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Real-Time Diffusion of Information on Twitter and the Financial Markets

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  • Ali Tafti
  • Ryan Zotti
  • Wolfgang Jank

Abstract

Do spikes in Twitter chatter about a firm precede unusual stock market trading activity for that firm? If so, Twitter activity may provide useful information about impending financial market activity in real-time. We study the real-time relationship between chatter on Twitter and the stock trading volume of 96 firms listed on the Nasdaq 100, during 193 days of trading in the period from May 21, 2012 to September 18, 2013. We identify observations featuring firm-specific spikes in Twitter activity, and randomly assign each observation to a ten-minute increment matching on the firm and a number of repeating time indicators. We examine the extent that unusual levels of chatter on Twitter about a firm portend an oncoming surge of trading of its stock within the hour, over and above what would normally be expected for the stock for that time of day and day of week. We also compare the findings from our explanatory model to the predictive power of Tweets. Although we find a compelling and potentially informative real-time relationship between Twitter activity and trading volume, our forecasting exercise highlights how difficult it can be to make use of this information for monetary gain.

Suggested Citation

  • Ali Tafti & Ryan Zotti & Wolfgang Jank, 2016. "Real-Time Diffusion of Information on Twitter and the Financial Markets," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0159226
    DOI: 10.1371/journal.pone.0159226
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    References listed on IDEAS

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    Cited by:

    1. Ahmad H. Juma’h & Yazan Alnsour, 2018. "Using Social Media Analytics: The Effect of President Trump’s Tweets On Companies’ Performance," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 17(1), pages 100-121, March.
    2. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    3. Peter Gabrovšek & Darko Aleksovski & Igor Mozetič & Miha Grčar, 2017. "Twitter sentiment around the Earnings Announcement events," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    4. Sangwon Chae & Sungjun Kwon & Donghyun Lee, 2018. "Predicting Infectious Disease Using Deep Learning and Big Data," IJERPH, MDPI, vol. 15(8), pages 1-20, July.
    5. Berny Carrera & Jae-Yoon Jung, 2018. "SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    6. Milla Siikanen & Kk{e}stutis Baltakys & Juho Kanniainen & Ravi Vatrapu & Raghava Mukkamala & Abid Hussain, 2017. "Facebook drives behavior of passive households in stock markets," Papers 1709.07300, arXiv.org, revised May 2018.
    7. Ana Fern'andez Vilas & Rebeca P. D'iaz Redondo & Keeley Crockett & Majdi Owda & Lewis Evans, 2023. "Twitter Permeability to financial events: an experiment towards a model for sensing irregularities," Papers 2312.11530, arXiv.org.
    8. Yu, Jing-Rung & Chiou, W. Paul & Hung, Cing-Hung & Dong, Wen-Kuei & Chang, Yi-Hsuan, 2022. "Dynamic rebalancing portfolio models with analyses of investor sentiment," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 1-13.

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