正则稀疏化的多因子量化选股策略

Multi-factor Quantitative Stock Selection Strategy Based on Sparsity Penalty

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。

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.