@article{
 title = {No Title},
 type = {article},
 id = {e81b6f21-4b3d-3a30-8840-d6b3f2ebbf3d},
 created = {2016-08-05T16:22:01.000Z},
 file_attached = {false},
 profile_id = {5f7f0dc9-6dfa-3461-b52f-08fd46b3e597},
 group_id = {22113f11-cc82-3072-acd0-28a9a369b882},
 last_modified = {2016-08-05T16:22:01.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
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 citation_key = {Ranco2015},
 source_type = {article},
 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 },
 bibtype = {article},
 author = {Ranco, Gabriele and Aleksovski, Darko and Caldarelli, Guido and Mozetic, Igor and Grcar, Miha}
}

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