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- Publisher
- Taylor & Francis
- Copyright
- © 2023 The Institute of Behavioral Finance
- ISSN
- 1542-7579
- eISSN
- 1542-7560
- DOI
- 10.1080/15427560.2023.2214262
- Publisher site
- See Article on Publisher Site
Abstract
Abstract We examine managerial behavior during conference calls vis-à-vis the informational impact on firm stock prices. Implementing an unsupervised machine learning algorithm, we document statistically and economically meaningful relationships between impromptu soft information divulged during calls and stock prices. Managers who choose to divulge more impromptu soft information in the Q&A session experience improved liquidity and less volatility, accompanied with lower abnormal returns. Conditioned on earnings surprise, large positive-surprise impromptu soft information results in larger returns and positive drift, indicating that investors do not completely trust the positive signal initially, with confidence growing over time evidenced as prices adjust to equilibrium.
Journal
Journal of Behavioral Finance – Taylor & Francis
Published: May 16, 2023
Keywords: Behavioral finance; Machine learning; Managerial impromptu information; Stock prices; Textual analysis; G1; G14; G40; G41