1. Evidence Regarding Market Efficiency From Studies

2. Background Information n Early 1970’s, Fama & MacBeth did a famous study testing the CAPM. n 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 Beta Return

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

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

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

7. Tie to a Random Walk n 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 n 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 n Keown and Pinkerton (1981): CARs for target firms around takeover attempt. n See graph on p. 371 in text

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

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

13. Abn. Returns: Use Historic Data Without a risk adjustment: Actual R it – Actual R m,t With a risk adjustment: Actual R it – [a i + B i [Actual R m,t ] Or, Actual R it – [Actual R match,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 n (Quarterly EPS Released – Forecasted Quarterly EPS) n Measure the abnormal risk-adjusted return after an earnings surprise. n Measure CAR: Actual R it – Predicted R i,t (Used CAPM)

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

17. Evidence: Quarterly Earnings Surprises For positive earnings surprises: n The larger the earnings surprise the higher the positive abnormal return. n 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: n The larger the negative earnings surprise the larger the loss as measured by the abnormal return. n 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 n 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!] n If the CAPM is wrong, then abnormal risk- adjusted returns using this model are wrong.

21. Interpretation: Mkts Not Efficient n Behavioral Finance: Psychological and behavioral elements lead to predictable biases. n Arbitrage: 1. Not always possible to execute arbitrage trades. 2. Arbitrage is risky and therefore limited

22. Evidence of Abn Risk Adj. Returns …. n After share repurchase announcements (Ikenberry (1995)) n After dividend initiations and omissions (Michaely (1995)) n After stock splits (Ikenberry (1995)) n 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 n January Anomaly: Most of the abnormal returns occur in January! (tax loss selling??) n 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 n Neglected by analysts and institutional investors, so is less information, which implies higher risk. n Less Liquidity: Higher trading costs as bid-ask spreads are wider, and broker commissions are larger.

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

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

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

29. Fama-French 1992

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

31. Fama-French Interpretation n 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) n 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 n 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 Average Monthly Returns (in Percent) AllLow-ββ-2β-3β-4β-5β-6β-7β-8β-9High-β All1.251.341.291.361.311.331.281.241.211.251.14 Small - ME1.521.711.571.791.611.50 1.371.631.501.42 ME-21.291.251.421.361.391.651.611.371.311.341.11 ME-31.241.121.311.171.701.291.101.311.361.260.76 ME-41.251.271.131.541.061.341.061.411.171.350.98 ME-51.291.341.421.391.481.421.181.131.271.181.08 ME-61.171.081.531.271.151.201.211.181.041.071.02 ME-71.070.951.211.261.091.181.111.240.621.320.76 ME-81.101.091.051.371.201.270.981.181.021.010.94 ME-90.950.980.881.021.141.071.230.940.820.880.59 Large - ME0.891.010.931.100.940.930.891.030.710.740.56

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

36. The Cross Section of Expected Stock Returns Table 5 Book-to-Market Portfolios AllLow23456789High All1.230.640.981.061.171.241.261.391.401.501.63 Small - ME1.470.701.141.201.431.561.511.701.721.821.92 ME-21.220.431.050.961.191.331.191.581.281.431.79 ME-31.220.560.881.230.951.361.30 1.401.541.60 ME-41.190.390.721.061.361.131.211.341.591.511.47 ME-51.240.880.651.081.471.131.431.441.261.521.49 ME-61.150.700.981.141.230.941.271.19 1.241.50 ME-71.070.951.000.990.830.991.130.991.161.101.47 ME-81.080.661.130.910.950.991.011.151.051.291.55 ME-90.950.440.890.921.001.050.930.821.111.041.22 Large - ME0.890.930.880.840.710.790.830.810.960.971.18

37. Value Puzzle n 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: n Value stocks have lower standard deviations and higher returns!

39. Fama-French Findings n Beta does not explain returns. n Small cap stocks have higher returns. Small cap stocks have higher betas, but it is size not beta driving higher returns. n 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 n 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!] n 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 n 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 n 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 n 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: n People look at gains and losses relative to some reference point rather than the levels of final wealth. n Display Loss Aversion! Outcome Typically Doesn’t follow standard von Neumann- Morgenstern rationality.

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

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

51. Behavioral Finance Framing Effects How data is presented can affect the decisions people make.

52. Framing Effects: Example n Investors will allocate more money to stocks rather than bonds when they see long-term cumulative wealth graphs than they will if you only show them volatile short-term stock returns.

53. Behavioral Finance Explanation for Quarterly Earnings Surprise n In this case, would argue that initially there is slow updating or “conservatism” as a reaction to the news released by the earnings surprise. [Short run under- reaction] n Eventually keep seeing good news so “representativeness” sets in [then get over-reaction].

54. Mkt Not Efficient? (Lakonishok, Shleifer and Vishney) These professors offer a different interpretation. Markets are inefficient. People overreact with a lag. Overprice firms with good recent returns (growth) and underprice firms with poor recent returns (value).

55. Long-Term Horizons: Test of Weak-Form EMH DeBondt and Thaler (1985): n Create Loser and Winner portfolios based on past 36 months of CARs. Top decile are Winners, bottom decile are Losers. n Examine CAR’s for next 36 months. n “Loser’s outperform “winners” Is an overreaction followed by a correction.

