Behavioral Finance Practice Exam

What is the formula for mean-variance utility?

U = E(r) – 0.05 × A × σ², where A is risk aversion coefficient. Choose the investment with the highest utility.

What is the utility function for log utility?

U(W) = ln(W) — Used to model risk aversion. Helps calculate expected utility and certainty equivalents.

How do you compute Certainty Equivalent (CE)?

Set U(CE) = E[U], then CE = e^(E[U]) when utility is logarithmic.

How do you calculate Risk Premium (RP)?

RP = E(W) – CEW — Difference between expected value and certainty equivalent.

What does exponential utility imply?

Constant Absolute Risk Aversion (ARA) and Increasing Relative Risk Aversion (RRA).

What does it mean if CE < EV?

Indicates risk aversion — the person prefers a guaranteed amount to a risky gamble.

What is the S-shape of the Prospect Theory value function?

Concave for gains, convex for losses, steeper for losses — shows loss aversion.

What is the weighting function in Prospect Theory?

Overweights small probabilities, underweights large ones — leads to risk-seeking in gains with low probabilities.

What is “segregation” in mental accounting?

Evaluating gains/losses separately (rather than as a net position).

What is “integration” in mental accounting?

Combining multiple outcomes into one mental account.

What is the Joint Hypothesis Problem in EMH?

Tests of EMH require assuming a correct pricing model — can’t separately test EMH and the model.

What are the 3 supports of market efficiency?

1. Investor rationality, 2. Uncorrelated errors, 3. Unlimited arbitrage — Only one needs to hold.

What is the disposition effect?

Selling winners too early, holding onto losers too long — reflects poor loss realization behavior.

What’s the difference between value and growth stocks?

Value stocks = low price relative to fundamentals. Growth stocks = high price due to future growth expectations.

What does anchoring bias lead to?

Sticking to initial estimates or beliefs even with new information — causes slow belief updating.

What is herding behavior?

Following others (the crowd), often irrationally, without independent analysis.

What are the types of overconfidence?

Miscalibration, Better-than-average effect, Illusion of control.

How does overconfidence affect trading behavior?

Leads to excessive trading, lower diversification, and worse portfolio performance.

How do emotions affect financial decisions?

Can lead to irrational risk-taking: e.g., House Money = risk-taking after gains; Break-Even = risk-taking after losses.

What is the house money effect?

People take greater risks with money they’ve recently gained, treating it like “casino” money.

What is the break-even effect?

After a loss, people take more risks to recover (break even), driven by emotion not logic.

What does “bounded rationality” mean?

People make satisficing decisions rather than optimal ones due to cognitive limitations.

What is the best way for a retail investor to diversify?

Buy a market index fund — easy and cost-effective diversification strategy.

What is an example of indirect overconfidence testing?

Analyzing trading volume and linking it statistically to overconfidence indicators.

What is an example of direct overconfidence testing?

Lab experiments measuring miscalibration or better-than-average effect.

Why do arbitrageurs face limits?

Limited wealth, fundamental risk, and noise trader risk — especially for managers handling others’ money (short horizons).

What is noise-trader risk?

Risk that mispricing worsens before correcting, discouraging arbitrage.

What is fundamental risk?

The risk that a stock’s value changes due to new information, making arbitrage risky.

What does research say about home bias?

Investors prefer local investments, often believing they have information advantages, but this is also due to familiarity bias.

What is “value” in market terms?

Intrinsic worth based on fundamentals (book-to-market, earnings, etc.)

What is “price” in market terms?

The market price — may deviate from value due to behavioral biases or noise.

Q1. Explain the difference between a good company and a good stock. Provide an example.

A good company has strong fundamentals (e.g., growth, good management), but it may be overvalued. A good stock is one that is undervalued relative to its intrinsic value, offering higher expected returns. Example: Tesla may be a good company, but if its stock is overpriced, it may not be a good investment. Conversely, an undervalued, struggling firm may be a good stock. Key insight: Price ≠ Value.

Q2. Compare and contrast Representativeness and Anchoring. How do they affect decision-making?

Representativeness: Judging probability based on similarity to stereotypes; can lead to base rate neglect. Anchoring: Relying too heavily on an initial value and adjusting insufficiently. Example: Representativeness = Believing a tech company will succeed because it resembles past successes. Anchoring = Sticking to an old price target despite new info.

