Behavioral finance

Behavioral Finance Ekrem Tufan (Visiting professor) Faculty of Business Administration in Karvina Silesian University ÇanakkaleOnsekiz Mart University School of Tourism and Hotel Management 2011 etufan@yahoo.com http://etufan.wordpress.com

Contents Financial decisions Financial decision approaches Traditional Approach and assumptions Expected Utility Theory Bayesian Logic Rational Expectations Theory Behavioural Finance Theory

Contents Behavioural Finance Short history of behavioural finance Behavioural Finance (BF) versus Efficient Market Hypothesis (EMH) EMH description, assumptions Weak Form Efficiency, examples Semi-Strong Form Efficiency, examples Strong Form Efficiency, examples

Contents Categories of BF Heuristic Decision Models Prospect Theory Editing phase Evaluation phase Logical Template Models

Reading list http://introduction.behaviouralfinance.net Eugene E. Fama, Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, Vol: 25, No:2, May 1970. Eugene E. Fama, Market Efficiency-Long Term Returns and behavioural Finance, February 1997, http://intraduction.behaviouralfinance.net/Rabi96.pdf Kahneman Daniel and Amos Tversky, Jugment under Uncertainty: Heuristics and Biases, Science, New series, Vol:185, Issue 4157, Sept. 27, 1974. Rabin Matthew, Psychology and Economics, Journal of Economics Literature, Vol: XXXVI, March 1998, http://introduction.behaviouralfiance.net/Rabi96.pdf

Reading list Shiller Robert J., Human Behaviour and Efficiency of the Financial System, 1999 in J.B. Taylor and M. Woodford, eds., Handbook of Macroeconomics, Vol:1, http://introduction.behaviouralfiance.net/Shil98.pdf Berggren Niclas, henrikJordahl and PanuPoutvaara, The looks of a winner: Beauty, Gender and electoral Success, Working Paper, No: 104, September 2006. Lo Andrew, Behavioural Finance: The science of Psychology, http://introduction.behaviouralfiance.net/vol5_text.pdf http://cepa.newschool.edu/het/profiles/neumann.htm http://economics.about.com/library/glossary/bldef-expected-utility-hypothesis.htm http://bayesian.org

Some article databases www.ssrn.com http://www.behaviouralfinance.net http://en.wikipedia.org

Lesson output Finding EU member countries stock exchanges main indexesclosing prices. It will be applied statistical methods and tested Weak Form Efficiency. Then it will be send it to an international symposium as a joint paper. OR Generating a questionnaire about behavioral finance and apply to undergraduate level students. Then it will be send it to an international symposium as a joint paper.

To Achieve Attending lesson is compulsory Delivering assignments (30 point) Presentations (50 point) Preparing paper (20 point)

Taking a decision How a human decide? What factors affect to decide? Have you affected someone? Is there psychological reasons? Do you think your are always rational when you take decisions about your life?

Taking a decisionSource: http://bio150.chass.utoronto.ca/pdgame/single.html

Taking a decisionSource: http://bio150.chass.utoronto.ca/pdgame/single.html If you chosen

If you have chosen Taking a decisionSource: http://bio150.chass.utoronto.ca/pdgame/single.html

Financial decisions Commonthing: Expectedcashflowsandinvestmentcost

Financial decision approaches Traditional Approach and assumptions Expected Utility Theory Bayesian Logic Rational Expectations Theory Behavioural Finance Theory

Traditional Approach and assumptions Traditional economic theory postulates an economic man, who, in the course of being economic is also rational.This man is assumed to have knowledge of the relevant aspects of his environment which, if not absolutely complete, is at least impressively clear and voluminous. He is assumed also to have a well-organized and stable system of preferences, and a skill in the computation that enables him to calculate, for the alternative courses of action that are avaible to him, which of these will permit him to reach to highest attainable point of his preference skill. (Simon A. Herbert, A Behavioral Model of rational Choice, The Quarterly Journal of Economics, Vol: 69, No: 1, Feb. 1955, p. 99)

Assumptions of modern portfolio theory and critics aboutthem* Traditional economic theory=Modern Portfolio Theory Asset returns are normally distributedrandom variables: In fact, it is frequently observed that returns in equity and other markets are not normally distributed. Large swings (3 to 6 standard deviations from the mean) occur in the market far more frequently than the normal distribution assumption would predict. While the model can also be justified by assuming any return distribution which isjointly elliptical all the joint elliptical distributions are symmetrical whereas asset returns empirically are not. *(http://en.wikipedia.org/wiki/Modern_portfolio_theory#Assumptions)

Continuation… Correlations between assets are fixed and constant forever:Correlations depend on systemic relationships between the underlying assets, and change when these relationships change. Examples include one country declaring war on another, or a general market crash. During times of financial crisis all assets tend to become positively correlated, because they all move (down) together. In other words, MPT breaks down precisely when investors are most in need of protection from risk.

Continuation… All investors aim to maximize economic utility (in other words, to make as much money as possible, regardless of any other considerations). This is a key assumption of the efficient market hypothesis, upon which MPT relies.

