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正则稀疏化的多因子量化选股策略
引用本文:舒时克,李路. 正则稀疏化的多因子量化选股策略[J]. 计算机工程与应用, 2021, 57(1): 110-117. DOI: 10.3778/j.issn.1002-8331.2002-0101
作者姓名:舒时克  李路
作者单位:上海工程技术大学 数理与统计学院,上海 201620
摘    要: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(ElasticNet)  SCAD  MCP  ADMM算法  逻辑回归  多因子选股  

Multi-factor Quantitative Stock Selection Strategy Based on Sparsity Penalty
SHU Shike,LI Lu. Multi-factor Quantitative Stock Selection Strategy Based on Sparsity Penalty[J]. Computer Engineering and Applications, 2021, 57(1): 110-117. DOI: 10.3778/j.issn.1002-8331.2002-0101
Authors:SHU Shike  LI Lu
Affiliation:School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China
Abstract: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.
Keywords:Elastic Net  Smoothly Clipped Absolute Deviation(SCAD)  Minimax Concave Penalty(MCP)  Alternating Direction Method of Multipliers(ADMM)algorithm  logistic regression  multi-factor stock selection
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