A Machine Learning Approach for the Short-term Reversal Strategy

Zheng Tan; Yan Li; Chulwoo Han

doi:doi:10.11648/j.ijdsa.20210706.13

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A Machine Learning Approach for the Short-term Reversal Strategy

Zheng Tan, Yan Li, Chulwoo Han

Published in International Journal of Data Science and Analysis (Volume 7, Issue 6)

Received: 25 October 2021 Accepted: 12 November 2021 Published: 17 November 2021

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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.

Published in	International Journal of Data Science and Analysis (Volume 7, Issue 6)
DOI	10.11648/j.ijdsa.20210706.13
Page(s)	150-160
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Finance, Artificial Intelligence, Reversal Trading, Stock Market

References

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APA Style

Zheng Tan, Yan Li, Chulwoo Han. (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

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ACS Style

Zheng Tan; Yan Li; Chulwoo Han. A Machine Learning Approach for the Short-term Reversal Strategy. Int. J. Data Sci. Anal. 2021, 7(6), 150-160. doi: 10.11648/j.ijdsa.20210706.13

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AMA Style

Zheng Tan, Yan Li, Chulwoo Han. A Machine Learning Approach for the Short-term Reversal Strategy. Int J Data Sci Anal. 2021;7(6):150-160. doi: 10.11648/j.ijdsa.20210706.13

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@article{10.11648/j.ijdsa.20210706.13,
  author = {Zheng Tan and Yan Li and Chulwoo Han},
  title = {A Machine Learning Approach for the Short-term Reversal Strategy},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {6},
  pages = {150-160},
  doi = {10.11648/j.ijdsa.20210706.13},
  url = {https://doi.org/10.11648/j.ijdsa.20210706.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210706.13},
  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.},
 year = {2021}
}

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TY  - JOUR
T1  - A Machine Learning Approach for the Short-term Reversal Strategy
AU  - Zheng Tan
AU  - Yan Li
AU  - Chulwoo Han
Y1  - 2021/11/17
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210706.13
DO  - 10.11648/j.ijdsa.20210706.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 150
EP  - 160
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210706.13
AB  - 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.
VL  - 7
IS  - 6
ER  -

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Author Information

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

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Plain Text BibTeX RIS

APA Style

Zheng Tan, Yan Li, Chulwoo Han. (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

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ACS Style

Zheng Tan; Yan Li; Chulwoo Han. A Machine Learning Approach for the Short-term Reversal Strategy. Int. J. Data Sci. Anal. 2021, 7(6), 150-160. doi: 10.11648/j.ijdsa.20210706.13

Copy | Download

AMA Style

Zheng Tan, Yan Li, Chulwoo Han. A Machine Learning Approach for the Short-term Reversal Strategy. Int J Data Sci Anal. 2021;7(6):150-160. doi: 10.11648/j.ijdsa.20210706.13

Copy | Download

@article{10.11648/j.ijdsa.20210706.13,
  author = {Zheng Tan and Yan Li and Chulwoo Han},
  title = {A Machine Learning Approach for the Short-term Reversal Strategy},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {6},
  pages = {150-160},
  doi = {10.11648/j.ijdsa.20210706.13},
  url = {https://doi.org/10.11648/j.ijdsa.20210706.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210706.13},
  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.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - A Machine Learning Approach for the Short-term Reversal Strategy
AU  - Zheng Tan
AU  - Yan Li
AU  - Chulwoo Han
Y1  - 2021/11/17
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210706.13
DO  - 10.11648/j.ijdsa.20210706.13
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 150
EP  - 160
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210706.13
AB  - 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.
VL  - 7
IS  - 6
ER  -

Copy | Download