Continuation… All investors are rational and risk-averse. This is another assumption of the efficient market hypothesis, but we now know from behavioral economics that market participants are not rational. It does not allow for "herd behavior" or investors who will accept lower returns for higher risk. Casino gamblers clearly pay for risk, and it is possible that some stock traders will pay for risk as well.

Continuation… All investors have access to the same information at the same time. This also comes from the efficient market hypothesis. In fact, real markets contain information asymmetry, insider trading, and those who are simply better informed than others.

Continuation… Investors have an accurate conception of possible returns, i.e., the probability beliefs of investors match the true distribution of returns:A different possibility is that investors' expectations are biased, causing market prices to be informationally inefficient. This possibility is studied in the field of behavioral finance, which uses psychological assumptions to provide alternatives to the CAPM such as the overconfidence-based asset pricing model of Kent Daniel, David Hirshleifer, and AvanidharSubrahmanyam (2001).

Continuation… There are no taxes or transaction costs:Real financial products are subject both to taxes and transaction costs (such as broker fees), and taking these into account will alter the composition of the optimum portfolio. These assumptions can be relaxed with more complicated versions of the model.

Continuation… All investors are price takers, i.e., their actions do not influence prices:In reality, sufficiently large sales or purchases of individual assets can shift market prices for that asset and others (via cross-elasticity of demand.) An investor may not even be able to assemble the theoretically optimal portfolio if the market moves too much while they are buying the required securities

Continuation… Any investor can lend and borrow an unlimited amount at the risk free rate of interest: In reality, every investor has a credit limit. All securities can be divided into parcels of any size: In reality, fractional shares usually cannot be bought or sold, and some assets have minimum orders sizes. More information: Markowitz, H.M. (March 1952). "Portfolio Selection". The Journal of Finance7 (1): 77–91

Modern Portfolio TheorySource: http://en.wikipedia.org/wiki/Modern_portfolio_theory#Mathematical_model

Modern Portfolio Theory The father of Modern Portfolio Theory! Prof. Dr. Harry Markowitz Source: www.afajof.org/association/historyfinance.asp (Here you can find also transcript of his speech, 2004)

Expected Utility Theory Description The expected utility hypothesis is the hypothesis that the utility of an agent facing uncertainty is calculated by considering utility in each possible state and constructing a weighted average, where the weights are the agent's estimate of the probability of each state. (Source: http://economics.about.com/library/glossary/bldef-expected-utility-hypothesis.htm)

Expected Utility Theory Description: In economics, game theory, and decision theory the expected utility hypothesis is a theory of utility in which "betting preferences" of people with regard to uncertain outcomes (gambles) are represented by a function of the payouts (whether in money or other goods), the probabilities of occurrence, risk aversion, and the different utility of the same payout to people with different assets or personal preferences. This theory has proved useful to explain some popular choices that seem to contradict the expected value criterion (which takes into account only the sizes of the payouts and the probabilities of occurrence), such as occur in the contexts of gambling and insurance. Daniel Bernoulli initiated this theory in 1738. Source: http://en.wikipedia.org/wiki/Expected_utility_hypothesis

Expected Utility Theory The theory simply solves decision making problem under uncertainty conditionsand uses utility and risk relationship.It claims that people take into consider utility of all conditions and predict theirprobabilities then calculate weighted average. (Source: http://economics.about.com/library/glossary/bldef-expected-utility-hypothesis.htm) Under expected utility theory, some people would be risk averse enough to prefer the sure thing, even though it has a lower expected value, while other less risk averse people would still choose the riskier, higher-mean gamble.

Expected Utility Theory Brief history: The expected utility theory deals with the analysis of choices among risky projects with (possibly multidimensional) outcomes. The expected utility model was first proposed by Nicholas Bernoulli in 1713 and solved by Daniel Bernoulli in 1738 as the St. Petersburg paradox. Bernoulli argued that the paradox could be resolved if decisionmakers displayed risk aversion and argued for a logarithmic cardinal utility function. The first important use of the expected utility theory was that of John von Neumann and Oskar Morgenstern who used the assumption of expected utility maximization in their formulation of game theory. Source: http://en.wikipedia.org/wiki/Utility

Expected Utility Theory Why we use utility but not profit?

Expected Utility Theory Because, amount of money which could have earned by everybody has different effects on people in real life. For example: Assume that Turkish government pays same extra money to all government officials which is 100 Euro. Mr. Tufan has a 3.000 Euro salary while Mr. Basarili has 500 Euro. Because of different life styles, 100 Euro will more satisfyto Mr. Basarili than Mr. Tufan. (100/3000=%3,33, 100/500=%20)

Expected Utility Theory The von Neumann–Morgenstern utility theorem provides necessary and sufficient "rationality" axioms under which the expected utility hypothesis holds. In 1944, John von Neumann and Oskar Morgenstern exhibited four relatively modest axioms of "rationality" such that any agent satisfying the axioms has a utility function. The expected utility hypothesis is that rationality can be modeled as maximizing an expected value, which given the theorem, can be summarized as "rationality is VNM-rationality (Source: http://en.wikipedia.org/wiki/Von_Neumann%E2%80%93Morgenstern_utility_theorem)

Expected Utility Theory The four axioms of VNM-rationality are then completeness, transitivity, continuity, and independence. Completeness assumes that an individual has well defined preferences: (Source: http://en.wikipedia.org/wiki/Von_Neumann%E2%80%93Morgenstern_utility_theorem)

Bayesian LogicSource: http://en.wikipedia.org/wiki/Bayes%27_theorem In probability theory and applications, Bayes' theorem shows the relation between two conditional probabilities which are the reverse of each other. Bayes' theorem can then be understood as specifying how an ideally rational person responds to evidence.

