LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU

ArXiv ID: 2409.08282 “View on arXiv”

Authors: Unknown

Abstract

Stock price prediction is a challenging problem in the field of finance and receives widespread attention. In recent years, with the rapid development of technologies such as deep learning and graph neural networks, more research methods have begun to focus on exploring the interrelationships between stocks. However, existing methods mostly focus on the short-term dynamic relationships of stocks and directly integrating relationship information with temporal information. They often overlook the complex nonlinear dynamic characteristics and potential higher-order interaction relationships among stocks in the stock market. Therefore, we propose a stock price trend prediction model named LSR-IGRU in this paper, which is based on long short-term stock relationships and an improved GRU input. Firstly, we construct a long short-term relationship matrix between stocks, where secondary industry information is employed for the first time to capture long-term relationships of stocks, and overnight price information is utilized to establish short-term relationships. Next, we improve the inputs of the GRU model at each step, enabling the model to more effectively integrate temporal information and long short-term relationship information, thereby significantly improving the accuracy of predicting stock trend changes. Finally, through extensive experiments on multiple datasets from stock markets in China and the United States, we validate the superiority of the proposed LSR-IGRU model over the current state-of-the-art baseline models. We also apply the proposed model to the algorithmic trading system of a financial company, achieving significantly higher cumulative portfolio returns compared to other baseline methods. Our sources are released at https://github.com/ZP1481616577/Baselines_LSR-IGRU.

Keywords: Long Short-Term Relationships, Improved GRU, Relationship Matrix, Secondary Industry Information, Algorithmic Trading, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (GRU, graph neural networks) with novel relationship matrices, indicating high mathematical complexity. It also demonstrates strong empirical rigor through extensive experiments on real-world stock market datasets (China and US), deployment in an algorithmic trading system, and release of source code.

  flowchart TD
    A["Research Goal: Improve Stock Trend Prediction by Integrating Long/Short-Term Relationships with Temporal Data"] --> B["Data Processing"]
    B --> C["Construct Long Short-Term Relationship Matrix"]
    B --> D["Input Financial Time Series Data"]
    C --> E["Improved GRU Model"]
    D --> E
    E --> F["Model Training & Prediction"]
    F --> G["Outcomes"]
    
    subgraph G ["Key Findings"]
        G1["Superior accuracy vs SOTA baselines"]
        G2["Higher cumulative returns in algorithmic trading"]
    end
    
    C --> C1["Long-term: Secondary Industry Info"]
    C --> C2["Short-term: Overnight Price Info"]
    E --> E1["Enhanced GRU Input Integration"]