Tweets and Trades: The Information Content of Stock Microblogs

Abstract

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 (i.e., bullishness) of tweets to be associated with abnormal stock returns and message volume to predict next-day trading volume. In addition, we analyze the mechanism leading to efficient aggregation of information in microblogging forums. Our results demonstrate that users providing above average investment advice are retweeted (i.e., quoted) more often and have more followers, which amplifies their share of voice in microblogging forums.

Keywords: Twitter, Microblogging, Stock Market, Investor Sentiment, Text Classification, Computational Linguistics

JEL Classification: G12, G14