More than just Noise? Examining the Information Content of Stock Microblogs on Financial Markets

Abstract

Scholars and practitioners alike increasingly recognize the importance of stock microblogs as they capture the market discussion and have predictive value for financial markets. This paper examines the extent to which stock microblog messages are related to financial market indicators and the mechanism leading to efficient aggregation of information. In particular, this paper investigates the information content of stock microblogs with respect to individual stocks and explores the effects of social influences on an interday and intraday basis. We collected more than 1.2 million stock-related messages (i.e., tweets) related to S&P 100 companies over a period of 7 months. Using methods from computational linguistics, we went through an elaborate process of message feature reduction, spam detection, language detection, and slang removal, which has led to an increase in classification accuracy for sentiment analysis. We analyzed the data on both a daily and a 15-min basis and found that the sentiment of messages is positively affected with contemporaneous daily abnormal stock returns and that message volume predicts 15-min follow-up returns, trading volume, and volatility. Disagreement in microblog messages positively influences stock features, both in interday and intraday analysis. Notably, if we give a greater share of voice to microblog messages depending on the social influence of microbloggers, this amplifies the relationship between bullishness and abnormal returns, market volume, and volatility. Following knowledgeable investors advice results in more power in explaining changes in market features. This offers an explanation for the efficient aggregation of information on microblogging platforms. Furthermore, we simulated a set of trading strategies using microblog features and the results suggest that it is possible to exploit market inefficiencies even when transaction costs are included. To our knowledge, this is the first study to comprehensively examine the association between the information content of stock microblogs and intraday stock market features. The insights from the study permit scholars and professionals to reliably identify stock microblog features, which may serve as valuable proxies for market sentiment and permit individual investors to make better investment decisions.

Keywords

analytics big data intraday analysis sentiment analysis social influence social media stock market trading strategy twitter

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