提出面向企业关系图的节点级注意力网络，提升长期股价预测效果。
NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction
- 基于节点级注意力机制建模企业间关系，简化结构提升泛化能力
- 在两个数据集上实现更优的长期股价预测性能
- 公开代码促进可复现性，适合金融图学习研究者参考
图表示学习方法已被广泛应用于金融领域，通过利用企业间关系来增强公司表征。然而，现有方法面临三大挑战：(1) 关系信息的优势被下游任务设计局限所掩盖；(2) 针对股价预测设计的图模型通常过于复杂且泛化能力差；(3) 基于经验构建的企业关系图缺乏对不同图结构的有效比较。为解决这些问题，我们提出一项长期股价预测任务，并开发了专用于企业关系图的节点级图注意力网络（NGAT）。此外，我们实证揭示了现有基于模型下游任务表现的图结构比较方法的局限性。在两个数据集上的实验结果一致表明，所提出的任务与模型具有显著有效性。项目已开源至GitHub，以促进可复现性与后续研究。
原文摘要 · Abstract (English)
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.
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