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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.06862 |
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| _version_ | 1866916516530749440 |
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| author | Yao, Jianhua Dong, Yuxin Wang, Jiajing Wang, Bingxing Zheng, Hongye Qin, Honglin |
| author_facet | Yao, Jianhua Dong, Yuxin Wang, Jiajing Wang, Bingxing Zheng, Hongye Qin, Honglin |
| contents | This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph and the extraction of temporal information from historical price sequences. It also highlights the design of a graph convolution operation and a temporal attention aggregator to model the macro market state. The integration of these features results in a comprehensive stock prediction model that addresses the challenges of utilizing stock relationship data and modeling hierarchical attributes in the stock market. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06862 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stock Type Prediction Model Based on Hierarchical Graph Neural Network Yao, Jianhua Dong, Yuxin Wang, Jiajing Wang, Bingxing Zheng, Hongye Qin, Honglin Machine Learning Computational Finance This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph and the extraction of temporal information from historical price sequences. It also highlights the design of a graph convolution operation and a temporal attention aggregator to model the macro market state. The integration of these features results in a comprehensive stock prediction model that addresses the challenges of utilizing stock relationship data and modeling hierarchical attributes in the stock market. |
| title | Stock Type Prediction Model Based on Hierarchical Graph Neural Network |
| topic | Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2412.06862 |