Efficient Market Hypothesis

Efficient Market Hypothesis www.pptmart.com

Efficient Market Hypothesis (EMH) Presentation Format • EMH A. Weak EMH B. Semi-strong EMH C. Strong EMH • Anomalies in the EMH A. Discovered before the 1980s B. Discovered during the 1980s www.pptmart.com

I. Efficient Market Hypothesis: Introduction • Eugene Fama published an empirical study in 1965. • He analyzed the stock price movements of all stocks that make up the DJIA and investigated daily price changes of 30 stocks over a 5-year period • He studied the rates of price change since the ave. rate of price change for most stocks do not change from year to year when measured over a representative sample period and it is easier to make comparisons between ave.rates of change since the typical stock’s ave. rate of price change remains constant. www.pptmart.com

I. Efficient Market Hypothesis: Introduction • The study was designed to measure the degree of randomness with which stock prices fluctuate. • He thought that the financial information arrived randomly and, assuming that prices responded efficiently to the new information, prices should fluctuate randomly too. • Fama delineated 3 levels of market efficiency. • Weak EMH • Semi- Strong EMH • Strong EMH www.pptmart.com

I. Efficient Market Hypothesis: Introduction • Weak EMH, assumes that all historical information is reflected in security prices. • Semi-strong EMH, assumes that all public information is reflected in security prices. • Strong EMH, assumes that all information is reflected in security prices www.pptmart.com

I. Efficient Market Hypothesis: Introduction www.pptmart.com

A. Weak EMH: Introduction • Markets where past prices provide no info. that would allow a trader to earn a return above what could be attained with a naïve buy-and-hold strategy. • While traders & speculators may earn positive rates of return, they will not beat a naïve buy-and-hold strategy with info. obtained from historical data. • Some may be luck/unlucky but general conclusions cannot be reached from specific cases, instead scientific analysis of massive empirical data are considered in an effort to reach general conclusions. www.pptmart.com

Weak EMH: (A.1) Filter Rules • Eugene Fama & Marshall Blume applied the filter test to each stock in the DJIA from 1957-1962. • They programmed their computer to trade through a mechanical security trading strategy called an x percent filter rule. • If the daily closing price of a security moves up at least x%, buy the security until its price moves down at least x% from the subsequent high, at which time simultaneously sell and go short. The short position should be maintained until the price rises at least x% above a subsequent low, at which time cover and buy. www.pptmart.com

Weak EMH: (A.1) Filter Rules • By varying the value of x, you can test an infinite number of filter rules. • Some filter rules using small values of x can earn the investor a return above the naïve buy-and-hold strategy. • If the commissions incurred in buying and selling are ignored. • The ½ of 1% (x=.005) filter rule tends to be the most profitable. • However, after commissions, they reported that none of the 30 DJIA stocks investigated outperformed the naïve strategy. • In fact, some ran up considerable losses. www.pptmart.com

Weak EMH: (A.1) Filter Rules • Richard Sweeney developed a filter rule that was able to earn modest profits. • He replicated Fama & Blume’s test and found that the part of their filter rule that resulted in short positions usually generated the losses, in contrast, the long positions were often profitable. • Sweeney programmed his computer to trade an x percent filter rule with no short positions. • If the price of a security rises at least x %, buy and hold the security until it drops at least x % from a subsequent high. www.pptmart.com

Weak EMH: (A.1) Filter Rules • Then liquidate the long position and invest the proceeds in a risk-free short-term bond until the price reaches its next trough and then rises x %. • He found that filter rule trading tended to be fairly consistently profitable in some stocks while being fairly consistently unprofitable in other stocks. • Addressing these issues, his filter rules could mechanically trade some stocks and earn a statistically significant rate of profit after the trading costs incurred by floor traders are deducted. If the higher commission rates that investors pay are deducted his filter rule was not profitable. www.pptmart.com

Weak EMH: (A.1) Filter Rules • In conclusion, some patterns do exist that can be used as basis for a profitable trading strategy. • But these patterns are so complex or weak that filter rules are unable to generate profits from every stock, or even substantial profits from the stocks that are profitable to trade. • This finding provides some support for the weak EMH. www.pptmart.com

Weak EMH: (A.2) Serial Correlation • It is the correlation of a variable with itself over successive time intervals. (autocorrelation) • Serial correlation is used to determine how well the past price of a security predicts the future price. • A significant positive serial correlation indicates the presence of trends. The presence of negative serial correlation indicates the existence of more reversals than might occur randomly. Numbers that are truly random will have zero serial correlation. www.pptmart.com

