A Sustainable Quantitative Stock Selection Strategy Based on Dynamic Factor Adjustment

Yi Fu, Shuai Cao and Tao Pang
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Yi Fu: School of Finance and Business, Shanghai Normal University, Shanghai 200234, China
Shuai Cao: School of Finance and Business, Shanghai Normal University, Shanghai 200234, China
Tao Pang: Department of Mathematics, North Carolina State University, Raleigh, NC 27695-8205, USA

Sustainability, 2020, vol. 12, issue 10, 1-12

Abstract: In this paper, we consider a sustainable quantitative stock selection strategy using some machine learning techniques. In particular, we use a random forest model to dynamically select factors for the training set in each period to ensure that the factors that can be selected in each period are the optimal factors in the current period. At the same time, the classification probability prediction (CPP) of stock returns is performed. Historical back-testing using Chinese stock market data shows that the proposed CPP quantitative stock selection strategy performs better than the traditional machine learning stock selection methods, and it can outperform the market index over the same period in most back-testing periods. Moreover, this strategy is sustainable in all market conditions, such as a bull market, a bear market, or a volatile market.

Keywords: stock selection; machine learning; classification probability prediction; back-testing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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