Forecasting Earnings with Predicted, Conditional Probability Density Functions

Abstract

This study provides empirical evidence for the efficacy of deriving firms' earnings forecasts from predictions of the complete, conditional probability density function (pdf). Relative to cross-sectional earnings forecasts based on OLS regressions, improvements of accuracy, bias and measures for the validity as an expectation's proxy amount to approximately two fifths, when conditional pdfs are obtained via quantile regressions. In turn, another fifth is gained substituting quantile regressions by artificial neural networks. Cross-sectional analyses are consistent with improvements deriving from taking into consideration pdfs of firms which are particular peculiar. Furthermore, also recent point estimation methods fall behind the pdf-based approach.