Stock price prediction for new energy vehicle companies based on multi-source data and hybrid attention structure

• Published in: Expert systems with applications Vol. 255; p. 124787
• Main Authors: Liu, Xueyong, Wu, Yanhui, Luo, Min, Chen, Zhensong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2024
• Subjects: Hybrid attention structure; Multi-source data; NEV companies; Stock price prediction; NEV companies; Multi-source data; Stock price prediction; Hybrid attention structure
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.124787

In recent years, with the rapid advancement of new energy vehicle (NEV) technology, an increasing number of individuals have begun to invest in the stock market of NEV companies. Accurate prediction holds great significance for investors as it not only mitigates investment risks but also enhances the potential returns. However, the accurate prediction of stock prices is subject to the influence of numerous factors from different aspects. Therefore, to improve the prediction accuracy of stock prices, this paper integrates multi-source data and introduces an LSTM-GRU-SA-AM model. The model comprises Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Self-Attention (SA), and the hybrid attention structure SA-AM, with SA representing self-attention and AM representing attention mechanism. The multi-source data consists of stock market trading data, stock data of pertinent companies, and financial news data. We have selected four stocks of Chinese NEV companies to validate the efficacy of utilizing multi-source data, and compared our LSTM-GRU-SA-AM model with six baseline models. The experimental results demonstrate that incorporating multi-source data significantly improves prediction accuracy. Moreover, among all the models examined, our proposed LSTM-GRU-SA-AM model exhibits superior performance.