A Survey of Behavioral Finance

1. A Survey of Behavioral Finance NICHOLAS BARBERIS University of Chicago RICHARD THALER University of Chicago Ender DemirBehavioral Finance

2. The traditional finance • Itseeks to understand financial markets using models in which agents are“rational”. Rationality 1) First, when they receive new information, agents update their beliefs correctly, in the manner described by Bayes’ law. 2) Second,given their beliefs, agents make choices that are normatively acceptable, in the sensethat they are consistent with Savage’s notion of Subjective Expected Utility (SEU).

3. Baye’s Rule • The probability that a woman at age 40 has breast cancer is 1%. According to the literature, the probability that the disease is detected by a mammographyis 80%. The probability that the test misdetects the disease although the patient does not have it is9.6%. • If a woman at age 40 is tested as positive, what is the probability that she indeed has breastcancer?

4. Thus, the probability of breast cancer is only 7.8%, while Eddy reports that 95 out of 100doctors estimated this probability to be between 70% and 80%.

5. BehavioralFinance • Behavioral finance is a new approach to financial markets that has emerged, at leastin part, in response to the difficulties faced by the traditional paradigm • In broad terms,it argues that some financial phenomena can be better understood using models inwhich some agents are not fully rational. • More specifically, it analyzes what happenswhen we relax one, or both, of the two tenets that underlie individual rationality.

6. Traditional vs. Behavioral • Traditional • Rational • Correct Bayesian Updating • Choices Consistent with Expected Utility • Behavioral • Some are Not Fully Rational • Relax One or Both Tenets of Rationality

7. Traditional vs. Behavioral • Behavioral • error-prone and emotional Homo sapiens • Relax One or Both Tenets of Rationality • Traditional • Homo economicus • perfectly rational decisions • applies unlimited processing powerto any available information • holds preferences well-described by standardexpected utility theory. Richard Thaler,a founding father of behavioral finance, captured the conflict in a memorable NationalBureau of Economic Research (NBER) conference remark to traditionalistRobert Barro: “The difference between us is that you assume people are as smartas you are, while I assume people are as dumb as I am.”

8. Traditional vs. Behavioral • Embedded within standard financeis the notion of “Homo Economicus,” or rational economic man. • It prescribes that humans make perfectly rational economic decisions atall times. Standard finance, basically, is built on rules about how investors“should” behave, rather than on principles describing how theyactually behave. • Behavioral finance attempts to identify and learn fromthe human psychological phenomena at work in financial markets andwithin individual investors.

9. The classic objection to behavioral finance • Even if some agentsin the economy are less than fully rational, rational agents will prevent them frominfluencing security prices for very long, through a process known as arbitrage. • Oneof the biggest successes of behavioral finance is a series of theoretical papers showingthat in an economy where rational and irrational traders interact, irrationality can havea substantial and long-lived impact on prices. • These papers, known as the literatureon “limits to arbitrage”, form one of the two buildings blocks of behavioral finance.

10. The classic objection to behavioral finance • To make predictions, behavioral models often need to specify the form ofagents’ irrationality. • How exactly do people misapply Bayes law or deviate fromSEU? Psychology is therefore the second building block of behavioralfinance

11. Behavioral Finance Limits to Arbitrage Psychology

12. Limits to arbitrage 2.1. Market efficiency • In the traditional framework where agents are rational and there are no frictions (no transaction costs),a security’s price equals its “fundamental value”. • This is the discounted sumof expected future cash flows, where in forming expectations, investors correctlyprocess all available information, and where the discount rate is consistent with anormatively acceptable preference specification. • The hypothesis that actual pricesreflect fundamental values is the Efficient Markets Hypothesis (EMH). Put simply,under this hypothesis, “prices are right”, in that they are set by agents who understandBayes’ law and have sensible preferences. • In an efficient market, there is “no freelunch”: no investment strategy can earn excess risk-adjusted average returns, or averagereturns greater than are warranted for its risk.

13. EMH - The Efficient Markets Hypothesis • Definition of Efficient Markets: Is a market that reacts quickly and relatively accurately to new public information, which results in prices that are correct, on average. • Assumptions; • A large number of rational, profit-maximizing investors exist, who actively participate in the market by analyzing, valuing, and trading securities. The markets must be competitive, meaning no one investor can significantly affect the price of the security through their own buying or selling. • Information is costless and widely available to market participants at the same time. • Information arrives randomly and therefore announcements over time are not serially connected. • Investors react quicklyand fully (and reasonably accurately) to the new information, which is reflected in stock prices. • EMH is the theory that markets are efficient and therefore, in its strictest sense, implies that prices accurately reflect all available information at any given time.

