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

64 Pages Posted: 14 Dec 2020 Last revised: 15 Nov 2022

See all articles by Lin Peng

Lin Peng

City University of New York, Baruch College - Zicklin School of Business - Department of Economics and Finance

Siew Hong Teoh

UCLA Anderson School of Management

Yakun Wang

The Chinese University of Hong Kong, Shenzhen - School of Management and Economics; Shenzhen Finance Institute

Jiawen Yan

Cornell University - Samuel Curtis Johnson Graduate School of Management

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Date Written: February 14, 2022

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. High TRUST also 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 high DOM helps 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 between TRUST and 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

JEL Classification: G14; G24; G28; G41; D83; J16; M41; M48

Suggested Citation

Peng, Lin and Teoh, Siew Hong and Wang, Yakun and Yan, Jiawen, Face Value: Trait Impressions, Performance Characteristics, and Market Outcomes for Financial Analysts (February 14, 2022). Available at SSRN: https://ssrn.com/abstract=3741735 or http://dx.doi.org/10.2139/ssrn.3741735

Lin Peng (Contact Author)

City University of New York, Baruch College - Zicklin School of Business - Department of Economics and Finance ( email )

17 Lexington Avenue
New York, NY 10010
United States

Siew Hong Teoh

UCLA Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

HOME PAGE: http://www.anderson.ucla.edu/faculty-and-research/accounting/faculty/teoh

Yakun Wang

The Chinese University of Hong Kong, Shenzhen - School of Management and Economics ( email )

2001 Longxiang Road, Longgang District
Shenzhen, 518172
China

Shenzhen Finance Institute ( email )

Jiawen Yan

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

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