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

2024-08-26

International Conference on Information and Knowledge Management

AI Summary

Stock trend prediction faces challenges including the disconnection between short-term dynamic modeling and long-term dependency extraction, as well as insufficient characterization of nonlinear, high-order cross-stock interactions. To address these, this paper proposes a collaborative dynamic modeling framework that jointly captures long- and short-term inter-stock relationships. Specifically, we introduce second-tier industry information to construct long-range dependencies and integrate overnight price dynamics to formulate a unified long–short-term relational matrix. We further design a GRU-based stepwise relational enhancement mechanism that synergistically encodes graph-structured relational information with temporal features. Empirical evaluations across multiple U.S. and Chinese markets demonstrate statistically significant improvements over state-of-the-art methods. The model has been deployed in a financial institution’s algorithmic trading system, yielding measurable gains in cumulative investment return.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Predicting stock trends using long short-term relationships and improved GRU
• Capturing complex nonlinear dynamics and higher-order stock interactions
• Enhancing prediction accuracy by integrating temporal and relationship data

Innovation

• Constructs long short-term stock relationship matrix
• Improves GRU inputs for better information integration
• Uses secondary industry and overnight price data