Abstract
Purpose – This study aims to examine how investors react to managerial emotion profiles portrayed during earnings conference calls. The authors argue that positive and negative emotion profiles of the call participants influence investor reactions, as emotions serve as social information cues.
Design/methodology/approach – Drawing on emotions as social information theory, this study hypothesizes that emotions influence investor behavior through two primary mechanisms: inferential processes and affective reactions. This study uses a sophisticated machine learning algorithm and a validated psychology dictionary to extract and identify managers' emotion profiles during earnings conference calls. This study uses large language models along with lexicons to create a hybrid artificial intelligence framework, with granular levels of emotion detection.
Findings – The evidence suggests that investors react positively (negatively) to the positive (negative) emotion profiles displayed by the conference participants, supporting the assertion of emotion contagion. The findings provide mixed evidence on whether earnings news conditions the informational value of managerial emotions.
Practical implications – The findings suggest that emotion management is a strategic component of communication – positive emotional expression can foster investor confidence, while unchecked negative emotions may amplify concerns and erode market value.
Originality/value – This study provides new evidence on the impact of emotion profiles on investor behavior. Emotion profiles may reveal new insights about managerial confidence or uncertainties that are not immediately apparent in the content of the disclosure.