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Journal of Quantitative Economics

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06-05-2022 | Original Article

An Entropy Approach to Measure the Dynamic Stock Market Efficiency

Authors: Subhamitra Patra, Gourishankar S. Hiremath

Published in: Journal of Quantitative Economics | Issue 2/2022

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Abstract

We measure stock market efficiency by drawing the comprehensive sample from Asia, Europe, Africa, North–South America, and Pacific Ocean regions and rank the cross-regional stock markets according to their level of informational efficiency. The study period spans from January 1, 1994, to August 3, 2017. We employ the approximate entropy approach and find that stock market efficiency evolves over the period. The degree and nature of evolution vary across regions and the development stage of the markets. The global, regional, domestic economic, and non-economic factors influence the adaptive nature of the stock markets. The emerging stock markets have improved efficiency by financial liberalization policy but are adversely affected by global shocks. The estimates validate the relevance of the adaptive market framework to describe the rejection of random walk without excess returns. The results suggest the growing presence of technical analysis and active portfolio managers. The emerging markets in Asia hold policy lessons for their peers. The findings suggest that global investors need to overcome the homogeneity bias as returns opportunities exist within the region and types of markets.

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For a detailed review on testing efficiency, see, Lo (2017).

For a detailed survey, see, Hiremath (2014).

Theodore (1996) considers such exploitable patterns of the price changes as the primary practice of market practitioners.

For a discussion on various methods to test efficiency, see Hiremath (2014).

Our selection of m and r is consistent with the literature. According to Yentes et al. (2013), APEn increases the self-matches with the increase in data length, and such self-matching bias can only be controlled with m = 2 and r $\ge$ 0.15. They argue that the value of entropy is stabilized with greater N. The minimum threshold for N must be greater than 200 data points. Lu et al. (2008) document that smaller r leads to the few self-matches due to low-pass filter. APEn estimation with 0.15 tolerance level is not biased by the increase or decrease in self-matches in the ${T}_{s}$ series. The estimates of the entropy with 0.1 and 0.2 tolerance levels are not reported in to save the space.

The begining date of a few indices varies based on the availability of the data.

Kristoufek and Vosvrda (2013) analyze the Japanese NIKKEI whereas we include Japanese Tokyo stock exchange as our sample market for the analysis. In this aspect, we argue that Japanese stock market remained the most efficient market over the years.

IFC, World Bank classifies the stock markets based on level of per capita gross national income (PGNI). Accordingly, the stock markets of the upper middle-income, lower middle income, and the lower-income economies having PGNI of below $12,235 are EMs whereas the stock markets of the higher income countries having PGNI of above $12,235 are considered as the developed markets.

We report the list of disaggregated markets in Appendix A.2.3.

We arrange the stock markets from Africa, and Pacific Ocean in one sample due to insufficient number of markets for a meaningful cluster analysis.

Euronext is a group of financial markets from France, Belgium, and the Netherland. The cooperation mechanism of Euronext monitors and supports the integration of operational systems for trading, clearing, and settlement-delivery and strives for harmonization and the unification of the market rules.

We argue that the lack of integration of the Euronext with the US and Asia makes the former region immune to the particular shocks and therefore indicate the better diversification opportunities for investors from the latter regions.

We have not reported these test statistics to save the space.

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Title: An Entropy Approach to Measure the Dynamic Stock Market Efficiency
Authors: Subhamitra Patra
Gourishankar S. Hiremath
Publication date: 06-05-2022
Publisher: Springer India
Published in: Journal of Quantitative Economics / Issue 2/2022
Print ISSN: 0971-1554
Electronic ISSN: 2364-1045
DOI: https://doi.org/10.1007/s40953-022-00295-x

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