the tracker's entire value depends on it actually getting updated. An eight-column sheet updated every Saturday is a functioning risk-management system. A twenty-two-column sheet updated every third Saturday because it takes ninety minutes is not.
PRACTICAL TOOL
Keep the tracker to exactly these eight columns and resist every urge to expand it. If you find yourself wanting to track something else — a stop-loss level, a target allocation, a note about a rumour — put it in a separate sheet or a separate document, not in the row that has to survive weekly updates for years.
Lesson 103.3 — The Sector-Concentration Dashboard
Chapters 59 through 62 built the case for sector allocation ceilings on NEPSE specifically, because the exchange's sector composition is unusually top-heavy in bank, financial institution, and life insurance counters compared to more diversified markets. Chapter 61 set the specific numbers most investors following this book use as defaults: no single sector above 25 percent of portfolio value, and — because commercial banks and development banks are both NRB-regulated deposit-taking institutions whose fortunes move together during a monetary tightening cycle or a liquidity crunch — a combined ceiling of 35 percent across the two sectors together, treated as one correlated risk bucket rather than two independent ones.
The problem this lesson solves is that no single purchase ever looks like the one that breaches a combined cap. You buy a development bank stock that is 2 percent of your portfolio, well under any individual position ceiling, and it looks completely reasonable in isolation. The danger is never any one purchase — it is the sum of several reasonable-looking purchases made over months, none of which individually triggers alarm, that together cross a line nobody was watching for because nobody was adding them up.
The dashboard is not a separate spreadsheet from the tracker — it is two summary rows sitting above the tracker's main table, built from the same sector column already in every row. The first summary row sums the weight column for every holding classified as a commercial bank. The second sums every holding classified as a development bank. A third row adds those two together and compares the total against the 35 percent combined ceiling, with the difference shown as headroom — how many more percentage points of portfolio value could go into this combined sector before the cap is reached.
REGULATORY DETAIL
NRB's institutional classification — Class A for commercial banks, Class B for development banks, Class C for finance companies, and Class D for microfinance institutions — is a convenient and consistent basis for the sector column, since every listed BFI already carries this classification and it rarely changes. Using NRB's own categories keeps your sector tags aligned with how the regulator itself groups correlated institutional risk, rather than inventing your own taxonomy that might miss the correlation NRB's classification is built to capture.
Suresh's dashboard has four correlated buckets he watches this way, each with its own combined ceiling drawn from Chapter 61: banking plus development banking at 35 percent, hydropower plus other energy infrastructure at 30 percent given its own correlated exposure to monsoon-dependent generation and PPA rate risk, life and non-life insurance combined at 20 percent, and hotels plus other tourism-linked counters at 15 percent given their shared sensitivity to tourist arrival numbers. Every one of his fourteen holdings falls into exactly one of these buckets or stands alone in a bucket of one.
Sector Bucket
Combined Cap
Current Exposure
Headroom
Commercial banks + development banks
35 percent
27 percent
8 points
Hydropower + energy infrastructure
30 percent
24 percent
6 points
Life + non-life insurance
20 percent
11 percent
9 points
Hotels + tourism-linked
15 percent
6 percent
9 points
The dashboard's value is entirely in the headroom column, because headroom is what should govern every new purchase decision in a way that the individual position-size ceiling alone cannot. Suresh's position-size calculator might tell him he could put another NPR 66,000 into a specific development bank stock without breaching his single-position ceiling for that category. But if his banking-plus-development-banking headroom is down to 2 points rather than 8, that purchase would breach the combined sector cap even though it clears the individual position check comfortably. Both tools have to say yes before he places the order — the position-size ceiling and the sector-cap headroom are independent checks, and a purchase that fails either one gets vetoed regardless of how attractive the stock looks on its own.
WARNING
A position-size ceiling that passes and a sector-cap headroom that fails is not a tie you resolve in favour of the trade you want to make. The sector cap exists precisely because individually reasonable positions can combine into an unreasonable concentration, which is the exact failure mode this dashboard is built to catch. Overriding it because "this one purchase is still small" is how the cap gets breached one small purchase at a time — which is the subject of the next lesson.
Lesson 103.4 — Rebalancing Triggers: Rules for When to Act
A tracker and a dashboard tell you where you stand. They do not tell you when to act on what they show you, and without a clear trigger rule, drift gets rationalised indefinitely — "it's only two points over," "I'll wait for a better exit price," "it's still a good company." This lesson sets the specific thresholds that convert observation into action, because a rule that only fires when you feel like it fires is not actually a rule.
There are two distinct triggers worth separating, because they call for different responses. The first is position drift: a single holding's weight has moved meaningfully away from where you intended it to sit, almost always because it has performed well and grown as a share of the portfolio rather than because you bought more of it. The second is cap breach: a position-size ceiling or a sector-cap headroom has actually been crossed, not just drifted toward.
For position drift, Suresh uses a simple band rule borrowed from institutional rebalancing practice and scaled down to retail size: if a holding's actual weight moves more than 30 percent away from its target weight — not 30 percentage points, but 30 percent of the target itself — it triggers a review, not necessarily an automatic sale. A stock he sized at a 6 percent target weight that has drifted to 8 percent has moved up by a third relative to its target, which crosses the 30 percent band and triggers a look. A stock at the same 6 percent target that has drifted to 6.8 percent has moved by roughly 13 percent relative to target, which stays inside the band and gets left alone. This proportional band matters more than a flat percentage-point rule would, because a two-point drift means something very different for a position targeted at 4 percent than for one targeted at 15 percent.
Trigger Type
Threshold
Response
Position drift (proportional band)
Actual weight moves more than 30 percent away from target weight
Scheduled review within the week — decide to trim, hold, or raise the target deliberately
Individual position-size ceiling breach
Weight exceeds the ceiling from the 103.1 calculator
Trim to bring weight back under ceiling within two weeks, barring an explicit documented decision to raise the ceiling
Sector-cap headroom exhausted
Combined sector exposure exceeds the Chapter 61 cap
No new purchases in that sector bucket until exposure is back under cap; consider trimming the largest holding in the bucket
Canon Score deterioration
Score falls by more than 15 points from the score at purchase
Full re-review of the investment thesis within the week, independent of price action
The response to a drift trigger is a review, not an automatic sale, because drift caused by strong performance is not itself a problem — it might simply mean the target was set conservatively and deserves to be raised deliberately, which is a legitimate outcome of the review. The response to an actual ceiling or cap breach is firmer, because those thresholds were set with the worst-case decline assumption already built in, and every day a breach persists is a day the portfolio is carrying more single-stock or single-sector risk than the drawdown budget from Chapter 97 was designed to tolerate.
CASE IN POINT
Suresh's largest single holding, a commercial bank he bought in 2020 at a modest position size, grew through a combination of share price appreciation and two bonus share issues to nearly 12 percent of his portfolio by late 2024 — well above his 8 percent ceiling for that category. He did not sell in a panic. He trimmed it back to 8 percent over three tranches across six weeks, partly to manage the tax consequence of a large single-year gain and partly because a NEPSE counter that size can move on its own selling pressure if dumped at once. The ceiling told him what his end state needed to be; it did not dictate that he had to get there in one transaction.
Rebalancing triggers need one further discipline layered on top, because acting on every trigger the instant it fires generates a cost that a slower, more deliberate response avoids. Every sale to trim a position is a taxable event under Nepal's capital gains framework — the Inland Revenue Department currently applies a lower rate to shares held more than 365 days and a higher rate to shares sold within a year of purchase — and every trade also carries SEBON-mandated broker commission on both the buy and the eventual sell leg. A trigger that fires and gets acted on within days, purely because a threshold was technically crossed by half a point, can cost more in avoidable short-term capital gains tax and commission than the risk it was managing.
WARNING
Treat every trigger threshold as the start of a decision, not the decision itself. A position that has drifted past its band or a sector that has edged past its cap deserves a deliberate, unhurried trim executed over one to three weeks — not a same-day sale that pushes a long-term holding into short-term capital gains territory for the sake of correcting a two-point overshoot. The goal is to get back inside the discipline, not to prove you reacted instantly.
Lesson 103.5 — How Small Purchases Build a Big Problem: A Worked Example
This is the scenario every tool in this chapter exists to catch, and it is worth walking through with Suresh's actual numbers because it never announces itself as a single bad decision. It arrives as a series of individually sensible ones.
In January of a given year, Suresh's banking-plus-development-banking bucket stood at 27 percent against his 35 percent combined ceiling — eight points of headroom, comfortably inside the cap, exactly as shown in the dashboard table in Lesson 103.3. Over the following seven months, four separate opportunities came across his radar, each involving a development bank paying an attractive dividend during a period when NEPSE's financial sector was recovering from a liquidity squeeze.
Month
Purchase
Amount
Position Weight Added
Bucket Total After Purchase
January (starting point)
—
—
—
27 percent
March
Development bank A, additional tranche
NPR 18,000
1.6 percent
28.6 percent
May
Development bank B, new position
NPR 22,000
2.0 percent
30.6 percent
June
Commercial bank C, bonus shares received (no cash outlay, price appreciation)
—
1.4 percent
32.0 percent
August
Development bank D, new position
NPR 24,000
2.1 percent
34.1 percent
Every one of these four events, examined on its own on the day it happened, passed every check Suresh had. Each cash purchase was well under his individual position-size ceiling for development banks — the March tranche was 1.6 percent against a 6 percent ceiling, the May position was 2.0 percent against the same ceiling, the August position was 2.1 percent. None of them looked remotely aggressive. The June entry was not even a purchase decision at all — it was a bonus share allotment on an existing commercial bank holding, the kind of passive increase that is easy to forget counts as new exposure at all, because no order was placed and no cash left his account.
By August, the combined bucket sat at 34.1 percent, still technically under the 35 percent cap but with headroom down to under one point — meaning a single further bonus allotment, or even ordinary price appreciation across a strong quarter for financials, would push the bucket over the line without Suresh doing anything at all. He caught this not because any one purchase alarmed him, but because his Saturday morning update in early September showed the dashboard's headroom column reading 0.9 percent, a number small enough to stop him mid-review.
CASE IN POINT
What made this catchable was not vigilance at the moment of each purchase — Suresh genuinely did not notice the cumulative effect while it was building, and said afterward that each individual decision felt completely disconnected from the others because they were made months apart for different reasons. What made it catchable was that the dashboard aggregated sector exposure automatically every single week regardless of what he was paying attention to that day, so the creeping total was visible the first Saturday he happened to look at that row, rather than remaining invisible until a much larger breach forced the issue.
His response followed the rebalancing trigger rules from Lesson 103.4 exactly as designed. Because headroom had not yet been breached — the bucket sat at 34.1 percent against a 35 percent cap, technically still inside the line — the trigger was a review, not a mandatory sale. But because headroom was down to under one point, meaning any further passive drift would cause a breach with no purchase decision involved at all, Suresh chose to trim the smallest and lowest-conviction of the four development bank positions, the May purchase, by about half, bringing the bucket back down to roughly 32 percent and restoring three points of headroom as a buffer against exactly the kind of passive bonus-share drift that had contributed to the problem in the first place.
The lesson generalises past this specific bucket and this specific investor. Concentration risk on NEPSE rarely arrives through one oversized purchase, because most investors who have read Chapters 59 through 62 already know not to make one oversized purchase. It arrives through several purchases spread over enough months that no single one triggers memory of the others, compounded by bonus shares and price appreciation that add exposure with no purchase decision to notice at all. A tool that only checks concentration at the moment of a new purchase order will miss the bonus-share and price-appreciation contribution entirely. A tool that recalculates the full combined-sector total every week, regardless of whether anything was bought that week, is the only version that catches this pattern before it becomes a genuine breach rather than a near miss.
WARNING
Bonus shares and price appreciation add to sector concentration exactly as much as cash purchases do, and they are far easier to overlook because no buy order marks the moment they happened. Any concentration dashboard that only recalculates when you execute a trade will systematically understate your real exposure. Recalculate the dashboard on a fixed weekly schedule regardless of trading activity, precisely so that passive drift gets caught on the same footing as active purchases.
Lesson 103.6 — Designing Tools You Will Actually Maintain
Every tool in this chapter can be built to a far higher level of sophistication than what has been described here. You could track intraday price movements instead of weekly closes. You could build a Canon Score that updates automatically from scraped disclosure data rather than manual quarterly review. You could add a Monte Carlo simulation layer to the position-size calculator instead of a fixed worst-case decline assumption. Investors with the technical background to build these things sometimes do, and there is nothing wrong with more sophistication if it is genuinely sustained.
The far more common outcome, and the one this closing lesson is written to prevent, is that an elaborate system gets built with enthusiasm over one long weekend and abandoned within six weeks because keeping it updated turns out to require more time than the investor actually has to give it on an ordinary Tuesday. A drawdown-budget discipline that runs for one quarter and then quietly lapses because the tracker became too tedious to update protects you for one quarter and then stops protecting you at all, usually without you noticing the exact week it stopped, which is worse in some ways than never having built it, because it creates a false sense that the discipline is still active when it is not.
KEY CONCEPT
The correct standard for any personal portfolio tool is not "the most sophisticated version I could build." It is "the simplest version that captures the discipline, updated on a schedule I will actually keep for years, not months." A four-tool system this chapter describes, updated for twenty-five minutes every Saturday, that runs continuously for a decade will catch more real problems than a far more elaborate system that runs brilliantly for two months and then sits untouched.
Suresh's version of all four tools fits on a single Google Sheet with four tabs, none of them using anything more complex than SUMIF and basic conditional formatting — no macros, no external data feeds, nothing that would break if Google changed something or a formula reference shifted. He chose this deliberately after his first attempt, built in 2019, involved a more ambitious tracker with live price feeds pulled through a script that broke twice in its first year and sat unrepaired for months each time, during which he was effectively flying blind on the exact concentration risk this chapter is built to catch. The current version pulls closing prices he types in by hand from NEPSE's own published data once a week, which takes an extra five minutes over an automated feed but has never once broken.
PRACTICAL TOOL
Before adopting any addition to your portfolio tools — a new column, an automated data feed, a more granular sector taxonomy — ask a single question: will I still be updating this the same way in eighteen months? If the honest answer is uncertain, build the simpler version instead. You can always add sophistication later once a simple version has proven it survives contact with an ordinary year. You cannot easily recover the months of missed reviews that follow from an elaborate system you quietly stopped maintaining.
The four tools in this chapter — the position-size calculator, the running tracker, the sector-concentration dashboard, and the rebalancing triggers — are designed to work together as one weekly routine rather than as four separate obligations. The calculator sets the ceiling before you buy. The tracker records what you actually hold and flags drift through its weight column. The dashboard aggregates sector exposure across every holding and catches the kind of gradual concentration that no single purchase would reveal. The triggers convert what the tracker and dashboard show into a scheduled, unhurried response rather than either panic or neglect. None of the four pieces does much on its own. Run together, every Saturday, for years, they do the entire job that Chapters 59 through 62 and Chapter 97 set out to accomplish — turning allocation principles and a drawdown formula into something that actually governs a real portfolio through real weeks, rather than remaining a good idea that lived only in the earlier chapters of this book.
Chapter recap
This chapter turned the position-sizing formula and drawdown-budget logic from Chapter 97, together with the allocation ceilings from Chapters 59 through 62, into four concrete tools built around Suresh Gurung's own working spreadsheet. The position-size calculator divides a fixed single-position drawdown budget by a category-specific worst-case decline assumption to produce a rupee ceiling for any new purchase, with commercial banks, development banks, and thinly traded counters each getting a different, appropriately conservative ceiling. The eight-column running tracker — holding, sector, cost basis, current price, weight, sector-cap headroom, Canon Score, and last review date — keeps that ceiling visible against every actual holding every week, rather than only at the moment of purchase. The sector-concentration dashboard sums correlated sector buckets, such as commercial banks plus development banks, against the combined caps set in Chapter 61, and the worked example showed exactly how four individually reasonable purchases and one bonus share allotment, spread across seven months, pushed that bucket from 27 percent to 34.1 percent without any single decision ever looking aggressive. The rebalancing triggers convert what the tracker and dashboard reveal into scheduled, deliberate action — a proportional drift band for ordinary review, and firmer but still unhurried responses for an actual ceiling or cap breach — while staying mindful of the real capital gains tax and brokerage commission cost of reacting too quickly to a small overshoot. The closing lesson made the case for keeping every one of these tools simple enough to survive years of ordinary Saturdays, on the grounds that a modest system maintained for a decade outperforms an elaborate one abandoned after two months.
Chapter 104, Performance Monitoring Tools, picks up where this chapter leaves off, moving from what you own and how much of it you own into how well the whole portfolio is actually performing. It will build an XIRR tracking template suited to a NEPSE portfolio with irregular cash flows — additional purchases, bonus shares, rights issues, and dividend receipts all landing at different times — and a portfolio performance dashboard that lets you judge your results against a realistic benchmark rather than against vague impressions of a good or bad year.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 104
Performance Monitoring Tools
First published 26 Aug 2026 · Last verified 29 Aug 2026
Sunil Maharjan had a number in his head, and for three years he was proud of it. A civil engineer supervising sub-contracts on a run-of-river hydropower project in Kathmandu, Sunil kept his investing simple: check the portfolio value in Meroshare once a month, compare it to what he remembered depositing, and feel good or bad accordingly. By the middle of 2026 his mental arithmetic told him something spectacular — his account had gone from roughly five lakh rupees to well over eleven lakh. More than doubled. At a family gathering during Dashain he said as much, a little proudly, to his brother-in-law, who happened to manage a small SACCOS branch in Lalitpur and asked one plain question: "Doubled from when, and did you only ever put in that five lakh?"
Sunil hadn't. Between that first deposit and the present moment he had paid for two rights share calls, added fresh cash twice after bonus announcements freed up demat space, and pulled out one dividend payment in cash rather than reinvesting it. When his brother-in-law sat down with him and actually listed every rupee that had moved in and out of the account, with dates, the "doubled my money" story fell apart. Sunil had not made a 130 percent return. He had made something much smaller, and it took a proper calculation to find out what it actually was.
This chapter is about that calculation, and about the habits that keep an investor from fooling themselves the way Sunil nearly did. It is not abstract finance theory. It is the specific arithmetic of tracking a real Nepali portfolio — one with rights issue payments, IPO allotments, dividend choices, and deposits made at irregular, inconvenient times — so that the number in your head matches the number that is actually true.
Lesson 104.1 — Why "My Portfolio Is Up" Is Not an Answer
Ask any ten NEPSE investors how they are doing and most will give you a version of Sunil's answer: a comparison between what the account is worth now and what they remember putting in, expressed as a percentage. This works fine in the one situation where nobody actually invests — a single lump sum on day one, left untouched, with the ending value checked on a single day at the end. The moment you add money, remove money, or receive shares outside of a normal purchase, that simple percentage stops measuring your return and starts measuring something else: a mixture of your return, the amount of money you had exposed to the market at various times, and pure luck of timing.
Nepali retail portfolios are almost never the single-lump-sum case. Rights issues are one of the biggest sources of irregular cash flow unique to this market. A company you already hold announces a 1:1 or 1:2 rights offering, and unless you let your entitlement lapse, you are expected to pay in fresh cash on a specific date to receive the new shares — cash that has nothing to do with market performance and everything to do with your own decision to keep your ownership percentage intact. Add to that IPO applications through ASBA, where money is blocked and then either allotted or refunded depending on the lottery result; dividend elections, where a company might pay partly in bonus shares and partly in cash, and you must decide whether to reinvest that cash or spend it; and ordinary deposits made whenever salary allows. Every one of these events changes how much money you have "at risk" in the market at a given time, and none of them should be counted as investment gain.
WARNING
A portfolio value that has grown mostly because you deposited more money, not because your holdings appreciated, will still feel like success. The feeling is real. The return is not. Before you compare this year to last year, or compare yourself to a friend, separate what you put in from what the market gave you.
The practical failure mode is this: an investor sees the account balance rise from Rs 5,00,000 to Rs 11,50,000 and mentally computes a 130 percent gain, when in fact Rs 2,90,000 of that increase came from rights payments and fresh deposits — money that was never "returned," it was simply added. Confusing contributed capital with investment profit is the single most common performance-tracking error among Nepali retail investors, and it compounds every year an investor keeps adding cash without correcting for it. The fix is not complicated, but it does require abandoning mental arithmetic in favour of two specific tools: the time-weighted return and the money-weighted return, better known by its common calculation method, XIRR.
Lesson 104.2 — Two Kinds of "Return," and Why You Need Both
Professional fund managers and individual investors face a subtly different measurement problem, and the industry has settled on two different tools because of it.
A mutual fund manager does not control when investors add or withdraw money from the fund. Someone might invest a large lump sum the day before a market crash, and someone else might invest the day after — neither event is the fund manager's doing, and it would be unfair to judge the manager's skill by the accident of when other people's money happened to arrive. So funds are judged by the time-weighted return, or TWR: a method that breaks the investment period into sub-periods bounded by each cash flow, calculates the return of each sub-period in isolation, and geometrically chains those sub-period returns together. The formula for chaining n sub-periods looks like this: TWR = [(1+R1) x (1+R2) x ... x (1+Rn)] - 1, where each Rn is calculated from the start and end value of that sub-period, with the cash flow itself excluded from the gain calculation. TWR answers the question: how good were the actual investment decisions and stock selections, independent of the size or timing of money moving in and out?
You, as an individual investor, control both things — the stock selection and the timing of your deposits and withdrawals. Both are your decisions, and both affect your actual lived financial outcome. So while TWR tells you whether you are a good stock-picker, it does not tell you whether you are managing your own cash flow well — whether, for instance, you kept adding money into an overheating market at the top, or whether your rights share payments happened to land at good or bad moments. The tool that captures your actual, lived experience — factoring in every rupee you put in and when — is the money-weighted return, most commonly computed as XIRR (the extended internal rate of return, so named because it works with cash flows on irregular, real calendar dates rather than assuming neat annual intervals).
KEY CONCEPT
Time-weighted return (TWR) measures how well the portfolio's holdings performed, stripped of the effect of your deposits and withdrawals — the fair way to judge whether your stock selection has skill. Money-weighted return (XIRR) measures the actual annualized return you personally experienced, given exactly how much money you had in the market and when. A serious investor tracks both, because they answer different questions, and the gap between them tells its own story.
For most individual Nepali investors, XIRR is the more urgent number to get right, because it is the one that answers "was this actually worth my money and my years," and it is the one almost nobody calculates correctly by hand. TWR matters more once you start comparing your stock selection to a benchmark like the NEPSE index — which we come to in Lesson 104.4 — because a benchmark comparison that ignores your cash flow timing will blame or credit you for the market's mood on the days you happened to deposit money, rather than for the quality of the shares you chose.
Lesson 104.3 — Calculating XIRR for a Real Portfolio
Here is the actual calculation Sunil's brother-in-law walked him through, using Sunil's real transaction history pulled from his broker's TMS statement and his Meroshare portfolio record. The mechanics generalise to any portfolio with irregular cash flows, which in Nepal means almost every portfolio.
XIRR works from a simple rule: every cash flow the investor makes into the portfolio is entered as a negative number (money leaving your pocket), and every cash flow out of the portfolio — including the current value of the portfolio, treated as if you liquidated it today — is entered as a positive number (money that would return to your pocket). Each cash flow gets a specific calendar date, not a rounded quarter or year. The XIRR is the single annualized rate of return that makes the net present value of every one of those cash flows equal exactly zero. It is solved by iteration — try a rate, see if the present values balance, adjust, try again — which is why nobody does this by hand in practice. Both Excel and Google Sheets have a built-in =XIRR(values, dates) function that does the iteration for you.
Date
Description
Cash Flow (NPR)
2023-01-15
Initial lump sum deposit into demat/trading account
-5,00,000
2023-07-10
Rights share payment, 1:1 rights call, hydropower holding
-1,50,000
2024-02-05
Fresh deposit after bonus shares freed up cash for new positions
-80,000
2024-08-20
Second rights call, separate listed company
-60,000
2025-03-12
Cash dividend received and withdrawn to bank (not reinvested)
+40,000
2026-08-25
Current portfolio value, treated as if sold today
+11,50,000
Feed those six rows into the XIRR function and the answer comes out to approximately 13.4 percent per year. That is Sunil's real, honest, money-weighted annual return across the roughly three years and seven months the account has existed.
Compare that to what Sunil's mental arithmetic was telling him. If you take only the first and last numbers — Rs 5,00,000 growing to Rs 11,50,000 over 3.61 years — and annualize that as a simple compound growth rate, you get a headline figure above 25 percent a year, nearly double the true return. The gap exists entirely because the naive calculation credits Sunil's own Rs 2,90,000 of rights payments and fresh deposits as if they were investment gains rather than contributed capital. XIRR, by treating each of those payments as a dated outflow in its own right, correctly recognises them as money he put in, not money the market gave him.
PRACTICAL TOOL
Build a running XIRR log in a spreadsheet with two columns: date and amount. Every deposit, every rights payment, every IPO application that gets allotted (the allotted amount, not the applied amount — refunds from unsuccessful ASBA applications are not investment cash flows and should never enter the log), and every withdrawal or cash dividend taken out, gets its own row with its own exact date. Add one final row today with today's date and your current total portfolio value as a positive number. Run =XIRR(range of amounts, range of dates) and that is your real annualized return, updated in minutes any time you want to check it.
Two details matter enough to flag before you build this for yourself. First, how you treat dividends changes the answer, and you must be consistent. A cash dividend you withdraw is an inflow on the day you receive it. A cash dividend you use to buy more shares should not appear as a separate row at all if the money never left your demat-linked account — but if you manually withdrew it and then manually redeposited it to buy something else, treat that as a wash and skip both entries, since including one side without the other will distort the calculation. Bonus shares and stock dividends are not cash flows at all — no cash moved, so nothing goes in the log; the increased number of shares just becomes part of your ending portfolio value, which is already captured by the final row.
Second, be honest about the ending value. If you are tracking XIRR quarterly, the "current value" row should reflect actual market prices from that day's NEPSE closing data on your holdings, not book cost, and it should be the only row you delete and re-add each time you update the calculation — every historical cash flow stays untouched.
REGULATORY DETAIL
Under Nepal's capital gains tax regime for listed shares, tax on realised gains is deducted at source by CDSC through your broker at the time of sale — currently 5 percent of the gain for shares held over 365 days and 7.5 percent for shares held less than a year, with the net proceeds credited to your bank account. If you are computing XIRR only on realised sales rather than treating your holdings as if liquidated today, use the net-of-tax proceeds you actually received, not the gross sale value — otherwise your calculated return will overstate what you actually kept.
CAUTION
XIRR assumes a single, consistent internal rate of return exists for your cash flow pattern. In the overwhelming majority of retail portfolios — one initial investment followed by a mix of additions and one final valuation — this holds fine. But a pattern with several large sign changes back and forth (heavy withdrawals followed by heavy deposits followed by more withdrawals) can mathematically produce more than one rate that satisfies the equation, and your spreadsheet function may return a result that looks plausible but is not the only valid answer. If your XIRR result seems wildly out of line with what a rough sanity check suggests, try supplying a different "guess" value to the function (its optional second argument) and see if the answer changes — if it does, treat the result with suspicion and simplify your cash flow log before trusting the number.
Lesson 104.4 — Building the Personal Performance Dashboard
Once you can calculate XIRR reliably, the next step is turning it from an occasional exercise into a standing dashboard — a single sheet you update every quarter that shows four things side by side: your time-weighted return, your money-weighted return, how both compare to the NEPSE index over the same period, and which sectors or holdings actually drove the result. Looking at these four together is what prevents any one of them from telling a misleading story on its own.
The time-weighted return tells you whether your specific stock selections are earning their keep, independent of your cash flow timing. The money-weighted return (XIRR) tells you the actual annualized rate your money experienced, given both your picks and your timing. The NEPSE benchmark comparison tells you whether either of those numbers is actually impressive, or just a reflection of a market that went up regardless of what you held. And the sector or per-position contribution breakdown tells you where the return actually came from — which is where uncomfortable truths tend to surface, as Lesson 104.5 shows.
Period
Time-Weighted Return
XIRR (Money-Weighted)
NEPSE Benchmark Return
Alpha vs NEPSE
Top Contributing Position
Q1 2025
4.2%
3.9%
3.1%
+1.1 pts
Hydropower sector, broad
Q2 2025
1.8%
1.6%
2.4%
-0.8 pts
Banking sector holdings
Q3 2025
6.5%
5.2%
4.8%
+1.7 pts
Microfinance holding
Q4 2025
-2.1%
-3.4%
-1.5%
-0.6 pts
Hydropower sector, broad
Q1 2026
3.3%
2.9%
3.0%
+0.3 pts
Insurance holding
Q2 2026
2.7%
2.4%
2.8%
-0.4 pts
Banking sector holdings
Notice that TWR and XIRR track each other closely quarter to quarter in this table but are never identical — the small gaps are exactly the effect of when Sunil's deposits or rights payments happened to land within that particular quarter relative to market movement. The NEPSE benchmark column uses the index's own percentage change over the identical calendar dates, calculated the same simple way the index itself is quoted, so the comparison is apples to apples. Alpha is just the XIRR column minus the benchmark column for that period, and it is this column, tracked over many quarters rather than any single one, that starts to answer the real question of the chapter: is there skill here, or not.
Per-sector or per-position contribution is best built as its own supporting worksheet, not crammed into the main dashboard row. For any period, contribution to return for a given holding is calculated as that holding's rupee gain or loss during the period, divided by the portfolio's total starting value for the period — not divided by the holding's own size. This is the detail that trips people up: a small position with a spectacular percentage gain contributes only a little to the overall portfolio return if it was a small slice of capital, while a large position with a modest percentage gain can dominate the total. Calculating it the right way is what exposes concentration in your return, which brings us to Sunil's actual reckoning.
Lesson 104.5 — The IPO Allotment That Wasn't Skill
Sunil's brother-in-law didn't stop at correcting the headline number. He asked Sunil to break 2024 down by position, because that year's XIRR — 42.6 percent — was so far above every other year that it deserved scrutiny before Sunil built any confidence around it.
Year
Sunil's XIRR
NEPSE Annual Return
Alpha vs NEPSE
Primary Driver
2023
11.2%
9.8%
+1.4 pts
Broad portfolio, ordinary stock-picking
2024
42.6%
14.3%
+28.3 pts
One hydropower IPO allotment
2025
8.1%
10.5%
-2.4 pts
Broad portfolio, underperformed market
2026 (YTD)
6.9%
7.4%
-0.5 pts
Broad portfolio, roughly tracking market
The 2024 figure came from a single event: Sunil had applied through ASBA for the IPO of a small hydropower company. The issue was heavily oversubscribed, so like every other successful applicant he was allotted the standard minimum of 10 kitta at the par value of Rs 100 — an allotted position of just Rs 1,000. It listed on NEPSE at more than four times its issue price within weeks amid strong retail demand for hydropower issues that year, and Sunil's Rs 1,000 position became worth roughly Rs 4,200 almost overnight — a gain that had nothing to do with analysis, patience, or any skill Sunil could take credit for. It was a lottery result. SEBON's ASBA allotment process distributes shares by random draw among applicants when an issue is oversubscribed, and Sunil's name simply came up.
CASE IN POINT
When Sunil ran the per-position contribution breakdown for 2024, the picture was less flattering than the headline suggested. The hydropower allotment quadrupled, but on a Rs 1,000 position that is a gain of about Rs 3,200 — a rounding error against a portfolio of roughly Rs 6,50,000. What actually drove the year was a concentrated bet on two commercial banks that re-rated sharply, while the rest of the portfolio he had carefully researched returned close to 14 percent, essentially identical to the NEPSE index itself. The IPO win felt like the story of the year because it was the most memorable event, not because it moved the number.
This is the trap that catches investors who look at only one good year, or who look at cumulative return without breaking down where it came from. Sunil's multi-year XIRR across 2023 through mid-2026 works out to approximately 13.4 percent, comfortably above the NEPSE's annualized return of roughly 10.5 to 10.6 percent over the same window — a genuinely respectable-looking record on the surface. But adjust that same multi-year figure by stripping out the two bank positions that drove 2024 — treating them as if they had simply matched the market that year instead of re-rating — and Sunil's adjusted multi-year XIRR falls to somewhere close to 9.5 to 10 percent, statistically indistinguishable from the NEPSE benchmark itself, and arguably trailing it once the small remaining variance is considered.
CAUTION
A single standout year, especially one built on a concentrated sector bet, a rumour-driven spike, or a rights entitlement you happened to hold before a favourable announcement, tells you almost nothing about your skill as an investor. Skill only shows up as a pattern that survives across several years and several different market conditions — a rising market, a falling one, and a flat one. One lucky draw dressed up as a strategy is how retail investors talk themselves into concentrating more money into speculative IPO applications and low-quality rights entitlements, believing a pattern exists where there is only a single coin flip that came up heads.
The honest reassessment changed how Sunil operated. He stopped describing himself, even privately, as someone who "beats the market." He stopped treating his 2024 result as evidence that his stock-picking process was unusually good, and instead treated his broad-portfolio returns — the ones excluding lottery-based IPO windfalls — as the only fair measure of his actual process. He kept applying for IPOs, because a free lottery ticket with government-mandated fair allotment odds is still worth entering, but he stopped increasing his ASBA application sizes on the theory that he had some special insight into which issues would perform, since the 2024 result had made clear that the outcome was allotment luck, not selection skill. And he began running the sector and position-level contribution breakdown every single quarter going forward, specifically so that the next lucky or unlucky outlier would be caught immediately rather than three years later at a family gathering.
Lesson 104.6 — The Quarterly Review, Tied to the Canon Score
None of this is worth building if it lives in a spreadsheet nobody opens after the first excited weekend. The dashboard from Lesson 104.4 needs a fixed cadence, and it needs to connect to the self-assessment discipline this book has already established, rather than existing as a separate, disconnected exercise.
The quarterly review is deliberately lightweight — thirty to forty-five minutes, four times a year, ideally on a fixed date like the close of each Nepali fiscal quarter so it does not get pushed aside. It has five steps, in order.
First, update the cash flow log with every deposit, rights payment, IPO allotment, and withdrawal from the quarter, and recompute XIRR. Second, recompute the time-weighted return for the same period using your broker's or your own sub-period valuations. Third, pull the NEPSE index's percentage change over the identical dates and compute your alpha. Fourth, run the per-position contribution breakdown and flag anything that contributed more than, say, 20 percent of the quarter's total return from a single holding — that is your early warning for exactly the kind of concentrated-luck outcome that fooled Sunil in 2024. Fifth, write down, in one or two sentences, what actually drove the quarter's result — not a feeling, a specific cause: a sector rotation, an earnings announcement, a rights entitlement, an IPO allotment, or ordinary market drift.
WARNING
A quarterly alpha number in isolation is nearly useless and can push you toward overconfidence or panic on the strength of a single three-month window. Never make a decision to increase risk, concentrate a position, or abandon a strategy based on one quarter's alpha reading alone. The number only earns your trust once you can look back across at least four to six consecutive quarters and see a pattern, not a blip.
This quarterly habit is not a new, separate obligation — it is the evidence base for the constitution review protocol and Canon Score discipline already established in Chapters 63 through 66 and revisited in Chapter 98. The constitution review asks whether you are still behaving in line with the investment rules you set for yourself; the performance dashboard is what tells you, in hard numbers rather than impressions, whether those rules are actually producing results worth keeping. A Canon Score review that only asks "did I follow my process" without also asking "and did that process's actual money-weighted return hold up against NEPSE over multiple periods" is checking your discipline but not your outcomes — you need both halves to know whether the constitution itself needs revision or simply needs more patience.
PRACTICAL TOOL
Keep a single tab in your Canon tracking sheet labelled "Quarterly Performance Log," with one row per quarter holding: TWR, XIRR, NEPSE return, alpha, top contributing position, and a one-line cause note. At your annual constitution review, this becomes the primary evidence exhibit — four rows of hard data rather than a year of vague impressions about how the portfolio "felt."
This quarterly rhythm feeds directly into the annual cadence established in Chapter 94. The four quarterly entries roll up into a single annual review, where the real question is not "was this quarter good" but "across the full year, and ideally across the several years the Canon Score process asks you to track, did the discipline actually produce a money-weighted return that justified the effort, relative to simply holding an NEPSE index-tracking allocation." An investor who cannot answer that question with a number, sourced from an actual XIRR calculation rather than a gut feeling, is in exactly the position Sunil was in before Dashain 2026 — proud of a number that was never real.
Chapter recap
This chapter built the specific arithmetic that separates a true investment return from the misleading gut-check most Nepali retail investors rely on. Simple percentage tracking breaks down the moment real cash flows enter the picture — rights share payments, IPO allotments, dividend elections, and irregular deposits — because it silently counts contributed capital as if it were investment profit, the way Sunil Maharjan's mental arithmetic turned Rs 2,90,000 of his own rights payments and deposits into an imagined 130 percent gain. The fix is XIRR: a dated log of every cash flow in and out of the portfolio, solved through a spreadsheet's built-in function, that produces the honest annualized return actually experienced — 13.4 percent in Sunil's worked example, against a naive headline figure nearly double that.
Alongside XIRR sits the time-weighted return, which strips out cash flow timing to judge stock selection on its own merits, and together they populate a quarterly personal performance dashboard with four columns worth tracking side by side: TWR, XIRR, the NEPSE benchmark return over the identical period, and a per-sector or per-position breakdown of exactly where the return came from. That last column is what caught Sunil's real lesson — his standout 2024 result, an apparent 28-point outperformance of NEPSE, traced back almost entirely to two concentrated bank positions that happened to re-rate, not to any skill in his broader, carefully researched holdings, which had simply tracked the market. Multi-year comparison against the NEPSE benchmark, not any single good year, is the only honest way to tell skill from luck — and the quarterly review habit, feeding into the Canon Score and constitution review protocol from Chapters 63 through 66 and 98, and rolling up into the annual cadence from Chapter 94, is what keeps that distinction from being forgotten the next time a lucky year arrives.
Chapter 105, "The Canon Data Pipeline," picks up exactly where this leaves off: the tools in this chapter are only as good as the transaction dates, cash flow amounts, and price data you feed into them, and the next chapter covers how to systematically collect, record, and organise that underlying data — from broker statements and Meroshare transaction histories to NEPSE index records — so that the XIRR log, the dashboard, and the quarterly review never depend on memory or guesswork again.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 105
The Canon Data Pipeline
First published 26 Aug 2026 · Last verified 29 Aug 2026
Sunita Rai keeps a battered Lenovo laptop on her desk at the structural engineering firm where she works in Kathmandu, and for the first four years she owned shares, that laptop was also her entire investment record. Broker statements sat in her email inbox, unsorted. Annual reports lived wherever she'd last downloaded them, if she'd downloaded them at all. When her accountant asked for the cost basis on a rights allotment from three years earlier, she spent an entire Saturday searching her Meroshare login, her Gmail, and a shoebox of paper TMS slips before she found it. That Saturday is when she decided that if she was going to run her portfolio like an investor rather than a gambler, she needed a system for the data itself, not just for the decisions she made with it. The checklists in Chapter 101, the financial models in Chapter 102, the position-sizing and tracking sheet in Chapter 103, and the XIRR log in Chapter 104 are only as good as what feeds them. Every number in those tools comes from somewhere. This chapter is about knowing where, and about building the unglamorous plumbing that gets the right number into the right cell without eating your weekends.
Sunita's system, three years on, is nothing exotic. It is a folder structure on her laptop, mirrored to a cloud drive, a short weekly routine, and a habit of writing the source next to every number she records. It took her a weekend to set up and now costs her perhaps twenty minutes a week plus a couple of focused hours each quarter. That is the standard this chapter holds itself to. If what follows takes you longer than that to run in practice, you have overbuilt it.
Lesson 105.1 — Where the Data Actually Lives
Before you can organise data, you need to know where it originates. Nepali retail investors often rely on secondhand sources — a Sharesansar headline, a Facebook investment group screenshot, a friend's WhatsApp forward — because the primary sources feel harder to reach. They are not, once you know the map. There are six places that matter, and each produces a different kind of number.
The Nepal Stock Exchange's own website is the first stop, and it is more useful than most investors give it credit for. NEPSE publishes daily trading reports covering turnover, traded shares, transaction counts, and the day's price movements across every listed scrip, alongside floor sheets that show the actual trade-by-trade tape. If you want the closing price history for a stock, the daily turnover for the market as a whole, or the exact volume that changed hands on a given day, NEPSE's site is the primary record, not a news aggregator's summary of it. NEPSE also publishes corporate disclosures directly, since listed companies are required to file material information with the exchange, including everything from board meeting notices to dividend declarations to auditor's reports.
The Securities Board of Nepal, SEBON, is the regulator, and its site is where you go for a different class of document: prospectuses for public issues, merchant banker reports, SEBON directives and circulars that change the rules investors operate under, and enforcement actions or penalties against companies or brokers. If a company changes its capital structure, issues rights shares, or gets flagged for a compliance lapse, the notice usually shows up at SEBON before it becomes market gossip.
Meroshare, run through CDSC, is your own transaction ledger and the closest thing to a single source of truth for what you personally hold. It carries your current holdings, your historical transaction record including IPO and rights allotments, and your dematerialized share balance. If a broker's statement ever seems to disagree with your own memory of what you bought, Meroshare is usually the tiebreaker, because it reflects the depository record rather than any one intermediary's bookkeeping.
Your broker's TMS, the Trading Management System each brokerage runs, is where your buy and sell orders actually execute, and it produces the transaction statements and contract notes that carry brokerage fees, SEBON fees, and the exact settlement amounts. These numbers are what you need for computing your real cost basis and your realised gains, since they include the fee drag that a simple price-times-quantity calculation misses.
Nepal Rastra Bank's website is where the macro backdrop comes from: policy rate decisions, the monetary policy statement issued each year, directives to banks and financial institutions, remittance and BOP data, and inflation figures. None of this tells you what to do with a specific stock, but it shapes the environment every stock trades in, and the position-sizing judgment in Chapter 103 leans on it.
Finally, company annual and quarterly reports, usually PDFs posted on the company's own website or filed through NEPSE, are the primary financial statements: balance sheet, profit and loss, cash flow, and the notes that explain restatements, related-party transactions, and provisioning. This is the document of record for the financial modelling work in Chapter 102. A media summary of an annual report is not the annual report.
KEY CONCEPT
Every number you use has a primary source and, usually, one or more secondary retellings of it. A news article's earnings summary, a group chat's dividend rumour, and a broker's WhatsApp update are all secondary sources. Build the habit of tracing any number back to NEPSE, SEBON, CDSC, NRB, or the company's own filing before you record it as fact.
Data Type
Primary Source
What You Get
Daily prices and turnover
NEPSE website
Closing prices, volume, floor sheet
Corporate disclosures
NEPSE, company website
AGM notices, dividend declarations, material events
Balance sheet, P&L, cash flow, notes and restatements
Sunita's early mistake, in her first year of investing, was treating Sharesansar and Merolagani as if they were primary sources. They are useful aggregators and she still reads them daily, but she now treats every figure she sees there as a pointer to go verify, not as the number she writes into her spreadsheet. The habit costs almost nothing once it is automatic, and it is the single biggest reason her records have held up over time.
Lesson 105.2 — The Folder and Naming Convention That Survives Years
The reason Sunita lost an entire Saturday hunting for a three-year-old cost-basis document was not that she failed to save it. She had saved it, in a folder called "Documents," under a filename her phone had generated automatically when she scanned it. A data pipeline that cannot be searched two years later is not a pipeline, it is a pile. The fix is not complicated, but it has to be consistent, because a folder system you abandon after three months is worse than none at all — you will have some documents in the system and some not, and you won't remember which.
Sunita's structure, adapted here as a template, is a top-level folder called something like "Investing" with a small number of subfolders underneath it, organised by function rather than by date, because you will usually be looking for a thing, not a moment.
Under "Investing," she keeps a "Disclosures" folder, subdivided by company ticker, so each listed company she holds or watches gets its own folder — NABIL, NLIC, HRL, and so on. Inside each company folder, every downloaded document, whether an AGM notice, a dividend declaration, or a NEPSE disclosure, gets saved with a filename that starts with the date in year-month-day order, followed by the company ticker and a short description. A dividend notice from NABIL filed in mid-2026 becomes something like 2026-07-15_NABIL_dividend-declaration.pdf. The year-first date format is deliberate: sorted alphabetically, the folder automatically sorts chronologically too, so a company's entire disclosure history reads top to bottom in the order it happened, with no extra effort.
A second folder, "Annual Reports," holds one subfolder per company, with each year's annual and quarterly report saved the same way: 2025-Q4_HRL_annual-report.pdf, 2026-Q1_HRL_quarterly-report.pdf. Because fiscal years in Nepal run mid-Ashadh to mid-Ashadh, she notes both the Nepali fiscal year and the corresponding English year range in a small text file at the top of each company folder, since this is the detail she has found herself forgetting fastest — whether a report labelled FY 2081/82 corresponds to the period she's modelling in Chapter 102.
A third folder, "Broker Statements," holds TMS transaction statements and contract notes, organised by broker if she has used more than one, then by year. A fourth, "Meroshare Exports," holds periodic CSV or PDF exports of her CDSC holdings and transaction history, dated the same way. A fifth, "Macro," holds NRB monetary policy statements and any directive she has needed to reference, organised by year rather than by topic, since she rarely needs more than the current and prior year's macro documents at once.
PRACTICAL TOOL
A folder-naming convention only works if you can describe it in one sentence and apply it without thinking. Sunita's is: date first in YYYY-MM-DD or YYYY-QX form, then company ticker, then a two-to-four-word description, all lowercase, hyphens instead of spaces. Write your own version of that sentence down and tape it, literally or figuratively, to the top of your Investing folder.
Folder
Example Subfolder
Example Filename
Disclosures
NABIL
2026-07-15_NABIL_dividend-declaration.pdf
Annual Reports
HRL
2025-Q4_HRL_annual-report.pdf
Broker Statements
Broker name, then year
2026_NIC-Asia-Securities_contract-notes.pdf
Meroshare Exports
By year
2026-06-30_meroshare_holdings-export.csv
Macro
By year
2026_NRB_monetary-policy-statement.pdf
The point of this system is not tidiness for its own sake. It is that the moment a discrepancy comes up — and it will, as the worked example in Lesson 105.4 shows — you need to go from "I remember there was a dividend notice about this" to the actual document in under a minute. A folder structure you have to think hard about to navigate fails exactly when you need it most, under time pressure, trying to settle a question before you decide whether to buy or sell.
One more discipline worth adopting alongside the folders: never rename a source document's content, only its filename, and never edit a PDF. If a company later restates a number, save the restated version as a new dated file rather than overwriting the old one. The old, wrong number is itself useful information — it tells you the company has a habit of restating, which belongs in your notes on that company's disclosure quality.
Lesson 105.3 — The Update Cadence: Daily, Weekly, Quarterly, Annual
The mistake that turns a data pipeline into a second job is trying to keep everything current all the time. NEPSE's floor sheet updates every trading day; you do not need to download it every trading day. The right cadence matches how often each tool in Chapters 101 through 104 actually consumes the data, not how often the data changes.
Daily, the only thing worth even glancing at is the closing price of stocks you hold or are actively watching for a buy, which takes under five minutes if you resist the urge to also read every headline and forum post attached to it. Sunita checks this from her phone on the bus home, using NEPSE's site or an aggregator, and does nothing else with it unless a price move is large enough to warrant checking whether a disclosure explains it.
Weekly, she spends about fifteen minutes updating the tracking sheet from Chapter 103: entering the week's closing prices, noting any dividends or corporate actions announced that week by checking the disclosure feeds for the handful of companies she holds, and glancing at NEPSE's weekly turnover summary to keep a feel for overall market activity. This is also when she scans for any SEBON circulars that might affect her holdings or her broker.
Quarterly, the workload steps up but stays bounded. When a company she holds files its quarterly report, she downloads it, saves it under the naming convention from Lesson 105.2, and updates the financial model inputs from Chapter 102 — revenue, net profit, EPS, and whatever ratios that model tracks. She also pulls a fresh Meroshare export of her holdings and transaction history as a reconciliation check against her own tracking sheet, and she reviews NRB's latest data release if one has come out in that window, since monetary policy shifts often land quarterly. This is the point where she also does her cross-checking pass, described in the next lesson, comparing what the company reported against what any news coverage said about it.
Annually, she does the heavier lift: downloading every annual report for every company she holds, re-verifying the XIRR calculation in Chapter 104 against a full-year Meroshare export, reviewing NRB's annual monetary policy statement in full rather than skimming it, and doing a full audit of her folder structure to catch anything that slipped through during the year, misfiled or missed. This is also when she prunes: companies she has fully exited get their folders archived rather than deleted, since she has more than once needed an old record for tax purposes.
Cadence
What to Pull
Which Tool It Feeds
Daily
Closing prices for held or watched stocks
Chapter 103 tracking sheet, mental checkpoint only
Weekly
Price updates, new disclosures, SEBON circulars
Chapter 103 tracking sheet
Quarterly
Quarterly reports, Meroshare export, NRB data release
Annual reports, full Meroshare export, NRB annual statement, folder audit
Chapter 102 model refresh, Chapter 104 XIRR verification
WARNING
A pipeline that demands daily attention to every data source will not survive contact with a busy month at your job. Design the cadence for the version of yourself that has a bad week, not the version that has a free Saturday. If the weekly step ever creeps past twenty minutes, something has been added that does not need to be there every week.
The discipline here is restraint as much as diligence. NEPSE, SEBON, and company sites will all happily give you more data than any four tools need. The cadence above is deliberately matched to the update frequency each of the earlier chapters' tools actually requires — there is no value in refreshing a quarterly EPS figure daily, and no value in letting a weekly price update slide for a month.
Lesson 105.4 — Cross-Checking and Data Quality
Numbers from Nepali capital market sources are not always clean on first pass, and treating any single source as automatically authoritative is a mistake, including this book's implicit ranking of primary over secondary sources. Companies restate prior-period figures, sometimes with little fanfare. PDFs get typos, especially in tables where a single misplaced decimal or an extra zero can survive multiple rounds of review. A news article summarising a quarterly result may be reporting a preliminary or unaudited figure and never issue a correction once the audited version differs. And straightforward transcription error, someone fat-fingering a number into a spreadsheet or an article, happens more often than anyone likes to admit.
CASE IN POINT
In late 2026, Sunita was updating her Chapter 102 model for a hydropower company she has held for two years, call it a stand-in for the sector, and found that the EPS figure she had recorded from a Sharesansar earnings summary the previous quarter, when the company released unaudited fourth-quarter results, was Rs 14.20. The company's subsequently released audited annual report, filed with NEPSE and posted on the company's own site, showed a restated full-year EPS of Rs 12.85, a meaningful difference for a stock whose valuation she had partly anchored to the earlier number. Because her folder held both the original unaudited disclosure, dated and filed under the company's ticker, and the later audited annual report, dated separately, she could see in about ten minutes that the restatement stemmed from a reclassification of a deferred tax provision between the unaudited and audited versions, a legitimate accounting adjustment rather than an error on anyone's part. Without the dated paper trail, she would have had to either accept the news figure at face value, guess at which number was current, or spend hours emailing the company's investor relations contact and waiting on a reply. Instead she updated her model with the audited figure, made a note in the company's folder explaining the restatement in one line, and moved on within the same sitting.
