Composite forecasting of annual earnings: an application of biased regression techniques

Abstract

In this study, composite earnings per share models are estimated for 35 chemical, food, and utility firms during the 1979-1980 period. It is generally held that financial analysts produce superior earnings forecast when compared to time series model forecasts, however, the results of this study indicate that the average mean square forecasting error of analyst forecasts may be reduced by combining analyst and univariate time series model forecasts. Moreover, despite the high degree o! correlation existing among analyst and time series forecasts, the ordinary least squares estimation of the composite earnings model is a better forecasting model than the composite earnings models estimated with ridge regression and latent root regression techniques. Standardization of regression variables also is addressed.