The President Reacts to News Channel of Government Communication

Abstract

Studying about 1,200 economy-related tweets of President Trump, we establish the “President reacts to news” channel of stock returns. Using high-frequency identification of market movements and machine learning to classify the topics and textual sentiment of tweets, we address the observed heterogeneity in the aggregate stock market response to these messages. After controlling for market trends preceding tweets, we find that 80% of tweets are reactive and predictable rather than novel and informative. The exceptions are trade war tweets, where the President has direct policy authority, and his tweets can reveal investable private information or information about his policy function.

Keywords: Government communication, Social media, Twitter, Machine learning, ETFs

JEL Classification: G10, G14, C58