NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908431467675648 |
|---|---|
| author | Niu, Yingjie Zhao, Mingchuan Poti, Valerio Dong, Ruihai |
| author_facet | Niu, Yingjie Zhao, Mingchuan Poti, Valerio Dong, Ruihai |
| contents | Graph representation learning methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships. However, current approaches face three key challenges: (1) The advantages of relational information are obscured by limitations in downstream task designs; (2) Existing graph models specifically designed for stock prediction often suffer from excessive complexity and poor generalization; (3) Experience-based construction of corporate relationship graphs lacks effective comparison of different graph structures. To address these limitations, we propose a long-term stock prediction task and develop a Node-level Graph Attention Network (NGAT) specifically tailored for corporate relationship graphs. Furthermore, we experimentally demonstrate the limitations of existing graph comparison methods based on model downstream task performance. Experimental results across two datasets consistently demonstrate the effectiveness of our proposed task and model. The project is publicly available on GitHub to encourage reproducibility and future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02018 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction Niu, Yingjie Zhao, Mingchuan Poti, Valerio Dong, Ruihai Statistical Finance Artificial Intelligence Machine Learning I.2.1 Graph representation learning methods have been widely adopted in financial applications to enhance company representations by leveraging inter-firm relationships. However, current approaches face three key challenges: (1) The advantages of relational information are obscured by limitations in downstream task designs; (2) Existing graph models specifically designed for stock prediction often suffer from excessive complexity and poor generalization; (3) Experience-based construction of corporate relationship graphs lacks effective comparison of different graph structures. To address these limitations, we propose a long-term stock prediction task and develop a Node-level Graph Attention Network (NGAT) specifically tailored for corporate relationship graphs. Furthermore, we experimentally demonstrate the limitations of existing graph comparison methods based on model downstream task performance. Experimental results across two datasets consistently demonstrate the effectiveness of our proposed task and model. The project is publicly available on GitHub to encourage reproducibility and future research. |
| title | NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction |
| topic | Statistical Finance Artificial Intelligence Machine Learning I.2.1 |
| url | https://arxiv.org/abs/2507.02018 |