Weak EMH: (A.2) Serial Correlation • There is a long-term upward trend of about 6% per year in the prices of the stock so if the period over which the returns are to be measured spans several years, a positive serial correlation will be observed. • If the period over which the returns are measured is short (monthly) and spans a substantial fraction of the length of the business cycle (say 3yrs.) then a significant negative serial correlation will be found as the stock market rises and falls with the business cycle. • But these are business cycle gyrations and rising long-run trends. www.pptmart.com

Weak EMH: (A.2) Serial Correlation • Studies of security price behavior have focused on the short-term patterns (daily, weekly, monthly) that might net a larger profit after commission than a naïve buy-and-hold strategy. • Many different securities, different lags (k), different sample periods have been studied. • The serial correlation studies failed to detect any significant patterns. • This finding provides support for the weak EMH. www.pptmart.com

Weak EMH: (A.3) Runs Tests • A runs test is used to determine if there are runs in the price changes. • A run occurs in a series of numbers whenever the changes in the numbers reverse sign. • The premise of conducting the runs test is that price changes may be random most of the time but occasionally become serially correlated for varying periods, which the filter rules and serial correlation cannot detect. www.pptmart.com

Weak EMH: (A.3) Runs Tests www.pptmart.com

Weak EMH: (A.3) Runs Tests • Runs can include any number of transactions. A security price that declines for 100 consecutive transactions will generate 100 negative price changes but only one negative run. • Mathematical statisticians are able to determine how many positive, negative, zero, or total runs may be expected to occur in a series of random numbers of any size. • Therefore, if a consecutive series of security price changes occurs either more frequently or less frequently than would be expected in a series of random numbers this is evidence of non-randomness. www.pptmart.com

Weak EMH: (A.3) Runs Tests • Published runs tests suggest that the runs in the price changes of various securities are not significantly different form the runs in a table of random numbers. • This suggest that active traders searching for various types of nonrandom trends from which to earn a profit will not be able to beat a naive buy-and-hold strategy on average. • This finding provides support for the weak EMH. www.pptmart.com

B. Semi-strong EMH: Introduction • Specifies that markets are efficient enough for prices to reflect all publicly available information. • This requires more evidence of market efficiency than the weak EMH since the latter only requires that security prices tend not to follow repetitive patterns. • This means that all information in publications, newspapers, company releases, brokerage reports, and investment advisory services contain nothing of value to investors. www.pptmart.com

Semi-strong EMH: (B.1) Announcement Effect • Interest rates affect security prices because market interest rates determine the discount rates that are appropriate to use in determining the present values. • Therefore changes in the Federal Reserve discount rate, announced after the Federal Reserve Board decides to change this monetary policy variable in confidential meetings may be expected to affect security prices. • Announcements of changes in the discount rate are so widely publicized in the financial press. www.pptmart.com

Semi-strong EMH: (B.1) Announcement Effect • Research into the effects of discount rate changes has shown that the average common stock’s price changes a small but statistically significant amount on the 1st trading day following the public announcement by the Federal Reserve of a change in the discount rate. • However, this change is not enough to yield a trading profit. • Most of the price change associated with the announcement seems to occur before the actual announcement. • Market seems to anticipate changes in the Fed’s discount rate policy • This finding provides support for the semi-strong EMH. www.pptmart.com

Semi-strong EMH: (B.2) Stock Splits & Dividends • Stock splits & stock dividends are essentially paper-shuffling operations that do not change the total value of the firm or investor’s wealth. • A 100% stock dividend, or equivalently, a 2-for-1 stock split means that twice as many shares will be outstanding and each share will be worth half as much. • If security markets efficiently equate security prices with security values, the total value of the firm’s outstanding shares will not be affected by these changes. • Stock splits and stock dividends are publicly announced events. www.pptmart.com

Semi-strong EMH: (B.2) Stock Splits & Dividends • Eugene Fama, Lawrence Fisher, Michael Jensen, and Richard Roll (FFJR) conducted a study to find out the speed and accuracy with which the market reacted to the announcement of stock splits and anticipated dividend increases. • The study was based on a sample of 940 stock splits and stock dividends that occurred on the NYSE between 1927 to 1959. • Their analysis centered on each stock’s behavior in the period 30 months before and after the split. www.pptmart.com

Semi-strong EMH: (B.2) Stock Splits & Dividends • The results showed a sharp upward trend of market prices prior to the split which can be inferred as the market being efficient in anticipating the pending split and subsequent dividend increases. • This finding provides support for the semi-strong EMH. www.pptmart.com

C. Strong EMH: Introduction • It holds that the present market prices of securities reflect all the information that can be known about a company, including privileged information that might be available to corporate insiders, specialists on the exchange, and analysts from in-depth studies. • It suggests that all information, public or not, is fully reflected on security prices. • This is an idealistic economic situation that results in a perfectly efficient market where prices and values are always equal as they fluctuate randomly together to the arrival of new information. www.pptmart.com

