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

Fani Tsapeli, Mirco Musolesi and Peter Tino

Physica A: Statistical Mechanics and its Applications, 2017, vol. 483, issue C, 139-155

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.

Keywords: Causality; Social media; Stock market; Sentiment tracking; Time-series (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:483:y:2017:i:c:p:139-155

DOI: 10.1016/j.physa.2017.04.101

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