Evidence Regarding Market Efficiency From Studies

1. Evidence Regarding Market Efficiency From Studies

2. Background Information • Early 1970’s, Fama & MacBeth did a famous study testing the CAPM. • They found weak evidence that portfolios of stocks with higher betas had higher returns, and found an intercept slightly higher than zero. (CAPM Assumes Alpha = 0)

3. Beta & Return of Portfolios Return Beta

4. Early Evidence • Early evidence basically supported the weak and the semi-strong form EMH.

5. Early Weak Form EMH Tests (+) Serial Correlation: • + returns follow + returns for a given stock or - returns follow - returns for a given stock • Called “momentum” or “inertia”

6. Early Weak Form EMH Tests (-) Serial Correlation: • + returns follow - returns for a given stock or - returns follow + returns for a given stock. • Called “reversals”

7. Tie to a Random Walk • If we find (+) or (-) serial correlation, this is evidence against the weak-form EMH as it implies that past prices can be used to predict future prices. (Technical analysis)

8. Early Weak Form EMH Tests In 1960s Fama showed that: 1. Stock Prices followed a random walk 2. No evidence of serial correlation. The price of a stock is just as likely to rise after a previous day’s increase as after a previous day’s decline.

9. Early Semi-Strong Form EMH Tests • Event studies in the 1960s & 1970s looked at stock prices around the release of new information to the public. (Fundamental analysis)

10. Graph of a Typical Study • Keown and Pinkerton (1981): CARs for target firms around takeover attempt. • See graph on p. 371 in text

11. Challenges to the EMH 1980s & 1990s: • Empirical evidence began to accumulate that provided evidence first against the semi-strong EMH and later against the weak form EMH • Initially any evidence against EMH called an anomaly.

12. More Recent Tests of the Semi-Strong Form EMH • Are abnormal risk-adjusted returns possible if you trade after information is made public? (fundamental analysts) • General Equation for Abn. Returns: • Actual Rit – Predicted Ri,t

13. Abn. Returns:Use Historic Data Without a risk adjustment: Actual Rit – Actual Rm,t With a risk adjustment: Actual Rit – [ai + Bi[Actual Rm,t] Or, Actual Rit – [Actual Rmatch,t]

14. Challenges to Testing Difficult to measure risk-adjusted returns a) Is beta the proper measure of risk? b) CAPM is forward looking and you are using historic data. c) Is your matched firm the best match?

15. Quarterly Earnings Surprises • (Quarterly EPS Released – Forecasted Quarterly EPS) • Measure the abnormal risk-adjusted return after an earnings surprise. • Measure CAR: Actual Rit – Predicted Ri,t (Used CAPM)

16. Quarterly Earnings Surprises • Rank from highest to lowest by magnitude of earnings surprises and place stocks into decile portfolios. • See if trading on earnings surprises results in subsequent abnormal returns. • (Cumulative Abnormal Returns (CARs) are the daily abnormal returns summed up over time)

17. Evidence: Quarterly Earnings Surprises For positive earnings surprises: • The larger the earnings surprise the higher the positive abnormal return. • The upward drift in the stock price continues a couple of months after the earning announcement!

18. Evidence: Quarterly Earnings Surprises For negative earnings surprises: • The larger the negative earnings surprise the larger the loss as measured by the abnormal return. • The downward drift in the stock price continues a couple of months after the earning announcement!

19. Interpretation: Mkts Efficient Measurement Errors Markets are efficient. The evidence of abn. risk-adjusted returns is due to various Measurement Errors when using the CAPM. (1) Benchmark Error: Beta & SML wrong (2) CAPM is a forward looking model & are testing it with historic or ex-post data.

20. Interpretation:CAPM Not Valid • Markets are efficient. The evidence of abnormal risk-adjusted returns (evidence against market inefficiency) is inconclusive as the CAPM may not be the proper risk adjustment model. [Joint or Dual Hypothesis Problem!] • If the CAPM is wrong, then abnormal risk-adjusted returns using this model are wrong.

21. Interpretation: Mkts Not Efficient • Behavioral Finance: Psychological and behavioral elements lead to predictable biases. • Arbitrage: • Not always possible to execute arbitrage trades. • Arbitrage is risky and therefore limited

22. Evidence of Abn Risk Adj. Returns …. • After share repurchase announcements (Ikenberry (1995)) • After dividend initiations and omissions (Michaely (1995)) • After stock splits (Ikenberry (1995)) • After seasoned equity offerings & after IPOs (Loughran and Ritter (1995))

23. Size Effect Portfolios of small cap stocks earn positive abnormal risk-adjusted returns (+ alphas):

24. Size Effect • January Anomaly: Most of the abnormal returns occur in January! (tax loss selling??) • Grossman/Stiglitz: Professionals move prices to efficiency. Don’t buy at the small cap end of the market much due to limits on portfolio positions.

