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A nonlinear impact: evidences of causal effects of social media on market prices

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  • Th'arsis T. P. Souza
  • Tomaso Aste

Abstract

Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior. We provide empirical evidence that suggests social media and stock markets have a nonlinear causal relationship. We take advantage of an extensive data set composed of social media messages related to DJIA index components. By using information-theoretic measures to cope for possible nonlinear causal coupling between social media and stock markets systems, we point out stunning differences in the results with respect to linear coupling. Two main conclusions are drawn: First, social media significant causality on stocks' returns are purely nonlinear in most cases; Second, social media dominates the directional coupling with stock market, an effect not observable within linear modeling. Results also serve as empirical guidance on model adequacy in the investigation of sociotechnical and financial systems.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:1601.04535
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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. Agarwal, Shweta & Kumar, Shailendra & Goel, Utkarsh, 2019. "Stock market response to information diffusion through internet sources: A literature review," International Journal of Information Management, Elsevier, vol. 45(C), pages 118-131.
    3. Jacopo Rocchi & Enoch Yan Lok Tsui & David Saad, 2016. "Emerging interdependence between stock values during financial crashes," Papers 1611.02549, arXiv.org.
    4. 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.
    5. 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.
    6. 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).
    7. Jacopo Rocchi & Enoch Yan Lok Tsui & David Saad, 2017. "Emerging interdependence between stock values during financial crashes," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-15, May.
    8. Clark, Andrew, 2022. "Causality in the aluminum market," Journal of Commodity Markets, Elsevier, vol. 27(C).

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