The tangled webs we weave: Examining the effects of CEO deception on analyst recommendations

Abstract

Research Summary: Organizations are punished by analysts and investors when material deceit by their CEO is uncovered. However, few studies examine analysts' responses to deceptive CEOs before their deceit is publicly known. We use machine learning (ML) models to operationalize the likelihood of CEO deception as well as analysts' suspicion of CEO deception on earnings calls. Controlling for analysts' suspicion of deception, we show that analysts are prone to assigning superior recommendations to deceptive CEOs, particularly those deemed as All-Star analysts. We find that the benefits of CEO deception are lower for habitual deceivers, pointing to diminishing returns of deception. This study contributes to corporate governance research by enhancing our understanding of analysts' reactions to CEO deception prior to public exposure of any fraud or misconduct. Managerial Summary: Undetected deception by CEOs can impact the stock market by influencing analysts' recommendations. Using an advanced ML model, our study measures the likelihood of deception more accurately than previous methods and identifies a tendency among financial analysts to favor deceptive CEOs, particularly high-status analysts. However, deception is less effective with analysts who are repeatedly exposed to deception. These findings underscore the importance of awareness of potential deception in CEO communications and the need for continuous scrutiny, learning, and adaptability among analysts.