融合动态演化与静态关系，提升股市趋势预测准确率
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
- 构建双关系图模型，分别捕捉股价时序变化与股票间静态关联
- 在NASDAQ和NYSE数据集上，预测精度显著超越现有方法
- 适合金融量化研究者与图神经网络应用开发者参考
时间图学习（TGL）对捕捉股市动态演变至关重要。传统方法常忽视股价时序变化与股票间静态关系的相互作用。为此，我们提出动态图表示对比学习框架（DGRCL），整合动态与静态图关系以提升股市趋势预测准确性。该框架包含两个核心模块：嵌入增强（EE）模块用于动态捕捉股价时序演化，对比约束训练（CCT）模块则基于股票关系施加静态约束，通过对比学习进行优化。这种双重关系机制使模型更全面理解市场动态。在两大美国股市数据集NASDAQ与NYSE上的实验表明，DGRCL显著优于当前主流TGL基线方法。消融实验验证了两个模块的关键作用。整体而言，DGRCL不仅提升了预测能力，还为动态图中时序与关系数据的融合提供了稳健框架。代码与数据公开可获取。
原文摘要 · Abstract (English)
Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and static relational structures between stocks. To address this issue, we propose the Dynamic Graph Representation with Contrastive Learning (DGRCL) framework, which integrates dynamic and static graph relations to improve the accuracy of stock trend prediction. Our framework introduces two key components: the Embedding Enhancement (EE) module and the Contrastive Constrained Training (CCT) module. The EE module focuses on dynamically capturing the temporal evolution of stock data, while the CCT module enforces static constraints based on stock relations, refined within contrastive learning. This dual-relation approach allows for a more comprehensive understanding of stock market dynamics. Our experiments on two major U.S. stock market datasets, NASDAQ and NYSE, demonstrate that DGRCL significantly outperforms state-of-the-art TGL baselines. Ablation studies indicate the importance of both modules. Overall, DGRCL not only enhances prediction ability but also provides a robust framework for integrating temporal and relational data in dynamic graphs. Code and data are available for public access.
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