Transcript Introduction to Behavioural Economics & Finance

Introduction to Behavioural
Economics & Finance
Birkbeck College, London
www.williamboot.net
[email protected]
Introduction
• Economics about allocating resources => trade-off => choices
• Decision-making is therefore central to understanding economics
• All economic theory assumes (needs) a theory of human behaviour
• Model of human behaviour in most economics is very simplified…
A motivating example - mobile phones
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Assume perfect competition, I.e. prices = costs
Phones cost £100 to make
Calls cost £.01/minute to supply
Behavioural model
– Users not sure how many minutes/month will be used
– Opens up possibility to extract rent in market for minutes: offer x
free minutes/month and extortionate charges if you go over this
limit. People will assume they can control their usage within limit
– Fierce competition in phones market to attract users
– Efficiency issues: buy too many phones; under use minutes
Anomalies (Richard Thaler, EP)
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Violations of law of one price (Shell Oil)
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Stock bubbles (Internet boom)
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Equity premium puzzle - risk premium too high
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Selling winning stocks, holding losers
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Individuals fail to save for retirement, despite employee contributions
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Dividends spent, capital gains not
Meet Homo Economicus
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Economics relies on an unrealistic model of human behaviour:
– Unlimited will power
– Unlimited rationality
– Unlimited selfishness
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In theoretical terms:
– Bayesian decision makers
– Stable preferences (know what we want)
– Maximise utility (self interest)
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Behavioural models use more realistic models of human behaviour by using
evidence from psychology as to how people really behave
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Methodology: experiments (psychology inspired); econometrics (esp in
finance) and increasingly formal modelling
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Discussion point: Friedman criticism
Plan for today
• Psychology: How do we form beliefs?
• What does U(x) look like?
• Do we really maximise ‘utility’?
• Implications for policy
Judgement, heuristics & biases
• Representativeness
• Law of small numbers
• Availability
• Anchoring
• Confirmatory bias of beliefs
Representativeness
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Overuse Bayes law; ignore base rates, e.g. see someone who looks like a criminal, we
tend to ignore data on percentage of people who are criminals when forming an
assessment of whether this person is a criminal. More reliance on similarity
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Engineer vs lawyers (Tversky & Kahneman, 1974). Give subjects a description of
individual together with base rate data, e.g. 70% lawyers, 30% engineers. Subjects
ignored the data
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Conjunctive effect:
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“Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a
student she was deeply concerned with issues of discrimination and social justice, and also
participated in anti-nuclear demonstration”
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Linda is a primary school teacher
Linda is a bank worker
Linda works in a bookstore and teaches yoga
Linda is active in the feminist movement
Linda is a psychiatric social worker
Linda is a bank worker and active in the feminist movement
Prob (A and B) must be less than Prob (A) pr (Prob B)!
Law of small numbers
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People assume that a small group will resemble parent population, or underlying
probability distribution that generates the group.E.g. in 10 coin tosses, how many heads?
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Kahneman & Tversky (1982a)
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A certain town is served by two hospitals. In the larger hospital, about 45 babies are born each
day, and 15 in the smaller hospital. You know that roughly 50% of babies born are boys, though
may be higher or lower on a particular day. Over 1 year each hospital recorded the days on
which more than 60% od babies born were boys. Which hospital recorded more such days?
Correct answer is: smaller hospital.
People exaggerate likelihood that a short sequence will resemble a longer sequence (gambler’s
fallacy)
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Simultaneously ignore relevance of large samples
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Misperception of regression to the team: read too much into short sequences;
exceptional performances will be followed by more ‘normal’ ones, but people ignore
this.
Availability
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Are planes more dangerous than cars?
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If an English word is chosen at random, is it more likely that:
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K is the first letter?
K is the third letter
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Heuristic relies on ease with which instances can be recalled from memory
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Ross (1977) Fundamental attribution error
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Over estimate role of individual behaviour and under estimate situational factors.
Overcome by forming thorough lists of outcomes; understand how you are biased
Anchoring
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Kahneman & Tversky (1974)
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Percentage of countries in the UN are African
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Spin Wheel of Fortune - generates random number
Guess whether the % is higher or lower than the random number
Guess % of African
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When random number = 10, Guess = 25
When random number = 60, Guess = 45
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Is the Mississippi more or less than:
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What is probability that Dow Jones > 12,000 or construct probability distribution for
various levels of the Dow Jones
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70 miles?
2000 miles?
Two procedures are formally equivalent but in first question, starting point is ones own guess
(e.g. use median to estimate). In second question, starting level is given, or may anchor on
50:50 odds
Framing effects, starting points, context etc. matter
Belief confirmation
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People are often less attentive to new information, once they have formed their own
beliefs, e.g. inattentive to information on alternative investment strategy if you become
convinced that you’re chosen strategy is the one.
