After coming into the 21st century, the theory develop rapidly to go further research. Lewellen and Shanken (2002) concluded that parameter uncertainty can be important for characterizing and testing market efficiency. Chen and Yeh (2002) investigated the emergent properties of artificial stock markets and show that the EMH can be satisfied with some portions of the artificial time series. Malkiel (2003) examined the attacks on the EHM and concludes that stock markets are far more efficient and far less predictable than some recent academic papers would have us believe. G. William Schwert showed that when anomalies are published, practitioners implement strategies implied by the papers and the anomalies subsequently weaken or disappear. In other words, research findings cause the market to become more efficient (Schwert, 2003). Malkiel (2005) showed that professional investment managers do not outperform their index benchmarks and provides evidence that by and large market prices do seem to reflect all available information. Wilson and Marashdeh (2007) demonstrated that co-integrated stock prices are inconsistent with the EMH in the short run, but consistent with the EMH in the long run. The elimination of arbitrage opportunities means that stock market inefficiency in the short run ensures stock market efficiency in the long run. In a paper on the global financial crisis Ball (2009) argued that the collapse of Lehman Brothers and other large financial institutions, far from resulting from excessive faith in efficient markets, reflects a failure to heed the lessons of efficient markets. Lee et al. (2010) investigated the stationary of real stock prices for 32 developed and 26 developing countries covering the period January 1999 to May 2007 and conclude that stock markets are not efficient.
Except CAPM model, there is a second class of models used to test market efficiency focuses on variance as the key characteristic. Among them are the model of Shiller (1981), who reported that stock prices were too volatile to be efficient when compared to subsequent dividend payouts, and the model of Marsh and Merton (1986), which showed that Shiller’s results could be reversed by a change in assumptions regarding the dividend model. The reply of Schwartz (1970) to the seminal paper of Fama (1970) could also be considered to fall into the category of variance efficient market models, as it propagated the use of models that tested for variance-based strategies to generate excess returns in capital markets. The first variance efficient market models in the early 1980s coincided with the advent of behavioral finance and behavioral market models, which soon started to erode the solid standing the efficient market hypothesis had (until that time) enjoyed in academic circles. A number of anomalies were discovered in empirical data, suggesting that the universal belief in the applicability of the efficient market theory had been overly optimistic. Today, evidence of widespread efficiency in developed markets coexists with well-recognized anomalies, both in these highly developed markets in industrialized countries and – much more frequently – in less developed market economies. These anomalies can be subsumed under a few broad categories, which are summarized in the following section.
- Theoretical background
The Efficient Markets Hypothesis (EMH) states that market prices reflect all available information. This theory is based on the operational and allocational efficiency of capital markets that have the following properties:
(1) Agents are rational and seek the optimization of their expected utility
(2) All agents are price takers
(3) Assets are divisible and marketable.
(4) Market is frictionless without transaction costs and taxes
(5) Information is costless and arrives to all agents at the same rate and time
As a result all shares on the market trade at their fair value. This price incorporates all known information and history price effects. No-arbitrage is realizable using public information.
Fama (1970, 1976) established an extremely important classification for this market efficiency:
Weak-Form Efficiency: No abnormal return can be realized based on previous price information and future price movement is totally random. The weak-form efficiency suggests that stock prices only change in response to new information and as new information follows random patterns thereby, the stock prices should follow a random walk (Preston and Collins 1966, pp. 154-162). The technical analysis fails in the presence of weak-form efficiency as the investor behavior facilitates the elimination of profit opportunities that are associated with patterns formed by stock price movements. The concept is that if it was possible to make substantial profits from following simple trends, every investor will follow the pattern and make huge profits; however, this activity in its very essence will eliminate any arbitrage profit as each investor instantly reacts to any patterns observed in the market.
