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Twitter sentiment around the Earnings Announcement events

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  • Peter Gabrovšek
  • Darko Aleksovski
  • Igor Mozetič
  • Miha Grčar

Abstract

We investigate the relationship between social media, Twitter in particular, and stock market. We provide an in-depth analysis of the Twitter volume and sentiment about the 30 companies in the Dow Jones Industrial Average index, over a period of three years. We focus on Earnings Announcements and show that there is a considerable difference with respect to when the announcements are made: before the market opens or after the market closes. The two different timings of the Earnings Announcements were already investigated in the financial literature, but not yet in the social media. We analyze the differences in terms of the Twitter volumes, cumulative abnormal returns, trade returns, and earnings surprises. We report mixed results. On the one hand, we show that the Twitter sentiment (the collective opinion of the users) on the day of the announcement very well reflects the stock moves on the same day. We demonstrate this by applying the event study methodology, where the polarity of the Earnings Announcements is computed from the Twitter sentiment. Cumulative abnormal returns are high (2–4%) and statistically significant. On the other hand, we find only weak predictive power of the Twitter sentiment one day in advance. It turns out that it is important how to account for the announcements made after the market closes. These after-hours announcements draw high Twitter activity immediately, but volume and price changes in trading are observed only on the next day. On the day before the announcements, the Twitter volume is low, and the sentiment has very weak predictive power. A useful lesson learned is the importance of the proper alignment between the announcements, trading and Twitter data.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0173151
    DOI: 10.1371/journal.pone.0173151
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    References listed on IDEAS

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    1. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    2. Cameron Truong, 2010. "Strategic timing of earnings announcements?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 50(3), pages 719-738, September.
    3. Th'arsis T. P. Souza & Tomaso Aste, 2016. "A nonlinear impact: evidences of causal effects of social media on market prices," Papers 1601.04535, arXiv.org, revised Mar 2016.
    4. Igor Mozetič & Miha Grčar & Jasmina Smailović, 2016. "Multilingual Twitter Sentiment Classification: The Role of Human Annotators," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-26, May.
    5. 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.
    6. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    7. Zheludev, Ilya & Smith, Robert & Aste, Tomaso, 2014. "When can social media lead financial markets?," LSE Research Online Documents on Economics 57376, London School of Economics and Political Science, LSE Library.
    8. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    9. Th'arsis Tuani Pinto Souza & Olga Kolchyna & Philip C. Treleaven & Tomaso Aste, 2015. "Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry," Papers 1507.00784, arXiv.org, revised Jul 2015.
    10. Timm O. Sprenger & Philipp G. Sandner & Andranik Tumasjan & Isabell M. Welpe, 2014. "News or Noise? Using Twitter to Identify and Understand Company-specific News Flow," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 41(7-8), pages 791-830, September.
    11. Petra Kralj Novak & Jasmina Smailović & Borut Sluban & Igor Mozetič, 2015. "Sentiment of Emojis," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
    12. A. Craig MacKinlay, 1997. "Event Studies in Economics and Finance," Journal of Economic Literature, American Economic Association, vol. 35(1), pages 13-39, March.
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    4. Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022. "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, vol. 114(C).

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