Information Theoretic Causality Detection between Financial and Sentiment Data

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Information Theoretic Causality Detection between Financial and Sentiment Data

Scaramozzino, R; Cerchiello, P; Aste, T; (2021) Information Theoretic Causality Detection between Financial and Sentiment Data. Entropy , 23 (5) , Article 621. 10.3390/e23050621. Green open access

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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 Standard & Poor (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 is 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.

Type:	Article
Title:	Information Theoretic Causality Detection between Financial and Sentiment Data
Open access status:	An open access version is available from UCL Discovery
DOI:	10.3390/e23050621
Publisher version:	http://dx.doi.org/10.3390/e23050621
Language:	English
Additional information:	This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords:	information theory; textual analysis; transfer entropy; financial news; causality; time series
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10128938