TY - JOUR
T1 - A Novel Interpretable Stock Selection Algorithm for Quantitative Trading
AU - Li, Zhengrui
AU - Lin, WeiWei
AU - Wang, James Z.
AU - Peng, Peng
AU - Lin, Jianpeng
AU - Chang, Victor
AU - Pan, Jianghu
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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. The results also demonstrate that the proposed ISSA solution can effectively filter out the most impactful features, potentially used for investment strategy.
AB - 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. The results also demonstrate that the proposed ISSA solution can effectively filter out the most impactful features, potentially used for investment strategy.
KW - Artificial Intelligence
KW - Interpretability
KW - Quantitative Trading
KW - Stock Selection
UR - https://www.igi-global.com/gateway/article/301589
UR - http://www.scopus.com/inward/record.url?scp=85132111268&partnerID=8YFLogxK
U2 - 10.4018/ijghpc.301589
DO - 10.4018/ijghpc.301589
M3 - Article
SN - 1938-0259
VL - 14
JO - International Journal of Grid and High Performance Computing
JF - International Journal of Grid and High Performance Computing
IS - 1
M1 - 89
ER -