| 正则稀疏化的多因子量化选股策略 |
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| 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 |
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| 作者姓名： | 舒时克 李路 |
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| 作者单位： | 上海工程技术大学 数理与统计学院，上海 201620 |
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| 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。
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| 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 |
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| 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 |
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| Authors: | SHU Shike LI Lu |
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| Affiliation: | School of Mathematics, Physics and Statistics, Shanghai University of Engineering Science, Shanghai 201620, China |
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| 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. |
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| 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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