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Critically Evaluating the Efficiency of Stock Markets


In the financial market, no issue would raise so many controversies among researchers and practitioners as the efficiency of markets (Gwilym 2010, p. 187). The in-depth research on the efficiency of financial markets has provided evidence that either supports or rejects market efficiency, making this issue a subject of strongly held views. Efficient markets reflect security information that later determines its price (Chen & Chen 2011, p. 208; De Bondt, Palm & Wolff 2004, p. 423).

Thus, the market efficiency concept proposes that investors lack access to privy information that can enable them to make extra profits. Therefore, security prices reflect market information available on particular security, making sure that one cannot beat efficient markets. Studies indicate that factors, which may affect financial market efficiency include limits to trading, the volume of market participants, rationality, and the availability of information (Dichtl & Drobetz 2014, p. 112).

Proponents of efficient markets criticize behavioral finance for its observational nature in explaining the factors that affect market efficiency. The criticism by modern financial analysts and behaviorists on the factors largely reflects the differences that exist in economics and psychology (Kim & Shamsuddin 2008, p. 518). Does one conclude that security markets are efficient? What do we know about market efficiency from the perspective of behaviorists and modern finance?

The degree of market participants

Stock market efficiency heightens when many participants are following the market. Most investors and analysts who buy and sell stock assume that the securities they are purchasing have a value that exceeds their stated prices, a key assumption of the behaviorist’s view (De Bondt, Palm & Wolff 2004, p. 423). They also assume that market stocks on sale value are lesser as compared to their selling prices. This makes the purchase and sale of securities aimed at outperforming markets a pure chance.

On the other hand, Fama argues that active markets consist of knowledgeable investors who price securities properly. Market efficiency includes large rational profit maximizers who actively compete against each other in predicting the values of each entity realized in future markets (Chen & Chen 2011, p. 208). This market also disseminates information freely to all participants. According to the authors, stock prices, which adjust instantly to show new information, create unpredictability in stock markets.

The burgeoning anomalous evidence in return behavior complicates the efforts of reconciling efficiency between behaviorists and modern finance theorists (Dichtl & Drobetz 2014, p. 112). The intelligent investors who flood security markets seek underpriced and over-priced securities from making purchases and sales. With impaired market efficiency, well-informed investors can outperform well-informed investors. The efficient market hypothesis argues that managers analyze and invest depending on various risk profiles.

The impact of rationality on market efficiency

Modern finance hinges on the efficient market hypothesis, which holds that markets can efficiently include correct data into market prices (Lee, Lee & Lee 2010, p. 49). The basis of this idea is the notion that rational economic beings make up the markets. These self-interested people engage their decision-making in cost and benefit tradeoffs using statistics-based marginal utilities and probabilities. Critics have relentlessly attacked the rationality assumptions of behaviorists and their significance to market efficiency (Shiller 2003, p. 83). Specifically, experimental economists and psychologists argue for market rationality based on behavioral biases.

These include uncertain decision-making, which often results in undesirable outcomes for most people’s economic welfare. Although there is a little doubt that humans have particular periodical behavioral idiosyncrasies, a clear consensus on its impact on finance is inefficient. Behavioral finance theorists and efficient market hypothesis advocates have pitched a battle that has little consensus on the winning side (Ma & Wohar 2014, p. 371). Even after years of research, amidst burgeoning studies, economists fail to achieve a happy medium on the efficiency of markets.

Limits to trading

Although behavioral theorists have proposed several explanations to supplant the efficient market hypothesis, none has managed to do so successfully (Edmans, Garcia & Norli 2007, p. 1967). This partly links to the huge impact of modern financial economics on financial theory, as well as practices during the last fifty years. Behaviorists have argued that market restrictions on short-selling stocks affect the operations of markets. Researchers rigorously tested the efficient market hypothesis with evidence supporting the efficiency of securities markets (Cronqvist, Makhija & Yonker 2012, p. 20).

Nevertheless, even as it gained dominance, there were exceptions to publicly available information, including the possibility that stocks with low earnings per price ratios outperformed markets. Behavioral finance, which tries to understand investor behavior, has persistently challenged the efficient market hypothesis. Behaviorists believe that human psychology determines the limits in trading, as well as movements in stock market prices (Edmans, Garcia & Norli 2007, p. 1967). They refute the assumptions of right market prices, with the belief that investor psychology drives market prices and trading limits. Behavioral finance advocates for support that an investor can gain on price inefficiencies resulting from behavioral biases. Behaviorists suggest that the irrational tendencies of investors have been behind the recent market collapses in recent times.

The availability of information

Evidence suggests that a perfectly informational efficient market is impossible, since perfect markets gain no profits from gathering information, creating little reason for trading and leading to many collapsed markets (El-Galfy & Forbes 2004, p. 617). Contrastively, the levels of market efficiency find the efforts that investors are willing to give to information gathering. Proponents of the efficient market hypothesis note that both behavioral biases and matching inefficiencies are considerably present, despite the limitation in their relevance and impact (Economou, Kostakis & Philippas 2011, p. 443).

