Abstract

     This article helps resolve the current unsatisfying and inclusive studies covering the efficiency of stock markets in developing countries. Previous studies have used limited data and partial statistical tests. We use a large, unique data set, across 12 countries, and a comprehensive set of traditional and recent statistical methods as well as powerful multiple-break unit root and spectral analysis tests, many of which have never been used to evaluate the efficient market hypothesis (EMH) in emerging markets. Our results confirm the rejection of the EMH for emerging markets. Our findings have important implications for investors and policy makers, suggesting the possibility for excess profits in these markets.

Introduction

     The idea of efficient markets has been a central topic in financial theory since Eugene Fama introduced it in 1970. The efficient market hypothesis (EMH) states that financial asset prices entirely reflect all available information, making it impossible for investors to beat the market. The theory has received significant support from academics: Shiller (1981), Carhart (1997), Lettau and Van Nieuwerburgh (2008), Fama and French (2010), Busse, Goyal, and Wahal (2010), and Bertone, Paeglis and Ravi (2015). The award of the Nobel Prize in Economics to Richard Thaler in 2017 has helped reignite this debate. Thaler, one of the founders of “behavioral finance” has put the notion of EMH in doubt and provided scientific explanations for the existence of irrational market behaviors. The empirical evidence is mixed and the research community is “torn” between the EMH and behavioral finance camps (Verheyden, Moor, and Bossche 2015).

     After extensive research of the EMH in developed markets, a widespread but not complete consensus exists that developed markets tend toward efficiency although there are periods of informational inefficiency and periods of speculative bubbles (behavioral finance), (French and Roll (1986), De Long and Becht (1992), Carhart (1997), Fama and French (2010), Busse, Goyal, and Wahal (2010), Bertone, Paeglis, and Ravi (2015).

Empirical Results

     Table 1 presents the summary statistics of the data. As found in many prior studies, all the series in the sample are not normally distributed on their associated J-B statistics. The stock markets in Taiwan, Thailand, and Mexico developed earlier, with starting dates in July 1998. Columbia, Malaysia, Indonesia, and Hong Kong began much later, beginning July 2005. The return series are quite similar, with the exception of Hong Kong, during the study period. We also examine the correlation matrix (results not shown) and observe that these return series are positively (and statistically significant) related, similar to those found in developed markets in several prior studies. The non-normality is significant because the very high kurtosis numbers indicate the likelihood of extreme returns. Also, the skewness numbers help reinforce that volatility with positive values indicates some extreme gains and many small losses, and vice versa for negative values.

     Table 2 displays the variance ratio test results. First, as revealed in the table, the null hypothesis of random walk is decidedly rejected in all countries in the sample at the 1% significance levels for periods 2 through 16. Second, the Wright (2000) tests, not shown (as discussed), yield nearly identical results for all countries. These results are similar to several of those reported in some of the reviewed articles, using older data sets.