25. Problem With CAPM? Possible sources of risk for small caps • Neglected by analysts and institutional investors, so is less information, which implies higher risk. • Less Liquidity: Higher trading costs as bid-ask spreads are wider, and broker commissions are larger.

26. Background Information • Back to Early 1970’s, Fama & MacBeth test of CAPM.

27. Fama MacBeth CAPM Test Early 1970’s Return Beta

28. Relationship Between Beta and Returns • Fama & French re-examined the earlier tests of the CAPM forming size decile portfolios.

29. Fama-French 1992

30. Beta & Return of Portfolios Small cap stocks Return Large cap stocks Beta

31. Fama-French Interpretation • See that small cap stocks have higher betas than large cap stocks. Fama and French concluded that size is driving the relationship between beta and return not beta!

32. Previous Slide (cont) • Also see that within the small cap groupings, portfolios of stocks with lower betas have higher returns! The same is true within the large cap groupings.

33. Interesting Fact • Fama, once a strong proponent of the CAPM now claimed that beta was dead. Beta was a rough proxy for size in his earlier tests!!

34. The Cross Section of Expected Stock Returns Table 1 Panel A

35. Interesting Result • Within each size group, the higher the beta the lower the return.

36. The Cross Section of Expected Stock ReturnsTable 5

37. Value Puzzle • It is not evident why value stocks should be riskier than growth stocks. Value stocks have lower standard deviations than growth stocks after controlling for size.

38. Value Puzzle Value Puzzle: • Value stocks have lower standard deviations and higher returns!

39. Fama-French Findings • Beta does not explain returns. • Small cap stocks have higher returns. Small cap stocks have higher betas, but it is size not beta driving higher returns. • Low P/E or high Book-to-Market of equity stocks have higher returns.

40. Explanations for Fama-French Results Alternative Explanations for their results? Market Semi-Strong Efficient: Small cap stocks and low P/E (high B/M) stocks generate higher returns because they are riskier. However, this risk is not captured by Beta!

41. Problem • Lack of a theoretical model to explain why size and style (value vs growth) are important risk factors. The CAPM had an elegant, logical theory underlying it, this has none!

42. Explanations for Fama-French Results Market Semi-Strong Efficient: Abnormal risk-adjusted returns for small cap stocks or for stocks with low P/E (or high B/M) are due to various measurement errors when using the CAPM. (1) Benchmark Error: Beta & SML wrong (2) CAPM is a forward looking model & we are testing it with a historic or ex-post data.

43. Explanations for Fama-French Results Market Semi-Strong Efficient. Abnormal risk-adjusted returns (evidence against market inefficiency) are inconclusive as the CAPM may not be the proper risk adjustment model. [Joint or Dual Hypothesis Problem!] • If the CAPM is wrong, then abnormal risk-adjusted returns using this model are wrong.

44. Explanations for Fama-French Results Market Not Semi-Strong Form Efficient: Can make abnormal returns using public information regarding market capitalization and P/E or B/M ratio. How can this persist?

45. Behavioral Finance • Decisions people make deviate from the maxims of economic rationality in predictable ways: 1. Attitudes towards Risk 2. Non Bayesian Expectation Formation 3. Framing Effects of Decisions

46. Attitudes Toward Risk: Example • 90% chance of $1 million; 10% chance of $0. I offer to buy you out for $900,000. Will you take my offer?

47. Attitudes Toward Risk: Example • 90% chance to lose $1 million; 10% chance of $0. I will take the bet if you pay me $900,000. Will you take my offer?

48. Behavioral Finance Attitudes Towards Risk: • People look at gains and losses relative to some reference point rather than the levels of final wealth. • Display Loss Aversion! Outcome Typically Doesn’t follow standard von Neumann-Morgenstern rationality.

49. Behavioral Finance Non-Bayesian Expectation Formation • Representativeness: Predict the future taking a short history of data and determine the model driving the data. (Too small a weight on chance.) • Conservatism: Slow updating to new information as have extrapolated a short earnings history too far into the future.

50. Non-Bayesian Expectations • 1st 2 winters here mild. Assumed they were always like that. • Investors may extrapolate short histories of rapid earnings growth too far in the future and may overprice “glamour” stocks.