股票生境与信息流:网络社区中不同的共同关注行为如何影响市场反应?

IF 6.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuhong Zhan , Chaoyue Gao , Alvin Chung Man Leung , Qiang Ye
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引用次数: 0

摘要

投资者越来越多地利用在线投资社区在进行交易决策前获取金融市场信息,以降低信息获取成本,获得更丰富的内容。由于注意力有限,投资者倾向于只关注与他们个人投资偏好相符的资产子集。因此,投资者在社区中的关注行为可以反映其关注趋势,预示未来的股票走势。与以往的研究主要关注投资者的常见搜索和观看行为不同,我们基于不同的常见关注行为数据(即投资者的常见关注行为和内容贡献者的常见提及行为)构建了股票聚类,并比较了它们对股票收益的预测能力。在控制了一些确定性因素后,我们验证了集群内股票(即股票栖息地)之间存在共动,发现投资者的共同关注行为比内容贡献者更能预测股票收益。为了探索这一机制,我们发现了不同股票生境之间信息流动的可能方向,并揭示了在线投资社区中内容贡献者的主导作用。本研究丰富了网上投资社区中股票生境与信息扩散的相关文献,为投资者的投资组合管理提供了实用的决策支持。此外,网络平台管理者也可以利用我们的结论为市场参与者提供更好的决策辅助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stock habitats and information flow: How do different co-attention behaviors in online communities shape market reactions?
Investors increasingly use online investment communities to acquire financial market information before making trading decisions to reduce the cost of information acquisition and get more abundant content. Due to limited attention, investors tend to focus their trading only on a subset of assets that align with their personal investment preferences. Thus, the attention behavior of investors in the communities can reflect their focus trends and indicate future stock movements. Unlike previous research that mainly focused on investor common search and viewing behaviors, we constructed stock clusters based on different common attention behaviors data (i.e., common follow behavior by investors and common mention behavior by content contributors) and compared their predictive capabilities on stock returns. After controlling for some deterministic factors, we verified the existence of comovement among stocks within the clusters (i.e., stock habitats) and found that investors' common attention behaviors can better predict stock returns compared to content contributors. To explore the mechanism, we found a possible direction of information flow between different stock habitats and revealed the leading role of content contributors in online investment communities. This study enriches the literature on stock habitats and information diffusion in online investment communities and provides practical decision support on portfolio management for investors. Moreover, online platform managers can also use our conclusions to provide better decision-making assistance for market participants.
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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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