基于多因子模型和CART分类回归树的证券市场的预测及应用

Forecasting and Application for Securities Market Based on Multi-Factor Model and CART Classification Regression Tree Algorithm

本文基于多因子模型和CART分类回归树算法,利用因子分析法,研究了证券市场的预测问题。首先依据Fama和French因子分析方法选取了8大类公司财务指标建立了多因子分析模型,其次利用统计学中的因子分析法对所选因子进行了特征提取,得到贡献最大的五个特征,并分析了所得市场特征的市场意义,然后根据提取的市场特征及其市场技术指标建立了预测市场季收益率的CART分类回归树模型,并随机选取了了上交所20家公司进行了实证分析,结果表明预测模型具有较好的预测精度,其次,依据预测结果构造了顺势投资策略,获得了优于市场平均收益的良好回报。

Based on Famma and French multi-factor model and CART classification regression tree algorithm, this paper forecasts the securities market. First, according to the Fama and French fmulti-factor analysis method, eight major types of company financial indicators were selected to make a multi-factor analysis. Second, the factor analysis method in statistics was adopted to extract the features of the selected factors, and five features that contribute the most weight were obtained. Then these five features and market risk indicator were taken to train a CART classification regression tree model for predicting market quarterly returns. Meanwhile 20 companies from the Shanghai Stock Exchange were randomly chosen for empirical analysis. The results show that the forecasting model has good forecasting accuracy. Especially, a simulation investment has made with history data and obtained a better performance than the average market.