Composite Earnings Forecasting Efficiency

Abstract

Composite earnings-per-share models were estimated for 35 chemical, food, and utility firms during the 1979–1980 period. It is generally held that financial analysts produce earnings forecasts superior 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. Despite the high degree of correlation existing among analyst and time-series forecasts, the ordinary least-squares model estimation of the composite-earnings model is a better forecasting model than the composite-earnings models estimated with ridge regression techniques.