COMM W. Suo Slide 1

COMM W. Suo Slide 2  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 Random Walk and Stock Prices Security Prices Time

COMM W. Suo Slide 3 Why do stock prices change? Why are price changes random?  Prices react to information  Flow of information is random Random Price Changes

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

COMM W. Suo Slide 5  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 Efficient Market Hypothesis (EMH)

COMM W. Suo Slide 6  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 Implications of Efficiency for Active or Passive Management

COMM W. Suo Slide 7  Event studies  Assessing performance of professional managers  Testing some trading rule Empirical Tests of Market Efficiency

COMM W. Suo Slide 8 Evidence Supporting Weakly 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

COMM W. Suo Slide 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

COMM W. Suo Slide 10 Using a 10% Filter Rule to Trade a Security

COMM W. Suo Slide 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

COMM W. Suo Slide 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

COMM W. Suo Slide 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

COMM W. Suo Slide 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

COMM W. Suo Slide 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

COMM W. Suo Slide Examine prices and returns over time Tests of Semi-Strong Efficiency: Event Studies 0+t-t Announcement Date

COMM W. Suo Slide Returns are adjusted to determine if they are abnormal Market Model approach a. R t = a + bR mt + e t (Expected Return) b. Excess Return = (Actual - Expected) e t = Actual - (a t + b t R mt ) How Tests Are Structured (cont’d)

COMM W. Suo Slide 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

COMM W. Suo Slide 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

COMM W. Suo Slide 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

COMM W. Suo Slide 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

COMM W. Suo Slide 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