A Survey of Behavioral Finance
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Transcript of A Survey of Behavioral Finance
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A Survey of Behavioral Finance
Nicholas BarberisRichard Thaler
Presented by Ryan Samson
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Traditional vs. Behavioral
• Traditional
– Rational
• Correct Bayesian Updating
• Choices Consistent with Expected Utility
• Behavioral
– Some are Not Fully Rational
– Relax One or Both Tenets of Rationality
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Behavioral Finance
Limits to Arbitrage Psychology
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Limits to Arbitrage vs.
Market Efficiency• EMH
– Prices Reflect Value– Mispricings Corrected by Arbitrageurs
• Limits to Arbitrage– Strategies May not be Arbitrage– Problems Entering Position?
• Correct Prices => No Free Lunch• No Free Lunch ≠> Correct Prices• Why Care?
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Theory Supporting Limits to Arbitrage
• Fundamental Risk – Negative Shock and no Perfect Substitute (e.g. Ford and GM)
• Noise Trader Risk – Continued Widespread Irrationality– Forced Liquidation (Separation of Brains and
Capital)– Horizon Risk– Trading in the Same Direction
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Theory Supporting Limits to Arbitrage 2
• Implementation Costs– Commission– Bid/Ask Spread– Price Impact– Short Sell Costs
• Fees• Volume Constraints• Legal Restraints
– Identification Cost• Mispricing ≠> Predictability
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Evidence Supporting Limits to Arbitrage
• Mispricings Hard to Identify– Test of Mispricing => Test of Discount Rate Model
• Twin Shares– Royal Dutch (60%) and Shell (40%)
• Only Risk is Noise Traders
– => PriceRD = 1.5*PriceS
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Evidence Supporting Limits to Arbitrage 2
• Index Inclusions– Stock Price Jumps Permanently– 3.5% Average
• Fundamental Risk– Poor Substitutes (best R2 < 0.25)
• Noise Trader Risk– Index Fund Purchases etc.
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Evidence Supporting Limits to Arbitrage 3
• Internet Carve-Outs– 3Com Sells 5% of Palm in IPO, Will Spin Off
Remainder in 9 Months– 1 Share of 3Com will own 1.5 Shares of Palm
– PPalm = $95
– 3Com should be ≥ $142
– P3Com = $81
– Value of 3Com Excluding Palm = -$60
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Evidence Supporting Limits to Arbitrage 4
• Why?
• Very Few Shares of Palm available to Short
• Arbitrage was Limited
• Mispricing Persisted
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Psychology
• Beliefs– Overconfidence
• 98% CI only captures 60%• 100% is actually 80% and 0% is actually 20%
– Optimism / Wishful Thinking• Unrealistic View of Personal Abilities / Prospects• 90% of Drivers Claim Above Average Skill• 99% of Freshman Claim Superior Intelligence
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Psychology 2
• Beliefs Continued– Representativeness
• Base Rates are Under-Emphasized Relative to Evidence
• Sample Size Neglect in Learning Distribution– (6 Tosses vs. 1000 Tosses)
• “Law of Small Numbers”– Gambler’s Fallacy
– Conservatism• Base Rates are Over-Emphasized Relative to
Evidence
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Psychology 3
• Beliefs Continued– Belief Perseverance
• Search for Contradictory Evidence• Treatment of Contradictory Evidence
– Anchoring• Initial Arbitrary Value and Make Adjustments
– Availability Biases• Recent or Salient Events
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Psychology 4
• Beliefs, Final Notes
– People Display Poor Learning in Application
– Experts Often do Worse
– Increasing Incentives Doesn’t Help
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Psychology 5
• Preferences– Expected Utility vs. Prospect Theory or
Ambiguity Aversion
• Prospect Theory– Value of a Gamble is: π(p)*v(x)+π(q)*v(y)– Utility Defined over Gains and Loses– Concave over Gains, Convex over Losses– Nonlinear Probability Transformation
• Especially Large Weight on Certain Outcomes