Bayesian LogicSource: http://en.wikipedia.org/wiki/Bayes%27_theorem

Bayesian Logic: Example Source: http://en.wikipedia.org/wiki/Bayes%27_theorem Suppose there is a school with 60% boys and 40% girls as its students. The female students wear trousers or skirts in equal numbers; the boys all wear trousers. An observer sees a (random) student from a distance, and what the observer can see is that this student is wearing trousers. What is the probability this student is a girl? The correct answer can be computed using Bayes' theorem. The event A is that the student observed is a girl, and the event B is that the student observed is wearing trousers. To compute P(A|B), we first need to know: P(B|A), or the probability of the student wearing trousers given that the student is a girl. Since girls are as likely to wear skirts as trousers, this is 0.5. P(A), or the probability that the student is a girl regardless of any other information. Since the observer sees a random student, meaning that all students have the same probability of being observed, and the fraction of girls among the students is 40%, this probability equals 0.4. P(B), or the probability of a (randomly selected) student wearing trousers regardless of any other information. Since half of the girls and all of the boys are wearing trousers, this is 0.5×0.4 + 1.0×0.6 = 0.8.

Continuation… Another, essentially equivalent way of obtaining the same result is as follows: Assume, for concreteness, that there are 100 students, 60 boys and 40 girls. Among these, 60 boys and 20 girls wear trousers. All together there are 80 trouser-wearers, of which 20 are girls. Therefore the chance that a random trouser-wearer is a girl equals 20/80 = 0.25. Put in terms of Bayes´ theorem, the probability of a student being a girl is 40/100, the probability that any given girl will wear trousers is 1/2. The product of these two is 20/100, but we know the student is wearing trousers, so one deducts the 20 students not wearing trousers, and then calculate a probability of (20/100)/(80/100), or 20/80. It is often helpful when calculating conditional probabilities to create a simple table containing the number of occurrences of each outcome, or the relative frequencies of each outcome, for each of the independent variables.

Rational ExpectationsTheory(http://en.wikipedia.org/wiki/Rational_Expectations_Theory) Rational expectations is a hypothesis in economics which states that agents' predictions of the future value of economically relevant variables are not systematically wrong in that all errors are random. The rational expectations assumption is used in many contemporary macroeconomic models, game theory and other applications of rational choice theory. Since most macroeconomic models today study decisions over many periods, the expectations of workers, consumers, and firms about future economic conditions are an essential part of the model.

Rational ExpectationsTheory(http://en.wikipedia.org/wiki/Rational_Expectations_Theory) Although the future is not fully predictable, agents' expectations are assumed not to be systematically biased and use all relevant information in forming expectations of economic variables. This way of modeling expectations was originally proposed by John F. Muth (1961) and later became influential when it was used by Robert E. Lucas Jr and others. Modeling expectations is crucial in all models which study how a large number of individuals, firms and organizations make choices under uncertainty.

Rational ExpectationsTheory(http://en.wikipedia.org/wiki/Rational_Expectations_Theory) Rational expectations theory defines this kind of expectations as being identical to the best guess of the future (the optimal forecast) that uses all available information. Thus, it is assumed that outcomes that are being forecast do not differ systematically from the market equilibrium results. In aAs a result, rational expectations do not differ systematically or predictably from equilibrium results. That is, it assumes that people do not make systematic errors when predicting the future, and deviations from perfect foresight are only random. n economic model, this is typically modelled by assuming that the expected value of a variable is equal to the expected value predicted by the model.

Rational ExpectationsTheory(http://en.wikipedia.org/wiki/Rational_Expectations_Theory) Rational expectations theories were developed in response to perceived flaws in theories based on adaptive expectations. Under adaptive expectations, expectations of the future value of an economic variable are based on past values. For example, people would be assumed to predict inflation by looking at inflation last year and in previous years. Under adaptive expectations, if the economy suffers from constantly rising inflation rates (perhaps due to government policies), people would be assumed to always underestimate inflation.

Rational ExpectationsTheory(http://en.wikipedia.org/wiki/Rational_Expectations_Theory) Rational expectations theory is the basis for the efficient market hypothesis (efficient market theory). If a security's price does not reflect all the information about it, then there exist "unexploited profit opportunities": someone can buy (or sell) the security to make a profit, thus driving the price toward equilibrium. In the strongest versions of these theories, where all profit opportunities have been exploited, all prices in financial markets are correct and reflect market fundamentals (such as future streams of profits and dividends). Each financial investment is as good as any other, while a security's price reflects all information about its intrinsic value.

