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基于股评的投资者情绪对股票市场的影响
Investor sentiment extracted from internet stock message boards and itseffect on Chinese stock market
摘要点击 396  全文点击 144    
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中文关键词  投资者情绪; 噪声交易者; 文本挖掘; Granger 因果检验
英文关键词  investor sentiment; noise trader; text mining; Granger causality test
基金项目  国家自然科学基金资助项目( 71671012; 71373001; 71531001; 71725002; 71471009) ; 北京航空航天大学青年科学家团队项目.
学科分类代码  
作者单位
部 慧 北京航空航天大学经济管理学院,北京100191 
解 峥 北京航空航天大学经济管理学院,北京100191 
李佳鸿 北京航空航天大学经济管理学院,北京100191 
吴俊杰 北京航空航天大学经济管理学院,北京100191;北京航空航天大学大数据科学与脑机智能高精尖创新中心,北京100191 
中文摘要
      探讨投资者情绪对我国股票市场的影响.为刻画投资者情绪,基于东方财富网股吧帖文与朴素贝叶斯方法,提出融合股评看涨看跌预期和投资者关注程度的投资者情绪度量指标.进一步,利用Granger因果检验、瞬时Granger因果检验、跨期回归分析等方法,探讨了投资者情绪对我国股票收益率、交易量和波动性是否具有预测能力及影响.实证结果揭示:虽然投资者情绪对股票市场收益率、交易量和波动性均无预测能力,但投资者情绪对股票收益率和交易量有当期影响;开盘前非交易时段的股评情绪对开盘价具有预测力,开盘后交易时段的股评情绪对收盘价和日交易量具有更显著的影响.此外,股票收益率是投资者情绪的Granger原因,即投资者情绪的形成依赖于前期市场收益率.这些实证结果为深入理解参与股吧评论的交易者的行为以及行为对市场产生的影响提供了证据.
英文摘要
      This paper examines whether investor sentiment expressed in online user generated content ( UGC) in the stock message boards of Easymoney. com has a predictive power for Chinese stock market. Naive Bayes classifier is used to obtain the expectation of every message,and then a new indicator of investor sentiment is put for ward that combines the idea of bullishness and investor attention. Granger causality test,instantaneous Granger causality test and intertemporal regression analysis are applied to study the predictability of investor sentiment for stock returns,volatility,and trading volume. It is found that investor sentiment has no predictable power for returns,volatility,or trading volume; while investor sentiment does have a significant positive effect on the contemporaneous stock price,and disagreement of investor sentiment can affect contemporaneous trading volume. Inventor sentiment using the messages before market opening has the predictive power of open price,and inventor sentiment from the trading hours is more valuable for closing price and trading volume. Moreover,stock returns are the Granger cause of investor sentiment. Our results can help to understand the behavior of investors and their effects on stock market.
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