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Psychology and Investments

Psychology and Investments. Andrei Simonov. Intro:Behavioral finance as discipline. Violations in expected utility theory Kahmnemann-Tversky Bounded rationality Classical Finance is based on the notion of Homo Chicagoan Rational

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Psychology and Investments

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  1. Psychology and Investments Andrei Simonov Behavioral Finance

  2. Intro:Behavioral finance as discipline • Violations in expected utility theory • Kahmnemann-Tversky • Bounded rationality • Classical Finance is based on the notion of Homo Chicagoan • Rational • Do keep track of all available investment opportunities and correctly assess probabilities of all and every event • Can process tons of information instantly and costlessly • We will focus on the second part. Behavioral Finance

  3. Outline • Investors’ perception of their knowledge, overconfidence and overoptimism • Trading behavor • Investors’ perception of stochastic processes of asset prices • Investors’ perception of value • Risk-return trade-off Behavioral Finance

  4. Word of caution: • BF is relatively new area. As such, some of the definitions are vague. Sometimes the same term refer to completely different phenomena. Behavioral Finance

  5. 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 Behavioral Finance

  6. Cooper et. al: Entrepreneurs’ perceived chances for success Behavioral Finance

  7. Overconfidence and Optimism (3) • 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

  8. 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

  9. 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

  10. Overconfidence and Individual Investors: De Bondt (1998) (3) • AAII members: • Overestimate precision of their knowledge (conf. Intervals) • Underestimate covariance structure of returns • Overoptimistic, especially for stocks they selected Behavioral Finance

  11. Overconfidence and Individual Investors (4): Illusion of Control • 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

  12. 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

  13. Perception of price movements • Extrapolation bias: what was going up yesterday will go up tomorrow. • Technical analysis • 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

  14. 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 Behavioral Finance

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

  16. 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. Behavioral Finance

  17. 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. 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

  19. How those decisions are done? • Investors are overconfident and overoptimistic. • Investors process information based on intuition and set of rules, “bounded rationality”, “satisficing” behavior. Information processing & memory limitations. • Lo&Repin NBER8508: Professinal traders make decisions based on intuition. They cannot rationally explain their decisions 10 min. past... • What affects investors sentiment? Generally, emotional state of investors. • January effect, September Effect • Weather: Hirshleifer & Shumway (2001) Good day sunshine positively correlated with daily stock return. No effect of rain and snow… Behavioral Finance

  20. How investors then select firms? • Availability bias: ”Familiarity breeds investment” • Huberman: people invest in the company they know. Baby bells stories • Moskowitz & Coval (2001): Mutual funds managers prefer to invest in companies that are close to the HQ. • Simonov-Giannetti: Same effect exists in Sweden • Bondaruk: Effect is economically significant for small investors. 1.5%-3.5% Behavioral Finance

  21. How investors then select firms? (2) • In search of excellence or mediocrity? Behavioral Finance

  22. They are bad, but returns are good... Behavioral Finance

  23. Is this example unique? No • Moody’s Bankrupt Bond Index shows that bankrupt bonds are superior investments that are normally abandoned by investors at the moment of bankruptcy Behavioral Finance

  24. Another example: Value vs. GrowthPast growth negatively correlated with future growth... Behavioral Finance

  25. Value vs. Growth(2)Forecasters’ errors are strongest for growth companies (extrapolation bias) Behavioral Finance

  26. And Returns confirm the story... Behavioral Finance

  27. Investors’ belief about risk & return • I would rather have in my stock portfolio just a few companies that I know well than many companies that I know little about [89% agreed ] • If you do not do your homework the investment success is unlikely [70%] • Investing in stocks is like buying lottery tickets. Luck is everything. [0%] • Because most investors do not like risk, risky stocks sell at lower market prices [7%] • The risk of the stock depends on whether price is correlated with the market [18%] Behavioral Finance

  28. 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

  29. Is it all behavioral? • No. Information availability plays important role. Neglected firms effect • 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

  30. 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. But exploiting those opportunities means to face some other risks (RDS case) Behavioral Finance

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