ORIGINAL RESEARCH article
Front. Appl. Math. Stat.
Sec. Mathematical Finance
Volume 11 - 2025 | doi: 10.3389/fams.2025.1588202
This article is part of the Research TopicFinancial Modeling with FrictionsView all 7 articles
Improving Stock Price Forecasting with M-A-BiLSTM: A Novel Approach
Provisionally accepted
- Ocean University of China, Qingdao, China
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Stock price prediction plays a crucial role in investment, corporate strategic planning, and government policy formulation. However, stock price prediction remains a challenging issue.To tackle this issue, we propose a novel hybrid model, termed M-A-BiLSTM, which integrates Attention mechanisms, Multi-Layer Perceptron (MLP), and Bidirectional Long Short-Term Memory (Bi-LSTM). This model is designed to enhance feature selection capabilities and capture nonlinear patterns in financial time series. Evaluated on stock datasets from Apple, ExxonMobil, Tesla, and Snapchat, our model outperforms existing deep learning methods, achieving a 15.91% reduction in Mean Squared Error (MSE) for Tesla and a 5.95% increase in R-squared (R 2 ) for Apple.Meanwhile, the MSE on the ExxonMobil dataset decreased to 1.8954, showing a significant reduction, while the R 2 increased to 0.9887. These results demonstrate the model's superior predictive power, offering a robust and interpretable approach for financial forecasting.
Keywords: Stock price predication, deep learning, Bi-LSTM, MLP, Attention
Received: 05 Mar 2025; Accepted: 10 Apr 2025.
Copyright: © 2025 Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zihan Liu, Ocean University of China, Qingdao, China
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