Factor & Behavioral Finance

Q1. (3 marks)

• Factor investing: an investment approach that targets specific, systematic drivers of return ("factors") — measurable characteristics shared across securities that explain differences in returns and risk. (2 marks) — the "why": returns are attributed to persistent risk/behavioral sources rather than individual stock picking.
• Two examples (any): value, momentum, quality, size, low-volatility. (1 mark)

Q2. (6 marks — 2 each: 1 for definition, 1 for metric)

• (a) Value: buying stocks that are cheap relative to fundamentals. Metric: P/E, P/B (book-to-market), P/CF, dividend yield.
• (b) Momentum: buying stocks that have performed well recently (and shorting/underweighting recent losers). Metric: trailing 6–12 month return (skipping the most recent month).
• (c) Quality: favouring financially healthy, profitable firms. Metric: ROE, gross profitability, low debt/earnings stability.

Q3. (5 marks) Model equation (2 marks): $R_i - R_f = \alpha_i + \beta_i (R_m - R_f) + s_i \cdot SMB + h_i \cdot HML + \varepsilon_i$ Naming three factors (2 marks): Market (excess return), SMB = Small Minus Big (size), HML = High Minus Low book-to-market (value). Two additional five-factor terms (1 mark): RMW (Robust Minus Weak profitability) and CMA (Conservative Minus Aggressive investment).

Q4. (4 marks) Fama-French three-factor expected return: $E(R) = R_f + \beta(R_m-R_f) + s\cdot SMB + h\cdot HML$ Substitute (1 mark setup): $= 2\% + 1.1(6\%) + 0.4(3\%) + 0.3(4\%)$

• Market term: $1.1 \times 6 = 6.6\%$ (1)
• SMB term: $0.4 \times 3 = 1.2\%$; HML term: $0.3 \times 4 = 1.2\%$ (1)
• Total: $2 + 6.6 + 1.2 + 1.2 = 11.0\%$ (1)

Answer: $11.0\%$

Q5. (4 marks — 2 each)

• Size factor: small-cap stocks tend to outperform large-cap stocks over the long run. Anomaly: CAPM predicts return depends only on market beta, but small caps historically earn a premium beyond their beta. (2)
• Low-volatility factor: low-volatility (low-beta) stocks have delivered higher risk-adjusted returns than high-volatility stocks. Anomaly: CAPM predicts higher beta → higher return, but empirically the relationship is flat or inverted. (2)

Q6. (4 marks)

• Smart beta: rules-based, transparent index strategies that weight securities by factor exposures (e.g., value, low-vol) or non-cap methods rather than market capitalization. (2)
• (a) vs cap-weighting: does not weight purely by market cap; it deliberately tilts toward factors. (1)
• (b) vs active management: it is systematic/rules-based and low-cost, not relying on manager discretion/forecasts. (1)

Q7. (5 marks — 1 each)

• (i) Anchoring → B
• (ii) Confirmation bias → D
• (iii) Herding → A
• (iv) Recency bias → C
• (v) Loss aversion → E

Q8. (4 marks)

• Disposition effect: the tendency of investors to sell winning positions too early while holding onto losing positions too long. (2)
• Link to loss aversion: because losses hurt more than equivalent gains give pleasure, investors avoid "realizing" a loss (holding losers hoping to break even) and rush to lock in gains — the asymmetry drives the behaviour. (2)

Q9. (5 marks)

• Weak form: prices reflect all past price/volume (historical market) data → technical analysis cannot yield excess returns. (1.5)
• Semi-strong form: prices reflect all publicly available information (financials, news) → fundamental analysis of public data cannot beat the market. (1.5)
• Strong form: prices reflect all information, public and private (insider) → even insiders cannot earn excess returns. (2)

[
  {"claim":"Q4: Fama-French 3-factor expected return equals 11.0%","code":"Rf=2; E=Rf+1.1*6+0.4*3+0.3*4; result=(E==11.0)"},
  {"claim":"Q4: market factor contribution is 6.6%","code":"result=(1.1*6==6.6)"},
  {"claim":"Q4: combined SMB+HML contribution is 2.4%","code":"result=(0.4*3+0.3*4==2.4)"},
  {"claim":"Q4: total minus risk-free premium equals 9.0%","code":"E=2+1.1*6+0.4*3+0.3*4; result=(E-2==9.0)"}
]