A New Measure of Earnings Forecast Uncertainty: Theory and Evidence

Abstract

By decomposing analysts’ forecast errors into common and idiosyncratic components, we develop a simple model aimed at explaining the relationship between forecast uncertainty and analyst dispersion. Under this framework, we propose a new measure of earnings forecast uncertainty as the sum of dispersion among analysts and the variance of mean forecast errors estimated by a GARCH model. The new measure gives an ex ante estimate of uncertainty arising from both analysts’ common and private information. Hence, it circumvents the limitations of other commonly-used proxies for forecast uncertainty in the literature. Using analysts’ earnings forecasts, we find direct evidence for the superior performance of the new measure.