Behavioural Finance Theory Descriptions: Behavioural finance is the study of the influence of psychology on the behaviourof financial practitioners and the subsequent effect on markets. Behavioural finance is of interest because it helps explain why and how markets might beinefficient. Behavioural finance and behavioural economics are closely related fields which apply scientific research on human and social cognitive and emotional biases to better understand economic decisions and how they affect market prices, returns and the allocation of resources."

Descriptions: The behavioral finance applies theories of human derived from psychology, sociology and anthropology to understand the behavior of the financial market. Source: CornicelloGiuseppe, BehaviouralFinanceandSpeculativeBubble, UniversitaCommercialeLuigi-Milano, PhDThesis, 2004, p.23 Behavioural Finance Theory

Behavioural Finance Theory What kind of researchs are being done by researchers ? Behavioral finance claims that human not always behave rational. Some examples… (Source: Tufan, Ekrem Davranışsal Finans, İmaj Yayınevi, 2008, p.19)

Behavioural Finance Theory Employers tend to pay much salary beautiful and handsome people then ordinary ones. (Mobius and Rosenblat (March 2006), Why Beauty Matters, The American Economic Review, Vol: 96, No:1) Beautiful and handsome PM candidates have more chance to be voted. (Source: Berggren Niclas, HenrickJordahl and PanuPoutvaara, (September 2006), The Looks of a Winner: Beauty, Gender and Electoral Success, Working Paper, No: 104, p.17) In a lottery, people give lower probability if that number has recently disavantager. (Source: Clotfelter Charles T. And Philip J. Cook, (1991), The "Gambler's Fallacy" in Lottery Play , NBER Working Paper No. W3769, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=226933

Behavioural Finance Theory What I have searched: TUFANEkremandBahattinHamarat, (2009) “Jinx Numbers Effect”, The ISE Review (Journal of Istanbul Stock Exchange), Vol:11, No:41. TUFAN Ekremand Bahattin HAMARAT, “Do Investors being Affected by The Weather Conditions: An Evidence from Istanbul Stock Exchange”, The ISE Review (Journal of Istanbul Stock Exchange), Vol: 31 TUFANEkrem, MirelaCristea and RalucaDracea, “Experience of Risk Taking Behavior on Insurance Market from two Developing Countries: Romania and Turkey”, StudiaUniversitatisBabeş-BolyaiStudiaEuropaea, LII, No:1.

Behavioural Finance Theory TUFAN Ekremand Bahattin HAMARAT, “Do Cloudy Days Affect Stock Exchange Return: Evidence from Istanbul Stock Exchange, Journal of Naval Science& Engineering, Vol.2, No.1 For more informationabout behavioral finance www.behaviouralfinance.net

Behavioural Finance Theory History The behavioral finance could be based on Adam Smith’s The Theory of Modern Sentiment book. In that book he says “we suffer more...when we fall from a better to worse situation, then we ever enjoy when we rise from a worse to a better…” This sentence explains the principle of loss aversion in behavioural finance. In same age, jeremy Bentham have published on articles about utility’s psychological dimensions. History of behavioralfinance Source: Cornicello Giuseppe, Behavioral Finance and Speculative Bubble, UniversitaCommerciale Luigi-Milano, PhD Thesis, 2004, p.23-24.

Behavioural Finance TheorySource: Cornicello Giuseppe, Behavioral Finance and Speculative Bubble, UniversitaCommerciale Luigi-Milano, PhD Thesis, 2004, p.23-24 History The researchers such as Keynes, Irving Fisher and Vilfredo have also investigated behavioura subjects but the subject has not been take into consider till middle of this century. Probably the main contributions to the growth of the modern behavioral finance were the articles of Tversky and Kahneman(1974) on the heuristics and their work of the (1979) on the prospect theory.

Behavioural Finance (BF) versus Efficient Market Hypothesis (EMH) EMH is based on human rationality and related with random walk. What is random walk? A price series where all subsequent price changes represent random departures from previous prices.The logic of the random walk idea is that if the flow of information is unimpeded and information is immediately reflected in stock prices, then tomorrow’s price change will reflect only tomorrow’s news and will be independent of the prices changes today. (Source: Malkiel, G. Burton, TheEfficient Market HypothesisandItsCritics, Journal of economicPerspectives, Vol: 17, No: 1, Winter 2003, pp.59-82.

Behavioural Finance (BF) versus Efficient Market Hypothesis (EMH) Because stock prices reflect all related information, nobody can predict future price changes with using past patterns of it. So, applying technical or fundamental analysis is useless. Do you think so? Behavioral finance supporters claim that future prices could be predicted! Some psychological factors affect to stock exchange prices.

