Information Theoretic Causality Detection between Financial and Sentiment Data

Entropy

Tomaso Aste¹  Roberta Scaramozzino²  Paola Cerchiello² 

[1] Department of Computer Science, University College London, Gower Street, London WC1E 6EA, UK;Department of Economics and Management, University of Pavia, Via San Felice 7, 27100 Pavia, Italy;

关键词: information theory;    textual analysis;    transfer entropy;    financial news;    causality;    time series;   

DOI  :  10.3390/e23050621

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown