Are All Experts Equally Good? A Study of Analyst Earnings Estimates

TL;DR

This study examines whether stock market analysts have varying levels of expertise and how their individual biases and differentiation impact the accuracy of earnings forecasts, revealing modest differentiation but significant bias.

Contribution

It introduces a method to measure analyst differentiation through aggregation improvements and applies it to historical earnings forecasts, providing new insights into analyst expertise.

Findings

Analysts have modest differentiation in their forecasts.

Significant individual biases exist among analysts.

Aggregation of forecasts improves accuracy by 20-30%.

Abstract

We investigate whether experts possess differential expertise when making predictions. We note that this would make it possible to aggregate multiple predictions into a result that is more accurate than their consensus average, and that the improvement prospects grow with the amount of differentiation. Turning this argument on its head, we show how differentiation can be measured by how much weighted aggregation improves on simple averaging. Taking stock-market analysts as experts in their domain, we do a retrospective study using historical quarterly earnings forecasts and actual results for large publicly traded companies. We use it to shed new light on the Sinha et al. (1997) result, showing that analysts indeed possess individual expertise, but that their differentiation is modest. On the other hand, they have significant individual bias. Together, these enable a 20%-30% accuracy improvement over consensus average.