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Eugene Fama was an undergraduate student at Tufts University when he first began to develop an interest for economic theory (Tuft’s E.Newz). Mr. Fama worked for a professor who was trying to develop a “buy - sell” formula for the securities market based on price momentum (News-School). The phenomena accompanied with his study, plus the skills that he acquired while evaluating stock market data, drew Mr. Fama to the University of Chicago where he would finally develop what is known today as the “Efficient Market Hypothesis”.

INTRODUCTION

This paper reviews the theoretical and empirical evidence on the “Efficient Markets Hypothesis”. After a discussion of the theory and its relevant forms, empirical work concerned with the adjustment of security prices to the three relevant information subsets is considered. First, weak form tests, in which the information set is just historical prices, are discussed. Then the semi-strong form tests, in which the concern is whether prices efficiently adjust to other information that is obviously publicly available, (e.g., announcements of annual earnings, stock splits, etc) are considered. Finally, strong form tests concerned with whether given investors or groups have monopolistic access to any information relevant for price formation is reviewed.




Next, this review will discuss some particular problems associated with testing the Efficient Market Hypothesis; specifically the setbacks associated with developing a benchmark (or expected return) when applying the CAPM model.

Furthermore, this review will identify some specific anomalies associated with the Efficient Market Hypothesis; illustrate the evidence of abnormal returns, and describe the empirical research performed to identify the causes of occurrence.

Finally, this review will introduce the recent evolution of “Behavioral Finance”, a concept that has been initiated in attempts to enhance the understanding of investors and the Efficient Market Hypothesis.

EFFICIENT MARKET HYPOTHESIS

According to the theory of “Stock Market Efficiency”, an efficient market fully and accurately reflects all of the relavent available information in the price of a security (Fama, 170). The nature of the information does not have to be limited to financial news or research alone; information about political, economic and social events, combined with how investors perceive such information will be reflected in a securities price. Furthermore, the securities market is flooded with thousands of intelligent, well-paid, and well-educated investors seeking under and over-valued securities to buy and sell(Fama, 11). The more participants and the faster the dissemination of information, the more efficient a market should be.

As security prices only respond to information available in the market, and because the information is available to everyone, no one has the ability to “out-profit” another. Security prices are therefore random, and this “random-walk” of price results in the failure of any active investment strategy aimed at beating the market consistently. A planned investment approachs cannot be successful.

ASSUMPTIONS

Perfect Information is one of the pre-conditions in a perfectly efficienty market. The term “Perfect Information” is frequently used in economics to identify the assumption that complete knowledge is available to all securities market participants(Fama, 170). The information is available at no cost, and trading on such information is free. Furthermore, it is the assumption that all market participants agree to the implications of such information and a securities price fully reflects the expected return.

In the “real-world” however, the assumption of Perfect Information is highly unlikely. There are transaction costs associated with acquiring/analyzing market information and secondly, trading securities in the market is often performed for a nominal fee (Varian, 18). Furthermore, even the most sophicsticated security investors do not receive information instantly, nor do they react to it instantly. There is a vast amount of information that travels through the avenues of financial firms daily, and the major decisions are taken by committees or small groups of people to discuss, analyze, and decide as to what to do (Varian, 18). The major financial institutions must analyze the market impact a large scale transaction will have when transferring their funds from one asset to another, all of which takes a considerable amount of time.

A “Random Walk” is a second assumption considered in the Market Efficiency Hypothesis (Fama, 165). The theory claims that a securities price will fluctuate randomly, without any influence by past price movements. Furthermore, the theory assumes that it is impossible to predict with any accuracy which direction the security will move at any given point; neither fundamental analysis nor technical analysis’ maintain any validity.

SUB-CLASSIFICATIONS

A more complex interpretation of the “Efficient Market Hypothesis” identifies three classifications of market efficiency. Each classification is aimed at reflecting the degree to which the theory can be applied to the security markets.

WEAK FORM OF MARKET EFFICIENCY � Suggests that all past prices of a security are reflected in today’s securty prices, therefore, technical analysis cannot be used to provide an investor with an advantage (Fama, 170); examining market trading data such as the history of past prices, trading volume, or short interst, and trend analysis is fruitless (Frankel & Froot, 10).

