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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.10540 |
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| _version_ | 1866913613239812096 |
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| author | Omranpour, Soroush Rabusseau, Guillaume Rabbany, Reihaneh |
| author_facet | Omranpour, Soroush Rabusseau, Guillaume Rabbany, Reihaneh |
| contents | In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a low-rank approximation of the potentially large attention tensor using tensor decomposition and employ kernel attention, reducing complexity to linear with respect to the data size. Additionally, we present an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Our experiments on the Stocknet dataset demonstrate the effectiveness of our method, highlighting its potential for enhancing stock movement prediction in financial markets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10540 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data Omranpour, Soroush Rabusseau, Guillaume Rabbany, Reihaneh Machine Learning Statistical Finance In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a low-rank approximation of the potentially large attention tensor using tensor decomposition and employ kernel attention, reducing complexity to linear with respect to the data size. Additionally, we present an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Our experiments on the Stocknet dataset demonstrate the effectiveness of our method, highlighting its potential for enhancing stock movement prediction in financial markets. |
| title | Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data |
| topic | Machine Learning Statistical Finance |
| url | https://arxiv.org/abs/2412.10540 |