基于EKPCA算法的多因子选股模型研究

Research on Multi-Factor Stock Selection Model Based on EKPCA Algorithm

摘要: 多因子选股模型是量化投资中的主流方法。本文首次引入高效的核主成分分析(Efficient Kernel Principal Component Analysis, EKPCA)算法，以高效的核主成分为自变量建立回归方程预测收益率，构建多因子选股模型。本文基于上证180的成分股进行实证分析，选取包含基本面、技术指标及投资者情绪指标等50多个影响因子，引用EKPCA算法确定基本模式，在高维特征空间提取高效核主成分。与经典KPCA算法对比，EKPCA算法具有更高的特征抽取效率。回测结果显示，构造的投资组合的贝塔系数和夏普比率在所选时间段内均优于市场基准水平，这表明该模型具有较好的选股效果。

Abstract: The multi-factor stock selection model is the mainstream method in quantitative investment. This paper introduces the Efficient Kernel Principal Component Analysis (EKPCA) algorithm for the first time. The high-efficiency kernel principal component is used as the independent variable to estab-lish the regression equation to predict the rate of return and construct a multi-factor stock selection model. Based on the empirical analysis of the constituents of SSE 180, this paper selects more than 50 impact factors including fundamentals, technical indicators and investor sentiment indicators, and uses the EKPCA algorithm to determine the basic model and extracts high-efficiency kernel principal components in the high-dimensional feature space. Compared with the classical KPCA algorithm, the EKPCA algorithm has higher feature extraction efficiency. The backtest results show that the beta coefficient and Sharpe ratio of the constructed portfolio are better than the market benchmark level in the selected time period, which indicates that the model has a better stock picking effect.