  Predicting investors reactions to financial and political news is important
for the early detection of stock market jitters. Evidence from several recent
studies suggests that online social media could improve prediction of stock
market movements. However, utilizing such information to predict strong stock
market fluctuations has not been explored so far. In this work, we propose a
novel event detection method on Twitter, tailored to detect financial and
political events that influence a specific stock market. The proposed approach
applies a bursty topic detection method on a stream of tweets related to
finance or politics followed by a classification process which filters-out
events that do not influence the examined stock market. We train our classifier
to recognise real events by using solely information about stock market
volatility, without the need of manual labeling. We model Twitter events as
feature vectors that encompass a rich variety of information, such as the
geographical distribution of tweets, their polarity, information about their
authors as well as information about bursty words associated with the event. We
show that utilizing only information about tweets polarity, like most previous
studies, results in wasting important information. We apply the proposed method
on high-frequency intra-day data from the Greek and Spanish stock market and we
show that our financial event detector successfully predicts most of the stock
market jitters.
