Volume 7 , Issue 6 , December 2021 , Pages: 150 - 160
A Machine Learning Approach for the Short-term Reversal Strategy
Zheng Tan, Institute of Information Science and Technology, Chengdu Polytechnic, Chengdu, P. R. China
Yan Li, R&D Department, Xiyuan Quantitative Technology, Chengdu, P. R. China
Chulwoo Han, Durham Business School, Durham University, Durham, UK
Received: Oct. 25, 2021;       Accepted: Nov. 12, 2021;       Published: Nov. 17, 2021
DOI: 10.11648/j.ijdsa.20210706.13        View        Downloads  
Abstract
The short-term reversal effect is a pervasive and persistent phenomenon in worldwide financial markets that has been found to generate abnormal returns not explainable by traditional asset pricing models. In contrast to the linear model employed in most studies on the short-term reversal, this article aims to establish a nonlinear framework to study the reversal anomaly, by using machine learning approaches. Machine learning methods including Random Forest, Adaptive Boosting, Gradient Boosted Decision Trees and extreme Gradient Boosting, are employed to test the profitability of the short-term strategy in the US and Chinese stock markets. Significant outperformances with extremely high Sharpe ratio, moderate kurtosis, and positive skewness are found, showing remarkable classification efficiency of the machine learning models and their applicability to various markets. Further studies reveal that the strategy returns can be weakened with the extension of the holding period. Notably, by comparing the performances of machine learning with our newly developed linear reversal strategy, the nonlinear methods are proved to be capable of providing a diversified model predictability with improved classification accuracy. Our research indicates the significant potential of machine learning in resolving the stock return and feature relationship, which can be helpful for quantitative traders to make profitable investment decisions.
Keywords
Finance, Artificial Intelligence, Reversal Trading, Stock Market
To cite this article
Zheng Tan, Yan Li, Chulwoo Han, A Machine Learning Approach for the Short-term Reversal Strategy, International Journal of Data Science and Analysis. Vol. 7, No. 6, 2021, pp. 150-160. doi: 10.11648/j.ijdsa.20210706.13
Copyright
Copyright © 2021 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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