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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.10695 |
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| _version_ | 1866917006909898752 |
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| author | Chen, Long Bai, Huixin Wang, Mingxin Huang, Xiaohua Liu, Ying Zhao, Jie Guan, Ziyu |
| author_facet | Chen, Long Bai, Huixin Wang, Mingxin Huang, Xiaohua Liu, Ying Zhao, Jie Guan, Ziyu |
| contents | Accurate modeling of inter-stock relationships is critical for stock price forecasting. However, existing methods predominantly focus on single-state relationships, neglecting the essential complementarity between dynamic and static inter-stock relations. To solve this problem, we propose a Dual Relation Fusion Network (DRFN) to capture the long-term relative stability of stock relation structures while retaining the flexibility to respond to sudden market shifts. Our approach features a novel relative static relation component that models time-varying long-term patterns and incorporates overnight informational influences. We capture dynamic inter-stock relationships through distance-aware mechanisms, while evolving long-term structures via recurrent fusion of dynamic relations from the prior day with the pre-defined static relations. Experiments demonstrate that our method significantly outperforms the baselines across different markets, with high sensitivity to the co-movement of relational strength and stock price. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations Chen, Long Bai, Huixin Wang, Mingxin Huang, Xiaohua Liu, Ying Zhao, Jie Guan, Ziyu Machine Learning Accurate modeling of inter-stock relationships is critical for stock price forecasting. However, existing methods predominantly focus on single-state relationships, neglecting the essential complementarity between dynamic and static inter-stock relations. To solve this problem, we propose a Dual Relation Fusion Network (DRFN) to capture the long-term relative stability of stock relation structures while retaining the flexibility to respond to sudden market shifts. Our approach features a novel relative static relation component that models time-varying long-term patterns and incorporates overnight informational influences. We capture dynamic inter-stock relationships through distance-aware mechanisms, while evolving long-term structures via recurrent fusion of dynamic relations from the prior day with the pre-defined static relations. Experiments demonstrate that our method significantly outperforms the baselines across different markets, with high sensitivity to the co-movement of relational strength and stock price. |
| title | Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.10695 |