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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.07923 |
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| _version_ | 1866910908525051904 |
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| author | Wu, Xian |
| author_facet | Wu, Xian |
| contents | This paper proposes a new algorithm -- Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks. It combines the strength of the traditional simulated method of moments (SMM) and recent machine learning techniques -- Graph Neural Network (GNN). It outperforms existing reduced-form methods with network centrality measures in prediction accuracy. The method can be used on networks with any structure, allowing for heterogeneity among both traders and assets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07923 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Trading Graph Neural Network Wu, Xian Trading and Market Microstructure Machine Learning General Economics Economics Pricing of Securities This paper proposes a new algorithm -- Trading Graph Neural Network (TGNN) that can structurally estimate the impact of asset features, dealer features and relationship features on asset prices in trading networks. It combines the strength of the traditional simulated method of moments (SMM) and recent machine learning techniques -- Graph Neural Network (GNN). It outperforms existing reduced-form methods with network centrality measures in prediction accuracy. The method can be used on networks with any structure, allowing for heterogeneity among both traders and assets. |
| title | Trading Graph Neural Network |
| topic | Trading and Market Microstructure Machine Learning General Economics Economics Pricing of Securities |
| url | https://arxiv.org/abs/2504.07923 |