Biased Forecasts or Biased Earnings? The Role of Reported Earnings in Explaining Apparent Bias and Over/Underreaction in Analysts' Earnings Forecasts

Abstract

We demonstrate the role of three empirical properties of cross-sectional distributions of analysts' forecast errors in generating evidence pertinent to three important and heretofore separately analyzed phenomena studied in the analyst earnings forecast literature: purported bias (intentional or unintentional) in analysts' earnings forecasts, forecaster over/underreaction to information in prior realizations of economic variables, and positive serial correlation in analysts' forecast errors. The empirical properties of interest include: the existence of two statistically influential asymmetries found in the tail and the middle of typical forecast error distributions, the fact that a relatively small number of observations comprise these asymmetries and, the unusual character of the reported earnings benchmark used in the calculation of the forecast errors that fall into the two asymmetries that is associated with firm recognition of unexpected accruals. We discuss competing explanations for the presence of these properties of forecast error distributions and their implications for conclusions about analyst forecast rationality that are pertinent to researchers, regulators, and investors concerned with the incentives and judgments of analysts.

Previously titled "Biased Forecasts or Biased Earnings? The Role of Earnings Management in Explaining Apparent Optimism and Inefficiency in Analysts' Earnings Forecasts"

JEL Classification: G29, M41, M43