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Does Investors’ Online Public Opinion Divergence Increase the Trading Volume? Evidence from the CSI 300 Index Constituents

Author

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  • Zihuang Huang

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Qing Xu

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Xinyu Wang

    (School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

We collected online public opinions on the CSI 300 index constituents and investigated the different impacts of online public opinion divergence on trading volume. Here, we find that online public opinions are helpful in improving the trading volume, but the online public opinion divergence of investors reduces the expected trading volume. In particular, non-financial and mid-cap stocks with high levels of discussion are more significantly influenced by online public opinion divergence. Through the classification of investors’ influence levels, we find that the divergence among high-level investors increases the trading volume, while the divergence among low-level investors exacerbates the decrease in trading volume. A reduction in divergence for both levels will have a greater impact. We believe that attention should be paid to regulating and guiding the online public opinions of “newcomers”. This will not only improve the quality of Guba but also contribute to the steady development of the Chinese stock market.

Suggested Citation

  • Zihuang Huang & Qing Xu & Xinyu Wang, 2024. "Does Investors’ Online Public Opinion Divergence Increase the Trading Volume? Evidence from the CSI 300 Index Constituents," JRFM, MDPI, vol. 17(8), pages 1-17, July.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:8:p:316-:d:1441449
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    References listed on IDEAS

    as
    1. Huang, Yuqin & Qiu, Huiyan & Wu, Zhiguo, 2016. "Local bias in investor attention: Evidence from China's Internet stock message boards," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 338-354.
    2. Samuel Jebaraj Benjamin & Pallab Kumar Biswas & M Srikamalaladevi M Marathamuthu & Murugesh Arunachalam, 2022. "Social Media Sentiments and Firm Value," Applied Economics, Taylor & Francis Journals, vol. 54(26), pages 2983-2997, June.
    3. Bolin Lei & Yuping Song, 2024. "Volatility forecasting for stock market incorporating media reports, investors' sentiment, and attention based on MTGNN model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1706-1730, August.
    4. J. Anthony Cookson & Marina Niessner, 2020. "Why Don't We Agree? Evidence from a Social Network of Investors," Journal of Finance, American Finance Association, vol. 75(1), pages 173-228, February.
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