Semi-Strong-Form Efficiency: No investor can earn excess return by trading on the publicly available information because the market adjusts rapidly to the information and in an unbiased fashion. The semi-strong form of market efficiency indicates that all the historical and public information is reflected into the stock market prices and can be tested by the use of event studies attempting to disclose how quickly and accurately markets respond to new information. Another, approach used by researchers is to test whether fundamental analysis is useful when predicting stock prices; if the fundamental analysis is found useful than markets do not instantly reflect all the historical and publicly available information in their prices. Event studies regarding semi-strong form efficiency examine the changes in prices and returns over time; especially before and after the release of new information. The tests involve measuring whether markets under-react or over-react to new information; moreover, they also intend to find if the reaction of markets to new information is delayed or it is around the actual event. Event studies can be applied to a large number of events including mergers, earning announcements, earnings surprises, issues of new stock, capital expenditures by firms and changes in the level of dividends. Renshaw (1984, pp. 48-51) found that investors often tend to over-react to negative news signals and may sell stock in a wave of panic resulting in the price to fall below its fundamental value. In such cases the market is not semi-strong efficient as the undervalued stocks can be identified and money can be made in the short-to-medium term.
Strong Form Efficiency: Share price reflects all available information and no investor can earn excess return. Not only do insider traders make money, but in most situations where insider trading takes place prior to the public release of the price move significantly on the public release of the information. Therefore the non-public information was not fully reflected in the price.
The literature argues for and against the theory from different point of views. Several classical papers document the presence of anomalies in the market pricing of shares. Other papers discuss the validity and the presence of information in the market movements. Anomalies in return have been reported during the past fifteen years. Although, they should be called regularities as suggested by Berk (1995). These anomalies were documented in the following papers:
Benz (1981) this paper was one of the first that documents empirical irregularities with the market pricing. It describes two important aspects of the prices: The logarithm of a stock price is an inverse predictor of its return. And when risk is controlled for by using an asset pricing model (CAPM for example) the market value has explanatory power over the part of return that is not explained by the model.
Puterba and Summers (1988) investigates the presence of a transitory price component in the price process. They notice that the presence of a negative serial correlation in the price process means that some previous erroneous market moves had been corrected or the negative serial correlation arises from variation in the risk factors over time. They aim in this paper at examining the hypothetical transient price component or the validity of mean reverting movement. In addition, they wanted to test whether the mean reverting movement is due to shifts in required return or resulting from changes in the interest rate. They could not reject the random walk hypothesis using VR tests but they find significant transitory price component that is responsible for a major part of price variance over time. The standard deviation of US price variance is 15-25% and this accounts for more than 50% of monthly return variance. They found out also positive serial correlation for prices on the short run but negative serial correlation over the long run. They suggest that noise trading provides a plausible explanation for transitory price components.
Lakonishock and Smidt (1988) use 90 years of daily return on DJIA to test for the existence of persistent seasonality patterns in the returns from 1898 until 1986. They find evidence of persistence anomalies of returns around the turn of the week, around the turn of the month, around the turn of the year and around holidays. The rate of return on Mondays was negative and the price increase around the turn of the month exceeds the total monthly price increase. The price increase from last trading day before Xmas to the end of the year is over 1.5%. However, there was no special pattern for end of the month if the month is not at the end of the year or end of quarter. Possibly these patterns are due to the inventory adjustment of different traders at the end of fiscal periods, timing of reporting by firms, seasonal patterns in cash flow to individual and institutional investors, tax-induced trading, hedge funds last minute trading, and window dressing induced by periodic evaluation of portfolio managers.
Lo and Mackinglay (1988) tests the RW hypothesis for weekly stock return by comparing the variance estimators derived from data sampled at different frequencies. They find out that RW model is generally not consistent with the stochastic behavior of weekly return especially for smaller cap stocks. Unlike FF (87) and Poterba and Summer (88), they find out that portfolio returns exhibits positive serial correlation but the individual stocks show negative correlation. The rejection cannot be completely explained by infrequent trading or time varying volatilities although they are largely due to the behavior of small stocks. In addition, they concluded that the price stationary mean reverting model discussed in Poterba and Summers (87) and FF (87) cannot account for all the variations observed in the empirical survey of weekly returns. However, they assert that the rejection of the RW does not mean that market prices are in-efficient but it should impose limits on the acceptable pricing models.