Strong EMH: (C.1) Trading on Inside Information • Federal law defines insiders as the directors, officers, consultants, significant shareholders, and any other persons who have access to material nonpublic information about a firm. • Federal law requires insiders to notify the SEC within 1 month all trades they have made in their corporation’s stock. • The SEC then publishes these insider trades in its monthly pamphlet, Official Summary of Insider Trading. • Jeffrey Jaffe analyzed this over 6 years to measure insiders’ trading profits. • The CAPM was used in this analysis. www.pptmart.com

Strong EMH: (C.1) Trading on Inside Information • He estimated the CAPM for different months using all NYSE stocks. • He assessed the value of the insiders’ trades in their own corporation’s stock. If the return from the trade exceeds the expected return based from that month’s CAPM then the residual error is positive, or vice versa for negative residuals. • He studied each month’s Official Summary of Insider Trading and selected the stocks in each month that had 3 more inside sellers than buyers– he labeled this event as selling plurality by insiders. 3 more insiders buying than selling in the same month was called buying plurality. www.pptmart.com

Strong EMH: (C.1) Trading on Inside Information • Appropriate buying and selling commissions were subtracted from each insider’s trade to obtain the net profit. • Then the average residuals after commissions were cumulated over 1,2 and 8 months after the month in which the plurality of insiders originally made their trade. www.pptmart.com

Strong EMH: (C.1) Trading on Inside Information • Statistically, this rate of insiders’ trading profit is significantly above zero. • Practically speaking, the average insider certainly is not getting rich by making investments based on their information. • This finding provides support for the strong EMH. www.pptmart.com

Strong EMH: (C.1) Trading on Inside Information • H.N. Seyhun analyzed insiders’ trading between 1975 and 1981 using a larger sample and a different methodology. • He extended Jaffe’s study by examining outsiders who traded on inside information purchased from one of the financial services that purveys data about insiders’ trading activities. • He found that, on average, outsiders who traded on the latest available information reported by insiders to the SEC were unable to earn positive profits from their trades. • The fact that insiders, on average, can earn trading profits from their information refutes the strong EMH. www.pptmart.com

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Anomalous Empirical Evidence About Pricing Efficiency Discovered before the 1980s • Low-Priced Stocks • Reported in 1936 by L.H. Fritzemeier. • He worked through numerous hand calculations to show that low-priced common stocks tended to earn higher rates of return than high-priced stocks. • Price- Earnings Ratio Effects • Basu’s study of stocks with low P/E ratio documents this anomaly but other studies have since confirmed that common stocks selling at low multiples of their EPS earn abnormally high rates of return. • The P/E ratio effect remains a robust and statistically significant determinant of common stock returns. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency • Cash Dividend Yield • Various studies do not reach a consensus, but most suggest that cash dividend yields have a positive but marginally significant effect on the market value of equity shares. • Cash dividend effects of any kind can be considered an anomaly in the efficient market theory. • January Effect • Tendency of stock prices to decline slightly during the last few trading days of December and then move up during January. • Much of the year’s price appreciation occur in January. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency • Business Cycle Effects • Stock market always declines months before the economy enters a recession. However, the stock market sometimes crashes when no recession follows. • Nevertheless, the relationship between stock prices and business activity can introduce trends, business cycle fluctuations, and other patterns that might be considered as blemish on the efficient market theory. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency Discovered during the 1980s • Within-the-Month-Effects • Stock price returns tends to be positive during the first half of the month, then they typically average zero from mid-month until the last few days of the month, they tend to become positive again on the last few days of the month. • Any particular month may deviate substantially from this pattern. • The Sales: Price Effect • Senchack & Martin suggested that sales per share/ price per share was a better indicator of superior returns than the PE ratio. • They provided credible empirical evidence that documents this imperfection in the efficient markets theory. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency • Effect of Cashflows • Cash flow per share has more influence on a stock’s price than EPS. • Unsystematic (Diversifiable) Risk Effects • Empirical studies report that diversifiable risk has significant explanatory power over stock prices. • Effects of Skewness • Skewness is a statistic that measures lopsidedness in a probability distribution. • Theoretical and empirical research suggests that assets which can contribute positive skewness to an investor’s portfolio are more desirable than assets that contribute negative skewness, if all other factors are equal. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency • Agency Effects • Jensen & Meckling suggested that corporate executives may not always try to make decisions to maximize the value of the firm for the firm’s owners. • Empirical evidence suggests that such agency costs tend to cause the average rate of return from common stocks to vary directly with the percentage of the corporation’s stock owned by the managers of the corporation. • Earnings Controversy Effects • Carvel & Strebel reported that a stock’s future returns may be positively related to controversy or uncertainty about the stock’s future EPS. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency • Effects of Earnings Surprises • Earnings surprise is a relatively large deviation from an earnings forecast. • The abnormal price moves are apparently the market’s reaction to important financial information that was not anticipated. • Effects of Trends in Analysts’ Earnings Forecasts • Empirical evidence suggests that stock prices are affected by trends in earnings forecasts. • A possible measure of a trend in a corporation’s forecasted EPS is the percentage change from the current consensus earnings forecasts to the 1-year ahead consensus earnings forecast. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency • Effects of an Earnings Torpedo • When the market has high expectations for a stock’s earnings and as a result has bid up that stock’s price in anticipation of high future earnings, the stock’s price can sink as if it were torpedoed if the actual earnings turn out to be significantly worse than expected. • Such price reactions can reach proportions that represent anomalies in an efficient pricing mechanism. • Effects of Relative Strength • Relative strength approach is a technical analysis tool that suggests that prices of some securities rise relatively faster in a bull market or decline relatively more slowly in a bear market than other securities. • Different empirical studies support the ability of the relative strength measures. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency • Residual Reversal Effects • Reversals refer to the sign changes in residual errors. • Empirical evidence has documented the tendency for the residuals to reverse signs more than would occur randomly. • Firm’s Size Effect (Banz, 1981) • Small firms tend to earn better rates of return than large firms. • Effect of Neglect • Neglected stocks are those that lack popularity with the large institutional investors, not followed by many professional security analysts, or stocks about which it is difficult to get information. • Research suggests that these stocks tend to earn better than average returns. www.pptmart.com

