NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction
This work addresses challenges in financial applications for investors and analysts by improving stock prediction through graph representation learning, though it appears incremental in method design.
The paper tackled the problem of long-term stock prediction by proposing a Node-level Graph Attention Network (NGAT) tailored for corporate relationship graphs, and experimental results across two datasets demonstrated its effectiveness.
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.