Psychology%20and%20Investments

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Psychology and Investments

• Andrei Simonov

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Introduction

• Classical Finance is based on the notion of Homo
  Chicagoan
• Rational
• Do keep track of all available investment
  opportunities
• Can process tons of information instantly
• For simplicity preferences are described by
  relatively simple utility function
• As amount of the gamble winning/loosing e cents
  (p1/2) decreases, sooner or later everyone plays

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Overconfidence and Optimism (1)

• Rule of thumbs I am 99 sure should be
  translated as I am 70-90 sure
• Empirical Results people do overestimate the
  precision of their knowledge
• Alpert Raiffa 82
• Stael von Holstein 1972 inv. bankers
• Cooper et. al. 88 - enterpreneurs

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Overconfidence and Optimism (2)

• People overestimate their ability to deal with
  task. The more important the task is the greater
  is the optimism (Frank 35)
• 82 of students are in top 30 of their class
  (Svenson)
• 81 of 2994 new business owners are sure that
  their firm has 70 or better chances of survival.
  Only 39 thought that the business like theirs
  has similar chances (Cooper).

BAD GOOD
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Overconfidence and Individual Investors Barber
Odean (1)

• Individual Investors Behavior
• H1 Overconfident investors buy transactions
  should underperform
• H2 Overconfident investors sell transactions
  should overperform
• Transaction cost for round-trip ?6 ?buys
  should overperform sells by 6
• Result of BarberOdean
• 4mo rBUY-rSELL ?-2.5
• 1 yr rBUY-rSELL ?-5.1
• 2 yr rBUY-rSELL ?-8.6

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Overconfidence and Individual Investors Barber
Odean (2)

• Turnover
• The more investors trade the more they reduce
  their return.
• Partitioning investors into quintiles
• Quitile that trades unfrequently underperform
  bu-and-hold strategy for 0.25 annually.
• Active traders underperformed 7.04
• Gender Boys will be boys
• Overall, men claim more ability than do women,
  but this difference emerges most strongly on
  masculine tasks Deaux Farris, 1977
• BarberOdean Men traded 45 more actively. The
  difference between returns of men and women is
  0.94

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Overconfidence and Individual Investors (3)

• Goetzmann Peles 1997
• AAII members(informed investors) survey
• On average investors overestimate the performance
  of their mutual funds by 3.4
• If investors have control over choosing the fund,
  their estimate exceed the real number by 8.6
  (vs. 2.4 for defined contributions plans)
• ?Illusion of control matters. Internet and online
  access provides that kind of illusion
• Barber and Odean Fast dies first Investors who
  switch to online trading underperform more than
  before
• Metrick (NBER2000) Thades done through online
  channel are unambiguously less profitable

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Overconfidence what to do?

• New year resolution list (Kaneman Riepe)
• Always analyse worst case scenario, avoid focus
  on upside
• Keep the list of past recommendation you made
  that did not work (Caesar, you are just a man...)
• Serious stuff
• Create sub-account in which investor trades
  (gambles) as he/she wish. Typical client invests
  5-7 of his portfolio himself with dismal
  results.
• Give em more control. Clients are wanting more
  details, more paper and more technology (Hurley
  2000)
• Education matters

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Confirmation Bias

• August 1987 saw a historically high valuation of
  dividends, beating out even that of 1929. The
  result was a 1,000 points crash
• (Prechter,1997)
• True, low DivY was followed by low returns in the
  following year 33 times in 1872-1999.
• But Low DivY high Ret 31 years
• High DivY low Ret 31 years
• High DivY high Ret 33 years

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Confirmation Bias(2)

• Cure Statistical analysis.
• 1year return no relation
• 10yr annualized 10yr returns strong positive
  correlation
• Ref. Due FisherStatman, JPM 2000

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An Example

• Initial endowment 300. Consider a choice
  between
• a sure gain of 100
• a 50 chance to gain 200, a 50 chance to gain
  0.
• Initial endowment 500. Consider a choice
  between
• a sure loss of 100
• a 50 chance to lose 200, a 50 chance to lose
  0.

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Reversal in Choice

• Case 1 72 chose option 1, 28 chose option 2.
• Case 2 36 chose option 1, 64 chose option 2.
• gt A reversal in Choice
• Problem framed as a gain decision maker is risk
  averse.
• Problem framed as a loss decision maker is risk
  seeking.

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Allais Paradox

• Case 1 consider a choice between
• 1 million with certainty.
• 5 million with prob 0.1, 1m with prob 0.89 and
  0 with prob 0.01
• Case 2 consider a choice between
• 1m with prob 0.11, 0 with prob 0.89.
• 5m with prob 0.10 and 0 with prob 0.90.

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Allais Paradox Explanation

• u(1m) gt 0.10u(5m) 0.89u(1m) 0.01u(0m)
• Add 0.89u(0m) - 0.89u(1m) to both sides.
• 0.11u(1m) 0.89u(0m) gt 0.10u(5m) 0.90u(0m)
  
• Violates Expected Utility Theorem!

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Prospect Theory

• Proposed by two psychologists Daniel Kahneman
  and Amos Tversky.
• Gambles are evaluated relative to a reference
  point.
• Decision maker analyzes gains and losses
  differently.
• Incremental value of a loss is larger than that
  of a loss.
• the hurt of a 1000 loss is more painful than
  the benefit of a 1000 gain.

