Welcome to the InfoSci Platform
A Novel Interpretable Stock Selection Algorithm for Quantitative Trading

A Novel Interpretable Stock Selection Algorithm for Quantitative Trading

Zhengrui Li, WeiWei Lin, James Z. Wang, Peng Peng, Jianpeng Lin, Victor Chang, Jianghu Pan
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 19
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781683180524|DOI: 10.4018/IJGHPC.301589
Cite Article Cite Article

MLA

Li, Zhengrui, et al. "A Novel Interpretable Stock Selection Algorithm for Quantitative Trading." IJGHPC vol.14, no.1 2022: pp.1-19. http://doi.org/10.4018/IJGHPC.301589

APA

Li, Z., Lin, W., Wang, J. Z., Peng, P., Lin, J., Chang, V., & Pan, J. (2022). A Novel Interpretable Stock Selection Algorithm for Quantitative Trading. International Journal of Grid and High Performance Computing (IJGHPC), 14(1), 1-19. http://doi.org/10.4018/IJGHPC.301589

Chicago

Li, Zhengrui, et al. "A Novel Interpretable Stock Selection Algorithm for Quantitative Trading," International Journal of Grid and High Performance Computing (IJGHPC) 14, no.1: 1-19. http://doi.org/10.4018/IJGHPC.301589

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In recent years, machine learning models have exhibited remarkable performance in the fourth industrial revolution. However, especially in the field of stock forecasting, most of the existing models demonstrate either relatively weak interpretability or unsatisfactory performance. This paper proposes an interpretable stock selection algorithm（ISSA) to achieve accurate prediction results and high interpretability for stock selection. The excellent performance of ISSA lies in its integration of the learning to rank algorithm LambdaMART with the SHapley Additive exPlanations (SHAP) interpretation method. Performance evaluation over the Shanghai Stock Exchange A-share market shows that ISSA outperforms regression and classification models in stock selection performance. Our results also demonstrate that our proposed ISSA solution can effectively filter out the most impactful features, potentially used for investment strategy.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.