14. EMH - The Efficient Markets Hypothesis 1. The “Weak” form contends that all past market prices and data arefully reflected in securities prices; that is, technical analysis is of littleor no value (technical) 2. The “Semistrong” form contends that all publicly available informationis fully reflected in securities prices; that is, fundamental analysisis of no value (fundamental) 3. The “Strong” form contends that all information is fully reflected insecurities prices; that is, insider information is of no value. (insider trading)

15. Limits to arbitrage 2.1. Market efficiency • Behavioral finance argues that some features of asset prices are interpreted as deviations from fundamental value, and that these deviations are broughtabout by the presence of traders who are not fully rational. • A long-standing objectionto this view that goes back to Friedman (1953) is that rational traders will quicklyundo any dislocations caused by irrational traders.How?

16. Limits to arbitrage 2.1. Market efficiency • 1) as soon asthere is a deviation from fundamental value – in short, a mispricing – an attractiveinvestment opportunity is created, 2) rational traders will immediately snap upthe opportunity, thereby correcting the mispricing. • Behavioral finance arguesthatwhen an asset is wildly mispriced, strategies designed to correct the mispricing canbe both risky and costly, rendering them unattractive. As a result, the mispricing canremain unchallenged.

17. Limits to arbitrage 2.1. Market efficiency • Irrational traders are often known as “noise traders” • Rational traders are typicallyreferred to as “arbitrageurs”. • Strictly speaking, an arbitrage is an investment strategythat offers riskless profits at no costwhich is thecaseaccordingtoFriedman. • Behavioral finance argues thatthis is not true: the strategies that Friedman would have his rational traders adopt arenot necessarily arbitrages; quite often, they are very risky.

18. Limits to arbitrage 2.1. Market efficiency Correct Prices => No Free Lunch No Free Lunch=> Correct Prices True in efficient market • just because pricesare away from fundamental value does not necessarily mean that there are any excessrisk-adjusted average returns for the taking. Correct Prices => No Free Lunch No Free Lunch ≠> Correct Prices

19. Example • Thefundamental value of a share of Ford is $20. • Imagine that a group of irrationaltraders becomes excessively pessimistic about Ford’s future prospects and through itsselling, pushes the price to $15. • Sowhatwillhappen? • Defenders of the EMH argue that rational traders,sensing an attractive opportunity, will buy the security at its bargain price and at thesame time, hedge their bet by shorting a “substitute” security, such as General Motors,that has similar cash flows to Ford in future states of the world. • The buying pressureon Ford shares will then bring their price back to fundamental value.

20. Limits to arbitrage 2.2. Theory • Fundamental risk - Negative Shock and no Perfect Substitute (ShortingGeneral Motors protects the arbitrageur somewhat from adverse news about the carindustry as a whole, but still leaves him vulnerable to news that is specific to Ford - news about defective tires) • Noise trader risk - Even if General Motorsis a perfect substitute security for Ford, the arbitrageur still faces the risk that thepessimistic investors causing Ford to be undervalued in the first place become evenmore pessimistic, lowering its price even further (Continued Widespread Irrationality). • Forced Liquidation (Separation of Brains and Capital) (professional portfolio managers) • Horizon Risk (If a mispricing that the arbitrageur is trying to exploit worsens in theshort run, generating negative returns, investors may decide that he isincompetent,and withdraw their funds. If this happens, the arbitrageur will be forced to liquidatehis position prematurely. Fear of such premature liquidation makes him less aggressivein combating the mispricing in the first place.)

21. Limits to arbitrage 2.2. Theory • Implementation Costs • Commission • Short Sell Costs • Fees • Volume Constraints • Legal Restraints • Identification Cost • Mispricing ≠> Predictability

22. Limits to arbitrage 2.2. Theory • In contrast, then, to straightforward-sounding textbook arbitrage, real world arbitrageentails both costs and risks, which under some conditions will limit arbitrage and allowdeviations from fundamental value to persist. • It is also important to note that for particular types of noise trading, arbitrageursmay prefer to trade in the same direction as the noise traders, thereby exacerbatingthe mispricing, rather than against them. • De Long et al. (1990)showthat if these noise traders push an asset’s priceabove fundamental value, arbitrageurs do not sell or short the asset. Rather, theybuy it, knowing that the earlier price rise will attract more feedback traders nextperiod, leading to still higher prices, at which point the arbitrageurs can exit at aprofit.

