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Textual Sentiment of Chinese Microblog Toward the Stock Market

Author

Listed:
  • Ning Wang

    (Department of Information Management, School of Management, Shanghai University, No. 333, Nanchen Rd., Shanghai, China)

  • Shanhui Ke

    (Department of Information Management, School of Management, Shanghai University, No. 333, Nanchen Rd., Shanghai, China)

  • Yibo Chen

    (#x2020;Shanghai Liangyou Asset Management Company Limited, Shanghai, China)

  • Tao Yan

    (#x2021;Department of Management Science, School of Management, University of Science and Technology of China, China)

  • Andrew Lim

    (#xA7;Department of Industrial Systems Engineering, National University of Singapore, Singapore)

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.

Suggested Citation

  • Ning Wang & Shanhui Ke & Yibo Chen & Tao Yan & Andrew Lim, 2019. "Textual Sentiment of Chinese Microblog Toward the Stock Market," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 649-671, March.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:02:n:s0219622019500068
    DOI: 10.1142/S0219622019500068
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    Cited by:

    1. Paola Zola & Paulo Cortez & Costantino Ragno & Eugenio Brentari, 2019. "Social Media Cross-Source and Cross-Domain Sentiment Classification," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1469-1499, September.

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