Title |
The Effects of Twitter Sentiment on Stock Price Returns
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Published in |
PLOS ONE, September 2015
|
DOI | 10.1371/journal.pone.0138441 |
Pubmed ID | |
Authors |
Gabriele Ranco, Darko Aleksovski, Guido Caldarelli, Miha Grčar, Igor Mozetič |
Abstract |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 16 | 30% |
India | 2 | 4% |
Australia | 2 | 4% |
United Kingdom | 2 | 4% |
Canada | 2 | 4% |
Korea, Republic of | 1 | 2% |
Japan | 1 | 2% |
Croatia | 1 | 2% |
Nigeria | 1 | 2% |
Other | 5 | 9% |
Unknown | 21 | 39% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 47 | 87% |
Practitioners (doctors, other healthcare professionals) | 3 | 6% |
Scientists | 3 | 6% |
Science communicators (journalists, bloggers, editors) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 3 | <1% |
United States | 3 | <1% |
Italy | 1 | <1% |
Brazil | 1 | <1% |
Spain | 1 | <1% |
Slovenia | 1 | <1% |
Japan | 1 | <1% |
Luxembourg | 1 | <1% |
Unknown | 566 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 100 | 17% |
Student > Ph. D. Student | 82 | 14% |
Student > Bachelor | 72 | 12% |
Researcher | 42 | 7% |
Student > Doctoral Student | 34 | 6% |
Other | 80 | 14% |
Unknown | 168 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Economics, Econometrics and Finance | 120 | 21% |
Business, Management and Accounting | 101 | 17% |
Computer Science | 85 | 15% |
Engineering | 29 | 5% |
Social Sciences | 15 | 3% |
Other | 41 | 7% |
Unknown | 187 | 32% |