# Evaluating Financial Relational Graphs: Interpretation Before Prediction

arXiv: https://arxiv.org/abs/2410.07216 · HTML: https://mlsanity.com/paper/2410.07216/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Temporal and Heterogeneous Graph Neural Network for Financial Time Series Prediction](https://mlsanity.com/paper/2305.08740/) (2023)
- [Learning to Generalize for Cross-domain QA](https://mlsanity.com/paper/2305.08208/) (2023)
- [Stock Embeddings: Learning Distributed Representations for Financial Assets](https://mlsanity.com/paper/2202.08968/) (2022)

## Built on by (most cited first, 5 of 5)

- [NGAT: A Node-level Graph Attention Network for Long-term Stock Prediction](https://mlsanity.com/paper/2507.02018/) (2025)
- [Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation](https://mlsanity.com/paper/2502.17011/) (2025)
- [GAPNet: Plug-in Jointly Learning Task-Specific Graph for Dynamic Stock Relation](https://mlsanity.com/paper/2602.00888/) (2026)
- [Relational Probing: LM-to-Graph Adaptation for Financial Prediction](https://mlsanity.com/paper/2604.10212/) (2026)
- [Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs](https://mlsanity.com/paper/2607.12067/) (2026)

## Abstract (arXiv)

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.