Information theoretic causality detection between financial and sentiment data

Roberta Scaramozzino (), Paola Cerchiello () and Tomaso Aste ()
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Roberta Scaramozzino: University of Pavia
Paola Cerchiello: University of Pavia
Tomaso Aste: Computer Science Department, University College London

No 202, DEM Working Papers Series from University of Pavia, Department of Economics and Management

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 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 are 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 (search for similar items in EconPapers)
Pages: 18
Date: 2021-04
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (3)

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