![]() ![]() DA, MG, and IM also acknowledge support of the Slovenian ARRS programme no. GC also acknowledges support of the EC projects SoBigData no. įunding: All the authors acknowledge support of the EC projects SIMPOL no. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedĭata Availability: The data used in the study available, including the DJIA30 Twitter sentiment and closing price data, used for the analyses, are available at. Received: JAccepted: AugPublished: September 21, 2015Ĭopyright: © 2015 Ranco et al. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.Ĭitation: Ranco G, Aleksovski D, Caldarelli G, Grčar M, Mozetič I (2015) The Effects of Twitter Sentiment on Stock Price Returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. 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 formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. ![]() This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. 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. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. Social media are increasingly reflecting and influencing behavior of other complex systems. ![]()
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