Stock Market Efficiency Overview

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Table of Contents

Background to the Study
Literature Review
Data Collection and Research Methodology
Analysis and Discussion
Conclusion
References

Background to the Study

In this literature review, the main aim is to discuss what previous studies have highlighted in their analysis of stock market efficiency. As one of the most authoritative theories of understanding how the stock market works, the Efficient Market Hypothesis (EMH), which is the main theory of understanding stock market efficiency, has received criticism and support in equal measure. One side comprises people who believe that stock market efficiency can be explained by the EMH theory while the other side consists of a class that believes that the existing theory is not effective and that other equally important variables have been left out. Despite the discussion on the effectiveness of the existing theories and approaches to stock market efficiency, economists are in consensus that stock market efficiency is an important tool and concept due to its implications on the investment sector and generally the economy.

Literature Review

Understanding Stock Market Efficiency

The ‘Efficient Market’ proposition assumes that if the stock bazaar is competent, it is impracticable to use elementary and technical analysis to succeed in the market since the prevailing share rates constantly integrate and replicate all the appropriate data. In such a scenario, it is impractical to make extraordinary proceeds above the stock bazaar returns. Hence, there is minimal or no arbitrage chance in the stock market sector. According to Jethwani and Achuthan, the hypothesis can be divided into three categories (35). The first category is the Weak Form Efficiency where the stock prices incorporate all information from trade statistics such as the rate of return, trading capacity, and chronological outlays. The second category is the Semi-Strong type effectiveness where security rates change quickly to liberate publicly obtainable facts. The last category is the Strong Form Efficiency where stock prices fully reflect all the relevant information such as past information, publicly available information, and insider facts that are accessible only to companies (Jethwani and Achuthan 35).

Three tenets determine market competence (Prentis 2). The first tenet of stock market efficiency is that markets are stable and that when unexpected events occur and cause imbalance, such disruption is temporary since markets are self-equilibrating. However, this argument is criticized and disputed in the literature. For instance, Kristoufek and Vosvrda assert that it is impossible to have a stock market that is always in equilibrium since traders and investors have different inclinations, endowments, and viewpoints (184). Further, other factors such as arbitrage expenses often push the market out of equilibrium.

The second tenet of market efficiency is the argument that stock prices fully reflect all the relevant information (Bhunia 72). However, many researchers and economists have disputed this argument in the past. Further, the tenet holds that asset prices accurately reflect their intrinsic value. Thus, such prices will always accurately signal capital allocation. However, some scholars assert that such an assumption in an efficient market is flawed since it does not reflect other important factors such as human nature and inherent mass mentality of traders in the market.

The last tenet of stock market efficiency points out that stock prices move unpredictably and that they are uncorrelated or independent of the price change during the prior period. In this case, the premise is that earning higher returns than what is possible in the stock market with lower risk is impossible when considering the past prices only as represented in the stock trading rules or stock charts.

The State of Stock Market Efficiency and Criticisms

Although the Stock Market Efficiency was highly popular in the 20^th century, many scholars such as Chong, Cheng, and Wong have questioned its application and validity in the recent past (235). At the start of the century, many economists began questioning some of the key tenets of the EMH where they argued that stock prices were partially predictable (Dow and Gorton 1087). The economists began by pointing to new variables that had not been highlighted in the previous theory. These variables included tenets such as psychological and behavioral elements of determining the stock price. In this case, the business analysts held that although potential stock charges are conventional based on the earlier data on stock price trend, other elementary assessment metrics were also important in the determination procedure.

Under-reaction to New Information and Temporary Impetus

Firstly, the existing experimental data that substantiates the EMH theory and the argument of the unpredictability of stock charges considered events such as the immediate relationship between consecutive changes in the stock rates. In this view, the claim was that past stock performance was not important or of any weight in determining how such stock would behave in the future. However, critics of this position point out that indeed quick fix associations in the same course are not zero. Besides, the presence of many consecutive trends in given stock rates is an important ground for eliminating the premise that stock rates behave haphazardly and hence volatile. Further, Kristoufek and Vosvrda assert that some statistical formulations and methodologies such as “head and shoulders,” as well as “double bottoms”, have some predictive power in determining stock prices (186).

Business analysts and psychologists in the behavioral field of business point out that temporary impetus draws a parallel with psychosomatic feedback systems. In this case, the school of thought claims that the bandwagon effect where people observe stock prices rising and dropping to inform their decision of purchasing, holding or selling their stock cannot be ignored. Further, apart from the bandwagon effect, the tendency of investors under-reacting to new information can also be an important factor of explaining short-term momentum (Harrison and Moore 78). The school holds that if the full impact of new information is only felt over a period, then stock prices are likely to display positive serial correlation. As such, the use of behavioral science has become increasingly prominent over randomness that is dictated by the EMH.

