Alternative methods of combining security analysts' and statistical forecasts of annual corporate earnings

Abstract This study provides empirical evidence on the accuracy of alternative methods of combining security analysts' and statistical forecasts of annual corporate earnings. Linear cross-sectional least squares regression models with and without constant terms, and constrained and unconstrained forecast weights, are used to form combination forecasts in addition to equally weighted combinations. The empirical analysis indicates that combination forecasts formed using a linear model with no constant term and no constraints on the forecast weights are superior to forecasts generated using the other combination methods. Additionally, improvement in the accuracy of security analysts' earnings forecasts is obtained by combining them with forecasts generated from statistical models fitted to past earnings series.

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