Dean's Office Official Model Question Paper

Answer: Investment: The commitment of current funds into financial or real assets for a long-term period in anticipation of receiving future cash flows that compensate for time, expected inflation, and risk, based on thorough fundamental analysis. Contrast with Speculation:

• Time Horizon: Investment focuses on medium-to-long-term horizons; speculation seeks quick gains over short-term price fluctuations.
• Risk Profile: Investors take calculated, moderate risk supported by asset value; speculators take high, aggressive risks driven by market rumors or momentum.

Answer: Security Market Line (SML): The graphical representation of the Capital Asset Pricing Model (CAPM) depicting the linear relationship between expected return and systematic risk (measured by Beta, $\beta$) for individual assets or portfolios. Equation:

$$E(R_i) = R_f + \beta_i [E(R_m) - R_f]$$

Where $R_f$ is risk-free rate, $E(R_m)$ is expected market return, and $[E(R_m) - R_f]$ is market risk premium.

Answer:

• Systematic (Market) Risk: Non-diversifiable risk stemming from macroeconomic factors that impact all securities simultaneously (e.g., nationwide inflation spikes, central bank interest rate hikes, political upheaval).
• Unsystematic (Company-Specific) Risk: Unique, diversifiable risk caused by microeconomic factors specific to an individual firm or industry (e.g., labor strike at a factory, product recall, CEO resignation).

Answer: Beta ($\beta$): A standardized measure of a security’s sensitivity or volatility relative to movements in the overall market portfolio:

$$\beta_i = \frac{\text{Cov}(R_i, R_m)}{\sigma_m^2}$$

Meaning of $\beta = 1.4$: The stock is 40% more volatile than the market benchmark. If the market index (NEPSE) rises by 10%, the stock is expected to gain 14%; if the market declines by 10%, the stock is expected to fall by 14%.

Answer: Macaulay Duration: The weighted average term to maturity of the cash flows from a bond, where the weights are the present values of each respective cash flow relative to the total bond price:

$$D = \sum_{t=1}^{n} \frac{t \times \text{PV}(\text{CF}_t)}{\text{Price}}$$

It quantifies both the effective maturity and the interest rate price sensitivity of the bond.

Solution:

1. Expected Return:
   $$E(R) = (0.60 \times 22\%) + (0.40 \times -8\%) = 13.2\% - 3.2\% = \mathbf{10.0\%}$$
2. Variance ($\sigma^2$):
   $$\sigma^2 = \sum P_i [R_i - E(R)]^2 = 0.60(22 - 10)^2 + 0.40(-8 - 10)^2$$
   $$\sigma^2 = 0.60(144) + 0.40(324) = 86.4 + 129.6 = \mathbf{216.0\%^2} \implies \sigma = \sqrt{216} = \mathbf{14.70\%}$$

Answer:

1. Weak Form: Stock prices fully reflect all past trading history and historical price/volume data (Technical analysis cannot generate abnormal returns).
2. Semi-Strong Form: Stock prices adjust rapidly to all publicly available information, including financial statements, earnings announcements, and macroeconomic news (Fundamental analysis cannot earn abnormal returns).
3. Strong Form: Stock prices reflect all information, public and private/insider information alike (Nobody, even corporate insiders, can earn abnormal returns).

Answer:

• Open-End Mutual Fund: Has no fixed maturity and variable capital; units are bought and sold directly through the fund manager at daily Net Asset Value (NAV).
• Closed-End Mutual Fund: Has a fixed maturity (e.g., 7 or 10 years) and fixed number of units; listed and traded on the stock exchange (NEPSE) between investors, often trading at a discount or premium to NAV.

Answer: Margin Trading: Buying securities by borrowing a portion of the purchase price from a broker/bank using the purchased securities as collateral.

• Initial Margin: The minimum percentage of equity cash that the investor must deposit upfront at the time of purchasing the stock (e.g., 50%).
• Maintenance Margin: The minimum equity percentage that the investor must maintain in the margin account after purchase before triggering a margin call.

Answer: Technical Analysis: A security analysis methodology that forecasts future price movements and trends based on historical price charts, trading volume, and market behavioral patterns rather than company intrinsic financials. Two Common Indicators:

1. Relative Strength Index (RSI): A momentum oscillator measuring overbought (>70) and oversold (<30) conditions.
2. Moving Average Convergence Divergence (MACD): A trend-following momentum indicator displaying relationship between two moving averages.