Behavioural Finance (BF) versus Efficient Market Hypothesis (EMH) Example for EMH Burton G. Malkiel, an economist professor at Princeton University and writer of A Random Walk Down Wall Street, performed a test where his students were given a hypothetical stock that was initially worth fifty dollars. The closing stock price for each day was determined by a coin flip. If the result was heads, the price would close a half point higher, but if the result was tails, it would close a half point lower. Thus, each time, the price had a fifty-fifty chance of closing higher or lower than the previous day. Cycles or trends were determined from the tests. Malkiel then took the results in a chart and graph form to a chartist, a person who “seeks to predict future movements by seeking to interpret past patterns on the assumption that ‘history tends to repeat itself’”.[5] The chartist told Malkiel that they needed to immediately buy the stock. When Malkiel told him it was based purely on flipping a coin, the chartist was very unhappy. Malkiel argued that this indicates that the market and stocks could be just as random as flipping a coin. (Source: http://en.wikipedia.org/wiki/Random_walk_hypothesis)

Efficient Market Hypothesis (EMH) In finance, the efficient-market hypothesis (EMH) asserts that financial markets are "informationally efficient". That is, one cannot consistently achieve returns in excess of average market returns on a risk-adjusted basis, given the information publicly available at the time the investment is made. (Source: http://en.wikipedia.org/wiki/Efficient-market_hypothesis) A market in which prices always "fully reflect" available informationis called "efficient.“ (Source: Fama F. Eugene, May 1970, EfficientCapitalMarkets: A Review of TheoryandEmpiricalWork, TheJournal of Finance, Vol: 25, No:2, p.283.)

Efficient Market Hypothesis (EMH) EMH has three type: Weak form efficiency Semi-strong form efficiency Strong form efficiency

Weak Form Efficiency(Source: http://en.wikipedia.org/wiki/Efficient-market_hypothesis) Future prices cannot be predicted by analyzing prices from the past. Excess returns cannot be earned in the long run by using investment strategies based on historical share prices or other historical data. Technical analysis techniques will not be able to consistently produce excess returns, though some forms of fundamental analysis may still provide excess returns. Share prices exhibit no serial dependencies, meaning that there are no "patterns" to asset prices. This implies that future price movements are determined entirely by information not contained in the price series. Hence, prices must follow a random walk. This 'soft' EMH does not require that prices remain at or near equilibrium, but only that market participants not be able to systematically profit from market 'inefficiencies'.

Weak Form Efficiency Weak form efficiency = Returnpredictability Lets read Fama’s article and find questions which should be answered before apply statistics such as randomwalkmeansseries withhas no serial dependencies. What does the meaning of serial dependence in statistics? How can we calculate with statistic / econometric programs? Fama F. Eugene, May 1970, EfficientCapitalMarkets: A Review of TheoryandEmpiricalWork, TheJournal of Finance, Vol: 25, No:2, p.383

A brief Literature Review on Weak Form Efficiency Stein J. Jeremy, Nov, 1989, Efficient Capital Markets, Inefficient Firms: A Model of Myopic Corporate Behaviour, The Quarterly Journal of Economics, Vol. 104, No. 4 (Nov., 1989), pp. 655-669 The academic argument is based on the tenetof efficient markets: since it is unlikely that the market can besystematically fooled by inflated earnings, managers will only lowerstock prices by undertaking actions that are not in the best long-runinterests of their companies. Hence, managers who areconcernedwith high stock prices will not behave myopically.

Continuation… Balaban has investigated daily anomalies for Turkish Stock Market and reported that significant day of the week effect for the Turkish market. Metin and et all. have examined the weak form efficiency of Istanbul Stock Exchange (ISE) by using random walk test and the day of the week effect. They have used data January 4, 1988 to December 27, 1996. They have reported Friday and Monday effect but Monday effect was not statistical significant. Bildik has investigated the day of the week effect in overnight interest rates in interbank market, overnight interest rates in interest rates of the Istanbul Stock Exchange (ISE) and daily closing values of the Istanbul Stock Exchange’s Composite Index. The researcher has reported that there is no significant difference between the repo rates occurred in the ISE repo Market and interest rates in Interbank Market. He also reported overnight interest rates decrease on Wednesdays and increase on Mondays relative to previous days. In stock market, he has found pattern of low or negative returns over the first part of the week (Monday through Tuesday) and high and positive returns over the second part of the week (Wednesday through Friday). Balaban, Ercan, Day of the Week Effect: New Evidence from an Emerging Stock Market, Applied Economics Letters,1995, Vol: 2, pp.139-143. Metin, Kıvılcım, Muradoglu G. and Yazıcı B., İstanbul Menkul Kıymetler Borsası’nda Gün Etkilerinin İncelenmesi, IMKB Dergisi, 1997, Vol:4, pp.15-25. Bildik, Recep, Day of the Week Effect in Turkish Stock and Money Markets, Annual Meeting of European Financial Management Association,Paris, 1999, pp. 1-49.

