Cross-Modal Temporal Fusion for Financial Market Forecasting
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915590653870080 |
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| author | Pei, Yunhua Cartlidge, John Mandal, Anandadeep Gold, Daniel Marcilio, Enrique Mazzon, Riccardo |
| author_facet | Pei, Yunhua Cartlidge, John Mandal, Anandadeep Gold, Daniel Marcilio, Enrique Mazzon, Riccardo |
| contents | Accurate forecasting in financial markets requires integrating diverse data sources, from historical prices to macroeconomic indicators and financial news. However, existing models often fail to align these modalities effectively, limiting their practical use. In this paper, we introduce a transformer-based deep learning framework, Cross-Modal Temporal Fusion (CMTF), that fuses structured and unstructured financial data for improved market prediction. The model incorporates a tensor interpretation module for feature selection and an auto-training pipeline for efficient hyperparameter tuning. Experimental results using FTSE 100 stock data demonstrate that CMTF achieves superior performance in price direction classification compared to classical and deep learning baselines. These findings suggest that our framework is an effective and scalable solution for real-world cross-modal financial forecasting tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13522 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Modal Temporal Fusion for Financial Market Forecasting Pei, Yunhua Cartlidge, John Mandal, Anandadeep Gold, Daniel Marcilio, Enrique Mazzon, Riccardo Machine Learning Neural and Evolutionary Computing Computational Finance Accurate forecasting in financial markets requires integrating diverse data sources, from historical prices to macroeconomic indicators and financial news. However, existing models often fail to align these modalities effectively, limiting their practical use. In this paper, we introduce a transformer-based deep learning framework, Cross-Modal Temporal Fusion (CMTF), that fuses structured and unstructured financial data for improved market prediction. The model incorporates a tensor interpretation module for feature selection and an auto-training pipeline for efficient hyperparameter tuning. Experimental results using FTSE 100 stock data demonstrate that CMTF achieves superior performance in price direction classification compared to classical and deep learning baselines. These findings suggest that our framework is an effective and scalable solution for real-world cross-modal financial forecasting tasks. |
| title | Cross-Modal Temporal Fusion for Financial Market Forecasting |
| topic | Machine Learning Neural and Evolutionary Computing Computational Finance |
| url | https://arxiv.org/abs/2504.13522 |