Application Research of the XGBoost-SVM Combination Model in Quantitative Investment Strategy

Financia1 data are non-stationary and nonlinear. Machine learning makes it easier to classify financial data than traditional models. With the development of machine learning, improving the accuracy of machine learning models for stock price prediction has gradually become a hot research topic. This paper uses the XGBoost (eXtreme gradient boosting) model and the SVM (support vector machine) model to predict the rising, falling and fluctuating of CSI 300, SSE 50 and CSI 500 stock index futures respectively. Then it constructs the XGBoost-SVM combination model and designs a quantitative investment strategy to trade stock index futures in order to research the effectiveness of the models in quantitative investment strategies. The research shows that the proposed method can stably outperform the benchmark returns by combining the investment strategies of the three-price-trend classifications. The constructed XGBoost-SVM model performs better than the original model. It gets higher returns.