Some Statistics Applications with SPSS Statistical Program A webcam connection with LecturerBahattin HAMARAT Canakkale Onsekiz Mart University School of Tourism and Hotel Management TURKEY

Semi-Strong Form Efficiency Semi-strong EMH claims both that prices reflect all publicly available information and that prices instantly change to reflect new public information. (Source: http://en.wikipedia.org/wiki/Semi-strong_form) Semi-strong form tests, in which the concernis whether prices efficiently adjust to other information that is obviouslypublicly available (e.g., announcements of annual earnings, stock splits, etc.)are considered. (Source: Fama F. Eugene, May 1970, EfficientCapitalMarkets: A Review of TheoryandEmpiricalWork, TheJournal of Finance, Vol: 25, No:2, p.283.)

Semi-Strong Form Efficiency Instead of semi-strong-form tests of the adjustment of prices to publicannouncements, I use the now common title, event studies. Event studies are now an important part of finance, especially corporatefinance. Using simple tools, this research documents interesting regularities in theresponse of stock prices to investment decisions, financing decisions, andchanges in corporate control.Lets read Fama’s article! (Source: Eugene F. Fama, December 1991, Efficient Capital Markets: II, The Journal of Finance, Vol: XLVI, No: 5, p.15751617.

Literature Review Journal of Financial and Quantitative Analysis (1986), 21: 1-25

Strong Form Efficiency Strong form test= Testsfor private information Exp: Insider trading Strongform tests concerned with whether given investorsor groups have monopolistic access to any information relevant forprice formation are reviewed. (Source: Fama F. Eugene, May 1970, Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, Vol: 25, No:2, p.383)

Contents Categories of BF Heuristic Decision Models Prospect Theory Editing phase Evaluation phase Logical Template Models (Source: Tufan Ekrem, Davranışsal Finans, 2008, İmaj Yayınevi, www.imajyayinevi.com, p.53.)

Heuristic Decision Models Representativeness Overconfidence Anchoring Gambler’s fallacy Availability bias

Heuristic Decision Models Representativeness: Under and Over reactions arise from the interaction of momentum traders and news watchers Momentum traders make partial use of the information continued in recent price trends, and ignore fundamental news Fundamental traders rationally use fundamental news but ignore prices. (Source: http://introduction.behaviouralfinance.net)

Heuristic Decision Models Representativeness: Representativeness refers to the tendency of decision makers to make decisions basedon stereotypes, that is to see patterns where perhaps none exist. Representativenessalso arises in the guise of the ‘law of small numbers’ whereby investors tend toassume that recent events will continue into the future. In financial markets this canmanifest itself when investors seek to buy ‘hot’ stocks and to avoid stocks which haveperformed poorly in the recent past. (Source: Brabazon Tony, 2000, Behavioral Finance: A New Sunsrise or False Dawn?, http://introduction.behavioralfinance.net/Brab00.pdf, p.2)

Heuristic Decision Models Representativeness: Representativeness heuristic (finds patterns in data too readily, tends to over react to information) and conservatism (clings to prior beliefs, under reacts to information). Interaction of representativeness heuristic and conservatism: explains short term under reaction and long term over reaction. Investor’s reaction to current information condition on past information. Investor tends to under react to information that is preceded by a small quantity of similar information and to over react to information that is preceded by a large quantity of similar information. (Source: http://introduction.behaviouralfinance.net)

Heuristic Decision Models Overconfidence: Overconfidence leads investors tend to overestimate their ‘predictive’ skills andbelieve they can ‘time’ the market. Studies have shown that one side effect of investoroverconfidence is excessive trading. Overconfidence is by no means limited toindividual investors. There is evidence that financial analysts are slow to revise theirprevious assessment of a company’s likely future performance, even when there isnotable evidence that their existing assessment is incorrect. (Source: Brabozan, 2000, a.g.e, p.2)

Heuristic Decision Models Overconfidence: What happens in financial markets when people are overconfident? Trading volume increases: overconfidence generates trading. Those who trade more frequently fare worse than those who trade less Overconfident traders hold under-diversified portfolios; riskier portfolios though they have the same degree of risk aversion Overconfident insiders improve price quality; overconfident noise traders worsen it Men are more overconfident than women; men trade more frequently (45% more) than women, men earn less returns than women (one percent less). Single men and single women the results are larger (67% more trading, 1.4% less) (Source: http://introduction.behaviouralfinance.net)

Heuristic Decision Models Overconfidence: Depending upon the success of failure, level of overconfidence changes A trader is not overconfident when he begins to trade Overconfidence increase over his first several trading periods early in his career These overconfident traders survive the threat of arbitrage, that is, they are not the poorest traders Initial success increases overconfidence Overconfidence declines thereafter (Source: http://introduction.behaviouralfinance.net)

Heuristic Decision Models Anchoring: Anchoring arises when a value scale is fixed or anchored by recent observations. Thiscan lead investors to expect a share to continue totrade in a defined range or to expectacompany’s earnings to be in line with historical trends, leading to possibleunderreaction to trend changes. (Source: Brabozan, 2000, a.g.e, p.2)

Heuristic Decision Models Anchoring: Anchoring and adjustment is a psychological heuristic that influences the way people intuitively assess probabilities. According to this heuristic, people start with an implicitly suggested reference point (the "anchor") and make adjustments to it to reach their estimate. A person begins with a first approximation (anchor) and then makes adjustments to that number based on additional information. The anchoring and adjustment heuristic was first theorized by Amos Tversky and Daniel Kahneman (Source: http://en.wikipedia.org/wiki/Anchoring)