Q3. Compare and contrast Emotion-Based and Rational Decision Making. Are they incompatible?

Rational decision-making follows logic and maximizes utility. Emotion-based decisions are driven by feelings (e.g., fear, regret, affect). While often viewed as opposites, they can be complementary. Emotion can guide intuitive decisions or interfere with logic. Traders may learn to manage emotion for optimal decision-making.

Q4. Explain the intuition behind the regression linking LTIV to size, B/M ratio, and MQ. What behavioral biases might this reveal?

The regression shows that investors assign higher long-term value to big firms and those with high management quality and low B/M ratios. This suggests they equate firm size and quality with investment value. Behavioral bias: Representativeness — investors assume past success = future returns. But empirically, small-cap value stocks outperform, not glamorous firms.

Q5. Explain the difference in weights given to winner and loser stocks in retirement portfolios. What biases explain this?

Investors tend to overweight winners and underweight losers, reflecting the Disposition Effect (selling winners early, holding onto losers), Self-Attribution Bias (crediting success to skill), and House Money Effect (taking more risk after gains). These behaviors distort optimal allocation in long-term savings.

Q6. Research shows market forecasters are overconfident. Do they learn from their mistakes? Explain.

Most forecasters show overconfidence (e.g., narrow confidence intervals). Some studies suggest partial learning (adjust intervals after outcomes), but Self-Attribution Bias often prevents full adjustment. Experience can reinforce overconfidence if success is misattributed to skill. So learning is slow and incomplete.

Q7A. Define and distinguish between Momentum and Reversal in asset returns.

Momentum = short-term return continuation (e.g., past winners keep winning for ~6 months). Reversal = long-term mean-reversion (e.g., past winners underperform, losers outperform after ~5 years). Opposing patterns across different time horizons.

Q7B. (Optional) Explain Mean-Reversion and Continuation in the BSV model.

The BSV model (Barberis, Shleifer, Vishny) uses biases like conservatism and representativeness to explain both continuation (momentum) and mean-reversion (reversal). Investors overreact in short term (momentum), then correct beliefs too slowly (mean-reversion).

Q7C. Define and distinguish Size Factor and Book-to-Market Factor in asset pricing.

Size Factor = small firms tend to earn higher returns (SMB = small minus big). B/M Factor = high book-to-market (value stocks) outperform low B/M (growth stocks). Both are part of the Fama-French 3-factor model, capturing size and value anomalies.

Q7D. Compare Risk-Based vs. Behavioral Explanations for Anomalies.

Risk-Based: Anomalies are compensation for unmeasured risk (e.g., small firms are riskier). Behavioral: Anomalies arise from biases (e.g., overconfidence, representativeness) and persist due to limits to arbitrage. Behavioral view explains irrational pricing patterns better in many contexts.

"How do you calculate Expected Utility (E[U])?"

"Use the formula E[U] = p1 * U(w1) + p2 * U(w2); for example if U(W) = ln(W), and there's a 50% chance of £22,000 or £18,000, then E[U] ≈ 0.5 ln(22000) + 0.5 ln(18000) ≈ 9.895."

"How do you calculate the Certainty Equivalent (CEW)?"

"Set U(CEW) = E[U] and solve using the inverse of the utility function; e.g. if E[U] = 9.895 and U(W) = ln(W), then CEW = e^9.895 ≈ £19,847."

"How do you calculate the Risk Premium (RP)?"

"Use RP = E(W) – CEW; for example if E(W) = £20,000 and CEW ≈ £19,847, then RP ≈ £153."

"What is the Mean-Variance Utility formula?"

"U = E(r) – 0.05Aσ²; for example if E(r) = 15%, σ² = 16, A = 2, then U = 15 - 0.05*2*16 = 13.4."

"How do you calculate the maximum price you’d pay for insurance?"

"Set E[U(With Insurance)] = E[U(Without)] and solve for the premium; e.g., without insurance: 0.5 √100 + 0.5 √64 = 9, solve √(100 - I) = 9 ⇒ I = 19."

"How do you calculate value under Prospect Theory?"

"Use V = π(p) * v(x); for example if π(0.001) = 0.011 and v(£5000) = 5000^0.8 ≈ 1799.26, then V = 0.011 * 1799.26 ≈ 19.79."