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

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.

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