COVID-19 related TV news and stock returns: Evidence from major US TV stations

We investigate a novel dataset of more than half a million 15 seconds transcribed audio snippets containing COVID-19 mentions from major US TV stations throughout 2020. Using the Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, we identify seven COVID-19 related topics...

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Detalles Bibliográficos
Autores principales: Möller, Rouven, Reichmann, Doron
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Board of Trustees of the University of Illinois. Published by Elsevier Inc. 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9721134/
https://www.ncbi.nlm.nih.gov/pubmed/36506906
http://dx.doi.org/10.1016/j.qref.2022.11.007
Descripción
Sumario:We investigate a novel dataset of more than half a million 15 seconds transcribed audio snippets containing COVID-19 mentions from major US TV stations throughout 2020. Using the Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, we identify seven COVID-19 related topics discussed in US TV news. We find that several topics identified by the LDA predict significant and economically meaningful market reactions in the next day, even after controlling for the general TV tone derived from a field-specific COVID-19 tone dictionary. Our results suggest that COVID-19 related TV content had nonnegligible effects on financial markets during the pandemic.

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