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When can social media lead financial markets?

Author

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  • Zheludev, Ilya
  • Smith, Robert
  • Aste, Tomaso

Abstract

Social media analytics is showing promise for the prediction of financial markets. However, the true value of such data for trading is unclear due to a lack of consensus on which instruments can be predicted and how. Current approaches are based on the evaluation of message volumes and are typically assessed via retrospective (ex-post facto) evaluation of trading strategy returns. In this paper, we present instead a sentiment analysis methodology to quantify and statistically validate which assets could qualify for trading from social media analytics in an ex-ante configuration. We use sentiment analysis techniques and Information Theory measures to demonstrate that social media message sentiment can contain statistically-significant ex-ante information on the future prices of the S&P500 index and a limited set of stocks, in excess of what is achievable using solely message volumes.

Suggested Citation

  • 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.
    • Handle: RePEc:ehl:lserod:57376
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    File URL: http://eprints.lse.ac.uk/57376/
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    Citations

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    Cited by:

    1. Scaramozzino, Roberta & Cerchiello, Paola & Aste, Tomaso, 2021. "Information theoretic causality detection between financial and sentiment data," LSE Research Online Documents on Economics 110903, London School of Economics and Political Science, LSE Library.
    2. 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.
    3. Marinč, Matej & Massoud, Nadia & Ichev, Riste & Valentinčič, Aljoša, 2021. "Presidential candidates linguistic tone: The impact on the financial markets," Economics Letters, Elsevier, vol. 204(C).
    4. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2016. "Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-14, January.
    5. Ilaria Gianstefani & Luigi Longo & Massimo Riccaboni, 2022. "The echo chamber effect resounds on financial markets: a social media alert system for meme stocks," Papers 2203.13790, arXiv.org.
    6. Tim Matthies & Thomas Lohden & Stephan Leible & Jun-Patrick Raabe, 2023. "To the Moon: Analyzing Collective Trading Events on the Wings of Sentiment Analysis," Papers 2308.09968, arXiv.org.
    7. Xiao-Qian Sun & Hua-Wei Shen & Xue-Qi Cheng & Yuqing Zhang, 2016. "Market Confidence Predicts Stock Price: Beyond Supply and Demand," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-10, July.
    8. Mario Gutiérrez-Roig & Carlota Segura & Jordi Duch & Josep Perelló, 2016. "Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.
    9. Roberta Scaramozzino & Paola Cerchiello & Tomaso Aste, 2021. "Information theoretic causality detection between financial and sentiment data," DEM Working Papers Series 202, University of Pavia, Department of Economics and Management.
    10. Swamy, Vighneswara & Lagesh, M.A., 2023. "Does happy Twitter forecast gold price?," Resources Policy, Elsevier, vol. 81(C).
    11. Heleen Brans & Bert Scholtens, 2020. "Under his thumb the effect of president Donald Trump’s Twitter messages on the US stock market," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-11, March.
    12. Jonathan Manfield & Derek Lukacsko & Th'arsis T. P. Souza, 2018. "Bull Bear Balance: A Cluster Analysis of Socially Informed Financial Volatility," Papers 1811.10195, arXiv.org.
    13. Souza, Thársis T.P. & Aste, Tomaso, 2019. "Predicting future stock market structure by combining social and financial network information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    14. Harish Kamath & Noor Firdoos Jahan, 2020. "Using Hidden Markov Model to Monitor Possible Loan Defaults in Banks," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(4), pages 1097-1107.
    15. Francisco Guijarro & Ismael Moya-Clemente & Jawad Saleemi, 2019. "Liquidity Risk and Investors’ Mood: Linking the Financial Market Liquidity to Sentiment Analysis through Twitter in the S&P500 Index," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    16. 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.
    17. 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.
    18. Siikanen, Milla & Baltakys, Kęstutis & Kanniainen, Juho & Vatrapu, Ravi & Mukkamala, Raghava & Hussain, Abid, 2018. "Facebook drives behavior of passive households in stock markets," Finance Research Letters, Elsevier, vol. 27(C), pages 208-213.
    19. Yukie Sano & Hideki Takayasu & Shlomo Havlin & Misako Takayasu, 2019. "Identifying long-term periodic cycles and memories of collective emotion in online social media," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-17, March.
    20. Tsapeli, Fani & Musolesi, Mirco & Tino, Peter, 2017. "Non-parametric causality detection: An application to social media and financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 139-155.
    21. 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.

    More about this item

    JEL classification:

    • N0 - Economic History - - General
    • E6 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook

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