Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (1): 110-117.DOI: 10.3778/j.issn.1002-8331.2002-0101

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Multi-factor Quantitative Stock Selection Strategy Based on Sparsity Penalty

SHU Shike,  LI Lu   

  1. School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China
  • Online:2021-01-01 Published:2020-12-31



  1. 上海工程技术大学 数理与统计学院,上海 201620


Aiming at the complexity between the characteristics of high-dimensional datasets. This paper proposes replace L1 penalty in LR-Elastic Net with SCAD(Smoothly Clipped Absolute Deviation) penalty and MCP(Minimax Concave Penalty), constructs LR-SCAD and LR-MCP models respectively, and uses ADMM(Alternating Direction Method of Multipliers)algorithm to solve. Simulation experiments show that LR-Elastic Net model is good at handling small sample data with correlation features, while LR-SCAD and LR-MCP models perform well in large sample data with correlation features. At the same time, the paper establishes LR-Elastic Net, LR-SCAD and LR-MCP strategies, and applies them to the data of the CSI 300 Index. Back-test results show that LR-SCAD and LR-MCP strategies perform better than LR-Elastic Net strategies in highly correlated data.

Key words: Elastic Net, Smoothly Clipped Absolute Deviation(SCAD), Minimax Concave Penalty(MCP), Alternating Direction Method of Multipliers(ADMM) algorithm, logistic regression, multi-factor stock selection


针对高维度数据集特征之间的复杂性,而传统的L1惩罚项不满足Oracle性质的无偏性,将逻辑回归弹性网(LR-Elastic Net)中的L1惩罚项替换为SCAD(Smoothly Clipped Absolute Deviation)和MCP(Minimax Concave Penalty)惩罚项,分别构建了LR-SCAD和LR-MCP模型,在保留稀疏性的同时满足了无偏性,并利用ADMM(Alternating Direction Method of Multipliers)算法进行求解。通过模拟实验发现,LR-Elastic Net模型能很好地处理特征存在相关性的小样本数据,而LR-SCAD和LR-MCP模型在特征存在相关性的大样本数据中表现较好;建立LR-Elastic Net、LR-SCAD和LR-MCP策略,并应用于沪深300指数成分股数据。回测结果显示,LR-SCAD和LR-MCP策略在股票相关性很强的数据中比LR-Elastic Net策略表现更好。

关键词: 弹性网(Elastic Net), SCAD, MCP, ADMM算法, 逻辑回归, 多因子选股