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“Blurring study”, Bruner & Potter (1964)
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90 subjects shown blurred picture that is gradually bought into focus. Subjects start viewing the
pictures at different states of blurriness. Those subjects who started viewing at early stages of
blurriness were much less likely to guess the picture than other subjects.
People using weak evidence to form initial hypothesis have difficulty correctly interpreting
subsequent better information that contradicts those initial hypotheses
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Not misinterpret new information, but ignore it. Alternative form is that evidence can be
misread as support for the hypothesis, when it should be rejected.
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Ambiguity of information seems important (also important for overconfidence); illusory
correlation; filtering
What does U(x) look like?
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Expected Utility theory - final states of wealth matter:Normative theory
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60% gain £10, 40% loss £10
EU: (0.6)u(w+10) + (0.4)u(w-10)
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But Allais paradox
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Kahneman & Tversky (1979), Prospect Theory: Descriptive theory
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Changes in wealth, not final states matter
Reference point matters
Subjective formation of probability, not actual
More averse to losses than attracted to similar sized gains (loss aversion)
Overweight low probabilities, underweight larger ones - certainty effect
Value function is convex for losses, convex for gains => diminishing marginal sensitivity for
losses
Different to risk aversion because slope changes at reference level
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Model predicts loss aversion; Thaler’s mugs (1990)
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Current research: where do we get reference levels from?
Probability weighting in prospect theory
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Choose either:
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A: 80% chance to win £4000
B: 100% chance of winning £3000
Now choose:
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C: 20% chance of winning £4000
D: 25% chance of winning £3000
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People generally prefer B > A and C > D
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But these choices are contradictory. Take first lottery.
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A=> (0.8)u(x+4000) + (0.2)u(x) < u(x+3000), so
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(0.2)u(x+4000) + (0.05)u(x) < (0.25)u(x+3000) - I.e. divided by 4
Adding (0.75)(u(x) to both sides:
(0.2)u(x+4000) + (0.8)u(x) < (0.25)u(x+3000) + (0.75)u(x) (I.e. now C < D)
But this contradicts result of second lottery
These kinds of results show certainty effect and under(over) weighting for probabilities
close to 1 (0)
Risk-loving for losses and risk-averse for
gains
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UK is preparing for outbreak of unusual Asian disease which is expected to kill 600
people. 2 alternative programs to combat disease have been proposed:
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A: 200 people will be saved
B: 1/3 probability that 600 people saved and 2/3 probability 0 people saved
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K&T found 67% prefer program A
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Now choose:
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A: 400 people will die for sure
B: 1/3 probability that 0 people die and 2/3 probability that 600 die
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Note that A=A and B=B
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Now people switch preferences, 32% prefer A.
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Framing effects therefore also important
Do we maximise our own utility?
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Is our economic behaviour motivated only by self interest?
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You are given a sum of money (£100) to split between yourself and an anonymous person. If
accepted refuses your allocation, neither of you get anything. What is your allocation?
Individuals don’t choose the rational division
People contribute to public goods
Monopoly pricing (Thaler, 1986a) - fairness can act as a constraint
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Becker approach: altruism enters our own utility function, so we are still maximising
our own utility (evolutionary explanation)
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Sen (2002) - 3 levels to consider
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Rabin (1993) - beliefs as a source of utility
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We maximise (something)
Something can be interpreted as self-interest
Self-interest is self-centred welfare
Players form a judgement of kindness/meanness of others, depending on whether others give
them more or less of a reference point
Players reciprocate in opposite directions: kindness meets kindness etc.
People are not simple altruists
Choices over time
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Traditionally assume that we are consistent in our choices (Samuelson - DU)
– On Jan 1 you can do 7 hours work on April 1, rest April 2, or
– Rest April 1, do 7.7 hrs work on April 2
– Now ask on March 31 - answer should be the same but preference reversals
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Read et al (1999) - choosing a video, “high brow” or “low brow”
– Pick for tonight, 66% choose low brown
– Next Thursday 37% choose low brow
– Second Thursday 29% choose low brow
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Thaler (1981) - You have won a lottery. Money held in bank. Can take it now or wait. If wait, then
how much you need if delay is x (also loss version involving fines)
– Discount rates (pure rate of time preference) decline with time => very impatient over short
term
– Discount rates decline with size of reward - larger reward => less impatient
– Discount rates for gains higher than for losses - prefer to pay fine now
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Hyperbolic discount rates rather than exponential - people have declining rate of time preference
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Thus people are dynamically inconsistent - have self control problems
– Instant gratification now
– ‘Behave’ in the future
– Naiives vs sophisticates - issues for procrastination
Putting it all together - applications
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Disposition effect: Sherif & Statman (1985) - people dislike incurring losses more than
they like getting gains. Combines with prospect theory: people willing to gamble in
domain of losses in order to ‘get back to purchase price’ (also misperception of mean
reversion). Genovese & Meyer (in press) - application to housing market. Odean (1998)
data from brokerage firm showed investors held losing stocks for median of 124 days
and winners for 104.
