Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/243280 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
SAFE Working Paper No. 314
Versionsangabe: 
September 27, 2021
Verlag: 
Leibniz Institute for Financial Research SAFE, Frankfurt a. M.
Zusammenfassung: 
We propose the "President reacts to news" channel of stock returns by studying the financial market impact of the Twitter account of the 45th president of the United States, Donald Trump. We use machine learning algorithms to classify topic and textual sentiment of 1,400 economy-related tweets to investigate whether they contain relevant information for financial markets. Analyzing high-frequency data, we find that after controlling for past market movements, most tweets are reactive and predictable, rather than novel and informative. The exceptions are tweet topics where the president has direct policy authority and his negative sentiment could adversely a↵ect economic outcomes.
Schlagwörter: 
Government communication
Social media
Twitter
Machine learning
ETFs
JEL: 
G10
G14
C58
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.