EMH proponents point out that there are limits to behavioral biases due to the incentives to find and exploit these occurrences. Although all people face a level of bias in their actions, EMH proponents argue that market forces often lower prices to normal levels. According to Onali and Goddard (2011, p. 59), this implies that irrational behavior that stock markets show has a negligible effect that makes them irrelevant. This conclusion assumes that market forces can overcome any behavioral biases. It also assumes that irrational beliefs lack a pervasive ability that can overwhelm arbitrary capital meant to handle these irrationalities.

Early studies show that financial markets, especially stock markets, do not react to information unbiased. El-Galfy and Forbes (2004 p. 617) state that the unbiased reaction to information formed the early inaccurate efficiency definitions, which implied rational investor behaviors in aggressive security markets. Although this evidence was inconsistent with the theory, Fama’s influential survey in the 1970s favored the efficient market hypothesis, making it a consistent area in observed economics (Shefrin 2007, p. 4).

Latter work failed to reach this extent of consistency in conclusions, with evidence showing that stock prices had a systematic under-reaction to announcements in earnings. On the other hand, impressive evidence shows a reversal in intense price changes, implying that prices can overreact to certain variables, for instance, earnings announcements (Fama 1998, p. 283). To the average person, these findings are irreconcilable since evidence in each scenario remains uncomplicated and digestible to investors, meaning that the market inefficiencies imply a disingenuous application of information. Yet, after accumulating the evidence, literature proves that indeed, stock prices show ingenious reactions to certain information categories.


While there is room for considerable disagreement, both behavioral finance and traditional finance present views that are all worth airing. From this paper’s analysis, limits to trading, the volume of market participants, rationality, and the availability of information have an impact on market efficiency that one cannot ignore. That anomalies in stock markets disappear with time does not suggest fully rational markets, because it is what one can expect from highly irrational markets. Financial analysts have to stay abreast of the factors that elevate weaknesses in an efficient market and keep an eclectic approach.

As finance practitioners, we have to remain wary of the impacts of limits to trading, the volume of market participants, rationality, and the availability of information in explaining the efficiency of markets. The debate about efficient stock markets will not end soon, even though two schools of thought are intersecting at a single point. The efficient market hypothesis makes significant contributions to modern finance by permeating and pervading the subject. Behaviorists also agree that an efficient market is what we must focus on to achieve. This paper thus concludes that the efficient markets theory is only incomplete, and not wrong as it only analyses the behavior of markets in light of the right conditions.

Reference List

Chen, T & Chen, C 2011, ‘Size effect in January and cultural influences in an emerging stock market: The perspective of behavioral finance,’ Pacific-Basin Finance, vol. 19, no. 2, pp. 208-229.

Cronqvist, H, Makhija, A & Yonker, S 2012, ‘Behavioural consistency in corporate finance: CEO personal and corporate leverage,’ Journal of Financial Economics, vol. 103, no. 1, pp. 20-40.

De Bondt, W, Palm, F & Wolff, 2004, ‘Introduction to the special issue on behavioral finance,’ Journal of Empirical Finance, vol. 11, no. 4, pp. 423-427.

Dichtl, H & Drobetz, W 2014, “Are stock markets really so inefficient? The case of the ‘Halloween Indicator’,” Finance Research Letters, vol. 11, no. 2, pp. 112-121.

Economou, F, Kostakis, A & Philippas, N 2011, ‘Cross-country effects in herding behavior: evidence from four south European markets,’ Journal of International Financial Markets, Institutions and Markets, vol. 21, 443-460.

Edmans, A, Garcia, D & Norli, O 2007, ‘Sports sentiments, and stock returns,’ Journal of Finance, vol. 62, no. 4, pp. 1967-1998.

Edmans, A., Garcia, D & Norli, O 2007, ‘Sports sentiments and stock returns,’ Journal of Finance, vol. 62, no. 4, pp.1967-1998.

El-Galfy, A & Forbes, W 2004, ‘Are forecasts of corporate profits rational? A note and further evidence,’ Journal of Empirical Finance, vol. 11, no. 4, pp. 617-626.

Fama, E 1998, ‘Market efficiency, long-term returns, and behavioral finance,’ Journal of Financial Economics, vol. 49, pp. 283-306.

Gwilym, R 2010, ‘Can behavioral finance models account for historical asset prices?’ Economic Letters, vol. 108, no. 2, pp. 187-189.

Kim, J.H. &Shamsuddin, A 2008, ‘Are Asian stock markets efficient? Evidence from new multiple variance ratio tests,’ Journal of Empirical Finance, vol. 15, no.3, pp. 518-532.

Lee, C., Lee, J & Lee, C 2010. ‘Stock prices and the efficient markets hypothesis: Evidence from a panel stationary test with structural breaks,’ Japan and the World Economy, vol. 22, no. 10, pp. 49-58.

Ma, J &Wohar, M 2014, ‘Determining what drives stock returns: Proper inference is crucial: Evidence from the UK,’ International Review of Economics and Finance, vol. 33, pp. 371-390.

Onali, E & Goddard, J 2011, ‘Are European equity markets efficient? New evidence from fractal analysis,’ International Review of Financial Analysis, vol. 20, no. 2, pp. 59-67.

Shiller, R 2003, ‘From efficient markets theory to behavioral finance,’ Journal of Economic Perspectives, vol. 17, no. 1, pp. 83-104.

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