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Information theoretic causality detection between financial and sentiment data

Roberta Scaramozzino, Paola Cerchiello and Tomaso Aste

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: information theory; textual analysis; transfer entropy; financial news; causality; time series; ES/K002309/1; EP/P031730/1; H2020-ICT-2018-2 825215 (search for similar items in EconPapers)
JEL-codes: C1 F3 G3 (search for similar items in EconPapers)
Pages: 22 pages
Date: 2021-05-16
New Economics Papers: this item is included in nep-big and nep-ict
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Citations: View citations in EconPapers (1) Track citations by RSS feed

Published in Entropy, 16, May, 2021, 23(5). ISSN: 1099-4300

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