A topic modeling perspective on investor uncertainty - Daniel Perico Ortiz; Matthias Schnaubelt; Oleg Seifert (Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute for Economics, 2023)
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.
Keywords: 330; Earnings Conference Calls; Option Implied Volatility; Natural Language Processing; Sentiment; Topic Modeling