Stock Price Prediction Using a Hybrid LSTM-GNN Model: Integrating Time-Series and Graph-Based Analysis

ArXiv ID: 2502.15813 “View on arXiv”

Authors: Unknown

Abstract

This paper presents a novel hybrid model that integrates long-short-term memory (LSTM) networks and Graph Neural Networks (GNNs) to significantly enhance the accuracy of stock market predictions. The LSTM component adeptly captures temporal patterns in stock price data, effectively modeling the time series dynamics of financial markets. Concurrently, the GNN component leverages Pearson correlation and association analysis to model inter-stock relational data, capturing complex nonlinear polyadic dependencies influencing stock prices. The model is trained and evaluated using an expanding window validation approach, enabling continuous learning from increasing amounts of data and adaptation to evolving market conditions. Extensive experiments conducted on historical stock data demonstrate that our hybrid LSTM-GNN model achieves a mean square error (MSE) of 0.00144, representing a substantial reduction of 10.6% compared to the MSE of the standalone LSTM model of 0.00161. Furthermore, the hybrid model outperforms traditional and advanced benchmarks, including linear regression, convolutional neural networks (CNN), and dense networks. These compelling results underscore the significant potential of combining temporal and relational data through a hybrid approach, offering a powerful tool for real-time trading and financial analysis.

Keywords: Long-Short-Term Memory (LSTM), Graph Neural Networks (GNN), Hybrid Model, Stock Market Prediction, Time Series Analysis, Equities / Stocks

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper combines advanced deep learning architectures (LSTM and GNN) requiring knowledge of sequential processing and graph theory, placing it on the higher end of math complexity. Empirical rigor is solid with an expanding window validation, MSE metrics, and benchmarking against multiple models, indicating backtest-ready methodology.

  flowchart TD
    A["Research Goal: Enhance Stock Price Prediction<br>using Temporal and Relational Data"] --> B["Data Input: Historical Stock Prices"]

    subgraph Methodology ["Methodology: Hybrid LSTM-GNN Model"]
        direction LR
        B --> C["LSTM Component<br>Captures Temporal Patterns"]
        B --> D["GNN Component<br>Models Inter-Stock Relations via Pearson Correlation"]
    end

    Methodology --> E["Training: Expanding Window Validation<br>Continuous Learning & Adaptation"]

    E --> F["Computational Process:<br>Integrate Time-Series & Graph-Based Analysis"]

    F --> G["Key Findings & Outcomes"]
    subgraph Outcomes [" "]
        direction LR
        G1["MSE: 0.00144<br>10.6% reduction vs. Standalone LSTM"]
        G2["Outperforms Benchmarks<br>Linear Regression, CNN, Dense Networks"]
    end