Stock market index prediction using deep Transformer model

Abstract

Applications of deep learning in financial market prediction have attracted widespread attention from investors and scholars. From convolutional neural networks to recurrent neural networks, deep learning methods exhibit superior ability to capture the non-linear characteristics of stock markets and, accordingly, achieve a high performance on stock market index prediction. In this paper, we utilize the latest deep learning framework, Transformer, to predict the stock market index. Transformer was initially developed for the natural language processing problem, and has recently been applied to time series forecasting. Through the encoder–decoder architecture and the multi-head attention mechanism, Transformer can better characterize the underlying rules of stock market dynamics. We implement several back-testing experiments on the main stock market indices worldwide, including CSI 300, S&P 500, Hang Seng Index, and Nikkei 225. All the experiments demonstrate that Transformer outperforms other classic methods significantly and can gain excess earnings for investors.