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  1. Market impact of government communication
    the case of presidential tweets
    Autor*in: Abdi, Farshid; Kormanyos, Emily; Pelizzon, Loriana; Getmansky, Mila; Simon, Zorka
    Erschienen: [2021]
    Verlag:  Leibniz Institute for Financial Research SAFE, Sustainable Architecture for Finance in Europe, Frankfurt am Main

    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... mehr

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    Resolving-System (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Standort:
    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
    Fernleihe:
    keine Fernleihe

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 431
    Fernleihe:
    keine Fernleihe

     

    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.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/243280
    Schriftenreihe: SAFE working paper ; no. 314
    Schlagworte: Government communication; Social media; Twitter; Machine learning; ETFs
    Umfang: 1 Online-Ressource (circa 86 Seiten), Illustrationen
  2. A modern take on market efficiency
    the impact of Trump's tweets on financial markets
    Autor*in: Abdi, Farshid; Kormanyos, Emily; Pelizzon, Loriana; Getmansky, Mila; Simon, Zorka
    Erschienen: [2021]
    Verlag:  Leibniz Institute for Financial Research SAFE, Sustainable Architecture for Finance in Europe, Frankfurt am Main

    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... mehr

    Zugang:
    Resolving-System (kostenfrei)
    Verlag (kostenfrei)
    Resolving-System (lizenzpflichtig)
    Verlag (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 431
    Fernleihe:
    keine Fernleihe

     

    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.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/233887
    Schriftenreihe: SAFE working paper ; no. 314
    Schlagworte: Market efficiency; Social media; Twitter; High-frequency event study; Machine learning; ETFs
    Umfang: 1 Online-Ressource (circa 70 Seiten), Illustrationen