Download - Psychology and behavioral finance
Psychology and Behavioral Finance
Outline
What is behavioral finance? A list of behavioral features/quirks Herding behavior Does this all explain bubbles?
Behavioral Finance
Acknowledges that investors are not perfectly rational
Allows for psychological factors of behavior Applies results from experiments on risk
taking
Behavioral Quirks
We all make mistakes Laboratory experiments indicate that these
can follow consistent patterns
Questions About Quirks
Do they apply in the real world (outside the laboratory)?
Do they aggregate?
Top Behavioral Issues for Finance
Overconfidence Loss aversion/house money Anchoring/representativeness Regret Mental accounting Probability mistakes Ambiguity Herd behavior
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
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)
Overconfidence and Risk taking
Overconfident investors take more risk Higher beta portfolios Smaller firms
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
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)
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?
Regret
“My intention was to minimize myfuture regret. So I split my contribution
50/50 between bonds and stocks.”
Harry Markowitz
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
Probability
Difficult for humans Conditional probabilities harder
Information -> Decisions
Uncertainty/ambiguity
Probability Mistakes
Medical tests DNA evidence Sports Game shows (Monty Hall)
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.
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”
Ambiguity: Risk and Uncertainty
Risk: Know all probabilities Uncertainty: Probabilities are not known Knight/Ellsberg
"Knightian uncertainty"
Casinos versus stock markets Securitized debt markets
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.”
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
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
Experiments
Asch experiments: obvious wrong answers (repeated with out physical proximity)
Milgram and authority Candid camera elevators
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
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