A machine learning approach for the short-term reversal strategy

Tan, Z.; Li, Y.; Han, C.

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.

Citation

Tan, Z., Li, Y., & Han, C. (2021). A machine learning approach for the short-term reversal strategy. International journal of data science and analysis, 7(6), 150-160. https://doi.org/10.11648/j.ijdsa.20210706.13

• Journal Article Type: Article
• Acceptance Date: Nov 12, 2021
• Online Publication Date: Nov 17, 2021
• Publication Date: 2021-12
• Deposit Date: Nov 24, 2021
• Publicly Available Date: Nov 24, 2021
• Journal: International Journal of Data Science and Analysis
• Print ISSN: 2575-1883
• Electronic ISSN: 2575-1891
• Publisher: Science Publications
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 6
• Pages: 150-160
• DOI: https://doi.org/10.11648/j.ijdsa.20210706.13
• Public URL: https://durham-repository.worktribe.com/output/1222200
• Publisher URL: http://www.ijdsa.org/article/367/10.11648.j.ijdsa.20210706.13