管理评论 ›› 2021, Vol. 33 ›› Issue (5): 281-294.

• 物理-事理-人理系统方法论(WSR) • 上一篇    下一篇

股市危机情境下社会媒体投资者情绪对股票市场的影响研究

裘江南, 葛一迪   

  1. 大连理工大学经济管理学院, 大连 116024
  • 收稿日期:2018-08-30 出版日期:2021-05-28 发布日期:2021-06-03
  • 通讯作者: 裘江南(通讯作者),大连理工大学经济管理学院教授,博士
  • 作者简介:葛一迪,大连理工大学经济管理学院硕士研究生。
  • 基金资助:
    国家自然科学基金项目(72074044;71573030);辽宁省社会科学界联合会项目(2021lslybkt-028);中央高校基本科研业务费专项资助项目(DUT20RW207)。

Research on the Influence of Emotion in Social Media on Stock Market in the Context of Stock Market Crash

Qiu Jiangnan, Ge Yidi   

  1. School of Economics and Management, Dalian University of Technology, Dalian 116024
  • Received:2018-08-30 Online:2021-05-28 Published:2021-06-03

摘要: 本研究以2015年下半年中国股市经历的股市危机为背景,通过分层采样的方式选取沪深300指数成分股中60只股票作为样本,爬取样本企业相关的新浪微博文本并提取各类细分情绪,对该特殊时期社交媒体中不同细分情绪对股票市场的影响进行了评估。研究基于“情绪-认知-行为”框架,通过使用隐马尔可夫模型挖掘出“市场认知”这一认知层次上的隐含状态,进而建立投资者情绪与股票市场收益率的间接联系,试图还原真实的决策过程,发现最终影响股票市场的主要情绪。研究表明,股市危机中投资者情绪通过影响市场认知的方式间接影响股票市场,其中正面情绪对市场认知发挥积极的调整作用,而以“怒”为代表的负面高唤醒情绪会恶化市场认知进而加重危机的影响。该研究结论能够指导企业经营者在危机中做出科学的情绪疏导策略,以减弱危机中投资者情绪对市场的影响。此外,本研究为企业经营者和相关研究提供了一种通过挖掘市场隐含状态进而探究情绪和股票市场指标间影响关系的新思路。

关键词: 股市危机, 隐马尔可夫模型, 投资者情绪, 有序逻辑回归, 社交媒体

Abstract: Based on the background of a typical stock market crash for Chinese stock market in the second half of 2015, this study selects 60 stocks in the Shanghai and Shenzhen 300 Index stocks as a sample through stratified sampling, and crawls the Sina Weibo texts related to the company for calculating the multi-category emotion in social media (or investor sentiment). This study evaluates the impact of investor sentiment on stock market during crash period. Based on the “Emotion-Cognition-Behavior” framework, the study mines the hidden state of “market cognition” for the stock market by using Hidden Markov Model (HMM), and then establishes an indirect relationship between investor sentiment and stock return. The study discovers that investor sentiment indirectly affects the stock market by the market cognition. To be more specific, positive sentiment plays a positive role in adjusting market cognition, while negative sentiment with high arousal (e.g. “Anger”) will worsen the market cognition toward the crash state. The results guide business operators to make scientific emotional counseling strategies to reduce the impact of investor sentiment in social media on the market during the crash period. In addition, this study also provides a new approach for business operators to explore the relationship between sentiment and stock market indicators by hidden states mining.

Key words: stock market crash, Hidden Markov Model, investor sentiment, ordered-logistic regression, social media