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Non-parametric causality detection: An application to social media and financial data

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  • Tsapeli, Fani
  • Musolesi, Mirco
  • Tino, Peter

Abstract

According to behavioral finance, stock market returns are influenced by emotional, social and psychological factors. Several recent works support this theory by providing evidence of correlation between stock market prices and collective sentiment indexes measured using social media data. However, a pure correlation analysis is not sufficient to prove that stock market returns are influenced by such emotional factors since both stock market prices and collective sentiment may be driven by a third unmeasured factor. Controlling for factors that could influence the study by applying multivariate regression models is challenging given the complexity of stock market data. False assumptions about the linearity or non-linearity of the model and inaccuracies on model specification may result in misleading conclusions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:139-155
    DOI: 10.1016/j.physa.2017.04.101
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    References listed on IDEAS

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    Cited by:

    1. Can, Umit & Alatas, Bilal, 2019. "A new direction in social network analysis: Online social network analysis problems and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    2. Satyam Kumar & Yelleti Vivek & Vadlamani Ravi & Indranil Bose, 2023. "Causal Inference for Banking Finance and Insurance A Survey," Papers 2307.16427, arXiv.org.

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