Various factors hinder economists from making a convincing interpretation that the empirical studies prove market inefficiency (Chong, Cheng, and Wong 237). For instance, although stock bazaars may not provide the right variables to demonstrate scientifically the ideal unpredictability that such studies want to highlight, it is very imperative to differentiate financial implication from arithmetical insinuations. In this case, the statistical variables that lead to momentum are very small and hence highly unlikely to allow investors to gain returns. In other words, the variables used in the studies are very small. Hence, very few individuals, if any, are likely to design a trading strategy based on such variables that will beat a buy-and-hold strategy. Further, Prentis confirms the existence of no excess returns for momentum investors (26). Indeed, other studies show that such financiers perform poorer relative to buy-and-hold shareholders, regardless of the arithmetical substantiation that reveals a certain positive impetus.

The Use of Wrong Data

According to Prentis, the stock market efficiency theory has major errors, which make it impossible to be used for guiding the stock market decision-making relating to buying and selling of stock among investors (24). One of these errors is evident in the methodology that guides the theory and related studies, which use daily stock prices changes of specific companies. The stock price movement is determined by both methodical and unsystematic risks. In this case, 50% of a company’s stock actions are random unsystematic risks, which emanate from the international situations in the company. The other 50% reflects the systematic conditions in the market. Since the systematic risk is the only part, which can be analyzed, then there is room for errors among supporters of the EMH theory since they leave the consideration of the unsystematic risk variations, which can also have a major effect on the stock prices of a given company (Rizvi et al. 91).

The Use of Faulty Data Analysis Method

According to Prentis, the methods that have been used to analyze the validity and application of the EMH theory are questionable (25). Hence, they cast distrust on the authority of the whole theory. In this case, the Prentis asserts that the use of the statistical inference method to test the independence of stock price data raises valid problems that have been recognized by other researchers (25). Firstly, it is difficult to differentiate a rootless series from a faint systematic quality. Secondly, Prentis further observes that it requires data of more than 5000 years to show independence in stock price using the statistical presumption technique (25). Such data is not available. Hence, the situation sheds more doubts on the validity of the theory.

Wrong Conclusions Based on Uncertain Information

Prentis further asserts that the use of daily individual company data to show the randomness of the stock prices for a company is faulty (25). In this case, the use of day-to-day company reports as required in the statistical inference tests leads to correlation coefficients, which are close to zero. As such, the findings support the assumptions of serial independence in the price data. This outcome can lead to a wrong conclusion that tenet number three as previously discussed is a valid assumption. Hence, the use of daily stock price movement is a wrong premise to focus on. Instead, economists are more interested in understanding the performance of the overall stock market in the long-term, an area that has not been addressed by the EMH supporters who, unfortunately, use the wrong statistical method.

Predictability of Stock Prices based on Valuation Parameters

The support for stock price randomness that is put forward by the EMH has been countered by new approaches that show that it is impossible to predict. However, new research shows that it is feasible to attain certainty using some assessment parameters such as price-earnings and manifold or share yields of the stock bazaar. Firstly, the use of the dividend yield of the market index shows that it is possible to predict stock prices. According to a study by Bhunia, dividends have a tendency to be low when interests go down and high when dividends go up (73). In this case, the capacity of preliminary yields to indicate potential profits can be viewed as a pointer of the capacity of the stock bazaar to change to the universal financial state of affairs. However, this argument has been criticized, especially in studies carried on the US Stock Market. The studies show that dividend yields have been 3% below the market predictions since the 1990s. Secondly, the deployment of price-earnings percentages has been declared a potential technique that can help to foresee the stock bazaar. According to Dow and Gorton, investors tend to earn larger returns on long-term stocks when they purchase at relatively low price-earnings multiples (1092). However, such an argument supports the buy-and-hold method that has been highly popular in the stock market, which the EMH seeks to disapprove.

Data Collection and Research Methodology

Any research has to deploy a strategically formulated methodology that defines how the researcher will obtain data whose analysis is used to draw conclusions on the subject that is being studied. Each research type has its best data collection methodology. It is crucial to note that a wrong data collection methodology is bound to give the researcher a hard time when trying to interpret the gathered data. This study applied a meta-analysis method, which focuses on the review of what other researchers have discussed concerning the subject under investigation. In this case, both critics and supporters of the EMH theory were reviewed to create a strong basis against which the report presents its findings. The use of the method is very important since it allows the viewing of the existing research to identify trends of the findings on the subject of stock market efficiency as addressed by other researchers. However, it is difficult to pinpoint the implications of such researches. As such, the paper has drawn its findings from eight previous studies that provide important conclusions, which help the researcher to present an unbiased conclusion on the current subject.

Analysis and Discussion

From the analysis of the various studies that have been used in the paper, it is evident that stock market efficiency is a major topic that has attracted immense study from economists who are keen on determining the predictability or the lack of predictability of the stock market. The findings have shown that indeed both sides of the divide have strong arguments that support the viewpoints of researchers and economists in the field.

