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How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash

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  • Yang, Xiaolan
  • Zhu, Yu
  • Cheng, Teng Yuan

Abstract

This study examines whether the sentiments expressed in the stock forum posted by individual investors lead to abnormal trading and impose a significant impact on stock price crashes. The history of world financial crises reveals that investor panic is one of the most important factors triggering market crashes. In this paper, we propose a measure to estimate investor panic and its effect on a stock price crash. Using Growth Enterprise Market firms in China's Shenzhen Stock Exchange as a sample, we utilize a computer text mining tool to analyze the contents of nearly one million posts from the largest Chinese stock message board. According to the posting contents, we built a daily Sentiment Index and a Panic Index at the firm level. The empirical results show that the high Sentiment Index leads to a significant following abnormal trading. After controlling public information announcements, this effect is still significant. Moreover, the Panic Index could predict the stock price crash. The effect of the Panic Index on the price crash is stronger either when the information disclosure is opaque or when the proportion of shares held by institutional investors is low.

Suggested Citation

  • Yang, Xiaolan & Zhu, Yu & Cheng, Teng Yuan, 2020. "How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:pacfin:v:59:y:2020:i:c:s0927538x19301349
    DOI: 10.1016/j.pacfin.2019.101245
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    Cited by:

    1. Zhang, Wei & Wang, Pengfei & Li, Yi, 2021. "Do messages on online stock forums spur firm productivity?," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    2. Xuejun Jin & Jiawei Yu, 2022. "Does communication increase investors’ trading frequency? Evidence from a Chinese social trading platform," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-32, December.
    3. Ruwei Zhao & Ruixin Fan & Xiong Xiong & Jianli Wang & Jitka Hilliard, 2023. "Media Tone and Stock Price Crash Risk: Evidence from China," Mathematics, MDPI, vol. 11(17), pages 1-14, August.
    4. Ziqin Yu & Xiang Xiao, 2022. "Innovation information disclosure and stock price crash risk‐based supervision and insurance effect path analysis," Australian Economic Papers, Wiley Blackwell, vol. 61(3), pages 534-590, September.
    5. Irfan Safdar & Michael Neel & Babatunde Odusami, 2022. "Accounting information and left-tail risk," Review of Quantitative Finance and Accounting, Springer, vol. 58(4), pages 1709-1740, May.
    6. Li, Yanshuang & Shi, Yujie & Shi, Yongdong & Xiong, Xiong & Yi, Shangkun, 2024. "Time-frequency extreme risk spillovers between COVID-19 news-based panic sentiment and stock market volatility in the multi-layer network: Evidence from the RCEP countries," International Review of Financial Analysis, Elsevier, vol. 94(C).
    7. Hu, Debao & Li, Xin & Xiang, George & Zhou, Qiyao, 2023. "Asset pricing models in the presence of higher moments: Theory and evidence from the U.S. and China stock market," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    8. Lu, Jing & Chen, Rongze, 2023. "Do individual investors pay attention to the information acquisition activities of institutional investors?," Finance Research Letters, Elsevier, vol. 58(PD).
    9. Wang, Gang-Jin & Xiong, Lu & Zhu, You & Xie, Chi & Foglia, Matteo, 2022. "Multilayer network analysis of investor sentiment and stock returns," Research in International Business and Finance, Elsevier, vol. 62(C).
    10. Sahar Arshad & Nikhar Azhar & Sana Sajid & Seemab Latif & Rabia Latif, 2024. "Cross-Lingual News Event Correlation for Stock Market Trend Prediction," Papers 2410.00024, arXiv.org.
    11. Atri, Hanen & Kouki, Saoussen & Gallali, Mohamed imen, 2021. "The impact of COVID-19 news, panic and media coverage on the oil and gold prices: An ARDL approach," Resources Policy, Elsevier, vol. 72(C).
    12. Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
    13. Saeed, Abubakr & Riaz, Hammad & Baloch, Muhammad Saad, 2022. "Does big data utilization improve firm legitimacy?," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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    More about this item

    Keywords

    Stock message board; Investor panic; Stock price crash; Text mining; Spiral of silence;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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