That worked example is the entire argument for this chapter in miniature. The pipeline did not prevent the discrepancy from existing, discrepancies between unaudited and audited figures are routine in Nepali reporting, since companies often release preliminary results ahead of the full audited annual report, and the two can legitimately differ. What the pipeline did was make the discrepancy cheap to resolve, because both source documents were dated, filed under the same company ticker, and sitting one folder-click away instead of buried in an inbox or a phone's downloads folder.
The general habit worth building from this is threefold. First, always note whether a figure you are recording is unaudited, provisional, or audited final, since Nepali companies frequently release results in stages and only the audited annual report should be treated as settled. Second, when a figure from a news source and a figure from the company's own filing disagree, trust the filing, but keep both, dated, so you can explain the gap later rather than simply overwriting one with the other and losing the history. Third, treat any number that looks unusually clean, a suspiciously round figure, an outlier growth rate, a ratio that jumps sharply from the prior period, as a prompt to check the source PDF directly rather than the aggregator's rendering of it, since transcription errors compound when an aggregator scrapes a number and a dozen forum posts copy the aggregator.
CAUTION
Do not assume the first number you see is the final number. Nepali companies commonly issue unaudited quarterly and annual results ahead of the audited version, and the audited figure, filed later with NEPSE and SEBON, is the one that should feed your financial models. Label every figure in your notes as unaudited or audited, and revisit unaudited entries once the audited version is out.
There is a second, subtler quality problem worth naming: cross-source disagreement that has nothing to do with restatement, simply human error somewhere in the chain. A dividend percentage reported as 15 percent in one aggregator and 1.5 percent in another is not a restatement, it is almost certainly a typo somewhere, and the only way to resolve it fast is to go to the company's own disclosure or the NEPSE filing directly, which is exactly the habit Lesson 105.1 pushes toward. Investors who skip building direct-source habits end up needing to resolve this kind of disagreement from scratch every time it happens, at whatever inconvenient moment it surfaces, usually right when they are deciding whether to act on the number.
REGULATORY DETAIL
SEBON requires listed companies to disclose material information, including quarterly and annual financial results, to the exchange in a defined format and timeline, and NEPSE publishes these disclosures on its own site as they are filed. This filing is the authoritative record of when a company reported what, and it is timestamped in a way that a news article rarely is with the same precision. When timing matters, such as determining whether an insider transaction happened before or after a material disclosure, the filing timestamp is what to rely on.
Lesson 105.5 — Backing Up What You've Built
A folder full of years of carefully dated, carefully named documents is worth exactly nothing the day a laptop's hard drive fails, and hard drives do fail, along with laptops getting stolen, dropped, or simply lost to a factory reset that nobody backed up first. This is not a hypothetical risk raised for the sake of thoroughness. It is one of the most common ways Nepali retail investors lose years of records, and it is entirely preventable with a habit that costs almost no ongoing effort once it is set up.
The minimum viable backup is a cloud sync of the entire "Investing" folder structure described in Lesson 105.2, using any mainstream cloud storage service, Google Drive, Dropbox, or a similar provider, configured to sync automatically rather than requiring a manual upload step. Automatic sync matters more than which provider you choose, because a backup that depends on remembering to do it manually will lapse exactly when life gets busy, which is also when you are least likely to notice a hard drive starting to fail. Sunita uses a folder that syncs continuously in the background, so a new disclosure she saves on a Tuesday evening is already backed up before she closes her laptop.
WARNING
A backup you have never tested is not a backup, it is a hope. Once a year, ideally at the same time as the annual review described in Lesson 105.3, deliberately try to recover a handful of files from your cloud backup on a different device, or after temporarily disabling local access, to confirm the sync has actually been capturing what you think it has. It is common for a sync tool to silently stop working after a password change, an app update, or a folder getting moved outside the synced directory, and the failure is invisible until the day you need it.
Beyond the folder of documents, the spreadsheet tools themselves, the tracking sheet from Chapter 103 and the XIRR log from Chapter 104, deserve their own explicit backup discipline, since these are working files you edit constantly rather than static documents you file once. If you keep these in a cloud-native spreadsheet tool, version history is usually built in and you get some protection automatically. If you keep them as local Excel files, the same automatic folder sync covers them, but it is worth occasionally saving a dated snapshot copy, a year-end copy renamed with the date, separately from the live working file, so that a spreadsheet formula error that silently corrupts months of entries does not also corrupt your only backup of the correct version.
A second layer worth considering, though not strictly necessary for most individual investors, is an occasional export to a second location entirely independent of your primary cloud provider, a periodic download to an external drive kept at a different physical location, or a second cloud account. This is insurance against the low-probability but non-zero case of an account lockout or provider-side data loss, and it costs perhaps an hour a year to maintain.
PRACTICAL TOOL
A simple backup checklist to revisit annually: confirm cloud sync is active and has recent activity, restore a sample file to verify the backup is real rather than assumed, save a dated snapshot copy of your live tracking and XIRR spreadsheets separately from the working file, and confirm you can still log into whatever account holds the backup, since a forgotten password on an account you touch once a year is a surprisingly common failure mode.
None of this is complicated, and that is deliberate. The investors who lose years of data are rarely undone by an exotic failure. They are undone by an ordinary one, a cracked screen, a stolen bag, a child who resets a tablet, on top of a backup habit that existed in intention but not in practice. Building the habit into the same rhythm as the quarterly and annual reviews already described means it happens automatically rather than needing to be remembered as a separate task.
Lesson 105.6 — Closing the Loop With the Rest of the Toolkit
The reason this chapter exists inside Part XVIII rather than standing alone is that a data pipeline is not useful in isolation. Its entire value is in how cleanly it hands off to the tools built in the four preceding chapters, and it is worth walking through that handoff explicitly so the whole Part reads as one system rather than four separate exercises plus a filing chore.
The pre-buy checklist from Chapter 101 draws directly on the Disclosures and Annual Reports folders: before initiating a new position, Sunita's checklist step of reviewing recent corporate actions and disclosure history is really just a prompt to open that company's folder and read the last year or two of filings in order, which the chronological filename sorting from Lesson 105.2 makes trivial. A company whose folder shows a pattern of late filings, frequent unaudited-to-audited restatements, or unexplained gaps is telling you something about its governance quality before you have looked at a single financial ratio.
The financial model in Chapter 102 consumes the audited annual and quarterly reports directly, and the discipline from Lesson 105.4, labelling every figure as unaudited or audited and preferring the audited version once available, is what keeps that model from being quietly built on a number that later gets restated out from under it. The quarterly update cadence from Lesson 105.3 is timed specifically so the model gets refreshed exactly when new audited or unaudited data becomes available, no faster and no slower.
The position-sizing and tracking sheet in Chapter 103 draws its price data from the daily and weekly cadence, and its transaction records from the Broker Statements and Meroshare Exports folders, reconciled quarterly. This reconciliation step, checking the tracking sheet's running record against a fresh Meroshare export, is where errors get caught before they compound. A transaction entered with the wrong quantity or price in week three will silently distort every subsequent calculation until someone checks it against the depository record, and quarterly is a reasonable frequency to catch that before it does real damage.
The XIRR log in Chapter 104 depends entirely on having a clean, complete, correctly dated transaction history, which is precisely what the Meroshare and broker statement folders are built to preserve. An XIRR calculation is only as trustworthy as its cash flow dates and amounts, and a data pipeline that has been quietly losing or misdating transactions for two years will produce an XIRR figure that looks precise and is actually wrong, with no obvious warning sign, since the formula will happily compute a confident-looking answer from bad inputs.
KEY CONCEPT
A data pipeline's job is not to generate insight. It is to make sure that when Chapters 101 through 104 ask for a number, the right number, from the right source, dated and labelled, is one folder-click away. Judge this chapter's system by how invisible it becomes in daily use, not by how sophisticated it looks.
Seen this way, the six lessons in this chapter are really one lesson applied six times: know where a number actually comes from, keep it somewhere you can find it years later, refresh it only as often as it needs refreshing, verify it before trusting it, protect it from the ordinary disasters that erase unbacked data, and route it to the tool that needs it. None of the four preceding chapters' tools require anything more elaborate than that to function well. A checklist, a model, a tracking sheet, and an XIRR log are all, at bottom, simple structures that do not reward being fed by an overengineered data operation any more than a well-run kitchen rewards a walk-in freezer for a single-person household.
Sunita's system today looks almost exactly as described in this chapter: a folder tree that took a weekend to set up, a weekly fifteen-minute update, a quarterly afternoon of reconciliation, an annual review, and a cloud backup she tests once a year. It is not more elaborate than that, and it does not need to be. The temptation, especially for an investor who enjoys the mechanics of organising data, is to keep adding layers, more folders, more automation, more sources tracked "just in case." Resist that temptation the same way you would resist overcomplicating a position-sizing rule or a checklist. The pipeline exists to serve the four tools around it, not to become a fifth hobby competing with the actual work of picking and holding good investments.
Chapter recap
This chapter mapped the six places Nepali market data actually originates: NEPSE's own site for prices, turnover, and disclosures; SEBON for regulatory filings and directives; Meroshare and CDSC for your personal holdings and transaction history; broker TMS statements for execution detail and fees; NRB's site for macro data and policy; and company annual and quarterly reports for the audited financial statements everything else ultimately rests on. It laid out a folder-and-naming convention, organised by company ticker with year-first dated filenames, designed to remain searchable years after the fact rather than only in the week a document was saved. It set an update cadence, daily price glances, weekly tracking-sheet updates, quarterly report downloads and reconciliation, annual full reviews, matched to how often the tools in Chapters 101 through 104 actually need fresh input rather than to how often the underlying data changes. It walked through a real cross-checking failure mode, an unaudited EPS figure later restated in the audited annual report, and showed how a dated, well-filed paper trail turned what could have been hours of confusion into a ten-minute resolution. It laid out a backup discipline built around automatic cloud sync, annual restore testing, and dated snapshots of the working spreadsheets, aimed squarely at the ordinary, common way years of manually collected data get lost. And it closed by tracing how this pipeline feeds directly into the pre-buy checklist, the financial model, the tracking sheet, and the XIRR log built in the four preceding chapters, arguing that the pipeline's only job is to stay simple enough to serve those tools without becoming a project in its own right.
Chapter 106, The NEPSE Data Handbook — Historical Almanac, turns from process to reference. It compiles a compact historical almanac of NEPSE data, the kind of index-level and market-wide figures worth having on hand rather than re-deriving from scratch each time a question about NEPSE's history comes up, so that the pipeline built in this chapter has a settled body of historical reference material to sit alongside the investor's own ongoing records.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 106
The NEPSE Data Handbook — Historical Almanac
First published 26 Aug 2026 · Last verified 29 Aug 2026
The NEPSE Data Handbook — Historical Almanac
Sunita Koirala keeps a single laminated sheet taped inside the cabinet door beside her desk in Kathmandu. On one side is a hand-drawn timeline of the NEPSE index going back to 2015, with arrows and dates scrawled in three colours of pen. On the other side is a small table of P/E ranges by sector, a column of AGM months, and a short list of regulatory changes she has lived through as an investor — the kind of changes that, at the time, felt like footnotes, and only years later revealed themselves as turning points. She built the sheet slowly, one correction and one recovery at a time, and she updates it every year using the same data pipeline described in Chapter 105. She does not read it the way she reads a book. She glances at it the way a sailor glances at a tide table — not for excitement, but to know roughly where she stands before deciding what to do next.
This chapter is that sheet, expanded into a full reference. It is not meant to be read once and shelved. It is meant to be returned to — before you buy into a rally that feels unprecedented, before you panic in a correction that feels like the end of the market, before AGM season, before a rights issue, before you assume a regulation you remember is still in force. Everything in this chapter should be treated as a set of approximate, illustrative ranges that build intuition for how the Nepali market has behaved historically — not as precise figures to bet on, and not as a forecast of what must happen again. Markets rhyme. They do not repeat exactly, and NEPSE's history is short enough, and its regulatory environment fluid enough, that yesterday's exact numbers are less useful than the underlying mechanics that produced them. Those mechanics — credit cycles and liquidity regimes — are what this chapter keeps coming back to, because they are what actually explain NEPSE's major swings, far more than any single company's earnings ever did.
Lesson 106.1 — The Shape of NEPSE's Cycles: Four Eras in Plain English
If you have read Chapters 0.1 through 0.3 and Chapter 11, you already know the core argument of this book: Nepal's stock market is, more than almost any comparable market in the region, a liquidity market before it is an earnings market. Share prices in Nepal move less because companies suddenly become more or less profitable, and more because the banking system's capacity and willingness to lend expands or contracts, because deposit growth outruns or falls behind credit growth, because interest rates rise or fall, and because margin lending amplifies whatever the underlying liquidity trend already is. NEPSE's history since the mid-2010s is, in effect, a case study in that argument playing out four times.
The first era is the 2016 rally and its peak. In the years leading up to it, the banking system was flush with liquidity, deposit growth was strong, and interest rates were low. Credit was cheap and available, and margin lending — loans taken against listed shares as collateral — expanded quickly. Retail investors could borrow against an existing portfolio to buy more shares, and as prices rose, the value of the collateral rose too, which allowed still more borrowing. This is the leverage spiral described in Chapter 11: rising prices create more borrowing capacity, more borrowing capacity buys more shares, and more buying pushes prices higher still. Add to that a general mood of post-earthquake reconstruction optimism and renewed confidence in the banking and hydropower sectors, and NEPSE ran hard through 2015 and into 2016, approaching and briefly touching levels in the high 1,800s on the index — treat that specific number as an illustrative approximation of where the peak sat, not a verified constant, and check the primary NEPSE historical series if you need the exact print. What matters for your intuition is less the exact index level and more the shape: a multi-year advance built substantially on liquidity and leverage, not on a corresponding multi-year advance in underlying corporate earnings.
The second era is the correction and consolidation that followed, running roughly from later 2016 through 2019. This is where the liquidity story reverses. Nepal Rastra Bank tightened as deposit growth failed to keep pace with credit growth, the credit-to-deposit ratio in the banking system came under pressure, and interest rates — especially fixed deposit rates offered by banks scrambling for deposits — rose sharply. When the cost of money rises and its availability tightens, two things happen to a leveraged market at the same time. First, margin loans that looked comfortable at low interest rates become expensive to carry, and some borrowers are forced to sell to service or repay them. Second, new buying power dries up, because banks have less room to extend fresh margin lending and depositors can earn attractive fixed returns elsewhere instead of chasing shares. NEPSE spent roughly three years in a wide trading range, generally described as the low-to-mid 1,100s to the 1,300s, an approximate band worth treating as a rough recollection of the period rather than an exact statistic. It was not a single sharp crash; it was a long, grinding, liquidity-starved consolidation, punctuated by short rallies whenever liquidity eased briefly.
The third era is the pandemic-era rally of 2020-2021 and its peak. This is the most dramatic example in NEPSE's short history of a liquidity-driven bull market, and it is worth understanding mechanically rather than as a story about optimism. When COVID-19 lockdowns hit, several things happened to Nepal's liquidity picture at once. Remittance inflows, which many analysts expected to collapse, instead held up and in some periods rose, partly because migrant workers unable to travel or spend abroad sent more of their earnings home, and partly because informal channels (hundi) were disrupted, pushing more remittance through formal banking channels where it showed up as deposits. Interest rates fell to some of the lowest levels in NEPSE's modern history as the banking system sat on ample deposits with fewer productive lending opportunities during lockdowns. At the same time, retail Nepalis stuck at home discovered online trading — dematerialized shares, the Trading Management System, and mobile-based Meroshare accounts made it possible to open a demat account and trade shares from a phone without visiting a broker's office. Cheap money, abundant liquidity, and a large new wave of retail participants converged, and margin lending expanded again, again amplifying the move in both directions. NEPSE rallied from roughly the 1,100s in 2020 to a peak commonly cited around the 3,200 mark in mid-2021 — again, treat that figure as an approximate, illustrative recollection rather than a number to quote precisely; pull the exact print from your own data pipeline if precision matters for a specific decision.
The fourth era is the 2022 correction, and it is close to a textbook mirror image of what triggered the 2016-2019 slide, compressed into a shorter and sharper period. Nepal faced balance-of-payments pressure and falling foreign exchange reserves through 2021 into 2022, partly from a surge in imports as the economy reopened. NRB responded with a tightening cycle: policy rates rose, import restrictions were introduced on certain goods, and banks faced credit-to-deposit ratio limits that forced many of them to slow lending and, in some cases, call in margin loans or refuse to renew them. Fixed deposit rates spiked into double digits as banks competed hard for scarce deposits. Margin-financed positions built during the 2021 rally came under pressure exactly when the ability to refinance them evaporated, and NEPSE fell back sharply, into roughly the high-1,900s to 2,000 range by the end of 2022, with further weakness into 2023.
The most recent era, running from roughly 2023 into 2026, has been one of gradual recovery and consolidation rather than a fresh dramatic rally. As NRB's tightening cycle eased, policy rates came down in steps, remittance and reserve positions stabilised, and interest rates on deposits and loans drifted lower again, NEPSE recovered in a choppier, more uneven way than either of its two prior rallies — advancing into the 2,400 to 2,800 range in various stretches, with real volatility around that recovery rather than a smooth climb, and still, as of this writing, below the 2021 peak. Whether that changes by the time you are reading this is exactly the kind of thing this almanac cannot tell you and your own updated data pipeline can.
KEY CONCEPT
Every one of NEPSE's four major eras traces the same underlying mechanism: bank liquidity (deposit growth relative to credit growth), the interest rate that liquidity sets, and the margin lending that amplifies whatever direction liquidity is already moving. Read NEPSE's history through that lens first, and through company-level news a distant second.
CASE IN POINT
The 2021 peak and 2022 correction happened within about eighteen months of each other, driven almost entirely by the reversal of NRB's monetary stance and the resulting swing in bank liquidity — not by any comparable swing in listed companies' underlying earnings over that same period. If you want one compact illustration of the credit-cycle argument in Chapters 0.1 to 0.3, this is it.
Lesson 106.2 — Valuation Ranges Across the Cycle: A Sector Reference Table
Because NEPSE's swings are liquidity-driven more than earnings-driven, the same company can trade at wildly different multiples of its own earnings and book value depending purely on where the credit cycle stands — with little change in the business itself. This is one of the most useful and most dangerous things to internalize as a Nepali investor. Useful, because it means valuation ranges are genuinely cyclical and can act as a rough compass. Dangerous, because it tempts people to treat a past cycle's peak or trough multiple as a hard ceiling or floor that a share "must" return to.
The table below gives approximate, historically-informed reference ranges for price-to-earnings (P/E) and price-to-book-value (P/BV) multiples across NEPSE's major sectors, split by roughly what "bull phase" conditions and "bear phase" conditions have historically looked like. These are not predictions, they are not current-as-of-publication quotes, and they should not be used as the sole basis for a buy or sell decision. They exist so that when you see a banking stock trading at 40 times earnings, or a hydropower counter trading below 10 times earnings, you have some sense of whether that sits inside or far outside the range this market has historically shown for that sector, and can ask why.
Sector
Typical Bull-Phase Range (illustrative)
Typical Bear-Phase Range (illustrative)
Commercial banking
P/E roughly 18-25x, P/BV roughly 2.5-4x
P/E roughly 7-12x, P/BV roughly 1-1.5x
Hydropower
P/E often 30x and higher (frequently speculative given low current-year earnings), P/BV roughly 3-6x
P/E roughly 12-20x, P/BV roughly 1.2-2x
Microfinance / development banks
P/E roughly 20-30x, P/BV roughly 3.5-6x
P/E roughly 8-14x, P/BV roughly 1.3-2x
Life and non-life insurance
P/E roughly 18-28x, P/BV roughly 3-5x
P/E roughly 9-15x, P/BV roughly 1.2-2x
Manufacturing, trading, and other
P/E roughly 14-20x, P/BV roughly 2-3x
P/E roughly 8-12x, P/BV roughly 1-1.5x
A few observations worth attaching to that table rather than reading it as a stand-alone fact sheet. First, hydropower multiples deserve their own caution: because many hydropower companies report low or lumpy current earnings (a plant not yet at full generation, a monsoon-dependent revenue swing, or a project still under construction reporting through an associate holding), P/E ratios in this sector can look extreme in both directions for reasons that have nothing to do with market sentiment and everything to do with where a specific plant sits in its own commissioning and hydrology cycle. Do not read a triple-digit hydropower P/E automatically as a bubble signal, and do not read a low one automatically as a bargain, without first checking whether the earnings base itself is temporarily depressed or inflated.
Second, microfinance multiples have historically compressed the hardest during liquidity tightening phases, because the sector's business model — small loans, often to borrowers with thinner collateral cushions, funded by wholesale borrowing from banks — is unusually sensitive to the same interest rate and credit-to-deposit pressures described in Lesson 106.1. When banks tighten, microfinance institutions' own cost of funds rises faster than most other borrowers', and their asset quality assumptions get tested hardest. That is part of why microfinance P/BV multiples have shown some of the widest bull-to-bear swings on the table.
Third, insurance multiples have historically carried their own quirk: this is a sector where regulatory paid-up capital increases (discussed further in Lesson 106.4) have periodically forced fresh capital raises that diluted per-share book value and earnings in the near term, distorting multiples independent of the credit cycle. When you see an unusual insurance multiple, check first whether a recent capital increase or bonus/rights issuance is the real explanation before assuming it reflects a market view on the business.
WARNING
Do not treat any cell in this table as a price target, a floor, or a ceiling. A sector trading below its historical bear-phase range is not automatically cheap, and a sector trading above its historical bull-phase range is not automatically overheated — it may simply mean the range itself has shifted, or that something in the underlying business (a capital raise, a regulatory change, an earnings distortion) explains the number better than sentiment does. Use this table to ask better questions, not to skip asking them.
Lesson 106.3 — The Fiscal Year Clock: AGMs, Book Closure, and Dividend Timing
Nepal's government fiscal year runs from Shrawan 1 to the end of Ashad — roughly mid-July to mid-July of the following Gregorian year — and nearly every listed company in Nepal reports its annual accounts on this same fiscal calendar rather than a January-to-December year. This single fact shapes an entire recurring rhythm in NEPSE that repeats every year, and that a Nepali investor benefits from knowing by heart rather than relearning each season.
Once a company's fiscal year closes at the end of Ashad, it takes time to finalise audited financial statements, get board approval, and secure regulatory sign-off (from NRB for banks and financial institutions, from the Insurance Board for insurers, and from SEBON more broadly) before the company can call its Annual General Meeting. In practice, this means AGM season for most Nepali listed companies clusters in the months roughly corresponding to Poush through Falgun — approximately December through March — though it is common, and normal, for some companies to hold AGMs later in the year, and for the exact clustering to shift somewhat year to year depending on regulatory processing times and each company's own pace. Do not assume every company will hold its AGM in the same month every year; treat the December-to-March window as the period when most AGM-related activity happens, not a fixed date on the calendar.
Before an AGM that will approve dividends (cash, bonus shares, or both), a company sets a book closure date. Shareholders who hold the stock as of that book closure date are the ones entitled to the dividend the AGM subsequently approves; anyone who buys after book closure, even by a single day, does not receive that dividend cycle's payout. This creates a very reliable, recurring pattern in NEPSE trading behaviour: volume and price often firm up in the days and weeks leading into an expected book closure, as investors position to capture an anticipated dividend, and it is common — though not universal or guaranteed — to see some profit-taking or price softness immediately after book closure, as short-term dividend-capture buyers exit. This is sometimes called a dividend-capture pattern, and while it is a real and recurring seasonal tendency, it is not a mechanical law; a stock's price can just as easily keep rising after book closure if the broader liquidity and sentiment backdrop is strong enough to override the seasonal effect. Treat it as one input among several, not a trading system on its own.
Once the AGM approves a dividend, cash dividends are typically credited to shareholders' bank accounts within a defined regulatory window after approval, and bonus shares go through a separate process of regulatory approval, allotment, and crediting to shareholders' demat accounts via CDSC, which historically has taken noticeably longer than cash dividend disbursement — sometimes stretching to weeks or months after the AGM itself. If you are counting on bonus shares to be tradeable by a specific date, build in a real buffer rather than assuming an immediate credit.
There is a second, quieter seasonal pattern tied to the same fiscal year-end: because Ashad-end (mid-July) is when banks and financial institutions close their books for regulatory ratios — capital adequacy, credit-to-deposit ratio, non-performing loan classification — there is a recurring tendency for banking system liquidity to tighten somewhat around fiscal year-end, as banks manage their balance sheets to meet regulatory ratios on the reporting date, and for interest rates on short-term instruments to firm up around the same period. Businesses across the economy also tend to settle tax obligations and loan repayments around fiscal year-end, adding to the seasonal liquidity squeeze. This is a modest, recurring seasonal pattern rather than a dramatic one, but it is worth having on your calendar alongside AGM season, because the two together roughly bookend the Nepali investing year: a liquidity-tightening period around Ashad-end (mid-July), followed some months later by a wave of AGMs, book closures, and dividend announcements clustering in the following Poush-to-Falgun window.
Calendar Marker (approximate)
What Typically Happens
Why It Matters to You
Ashad end (mid-July)
Fiscal year-end for government and most listed companies; banks close books for regulatory ratio reporting
A recurring seasonal liquidity tightening; short-term rates often firm up around this period
Shrawan-Bhadra (mid-July to mid-September)
Audited financial statements prepared and finalised
Limited new AGM-related activity yet; results begin trickling into public disclosure
Poush-Falgun (roughly December-March)
Peak AGM season across most sectors
Book closure dates announced, dividend decisions made public, dividend-capture trading pattern often visible
Scattered through the year
Some companies hold AGMs outside the typical window
Always check each specific company's own disclosure rather than assuming the general pattern applies
PRACTICAL TOOL
Keep a running calendar — inside the same spreadsheet or data pipeline described in Chapter 105 — of book closure dates and AGM dates for every company you hold. Update it every time a company issues a notice, and review it at the start of each fiscal year. A five-minute habit here prevents the common and avoidable mistake of missing a dividend entitlement by buying a day too late, or panicking at a post-book-closure price dip that is simply the normal dividend-capture pattern playing out.
Lesson 106.4 — A Decade of Regulatory Turning Points
NEPSE today looks and functions differently than it did a decade ago, and almost none of that change came from the market itself — it came from regulatory decisions made by NRB, SEBON, and NEPSE's own management, often in direct response to the credit cycles described in Lesson 106.1. Understanding these turning points matters for two reasons. First, several of the mechanical shifts described elsewhere in this book — margin lending amplification, dividend timing, the ease of retail participation — exist in their current form only because of specific regulatory decisions, and those decisions can and do change again. Second, when you read older commentary, older data, or even older chapters of financial history about NEPSE, you need to know which regulatory regime that commentary was written under, because a rule that was true in 2015 may not be true today, and a rule that is true today may not hold by the time you are reading this.
The shift to electronic trading was one of the foundational changes. NEPSE moved from an open-outcry floor trading system to a fully electronic Trading Management System, changing execution speed, transparency, and the practical barrier to entry for ordinary investors. Around the same broad period, dematerialization of shares became mandatory, meaning physical share certificates were retired in favour of electronic holdings recorded through CDSC (the Central Depository System and Clearing company), with investors holding shares in demat accounts rather than paper certificates. Together, these two changes are the infrastructure that made everything else in this chapter — online trading, mobile-based participation, faster settlement — possible. Before this shift, participating in NEPSE meant physical certificates, broker floor visits, and settlement timelines that would feel unrecognizably slow today.
A second major turning point was a wave of NRB-driven capital adequacy changes for banks and financial institutions, most visibly a mandated increase in minimum paid-up capital for commercial banks (and, on a different schedule, for development banks, finance companies, and other BFI categories) that forced a wave of mergers and acquisitions across the banking sector, along with a wave of rights issues and bonus share issuances as banks scrambled to build up capital organically and through the market. For an investor, this mattered on two levels: it changed which specific bank shares existed on NEPSE at all (many older bank names disappeared into mergers), and it produced a sustained period of share count expansion across the banking sector, which is part of why any long-run per-share comparison of Nepali bank shares has to account for repeated bonus and rights dilution, not just price movement.
A third turning point was the tightening and loosening cycle in margin lending rules, discussed mechanically in Lesson 106.1. After the 2016 peak, regulators moved to tighten how margin lending was valued and concentrated — adjusting the basis on which collateral shares are valued for margin purposes, and limiting how concentrated a margin book could be in a small number of scrips — specifically because excessive margin-fuelled concentration had been a visible amplifier of the preceding rally and was seen as a systemic risk once the market turned. A similar tightening occurred again around 2022, this time bound up with the broader NRB credit-to-deposit ratio squeeze, including reduced loan-to-value allowances on margin lending and higher interest rates on margin loans themselves. In both cases, the tightening arrived after the peak, not before it — a pattern worth remembering, because it means margin lending rule changes have historically been a lagging confirmation of a cycle turn rather than an early warning of one.
A fourth turning point has been the ongoing reform of the IPO and rights issue application process, moving from older, more cumbersome application methods toward ASBA (Application Supported by Blocked Amount) and its later online form, C-ASBA, alongside the broader rollout of Meroshare as the standard portal for demat account holders to apply for IPOs, receive allotments, and manage their holdings. These reforms, taken together with SEBON's periodic adjustments to IPO allotment methodology (including lottery-based allotment for oversubscribed issues and minimum retail allocation rules), have made IPO participation dramatically more accessible to ordinary retail investors than it was a decade earlier, while also meaning that IPO allotment odds and processes you may remember from several years ago may already be out of date.
REGULATORY DETAIL
Margin lending rules in Nepal are not a single fixed policy — they are a lever NRB and NEPSE's regulators have adjusted repeatedly, tightening the loan-to-value ratio and collateral valuation basis after periods of rapid market appreciation, and loosening them again once liquidity conditions and market conditions cool. Any specific margin lending rule you know today should be treated as current only as of today, not as a permanent feature of the market.
Approximate Period
Regulatory / Structural Change
What It Meant for Investors
Roughly early-to-mid 2010s
Shift from open-outcry floor trading to electronic trading (TMS); mandatory dematerialization of shares via CDSC
Faster, more transparent execution; elimination of physical certificate risk; groundwork for online participation
After the 2016 peak
Margin lending valuation basis and concentration limits tightened
Reduced leverage-driven speculation in individual scrips; contributed to the length of the following correction
Mid-2010s (staggered by BFI category)
NRB-mandated increase in minimum paid-up capital for banks and other BFIs
Wave of bank mergers and acquisitions; wave of rights and bonus issuances diluting per-share figures across the sector
Roughly 2019-2021
Rollout and expansion of ASBA / C-ASBA and Meroshare for IPO applications and demat management
Broader, faster, less paperwork-heavy retail access to IPOs and share holdings
Forced deleveraging by margin borrowers; amplified the 2022 correction
Recent years
Continued digitization of trading and periodic SEBON adjustments to IPO allotment methodology and retail quotas
Broader access, but the same underlying liquidity-driven cyclicality remains; specific allotment rules should always be checked against current SEBON notices
CAUTION
Every date and figure in this chapter, including the regulatory table above, is presented as an approximate, illustrative marker to build your intuition for the sequence and mechanics of change — not as a verified historical record. Regulatory notices from NRB and SEBON, and NEPSE's own official historical data, are the primary sources to consult before relying on any specific date, threshold, or ratio for a real decision.
Lesson 106.5 — Reading the Cycle in Real Time: Signals Worth Tracking
An almanac is only useful if it also tells you how to place today somewhere on the map it describes. The four eras in Lesson 106.1 were each recognizable, in real time, to anyone tracking the right handful of signals — not with certainty, and not early enough to catch the very first move, but early enough to matter. The same signals are worth tracking today, on an ongoing basis, using the data pipeline built in Chapter 105.
The single most important signal is the credit-to-deposit ratio across the banking system, and its trend rather than its level. When CD ratios across major banks are comfortably below the regulatory ceiling and trending down (deposits growing faster than credit), the banking system generally has room to extend fresh lending, including margin lending, and liquidity conditions tend to be easy. When CD ratios are pressed up against the ceiling and trending up, banks are constrained, deposit competition intensifies, interest rates rise, and margin lending capacity shrinks. This single ratio, tracked over time, foreshadowed both the 2016-2019 tightening and the 2022 correction well before NEPSE's index confirmed the turn.
The second signal is the trend in fixed deposit interest rates offered by commercial banks. Rates that are falling, or have recently fallen to multi-year lows, are consistent with an easy-liquidity environment that has historically supported NEPSE rallies; rates that are rising, especially rising quickly, are consistent with the tightening environment that has historically preceded or accompanied corrections. This is a signal ordinary investors can track without any specialised data source — bank FD rates are publicly advertised — and it is one of the most reliable, low-effort indicators available to a retail investor in Nepal.
The third signal is remittance inflow growth, published periodically by NRB, because remittances are one of the primary sources of deposit growth in the Nepali banking system and therefore a leading input into the CD ratio and liquidity story above. A period of strong remittance growth tends to feed through into stronger deposit growth, easier system liquidity, and — with a lag — friendlier conditions for equities; a period of weak or negative remittance growth tends to work in the opposite direction.
The fourth signal is margin lending outstanding as a share of total bank lending, where data is available, and more generally the qualitative tone of NRB and SEBON communication about margin lending — whether it is being loosened or tightened. Because margin lending amplifies the underlying liquidity trend in both directions, a rapid expansion of margin lending during a rally is historically a sign that the rally has entered its more fragile, leverage-dependent phase, not a sign that it is safe to lean in harder.
None of these four signals, individually or together, tells you exactly when a peak or trough will arrive. What they do is let you locate roughly which phase of the credit cycle the market is currently in, and therefore what kind of valuation multiples (Lesson 106.2), what kind of dividend behaviour (Lesson 106.3), and what kind of regulatory posture (Lesson 106.4) are more or less likely to be operating at that moment. That is the entire and modest purpose of tracking them.
KEY CONCEPT
You cannot reliably time the exact top or bottom of a NEPSE cycle from these signals, and you should not try to. What you can do is avoid being the last person still leveraged into a rally after CD ratios, interest rates, and margin lending have already told you the liquidity tide has turned — and avoid being too fearful to participate after those same signals have clearly turned back in the other direction.
Lesson 106.6 — Using This Almanac: A Living Reference, Not a One-Time Read
This chapter was never meant to be read once. Sunita's laminated sheet works because she looks at it every year, updates the numbers that have changed, and adds a line for whatever new regulatory shift or cyclical marker occurred since her last update. That habit — not the specific numbers on the sheet at any given moment — is the actual value of an almanac chapter like this one.
Use it in three concrete ways. First, before you make a significant portfolio decision — adding a large new position, taking on margin, or panic-selling into a drawdown — glance at Lesson 106.1 and ask which era this current moment most resembles: easy liquidity and rising leverage, or tightening liquidity and forced deleveraging. Second, before you judge whether a stock's current valuation looks rich or cheap, glance at the ranges in Lesson 106.2 and ask whether the multiple in front of you sits inside or outside historical norms for that sector, and why. Third, keep the fiscal year calendar in Lesson 106.3 live in your own tracking sheet, and revisit the regulatory table in Lesson 106.4 whenever you read older material about NEPSE, so you can judge whether the rule being described is still the current rule.
None of this replaces the primary sources. NRB's periodic reports on monetary policy, banking sector liquidity, and remittance flows; SEBON's notices on IPO, rights issue, and margin lending regulation; and NEPSE's own published historical index and turnover data are the ground truth, and this chapter is a compass pointing you toward them, not a replacement for checking them. The Canon Data Pipeline built in Chapter 105 is precisely the tool for keeping this almanac current: the same spreadsheet or system you built there to pull NEPSE index data, sector indices, and company disclosures is where you should also be logging book closure dates, AGM outcomes, interest rate trends, and any regulatory notice that touches margin lending, capital adequacy, or IPO process — so that a year from now, or five years from now, this chapter's approximate figures can be checked, updated, and corrected against your own running record rather than trusted blindly.
PRACTICAL TOOL
Once a year — a natural anchor point is shortly after Ashad-end, when fiscal year data starts to firm up — spend one sitting updating your own version of this almanac: refresh the index-level markers for the current cycle, note any change to margin lending or capital adequacy rules you have observed, and add the year's AGM and book closure dates for the companies you hold. Treat it as routine maintenance, the same way you would service a vehicle, rather than as optional research.
Chapter recap
NEPSE's history since the mid-2010s is best understood as a sequence of liquidity-driven cycles rather than a sequence of earnings-driven ones: the 2016 rally and peak, built on cheap credit and expanding margin lending; the multi-year correction and consolidation that followed as NRB tightened and interest rates rose; the 2020-2021 pandemic-era rally, driven by resilient remittances, historically low interest rates, and a wave of new retail participation through demat and online trading; the sharp 2022 correction, driven by balance-of-payments pressure, NRB tightening, and renewed margin lending restrictions; and the more recent recovery and consolidation phase as conditions gradually eased again. Valuation multiples across banking, hydropower, microfinance, insurance, and manufacturing/trading sectors have historically swung wide between these bull and bear phases, and the reference ranges in this chapter should be used to ask better questions about a given valuation, never as a predictive target. Nepal's fiscal year, running Shrawan to Ashad, drives a predictable annual rhythm of AGM season, book closure dates, and dividend timing that every Nepali investor should track on a running calendar, alongside a modest, recurring liquidity tightening around fiscal year-end itself. A decade of regulatory turning points — the shift to electronic trading and dematerialization, NRB's capital adequacy-driven bank merger wave, repeated tightening and loosening cycles in margin lending rules, and the ongoing modernisation of IPO and rights issue processes through ASBA, C-ASBA, and Meroshare — has reshaped how this market functions, and will likely keep changing further. Every figure in this chapter is approximate and illustrative by design; treat it as a compass for intuition, not a precise historical record, and keep it updated using the Canon Data Pipeline from Chapter 105 rather than filing it away and forgetting it. Chapter 107, Keeping the Canon Current, turns to the broader version of that same habit — how you, as the reader, should go about updating this book's own frameworks, ranges, and assumptions as Nepal's regulations and market conditions continue to evolve after the point of publication.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 107
Keeping the Canon Current
First published 26 Aug 2026 · Last verified 29 Aug 2026
Rajendra Khanal has been investing in NEPSE-listed shares since 2009, and for almost as long he has kept what he privately calls his "own book" — a spreadsheet, later a small Access database, now a set of linked Google Sheets, that scores every stock he considers against a checklist of his own devising. He works as a senior branch manager for a development bank in Butwal, which means he reads NRB circulars for a living and has for years brought that same habit home to his personal portfolio. His checklist, built years before this Canon existed, tracked capital adequacy ratios, credit-to-deposit ceilings, promoter shareholding limits, and dividend tax treatment — the same categories this book's Canon Score covers in Chapters 63 through 66. He has had to revise his own thresholds at least four times since he first wrote them down: once when NRB moved from the old credit-to-core-capital-plus-deposit formula to a simplified CD ratio ceiling, once when the capital adequacy floor for commercial banks was adjusted under a monetary policy update, once when capital gains tax treatment for long-held versus short-held shares changed, and once when SEBON tightened margin lending rules after a market run-up. Each time, Rajendra did not throw out his checklist and start over. He opened the relevant cell, checked the new official number against the old one, updated it, and moved on. That habit — small, disciplined, annual — is the entire subject of this chapter.
This book will go stale in exactly the same way Rajendra's private spreadsheet went stale, for exactly the same reasons, and the fix is exactly the same. Nepal's regulatory environment is not static. NRB issues a monetary policy statement every mid-July and revises its unified directives to banks and financial institutions throughout the year. SEBON issues circulars adjusting margin rules, disclosure requirements, and market conduct standards. The Inland Revenue Department adjusts tax rates and thresholds, sometimes through the annual budget, sometimes through separate notices. NEPSE itself changes trading mechanisms, circuit breaker bands, and settlement cycles. Every one of these bodies has, at some point in the last fifteen years, changed a number that matters to how Nepali retail investors evaluate a stock. None of them asked this book's permission first, and none of them will ask permission before the next change either.
This chapter is not about specific rule changes you should make — it cannot be, since the whole point is that the specific numbers will differ depending on when you are reading this. Instead it teaches the discipline of updating: how to tell what in this Canon is a permanent principle and what is a number with an expiration date, how to run an annual check against the actual regulators rather than gossip or outdated summaries, and how to make the update itself — small, mechanical, and completely separate from questioning the underlying logic of the framework.
Lesson 107.1 — The Book Is a Snapshot, Not a Statute
Every book about investing in a specific market, written at a specific time, is a photograph of a moving object. The photograph is accurate the moment it is taken. It becomes progressively less accurate as the object keeps moving, even though the photograph itself never changes. The mistake readers make with financial books is treating the photograph as though it were a live video feed — assuming that because a number was correct in the edition they are holding, it remains correct indefinitely. The other mistake, just as damaging, is treating the entire photograph as unreliable the moment one detail in it is proven outdated, and discarding the whole thing.
The way out of both mistakes is to separate what this Canon actually contains into two categories, and to get comfortable identifying which category any given claim belongs to.
The first category is durable principle. This is the reasoning that does not depend on what year it is or what NRB's current directive says. The behavioural material in Part III on loss aversion, herding, anchoring, and disposition effect describes how human brains process risk and reward; that description does not change because a circular was issued. The valuation logic in the chapters on price-to-earnings ranges, dividend discount reasoning, and book value assessment is arithmetic and reasoning applied to cash flows and growth assumptions; the logic of discounting a future cash flow back to a present value is true regardless of what the risk-free rate happens to be this quarter. Position-sizing math — the idea that you size a position according to your conviction, your portfolio's total risk budget, and the correlation between your holdings — is a structural discipline, not a Nepal-specific rule. And the general shape of the Canon Score itself — the idea of scoring a company across capital strength, earnings quality, governance, liquidity, and valuation, then weighting those scores into a single comparable number — is a methodology, not a set of numbers. That shape was built to survive rule changes precisely because it was designed as a container into which current numbers get poured, rather than as a fixed set of numbers itself.
The second category is perishable specifics: the actual figures that fill that container today. NRB's current capital adequacy ratio floor for commercial banks. The current CD ratio ceiling. The current cash reserve ratio and statutory liquidity ratio. SEBON's current margin lending limits and current disclosure thresholds. The Inland Revenue Department's current capital gains tax rates for shares held under a year versus over a year, and the current dividend withholding tax rate. NEPSE's current circuit breaker bands and current settlement cycle. Every one of these numbers appears somewhere in this Canon because at the time of writing they were the governing figures. Every one of them is a candidate for revision, and several of them have already been revised more than once in Nepal's market history since NEPSE automated trading.
KEY CONCEPT
A durable principle tells you what to look at and why it matters. A perishable specific tells you the exact number that currently defines a pass or a fail on that criterion. The Canon Score's five-category structure is durable; the threshold that separates a strong CAR reading from a weak one inside that structure is perishable. Keep the first. Check the second every year.
A simple test helps distinguish the two when you are unsure which category a given passage belongs to. Ask: if NRB issued a new directive tomorrow changing this exact figure, would the surrounding reasoning in the chapter still make sense with the new figure substituted in? If yes — if the chapter's argument is "compare the bank's CAR to the regulatory floor, and treat a bank sitting close to that floor as more fragile than one with a comfortable buffer" — then the number is perishable and the reasoning is durable. You substitute the new floor and the lesson still holds perfectly. If no — if changing the number would also invalidate the argument itself, not just the arithmetic — then you are dealing with something more serious than a stale figure, and that is worth flagging separately, which Lesson 107.4 covers directly.
Rajendra's own experience illustrates this well. When NRB replaced the old credit-to-core-capital-plus-deposit ratio with a simplified credit-to-deposit ratio some years into his investing life, he did not need to rebuild his understanding of why banks with aggressive lending relative to their deposit base are riskier during a liquidity squeeze — that reasoning was untouched. He needed to change one formula in one cell of his spreadsheet, and confirm the new ceiling NRB had set. The rest of his checklist, built around governance red flags, promoter pledging, and dividend history, needed no attention at all.
Lesson 107.2 — Reading the Rulebook Behind the Rules
Before you can update anything, you need to know where the actual rules live, because the Canon's specific numbers were themselves drawn from primary regulatory sources, and those are the same sources you will return to when a figure needs checking. Three institutions generate almost every regulatory number this book cites, and each publishes its own material directly.
Nepal Rastra Bank is the source for anything involving bank and financial institution soundness: capital adequacy ratio requirements, the CD ratio ceiling, cash reserve ratio, statutory liquidity ratio, provisioning norms for loan classification, and the broad monetary stance that shapes interest rates and credit growth across the whole listed banking and finance sector. NRB communicates these through two main channels. The mid-July monetary policy statement sets the year's overall direction — growth targets, inflation targets, and any structural changes to the ratios above. The unified directives to banks and financial institutions, revised and reissued periodically through the year, contain the granular operational rules, including the exact capital adequacy floors, provisioning percentages, and single obligor limits that feed directly into Canon Score's capital strength category. Both are published on NRB's own website, in Nepali and often in English summary, and both are free.
The Securities Board of Nepal is the source for anything involving how shares are issued, traded, disclosed, and margined: IPO and rights issue procedures, insider disclosure obligations, margin lending caps, broker conduct rules, and market surveillance actions. SEBON issues circulars on a rolling basis rather than one annual statement, so checking SEBON means periodically reviewing its circular archive rather than waiting for a single yearly release. Several Canon Score inputs around governance disclosure and promoter shareholding limits trace back to SEBON directives rather than NRB ones, and it is worth being clear in your own mind which regulator governs which number, since looking for a SEBON-governed figure in an NRB directive wastes time.
The Inland Revenue Department is the source for tax rates: capital gains tax on listed share disposals, differentiated by holding period, and the withholding tax rate on dividends. Tax rates change less frequently than NRB's monetary ratios but not never, and they are usually adjusted through the annual budget announcement or a specific IRD notice, both of which IRD publishes directly.
A fourth source, NEPSE itself, governs market mechanics rather than prudential rules: circuit breaker bands, trading hours, settlement cycle length (T+1, T+2, or whatever cycle is current when you are reading this), and the index composition and free-float methodology. NEPSE publishes market announcements and circulars on its own site, separate from SEBON's regulatory circulars, and the two are sometimes confused because both concern "the market" in a loose sense.
Regulator
Governs
Where the Canon cites it
Nepal Rastra Bank
CAR floor, CD ratio ceiling, CRR, SLR, loan provisioning, monetary stance
Chapters 63-66 capital strength scoring; sector playbooks for BFIs
Securities Board of Nepal
Margin lending caps, disclosure rules, IPO and rights procedures, broker conduct
Governance scoring; new-issue chapters
Inland Revenue Department
Capital gains tax rates, dividend withholding tax
Tax-adjusted return chapters; almanac tax tables
NEPSE
Circuit breakers, trading hours, settlement cycle, index rules
Execution and mechanics chapters
REGULATORY DETAIL
NRB's unified directives are the single most important document for keeping the Canon Score's capital strength category current, because they contain the exact capital adequacy and CD ratio figures the score checks a bank against. They are revised more often than the monetary policy statement itself, so checking only the July statement and skipping the directive updates through the year is a common way investors miss a mid-year change.
Knowing which regulator to check is half the work. The other half, covered next, is doing the check on a schedule rather than only when something feels off — because things that have quietly changed rarely feel off until a mistake has already been made.
Lesson 107.3 — The Annual Regulatory Refresh
The discipline that keeps a personal Canon Score model useful for fifteen years, the way Rajendra's has stayed useful, is not vigilance in the sense of constant monitoring. It is a fixed, boring, once-a-year (plus one light mid-year pass) routine that takes an afternoon and catches the changes that matter before they cause a mistake. Trying to track every announcement as it happens is exhausting and unnecessary for a retail investor who is not trading on regulatory news. A calendar-based refresh is enough.
The natural anchor point is NRB's monetary policy statement, published each year around mid-July, near the start of the Nepali fiscal year. This is when NRB is most likely to announce structural changes to capital adequacy requirements, CD ratio ceilings, and broad credit growth targets, so treating this as the primary annual checkpoint for the Canon Score's banking and finance inputs makes sense. A second, lighter check in mid-January, roughly six months later, catches any unified directive revisions or SEBON circulars issued outside the July cycle, since both bodies do sometimes act mid-year in response to market conditions.
The refresh itself is a short checklist, not a research project.
Step
Source to check
What you are looking for
1
NRB's latest monetary policy statement
Any change to CAR floor, CD ratio ceiling, CRR, SLR, or provisioning norms
Granular rule changes not mentioned in the monetary policy statement itself
3
SEBON's circular archive since your last check
Margin lending changes, disclosure rule changes, new issue procedure changes
4
Inland Revenue Department's published rate notices
Capital gains tax rate or bracket changes, dividend withholding tax changes
5
NEPSE's market announcements
Circuit breaker, settlement cycle, or trading hour changes
6
Your own checklist and spreadsheet
Update any cell that references one of the above; note the date and source of the change
PRACTICAL TOOL
Keep a single tab in your Canon Score spreadsheet labelled "Regulatory Log." Every time you update a threshold, add one row: the date, the old value, the new value, and the source document you checked. After a few years this log becomes more valuable than the model itself, because it lets you see at a glance which numbers move often (CD ratio, provisioning norms) and which barely move at all (the broad tax treatment of dividends), which in turn tells you where to focus your attention each year.
Rajendra's own version of this log, which he has kept since roughly 2013, shows a pattern worth noting: capital adequacy and CD ratio figures have moved several times, sometimes tightened and sometimes loosened depending on NRB's credit growth concerns in a given year; capital gains tax treatment moved once in that period; dividend withholding tax has been comparatively stable; and NEPSE's settlement cycle has shortened over the years as market infrastructure modernised. None of these changes required him to rethink why he checks CAR, why he checks tax-adjusted returns, or why settlement speed matters for his cash management — only to update the number attached to each.
The refresh does not require reading every word of a lengthy directive. NRB's monetary policy statements typically include a summary section or press release highlighting the year's key changes, and financial newspapers with a track record of accurate regulatory reporting (rather than social media summaries, which Lesson 107.6 addresses) usually flag the headline changes within days. The annual discipline is less about reading comprehension and more about making sure you actually sit down once or twice a year and ask the specific question: has anything changed that affects a number in my model? If the answer is no, the refresh takes ten minutes. If the answer is yes, it takes an afternoon to trace the change to its source and update accordingly.
