It is generally accepted that news, often defined as news stories in a professionally edited newspaper, moves stock prices. By exploring data from an online stock forum our study presents a novel approach to identify stock-related news events from an investor perspective as an alternative to...

This study investigates whether microblogging messages on Twitter validly mirror the political landscape offline and can be used to predict election results. In the context of the 2009 German federal election, we conducted a sentiment analysis of over 100,000 messages containing a reference to...

Delineating industry groups of related firms and identifying strategic peers is important for both financial practitioners and scholars. Our study explores whether the degree to which pairs of companies are associated with each other in an online stock forum is related to the comovement of their...

Microblogging forums have become a vibrant online platform to exchange trading ideas and other stock-related information. Using methods from computational linguistics, we analyze roughly 250,000 stock-related microblogging messages, so-called tweets, on a daily basis. We find the sentiment...

This study presents a methodology for identifying a broad range of real-world news events based on microblogging messages. Applying computational linguistics to a unique dataset of more than 400,000 S&P 500 stock-related Twitter messages, we distinguish between good and bad news and demonstrate...