关键词: XGBoost, 网格搜索, 梯度增强决策树（GBDT）, 支持向量机（SVM）, 股价预测

Abstract: With the continuous advancement of the times, people’s living standards have been increasing. In addition to solving the problem of food and clothing, there is surplus money available for investment. More and more people are turning their attention to stock market investment, which provides financial conditions for the development of the stock market. However, in the complicated stock market, how to find the optimal stock has become an urgent problem to be solved. This is not only a unilateral confusion for investors, but also a focus of scholars in the field of stock forecasting. In this paper, the grid prediction algorithm is used to optimize the XGBoost model to construct the financial forecasting model of GS-XGBoost, and the model is applied to short-term stock forecasting. The daily closing prices of China Ping An, China State Construction Engineering Corporation, CRRC Corporation Limited, IFLYTEK and SANY HEAVY INDUSTRY from April 2005 to December 28, 2018 are used as experimental data. Through experimental comparison, compared with the original XGBoost model, GBDT model and SVM model, the GS-XGBoost model shows good prediction results on the three evaluation indexes of MSE, RMSE and MAE. It is verified that the GS-XGBoost financial forecasting model has better fitting performance in short-term stock forecasting.

Key words: XGBoost, grid search, Gradient Boosting Decision Tree（GBDT）, Support Vector Machine（SVM）, stock price forecast