Stock Type Prediction Model Based on Hierarchical Graph Neural Network
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916516530749440 |
|---|---|
| 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 |