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
 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

More willing to risk recent gains
 Loss


money
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





Representativeness/familiarity




Current stock price, or recent performance
Price of other stocks
Loss aversion
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 my
future 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 Fallacy
Law of Small Numbers
 Decisions


Hot Hands
Mutual funds
 Patterns

 Is


made on short data sets
seen in short data sets
Technical trading
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


Epidemics and information




Does the right restaurant survive?
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


Key questions remain




Especially when uncertainty is involved
Aggregation
Bubbles
Investment strategies
Keep in mind:

The real world is very complex