Evaluating Financial Relational Graphs: Interpretation Before Prediction

ArXiv ID: 2410.07216 “View on arXiv”

Authors: Unknown

Abstract

Accurate and robust stock trend forecasting has been a crucial and challenging task, as stock price changes are influenced by multiple factors. Graph neural network-based methods have recently achieved remarkable success in this domain by constructing stock relationship graphs that reflect internal factors and relationships between stocks. However, most of these methods rely on predefined factors to construct static stock relationship graphs due to the lack of suitable datasets, failing to capture the dynamic changes in stock relationships. Moreover, the evaluation of relationship graphs in these methods is often tied to the performance of neural network models on downstream tasks, leading to confusion and imprecision. To address these issues, we introduce the SPNews dataset, collected based on S&P 500 Index stocks, to facilitate the construction of dynamic relationship graphs. Furthermore, we propose a novel set of financial relationship graph evaluation methods that are independent of downstream tasks. By using the relationship graph to explain historical financial phenomena, we assess its validity before constructing a graph neural network, ensuring the graph’s effectiveness in capturing relevant financial relationships. Experimental results demonstrate that our evaluation methods can effectively differentiate between various financial relationship graphs, yielding more interpretable results compared to traditional approaches. We make our source code publicly available on GitHub to promote reproducibility and further research in this area.

Keywords: graph neural networks, stock relationship graph, dynamic graphs, SPNews dataset, S&P 500, equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders

  flowchart TD
    A["Research Goal<br>How to evaluate stock relationship graphs<br>independently of neural network performance?"]

    subgraph B["Methodology"]
        direction LR
        B1["Construct Dynamic Graphs<br>from SPNews dataset"]
        B2["Propose Evaluation Metrics<br>independent of downstream tasks"]
    end

    C["Data: SPNews Dataset<br>S&P 500 stocks + news relationships"]

    subgraph D["Process"]
        D1["Explain Historical Events<br>using graph relationships"]
        D2["Assess Graph Validity<br>before GNN construction"]
    end

    E["Key Findings"]
    E1["Effective differentiation<br>between graph types"]
    E2["Interpretable results<br>vs. traditional methods"]
    E3["Code available for reproducibility"]

    A --> B
    B --> C
    C --> D
    D --> E