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Psychology 6
• Ambiguity Aversion– People Avoid Uncertain Probability
Distributions– Aversion Changes Based on Perceived
Competence at Assessing Relevant Distribution
• Preference for Familiar
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Application 1: Aggregate Stock Market
• 3 Puzzles:
– Equity Premium
– High Volatility in Returns and P/D Ratios
– Predictable Returns (D/P alone R2 = 0.27)
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Equity Premium
• Risk Premium Seems too High• Possible Explanations Under Prospect
Theory– Benartzi and Thaler
• Eπv[(1-w)Rf,t+1 + wRt+1 – 1], π and v as before• Given Historical Returns, Investors are Indifferent
to w = 1 and w = 0• Calculate Implied Length of t• 1 Year (Taxes? Annual Reports?)• Result is Myopic Loss Aversion
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Equity Premium 2
• Possible Explanations Under Prospect Theory Continued– Need Intertemporal Model– Barberis, Huang, Santos
• Utility From Consumption (Source 1) AND Utility From Changes in Value of Risky Assets (Source 2)
• Utility From Source 2 Captures Loss Aversion (Not Convexity, Concavity, or Nonlinearity of π)
• Explanatory power based on weight of Source 2
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Equity Premium 3
• Possible Explanations Under Prospect Theory, Final Notes– Why?– Regret– Bounded Rational:
• P(C(Labor Income, Stock Returns) < Habit)• P(C(Stock Returns) < Habit)
– t = 1 Year Based on Presentation
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Equity Premium 4
• Explanations Under Ambiguity Aversion– Max[Min[E[U]]] (i.e. Playing Malevolent
Opponent)– Requires High Equity Premium
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Volatility
• Rational Approaches Must Focus on Changing Risk Aversion to Explain Volatility
• Explanations Under Beliefs– Overreaction to Dividend Growth Volatile Prices
• Law of Small Numbers• Overconfidence in Opinion
– Overreaction to Returns• Law of Small Numbers
– Confusion Between Real and Nominal Rates
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Volatility
• Explanations Under Preferences– Same Model as Used for Equity Premium
– Add zt, a State Variable, to Source 2 of Utility
– Several Price Increases Less Scared– Price Decreases Scared
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Application 2:Cross-Section of Average Returns
• You Can Form Groups of Stocks w/ Different Average Returns, Not Explained by CAPM– Size Premium (Small Stocks +0.74%/month)– Long Term (3 Yr) Reversal (8%/Yr)– Price Ratios
• B/M (High B/M +1.53%/month)• P/E (High P/E +0.68%/month)
– Momentum (6 Month Winners +10%/Yr)
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Cross-Section of Average Returns
• Anomalies Continued– Earnings Announcements (Over 60 Days +4%
for Good Over Bad)– Dividend Initiation / Omission– Stock Repurchases
• Problems w/ Anomalies– Difficult Statistics (Cross-Sectional
Correlation)– Data-Mining (Test Out of Sample)
• Multi-Factor Models
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Cross-Section of Average Returns
• Explanations Under Beliefs– Conservatism (Underweight New Info)
• React Slowly to Earnings Reports
– Representativeness• Overreact Now, Reversal Later
– Overconfidence• Ignore Unfavorable Public Info Reversal• Too Much Attention to Favorable Public Info
Momentum
– All Imply P Around Earnings Report
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Cross-Section of Average Returns
• Belief Based Continued– Positive Feedback
• Momentum• Post Earnings Drift• Long Term Reversal• A Result of Law of Small Numbers?
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Cross-Section of Average Returns
• Belief Based w/ Institutional Friction (i.e. Short Sell Constraints)– Bearish Cannot Short Reversal or
Momentum– Effect of Higher Incentives on Short Prices
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Cross-Section of Average Returns
• Preference Based Explanations– Same BHS Model Applied to Individual
Stocks• Price Reversal (Not Momentum)