WARNING
Do not rely on a headline alone to update a threshold. A news report saying "NRB raises capital adequacy requirement" without specifying the exact new percentage, or without specifying which category of bank or finance company it applies to, is not enough to update a model correctly. Trace every headline back to the actual directive or monetary policy statement before changing a number in your spreadsheet. A close paraphrase is not a substitute for the primary figure.
Lesson 107.4 — One Number Changing Does Not Mean the Framework Is Wrong
The most common overreaction readers have to a stale figure is treating it as evidence that the whole book has failed them. This reaction is understandable — if you catch one number wrong, it is natural to wonder what else might be wrong — but it confuses two very different situations, and telling them apart is one of the more important judgment calls this chapter can teach.
A number becoming stale means the world changed after the number was written down, and the number needs to be brought up to date. This is expected, routine, and says nothing negative about the quality of the reasoning that used the number. If NRB raises the CD ratio ceiling from one level to a slightly higher one, the Canon Score's capital strength category does not need to be redesigned — it needs the ceiling figure updated, and every bank's score recalculated against the new ceiling. The category still measures the right thing; it is measuring it against a new bar.
A framework being wrong means the underlying logic itself was flawed from the start, or has been invalidated by a structural change in how the market works, not merely a numeric adjustment within an existing structure. An example of an actual framework failure would be if NRB abolished the CD ratio concept entirely and replaced it with a fundamentally different liquidity metric that measures something categorically different — in that case, updating the old ceiling number would not fix anything, because the old category would no longer correspond to anything NRB actually regulates. That would require rebuilding the category, not editing a cell.
The distinguishing question is simple: does the new information require you to change a number inside an existing category, or does it require you to change what the category measures in the first place? The first is a refresh. The second is a redesign, and redesigns are rare — rarer than most readers assume when they first encounter a stale figure and feel a flash of distrust toward the whole book.
CAUTION
Resist the urge to discard an entire chapter's framework because one figure in it is out of date. A stale number is a maintenance task. A wrong framework is a design failure. In fifteen years of Nepali market regulation, structural redesigns of this kind — NRB or SEBON abolishing an entire regulatory concept rather than adjusting its threshold — have been far rarer than routine threshold adjustments. Assume you are dealing with a maintenance task unless you have specific evidence that the underlying concept itself has been retired, not just recalibrated.
There is a related trap worth naming directly: assuming that because one regulator changed one number, every other number in the same table must also be suspect, and abandoning the whole checklist out of an excess of caution. This is inefficient and unnecessary. Regulatory changes are typically narrow and specific — a single directive amendment to a single ratio — not sweeping simultaneous overhauls of every rule at once. When you find one stale number during your annual refresh, check that number's category and close neighbours, but there is no need to re-verify every unrelated figure in the book from scratch each time. The regulatory log described in Lesson 107.3 helps here, because it shows you, over time, that most figures move rarely, and lets you calibrate how much re-checking a given change actually warrants.
KEY CONCEPT
Treat every specific number in this Canon as accurate as of the date this edition was finalised, and treat every principle as accurate regardless of date. When you find a discrepancy between the book's number and today's regulation, the fix is almost always: update the number, keep the principle, move on. Only rebuild the framework itself when the regulator has retired the underlying concept, not merely adjusted its value.
Lesson 107.5 — Case Study: Rebuilding a Score After a Capital Adequacy Revision
Rajendra's most instructive update happened a few years into using his own personal Canon-style model, when NRB revised the capital adequacy floor for commercial banks as part of a monetary policy statement aimed at tightening credit growth after a period of rapid loan expansion. He walks through the update in detail because it shows exactly what a proper refresh looks like in practice — mechanical, source-anchored, and narrow in scope.
He first noticed the change not by reading the full monetary policy statement cover to cover, but because a financial newspaper he trusts ran a short piece the week of the statement's release, headlined around NRB tightening capital buffers for commercial banks. Rather than updating his model off that headline, he went to NRB's own website and located the actual monetary policy statement and, once it was published, the revised unified directive covering capital adequacy. He confirmed three things directly from the primary source: the exact new CAR floor for commercial banks, the effective date from which banks were required to comply, and whether the change applied uniformly to all commercial banks or was differentiated by bank size or systemic importance (some NRB changes apply only to systemically important banks, a distinction easy to miss from a secondhand summary).
With the confirmed figure in hand, he made three specific changes to his model, and no others.
First, in the capital strength category of his personal Canon Score, he updated the threshold cell that separates a "strong" CAR reading from a "watch" reading and a "watch" reading from a "weak" one. His model, like the one described in Chapters 63 through 66, does not simply check pass or fail against the regulatory floor — it scores a buffer above the floor, because a bank sitting exactly at the minimum is riskier than one with headroom. When the floor itself moved, the buffer bands built on top of it needed to shift by the same amount, so a bank that previously scored as having a comfortable two-percentage-point buffer above the old floor needed to be re-evaluated against its buffer above the new floor.
Second, he re-ran every commercial bank in his watchlist through the updated capital strength category. This was mechanical — pull each bank's most recently reported CAR, compare it to the new floor and new buffer bands, and update the category score. Two banks in his watchlist that had previously scored comfortably in the "strong" band moved down to "watch" because their existing buffer, adequate against the old floor, was now thinner against the new one. This did not mean anything had gotten worse at those banks operationally — their actual capital position had not changed overnight — it meant the bar they were being measured against had moved, and his scoring correctly reflected that they now had less room to maneuver relative to the new regulatory expectation.
Third, and this is the step people skip, he explicitly checked whether anything else in his model needed to change as a consequence, and confirmed that nothing did. The governance category, the earnings quality category, the liquidity category built around the CD ratio, and the valuation category were all untouched by a CAR floor revision, because none of them measure capital adequacy. He did not re-examine promoter shareholding data, dividend history, or price multiples, because the change he had confirmed from NRB's directive was specific to one ratio in one category, and there was no indication — in the directive itself or in any related SEBON or IRD notice around the same time — that anything else had moved. He logged the change in his regulatory log with the date, the old floor, the new floor, and a note confirming that the CD ratio, provisioning norms, and other capital-strength inputs had not been altered by the same directive.
CASE IN POINT
Rajendra's update touched exactly one number in one category, took him roughly ninety minutes including the time spent locating the primary directive on NRB's site, and required him to re-score two banks whose buffer against the new floor was thinner than their buffer against the old one. It did not require him to touch his governance scoring, his valuation multiples, his dividend history tracking, or a single line of his position-sizing rules. This is what a correct regulatory refresh looks like: narrow, sourced, and bounded to the category the actual regulatory change affects.
This same process applies to the reader working through this Canon. When you learn that NRB has adjusted a figure this book cites — a CAR floor, a CD ratio ceiling, a provisioning percentage — the correct response has the same shape every time: confirm the exact new figure and its effective date directly from NRB's own publication; update the specific threshold cell in your own Canon Score model or checklist that corresponds to that figure; re-score whichever holdings sit close enough to the old threshold that the new one might change their category; and explicitly confirm, rather than assume, that no other category in your model is affected by the same regulatory change. Skipping that last step is how small updates quietly turn into models that drift out of internal consistency over time, with some categories reflecting last year's rules and others reflecting this year's.
PRACTICAL TOOL
When updating a threshold after a confirmed regulatory change, re-score only the holdings whose prior score sat within one band of the old threshold — these are the only positions where a shifted threshold can plausibly change the outcome. A bank that scored deep in the "strong" band under the old floor, with a large buffer, will almost certainly still score "strong" under a modestly revised floor, and does not need urgent re-checking. This saves time without sacrificing accuracy, since the marginal cases are exactly the ones a threshold change is designed to catch.
Lesson 107.6 — Where the Truth Actually Lives
The single most reliable predictor of whether an investor's personal model stays accurate over the years is not how sophisticated the model is — it is where the investor goes to check facts. Rajendra's habit of going directly to NRB's own website rather than trusting a headline, a forwarded message, or a social media post is the entire reason his model has survived four rule changes without ever drifting into error. This lesson is about building that same habit.
Primary sources, in order of authority for the kinds of figures this Canon relies on, are Nepal Rastra Bank's own website for anything involving capital adequacy, CD ratio, CRR, SLR, provisioning, and the broad monetary stance; the Securities Board of Nepal's own website and circular archive for anything involving margin rules, disclosure obligations, IPO and rights procedures, and broker or market conduct; the Inland Revenue Department's own published notices for capital gains and dividend tax rates; and NEPSE's own market announcements for trading mechanics, circuit breakers, and settlement cycles. Each of these institutions publishes its own primary documents, usually as downloadable PDFs of the actual directive, circular, or notice, dated and often numbered for reference. These primary documents are the only place a figure should be considered confirmed.
Secondary sources — financial newspapers, investment newsletters, brokerage research notes — are useful for noticing that something has changed and for plain-language explanation of what a change means in practice. Reputable Nepali financial press generally does a competent job flagging the headline direction of an NRB or SEBON move quickly. But a secondary source summarising a rule is still one step removed from the rule itself, and summaries can compress away exactly the kind of detail that matters for a precise model update — which category of institution a rule applies to, the effective date, whether a change is phased in over multiple quarters, or whether an apparently sweeping headline actually applies only to a subset of banks. Use secondary sources to know when to look, not as the final word on what to change.
Tertiary sources — social media posts, forwarded messages, unofficial forums, and secondhand paraphrases circulating among investors — are the least reliable and, unfortunately, often the fastest-moving and most visible. A number circulating on social media claiming to be the new CAR floor or the new capital gains tax rate should never be entered into a spreadsheet until it has been checked against the actual NRB directive, IRD notice, or SEBON circular. Misquoted figures, outdated figures still being shared long after a further revision, and rules described out of context (a systemically important bank rule presented as applying to all banks, for instance) circulate constantly in informal investor discussion, and a model built on that kind of input degrades quietly over time in a way that is hard to detect until a real mistake surfaces.
WARNING
Never update a Canon Score threshold, a tax assumption, or a regulatory ceiling based on a social media post, a forwarded message, or an unsourced claim, no matter how confidently it is stated or how many people are sharing it. Trace every figure back to NRB's, SEBON's, IRD's, or NEPSE's own published material before it goes into your model. This single habit, more than any other in this chapter, is what separates an investor whose model stays trustworthy for a decade from one whose model quietly accumulates errors.
The discipline this chapter asks for is not glamorous, and it will never feel urgent in the way a stock price move feels urgent. It is closer to the discipline of servicing a vehicle on a schedule rather than waiting for it to break down. Once or twice a year, sit with NRB's latest monetary policy statement and its unified directives, SEBON's circular archive, and IRD's published rates; check them against the figures currently sitting in your own checklist and Canon Score spreadsheet; update what has moved; confirm what has not moved needs no attention; and log the change with its date and source. Do this consistently, the way Rajendra has for over a decade, and the specific numbers in this book aging out of date will never become a problem — because you will have quietly kept your own working copy current the whole time, one confirmed figure at a time.
Chapter recap
This chapter treated the Canon itself as a subject of study: a book written at a specific moment, describing a market regulated by bodies that keep changing specific numbers even while the underlying logic they regulate stays largely the same. The core distinction to carry forward is between durable principle and perishable specific — the behavioural psychology, valuation logic, position-sizing math, and the general five-category shape of the Canon Score are built to last, while exact figures like NRB's capital adequacy and CD ratio ceilings, SEBON's margin and disclosure rules, and IRD's capital gains and dividend tax rates are snapshots that will be revised, sometimes more than once, over the life of this book. An annual regulatory refresh — anchored to NRB's mid-July monetary policy statement with a lighter mid-year check, working through NRB's directives, SEBON's circulars, IRD's rate notices, and NEPSE's own announcements in turn — is enough to keep a personal model current, as it has been for the long-time reader whose updating habit anchored this chapter. A single stale number is a maintenance task, not evidence the framework has failed; the two are confused far too often, and the worked example here showed what a correctly bounded update actually looks like — one threshold changed, the affected holdings re-scored, and every unrelated category explicitly confirmed as untouched. And the sources worth trusting for any of this are the regulators' own publications — NRB, SEBON, IRD, and NEPSE directly — never a headline, a forwarded message, or a social media claim standing in their place. Chapter 108, "Adapting to Market Evolution," moves from regulatory upkeep to something broader: how to adjust the Canon's frameworks as NEPSE itself changes shape over the coming years — new instruments, new market segments, and new categories of market participant that do not yet exist in the market this book was written to describe.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 108
Adapting to Market Evolution
First published 26 Aug 2026 · Last verified 29 Aug 2026
Sabitri Gurung sold her first hundred shares of Nepal Bank Limited in 2009, standing in a queue outside a broker's office in Pokhara with a paper share certificate folded inside a plastic file, the same way she had once carried her citizenship certificate to the district office. She is a retired schoolteacher now, in her early sixties, and she has kept a small equity portfolio running continuously since 2006. What strikes her, when she talks about those years, is not any single trade she made. It is how differently the whole apparatus of investing works today compared to when she began. The share certificate she once guarded like a land deed no longer exists in any physical form. The floor in Kathmandu where brokers once shouted orders across a crowded hall is gone, replaced by a system she operates from a laptop in her sitting room. The IPO application that once meant standing in a queue with a bank draft is now a few taps in a mobile app linked to her demat account. Nothing about the companies she owns changed in any of this. What changed was the machinery underneath them.
This chapter is about that machinery, and about how a disciplined investor should think about the fact that it keeps changing. Chapter 107 covered the concrete, near-term task of keeping your numbers current — circuit bands, tax rates, margin rules, the SEBON and NRB directives that get revised and republished. This chapter asks a different question. It is not about updating a number in a spreadsheet. It is about how you hold your own investment framework loosely enough to absorb genuine structural change in the Nepali capital market, without either freezing into a stance that assumes NEPSE is finished evolving, or abandoning sound principles every time something new is introduced with a promising name.
Sabitri's decade and a half in the market gives us a useful vantage point, because she has already lived through several rounds of exactly this kind of change. Nothing in this chapter asks you to speculate wildly about what NEPSE might become. It asks you to notice the pattern in what it already has become, and to build a habit of mind that can meet the next round of change the same way — curious, unhurried, and anchored in the valuation and risk logic this book has spent a hundred and seven chapters building.
Lesson 108.1 — What Dematerialization Actually Changed
When Sabitri bought those Nepal Bank shares, ownership was evidenced by a paper certificate issued by the company's share registrar. If you wanted to sell, you carried that certificate to your broker, who verified it, processed a transfer, and eventually a new certificate was issued to the buyer. Settlement took days, sometimes longer if a certificate was damaged, misplaced, or under a name that no longer matched a citizenship document after a marriage or a migration to a new address. Fraud was a real risk. Duplicate certificates, forged transfer instruments, and disputes over lost paper were not rare stories — they were a known cost of participating in the market at all.
The Central Depository System and Clearing Limited, CDSC, was established to solve this, and the shift to dematerialized shares — shares that exist only as electronic entries in a depository system rather than as physical paper — unfolded over several years, gathering pace through the early-to-mid 2010s until the paper certificate essentially disappeared from active trading. Every investor was required to open a demat account, linked to a depository participant, usually the same brokerage or bank already handling their trading. Ownership became a ledger entry rather than a document in a drawer.
Old mechanism
What it required of the investor
What replaced it
Paper share certificates
Physical safekeeping, in-person transfer at broker or registrar
Standing in queue, arranging a draft, waiting for allotment notice by post or newspaper
ASBA and later C-ASBA through Meroshare, done from a bank account or phone
Open-outcry floor trading
Physical presence or a broker relaying orders by phone from the floor
Fully electronic order matching from any internet connection
This is not a nostalgia exercise. The point of walking through it is that dematerialization was not a cosmetic upgrade. It removed an entire category of risk — the risk of physical loss, forgery, and settlement failure tied to paper — that investors of Sabitri's generation had simply built into their mental model of what owning shares involved. An investor who started after 2015 has probably never thought about this risk at all, because the structural change absorbed it before they arrived. That is exactly what structural evolution in a market does: it does not just add a new feature, it can quietly retire an old risk that the previous generation of investors had to price in and work around.
KEY CONCEPT
Structural evolution in a market is not the same as a regulatory number update. A number update changes a parameter within a stable system — a circuit band moves from 4 percent to 5 percent. Structural evolution changes the system itself — how ownership is recorded, how trades are matched, how applications are submitted — and it can eliminate risks and frictions that investors had previously treated as permanent features of participating in NEPSE.
Sabitri's own habit, formed without her ever putting it in these words, was to keep asking a simple question whenever the depository, the broker association, or SEBON announced a change to the plumbing of the market: what old inconvenience or risk does this remove, and what new dependency does it create in its place? Dematerialization removed the risk of a lost certificate. It created a new dependency on the depository system itself functioning correctly, and on your account credentials being kept secure — a different, narrower risk, but a real one, which is why this book has devoted attention elsewhere to demat account security and to verifying your holdings statement periodically. Structural change rarely eliminates risk outright. It usually trades one category of risk for a smaller, more contained one. Learning to see that trade clearly, rather than assuming either that the old risk is still there or that the new arrangement is risk-free, is the first habit this chapter wants to build.
Lesson 108.2 — From the Trading Floor to the Screen
Before full electronic trading, NEPSE operated a floor-based system in which licensed broker representatives physically gathered to place and match orders, a structure inherited from an earlier era of exchange design used across many emerging markets before automation became standard. Retail investors did not go to the floor themselves. They placed orders with a broker, who transmitted them to a representative on the floor, who then executed against a matching order from another broker's representative, all within trading hours that were shorter and thinner than what exists today. Price discovery depended on how many representatives were paying attention to a given counter at a given moment, and a client's order could sit unexecuted simply because the broker's floor representative was occupied with other business.
The transition to a fully electronic, automated trading system removed the floor as a physical bottleneck. Orders now enter directly into a central matching engine, whether placed by a broker on a client's behalf or, since the introduction of online trading capabilities and later fully mobile trading apps, directly by the investor. This has had several effects worth naming plainly.
First, the geography of who can meaningfully participate widened. A floor-based system, in practice, favoured people who were near Kathmandu or who had a broker with strong floor representation, because presence and relationships on the floor mattered for how quickly and fairly an order got attention. Electronic trading made the physical location of the investor almost irrelevant to execution — an order from Pokhara, Butwal, Biratnagar, or Dhangadhi enters the same matching engine as an order from New Road. This did not instantly erase Kathmandu's advantages in information and broker relationships, but it removed the mechanical disadvantage of distance from the trading floor itself.
Second, transparency improved. A floor system depends on trust in the honesty and diligence of the specific human representative handling your order. An electronic matching engine executes according to a fixed, auditable rule — price-time priority, typically — that does not depend on which broker's representative happened to be standing where on a given day. This does not eliminate broker-related risk (a broker can still mishandle a client's instructions or misuse a client's account, which is why this book has separately covered how to audit your broker relationship), but it removes an entire layer of floor-specific discretion.
Third, and this is the one investors underestimate, electronic trading changed the pace and psychology of the market itself. A floor system has natural friction built in — the time it takes to relay an order, the limited number of representatives, the physical constraints of shouting across a hall. Electronic trading removed that friction, which is part of why NEPSE today can see faster price swings within a session, and why circuit filters and other trading-mechanism safeguards (covered in Chapter 107 for their current parameters) exist at all — they are, in part, compensating for the very speed the electronic system introduced.
CASE IN POINT
Sabitri remembers a specific afternoon in the early 2010s when her broker's floor representative was tied up processing a large institutional order, and her own modest sell order for a hydropower counter sat unexecuted for the better part of a session while the price drifted down. There was no wrongdoing involved — it was simply how a floor system distributed attention under load. She says the memory has stayed with her mainly because it explains, to her, why she no longer worries about that particular kind of delay with her current app-based trading account. The friction she used to plan around simply does not exist for that specific failure mode anymore.
The broader lesson is that automation of the trading mechanism itself is a category of structural change independent of anything happening to individual companies or sectors. It changes execution speed, it changes who can practically participate, and it changes the kind of safeguards the exchange needs to build around the trading mechanism. An investor who understands their valuation and risk framework but has not updated their mental model of how orders actually get matched is operating with an incomplete picture of the market they are in.
Lesson 108.3 — ASBA and the Broadening of Participation
The Application Supported by Blocked Amount system, ASBA, changed how ordinary investors apply for IPOs, FPOs, and rights shares. Under the older method, an applicant filled out a paper form and submitted a bank draft or cheque for the full application amount, which was then held by the receiving bank until allotment was finalised — meaning an investor's money was fully withdrawn from their own use for the duration of the process, whether or not they received an allotment. ASBA changed this by blocking the required amount directly in the applicant's own bank account rather than transferring it out, releasing the block automatically once allotment (or non-allotment) is determined. The practical effect was that an investor's funds remained theirs, earning whatever interest or remaining available for other use the account structure allowed, right up until the block was actually needed.
This might sound like a narrow banking mechanic, but its consequences for participation were significant. The paper-and-draft method imposed a real cost and inconvenience on applying from outside the Kathmandu Valley — arranging a draft through a local bank branch, physically submitting a form, tracking an allotment notice that might be published in a newspaper or posted by mail. ASBA, especially once it was extended into the fully online C-ASBA system accessible through Meroshare, reduced the entire IPO application process to something an investor could complete from a mobile phone anywhere with a bank account and internet access. An investor in a district headquarters far from any broker's office gained essentially the same practical ability to apply for a popular IPO as an investor sitting three minutes' walk from NEPSE's building.
REGULATORY DETAIL
ASBA and its electronic successor were introduced and refined by SEBON in coordination with CDSC and the banking system, and the specific mechanics — which banks participate, how blocking and release work, what the current application windows look like — are exactly the kind of operational detail that gets revised periodically and that Chapter 107 asked you to keep current. This chapter is concerned with the structural shift ASBA represents, not with memorising its present-day procedural specifics, which you should always verify against current SEBON and CDSC guidance rather than relying on any book's snapshot of them.
The broadening of participation beyond the Kathmandu Valley is itself a structural trend worth naming directly, because it did not happen through any single reform. It has been the cumulative effect of dematerialization removing the need to physically handle paper certificates near a central registrar, electronic trading removing the floor's geographic bias, ASBA and online application systems removing the friction of applying for new issues from a distance, and the broader spread of internet access and mobile banking across Nepal over the same period. Sabitri's own observation, watching her adult children and their friends in Pokhara begin trading accounts of their own over the past decade, is that the population of NEPSE participants she now encounters is visibly less concentrated in Kathmandu than it was when she began. This has real consequences for market behaviour — a more geographically dispersed retail base can change how quickly sentiment about a particular counter spreads, how concentrated demand is during an IPO window, and how much any single physical location's rumour mill actually drives the tape.
KEY CONCEPT
Broadened participation is a structural trend, not a one-time event, and it interacts with almost everything else in this book. A market with a wider, more geographically dispersed retail base does not automatically become more efficient — the herd behaviour and momentum-chasing patterns this book has warned about (see the chapters on market psychology) can spread just as easily, or faster, through a wider network of participants connected by mobile apps and social media as they did through word of mouth in Kathmandu tea shops. Wider participation changes who is in the market. It does not by itself change the discipline required to invest well within it.
The three shifts covered so far — dematerialization, electronic trading, and ASBA-driven participation — share a common shape. Each removed a specific, identifiable friction or risk that investors of an earlier era had built into their working assumptions about the market. Each took years to fully unfold rather than arriving as a single announcement. And each was, in hindsight, a fairly clear improvement in the mechanics of the market without changing what a share of a company actually represents or what makes one investment sound and another unsound. That last point is the bridge to the rest of this chapter: structural evolution so far has mostly upgraded the pipes, not rewritten the logic of what flows through them. The honest question for a forward-looking investor is whether future evolution will stay in that category, or eventually introduce something that genuinely does require new logic.
Lesson 108.4 — Categories of Change Worth Watching, Without Overpredicting
This book will not pretend to know exactly what NEPSE will look like in ten or fifteen years, and you should be suspicious of any source that claims confident specifics about the future shape of a market this young and this actively being built out by its regulators. What is more useful than a prediction is a short list of categories where change is plausible, grounded in what other markets at a similar stage of development have typically added, and in directions Nepali regulators have themselves signalled interest in from time to time. Holding these as open categories to watch, rather than as forecasts to bet on, is the right posture.
New listed instrument types. NEPSE today is overwhelmingly a market for common equity, with debentures and a small number of mutual fund units as the main variations. Many more developed markets eventually add exchange-traded funds, which let an investor buy a basket of securities as a single listed unit, and some add derivative instruments such as futures or options, which derive their value from an underlying security or index rather than representing direct ownership. Nepal's regulatory and market infrastructure has been gradually built toward some of these possibilities over time, and it would not be surprising if, within an investing lifetime, NEPSE or a related exchange in Nepal listed an ETF-like product or introduced a formal derivatives market. It would be equally unsurprising if this timeline slipped by years relative to any current expectation, since building the legal, clearing, and risk-management infrastructure for derivatives in particular is a substantial undertaking that SEBON, CDSC, and NRB would need to coordinate carefully, given the systemic risk derivatives can introduce if margin and settlement rules are not robust.
Changes in foreign investor participation. Nepal's rules on foreign participation in NEPSE-listed equities have historically been restrictive compared to many regional markets, tied to broader capital account and foreign exchange management policy administered through NRB. Any loosening of these rules — even a modest, carefully bounded one — would represent a structural change in who can hold Nepali shares and in what new sources of demand (and new sources of capital flight risk during global shocks) the market would be exposed to. This is a category where change, if it comes, is likely to be gradual and hedged with safeguards, because it touches Nepal's foreign exchange reserve management directly, a subject this book has covered in its NRB-focused chapters.
Market-making or liquidity-provision mechanisms. Chapter 11 covered the liquidity problem in NEPSE at length — how thin trading in many counters can mean wide bid-ask spreads, difficulty exiting a position at a fair price, and price moves driven by a handful of orders rather than deep two-sided interest. Many exchanges address this kind of problem by licensing designated market makers, firms obligated to continuously quote both a buy and a sell price for a given security in exchange for certain privileges, which narrows spreads and deepens the order book. NEPSE and SEBON have discussed liquidity-enhancement measures in various forms over time, and some structured mechanism along these lines is a plausible, if unconfirmed, direction for the market's development. If it arrives, it would be a direct structural answer to a problem this book has already asked you to manage around rather than assume away.
Changes to circuit filter and trading mechanism design. Chapter 107 gave you the current circuit band percentages and reminded you to verify them periodically. Separately from any single number changing, the design of the mechanism itself could evolve — for instance, a shift toward index-level circuit breakers that pause the whole market rather than only individual counters, staggered or dynamic bands that widen as a stock's volatility profile is reassessed, or auction-based mechanisms for reopening trading after a halt. These are mechanism-design questions distinct from the specific percentage thresholds, and exchanges around the world have iterated on this design space continuously as they learn from episodes of extreme volatility.
Category of possible change
What problem it would address
What it would NOT change
New instruments (ETFs, derivatives)
Limited ways to get diversified or hedged exposure through a single listed product
The underlying logic of valuing the companies or assets the instrument is built on
Foreign investor rules
Limited external demand and capital access for Nepali equities
NRB's overall mandate to manage foreign exchange stability, which any change would still respect
Market-maker or liquidity mechanisms
Thin order books and wide spreads in many counters (Chapter 11's liquidity problem)
The fundamental quality of the underlying business — a market maker adds liquidity, not earnings
Circuit filter and mechanism redesign
How the exchange manages extreme volatility and disorderly trading
Your own responsibility to size positions and set exit rules independent of any filter
WARNING
None of the four categories above is a prediction. They are areas where structural change is plausible based on patterns elsewhere and on directions Nepali regulators have shown interest in at various points, not a roadmap this book is asserting SEBON, NEPSE, or NRB will follow on any particular timeline. Do not make investment decisions today based on an assumption that any of these changes is imminent. Treat this list as a set of categories to recognise and evaluate calmly if and when something in it actually happens, not as a basis for positioning your portfolio in advance of an announcement that may never come, or may come in a very different form than imagined here.
Lesson 108.5 — A Framework for Evaluating Anything Genuinely New
The useful skill is not predicting which of these changes will happen. It is having a steady way to evaluate whichever one actually does, when it does, without either dismissing it reflexively or getting swept up in it uncritically. The framework below is deliberately simple, because in a moment of genuine novelty — a new instrument type trading for the first time, a new participation rule just announced — simple, well-rehearsed questions serve you better than an elaborate new analytical apparatus you are inventing on the spot.
The first question is what does this instrument or mechanism actually represent as a claim on value or as a risk exposure. Strip away the new name and the promotional language around its launch, and ask what you are actually being offered — a direct ownership claim on a business's future cash flows, a claim on a basket of other things, a bet on a price movement without ownership of anything, or a change in the mechanics of trading something you already understand. This question alone resolves a surprising number of cases. An ETF tracking a basket of NEPSE-listed equities, if one is ever introduced, is still fundamentally a claim on the earnings and growth of the underlying companies — the valuation logic this book has built for individual equities (Part III's work on ratios, earnings quality, and business fundamentals) still applies, just aggregated across a basket instead of concentrated in one name. A derivative contract on the NEPSE index, by contrast, represents something categorically different — a bet on price movement with no direct ownership claim at all, amplified by leverage and subject to margin calls, which is a risk profile this book's core equity framework was never built to handle and which would require genuinely new material on leverage, margin risk, and time-decay if you intend to use it.
The second question is whether the existing risk controls in this book still function against the new thing, or whether they need modification. Position sizing, diversification across sectors, a maximum allocation to any single holding, a rule about not investing borrowed money in volatile assets — these are the load-bearing risk rules of this book. Ask specifically whether each one still does its job unmodified against the new instrument. A liquidity-enhancing market-maker mechanism, for instance, does not require you to change any of your risk rules at all — it simply makes exiting a position at a fair price somewhat easier than before, which if anything makes your existing rules easier to execute, not harder. A leveraged derivative product, on the other hand, can blow straight through a position-sizing rule calibrated for unleveraged equity, because a small adverse price move on a leveraged position can produce a loss many times larger than the same move would produce on an equivalent unleveraged holding. If a risk rule needs modification rather than simple application, that is your signal that you are dealing with something that requires new analysis, not an extension of familiar analysis.
The third question is who bears the downside if this goes wrong, and how well understood is that downside by the regulator overseeing it. A genuinely new instrument introduced by a mature, well-tested regulatory framework (SEBON having spent years developing rules, disclosure requirements, and investor-protection mechanisms specifically for it) carries a different risk profile than the same instrument introduced quickly with thin rules and untested clearing arrangements. This is not a reason to avoid everything new — it is a reason to ask the question explicitly rather than assuming that because something is listed on NEPSE, it carries the same regulatory maturity as the equity market that has had decades to develop.
PRACTICAL TOOL
When something genuinely new appears — a new instrument type, a new participation channel, a new trading mechanism — write down brief answers to three questions before committing any money: (1) What does this actually represent — ownership, a basket of ownership, or a bet on price movement without ownership? (2) Which of my existing risk rules apply unmodified, and which would need to change? (3) How mature is the regulatory and clearing infrastructure behind this specific instrument, as best you can determine from SEBON and CDSC's own public guidance? If you cannot answer all three plainly, that is itself useful information — it means you do not yet understand the thing well enough to size a position in it, regardless of how it is being marketed.
The fourth question, which follows naturally from the first three, is simply this: does this fit inside the existing logic of this book, or does it require a genuinely new book. Most changes that will actually reach NEPSE in the coming years will turn out to be plumbing changes like the ones covered in Lessons 108.1 through 108.3 — they will change how you access or execute something, not what that something fundamentally is. A smaller number will turn out to be genuinely new claims or exposures that need their own dedicated risk logic layered on top of, not instead of, everything this book has already taught you. Knowing which category you are in before you commit capital is the entire point of the framework.
Lesson 108.6 — Sabitri and the ETF Question, and the Two Extremes to Avoid
To make the framework concrete rather than abstract, consider how Sabitri actually applied something like it, a few years back, when a fund manager began publicly discussing plans to launch what would be described as an exchange-traded fund tracking a basket of Nepali blue-chip stocks — a hypothetical illustration of exactly the kind of new-instrument moment this chapter has been building toward, worked through the way an investor should actually work through it rather than treated as a separate universe with its own brand-new rulebook.
Her first instinct, on hearing the term used by acquaintances who had picked it up from financial news coverage abroad, was mild alarm — she associated the phrase with complex global finance she had read about in the context of the 2008 crisis, and her first impulse was to treat it as inherently exotic and risky, something that belonged to a different, more dangerous category of investing than the equity holdings she understood. That impulse is worth naming honestly, because it is the first extreme this chapter wants to warn against: reflexively treating anything new and unfamiliar as automatically more dangerous or more sophisticated than it actually is, simply because the label is unfamiliar.
She slowed down and applied something close to the four questions above, working through them roughly as follows. What does it actually represent? A basket of shares in the same handful of large, well-known Nepali companies she already owned individually — Nepal Investment Bank, a couple of the larger hydropower names, an insurance company — packaged into a single listed unit that she could buy or sell in one transaction instead of managing each holding separately. It was not a bet on price movement without ownership. It was ownership, just bundled. That answer alone dissolved most of her initial alarm.
Which of her existing risk rules applied unmodified? Her diversification rule — no single position becoming too large a share of her total portfolio — applied cleanly, since the ETF unit itself would need to be sized as a position like any other holding, though she noted with interest that buying the ETF gave her instant diversification across its underlying basket in a way a single stock purchase never could, which if anything made concentration risk within that portion of her portfolio easier to manage, not harder. Her rule about understanding a company's earnings before buying its shares needed a small adaptation rather than abandonment — instead of evaluating one company's earnings, she needed to understand the earnings quality of the basket as a whole and, importantly, understand what the fund's expense ratio or management fee would cost her annually, a cost that does not exist when holding individual shares directly and that she had to research specifically for this product rather than assume it worked like her regular brokerage costs.
How mature was the regulatory and clearing infrastructure? This was the question she spent the most time on, checking what SEBON had published about the specific approval and disclosure framework for the product, rather than relying on the fund manager's own marketing material, and satisfying herself that the fund's underlying holdings and their custody arrangement through CDSC were clearly documented before she considered a position.
Having gone through that process, her conclusion was neither of the two extremes this lesson is built around. She did not conclude that the product was too novel and foreign a concept for a NEPSE investor to touch, which would have meant permanently closing herself off to a legitimate diversification tool the moment it existed simply because it carried an unfamiliar three-letter name. She also did not conclude that because it was new, sophisticated-sounding, and being discussed by fund managers as the modern evolution of investing, it was automatically superior to the individual blue-chip holdings she already owned and understood well — which would have meant chasing a product for its novelty rather than evaluating it on its actual merits relative to what she already held. She treated it as what the framework in Lesson 108.5 said it was: a familiar type of exposure — ownership in companies she already knew — wrapped in a new, and in some ways more convenient, packaging, worth a modest allocation once she understood the fee structure and the custody arrangement, but not worth abandoning her existing individually-selected holdings for, since she had already done the specific company-level work on several of the names in the basket and had views about some of them that the basket approach would have diluted.
CASE IN POINT
Sabitri's actual decision, once she had worked through the questions, was to leave her existing individual holdings in the two companies she had researched most thoroughly untouched, and to consider a small allocation to the pooled product only for the portion of her portfolio she had previously left in a low-conviction, lightly-researched state — treating the new instrument as a better tool for the passive, diversified portion of her holdings than for the concentrated, high-conviction portion she had built through her own company-level work. This is the outcome the framework is meant to produce: neither blanket rejection nor blanket enthusiasm, but a specific, reasoned allocation decision based on what the new thing actually was.
This example is deliberately built around a hypothetical instrument rather than a confirmed, currently-listed one, because the point of the lesson is the reasoning process, not the specific product. Whatever the first genuinely new instrument type actually turns out to be when it arrives on NEPSE or an associated Nepali exchange — and it may look nothing like an ETF — the same four questions apply, and the same two extremes remain the ones to avoid.
The first extreme is assuming NEPSE is essentially finished evolving and that this book's current chapters describe a fixed and permanent landscape. This is the more comfortable extreme to fall into, because it requires nothing further of you — you learn the rules as they stand today and stop paying attention to how the underlying structure might keep changing. But you have just read three concrete examples in this chapter — dematerialization, electronic trading, ASBA — of exactly how much the mechanics of this market have already changed within a single investor's working lifetime, and there is no principled reason to believe the pace of that change has now stopped. An investor who assumes NEPSE is a finished, static system will be the last to understand a genuinely useful new mechanism when it appears, and may keep bearing costs and frictions — thin liquidity, restricted instrument choice — that a structural improvement has already begun to solve, simply because they were not paying attention.
CAUTION
Assuming the market has stopped evolving is a quiet, low-drama mistake — it does not cause any single bad trade, so it is easy to hold for years without ever being confronted with its cost. Its cost shows up as opportunity foregone: a liquidity mechanism you never used because you did not know to look for it, a diversification tool you dismissed unexamined because it sounded unfamiliar, a participation channel that would have suited you that you never investigated because your mental model of "how NEPSE works" had quietly stopped updating a decade earlier.
The second extreme is the opposite failure: treating every new product, mechanism, or piece of market-structure news as automatically an upgrade over the tools in this book, and rushing toward it uncritically simply because it is new and being talked about. This is the noisier, more damaging extreme, because it produces actual bad trades — money committed to an instrument whose risk profile was never actually understood, simply because it was marketed as the sophisticated, modern alternative to plain equity investing. Nepal has, at various points, seen enthusiasm around new financial products and schemes that traded on novelty and sophistication-signalling more than on any real, examined benefit to the investor, and the same pattern — this book has discussed it in the context of scheme evaluation and fraud awareness elsewhere — applies just as much to a genuinely regulator-approved new instrument type as it does to an outright fraudulent scheme. Newness is not evidence of quality. A new derivative product introduced with thin margin rules and untested clearing arrangements can be objectively more dangerous than the plain equity holdings this book has spent a hundred chapters teaching you to evaluate soundly, regardless of how modern it sounds.
WARNING
A product's novelty tells you nothing about its quality or its suitability for you. The only things that tell you that are the four questions in Lesson 108.5 — what it actually represents, which of your risk rules apply, how mature its regulatory infrastructure is, and whether it fits inside your existing framework or requires a genuinely new one. Apply those questions to every new instrument or mechanism exactly as rigorously as you would apply your existing valuation checklist to an unfamiliar stock, and treat marketing enthusiasm from the product's own promoters as input to be checked, not as a substitute for your own evaluation.
The steady middle path between these two extremes is simply active, unhurried attention. You do not need to predict what NEPSE will introduce next. You need to keep half an eye on SEBON and CDSC announcements, on NEPSE's own communications about its systems and instrument offerings, and on NRB's stance toward foreign participation and capital account matters, the same unhurried way Sabitri has followed these institutions for close to two decades without ever needing to act urgently on any single piece of news. When something genuinely new actually appears, you will not be caught flat-footed by an unfamiliar term, and you will not be swept into a decision before you have applied the same calm evaluation this book has asked you to bring to every other investment decision.
It's worth being explicit about one more thing: the fact that this framework worked cleanly for Sabitri's ETF example does not mean it will always produce such a comfortable answer. Sometimes the honest answer to "does this fit inside my existing framework" will be no, this needs genuinely new analysis I don't currently have — and in that case, the right response is not to force the new thing into old categories where it does not belong, but to slow down, seek out dedicated material on that specific new risk (a derivatives-specific risk education resource, for instance, if and when derivatives trading becomes available on a Nepali exchange), and treat your existing framework as a foundation to build on rather than a complete answer to everything the market might ever offer. Adaptability, in other words, is not the same as flexibility of belief — it is the discipline to correctly sort each new thing into "familiar logic, new packaging" or "genuinely new risk requiring genuinely new study," and to only proceed once you know which bucket you are in.
Chapter recap
Sabitri Gurung's investing life traces the shape of NEPSE's own structural evolution — from paper share certificates guarded like land deeds to electronic holdings in a CDSC demat account, from floor-based trading dependent on a broker's physical representative to a fully electronic matching engine reachable from anywhere in Nepal, from paper IPO application forms and bank drafts to ASBA and the fully electronic C-ASBA system through Meroshare, and alongside all of it, a genuine broadening of who can practically participate beyond the Kathmandu Valley. None of these changes altered what a share of a company actually represents or what makes an investment sound. They changed the machinery investors operate within, generally by removing a specific, identifiable friction or risk that an earlier generation of investors had to plan around.
Looking forward, plausible categories of further structural change include new listed instrument types such as ETFs or derivatives if the regulatory and clearing infrastructure develops to support them, changes in the rules governing foreign investor participation tied to NRB's broader foreign exchange management mandate, market-maker or liquidity-provision mechanisms that could directly address the thin-liquidity problem covered in Chapter 11, and evolution in circuit filter and trading mechanism design beyond the specific percentage thresholds covered in Chapter 107. None of these is a confirmed prediction — they are categories worth watching calmly, not bets worth positioning a portfolio around in advance.
When something genuinely new does appear, evaluate it with four steady questions: what does it actually represent as a claim on value or a risk exposure; which of your existing risk rules apply unmodified and which would need real modification; how mature is the regulatory and clearing infrastructure standing behind it; and does it fit inside the valuation and risk logic this book has already built, or does it require genuinely new analysis layered on top. Sabitri's hypothetical evaluation of an ETF-like product when it was first floated showed this process in action — neither reflexive rejection of something unfamiliar, nor uncritical enthusiasm for something merely because it was new and modern-sounding, but a specific, reasoned decision based on what the product actually was once she looked past its label.
The two extremes to avoid are assuming NEPSE has finished evolving and therefore ignoring genuine developments as they arrive, and chasing every new product as automatically superior to the tools this book has taught simply because it is unfamiliar and being promoted as sophisticated. The steady middle path is unhurried, ongoing attention to what SEBON, CDSC, NEPSE, and NRB are actually doing, paired with a firm habit of running anything genuinely new through the same evaluative discipline you would apply to any other investment decision.
This forward-looking adaptability is itself one expression of a larger habit of mind — the willingness to keep learning, keep questioning your own assumptions, and keep updating your understanding of the market you operate in without ever abandoning the core discipline that makes you a sound investor in the first place. Chapter 109, "The Lifelong Investor's Mindset," takes up that larger habit directly, moving from the specific question of market-structure change addressed here to the broader psychological and intellectual practices — patience, humility, continuous learning, and emotional discipline sustained across an entire investing lifetime — that keep a Nepali investor grounded and effective no matter how many more rounds of structural evolution NEPSE goes through in the years ahead.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 109
The Lifelong Investor’s Mindset
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 109.1 — Twenty Years, One Ledger: Krishna Bahadur Rai's Story
In a small tin-roofed house near Traffic Chowk in Biratnagar lives a retired schoolteacher named Krishna Bahadur Rai. For thirty-one years he taught mathematics and science to teenagers at a government secondary school. He is seventy-one years old now, and he still keeps, in a locked wooden almirah behind his study table, seventeen thin notebooks. Each notebook covers roughly a year. Inside, in his neat teacher's handwriting, is a record of every share he has ever bought, every share he has ever sold, every dividend he has received, and every mistake he has ever made in the stock market.
Krishna sir, as his former students still call him, opened his first demat account in late 2006, when he was thirty-nine years old. A demat account is simply an electronic locker for your shares — before demat accounts became common, share ownership in Nepal was tracked using paper certificates, which could be lost, damaged, or forged. Krishna sir's first investment was two hundred shares of a commercial bank, bought with money he had saved over four years from his teaching salary and a small tuition side-business.
This chapter is not about which shares to buy today. Earlier chapters of this book have already covered fundamental analysis, technical charts, sector rotation, and portfolio construction. This chapter is about something harder to teach and far more important than any single stock pick: the mindset required to remain a sensible, steady investor for twenty, thirty, or forty years — through booms that make you feel like a genius and crashes that make you feel like a fool, through the birth of children, the marriage of daughters, the retirement of a career, and the slow, patient accumulation of wealth that outlives any single market cycle.
KEY CONCEPT
A lifelong investor's mindset means treating your NEPSE portfolio the way a farmer treats a fruit orchard, not the way a fisherman treats a single net cast into a river. An orchard is planted once and tended for decades; it gives small harvests every year and grows more valuable as the trees mature. A single net cast, by contrast, either catches fish today or it does not. Investors who treat NEPSE like a net — expecting quick profit from every trade — burn out within a few years, either from losses or from exhaustion. Investors who treat it like an orchard are still there, calmly harvesting, twenty years later.
Krishna sir did not start out as a patient orchard-keeper. He started, like most first-time investors, as an excited gambler. His own notebooks record this honestly. In his first entry, dated Mangsir 2063 (November 2006 in the Gregorian calendar), he wrote only the name of the bank share, the price, and the number of shares. There is no reason recorded for the purchase — no analysis of the bank's loan book, no comparison with other banks, nothing about the price relative to the bank's earnings. He simply bought it because a fellow teacher at his school said the price would double within a year.
It did, in fact, nearly double within a year. NEPSE, the Nepal Stock Exchange, was in the early stages of a multi-year bull run at that time, driven partly by growing confidence after the end of the armed conflict, partly by rising remittances flowing into the country. Remittances are the money that Nepali workers abroad — in the Gulf countries, Malaysia, and elsewhere — send home to their families. A large share of that money eventually finds its way into bank deposits, real estate, and, for a growing number of households, the stock market.
By 2008, Krishna sir's small holding of two hundred shares had grown, through bonus shares and one rights issue, into a portfolio worth several times his original investment. A bonus share is a free additional share that a company gives to existing shareholders, usually paid out of the company's retained profits instead of cash; a rights issue is an offer that lets existing shareholders buy new shares at a discounted price, usually so the company can raise fresh capital. Krishna sir, in his own words years later, said: "I thought I had discovered a machine that only produced money. I did not yet understand that the machine could also grind money into dust."
That lesson arrived soon enough, and it is the subject of the next lesson in this chapter. But before moving on, it is worth stating plainly what separates an investor like Krishna sir today from the excited young teacher of 2006: not a smarter set of stock picks, but a completely different relationship with time, with risk, and with his own emotions. That relationship did not arrive overnight. It was built slowly, through the specific events described in the rest of this chapter, and it is available to any Nepali investor willing to learn from someone else's twenty years instead of only their own.
Lesson 109.2 — Learning From the Crashes You Survive
Every long-serving NEPSE investor carries scars from at least one crash. Krishna sir carries scars from two large ones, and a smaller third one more recently. Understanding how he experienced each of them — and, more importantly, how his response changed between the first and the later ones — is the clearest way to teach the difference between an investor who is destroyed by a crash and one who is merely educated by it.
The first crash Krishna sir lived through began in 2008. Global financial markets were in turmoil that year because of the collapse of large banks in the United States, an event usually called the global financial crisis. NEPSE itself was not directly connected to Wall Street — Nepal's stock market was, and largely still is, dominated by domestic banks, hydropower companies, microfinance institutions, and insurance companies, not by companies with large foreign holdings. But confidence is contagious, and by 2009 the exuberance of the mid-2000s bull run had faded. More importantly, a purely domestic problem was brewing: bank credit had expanded very fast in the preceding years, a large share of that credit had gone into real estate and share-margin lending, and by 2010 and 2011 NEPSE entered a long, grinding decline that lasted for roughly four years.
Krishna sir had, by 2008, expanded his portfolio using margin lending — a facility where a bank or broker lends you money against the value of the shares you already own, so you can buy more shares than your own cash would allow. It works exactly like borrowing against the value of your house to buy a second house: as long as prices keep rising, you look brilliant, because your gains are calculated on borrowed money as well as your own money. But if prices fall, the lender can force you to sell your existing shares to repay the loan, often at exactly the moment when prices are lowest and everyone else is also being forced to sell.
CASE IN POINT
Between 2010 and 2013, Krishna sir received two margin calls — demands from his bank to either deposit more cash or have his shares sold to cover the loan — within eighteen months. On both occasions he had to sell shares at roughly forty percent below what he had paid for them, simply because the loan repayment was due, not because he had decided the companies were bad investments. He later calculated that the interest he paid on the margin loan, combined with the forced losses, had erased almost three years of his teaching salary savings.
This is the single most important early lesson in Krishna sir's story, and it deserves its own explanation. Margin lending is not inherently evil — used carefully, in small amounts, by an investor who fully understands the risk, it can be one tool among many. But it converts a normal market decline, which a patient, debt-free investor can simply wait out, into a forced sale at the worst possible time. A patient orchard-keeper who owns his trees outright can survive three bad harvest years in a row and wait for the fourth good one. A farmer who has mortgaged the orchard itself to buy more trees can be thrown off the land during the very first bad year, before the good year ever arrives.
WARNING
Borrowed money removes your ability to wait. In the stock market, the ability to wait is often the single most valuable asset an ordinary investor has, more valuable than any single piece of market analysis. Never take on debt to buy shares in an amount large enough that a forty or fifty percent price decline would force you to sell before you are ready.
The second major cycle Krishna sir lived through was the run-up to 2016 and the bust that followed. NEPSE's benchmark index rose sharply through 2014, 2015, and into mid-2016, reaching what was at the time an all-time high, driven by a wave of new retail investors, easier bank lending again, and enormous public enthusiasm — newspapers ran daily front-page stories about ordinary people making fortunes, tea-shop conversations everywhere in the country turned to share prices, and Krishna sir remembers colleagues at his school who had never owned a single share suddenly opening demat accounts and buying whatever their brother-in-law recommended.
By this point, Krishna sir was a different investor than he had been in 2008. He had stopped using margin lending entirely after his earlier losses. He had also begun, slowly, to read company financial statements instead of relying only on rumour — an evolution that later lessons in this chapter will describe in more detail. Because of this, when the 2016 peak arrived, Krishna sir did something that felt deeply uncomfortable at the time: he sold roughly a third of his holdings, specifically the shares that had risen the fastest and that he judged to be priced well above what the underlying company's profits could justify. He did not sell everything, and he did not try to guess the exact top of the market — he simply recognised that prices had run far ahead of the businesses behind them, and he reduced his exposure accordingly.
The bust that followed lasted, in various forms, from late 2016 through 2019, with the index eventually falling by more than half from its peak. Investors who had bought heavily during the euphoria, especially those using margin loans, suffered severe losses. Krishna sir's own portfolio fell in value too — nobody escapes a broad market decline entirely — but because he had trimmed his most overvalued holdings beforehand and carried no debt, he was able to do something almost no panicked investor manages to do: he kept buying small amounts of fundamentally sound companies throughout 2017 and 2018, while their prices were depressed, using his ongoing teaching salary.
This single decision — to keep buying steadily through a bear market instead of freezing in fear or fleeing entirely — is perhaps the clearest marker of the lifelong investor's mindset that this chapter is trying to teach. It is easy to buy when everyone around you is buying. It is extraordinarily difficult, and extraordinarily valuable, to keep buying small amounts when everyone around you has sworn off the stock market forever.
