Psychology and behavioral finance

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Psychology and behavioral finance

Published in: Business, Economy & Finance

Psychology and behavioral finance

  1. 1. Psychology and Behavioral Finance
  2. 2. Outline <ul><li>What is behavioral finance? </li></ul><ul><li>A list of behavioral features/quirks </li></ul><ul><li>Herding behavior </li></ul><ul><li>Does this all explain bubbles? </li></ul>
  3. 3. Behavioral Finance <ul><li>Acknowledges that investors are not perfectly rational </li></ul><ul><li>Allows for psychological factors of behavior </li></ul><ul><li>Applies results from experiments on risk taking </li></ul>
  4. 4. Behavioral Quirks <ul><li>We all make mistakes </li></ul><ul><li>Laboratory experiments indicate that these can follow consistent patterns </li></ul>
  5. 5. Questions About Quirks <ul><li>Do they apply in the real world (outside the laboratory)? </li></ul><ul><li>Do they aggregate? </li></ul>
  6. 6. Top Behavioral Issues for Finance <ul><li>Overconfidence </li></ul><ul><li>Loss aversion/house money </li></ul><ul><li>Anchoring/representativeness </li></ul><ul><li>Regret </li></ul><ul><li>Mental accounting </li></ul><ul><li>Probability mistakes </li></ul><ul><li>Ambiguity </li></ul><ul><li>Herd behavior </li></ul>
  7. 7. Overconfidence <ul><li>Driving surveys: 82% say above average </li></ul><ul><li>New businesses </li></ul><ul><ul><li>Most fail </li></ul></ul><ul><ul><li>Entrepreneurs believe 70% chance of success </li></ul></ul><ul><ul><li>Believe others have 30% chance of success </li></ul></ul><ul><li>Investors believe they will earn above average returns </li></ul>
  8. 8. Overconfidence and Investor Behavior <ul><li>Conjecture: Overconfident investors trade more (higher turnover) </li></ul><ul><ul><li>Believe information more precise than is </li></ul></ul><ul><li>Psychology: Men more overconfident than women </li></ul><ul><li>Data: Men trade more than women </li></ul><ul><li>Data: High turnover traders have lower returns (net transaction costs) </li></ul>
  9. 9. Overconfidence and Risk taking <ul><li>Overconfident investors take more risk </li></ul><ul><ul><li>Higher beta portfolios </li></ul></ul><ul><ul><li>Smaller firms </li></ul></ul>
  10. 10. Loss Aversion/House Money <ul><li>House money </li></ul><ul><ul><li>More willing to risk recent gains </li></ul></ul><ul><li>Loss aversion </li></ul><ul><ul><li>More risk averse after a recent loss </li></ul></ul><ul><ul><li>General heavier weight on losses (not mean-variance) </li></ul></ul><ul><li>Difficulty : Aggregation </li></ul>
  11. 11. Anchoring/ Representativeness <ul><li>Arbitrary value that impacts decision </li></ul><ul><li>Information shortcut </li></ul><ul><li>Quantitative anchor </li></ul><ul><ul><li>Current stock price, or recent performance </li></ul></ul><ul><ul><li>Price of other stocks </li></ul></ul><ul><ul><li>Loss aversion </li></ul></ul><ul><li>Representativeness/familiarity </li></ul><ul><ul><li>Story telling </li></ul></ul><ul><ul><li>Qualities of good companies </li></ul></ul><ul><ul><li>Own company/local phone companies/home bias </li></ul></ul><ul><li>Status Quo Bias (401K matching funds) </li></ul>
  12. 12. Regret <ul><li>Pain from realizing past decisions were wrong </li></ul><ul><li>Disposition </li></ul><ul><ul><li>Investors hold losers too long, and </li></ul></ul><ul><ul><li>Sell winners too soon </li></ul></ul><ul><ul><li>Evidence: Higher volume on recent winners, lower for losers </li></ul></ul><ul><ul><li>Real estate: Sellers with losses set higher initial bid prices/ wait longer to sell </li></ul></ul><ul><li>Impact on bubbles? </li></ul>
