标题： 分解金融新闻影响：一种基于几何超图的新型人工智能方法 显示英文标题

标题： Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs

摘要： 在瞬息万变且波动剧烈的金融市场中，基于金融新闻准确预测股票走势对投资者和分析师至关重要。传统模型往往难以捕捉新闻事件与市场反应之间复杂且动态的关系，从而限制了它们提供可操作见解的能力。本文介绍了一种通过开发几何超图注意力网络（GHAN）利用可解释人工智能（XAI）的新方法，以分析金融新闻对市场行为的影响。几何超图通过允许边连接多个节点来扩展传统图结构，有效建模金融实体和新闻事件之间的高阶关系和交互作用。这种独特能力使得能够捕获复杂的依赖关系，例如单一新闻事件同时对多支股票或多个行业产生的影响，这是传统模型常常忽略的。通过在超图中引入注意力机制，GHAN增强了模型聚焦于最相关信息的能力，确保更准确的预测和更好的可解释性。此外，我们采用基于 BERT 的嵌入来捕捉金融新闻文本的语义丰富性，提供对内容的细致理解。使用综合的金融新闻数据集，我们的 GHAN 模型解决了金融新闻影响分析中的关键挑战，包括高阶交互的复杂性、模型可解释性的必要性以及金融市场的动态特性。在 GHAN 中整合注意力机制和 SHAP 值确保了透明度，突出驱动市场预测的最重要因素。实证验证表明，我们的方法比传统的 sentiment analysis 和时间序列模型更为有效。 显示英文摘要

摘要： In the fast-paced and volatile financial markets, accurately predicting stock movements based on financial news is critical for investors and analysts. Traditional models often struggle to capture the intricate and dynamic relationships between news events and market reactions, limiting their ability to provide actionable insights. This paper introduces a novel approach leveraging Explainable Artificial Intelligence (XAI) through the development of a Geometric Hypergraph Attention Network (GHAN) to analyze the impact of financial news on market behaviours. Geometric hypergraphs extend traditional graph structures by allowing edges to connect multiple nodes, effectively modelling high-order relationships and interactions among financial entities and news events. This unique capability enables the capture of complex dependencies, such as the simultaneous impact of a single news event on multiple stocks or sectors, which traditional models frequently overlook. By incorporating attention mechanisms within hypergraphs, GHAN enhances the model's ability to focus on the most relevant information, ensuring more accurate predictions and better interpretability. Additionally, we employ BERT-based embeddings to capture the semantic richness of financial news texts, providing a nuanced understanding of the content. Using a comprehensive financial news dataset, our GHAN model addresses key challenges in financial news impact analysis, including the complexity of high-order interactions, the necessity for model interpretability, and the dynamic nature of financial markets. Integrating attention mechanisms and SHAP values within GHAN ensures transparency, highlighting the most influential factors driving market predictions. Empirical validation demonstrates the superior effectiveness of our approach over traditional sentiment analysis and time-series models.