Linking Twitter Events With Stock Market Jitters

TL;DR

This paper introduces a novel Twitter-based event detection method that predicts stock market jitters by analyzing financial and political events, using a classifier trained on market volatility data without manual labeling.

Contribution

The study presents a new event detection approach combining bursty topic detection and classification tailored for financial and political events influencing stock markets.

Findings

Successfully predicts stock market jitters in Greek and Spanish markets

Utilizes rich Twitter features beyond polarity, such as geography and author info

Achieves high accuracy in real-time intra-day event detection

Abstract

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.