HARVARD UNIVERSITY Department of Economics

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1 HARVARD UNIVERSITY Department of Economics Economics 970 Behavioral Finance Science Center 103b Spring 2002 M, W 7-8:30 pm Mr. Evgeny Agronin Teaching Fellow agronin@fas.harvard.edu (617) Course Description One of the most intriguing questions that anyone has ever asked himself is whether it is possible to make money in the stock market. Some of those who tried indeed did, while many others found themselves very disappointed with their experience. How should an individual choose his portfolio of financial assets? What are the assets that generate the highest return? How can we explain that the returns on some assets are higher than on other assets? Are these differences in returns are due to risk or mispricing? We will first learn the classical approach to how assets are priced. This approach implies that differences in returns on different stocks exist due to different levels of risk associated with investment into these stocks. We will talk about the economic models of portfolio choice, implications of these models for the prices of assets, and the empirical evidence that addresses validity of these models. Differences in the performance of certain strategies have, however, difficulties to be explained by just different levels of risk associated with these strategies. Indeed, we will learn about the investment strategies that usually result in abnormally high return and are not very risky in the same time. This fact suggests that some assets are probably mispriced. Some assets are too expansive relative to the fundamental values of underlying companies. Other assets are too cheap. Why does mispricing exist in the stock market? Why do rational arbitrageurs, whose work is to make money on the mispricing of stocks, not eliminate it? The behavioral approach suggests that the participants in the stock market have certain psychological biases. They under- or overreact to news; they can be overconfident about their private information; they are averse not only to risk, which represents the uncertainty about an asset s going up or down, but also to losses; on the one hand, people put to much weight on the recent information in their decision; on the other hand they are too conservative. We will learn, how some of these biases can explain extremely good performance of certain strategies and extremely bad performance of other ones.

2 Course Requirements Class participation. 25% of grade. Attendance is mandatory. Class discussions are an essential component of the sophomore tutorial. Your full participation is expected in the discussion of assigned readings and general course themes. Response papers. 15% of grade divided among 5 papers (2-3 pp. each). These papers are intended to facilitate class participation. Topics will range from summarizing assigned readings to raising ideas for discussion. Essays. 15% of grade dividend among 3 papers (4-6 pp. each). Assigned topics will be based on class discussions and readings. Topics will include criticizing assigned readings an proposing solutions to economic problems. Empirical exercise. 15% of grade (5-7 pp.) You will construct an econometric model, report your findings, and analyze the results. Final paper. 30% of grade. (15-15 pp.) You will choose (or will be assigned) a pattern of behavior in the stock returns (among those which we will discuss in class) and write a final paper, which compares the classical explanations of this pattern vs. behavioral explanations. The comparison will be based on the literature discussed in class, and other related literature that you find yourself. You will try to find strong and weak sides of both approaches in the explanation of the pattern and conclude, which approach is more appropriate for the explanation. Course Policies Attendance. Attendance is mandatory at all regular class meetings. Exceptions for personal or family emergencies will be granted on a case-by-case basis. Tardiness. No assignment will be accepted beyond the announced deadline. As with attendance, exceptions for personal or family emergencies will be allowed on a case-bycase basis. Office hours. I will meet with students on the individual basis (we can set the time of the meeting either after the class, or via ).

3 Readings Part 1. Classical Approach to Asset Pricing Meeting Expected Utility and Risk Aversion 2. The Allias Paradox Varian, H.R., Intermediate microeconomics. A modern approach, Second Edition, W.W. Norton &Company, 1990; Ch. 11 (Asset Markets) and 12 (Uncertainty) Meeting Portfolio Choice Theory 2. CAPM Varian, H.R., Intermediate microeconomics. A modern approach, Second Edition, W.W. Norton &Company, 1990; Ch. 13 (Risky Assets) Sharpe, W., Capital asset prices: a theory of market equilibrium under conditions of risk, Journal of Finance 19, (JSTOR) Meeting Econometrics Overview 2. Empirical Tests of CAPM: cross-section and time-series approaches. Cochrane, J. H., Asset Pricing, Princeton University Press, 2001 Handouts Assignment 1. A response paper on what assumptions of CAPM are, and why it does not work (1 page). Meeting Consumption CAPM and EPP. The Notion of Risk. 2. SDF (by analogy with CAPM) and how we get to CAPM through SDF (CAPM as one factor model) 3. Does CAPM work? Fama, E. F. and K. R. French, 1992, The cross-section of expected stock returns, Journal of Finance 47, (JSTOR) Meeting 5. New and Old Facts in Finance Cochrane, J. H., 1997, Where is the market going? Uncertain facts and novel theories, Economic Perspectives 21 (6), Part 1 Cochrane, J. H., 1999, New facts in finance, Economic Perspectives 23 (3), Meeting Value Stocks vs. Growth Stocks