Proponents of the stock market efficiency have undertaken several researches to support their claims of the impossibility to beat the market since stock market efficiency ensures the incorporation of all existing information to guarantee the availability of the relevant information for decision-making purposes. In this case, it is not possible for investors to make high returns unless they invest in very high-risk stocks. Consequently, no difference exists between experienced and new stock investors since the relevant information for making decisions on a given stock price is available. On the other side of the divide are those who have argued against the validity of the stock market efficiency theory. In this case, the main claim is that the use of stock market efficiency has left out variables that are equally important in determining the stock market prices. For instance, as revealed by behavioral finance theorists, the “bandwagon effect” is a central aspect that clearly makes stock market prices predictable. In this case, it is common for investors to under-react or overreact to new information, which consequently determines the trend in the stock market prices. In this case, the bandwagon effect of mass mentality can easily lead to increased stock prices or drop in the prices. As such, it is very important for stock market economists to consider the influence of behavioral factors on the predictability of the stock market process.

Secondly, the use of new methods of data analysis has shown that indeed new factors of predicting stock market prices show some inescapability, as opposed to what the EMH implies. For instance, the economists in support of the new approaches point to the ineffectiveness of the stock market efficiency argument, which uses wrong statistical inferences to disapprove predictability. In this case, the EMH deploys a statistical inference method that uses the day-to-day data to test the independence of stock market prices. The argument is that it is impossible to differentiate between rootless series from systematic quality. The large amount of information that is required to predict the independence of the stock prices of an individual company is impossible to obtain. This finding confirms that the method and the findings are faulty. The use of everyday data is also faulty since it focuses on short-term momentum, as opposed to the long-standing impetus, which is more significant to investors.

The significance of unsystematic risks is equally important in the determination of stock market prices. However, the EMH considers only the systematic risks. It leaves out an important variable. Consequently, there is the need to review the validity of the new methods that have been suggested by theorists since they also offer important suggestions that should be incorporated in the determination of stock market prices in the current world. New methods that have come up to predict the stock market such as the price-earnings multiple or dividend yields of the stock market have managed to predict stock market prices with some degree of certainty, hence disapproving the authority of stock market efficiency theory.

Conclusion

The above analysis and discussion section has shown that while the use of stock market efficiency and its related theory, namely the efficient market hypothesis, dominated the 20^th century, critics of the premise emerged in the 21^st century. The main argument of the theory is based on the assumption that all the relevant information concerning stock is available and that no investor can “beat the market”. In such a scenario, no investor can make abnormal returns above what the market can offer unless such returns are from high-risk investments. However, as pointed by critics, the theory is based on wrong variables and data, which cannot be relied on. The use of the everyday data to draw inferences on the stock prices is wrong since it focuses on temporary momentum, as opposed to long-term predictability, which is more valuable and relevant to investors. Further, the importance of behavioral actions such as the bandwagon effect cannot be ignored. In many instances, investors often make investment decisions based on the current perceptions or views on a given company’s stock. Other new techniques of foreseeing the stock bazaar such as the price-earnings manifold or share returns have come up. In the end, the issue of validity and application of the EMH has been challenged and hence the need for more research to prove or disapprove such criticisms.

References

Bhunia, Amalendu. “Stock Market Efficiency in India: Evidence from NSE.” Universal Journal of Marketing and Business Research 1.2(2012): 72-78. Print.

Chong, Tai-Leung, Sam Ho-Sum Cheng, and Elfreda Nga-Yee Wong. “A Comparison of Stock Market Efficiency of the BRIC Countries.” Technology and Investment 1.1(2010): 235-238. Print.

Dow, James, and Gary Gorton. “Stock Market Efficiency and Economic Efficiency: Is There a Connection.” The Journal of Finance 52.3(1997): 1087-1179. Print.

Harrison, Barry, and Winston Moore. “Stock market efficiency, non-linearity, thin trading and asymmetric information in MENA stock markets.” Economic Issues 17.1(2012): 77-93. Print.

Jethwani, Kinjal, and Sarla Achuthan. “Stock Market Efficiency and Crisis: Evidence from India.” Asia-Pacific Finance and Accounting Review 1.2(2013): 38-43. Print.

Kristoufek, Ladislav, and Miloslav Vosvrda. “Measuring capital market efficiency: Global and local correlations structure.” Physica A: Statistical Mechanics and its Applications 392.1(2013): 184-193. Print.

Prentis, Eric. “Early Evidence on US Stock Market Efficiency: “Market vs. State” Debate and Deregulation Implications.” Economics and Finance Review 2.8(2012): 23-34. Print.

Rizvi, Syen, Ginanjar Dewandaru, Obiyathulla Bacha, and Mansur Masih. “An Analysis of Stock Market Efficiency: Development vs Islamic Stock Markets using MF-DFA.” Physica A: Statistical Mechanics and its Applications 407(2014): 86-99. Print.

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