Heuristic Decision Models Anchoring:During normal decision making, individuals anchor, or overly rely, on specific information or a specific value and then adjust to that value to account for other elements of the circumstance. Usually once the anchor is set, there is a bias toward that value. Take, for example, a person looking to buy a used car. He or she may focus excessively on the odometer reading and model year of the car, and use those criteria as a basis for evaluating the value of the car, rather than considering how well the engine or the transmission is maintained. (Source: http://en.wikipedia.org/wiki/Anchoring)

Heuristic Decision Models Anchoring: "In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer. The initial value, or starting point, may be suggested by the formulation of the problem, or it may be the result of a partial computation. In either case, adjustments are typically insufficient (Slovic & Lichtenstein, 1971). That is, different starting points yield different estimates, which are biased toward the initial values. We call this phenomenon anchoring.“(Source:Tversky and Kahneman (September1974), Jugment under Uncertainty: Heuristics and Biases, Science, New series, Vol: 185, Issue: 4157, pp.1124-1131)

Heuristic Decision Models Gambler’s fallacy: Gamblers’ fallacy arises when people inappropriately predict that a trend will reverse. This tendency may lead investors to anticipate the end of a run of good (or poor)market returns. Gamblers’ fallacy can be considered to be an extreme belief inregression to the mean. Regression to the mean is found in many human systems andimplies that an extreme trend will tend to move closer to the mean over time.Sometimes regression to the mean is incorrectly interpreted as implying that, forexample, an upward trend must be followed by a downward trend in order to satisfy alaw of averages. (Source: Brabozan, 2000, a.g.e, p.2)

Heuristic Decision Models The Gambler's fallacy, also known as the Monte Carlo fallacy (because its most famous example happened in a Monte Carlo casino in 1913)[1] or the fallacy of the maturity of chances, is the belief that if deviations from expected behaviour are observed in repeated independent trials of some random process then these deviations are likely to be evened out by opposite deviations in the future. For example, if a fair coin is tossed repeatedly and tails comes up a larger number of times than is expected, a gambler may incorrectly believe that this means that heads is more likely in future tosses. (Source: http://en.wikipedia.org/wiki/Gambler's_fallacy)

Literature Review Why do gamblers over-report wins? An examination of social factors John Jamieson, Chris MushquashandDwight Mazmanian The role of social factors in gamblers' over-reporting of wins was explored using a survey administered via the Internet. One hundred and fifteen gamblers (average age 36.9) completed the survey. The majority of gamblers reported that they do not over-report wins, and would not do so for social reasons. However, they believe that other gamblers do mislead people about their losses for a variety of social reasons, such as a desire to appear skilled or to be popular. As well, the majority of gamblers report not feeling urges to gamble when hearing about wins, although younger people, males, and those with gambling problems were significantly more likely to report feeling and/or acting on urges to gamble when hearing about others' wins. The discrepancy between their views of themselves and of other gamblers may be due to cognitive distortions specific to gamblers, or may reflect a general self-presentation bias. (Source: http://epe.lac-bac.gc.ca/100/201/300/jrn_gambling_issues/html/2005/no15/issue9/research/jamieson)

Heuristic Decision Models Availability bias:Availability bias emerges when people place undue weight on [easily] availableinformation in making a decision.(Source: Brabozan, 2000, a.g.e, p.2) The availability heuristic is a phenomenon (which can result in a cognitive bias) in which people predict the frequency of an event, or a proportion within a population, based on how easily an example can be brought to mind. This phenomenon was first reported by psychologistsAmos Tversky and Daniel Kahneman, who also identified the representativeness heuristic. (Source: http://en.wikipedia.org/wiki/Availability_heuristic)

Examples A person argues that cigarette smoking is not unhealthy because his grandfather smoked three packs of cigarettes a day and lived to be 100. The grandfather's health could simply be an unusual case that does not speak to the health of smokers in general. A politician says that walnut farmers need a special farm subsidy. He points to a farmer standing nearby and explains how that farmer will benefit. Others who watch and discuss later agree that the subsidy is needed based on the benefit to that farmer. The farmer, however, might be the only person who will benefit from the subsidy. Walnut farmers in general may not necessarily need this subsidy. A person claims to a group of friends that drivers of red cars get more speeding tickets. The group agrees with the statement because a member of the group, "Jim," drives a red car and frequently gets speeding tickets. The reality could be that Jim just drives fast and would get a speeding ticket regardless of the colour of car that he drove. Even if statistics show fewer speeding tickets were given to red cars than to other coloursof cars, Jim is an available example which makes the statement seem more plausible. (Source: http://en.wikipedia.org/wiki/Availability_heuristic)

Literature Review Lets have a look to Tversky Amos and Daniel Kahneman’s (1973) presentation. http://www.posbase.uib.no/posbase/Presentasjoner/P_Tversky%20&%20Kahneman%20(1973).ppt

