Improving stock price forecasting with M-A-BiLSTM: a novel approach
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, Mu...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2025.1588202/full |
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| author | Zihan Liu |
| author_facet | Zihan Liu |
| author_sort | Zihan Liu |
| collection | DOAJ |
| description | 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 (R2) for Apple. Meanwhile, the MSE on the ExxonMobil dataset decreased to 1.8954, showing a significant reduction, while the R2 increased to 0.9887. These results demonstrate the model's superior predictive power, offering a robust and interpretable approach for financial forecasting. |
| format | Article |
| id | doaj-art-b918f10fb9be42e0a02659f290a9c934 |
| institution | DOAJ |
| issn | 2297-4687 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj-art-b918f10fb9be42e0a02659f290a9c9342025-08-20T03:07:21ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872025-06-011110.3389/fams.2025.15882021588202Improving stock price forecasting with M-A-BiLSTM: a novel approachZihan LiuStock 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 (R2) for Apple. Meanwhile, the MSE on the ExxonMobil dataset decreased to 1.8954, showing a significant reduction, while the R2 increased to 0.9887. These results demonstrate the model's superior predictive power, offering a robust and interpretable approach for financial forecasting.https://www.frontiersin.org/articles/10.3389/fams.2025.1588202/fullstock price predictiondeep learningBi-LSTMMLPattention |
| spellingShingle | Zihan Liu Improving stock price forecasting with M-A-BiLSTM: a novel approach Frontiers in Applied Mathematics and Statistics stock price prediction deep learning Bi-LSTM MLP attention |
| title | Improving stock price forecasting with M-A-BiLSTM: a novel approach |
| title_full | Improving stock price forecasting with M-A-BiLSTM: a novel approach |
| title_fullStr | Improving stock price forecasting with M-A-BiLSTM: a novel approach |
| title_full_unstemmed | Improving stock price forecasting with M-A-BiLSTM: a novel approach |
| title_short | Improving stock price forecasting with M-A-BiLSTM: a novel approach |
| title_sort | improving stock price forecasting with m a bilstm a novel approach |
| topic | stock price prediction deep learning Bi-LSTM MLP attention |
| url | https://www.frontiersin.org/articles/10.3389/fams.2025.1588202/full |
| work_keys_str_mv | AT zihanliu improvingstockpriceforecastingwithmabilstmanovelapproach |