  13. 13. Regret “ My intention was to minimize my future regret. So I split my contribution 50/50 between bonds and stocks.” Harry Markowitz
  14. 14. Mental Accounting <ul><li>You can go on vacation. Would you like to pay for it with </li></ul><ul><ul><li>$200 month for the 6 months before the vacation </li></ul></ul><ul><ul><li>$200 month for the 6 months after the vacation </li></ul></ul>
  15. 15. Probability <ul><li>Difficult for humans </li></ul><ul><li>Conditional probabilities harder </li></ul><ul><ul><li>Information -> Decisions </li></ul></ul><ul><li>Uncertainty/ambiguity </li></ul>
  16. 16. Probability Mistakes <ul><li>Medical tests </li></ul><ul><li>DNA evidence </li></ul><ul><li>Sports </li></ul><ul><li>Game shows (Monty Hall) </li></ul>
  17. 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. 18. Gambler’s Fallacy Law of Small Numbers <ul><li>Decisions made on short data sets </li></ul><ul><ul><li>Hot Hands </li></ul></ul><ul><ul><li>Mutual funds </li></ul></ul><ul><li>Patterns seen in short data sets </li></ul><ul><ul><li>Technical trading </li></ul></ul><ul><li>Is this really irrational? </li></ul><ul><ul><li>Econometrics and regime changes </li></ul></ul><ul><ul><li>“ New Economy” </li></ul></ul>
  19. 19. Ambiguity: Risk and Uncertainty <ul><li>Risk: Know all probabilities </li></ul><ul><li>Uncertainty : Probabilities are not known </li></ul><ul><li>Knight/Ellsberg </li></ul><ul><ul><li>&quot;Knightian uncertainty&quot; </li></ul></ul><ul><li>Casinos versus stock markets </li></ul><ul><li>Securitized debt markets </li></ul>
  20. 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. 21. Herding <ul><li>Group technologies </li></ul><ul><ul><li>News media </li></ul></ul><ul><ul><li>Personal contacts </li></ul></ul><ul><ul><li>Telephones (20’s) </li></ul></ul><ul><ul><li>Internet (90’s) </li></ul></ul><ul><ul><li>Investment clubs </li></ul></ul><ul><li>Investors watch what others our doing and investing in more than fundamentals </li></ul>
  22. 22. Internet Stocks and Herding <ul><li>eToys versus Toys R Us </li></ul><ul><li>Toys-R-Us </li></ul><ul><ul><li>Market value $6 billion </li></ul></ul><ul><ul><li>Earnings $376 million </li></ul></ul><ul><li>eToys </li></ul><ul><ul><li>Market value $8 billion </li></ul></ul><ul><ul><li>Earnings -$28 million, sales $30 million </li></ul></ul>
  23. 23. Experiments <ul><li>Asch experiments: obvious wrong answers (repeated with out physical proximity) </li></ul><ul><li>Milgram and authority </li></ul><ul><li>Candid camera elevators </li></ul>
  24. 24. Information Cascades <ul><li>Restaurant A versus B </li></ul><ul><ul><li>Does the right restaurant survive? </li></ul></ul><ul><li>Epidemics and information </li></ul><ul><ul><li>Infection rate, removal rate </li></ul></ul><ul><ul><li>Logistic curve </li></ul></ul><ul><ul><li>Messy in finance and social systems (doesn’t work like a disease) </li></ul></ul><ul><li>Theory of mind </li></ul><ul><ul><li>Lot’s of hypotheses </li></ul></ul><ul><ul><li>Narrow down to those others have </li></ul></ul>
  25. 25. Summary <ul><li>Humans often behave in somewhat irrational fashions </li></ul><ul><ul><li>Especially when uncertainty is involved </li></ul></ul><ul><li>Key questions remain </li></ul><ul><ul><li>Aggregation </li></ul></ul><ul><ul><li>Bubbles </li></ul></ul><ul><ul><li>Investment strategies </li></ul></ul><ul><li>Keep in mind: </li></ul><ul><ul><li>The real world is very complex </li></ul></ul>

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