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We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication a↵ects the aggregate stock markets. To measure this e↵ect, we concentrate on one of the most prominent Twitter users, the 45 th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they a↵ect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump's tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump's decision to tweet about the economy.

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