基于媒体关注和情感因果推理的股指预测集合模型

IF 3.4 3区 经济学 Q1 ECONOMICS
Juanjuan Wang, Shujie Zhou, Wentong Liu, Lin Jiang
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引用次数: 0

摘要

电子和数字交易模式使股票交易更加便捷,从而导致交易数据呈指数级增长。有了大量可用的交易数据,研究人员发现了通过揭示股价走势和市场动态中的模式来提取有价值见解的机会。深度学习模型越来越多地被用于股价预测。虽然与传统统计方法相比,神经网络具有更强的计算能力,但其结果往往缺乏可解释性,限制了其在解释股价波动和投资行为方面的实用性。为了应对这一挑战,我们提出了一种基于因果关系的方法,该方法采用多元方法,整合了新闻事件关注序列和情绪指数序列。我们的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、语言和通全球事件数据库说明了这一提议方法的应用,通过分析不同类别新闻事件的关注序列和媒体情绪指数序列,展示了这一方法的优势。这项研究不仅为进一步探索指明了方向,还为做出明智的投资决策提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An ensemble model for stock index prediction based on media attention and emotional causal inference

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.

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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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