McGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, Inc., All Rights Reserved. The Efficient Market Hypothesis CHAPTER 8

RANDOM WALKS AND THE EFFICIENT MARKET HYPOTHESIS

8-3 Efficient Market Hypothesis (EMH) Do security prices reflect information Why look at market efficiency –Implications for business and corporate finance –Implications for investment

8-4 Random Walk - stock prices are random –Randomly evolving stock prices are the consequence of intelligent investors competing to discover relevant information Expected price is positive over time Positive trend and random about the trend Random Walk and the EMH

8-5 Random Walk – Model –Model can be of prices P, –More commonly model in logs, LN(P)

8-6 SecurityPrices Time Random Walk with Positive Trend

8-7 Random Walk – Why Logarithms? Transform data to natural logarithms so model is about percentage changes. The change in the natural log, LN(P t )-LN(P t-1 ) is a good approximation to the percentage change

8-8 More on Logarithms

8-9 Why are price changes random –Prices react to information –Flow of information is random –Therefore, price changes are random Random Price Changes

8-10 Figure 8.1 Cumulative Abnormal Returns Before Takeover Attempts

8-11 Figure 8.2 Stock Price Reaction to CNBC Reports

8-12 EMH and Competition Stock prices fully and accurately reflect publicly available information Once information becomes available, market participants analyze it Competition assures prices reflect information

8-13 Versions of the EMH Weak: can’t predict future stock price movements based on lagged stock price Semi-strong: can’t predict future stock price movements based on any publicly known lagged data Strong: can’t predict future stock price movements based on any lagged data

IMPLICATIONS OF THE EMH

8-15 Types of Stock Analysis Technical Analysis - using prices and volume information to predict future prices; search for patterns in the data –Weak form efficiency & technical analysis; EMH (weak form) suggests technical analysis is futile. Fundamental Analysis - using economic and accounting information to predict stock prices –Semi strong form efficiency & fundamental analysis; EMH (semi-strong) suggests fundamental analysis is futile.

8-16 Active Management –Security analysis –Timing Passive Management –Buy and Hold –Index Funds Implications of Efficiency for Active or Passive Management

8-17 Even if the market is efficient a role exists for portfolio management: –Appropriate risk level –Tax considerations –Other considerations The Role of Portfolio Management in an Efficient Market

8-18 Information Processing is not Costless EMH requires all to have the relevant information and process it costlessly and flawlessly. Economic theory suggests an issue if there is a cost to data acquisition and/or processing. “The Impossibility of Informationally Efficient Markets” But…EMH in the extreme makes clear how difficult it can be for various active management policies to work as intended or as claimed.

ARE MARKETS EFFICIENT

8-20 Magnitude Issue –If managers are successful and raise returns by a small amount, can we detect it? –If managers identify stock prices that are mispriced by a small amount, can we detect this? –Instead of asking ‘Are markets efficient?’ maybe we should ask ‘How efficient are markets?’ Selection Bias Issue –The outcomes we observe have been preselected in favor of failed attempts (because true market-beating schemes are not reported) –Cannot evaluate the true ability of portfolio managers Lucky Event Issue –Just because an investor or investment scheme has been successful in the past, even for a number of years, is no proof that it was not merely good luck instead of good management or superior ability to forecast. Empirical Tests of Market Efficiency

8-21 Weak-Form EMH Tests: Patterns in Stock Returns Returns over short horizons –Weak-Form EMH claims no serial correlation in stock returns. –Very short time horizons small magnitude of positive trends –3-12 month some evidence of positive momentum Returns over long horizons – pronounced negative correlation –Dramatic evidence; could be fads –But, could just indicate changes in risk premium over time. Evidence on Reversals –Loser Portfolio (35 stocks with worst 5-yr performance) outperforms the ‘Winner portfolio’ over the next 3-yr period by 25%. Evidence for a contrarian approach.

8-22 Predictors of Broad Market Returns Fama and French –Aggregate returns are higher with higher dividend ratios (dividend/price) Campbell and Shiller –Earnings yield can predict market returns (earnings/price) Keim and Stambaugh –Bond spreads can predict market returns (interest on high grade minus interest on low grade corporate bonds)

8-23 P/E Effect: Portfolios with low P/E stocks have higher returns than portfolios with high P/E stocks (even if adjusted for beta!) –But could be that P reflects risk differences Small Firm Effect (January Effect) –Invest in low-capitalization stocks –Earn excess returns Semi-Strong Tests: Market Anomalies

8-24 Figure 8.3 Returns in Excess of Risk- Free Rate and in Excess of the SML

8-25 Semi-Strong Tests: Market Anomalies (Con’t) Neglected Firm –Small firms tend to be neglected by large institutional traders Book-to-Market Ratios –Beta seems to have no power to explain average security returns after controlling for size and B-to-M ratios

8-26 Figure 8.4 Average Annual Return as a Function of Book-to-Market

8-27 Semi-Strong Tests: Market Anomalies (Con’t) Post-Earnings Announcement Drift –There is a large abnormal return on the earnings announcement day –Drift in returns even after announcement

8-28 Figure 8.5 Cumulative Abnormal Returns in Response to Earnings Announcements

8-29 Strong-Form Tests: Inside Information The ability of insiders to trade profitability in their own stock has been documented in studies by Jaffe, Seyhun, Givoly, and Palmon SEC requires all insiders to register their trading activity

8-30 Interpreting the Evidence Risk Premiums or market inefficiencies— disagreement here –Fama and French argue that these effects can be explained as manifestations of risk stocks with higher betas –Lakonishok, Shleifer, and Vishney argue that these effects are evidence of inefficient markets

8-31 Figure 8.6 Return to Style Portfolio as a Predictor of GDP Growth

8-32 Interpreting the Evidence (Con’t) Anomalies or Data Mining –Rerun the computer database of past returns over and over and examine stock returns along enough dimensions: Simple chance may cause some criteria to appear to predict returns

MUTUAL FUND AND ANALYST PERFORMANCE

8-34 Stock Market Analysts Do analysts add value—mixed evidence –Womack study found that positive changes are associated with increased stock prices of about 5% –Negative changes result in average price decreases of 11% –Are prices change due to analysts’ information or through pressure brought on by the recommendations themselves

8-35 Mutual Fund Managers Some evidence of persistent positive and negative performance Potential measurement error for benchmark returns –Style changes –May be risk premiums Superstar phenomenon –Peter Lynch (Fidelity Magellan), Warren Buffet (Berkshire Hathaway), John Templeton, John Neff (Vanguard Windsor)

8-36 Figure 8.7 Estimates of Individual Mutual Fund Alphas

8-37 Table 8.1 Performance of Mutual Funds Based on Three-Index Model

8-38 Figure 8.8 Persistence of Mutual Fund Performance

8-39 Table 8.2 Two-Way Table of Managers Classified by Risk-Adjusted Returns over Successive Intervals