The impact of the infodemic on the stock market under the COVID-19: taking the investors’ information infection index as the intermediary variable
Abstract
The outbreak of COVID-19 is synchronized with the outbreak of the infodemic, which directly affected the sentiment and behaviours of investors and thus affected the stock market. At the same time, the outbreak of the infodemic has led to the information infection of the public. With the information infection, panic, anxiety, and other emotions have spread among the public, affecting the behaviours of investors, and thus affecting the stock returns. This paper explores the impact of the infodemic on the stock market by selecting keywords related to the “epidemic situation”, using the Baidu information index as an indicator to measure the infodemic, and the Baidu search index as an indicator to measure the degree of information infection. The empirical findings reveal that: First, the more serious the infodemic, the more severe the information infection; Second, the deeper the infodemic, the lower the stock returns of A-share listed companies; Third, there is a phenomenon that the infodemic affects the stock returns through the intermediary of information infection in the stock market.
First published online 20 February 2023
Keyword : COVID-19, infodemic, information infection, emotional contagion, intermediary effect
How to Cite
Xie, W., Tang, Y., Xu, Z., Zhang, X., & Lai, D. (2023). The impact of the infodemic on the stock market under the COVID-19: taking the investors’ information infection index as the intermediary variable. Technological and Economic Development of Economy, 29(2), 653–676. https://doi.org/10.3846/tede.2023.18571
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Yang, Z. H., Chen, Y. T., & Zhang, P. M. (2020). Macroeconomic impact, financial risk transmission and governance response under major public emergencies. Journal of Management World, 36(05), 13–35 + 7. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=GLSJ202005004&DbName=DKFX2020
Yi, C. H., & Li, S. (2021). Analysis on the generation of netizens’ emotion and its impact on the government image in the public opinion crisis. Public Administration and Policy Review, 10(04), 73–83. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=GGZC202104010&DbName=CJFQ2021
You, J. X., & Wu, J. (2012). Silent spiral: Media sentiment and asset mispricing. Economic Research Journal, 47(07), 141–152. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=JJYJ201207012&DbName=CJFQ2012
Zhang, C., & Huang, W. S. (2020). The concept, characteristics and coping strategies of “infodemic” of COVID-19. Chinese Journalist, 5, 59–61. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=ZGJZ202005017&DbName=CJFQ2020
Zhang, Y. M., He, X., Du, C. C., & Su, Y. Y. (2020). Research on IESR model of internet users’ emotional communication under the cumulative effect of negative emotions. Information Science, 38(10), 29–34. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=QBKX202010005&DbName=DKFX2020
Zuckerman, M. (1984). Sensation seeking: A comparative approach to a human trait. The Behavioral, and Brain Sciences, 7(3), 413–434. https://doi.org/10.1017/S0140525X00018938
Bikhchandani, S., & Sharma, S. (2001). Herding behavior in financial markets. IMF Staff Papers, 47(3), 279–310.
