Perico Ortiz D, Schnaubelt M, Seifert O (2025)
Publication Type: Journal article
Publication year: 2025
DOI: 10.1080/15427560.2025.2528025
We leverage 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. Our approach enables us to disentangle the overall effect into tone and topic effects, with executive statements’ topics having the largest net effect, whereas tones from analyst statements are particularly relevant for pricing call options.
APA:
Perico Ortiz, D., Schnaubelt, M., & Seifert, O. (2025). A Narrative Perspective on Investor Uncertainty: Evidence from Earnings Conference Calls. Journal of Behavioral Finance. https://doi.org/10.1080/15427560.2025.2528025
MLA:
Perico Ortiz, Daniel, Matthias Schnaubelt, and Oleg Seifert. "A Narrative Perspective on Investor Uncertainty: Evidence from Earnings Conference Calls." Journal of Behavioral Finance (2025).
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