Prospect Theory The theory allows one to describe how people make choices in situations where they have to decide between alternatives that involve risk (e.g., in financial decisions). Starting from empirical evidence, the theory describes how individuals evaluate potential losses and gains. In the original formulation the term prospect referred to a lottery. The theory describes such decision processes as consisting of two stages, editing and evaluation. In the first, possible outcomes of the decision are ordered following some heuristic. In particular, people decide which outcomes they see as basically identical and they set a reference point and consider lower outcomes as losses and larger as gains. In the following evaluation phase, people behave as if they would compute a value (utility), based on the potential outcomes and their respective probabilities, and then choose the alternative having a higher utility. (Source: http://en.wikipedia.org/wiki/Prospect_theory)

Prospect Theory Editing phase: It consists of a preliminary analysis of the offered prospects, which often yields a simpler representation of these prospects.The major operations of the editing phase are: Coding: People normally perceive outcomes as gains and losses, rather than as final states of wealth or welfare. Gains and losses, of course, are defined relative to some neutral reference point. The reference point usually corresponds to the current asset position, in which cases gains and losses coincide with the actual amountsthat are received or paid.

Prospect Theory(Source: KahnemanDanieland Amos Tversky, Prospect Theory: An Analysisof Decision Under Risk, Econometrica (pre-1986); Mar 1979; 47, 2; ABI/INFORM Global, pg. 263) Combination: Prospects can sometimes be simplified by combining the probabilities associated wit identical outcomes. Segration: Some prospects contain a riskless component that is segregated from the risky component in the editing phase. Cancellation: Theessence of the isolation effects is the discarding of components that are shared by the offered prospects. Reflection: People are risk averse when a certain gain or taking risk to more positive gain while taking risk certain losses or taking risk to escape from negative (losses) possibilities.

Prospect Theory Some evidences against Expected Utility Theory: Certainty effect: People underweight outcomes that are merely probable in comparison with outcomes that are obtained with certainty. This tendency, called the certainty effect, contributes to risk aversion in choices involving sure gains and risk seeking in choices involving sure losses. Isolation effect:People generally discard components that are shared by all prospects under consideration. It leads to inconsistent preferences when the same choice is presented in different forms. (Source: KahnemanDanieland Amos Tversky, Prospect Theory: An Analysisof Decision Under Risk, Econometrica (pre-1986); Mar 1979; 47, 2; ABI/INFORM Global, pg. 263)

Prospect Theory LetsreadKahnemanandTversky’sarticle! (First page) KahnemanDanieland Amos Tversky, Prospect Theory: An Analysisof Decision Under Risk, Econometrica (pre-1986); Mar 1979; 47, 2; ABI/INFORM Global, pg. 263

Prospect Theory Problem 1: Please choose between A: 2.500 € with probability 33%B: 2.400 € with certainty 2.400 € with probability 66% 0 € with probability 1% Problem 2:Please choose between C: 2.500 € with probability 33% D: 2.400 € with prob. 34% 0 € with probability 67% 0 € with probability 66% Lets continuo to read page 266 and see Kahneman and Tversky’s comments…

Logical Template Models(Source: BrabazonTony, BehavioralFinance: A New Sunriseor a FalseDawn?, 2000, http://wenku.baidu.com/view/03d9b56baf1ffc4ffe47ace7.html To be risk averse because of loss phobia: Loss aversion is based on the idea that the mentally associated with a given loss is greater than the mental reward from a gain of the same size. Regret Aversion: It arises because of peoples’ desire to avoid feeling the pain of regret resulting from a poor investment decision. This aversion can encourage investors to hold poorly performing shares as avoiding their sale also avoids the recognition of the associated loss.

Logical Template Models Mental accounting:It is the name given to the propensity of individuals to organize their world into separate mental accounts. For example: An individual can borrow at high interest to buy a consumer item whilst simultaneously saving at lower interest rates for a child’s college fund.(Source: Brabazon, a.g.e….) Self control: After controlling for the degree of investor overconfidence, firmsin a sector with a lower average return correlation tend to have more pronouncedoverreaction-driven return predictability, such as long-run price reversals and short-termprice momentum. However, ignored information in public domain, such as certainvariables in firms’ financial statements, is less effective in predicting the future returns ofthese firms. (Source: Lin Penga, Wei Xiong, 2006, Investor attention, overconfidenceand category learning,Journal of Financial Economics 80,pp.563–602

HerdBehavior Anomaly:Istanbul Stock Exchange (ISE)(Source: Tufan Ekrem, Davranışsal Finans, 2008, İmaj Yayınevi, pp.93-109. Aim of the study: Investigate herd behavior in ISE with using foreign investors and local investors bur and sell volumes Data covers: January 1995 to June 2007 Methodology: Johansen Cointegration and Granger Causality Tests Result: There is a one way relationship between foreign investors trading volume and local investors trading volume. The way of the relationship is from foreigner to local. So, there is a herd behavior in ISE. Local investors follow to foreign investors.

Thank you very much… http://etufan.wordpress.com etufan@yahoo.com

Behavioral finance

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