Face Value: Trait Impressions, Performance Characteristics, and Market Outcomes for Financial Analysts

LIN PENG, SIEW HONG TEOH, YAKUN WANG, JIAWEN YAN

Journal of Accounting Research · 2022 · Volume 60 · Issue 2

Abstract

Using machine learning–based algorithms, we measure key impressions about sell-side analysts using their LinkedIn photos. We find that impressions of analysts’ trustworthiness (TRUST) and dominance (DOM) are positively associated with forecast accuracy, especially after recent in-person meetings between analysts and firm managers. HighTRUSTalso enhances stock return sensitivity to forecast revisions, especially for stocks with high institutional ownership. In contrast, the impression of analysts’ attractiveness (ATTRACT) is only positively associated with accuracy for new analysts or when a firm has a new CEO or CFO. Furthermore, while highDOMhelps male analysts’ chances of attaining All-Star status, it reduces female analysts’ accuracy and the likelihood of winning the All-Star award. In addition, the relation betweenTRUSTand accuracy is modulated by the disclosure environment and is attenuated by Regulation Fair Disclosure. Our results suggest that face impressions influence analysts’ access to information and the perceived credibility of their reports.

Keywords

machine learning, facial recognition, trait impressions, analysts, gender discrimination, EPS forecasts, All-Star Analysts, forecast revision, social interactions

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