Carl R. Beidleman

A new look at forecasting annual corporate earnings in the U.S.A.

Abstract In this study, composite earnings per share models are estimated for 35 chemical, food, and utility firms during the 1981–1982 period. Although it is generally held that financial analysts produce superior earnings forecasts when compared to time series model forecasts, the results of this study indicate that analysts fared very poorly in 1982 and the average mean square forecasting error of analyst forecasts may be reduced by 74.2 percent by combining analyst and univariate time series model forecasts. This reduction is very interesting when one finds that the univariate time series model forecasts do not substantially deviate from those produced by random walk drift models, the ARIMA (0, 1, 1) process. Moreover, despite the high degree of 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.

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

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.