56. Efficient Market Believers Say.... n Evidence is due to market risk premiums varying over time. Is not overshooting & correction but instead a rational response to changes in the discount rate.

57. Short Horizons (Tests of Weak Form EMH) n Lo and MacKinlay (1988) test to see if there is serial correlation of weekly stock returns for NYSE stocks.

58. Lo & MacKinlay Stock Price Period 12 + momentum - momentum reversal

59. Lo & MacKinlay n If momentum is present, the variance of returns should increase as the number of periods used is increased. n If there is no momentum, gains or losses will tend to reverse, keeping the variance of returns from becoming wider.

60. Evidence: Lo & MacKinlay n Lo and Mackinlay (1988) find serial correlation of weekly stock returns for NYSE stocks as the variance of returns increases as the return interval is lengthened. Implies there is inertia in the short run.

61. Evidence: Lo & MacKinlay n The effect is the strongest in the small cap stocks. n Not clear if abnormal returns are possible by exploiting this information.

62. Intermediate Horizons: Test of Weak-Form EMH Study by Jegadeesh and Titman.

63. Intermediate Horizons 1.Measure stock rates of return over the past 6 months. 2.Rank the stocks from highest to lowest past 6 month return and then divide the sample into deciles. “Losers” are the bottom decile and “winners” are the top decile

64. Jegadeesh and Titman 3. For the next 36 months, every time one of the winners or losers reports quarterly earnings, record 3-day returns starting 2 days before the earnings announcement and ending the day of the announcement. 4.Observe the difference in 3-day returns between the winners and losers reporting earnings in each month.

65. Evidence: Jegadeesh and Titman n For the 1st 7 months, the market is pleasantly surprised by the earnings announcements of the winners and disappointed by the earnings announcements of the losers. (momentum in the short run)

66. Evidence: Jegadeesh and Titman n From months 9 - 36, the market is pleasantly surprised by the earnings announcements of the losers and disappointed by the earnings announcements of the winners. (Reversals in the intermediate term) n If the stock market is efficient, it should be able to anticipate the good or bad reports in advance.

67. Evidence: Jegadeesh and Titman n Abnormal profit opportunities. n Reversion to the mean. n The market overreacts with a lag. Consistent with “Representativeness and Conservatism.” n Short Run: Inertia n Intermediate Run: Reversals

68. Technical Analysts n Technical analysts claim to exploit these trends or patterns.

69. Mutual Fund Performance n If the stock market is not weak or semi- strong form efficient, then professional portfolio managers should be able to achieve abnormal risk-adjusted returns!

70. Evidence: Mutual Funds Malkiel (1995) examined the alphas of mutual funds. Recall Regression Model (R i,t – RFR t ) =  i +  i (R m,t - RFR t ) + e i,t If market is efficient what should we find regarding the multiple-period alpha?

71. Evidence: Mutual Funds n WSJ Article, “Stock Funds Just Don’t Measure Up”. Oct. 5, 1999 n After adjusting for size and survivorship bias, funds trailed the S&P 500 by 1.4% per year which is on average what they charge for annual expenses.

72. Evidence: Mutual Funds n Other studies: 1970’s – 1990’s: After expenses & commissions, only 1/3 beat the market on a risk-adjusted basis.

73. STRONG FORM EMH TESTS n Are abnormal risk-adjusted returns possible if you trade using private information?

74. Evidence on Insiders n Corporate insiders are required to report their transactions to the SEC. n They are not supposed to trade when in the possession of “material” information. n Even with regulation, they achieve positive risk-adjusted abnormal returns.

75. Market Crash of Oct. 1987 23% Drop in One Day?? n No large release of news n Efficient Market explanation: Due to chance. Are outliers in the distribution. Just an outlier observation in a random process. n Panic & Crowd Psychology (behavioral finance explanation)

76. Internet Bubble n Some companies saw their stock price go up just by adding dotcom to their names n When 3-Com spun off Palm Pilot, but kept 95% of the shares, The 95% of Palm owned by 3-Com were worth more than the market cap of 3-Com. Implies negative value for the rest of 3-Com!

77. Internet Bubble n It is obvious now that the 1998-March 2000 tech run-up was a bubble, but was this market inefficiency, or merely poor valuations? n How do you know a bubble when you are in it? n Should you try to short a bubble if you don’t know when it will burst?

78. Limits of Arbitrage n Just because you know something is overvalued or undervalued, doesn’t necessarily mean you can make money off it n Classic Example: We know that someday the sun will explode, but you can’t short the Earth

79. Shleifer and Vishny (1997) Paper n Most arbitrage is not carried out by small investors, but by large money managers. n They usually manage OPM (other people’s money) n Most arbitrage in the real world is actually “risk arbitrage” and requires capital

80. n If money managers observe a price discrepancy and commit capital to an arbitrage position based on convergence, the initial movement may be away from convergence, but that merely means there is a greater opportunity for profit, and more capital should be committed.

81. n But that is exactly when investors are most likely to pull out. n Investors invest based on PBA (Performance Based Arbitrage) rather than expected returns n This lack of capital prevents arbitrage from taking place

82. n This is often given as an explanation for the collapse of LTCM (Long-Term Capital Management). n Amazingly, the Shleifer and Vishny paper came out about a year prior to the LTCM collapse