The use of stastical investigations on time series data is applied to test the weak form of market efficiency. Charting and techincal analysis both apply a series of numerical values such as price and volume to predict future trends, and while widely used by both professional and amateur traders this type of analysis implicitly rejects the efficiency of the market as understood in the efficiency market hypothesis.

With the innovation of computer software new types of technical analysis applications have been developed to allow the users to design their own indicators and to optimise them by testing their profitability (Tung, 1). Analysts or chartists believe that by analyzing the history of a securities price they can develop probabilities and anticipate future events. This type of technical analysis applies statistical data to search for meaningful signals amongst the apparent random fluctuation of stock price movements, and although technical analysis has produced mixed results, it is commonly practiced in the securities market.

Two commonly used types of technical analysis included

BAR CHARTS Bar charts are often used to identify patterns in price, volume, and the current trend of a securities price, and can be customized to illustrate price fluctuations throughout the day, month, or even year. With the use of a Bar Chart, an analyst can attempt to identify any trend in the securities price and therefore forecast its future based on past information illustrated in the graph. The items highlighted in Blue identify the day-to-day price fluctuations, which are currently on a downward trend. The Green and Red lines identify the trading activity or volume, which takes place on a day-to-day basis.

MOVING AVERAGE CHART

The moving average chart identifies the average closing value of the securities price. This information is generated by combining the sum of closing prices and dividing them by the number of days under observation. This type of analysis is often used to identify the direction of a trend and to smooth out price and volume fluctuations. The Red Circle ( Left Side) identifies a drop in the securities price below its 50 day average (Green Line), this trend continues until January, where the second Red Circle (Right Side) identifies an increase in the stocks actual price relatively larger then the 50 day average. This information combined with historical data could identify a trend in the securities price during the month of January, sometimes called the January Effect.

SEMI-STRONG FORM OF MARKET EFFICIENCY � Suggests that all public information is calculated into the securties current price. No form of “fundemental” or “techenical” analysis can be used to provide an investor with an advantage (Fama, 170).

To test the semi-strong form of market efficiency, an analysist researches any types of adjustments to a securities price after unknown news has been released. Any types of “consistent fluctuations” in a securities price after the initial change in price suggest that investors have interpreted the “New Information” inefficiently (Frankel & Froot, 10).

By applying a Fundamental analysis, an investor attempts to value a security by analyzing the companies accounting information. Fundamental Analysis is a method that uses earnings and dividends forecasts, economic analysis, quality of the firm’s management, the firm’s standing within the industry, and the prospects for the industry as a whole. The hope is to attain insight into the future performance of the firm that is not recognized by the rest of the market(Frankel & Froot, 10). Although the results from this type of analysis have varied in the past, investment firms like Meryll Lynch, Smith Barney, and Morgan Stanley Dean Whitter continuously apply fundamental analysis to develop strategic investment portfolios for their clients.

Three types of Fundamental strategies often applied include

Value Strategy The value strategy involves selecting certain securities which appear to be underpriced in relation to their fundamentals. By evaluating a firms Price � Earnings ratio, Price �Book Ratio, Dividend Yield, Price to Sales Ratio, Dividend �Earnings Ratio, and Earnings to Sales Ratio an analyst can determine whether or not a security is undervalued in comparison to its intrinsic value.

Momentum Strategy The momentum strategy involves the selection of certain securities which show a strong appreciation in price, either in absolute or relative terms. By evaluating a firm’s Mean Return, Past and Current Performance, Trend, and Continuous Momentum, an analyst may try to identify any persistence of the forces that drive the particular market as well as the security.

Growth Strategy The growth strategy involves the selection of securities that show a continuous growth in both earnings and sales. By simply evaluating the growth of a firms Earnings Per Share, and Sales in association to the current market condition an analyst can determine whether or not the firm is likely to continue its growth pattern.

STRONG FORM OF MARKET EFFICIENCY �Suggests that all information whether public or private, is accounted for in a securities price. Although private information, also known as “inside information” is held only by the directors, managers, and professional advisors of an organization, a security price is still at its intrinsic value (Fama, 170).