Solution: Markowitz Two-Asset Portfolio Analysis

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Part (a): Portfolio Expected Return

$$E(R_p) = w_A E(R_A) + w_B E(R_B) = (0.60 \times 14\%) + (0.40 \times 20\%) = 8.4\% + 8.0\% = \mathbf{16.40\%}$$

(Expected return is a linear weighted average unaffected by correlation).

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Part (b): Portfolio Standard Deviation

The portfolio variance equation is:

$$\sigma_p = \sqrt{w_A^2 \sigma_A^2 + w_B^2 \sigma_B^2 + 2 w_A w_B \sigma_A \sigma_B \rho_{AB}}$$

• $w_A^2 \sigma_A^2 = (0.60)^2 (18)^2 = 0.36 \times 324 = 116.64$
• $w_B^2 \sigma_B^2 = (0.40)^2 (26)^2 = 0.16 \times 676 = 108.16$
• $2 w_A w_B \sigma_A \sigma_B = 2(0.60)(0.40)(18)(26) = 224.64$

1. Scenario 1: $\rho_{AB} = +1.0$ (Perfect Positive Correlation)

$$\sigma_p = \sqrt{116.64 + 108.16 + 224.64(1.0)} = \sqrt{449.44} = \mathbf{21.20\%}$$

(Note: Exactly equal to weighted average: $0.60(18) + 0.40(26) = 10.8 + 10.4 = 21.20\%$. No risk diversification).

2. Scenario 2: $\rho_{AB} = 0.0$ (Zero Correlation / Independent)

$$\sigma_p = \sqrt{116.64 + 108.16 + 0} = \sqrt{224.80} = \mathbf{14.99\%}$$

(Portfolio risk drops below both individual stocks’ weighted average and even below Stock A’s individual risk!).

3. Scenario 3: $\rho_{AB} = -1.0$ (Perfect Negative Correlation)

$$\sigma_p = \sqrt{116.64 + 108.16 - 224.64} = \sqrt{0.16} = \mathbf{0.40\%} \approx \mathbf{0\%}$$

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Part (c): Fundamental Markowitz Diversification Principle

This demonstrates that portfolio risk is driven fundamentally by the correlation coefficient ($\rho$) between assets, not merely individual asset risks.

• When correlation is less than $+1.0$, diversification eliminates unsystematic risk without sacrificing portfolio expected return.
• When correlation reaches $-1.0$, risk can be virtually eliminated while still achieving a lucrative 16.4% expected return.

Solution: CAPM Security Valuation

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Part (a) & (b): Computations under CAPM

Market Risk Premium $= E(R_m) - R_f = 13\% - 5\% = 8\%$. CAPM Required Return: $k_i = R_f + \beta_i [E(R_m) - R_f] = 5\% + \beta_i(8\%)$. Jensen’s Alpha: $\alpha_i = \text{Estimated Return } (R_i) - \text{Required Return } (k_i)$.

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Part (c): Strategic Evaluation

• Stock X (Alpha = +0.60%): Generates an expected return (12.0%) higher than its required risk-adjusted benchmark (11.4%). Its current price is too low; investors should BUY Stock X.
• Stock Y (Alpha = 0.00%): Accurately priced for its systematic market risk; investors should HOLD.
• Stock Z (Alpha = -1.20%): Offers only 15.0% return while requiring 16.2% for its high systematic risk ($\beta = 1.4$). Its market price is inflated; investors should SELL or short-sell Stock Z.

Solution: Semi-Annual Bond Valuation

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Given:

• Par Value ($M$): Rs. 1,000
• Annual Coupon Rate: 8% $\implies$ Annual Coupon $= \text{Rs. } 80$
• Semi-annual Coupon ($C/2$): $\frac{80}{2} = \text{Rs. } 40$
• Current Market Price ($V_0$): Rs. 950
• Maturity ($n$): 6 years $\implies$ Semi-annual periods ($2n$) = 12 periods

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Part (a): Current Yield

$$\text{Current Yield} = \frac{\text{Annual Coupon Payment}}{\text{Current Market Price}} = \frac{\text{Rs. } 80}{\text{Rs. } 950} = \mathbf{8.42\%}$$

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Part (b): Approximate Yield to Maturity (YTM)

