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CFO narcissism and the power of persuasion over analysts: a mixed-methods approach

Review of Accounting Studies 2025 30(3), 2419-2467 open access
Abstract We study the role of CFO narcissism in the intent and ability to positively influence sell-side analysts’ perceptions of the firm. Consistent with narcissists casting favorable impressions on others, we find CFO narcissism is associated with overly optimistic analyst valuations. We then study public persuasion attempts by analyzing conference call transcripts and private persuasion attempts through a laboratory study. In the conference call setting, we show that narcissistic CFOs use more persuasive language and are more inclined to call on bearish analysts, both of which we link to price target revisions following the call. In the lab study, we simulate a one-on-one conversation and find that narcissists are especially more likely to use coercive methods to induce higher valuations from analysts. Collectively, we show that narcissistic CFOs use persuasion to favorably influence analysts’ perceptions of firm value.

Crypto-influencers

Review of Accounting Studies 2024 29(3), 2254-2297 open access
Abstract This study examines the investment value of information provided by crypto-influencers, that is, social media influencers covering crypto assets on Twitter. We examine the returns associated with approximately 36,000 tweets issued by 180 of the most prominent crypto social media influencers covering over 1,600 crypto assets for the two years spanning through December 2022. Our primary results indicate that crypto-influencers’ tweets are initially associated with positive returns. However, these tweets are followed by significant negative longer-horizon returns, suggesting they generate minimal long-term investment value. These effects are most pronounced for tweets issued by crypto-influencers proclaiming to be crypto experts, for smaller cap crypto asset securities and for self-described experts with many Twitter followers. In an additional analysis, we use machine-learning methods to classify tweets and find that this pattern of results strengthens when the tweets have a more positive sentiment or relate to buy recommendations.