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

Rouven Möller and Doron Reichmann

The Quarterly Review of Economics and Finance, 2023, vol. 87, issue C, 95-109

Abstract: 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.

Keywords: Stock returns; COVID-19 TV news; Natural language processing; Topic modeling (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:quaeco:v:87:y:2023:i:c:p:95-109

DOI: 10.1016/j.qref.2022.11.007

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