Information Theoretic Causality Detection between Financial and Sentiment Data.

Roberta Scaramozzino,Paola Cerchiello,Tomaso Aste

doi:10.3390/e23050621

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.

Highlights

Causality is hard to detect from observations
In order to statistically quantify the significance of such overlap between the networks, we compute the hypergeometric probability to have a certain number or more of overlapping edges in two directed graphs
We find that there is no statistical significance in terms of p-value for the thresholds Z > 2.5 and News > 20

Summary

Introduction

Causality is hard to detect from observations. This is because the occurrence of two events, one after the other, does not necessarily imply that the first caused the second.In 1969, Granger [1] first proposed to look at causality in terms of the amount of extra information that the observation of a variable provides about another variable. Shreiber [23] theorizes the concept of transfer entropy as a measure of oriented coherence statistics between systems that evolve over time and Marschinski and Kants [24], following this concept, analyze the flow of information between two time series: Dow Jones and DAX stock index. They introduce a modified estimator able to perform well in the case of short temporal series. For the implementation of the analysis, they used general and partial Granger causality, the latter correlated to representative measures of the general economic condition

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