Karafiath (88 and 94) approached the issue from a different point of view and contributed some methodological innovations to the testing methods. In Karafiath 88 paper, he introduced the concept of using dummy variables in the even study procedure because it offers a convenient procedure to obtain cumulative prediction errors and related test statistics all in one step. In his 94 paper, Karafiath uses Monte Carlo simulations to investigate whether FGLS (Feasible Generalized Least Square), (Weighted Least Square) WLS, or (Consistent Estimator Least Square) CLS accounts better for heteroskedasticity and crossessional correlation in return than (Ordinarily Least Square) OLS. The paper concludes that FGLS is well specified if the number of the time series observation is much larger than the number of securities but this model does not have greater power than the WLS (which is the FGLS with off-diagonal elements of the covariance matrix set to zero). The OLS is well specified in the MC simulation as well and the CLS have similar power to OLS. In summary, WLS, CLS, OLS are well specified under the simulation and WLS has better power than OLS or CLS. This extra power decreases as the number of securities increases.
Berk (1995) examines size related anomalies and suggests that the observation violating the RW hypothesis should be treated as regularities in an economy in which all asset returns satisfy any of the adopted asset pricing models (APM). In addition, the paper asserts that size of the firm can account for some of the return risk of a firm and is usually recognized as the most prominent contradiction to the AP paradigm. Schwert (83) notes that observed relation between the anomaly variables and return implies that these variables proxy for risk. Little success is achieved in explaining these regularities and their interaction with risk and return. The author assumes, for the sake of argument, that all companies have the same size (same expected value) and the end of period cash flow is the same. However, the risk of every firms CF is different and this means that the market value of each firm is different. Riskier firms have lower market value and should yield higher expected return on holding their assets. Consequently if the market value is used as a measure of risk, it will predict a component of return. As a conclusion, the author thinks that it is misleading to refer to the size relation with return as an anomaly. On the other hand, the author thinks that it would be an anomaly if a negative relation is not found between size and expected return and this is why size should be used in cross sectional regression to detect mis-specifications of the model.
Fama and French (1996) Based on previous conclusions in the literature, FF (93) developed an innovative model for risk-return relations using three factors that incoporate risks, size and growth (E(Ri) = b[E(Rm) - rf] + s* E[SMB] + h*E[HML]). This model could not prove its viability had not the size represent a major factor in risk and return prediction. FF (96) asserts that this model would not be able to predict return on all securities especially when there is a momemtum effects. However, the authors conclude that size, E/P, growth, CF/P, B/M, long term past return, and short term past return play all an important role in predicting future movement of prices. Hence, they are not anomalies and should be considered as essential factors even CAPM does not incorporate them. With this model, most of the anomalies disappear from the return process.
Barber and Lyon (1997) analyze the power and specification of test statistics in event studies designed to detect long term abnormal returns. They find out that test statistics based on abnormal returns calculated using a reference portfolio are mis-specified because of three main reasons:
(1) New listing bias: New companies are in and out of the index on a monthly basis and this might happen after the event
(2) Re-balancing bias: The compounded return of the reference portfolio is rebalanced every month but for individual companies in the tested sample are not
(3) Skewness bias: Long term abnormal returns are right skewed.
The cumulative abnormal return is mostly affected by the new listing bias, and, therefore, the long run cumulative abnormal return is positively biased in general. On the other hand, the long run buy and hold abnormal return is affected by the re-balancing bias and the skewness bias which is a negative bias.
According to the efficient markets view, the low number of home mortgage loans in low- and moderate-income neighborhoods is to be understood within the context of business of depository institutions, where profit-seeking institutions seek to meet the demands of creditworthy borrowers. In this view, the small population, the high proportion of renters, and the often distressed nature of the lowand moderate-income neighborhoods, with lower holdings of financial assets, the relatively small number of creditworthy borrowers, the lower demand, the lower return, and the relatively small supply of owner-occupied housing stocks together with a nondiscriminatory use of underwriting criteria that reflect the credit risk or the cost of extending credit to individual applicants translates into few opportunities to make profitable home purchase loans. In this view, as long as mortgage credit is extended in a non-discriminatory and competitive manner the market is best suited to determine which lenders and how many are needed to serve the borrowers.