4 2. Explanations: Extrapolation vs. Risk in one factor model 3. Multifactor Models Lakonishok, J., A. Shleifer, and R. W., Vishny, 1994, Contrarian investment, extrapolation, and risk, Journal of Finance 49, (JSTOR) Fama, E. F., and K. R. French, 1996, Multifactor explanations of asset pricing anomalies, Journal of Finance 51, (JSTOR) Assignment 2. A short essay on the comparison of the competing behavioral and risk explanation (2-4 pages). Meeting Discussion of the essays 2. Guidelines for the empirical exercise 3. Discussion on various heuristics and biases Assignment 3. Empirical exercise on pricing assets with factors. Cross-section and timeseries approaches. Part 2. Investors Psychology and Asset Prices Meeting 8. Overview of Heuristics and Biases (continued) Hirshleifer, D., 2001, Investor psychology and asset pricing, forthcoming, Journal of Finance. Shiller, R. J., 2001, Bubbles, human judgment, and expert opinion, Cowles Foundation Discussion Paper No find a button click here to download the document Meeting 9. Prospect Theory and Applications Kahneman, D. and A. Tversky, 1979, Prospect theory: an analysis of decision under risk, Econometrica (JSTOR) Benartzi, S. and R. Thaler, 1997, Myopic loss aversion and the equity premium puzzle, Quarterly Journal of Economics Gneezy, U. and J. Ptters, 1997, An experiment in risk taking and evaluation periods, Quarterly Journal of Economics Meeting 10. Limited Diversification: people do not hold the market portfolio S. Benartzi, and R. Thaler, Naןve Diversification Strategies in defined contribution saving plans, American Economic Review 91:79-98, March S. Benartzi, Excessive extrapolation and the allocation of 401(k) accounts to company stock, Journal of Finance, October 2001.

5 Assignment 4. A response paper on how people diversify. Which psychological biases can be responsible for naive diversification? Meeting 11. Limited Participation Hong, H., Kubik, J.D., and J.C. Stein, 2001, Social interaction and stock-market participation, Mimeo. Part 3. Market Efficiency Meeting 12. Efficient Markets Hypothesis The Harvard College Economist, Spring 2001, An interview with Professor Andrey, Volume 1, Issue 1. University Press, Ch. 1 Assignment 5. A response paper on what is implied by the market efficiency, and whether markets are efficient. Meeting 13. Limited Arbitrage University Press, Ch. 2 & 3. Pontiff, J., 1996, Costly arbitrage: evidence from closed end funds, Quarterly Journal of Economics Meeting 14. Evidence of Limited Arbitrage Wurgler, J., and E. Zhuravaskya, 1999, Does arbitrage flatten demand curves for stocks, Harvard Mimeo. find a button click here to download the document Rashes, M., 1999, Massively confused investors making conspicuously ignorant choices (MCI-MCIC), Harvard Mimeo. Froot, K., and E. Dabora, 1999, How are stock prices affected by the location of trade, Journal of Financial Economics. Vol. 53 (2). P Meeting 15. Professional Arbitrage University Press, Ch. 4. Scholes, Myron S. Crisis and Risk Management, American Economic Review. Vol. 90 (2). p May 2000

6 Assignment 6. An essay on why arbitrage is possible. Can we make money on the stock market, without being exposed to risk? (2-4 pages) Part 4. Predictability of Returns Meeting 16. Predictability of Returns with Financial Ratios Goetzman, W.N., and P. Jorion, 1995, Testing the predictive power of dividend yields, Journal of Finance (JSTOR) Kothari, S.P., and J. Shanken, 1997, Book-to-market, dividend yield, and expected market returns: a time-series analysis, Journal of Financial Economics 44, Lamont, O., 1998, Earnings and expected returns, Journal of Finance 53, Assignment 7. A response paper on whether stock returns are predictable with the financial ratios. Meeting 17. Under- and Overreaction De Bond, Werner F. M., and R. Thaler, 1985, Does the stock market overreact?, Journal of Finance 40, (JSTOR) Jegadeesh, N., and S. Titman, 1993, Returns to buying winners and selling losers: Implications for stock market efficiency, Journal of Finance 48, (JSTOR) Chan, L., N. Jegadeesh and J. Lakonishok, 1996, Momentum Strategies, Journal of Finance, 1996 (JSTOR) Meeting 18. Post earnings announcement drift Bernanrd, V., 1993, Stock price reactions to earnings announcements: a summary of recent anomalous evidence and possible explanations, in Richard H. Thaler, ed., Advances in Behavioral Finance, Russell Sage Foundation Lee, C., and B. Swaminathan, 2000, Do stock prices overreact to earnings news?, Mimeo, Cornell University Assignment 8. An essay on how we can make money in the stock market (one should give a detailed description of the strategies; 2-4 pages). Meeting 19. Predictability and Investor Sentiment University Press, Ch. 4. La Porta, R., 1996, Expectations and the cross-section of stock returns, Journal of Finance (JSTOR) Meeting 20. Overconfidence

