1. Psychology and Investments
Andrei Simonov
Behavioral Finance
18/11/2018

2. 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 (p=1/2) decreases, sooner or later everyone plays
Behavioral Finance
18/11/2018

3. 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 enterpreneurs
Behavioral Finance
18/11/2018

4. 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
Behavioral Finance
18/11/2018

5. 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 Barber&Odean
4mo: rBUY-rSELL -2.5%
1 yr: rBUY-rSELL -5.1%
2 yr: rBUY-rSELL -8.6%
Behavioral Finance
18/11/2018

6. 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
Barber&Odean: Men traded 45% more actively. The difference between returns of men and women is 0.94%
Behavioral Finance
18/11/2018

7. 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
Behavioral Finance
18/11/2018

8. 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
Behavioral Finance
18/11/2018

9. Confirmation Bias
August 1987 saw a historically high valuation of dividends, beating out even that of 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
But: Low DivY – high Ret =31 years
High DivY – low Ret =31 years
High DivY – high Ret =33 years
Behavioral Finance
18/11/2018

10. Confirmation Bias(2) Cure: Statistical analysis.
1year return: no relation
10yr annualized 10yr returns: strong positive correlation
Ref. Due: Fisher&Statman, JPM 2000
Behavioral Finance
18/11/2018

11. 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.
Behavioral Finance
18/11/2018

12. Reversal in Choice
Case 1: 72% chose option 1, 28% chose option 2.
Case 2: 36% chose option 1, 64% chose option 2.
=> A reversal in Choice
Problem framed as a gain: decision maker is risk averse.
Problem framed as a loss: decision maker is risk seeking.
Behavioral Finance
18/11/2018

13. 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.
Behavioral Finance
18/11/2018

14. Allais Paradox: Explanation
u(1m) > 0.10*u(5m) *u(1m) *u(0m)
Add 0.89*u(0m) *u(1m) to both sides.
0.11*u(1m) *u(0m) > 0.10*u(5m) *u(0m)
Violates Expected Utility Theorem!
Behavioral Finance
18/11/2018

15. 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”.
Behavioral Finance
18/11/2018

16. 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.
Behavioral Finance
18/11/2018

17. 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!
Behavioral Finance
18/11/2018

18. 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 90’s is acceptable (w.r.t. ”anchoring level of 1980’s)
Money illusion (counting nominal and not real money)
Stop orders might be useful, statistical analysis is important.
Behavioral Finance
18/11/2018

19. 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 90:10...
Effect of framing for current market entrants.
Opportunity example: covered calls
Framing: one should use the broader possible frame. Role of education.
Behavioral Finance
18/11/2018

20. 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 <1 yr. Into the future
11% hedged transactions >1 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.
Behavioral Finance
18/11/2018

21. 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.
Behavioral Finance
18/11/2018

22. 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).
Behavioral Finance
18/11/2018

23. Gambler’s Fallacy
Investors may apply law of large numbers to small sequences.
Example: fair coin tossing.
THTHTHHHHHH -> P(T) = ?, P(H) = ?.
Which of the 2 sequences is more likely to occur in a fair coin tossing experiment?
HHHHHHTTTTTTHHHHHH
HHTHTHHTHTTHTHHTTH
Behavioral Finance
18/11/2018

24. 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.
Behavioral Finance
18/11/2018

25. Can arbitrage opportunities exist?
Yes!
Real-world arbitrage is always risky. No riskless hedge for the arbitrageur.
Arbitrageur faces“noise 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.
Behavioral Finance
18/11/2018

26. 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.
Behavioral Finance
18/11/2018

27. 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
Behavioral Finance
18/11/2018

28. 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 (100Y=1$)
$/€ is another good example.
Shiller (88): Investors sold in 87 because they believed that the market is going to decline further.
”Bigger sucker” theory.
Behavioral Finance
18/11/2018

29. 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.
Behavioral Finance
18/11/2018

30. “Irrational” Behavior of Professional Money Managers
May choose a portfolio very close to the benchmark against which they are evaluated (for example: S&P500 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.
Behavioral Finance
18/11/2018

31. 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.
Behavioral Finance
18/11/2018