Blendon, R. J., Benson, J. M., DesRoches, C. M., Raleigh, E., & Taylor-Clark, K. (2004). The public’s response to severe acute respiratory syndrome in Toronto, and the United States. Clinical Infectious Diseases, 38(7), 925–931. https://doi.org/10.1086/382355
Chen, L., & Qu, X. H. (2020). Research on market response to infectious public health events – Based on the impact of COVID-19 epidemic on China’s stock market. Financial Forum, 25(07), 25–33 +65. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=CSJR202007004&DbName=CJFQ2020
Chen, Q., Song, S. J., & Zhao, Y. X. (2020a). The impact of information overload on users’ information evasion behavior in public health emergencies: An empirical study based on COVID-19 information epidemic. Information and Documentation Services, 41(03), 76–88. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=QBZL202003015&DbName=CJFQ2020
Chen, S. H., Dai, J. M., Hu, Q., Chen, H., Wang, Y., Gao, J. L., Zheng, B. B., & Fu, H. (2020b). Public anxiety and its influencing factors under the outbreak of COVID-19 in 2019. Fudan University Journal of Medical Sciences, 47(03), 385–391. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=SHYK202003015&DbName=CJFQ2020
Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society, 20(5), 1–20. https://doi.org/10.1080/01972240490507974
He, C. Y., Wen, Y. C., Chang, Y. L., & Geng, X. X. (2020). Measurement and analysis of the impact of novel coronavirus pneumonia on China’s economy. The Journal of Quantitative & Technical Economics, 37(05), 3–22. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=SLJY202005001&DbName=CJFQ2020
Iliyasu, G., Ogoina, D., Out, A. A., Dayyab, F. M., Ebenso, B., Otokpa, D., Rotifa, S., Olomo, T. O., & Habib, A. G. (2015). A multisite knowledge attitude and practice survey of Ebola virus disease in Nigeria. PloS One, 10(8), e0135955. https://doi.org/10.1371/journal.pone.0135955
Lan, B., & Zhuang, L. (2021). Research on the impact of COVID-19 on financial market. Statistics & Decision Making, 37(05), 129–133. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=TJJC202105029&DbName=DKFX2021
Latiff, L. A., Parhizkar, S., Zainuddin, H., Chun, G. M., Liyana, M. A. A. R. N., Ramli, N., & Yun, K. L. (2012). Pandemic influenza A (H1N1) and its prevention: A cross sectional study on patients, knowledge, attitude and practice among patients attending primary health care clinic in Kuala Lumpur, Malaysia. Global Journal of Health Science, 4(2), 95–102. https://doi.org/10.5539/gjhs.v4n2p95
Leung, G. M., & Leung, K. (2020). Crowdsourcing data to mitigate epidemics. The Lancet Digital Health, 2(4), 156–157. https://doi.org/10.1016/S2589-7500(20)30055-8
Li, Y., & Wu, F. F. (2020). Research on investor sentiment, idiosyncratic risk and A+H stock price difference. Financial Regulation Research, 12, 50–63. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=JRJG202012004&DbName=CJFQ2020
Liu, Y. Z., & Wang, C. H. (2020). Epidemic analysis from the perspective of behavior: A review of causes, impacts, and countermeasures. Journal of Financial Research, 6(19), 1–19. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=JRYJ202006001&DbName=CJFQ2020
Lu, F., & Xing, X. W. (2022). Divergence in investor sentiment and stock price bias in the recovery phase of major public events. Contemporary Economic Management, 44(10), 91–96. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=DJGL202210011&DbName=CJFQ2022
Lu, Q. Y., & Chen, H. (2020). Media reports, investor sentiment and stock price fluctuations. Research on Financial and Economic Issues, 3, 60–67. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=CJWT202103007&DbName=CJFQ202
Luo, Z., Wang, C. F., & Fang, Z. M. (2018). Social interaction, investor sentiment contagion and asset foam – An empirical study based on the post of stock forum. Operations Research and Management Science, 27(02), 124–132. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=YCGL201802017&DbName=CJFQ2018
McDougall, W. (1923). Purposive or mechanical psychology? Psychological Review, 30(4), 273–288. https://doi.org/10.1037/h0074955
Rothkopf, D. (2003, May 18). SARS, fear, rumors feed unprecedented infodemic. Washington Post.
