Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/233887
Authors: 
Abdi, Farshid
Kormanyos, Emily
Pelizzon, Loriana
Getmansky, Mila
Simon, Zorka
Year of Publication: 
2021
Series/Report no.: 
SAFE Working Paper No. 314
Abstract: 
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.
Subjects: 
Market efficiency
Social media
Twitter
High-frequency event study
Machine learning
ETFs
JEL: 
G10
G14
C58
Document Type: 
Working Paper

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