Multi-factor Stock Forecasting Model Based on Independence Criterion Weighted Bagging Algorithm

Abstract

Stock price prediction and the selection of important factors are a hot issue for statisticians. Machine learning methods need to ensure the prediction accuracy of the model in high-dimensional data scenes on the one hand, and the interpretability of the output of the prediction model on the other. If a single model is used for forecasting, it is easy to cause the problems of deviation and variance imbalance of the forecasting model (over-fitting and under-fitting). Based on this, we propose an interpretable forecasting model weighted bagging based on independence criterion. On the one hand, the proposed method solves the problem that a single model is easy to fall into over-fitting and under-fitting by using the idea of model averaging. On the other hand, when the sub-model is an interpretable machine learning method in Bagging framework, the proposed method can also output the weighted feature importance of each factor. In addition, the choice of independence criterion and sub-model is free. The actual data analysis shows that the proposed method has smaller prediction error compared with the comparison methods such as LASSO, ridge regression, random forest and XGBoost. The factor selection model under the minimum prediction error criterion will be more interpretable.