Reading the Tea Leaves: Model Uncertainty, Robust Forecasts, and the Autocorrelation of Analysts' Forecast Errors

Abstract

We put forward a model in which analysts are uncertain about a firm's earnings process. Faced with the possibility of using a misspecified model, analysts issue forecasts that are robust to model misspecification. We estimate that this mechanism explains approximately 60% of the autocorrelation in analysts' forecast errors. The remainder stems from the cross-sectional variation in mean forecast errors and in analysts' estimation errors of the persistence of earnings growth shocks. Consistent with our model, we find that analysts learn about some features of the earnings process but not others, and this learning reduces, but does not eliminate, the auto- correlation of forecast errors as firms age. Other potential explanations for the autocorrelation of analyst's forecast errors are rejected. Our model of robust forecasting applies not only to analysts' forecasts but to all model-based forecasts.

Keywords: Model uncertainty, parameter uncertainty, forecasting, robustness, financial analysts

JEL Classification: G14, G24