A Note On Earnings Forecast Source Superiority

Abstract

We examine the forecast accuracy of Value Line analysts relative to the Brown-Rozeff (100)X(011)₄ ARIMA model. We find that for a surprising percentage (35-41%) of our sample of small firms that time series-based earnings per share predictions are more accurate than those obtained from The Value Line Investment Survey. Further, we document exploitable characteristics of each subgroup that are associated with forecast origin. In those instances where the seasonal, univariate earnings forecast model identified by Brown and Rozeff (1979) produces more accurate forecasts than Value Line, we find significant differences in firm size, degree of diversification, magnitudes of the autoregressive and seasonal moving-average parameters, residual standard errors, and magnitude of the Ljung-Box Q-statistic. We use probit regressions to identify ex ante those firms likely to be accurately forecast by each source. We achieve a marginal improvement in forecast accuracy, which suggests there is potential for using ex ante decision rules to improve forecast accuracy.