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http://dx.doi.org/10.6109/jkiice.2021.25.1.1

Sensitivity of abacus and Chasdaq in the Chinese stock market through analysis of Weibo sentiment related to Corona-19  

Li, Jiaqi (Department of Business Administration, Sungkyunkwan University)
Oh, Hayoung (Global Convergence, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.1, 2021 , pp. 1-7 More about this Journal
Abstract
Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.
Keywords
Social media; Investor sentiment; Text mining; Chinese stock market;
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