Uncovering the relationship between incidental emotion toward a disaster and stock market fluctuations: Evidence from the US market

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Tao Yang , T. Robert Yu , Huimin Zhao
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引用次数: 0

Abstract

Despite having potentially important implications, there has been little research on the relationship between the public's incidental emotion and the stock market. To that end, we construct a valence-based measure of incidental emotion using BERTweet's sentiment analysis and empirically investigate the association between collective incidental emotion toward the COVID-19 pandemic and the U.S. stock market. We employ multivariate time series autoregressive models to test the relationship between emotion polarity and stock market returns or trading volumes. The results reveal that societal sentiment toward the pandemic has a significant effect on the returns of the Dow Jones Industrial Average and S&P 500. In contrast, the macro-level emotion does not significantly affect the return for NASDAQ 100. The findings also suggest a significant association between incidental emotion and trading volumes. We conduct a battery of sensitivity tests that further support our conjecture. The study underscores the robust role of incidental emotion in investment decision-making, highlighting its significance as a distinctive feature that should be incorporated into financial decision support systems.

揭示对灾难的偶然情绪与股市波动之间的关系:来自美国市场的证据
尽管具有潜在的重要影响,但有关公众偶然情绪与股票市场之间关系的研究却很少。为此,我们利用 BERTweet 的情感分析方法构建了一种基于价态的偶发情绪测量方法,并对 COVID-19 大流行病的集体偶发情绪与美国股市之间的关系进行了实证研究。我们采用多变量时间序列自回归模型来检验情绪极性与股市收益或交易量之间的关系。结果显示,社会对大流行病的情绪对道琼斯工业平均指数和 S&P 500 指数的收益率有显著影响。相比之下,宏观层面的情绪对纳斯达克 100 指数的回报率影响不大。研究结果还表明,偶发情绪与交易量之间存在显著关联。我们进行了一系列敏感性测试,进一步证实了我们的猜想。本研究强调了偶发情绪在投资决策中的重要作用,突出了偶发情绪作为一种独特特征的重要性,应将其纳入金融决策支持系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
期刊介绍: The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).
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