标题： EP-GAT：基于能量的并行图注意力神经网络用于股票趋势分类 显示英文标题

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

摘要： 图神经网络在预测股票走势方面表现出色，这源于学习股票之间的复杂相互依赖关系以及股票内部的动力学。 基于图神经网络的现有方法通常依赖于静态或手动定义的因素来建模股票之间的变化相互依赖关系。 此外，这些工作往往难以保留股票内的层次特征。 为弥合这些差距，本工作提出了基于能量的并行图注意力神经网络，这是一种用于预测多只股票未来走势的新方法。 首先，它通过股票之间的能量差和玻尔兹曼分布生成动态股票图，捕捉股票之间的演变相互依赖关系。 然后，提出了一种并行图注意力机制以保留股票内的层次内部动力学。 在五个真实世界数据集上进行了广泛的实验，以验证所提出的方法，涵盖美国股票市场（NASDAQ、NYSE、SP）和英国股票市场（FTSE、LSE）。 实验结果表明，在各种指标上，EP-GAT在测试期间始终优于五种竞争基线。 消融研究和超参数敏感性分析进一步验证了所提方法中每个模块的有效性。 显示英文摘要

摘要： 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.