In evaluating the theory of a strong-form of market efficiency, studies on the US stock market have shown that people who do trade on inside information sometimes make enormous profits (Kyle, 185). Furthermore, it was also found that investors who monitor the trading activity of insiders, and thus follow their activity have sometimes enjoyed the same type of profits (Tung, 1).



Exhibit A



Exhibit B



Exhibit A and Exhibit B illustrate the effects of individuals who, by virtue of their possession retain nonpublic information and use it without restraint in security trading. As Exhibit A demonstrates, the magnitude of profits earned by corporate insiders exceeds the profits available once the information is released to the public (Exhibit B), thus rejecting the strong form of market efficiency.

TESTING THE THEORY

Testing the Efficient Market Hypothesis can be classified into three main categories. The first type of test identifies whether abnormal return are independent of newly released information. The second type of test exposes any form of abnormal returns after taking into account the transaction costs and risk associated with the trading of a security. The third type of test determines whether the market price of a security is equal to its intrinsic value.

Testing the Weak Form of Market Efficiency

The statistical analysis of independence between rates of return and past security prices is applied to test the weak form of market efficiency. Throughout history two conventional types of tests have been applied. The first type of test ,Serial Correlation Test, identifies any positive or negative serial correlation between returns over a period of time (Lyon & Tsai, 1). The second type of test ,Run Test, compares the actual values of a securities price to its expected (forecasted) value (Lyon & Tsai, 1). Consistency in serial correlation or accurate forecasting would identify an imperfection in the weak form of market efficiency and thus reject the hypothesis, however the evidence generated from these tests suggests that the weak form of market efficiency is accurate.

Testing the Semi-Strong Form of Market Efficiency

Testing the semi strong form of market efficiency requires the examination of economic, industry, and firm specific information and its affect on a securities price. Specifically, the analysis distinguishes whether or not a securities price fully reflects all publicly available information.

An important breakthrough in testing the Efficiency Market Theory came with the advent of the Event Study method. The first Event Study was applied to stock splits, however similar tests have been structured to evaluate dividend announcements, earnings announcements, large transactions, repurchase tender offers, and other public announcements. Secondly, Time Series Analysis Tests are performed to evaluate the affect of public information on a securities price and to determine whether or not specific information will provide a superior estimate of returns for a short horizon (1-6 months) or a long horizon (1 � 5 years).

The continuous examination of Event Studies and Time Series Analysis had supported the Efficiency Hypothesis (Stanton & Gabriel, 1). However, contradicting evidence (market anomalies) has been discovered leading to mixed results in the Semi-Strong theory.

Testing the Strong-Form of Market Efficiency

Testing the strong form of market efficiency incorporates the investigation of insider trading and its contribution to abnormal returns. The strong form suggests that all information, whether public or private, is accounted for in a securities price, thus investors trading on “non-public” information will not earn abnormal profits.

Numerous tests have been performed to identify insider trading and the release of new information (Allen, 18). As mentioned before, evidence suggests that insiders do earn superior returns on securities when trading on the basis of inside information, thus rejecting the strong form of market efficiency.

PROBLEMS TESTING EFFICIENT MARKET HYPOTHESIS

In an efficient market, no information or analysis can be expected to result in the out-performance of an appropriate benchmark, or “expected return”. Appropriate benchmarks refer to comparable securities of similar characteristics. The difficulty lies with the lack of ability to identify an accurate benchmark to measure actual and expected returns.

CAPM Model

Earlier investigations of the Efficient Market Hypothesis utilize the Capital Asset Pricing Model (CAPM) to generate an “expected return”. The Capital Asset Pricing Model is an economic model for valuing stocks by relating risk and expected return (Reinganum, 181). The assumption is based on the belief that investors demand additional expected return on assets that have a higher measure of risk.

Although the results proved to be accurate there is particular criticism to applying the CAPM model because it too is a hypothesis. Arguments against the use of the CAPM model suggest that the expected returns are unobservable therefore the evidence is inconclusive (Reinganum, 181). Secondly, estimates of the firm specific risk is imprecise, which creates a measurement error problem when applied to calculate an expected return. The two aforementioned arguments represent the Joint Hypothesis Problem, which states that “tests of the Efficient Market Hypothesis are joint tests of market efficiency and the Capital Asset Pricing Model”. Thus if the CAPM model is incorrect so is its evaluation of the Efficient Market.

