Market Efficiency & Anomalies

1. Market Efficiency & Anomalies

2. Security Prices Time Random Walk and Stock Prices • Random Walk - stock price change unpredictably • Actually stock prices follow a positive trend • Expected price is positive over time • Positive trend and random around the trend

3. Random Price Changes Why do stock prices change? Why are price changes random? • Prices react to information • Flow of information is random

4. Over-reaction Efficient Learning Lag Market Price, $ Learning Lag Efficient Over-reaction t Trading Days Information Good News Bad News New information arrives in the market on day t.

5. Efficient Market Hypothesis (EMH) • Basic question: • Do security prices reflect information ? • Why look at market efficiency • Implications for business and corporate finance • Implications for investment • Forms of efficient market hypothesis • Weak • Semi-strong • Strong

6. Implications of Efficiency for Active or Passive Management • Types of Stock Analysis • Fundamental Analysis • Using economic and accounting information to predict stock prices • Semi strong form efficiency & fundamental analysis • Technical Analysis • Using prices and volume information to predict future prices • Weak form efficiency & technical analysis • Active Management • Security analysis • Timing • Passive Management • Buy and Hold • Index Funds • Even if the market is efficient a role exists for portfolio management • Diversification • Appropriate risk level • Tax considerations

7. Empirical Tests of Market Efficiency • Event studies • Assessing performance of professional managers • Testing some trading rule

8. Evidence SupportingWeakly Efficient Hypothesis • Is it possible that security prices do not reflect all historical information? • Which is easy to obtain and cheap • Technicians focus on past security prices • Look for meaningful trends in historical security prices • Attempt to extract predictions from whatever patterns they find

9. Filter Rules • An X% filter is a mechanical trading rule • If a security’s price rises by at least X%, buy and hold until the price peaks and falls by at least X% • When price decreases from a peak level by X%, liquidate long position and sell short • Hold short position until price reaches a low point and then begins to rise • If (when) the price rises above X%, cover the short position and go long

10. Using a 10% Filter Rule to Trade a Security

11. Filter Rules • Different filter rules can be testing by changing the X value • If stock prices fluctuate randomly, filter rules should not outperform randomly chosen stocks • Filters ranging from .05% to 50% have been tested • In general, filter rules generate large commissions (especially those with small X values) • After deducting for commissions, filter rules do not outperform naïve buy-and-hold strategy • Some filters result in large net losses after deducting commissions

12. Serial Correlation Tests • Serial correlation (autocorrelation) tests should be able to determine if security prices move in trends or reversals • Measures the correlation coefficient in a series of numbers with lagged values in the same series • Lags of any length can be used • Stock prices exhibit a long-run upward trend of about 6.6% a year in the U.S. • Thus, some positive serial correlation is found • But, technical analysts focus on short-term trends

13. Serial Correlation Tests • Do daily or weekly price change trends exist and, if so, can they be used to earning a trading profit after commission? • Many studies have failed to detect statistically significant serial correlations on a daily, weekly or monthly basis • Scientific evidence supporting weak form efficiency • Some conflicting evidence exists • DeBondt & Thaler (1985) find evidence of long-term stock price overreaction and negative serial correlation for individual stocks • Lo & MacKinlay (1988) found positive serial correlation for a diversified portfolio of stocks • Conrad & Kaul (1993) suggest that the above results are due to statistical measurement errors

14. Runs Tests • A “runs” test can be performed to determine if irregular trends occur in price changes • A run occurs when the changes between consecutive numbers switch direction • A series of random numbers is expected to generate a certain amount of positive, negative or zero runs • By comparing the actual number of runs to the expected number, we can determine if a non-random number of runs occurred • Results suggest that actual number of runs do not differ statistically from the number of expected runs

15. Anomalies in Weakly Efficient Hypothesis • Day-of-the-Week Effects • the stock market tends to fall on Mondays and rise the rest of the week • Holiday effect • Returns on the day before holiday weekends are 9 – 13 times higher than the average daily return • About 1/3 of the average stock’s annual return was earned in pre-holiday trading days • Friday to Monday • Negative (positive) returns on a Friday are usually followed by large negative (positive) returns on Monday • The large commissions paid (relative to the small positive daily returns) will more than offset the potential benefit of this knowledge • January Effect • average stock’s return in January is more than 5 times the mean monthly return • A large part of the typical stock’s annual return is generated during January • This is a larger anomaly than the day-of-the-week effects • Can yield net trading profits after deducting transaction costs • Buy stocks before Christmas and sell at the end of January

16. -t 0 +t Announcement Date Tests of Semi-Strong Efficiency: Event Studies 1. Examine prices and returns over time

17. How Tests Are Structured (cont’d) 2. Returns are adjusted to determine if they are abnormal Market Model approach a. Rt = a + bRmt + et (Expected Return) b. Excess Return = (Actual - Expected) et = Actual - (at + btRmt)

18. Stock Splits and Stock Dividends • Neither of these events change the total value of the firm or investor’s wealth • If security markets are efficient, the firm’s market capitalization should not be impacted by a stock split or stock dividend • In the long-run, stock splits and stock dividends do not seem to impact • The liquidity of the split stocks • The market value of the firm • Investors’ returns • If an investor can correctly predict which companies are going to split, it may be possible to earn excess returns • Studies involving stock splits and stock dividends appear to support the semi-strong efficient market hypothesis

19. Anomaly: Size Effect • Research shows that small company stocks earned higher rates of return than large company stocks, on average • Size based on market capitalization • Found that small cap stocks were also riskier, but even after adjusting for risk the size effect remained • Even after adjusting for the impact of infrequent price changes the size effect remained

20. Growth-Value Anomaly • Semi-strong form of EMH suggests that money managers who use a particular management style should not consistently outperform managers using another management style • Value managers • Growth manager • Value stock investors have historically outperformed growth stock investors on a risk-adjusted basis over extended periods of time • Constitutes an anomaly to the semi-strong form of efficient market hypothesis

21. Equity Premium Puzzle • Rewards for bearing risk appear too excessive • Possible causes: • Unanticipated capital gains • Survivorship bias • Survivorship bias also creates the appearance of abnormal returns in market efficiency studies

22. The Paradox • Grossman and Stiglitz • In a world where it cost money to analyze securities, analysts will be able to identify mispriced securities • Investors will do just as well using passive investment strategy where they simply but the securities in a particular index and hold unto those investments