COVID-19 related TV News and Stock Returns: Evidence from Major US TV Stations

48 Pages Posted: 18 Nov 2021 Last revised: 28 Nov 2022

See all articles by Rouven Möller

Rouven Möller

Ruhr University of Bochum - Department of Finance and Banking

Doron Reichmann

Ruhr University of Bochum - Department of Finance and Banking

Date Written: November 23, 2022

Abstract

We investigate a novel dataset of more than half a million 15 second 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.

Keywords: Stock Returns; COVID-19 TV News; Natural Language Processing; Topic Modeling

JEL Classification: G12, G10

Suggested Citation

Möller, Rouven and Reichmann, Doron, COVID-19 related TV News and Stock Returns: Evidence from Major US TV Stations (November 23, 2022). Quarterly Review of Economics and Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3944034 or http://dx.doi.org/10.2139/ssrn.3944034

Rouven Möller

Ruhr University of Bochum - Department of Finance and Banking ( email )

Universitätsstraße 150
Bochum, 44780
Germany
+49(0)234 32-27683 (Phone)

Doron Reichmann (Contact Author)

Ruhr University of Bochum - Department of Finance and Banking ( email )

Germany

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