Are All Experts Equally Good? A Study of Analyst Earnings Estimates
This addresses the problem of improving prediction accuracy in financial forecasting for investors and analysts, though it is incremental as it builds on prior work like Sinha et al. (1997).
The study examined whether stock-market analysts have differential expertise in earnings forecasts, finding that while individual expertise is modest, significant individual biases exist, enabling a 20%-30% accuracy improvement over consensus averages.
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.