MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914667463442432 |
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| author | Su, Junwei Wu, Shan Li, Jinhui |
| author_facet | Su, Junwei Wu, Shan Li, Jinhui |
| contents | In this study, we explore the synergy of deep learning and financial market applications, focusing on pair trading. This market-neutral strategy is integral to quantitative finance and is apt for advanced deep-learning techniques. A pivotal challenge in pair trading is discerning temporal correlations among entities, necessitating the integration of diverse data modalities. Addressing this, we introduce a novel framework, Multi-modal Temporal Relation Graph Learning (MTRGL). MTRGL combines time series data and discrete features into a temporal graph and employs a memory-based temporal graph neural network. This approach reframes temporal correlation identification as a temporal graph link prediction task, which has shown empirical success. Our experiments on real-world datasets confirm the superior performance of MTRGL, emphasizing its promise in refining automated pair trading strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14199 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning Su, Junwei Wu, Shan Li, Jinhui Machine Learning General Economics Economics Trading and Market Microstructure In this study, we explore the synergy of deep learning and financial market applications, focusing on pair trading. This market-neutral strategy is integral to quantitative finance and is apt for advanced deep-learning techniques. A pivotal challenge in pair trading is discerning temporal correlations among entities, necessitating the integration of diverse data modalities. Addressing this, we introduce a novel framework, Multi-modal Temporal Relation Graph Learning (MTRGL). MTRGL combines time series data and discrete features into a temporal graph and employs a memory-based temporal graph neural network. This approach reframes temporal correlation identification as a temporal graph link prediction task, which has shown empirical success. Our experiments on real-world datasets confirm the superior performance of MTRGL, emphasizing its promise in refining automated pair trading strategies. |
| title | MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning |
| topic | Machine Learning General Economics Economics Trading and Market Microstructure |
| url | https://arxiv.org/abs/2401.14199 |