The Effects of Twitter Sentiment on Stock Price Returns

TL;DR

This study explores how Twitter sentiment and volume relate to stock returns, finding that while overall correlation is low, significant effects occur during peaks, with sentiment polarity predicting the direction of abnormal returns.

Contribution

The paper adapts the event study methodology to Twitter data, enabling automatic detection of volume peaks and analysis of sentiment impact on stock returns.

Findings

Twitter volume peaks correlate with abnormal stock returns during specific events.

Sentiment polarity at peaks predicts the direction of cumulative abnormal returns.

Dependence between Twitter sentiment and stock returns is statistically significant over several days.

Abstract

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-know micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events.