Cycle
Krishna Sir's Age and Stage
What He Did
What He Learned
2008 to 2013 downturn
Late 30s, early years as investor
Used margin loans, was forced to sell at a loss twice
Never borrow against shares you are not prepared to lose control of
2014 to 2016 run-up and 2017 to 2019 bust
Late 40s, mid-career
Trimmed overpriced holdings near the peak, kept buying quality shares during the bust
Selling some strength near a euphoric peak is not the same as trying to time the market perfectly
2020 to 2022 pandemic dip and rally
Early 50s, senior teacher
Held steady through the pandemic panic, added carefully during the 2021 to 2022 rally, trimmed again near new highs
Extreme events pass; a portfolio built on real businesses recovers if you do not sell in panic
2022 to 2023 correction and recent years
Late 60s, retired
Shifted a larger share of the portfolio toward dividend-paying banks and insurance companies, reduced trading frequency sharply
As income needs replace growth needs, the portfolio itself should change shape
Lesson 109.3 — The Discipline That Compounds: Small, Regular, Boring
If the previous lesson was about surviving crashes, this lesson is about what Krishna sir did during the long, quiet years in between the crashes — because those quiet years, not the dramatic ones, are where most of his wealth was actually built.
Compounding is a word used often in investment writing, and it is worth defining plainly here rather than assuming every reader already understands it fully. Compounding means that the returns you earn in one year themselves start earning returns in the following years, so your wealth grows faster and faster over time, the way a snowball rolling downhill picks up more snow and grows bigger with every rotation. A small amount invested steadily over twenty years, with the profits reinvested each year rather than spent, can grow into a far larger sum than a much bigger amount invested for only three or four years. Time, not the size of any single investment, is the main ingredient.
Krishna sir's notebooks show a habit that he began around 2013, after his margin-lending losses had taught him caution, and that he maintained without interruption for the rest of his career: every month, on the day his teaching salary was deposited, he set aside a fixed small amount — initially fifteen hundred rupees, later increasing as his salary grew — into a separate savings account earmarked only for share purchases. He did not invest this money the moment he received it. Instead, he accumulated it for two or three months, then looked for a reasonably priced share among companies he already understood, usually a commercial bank, a life or non-life insurance company, or occasionally a hydropower company with an operating plant rather than one still under construction.
PRACTICAL TOOL
Krishna sir calls this his teacher's rule, and any reader of this book can adopt a version of it. First, decide a fixed rupee amount you can invest every month without it affecting your household's food, rent, or children's school fees. Second, keep that money in a separate account so it is not accidentally spent. Third, invest it on a regular schedule rather than trying to guess the perfect moment — buying a little in good months and a little in bad months averages out your purchase price over time, a practice sometimes called rupee-cost averaging. Fourth, and most importantly, never skip a year just because the market feels frightening; the frightening years are often when the rule matters most.
A second habit that compounded Krishna sir's wealth, often invisibly, was his treatment of dividends. A dividend is a portion of a company's profit that it pays out directly to shareholders, usually once a year, either as cash or as additional bonus shares. Many new investors treat dividend cash as spending money — a pleasant little bonus to be used for a family dinner or a new mobile phone. Krishna sir, from around 2012 onward, reinvested nearly every rupee of dividend income back into more shares, rather than spending it. Over a decade, this single habit — treating dividends as seed money rather than pocket money — meaningfully increased the size of his final holdings, because each reinvested dividend rupee then went on to earn its own future dividends.
REGULATORY DETAIL
Dividend income in Nepal is subject to tax at source, meaning the tax is deducted automatically before the dividend reaches the shareholder's account, at a rate set under Nepal's income tax rules and periodically clarified by NRB, Nepal Rastra Bank, the country's central bank, in coordination with tax authorities. Capital gains on shares — the profit made when you sell a share for more than you paid for it — are also taxed, with the rate depending on how long the share was held before sale, with a lower rate typically applying to shares held longer than a set holding period. Because these rates and rules are adjusted from time to time through the national budget and subsequent notices, an investor should always confirm the current rate with their broker or a tax advisor before assuming an older rate still applies; treating outdated tax figures as current is a common and avoidable mistake.
Krishna sir's approach to buying shares also evolved considerably over time, in a way tightly connected to the compounding discipline described above. In his first several years, he bought shares primarily by placing an order through a broker over the telephone, often with limited information about the company's actual financial condition. Starting around 2012, as Nepal's capital market infrastructure matured, he began using ASBA, which stands for Application Supported by Blocked Amount. ASBA is a system, introduced under SEBON's oversight, that allows an investor applying for newly issued shares — for example, an initial public offering, commonly called an IPO, when a company sells shares to the public for the first time — to have the application amount simply blocked in their own bank account rather than physically transferred out immediately. If the application is unsuccessful, the blocked amount is released back to the investor without any transfer having occurred at all, which is faster, safer, and removes the old risk of refund delays.
SEBON, the Securities Board of Nepal, is the government regulator responsible for overseeing the securities market, including how new share issues are conducted, how brokers behave, and how investor complaints are handled. Krishna sir's notebooks show that he applied for nearly every hydropower and microfinance IPO offered between 2012 and 2020, in modest amounts, using ASBA. Not every application was successful, since share allotment for oversubscribed IPOs in Nepal is usually done by lottery when demand exceeds supply, but the low cost and near-zero risk of applying meant it was a sensible habit to repeat, year after year, as one small part of a much larger portfolio strategy.
None of these individual habits — the fixed monthly amount, the reinvested dividends, the routine IPO applications through ASBA — are exciting. None of them would make an interesting headline. That, in fact, is precisely the point of this lesson. The lifelong investor's mindset is built overwhelmingly out of boring, repeated, unglamorous actions, taken consistently across market cycles that this chapter's other lessons describe as anything but boring.
Lesson 109.4 — Taming the Crowd Inside Your Own Head
The hardest part of investing for decades is not analysis. It is emotional control, especially the specific emotions that a crowd of other investors can trigger inside you even when you know, intellectually, that you should not be influenced by them. This lesson uses Krishna sir's experience to explain the two emotions that damage long-term investors most — greed during booms and fear during busts — and the practical guardrails he built against both.
Think of a village well during a drought. If one household starts drawing extra water because they fear the well will run dry, their neighbours, seeing the buckets moving quickly, often start drawing extra water too, even if their own storage is already full — not because they need it, but because everyone else appears to be acting urgently. The well can run dry faster because of this collective fear than it would have from actual water scarcity. Stock markets behave the same way. When share prices rise quickly, buyers who see others profiting rush in, pushing prices higher still, regardless of whether the companies' actual profits justify the new price. When prices fall quickly, sellers who see others panicking rush to sell too, pushing prices lower still, regardless of whether the companies' actual businesses have changed at all. This is often called herd behaviour, and NEPSE, being a relatively small and retail-dominated market, is especially prone to it.
Krishna sir experienced the greed side of herd behaviour most strongly in 2016, and again briefly during the 2021 to 2022 rally that followed Nepal's recovery from the covid-19 pandemic. During that later rally, NEPSE's index rose from around fourteen hundred points in mid-2020 to well above three thousand points by mid-2021, an extraordinarily fast climb driven by low interest rates, a flood of new young investors trading through mobile apps for the first time, and a general sense that shares were now a shortcut to fast wealth. Krishna sir recalls a former student, by then a young bank employee in his late twenties, calling him for advice, having already taken a personal loan to buy shares on margin, convinced that prices would keep doubling every few months. Krishna sir tried to warn him, using almost the exact language his own senior colleague should have used with him back in 2008, and the young man did not listen, and lost a significant portion of his savings when the market corrected sharply in 2022.
CASE IN POINT
The former student's losses in the 2022 correction were not caused by picking bad companies — several of the shares he held were reasonably solid banks and hydropower firms. His losses were caused entirely by the combination of margin debt and buying at euphoric prices, the same two mistakes Krishna sir himself had made fourteen years earlier, in 2008. A market lesson not passed on, or not accepted when it is offered, tends to repeat itself in the next generation of investors almost exactly.
The fear side of herd behaviour is, in some ways, even more dangerous for a lifelong investor, because it strikes exactly when your portfolio is already down in value, making the temptation to "just get out and stop the pain" feel entirely rational. NRB and SEBON have, over the years, introduced circuit breakers as one tool to slow this kind of panic. A circuit breaker is a rule that automatically halts trading, or limits how far a price can move in a single day, once the market or an individual share has moved by a certain percentage. The purpose is not to prevent losses altogether, but to give panicked investors a forced pause — a chance to breathe, gather actual information, and avoid making an irreversible decision purely out of adrenaline.
REGULATORY DETAIL
NEPSE applies daily circuit breaker limits both at the level of the overall market index and at the level of individual company shares, with trading halted for the remainder of the session if the relevant threshold is breached; the specific percentage thresholds have been adjusted by NEPSE and SEBON at various points, so an active investor should check the current rule in force rather than relying on a figure from an old notebook or an outdated article, exactly as with the tax rates discussed earlier in this chapter.
Krishna sir built two personal guardrails over the years to protect himself from his own crowd instinct, and both are simple enough for any reader to copy. The first guardrail was a rule that he would never make a buy or sell decision on the same day he felt strong emotion about it — if a share's price movement excited or frightened him, he would write the situation down in his notebook and wait at least three days before acting, by which time the initial adrenaline had usually faded and a clearer judgment could take its place. The second guardrail was to deliberately avoid checking share prices every single day during periods of high volatility, choosing instead to review his portfolio on a fixed weekly or monthly schedule, much as a farmer checks on a slow-growing tree once a week rather than staring at it every hour waiting to see it grow.
CAUTION
Checking share prices constantly during a sharp market swing does not give you better information — NEPSE's fundamentals do not change hour by hour — it only multiplies the number of chances you give yourself to make an emotional decision. Treat frequent price-checking during a panic the way you would treat repeatedly re-reading a worrying text message: it produces anxiety, not clarity.
KEY CONCEPT
The lifelong investor's real opponent is rarely a bad company or a falling market. It is the investor's own mind, reacting to the behaviour of the crowd around them. Every tool described in this book — fundamental analysis, technical charts, diversification, this chapter's discipline habits — exists partly to give an investor something concrete to lean on instead of the crowd's mood.
Lesson 109.5 — Building Systems So Discipline Does Not Depend on Willpower
By his late fifties, Krishna sir had come to a realisation that shaped the final and most mature phase of his investing life: relying on willpower alone to stay calm and disciplined would eventually fail, because willpower is a limited resource that weakens under stress, illness, family emergencies, or simple old age. What does not weaken as easily is a system — a set of habits, records, and rules built in advance, during calm periods, so that they can be followed almost automatically during difficult ones. This lesson describes the systems Krishna sir built, which any Nepali investor at any stage of life can adapt.
The first system was his notebook habit itself, mentioned at the start of this chapter. Each entry recorded not only the transaction — company name, number of shares, price, date, broker — but also, crucially, the reason for the decision, written in a sentence or two. Reading these reasons back years later taught Krishna sir far more than the profit-and-loss numbers alone ever could, because it let him see which types of reasoning had led to good outcomes and which types of reasoning, such as "my colleague said it would double," had reliably led to poor ones.
PRACTICAL TOOL
Keep a simple investing diary, on paper or in a phone notes app, with one line per transaction covering four things: the date, what you bought or sold and at what price, why you made the decision, and how you felt while making it. Review this diary once a year, ideally around your birthday or the Nepali new year, and honestly ask which of your past reasons for buying or selling actually held up, and which were simply excitement or fear wearing the disguise of analysis.
The second system was diversification across sectors, deliberately maintained rather than left to chance. Because NEPSE's listed companies are concentrated in a handful of major sectors — commercial banks, development banks, finance companies, microfinance institutions, life and non-life insurance companies, hydropower, and a smaller number of manufacturing, hotel, and trading companies — an investor who buys shares purely on tips, without tracking sector exposure, can easily end up owning ten different companies that are all, in effect, the same bet, because they all rise and fall together with bank lending conditions or with monsoon rainfall in the case of hydropower. Krishna sir, from around 2015 onward, set an informal personal rule that no single sector should make up more than about forty percent of his total portfolio value, and he checked this roughly twice a year, trimming and adding as needed to stay within that boundary.
The third system concerned recordkeeping with his broker and depository details, an unglamorous but essential piece of financial housekeeping. Every Nepali investor's shareholding is tracked electronically against a BOID, or Beneficiary Owner Identification number, which is the unique account number assigned to an investor's demat account. Krishna sir kept a laminated card with his BOID, his broker's contact details, and his bank account information for dividend and sale proceeds, in the same drawer as his citizenship documents, so that his wife and adult children would be able to locate and act on this information without confusion if he were ever unable to manage it himself. This is a small act of preparation that matters enormously for a lifelong investor, because a stock portfolio built carefully over decades is worth little to a family that does not know it exists or cannot access it.
REGULATORY DETAIL
Opening and maintaining a demat account in Nepal requires completing know-your-customer, commonly called KYC, documentation with a depository participant, typically a bank or a licensed brokerage, and the account is linked to the investor's citizenship or other approved identity document along with a designated bank account for fund transfers. SEBON and NRB have, over time, tightened these KYC requirements and periodically require investors to update or renew their documentation, particularly after a period of prolonged inactivity in the account; an investor who moves house, changes their phone number, or lets a document expire should proactively update their broker's records rather than waiting for a rejected transaction to reveal the problem.
The fourth system, which Krishna sir developed only in the last decade, was a periodic rebalancing schedule tied to his life stage rather than to market conditions. In his forties and early fifties, while still earning a full teaching salary and with two decades of working life still ahead of him, his portfolio leaned toward growth-oriented sectors, including hydropower projects still under construction and smaller microfinance companies with higher risk and higher potential return. As he approached retirement in his mid-sixties, he gradually shifted a larger share of his holdings toward established commercial banks and insurance companies with a long history of steady dividend payments, accepting slower growth in exchange for more predictable income, since his need at that stage was less about building wealth and more about drawing a reliable supplement to his pension.
Life Stage
Primary Financial Goal
Typical Portfolio Tilt
Risk Tolerance
Early career, 20s to mid 30s
Build savings habit, learn the market
Small regular purchases, mix of banks and select hydropower, minimal debt
Higher, since time is available to recover from mistakes
Mid career, late 30s to 50s
Grow wealth steadily, fund children's education
Diversified across banks, insurance, hydropower, some finance and microfinance companies
Moderate, growth-focused but debt-free
Pre-retirement, 50s to mid 60s
Protect accumulated wealth, prepare for reduced income
Shift toward established dividend-paying banks and insurers, reduce speculative holdings
Lower, prioritizing capital preservation
Retirement, mid 60s onward
Generate steady income, preserve capital for family
Concentrated in stable dividend payers, minimal trading, clear estate documentation
Lowest, income and simplicity prioritized over growth
This kind of stage-based rebalancing is not a rigid formula to be copied exactly by every reader — a younger investor with heavy family obligations might need a more conservative approach than the table suggests, and an older investor with a strong pension and no dependents might comfortably keep more growth exposure. The system's value lies in the habit of periodically asking the question at all, rather than leaving a portfolio's composition to drift unexamined for years at a time, shaped only by whichever shares happened to rise or fall the most.
Lesson 109.6 — Passing the Orchard to the Next Generation
The final piece of Krishna sir's story, and the natural closing note for a chapter on lifelong investing, concerns what happens to decades of careful investing once the investor themselves is no longer able to manage it — through old age, illness, or death. Many Nepali families, despite substantial accumulated savings, handle this poorly, either because the topic feels inauspicious to discuss openly or because financial matters were traditionally kept private within a household, often known fully only to the family's senior male member.
Krishna sir approached this differently, partly because of a specific incident. A close friend and fellow retired teacher passed away suddenly in 2019, and it took his widow and children nearly eight months to locate all of his shareholdings, because no family member other than him had known which broker he used, what his BOID was, or even a reasonably complete list of which companies he had invested in over the years. Dividends went unclaimed, one rights issue offer expired unused because no family member knew to respond to it, and a portion of the family's wealth was, for practical purposes, frozen and nearly lost.
Watching this unfold, Krishna sir made two changes to his own practice. First, as already described, he consolidated his account details onto a single card kept with his other important documents. Second, and more significantly, he began involving his adult children directly in his investing decisions, treating it as a form of ongoing financial education rather than a private matter to be revealed only after his death. His elder daughter, now in her thirties and working in Kathmandu, opened her own demat account in her late twenties with Krishna sir's guidance, and the two of them still discuss NEPSE developments during her visits home, comparing notes on which sectors look attractive and which regulatory changes from NRB or SEBON might affect their holdings.
CASE IN POINT
Krishna sir's late friend had, by conservative estimate, built a share portfolio worth several years of an average Nepali household's income, entirely from teaching-salary savings similar to Krishna sir's own. The value of that portfolio to his family was sharply reduced not because of any investing mistake, but purely because of poor information-sharing within the family. A lifelong investor's responsibility does not end with choosing good companies; it extends to making sure the fruits of that choice can actually reach the people meant to inherit them.
This is, in the end, the deepest meaning of the phrase "lifelong investor's mindset." It is not only about the investor's own lifetime. A well-tended orchard is meant to outlive the person who planted it, providing fruit to children and grandchildren who may never have met the original farmer. Krishna sir, now in his early seventies, no longer trades often. He still reads his weekend newspaper's business section, still attends his broker's occasional investor seminars, and still adds his small monthly amount when his pension allows it, but the more meaningful part of his investing life now is teaching, once again, just as he did for thirty-one years in the classroom — except now his students are his own children, and the subject is not mathematics but patience.
WARNING
Do not assume your family will be able to sort out your investments after you are gone simply because they are intelligent, capable people. Locating scattered shareholdings across multiple brokers, without a BOID, without account records, and without knowledge that the investments even exist, is a genuinely difficult administrative task, even for educated family members living in Kathmandu with time to spare. Prepare this information while you are healthy, and revisit it every year or two as details change.
An investor who applies every lesson in this chapter, but who never shares what they have built or how they built it with the people who will one day inherit it, has completed only half the task that a truly lifelong investing mindset requires. The other half is the quiet, unglamorous work of documentation and teaching, exactly the kind of boring, repeated action that Lesson 109.3 identified as the true engine of long-term compounding. Wealth compounds in a bank ledger. Wisdom compounds only if it is deliberately passed from one generation to the next.
Chapter recap
This chapter followed the twenty-year investing journey of Krishna Bahadur Rai, a retired schoolteacher from Biratnagar, to illustrate what it actually takes to remain a sensible NEPSE investor across decades rather than merely across a single good year. His story showed how early enthusiasm and margin borrowing led to painful forced losses during the 2008 to 2013 downturn, how hard-won caution allowed him to trim overvalued holdings near the 2016 peak and keep buying steadily through the bear market that followed, and how the same lessons about debt and crowd behaviour repeated themselves, this time in a former student's losses, during the 2021 to 2022 rally and its subsequent correction. The chapter then detailed the specific habits that compounded his wealth quietly over time — fixed monthly purchases, disciplined reinvestment of dividends, and steady use of ASBA for new share applications — before turning to the emotional guardrails he built against herd behaviour, the practical systems of recordkeeping, sector diversification, and BOID documentation that made his discipline durable rather than dependent on willpower, and finally the family communication that ensures decades of careful investing are not lost to poor information-sharing after he is gone. Across all six lessons, the same message recurs in different forms: an orchard, not a fishing net; boring and repeated, not exciting and occasional; a system built in calm times, not willpower summoned in a crisis.
The next chapter, Chapter 110, "The NEPSE Regulatory Change Tracker," moves from mindset to method. It will introduce a structured system, suitable for any ordinary Nepali investor to maintain, for tracking regulatory changes issued over time by NRB and SEBON — from adjustments to margin lending rules and circuit breaker thresholds to changes in dividend and capital gains tax treatment, IPO allotment procedures, and KYC requirements referenced throughout this chapter — so that an investor's own long-term discipline is never undermined by simply failing to notice that the rules of the game have changed.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 110
The NEPSE Regulatory Change Tracker
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 110.1 — Why a Tracker, Not Just a Habit of Reading News
Damber Prasad Sharma spent twenty-six years inside Nepal Rastra Bank, the central bank of Nepal, before he retired to Biratnagar to manage his own household's savings. For most of his career he was on the inside of the machine that ordinary investors only see from the outside — the circulars, the directives, the monetary policy statements that come out of NRB and land on brokerage floors, bank counters, and eventually on the NEPSE ticker. Now, sitting in his small study with a view of his mustard field, he does something that most retail investors in Nepal never think to do. He keeps a written, dated, organised record of every regulatory change that could touch his portfolio. Not headlines. Not a mental note. A written system.
Damber likes to explain this with an analogy from his own field. A farmer does not simply notice that it rained yesterday and shrug. A serious farmer writes down when the rain came, how much fell, what he planted, and what happened next — because next season, that written record tells him when to plant, when to expect the monsoon, when to worry about drought. A regulatory tracker is the same kind of record, but for money instead of rain. The rules that govern banks, hydropower financing, insurance capital, and stock market margin lending change often in Nepal. If you do not write down when a rule changed and what you did about it, you are farming from memory. Some years that works. Eventually it fails you, usually in the year it matters most.
This chapter is not about keeping this book current — that was Chapter 107, which described how the Canon itself gets updated as Nepal's laws and institutions evolve. This chapter is about you, the reader, building your own tracker: a personal, repeatable system for logging changes coming out of Nepal Rastra Bank and the Securities Board of Nepal, so that six months or six years from now, you can look back and see exactly what changed, when, and how you responded.
KEY CONCEPT
A regulatory tracker is not a news feed. A news feed tells you what happened today. A tracker is a running, dated, personal record of regulatory changes and your own reactions to them, built so that you can look backward and learn, not just forward and react.
Why does this matter so much for a NEPSE investor specifically? Because Nepal's stock market does not move only on company earnings. It moves heavily on liquidity, on interest rates, and on rules set by two institutions that most retail investors barely think about until a rule bites them. NRB controls the money supply and the banking system's lending capacity. SEBON regulates the stock market itself — brokers, listed companies, disclosure requirements, and margin lending against shares. A single NRB circular changing how much capital banks must hold, or a single SEBON directive on portfolio disclosure, can move the entire NEPSE index within days. Investors who were tracking the signal saw it coming. Investors who were not got surprised.
Consider an ordinary example from household life. In a joint family in Nepal, decisions about spending are often made collectively, and a wise family keeps a ledger — who borrowed what, who repaid what, what was agreed for the next Dashain. Families that keep no ledger end up arguing about what was decided, because memory is unreliable and convenient. The regulatory environment around NEPSE is like an extended joint family whose decisions affect your money whether you attended the meeting or not. NRB and SEBON hold the meetings. You were not in the room. But if you keep a ledger of what came out of those meetings, you at least know what was decided, and you can act accordingly instead of discovering the decision three weeks later when your margin call arrives.
The goal of this chapter is to teach you a structured, repeatable system — not a vague resolution to "keep up with the news." A system has three parts: sources you check on a schedule, a format you use every time you log something, and a rhythm of review so that logging does not become a drawer full of dead paper. We will build all three, using Damber's own practice as our worked example throughout.
Lesson 110.2 — The Two Sources You Must Watch, and What Each One Controls
Before you can track regulatory change, you need to know exactly where regulatory change comes from. In Nepal, for a NEPSE investor, there are two primary sources, and they are not interchangeable. Confusing them, or watching only one, is one of the most common mistakes new trackers make.
Nepal Rastra Bank, or NRB, is the central bank of Nepal. Its job is to manage the country's money supply, oversee commercial banks and financial institutions, and keep the banking system stable. NRB does not regulate the stock exchange directly. But because most listed companies on NEPSE are banks, financial institutions, hydropower companies financed by bank loans, and insurance companies, NRB's decisions about lending, capital, and interest rates ripple straight into share prices.
Think of NRB as the person who controls the main water valve for an entire irrigation canal system. NRB does not decide what any individual farmer plants in his own field — that decision belongs to the farmer, or in our world, to the individual company and its shareholders. But NRB decides how much water flows into the canal system at all, and how tightly the valve is turned. When NRB turns the valve open, more water — meaning more credit, more lending capacity, more liquidity — flows to every field along the canal, including the fields that grow bank shares, hydropower shares, and finance company shares. When NRB tightens the valve, every field downstream gets less water, whether that field needed it badly or not.
The Securities Board of Nepal, or SEBON, is a completely different kind of regulator. SEBON does not control money supply. SEBON regulates the securities market itself — how NEPSE and its listed companies must behave, how brokers must conduct business, what companies must disclose to shareholders, how initial public offerings are priced and allotted, and how margin lending against shares is structured from the market side. If NRB is the valve controlling how much water flows into the canal, SEBON is the inspector who walks the canal banks making sure nobody is stealing water out of turn, that the canal walls are not leaking, and that every farmer gets an honest, disclosed accounting of how much water actually reached his field.
REGULATORY DETAIL
NRB tools that most affect NEPSE indirectly include the CAR floor (capital adequacy ratio — the minimum percentage of a bank's own capital it must hold against its loans, meant to absorb losses), the CD ratio ceiling (credit to deposit ratio — the maximum percentage of deposits a bank may lend out, meant to prevent overlending), the CRR (cash reserve ratio — the percentage of deposits banks must keep idle at NRB), and the SLR (statutory liquidity ratio — the percentage of deposits banks must hold in safe, liquid assets like government securities). Tightening any of these reduces the money available for banks to lend, including margin lending for share purchases. Loosening them does the opposite.
REGULATORY DETAIL
SEBON tools that affect NEPSE directly include margin lending caps specific to brokers and merchant bankers (limits on how much a client can borrow against shares already held), disclosure rules (what listed companies must report and when, including material events, related-party transactions, and insider holdings), IPO and rights share regulations, and broker conduct rules. These do not change the total money supply, but they change how that money is allowed to move inside the market, and how much investors are told before they move it.
Why does this distinction matter practically? Because your reaction to an NRB change and your reaction to a SEBON change should usually be different in character. An NRB tightening of the CD ratio ceiling is a slow, system-wide tightening — it affects liquidity across the whole market gradually, sector by sector, mostly hitting banking and finance shares first, then spreading. A SEBON change to margin lending rules can be sudden and sharp — it can force margin-financed positions to be unwound within days if leverage caps drop, creating forced selling pressure concentrated in whichever stocks were most heavily margined.
Damber keeps these two sources in physically separate sections of his tracker, which we will build in the next lesson, precisely because mixing them made his early tracking attempts confusing. He told his tracker groups in Biratnagar, only half joking, that NRB is the slow monsoon and SEBON is the sudden hailstorm. Both affect the harvest. But a farmer prepares for them differently.
There is a third, smaller source worth a brief mention here even though this chapter focuses on the two primary regulators: the Ministry of Finance, which occasionally issues budget-related announcements affecting capital gains tax on shares, dividend tax treatment, or sector-specific incentives such as hydropower tax holidays. These come once a year mainly around the national budget announcement in Jestha or Ashadh, and Damber logs them in a separate small section since they are annual and predictable rather than continuous.
Lesson 110.3 — Building the Tracker: The Five-Column Log
Now we build the actual tool. A tracker only works if it is simple enough that you will actually use it every week, and structured enough that a random note six months old still means something to you when you reread it. Damber's system, refined over several years, uses five essential fields for every entry. You can keep this in a physical notebook, a simple spreadsheet, or a phone notes app — the format matters far more than the medium.
Date Noticed
Source
Change Summary
Sectors or Companies Affected
Action Taken
2024-03-14
NRB, Monetary Policy Mid-Term Review
CD ratio ceiling tightened from 90 percent to 85 percent for commercial banks
Paid down existing margin balance ahead of deadline; reviewed collateral concentration
2024-11-19
NRB, Circular on Capital Adequacy
CAR floor raised by fifty basis points for finance companies specifically, phased over two quarters
Development banks and finance companies
Flagged three finance company holdings for capital-raising risk; watched for rights issue announcements
2025-02-08
SEBON, Disclosure Directive
Listed companies required to disclose related-party transactions above a lower rupee threshold, and within a shorter time window
All listed companies, especially conglomerate-linked groups
Added a disclosure-quality check to annual report review checklist
This table is not decorative. It is the actual spine of the system. Notice what each column is doing.
Date Noticed is the date you personally became aware of the change, not necessarily the date the circular was issued. This distinction matters because there is often a lag between when NRB or SEBON issues something and when it becomes broadly known to retail investors. If you only ever log the official issue date, you lose the ability to measure your own information lag — which is itself useful information, because it tells you whether your sources are fast enough.
Source is the specific document type and issuing body — not just "NRB" but "NRB, Monetary Policy Mid-Term Review" or "SEBON, Directive on Margin Lending." Precision here matters because six months later, if you want to verify or re-read the original document, a vague note saying "NRB changed something about banks" is useless. A precise source lets you or your future self go find the original circular again.
Change Summary is written in your own plain words, not copied legal language. Damber insists on this because legal language from a circular is often dense and precise but does not tell you, in the moment, what it means for your holdings. Translating it into your own words at the time of logging forces you to actually understand it, rather than filing it away unread.
Sectors or Companies Affected is where you connect the abstract regulatory change to your actual portfolio. This is the step most investors skip, and it is the single most valuable column. A CD ratio ceiling change means nothing until you ask: which of my holdings are commercial banks, finance companies, or businesses that depend heavily on bank credit? Writing this down at the time, rather than trying to reconstruct it later, is what makes the tracker actionable rather than just historical.
Action Taken is what you actually did, if anything, and this is the column that turns the tracker from a diary into a decision record. Sometimes the honest entry is "no action taken, monitoring only" — and that is a perfectly valid entry, because it tells future you that you considered the change and made a deliberate choice not to react, rather than simply forgetting about it.
PRACTICAL TOOL
Keep the five columns in this order every time: Date Noticed, Source, Change Summary, Sectors or Companies Affected, Action Taken. Consistency of format matters more than the tool you use to keep it. A spreadsheet lets you sort and filter by sector later; a notebook is fine if you are willing to flip pages. Choose whichever you will actually maintain weekly, not the one that looks most impressive.
Damber adds one more habit that is not a column but a discipline: he never edits an old entry to make it look like he predicted something he did not. If his original action turned out to be wrong, he adds a new dated line noting the correction, rather than rewriting history. This is the same principle serious traders apply to a trading journal, and it matters even more for a regulatory tracker, because the entire value of the tool is honest hindsight. A tracker that has been quietly cleaned up to make its owner look prescient is worse than no tracker at all, because it creates false confidence.
Lesson 110.4 — Reading a Circular: From Notice to Action, Step by Step
Let us now walk through exactly how Damber processed one real-feeling example, start to finish, so you can see the system in motion rather than only in table form.
In late 2024, NRB issued a circular raising the capital adequacy ratio floor specifically for development banks and finance companies — a smaller, second tier of Nepal's banking system below the large commercial banks. The capital adequacy ratio, or CAR, is the minimum percentage of risk-weighted assets that a financial institution must hold as its own capital, rather than depositor money, so that if loans go bad, the institution's owners absorb losses before depositors or the wider system do. Think of CAR the way you would think of a household's own savings cushion before it borrows for a big purchase. A family with a thick cushion of its own savings can absorb a bad month without collapsing. A family that borrows everything with no cushion is one bad month away from crisis. NRB was, in effect, telling Nepal's finance companies: your cushion needs to be thicker than it currently is.
Damber first noticed this change not from NRB's own website, which he checks weekly but which can be slow to post plain-language summaries, but from a business news brief he reads every morning that referenced the circular directly by number. His Date Noticed entry was the date he read that brief, three days after NRB's actual issue date — a three-day lag he considers acceptable for a change of this type, since CAR floor changes are typically phased in over quarters rather than triggering instant market moves.
His Change Summary, in his own words, read: finance companies must raise their capital ratio in stages over two quarters; smaller, thinly capitalised finance companies will likely need to either raise new capital, shrink their loan books, or merge with stronger peers.
For Sectors or Companies Affected, Damber went through his actual holdings list — this is the step that requires you to already know what you own, which is why a regulatory tracker only works alongside a clear portfolio record, not in isolation. He identified three finance company holdings in his portfolio that fell squarely into the affected category, and noted that his commercial bank holdings were untouched because the circular specifically targeted the finance company tier, not commercial banks.
CASE IN POINT
When Damber cross-checked his three finance company holdings against the new CAR floor, he found that one of the three already reported a capital ratio comfortably above the new requirement — no action needed there. A second was close to the new floor and would likely need a modest rights issue within two quarters. A third was meaningfully below the new floor and, in his judgment, carried real risk of either a dilutive rights issue or a forced merger. He reduced his position in the third company by half over the following month, not in panic, but as a deliberate, logged decision.
Notice what did not happen here. Damber did not sell all finance company shares in a single reflexive move the day he read the news. He also did not ignore the circular because "these things get delayed anyway," which is a common and dangerous rationalisation. He read the actual mechanism — a capital cushion requirement — mapped it individually onto each holding's actual financial position, and took a differentiated action: hold one, watch one, reduce one. That differentiation is only possible because he keeps a portfolio record detailed enough to check each company's existing capital ratio quickly, which we covered in earlier chapters on portfolio record-keeping.
WARNING
Never act on a regulatory change based on a headline or a friend's summary alone. Business news briefs, WhatsApp groups, and brokerage floor gossip frequently simplify or distort the actual text of a circular. Before you write your Action Taken entry, find the original NRB or SEBON document, or at minimum a summary from a source you trust to have read the original, and confirm the effective date, the exact scope of institutions covered, and whether the change is immediate or phased in over time.
This warning exists because Damber has seen, in his own investing circle in Biratnagar, investors sell a bank stock in a panic based on a rumour that NRB was raising the CD ratio ceiling requirement, when in fact the actual circular applied only to a narrow category of rural development banks and did not touch the large commercial bank the investor owned at all. The rumour cost that investor a needless loss, crystallised by selling into a dip that reversed within two weeks once the actual circular text became widely understood. A tracker with a discipline of checking the source before logging the action would have caught this.
Once Damber completes an entry, he does one final thing that is not part of the five columns but is part of his personal rhythm: he sets a calendar reminder for the effective date of any phased change, so that when the second quarter of the CAR phase-in arrives, he is not caught off guard rechecking his affected holdings only when the deadline is already upon him.
Lesson 110.5 — Rating the Impact: Telling Noise from Signal
Not every circular deserves the same weight in your reaction, even though every circular deserves an entry in your log. One of the hardest skills to build is distinguishing between a change that is merely procedural — administrative housekeeping that will not move prices — and a change that genuinely shifts the economics of a sector or the liquidity available to the whole market.
Damber uses a simple three-level severity scale, written as a single word in the margin of his Change Summary column: Watch, Act, or Urgent.
Watch means the change is real but its effects are distant, small, or uncertain enough that no portfolio action is needed yet — only continued attention. Most disclosure rule changes fall here initially, because they change what companies must report, not what companies may do, and the market impact is usually gradual as better information slowly gets priced in.
Act means the change has a clear, identifiable effect on specific holdings or sectors, and a deliberate decision — even if that decision is "hold as is" — should be made and logged within a reasonably short window, typically within the same month.
Urgent means the change has an immediate, mechanical effect that could force price movement or require you to act before a specific deadline — most commonly a margin lending cap change with a short compliance window, since this can trigger forced selling across the market within days, not months.
CASE IN POINT
The SEBON margin lending directive Damber logged in mid-2024, reducing allowable margin against single-scrip hydropower collateral, was rated Urgent in his log, because brokerage clients holding margin loans against hydropower shares above the new limit had a compliance deadline of only a few weeks. Damber held some margin exposure himself at the time and paid down his balance well ahead of the deadline, avoiding the scramble that hit investors who waited until the final week, when hydropower share prices dipped as heavily margined investors sold in unison to meet the new limit.
This is worth dwelling on, because it illustrates exactly why the Urgent category exists separately from Act. A CAR floor change phased over two quarters gives you months to adjust. A margin lending cap change with a compliance deadline of weeks does not. The mechanism is the same broad category — a regulator tightening a lending-related rule — but the time pressure is entirely different, and your tracker's severity rating should reflect that difference immediately, at the moment you log the entry, not after the fact.
CAUTION
Do not rate every new circular as Urgent out of anxiety. A tracker that treats everything as an emergency is a tracker its owner will eventually stop maintaining, because constant false alarms are exhausting. Reserve Urgent for changes with a hard compliance deadline that could mechanically force trading activity — margin calls, forced deleveraging, mandatory position unwinding. Reserve Act for changes with clear sector impact but no forced-selling mechanism. Reserve Watch for everything else, including most disclosure and reporting rule changes.
There is a second dimension worth tracking alongside severity, which is breadth: does the change affect a single company, a whole sector, or the entire market? A disclosure rule targeting related-party transactions in conglomerate-linked groups is narrow in breadth even if eventually significant for those specific companies. A CRR or SLR change from NRB is market-wide in breadth, because it changes the total liquidity available to the entire banking system, and through it, to every sector that depends on bank credit, which in Nepal's NEPSE is most of them. Damber notes breadth as a second word next to severity — for example, "Act, Sector-Wide" or "Watch, Single-Company" — so that a quick scan of his log years later immediately tells him how big a ripple each entry actually caused, without needing to reread the full change summary.
Severity Level
What It Means
Typical Trigger
Your Response Window
Watch
Real change, distant or uncertain effect
Disclosure rules, minor administrative circulars
No immediate action; note and revisit at next review
Act
Clear effect on identified holdings or sector
CAR floor revisions, CD ratio changes, sector-specific lending rules
Deliberate decision within the current review cycle, typically weeks
Urgent
Immediate mechanical effect with a hard deadline
Margin lending cap reductions, forced compliance deadlines
Action before the compliance deadline, typically days
Building the instinct to sort incoming news into these three buckets is, over time, far more valuable than any single correct prediction. Markets reward investors who can tell the difference between a headline and a mechanism. The severity scale is simply a disciplined habit for making that distinction every single time, rather than relying on gut feeling that shifts with your mood on a given day.
Lesson 110.6 — The Review Rhythm: Weekly Scan, Quarterly Reckoning
A tracker that is only ever added to, and never reviewed, becomes a graveyard of forgotten entries. The final piece of the system is a rhythm — a fixed schedule for checking sources, and a separate fixed schedule for reviewing what you have already logged.
Damber's weekly rhythm is deliberately light, because a heavy weekly ritual is one most people abandon within a month. Every Saturday morning, he spends roughly thirty minutes doing three things: checking the NRB website's notices and circulars section, checking SEBON's website for new directives and notices, and skimming one business news source that specialises in financial regulation coverage, to catch anything the primary sources have not yet posted in plain language. If nothing new appears, he writes nothing — a tracker does not require an entry every week, only every time there is something real to log. If something appears, he processes it through the five-column format from Lesson 110.3, and rates it using the severity scale from Lesson 110.5, all within that same thirty minutes if possible, so the task does not pile up.
PRACTICAL TOOL
Fix a specific day and a specific time for your weekly regulatory scan, the same way you would fix a specific day for grocery shopping. Saturday morning, Sunday evening, whatever suits your week — the specific day matters less than its fixedness. A scan you do "whenever I remember" quietly becomes a scan you do never. Pair it with your existing portfolio review habit from earlier chapters if you already have one, so you are not creating an entirely new weekly obligation from scratch.
Beyond the weekly scan, Damber holds a quarterly reckoning — a longer session, usually ninety minutes, at the end of each Nepali fiscal quarter, where he does not look for new information but instead rereads everything he logged in the past three months. This quarterly review has three specific purposes.
First, he checks for overdue actions. Any entry where the Action Taken column says "monitoring only" or names a future deadline gets re-examined: has the situation changed? Is the deadline approaching? Should "watch" now become "act"?
Second, he checks his own accuracy, honestly. For each entry rated Act or Urgent, he asks whether his logged action, in hindsight, was the right one. Not to punish himself for being wrong sometimes — regulatory effects are genuinely hard to predict with certainty — but to notice patterns. Does he tend to overreact to SEBON disclosure rules and underreact to NRB liquidity changes? Patterns like this, visible only across a full quarter of entries, are exactly what a single week's log cannot show you.
Third, he looks across sectors. Because his tracker records Sectors or Companies Affected for every entry, a quarterly review lets him ask a question no single entry can answer alone: which sector has absorbed the most regulatory pressure this quarter — banking, hydropower, insurance, or manufacturing? A sector accumulating multiple tightening changes across a single quarter, even individually modest ones, may be facing a cumulative squeeze that no single circular reveals on its own. This is the same logic as noticing that a field has had several small dry spells in a row — no single dry week caused a crisis, but three in sequence might.
WARNING
A single quarter of entries is rarely enough to draw firm conclusions about your own judgment or about a sector's trajectory. Resist the temptation to overhaul your entire investing approach based on one quarterly review. The value of this rhythm compounds over multiple years, the same way a farmer's decades of rainfall notes become more valuable than any single season's numbers. Treat each quarterly reckoning as one more data point, not a verdict.
There is one more habit worth naming before we close this chapter: sharing selectively. Damber occasionally discusses specific tracker entries with a small circle of fellow retired professionals in Biratnagar who also invest in NEPSE, not to crowdsource decisions, but to stress-test his own reading of a circular against someone else who read the same document independently. This is not the same as following brokerage floor rumours uncritically, which we warned against earlier. The difference is source discipline — he only discusses entries where both parties have actually read the original NRB or SEBON text, not secondhand summaries. A tracker built and maintained entirely alone can develop blind spots. A small, disciplined circle of fellow trackers, each independently reading primary sources, catches misreadings that a solitary tracker might miss.
Building this system costs you perhaps thirty minutes a week and ninety minutes a quarter — a modest, sustainable commitment, not a full-time research job. What it buys you, over years, is something no amount of reactive news-reading can replicate: a written, honest, dated record of exactly how Nepal's regulatory environment has moved, and exactly how you responded each time, so that the next CAR floor revision, the next margin lending cap, the next disclosure rule does not arrive as a surprise, but as one more entry in a system you already trust because you built it yourself, one Saturday morning at a time.
Chapter recap
This chapter taught you to build your own personal regulatory tracker for NEPSE investing, distinct from any external resource — a structured, repeatable system rather than a vague habit of reading the news. You learned to distinguish Nepal Rastra Bank, which controls system-wide liquidity through tools like the CAR floor, CD ratio ceiling, CRR, and SLR, from the Securities Board of Nepal, which regulates the market's own conduct through margin lending caps and disclosure rules — and why these two sources typically call for different kinds of reactions, one slow and system-wide, the other sometimes sudden and sharply timed. You built a five-column log — Date Noticed, Source, Change Summary, Sectors or Companies Affected, Action Taken — and walked through Damber Prasad Sharma's worked example of a CAR floor revision, from first notice through differentiated action across three affected holdings. You learned a three-level severity scale, Watch, Act, and Urgent, to separate genuine mechanism from noise, and a two-part review rhythm — a light weekly scan and a deeper quarterly reckoning — that turns a pile of logged entries into an honest, improving record of your own judgment over time.
The next chapter, Chapter 111, Building Your Personal Research Library, turns from tracking regulatory change to organising everything else an ongoing NEPSE investor accumulates: annual reports, company disclosures, sector notes, and your own reference materials. Where this chapter gave you a system for watching what changes from outside, the next chapter gives you a system for organising what you already know, so that years of accumulated research become a library you can actually search and use, rather than a pile of forgotten PDFs.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 111
Building Your Personal Research Library
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 111.1 — Why Every Serious Investor Needs a Library
Prakash Adhikari runs a small pharmacy called Adhikari Health Pharmacy in Chipledhunga, Pokhara. He has sold cough syrup and blood pressure tablets for over twenty years. But every Saturday morning, after the shop opens and the first rush of customers has passed, he sits at the small table behind the counter with a cup of milk tea and reads. Not medical journals. Annual reports of companies listed on the Nepal Stock Exchange, or NEPSE.
Prakash has been investing in NEPSE-listed shares since 2003. He is not a stockbroker, not an economist, not a chartered accountant. He is a pharmacist who treats investing as a serious hobby. Over more than two decades, he has built something that most Nepali investors never build: a personal research library. It is not a website. It is not a folder of downloaded PDF files sitting forgotten on a laptop. It is a living, growing, organised collection of documents, notes, and observations that he has curated with his own hands, year after year.
This chapter is about building that same kind of library for yourself.
Before we go further, we must be clear about one distinction, because readers of this book will remember Chapter 105, "The Canon Data Pipeline." That chapter taught you how to pull raw data from NEPSE — daily prices, trading volumes, floorsheets — and store it in a structured, repeatable way, almost like setting up plumbing so that clean water reaches your tap every day. That was about data flowing in continuously, in numbers.
This chapter is different. This chapter is about something slower, older, and in some ways more valuable: the accumulation of knowledge over years. Annual reports. Research notes written by brokerage analysts. Sector studies. Your own handwritten or typed observations about a company you have followed for a decade. This is not water flowing through a pipe. This is water stored carefully in a large clay pot, or "gagri," so that it is there for you months or years later when you need it, when the taps run dry, when nobody else remembers what happened the last time hydropower stocks crashed or the last time a microfinance company's board was reshuffled.
Why does this matter so much in the Nepali context specifically? Because our capital market is still young, our public information systems are still incomplete, and our institutional memory is short. NEPSE itself was established in 1993 with trading beginning in 1994. That is little more than three decades of market history. Many company websites do not keep old annual reports available for download. The Securities Board of Nepal, or SEBON, and NEPSE both publish disclosures, but these disclosures are often scattered, not compiled, and can disappear from a website when it is redesigned. If you do not save a document yourself, the assumption that "it will always be online somewhere" is dangerous. Many investors have discovered, to their frustration, that a five-year-old annual report they need for a comparison is now nowhere to be found, because the company's website only keeps the last two years posted.
WARNING
Do not assume that because a document was once published on a company's website or on the NEPSE website, it will remain there forever. Company websites get redesigned, links break, and archives get deleted with no notice. If a document matters to your research, download it and store your own copy the day you find it.
There is a second reason a personal library matters, and it has to do with how understanding actually develops in a person's mind. Reading one annual report tells you what a company looked like in one year. Reading the same company's annual reports for ten years in a row, side by side, tells you a story — how its profits grew or shrank, how its dividend policy changed, how management explained good years and bad years differently, whether promises made in one year's report were kept by the following year. This kind of pattern recognition cannot happen if each year's document is read once and thrown away. It requires that the documents sit together, on a shelf or in a folder, waiting to be compared.
Think of it like a family that keeps old photographs. A single photograph of a child tells you almost nothing about how that child is growing. But an album with a photograph taken every year on the same date tells you a rich story of growth, health, and change. Your research library is that photograph album, except the subject is a company, a sector, or the whole Nepali economy.
KEY CONCEPT
A personal research library is not a data feed. A data feed like the one built in Chapter 105 gives you numbers, updated continuously. A research library gives you context, judgment, and history — the kind of material a human being wrote to explain what the numbers meant. Both are necessary. Neither replaces the other.
Prakash's own path illustrates the payoff clearly. In 2015, he had been reading annual reports of Nepali hydropower companies for about four years. He had a habit: every year, when a hydropower company released its annual report, he would write, on the first page of his notebook entry for that company, three numbers in large digits — installed capacity in megawatts, actual electricity generated that year in units, and the ratio between the two, which tells you how efficiently the plant actually ran compared to its theoretical maximum. Most investors look only at profit. Prakash looked at this ratio, called the plant load factor or capacity utilisation, because his training as a pharmacist had taught him to think in terms of dosage delivered versus dosage prescribed — the difference between what should happen and what actually happens.
By 2015, he had four years of this ratio recorded for a mid-sized hydropower company he was following. He noticed the ratio was falling steadily, year after year, even though the company's reports always mentioned "normal operations" in the chairman's message. A single year's report would not have shown this. Four years side by side did. He sold his position before the company later disclosed serious turbine wear issues and a costly repair that hurt two years of dividends. He did not predict the mechanical failure. He noticed a trend that management's cheerful language was not fully explaining, and his notebook made that trend visible to him in a way that memory alone never could have.
This is what a research library is for. It does not predict the future. It preserves the past accurately enough that patterns become visible, and it forces you to write down, in your own words, what you understood at the time — which is often more valuable later than what you remember feeling.
Lesson 111.2 — The Five Pillars: What Belongs in Your Library
A common mistake among new investors is trying to collect everything. This leads to a library that is really just a digital junk drawer — hundreds of PDF files with confusing names, never organised, never reviewed. A useful library is built from a small number of clearly defined categories, collected consistently, not a chaotic pile of everything you happened to download.
We recommend organising around five pillars.
The first pillar is annual reports. Every company listed on NEPSE is required to publish an annual report, which includes audited financial statements, the board of directors' message to shareholders, an auditor's report, and disclosures about related-party transactions, risk factors, and corporate governance compliance. This is the single most important document type for long-term investors, because it is the one document a company is legally required to produce carefully, get audited, and present to its own owners — the shareholders. Annual reports are usually available from the company's own website, from NEPSE's corporate disclosure section, and sometimes are handed out physically at the company's Annual General Meeting, commonly called the AGM.
The second pillar is brokerage and analyst research notes. Nepali stockbrokers, and a small number of independent research firms, occasionally publish notes on specific companies or sectors — for example, a note comparing the profitability of the major commercial banks, or an assessment of a hydropower company ahead of a rights share issue. These notes are not neutral; the brokerage may have a business relationship with the company it is writing about. But even a biased note contains useful raw material: numbers, comparisons, and industry context that took someone else time to compile. Read them critically, but do not discard them.
The third pillar is sector reports and regulatory publications. Nepal Rastra Bank, our central bank often called NRB, publishes regular reports on the banking and financial sector, including its "Financial Stability Report" and "Monetary Policy" statements, which affect interest rates and therefore the profitability of banks and finance companies. SEBON publishes an annual report on capital market conditions. The Insurance Board, sometimes called Beema Samiti, publishes data on the insurance sector. Nepal Electricity Authority, or NEA, publishes data relevant to hydropower. These publications rarely mention specific companies but tell you about the "weather" the whole sector is operating in.
The fourth pillar is news and disclosure archives — clippings and saved articles about specific events: a bonus share announcement, a merger, a change in company leadership, a regulatory penalty, a new branch opening. A single newspaper clipping means little. A folder of clippings collected over five years about the same company, in chronological order, becomes a timeline of the company's real behaviour, distinct from what its own annual report chooses to emphasise.
The fifth pillar, and in some ways the most important, is your own personal notes — your own conclusions, questions, doubts, and predictions, written in your own words, dated, and never edited afterward to make yourself look smarter in hindsight. This pillar is what turns a pile of other people's documents into your library, because it records your thinking, not just other people's.
REGULATORY DETAIL
Under SEBON's disclosure rules, listed companies must submit their audited annual reports and periodic financial statements through NEPSE's online corporate disclosure system, and these become part of the public record. However, "public record" does not mean "permanently and easily searchable" — older filings can be difficult to retrieve later, which is exactly why keeping your own copies matters.
