Intraday trend prediction of stock indices with machine learning approaches

Abstract

In recent years, as research at the intersection of machine learning and finance has grown, predicting stock price movements has become a particularly intriguing issue. Current research focuses primarily on using historical data of the previous day to predict stock movements for the following day, whereas fewer studies use the trading day’s opening data to predict market movements for the current day. We predict intraday price movements of the SSE-50 (Shanghai Securities 50 Index) using stock market opening data as input. Specifically, decision tree, extreme gradient boosting (XGBoost), random forest, support vector machines (SVM), and long-short-term memory are developed to predict the movements of the SSE-50 index utilizing opening price data of various time intervals. We also design three trading strategies when different time frequencies of data are used. At the same time-frequency, the results demonstrate that SVM with Gaussian and linear kernels outperform others. The forecasting accuracy at 10-min frequency approaches 70%, which is close to the results at longer time intervals, indicating that intraday trend can be determined by opening price fluctuations and the first 10-min data contains sufficient information to predict the trend for the entire trading day. In addition, trading methods based on the forecast of daily, weekly, and monthly SSE-50 price movement outperform buy-and-hold strategies. Daily trading performs better than the other two strategies. The outcomes of this research can expand the use of machine learning in quantitative trading and enrich intraday trading techniques further.

Additional information

Funding

The authors gratefully acknowledge financial support from the National Social Science Fund of China (No. 19CJL028).

Notes on contributors

Pan Tang

Pan Tang received the Ph.D. degree in Faculty of Science in 2011 from National University of Singapore. He is now an associate professor in the School of Economics and Management at the Southeast University. His current research is concerned with the joint area of financial economics and machine learning. He has published research articles on the application of machine learning in finance.

Xin Tang

Xin Tang is currently a postgraduate student in the School of Economics and Management at Southeast University. Her current research is the joint area of financial economics and machine learning.

Wentao Yu

Wentao Yu is currently a postgraduate student in the School of Cyber Science and Engineering at Southeast University. His research focuses on the intersection of finance and generating adversarial networks.