Abstract
In this paper, text mining and statistical models are deployed to explore the relationship between the Shanghai Stock Exchange Composite Index (SSECI) and the collective emotions of individual investors. The emotions of individual investors are quantified by extracting and aggregating investor online posts that contain finance-related keywords. To identify a set of finance-related keywords, three years of blogs from a famous financial blog site are segmented by an automatic text segmentation method; meanwhile, in the literature of social media, people typically select keywords manually. Posts that discuss the keywords are extracted out of all types of topics from Sina Weibo, the largest microblog platform in China. Statistical results reveal the relationship between daily posts and daily opening prices with a one-day lag, which indicates the existence of information (news) propagation lag. This study contributes to the existing literature by demonstrating that the microblog sentiment level reports can be quantitatively incorporated as a proxy to provide valuable support to portfolio decision making.
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