A Challenge to Market Efficiency Behavioral Economics Dr. D. Kuebler, SS2003 Behavioral Finance – A Challenge to Market Efficiency Henry Fiebelkorn

Structure I II III IV V Introduction Behavioral Finance and Overview Market Phenomena - Anomalies III Financial Asset Pricing Theory – Behavioral Models IV Applying Behavioral Finance V Summary and Outlook

Introduction „Modern finance theorists have turned finance into a science, but they forgot that it is a social science!“ BF:is the application of psychology to financial behavior – the behavior of practioners. Aim: recognize, understand and avoid mistakes. State of Modern Finance: Perfect Markets & Perfect People Current State: Imperfect Markets & Perfect People Can we indeed say that investors behave rational? Rational procedure of choices d.o. Alternatives, ecpectations, preferences People act to reference points and perceive utility as value in changes relative to this Example: Siamese-twin-Stocks: Shell / RoyalDutch: shares are claimed to the same CF but trade in different locations: 60% CashFlows by RoyalDutch, 40 % by Shell -> MV (Royal) / MV (Shel) = 1,5 But big deviation in Reality:more then 30 % mispricing despite even fundamental identical securities offers Abitrage-possibilities, Abitrage doesn´t work very well, why? NoiseTrader risk CAPM= E(Ri)= Rf+[b*(E(Rm) – Rf)] -> Grafik zeichnen, riskis measured in terms of variance CAPM is not meant to explain investors behavior, you can´t draw any conclusion about that Behavioral Finance: Imperfect Markets & Imperfect People

Classification Behavioral Finance Heuristics Self-Concept Framing Aspects Representativeness Availability Anchoring Ambiguity Aversion Overconfidence Self-Attribution Cognitive Dissonanz Self-Control Confirmation Bias Herding Conservatism Mental Accounting Prospect Theory Framing Aspects: The way people behave d.o. The way that their decesion problems are framed Heuristics: Processes to find out things by yourselves, f.e. rules of thumb for a speedy decesion to safe time and effort Heuristic driven Biases: Representativness: reliance on stereotypes, future returns representated by historical average, follow the rules of association instaed of probability Availability: people rely on information available and do only a little search expl.: trowing a coin, 5times head Anchoring / Conservatism: DM based on irrelevant frames due to reference points, adapt earnings to slow, rely on regimes Aversion to Ambiguity: people prefer the familiar to the unfamilar- certainty effect (100$ for 100% > 200$ for 55%) Underlying Concepts: Bounded Rationality Emotions

Structure I II III IV V Introduction Behavioral Finance and Overview Market Phenomena - Anomalies III Financial Asset Pricing Theory – Behavioral Models Applying Behavioral Finance IV Summary and Outlook V

Anomalies Volume Volatility Anomalies are consistent 2. Violate the Efficient Market Hypothesis Dividends Equity Premium Puzzle Volume: in the sense of standard finance: market participants trade very little because trader are rational, but partly 700 mio shares a day traded at Nyse – which induces cognitive errors of noise trader, f.e. overconfidence etc. Volatility: asset price vary too much to fundamentals according to rational efficient market theory: Behavioralists: changes in beliefs about demand curves of other investors but not due to change in fundamental values Dividens: Modigliani / Miller: dividend policy is irrelevant in efficient markets, investors can do it by themselves, often share prices rise after announcement of dividends, following the Prospect Theory d. are framed in different mental accounts EquityPremium Puzzle: abnormal returns on equity: difference between market index returns and return of a risk free security to high, risk premium of 7% per year emprically observed, much too high to be explained by risk alone Book-2-market-ratio: B2M-ratio and size is priced in the markets contrary to the standard finance: f.e.: value-based stocks have higher returns than growth-based stocks StockMarketcrash: In 1987 stocks at Nyse fell 29 % in a single day in the absence of any significant fundamental news, liqidity and diversification as protections to reduce risk exposure failed, pesimistic investors failed to materialize as buyers and could be seen as very pesimistic relative to the prices by the market, therefore optimistic investors didinot by in as the market fell, price changes derives from changing beliefs about demand curve of other investors Calendar effects: f.e. Monday-effect: US-stock market has risen, but Friday-to-Monday 3 day return is negative, effect not large enough for profitable trading strategy, abitrage too risky Book-to-Market Ratio

Structure I II III IV V Introduction Behavioral Finance and Overview Market Phenomena - Anomalies III Financial Asset Pricing Theory – Behavioral Models IV Applying Behavioral Finance V Summary and Outlook

Model of Investor Sentiment Existing Approaches Model of Investor Sentiment “Inefficient Markets” by Shleifer (2000) Information Trader Noise Trader Abitrageurs: Follow Standard CAPM: No cognitive errors Utility expressed motives Try to exploit Noise Trader Outside CAPM (BAPM): Cognitive errors: listen to gurus, follow rumors Value expressed motives = Investor Sentiment Investors sentiment reflects common judgement errors from substantial number of investors