- Criticism
The efficient market hypothesis is associated with the idea of a “random walk,” which is a term loosely used in the finance literature to characterize a price series where all subsequent price changes represent random departures from previous prices. The logic of the random walk idea is that if the flow of information is unimpeded and information is immediately reflected in stock prices, then tomorrow’s price change will reflect only tomorrow’s news and will be independent of the price changes today. But news is by definition unpredictable and, thus, resulting price changes must be unpredictable and random. As a result, prices fully reflect all known information, and even uninformed investors buying a diversified portfolio at the tableau of prices given by the market will obtain a rate of return as generous as that achieved by the experts.
The original empirical work supporting the notion of randomness in stock prices looked at such measures of short-run serial correlations between successive stock-price changes. Some ideas support the view that the stock market has no memory – the way a stock price behaved in the past is not useful in divining how it will behave in the future; for example, see the survey of articles contained in Cootner (1964). More recent work by Lo and MacKinlay (1999) finds that short-run serial correlations are not zero and that the existence of “too many” successive moves in the same direction enable them to reject the hypothesis that stock prices behave as random walks. There does seem to be some momentum in short-run stock prices. Moreover, Lo, Mamaysky and Wang (2000) also find, through the use of sophisticated nonparametric statistical techniques that can recognize patterns, some of the stock-price signals used by “technical analysts” such as “head and shoulders” formations and “double bottoms”, may actually have some modest predictive power.
An overpriced stock today can become even more overpriced tomorrow, bringing losses to even the cleverest short-seller. A bargain today can become an even better bargain next month, bringing grief to a value investor. Even the shrewdest investors must bear these risks and so lose money on occasion. Some of the risks they face can be hedged, but many can't. Because rational arbitrage is always risky, it is inherently limited in its ability to bring prices to their true values. A free, competitive market is almost necessarily inefficient. To illustrate this point, consider how efficient markets theory goes wrong. One very clear example is the pricing of the shares of Royal Dutch and Shell. Royal Dutch and Shell are independently incorporated in the Netherlands and England, respectively. In 1907, they formed an alliance agreeing to merge their interests on a 60-40 basis while remaining separate and distinct entities. All their profits, adjusting for corporate taxes and control rights, are effectively split into these proportions. The inefficiency in the pricing of Royal Dutch and Shell is a fantastic embarrassment for the efficient markets hypothesis because the setting is the best case for that theory. The same cash flows should sell for the same price in different markets. It shows that deviations from efficiency can be large and persistent, especially with no catalysts to bring markets back to efficiency. It also shows that market forces need not be strong enough to get prices in line even when many risks can be hedged, and that rational and sophisticated investors can lose money along the way, as mispricing deepens.
In inefficient markets, active investment management pays off in the long run. Contrarian strategies -- betting against the mispricing -- do better over the longterm than indexation. Value stocks have in fact outperformed growth stocks over long periods in the U.S. and European markets. But these strategies are inherently risky precisely because markets can move further away from efficiency. The Internet bubble of 1998-99 killed the relative performance of value investors; Mr. Robertson was only one of the victims. The question for active investors is whether they can take the pain of volatility long enough before the bubble bursts.
The fact that markets aren't efficient doesn't imply that the government should regulate them. Far from it, there are many benefits of inefficient markets. The Internet boom would not have been possible -- at least not on the same scale -- without financing from irrationally exuberant investors. The millions of Americans now benefitting from stocks might have stuck with savings accounts without the boom. The proposals to reform Social Security -- both Democratic and Republican -- would not have even started if markets were moribund. Yet to keep the government away from markets, we do not need to proclaim that "markets know best." The weaker but more accurate proposition, that the market knows better than the government, is more than sufficient.
- Conclusion
Just under half of the papers reviewed support market efficiency, with most of the attacks on the EMH coming in the 1980s and 1990s. Recall that a market is said to be efficient with respect to an information set if the price ‘fully reflects’ that information set (Fama, 1970). On the one hand, the definitional ‘fully’ is an exacting requirement, suggesting that no real market could ever be efficient, implying that the EMH is almost certainly false. On the other hand, economics is a social science, and a hypothesis that is asymptotically true puts the EMH in contention for one of the strongest hypothesis in the whole of the social sciences. Strictly speaking the EMH is false, but in spirit is profoundly true. Besides, science concerns seeking the best hypothesis, and until a flawed hypothesis is replaced by a better hypothesis, criticism is of limited value.
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