A word of caution about the fourth pillar, news clippings. Nepali financial journalism, like financial journalism everywhere, sometimes repeats a company's own press release without independent checking. A news clipping tells you what was reported, not necessarily what was true. Keep clippings, but label them clearly as "reported claim," and check them later against the annual report or regulatory filing when one becomes available. Do not let a hopeful newspaper headline substitute for an audited number.
CAUTION
A newspaper article announcing a company's rosy future project, or a bank's plan to expand into twenty new districts, is a claim, not a fact. File it under "claims to verify later," and go back to it when the next annual report is published to see whether the claim came true. This habit alone will make you a sharper reader of financial news within a year or two.
Lesson 111.3 — Choosing Your System: Physical Shelf, Digital Folder, or Both
Once you know what to collect, you must decide how to store it so that you can actually find it again three years later. There is no single correct answer — what matters is consistency, not sophistication. A simple system followed faithfully for ten years beats an elaborate system abandoned after three months.
Prakash uses a hybrid system, and it is worth describing in detail because it works well for someone without a technical background.
For physical documents — printed annual reports he receives at AGMs, or documents he prints because he finds reading on paper easier for long financial statements — he uses ordinary lever-arch box files, the kind sold in any stationery shop in Pokhara's New Road area. He keeps one box file per sector: one for commercial banks, one for hydropower, one for microfinance institutions, one for insurance, one labelled "others" for manufacturing, hotels, and everything else. Inside each box file, he uses cardboard dividers with a company's short stock symbol written on top in thick marker — for example, "NABIL" for Nabil Bank, or "UPPER" for Upper Tamakoshi Hydropower. Within each company's section, documents are simply stacked with the newest year on top. This is the entire physical system: sector box, company divider, newest on top. No further complexity.
For digital documents — PDFs downloaded from company websites, brokerage notes received by email, regulator reports downloaded from NRB or SEBON websites — he keeps a folder structure on his laptop that mirrors the physical system exactly. A top-level folder called "NEPSE Library," inside it sector folders with the same names as his physical box files, inside each sector folder a folder per company named with the stock symbol, and inside each company folder, files named with a strict pattern: year, then document type, then a short description. For example, "2081_AnnualReport_UpperTamakoshi.pdf" or "2080_BrokerNote_NIC_Asia_Q3Review.pdf." He backs this folder up twice a year onto a small external hard drive, and once a year he emails a compressed copy to himself so that a copy exists outside his own house.
PRACTICAL TOOL
Adopt a strict, boring file naming convention before you save a single document, and never deviate from it. A pattern such as YEAR_DOCUMENTTYPE_COMPANYNAME works well because it sorts correctly by year automatically in any file browser, and it tells you what the file is without opening it. The single biggest cause of an unusable digital library is inconsistent naming done in a hurry.
The mirroring between physical and digital matters more than either system alone. When Prakash wants to check something about a hydropower company while travelling, he can search his laptop folder. When he wants to sit down for a serious Saturday reading session, he prefers the physical box file, because he finds that flipping pages by hand and writing in the margins with a pencil helps him think more slowly and carefully than scrolling a screen. Both systems serve him; neither replaces the other.
You do not need to copy this exact structure. What matters is answering three questions clearly, before you save anything:
First, how will documents be grouped — by sector, by company, by year, or by document type? Choose one primary grouping and stick to it; you can always search within a group, but you cannot easily search across an inconsistent grouping.
Second, how will files be named or labelled so that their contents are obvious without opening them? A name like "Scan001.pdf" is useless a year later. A name like "2081_AnnualReport_NIMB.pdf" tells you everything at a glance.
Third, where does the backup copy live, and how often is it refreshed? A library that exists only on one laptop, with no backup, is one hard drive failure away from being permanently lost. This has happened to real investors in Nepal — years of collected annual reports gone because a laptop was stolen or a hard drive failed, with no second copy anywhere.
WARNING
A research library with no backup is not really a library. It is a temporary pile of files waiting for one accident to erase years of work. At minimum, keep one backup copy on a separate physical device, and ideally a second copy in cloud storage or emailed to yourself, updated at least twice a year.
For readers who prefer an entirely digital approach with no physical filing at all, the same three principles apply. Cloud storage services accessible from Nepal, simple note-taking applications, or even a well-organised set of folders synced across a phone and a laptop, all work fine. The technology matters far less than the discipline of consistent naming, consistent grouping, and consistent backup.
Approach
Best For
Main Risk
Mitigation
Physical box files only
Readers who think better on paper, limited internet access
Fire, flood, termites, physical loss
Keep in a dry, elevated place; consider a fireproof box for oldest, rarest documents
Digital folders only
Readers comfortable with computers, frequent travellers
Hard drive failure, accidental deletion, forgotten passwords
Two backups in two different locations, at least one offsite or cloud-based
Hybrid (physical plus digital)
Most disciplined long-term investors
Extra time needed to maintain both
Keep the two systems mirrored in structure so switching between them is effortless
Lesson 111.4 — The Company Dossier: One File, Many Years
The single most powerful unit inside your research library is what we will call the company dossier — a single running file, physical or digital, dedicated to one company, that you add to every single year for as long as you hold, or are considering holding, that company's shares.
Think of the company dossier the way a family doctor keeps a patient's file. A doctor does not throw away last year's blood test results just because a new one has arrived. The old results, placed next to the new ones, reveal trends — is blood pressure rising over five years, is weight increasing steadily, is a particular medicine working. A single visit's numbers, without history, tell the doctor much less than the same numbers seen against the pattern of the last several years. Your company dossier does the same job for a business.
A well-built company dossier for a NEPSE-listed company should contain, in this order: the company's basic profile — when it was established, when it listed on NEPSE, its paid-up capital history, and its sector; then, year by year, its annual report, or at minimum its audited financial highlights if the full report is unavailable; then a simple table you maintain yourself, tracking the same handful of numbers every single year so they can be compared at a glance; and finally, your own dated notes, written after each year's reading, recording what surprised you, what confirmed your earlier view, and what you plan to watch for next year.
The self-maintained table is worth describing carefully, because it is the heart of the dossier. Choose a small number of figures — five to eight is usually enough — that matter most for that company's sector, and track exactly the same figures every year without changing your mind about what to track. For a commercial bank, useful figures might include net profit, distributable profit, net interest margin, non-performing loan ratio, capital adequacy ratio, and dividend percentage declared. For a hydropower company, useful figures might include installed capacity, actual units generated, plant load factor, and interest coverage ratio, since hydropower companies typically carry significant debt during construction and early operation. For a microfinance institution, useful figures might include loan portfolio size, portfolio at risk, and the interest rate spread it earns between what it charges borrowers and what it pays depositors.
KEY CONCEPT
A company dossier turns twelve months of forgetting into ten years of remembering. Anyone can read one annual report. Almost nobody, without a system, can accurately recall what five consecutive annual reports actually said, in what order, and how the company's own story about itself changed from year to year. The dossier exists to defeat this forgetting.
Here is a case that shows the value of this discipline concretely. A retired schoolteacher in Biratnagar, whom Prakash corresponds with occasionally through an investors' discussion group, held shares in a small finance company for six years without any dossier at all — she simply kept the share certificates and checked the share price occasionally. When the company's share price fell sharply after a disappointing year, she had no record to consult about whether this was a one-year problem or the continuation of a longer decline, because she had never written down the company's numbers from earlier years. She had to guess, under the stress of a falling price, whether to sell or hold — exactly the wrong moment to be reconstructing history from memory. Prakash, who did maintain such tables for the companies he owned, was able to look back at his own hydropower dossier during a similar price fall and confirm calmly that the underlying generation numbers were still healthy, that the price fall was driven by general market sentiment rather than company-specific trouble, and he held his position, which recovered within a year.
CASE IN POINT
Two investors, both holding shares in companies that suffered a bad year, faced the same falling price. One had a personal dossier with five years of tracked numbers and could immediately tell whether the bad year was an isolated event or part of a trend. The other had no such record and had to decide under emotional pressure, from memory alone. The dossier did not change the company's fortunes, but it changed the quality of the decision each investor was able to make.
Building a company dossier requires patience. You cannot build ten years of history in one afternoon; you build it one year at a time, starting today, with whatever historical annual reports you can still find for past years, and continuing forward faithfully every year after that. A dossier started today, for a company you plan to hold for fifteen years, will be extraordinarily valuable by year ten — but only if you actually keep adding to it every single year, including the boring years when nothing dramatic happened. The boring years matter too, because they establish the normal pattern against which a genuinely unusual year can be recognised.
We recommend maintaining full dossiers for no more than fifteen to twenty companies at a time — the companies you actually own, or are seriously studying for a possible future purchase. Trying to maintain deep dossiers on every one of the roughly two hundred fifty companies listed on NEPSE is not realistic for an individual investor with a full-time job or business, and it dilutes the depth of attention any single dossier receives. Depth on a smaller number of companies beats shallow coverage of everything.
Lesson 111.5 — Building Sector Knowledge, Not Just Company Knowledge
A library built only from individual company dossiers has a blind spot: it cannot easily tell you whether a company is doing well because of its own management decisions, or simply because its entire sector is having a good year, or a bad one, for reasons outside any single company's control. To catch this, your library needs a second layer, organised by sector rather than by company.
Consider commercial banks. Nepal's commercial banks, sometimes called "A class" banks under Nepal Rastra Bank's licensing categories, are all affected together by NRB's monetary policy decisions — changes to the cash reserve ratio, the statutory liquidity ratio, and policy interest rates. When NRB tightens monetary policy to control inflation, loan growth typically slows across all banks simultaneously, and interest spreads may compress. If you only track individual bank dossiers, you might wrongly conclude that a particular bank's slower profit growth reflects poor management, when in fact every bank in the sector experienced the same slowdown that year. Only a sector-level view, tracking industry-wide figures over time, lets you correctly separate "this bank underperformed its peers" from "the whole sector faced a difficult year."
The same logic applies powerfully to hydropower. Nepal's hydropower generation depends heavily on river flow, which varies by season — the monsoon months from roughly June to September bring high water flow and high generation, while the dry winter and spring months bring much lower flow and lower generation for run-of-river plants, which make up most of Nepal's listed hydropower companies. A single hydropower company's weak quarter might simply reflect a dry season common to the entire sector, not a company-specific problem. Keeping a sector-level folder with data on national hydropower generation trends, rainfall patterns, and NEA purchase agreements lets you judge each company's own report against a realistic sector backdrop, rather than in isolation.
For microfinance institutions, sector-wide knowledge about loan portfolio quality across the whole industry, and about NRB's evolving regulations for microfinance mergers and capital requirements, similarly helps you judge whether one institution's rising bad loans reflect its own weak lending discipline or an industry-wide stress affecting all microfinance lenders at once — which has, in fact, happened in Nepal in certain years when overall microfinance sector loan quality weakened broadly.
REGULATORY DETAIL
Nepal Rastra Bank periodically issues directives affecting entire categories of financial institutions at once — for example, minimum capital requirements, provisioning rules for bad loans, or restrictions on certain types of lending. These directives are published in NRB's "Unified Directives," reissued and updated periodically. A sector folder that includes copies of the relevant year's directive helps you understand, later, why an entire group of companies changed behaviour in the same year — information that a single company's annual report will rarely explain in full.
Building sector knowledge in your library does not require separate elaborate research. It simply requires that, alongside your company dossiers, you keep a small number of sector-level documents each year: the relevant regulator's annual or periodic report, a short note you write yourself summarising what changed in the sector's operating environment that year, and any brokerage sector comparison notes you come across. Over time, this sector folder becomes a second, complementary photograph album — this time of an entire industry's health, rather than one company's.
Prakash keeps exactly this kind of sector folder for hydropower, and it proved useful in 2021, when several hydropower companies simultaneously reported weaker-than-expected quarterly generation. A less experienced investor, checking only individual company dossiers, might have panicked and sold across the board, assuming something specifically wrong at each company. Prakash's sector folder contained rainfall and river flow data he had been tracking, which showed a genuinely unusual dry spell affecting the whole Gandaki and Koshi river basins that year. He correctly attributed the weak quarter to weather, not mismanagement, held his hydropower positions, and watched generation numbers recover to normal the following wet season as his sector-level data had suggested they would.
PRACTICAL TOOL
For each sector you invest in, keep one simple running note answering a single question every year: what changed in this sector's external environment this year — regulation, weather, interest rates, government policy — that affected every company in it, not just one? Answering this question every year, even briefly, builds sector judgment far faster than reading company reports alone.
It is worth adding a caution here about comparing companies within a sector using numbers alone, without understanding differences in accounting choices or business models. Two hydropower companies may both report "installed capacity," but one may be a purely run-of-river project with large seasonal swings in generation, while another may have limited storage capability that smooths out some of that seasonal variation. Comparing their generation figures directly, without noting this structural difference, can lead to unfair conclusions about which company is "better run." Your sector notes should record these structural differences once, clearly, so you do not have to relearn them every time you make a comparison.
CAUTION
When comparing companies within the same sector using numbers from your dossiers, always check first whether the companies are genuinely similar in structure — for instance, whether a hydropower project is run-of-river or has a reservoir, or whether a bank is primarily focused on corporate lending or retail lending. Comparing dissimilar business models using the same yardstick produces misleading conclusions, however precise the numbers look.
Lesson 111.6 — Maintaining the Library for the Long Run
A library that is built once and never tended will slowly become useless — not because the old documents lose their value, but because without regular maintenance, new documents stop being added consistently, the filing system drifts into inconsistency, and eventually you stop trusting the library enough to actually use it when making decisions. Maintenance is not a separate chore from research; it is the research habit itself, repeated.
We recommend three simple maintenance rituals, each performed at a different frequency.
The first ritual is a weekly or monthly filing session — a fixed time, ideally the same day each week or month, set aside purely for filing new documents that have accumulated: sorting downloaded PDFs into the correct folders, filing away printed documents into the correct box file section, and clearing your desk or downloads folder of anything not yet filed. Prakash does this every Saturday morning, which is also when he does his main reading, so filing and reading happen together as one habit rather than two separate chores competing for his time.
The second ritual is an annual review, ideally done around the Nepali new year or around the time most companies hold their AGMs, when a natural wave of new annual reports arrives. During this annual review, go through each active company dossier and ask three questions: did this company's numbers move in the direction I expected last year, did anything happen that changes my view of its management's honesty or competence, and is this still a company worth tracking closely, or should its dossier be moved to an inactive or "watch occasionally" section. This annual review is also the right moment to update your sector notes, and to check whether your regulatory reference documents — NRB directives, SEBON rules — are still current, since these do get revised periodically.
The third ritual, less frequent but important, is periodic weeding — perhaps once every three to five years, going through your library and asking honestly whether it has become cluttered with material you never actually use: brokerage notes for companies you sold years ago and have no interest in revisiting, duplicate downloads, or news clippings about rumours that never materialised into anything. Weeding is not about discarding history casually — annual reports and your own dated notes should almost never be deleted, since they are the permanent record that gives the library its value over decades. Weeding is about clearing away the noise that accumulates around that permanent record, so the important material remains easy to find.
KEY CONCEPT
A research library is maintained through small, regular habits, not occasional heroic reorganizing efforts. A weekly filing habit, an annual review tied to AGM season, and an occasional weeding every few years will keep a library useful for decades. Long gaps followed by frantic catch-up sessions are how most personal libraries quietly die.
There is one more dimension to long-term maintenance worth discussing: what happens to your library as time passes and your own life circumstances change. A library built carefully over twenty years is not just a personal tool; for a family, it can become a kind of inherited financial wisdom, similar to how households in Nepal often pass down knowledge about land, gold, or family businesses from one generation to the next. Prakash has begun involving his daughter, who is studying commerce at a college in Pokhara, in his Saturday filing sessions, explaining to her why each document matters and how to read the tables he has built. This is not sentimental; it is practical. A well-organised library that only its creator understands is fragile — if that person becomes unavailable, through illness, travel, or simply old age, decades of careful work can become an incomprehensible pile of paper to anyone else. Writing clear notes, in plain language, and occasionally explaining your system to someone else, protects the value of everything you have built.
WARNING
A research library that exists only in your own head, understood by no one else, is vulnerable to being wasted the moment you are unavailable to explain it. Keep your notes and organising system simple enough that another literate adult — a spouse, a grown child, a trusted friend — could understand the filing logic within an hour of being shown around it.
Finally, remember why you are building this library at all. It is not a collection for its own sake, and it is not meant to impress anyone. Its entire purpose is to make your future investment decisions better informed than they would be from memory alone, and to let patterns become visible across years that no single year's document could reveal. Every annual report filed, every dossier table updated, every dated note written down honestly, is a small deposit into a resource that compounds in value the way a disciplined investment portfolio compounds in value — slowly, quietly, and then, after enough years, quite powerfully. Prakash's decision to sell his weakening hydropower holding in 2015, and his decision to hold calmly through the sector-wide dry spell of 2021, both came from the same source: years of unglamorous, consistent filing and note-writing, paying off exactly when it mattered most.
Chapter recap
This chapter taught you how to build a personal research library as a long-term companion to your NEPSE investing, distinct from the raw data pipeline covered in Chapter 105. You learned the five pillars worth collecting — annual reports, brokerage and analyst notes, sector and regulatory reports, news and disclosure clippings, and your own personal notes — and why each pillar serves a different purpose. You learned how to choose and mirror a physical and digital filing system built on three simple principles: consistent grouping, consistent naming, and reliable backup. You learned to build a company dossier, a single running file per company updated every year with the same tracked figures, which turns twelve months of forgetting into a decade of visible pattern. You learned to complement company dossiers with sector-level knowledge, so that a company-specific problem is never confused with an industry-wide condition affecting every company at once, whether that is an NRB policy shift affecting all commercial banks or a dry season affecting all run-of-river hydropower plants together. And you learned that a library survives only through regular maintenance — weekly filing, an annual review timed to AGM season, and occasional weeding — and that its value should be shared clearly enough that it does not die with its creator.
The next chapter, Chapter 112, "Technology Tools for the NEPSE Investor," moves from paper and personal discipline to the apps, software, and digital platforms available to Nepali investors today. It will survey the tools that can support the library-building habits taught in this chapter — from portfolio tracking applications and mobile trading apps such as those used for the Trading Management System, or TMS, to online research platforms, spreadsheet templates, and digital note-taking tools — helping you choose technology that strengthens your research discipline rather than distracting from it.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 112
Technology Tools for the NEPSE Investor
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 112.1 — The Investor's Digital Toolbox: Why These Tools Matter
Bikash Shrestha works as a software quality tester for a small IT company in Kathmandu, in an office near Jawalakhel. He is thirty-one years old, married, and has been investing in NEPSE-listed shares for almost six years. When he started, in 2020, he did almost everything on paper. He carried a physical share certificate folder to his broker's counter. He stood in line to apply for IPO shares. He asked his broker's staff to read out his portfolio value over the phone because he had no way to check it himself.
Today Bikash does none of that. He applies for IPOs from his phone while riding the local bus. He places buy and sell orders from his laptop during his lunch break. He tracks his entire portfolio, along with his profit and loss, in a spreadsheet he built himself and updates in ten minutes every evening. Nothing about what Bikash owns has changed in principle — he still owns shares in real companies listed on the Nepal Stock Exchange, still bears real risk, still needs the same patience and study as any investor. What changed is the plumbing. The pipes that carry his money, his shares, and his information now run through digital systems instead of paper and queues.
This chapter is about that plumbing. Think of investing in NEPSE as running a small farm. The land is the stock market itself — the companies, the prices, the value that grows or shrinks over seasons. But a farmer also needs tools: a spade to dig, a cart to carry produce to market, a ledger to record the harvest. Good tools do not make bad land productive, and bad tools cannot ruin good land completely, but the right tools save enormous time, prevent mistakes, and let a small farmer compete fairly with a big one. Technology tools for the NEPSE investor work the same way. They do not create profit by themselves. They remove friction, reduce errors, and give an ordinary investor in Nepal — whether in Kathmandu, Chitwan, Biratnagar, or Dhangadi — the same basic access to the market that once required personal connections or physical presence in the capital.
There are four broad categories of tools this chapter will cover, and it helps to see them as one connected chain before going deeper into each one.
The first is Meroshare, the system operated by CDS and Clearing Limited (commonly written as CDSC), which is the company responsible for holding investors' shares in electronic form and processing corporate actions like dividends, IPOs, and rights issues. Meroshare is where an investor's dematerialized shares — meaning shares converted from paper certificates into electronic entries — actually live. Think of Meroshare as the almirah, the steel cupboard at home, where all your important documents are stored safely and where you go to file new documents in or take them out.
The second is the TMS portal, short for Trading Management System, which is software provided by each individual broker so investors can place buy and sell orders on NEPSE without physically visiting the broker's office or phoning in an order. If Meroshare is the almirah where shares rest, the TMS portal is the shop counter where buying and selling actually happens.
The third is the wider world of mobile apps and market data sources — apps and websites that show live or delayed prices, market indices, company announcements, and news, letting an investor watch the market the way a farmer checks the sky for weather.
The fourth is the humble spreadsheet, or a similar simple record-keeping tool, which is not provided by any government body or broker but which every serious investor eventually builds for themselves, because no official system will organise your personal financial story exactly the way you need it organised.
KEY CONCEPT
Meroshare holds your shares. The TMS portal lets you trade them. Market data apps let you watch the market. A spreadsheet lets you understand your own results. These are four different jobs, and no single tool in Nepal currently does all four well. A complete investor uses all four together, the way a household uses a bank account, a shop, a radio, and a notebook, and does not expect one object to replace the other three.
Before going further, it is worth being honest about what technology cannot do. A fast TMS portal will not tell you whether a company's profits are genuine. A well-organised spreadsheet will not stop you from panicking when the market falls. Meroshare will not warn you that an IPO company has weak fundamentals. These tools are neutral. They make it easier to do whatever you were already planning to do, wise or unwise, quickly and at scale. This is exactly like a sharp kitchen knife: it makes a good cook faster and a careless cook more dangerous. The judgment discussed in earlier chapters of this Canon — on valuation, on patience, on risk — still has to come from you. This chapter only makes sure that once you have made a wise decision, nothing clumsy in your tools gets in the way of executing it.
With that foundation laid, the rest of this chapter follows Bikash Shrestha through his actual monthly routine: opening Meroshare to apply for a rights issue, logging into his broker's TMS portal to place a trade, checking a market app during a slow afternoon at work, and updating his spreadsheet before bed. By the end, you should be able to set up the same basic workflow for yourself, regardless of which broker you use or which city you live in.
Lesson 112.2 — Meroshare: Your Demat Account's Front Door
To understand Meroshare, you first need to understand dematerialization, a word that sounds complicated but describes something simple. In the old system, when you bought shares in a Nepali company, you eventually received a paper certificate, like a formal letter, proving you owned a certain number of shares. These certificates had to be physically stored, physically transferred when sold, and physically produced for many transactions. This created enormous room for loss, forgery, and delay — imagine if every time you wanted to sell a sack of rice, you had to produce the original purchase receipt from years ago, undamaged and unforged.
Dematerialization means converting that paper ownership into an electronic entry, similar to how your bank balance is not a pile of cash sitting in a labelled drawer with your name on it, but a number in a computer system that the bank guarantees is yours. In Nepal, the organisation that maintains these electronic entries for shares is CDS and Clearing Limited, a subsidiary company set up under the framework of the Securities Board of Nepal, commonly called SEBON, which is the government regulator for the entire securities market, similar in role to a central inspector who makes sure every shop in the market follows fair rules.
Every investor who wants to hold shares electronically must first open what is called a demat account, short for dematerialized account, through a broker or a depository participant. Once that demat account exists, Meroshare is the online portal where the investor manages it. Meroshare is not a trading platform. You cannot buy or sell shares directly on Meroshare. Instead, Meroshare handles four main jobs: viewing your current share holdings and their electronic statements, applying for new share issues such as IPOs (initial public offerings, meaning a company selling shares to the public for the first time) and rights issues (meaning an already-listed company offering additional new shares to its existing shareholders), transferring shares between accounts in specific permitted situations, and dematerializing old paper certificates into electronic form if you still hold any.
REGULATORY DETAIL
Meroshare is operated centrally by CDS and Clearing Limited under SEBON's regulatory framework, but each investor's individual account within Meroshare is linked to a specific participant, usually the brokerage house where they opened their demat account. If you ever change your primary broker, you generally need to work through your depository participant to migrate your demat account details rather than assuming Meroshare treats all brokers identically.
Let us follow Bikash Shrestha through a real use of Meroshare. In early 2026, a hydropower company he already held shares in announced a rights issue, offering existing shareholders the chance to buy one new share for every two shares they already held, at a discounted price. Rights issues are explained more fully elsewhere in this Canon, but for this chapter, the point is simply how Bikash applied for his allotment using Meroshare.
First, Bikash logged into Meroshare using his username and password, plus a one-time verification step, since Meroshare (like most financial systems in Nepal now) uses two-factor authentication, meaning you prove your identity in two separate ways — something you know, like a password, and something you have, like a code sent to your registered phone. He navigated to the section for "My ASBA," where ASBA stands for Applications Supported by Blocked Amount, a system where the money for a share application is not actually withdrawn from your bank account immediately but is instead frozen or blocked in place until the share allotment is finalised. This protects the investor: if you do not receive the shares you applied for, in full or in part, the blocked amount is released back to you rather than making you wait for a slow refund process.
Bikash selected the rights share offering from the list of open issues, entered how many shares he wanted to apply for, confirmed the bank account linked to his Meroshare profile, and submitted the application. Within a minute, the amount was blocked in his bank account, and a confirmation appeared in Meroshare showing his application number. He did not need to visit his broker's office, fill out a paper form, or stand in any queue. The entire process took him about four minutes, done while waiting for a kettle to boil at home.
CASE IN POINT
Before ASBA and Meroshare became standard practice, applying for an IPO in Nepal often meant physically depositing a bank draft or cash at a collection centre, sometimes travelling to another town if you lived outside a district with a collection centre. Investors in remote areas were structurally disadvantaged compared to those in Kathmandu or other major cities. Bikash's father, who invested in the 1990s, once told him about travelling two hours by bus just to submit an IPO form before a deadline. Bikash's own rights-issue application, submitted from his phone in under five minutes, illustrates one of the most significant improvements Nepal's capital market technology has delivered to ordinary savers: geography now matters far less than it once did.
A related caution deserves mention here. Meroshare has application windows and deadlines for every IPO and rights issue, and these deadlines are strictly enforced by the system itself; there is no manual override for a late application, no matter how sympathetic the circumstance. If Bikash had tried to submit his rights application one minute after the portal closed, no clerk anywhere could have accepted it on his behalf.
WARNING
Meroshare closes IPO and rights issue application windows automatically and does not accept late submissions under any circumstance, including technical problems on your own device. Do not wait until the final hour of the final day to apply, especially during periods of heavy internet traffic, when many investors are trying to submit applications simultaneously and the system can slow down.
Meroshare also lets Bikash check his consolidated shareholding statement at any time, a single screen listing every company he holds shares in, how many shares of each, and their current electronic status. This single screen replaces what used to require separate paper certificates for every company, potentially stored in different folders or even different physical locations. When dividends are announced by companies Bikash holds shares in — and dividends in Nepal are frequently paid partly or wholly as bonus shares, meaning additional free shares rather than cash — those bonus shares also appear automatically in his Meroshare holding statement once the company processes the distribution, without Bikash needing to do anything at all.
One frequent point of confusion for newer investors is the difference between their Meroshare login and their broker's TMS login. These are two separate systems with two separate usernames and passwords, even though both relate to the same underlying demat account. Meroshare is a shared, centralised system used by essentially every investor in Nepal, run by CDS and Clearing Limited. The TMS portal, covered next, belongs individually to each brokerage house. Confusing the two, or assuming a password reset on one automatically fixes the other, is a common early mistake.
PRACTICAL TOOL
Keep your Meroshare login and your broker's TMS login recorded separately, clearly labelled, in whatever password manager or secure notebook you use. Many new investors waste time during time-sensitive events, like a closing IPO window, because they mix up which password belongs to which system.
Lesson 112.3 — The TMS Portal: Placing and Tracking Trades Online
If Meroshare is the almirah where your shares rest, the TMS portal is the shop counter. TMS stands for Trading Management System, and in Nepal, every licensed brokerage firm operates its own version of this system, usually as both a website and a mobile app, though the underlying software is often licensed from a small number of technology vendors and looks broadly similar from one broker to the next.
Before TMS portals became widespread, placing a trade on NEPSE meant physically visiting your broker's office, or at best phoning in an order to a broker's staff member, who would then key it into the exchange's system on your behalf. This created delays, added a layer of potential miscommunication, and made it hard to react quickly to price movements. NEPSE itself operates during specific market hours, and a physical or phone-based order placed even a few minutes before market close could easily be missed.
A TMS portal changes this by letting the investor place buy and sell orders directly, from a computer or phone, which then flow electronically into NEPSE's trading system. Bikash uses his broker's TMS portal several times a month, usually a mix of small planned purchases and occasional sales when he decides to trim a position.
Here is how a typical order works for Bikash. Suppose he decides, after reviewing a company's quarterly results, that he wants to buy 50 shares of a commercial bank at a price no higher than 285 rupees per share. He logs into his TMS portal using his broker-assigned username and password (again, separate from his Meroshare credentials). He navigates to the buy order screen, searches for the company by its trading symbol — a short code, usually three to five letters, that NEPSE assigns to every listed company, similar to how a shop in a crowded bazaar might have a shorthand nickname everyone uses instead of its full legal name. He enters the quantity, 50 shares, and the price, 285 rupees, and submits the order.
This is what is called a limit order, meaning Bikash has set a maximum price he is willing to pay, and the order will only execute at that price or better. If the share is trading at exactly 285 or lower when matched against a seller, the trade completes. If the price never falls to his limit during that trading session, the order simply does not execute, and Bikash can choose to modify or cancel it. This is different from what is called a market order, where an investor accepts whatever price is currently available in exchange for near-certain, immediate execution — useful when speed matters more than an exact price, but risky in a fast-moving or thinly traded stock, since the executed price could be considerably worse than expected.
Feature
Meroshare
TMS Portal
Operated by
CDS and Clearing Limited (CDSC), centrally, under SEBON's regulatory framework
Individually by each licensed brokerage house
Main purpose
Holding shares electronically; applying for IPOs and rights issues via ASBA; viewing holding statements
Placing buy and sell orders on NEPSE; tracking order status and trade history
Login credentials
One Meroshare username and password per investor, shared across all their linked accounts
Separate username and password per broker, specific to that broker's TMS
What it cannot do
Cannot execute a buy or sell trade
Cannot hold IPO or rights applications; cannot store dematerialized shares directly
Typical investor use
Occasional: IPO season, rights issues, checking holdings
Frequent: any time you want to buy or sell
After Bikash's order is placed, the TMS portal shows its status, usually moving from pending to either fully executed, partially executed, or expired at the end of the trading session if unmatched. Once executed, the trade needs to formally settle, meaning the shares move into Bikash's demat account and the money moves out of his bank account. In Nepal, CDSC currently operates a T+2 settlement cycle, meaning the transaction fully clears two working days after the trade date, though exact settlement timelines are periodically adjusted by regulatory decision and should always be confirmed against your broker's current published cycle rather than assumed from memory.
REGULATORY DETAIL
Settlement cycles, brokerage commission rates, and daily circuit limits (the maximum percentage a share price is allowed to move up or down in a single session) are all set or adjusted by NEPSE and SEBON from time to time, not by individual brokers. Your TMS portal simply reflects whatever the current rule is; it is not the source of that rule. When something in your TMS portal seems to have changed unexpectedly, such as a new commission deduction or a stricter price limit, the likely explanation is a regulatory update rather than a technical glitch specific to your broker.
Most TMS portals also show a running ledger of an investor's cash balance held with the broker, any pending obligations, and a history of past trades. Bikash makes a habit of downloading his trade history as a spreadsheet-compatible file, usually a CSV file (a simple text format that any spreadsheet program can open, standing for comma-separated values), once a month. This becomes the raw material for the personal spreadsheet described later in this chapter.
A word of caution belongs here about order mistakes, which are extremely common among new TMS users and almost always avoidable. It is easy to accidentally add an extra zero to a quantity field, turning an intended order for 50 shares into an order for 500 shares, or to place a sell order when you meant to place a buy order, especially on a small phone screen. Because TMS portals execute quickly and automatically, such mistakes are not always reversible once matched with a counterparty.
CAUTION
Before submitting any order on a TMS portal, pause and re-read the three critical fields out loud to yourself: the company symbol, the quantity, and whether it is a buy or a sell. This costs three extra seconds and has likely prevented more financial damage among ordinary Nepali investors than any other single habit this chapter can recommend. Many portals also show a confirmation screen before final submission; never click through a confirmation screen without actually reading it.
Bikash also keeps in mind that a TMS portal is a tool for execution, not for research. The temptation, especially for newer investors, is to sit and watch live price movements on the TMS screen throughout the working day, refreshing constantly. This behaviour mimics gambling more than investing, and several earlier chapters in this Canon on discipline and temperament address why constant price-watching tends to produce worse decisions, not better ones. Bikash deliberately checks his TMS portal only when he has a specific order to place, rather than leaving it open as background entertainment during work hours.
Lesson 112.4 — Mobile Apps and NEPSE Data: Watching the Market from Your Pocket
Separate from Meroshare and any single broker's TMS portal, there is a wider ecosystem of apps and websites that show NEPSE market data: the daily index level (a single number summarising the overall direction of the market, similar to how a village might describe the whole harvest season as "good" or "poor" in one word even though every farmer's individual result varied), individual company prices, trading volumes, company announcements, and general financial news relevant to Nepal.
Some of these tools are official or semi-official, such as NEPSE's own website, which publishes daily trading summaries, company disclosures, and floor sheet data (a floor sheet is the complete public record of every single trade executed on a given day, showing which broker bought, which broker sold, at what price, and in what quantity — useful for anyone who wants to verify market activity in detail rather than relying on a summarised headline number). Other tools are private apps and websites built by third parties that aggregate this same public data into a more convenient, mobile-friendly format, often adding features like price alerts, watchlists, and simple charts.
Bikash uses a market data app on his phone the way many Nepali households use a radio for weather and news: as background awareness, not as an instruction to act. Each morning, before market open, he glances at the previous day's closing index and any major company announcements affecting shares he owns. During the day, since his job keeps him busy, he mostly ignores live price movement. In the evening, he might spend ten minutes reviewing the day's overall market summary.
KEY CONCEPT
A live price ticker showing numbers changing every few seconds creates a powerful psychological pull toward action, even when no action is actually warranted. This is similar to how standing at a vegetable market watching prices being called out repeatedly can make a shopper feel they must buy something immediately, even if they walked in only planning to look. Recognising that market data apps are designed to be engaging, sometimes deliberately so to increase how long you spend inside the app, helps an investor use them as a tool rather than be used by them.
It is worth being specific about what these apps are good for and where their limits lie. They are excellent for checking whether a company you own has released its quarterly results, whether a dividend or bonus share has been announced, or what the broad market did on a given day. They are far less reliable as a source for deep company research; detailed financial statements, annual reports, and regulatory filings are better sourced from the company's own disclosures filed with NEPSE and SEBON, or from the company's own investor relations pages, rather than a summarised app notification. Chapters earlier in this Canon on fundamental analysis cover how to read those primary documents properly; this chapter's concern is only the delivery mechanism.
A second limitation worth flagging clearly: not every app pulling NEPSE-related data is officially sanctioned, accurate, or safe. Nepal's growing smartphone market has attracted a range of independent developers, some careful and reliable, others less so, building apps that display market information. A poorly maintained app might show stale prices, miscalculate a portfolio value, or in worse cases, request unnecessary permissions or personal information unrelated to its stated purpose.
CAUTION
Before installing any third-party market data app, check who publishes it, how recently it has been updated, and what permissions it requests on your phone. An app that only needs to display public market data has no legitimate reason to request access to your contacts, your messages, or your call history. If in doubt, prefer apps recommended directly by your broker or ones with a long, visible track record among other Nepali investors, and never enter your TMS or Meroshare password into any app other than the official one from your broker or CDS and Clearing Limited itself.
Bikash also follows announcements from NEPSE and SEBON directly, rather than relying entirely on secondhand summaries, particularly for anything involving regulatory changes, such as adjustments to margin lending rules (borrowing money from a broker against existing shares as collateral) or circuit breaker limits. He compares this habit to a farmer who listens to the government agricultural office's own seasonal advisory directly, rather than only hearing about it secondhand from a neighbour, since details can get lost or distorted after passing through several people.
Finally, it is worth mentioning basic tools like price alerts, available in many market apps and even some TMS portals, which notify an investor by phone notification when a chosen stock crosses a certain price. Bikash uses this feature sparingly, mainly for a handful of companies on his long-term watchlist that he does not currently own but would consider buying if the price fell to a specific level he has already calculated using the valuation methods discussed elsewhere in this Canon. Used this way, a price alert is simply a patient reminder, not a trading signal in itself; the actual decision still rests on the analysis Bikash did in advance, calmly, not on the emotion of the moment the alert arrives.
Lesson 112.5 — Spreadsheets and Simple Recordkeeping: Building Your Own Dashboard
No official Nepali system — not Meroshare, not any broker's TMS portal, not any market data app — is designed to answer one of the most important questions an investor needs answered: across everything I own, bought at different times and different prices, am I actually making money, and how much, after all costs?
Meroshare shows current holdings but not your original purchase price or your overall return. A TMS portal shows individual trade history but usually does not automatically combine that with dividends received, bonus shares issued, or a clean overall profit and loss picture across your full holding period. This is exactly why Bikash, like most disciplined Nepali investors eventually do, built his own spreadsheet.
Think of this the way a small shop owner in Chitwan keeps a personal ledger even though the wholesale market has its own price board. The market price board tells the shop owner what things generally cost today. It does not tell them, by itself, whether their own shop, with its own particular purchase costs and particular customers, is profitable this month. Only their own ledger answers that.
Bikash's spreadsheet, built in a free spreadsheet program, has several simple columns: the date of each purchase, the company symbol, the quantity bought, the price per share, the total cost including brokerage commission, and a separate section tracking dividends and bonus shares received over time for each holding. Once a month, he pulls his trade history file from his broker's TMS portal, described in the previous section, and adds any new transactions to this spreadsheet by hand or with simple copy and paste.
PRACTICAL TOOL
A spreadsheet does not need to be complicated to be useful. Four or five columns tracking date, company, quantity, price, and total cost, kept consistently, will answer almost every practical question a long-term NEPSE investor needs answered. Complexity can be added later if genuinely needed; most investors abandon overly elaborate tracking systems within a few months, while a simple one gets maintained for years.
Bikash also built one additional simple calculation into his spreadsheet: his current portfolio value, calculated by multiplying his current share quantities by their latest closing prices, which he updates weekly by copying prices from a market data app or NEPSE's own published closing data. Comparing this current value against his total invested cost gives him an overall unrealized profit or loss figure — unrealized meaning the shares have not actually been sold yet, so the gain or loss exists only on paper until a sale is made.
This is an important distinction worth explaining plainly. A rise in the value shown in a spreadsheet is not the same as money in hand. Only when Bikash actually sells shares does a gain or loss become realised, meaning locked in and reflected in his actual bank balance. Before that point, prices can still move in either direction. Confusing an unrealized gain, still sitting inside share ownership, with money already earned and safe to spend, is a common and sometimes costly error among newer investors, and a well-kept spreadsheet, showing both figures clearly side by side, helps prevent that confusion by making the distinction visible every time you look at it.
WARNING
Never treat an unrealized gain shown in your spreadsheet or your TMS portal as money you can safely spend, borrow against casually, or count as guaranteed. Share prices can fall as quickly as they rose. Only a completed sale, with funds actually settled into your bank account, represents money genuinely in hand.
Bikash's spreadsheet also plays a quieter, longer-term role: it is his own private, permanent financial record, independent of any single broker or app. If Bikash ever changed brokers, or if some future technical problem affected any single official system temporarily, his own spreadsheet would remain untouched, stored on his own laptop and backed up to a personal cloud storage account he controls. This is not meant to suggest that official Nepali systems are unreliable; CDS and Clearing Limited and NEPSE maintain the authoritative records of what an investor actually owns, and in any conflict, those official records govern, not a personal spreadsheet. Rather, a personal spreadsheet is simply good practice, the same way a household keeps its own copy of an important document even though a government office also holds the official version, purely as a convenience and a safeguard against needing to reconstruct history from memory.
A modest final point: for investors who prefer not to build a spreadsheet from scratch, some brokers and third-party apps in Nepal now offer basic built-in portfolio tracking features that calculate average cost and unrealized gain automatically. These can be a reasonable starting point, especially for a newer investor. Bikash's own view, formed after using both, is that a spreadsheet he built and understands completely gives him more confidence in the numbers than a black-box calculation inside someone else's app, because he can see exactly how every figure was derived and correct it immediately if something looks wrong. Which approach an individual investor chooses matters less than actually maintaining one of them consistently.
Tool
Best For
Main Limitation
Meroshare
Viewing dematerialized holdings; applying for IPOs and rights issues via ASBA
Cannot execute trades; does not show purchase price history or profit and loss
TMS portal
Placing and tracking buy and sell orders; viewing broker-held cash and trade history
Different login per broker; usually does not combine dividends and bonus shares into a full return picture
Market data app
Watching daily index moves, company announcements, and price alerts
Can encourage overtrading; third-party apps vary widely in reliability
Personal spreadsheet
Understanding your true overall cost, current value, and realised versus unrealized profit
Requires manual discipline to update; not an official record of ownership
Lesson 112.6 — Digital Hygiene: Passwords, Security, and Avoiding Scams
Everything covered so far in this chapter assumes that the accounts involved — Meroshare, the TMS portal, and any linked bank account — remain securely in the investor's own control. This final lesson addresses how to keep it that way, because as more of an investor's money and shares move into digital systems, digital security stops being a technical afterthought and becomes as important as any investing decision covered elsewhere in this Canon.
Begin with passwords. Bikash uses a different, reasonably long password for his Meroshare account, his TMS account, and his email account, rather than reusing one password everywhere. This matters because if a password is ever exposed in one place — for example, if some unrelated website he used years ago suffers a data breach — a reused password would let an attacker try that same combination against his financial accounts too. Using different passwords for different systems is like using different keys for your house, your shop, and your motorbike; losing one key does not hand a thief access to everything else you own.
KEY CONCEPT
A password is only as strong as its weakest reuse. It does not matter how complicated a password is if the exact same password is also used on a minor, less secure website that later gets compromised. Unique passwords per important account, combined with two-factor authentication wherever it is offered, are the two single most effective, low-cost protections available to an ordinary investor.
Two-factor authentication, mentioned earlier in this chapter regarding Meroshare, deserves emphasis here as a general habit, not just a Meroshare-specific feature. Most TMS portals and banking apps in Nepal now offer some form of it, typically a code sent by SMS or generated by an authenticator app. Bikash enables this wherever it is available, even though it adds a small amount of friction to logging in, because it means that even if someone somehow learned his password, they would still need physical access to his registered phone to actually get in.
Next, consider the specific scams that have targeted Nepali investors as digital investing has grown more common. A frequent pattern involves messages, often sent through social media or messaging apps, impersonating a broker, an official from SEBON or CDS and Clearing Limited, or a fellow investor, offering "guaranteed returns," insider tips on upcoming IPO allotments, or claiming a problem with the investor's account that requires urgently sharing a password or one-time code to resolve.
WARNING
No genuine broker, no representative of CDS and Clearing Limited, and no SEBON official will ever contact you asking for your password or a one-time verification code over phone, SMS, or messaging app. These credentials should never be shared with anyone under any circumstance, including someone claiming to be resolving an account problem on your behalf. If you receive such a request, treat it as an attempted fraud and verify independently by contacting your broker directly through their officially published phone number, not any number provided within the suspicious message itself.
Bikash also stays alert to a subtler version of this same problem: fake apps or fake websites designed to closely resemble Meroshare's or a broker's real login page, sometimes distributed through links shared in messaging groups promising urgent IPO news or limited-time investment opportunities. The safest habit, which Bikash follows consistently, is to always navigate to Meroshare or his broker's TMS portal by typing the address directly or using a bookmark he saved himself after confirming the correct address once, rather than clicking links sent to him by others, however official those links might appear.
A related, quieter risk involves shared or public devices. Bikash occasionally sees colleagues checking their portfolio on a shared office computer or public library computer, sometimes forgetting to log out afterward. Any financial account left logged in on a device others can access is a meaningful, avoidable risk.
CAUTION
Avoid accessing Meroshare, your TMS portal, or your linked bank account from shared or public computers whenever possible. If it is genuinely unavoidable, make sure to fully log out afterward and clear the browser session, rather than simply closing the browser window, which does not always end an active login session.
Finally, Bikash keeps his devices themselves reasonably secure: a lock code on his phone, operating system and app updates installed promptly rather than postponed indefinitely, and caution about which apps he grants sensitive permissions to, echoing the earlier point in this chapter about market data apps requesting unnecessary access. He also periodically reviews his Meroshare and TMS account activity logs, where available, checking for any login or application he does not personally recognise, the digital equivalent of periodically checking that your almirah lock has not been tampered with even if nothing appears to be missing.
None of these precautions are complicated or expensive. They cost a few extra minutes of attention, much like remembering to lock your front door is only a small daily habit, yet it is precisely the habit that prevents the rare but serious loss. As more of an ordinary Nepali household's savings and share ownership moves into digital systems, this kind of basic digital hygiene deserves the same seriousness that earlier generations gave to safely storing a paper share certificate or a passbook in a physical almirah.
Chapter recap
This chapter followed Bikash Shrestha, an IT professional in Kathmandu, through the practical digital toolkit that a Nepali NEPSE investor uses today: Meroshare, operated by CDS and Clearing Limited under SEBON's oversight, which holds dematerialized shares electronically and processes IPO and rights issue applications through the ASBA system, blocking rather than immediately withdrawing an applicant's funds; the TMS portal, operated individually by each brokerage house, where actual buy and sell orders are placed and tracked, distinct from Meroshare and requiring separate login credentials; mobile apps and market data sources that let an investor watch index levels, company announcements, and prices without encouraging the kind of constant, anxious checking that undermines good decision-making; and the personal spreadsheet, which no official system provides but which every serious investor eventually needs, to see their true overall cost, current value, and the crucial difference between unrealized and realised gains. A closing lesson on digital hygiene tied these tools together with practical security habits: unique passwords, two-factor authentication, wariness of impersonation scams, and care around shared devices and unofficial apps, since none of the convenience these tools offer is worth anything if an account falls into the wrong hands.
Taken together, these four tools, used the way Bikash uses them, form a complete, ordinary, un-glamorous operating routine: check Meroshare occasionally for holdings and share issues, use the TMS portal deliberately whenever an actual trade decision has been made, glance at market data without becoming absorbed by it, and keep an honest personal ledger of true results. None of this requires special access, expensive software, or technical expertise beyond what an ordinary smartphone or basic computer user already has. It requires only consistency.
This chapter completes Part XVIII, Operational Tools, Data Reference, and Almanac. Across Chapters 101 through 112, this part has assembled the practical, day-to-day operating toolkit of a Nepali investor: how to read financial statements and market data, how to work with brokers and the exchange's mechanics, how corporate actions like dividends, bonus shares, and rights issues are actually processed, and now, in this final chapter, the digital tools that carry all of it. Where earlier parts of this Canon addressed why and what to invest in, and how to think about value, risk, and temperament, this part has addressed the more mundane but equally essential question of how the machinery actually works in practice. With the operating toolkit now complete, the Canon moves forward into its next part, returning to the deeper questions of strategy and judgment that this operational foundation exists to serve.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 113
The Annual Financial Calendar for NEPSE
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 113.1 — The Shape of the Nepali Financial Year
Every farmer in Nepal knows the rhythm of the land without needing to check a diary. Maize goes in after the first monsoon showers. Paddy is transplanted in Ashad and Shrawan. Rice is harvested in Kartik. Mustard and wheat follow in the winter fields. A farmer who plants paddy in Poush, out of season, will get nothing for the effort, no matter how hard he works. The land has its own calendar, and working with that calendar, not against it, is what separates a good harvest from a wasted one.
NEPSE, the Nepal Stock Exchange, has a calendar too. It is not written on any wall calendar sold in New Road, but it repeats every year with the same reliability as Dashain and Tihar. The national budget is read out in Jestha. The fiscal year closes at the end of Ashad. Companies close their books and call their shareholders to Annual General Meetings between Kartik and Falgun. Nepal Rastra Bank, the central bank that regulates all banks and sets the country's monetary direction, announces its big annual monetary policy at the start of the new fiscal year and reviews it again six months later. Dividends and bonus shares arrive in waves, not in a steady drip. Festivals pull money out of the market before they arrive and sometimes bring it back afterward. Tax deadlines fall on fixed days every single year.
An investor who does not know this calendar is like the out-of-season farmer. He buys shares in a panic during Dashain because trading has gone quiet and he mistakes silence for danger. He misses an AGM notice because he was not looking in Mangsir. He forgets to file his tax return by the Ashwin deadline and pays a fine that eats into the very dividend he was celebrating. An investor who does know the calendar, on the other hand, moves through the year with a plan, the same way a farmer moves through the seasons with a plan.
This chapter is that calendar, laid out month by month, in the order the Nepali fiscal year actually runs — starting from Shrawan, not from Baisakh. This matters because Nepal's government, its companies, and its regulators do not organise their financial life around the Nepali New Year in Baisakh. They organise it around the fiscal year, which begins on the first day of Shrawan (mid-July) and ends on the last day of Ashad (mid-July the following year). Shrawan to Ashad is to a Nepali company's finances what a full crop cycle is to a farmer's field: everything is measured from one Ashad-end to the next.
To make this concrete, this chapter will follow one person through a full year of investing. His name is Devi Prasad Wagle. He is fifty-four years old, and he works as a Section Officer in a government ministry in Kathmandu, a modest but steady job he has held for close to three decades. Devi Prasad is not a big trader. He does not sit refreshing share prices on his phone all day. But over fifteen years he has built a portfolio of bank shares, a couple of hydropower companies, and one insurance company, using savings from his salary and a portion of his provident fund. He is six years from retirement, and he has one clear goal: to turn this portfolio into a dependable stream of dividend income for the years when his government pension alone will not be enough. Devi Prasad has learned, slowly and sometimes expensively, that the way to manage this portfolio well is to know exactly what tends to happen in each month of the year, and to plan around it rather than be surprised by it.