Model of Investor Sentiment Existing Approaches Model of Investor Sentiment Abitrage is limited because: Securities don´t have obvious substitutes Abitrage is risky (Risk Aversion) Noise Trader Risk Uncertainty about beliefs of next period noise trader No perfect substitute available because noise trader keeps u (unsafe asset) from driving down to fundamental values Model Assumptions: Overlapping generation structure: generates continuity Fixed supply of unsafe asset: prevents arbitrageurs from money strategies (conversion safe asset into unsafe asset) Systematic nature of noise trader risk: fundamental uncorrelated securities have correlated returns Price changes in absence of fundamental news! Belief about Noise Trader determines price

Model of Investor Sentiment Existing Approaches Model of Investor Sentiment Price Determination: Two Earning Regimes: News News Prior views No revaluation due to give up old model Attach to new model due to NoiseTrader: new information: Conservatism Representativeness Underreaction Overreaction

Investors: 2 Earning Regimes: Existing Approaches BSV - Model Captures 2 Judgement Biases: Barberis, Shleifer, Vishny (1998) Investors: 2 Earning Regimes: Barberis, Shleifer, Vishny (1998) (+) Firms´ earnings are trending Long-term- Change Representative-ness Bias (-) Conservatism Earnings are meanreverting Change temporarely Overreaction of Stock Prices Underreaction of Stock Prices

Captures 2 Judgement Biases: Special Prediction: Selective Items Existing Approaches DHS - Model Captures 2 Judgement Biases: Daniel, Hirshleifer, Subramanyam (1997) Overconfidence Self-Attribution (-) (+) Exaggerate Privat Information Downweight Public Information stock prices are determined by the informed investors, overconfidence: leads them to exaggerate the precision of their private signals about a stock´s value self attribution: causes them to downweight public signals about value overreaction to private information and underreaction to public information tend to produce short term continuitation of stock returns but long term reversals as public information eventually overwhelms the behavioral biases selective events: events that occur to take advantage of the misspricing of a firm´s stock Special Prediction: Selective Items

Behavioral Asset Pricing Model (BAPM) Existing Approaches Behavioral Asset Pricing Model (BAPM) BAPM Enable user to identify value of products value expressed characteristics Utilitian characteristics Rational Utility Strong: Jewelry Less: Automobiles Absent: Laundry Risk: Automobiles : Laundry “Behavioral Asset Pricing Theory” by Shefrin, Statmen (1994) Utilitarian characteristics vs. Value expressed characteristics (Timex /Rolex example) 16

Model Requirements Behavioral Asset Pricing Model: Should include: Identification of preferences of the buyers/sellers Characteristics capturing value expressive (VEC) & utilitarian preferences (UC) Behavioral Asset Pricing Model: Should include: What investors think How they asses risk How they forecast growth What rules they follow Conclude with equilibrium prices New Model will be an old demand/ and Supply model, not as beatiful but more robust Will teach us a lot about determinants of expected returns Behavioral Portfolio Theory: BPT describes what is thouhgt and observed – i.O. to the CAPM – refers to menatal accounting: every segment has own values PF from two parts: downside proetection and upside potential vs. CAMP: measures return over risk

Structure I II III IV V Introduction Behavioral Finance and Overview Market Phenomena - Anomalies III Financial Asset Pricing Theory – Behavioral Models IV Applying Behavioral Finance V Summary and Outlook

Applying Behavioral Finance Investors Limitations External Biases information Limitation of time &resources Practical restrictions Internal Mental Accounts Heuristics Self-Deception

Applying Behavioral Finance Emotional Feelings Performance Pressure Overload of Information Biased Information Investor Heuristics Time & Resource Constraints Practical Restrictions Uncertainty

Coping with Limited Rationality Applying Behavioral Finance Coping with Limited Rationality 1. Identify / Framing „People are intendedly rational but limited to do so!“ 2. Editing 3. Decomposition 1: identify, frame in different accounts 2: Simplifying 3. Divide in parts, single evaluation 4. Rules of thumb Example: want to go into cinema: A) bought 2 tickets for 40 € - lost! Will you buy new one? B) Want to buy tickets, loose 40 € out of the pocket According to Modigilani/ Miller: both is equal, but: A): transaction costs: effort for buying the tickets, tickets booked on leisure account, hurts more! 4. Heuristics

Applying Behavioral Finance Investment Strategies Value Investing Mean Reversion Strategy Momentum Strategy Event Studies Earning Revision Strategies Combination Studies Behavior: Overreaction Underreaction Extrapolation Herd Behavior Two examples: Mean Reversion strategy (overreaction) Momentum Strategy (Trending strategy: Herd Behavior)

Behavioral Finance leads to product development Applying Behavioral Finance Behavioral Finance leads to product development Guaranteed products RenteMaXX Best of World Garant Fund Loss Aversion Overconfidence Daytrading Hedge Funds: Event driven Opportunistic Relative value Absolute Return

Structure I II III IV V Introduction Behavioral Finance and Overview Market Phenomena - Anomalies III Financial Asset Pricing Theory – Behavioral Models IV Applying Behavioral Finance V Summary and Outlook

What lessons does Behavioral Finance teach us? Summary What lessons does Behavioral Finance teach us? ll Investor Market Be aware of information biases: seek and screen information actively 2. Avoid narrow framing, anchoring, overconfidence 3. Follow rules of decision making under uncertainty Market and people are imperfect There are systematic and recurring market inefficiencies Anomalies are consistent and can´t be ignored Sensible implementation of irrational human behavior into asset pricing models necessary VEC as well as UC must be included Thanks for your attention!