Arbeitspapier
A topic modeling perspective on investor uncertainty
We leverages computational linguistics to determine how the narrative content of earnings conference calls influences investors' uncertainty about a firm's future valuation. 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.
- Sprache
-
Englisch
- Erschienen in
-
Series: FAU Discussion Papers in Economics ; No. 04/2023
- Klassifikation
-
Wirtschaft
- Thema
-
Earnings Conference Calls
Option Implied Volatility
Natural Language Processing
Sentiment
Topic Modeling
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Perico Ortiz, Daniel
Schnaubelt, Matthias
Seifert, Oleg
- Ereignis
-
Veröffentlichung
- (wer)
-
Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics
- (wo)
-
Nürnberg
- (wann)
-
2023
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Perico Ortiz, Daniel
- Schnaubelt, Matthias
- Seifert, Oleg
- Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics
Entstanden
- 2023
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