Market Signals from Social Media

Département: Finance

Intervenant: Anthony Cookson (U. Colorado)

Salle: TBD

Abstract

This paper develops daily market-wide sentiment and attention indexes derived from millions of posts across major investor social media platforms. We find that sentiment extrapolates from past market-wide returns and exhibits a strong reversal. In contrast, attention predicts negative returns as a continuation of previous trends. The two indexes have distinct predictions for aggregate trading: abnormal trading rises when sentiment is low and attention is high. To identify the drivers of attention and sentiment, we use a shock to data sharing networks: We find sentiment spreads through real firm connections while attention does not. Moreover, attention rises after abnormally high trading, while sentiment rises after abnormally high returns. This extrapolative return pattern is asymmetric, primarily driven by negative market jumps. These findings provide new evidence on the daily market dynamics of sentiment and attention.