Think of the fiscal year as a length of pipe carrying water from a village tap. Water does not arrive at every point in the pipe at the same time; it moves in a sequence, and if you know the sequence, you know exactly when to place your bucket. The budget announcement in Jestha is the moment the tap is turned on for the coming year, deciding tax rates and sector priorities. The fiscal year-end in Ashad is when the pipe is checked and the year's flow is measured and recorded. The AGM season from Kartik to Falgun is when that measured water is actually handed out to shareholders as dividends and bonus shares. Understanding this sequence is the single most useful habit a NEPSE investor can build, more useful, in Devi Prasad's experience, than trying to predict which stock will rise in a given week.
Nepali Month
Approximate Gregorian Months
Typical Financial and Market Events
Shrawan
Mid-July to Mid-August
Fiscal year begins; NRB announces the annual Monetary Policy; new budget takes effect; companies begin preparing year-end accounts
Bhadra
Mid-August to Mid-September
Audited financial statements start getting finalised; early book closure notices for some companies; monsoon season, generally quieter trading
Ashwin
Mid-September to Mid-October
Income tax return filing deadline for the previous fiscal year; Dashain preparations begin; trading volume often dips before the festival
Kartik
Mid-October to Mid-November
Dashain and Tihar festivals; market holidays; remittance inflows rise sharply; first wave of AGM notices and book closure dates
Mangsir
Mid-November to Mid-December
Peak AGM season begins; dividend and bonus announcements accelerate; post-festival trading often picks up
Poush
Mid-December to Mid-January
First instalment of advance tax due at month-end; continued AGM and dividend announcements; NRB begins preparing mid-year review
Magh
Mid-January to Mid-February
NRB publishes the mid-year review of Monetary Policy; more AGMs, particularly for companies with later book closures
Falgun
Mid-February to Mid-March
IPO and rights issue activity often picks up; remaining AGMs for the year; continued dividend bookings
Chaitra
Mid-March to Mid-April
Second instalment of advance tax due at month-end; final push of AGMs before the fiscal year-end deadline; pre-New Year positioning
Baisakh
Mid-April to Mid-May
Nepali New Year; historically a month of renewed retail buying interest; companies begin planning for the coming AGM cycle
Jestha
Mid-May to Mid-June
National budget announced around Jestha 15; markets react to sector-specific tax and policy changes
Ashad
Mid-June to Mid-July
Fiscal year-end and book closure for annual accounts; final advance tax instalment due; rights issues and capital increases often rushed to meet year-end deadlines
KEY CONCEPT
The Nepali fiscal year runs from the first day of Shrawan to the last day of Ashad, roughly mid-July to mid-July of the following year. Almost every recurring financial event described in this chapter, the budget, book closure, AGMs, dividends, and tax deadlines, is anchored to this cycle rather than to the Baisakh-to-Chaitra calendar year that marks festivals and personal birthdays. Learning to think in fiscal years, not calendar years, is the first skill of a NEPSE investor.
Devi Prasad keeps a small notebook, the kind sold for ten rupees at any stationery shop, in which he has written out this twelve-month cycle by hand. Every Shrawan, he opens to a fresh page and writes the year's plan at the top. He does not need to guess when the AGM season will begin or when his tax instalment is due. He already knows, because the pattern repeats. The rest of this chapter walks through that same pattern, quarter by quarter, the way Devi Prasad experiences it every single year.
Lesson 113.2 — Shrawan and Bhadra: A New Fiscal Year, Monetary Policy, and Setting the Plan
For Devi Prasad, the first day of Shrawan feels a little like the first day of Baisakh feels for everyone else, except quieter. There is no tika, no new clothes, no family gathering. But inside the Ministry where he works, and inside every company listed on NEPSE, Shrawan 1 is when the books reset to zero. The old fiscal year's revenue and expense counters go back to nil, and a new twelve-month accounting period begins.
Within the first few weeks of Shrawan, Nepal Rastra Bank, the country's central bank, publishes its annual Monetary Policy statement. In plain language, this is the central bank's yearly instruction manual for how much money should flow through the banking system over the coming year, what interest rates commercial banks should broadly work within, how much capital banks must hold in reserve, and what limits apply to different kinds of lending, including margin lending against shares. Think of it as the central bank turning a large tap that controls how freely water, meaning credit and liquidity, flows into the economy. Turn the tap open a little more, and banks lend more freely, businesses expand, and often share prices respond well. Turn the tap tighter, and credit becomes scarcer, loan interest rates rise, and the stock market often cools.
Devi Prasad has learned to read the Monetary Policy statement not as a technical document for bankers, but as a weather forecast for the year ahead. If the policy signals a loosening of margin lending rules, meaning the rules that govern how much money investors can borrow against their existing shares to buy more shares, he expects more retail money to enter NEPSE in the following months, which often pushes prices for popular sectors like banking and hydropower upward. If the policy tightens capital adequacy requirements for banks, meaning it requires banks to hold more of their own safety cushion relative to what they lend out, he expects some banking shares to come under pressure, and he watches for signs that certain banks may need to raise fresh capital through a rights issue later in the year.
CASE IN POINT
A few years ago, the Monetary Policy statement in Shrawan tightened the rules on how much banks could lend against pledged shares. Devi Prasad noticed that several of his neighbours, who had borrowed heavily against their share portfolios to buy more shares, were forced to sell a portion of their holdings within weeks to meet the new margin requirements. Devi Prasad, who had never borrowed against his shares, was unaffected and used the resulting dip in prices to add a small position in a bank he had been watching. The lesson he draws from this every year is simple: read the Monetary Policy statement first, decide what it means for credit conditions, and only then decide whether it is a month to buy, hold, or wait.
Bhadra, the month that follows, is usually quieter on the surface, but it is when the real preparation work happens behind the scenes at listed companies. Auditors are going through the books. Finance departments are finalising the numbers that will eventually be presented to shareholders. A few companies with early accounting cycles begin issuing their first book closure notices in this period, though the bulk of that activity is still a few months away.
A book closure is a term every NEPSE investor must understand clearly, because so much of the annual calendar revolves around it. When a company decides to pay a dividend, meaning a share of its profit, in cash, in bonus shares, meaning additional free shares given out of the company's reserves, or both, it must first decide exactly who counts as a shareholder eligible to receive it. Since shares change hands every trading day, the company needs a fixed date to draw the line. It announces a book closure date, and whoever is registered as the owner of a share in the company's official shareholder ledger as of that date receives the dividend or bonus, regardless of whether they sell the very next day. Anyone who buys the share after that date misses out on that particular payment, even if they hold it for years afterward.
REGULATORY DETAIL
A book closure date is fixed by the company, published through NEPSE and the company's own notices, and typically falls at least a few days after the announcement to give the market time to react. Because Nepal's settlement cycle for share trades takes a short number of business days to complete, a buyer generally needs to purchase the share a few trading days before the book closure date itself in order to be registered as the owner in time. Waiting until the book closure date to buy is usually too late; the purchase will not settle in time to count. Investors who want a specific dividend or bonus should always check the exact settlement cutoff published alongside the book closure notice, rather than assuming the book closure date itself is the last day to buy.
For Devi Prasad, Shrawan and Bhadra are planning months, not action months. He rereads his notebook, checks which of his holdings reported strong or weak previous-year results, and makes a short list of companies whose book closure and AGM announcements he expects to watch closely later in the year. He compares this year's Monetary Policy statement to last year's, noting what has changed. He does not buy or sell heavily in these two months. He prepares, the way a farmer checks his tools and seed stock before the real planting season begins.
Lesson 113.3 — Ashwin and Kartik: Dashain, Tihar, and the Festival Liquidity Cycle
If Shrawan and Bhadra are quiet preparation months, Ashwin and Kartik are the two months when the ordinary rhythm of Nepali life visibly collides with the stock market. This is Dashain and Tihar season, the two largest festivals of the year, and both have real, measurable effects on how NEPSE behaves.
The first effect is on trading volume, meaning the total value or number of shares changing hands each day. In the weeks leading up to Dashain, many investors, especially those who also run small businesses or farms, pull money out of savings and out of shares to cover festival expenses: new clothes for children, meat and goods for family gatherings, gifts, and travel back to home villages. Devi Prasad has observed this pattern for fifteen years running. Trading volume on NEPSE tends to thin out in the fortnight before Dashain, and it is common to see several days of little movement as the exchange itself closes for the main festival holidays.
WARNING
A quiet, low-volume market in the days before Dashain is not usually a sign that something is wrong with the economy or with a particular company. It is simply the predictable effect of festival spending pulling cash out of the market temporarily. New investors sometimes panic when they see thin trading and falling prices in this period and sell at a loss, mistaking a seasonal lull for a genuine downturn. Before reacting to a quiet Ashwin or early Kartik market, first ask whether the calendar, not the company, explains what you are seeing.
The second effect runs in the opposite direction. Dashain and Tihar are also the two festivals when remittance inflows, meaning money sent home by Nepali workers abroad, typically peak. Families working in the Gulf countries, Malaysia, and elsewhere send extra money home so that their relatives can celebrate properly. Once the festivals pass and the immediate spending need is satisfied, a portion of this remittance money historically finds its way into savings, and a smaller portion into share purchases, particularly from Kartik into Mangsir. Devi Prasad thinks of this the way he thinks of monsoon rain filling a village pond: the rain, in this case the remittance inflow, arrives heavily around the festivals, some of it is used immediately for the fields, meaning festival expenses, and what remains slowly fills the pond that other activities, including share buying, can later draw from.
Tihar itself, following close behind Dashain in Kartik, brings its own short closure of the exchange for the main festival days, plus Laxmi Puja, when many households worship wealth and prosperity directly, a cultural moment that Devi Prasad half-jokingly calls the most appropriate day of the year to review one's investment portfolio, even though the exchange itself is closed.
It is also in Kartik that the first real wave of the AGM season begins. Some companies with earlier accounting cycles or faster audit completion start issuing book closure notices and AGM invitations as early as late Kartik, right after the festival dust settles. This is often the first genuine signal of the year that dividend season has begun, and Devi Prasad treats the first two or three AGM notices each year as an early indicator of how generous the overall dividend season is likely to be.
Sitting alongside the festivals, and easy to forget in the excitement of Dashain preparations, is one of the most important fixed deadlines of the entire fiscal calendar: the income tax return filing deadline, which falls at the end of Ashwin, roughly three months after the fiscal year closed at the end of the previous Ashad. Any investor who earned taxable income, including capital gains from selling shares or dividend income above certain thresholds, needs to be sure their tax affairs for the previous fiscal year are filed correctly by this date, or apply through the proper channel for an extension where one is available.
WARNING
Missing the Ashwin-end tax filing deadline can result in fines and interest charges from the tax office that are entirely avoidable with basic planning. Because share-related capital gains tax in Nepal is typically deducted at source by the broker or depository at the time of sale, many small investors assume they have nothing further to file. This is not always correct, particularly once other income sources are combined. Devi Prasad's habit is to gather every annual tax deduction certificate connected to his share transactions from his broker each Shrawan, so that by the time Ashwin arrives, the paperwork is already sorted rather than being assembled in a last-minute rush.
For Devi Prasad personally, Ashwin and Kartik are months of patience rather than action. He does not rush to buy into thin, quiet pre-Dashain trading, and he does not panic-sell either. He uses the festival lull to finish his own tax filing early, well before the crowd at the tax office builds up in the final week of Ashwin. He watches the first AGM notices that begin appearing in Kartik as an early weather signal for the dividend season ahead, jotting the details into his notebook, but he generally waits until Mangsir before making any real buying or selling decisions.
Lesson 113.4 — Mangsir through Magh: AGM Season, the Dividend Wave, and the Mid-Year Policy Review
If Ashwin and Kartik are the quiet, festival-heavy months, Mangsir through Magh is when the NEPSE calendar becomes genuinely busy, and it is Devi Prasad's favourite stretch of the year, because it is when the work of the previous months finally turns into cash in his bank account.
An Annual General Meeting, usually shortened to AGM, is the yearly meeting every listed company is legally required to hold, where the board of directors presents the past year's audited financial results to shareholders, proposes a dividend or bonus share distribution, and asks shareholders to vote on various resolutions, including sometimes the appointment of auditors or changes to company rules. Under the governing company law, a company must hold its AGM within a set period after its fiscal year-end, and in practice this pushes most AGMs into the window between Kartik and Falgun, with Mangsir, Poush, and Magh carrying the heaviest concentration.
REGULATORY DETAIL
Companies registered under the Companies Act are generally required to hold their Annual General Meeting within six months of the close of the fiscal year, meaning by roughly the end of Poush for a fiscal year that ended at the end of the previous Ashad, although extensions are sometimes granted through the regulator. SEBON, the Securities Board of Nepal, is the regulatory body responsible for overseeing securities markets, protecting investors, and ensuring listed companies follow proper disclosure practices, including timely AGMs and accurate dividend announcements. When a company delays its AGM well past this window, it is worth asking why, since a repeated pattern of delay can sometimes signal internal difficulties.
The mechanics work like this. First, the board proposes a dividend, whether in cash, in bonus shares, or a combination of both, based on the profits and reserves shown in the audited accounts. Second, the company announces a book closure date, the cutoff described in the previous lesson, fixing exactly who is entitled to receive that dividend. Third, the AGM itself is held, where shareholders formally approve the proposal, sometimes with adjustments. Fourth, once approved, the actual cash is credited to shareholders' bank accounts linked to their demat account, meaning their electronic share-holding account, and bonus shares are credited to the same demat account, typically within a defined period after the AGM.
Devi Prasad thinks of this whole sequence like a joint family's harvest festival. The crop, meaning the company's annual profit, is gathered and measured after Ashad. The family elders, meaning the board of directors, then decide how much of that harvest to distribute to each family member now, in this case as dividend, and how much to keep stored for next season's seed and hard times, meaning retained reserves. The formal family gathering where this division is announced and confirmed is the AGM. And just as in a real joint family, not every member receives an equal share; it depends on how many shares, meaning how large a stake in the family's collective land, each person holds.
PRACTICAL TOOL
Devi Prasad maintains a simple table in the back of his notebook with one row for every company he owns shares in. The columns are: company name, last year's book closure date, last year's AGM date, and last year's dividend or bonus percentage. Every year in Bhadra, before the new season of announcements begins, he updates this table with the previous year's actual dates and figures. This lets him roughly predict, within a few weeks, when each company in his portfolio is likely to announce its book closure this year, simply because most companies are creatures of habit and tend to repeat a similar schedule year after year.
Poush brings with it a second fixed deadline that has nothing to do with dividends directly but matters enormously to any investor with taxable income: the first instalment of advance tax. Taxpayers whose income is not fully covered by tax deducted at source are required to pay advance tax in three instalments across the fiscal year, calculated as a percentage of their estimated annual tax liability. The first instalment, covering forty percent of the estimated liability, falls due at the end of Poush. Devi Prasad, whose government salary is taxed at source but whose share-related income sometimes requires separate estimation, treats this deadline with the same seriousness as the Ashwin tax filing deadline, because penalties for underpayment can accumulate quietly over the year if ignored.
Magh brings the second major NRB event of the calendar: the mid-year review of the Monetary Policy. Roughly six months into the fiscal year, once half a year of actual economic data is available, Nepal Rastra Bank reviews its original Shrawan policy statement and adjusts it if circumstances have changed, tightening or loosening specific provisions based on how inflation, remittances, bank liquidity, and credit growth have actually behaved so far that year. Devi Prasad reads this review the same way he reads the original Shrawan statement, as an updated weather forecast, except now it is a mid-season correction rather than a full forecast, and it often has a sharper, more immediate effect on bank and finance company shares because it responds to real numbers rather than projections.
By the end of Magh, Devi Prasad's notebook usually shows several completed rows: dividends received, bonus shares credited, tax instalment paid, monetary policy review noted. This is the busiest and, for him, most rewarding stretch of the year, because it is when patient holding over the previous months converts into visible, bankable results.
Lesson 113.5 — Falgun through Chaitra: The Late AGM Rush, IPOs, Rights Issues, and the Second Tax Instalment
By the time Falgun arrives, the heaviest wave of AGMs has usually passed, but the season is not over. Companies that delayed their audits, or that simply have accounting cycles running slightly later, hold their AGMs in Falgun and into Chaitra, right up against the six-month regulatory deadline described earlier. Devi Prasad treats this period as the closing innings of the dividend season: fewer announcements arrive each week than in Mangsir, but they still matter, and he keeps checking his notebook of predicted dates so that no late book closure catches him unprepared.
Falgun and Chaitra are also, historically, the months when Devi Prasad sees the most activity in new share issuance, meaning IPOs, short for Initial Public Offerings, where a company sells shares to the public for the first time, and rights issues, where an already-listed company offers additional new shares to its existing shareholders, usually at a discounted price, in proportion to what they already hold, in order to raise fresh capital.
There is a practical reason so much of this activity clusters in the second half of the fiscal year rather than spreading evenly across all twelve months. Companies, particularly banks and financial institutions that must meet regulatory capital requirements, and hydropower companies that need funds to complete construction of a project, often plan their capital-raising to be completed and formally recorded before the fiscal year closes at the end of Ashad. Since the whole approval chain, board decision, regulatory clearance from SEBON, allotment, and listing, takes real time to complete, Falgun and Chaitra become the natural window to start that process if a company wants everything finished before Ashad-end. Waiting until Baisakh or Jestha to begin the same process risks missing the fiscal year-end deadline entirely.
For a rights issue specifically, an existing shareholder like Devi Prasad receives a formal notice of how many new shares he is entitled to buy, at what price, and by what deadline, based on how many shares he already holds on the relevant book closure date for the rights issue. He then applies to subscribe, in Nepal now most commonly done electronically through a system where a shareholder's linked bank account is used to apply, and if he does not apply within the deadline, his right to buy those discounted shares simply lapses; it is not automatically carried forward or refunded as shares.
CAUTION
A rights issue subscription deadline is unforgiving. Unlike a market order that can be placed at any time the exchange is open, a rights issue has one fixed closing date, and once it passes, the opportunity to buy those particular discounted shares is gone permanently for that issue. Devi Prasad once let a rights issue deadline pass by a single day because he was travelling for a family function and had not checked his messages, and he lost the chance to buy shares at a meaningful discount to the prevailing market price. His rule since then is to mark every rights issue deadline on the first page of that month's section in his notebook, not buried somewhere in the middle, precisely because Falgun and Chaitra are the months most likely to carry this kind of notice.
Chaitra also carries the second instalment of advance tax, covering up to seventy percent of the estimated annual liability, cumulative with the forty percent already paid at the end of Poush. Devi Prasad treats this the same way he treated the Poush instalment: a fixed date, not a flexible suggestion, with a real financial penalty attached to missing it.
As Chaitra draws to a close, Devi Prasad also begins a quieter kind of preparation, thinking ahead to Baisakh and the Nepali New Year. He does not treat the New Year as a market event in the way Dashain or the budget are market events, but he has noticed, over many years, that retail buying interest in NEPSE often picks up in the weeks around Baisakh, as investors treat the new year psychologically as a fresh start and put new savings to work. He does not chase this pattern blindly, but he keeps it in mind as he decides whether to hold cash in reserve heading into Baisakh or deploy it earlier while prices in Chaitra are sometimes comparatively quieter.
KEY CONCEPT
IPO and rights issue activity on NEPSE is not evenly spread across the twelve months of the fiscal year. It clusters heavily in the second half of the fiscal year, from Falgun through Ashad, because companies that need to raise or increase capital, particularly banks meeting regulatory requirements and hydropower companies funding construction milestones, aim to complete the process before the fiscal year closes at the end of Ashad. An investor who understands this pattern can budget cash reserves in advance for this period rather than being caught short when three or four new offerings appear within the same few weeks.
Lesson 113.6 — Baisakh through Ashad: New Year, the National Budget, and Closing the Books
The final stretch of the fiscal year, Baisakh through Ashad, brings the calendar full circle, ending exactly where the next year's Shrawan will begin.
Baisakh 1 marks the Nepali New Year, a cultural and personal milestone far more than a market event, but Devi Prasad has come to see it as a natural moment to review the whole portfolio from a fresh vantage point. He treats it the way many households treat New Year's Day itself: not a day to trade, but a day to sit down, look at everything acquired over the past year, dividends received, bonus shares credited, any new positions bought during rights issues, and honestly assess what worked and what did not.
Jestha is the month every serious NEPSE investor watches most closely of all, because this is when the Government of Nepal presents its national budget, usually on Jestha 15, roughly one month before the new fiscal year begins. The budget speech sets out the government's spending priorities, revenue targets, and, crucially for investors, any changes to tax policy affecting specific sectors. A change to the capital gains tax rate on shares, a new customs duty affecting imported raw materials for a manufacturing company, incentives announced for hydropower or tourism, or changes to the tax treatment of dividends can all move share prices within days of the budget speech, sometimes even within hours, as investors reprice their expectations for affected sectors.
Devi Prasad has learned to read the budget the way a farmer reads the first announcement of the government's fertiliser subsidy for the coming season: it tells him which crops, meaning which sectors, are likely to be favoured and which may face new costs. He does not act impulsively on budget day itself, when trading can be volatile and prices can overreact in both directions, but he reads the full budget document carefully over the following days, checking specifically for any provisions touching banking, hydropower, insurance, and manufacturing, the four sectors where his own holdings sit.
REGULATORY DETAIL
The national budget is presented by the Minister of Finance to the Federal Parliament, and while the exact date can shift slightly from year to year, it has for many years fallen on or around Jestha 15, roughly a month before the new fiscal year begins on Shrawan 1. Because the budget can alter tax rates, customs duties, and sector-specific incentives with effect from the new fiscal year, its announcement is one of the most closely watched single days on the entire NEPSE calendar, often producing sharper single-day price movements in specific sectors than almost any other scheduled event of the year.
Ashad, the final month of the fiscal year, is when everything comes to a close. Companies finalise their accounts for the year that is ending, several use this exact window to hold their own book closure for annual accounts purposes, and the third and final advance tax instalment, covering the remaining balance up to the full estimated liability, falls due at the very end of the month. It is also, historically, a month of last-minute rushes: banks and finance companies racing to complete a capital increase before the fiscal year-end deadline that regulators have set, and companies finalising any rights issue or bonus share process that needs to be reflected in the closing accounts.
CAUTION
Ashad is often the busiest single month of the fiscal year for regulatory deadlines arriving all at once: the final advance tax instalment, the fiscal year-end itself, and, in some years, capital increase deadlines for banks and financial institutions. Investors sometimes see unusual volatility in specific bank or finance company shares during Ashad as institutions rush to complete share allotments or capital transactions before the year closes. This is a predictable, recurring pattern tied to the calendar, not necessarily a signal of anything wrong with the company itself, though it is always worth checking the specific reason behind any last-minute announcement.
By the last week of Ashad, Devi Prasad's notebook for the year is nearly full. He totals up the dividends received, the bonus shares credited, the tax instalments paid across Poush, Chaitra, and Ashad, and the net change in the value of his holdings across the twelve months. He closes the notebook, and on the first day of the new Shrawan, he opens a fresh page and begins again, because the fiscal year, like the agricultural year his grandparents once lived by, does not truly end. It only turns over into the next cycle of the same recurring calendar.
Chapter recap
This chapter walked through a full Nepali fiscal year, from Shrawan to Ashad, as it actually unfolds for a NEPSE investor. Shrawan and Bhadra open the year with Nepal Rastra Bank's annual Monetary Policy statement and quiet preparation as companies finalise their previous year's accounts. Ashwin and Kartik bring Dashain and Tihar, festival-driven thinning of trading volume, a surge in remittance inflows, the first early AGM notices, and the fixed income tax return filing deadline at the end of Ashwin. Mangsir through Magh form the heart of the dividend season, with the bulk of Annual General Meetings, book closures, and dividend and bonus announcements, alongside the first advance tax instalment in Poush and NRB's mid-year Monetary Policy review in Magh. Falgun and Chaitra carry the late AGM rush, the busiest window for IPOs and rights issues as companies race to raise capital before the fiscal year closes, and the second advance tax instalment. Baisakh through Ashad close the loop, with the Nepali New Year, the national budget announcement in Jestha that can reshape sector-level tax expectations overnight, and the fiscal year-end itself in Ashad, complete with the final advance tax instalment and a last rush of capital transactions. Running through all of it was the disciplined, notebook-keeping habit of Devi Prasad Wagle, who treats this recurring calendar not as background noise but as the main structure around which every buying, holding, and selling decision is made.
Knowing this calendar answers the question of when things happen. The next chapter, Chapter 114, Communication Protocols — Broker, AGM, SEBON, answers the equally important question of how to act once you know the timing: how to communicate clearly and effectively with your broker when placing orders or resolving problems, how to participate meaningfully in an Annual General Meeting rather than treating the notice as a formality to ignore, and how to raise a complaint or query with SEBON, the securities regulator, when something about a company's disclosure or a broker's conduct does not seem right. Together, this chapter's calendar and the next chapter's communication protocols give an ordinary Nepali investor both the timing and the voice needed to participate in NEPSE with confidence.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 114
Communication Protocols — Broker, AGM, SEBON
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 114.1 — Why Communication Is a Skill Every Investor Needs
Sunita Tamang runs a cloth shop near Adarsh Nagar in Birgunj. For eleven years she has sold saree, kurta, and school uniforms to families across Parsa district. In 2019, on the advice of her nephew who worked in Kathmandu, she opened a demat account — a demat account is simply an electronic locker where your shares are kept, the same way a bank account is an electronic locker for your cash — and began buying shares of a few commercial banks and a hydropower company listed on NEPSE, the Nepal Stock Exchange.
Sunita is not an economist. She did not study finance in college. But over six years of investing, she learned something that most investment books never mention: knowing which shares to buy is only half the job. The other half is knowing how to talk — to your broker, to a company's management at its Annual General Meeting, and, when something goes badly wrong, to the regulator that oversees the whole market, the Securities Board of Nepal, known everywhere by its short name, SEBON.
Think of investing like farming a rented field. Choosing the right seed and the right season is one skill. But you also need to know how to speak to the landlord when the boundary is disputed, how to speak to the village council when water rights are unclear, and how to speak to the district agriculture office when you need a subsidy released. A farmer who plants well but cannot communicate when trouble comes will lose crops to arguments, not to weather.
This chapter teaches that second skill. It is organised around three relationships every NEPSE investor eventually has to manage:
First, the daily relationship with your stockbroker — the licensed firm through which you place buy and sell orders on the exchange. You cannot trade directly on NEPSE; every order must pass through one of the licensed brokers, identified by a broker number such as Broker 34 or Broker 61.
Second, the once-a-year relationship with the companies you own shares in, expressed through the Annual General Meeting, or AGM — the yearly gathering where a company's board and management report to shareholders and answer their questions.
Third, the relationship with SEBON, the government body that licenses brokers, regulates listed companies, and exists specifically to protect ordinary investors like Sunita when the first two relationships fail.
KEY CONCEPT
A broker executes your trades on NEPSE and charges a commission for it. A DP, or Depository Participant, is a separate but often overlapping service that keeps your shares in electronic form through CDSC, the Central Depository System and Clearing Limited. Many firms in Nepal offer both broker and DP services under one roof, which is why people use the words interchangeably — but when you complain about something, you must know which hat the firm was wearing, because the complaint process differs.
Sunita's own story will run through this whole chapter. You will see the day her broker's staff placed a sell order she never authorized, how she escalated it correctly and got it corrected within twelve days. You will see the question she asked at a hydropower company's AGM in Kathmandu that made the company's finance director visibly uncomfortable, and why that question mattered. And you will see the letter she eventually wrote to SEBON — not out of anger, but because she had learned that anger without a proper written complaint changes nothing, while a calm, well-documented complaint changes a great deal.
Communication in the stock market is not about being aggressive. It is about being precise, being calm, and putting things in writing at the right moments. That is the entire content of this chapter, and it applies whether you hold ten shares or ten thousand.
Lesson 114.2 — Talking to Your Broker: Questions That Get Answers
Most disputes between investors and brokers in Nepal do not start with fraud. They start with confusion, and confusion grows because the investor did not ask the right question at the right time, or did not get the answer in writing.
Think of your broker relationship like ordering vegetables from a wholesaler for your shop. You do not simply say "give me vegetables." You specify quantity, price, delivery date, and what happens if the vegetables arrive spoiled. A vague order invites a vague, and sometimes convenient-for-the-seller, response.
The same discipline applies to placing an order on NEPSE. When Sunita calls her broker's office, or logs into their TMS — Trading Management System, the online portal through which most retail investors in Nepal now place orders directly — she has trained herself to always confirm four things before an order goes in:
The exact scrip, meaning the short code the company trades under, such as NABIL for Nabil Bank or CHCL for Chilime Hydropower. Two companies can have similar-sounding names, and mixing them up is a common, costly mistake.
The exact price and quantity, stated as a number, never as "around" or "whatever is fair." NEPSE trading has a price band, an upper and lower limit each share can move in a single day, so vague instructions can be executed at a price far from what you intended.
The order type — whether it is a limit order, which only executes at your specified price or better, or a market order, which executes immediately at whatever price is available. Confusing these two is the single most common source of "I did not mean to sell at that price" disputes.
The settlement timeline, which in Nepal is T+2 — meaning the trade settles, and money or shares actually move, two working days after the transaction date. Knowing this prevents panicked calls asking "where is my money" one day after a sale.
PRACTICAL TOOL
Before placing any order by phone, say this sentence to your broker's dealer and ask them to read it back: "Buy/Sell [quantity] shares of [scrip code] at [price], limit order, confirm." Then ask for the order number. Write the order number, date, and time in a notebook. This thirty-second habit is the single strongest protection against disputes, because it converts a verbal instruction into a specific, checkable record.
Sunita keeps a physical notebook — the same kind she uses for her shop's credit accounts — next to her phone. Every order she places by phone, she writes down: date, time, scrip, quantity, price, and the order number the dealer reads back to her. When she places orders herself through the TMS app, the app generates its own record automatically, but she still notes the order number in her book, because TMS systems occasionally go down for maintenance and she has learned not to depend on any single system holding her history.
This is not paranoia. It is the same instinct that makes a shopkeeper keep a khata, a running ledger of credit given and received. No sensible shopkeeper in Nepal extends credit without writing it down, no matter how well she knows the customer. Extend that same instinct to your broker relationship.
Beyond placing orders, a good investor also asks a small set of standing questions at the start of the relationship, before any problem occurs:
What is your brokerage commission rate, and is it the same for buy and sell, or different? Commission rates are regulated with a ceiling set by SEBON, but brokers can charge less, and some negotiate lower rates for larger or more frequent traders.
How do I receive contract notes — the official receipt for each trade — and how long are they retained? A contract note is your proof of a trade's exact terms and must be issued for every executed order.
What is the process if I want to transfer my demat account to a different broker or DP? You are never locked into one broker for life, and knowing the exit process removes a source of anxiety that keeps some investors silent about problems for fear of "losing" their account.
WARNING
Never accept a purely verbal promise about your money or your shares as final. If a broker's staff tells you "your dividend will arrive next week" or "we will fix the mistake, don't worry," politely ask them to confirm it in writing — an SMS, an email, or a message on the TMS platform is enough. Verbal reassurance evaporates the moment a dispute becomes serious; written confirmation does not.
Sunita learned this lesson the hard way in 2021, which brings us to the central case study of this chapter.
Lesson 114.3 — When the Relationship Breaks: Escalating a Broker Dispute Properly
In March 2021, Sunita placed a phone order to sell 100 shares of a bank she had held for two years, at a limit price of 410 rupees per share. She wrote it in her notebook as always. Three days later, checking her Meroshare account — Meroshare is the online portal, run by CDSC, where investors see their share holdings and can apply for IPOs and right shares — she discovered that 150 shares had been sold, not 100, and at 395 rupees, not 410.
Her first instinct was anger. Her second, better instinct — one she had cultivated by then — was to write down exactly what she saw, print the Meroshare screen if possible, and call the broker's customer service line, not the individual dealer she usually spoke to.
This distinction matters enormously, and it is worth explaining as a general principle before returning to Sunita's case.
Every licensed brokerage firm in Nepal is required to have a customer service or grievance-handling point of contact separate from the individual trading floor staff who take your orders. Going first to the same dealer who may have made the mistake often produces defensiveness rather than resolution, because that individual has a personal incentive to minimise the error. Going to customer service, and asking for the matter to be logged with a complaint or reference number, creates an institutional record that the firm itself is obligated to track.
CASE IN POINT
Sunita called her broker's Birgunj branch customer service number and said: "On March 14, I placed an order for 100 shares of [bank] at 410. Your system shows 150 shares sold at 395. Please register this as a formal complaint and give me a complaint number." The staff initially tried to explain it away as "market movement." Sunita repeated the same two sentences calmly, without raising her voice, and asked again for a complaint number. She received one within the same call. That complaint number became the anchor for everything that followed.
If the branch-level customer service does not resolve a complaint within a reasonable time — most firms aim for three to seven working days for straightforward order-execution disputes — the next step is the firm's compliance officer. Every licensed broker in Nepal is required by SEBON's directives to designate a compliance officer, a senior staff member responsible for handling regulatory and investor-protection matters, distinct from ordinary sales or dealing staff. Asking specifically "may I speak with your compliance officer" is a phrase that signals you know the proper channel exists, and firms generally respond to it with more seriousness than to a general complaint.
In Sunita's case, the branch could not explain the discrepancy within a week. She then asked, by name of the position rather than a person, to escalate to the compliance officer at the firm's head office in Kathmandu. She sent a short written email — copying her complaint number, the order details from her notebook, and a screenshot of her Meroshare holdings — and asked for a written response within seven working days, which is a reasonable and common industry expectation to state explicitly in such a letter.
The compliance officer's investigation found that a dealer had accidentally merged Sunita's order with another client's similarly timed order due to a data entry error, executing an aggregated 150-share lot instead of two separate 100 and 50 share orders. The firm corrected her account, credited the price difference, and issued a written apology. The entire process, from the discovery of the error to its correction, took twelve working days — well within what she would have needed to escalate further, to SEBON itself.
Step
Where the Complaint Goes
Typical Timeline to Expect
Step 1
Broker's branch or customer service desk — state facts, request a complaint number
3 to 7 working days for acknowledgment and initial response
Step 2
Broker's designated compliance officer, in writing, referencing the complaint number
7 to 15 working days for a written resolution or explanation
Step 3
SEBON's investor grievance channel, only if Steps 1 and 2 fail or the firm is unresponsive
SEBON typically seeks a response from the firm within 15 to 30 days of receiving a complaint, though complex cases can take longer
REGULATORY DETAIL
SEBON does not want to be your first stop for every trading disagreement — it wants brokers to resolve straightforward errors themselves, and it can penalise a firm found to be ignoring legitimate internal complaints. Approaching SEBON only after Steps 1 and 2 also strengthens your case, because you can show the regulator a documented, dated trail proving you gave the firm a fair chance to fix the problem itself.
Sunita never needed Step 3 for this particular dispute — but as you will see in Lesson 114.5, she did need it two years later, for a different and more serious matter involving a delayed dividend payment.
The broader lesson of this section is simple. A dispute resolved well is rarely resolved through the loudest voice in the room. It is resolved through a documented sequence: facts written down at the time they happened, a complaint number obtained early, escalation through the proper internal channel, and a reasonable, explicitly stated timeline given to the other side before you take the next step.
Lesson 114.4 — Finding Your Voice at the AGM
An Annual General Meeting, or AGM, is the one formal occasion each year when a listed company's board of directors and senior management must stand in front of ordinary shareholders and answer questions. In Nepal, companies are required to hold their AGM within a set period after their fiscal year ends, and must give shareholders written notice — typically at least 21 days in advance — stating the date, venue, and agenda.
Many small investors in Nepal never attend an AGM. Some feel it is only for large shareholders or for people who understand accounting. Some assume their one vote, tied to a small shareholding, cannot possibly matter next to a promoter family holding a controlling stake. Both of these assumptions are only partly true, and neither is a good reason to stay silent.
Think of an AGM like a village tole gathering about the shared irrigation canal. Even a farmer with the smallest plot has a right to ask why the canal was diverted, why maintenance funds were spent the way they were, and to hear the answer in front of everyone else who depends on the same water. The answer given in public, to one person's question, benefits every other shareholder listening — including the ones who never spoke at all.
KEY CONCEPT
Book closure is the date a company fixes its list of shareholders for a specific purpose — usually to determine who receives a dividend, bonus share, or right share, or who is entitled to attend and vote at the AGM. If you sell your shares before the book closure date for an AGM, you lose your right to attend and vote at that particular meeting, even if you held the shares for the entire preceding year.
Sunita began attending AGMs in 2020, initially just to understand what happened at one, sitting quietly in the back row of a hotel hall in Kathmandu where a hydropower company held its meeting. By 2023, she had learned to prepare a single, focused question in advance rather than arriving with vague curiosity, because vague questions get vague answers, and a crowded AGM hall gives you perhaps one real opportunity to speak.
Her preparation routine, which she now recommends to other small investors from Birgunj who travel up for AGMs together, follows a simple pattern:
Read the annual report before the meeting, especially the auditor's notes and any section describing related-party transactions — deals between the company and its own directors, promoters, or their other businesses. These are the sections where problems most often hide, precisely because they are the least glamorous pages and most shareholders skip them.
Pick one specific number that seems unusual, rather than asking a broad question like "how is the company doing." A specific number invites a specific answer; a broad question invites a rehearsed, general reassurance.
Write the question down in one or two sentences beforehand, so that nervousness in the room does not turn a sharp question into a rambling one.
CASE IN POINT
At a hydropower company's AGM, Sunita had noticed in the annual report that the company's receivables — money owed to it, in this case mostly by the state utility that purchases its electricity — had grown much faster than its revenue that year. She stood and asked: "Our receivables grew by around 40 percent while revenue grew by about 12 percent. Can management explain the reason for this gap, and when this amount is expected to be collected?" The finance director's answer, that a portion of the increase was due to a tariff dispute still under negotiation, was new information not clearly stated anywhere in the annual report itself. Several other shareholders in the hall began asking follow-up questions on the same point once she had opened it.
This example illustrates the deeper value of AGM participation. Sunita's question did not accuse anyone of wrongdoing. It simply asked management to explain a number that any careful reader of the annual report could have noticed. The answer she received became useful information for every shareholder in that hall, and arguably for every shareholder who later read the meeting minutes, which listed shareholders in Nepal are entitled to request from the company.
A few practical points of AGM etiquette matter here, because how a question is asked affects whether it gets a genuine answer:
Identify yourself briefly — your name and, if you wish, your city — before asking your question. This is normal practice and signals that you are a serious, engaged shareholder rather than a heckler.
Ask about the company's numbers and decisions, not about personal matters concerning the directors. A question about a related-party loan is fair; a personal accusation is not, and will likely be ruled out of order by the chairperson.
If your question is not fully answered in the room, it is entirely acceptable to ask, politely, that the company respond in writing afterward, and to request that the response be recorded in the AGM minutes.
WARNING
Do not mistake a loud, confrontational tone for a strong question. Nepali AGMs, like most formal gatherings, are chaired by a person with authority to limit or close off disorderly conduct. A shareholder who shouts often gets removed from the discussion procedurally, while a shareholder who asks a sharp, calm, specific question is far harder to dismiss and far more likely to get a complete answer recorded in the minutes.
Attending an AGM costs a day's travel and time, which is a real cost for a shop owner like Sunita. But she has come to see it as part of the return on her investment, in the same way that visiting a rented-out property occasionally is part of being a responsible landlord, even when the tenant pays rent reliably every month.
Lesson 114.5 — Writing to SEBON: The Complaint That Actually Gets Read
SEBON, the Securities Board of Nepal, is the government regulator responsible for overseeing the securities market — brokers, listed companies' disclosure obligations, merchant bankers, and the exchange itself. Its role, in plain terms, is closest to a district administration office that a citizen approaches when a local dispute cannot be resolved through ordinary channels. SEBON does not replace the courts, and it does not adjudicate every private disagreement, but it has real authority to investigate licensed market intermediaries and listed companies, and to penalise them for violations.
Understanding what SEBON can and cannot help with saves an investor enormous frustration.
Type of Problem
Is SEBON the Right Address
Better or Additional Address
Broker delayed executing your order or made an execution error, and would not resolve it after Steps 1 and 2
Yes, after internal escalation is exhausted
None needed beyond SEBON
Dividend declared by a company but not credited to your bank account for months
Yes, SEBON can direct the company and registrar to explain the delay
Also contact the company's share registrar directly, as delays are often administrative
Suspected insider trading or price manipulation in a scrip
Yes, this is a core SEBON surveillance responsibility
NEPSE's own surveillance department can also be alerted
A private dispute over inheritance of shares between family members
No, this is a civil law matter
District court or a lawyer specialising in succession matters
Company's annual report omits or misrepresents required disclosures
Yes, SEBON enforces listed company disclosure rules
The Office of the Company Registrar for company law matters
CAUTION
SEBON's jurisdiction covers licensed market participants and listed companies acting in their capacity as such. It is not a forum for resolving family property disputes, employment disagreements at a listed company, or general consumer complaints unrelated to securities transactions. Sending SEBON a complaint clearly outside its authority delays the attention available for complaints it can actually act on.
Sunita's SEBON complaint arose two years after her broker dispute, in a different situation entirely. She had held shares in a company that declared a cash dividend at its AGM, with a board resolution date and a promised payment timeline stated in the AGM minutes. Five months passed, and the money never arrived in her bank account, despite the dividend having been publicly declared and despite other shareholders in her informal investment group in Birgunj confirming they had also not received theirs.
She followed the same disciplined sequence she had learned from her broker dispute, adapted to this new situation:
First, she contacted the company's share registrar — the entity, sometimes an in-house department and sometimes an outside registrar firm, responsible for actually processing dividend payments to the shareholder list. She asked, in writing by email, for the specific reason for the delay and a specific date by which payment would be made.
When the registrar's reply was vague — citing unspecified "banking process delays" with no committed date — she and four other shareholders from her group decided to write to SEBON together, since a complaint representing several affected shareholders, all citing the same company and the same delayed dividend, carries more evident pattern and weight than one individual's letter.
PRACTICAL TOOL
A SEBON complaint letter should be short and structured, not emotional or lengthy. Include: your full name, citizenship number, and demat account number; the company or broker name and its registration or scrip code; the specific dates of the events in order; copies of any prior correspondence with the company or broker, including their responses if any; and a clear, single-sentence statement of what you are asking SEBON to do. End with your contact number and address. A one-page letter with attachments is far more likely to be acted on quickly than a five-page letter narrating every feeling involved.
Their letter to SEBON stated the AGM dividend declaration date, the promised payment timeline from the AGM minutes, the date the registrar was contacted, the vague response received, and a request that SEBON direct the company to state a firm payment date and confirm compliance with dividend distribution timelines. They attached the AGM minutes excerpt, the registrar email exchange, and a shared list of the affected shareholders' demat account numbers.
REGULATORY DETAIL
Complaints to SEBON can be submitted in writing to its office, and SEBON has also developed online and telephone-based investor grievance channels to make filing easier for investors outside Kathmandu. A complaint should always be dated and, where possible, retained with a receipt of submission or an acknowledgment number, exactly as with the broker-level complaint discussed in Lesson 114.3.
Within about three weeks, SEBON's inquiry to the company produced a response: the delay had in fact been caused by an internal reconciliation problem in the company's dividend disbursement bank file, unrelated to any dispute over the dividend itself, but the company had been slow to communicate this to shareholders. The dividends were processed within the following month, and the company subsequently improved its practice of proactively notifying shareholders of any disbursement delays.
This case illustrates the real, practical value of SEBON's complaint mechanism — it does not require proving fraud or wrongdoing to be useful. It exists for exactly this kind of situation: a legitimate administrative failure that a company was not treating with urgency until a regulator asked it to explain itself in writing.
REGULATORY DETAIL
NRB, the Nepal Rastra Bank, is the central bank and regulates banks and financial institutions as businesses — their capital adequacy, lending practices, and licensing. SEBON regulates those same banks' behaviour as listed companies on the stock exchange — their disclosures, dividend practices, and securities issuances. A complaint about a bank's loan or account service belongs with NRB; a complaint about a bank's conduct as a listed company, such as delayed dividends or disclosure problems, belongs with SEBON. Knowing this distinction saves you from sending a letter to the wrong regulator entirely.
Lesson 114.6 — Building a Reputation as a Reasonable, Informed Shareholder
There is a quieter lesson underneath everything covered in this chapter, and it is worth stating directly. Every broker, every company's investor relations staff, and even SEBON's own officers deal with a large volume of complaints and questions. Over time, they learn to recognise which correspondents are careful, factual, and fair, and which are not. An investor who develops a reputation for calm, specific, well-documented communication finds that their concerns get taken seriously faster, every single time, than an investor who is known for exaggeration or hostility.
This is not different from how reputation works in a joint family or a small business community. A shopkeeper in Birgunj who always pays her wholesaler on time, and who raises a genuine quality complaint clearly and fairly when it happens, gets faster, more generous treatment over the years than one who complains constantly about small things or who is known to shout. The wholesaler starts to trust that when this particular shopkeeper raises an issue, it is real and worth addressing quickly.
Sunita has noticed the same pattern with her broker's Birgunj branch. Staff there now recognise her voice on the phone and know she keeps records, asks for order numbers as routine, and only escalates when genuinely necessary. This has, if anything, made her ordinary day-to-day dealings smoother, not more adversarial, because the branch knows that a careless mistake with her account will be caught and documented quickly, so they are more careful with her orders from the start.
KEY CONCEPT
Investor communication, done well, is preventive as much as it is corrective. A broker who knows a client checks contract notes carefully is less likely to make careless errors with that client's account in the first place. A company that knows its shareholders read the annual report closely and ask about the numbers at AGMs tends to write clearer, more complete disclosures the following year. Good communication habits change the other side's behaviour before any dispute even arises.
A few closing habits worth adopting, drawn directly from the practices described across this chapter:
Keep a simple physical or digital notebook of every trade order, every AGM attended with the questions you asked, and every written complaint you have sent, along with dates and reference numbers. This single habit underlies every successful resolution described in this chapter.
Always move from informal conversation to written communication at the point a matter becomes a genuine disagreement, not before, and not long after. Moving too early makes every small misunderstanding feel like a formal dispute; moving too late means valuable time and evidence is lost.
Treat every AGM as an opportunity, not an obligation. Even one well-prepared question a year, asked calmly and specifically, makes you a more informed shareholder and often benefits every other shareholder in the room.
Know the boundary of SEBON's authority before writing to it, so that your complaint reaches the right desk the first time, and use the escalation sequence — broker branch, then compliance officer, then SEBON — in that order, giving each step a fair and explicitly stated amount of time to respond.
None of this requires financial expertise. It requires patience, a notebook, and the willingness to write things down clearly and calmly at the moment they happen, rather than relying on memory or emotion later. Sunita Tamang did not become a better investor in the technical sense through any of these episodes — she did not learn a new valuation formula or a new chart pattern. She became a better investor in the practical sense, the sense that actually protects a family's savings over decades: she learned to make sure that when something went wrong, it got noticed, documented, and corrected, instead of being absorbed silently as an unavoidable cost of participating in the market.
Chapter recap
This chapter treated communication as a core investing skill rather than a side matter, using Sunita Tamang, a cloth shop owner in Birgunj, as a guide through three relationships every NEPSE investor must manage. With her broker, the key lesson was precision and record-keeping: confirming scrip, price, quantity, and order type before every trade, obtaining a complaint number the moment something goes wrong, and escalating in the proper order from branch customer service to the firm's compliance officer before ever approaching the regulator. With a company at its AGM, the key lesson was preparation: reading the annual report in advance, identifying one specific, well-chosen number to question, and asking it calmly and clearly, since a single sharp question benefits every shareholder in the room. With SEBON, the key lesson was knowing the boundary of its authority and writing a short, structured, evidence-backed complaint rather than a long emotional one, as Sunita and her fellow shareholders did successfully over a delayed dividend. Underlying all three relationships is the same discipline: write things down at the time they happen, move to written communication at the right moment, and remain calm and factual, because a reputation for reasonable, documented communication produces faster and fairer treatment over years of investing.
Chapter 115, Multi-Generational Wealth Building Through NEPSE, turns from these day-to-day communication skills toward a longer horizon. It will examine how a Nepali family can use NEPSE investing not just to grow one person's savings, but to build and transfer wealth across generations — covering how joint family shareholding is typically structured, how demat accounts and nominations work when a shareholder passes away, how parents can begin investing on behalf of children, and how the habits of patience, documentation, and informed participation described in this chapter become even more valuable when the investments in question are meant to outlive the investor who first bought them.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 115
Multi-Generational Wealth Building Through NEPSE
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 115.1 — Thinking in Generations, Not Just in Years
Dilip Prasad Sharma bought his first NEPSE shares in 1994, the year Nepal's stock market opened its trading floor to ordinary citizens instead of only to a handful of large institutions. He was a schoolteacher in Biratnagar then, forty-six years old, earning a modest government salary, and he put a small amount of savings into a development bank's shares because a colleague told him it was "safer than hiding money under the mattress." Dilip is ninety-one this year. His granddaughter, Ashmita Sharma, is thirty-one, an engineer working in Kathmandu, and she now sits beside her grandfather twice a month to review the family's NEPSE holdings together. Between them sits Dilip's son and Ashmita's father, Prakash Sharma, a bank branch manager in Biratnagar who inherited some of his father's caution about risk but almost none of his father's patience for reading annual reports. Three generations, one portfolio, one long story. This chapter is built around their family, because the questions they have faced are the same questions almost every NEPSE investing family in Nepal eventually faces.
Most investing books, and most investing habits in Nepal, are built around a single lifetime. You save, you invest, you hope the value grows, and you spend it in your old age. That is a useful way to think about your own money. But it is an incomplete way to think about your family's money, especially in a country where joint family structures are still common, where land and shares are often held with the unspoken understanding that they belong to the family rather than to one person, and where three generations frequently share a single household or at least a single set of financial decisions.
Think of a farmer preparing a field. If he only wants a harvest for himself this season, he plants what grows fastest. If he wants his grandchildren to eat from the same land, he also plants fruit trees that will not bear fruit for ten or fifteen years, and he digs an irrigation channel that will still be carrying water long after he is gone. NEPSE investing can be treated the same way. Some shares you buy for near-term needs — a daughter's wedding in five years, a house renovation. Other shares, and more importantly the habits and the systems around those shares, you plant for a harvest you yourself may never personally collect. A blue-chip bank share bought today and held with care can still be paying dividends to your grandchildren in 2060. But only if the "irrigation channel" — the legal and family systems that carry ownership from one generation to the next without leaking or breaking — is properly built.
Multi-generational wealth building through NEPSE, then, is not really about picking better shares than everyone else. It is about answering four practical questions, each of which we will unpack across this chapter.
First, how do you involve the next generation early enough that they are ready to manage the portfolio, rather than inheriting it as strangers to it. Second, what legal tools exist in Nepal today — particularly the nomination facility inside your Meroshare demat account — that make the handover simpler when the time comes. Third, what actually happens, step by step, when a shareholder dies and the shares must move to the family. Fourth, how a joint family can make investment decisions together without the process turning into resentment or conflict, and without one generation's inheritance later becoming ground for a family feud.
