Source: Peng, Teoh, Wang & Yan (2022) — “Face Value: Trait Impressions, Performance Characteristics, and Market Outcomes for Financial Analysts”
At first this sounds like a technical finance question. Then you look closer and realise it is also a question about attention, incentives, and whether people are using information or just being impressed by it.
This paper sits at an uncomfortable intersection of psychology, finance, and AI measurement. The core finding: trait impressions derived from analyst facial photographs — using machine learning to score perceived competence, trustworthiness, and dominance — predict aspects of analyst career trajectories and market influence, even after controlling for actual forecast performance. Analysts who score higher on AI-derived facial competence measures receive more media coverage, accumulate more followers, and have their forecasts responded to more strongly by markets. These effects are not fully explained by actual forecast accuracy or recommendation quality — they reflect genuine bias in how analysts are perceived and how much attention they receive. The researchers are careful about the mechanism. Facial impressions don’t just affect individual interactions — they affect aggregate market outcomes. If the market weights analyst forecasts partly based on how the analyst looks, that’s a market efficiency problem. The information content of forecasts should drive market reactions, not the analyst’s perceived facial competence. What I find interesting about this paper from an AI measurement perspective is that it works in two directions.
In plain English, that is why the result matters beyond the chart. It changes where people should look, what they should question, and which comfortable assumption probably needs to be retired.
So I would not read this as a neat technology story. It is a messy information story. The tools improve, but people still have to decide what deserves attention. Annoying, but true.