For semi-annual payments, let semi-annual yield be $YTM_{semi}$:

$$YTM_{semi} \approx \frac{(C/2) + \frac{M - V_0}{2n}}{\frac{M + 2V_0}{3}} = \frac{40 + \frac{1,000 - 950}{12}}{\frac{1,000 + 2(950)}{3}}$$

$$\text{Numerator} = 40 + \frac{50}{12} = 40 + 4.167 = 44.167$$

$$\text{Denominator} = \frac{1,000 + 1,900}{3} = \frac{2,900}{3} = 966.67$$

$$YTM_{semi} \approx \frac{44.167}{966.67} = 0.04569 = 4.569\% \text{ per six months}$$

$$\mathbf{\text{Nominal Annual YTM}} = 2 \times YTM_{semi} = 2 \times 4.569\% = \mathbf{9.138\% \approx 9.14\%}$$

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Part (c): Effective Annual Yield (EAY)

$$\mathbf{\text{EAY}} = (1 + YTM_{semi})^2 - 1 = (1 + 0.04569)^2 - 1 = (1.04569)^2 - 1 = 1.09347 - 1 = \mathbf{9.35\%}$$

Solution: Two-Stage Dividend Discount Valuation

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Given:

• Current Dividend ($D_0$): Rs. 10.00
• Supernormal Growth Rate ($g_s$): 20% (for Years 1 to 3)
• Stable Long-term Growth Rate ($g_n$): 6% (from Year 4 onward)
• Required Rate of Return ($k_e$): 14% ($0.14$)

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Step 1: Forecast Dividends during Supernormal Period (Years 1–3)

• $D_1 = D_0(1 + g_s) = 10 \times (1 + 0.20) = \text{Rs. } 12.00$
• $D_2 = D_1(1 + g_s) = 12 \times (1 + 0.20) = \text{Rs. } 14.40$
• $D_3 = D_2(1 + g_s) = 14.40 \times (1 + 0.20) = \text{Rs. } 17.28$

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Step 2: Present Value of Supernormal Dividends at $k_e = 14\%$

• $PV(D_1) = \frac{12.00}{(1.14)^1} = \frac{12.00}{1.14} = \text{Rs. } 10.53$
• $PV(D_2) = \frac{14.40}{(1.14)^2} = \frac{14.40}{1.2996} = \text{Rs. } 11.08$
• $PV(D_3) = \frac{17.28}{(1.14)^3} = \frac{17.28}{1.48154} = \text{Rs. } 11.66$$\text{Total PV of Dividends (Years 1–3)} = 10.53 + 11.08 + 11.66 = \mathbf{\text{Rs. } 33.27}$$

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Step 3: Compute Terminal Stock Price ($P_3$) at End of Year 3

First, determine $D_4$ under stable growth ($g_n = 6\%$):

$$D_4 = D_3(1 + g_n) = 17.28 \times (1 + 0.06) = \text{Rs. } 18.3168$$

$$P_3 = \frac{D_4}{k_e - g_n} = \frac{18.3168}{0.14 - 0.06} = \frac{18.3168}{0.08} = \mathbf{\text{Rs. } 228.96}$$

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Step 4: Present Value of Terminal Price ($P_3$)

$$PV(P_3) = \frac{P_3}{(1.14)^3} = \frac{228.96}{1.48154} = \mathbf{\text{Rs. } 154.54}$$

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Step 5: Total Intrinsic Value of the Stock ($P_0$)

$$\mathbf{P_0} = \text{PV of Dividends (1–3)} + PV(P_3) = \text{Rs. } 33.27 + 154.54 = \mathbf{\text{Rs. } 187.81}$$

1. The Top-Down E-I-C Valuation Framework

Fundamental analysis follows a hierarchical top-down approach to determine whether an equity is fundamentally sound and fairly valued.

                  [ 1. Economic Analysis ]
                  - GDP, Remittance, Interest Rates, Inflation
                             |
                             v
                  [ 2. Industry Analysis ]
                  - Industry Life Cycle, Regulations, Porter's Forces
                             |
                             v
                  [ 3. Company Analysis ]
                  - Financial Statements, Management, Governance, Competitive Moat

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2. Application to NEPSE Stock Selection

A. Economic Analysis (Macro Environment)

• Monetary Policy & Liquidity: Bank liquidity directly dictates stock market cycles. In Nepal, when the banking sector experiences a liquidity crunch and interbank rates jump to 8%–10%, equity valuations contract. When liquidity is flush and interest rates plunge, equity markets rally.
• Remittance Inflows: Surging worker remittances stimulate household disposable income, driving consumption and capital market retail investments.