7 Odean, T., 1998, Volume, volatility, price and profit when all traders are above average, Journal of Finance 53, Barber, Brad M; Odean, Terrance. Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors, Journal of Finance Vol. 55 (2). p April Meeting 21. University Press, Ch. 6. Daniel, K. D., D. Hirshleifer, and A. Subrahmanyam, 1998, Investor psychology and security market under- and over-reactions, Journal of Finance 53, Hong, H., and J. Stein, A unified theory of underreaction, momentum trading, and overreaction in asset markets, Journal of Finance 54, Assignment 9. A response paper on how the anomalies like momentum and reversal are explained with behavioral biases. Part 5. Other Topics Meeting 22. Stock Price Movement without News and Market Crashes Romer, D., 1993, Rational asset-price movements without news, American Economic Review, 83, (JSTOR) Hong, H., and J. Stein, 1999, Differences of Opinion, rational arbitrage and market crashes, Mimeo. Chen, J., Hong, H., and J. Stein, 2000, Forecasting crashes: trading volume, past returns and conditional skewness in stock prices, Mimeo. Meeting Real Effects of Inefficient Markets 2. Conclusions Morck, R., Shleifer, A., and R. Vishny, 1990:2, The stock market and investment: is the market a sideshow?, Brookings Papers on Economic Activity (JSTOR) Baker, M. and J. Wurgler, 1999, The equity share in new issues and aggregate stock returns, Journal of Finance find a button click here to download the document University Press, Ch. 7.

8 Economics 970 Empirical Exercise Mr. Evgeny Agronin Spring 2002 In this exercise you will learn how to test CAPM using the famous Fama-MacBeth crosssectional approach that we studied in class. This approach is widely used by financial economists in testing not only CAPM, but also many other important classes of models, designed to price financial assets. You will use a data set crspdata.xls distributed in class. It contains 40 yearly observations on the portfolios of stocks formed by size (market capitalization) of the stocks. The portfolio in the first decile contains the biggest stocks, while the portfolio in the tenth decile contains the smallest stocks. The data set also contains 40 observations on the value-weighted return on the NYSE (New York Stock Exchange) index, which is often used as the market portfolio. If you know how to program in Matlab or Stata, I would recommend using this software, since you will save time if you use loops: you are expected to run 10 regressions in the first part of the exercise and 40 regressions in the second part. Both Stata and Matlab use the command regress to run regression. If you do not know how to program loops in any software familiar to you, running regressions in Excel or Stata (regression by regression) will not take much more time. Answer to each of the 10 questions (a e in the part 1, and a e in the part 2) gives you maximum 10% of the grade. 1. Take the time series of the biggest stocks (40 observations) and run this variable on the constant and the market returns (return on NYSE index). Then take the next size portfolio and perform the same procedure. Return to these steps until you run a regression for each portfolio. Store the coefficients that you get from the regressions. a. What is the β of each portfolio of stocks? b. Plot β as a function of size of the portfolios. What can you tell about the relation between β and size? Which portfolios of stocks, in your opinion, are riskier? c. What is the average return on each portfolio? Investment into which portfolios is more profitable on average? d. According to you answers to a, b and c, how is average return on a portfolio of stocks related to risk of the investment in this portfolio? e*. You probably know that in order to sell an asset you do not necessarily have to own this asset in the first place. Instead, you can borrow the asset and sell it. This is called short selling. Suppose, you do not have much money, and you also do not own any stock. Given your answers to a-d, which strategy would you take in order to make money? Try to describe your strategy in detail, what would you do in each step. Can you call such a strategy arbitrage?

9 2. For each year, you have a cross-section of 10 returns on the portfolios and 10 β-s corresponding to these portfolios. For each year, run a regression of returns on the constant and the β-s. Store the coefficients of β for each regression (thtotal of 40). Then calculate the mean of the coefficients and the variance of the mean. To calculate the variance use the following formula: σ = λ 2 40 t= 1 where λ t and λ are the coefficient from the regression run for the time t, and the mean of the coefficients over 40 years, respectively. 40 ( λ t λ) a. Notice, that the formula for the variance is not standard. Why should we divide by 40 2 and not 40, as usual? (Hint: use the logic of the central limit theorem) λ b. Calculate t-statistic of the regression: =. Is λ significantly different from zero? Does β affect return? c. Is the significance of λ enough to conclude that CAPM works? Discuss. d. Which steps would you add to the procedure above to test CAPM? Do not make the computation, just describe the intuition. e*. If you are asked to test CAPM using the only time series regressions (similar to what you have done in step 1), how would you proceed? Do not make the computation, just describe the intuition. t stat. 2 σ λ