Fear Cautions, Anger Commands: Information in Managerial Vocal Emotion
Publication Date
11-10-2025
Abstract
We examine whether managerial vocal fear and anger—captured from speech during earnings conference calls—convey information about internal beliefs and firm outcomes. Using a machine learning algorithm trained to detect emotions from vocal cues, we construct novel firm-quarter measures of managerial vocal fear and anger for S&P 500 firms. We find that vocal fear elicits negative investor reactions and predicts future underperformance, especially when paired with positive earnings news, consistent with fear signaling latent concerns and investors discounting the contradictory earnings signal. Fear is also associated with cautious managerial behavior (reduced insider buying, investment, confirmatory guidance) and adverse material events, particularly following bad earnings news, suggesting that fear also acts as a reinforcing signal of downside risk. In contrast, vocal anger predicts positive market reactions, future firm growth, confident and assertive behavior (more insider buying, investment, upward guidance), and positive firm developments, especially when earnings news is strong, implying that anger reflects conviction to sustain or build on positive firm momentum. Our findings highlight how involuntary vocal cues offer incremental soft information, with fear and anger – despite both being negative affect – conveying distinct signals about managerial expectations and firm trajectory shaped by the broader new context.
Document Type
Article
Keywords
Vocal Emotion, Conference Calls, Machine Learning, Managerial Communication, Soft Information, Disclosure, Earnings Surprise
Disciplines
Accounting
Source
SMU Cox: Accounting (Topic)
Language
English