B. Industry Analysis (Sector Selection)

• Identify sectors poised to benefit from current economic conditions:
  • Hydropower Sector: Benefit from government power purchase agreements (PPA) and export treaties with India.
  • Commercial Banks: Mergers reducing overheads, though constrained by asset quality and loan-loss provisions.
  • Tourism/Hospitality: Expanding post-crisis tourism arrivals in Pokhara, Chitwan, and Kathmandu.

C. Company Analysis (Firm Selection)

• Financial Statement Ratios: Evaluate Return on Equity (ROE), Net Interest Margin (NIM), Non-Performing Loans (NPL ratio), Price-to-Earnings (P/E) multiple, and Price-to-Book (P/B) ratio.
• Management Integrity & Corporate Governance: Evaluate promoter reputation, transparency in quarterly reporting, and consistency in dividend payout track records.

Conclusion: Buying a great company operating in a collapsing industry during a macroeconomic recession leads to losses. The E-I-C framework ensures that capital is deployed only when economic tailwinds, industry strength, and firm-level superiority align.

1. The Three Portfolio Performance Measures

                     Portfolio Performance Metrics
                                   |
     +-----------------------------+-----------------------------+
     |                             |                             |
Sharpe Ratio                 Treynor Ratio                 Jensen's Alpha
Total Risk Benchmark (sigma) Systematic Risk Benchmark (beta) Abnormal Return vs SML

A. Sharpe Ratio ($S_p$)

Measures the excess return earned per unit of Total Risk (standard deviation, $\sigma_p$):

$$S_p = \frac{\bar{R}_p - R_f}{\sigma_p}$$

• Evaluates the steepness of the Capital Allocation Line (CAL).
• Higher Sharpe ratio indicates superior risk-adjusted performance.

B. Treynor Ratio ($T_p$)

Measures the excess return earned per unit of Systematic Risk (beta, $\beta_p$):

$$T_p = \frac{\bar{R}_p - R_f}{\beta_p}$$

• Assumes the portfolio is already completely diversified, so unsystematic risk has been eliminated.

C. Jensen’s Alpha ($\alpha_p$)

Measures the absolute abnormal return generated by the fund manager over and above the return predicted by the CAPM:

$$\alpha_p = \bar{R}_p - [R_f + \beta_p (\bar{R}_m - R_f)]$$

• If $\alpha > 0$: Manager demonstrated superior stock selection and market timing skill.
• If $\alpha < 0$: Manager underperformed the benchmark.

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2. When to Use Sharpe vs. Treynor

• Use Sharpe Ratio: When evaluating an investor’s entire wealth portfolio (or when the portfolio is poorly diversified). Since total risk ($\sigma$) encompasses both systematic and unsystematic risk, Sharpe rewards proper diversification.
• Use Treynor Ratio: When evaluating a specific mutual fund that will be added as a small component to an already well-diversified macro portfolio. Here, unique risk will be diversified away, so only systematic risk ($\beta$) matters.

Solution: Markowitz Portfolio Optimization

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Part (a): Minimum Variance Portfolio (MVP) Formulation

For a two-asset portfolio, the weight of Asset X that minimizes total portfolio variance is:

$$w_X^* = \frac{\sigma_Y^2 - \text{Cov}(X, Y)}{\sigma_X^2 + \sigma_Y^2 - 2 \text{Cov}(X, Y)}$$

Where:

• $\sigma_X^2 = (16)^2 = 256$
• $\sigma_Y^2 = (24)^2 = 576$
• $\text{Cov}(X, Y) = \rho_{XY} \sigma_X \sigma_Y = (-0.20) \times 16 \times 24 = -76.80$

