Information theoretic causality detection between financial and sentiment data

Scaramozzino, Roberta, Cerchiello, Paola and Aste, Tomaso (2021) Information theoretic causality detection between financial and sentiment data. Entropy, 23 (5). ISSN 1099-4300

Text (entropy-23-00621-v3) - Published Version Available under License Creative Commons Attribution. Download (3MB)

• Author

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.

Item Type:	Article
Official URL:	https://www.mdpi.com/journal/entropy
Additional Information:	© 2021 The Authors
Divisions:	Systemic Risk Centre
Subjects:	H Social Sciences > HG Finance H Social Sciences > HA Statistics
Date Deposited:	22 Jun 2021 12:30
Last Modified:	17 Apr 2024 06:45
URI:	http://eprints.lse.ac.uk/id/eprint/110903

Actions (login required)

View Item