23. Limits to arbitrage2.3. Evidence • Twin shares (Royal Dutch and Shell Transport) Homeworkfor a casestudy

24. Limits to arbitrage2.3. Evidence (index) • Whenone of the companies in the S&P 500 is taken out of the index becauseof a merger or bankruptcy, and is replaced by another firm. • Harris and Gurel (1986) and Shleifer (1986), document a remarkablefact: when a stock is added to the index, it jumps in price by an average of 3.5%, andmuch of this jump is permanent. In one dramatic illustration of this phenomenon, whenYahoo was added to the index, its shares jumped by 24% in a single day.

25. Limits to arbitrage2.3. Evidence (index) • The fact that a stock jumps in value upon inclusion is once again clear evidenceof mispricing: the price of the share changes even though its fundamental value doesnot. • Standard and Poor’s emphasizes that in selecting stocks for inclusion, they aresimply trying to make their index representative of the U.S. economy, not to conveyany information about the level or riskiness of a firm’s future cash flows.

26. Limits to arbitrage2.3. Evidence (index) • When one thinks about the risks involved in trying to exploit the anomaly,its persistence becomes less surprising. An arbitrageur needs to short the includedsecurity and to buy as good a substitute security as he can. • This entails considerable fundamental risk because individual stocks rarely have good substitutes. It also carriessubstantial noise trader risk: whatever caused the initial jump in price – in alllikelihood, buying by S&P 500 index funds – may continue, and cause the price to risestill further in the short run; indeed, Yahoo went from $115 prior to its S&P inclusionannouncement to $210 a month later. • This example of a deviation from fundamental value is also evidence of limitedarbitrage.

27. 3. Psychology • The theory of limited arbitrage shows that if irrational traders cause deviations fromfundamental value, rational traders will often be powerless to do anything about it. • In order to say more about the structure of these deviations, behavioral models oftenassume a specific form of irrationality.

28. 3.1. Beliefs • A crucial component of any model of financial markets is a specification of how agentsform expectations. We now summarize what psychologists have learned about howpeople appear to form beliefs in practice • Overconfidence • Optimism / Wishful Thinking • Representativeness • Conservatism • Belief Perseverance • Anchoring • Availability Biases

29. 3.1. Beliefs, Final Notes • Economistsbelieve (i) that people, through repetition, will learn their way out of biases; (ii) thatexperts in a field, such as traders in an investment bank, will make fewer errors; and(iii) that with more powerful incentives, the effects will disappear. • However, • People Display Poor Learning in Application (people often understand it, but thenimmediately proceed to violate it again in specific applications.) • Experts Often do Worse • Increasing Incentives Doesn’t Help

30. 3.2. Preferences3.2.1. Prospect theory • An essential ingredient of any model trying to understand asset prices or tradingbehavior is an assumption about investor preferences, or about how investors evaluaterisky gambles. • The vast majority of models assume that investors evaluate gamblesaccording to the expected utility framework, EU henceforth. • The theoretical motivationfor this goes back to Von Neumann and Morgenstern (1944), VNM henceforth,who show that if preferences satisfy a number of plausible axioms – completeness,transitivity, continuity, and independence – then they can be represented by theexpectation of a utility function.

31. Von Neumann and Morgenstern (1944) - EU Axioms -A set of outcomes, X -Probability distributions p,q...on X -Let P be a convex set of probability distributions p,q,... defined on the -set X of outcomes -(convexity of P means that if p,q ∈ P and 0≤λ≤1 then λp+(1-λ)q is in P)

32. Von Neumann and Morgenstern (1944) - EU Axioms • For all p,q,r ∈ P and all 0<λ<1, * completeness *Continuity *independence *transitivity

33. 3.2. Preferences3.2.1. Prospect theory • Unfortunately, experimental work in the decades after VNM has shown that peoplesystematically violate EU theory when choosing among risky gambles. • Recent work in behavioral finance has argued that some of the lessons we learn fromviolations of EU are central to understanding a number of financial phenomena. • Of all the non-EU theories, prospect theory may be the most promising for financial applications.

34. 3.2. Preferences3.2.1. Prospect theory • Kahneman and Tversky (1979)lay out the original version ofprospect theory, designed for gambles with at most two non-zero outcomes.