Data Mining

The rapid evolution of computer technology has provided investors with the capabilities to access and analyze vast amounts of financial data (Investor Home, 00). But the financial data provided to the market is not standardized, therefore there are numerous approaches to both producing and evaluating this information. The difficulty with data mining is the quality of sources of the data. The recording of revenues, net income, shares outstanding, earnings per share, cash flows, total returns, and many other important equity variables are highly source dependent (Loughran, 1). All multifactor equity models work on the assumption that the data upon which the model was constructed is comparable to other inputs, but due to the multiple accounting practices this may not be so. Therefore, the return predictions generated from a data mine source may be inaccurate and thus unobservable.

Assessment of Risk

An accurate assessment of risk needs to be established to properly identify the expected returns of a security’s value. An estimation of risk refers to identifying the investor’s uncertainty about the parameters of the return on a security; these parameters can differ significantly from the properties perceived by rational investors (Reinganum, 181). Therefore, actual returns can deviate from the expected. Thus producing a perception of excess returns when in reality the returns are normal.

Changes in Risk-Free Rates

Continuously changing risk-free rates are another problem in evaluating the Efficient Market Hypothesis. Fluctuations occur daily and therefore if the risk-free rate is an important factor in identifying the “expected return”, a continuous change in risk-free rates could produce an inaccurate estimation of the “expected return” (Reinganum, 181).

ANOMALIES

Despite the strong evidence that the securities market is highly efficient, numerous studies have been conducted to identify long-term historical anomalies that appear to contradict the Efficient Market Hypothesis. An anomaly is an “exception to the rule, or a deviation from what is expected”, therefore an exception to the Efficient Market Hypothesis (Jenson, 178). Furthermore, although the abnormal returns earned by anomalies associated with the securities market are not constant, they are all associated with above-average returns over a long period of time.

The anomalies listed below have all become well documented during the last few years and sometimes indicate inefficient market efficiency, thus offering potentially profitable opportunities to investors.

January Effect The January Effect refers to a phenomenon in which securities (usually small-cap), recognize higher rates of return in the month of January. Since 16, research has shown that very small securities are the prime beneficiaries, having on average recorded excess returns of approximately 10%. Midsize securities have recorded average excess returns of %, and approximately 1% for larger securities (Stanton & Gabriel, 1).

One theory suggests that the January effect is attributed to small-firms rebounding from a year-end tax sale by investors (Stanton & Gabriel, 1). The assumption is that relatively low year-end securities are usually sold for investors to realize a tax-loss. Once the tax calendar has ended the investors then reinvest their money in the market, thus causing the security prices to increase. A second theory suggests that individuals are simply compensated for the extra risk associated to investing in the small firms (Investopedia.com, 00).

Small Firm Effect First introduced by Rolf W. Banz in 181, the “Small Firm Effect” asserts that small capitalization (small-cap) securities appear to provide greater than average returns without a corresponding increase in risk (Market Volume Analysis, 00). Research indicated that risk-adjusted returns for smaller firms exceed the returns of larger firms by approximately 1% per year on average from 16 to 180 (Statman, 180). The highest returns were recognized in times of strong economic growth and during strong recovery following a recession. On the other hand, when economic times were poor, smaller firms generally under-performed their larger counterparts (Statman, 180).

Although there is no simple explanation for the Small Firm Effect, researchers suggest it could be caused by one or all of the following (1) Information uncertainty � which asserts that researchers are less likely to thoroughly investigate small-cap securities and the lack of information amounts to relatively higher risk, thus providing a potential opportunity to exploit market miss pricing (Sloan, 16). () Higher Transaction Costs and Illiquidity � which asserts that small-cap securities can be more expensive to trade or subject to wider bid-ask spreads due to the lack of liquidity, thus excess returns are possibly due to the compensation required by investors (Brau & Heaton, 00).

