Adaptive Market Hypothesis and Time-varying Contrarian Effect: Evidence From Emerging Stock Markets of South Asia

This study contributes to the growing debate on the relation between varying stock market conditions and the profitability of stock market anomalies. We investigate the effect of changed market conditions on time-varying contrarian profitability in order to examine the presence of the Adaptive Market Hypothesis (AMH) in South Asian emerging stock markets. The empirical findings reveal that a strong contrarian effect holds in all the emerging markets. We also find the stock return opportunities vary over time based on contrarian portfolios. We show that contrarian returns strengthen during the down state of market, higher volatility and crises periods, particularly during the Asian financial crisis. Interestingly, the market state instead of market volatility is the primary predictor of contrarian payoffs, which contradicts the findings of developed markets. We argue that the linkage arises from structural and psychological differences in emerging markets that produce unique intuitions regarding stock market anomalies returns. The overall findings on the time-varying contrarian returns in this study provide partial support to AMH. Another significant outcome of this study implies that investors in South Asian emerging markets, like investors in the developed markets, could not adapt to evolving market conditions. Therefore, contrarian profits often exist, and persistent weak-form market inefficiencies prevail in these markets.

Mood Sensitive Stocks and Sustainable Cross-Sectional Returns During the COVID-19 Pandemic: An Analysis of Day of the Week Effect in the Chinese A-Share Market

This study examines two stock market anomalies and provides strong evidence of the day-of-the-week effect in the Chinese A-share market during the COVID-19 pandemic. Specifically, we examined the Quality minus Junk (QMJ) strategy return on Monday and FridayQuality stocks mean portfolio deciles that earn higher excess returns. As historical evidences suggest that less distressed/safe stocks earn higher excess returns (Dichev, 1998).. The QMJ factor is similar to the division of speculative and non-speculative stocks described by Birru (2018). Our findings provide evidence that the QMJ strategy gains negative returns on Fridays for both anomalies because the junk side is sensitive to an elevated mood and, thus, performs better than the quality side of portfolios on Friday. Our findings are also consistent with the theory of investor sentiment which asserts that investors are more optimistic when their mood is elevated, and generally individual mood is better on Friday than on other days of the week. Therefore, the speculative stocks earned higher sustainable stock returns during higher volatility in Chinese market due to COVID-19. Intrinsically, new evidence emerges on an inclined strategy to invest in speculative stocks on Fridays during the COVID-19 pandemic to gain sustainable excess returns in the Chinese A-share market.

Design of data mining algorithm based on rough entropy for us stock market abnormality

The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a “barometer” of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although data mining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market anomaly, this paper uses data mining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of data mining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market.