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.