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Keywords:

  • Twitter;
  • microblogging;
  • stock market;
  • investor sentiment;
  • text classification;
  • computational linguistics

Abstract

Microblogging forums (e.g., Twitter) have become a vibrant online platform for exchanging stock-related information. Using methods from computational linguistics, we analyse roughly 250,000 stock-related messages (so-called tweets) on a daily basis. We find an association between tweet sentiment and stock returns, message volume and trading volume, as well as disagreement and volatility. In contrast to previous related research, we also analyse the mechanism leading to an 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.