Information theoretic causality detection between financial and sentiment data
Autor: | Roberta Scaramozzino, Paola Cerchiello, Tomaso Aste |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Index (economics)
causality HG Finance Science QC1-999 General Physics and Astronomy 02 engineering and technology Information theory Astrophysics 01 natural sciences Article 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Econometrics Economics Social media financial news HA Statistics 010306 general physics information theory Physics transfer entropy Causality Stock price QB460-466 textual analysis 020201 artificial intelligence & image processing Transfer entropy Stock market Causal information time series |
Zdroj: | Entropy Volume 23 Issue 5 Entropy, Vol 23, Iss 621, p 621 (2021) |
Popis: | 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. |
Databáze: | OpenAIRE |
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