Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations

ArXiv ID: 2412.04034 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Temporal Graph Learning (TGL), Contrastive Learning, Dynamic Graph Representation, Embedding Enhancement, Stock Trend Prediction, Equities (Stocks)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper involves advanced graph neural networks, contrastive learning, and Fourier transforms, requiring dense mathematical notation (high math). It includes extensive experiments on two real market datasets (NASDAQ, NYSE), ablation studies, and offers public code/data, demonstrating strong empirical validation.

  flowchart TD
    A["Research Goal<br>Predict Stock Trends by Integrating<br>Dynamic Temporal & Static Relational Data"] --> B["Data Input<br>NYSE & NASDAQ Stock Market Datasets"]
    
    B --> C["Proposed Framework: DGRCL<br>Dynamic Graph Representation with Contrastive Learning"]
    
    C --> D["Dynamic Component<br>Embedding Enhancement EE Module<br>Captures Temporal Evolution"]
    C --> E["Static Component<br>Contrastive Constrained Training CCT Module<br>Enforces Stock Relation Constraints"]
    
    D & E --> F["Computational Process<br>Contrastive Learning Integration<br>Refines Dynamic & Static Representations"]
    
    F --> G["Outcomes<br>Superior Prediction Accuracy vs SOTA Baselines"]
    F --> H["Outcomes<br>Robust Framework for Dynamic Graph Analysis"]
    F --> I["Outcomes<br>Public Code & Data Availability"]