Revising Earnings Per Share Forecasts: An Empirical Test

Abstract

In this study, we postulate that forecasters desire to improve their performance by studying their past forecasting errors. To improve performance, forecasters may measure their past mistakes and revise their forecasts by forecast revision techniques. In an empirical test, forecasts of fifty firms' EPS were prepared by seven forecasting models. The initial forecasts were corrected by the Theil optimal linear correction technique and a Bayesian revision adjustment. Results indicate that the optimal linear correction technique is superior to not correcting for past forecast error.