Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (15): 86-91.DOI: 10.3778/j.issn.1002-8331.1908-0450

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Multi-factor Quantitative Stock Selection Strategy Based on gcForest

WANG Lun, LI Lu   

  1. School of Mathematics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China
  • Online:2020-08-01 Published:2020-07-30



  1. 上海工程技术大学 数理与统计学院,上海 201620


In order to obtain higher excess returns in the stock market and improve the accuracy of stocks’ ups and downs, this paper introduces the gcForest(deep forest) algorithm into the stock investment market, and establishes a multi-factor quantitative investment strategy based on gcForest, which is composed of 300 constituent stocks at the end of each month. The gcForest algorithm is used to predict the top 30 stocks and the backtest is conducted. The research results show that the annualized rate of return of the gcForest algorithm is 29. 2%, far exceeding the benchmark annualized rate of return of 15. 0%, and an excess of 15. 8% is obtained. At the same time, the gcForest algorithm is compared with the random forest and support vector machine algorithm. From the comprehensive analysis of various technical indicators, the gcForest algorithm has obvious advantages over other algorithms in the stable and rising stock market.

Key words: multi-factor stock selection, multi-granularity scanning, gcForest algorithm



关键词: 多因子选股, 多粒度扫描, gcForest算法