A Stock Price Prediction Method Based on BiLSTM and Improved Transformer

Abstract

How to maximize shareholder returns has always been a focus of research in the financial field. In order to improve the accuracy and stability of stock price prediction, this article proposes a new method, BiLSTM-MTRAN-TCN. Improve the transformer model and introduce TCN (Temporary Revolution Network) to construct a new transformer model (MTRAN-TCN), making it suitable for stock price prediction. This method consists of BiLSTM (Bi-directional Long Short-Term Memory) and MTRAN-TCN, which can fully utilize the advantages of the three models: BiLSTM, transformer and TCN. Transformer is good at obtaining full range distance information, but its ability to capture sequence information is weak. BiLSTM can capture bidirectional information in sequences, while TCN can capture sequence dependencies and improve the model’s generalization ability. Not only did the improvement effect of the transformer and the effectiveness of introducing the BiLSTM model be verified, but the effectiveness of the method was also verified using 5 index stocks and 14 Shanghai and Shenzhen stocks. Compared with other existing methods in the literature, this method has the best fit on each index stock, and the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of this method is the best in 85.7% of the stock dataset. RMSE decreases by 24.3% to 93.5%, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> increases by 0.3% to 15.6%. In addition, this method has relatively stable prediction performance at different time periods and does not have timeliness issues. The results indicate that the BiLSTM-MTRAN-TCN method performs better in predicting stock prices, with high accuracy and generalization ability.