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Information theoretic causality detection between financial and sentiment data

Author

Listed:
  • Roberta Scaramozzino

    (University of Pavia)

  • Paola Cerchiello

    (University of Pavia)

  • Tomaso Aste

    (Computer Science Department, University College London)

Abstract

The interaction between the flow of sentiment expressed on blogs and media and the dynamics of the stock market prices are analyzed through an information-theoretic measure, the transfer entropy, to quantify causality relations. We analyzed daily stock price and daily social media sentiment for the top 50 companies in the S&P index during the period from November 2018 to November 2020. We also analyzed news mentioning these companies during the same period. We found that there is a causal flux of information that links those companies. The largest fraction of significant causal links are between prices and between sentiments, but there is also significant causal information which goes both ways from sentiment to prices and from prices to sentiment. We observe that the strongest causal signal between sentiment and prices is associated with the Tech sector.

Suggested Citation

  • 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.
    • Handle: RePEc:pav:demwpp:demwp0202
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    File URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0202.pdf
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    References listed on IDEAS

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    3. Nicola, Giancarlo & Cerchiello, Paola & Aste, Tomaso, 2020. "Information network modeling for U.S. banking systemic risk," LSE Research Online Documents on Economics 107563, London School of Economics and Political Science, LSE Library.
    4. 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.
    5. Z. Keskin & T. Aste, 2019. "Information-theoretic measures for non-linear causality detection: application to social media sentiment and cryptocurrency prices," Papers 1906.05740, arXiv.org, revised Jun 2019.
    6. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    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. 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.
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    Citations

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    Cited by:

    1. Caferra, Rocco, 2022. "Sentiment spillover and price dynamics: Information flow in the cryptocurrency and stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
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    3. Agosto, Arianna & Cerchiello, Paola & Pagnottoni, Paolo, 2022. "Sentiment, Google queries and explosivity in the cryptocurrency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).

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    More about this item

    Keywords

    Information theory; Textual analysis; Transfer Entropy; Financial news; Causality; Time Series;
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