EP-GAT: Energy-based Parallel Graph Attention Neural Network for Stock Trend Classification

• URL: http://arxiv.org/abs/2507.08184v1
• Date: Thu, 10 Jul 2025 21:45:09 GMT
• Title: EP-GAT: Energy-based Parallel Graph Attention Neural Network for Stock Trend Classification
• Authors: Zhuodong Jiang, Pengju Zhang, Peter Martin,
• Abstract summary: This work presents the Energy-based Parallel Graph Attention Neural Network, a novel approach for predicting future movements for multiple stocks.<n>Experiments on five real-world datasets are conducted to validate the proposed approach.
• Score: 0.49157446832511503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph neural networks have shown remarkable performance in forecasting stock movements, which arises from learning complex inter-dependencies between stocks and intra-dynamics of stocks. Existing approaches based on graph neural networks typically rely on static or manually defined factors to model changing inter-dependencies between stocks. Furthermore, these works often struggle to preserve hierarchical features within stocks. To bridge these gaps, this work presents the Energy-based Parallel Graph Attention Neural Network, a novel approach for predicting future movements for multiple stocks. First, it generates a dynamic stock graph with the energy difference between stocks and Boltzmann distribution, capturing evolving inter-dependencies between stocks. Then, a parallel graph attention mechanism is proposed to preserve the hierarchical intra-stock dynamics. Extensive experiments on five real-world datasets are conducted to validate the proposed approach, spanning from the US stock markets (NASDAQ, NYSE, SP) and UK stock markets (FTSE, LSE). The experimental results demonstrate that EP-GAT consistently outperforms competitive five baselines on test periods across various metrics. The ablation studies and hyperparameter sensitivity analysis further validate the effectiveness of each module in the proposed method.

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