Anomalous Empirical Evidence About Pricing Efficiency Day-of-the-Week & Time-of-the-Day Effects (French, 1982) • Stock prices tend to rise on Fridays more often than any other day of the week and have risen least often on Mondays. • Not only do a number of losses occur on Monday, most of these losses take place before lunch. • Effects of the Book-Value-to-Price Ratio • Empirical evidence has been published showing that stocks having high BV per share relative to their market price tend to perform well. www.pptmart.com

Conclusion: • Despite a century-and-a-half of think about the way security prices fluctuate, it was not until the 1960s that the power of the computer allowed financial analysts to test theories with massive empirical data. • Much of the research during the 1960s and 1970s was devoted to substantiating the theory of efficient markets. • Numerous scientific empirical studies confirmed a surprisingly large number of implications on the EMH, however, some anomalies in the efficient markets theory also emerged. www.pptmart.com

Conclusion: • These anomalies are difficult for investors to capture because of computational problems, nevertheless, the number of persistent anomalies blemish the efficient markets theory as a description of reality. • We know that it is possible to gain insights from some forms of technical analysis, however, it is costly to implement trading strategies that are designed to benefit from the anomalies of the efficient markets theory. • Without the sophisticated and costly resources needed to implement these strategies, investors are advised to act as if the efficient markets theory were descriptive of reality. www.pptmart.com

Learning Aids 1. Types of Security Analysis 2. More on Correlation (Weak EMH) 3. CARs for Stock Splits and Dividends (Semi-strong EMH) 4. Monthly average returns to Anomalies www.pptmart.com

1. Types of Security Analysis • Technical Analysis • Use prices and volume information to predict future prices • Mainly for market timing purpose • Related to the weak form efficiency • Fundamental Analysis • Use economic and accounting information to predict stock prices • Mainly for stock selection purpose • Related to the semi-strong form efficiency • This includes • Economic Analysis • Industry Analysis • Security Analysis www.pptmart.com

2. More on Serial Correlation • A correlation coefficient is a number between -1 and 1 which measures the degree to which two variables are linearly related. • If there is perfect linear relationship with positive slope between the two variables, we have a correlation coefficient of 1 • If there is a perfect linear relationship with negative slope between the two variables, we have a correlation coefficient of -1. • A correlation coefficient of 0 means that there is no linear relationship between the variables. www.pptmart.com

2. More on Serial Correlation www.pptmart.com

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2. More on Serial Correlation Karl Pearson Coefficient of correlation • It is the degree or numerical measure of linear relationship between two variables. • Let X & Y be two random variables ; • x , y be the std. dev. of X , Y and Cov ( X , Y ) be the covariance of X & Y then, the Correlation Coefficient between X & Y denoted by ‘r’ or r ( X , Y ) is defined as: r = r ( X , Y ) = Cov ( X , Y ) / (x y) www.pptmart.com

Efficient Market Hypothesis