2015/06/30 by Gabriele Ranco, Darko Aleksovski, Guido Caldarelli +3 · 395 citations
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Artificial intelligence #Biology #Complex Systems and Time Series Analysis #Computer science #Econometrics #Economics #Event (particle physics) #Financial Markets and Investment Strategies #Financial economics #Granger causality #History #Physics #Sentiment analysis #Series (stratigraphy) #Social media #Stock (firearms) #Stock Market Forecasting Methods #Stock price #Volume (thermodynamics) #World Wide Web #cs.CY #cs.SI
paper · pdf · doi:10.1371/journal.pone.0138441
published in PLoS ONE 10(9), e0138441 (Public Library of Science)
arxiv created 2015/08/11 · openalex publication_date 2015/09/21 · arxiv updated 2015/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events.