Market Over/Under Reaction Research indicates that security prices overreact to “firm specific” information, and under-react to “common factor” market information (Abar & Banell, 1). This over/under reaction is subsequently followed by a reversal in the securities price; the more extreme the initial price movement, the more extreme the following adjustment.

Earlier studies suggest that security investors are overly optimistic or pessimistic about the effects of new information and therefore overestimate the correlation on returns (DeBont & Thaler, 185). However, a recent study has suggested that when these returns are adjusted for differences in characteristics between winner and loser stocks, such as size, book to market, and trading volume there is no irregularity (Ball & Shanken, 15).

Post Earnings Announcement Drift Security price changes tend to persist after initial earnings announcements. Securities with positive announcements tend to drift upward, and those with negative surprises tend to drift downward (Bamber, 17).

The theory suggests that individual investors who are less sophisticated than institutional investors cause Post Earning Drift, and that the trading by individual investors causes market inefficiency. Investors naively assume earnings follow a seasonal random walk and fail to understand the implications of current earnings for future earnings (Bernard & Thomas, 18). On the other-hand, a second theory suggests that investors are not less sophisticated, but they underestimate the degree of correlation of current earnings for future earnings (Ball & Bartov, 16).

Behavioral Finance

The Efficient Market Hypothesis is based on the belief that investors act rationally and consider all available information in the decision making process. However modern research has exposed a considerable amount of evidence identifying repeated patterns of irrationality, inconsistency, and incompetence in the ways investors make decisions when faced with uncertainty. Such evidence is often referred to as market anomalies.

Since the 180’s there has been a movement to incorporate behavioral science into finance. Behavioral finance is the study of how investors interpret and act on information to make informed investment decisions (Shleifer, 1). By including behavioral science into the study of finance, researchers are able to improve the research on the Efficient Market Hypothesis by incorporating psychology-based theories to explain market anomalies (Varian, 18).

The recent evolution of Behavioral finance stems from attempts by researchers to better understand and explain how emotions and cognitive errors influence investors in the decision making process. Researchers believe that the study of psychology and other social sciences can identify current unexplained influences that cause security market anomalies.

The Efficiency Market Hypothesis assumes that investors act rationally and unbiased to available information. Behavioral research, however, has suggested that investors are commonly biased in several regards. For example, investors tend to be overconfident in their predictions in the future of the securities market. Between 17 and 10, security analysts have been substantially inaccurate in the forecasts (errors between 5% and 65%) of actual returns (Barber & Odean, 001).

Secondly, “Frame Dependence” asserts that investor decisions are often affected by a reference point of the given security (Olsen, 00). That is, at which point the investor identifies and evaluates the security. By just focusing on the recent performance of a security, investors often observe order where in reality it does not exist. Thus interpreting an accidental success to be the result of skill.

Furthermore, the study of behavioral finance illustrates the belief that investors often trade on information they believe is superior and relevant when in fact the information is already reflected in the securities price (Thaler & Berartzi, 001). This results in high trading volumes in the financial markets, a phenomenon that has often puzzled many researchers.

Although the security markets are not entirely efficient, the increasing popularity of behavioral finance has produced numerous explanations for the occurrence of market anomalies. The wide range of information has demonstrated the growing success of behavioral approaches to understanding the confusion of financial markets, and as research continues it is expected that further explanations will be exposed.

CONCLUSIONS

The introduction of the Efficient Market Hypothesis has certainly become a milestone for the financial research. The hypothesis has provided a powerful framework to analyze and understand security prices. Research has indicated that both the Weak and Semi-Strong Forms of Market Efficient are accurate, but conflicting evidence has suggested that the Strong Form of Market efficiency does not exist.

On another note, despite the strong evidence supporting the Efficient Market Hypothesis, some anomalies do exist. This has lead researchers into two different directions. Some researchers suggest that there could be considerable setbacks associated with the development of benchmarks (or expected returns), and others attempt to develop a better understanding of investors and the security markets by applying Behavioral Science.

Nonetheless, the introduction of the Efficient Market Hypothesis has become a world-renowned philosophy that has shaped the way many investors and researchers view the markets today. And with the introduction of Behavioral Science, researchers can expect to grasp a better understanding of investors, the security markets, and the influences affecting them.

References

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