Substitute into the formula:

$$w_X^* = \frac{576 - (-76.80)}{256 + 576 - 2(-76.80)} = \frac{576 + 76.80}{832 + 153.60} = \frac{652.80}{985.60} = \mathbf{0.6623 \implies 66.23\%}$$

$$w_Y^* = 1 - w_X^* = 1 - 0.6623 = \mathbf{0.3377 \implies 33.77\%}$$

Expected Return of MVP:

$$E(R_{\text{MVP}}) = w_X^* E(R_X) + w_Y^* E(R_Y) = (0.6623 \times 12\%) + (0.3377 \times 20\%)$$

$$E(R_{\text{MVP}}) = 7.948\% + 6.754\% = \mathbf{14.70\%}$$

Standard Deviation of MVP:

$$\sigma_{\text{MVP}}^2 = w_X^2 \sigma_X^2 + w_Y^2 \sigma_Y^2 + 2 w_X w_Y \text{Cov}(X, Y)$$

$$\sigma_{\text{MVP}}^2 = (0.6623)^2(256) + (0.3377)^2(576) + 2(0.6623)(0.3377)(-76.80)$$

$$\sigma_{\text{MVP}}^2 = (0.4386 \times 256) + (0.1140 \times 576) - 34.364 = 112.28 + 65.66 - 34.36 = 143.58\%^2$$

$$\sigma_{\text{MVP}} = \sqrt{143.58} = \mathbf{11.98\%}$$

(Notice that the MVP risk of 11.98% is substantially lower than both Asset X’s 16% and Asset Y’s 24%!).

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Part (b): Portfolio with $w_X = 0.60$ and $w_Y = 0.40$

1. Expected Return:
   $$E(R_p) = (0.60 \times 12\%) + (0.40 \times 20\%) = 7.2\% + 8.0\% = \mathbf{15.20\%}$$
2. Variance & Standard Deviation:
   $$\sigma_p^2 = (0.60)^2(256) + (0.40)^2(576) + 2(0.60)(0.40)(-76.80)$$
   $$\sigma_p^2 = (0.36 \times 256) + (0.16 \times 576) - 36.864 = 92.16 + 92.16 - 36.86 = 147.46\%^2$$
   $$\sigma_p = \sqrt{147.46} = \mathbf{12.14\%}$$

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Part (c): Capital Allocation Line (CAL) and Two-Fund Separation

• Definition of CAL: The graphical line created by plotting expected return against risk (standard deviation) for all possible combinations of the risk-free asset ($R_f$) and an optimal risky portfolio ($P^*$).
• Slope of CAL: Represents the Sharpe Ratio of the optimal tangency portfolio:
  $$\text{Slope } (S) = \frac{E(R_{P^*}) - R_f}{\sigma_{P^*}}$$
• Tobin’s Separation Theorem: The portfolio choice problem separates into two independent decisions:
  1. Technical Investment Decision: Identifying the unique optimal risky tangency portfolio ($P^*$) which maximizes the Sharpe ratio. This portfolio is identical for all investors regardless of risk aversion.
  2. Financing / Personal Preference Decision: Choosing the proportion allocated to risk-free lending versus risky portfolio $P^*$ based on individual risk tolerance (utility indifference curves). Highly conservative investors hold mostly $R_f$ and little $P^*$; aggressive investors borrow at $R_f$ to leverage portfolio $P^*$.

Solution: Bond Duration, Volatility, and Convexity

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Part (a): Current Market Price at YTM = 10%

• Annual Coupon ($C$): $\text{Rs. } 1,000 \times 9\% = \text{Rs. } 90$
• Par Value ($M$): Rs. 1,000; Maturity ($n$): 4 years; $y = 10\%$ ($0.10$)

$$P_0 = 90 \times \text{PVIFA}_{10\%, 4} + 1,000 \times \text{PVIF}_{10\%, 4}$$

$$\text{PVIFA}_{10\%, 4} = 3.16987, \quad \text{PVIF}_{10\%, 4} = 0.68301$$

$$P_0 = (90 \times 3.16987) + (1,000 \times 0.68301) = 285.29 + 683.01 = \mathbf{\text{Rs. } 968.30}$$

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Part (b): Macaulay Duration ($D$) and Modified Duration ($D^*$)

$$\mathbf{\text{Macaulay Duration } (D)} = \frac{\sum t \times \text{PV}(CF_t)}{P_0} = \frac{\text{Rs. } 3,411.36}{\text{Rs. } 968.30} = \mathbf{3.523 \text{ Years}}$$

$$\mathbf{\text{Modified Duration } (D^*)} = \frac{D}{1 + y} = \frac{3.523}{1 + 0.10} = \frac{3.523}{1.10} = \mathbf{3.203 \text{ Years}}$$

