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Faculty of Economics

A University of Cambridge academic has applied social psychology and machine learning techniques to study the importance of first impressions about Financial Analysts. Professor Peng at the Faculty of Economics has found that analysts’ perceived trustworthiness and dominance improve forecast accuracy, whereas attractiveness improves accuracy only for new analysts or in firms with new management.

 

Professor Lin Peng

The new paper “Face Value: Trait Impressions, Performance Characteristics, and Market Outcomes for Financial Analysts” is being published in a top accounting journal, the Journal of Accounting Research.

“This was a fun project, but with a serious underside,” says co-author, Lin Peng, a Visiting Professor and Director of Research at the Faculty of Economics, part of the University of Cambridge.

Using machine learning–based algorithms, the researchers measure key impressions about sell-side analysts using their LinkedIn photos.

“We use facial recognition and neural network techniques to extract peoples’ first impressions – of ‘Face Value’ of the analysts, using their LinkedIn photo,” says Professor Peng.

Humans form what are called ‘face impressions´ or first impressions about other people from their faces spontaneously within milliseconds, and these have powerful effects on visual attention, inferences, and judgments, a long time before they get to see how someone performs.

“First impressions have been shown to affect outcomes such as teaching ratings, online dating, and judicial sentencing. One would normally expect those first impressions to be less important in financial markets, which is dominated by sophisticated, rational, market participants and provides adequate high-frequency data for objectively measurable benchmarks,” says Professor Peng. “The fact that we found a strong association between first impressions and financial analysts’ outcomes were quite surprising. Impressions of analysts’ trustworthiness and dominance are positively associated with forecast accuracy, especially after recent in-person meetings between analysts and firm managers.”

 

Financial Analysts

 

The researchers also examined how perceived attractiveness impacted their first impression, and in contrast found it was only positively associated with accuracy for new analysts or when a firm has a new CEO or CFO.

The researchers have additionally discovered hints of gender bias. “Perceptions of dominance improve male analysts’ career outcomes, but substantially hurts female analysts’ career outcomes,” says Professor Peng.

While being perceived as more dominant helps male analysts’ chances of attaining what is called ‘All-Star status’ as an analyst, it actually reduces female analysts’ accuracy and the likelihood of winning the All-Star award.

The research has implications for the Financial Services Industry. “It suggests that face impressions influence analysts’ access to information and the perceived credibility of their reports, as opposed to the reader valuing the contents of the report itself verbatim,” she says.

“More importantly, the paper suggests that the impression effects grant certain analysts privileged access to information, and the initial impressions can be reinforced through self-fulfilling prophecy effects and therefore can be long-lasting, even in the setting of a sophisticated marketplace. In addition, the results speak to the role of regulation on disclosure policy,” says Professor Peng. “The effect of trustworthiness impression on analysts’ information access is attenuated after the U.S. Securities and Exchange Commission introduced Regulation Fair Disclosure in October 2000, which requires firms to provide equal disclosure of material information to all investors simultaneously.”

Co Authors include Siew Hong Teoh, Yakun, Wang and Jiawen Yan. Financial Support was provided by the Keynes Fund. The Keynes Fund for Applied Economics promotes innovative and path breaking quality research in economics and finance. A key part of the Faculty of Economics, it provides resources for early stage researchers as well as for established academics in Cambridge, with a special emphasis on understanding key distortions that affect market allocations and create inefficiencies, and on finding policy solutions to correct such inefficiencies.

The full paper is available at: https://onlinelibrary.wiley.com/doi/full/10.1111/1475-679X.12428

 

Tags:

Financial Analysts

Machine Learning

Social Psychology

Forecasting

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