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

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Presentation on theme: "Psychology and Behavioral Finance Fin254f: Spring 2010 Lecture notes 3 Readings: Shiller 8-9, Nofsinger, 1-5."— Presentation transcript:

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 Fallacy Law 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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