A Two-Stage ARIMA Model via Machine Learning and its Application in Stock Price Prediction

Abstract

Stock price prediction has always been a hot issue in the financial sector and quantitative investment. Since stock price time series data tends to have linear and nonlinear features, traditional ARIMA models exhibit certain limitations in modeling such data. Based on this, this paper innovatively uses intraday transaction data of the stock market as auxiliary information, and proposes an improved ARIMA stock price prediction model based on machine learning methods. The specific principle is to use the ARIMA model to predict the linear information of the data, and machine learning-related algorithms (RF, XGBoost, LSTM) are used to predict the nonlinear residual information. The empirical results show that compared with the traditional ARIMA model, the model can effectively improve the prediction accuracy and is robust in stock price prediction. Finally, because this framework is very flexible in content, it can be equipped with machine learning methods with the best prediction accuracy for different practical application scenarios. In addition, we can use the model averaging method in the two-stage framework to improve the accuracy, and the mixed or high-frequency data can be further mined.