Analysis of Stock Price Based On the XGBoost Algorithm With EMA-19 and SMA-15 Features

Abstract

At present, the prediction of stock market is one of the most popular and valuable research fields in the financial field. More and more scholars are engaged in the research of stock market forecast, exploring the law of stock market development, and new science and technology are constantly applied to the stock price forecast. In this paper, we proposed a stock closing price prediction model based on the XGBoost and Grid SearchCV algorithms. Experimental results show that our idea represents better performance than the other machine learning methods. Specifically, the RMSE value is 1.39%, 2.43% and 8.33% lower than SVM algorithm, neural network algorithm and LightGBM algorithm, respectively. In addition, we also give the importance ranking of the characteristics that affect the stock closing price, and obtain some interesting and instructive suggestions. For example, the “EMA-9” and “SMA-15” feature has the biggest and smallest impact on stock prices, which can guide our future work.