Psychology and behavioral finance
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Psychology and behavioral finance

Psychology and behavioral finance

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Psychology and behavioral finance Presentation Transcript

  • 1. Psychology and Behavioral Finance
  • 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