Paper: arXiv 2507.02018
Authors: Yingjie Niu, Mingchuan Zhao, Valerio Poti, Ruihai Dong
Abstract
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.
Complexity vs Empirical Score
- Math Complexity: 7.5/10
- Empirical Rigor: 7.0/10
- Quadrant: Holy Grail — high math complexity, high empirical rigor
Why this score: The paper introduces a specialized Node-level Graph Attention Network (NGAT) with node-specific attention mechanisms, involving advanced graph theory and deep learning concepts, which drives a high math score. It demonstrates empirical rigor through multi-dataset experimentation, public code release on GitHub, and statistical evaluation of model performance, though it lacks the extreme data/implementation intensity of full-scale backtesting.
Research Flowchart
flowchart TD
A["Research Goal: Predict long-term stock price movements using corporate relationship graphs"] --> B
subgraph B ["Methodology: Node-level Graph Attention Network NGAT"]
B1["Node-level Attention"]
B2["Graph Structure Learning"]
B3["Long-term Prediction Task"]
end
C["Input Data: Corporate Relationship Graphs<br/>(2 Datasets with Inter-firm Relations)"] --> D
subgraph D ["Computational Process"]
D1["Graph Embedding via NGAT"]
D2["Temporal Pattern Extraction"]
D3["Long-horizon Forecasting"]
end
B --> D
D --> E
subgraph E ["Key Findings & Outcomes"]
E1["NGAT Outperforms Baseline Models"]
E2["Long-term Task Reveals Relational Benefits"]
E3["Limitations of Existing Graph Comparison Methods Exposed"]
E4["Public GitHub Repository for Reproducibility"]
end
style A fill:#e1f5e1,stroke:#2e7d32
style E fill:#fff3e0,stroke:#e65100
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1 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.