KEY CONCEPT
Multi-generational wealth building means treating your NEPSE portfolio as something built to be handed down in an orderly way, not just an account you personally use during your lifetime. The goal has two equal parts: growing the money, and making sure it moves cleanly to the next generation.
Dilip did not think in these terms in 1994. Nobody handed him a book like this one. He learned the hard way, as we will see later in this chapter, when his own father's shares became difficult to transfer after his father's death. That hard lesson is exactly why Dilip, forty years later, has already filled in every nomination form his broker and his demat account allow, and why he insists that Ashmita attend every family portfolio review rather than being told about the shares only after he is gone.
Lesson 115.2 — Bringing the Next Generation Into the Room Early
There is an old habit in many Nepali households: money matters, especially investment matters, are discussed only among the senior male members of the family, often behind closed doors, and children — sons and daughters alike — are told almost nothing until they need to be told something, usually because a parent or grandparent has died. This habit made some sense when investing was rare, when most family wealth was land and gold held quietly, and when very few people understood shares at all. It makes much less sense today, when NEPSE has hundreds of thousands of demat account holders and when financial literacy is something every young person can learn on their own phone.
Ashmita's own introduction to the family's NEPSE holdings did not happen when she inherited anything. It happened when she was nineteen, still an engineering student, and her grandfather asked her to help him check his Meroshare account because his eyesight was failing and he found the portal's small text difficult to read. Meroshare is the online portal operated by CDS and Clearing Limited, the company that maintains Nepal's electronic register of who owns which shares, that lets a shareholder view holdings, apply for new share issues, and manage account details without visiting an office in person. What began as Ashmita simply reading numbers off a screen for her grandfather slowly became something more. She started asking why he held twelve different company shares instead of just two or three. She started asking what a dividend was, why some companies gave bonus shares instead of cash, and why the price of a hydropower company's shares moved so much after the monsoon compared to the dry season. Dilip, delighted to have an audience, explained patiently, the way he once explained fractions to schoolchildren.
By the time Ashmita finished university and started her own job, she already had her own small demat account, opened with her grandfather's help, holding a handful of shares she had chosen herself after watching him for years. She was not inheriting a stranger's portfolio. She was continuing a habit she already understood.
Contrast this with Prakash, Dilip's son. Prakash grew up in a household where his father invested quietly and rarely explained his reasoning, partly because in the 1990s and 2000s there was less to explain — the market was thinner, information was harder to get, and Dilip himself was still learning. Prakash inherited a general sense that shares were "his father's hobby" rather than a family responsibility. He is comfortable with his bank job and his fixed deposits, and he still finds his father's portfolio a little intimidating, even now, in his fifties. This is not a criticism of Prakash. It is simply what happens when one generation is not brought into the process early. The skip does not have to repeat itself with the next generation, which is exactly why Dilip made a deliberate choice to include Ashmita from a young age, to close the gap that had opened between himself and his son.
There are concrete, practical ways any Nepali family can do what Dilip eventually did on purpose.
Start by simply narrating decisions out loud, the way you might explain to a child why you water certain plants in the vegetable patch more than others. When you decide to buy shares of a commercial bank because it has paid a steady dividend for ten years, say that reasoning aloud in front of your children or grandchildren, even if they are only twelve or fourteen years old. When you decide to sell a share because the company's fundamentals have weakened, explain that too. Children absorb financial habits the same way they absorb language — through repeated exposure, not through a single lecture.
Second, consider opening a small demat account in a minor's name, held and operated by a parent or guardian as required under the rules, so that even a young family member has some shares that are, in spirit, "theirs," with real money and real outcomes attached, however small. Watching ten thousand rupees of shares rise and fall teaches a teenager more about patience and risk than any classroom lesson.
Third, once a family member becomes an adult and opens their own demat account through Meroshare, treat that as a milestone worth a family conversation, not just a paperwork exercise. Sit down together and review, in plain language, what a BOID is — the Beneficiary Owner Identification number, a unique code that identifies a person's demat account, similar in spirit to a bank account number but specifically for share ownership — and how it connects to their citizenship documents and bank account for dividend payments.
Fourth, hold regular, scheduled family portfolio reviews rather than only urgent, crisis-driven conversations. The Sharma family settled into a rhythm of meeting on the first Saturday of every month, a short session, often no more than forty-five minutes, where Dilip, Prakash, and Ashmita go through the portfolio together: what shares are held, what dividends have arrived, what news has come out about the companies they hold, and whether any action is needed. This simple, boring, repeated ritual is doing more for the family's multi-generational wealth than almost any single investment decision they have made.
PRACTICAL TOOL
A simple family portfolio review agenda, usable by any household: one, list every share currently held and its current price. Two, note any dividends or bonus shares received since the last meeting. Three, discuss any company news that might affect a holding. Four, decide together on any buying, selling, or nomination updates needed. Five, end by asking the youngest present what they learned. Keep it under an hour.
The purpose of bringing the next generation in early is not merely education for its own sake. It is risk management. A portfolio understood only by one aging person is fragile. If that person becomes ill, loses mental clarity, or dies suddenly, decisions must be made by people who do not know why any particular share was bought, what price it was bought at, or what the family's broader goals were. A portfolio understood by three generations, even imperfectly, survives the loss of any one member far better.
Lesson 115.3 — The Nomination Facility: The Simplest Tool You Are Probably Ignoring
Here is a plain, important fact that many NEPSE investors in Nepal do not act on even though it costs nothing and takes only a few minutes: every demat account opened through Meroshare allows the account holder to name a nominee, a person designated in advance to receive the shares in that account if the account holder dies. This is called the nomination facility. It is one of the single most useful and most underused tools available to any Nepali investing family.
Think of the nomination facility like the address label you write on a parcel before sending it through the postal service. Without a label, the parcel might still eventually reach the right person, but only after clerks open it, check its contents, ask questions, and try to work out where it belongs, at the cost of considerable time and paperwork. With a clear label already attached, the parcel moves through the system quickly, because everyone along the way already knows exactly where it is meant to go. A completed nomination form is that address label for your shares.
Nomination is different from a will, and different from ordinary inheritance law, and it is worth being precise about the difference because confusion here causes real problems for families later.
KEY CONCEPT
Transmission of shares means moving ownership of shares from a deceased person's demat account to their legal heirs or nominee. This is different from transfer of shares, which is the ordinary buying and selling of shares between two living parties on the exchange. Transmission happens once, after a death, and follows its own separate procedure through the depository participant rather than through normal NEPSE trading.
A nominee named in a Meroshare account is, broadly, the person the depository system will recognise as entitled to receive the shares after the account holder's death, which can make the transmission process considerably faster and simpler, because the identity of the intended recipient is already on record. However, nomination inside CDS and Clearing Limited's system operates within the framework of Nepal's broader inheritance and succession law, and family members with a legal claim under that law are not automatically cut out simply because someone else's name sits in the nominee field. In practice, this means nomination is best understood as a powerful administrative shortcut that dramatically eases the process, not as a replacement for a proper family understanding, and ideally a will, about how the shares are ultimately meant to be shared among heirs.
REGULATORY DETAIL
The nomination facility for demat accounts is administered by CDS and Clearing Limited (commonly called CDSC), the central depository that operates the Meroshare portal under regulations set by SEBON, the Securities Board of Nepal, which is the government body responsible for regulating Nepal's securities market. An account holder can typically add or update a nominee from within their Meroshare profile settings, or by submitting the relevant form through their depository participant, which is usually the brokerage or bank branch where the demat account was opened.
Dilip filled in his nomination form for the first time only in 2016, more than twenty years after he began investing, and he did it because of what had happened three years earlier with his own father's estate, an episode we will walk through in detail in the next lesson. He named Prakash as his primary nominee, since Prakash is his only son, but he made a point of telling both Prakash and Ashmita, in front of each other, exactly what he had done and why, so there would be no confusion or suspicion later about hidden intentions.
There are a few practical points every Nepali investor should act on immediately after reading this chapter, regardless of age or portfolio size.
Check whether a nominee is currently listed on your demat account at all. Many older accounts, opened years ago when the nomination facility was newer or less emphasised, still have no nominee recorded. If you have never explicitly filled in a nomination form, assume none exists.
If you already have a nominee listed, check whether that information is still correct. Family situations change. A nominee named fifteen years ago may have since passed away, moved abroad permanently, or the family relationship may have shifted in ways that make a different family member the more sensible choice today.
Make sure your bank account details linked for dividend payments, and your citizenship and contact details on file with your depository participant, are current. A returned dividend cheque or a bounced electronic payment because of an outdated bank account is a small but entirely avoidable headache that multiplies during an already stressful time after a death.
Tell your family, in plain words, that you have completed this step. A nomination filled in secretly and never mentioned to anyone provides only half its value. The other half of its value comes from removing uncertainty and anxiety among family members while you are still alive to reassure them.
WARNING
A nomination form filled in once and forgotten is not the same as a nomination form kept current. Life changes — remarriage, a nominee's death, migration abroad, estrangement — can all make an old nomination outdated or even inappropriate. Review your nominee details at least once every few years, and immediately after any major family change.
Lesson 115.4 — When a Shareholder Dies: Walking Through Transmission Step by Step
In 2013, Dilip's father, Tul Bahadur Sharma, passed away in the small town outside Biratnagar where he had lived his whole life. Tul Bahadur had bought a modest number of shares in his seventies, in the early 2000s, after Dilip persuaded him that a bank fixed deposit alone was not doing enough to protect his savings from inflation. Tul Bahadur understood shares only vaguely, trusted his son's judgment, and never filled in any nomination form, largely because at that time bank staff rarely explained the option clearly and Tul Bahadur, in his old age, was not inclined to chase down paperwork he did not fully understand.
When Tul Bahadur died, his shares sat in a demat account with no nominee on record. What followed took the Sharma family nearly eight months to resolve, and it is worth walking through exactly why, because most families discover these obstacles only when they are already grieving and already under pressure, which is the worst possible time to be learning a new bureaucratic process.
Without a nominee, the depository participant — the brokerage firm through which Tul Bahadur's demat account had been opened — required the family to establish, through documents, exactly who Tul Bahadur's legal heirs were and in what shares they were entitled to inherit his property. This meant obtaining a death certificate from the local ward office, which is the local government administrative unit in Nepal responsible for registering births, deaths, and issuing various local certificates. It meant obtaining what is commonly called a legal heir certificate, sometimes called a relationship certificate or "naata pramanit patra" in Nepali, also issued by the ward office, which formally lists who the deceased person's legal heirs are according to family relationship — spouse, children, and so on. It meant collecting citizenship documents for every listed heir, and in Dilip's family's case, because Tul Bahadur had more than one surviving child, it meant getting written consent or a formal relinquishment from Dilip's sister, who lived in Kathmandu, confirming how the shares would be divided among the siblings, since disagreement among heirs at this stage can stall the entire process indefinitely.
Every one of these documents had to be gathered, often requiring multiple visits to the ward office, sometimes with small clerical errors in a name or date requiring a document to be reissued. Then the full set had to be submitted, along with a formal application, to the depository participant, which forwarded it to CDS and Clearing Limited for the shares to actually be transmitted into the names of the recognised heirs. Only after this was complete could the family decide, together, whether to keep the shares, sell them, or divide them among themselves.
Document
What It Is
Where to Get It
Death certificate
Official record confirming the date and fact of death
Ward office where the death was registered
Legal heir certificate (naata pramanit patra)
Official document listing the deceased's recognised legal heirs and their relationship to the deceased
Ward office of the deceased's permanent residence
Citizenship certificates of all heirs
Proof of identity for every person named as an heir
Individually held by each heir; reissued at District Administration Office if lost
Relationship proof or family registration document
Supporting evidence connecting the heirs to the deceased, sometimes required alongside the heir certificate
Ward office; sometimes supplemented by a marriage certificate or birth certificate
Application to the depository participant
Formal request asking the broker to process transmission of the shares
Depository participant (the brokerage or bank branch holding the demat account)
Consent or relinquishment letter from co-heirs
Written agreement among heirs on how shares are to be divided, especially when more than one heir exists
Drafted by the family, often with notarization or ward office attestation
Original share certificates or demat account statement
Proof of the actual shareholding being claimed
Depository participant or CDS and Clearing Limited records
CASE IN POINT
When Tul Bahadur Sharma died without a nominee on his demat account, his family needed nearly eight months and several rounds of ward office visits to complete transmission of his shares, partly because no nominee was listed and partly because multiple heirs needed to formally agree on the division. Watching this process firsthand is exactly what convinced his son Dilip to complete his own nomination form immediately afterward.
Compare this to how Dilip has arranged his own affairs today. He has a nominee listed and confirmed with the depository participant. He has told both his son and his granddaughter in plain terms what the nomination says. He has kept his citizenship documents, his Meroshare login details, and a simple written note about which broker holds his account, together in one folder that both Prakash and Ashmita know the location of. When the time eventually comes, and Dilip is realistic that it will not be long now, given his age, the family expects the process to take weeks rather than months, largely because a clear nominee already exists on record, reducing the burden of establishing consensus among multiple heirs before the depository will act.
Situation
What Typically Happens
Typical Time Required
Nominee clearly listed and details current
Depository participant processes transmission to the nominee relatively quickly once the death certificate and basic documents are submitted
Generally a matter of weeks
No nominee listed, single clear heir
Family must obtain a death certificate and legal heir certificate before the depository will act, even with only one heir
Roughly one to a few months
No nominee listed, multiple heirs
Family must additionally agree in writing on division of shares among all heirs, and any disagreement can stall the process significantly
Often several months to over a year
It is worth being honest that even with a nominee, the process is not instant, and it is not entirely free of paperwork. A death certificate and some basic verification will always be required, because a nominee is a designated recipient, not a person who can bypass every safeguard meant to confirm that a death genuinely occurred and that the claimant is genuinely who they say they are. But the difference between "weeks" and "over a year," between one visit to a broker's office and eight months of ward office paperwork during a period of grief, is entirely the difference the nomination facility makes.
PRACTICAL TOOL
Keep one simple physical folder, known to at least two family members, containing your citizenship certificate copy, your Meroshare login username, the name of your depository participant and broker, a note of your BOID, and confirmation of who your nominee is. This single folder can save your family months of confusion.
Lesson 115.5 — Joint Family Decision-Making Without Losing Harmony
Nepal's joint family structure, where grandparents, parents, and adult children often share a household, or at least share close financial ties even when living separately, can be either a great strength or a great source of tension when it comes to investing. The strength is obvious: more eyes watching the portfolio, more hands available to handle paperwork, shared resources for larger investment opportunities, and the natural transmission of financial habits across generations that we discussed earlier in this chapter. The tension is equally obvious to anyone who has watched a family argue over money: different generations often have very different appetites for risk, different levels of financial literacy, and sometimes different, unspoken assumptions about who has the final say.
Think of joint family financial decision-making like several farmers sharing one irrigation channel that runs through all their fields. If everyone agrees in advance on how much water flows to whose field and when, the system works smoothly and every field gets watered fairly across the season. If there is no agreement, and each farmer simply takes water whenever he feels his field needs it most, the channel runs dry for someone downstream, resentment builds, and eventually somebody blocks the channel out of frustration. Family investment decisions work the same way. Clear, agreed rules about who decides what, and how disagreements get resolved, keep the "water" — the money and the trust — flowing fairly to everyone.
The Sharma family's monthly review meeting, described earlier, is one part of their system. But a meeting alone is not enough if it is not clear who actually has authority over which decisions. Over time, the Sharma family settled into an informal but workable arrangement, roughly as follows.
Dilip, as the founder of the family portfolio and the person whose name is on most of the older holdings, retains the final say over decisions involving his own core holdings while he is alive, out of simple respect and because it is legally his money. But he has explicitly, verbally and in writing to his son and granddaughter, invited both of them to challenge his decisions and ask questions, rather than treating disagreement as disrespect. Prakash, who is more conservative by temperament, has taken on the role of handling the practical administrative side — renewing citizenship documents, keeping records organised, dealing with the bank for dividend payments — even though he participates less in choosing which shares to buy. Ashmita, who has the most time, the most comfort with technology, and the most recently updated financial knowledge, does the research: reading company annual reports, checking NEPSE announcements, and bringing recommendations to the monthly meeting for the other two to discuss and approve together.
This division of labor did not happen automatically. It happened because the family talked about it explicitly rather than letting roles form by default or by assumption. A frequent source of trouble in Nepali households, and certainly not unique to the Sharma family, is the assumption that because one member is older, or male, or the eldest son, that person automatically has full and permanent authority over all financial decisions, regardless of who actually has the time, the interest, or the updated knowledge to make good decisions. This assumption, left unexamined, can quietly push capable family members, very often daughters and daughters-in-law, out of decisions that affect them directly.
CAUTION
Under Nepal's current inheritance law, daughters have equal inheritance rights alongside sons; this is a meaningful shift from older customary practice that sometimes favoured sons in the informal handling of family property. Families that continue to make investment and inheritance decisions as though only sons count are not only acting unfairly, they are setting up their own arrangements for legal challenge later.
A successful example from the Sharma family's own history illustrates how joint decision-making, done well, can outperform any single person's judgment. In early 2020, as news of a global pandemic began reaching Nepal and NEPSE's index fell sharply over a period of a few weeks, Prakash panicked and wanted to sell a large portion of the family's bank and hydropower shares immediately, worried that prices would keep falling indefinitely. Dilip, drawing on decades of having seen NEPSE fall and recover multiple times before, including during periods of political instability in the 2000s, argued for patience. Ashmita, rather than simply picking a side, suggested they look together at the actual businesses behind the shares: were the banks still collecting deposits and processing loans, was electricity still being generated and sold by the hydropower companies, had anything about the underlying businesses actually broken, or had only the price fallen. Together, they concluded the businesses were fundamentally intact, panic was driving the price fall more than any lasting business damage, and they agreed to hold rather than sell, with Ashmita additionally proposing they use some spare cash to buy a small additional quantity of a strong bank's shares at the lower price. Within about a year, the shares had not only recovered but risen well above where they had stood before the fall, and the family's decision to buy more during the dip added meaningfully to their long-term holdings.
What made this moment work was not that any one person was right and the others wrong. It was that three different temperaments and three different kinds of knowledge — Dilip's long memory, Prakash's caution, and Ashmita's willingness to dig into details — were all allowed into the room, and the family had already built the habit, through years of monthly meetings, of listening to one another rather than one person simply overruling the rest.
KEY CONCEPT
Good joint family investment decision-making is not about everyone agreeing instantly. It is about having an agreed process — regular meetings, clear roles, and a habit of listening to every generation's perspective — so that disagreements get resolved through discussion rather than through one person's authority or through silent resentment.
Lesson 115.6 — Preventing Disputes Over Inherited Shares
Even families that manage their living investments well can fall apart over inherited shares after a death, and it is worth being direct about why this happens so often, because prevention is far easier than repair once a dispute has hardened into a grudge.
The most common cause of dispute is simple lack of information. If only one family member knew the full details of what shares existed, at what value, and where the account records were kept, then after that person's death, other heirs are left guessing, and guessing breeds suspicion. A sibling may wonder whether another sibling quietly sold some shares before informing the rest of the family, even when nothing improper occurred, simply because nobody had visibility into the account while the deceased was alive.
The second most common cause is unequal treatment, whether real or perceived, especially between sons and daughters, or between a child who stayed close to the parents and a child who moved away, often for work, sometimes abroad. Nepal has an enormous number of families with at least one member working overseas, in the Gulf countries, Malaysia, or elsewhere, sending remittances home. A family member abroad who was not present during a parent's later years, but who is legally entitled to an equal share of inheritance, can easily feel shut out of decisions made entirely by those who stayed behind, even when those decisions were made with good intentions.
The third common cause is simply the absence of any prior conversation about intentions. A parent may have quietly intended certain shares to eventually support a specific grandchild's education, or may have meant certain holdings to be split evenly, but if this was never said aloud or written down, the family is left reconstructing intentions from fragments after the fact, and different family members will often reconstruct different, self-serving versions of what they believe was meant.
There are practical steps any Nepali family can take now, while everyone involved is alive and reasonably calm, that dramatically reduce the odds of a bitter dispute later.
Hold at least occasional family conversations, not only about the mechanics of the portfolio, but about intentions. If a grandparent wants certain shares eventually used for a grandchild's education, or wants the portfolio split in a particular way among children, saying this clearly, in front of the people affected, while everyone can still ask questions and register any objection calmly, prevents an enormous amount of later conflict.
Put important intentions in writing, ideally alongside proper legal advice about wills and succession under Nepali law, rather than relying purely on verbal promises remembered differently by different people afterward. A will, even a simple one, prepared with appropriate legal guidance, is not a sign of distrust among family members. It is a gift to them, sparing them from having to guess or argue about what a deceased person "would have wanted."
Keep records transparent while the account holder is alive. There is no good reason, in most families, for adult children to be kept entirely in the dark about what shares exist, especially once they are old enough to be trusted with financial information generally. Transparency during life is the best inoculation against suspicion after death.
Treat daughters and sons, and family members near and far, according to their actual legal entitlement and according to explicitly discussed family agreements, not according to old habits of favouring whoever is physically present or male. A family that quietly assumes only sons will inherit shares, without ever revisiting that assumption in light of current law and current fairness, is inviting a dispute that a court, if it ever reaches one, is unlikely to resolve in favour of that old assumption.
When disagreement does arise among heirs, seek to resolve it through calm family discussion first, involving a respected elder or a neutral family friend as a mediator if needed, before it hardens into a legal dispute. Court processes over inherited property in Nepal can take years and can cost far more, in both money and family relationships, than almost any dispute is worth. Many disputes that end up in court began as a misunderstanding that thirty minutes of honest conversation, at the right time, could have resolved.
WARNING
Disputes over inherited shares very rarely start with the shares themselves. They usually start with a feeling of being uninformed, undervalued, or unfairly treated, which then attaches itself to whatever asset is being divided. Preventing the feeling prevents most of the dispute, regardless of how the shares themselves are eventually split.
Dilip has taken this lesson seriously in his own planning. He has told Prakash and Ashmita, together, in the same conversation, exactly how he intends his remaining shares to be divided between his son and his daughter in Kathmandu, has confirmed this intention matches what a lawyer has advised him is consistent with proper succession practice, and has made sure both his son and his daughter have heard this directly from him rather than secondhand. He does not expect this guarantees perfect harmony after he is gone. But he has given his family the clearest possible starting point, built on information rather than guesswork, which is the most any one person can really do.
CASE IN POINT
By explicitly discussing his intended division of shares with both his son and his daughter together, rather than separately or not at all, Dilip Sharma removed the single biggest cause of family disputes over inheritance: the feeling that decisions were made in secret. Whatever disagreements his children may still have, they will not include confusion about what their father actually intended.
Chapter recap
This chapter looked at NEPSE investing across generations rather than across a single lifetime, using the Sharma family of Biratnagar and Kathmandu as a working example. We saw why bringing children and grandchildren into portfolio discussions early, the way Dilip Prasad Sharma did with his granddaughter Ashmita, builds a family that understands its own holdings rather than inheriting them as strangers. We looked closely at the nomination facility available through Meroshare and CDS and Clearing Limited, a simple but underused tool that can turn a months-long transmission process into a matter of weeks, illustrated by the contrast between Tul Bahadur Sharma's difficult, undocumented estate and Dilip's own carefully arranged nomination. We walked step by step through what transmission of shares after a death actually requires in Nepal, including the death certificate, the legal heir certificate, and the documents listed in this chapter's tables. We examined how a joint family can make investment decisions together, dividing roles according to time, temperament, and knowledge rather than according to age or gender alone, and we saw this approach succeed during the market shock of 2020. Finally, we looked at why disputes over inherited shares happen, almost always rooted in a lack of information or a feeling of unfair treatment, and at the concrete steps — transparency, written intentions, equal treatment under current law, and calm family conversation — that prevent those disputes before they start.
The next chapter, Chapter 116, "Teaching the Canon — Sharing Knowledge Responsibly," moves from your own family to the wider circle of newer investors you may find yourself mentoring, whether a younger colleague, a neighbour, or a relative outside your immediate household. It will address how an experienced investor like Dilip, or increasingly Ashmita, can share hard-earned NEPSE knowledge generously and honestly, without creating an unhealthy dependency where a newer investor simply waits to be told what to buy, and without crossing the line into giving advice that properly belongs to a licensed professional. Teaching well, it turns out, requires almost as much discipline as investing well.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 116
Teaching the Canon — Sharing Knowledge Responsibly
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 116.1 — Why Teach at All
Sanu Maya Rai has been buying and selling shares on the Nepal Stock Exchange for nineteen years. She teaches biology at a secondary school in Dharan, and every Saturday morning, six or seven neighbours gather in her small front yard with tea and notebooks. Some are retired army pension holders. Some are young cousins who just opened their first demat account. She calls it, half-jokingly, her "Saturday Sabha" — sabha meaning gathering or assembly, a word every Nepali knows from ward meetings and school functions.
Sanu Maya did not set out to become a teacher of investing. It happened the way most informal teaching happens in Nepal — someone asked her a question at a wedding, she answered it well, and word spread. Soon her phone was full of messages: "Didi, should I buy this share?" "Dai's friend told me to buy NIC Asia, is that good?" "My son is asking me to send money for IPO, is it safe?"
This chapter is about what Sanu Maya learned by trial and error over nineteen years of answering those questions — and about the invisible line she had to learn to walk. That line runs between teaching someone to think, which is a gift, and telling someone what to do, which is a risk both to them and to you.
Think of financial knowledge the way you would think of a skill passed down in a family trade. A carpenter's teacher does not carve every piece of furniture the apprentice will ever make. The teacher shows how to read the grain of the wood, how to hold the chisel, how to know when a joint is weak before it breaks. After that, the apprentice makes their own furniture — some pieces beautiful, some flawed, all of them theirs. If the teacher carved every piece for the apprentice, the apprentice would never become a carpenter. They would only ever be a pair of hands waiting for instructions.
Investing knowledge works the same way. If you tell your cousin "buy 50 shares of Nabil Bank tomorrow at 10 AM," you have not taught your cousin anything. You have simply moved the decision from your cousin's head into yours, and if the trade goes wrong, the disappointment — and sometimes the blame — moves back onto you. Worse, your cousin learns nothing except that decisions belong to other people. This is how financial dependency starts in families and communities, and it rarely ends well.
KEY CONCEPT
Teaching means transferring a way of thinking. Advising means making a decision for someone else. A mentor's job in personal finance is almost always teaching, not advising — because advising carries legal weight, financial risk, and emotional debt that most informal helpers are not equipped to carry.
There is also a real regulatory reason to be careful here, not just an ethical one.
REGULATORY DETAIL
In Nepal, only entities licensed by SEBON (the Securities Board of Nepal, the government body that regulates the stock market) may operate as registered investment advisors or portfolio managers under the Securities Businessperson Regulations. An ordinary investor — even a very experienced one — giving specific buy-or-sell recommendations to others for compensation, or in a way that creates a pattern of clients relying on that person's calls, risks operating as an unlicensed advisor. Free, general, one-off conversation among friends is normal and legal. Repeated, personalized, directive advice — especially if money or gifts change hands — moves into a grey zone SEBON has flagged as a public concern in past investor-awareness notices.
Sanu Maya's rule, refined over years, is simple: she will teach anyone who wants to learn, for free, for as long as they want to keep coming to the Saturday Sabha. But she will not tell anyone what to buy. Not her sister, not her best student, not even her own son. "I show them how to fish," she tells new arrivals to the group, borrowing the old saying everyone in Nepal has heard in some form. "I do not hand them the fish. If I hand them the fish today, they come back hungry tomorrow, and the day after, and one day I am not here to hand them anything, and they have learned nothing."
This chapter will walk through how she does this in practice: how to teach principles instead of picking stocks, how to gently correct the herd behaviour and tip-chasing habits that are extremely common in Nepali investing circles — at the tea shop, in the office lunchroom, in family WhatsApp groups — and how to know where your responsibility as an informal mentor ends and where a person's own responsibility for their own money must begin.
Lesson 116.2 — The Difference Between Teaching and Advising
Let's make the distinction from Lesson 116.1 completely concrete, because in the heat of a real conversation — especially with someone you love, who is anxious about money — the line gets blurry fast.
Imagine water flowing down from the hills into a village. A good irrigation teacher shows farmers how to build channels, how to read the slope of land, how to know when the monsoon will come and when it won't. The farmers then decide, field by field, how much water to divert to their own land, based on their own crop, their own soil, their own family's needs. A bad irrigation "helper" simply grabs the shovel and digs the channel to one farmer's field for him — and now that farmer never learns to read the land himself, and blames the helper if the channel silts up.
Teaching NEPSE investing works the same way. You can teach the shape of the land — how the market moves, what a price-to-earnings ratio means, why dividends matter, how NRB's monetary policy affects bank liquidity and therefore share prices — without ever telling a specific person to put their specific rupees into a specific company on a specific day.
Teaching Principles (Safe, Appropriate)
Giving Personalized Advice (Risky, Inappropriate for Informal Mentors)
Why the Difference Matters
"A price-to-earnings ratio compares a share's price to the company's profit per share — here is how to calculate it and what a high or low number might suggest."
"Buy Company X because its P/E ratio is low right now."
The first builds a permanent skill. The second is a single bet made on your authority, not theirs.
"Many people panic-sell when the NEPSE index drops sharply. Here is why that historically has often hurt more than helped."
"Don't sell your shares, hold on, trust me, it will recover by next month."
The first teaches a pattern to recognise for life. The second is a specific market-timing prediction you cannot actually guarantee.
"Diversification means not putting all your money in one sector, the way a farmer doesn't plant only one crop in case of one disease. Here is how to think about spreading risk."
"Sell your hydropower shares and move everything into this microfinance company."
The first is a risk-management principle usable in any market, any year. The second is a specific reallocation instruction with real financial consequences you'd be responsible for suggesting.
"IPOs (Initial Public Offerings, when a company first sells shares to the public) are allotted partly by lottery in Nepal — here is how the process works and what to expect."
"Apply for this IPO, I heard it will list high."
The first explains a public, verifiable mechanism. The second is a rumour dressed as a tip, exactly the herd behaviour this book warns against.
"Here is how to read a company's annual report and find the debt-to-equity figure."
"This company's management are good people, I know them, just trust them."
The first is a transferable research skill. The second substitutes your personal trust for the person's own diligence.
Notice the pattern in every row of that table. The safe column teaches a method the learner can reapply forever, on any company, in any year, without you present. The risky column hands over a conclusion the learner cannot reproduce or verify on their own — which means next time, they will need you again, or someone else's tip again.
WARNING
A common trap for generous, knowledgeable mentors is answering the question the person actually asked ("should I buy this?") instead of the question they should have asked ("how do I decide what to buy?"). Redirecting the conversation from the first question to the second, every single time, is the single most important habit of responsible informal teaching.
Sanu Maya has a specific phrase she uses at the Saturday Sabha whenever someone asks her a direct "should I buy" question. She says, in Nepali, "Ma tapaiko paisa ko jimmedar hoina" — "I am not responsible for your money." She does not say it coldly. She says it warmly, almost like a proverb, and then she immediately follows it with "but let's work out together how you would decide this yourself." That single sentence does two things at once: it protects her from being blamed later, and it protects the learner from becoming dependent on her.
Lesson 116.3 — A Worked Example: Teaching Bishnu Without Overstepping
Let's follow one real thread through Sanu Maya's Saturday Sabha, because abstract principles land better with a concrete story.
Bishnu is her husband's younger cousin, twenty-six years old, working at a mobile phone shop in Dharan bazaar. He came to the Sabha for the first time after his monthly salary finally let him save enough to open a demat account (the electronic account required to hold NEPSE shares, replacing the old paper share certificates). He arrived with a single question: "Sanu Maya didi, everyone at my shop is talking about a company. They say buy now before it goes up. Should I buy?"
Here is what Sanu Maya did not do. She did not say yes or no. She did not look up the company's price on her phone and give an opinion on the number. She did not say "I bought that one too, it's fine."
Here is what she did instead, step by step, over about twenty minutes:
First, she asked Bishnu what he actually knew about the company — not the price, but the business. What does it make or do? Is it a bank, a hydropower producer, an insurance company, a manufacturing company? Bishnu did not know. This told Sanu Maya the real problem was not which stock to pick, but that Bishnu had absorbed the habit of following talk instead of forming his own judgment.
Second, she taught him — using only publicly available information anyone can pull up on the NEPSE website or through a broker's app — how to find three basic things about any listed company: its recent profit trend over the last three to five years, its dividend history (whether and how much it has paid shareholders in cash or bonus shares), and its sector (banking, hydropower, insurance, manufacturing, hotels, and so on).
Third, she explained sector concentration risk using an analogy Bishnu understood instantly, because his own family are tea garden workers near Dharan: "If your whole family's income depends only on one tea garden, and that garden has a bad season — drought, pest, whatever — your whole family suffers together. If some of you also work in town, or farm vegetables, one bad season somewhere doesn't sink the whole family. A share portfolio is the same. If you only own hydropower shares, one dry winter with low water flow can hurt all of them at once, because they share the same weakness."
Fourth — and this is the important part — she never told Bishnu what to buy. At the end, Bishnu still had to decide for himself whether to buy the company his shop coworkers were discussing. What changed was that he now had a method: check the profit trend, check the dividend history, check what sector it's in and whether he already owns other things in that sector, and only then decide with his own money.
Three weeks later, Bishnu came back and reported, a little sheepishly, that he had looked up the company his coworkers were excited about and found it had reported declining profits for two straight years. He decided not to buy. Nobody told him not to. He worked it out himself, using a method someone had taught him. That is the entire goal of responsible mentorship in one small story.
CASE IN POINT
Bishnu's story shows the test of good teaching: three weeks later, he made a decision alone, using a method, without needing to ask Sanu Maya "should I buy this?" again for that specific situation. If he had still needed to ask her, the teaching would have failed, no matter how correct her original answer might have been.
Notice also what Sanu Maya avoided: she never disparaged Bishnu's coworkers by name, never called them foolish, and never made Bishnu feel embarrassed for almost following the crowd. She simply gave him a better tool. This matters enormously in a small-city, close-knit context like Dharan, where the people giving bad tips at the phone shop are also Bishnu's daily coworkers and possibly his friends. A mentor who makes a student feel ashamed of their social circle creates conflict in the student's daily life long after the mentor has gone home.
Lesson 116.4 — Correcting Herd Behaviour Without Wounding Pride
Herd behaviour — the tendency to buy or sell because everyone around you is doing it, rather than because of your own analysis — is probably the single most common bad habit an informal mentor in Nepal will encounter. It shows up everywhere: tea shop conversations where one loud voice names a stock and three others nod along, office lunch tables where a colleague's "sure thing" tip spreads department to department, and family gatherings — a wedding, a bratabandha, a puja — where a relative just back from a good trade holds court while everyone listens.
Herd behaviour feels good in the moment because it is social. Buying what everyone else is buying makes you feel like part of the group, not alone with your decision. This is precisely why it is hard to correct — you are not just correcting a financial mistake, you are asking someone to feel comfortable standing apart from people they see every day.
Think of it like buying vegetables at the haat bazaar, the weekly open market. If you see a long queue at one vegetable seller's stall, your instinct is that the vegetables must be fresher or cheaper there, so you join the queue too — even without checking the price or the quality yourself. Sometimes the queue is long because the vegetables really are good. But sometimes the queue is long simply because it started forming and people kept joining without checking. The stock market's herd behaviour is exactly the same instinct, just with rupees instead of radish.
KEY CONCEPT
Herd behaviour is not stupidity — it is a completely normal human shortcut for making decisions under uncertainty, by copying others instead of gathering your own information. Correcting it works best when you validate the instinct as understandable, and then offer a better shortcut, rather than criticising the person for having the instinct at all.
Sanu Maya's approach to correcting herd behaviour has three steps she has repeated for years, refined after some early attempts that did not go well. Early on, she once told a Saturday Sabha member bluntly, "You're just following the crowd, that's not investing." The woman did not come back for two months, and when she returned, she admitted she had felt scolded in front of the group. Sanu Maya learned from that.
Her current approach:
Step one: acknowledge the social pressure out loud, without judgment. "It's very natural to feel that if five people around you are buying something, they must know something you don't." This takes the sting out of the moment — the person does not feel accused of being foolish, because the mentor has just described a universal human tendency, not a personal flaw.
Step two: ask a curious question rather than issuing a correction. Not "why are you following the crowd," which sounds like an accusation, but "what do those five people actually know that made them buy — did they check the company's numbers, or did they hear it from someone else who heard it from someone else?" Very often, when someone traces the tip backward, they discover it is a rumour with no origin anyone can name — the financial equivalent of a story that changed each time it was retold at the tea shop, until nobody remembers who started it or whether it was ever true.
Step three: replace the herd's authority with a checkable fact. Instead of arguing against the crowd's opinion, she hands the person one specific, verifiable thing to check — the company's latest quarterly report, its dividend history, its price movement over the past year compared to the whole NEPSE index. A fact the person can check themselves is far more persuasive than any argument the mentor can make, because it produces the person's own conclusion instead of asking them to trade one authority (the crowd) for another (the mentor).
PRACTICAL TOOL
When someone tells you "everyone says buy this," ask them one question in return: "What is the one number or fact, from the company itself or from NEPSE's own disclosures, that made them say that?" If no one can answer, the tip has no foundation. This single question, asked gently and without sarcasm, is often enough to make a person pause on their own — you don't need to argue against the tip at all, just ask where it came from.
There is one more layer to correcting herd behaviour gently in the Nepali context specifically: face, or ijjat. Publicly contradicting someone's investment decision at a family gathering, in front of others, can feel to them like a loss of face, especially if the person who gave the original tip is older or more senior. Sanu Maya handles this by never correcting anyone in the moment, in front of the group that pressured them. She waits, and raises it privately, one-on-one, sometimes days later over tea, framed as her own curiosity rather than a correction: "I was thinking about what your uncle said the other day — has anyone actually looked at the company's report?" This preserves everyone's dignity while still doing the real work of teaching.
Lesson 116.5 — Boundaries: What a Mentor Should Never Do
Every trade or craft passed informally from an experienced person to a beginner has certain boundaries the teacher must never cross, precisely because crossing them harms the student, not just the teacher. A driving instructor should never grab the wheel and drive the car themselves while calling it a "lesson." A cooking teacher should never simply hand a finished dish to a hungry student and call it teaching. In investing mentorship, the boundaries are just as concrete, and Sanu Maya has hard rules for each one.
Rule one: never manage another person's account, password, or trading decisions directly. If a mentor logs into a friend's TMS (the Trading Management System used by NEPSE brokers to place buy and sell orders online) and places trades on the friend's behalf, the mentor has stopped teaching and started operating as an unlicensed portfolio manager — exactly the line SEBON regulation draws. Even with good intentions, and even for free, this exposes both people to real risk: if the trade loses money, the friendship carries the loss; if it gains money, the friend never learns anything except to ask again next time.
Rule two: never accept money, gifts, or commission tied to a specific recommendation. If Sanu Maya ever accepted even a small "thank you" gift explicitly linked to a stock tip that worked out — say, someone bringing her mustard oil or a sari because "your suggestion made me profit" — she would be edging toward compensated advisory activity without a license, and she would also be creating an incentive to tell people what they want to hear rather than what is true. She happily accepts tea, dinner invitations, and general goodwill for running the Saturday Sabha as a whole, since this is ordinary community reciprocity, not payment for a specific financial recommendation.
REGULATORY DETAIL
SEBON's investor protection guidance repeatedly emphasises that individuals should verify whether anyone offering investment advice is registered, and warns the public against unregistered persons or groups — including social media personalities and unlicensed "investment gurus" — who solicit fees, commissions, or gifts in exchange for specific buy/sell calls. Community mentors who keep their role clearly educational, and refuse any compensation tied to outcomes, stay outside this concern entirely.
Rule three: never predict specific prices or specific timing. "This will hit 800 rupees by Dashain" is a prediction no one can honestly make, no matter how experienced they are, and if it turns out wrong, the mentor's credibility and the student's trust in the entire process — not just in that one prediction — can collapse together. Sanu Maya teaches students to think in ranges and scenarios, never in confident single numbers: "if the company keeps growing profit at this rate, and if the sector stays stable, here is roughly what a reasonable valuation might look like" — always qualified, always showing the reasoning, never delivered as a bare prophecy.
Rule four: never diagnose or manage someone else's overall financial life without qualification. A person's investing decisions do not exist in isolation — they are tangled up with debt, family obligations, insurance needs, retirement timelines, and risk tolerance that only a licensed financial planner is trained to assess holistically. If a mentee says "I want to take a loan against my house to buy shares," this is far beyond stock-picking advice — it touches debt risk, family security, and legal obligation. Sanu Maya's answer to this kind of question is always the same: "That is a bigger decision than I can help you with fairly. Talk to your family, and consider a proper financial advisor or your bank's loan officer, before doing anything with the house." She resists the pull to feel flattered that someone trusts her judgment on something this large, because that flattery is exactly what leads mentors past their depth.
CAUTION
Watch for the moment a mentee's question moves from "how do markets work" to "what should I do with my life savings, my loan, my retirement, my child's education fund." That shift is a signal to redirect the person toward a licensed professional — a SEBON-registered advisor, a bank's financial officer, or in complex cases a chartered accountant — rather than answering from personal opinion, however well-meant.
Rule five: never let a teaching relationship become a source of guilt or blame. This is subtle but important. If a mentee loses money after a conversation with you — even a conversation where you carefully only taught principles and never gave a specific instruction — some people will still, emotionally, associate the loss with you, especially if you are a respected elder or relative. Sanu Maya handles this by stating her boundary out loud at the very first meeting with any new person, not after something goes wrong: "I will teach you everything I know. I will never tell you what to buy. Whatever you decide, the result is yours — good or bad — because the decision was always yours." Saying this early, before any money has changed hands or any trade has happened, protects both people from a painful conversation later.
WARNING
A mentor who waits until after a loss to say "I never told you to buy that" will sound like they are dodging blame, even if it is technically true. Establishing the boundary before any advice is exchanged — as a stated ground rule of the relationship — is far healthier than invoking it defensively afterward.
Lesson 116.6 — Building a Healthy Teaching Circle, Not a Dependent One
The final piece of responsible teaching is thinking about the shape of the group itself, not just individual conversations. A teaching circle can be built in a way that creates independent thinkers, or it can be built — even accidentally, even with good intentions — in a way that creates a cluster of people who all just wait for the mentor's opinion before acting. Sanu Maya has watched both patterns happen in Dharan over the years, in her own group and in others, and she has strong opinions about what separates them.
A dependent circle has a single centre. Everyone brings questions to one person, that person answers, and the group's only real activity is receiving answers. Over time, the members become less capable, not more, because every capable action — checking a report, questioning a rumour, deciding to buy or not buy — has been quietly outsourced to the centre of the circle. When that central person is unavailable — busy, unwell, or simply gone one Saturday — the whole group is lost, because nobody in it has practiced deciding for themselves.
A healthy circle has many centres. Members bring questions to the group as a whole, and the mentor's job shifts from answering to facilitating other members answering each other. Sanu Maya has deliberately cultivated this at her Saturday Sabha over the years. When a new question comes up — "what do people think about this company's latest results" — she often does not answer first. She asks who in the group has already looked at the report. She asks the newest member what they think before the most experienced member speaks, so newer voices are not simply drowned out by the ones people are used to deferring to. She has, over nineteen years, watched three or four of her early students become confident enough to run their own small study circles elsewhere in Dharan and even in Itahari — which she counts as her biggest success, not a loss of her own group's members.
PRACTICAL TOOL
A simple test for whether a teaching circle is healthy: ask what would happen to the group's decision-making quality if the most experienced person disappeared for six months. In a healthy circle, the group would slow down but keep functioning, because knowledge and habits of independent checking are distributed across many members. In a dependent circle, the group would effectively stop functioning, because all judgment was concentrated in one person.
There is also a broader community responsibility worth naming here, beyond any single circle. Nepal's investing culture is young compared to markets with a century or more of history, and NEPSE itself has grown enormously in the number of ordinary retail investors — including many first-time investors from remittance-earning families, newly employed young people, and retirees relying on pension lump sums. Informal peer teaching, of the kind Sanu Maya does every Saturday and of the kind countless unnamed people do at tea shops and family gatherings across the country, is quietly one of the most important forces shaping whether this new generation of investors becomes financially literate or becomes rumour-driven. A responsible mentor understands they are not just helping one cousin or one neighbour — in aggregate, thousands of small, careful, boundary-respecting conversations like the ones described in this chapter are what stand between a healthier NEPSE culture and one dominated by tips, panic, and herd stampedes.
This is, in the end, the deepest reason to teach principles instead of picking stocks for people. A stock tip helps one person for one trade. A well-taught principle — how to read a report, how to question a rumour, how to sit with uncertainty instead of chasing the crowd — travels. It travels from Sanu Maya to Bishnu, and someday, if the pattern holds, from Bishnu to someone younger who asks him the same anxious question he once asked her: "Should I buy this?" And if Bishnu answers the way he was taught, the chain continues, one careful conversation at a time, long after any single teacher is gone.
KEY CONCEPT
The measure of successful informal mentorship is not how many good tips you gave, but how many people no longer need to ask you for tips at all — because they learned to think the problem through themselves.
Chapter recap
This chapter examined the responsibilities and boundaries of informal financial mentorship within Nepali communities — the tea shop conversations, family gatherings, and neighbourhood study circles where investing knowledge naturally spreads from experienced investors to newer ones. Using Sanu Maya Rai's Saturday Sabha in Dharan as a recurring example, the chapter distinguished teaching principles (methods anyone can reapply independently, such as reading a profit trend or questioning where a tip originated) from giving personalized advice (specific buy-or-sell instructions, price predictions, or account management that create dependency and carry real regulatory weight under SEBON's rules for licensed advisors). It walked through a concrete example of teaching a young relative to evaluate a stock himself rather than following coworker rumours, and a method for gently correcting herd behaviour — acknowledging the social pressure, asking where a tip actually came from, and replacing crowd authority with a checkable fact — without wounding pride or causing loss of face, a genuine concern in close-knit Nepali social settings. It set out firm boundaries an informal mentor should never cross: managing someone's trades directly, accepting compensation tied to specific recommendations, predicting prices or timing, or advising on major financial decisions like loans that belong with licensed professionals. Finally, it distinguished a healthy teaching circle, where knowledge and independent judgment spread across many members, from a dependent one, centred entirely on a single person's opinions.
The next chapter, Chapter 117, "The Canon Audit — Annual Self-Assessment," turns this same disciplined, principle-based thinking back onto the reader's own investing life. It introduces a structured, once-a-year process for auditing your own portfolio decisions, habits, and discipline against everything taught across this entire book — from the earliest lessons on reading a company's fundamentals to the later lessons on managing emotion, avoiding herd behaviour, and now, teaching others responsibly. Just as this chapter asked you to help others build the habit of independent, checkable thinking, the next chapter asks you to turn that same honest, evidence-based scrutiny on yourself.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 117
The Canon Audit — Annual Self-Assessment
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 117.1 — Why Audit Yourself: The Logic of the Annual Review
Every Nepali household with farmland knows a simple truth: you do not wait until the paddy has failed to check on it. You walk the field every few weeks. You check the water level in the canal, you look for pests on the leaves, you see whether the bund, the small earthen wall that holds the water in, has cracked anywhere. Investing works the same way, except most people never walk their own field. They buy shares, they watch the NEPSE index move up and down on their phone, and they never once sit down and ask themselves plainly: how did I actually perform this year, not just in returns, but in behaviour?
This chapter is about building that habit. We are going to call it the Canon Audit, a once-a-year, structured, honest self-assessment where you compare what you actually did as an investor against what you promised yourself you would do. It is not a tax audit and it is not a SEBON inspection. Nobody is checking your homework except you. That is exactly what makes it hard, and exactly why it matters.
Think of it like the annual health checkup that many salaried Nepalis now get through their office insurance. You do not wait for chest pain to check your blood pressure. You get blood drawn, you get your weight and sugar checked, once a year, on a schedule, whether you feel fine or not. The value of the checkup is not that it always finds something wrong. The value is that it finds problems while they are still small and fixable, long before they become a heart attack. A portfolio works exactly the same way. Small mistakes, a slightly oversized position here, a skipped quarterly report there, do not sink an investor in one year. They compound quietly, year after year, until one bad year exposes all of them at once. The Canon Audit is the checkup that catches the drift early.
Let us meet the person who will guide us through this chapter. Her name is Sunita Basnyat, and she is a civil engineer working for a private construction firm in Kathmandu, living in Anamnagar. Sunita started investing in NEPSE shares nine years ago, initially through her father's demat account and then through her own once she turned twenty-five. Somewhere in her fourth year of investing, after a particularly painful year where she chased a hydropower IPO rumour on a friend's tip and lost badly on a stock she never researched, she decided she needed a system. She wrote out what this Canon calls an investment constitution, the personal, written set of rules an investor commits to before market excitement can override good judgment: how much of her portfolio could sit in any one sector, when she would and would not use margin lending, how often she would read company reports, what she would do if a stock fell thirty percent.
But a constitution written and forgotten is worthless. So Sunita also built a ritual: every year, on Baisakh 1, the first day of the Nepali new year, before she does anything else related to money, she sits down for what she now calls her Canon Audit. It usually takes her a full Saturday. She makes tea, she opens a spreadsheet, and she goes through the past year of her investing life the way a mechanic goes through a vehicle before a long highway journey, checking every part whether or not it seems to be making noise.
KEY CONCEPT
A Canon Audit is a scheduled, structured, once-a-year comparison of your actual investing behaviour against the rules you set for yourself in your investment constitution. Its purpose is not to judge your returns alone but to judge your discipline, because discipline is the thing you control and returns are the thing the market controls.
Why does this need to be scheduled and structured, rather than something you do whenever you feel like it? Because human memory is a liar, especially about our own mistakes. If you ask most investors, at any random moment, whether they have been following their own rules, almost everyone will say yes. It is only when you force yourself to lay out the actual transaction history, actual sector allocation, actual reading log, side by side with the rules, that the gaps become visible. This is exactly why a farmer walks the whole field on a schedule rather than only looking at the parts that catch his eye. The parts that catch your eye are usually the ones already doing fine. The weak corner of the field, the one quietly losing water through a crack in the bund, is the one you would walk right past if you were not making yourself look.
There is a second reason the audit must happen every year, not sporadically. NEPSE itself changes constantly: new IPOs get listed, SEBON, the Securities Board of Nepal, which is the government regulator overseeing the stock market and brokers, issues new directives, NRB, Nepal Rastra Bank, the central bank that regulates the banking and lending system, adjusts its rules on margin lending and bank capital, and companies you hold merge, issue bonus shares, or change their business entirely. An investor whose last serious self-review was three years ago is not just undisciplined, they are also very likely out of date. The audit is where you refresh your understanding of the current rules at the same time as you review your own conduct.