35. 3.2. Preferences3.2.1. Prospect theory

36. 3.2. Preferences3.2.1. Prospect theory u(0)=0 Problem 1 impliesthat u(2400) > 0.33 u(2500) + 0.66 u(2400) or 0.34 u(2400) > 0.33 u(2500) Problem 2 showsthat 0.33 u(2500) > 0.34 u(2400) Problem 2 is obtainedfrom Problem 1 byeliminating a 0.66 chance of winning 2400 frombothprospects

37. 3.2. Preferences3.2.1. Prospect theory C = (4,000, .20) can be expressed as (A, .25) D = (3,000, .25)can be rewritten as (B,.25). Utility theory asserts that ifB is preferred to A, then any (probability) mixture (B, p) must be preferred to themixture (A, p). Certaintyeffect

38. 3.2. Preferences3.2.1. Prospect theory

39. 3.2. Preferences3.2.1. Prospect theory • This formulation has a number of important features. • First, utility is defined overgains and losses rather than over final wealth positions, an idea first proposed byMarkowitz (1952). • This fits naturally with the way gambles are often presented anddiscussed in everyday life. More generally, it is consistent with the way peopleperceive attributes such as brightness, loudness, or temperature relative to earlierlevels, rather than in absolute terms. • Kahneman and Tversky (1979) also offer thefollowing violation of EU as evidence that people focus on gains and losses. Subjectsare asked:

40. 3.2. Preferences3.2.1. Prospect theory Note that the two problems are identical in terms of their final wealth positions andyet people choose differently. The subjects are apparently focusing only on gains andlosses. Indeed, when they are not given any information about prior winnings, theychoose B over A and C over D.

41. The second important feature is the shape of the value function v, namely itsconcavity in the domain of gains and convexity in the domain of losses. Put simply,people are risk averse over gains, and risk-seeking over losses. Simple evidence forthis comes from the fact just mentioned, namely that in the absence of any informationabout prior winnings. The v function also has a kink at the origin, indicating a greater sensitivity to lossesthan to gains, a feature known as loss aversion.

42. The intuition is that the 20% jump in probability from 0.8 to 1 is more striking topeople than the 20% jump from 0.2 to 0.25. In particular, people place much moreweight on outcomes that are certain relative to outcomes that are merely probable, afeature sometimes known as the “certainty effect”.

43. Prospect theory attempts to incorporate the observed violations of expected utility into an alternative theory of risky choice. It distinguishes two phases in the choice process: 1 - The editing phase involves a preliminary analysis of the choice problem. It includes the identification of the options available to the actor, the possible outcomes or consequences of each, and the values and probabilities associated with each of these outcomes. It also includes the organization and reformulation of perceived options so as to "simplify subsequent evaluation and choice” (coding, combination, segregation, cancellation) 2 - In the evaluation phase, the edited prospects are evaluated and the preferred prospect is selected.

44. 3.2. Preferences3.2.1. Prospect theory

46. 3.2. Preferences3.2.2. Ambiguity aversion • Our discussion so far has centered on understanding how people act when the outcomesof gambles have known objective probabilities. In reality, probabilities are rarelyobjectively known. • To handle these situations, Savage (1964) develops a counterpart toexpected utility known as subjective expected utility, SEU henceforth. • Experimental work in the last few decades has been as unkind to SEU as it was toEU.

47. The experiment suggests that people do not like situations where they are uncertainabout the probability distribution of a gamble. Such situations are known as situationsof ambiguity, and the general dislike for them, as ambiguity aversion.

48. Applications 4. Application: The aggregate stock market • The equity premium puzzle • The volatility puzzle 5. Application: The cross-section of average returns • Belief-based models • Belief-based models with institutional frictions • Preferences 6. Application: Closed-end funds and comovement • Closed-end funds • Comovement 7. Application: Investor behavior • Insufficient diversification • Naive diversification • Excessive trading • The selling decision • The buying decision 8. Application: Corporate finance • Security issuance, capital structure and investment • Dividends • Models of managerial irrationality

49. The equity premium puzzle • Over two decades ago, Mehra and Prescott (1985) challenged the profession with a poser: the historical US equitypremium, (the return earned by a risky security in excess of that earnedby a relatively risk free US T-bill) is an order of magnitude greaterthan can be rationalized in the context of the standard neoclassicalparadigm of financial economics.Simply, historicequitypremium has beenverylarge.

50. The equity premium puzzle