Perico Ortiz H, Schnaubelt M, Seifert O (2023)
Publication Language: English
Publication Type: Other publication type
Publication year: 2023
URI: https://www.iwf.rw.fau.de/files/2023/04/04_2023.pdf
We leverages computational linguistics to determine how the narrative content of
earnings conference calls influences investors’ uncertainty about a firm’s future valu-
ation. By applying statistical topic modeling to a corpus of 18,254 conference calls,
we extract topics and tones from both analyst questions and executive responses.
Our findings show that incorporating the estimated topics significantly increases the
explained variance of implied volatility changes of equity options. Furthermore, our
approach enables us to disentangle the overall effect into tone and topic effects, with
executive statements’ topics having the largest net effect, while tones from analyst
statements are particularly relevant for pricing call options.
APA:
Perico Ortiz, H., Schnaubelt, M., & Seifert, O. (2023). A Topic Modeling Perspective on Investor Uncertainty.
MLA:
Perico Ortiz, Hector, Matthias Schnaubelt, and Oleg Seifert. A Topic Modeling Perspective on Investor Uncertainty. 2023.
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