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Loss aversion and return patterns

• Barberis et. al (99) money in the bank affects
  the level of risk aversion.
• Investors who make money feel rich, they
  exhibit smaller loss aversion?.
• Investors overinvest in stock market, further
  pushing the prices up
• Equity premium is low
• Investors who loose money exhibit higher risk
  aversion, move out of the market
• Simple model of investor sentiment.

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What to do ?

• Investigate your clients loss aversion
• Use derivative instruments (may be, custom-build)
• Equity-linked structured notes
• Equity-linked annuities
• Protective puts on index
• Opportunities for investment advisors one size
  does not fit all!

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Disposition Effect, Regret Avoidance and Anchoring

• Barber and Odean (again!)
• Investors hold on loosers and sell winners
• Anchoring
• NASDAQ is down from its highs (No questions how
  reasonable high was)
• P/E level in Japan in 90s is acceptable (w.r.t.
  anchoring level of 1980s)
• Money illusion (counting nominal and not real
  money)
• Stop orders might be useful, statistical analysis
  is important.

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Framing

• Benartzi Thaler (96)
• When shown series of 30 one-year return, people
  allocate 40 to stocks and 60 to bonds.
• When shown just cummulative 30 yr. return, the
  allocation was 9010...
• Effect of framing for current market entrants.
• Opportunity example covered calls
• Framing one should use the broader possible
  frame. Role of education.

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Mental Compartments

• Hedging people hedge not against the risk of
  future cash flows but against the risk of a
  particular transaction
• Usage of derivatives by firms
• 50 hedged transactions lt1 yr. Into the future
• 11 hedged transactions gt1 yr. Into the future
• Long-term short-term investments compartment.
• It is difficult to ask client to sell the
  security designated as long-term investment.
  Way out covered calls.

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Role of Investor Behavior

• Bounded Rationality satisficing behavior.
  Information processing limitations. Example
  memory limitations.
• Investor Sentiment beliefs based on heuristics
  rather than Bayesian rationality.
• Investors may react to irrelevant information
  and hence may trade on noise rather than
  information.

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Behavioral Heuristics and Decision-Making Biases

• What strategies do decision makers use when faced
  with difficult decisions, especially ones that
  involve uncertainty?
• Commonly Used Heuristics
• Availability familiarity breeds investment.
• Representativeness judgment based on similarity.
  Patterns in random sequences.
• Reliance on the judgment of other people (Keynes
  beauty contest analogy).

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Gamblers Fallacy

• Investors may apply law of large numbers to small
  sequences.
• Example fair coin tossing.
• THTHTHHHHHH -gt P(T) ?, P(H) ?.
• Which of the 2 sequences is more likely to occur
  in a fair coin tossing experiment?
• HHHHHHTTTTTTHHHHHH
• HHTHTHHTHTTHTHHTTH

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Fashions and Fads

• People are influenced by each other. There is a
  social pressure to conform.
• Herding behavior safety-in-numbers.
• Informational Cascades
• Positive Feedback
• Example excessive demand for internet IPOs.
  Extremely high opening day returns.

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Can arbitrage opportunities exist?

• Yes!
• Real-world arbitrage is always risky. No
  riskless hedge for the arbitrageur.
• Arbitrageur facesnoise trader risk mispricing
  can become worse before it disappears.
• Close substitutes (needed for arbitrage
  positions) may not be available.
• Fundamentally identical assets may NOT sell at
  identical prices.

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Behavioral Finance Two Major Foundations

• Investor Sentiment creates disturbances to
  efficient prices.
• Limited arbitrage arbitrage is never riskfree,
  hence it does not counter irrational
  disturbances.
• Prices may not react to information by the
  right amount.
• Prices may react to non-information.
• Markets may remain efficient.

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Example of Investor sentiment Rose.com

• 63 companies that change the name from Widget to
  Widget.com/.net within Jan-Mar 98
• 80 announcement effect
• Renaming the company attracts investors with
  bullish sentiment towards internet stock.
• Rule of thumbs thinking change of name
  change in strategy.
• Reacting to non-information

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Investor Sentiment, Bubbles and Crashes

• Case Shiller(88)
• Expectations about future house value
  appreciation is an increasing function of
  previous period appreciation.
• Affects the decision to purchase the new house.
• Frankel Froot
• Long-term is overvalued w.r.t. Y, but short
  term it will go up.
• Effect of magical thresholds (100Y1)
• / is another good example.
• Shiller (88) Investors sold in 87 because they
  believed that the market is going to decline
  further.
• Bigger sucker theory.

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Investor sentiment and funds flow

• Goetzmann, Massa(99,Y2K)
• behavioral factors can explain 45 in
  cross-sectional variation in mutual funds
  returns
• Mf flow is by itself responsible for significant
  of the resent market run.
• Those inflows are heavily affected by the opinion
  of experts and behavioral factors.

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Irrational Behavior of Professional Money
Managers

• May choose a portfolio very close to the
  benchmark against which they are evaluated (for
  example SP500 index).
• Herding may select stocks that other managers
  select to avoid falling behind and looking
  bad.
• Window-dressing add to the portfolio stocks that
  have done well in the recent past and sell stocks
  that have recently done poorly.

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Summary

• Investor behavior does have an impact on the
  behavior of financial markets. How much? Not
  clear!
• Both social and psychological must be taken
  into account in explaining the behavior of agents
  in financial markets.
• Market anomalies may be widespread.
• Behavioral Finance does not replace but
  complements traditional models in Finance.
  Finally, noise risk is just another risk
  factor...
• Biases are not necesserily problems. They might
  provide you opportunities as well.