Sunita's audit ritual has five fixed parts, which map onto the five lessons that follow in this chapter: gathering the evidence, checking constitutional compliance, checking sector and leverage discipline, checking research and regulatory habits, and finally scoring the year and setting next year's targets. We will walk through her actual audit from three years ago, the year she discovered a serious problem hiding inside what looked, at first glance, like a very good year.
That year, 2080 in the Nepali calendar, Sunita's portfolio had grown handsomely. NEPSE had a strong run, and on paper she was up nearly forty percent. Most investors, seeing that number, would have closed the laptop and gone out for momos to celebrate. Sunita did that too, but first she sat down for her audit, and what she found underneath the happy headline number was a portfolio that had quietly broken two of her own rules for most of the year without her noticing. We will return to exactly what she found, and how she fixed it, as we go through each stage of the process below.
Lesson 117.2 — Gathering the Evidence: Rebuilding the Year from Your Data Pipeline
You cannot audit what you cannot see clearly, and memory alone is not evidence. This is why the first stage of the Canon Audit is not judgment at all, it is simply assembling the raw facts of the year. Think of this like preparing your documents before visiting a government office in Nepal: you do not walk into the Malpot office, the land revenue office, hoping to explain your case verbally. You bring the citizenship copy, the land ownership certificate, the tax clearance. Evidence first, argument second. The Canon Audit works the same way.
This is where the data pipeline you built earlier in this Canon becomes essential. Recall that a data pipeline, in the way this book has used the term, is simply the organised, repeatable system you use to pull in and store information about your holdings: your broker's transaction history, the daily or weekly closing prices of your stocks, company announcements from the Nepal Stock Exchange website, and dividend or bonus share records. If your data pipeline has been running properly all year, gathering the evidence for your audit takes an afternoon. If it has not, gathering the evidence can take a week, and that delay itself is a finding, because a broken data pipeline is one of the most common silent failures an otherwise careful investor can have.
Sunita's evidence-gathering step has four parts, each answering a different question about the year.
First, the transaction ledger. She exports every buy and sell order from her broker account for the full year and lists them in date order. This tells her not just what she bought, but when, and crucially, why, because she keeps a one-line note next to every trade explaining her reason at the time. This is the single habit she credits most for making her audits honest, because it stops her from quietly rewriting history in her own favour. It is very easy, a year later, to convince yourself you bought a stock for a sound reason when actually you bought it because a relative mentioned it at a wedding. The contemporaneous note does not let you lie to yourself.
Second, the portfolio snapshot history. Rather than looking only at where her portfolio stands today, Sunita pulls up snapshots from four points in the year, roughly every three months, showing exactly what percentage of her total invested money sat in each sector: commercial banks, development banks, hydropower, life and non-life insurance, microfinance, hotels, and so on. A single end-of-year snapshot can hide a lot. A stock that spiked in the middle of the year and then fell back by year end will look unremarkable at the final snapshot, even though for several months it represented a dangerously large slice of her portfolio. Quarterly snapshots catch drift that an annual snapshot alone would miss entirely.
Third, the reading log. Earlier chapters of this Canon urged every investor to keep a research library, a personal, organised collection of the annual reports, quarterly financial disclosures, and management commentary for every company they hold or are considering. Sunita's reading log is simply a dated list of what she actually read that year: which quarterly report of which company, which SEBON circular, which NRB monetary policy statement. If a company she holds shares in published four quarterly reports that year and her log shows she only read one of them, that is evidence, not an opinion.
Fourth, the regulatory tracker. This is the running list, also built earlier in this Canon, of regulatory changes relevant to her holdings: changes in the capital adequacy requirements NRB sets for banks, changes in margin lending rules, new SEBON directives on IPO allotment or insider trading disclosure, changes in dividend distribution rules. Sunita checks whether her tracker was actually updated through the year or whether, as sometimes happens, she let it go stale for months at a stretch.
PRACTICAL TOOL
Before you can judge a year, rebuild it in four documents: the annotated transaction ledger, quarterly portfolio sector snapshots, the reading log of company and regulatory documents you actually opened, and the regulatory tracker showing whether it was kept current. Treat these four as your evidence exhibits, not your opinions.
It was while assembling exactly these four exhibits, for the 2080 audit, that Sunita first noticed something odd. Her transaction ledger showed she had bought shares of two different hydropower companies in Chaitra, the last month of the Nepali fiscal year, both times with the same note attached: "everyone at office is buying hydropower, index is rallying." Not a single note referencing an annual report, a project completion timeline, or a power purchase agreement with the Nepal Electricity Authority. That one detail, sitting quietly in her own ledger, was the first thread that, once pulled, unraveled a much bigger problem, which we will trace fully in Lesson 117.4.
For now, the discipline to notice is this: gathering evidence is not a formality to rush through before the "real" audit begins. Often, as happened to Sunita, the evidence-gathering stage itself reveals the finding, if you read your own ledger honestly instead of skimming it.
WARNING
If you find that assembling your year's transaction ledger, sector snapshots, reading log, and regulatory tracker takes more than a day because the records simply were not kept, that difficulty is itself the audit's first and most important finding. A record-keeping breakdown almost always means a discipline breakdown happened alongside it.
One more point on evidence-gathering deserves emphasis, because it trips up even careful investors. When you pull your portfolio snapshot, use invested cost or, better, actual market value at each snapshot date, not a rough guess from memory. NEPSE prices move quickly, and a stock that felt like a small position when you bought it can become a large position purely through price appreciation, without you ever placing another order. This is precisely the kind of drift that a memory-based review misses and that a document-based review catches every time.
Lesson 117.3 — The Constitution Check: Did You Follow Your Own Rules?
With the evidence assembled, the next stage is to place it directly beside your investment constitution and ask, clause by clause, whether you actually did what you said you would do. This is the heart of the Canon Audit, and it is worth explaining why this comparison, and not simply "did I make money," is the correct question to be asking.
Imagine two investors in the same year. The first followed every rule in her constitution: she never exceeded her sector limits, she never used margin lending beyond her stated ceiling, she sold a stock exactly when it hit her predetermined stop-loss level, and she still lost money, because the whole market fell that year. The second investor broke every one of his rules, chased tips, overloaded on one hot sector, and made spectacular returns because that sector happened to rally. Which investor had the better year?
The Canon's answer, and it is a deliberately uncomfortable one, is the first investor. She had the better year, because investing is a long game played across many years, and the habits that produce good outcomes over decades are not the same as the habits that produce good outcomes in any single lucky year. The second investor's approach will eventually meet a year where the hot sector collapses instead of rallying, and because he has no rules restraining his exposure, that collapse will be severe. The first investor's discipline is what will let her survive fifteen or twenty market cycles instead of blowing up in one of them. This is why the constitution check does not ask about your percentage return at all. It asks only: did you follow your own rules.
KEY CONCEPT
A good year measured only in returns can hide bad discipline, and a bad year measured only in returns can hide excellent discipline. The constitution check evaluates behaviour against your written rules, not outcome against the market, because behaviour is the only thing you actually control.
The mechanical process is simple. Sunita keeps her investment constitution as a numbered list of perhaps fifteen rules. For 2080's audit, she went through each one and marked it Followed, Partially Followed, or Broken, with a specific piece of evidence from her four exhibits supporting the mark. Rules like "I will not invest in an IPO or a newly listed company until at least two quarterly reports have been published" are easy to check against the transaction ledger. Rules like "I will read the annual report of every company before increasing my position by more than twenty percent" require cross-referencing the ledger against the reading log.
This is where Sunita's 2080 audit produced its second major finding, connected to the hydropower purchases we noted in the previous lesson. Her constitution contained a clear rule: no new position exceeding five percent of total portfolio value without at least one full quarterly or annual report reviewed first. When she checked her two Chaitra hydropower purchases against her reading log, neither company had a single entry. She had broken her own rule, twice, in the same month, during a period when the broader NEPSE hydropower sub-index was rallying hard on general enthusiasm rather than on anything specific to those two companies.
What made this a genuine constitution check finding, rather than just an isolated bad decision, was that when Sunita looked further back, she found the pattern had actually started three months earlier. In Poush, she had made a similar unresearched purchase in a third hydropower name, also without a reading log entry, also justified in her notes only by reference to what colleagues were discussing. Seen in isolation at the time, each purchase felt small and forgivable. Seen together in the audit, they showed a clear erosion of a specific rule over a specific three-month window, coinciding with the exact period the sector was most exciting to talk about at the office.
CASE IN POINT
Sunita's 2080 audit found three hydropower purchases across three months, each individually small, none accompanied by any research log entry, all justified only by reference to what colleagues were buying. No single purchase looked alarming on its own. Only the audit's side-by-side view revealed a genuine, sustained breach of her research rule.
This is exactly why the constitution check must be done as a full-year review rather than trade by trade in real time. In the moment, each individual decision can be rationalised. It is only the aggregated, dated record, reviewed with a full year's distance, that exposes the pattern. This is the same reason a joint family's monthly household accounts often look fine but the yearly total suddenly shows spending has crept up badly. Nobody spent recklessly on any single day. The drift only becomes visible in the annual total.
The constitution check should end with a simple tally: how many rules were fully followed, how many partially followed, how many broken outright. This tally feeds directly into the canon score we will calculate in Lesson 117.6, but even before scoring, the tally itself is informative. Sunita's 2080 tally showed twelve of fifteen rules followed, two partially followed, and one broken, the research-before-buying rule specifically in hydropower. A single broken rule out of fifteen might sound minor, but as we will see in the next lesson, this particular broken rule connected directly to a second, larger problem in her sector allocation, which is exactly the kind of compounding risk the audit exists to catch.
Lesson 117.4 — The Concentration and Margin Check: Sector Limits and Leverage Discipline
Two of the most dangerous risks in NEPSE investing are also two of the easiest to drift into without noticing: sector concentration and margin lending. Both deserve their own dedicated stage in the Canon Audit, because both tend to build up slowly, quietly, and often for entirely innocent reasons, exactly like water pressure building behind a dam wall that looks fine from the outside.
Sector concentration limits, introduced earlier in this Canon, are simply a rule you set for yourself capping how much of your total portfolio can sit in any single sector, such as commercial banks, hydropower, life insurance, or microfinance. The reasoning behind this rule is straightforward. Nepal's economy and its stock market are both still relatively small and concentrated, and entire sectors can move together for reasons that have nothing to do with any individual company's quality. When NRB tightens capital adequacy requirements, every commercial bank stock can fall together. When monsoon rainfall is poor or a major transmission line project is delayed, every hydropower stock can fall together. An investor who is unknowingly eighty percent concentrated in one such sector is not really diversified at all, no matter how many different company names appear in the demat account.
Continuing Sunita's 2080 audit, this is exactly where her earlier hydropower finding turned out to be more serious than it first appeared. Recall she had found three unresearched hydropower purchases across three months. When she pulled her quarterly portfolio snapshots, as described in Lesson 117.2, she saw the following picture. At the start of the year, her hydropower allocation sat at eleven percent of her total portfolio, comfortably within her constitution's fifteen percent sector cap. By her third-quarter snapshot, after the three unresearched purchases and after strong price appreciation in the names she already held, her hydropower allocation had risen to twenty-three percent, well above her own limit. And because the sector had been rallying, it did not feel like a problem. It felt like success. This is precisely the trap: a breach of a concentration limit caused by price appreciation feels good in the moment and is therefore the hardest kind of breach to notice without a scheduled audit.
WARNING
A sector concentration breach caused by a rallying sector often feels like a reward rather than a risk, because the portfolio is going up. This is exactly why it is dangerous: the investor has no emotional signal telling them anything is wrong, only the disciplined act of checking their own rules against their actual numbers.
The margin lending check follows the same logic and deserves equal seriousness, arguably more, because margin lending, where an investor borrows against the value of shares already held in order to buy more shares, is a leverage tool that can turn an ordinary bad year into a devastating one. NRB sets rules on how much banks and finance companies can lend against share collateral, and these rules change from time to time, which is one more reason the regulatory tracker checked in the next lesson matters. But regardless of what NRB currently permits at the system level, your own investment constitution should set a personal ceiling well inside the regulatory limit, because the regulatory limit is the maximum the system allows, not a recommendation for how much any individual investor should actually use.
Audit Area
Question to Ask Yourself
Pass/Fail Signal
Sector concentration
At every quarterly snapshot, did any single sector exceed my constitution's stated cap?
Pass: all four snapshots within cap. Fail: any snapshot above cap, even briefly.
Margin lending discipline
Did my outstanding margin loan, at any point, exceed the ceiling my constitution sets, regardless of what my broker or lender would have allowed?
Pass: never exceeded personal ceiling. Fail: exceeded it even once, even briefly.
Research before buying
For every purchase increasing a position by more than the threshold set in my constitution, does my reading log show a report reviewed first?
Pass: reading log entry exists for every qualifying purchase. Fail: any qualifying purchase with no matching entry.
Regulatory tracker currency
Was my regulatory tracker updated at least once per quarter with NRB and SEBON changes relevant to my holdings?
Pass: four or more updates through the year. Fail: tracker untouched for two or more consecutive quarters.
Record-keeping completeness
Can I reconstruct this year's full transaction history, sector snapshots, and reading log without gaps or guesswork?
Pass: all four exhibits assembled within a day. Fail: significant gaps requiring reconstruction from memory.
Sunita's margin check that year turned out clean; she had not used margin lending at all in 2080, having decided two years earlier, after a smaller scare, that margin lending simply did not suit her temperament. This is worth noting precisely because it shows the audit is not designed to always produce bad news. Some areas will pass cleanly, and confirming that is just as valuable as finding the problems, because it tells you where your existing safeguards are holding and do not need new rules layered on top of them.
REGULATORY DETAIL
NRB periodically revises the margin lending limits that banks and finance companies may extend against share collateral, and these limits can tighten sharply during periods NRB judges the market to be overheated. A personal constitution ceiling set safely below the current regulatory maximum protects you even when NRB itself has not yet acted.
Having found the sector concentration breach, Sunita's next task was corrective action, not self-punishment. She trimmed her hydropower holdings back down to just under her fifteen percent cap over the following two months, selling first the two positions she had bought without any research, since those were the weakest holdings by her own rule, and keeping the older, researched positions. She did not panic-sell the entire sector, because the audit's finding was about position sizing discipline, not about hydropower being a bad sector to hold at all. This distinction matters. The Canon Audit should always produce a specific, targeted correction tied to the specific rule that was broken, never a broad emotional overreaction that swings the portfolio to some other extreme.
CAUTION
When an audit uncovers a breach, correct the specific breach with a specific, measured action. Do not overcorrect by abandoning an entire sector or asset class in a burst of self-criticism. The goal is restoring the rule, not punishing yourself for having broken it.
Lesson 117.5 — The Research and Regulatory Habits Check: Library and Tracker Review
The third major area of the Canon Audit turns away from portfolio numbers and toward habits: specifically, the health of your research library and the currency of your regulatory tracker. These two tools, introduced earlier in this Canon, are the quiet infrastructure behind every good investment decision, and like any infrastructure, they decay silently if not maintained. A water tank on a rooftop does not announce that it has developed a slow leak. You only discover it when you notice the water bill climbing or, worse, when the tank runs dry exactly when you need it most.
Your research library, recall, is your organised personal collection of annual reports, quarterly disclosures, prospectuses, and management commentary for the companies you hold or are considering. A healthy research library grows steadily through the year: as each company you hold publishes its quarterly results, you read them and file them. An unhealthy one has gaps, sometimes gaps you do not notice until you go looking for a specific document and cannot find it, or worse, realise you never downloaded it at all.
The audit question here is straightforward: for every company held for more than one quarter during the year, does the reading log show that quarter's report was actually reviewed. Sunita's 2080 audit, continuing the theme from the previous two lessons, found exactly the gap you would expect. Her reading log showed strong coverage for the eight companies she had held for several years, the ones she considered her core, long-term positions. But for the three hydropower names bought impulsively that year, the coverage was zero, confirming again, from a different angle, the same underlying weakness the constitution check had already surfaced. This is a useful pattern to watch for in your own audits: genuine weaknesses tend to show up in more than one exhibit, because a real behavioural gap leaves fingerprints across several different records, not just one.
KEY CONCEPT
A research library is not a filing cabinet you fill passively. It is a habit you either maintain every quarter or let decay. The audit's job is to measure that decay directly, company by company, rather than assume the habit held simply because it held in the past.
The regulatory tracker check works similarly but looks outward rather than inward, at the rules of the system you are investing within rather than at your own portfolio. Over the course of any given year, NRB typically issues its monetary policy statement along with periodic directives affecting bank capital requirements, interest rate spreads, and margin lending limits. SEBON, meanwhile, issues directives on matters like IPO share allotment procedures, insider trading disclosure requirements, broker conduct, and listing requirements for new companies. An investor holding bank shares who has not tracked NRB's latest capital adequacy directive, or an investor participating in IPOs who has not tracked SEBON's latest allotment rule changes, is investing with an outdated map of the terrain.
Sunita keeps her regulatory tracker as a simple running log: date, source, either NRB or SEBON, a one-line summary of the change, and a note on whether it affects any of her current holdings. Checking it for currency simply means looking at the dates and asking whether there is a long unexplained gap. In 2080, her tracker showed regular updates through the first two quarters, then a gap of nearly five months with no entries at all, precisely overlapping with the period she was also busiest at her engineering firm on a major project deadline. This was not a coincidence, and recognising that connection turned out to be one of the more useful outcomes of that year's audit, because it told her something about her own limits: when her professional workload spikes, her investing discipline is the first thing to slip, and it slips quietly, without her noticing in the moment.
CASE IN POINT
Sunita's regulatory tracker went five months without an entry during a period of heavy work deadlines. The gap itself, visible only because she kept dated records, told her more about her actual risk during busy periods than any single missed regulatory update did.
This finding led to a specific, practical corrective action, distinct from the sector trimming discussed in the previous lesson. Rather than trying to will herself into being equally attentive every month regardless of work pressure, which she recognised as unrealistic, Sunita instead built a lighter-weight backup habit: a recurring reminder on the first Saturday of every month to spend just thirty minutes reading whatever NRB and SEBON had published that month, regardless of how busy work was. Thirty minutes is a small enough commitment to survive even a demanding month, and it prevents the kind of five-month total gap that a more ambitious but fragile habit had allowed.
PRACTICAL TOOL
If a habit fails specifically during busy periods, do not just resolve to try harder. Replace it with a smaller, more resilient version of the same habit, one small enough to survive your worst month, not just your best one.
One further dimension belongs in this stage of the audit: record-keeping completeness itself, which is really a check on whether your entire evidence-gathering system from Lesson 117.2 functioned properly all year, not just at the two moments already discussed. Ask directly: were there any months where you simply stopped logging transactions with reasons, stopped updating the data pipeline, or let the demat statements pile up unopened. Sunita's answer that year was that her transaction ledger had, in fact, stayed current throughout, which was a genuine pass, and worth noting as a pass rather than assuming everything must have failed simply because two other areas had. An honest audit records passes as carefully as it records failures, because an inflated sense of total failure is just as inaccurate, and just as unhelpful for planning next year's corrections, as an inflated sense of total success.
Lesson 117.6 — Scoring the Year: Canon Score, Corrective Action, and Setting Next Year's Targets
The final stage of the Canon Audit brings everything from the previous four lessons together into a single number, the canon score, a concept introduced earlier in this book as a simple, personal measure of how closely your actual investing behaviour matched your own stated principles over a given period. The canon score is not a market performance metric. It says nothing about whether your portfolio beat NEPSE's index that year. It says only how disciplined you were, which, as argued in Lesson 117.3, is the thing that actually determines whether you survive and compound successfully across many years rather than just one.
Calculating a canon score does not require complicated mathematics. Sunita's method, which works well for most individual investors, is to score each of the major areas covered by this chapter, constitution compliance, sector concentration discipline, margin lending discipline, research library health, regulatory tracker currency, and record-keeping completeness, on a simple one-to-five scale, with five meaning fully maintained all year and one meaning essentially abandoned. These six area scores are then averaged into a single overall canon score out of five for the year.
Audit Section
What a Score of 5 Looks Like
What a Score of 1 Looks Like
Constitution compliance
Every written rule followed all year, verified against dated evidence
Constitution ignored or not consulted at all during the year
Sector concentration discipline
No sector exceeded its cap at any of the four quarterly snapshots
One or more sectors far above cap for most of the year
Margin lending discipline
Personal margin ceiling never exceeded, or margin not used at all
Margin used well beyond personal ceiling, close to or at regulatory maximum
Research library health
Every held company's quarterly report read and filed before any position increase
Major positions increased with no supporting report ever read
Regulatory tracker currency
NRB and SEBON updates logged every quarter without gaps
Tracker abandoned for half the year or more
Record-keeping completeness
Full ledger, snapshots, and logs reconstructable in under a day
Records so incomplete the audit itself cannot be properly done
For 2080, Sunita's six scores worked out to: constitution compliance, four out of five, given the one clear breach; sector concentration, three out of five, given the confirmed breach that lasted roughly two quarters before correction; margin lending, five out of five, since she had not used margin at all; research library, three out of five, reflecting the specific gap in the three hydropower names against otherwise strong coverage; regulatory tracker, three out of five, reflecting the five-month gap; and record-keeping, five out of five. Averaged, this produced an overall canon score of just under four out of five for that year, a solid but not perfect result, and one that told a clear, specific story rather than a vague feeling of having done "pretty well, I think."
KEY CONCEPT
The canon score is a discipline metric, not a returns metric. A high canon score in a losing market year still represents success, because it means your process held even when the outcome did not. A low canon score in a winning market year is a warning sign hiding behind good luck.
The number itself, however, is the least important output of the audit. What matters far more is the specific, written corrective plan for the year ahead, built directly from the findings. Sunita's plan following the 2080 audit had three concrete items, each tied to a specific finding rather than a vague resolution. First, a hard rule addition to her constitution: no purchase in any sector already representing more than ten percent of portfolio value without a mandatory two-day cooling-off period and a completed reading log entry, closing the exact loophole that had allowed the unresearched hydropower purchases. Second, the monthly thirty-minute regulatory reading reminder described in Lesson 117.5, specifically designed to survive busy work periods. Third, a mid-year checkpoint, a lighter, thirty-minute version of the full annual audit performed at the Nepali mid-year point, roughly around Kartik, specifically to catch sector concentration drift earlier than a once-a-year check would allow, since that particular breach had been allowed to run for two full quarters before the annual audit caught it.
PRACTICAL TOOL
Every corrective action coming out of a Canon Audit should trace back to one specific finding and should be specific and checkable itself, not a vague intention. "Read more" is not a corrective action. "Mandatory two-day cooling-off period and logged report before increasing any position in a sector already above ten percent" is a corrective action, because next year's audit can check directly whether it happened.
It is worth being honest about what the Canon Audit cannot do. It cannot predict the market. It cannot guarantee that following every rule perfectly will produce good returns in any given year, because NEPSE, like any market, is subject to forces well beyond any individual investor's discipline: NRB monetary policy shifts, remittance inflow trends, monsoon-dependent hydropower output, global commodity prices affecting import-heavy sectors, and simple crowd psychology among thousands of other retail investors. What the audit can do, reliably, is ensure that when a bad year does come, and eventually one will, it finds an investor who was not also carrying unnecessary self-inflicted risk on top of ordinary market risk. Sunita's correction of her sector concentration in 2080 meant that when hydropower sentiment cooled sharply the following year, her portfolio absorbed a much smaller shock than it would have if the twenty-three percent concentration had been left standing.
A final, practical point on timing and ritual. Sunita chose Baisakh 1, the Nepali new year, deliberately, because it is a date already culturally marked as a moment of renewal and fresh starts, making it psychologically easier to sit down and look honestly at the past year's mistakes than an arbitrary date would be. Some investors may prefer their birthday, the anniversary of opening their first demat account, or the start of the Nepali fiscal year in Shrawan. The specific date matters far less than the fact that it is fixed, recurring, and treated as non-negotiable, exactly like the annual health checkup it was compared to at the start of this chapter. An audit that only happens "when I get around to it" will, for most people, simply never happen, because there is always something more urgent competing for a Saturday afternoon than a quiet, sometimes uncomfortable review of your own mistakes.
WARNING
An audit without a fixed, recurring date on the calendar tends to quietly disappear within two or three years, exactly like any health checkup people mean to schedule "sometime soon." Fix the date now, put it on the calendar as a firm commitment, and treat it with the same seriousness as a bill payment deadline.
The Canon Audit closes each year not with judgment but with renewal: a scored year filed away as a reference point, a short list of specific corrections carried forward, and a fresh copy of the investment constitution ready to be tested again over the twelve months ahead. Sunita now has nine years of these audits filed in one folder, and rereading the earliest ones, she says, is humbling in a useful way. The mistakes of her fourth year as an investor look almost naive to her now. But she is quick to add that this is exactly the point. The audit is not there to make her feel clever about how far she has come. It is there to make sure the mistakes of this year get caught before they can compound into a genuinely damaging one, the way the sector concentration breach was caught and corrected long before it had the chance to combine with a genuine sector downturn.
Chapter recap
This chapter built the Canon Audit, a disciplined, once-a-year self-assessment that compares your actual investing behaviour against the rules in your own investment constitution, rather than against your portfolio's raw returns. We followed civil engineer Sunita Basnyat's annual Baisakh ritual through a real example: how gathering plain evidence, an annotated transaction ledger, quarterly sector snapshots, a reading log, and a regulatory tracker, exposed a hidden sector concentration breach in hydropower shares, traced it back to a broken research habit that had quietly failed during a busy work period, and led to specific, checkable corrections rather than vague resolutions. We showed how to score a year using the canon score across six areas, constitution compliance, sector concentration discipline, margin lending discipline, research library health, regulatory tracker currency, and record-keeping completeness, and why a high canon score in a losing year matters more than a high return in an undisciplined one. The core lesson throughout is that small breaches of discipline rarely announce themselves; they hide inside good years and only a scheduled, evidence-based audit reliably catches them while they are still small.
This is the second-to-last chapter of The Investor's Canon. Chapter 118, Final Synthesis — The Complete Canon Investor, will bring together everything taught across all one hundred and eighteen chapters, from the earliest lessons on what a share actually represents, through the investment constitution, sector concentration limits, margin lending discipline, the data pipeline, the research library, and now the Canon Audit itself, into one final, unified portrait of what a complete, disciplined NEPSE investor looks like in practice, and how all these separate tools and habits fit together as a single, coherent way of managing money for an entire investing lifetime.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.
Part XVIII · Chapter 118
Final Synthesis — The Complete Canon Investor
First published 26 Aug 2026 · Last verified 29 Aug 2026
Lesson 118.1 — The Orchard at Dusk
In Bhaktapur, on the terraced land behind his family's old brick house, a seventy-one-year-old retired headmaster named Basanta Prasad Adhikari climbs a low stone wall every evening at dusk and walks among his apple and walnut trees. He planted the oldest of them thirty-four years ago, in 1992, the same year he opened his first account with a stockbroker in New Road, Kathmandu, a year before the Nepal Stock Exchange, or NEPSE, the country's only stock exchange, even had a working trading floor. He was twenty-seven then, a young school teacher with a small salary and a smaller appetite for risk, and he bought two hundred shares of a commercial bank because his brother-in-law told him banks were the safest thing in the world. He did not understand what a balance sheet was. He did not know what the central bank, Nepal Rastra Bank, or NRB, actually did. He simply trusted a relative, the way most Nepali investors began in those years, and the way many still begin today.
Now, at seventy-one, Basanta Sir, as his former students still call him, manages a portfolio built across four decades, survived three stock market bubbles, two earthquakes, one pandemic, and more changes of government than he can accurately count. His grandchildren study in Kathmandu and Melbourne. His orchard still gives him apples every autumn. And when young relatives or former students come to him now, the way he once went to his brother-in-law, he no longer gives them a stock tip. He gives them something slower and more valuable: a way of seeing.
This final chapter follows Basanta Sir's evening walk through his orchard, tree by tree, because his own private habit, after decades of investing, is to think of each part of what he learned as a different kind of tree in the same orchard. Some trees give shade. Some give fruit only after many years. Some need pruning every season or they choke themselves. None of them, alone, would feed his family. Together, tended patiently, they have.
This chapter does not teach anything new. Its job is different, and in some ways harder. It must gather everything taught across four volumes and one hundred and seventeen prior chapters, and show how those separate lessons fit into a single, working, breathing whole, the way a collection of trees becomes an orchard, and the way a collection of habits becomes a life.
KEY CONCEPT
The Canon investor is not a person who knows the most facts about NEPSE. It is a person in whom macro understanding, analytical skill, behavioural discipline, and patient structure have become one integrated habit of mind, applied automatically, the way a farmer no longer thinks separately about soil, weather, and season, but simply farms.
Before walking the orchard, it is worth remembering why this matters. Nepal is a young capital market inside a young republic. NEPSE itself was established in 1993 and opened its trading floor in 1994. SEBON, the Securities Board of Nepal, the regulator that licenses brokers, oversees disclosure, and polices market conduct, was established in 1993 as well, to keep the market honest. Automated trading did not arrive until 2007. The demat system, which replaced paper share certificates with electronic records held through the Central Depository System and Clearing Limited, or CDSC, only became compulsory in the mid-2010s. In other words, nearly everything a reader of this book now takes for granted, the TMS trading account used to place orders online, the demat account that safely holds shares, the broker apps on a mobile phone, did not exist for most of Basanta Sir's investing life. He watched the market build its own house while he was already living in it.
That is the spirit in which this final chapter is written. Not as a technical summary, but as a walk through a life, because in the end that is what disciplined investing in NEPSE actually is: not a set of tricks, but a life lived a certain way, patiently, over a very long time.
Lesson 118.2 — Volume I Revisited: The Weather Before the Crop
Basanta Sir stops first beneath his oldest walnut tree, the one his father planted before him, because walnut trees are stubborn about weather. They do not fruit well in a year of poor rain, no matter how well you have pruned them. This is where he begins his own retelling of the Canon, because Volume One, FOUNDATIONS, taught exactly this lesson: no company, however well run, grows in isolation from the larger economic weather around it.
The first thing Volume One asked the reader to understand was Nepal Rastra Bank, the central bank, and its enormous invisible influence over every share price on the board. NRB does not buy or sell shares. It does not pick winners. But through its monetary policy, the tools it uses to control how much money and credit flow through the economy, it decides how easy or hard it is for businesses, and for stock market investors themselves, to borrow. When NRB tightens policy, for instance by raising the cash reserve ratio, the CRR, the share of deposits that commercial banks must keep locked away and cannot lend out, banks have less money to lend. Loans become scarcer and more expensive. Businesses slow their expansion. And margin lending, the practice of borrowing against one's own shares to buy more shares, becomes harder to get and more dangerous to hold. This is precisely what happened in 2016 and 2017, when a NEPSE bull run built substantially on margin borrowing collapsed once NRB and banking sector liquidity tightened, wiping out investors who had confused a rising market with a permanent one.
REGULATORY DETAIL
NRB's key tools include the cash reserve ratio, or CRR, the statutory liquidity ratio, or SLR, requiring banks to hold a portion of deposits in safe liquid assets, the policy rate at which it lends to banks, and open market operations, buying or selling government securities to add or remove money from the banking system. A Canon investor watches these the way a farmer watches the sky before deciding whether to plant.
Volume One also taught the credit cycle, the recurring pattern in which loose lending fuels an economic and stock market boom, which eventually produces bad loans, which forces banks to tighten again, which produces a bust, after which the cycle slowly begins again. Nepal's own economic history since the 1990s has moved through several such cycles, shaped further by remittance inflows from Nepali workers abroad, by monsoon-dependent agricultural output, by earthquakes and pandemics that interrupt everything at once, and by a political landscape that has changed government more often than most countries change a decade's worth of budgets. A reader who does not understand this weather, Volume One insisted, will mistake a temporary credit-fuelled rally for genuine, lasting prosperity, and will be caught outside with no shelter when the rain finally comes.
Alongside the macroeconomic weather, Volume One built the reader's understanding of market structure itself: how NEPSE actually functions, how a floor sheet records every trade of the day, how the NEPSE index is calculated as a market-weighted measure of overall market value, how circuit breakers, rules that automatically halt trading when the index moves too far in one day, exist to stop panic from feeding on itself, and how an initial public offering, or IPO, the first sale of a company's shares to the public, differs from a further public offering, or FPO, a later sale of additional shares by an already listed company. It taught the reader to open a demat account, the electronic account that legally holds one's shares, and a TMS account, the trading system through which buy and sell orders are actually placed, and to understand why both exist and how they protect an ordinary investor from the kind of fraud and confusion that plagued Nepal's markets in their early, more informal decades.
Basanta Sir remembers those informal decades personally. He remembers when share ownership was proven by a paper certificate that could be lost, stolen, or damaged by monsoon damp, long before CDSC digitized it all. He remembers brokers who operated more on reputation and rumour than on regulation, before SEBON's disclosure rules matured. The market he invests in today is a different, sturdier structure than the one he entered, and Volume One's deepest lesson, in his own words to his grandchildren, is this: understand the ground you are standing on before you plant anything in it.
CASE IN POINT
In the 2016 to 2017 NEPSE bull run, the index nearly doubled in under two years, driven heavily by margin-financed buying. When NRB and the banking sector tightened liquidity in 2018, the index fell by more than a third over the following months. Investors who had borrowed heavily against their portfolios were forced to sell at the worst possible time, a direct, painful demonstration of the credit cycle Volume One describes.
Volume One, in short, gave the reader eyes. It did not teach how to pick a winning company. It taught how to see the larger climate in which every company, winning or losing, must survive.
Lesson 118.3 — Volume II Revisited: Learning to Read the Soil
Further along the terrace, Basanta Sir keeps his vegetable beds, and here his metaphor shifts from weather to soil, because Volume Two, ANALYSIS, is where the Canon taught the reader to examine the actual ground beneath any individual company, not the sky above the whole market.
A farmer who understands the weather but never tests the soil will still plant in the wrong place. Volume Two therefore taught the reader to open a company's financial statements, the balance sheet, which shows what a company owns and owes at a single point in time, the profit and loss statement, which shows what it earned and spent over a period, and the cash flow statement, which shows whether the cash moving through the business actually matches the profits it reports on paper. This last point mattered enormously in the Nepali context, where the book returned repeatedly to cases of listed companies, particularly some finance companies and smaller manufacturing firms, whose reported profits looked healthy on paper for years while their actual cash position quietly deteriorated, a warning sign only visible to a reader patient enough to check the cash flow statement rather than trust the profit and loss statement alone.
Volume Two then built the reader's vocabulary of ratios, the shorthand tools that let one company be compared fairly against another. The price to earnings ratio, or P/E, comparing a share's price to the profit it earns per share, told the reader roughly how expensive a share was relative to its earnings. The price to book ratio compared a share's price to its net accounting value, useful especially for banks and financial institutions, where book value tends to matter more than for manufacturing firms. Return on equity, showing how effectively a company turns shareholder money into profit, and the debt to equity ratio, showing how much of a company's operations were funded by borrowing rather than by owner capital, together let the reader judge not just whether a company was profitable, but whether that profit was earned safely or built on borrowed risk.
KEY CONCEPT
Margin of safety means buying a share for meaningfully less than your honest estimate of its true worth, so that even if your analysis is somewhat wrong, or the future turns out worse than expected, you are protected from serious loss. It is the single most important idea in Volume Two, and arguably in the entire Canon.
Volume Two also took the reader sector by sector through the specific character of Nepal's own listed industries: the commercial banks and development banks that dominate NEPSE's total market weight and whose fortunes rise and fall with the credit cycle described in Volume One; the hydropower companies whose earnings depend on monsoon rainfall, river flow, and the price at which the state utility, the Nepal Electricity Authority, buys their power under long-term power purchase agreements; the microfinance institutions serving borrowers at the very base of the rural economy, sensitive to both interest rate caps and to the repayment capacity of poor households; the insurance companies whose profits depend on actuarial discipline rather than growth alone; and the small but growing group of manufacturing, hospitality, and trading companies whose fortunes track the broader consumer economy and remittance-driven spending.
The reader who works through Volume Two learns to distinguish a share that is merely cheap in price from a share that is genuinely undervalued relative to its worth, and to distinguish a share that is popular from a share that is sound. Basanta Sir, tending his vegetable beds, puts it more plainly to his former students: cheap soil is not the same as good soil. Sometimes land is cheap because nobody wants it. Sometimes it is cheap because it is genuinely fertile and simply overlooked. Learning the difference is the entire craft of analysis, and it cannot be rushed.
Volume
Core Question It Answers
What It Gave the Investor
Volume One, FOUNDATIONS
What is the larger economic and market weather around every investment decision?
Understanding of NRB policy, the credit cycle, NEPSE market structure, and regulatory safeguards
Volume Two, ANALYSIS
Is this specific company, and this specific price, actually sound?
Skill in reading financial statements, valuation, and sector-specific judgment
Volume Three, EXECUTION
Can I actually act on what I know, calmly and consistently, over time?
Behavioural discipline, portfolio construction, and practical operational tools
Volume Four, MASTERY
Can this way of investing outlast me, and serve more than just my own account?
A lifelong, multi-generational mindset and a durable legacy of habits
Volume Two, then, gave the reader craft. It turned a person who could merely read a stock ticker into a person who could read a company.
WARNING
A share price falling does not automatically mean it has become a bargain, and a share price rising does not automatically mean the underlying business has improved. Price and value are related but not identical. Confusing the two is the single most common analytical error new NEPSE investors make.
Lesson 118.4 — Volume III Revisited: The Hands That Do the Work
Understanding weather and testing soil, Basanta Sir tells his grandchildren, will still leave your hands empty if you never actually pick up the hoe. Volume Three, EXECUTION, is where the Canon turned from knowledge into action, and it is, in his own experience, the volume that separated those of his generation who genuinely built wealth from those who merely talked about markets at tea shops for thirty years without ever changing their behaviour.
Execution began with behavioural discipline, the unglamorous but decisive skill of managing one's own mind. The Canon taught the reader to recognise herd behaviour, the tendency to buy because everyone around you is buying, which drove much of the 2016 to 2017 bubble and again the extraordinary retail rally of 2020 to 2021, when NEPSE's index climbed to record highs partly on a wave of new, first-time investors trading from home during pandemic lockdowns. It taught the reader to recognise loss aversion, the very human tendency to feel a loss far more painfully than an equivalent gain, which causes investors to sell winning shares too early to "lock in" a small gain while holding losing shares far too long, hoping they will merely return to breakeven. It taught anchoring, the tendency to fixate on the price one originally paid as though it were meaningful to the market, when the market itself has no memory of your purchase price at all.
CAUTION
A share does not know what you paid for it. Its future performance depends on the business behind it and the price others are willing to pay today, never on your personal purchase history. Holding a share purely because "it will come back to what I paid" is hope dressed up as strategy.
From behaviour, Volume Three moved to portfolio construction, the discipline of building a considered collection of holdings rather than a scattered pile of impulsive bets. It taught diversification across sectors, so that a downturn in banking shares would not sink an entire portfolio alongside it, and position sizing, deciding in advance how much of one's total capital any single share deserved, so that even a badly wrong decision would not be a fatal one. It taught the reader to distinguish core holdings, meant to be held for years through a company's fundamental strength, from satellite or tactical positions, smaller and more speculative bets sized so that losing them entirely would not threaten the whole portfolio.
Execution also gave the reader operational tools, the unglamorous plumbing of actual investing life. It taught how to maintain an investment ledger or journal, recording not just what was bought and sold but why, so that decisions could be honestly reviewed later rather than remembered through the flattering fog of hindsight. It taught how to track dividends, the portion of profit a company distributes to shareholders, and bonus shares, additional shares issued to existing shareholders instead of cash, both common features of the Nepali market, and how to keep records straight for tax purposes with the Inland Revenue Department. It taught the practical mechanics of placing orders through a TMS account without making the costly small errors, wrong quantity, wrong price limit, wrong script code, that have cost careless investors real money on real trading days.
PRACTICAL TOOL
An investment journal need not be elaborate. A simple notebook or spreadsheet recording the date, the share, the price, the quantity, and one honest sentence explaining your reasoning at the time, is enough to transform investing from a blur of impulsive memory into a body of evidence you can actually learn from.
Basanta Sir keeps such a journal still, in his own careful teacher's handwriting, forty years of entries filling a shelf of notebooks in his study. He does not read it often. But he read it closely in 2021, when the market was euphoric and every acquaintance seemed to be doubling their money in hydropower and finance shares within months, and he pulled down his notebook from the 2016 to 2017 bubble to remind himself, in his own younger handwriting, exactly what that kind of euphoria had felt like the last time, and exactly how it had ended. He sold nothing in a panic. He simply stopped adding new money to shares trading well beyond any reasonable estimate of their worth, and waited. When the index corrected sharply through 2022, he was neither destroyed nor surprised. His journal had already told him what was coming, because it had already shown him what had come before.
Volume Three, in short, gave the reader hands. It turned understanding into behaviour that could survive contact with a real, emotional, unpredictable market.
Lesson 118.5 — Volume IV Revisited: Planting for Grandchildren
At the far end of the orchard stand the youngest trees, planted only in the last decade, too young yet to give much fruit, planted knowing that Basanta Sir himself may not be the one who eats their best harvest. This is where his metaphor turns to Volume Four, MASTERY, the part of the Canon concerned not with the investor's own lifetime, but with what outlasts it.
Volume Four asked a harder question than any before it: what happens to everything you have learned when you are no longer the one making the decisions? It taught estate and succession planning, the practical steps of ensuring that shares, demat accounts, and investment records pass cleanly to one's heirs rather than becoming tangled in confusion or dispute, a particular concern in Nepal where jointly held family property and unclear documentation have historically caused painful and expensive disputes after a family member's death. It taught the importance of clearly naming a nominee on demat and bank accounts, of keeping records organised and accessible rather than locked away in memory alone, and of actually discussing money and investing openly with one's children and grandchildren while still alive to explain it, rather than leaving them a portfolio with no context and no understanding of why any of it was built the way it was.
Volume Four also returned to a theme first raised gently in Volume One but only fully matured here: the extraordinary, almost invisible power of compounding across an entire lifetime rather than a single decade. A modest sum invested soundly in one's twenties and left to compound quietly for forty years produces a dramatically larger result than a much larger sum invested only in one's fifties, not because the later investor was less capable, but because time itself is the single largest multiplier compounding offers, larger than any individual stock pick, larger than any single bull market. This is precisely the lesson referenced in Chapter 109 through the story of Krishna Bahadur Rai, whose disciplined decades-long habit of reinvesting dividends rather than spending them became, in his final account, one of the largest components of his eventual wealth. Basanta Sir's own orchard makes the same point every autumn: the walnut tree his father planted, tended for decades before it ever mattered much, now gives more each year than any tree he has planted since.
KEY CONCEPT
Compounding rewards patience more generously than it rewards skill. An investor with modest analytical ability who starts young and stays disciplined for forty years will very often finish ahead of a brilliant analyst who starts late, trades often, and interrupts the process repeatedly.
Volume Four's final lessons turned to legacy in the fullest sense, not merely money passed down, but a way of thinking passed down. It taught the reader to write, even informally, a simple statement of their own investment principles, so that a spouse, child, or grandchild inheriting a portfolio would inherit the reasoning behind it as well, and would not be tempted to sell everything in a panic at the first market downturn simply because they never understood why it was bought in the first place. This is precisely why Basanta Sir walks his own grandchildren through his orchard and his notebooks together, tree by tree, page by page, rather than simply naming them as beneficiaries in a will and leaving the rest to chance.
CAUTION
Wealth transferred without understanding is often wealth lost within a single generation. A portfolio inherited by someone who was never taught why it was built the way it was is far more likely to be liquidated in fear, spent unwisely, or mismanaged out of simple unfamiliarity, no matter how soundly it was originally constructed.
Volume Four, in the end, gave the reader something larger than a strategy. It gave a horizon longer than one human life, and the humility to plant trees whose best fruit one may never personally taste.
Lesson 118.6 — The Complete Canon Investor
Now Basanta Sir reaches the centre of the orchard, where the paths from every terrace meet, and this is where the four volumes of the Canon must finally be seen not as four separate lessons but as one single, working person.
Trait of the Complete Canon Investor
What It Looks Like in Daily Practice
What It Protects Against
Macro awareness
Watching NRB policy signals and credit conditions, not just share prices
Being blindsided by a liquidity-driven boom or bust
Analytical patience
Reading financial statements fully before buying, not acting on tips alone
Overpaying for popularity rather than value
Behavioural steadiness
Following a written plan through both euphoria and panic
Herd behaviour, panic selling, and loss aversion
Structural discipline
Diversifying, sizing positions deliberately, keeping honest records
A single bad decision becoming a ruinous one
Generational thinking
Teaching heirs the reasoning, not just the numbers
Wealth and wisdom being lost within one generation
A person who has only the first trait, macro awareness, without the others, becomes a nervous commentator who understands the economy perfectly but never actually invests, too frightened of every headline to hold anything at all. A person who has only analytical skill, without behavioural steadiness, becomes exactly the kind of investor who correctly identifies an undervalued share and then sells it in a panic during the first ordinary correction, destroying through fear what careful analysis had built. A person who has structural discipline and diversification without any real analytical understanding of what they hold merely spreads their ignorance across more companies instead of concentrating it, which is not wisdom, only a wider net cast at random. And a person who builds wealth brilliantly across a single lifetime, without ever teaching anyone else how or why, has built something that will very likely not survive them.
The Canon investor is the rare integration of all five. Not perfect at any single one of them, necessarily, but never entirely missing any of them either. This is why the book has taken one hundred and eighteen chapters to describe a way of investing that could, in outline, be summarised on a single page. The outline is easy. The integration, lived out consistently across decades, through actual bull markets and actual crashes, through actual family pressure and actual market gossip, is the genuinely difficult part, and it cannot be taught quickly because it is not fundamentally an intellectual skill. It is a character built slowly, the way an orchard is built slowly, one season of discipline at a time.
WARNING
The greatest danger to a long, successful investor is not a market crash. It is complacency after long success, the quiet belief that because a method has worked for many years, it no longer needs the discipline that made it work in the first place. Every generation of Nepali investors has produced people who forgot this at exactly the wrong moment.
Basanta Sir is candid with his grandchildren about his own mistakes, because a genuine portrait of the complete Canon investor is not a portrait of someone who never erred. In 2008, during Nepal's first great NEPSE bull run following the political changes of that era, he bought too much of a single finance company on pure momentum, ignoring his own rule about position sizing, and watched it lose most of its value in the correction that followed. He does not hide this story from younger relatives. He tells it more often than his successes, because he believes a young investor learns more from an elder's honestly examined failure than from a polished account of unbroken triumph. This, too, is part of what Volume Four calls legacy: not a myth of perfection handed down, but an honest, usable record of a real, imperfect, lifelong education.
REGULATORY DETAIL
SEBON's investor protection framework, strengthened considerably since the 2016 to 2017 bubble, includes stricter disclosure requirements for listed companies, tighter rules around margin lending, and ongoing efforts toward greater transparency in IPO allotment. A Canon investor stays aware that the regulatory environment itself continues to mature, and reads SEBON and NRB circulars as part of ordinary practice, not as an occasional afterthought.
What, then, does the complete Canon investor actually do, on an ordinary Tuesday, decades into this practice? Basanta Sir's own answer, offered to a former student who asked him precisely this question last year, was disarmingly simple. He checks NRB's latest monetary policy statements and banking sector data a few times a year, not daily. He reads the annual reports of the companies he holds, cover to cover, once a year, along with their quarterly results as they are published. He rebalances his portfolio occasionally, trimming a position that has grown too large relative to the rest, adding to one that has become undervalued relative to its worth. He writes in his journal after every decision, however small. He does not check the NEPSE index every day, and he has never once made an investment decision because of something he overheard at a wedding or a tea shop. He talks to his children and grandchildren about all of it, openly, without pretending investing is either magic or mystery. And every evening, weather permitting, he walks his orchard.
CASE IN POINT
When the NEPSE index reached its record high in mid-2021, driven by an unprecedented wave of new retail investors and easy pandemic-era liquidity, Basanta Sir made no new purchases for nearly a year, holding his existing positions but refusing to chase valuations he judged, based on the same analytical tools taught throughout this Canon, to have detached from underlying company earnings. When the index fell by more than a third over the following eighteen months, his portfolio declined far less than the broader market, and he had cash available to add selectively to sound companies at considerably lower prices, precisely the outcome patient discipline is meant to produce.
This is the final image the Canon leaves its reader with, deliberately quiet rather than triumphant. Not a chart racing upward. Not a headline number. An ordinary elderly man in Bhaktapur, walking slowly among trees he planted decades ago, some of which he will never see reach their fullest fruit, at peace with a process he trusts precisely because he built it patiently, tested it honestly against real losses and real fear, and passed it on to people he loves in language plain enough for them to actually use.
That, in the end, is what one hundred and eighteen chapters have been building toward. Not a formula. A character. Not a shortcut to wealth. A way of living with money, with risk, with time, and with family, that a person could actually sustain for an entire lifetime and then hand to the next one intact.
Chapter recap
Across four volumes and one hundred and eighteen chapters, this Canon set out to build something more durable than a trading strategy. It set out to build an investor: someone who understands the weather of the economy through the lens of NRB policy and the credit cycle, who reads the soil of individual companies through patient financial analysis, whose hands and habits can actually execute calmly through both euphoria and fear, and whose thinking stretches far enough beyond their own lifetime to plant trees for grandchildren they may never see harvest. NEPSE will keep changing. New companies will list, old ones will fade, regulations will tighten and loosen again, and the market will surely produce more bubbles and more crashes in the decades ahead, just as it has in every decade behind us. None of that changes what this book has tried to give you, which was never a prediction about where any particular share is headed, but a way of thinking that can meet whatever comes next with patience instead of panic, and with understanding instead of guesswork.
If you take only one thing from everything written here, let it be this: invest like you are planting an orchard, not like you are placing a bet. Choose your ground carefully. Tend it honestly, season after season, especially when no one is watching and nothing seems to be happening. Expect storms, and do not mistake them for the end of the story. And when the fruit finally comes, whether to you or to those who come after you, remember that it was never really about the fruit at all. It was about the patient, disciplined, deeply human work of tending something worth tending, for longer than most people have the courage to try. Go tend your own orchard now. This Canon's work here is done; yours, happily, is only just beginning.
Primary data sources
Figures, rates and rules referenced in this chapter can be verified against the primary sources:
Nepal Rastra Bank (monetary policy, credit and BFI data),
SEBON (regulation and issue approvals),
NEPSE (prices, indices and turnover),
CDSC (settlement and demat data) and
Inland Revenue Department (tax rates and rulings).
If a figure here disagrees with the primary source, trust the primary source and
tell me.