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Psychology and Behavioral Finance

Psychology and Behavioral Finance. Fin254f: Spring 2010 Lecture notes 3 Readings: Shiller 8-9, Nofsinger, 1-5. Outline. What is behavioral finance? A list of behavioral features/quirks Herding behavior Does this all explain bubbles?. Behavioral Finance.

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Psychology and Behavioral Finance

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  1. Psychology and Behavioral Finance Fin254f: Spring 2010 Lecture notes 3 Readings: Shiller 8-9, Nofsinger, 1-5

  2. Outline • What is behavioral finance? • A list of behavioral features/quirks • Herding behavior • Does this all explain bubbles?

  3. Behavioral Finance • Acknowledges that investors are not perfectly rational • Allows for psychological factors of behavior • Applies results from experiments on risk taking

  4. Behavioral Quirks • We all make mistakes • Laboratory experiments indicate that these can follow consistent patterns

  5. Questions About Quirks • Do they apply in the real world (outside the laboratory)? • Do they aggregate?

  6. Top Behavioral Issues for Finance • Overconfidence • Loss aversion/house money • Anchoring/representativeness • Regret • Mental accounting • Probability mistakes • Ambiguity • Herd behavior

  7. Overconfidence • Driving surveys: 82% say above average • New businesses • Most fail • Entrepreneurs believe 70% chance of success • Believe others have 30% chance of success • Investors believe they will earn above average returns

  8. Overconfidence and Investor Behavior • Conjecture: Overconfident investors trade more (higher turnover) • Believe information more precise than is • Psychology: Men more overconfident than women • Data: Men trade more than women • Data: High turnover traders have lower returns (net transaction costs)

  9. Overconfidence and Risk taking • Overconfident investors take more risk • Higher beta portfolios • Smaller firms

  10. Loss Aversion/House Money • House money • More willing to risk recent gains • Loss aversion • More risk averse after a recent loss • General heavier weight on losses (not mean-variance) • Difficulty : Aggregation

  11. Anchoring/Representativeness • Arbitrary value that impacts decision • Information shortcut • Quantitative anchor • Current stock price, or recent performance • Price of other stocks • Loss aversion • Representativeness/familiarity • Story telling • Qualities of good companies • Own company/local phone companies/home bias • Status Quo Bias (401K matching funds)

  12. Regret • Pain from realizing past decisions were wrong • Disposition • Investors hold losers too long, and • Sell winners too soon • Evidence: Higher volume on recent winners, lower for losers • Real estate: Sellers with losses set higher initial bid prices/ wait longer to sell • Impact on bubbles?

  13. Regret “My intention was to minimize my future regret. So I split my contribution 50/50 between bonds and stocks.” Harry Markowitz

  14. Mental Accounting • You can go on vacation. Would you like to pay for it with • $200 month for the 6 months before the vacation • $200 month for the 6 months after the vacation

  15. Probability • Difficult for humans • Conditional probabilities harder • Information -> Decisions • Uncertainty/ambiguity

  16. Probability Mistakes • Medical tests • DNA evidence • Sports • Game shows (Monty Hall)

  17. Linda is 31 years old, single, outspoken, and very bright. She majored in environmental studies. She is an avid hiker, and also participated in anti-nuclear rallies. Which is more likely? A.) Linda is a bank teller. B.) Linda is a bank teller and a member of Green Peace.

  18. Gambler’s FallacyLaw of Small Numbers • Decisions made on short data sets • Hot Hands • Mutual funds • Patterns seen in short data sets • Technical trading • Is this really irrational? • Econometrics and regime changes • “New Economy”

  19. Ambiguity: Risk and Uncertainty • Risk: Know all probabilities • Uncertainty: Probabilities are not known • Knight/Ellsberg • "Knightian uncertainty" • Casinos versus stock markets • Securitized debt markets

  20. Donald Rumsfeld on Ambiguity “Reports that say that something hasn't happened are always interesting to me, because as we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don't know we don't know.”

  21. Herding • Group technologies • News media • Personal contacts • Telephones (20’s) • Internet (90’s) • Investment clubs • Investors watch what others our doing and investing in more than fundamentals

  22. Internet Stocks and Herding • eToys versus Toys R Us • Toys-R-Us • Market value $6 billion • Earnings $376 million • eToys • Market value $8 billion • Earnings -$28 million, sales $30 million

  23. Experiments • Asch experiments: obvious wrong answers (repeated with out physical proximity) • Milgram and authority • Candid camera elevators

  24. Information Cascades • Restaurant A versus B • Does the right restaurant survive? • Epidemics and information • Infection rate, removal rate • Logistic curve • Messy in finance and social systems (doesn’t work like a disease) • Theory of mind • Lot’s of hypotheses • Narrow down to those others have

  25. Summary • Humans often behave in somewhat irrational fashions • Especially when uncertainty is involved • Key questions remain • Aggregation • Bubbles • Investment strategies • Keep in mind: • The real world is very complex

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