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Equity premium effect: long-term premium on equities around 8% - for extra risk? But
Mehra & Prescott (1985): level of risk aversion implies person would be indifferent
between coin flip giving $50k or $100k, or $51k for certain I.e. absurd level of risk
aversion
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Benartzi & Thaler (1997) - investors are myopic and have prospect theoretic utility function =>
investors don’t care about variability of returns but chance of losses. Annual stock returns are
negative more often than bonds so investors demand more compensation for holding them.
Wouldn’t matter over long-term but myopia means investors check portfolios very often - much
more likely then that stocks are in negative return territory. Showed that over a 1-year horizon,
prospect theory can account for 8% premium
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Nominal wage rigidity - anchoring? May be ‘near rational’ Akerlof & Yellen (1987)
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Overreaction to recent news relative to LT trend => volatile share prices. Applies to
professionals. De Bondt & Thaler (1985, 1990)
Putting it all together - applications
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Retirement saving: people save too little. US: people forego employer
contributions which are essentially free. Default option on form is often to not
join - framing effects and status quo bias.
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Labour supply: positively related to expected wage. Hence taxi drivers should
quit early on poor days and work harder on good days. Camerer et al (1997)
found the opposite - inexperienced drivers using a daily income targeting
heuristic - daily target is reference point in prospect theoretic function
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Gabaix & Laibson (2006) “Shrouded attributes” - high charges for marginal
services e.g. hotel telephone/mini-bar; bank charges. Exist because people
have self-control problems - anticipate they won’t give in to mini-bar/go
overdrawn. Not competed away because a firm revealing hidden prices does
not attract customers? Why, because myopic customers think prices are too
expensive; sophisticates benefit from exploiting myopic customers and
substitute away from add-ons anyway.
Think about the following
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Saving and consumption: People assumed to spend constant fraction of
‘permanent’ income. But young spend a lot; middle-age earn more; retirees
reduce consumption. Consumption should also relate to expected wage
increase. This doesn’t happen; why?
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Caskey (1994) estimates 10%-20% of US households have no bank accounts.
These people also tend to save very little and rely on cheque-cashers (high
fees). Rational explanation rests on cost/benefit analysis. What might
behavioural explanation look like? What policies might get the poor to use
banks?
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Self-control & ‘bad behaviour’, e.g. saving too little; smoking. Is there a role
for an agent (e.g. government) to ‘help’ individuals make the ‘right’ choice?
Does it conflict with basic liberty?
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How would you explain the existence of a ‘fat club’ using behavioural theory?
Discussion
• Saving and consumption, Shea (1995); Bowman et al (1999)
– Consumption is reference dependent
– Marginal utility of getting to reference level is > MU of exceeding
it (loss averse)
– MU of consumption rises from below as you reach reference point
(reflection effect)
– Suppose consuming at reference point and get bad news about
future wages; don’t cut consumption because too painful because
a) loss-averse (it hurts) and b) might be better next year, so take a
gamble on either consuming way below reference point, or at it.
Discussion
• Why don’t the poor have bank accounts?
– Status quo/defaults - for poor often does not involve direct deposit
– Confusion/overwhelmed by choice
– Channel factors - e.g. map
– Framing - advertising, ‘not for me’
• First Account program in Chicago from 2002
– People reported not taking up bank account because of time
management issues
– More people completed forms when bank rep was present
– Presence also correlated with take-up of other services and general
account use
Discussion
• (Assymetric/Libertarian) Paternalism
– Which self dominates, now (eager) or later (regretful)?
– Can the market be relied on, e.g. Swedish social security system?
– Better than the current alternatives?
Class discussion
• Tendency of people to save in ‘buckets’ e.g. ‘rainy day’, ‘holiday’,
‘school fees’. People have different propensity to consume from
different buckets…but money is fungible. What about company
budgets across departments?
• Are people irrational across all situations? E.g. is buying a pension the
same as buying milk? What differences in the goods/markets might
affect how we behave?
• Heuristics and biases: Can people learn to overcome their ‘mistakes’?
• The irrational behaviours appear intuitively the ‘right’ thing to do, so
should they be taken into account when designing policy, or should we
leave people to their own fate?
Further reading
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Kahneman, Daniel, and Amos Tversky, “Prospect Theory: An analysis of
decision under risk” - Econometrica 47 (2), (1979): 263-292
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Thaler, Richard H. “Mental accounting matters”, Journal of Behavioural
Decision making 12 (1999): 183-206
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Cronqvist, Henrik and Richard H. Thaler,"Design Choices in Privatized
Social-Security Systems: Learning from the Swedish Experience" The
American Economics Review 94 (2), (2004): 424-428
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Sunstein, Cass & Richard H. Thaler “Libertarian Paternalism is not an
oxymoron” -, University of Chicago Law Review 70 (4), (2003): 1159-1202.