IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/57376.html
   My bibliography  Save this paper

When can social media lead financial markets?

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/57376/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. Swamy, Vighneswara & Lagesh, M.A., 2023. "Does happy Twitter forecast gold price?," Resources Policy, Elsevier, vol. 81(C).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ehl:lserod:57376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.