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Social Media, News Media and the Stock Market

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  • Andre Veiga
  • Ansgar Walther

Abstract

We contrast the impact of traditional news media and social media coverage on stock market volatility and trading volume. We develop a theoretical model of asset pricing and information processing, which allows for both rational traders and a variety of commonly studied behavioral biases. The model yields several novel and testable predictions about the impact of news and social media on asset prices. We then test the model’s theoretical predictions using a unique dataset which measures coverage of individual stocks in social and news media using a broad spectrum of print and online sources. Stocks with high social media coverage in one month experience high idiosyncratic volatility of returns and trading volume in the following month. Conversely, stocks with high news media coverage experience low volatility and low trading volume in the following month. These effects are statistically and economically significant and robust to controlling for stock and time fixed effects, as well as time-varying stock characteristics. The empirical evidence on news media is consistent with a market in which some traders are overconfident when interpreting new information. The evidence on social media is consistent with Tetlock (2011)’s “stale news†hypothesis (investors treat repeated information on social networks as though it were new) and with a model where investors’ perceptions are subject to random sentiment shocks.

Suggested Citation

  • Andre Veiga & Ansgar Walther, 2016. "Social Media, News Media and the Stock Market," Economics Series Working Papers Paper-805, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:paper-805
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    5. Tanaka, Yoshitaka & Managi, Shunsuke, 2023. "Attention-Grabbing ESG," MPRA Paper 116786, University Library of Munich, Germany.
    6. Alex Frino & Caihong Xu & Z. Ivy Zhou, 2022. "Are option traders more informed than Twitter users? A PVAR analysis," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(9), pages 1755-1771, September.
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    9. Xiaoman, Jin & Qing, Li & Jun, Wang & Jingmei, Zhao, 2023. "Voice or noise? Repetitive information and stock performance," Finance Research Letters, Elsevier, vol. 52(C).
    10. Haque, Md Ziaul & Qian, Aimin & Hoque, Md Rakibul & Lucky, Suraiea Akter, 2022. "A unified framework for exploring the determinants of online social networks (OSNs) on institutional investors’ capital market investment decision," Technology in Society, Elsevier, vol. 70(C).
    11. Alomari, Mohammad & Al Rababa’a, Abdel Razzaq & El-Nader, Ghaith & Alkhataybeh, Ahmad & Ur Rehman, Mobeen, 2021. "Examining the effects of news and media sentiments on volatility and correlation: Evidence from the UK," The Quarterly Review of Economics and Finance, Elsevier, vol. 82(C), pages 280-297.
    12. Abdi, Farshid & Kormanyos, Emily & Pelizzon, Loriana & Getmansky, Mila & Simon, Zorka, 2021. "Market impact of government communication: The case of presidential tweets," SAFE Working Paper Series 314, Leibniz Institute for Financial Research SAFE, revised 2021.
    13. Justina Deveikyte & Helyette Geman & Carlo Piccari & Alessandro Provetti, 2020. "A Sentiment Analysis Approach to the Prediction of Market Volatility," Papers 2012.05906, arXiv.org.
    14. Ndubuisi, Gideon & Urom, Christian, 2023. "Dependence and risk spillovers among clean cryptocurrencies prices and media environmental attention," Research in International Business and Finance, Elsevier, vol. 65(C).
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    17. Yongan Xu & Jianqiong Wang & Zhonglu Chen & Chao Liang, 2023. "Sentiment indices and stock returns: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 1063-1080, January.
    18. Qingchong Chen & Xiong Xiong & Ya Gao, 2021. "Is information really efficient for the market? Evidence of confirmatory bias in China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 5965-5997, December.
    19. Tumasjan, Andranik & Braun, Reiner & Stolz, Barbara, 2021. "Twitter sentiment as a weak signal in venture capital financing," Journal of Business Venturing, Elsevier, vol. 36(2).
    20. Tripathi, Abhinava & Pandey, Ashish, 2021. "Information dissemination across global markets during the spread of COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 103-115.
    21. Xiaohong Shen & Gaoshan Wang & Yue Wang & Alfred Peris, 2021. "The Influence of Research Reports on Stock Returns: The Mediating Effect of Machine-Learning-Based Investor Sentiment," Discrete Dynamics in Nature and Society, Hindawi, vol. 2021, pages 1-14, December.
    22. Thomas Boulton & Bill B. Francis & Thomas Shohfi & Daqi Xin, 2021. "Investor awareness or information asymmetry? Wikipedia and IPO underpricing," The Financial Review, Eastern Finance Association, vol. 56(3), pages 535-561, August.

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    More about this item

    Keywords

    Social media; news media; behavioral finance; volatility; trading volume;
    All these keywords.

    JEL classification:

    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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