Electronic and digital trading models have made stock trading more accessible and convenient, leading to exponential growth in trading data. With a wealth of trading data available, researchers have found opportunities to extract valuable insights by uncovering patterns in stock price movements and market dynamics. Deep learning models are increasingly being employed for stock price prediction. While neural networks offer superior computational capabilities compared with traditional statistical methods, their results often lack interpretability, limiting their utility in explaining stock price volatility and investment behavior. To address this challenge, we propose a causality‐based method that incorporates a multivariate approach, integrating news event attention sequences and sentiment index sequences. The goal is to capture the intricate and multifaceted relationships among news events, media sentiment, and stock prices. We illustrate the application of this proposed approach using a Global Database of Events, Language, and Tone global event database, demonstrating its benefits through the analysis of attention sequences and media sentiment index sequences for news events across various categories. This research not only identifies promising directions for further exploration but also offers insights with implications for informed investment decisions.

中文翻译：

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基于媒体关注和情感因果推理的股指预测集成模型

电子化、数字化的交易模式使得股票交易变得更加便捷和便捷，导致交易数据呈指数级增长。凭借大量可用的交易数据，研究人员找到了通过揭示股票价格走势和市场动态模式来提取有价值见解的机会。深度学习模型越来越多地用于股票价格预测。虽然与传统统计方法相比，神经网络提供了卓越的计算能力，但其结果往往缺乏可解释性，限制了其在解释股票价格波动和投资行为方面的效用。为了应对这一挑战，我们提出了一种基于因果关系的方法，该方法结合了多元方法，整合了新闻事件注意力序列和情绪指数序列。目标是捕捉新闻事件、媒体情绪和股票价格之间错综复杂的多方面关系。我们使用全球事件数据库、语言和语气全球事件数据库来说明所提出的方法的应用，通过分析不同类别的新闻事件的注意力序列和媒体情绪指数序列来展示其好处。这项研究不仅确定了进一步探索的有希望的方向，而且还提供了对明智的投资决策具有影响的见解。