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Part (c): Duration-Estimated Price Change for $\Delta y = -1.50\%$ (-0.015)

$$\%\Delta P \approx -D^* \times \Delta y = -3.203 \times (-0.015) = +0.04804 = \mathbf{+4.80\%}$$

$$\text{Estimated Price Increase} = \text{Rs. } 968.30 \times 4.804\% = \text{Rs. } 46.52$$

$$\mathbf{\text{Estimated New Bond Price}} = \text{Rs. } 968.30 + 46.52 = \mathbf{\text{Rs. } 1,014.82}$$

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Part (d): Exact Bond Price at YTM = 8.50% and Convexity Explanation

At new yield $y = 8.50\%$ ($0.085$):

• $\text{PVIFA}_{8.5\%, 4} = \frac{1 - (1.085)^{-4}}{0.085} = 3.2753$
• $\text{PVIF}_{8.5\%, 4} = (1.085)^{-4} = 0.72157$$\text{Actual } P_0' = (90 \times 3.2753) + (1,000 \times 0.72157) = 294.78 + 721.57 = \mathbf{\text{Rs. } 1,016.35}$$
  $$\text{Actual Percentage Change} = \frac{1,016.35 - 968.30}{968.30} = \frac{48.05}{968.30} = \mathbf{+4.96\%}$$

Comparison & Convexity Explanation:

• Duration estimated price $= \text{Rs. } 1,014.82$ ($+4.80\%$)
• Actual market price $= \text{Rs. } 1,016.35$ ($+4.96\%$)
• The duration rule underestimates the price increase by Rs. 1.53 because duration is a linear tangent approximation of a curved function. The price-yield relationship of a standard option-free bond is convex (curving upward).
• When interest rates decline, Convexity works in favor of bondholders: prices rise faster than linear duration predicts, and when rates rise, prices fall slower than duration predicts.

1. The Behavioral Finance Revolution in Emerging Markets

Traditional finance assumes market participants are fully rational, risk-averse utility maximizers who process all market information instantaneously. In contrast, Behavioral Finance integrates cognitive psychology to explain why actual market prices deviate systematically from intrinsic fundamental values due to psychological biases and irrational decision heuristics.

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2. Major Behavioral Biases Prevalent on NEPSE

                  Behavioral Anomalies Observed on NEPSE
                                    |
    +-------------------+-----------+-----------+-------------------+
    |                   |                       |                   |
Herd Behavior &        Disposition Effect       Overconfidence &    Anchoring &
Social Media Hype      & Loss Aversion          Illusion of Skill   Framing Bias

1. Herd Behavior (Bhedi Bheda Chal)

• In Nepal, retail investors heavily follow anonymous social media groups (Viber, Facebook stock chatrooms, Clubhouse) and prominent market speculators.
• Retailers aggressively buy low-cap microfinance and hydropower stocks without examining debt burdens or audited balances merely because everyone else is buying, creating speculative price bubbles disconnected from fundamentals.

2. The Disposition Effect (Mental Accounting & Loss Aversion)

• Propounded by Shefrin and Statman: Investors rush to sell winning stocks prematurely to lock in modest gains ("greed for small profit"), while stubbornly holding onto deeply losing stocks for years hoping they will break even.
• This creates severe capital misallocation, leaving retail portfolios dominated by financially distressed companies.

3. Overconfidence and Self-Attribution Bias

• During bull markets (e.g., NEPSE rising toward 3,200 index levels), beginner investors misattribute market-wide gains to their personal investment genius.
• Overconfident investors borrow heavily via margin lending and cooperative loans to amplify positions, making them vulnerable when liquidity tightens.

4. Gambler’s Fallacy & Lottery Preference

• Intense retail obsession with 10-unit IPO applications and Right Shares (Hakprada), regardless of corporate fundamentals. Retailers treat right share offerings as "free bonus gifts" rather than equity dilution.

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3. Impact on Market Volatility in Nepal

These synchronized cognitive biases create extreme price distortion:

• Unprofitable hydropower entities with negative net worth have traded at P/E multiples exceeding 100$\times$.
• When liquidity contracts, panic-selling takes hold; herd sentiment reverses completely, triggering limit-down circuit breakers and wiping out billions in household savings.

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4. Regulatory and Policy Interventions for SEBON and NEPSE

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5. Conclusion

A resilient capital market requires both structural institutional modernization and investor behavioral maturity. By empowering SEBON’s surveillance mechanisms and fostering an investment culture rooted in fundamental cash flows rather than speculative gossip, Nepal can transform NEPSE into a trusted engine for national capital formation.