Multimodal Stock Price Forecasting Using Attention Mechanism Based on Multi-Task Learning

This paper proposes a Multi-Task Attention-based Stock Prediction Model (MTASPM) to tackle the challenges of stock price forecasting in the Chinese market, characterized by solid volatility and numerous influencing factors. Employing multimodal information from stock correlation, historical trading data, company news, and government policies, MTASPM leverages multi-task learning and attention mechanism to enhance predictive accuracy and capture data patterns for stock price forecasting. Experimental results on the Shanghai Exchange Stock Price Dataset (SHESPD) and the Shenzhen Exchange Stock Price Dataset (SZESPD) demonstrate that MTASPM outperforms eight baseline models. Specifically, MTASPM achieves improvements of 42.16% in MSE, 25.18% in RMSE, and 6.88% in MAE on SHESPD, and improvements of 16.95% in MSE, 8.64% in RMSE, and 6.12% in MAE on SZESPD. Overall, this study presents an effective approach for accurate stock price prediction that considers multiple influencing factors and utilizes multimodal information.