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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 affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th 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 affect 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.
We analyze the market reaction to the sentiment of the CEO speech at the Annual General Meeting (AGM). As the AGM is typically preceded by several information disclosures, the CEO speech may be expected to contribute only marginally to investors’ decision making. Surprisingly, however, we observe from the transcripts of 338 CEO speeches of German corporates between 2008 and 2016 that their sentiment is significantly related to abnormal stock returns and trading volume around the AGM. By adapting a finance-specific German dictionary based on Loughran and McDonald (2011), we find a negative association of the post-AGM returns with the speeches’ negativity and a positive association with the speeches’ relative positivity (i.e. positivity relative to negativity). Relative positivity moreover corresponds with a lower trading volume around the AGM. Investors hence seem to perceive the sentiment of CEO speeches at AGMs as a valuable indicator of future firm performance. Our results are robust against different weighting schemes and our dictionary appears to be better suited to grasp the sentiment of German business documents compared to general dictionaries.