Sun, K. P., & Xiao, X. (2018). Influence mechanism of internet social media on investor sentiment contagion and stock price collapse risk. Journal of Technology Economics, 37(06), 93–102. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=JSJI201806012&DbName=CJFQ2018
Tian, J. F., Yang, X. T., Xue, R., & Wang, C. (2020). Uncertain events, investor concerns and heterogeneous characteristics of the stock market – Taking COVID-19 concept stocks as an example. Journal of Finance and Economics, 46(11), 19–30. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=CJYJ202011003&DbName=CJFQ2020
Wang, J., Lu, Y., Li, G. D., & Zhuang, X. T. (2019a). Emotional contagion and “foolish money effect” in fund investment. Securities Market Herald, 10(7), 60–66. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=ZQDB201910009&DbName=CJFQ2019
Wang, Q., Wang, Z. L., Li, S. X., & Xue, F. Z. (2020). The short-term impact of the “COVID-19” Epidemic on the Price Fluctuation of China’s Stock Market. Review of Economy and Management, 36(06), 16–27. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=SDJJ202006002&DbName=CJFQ2020
Wang, S. W. (2020). On the ten characteristics of “Information Epidemic”. Library Journal, 39(03), 19–23. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=TNGZ202003002&DbName=CJFQ2020
Wang, S., Wang, Y., & Li, F. (2019b). The impact of information overload on users’ negative use behavior in the context of socialized e-commerce – Mediation based on burnout and resistance. Enterprise Economy, 38(03), 50–57. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=QUIT201903007&DbName=CJFQ2019
Wang, Z. J., Hou, Y. R., Kuang, Y., Tang, H. Y., Zhao, Z. Z., & Chen, H. X. (2017). Amplification effect of group shared emotion. Advances in Psychological Science, 25(04), 662–671. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=XLXD201704011&DbName=CJFQ2017
Wei, J., Huang, Y. J. H., & Zhu, H. M. (2019). Research on social network public opinion communication model based on coupling network. Journal of Modern Information, 39(10), 110–118.
Wen, L., & Li, H. B. (2022). Epidemic prevention and control and stock price volatility: Empirical evidence from Chinese listed companies. Management Review, 34(08), 54–64. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=ZWGD202208005&DbName=CJFQ2022
Wen, Z. L., & Ye, B. J. (2014). Intermediary effect analysis: Method and model development. Advances in Psychological Science, 22(05), 731–745. https://kns.cnki.net/kcms2/article/abstract?v=inVe5WWz2m6679y6za_fUpo3JMrfolWujlaSrIGYM_KcNUDBIZfry_KQ_zCB4C-O_CSFgmaYxWKDLSOdrLnhYBAQIpkdC8P_clwr7YVM5zDWhQq_Vr0Abw==&uniplatform=NZKPT&language=CHS
Wilson, T. D. (2000). Human information behavior. Informing Science: The International Journal of an Emerging Transdiscipline, 3(02), 49–56. https://doi.org/10.28945/576
World Health Organization. (2020). Novel Coronavirus (2019-nCoV) situation report-12. 1st February 2020. https://www.who.int/emergencies/diseases/nevel-coronavirus-2019/situation-reports
Xiong, X., Liu, M. H., & Chen, S. N. (2018). Dynamic characterization of the influence of investor sentiment and market interest rate on the return of securities market index. Operations Research and Management Science, 27(09), 156–169. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=YCGL201809021&DbName=CJFQ2018
Xu, J., & Qian, Y. F. (2020). Definition, dissemination and management of “Information Epidemic”. Journal of Shanghai Jiaotong University (Philosophy and Social Sciences), 28(05), 121–134. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=SHJX202005013&DbName=CJFQ2020
Yang, Z. H., Chen, Y. T., & Zhang, P. M. (2020). Macroeconomic impact, financial risk transmission and governance response under major public emergencies. Journal of Management World, 36(05), 13–35 + 7. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=GLSJ202005004&DbName=DKFX2020
Yi, C. H., & Li, S. (2021). Analysis on the generation of netizens’ emotion and its impact on the government image in the public opinion crisis. Public Administration and Policy Review, 10(04), 73–83. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=GGZC202104010&DbName=CJFQ2021
You, J. X., & Wu, J. (2012). Silent spiral: Media sentiment and asset mispricing. Economic Research Journal, 47(07), 141–152. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=JJYJ201207012&DbName=CJFQ2012
Zhang, C., & Huang, W. S. (2020). The concept, characteristics and coping strategies of “infodemic” of COVID-19. Chinese Journalist, 5, 59–61. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=ZGJZ202005017&DbName=CJFQ2020
Zhang, Y. M., He, X., Du, C. C., & Su, Y. Y. (2020). Research on IESR model of internet users’ emotional communication under the cumulative effect of negative emotions. Information Science, 38(10), 29–34. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=QBKX202010005&DbName=DKFX2020
Zuckerman, M. (1984). Sensation seeking: A comparative approach to a human trait. The Behavioral, and Brain Sciences, 7(3), 413–434. https://doi.org/10.1017/S0140525X00018938