TY  - UNPD
A1  - Abdi, Farshid
A1  - Kormanyos, Emily
A1  - Pelizzon, Loriana
A1  - Getmansky, Mila
A1  - Simon, Zorka
T1  - A modern take on market efficiency: the impact of Trump’s tweets on financial markets
T2  - SAFE working paper ; No. 314
N2  - 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.
T3  - SAFE working paper - 314 
KW  - Market efficiency
KW  - Social media
KW  - Twitter
KW  - High-frequency event study
KW  - Machine learning
KW  - ETFs
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/59156
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-591560
IS  - May 10, 2021
PB  - SAFE
CY  - Frankfurt am Main
ER  -