![]() A relation between the daily number of queries for a particular stock, and daily trading volume of the same stock has been studied by. There are several analyses of search engine queries. They study: (i) the connection of exogenous news with price movements, (ii) the stock price reaction to news (iii) the relations between mentions of a company in financial news, or the pessimism of the media, and trading volume (iv) the relation between the sentiment of news, earnings and return predictability, (v) the role of news in trading actions, especially of short sellers (vi) the role of macroeconomic news in stock returns and finally (vii) the high-frequency market reactions to news. Regarding news, various approaches have been attempted. Three major classes of data are considered: web news, search engine queries, and social media. We briefly review the state-of-the-art research which investigates the correlation between the web data and financial markets. Therefore, the possibility to anticipate anomalous collective behavior of investors is of great interest to policy makers because it may allow for a more prompt intervention, when appropriate. Indeed, financial contagion and, ultimately, crises, are often originated by collective phenomena such as herding among investors (or, in extreme cases, panic) which signal the intrinsic complexity of the financial system. We believe that social aspects as measured by social networks are particularly useful to understand financial turnovers. Ultimately, in this vast repository of Internet activity we can find the interests, concerns, and intentions of the global population with respect to various economic, political, and cultural phenomena.Īmong the many fields of applications of data collection, analysis and modeling, we present here a case study on financial systems. The interaction with technological systems is generating massive datasets that document collective behavior in a previously unimaginable fashion. The constantly increasing use of the Internet as a source of information, such as business or political news, triggered an analogous increasing online activity. The recent technological revolution with widespread presence of computers and Internet has created an unprecedented situation of data deluge, changing dramatically the way in which we look at social and economic sciences. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. 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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