Adjusting Positive Earnings Forecasts for Bias: A Multiple Discriminant Analysis Approach

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Request a copy of the paper from the author: Hakan Saraoglu


In this study, a methodology is tested to adjust positive forecasts that are predicted to correspond to negative earnings outcomes. The methodology involves using consensus forecasts of annual earnings with the sum of the first three quarters' earnings to predict the sign of an earnings announcement. First, the coefficients and the cut-off discriminant values from a multiple discriminant analysis (MDA) for each annual sample period are estimated between 1984 and 1990. OLS regression parameters of the forecast errors against the discriminant scores are obtained for those earnings predicted as negative by MDA in the estimation period. Coefficient values of the MDA function and cut-off discriminant scores are then used in an out-of-sample test period to predict the sign of actual earnings outcomes. An adjustment factor is obtained by using the previously estimated regression parameters of the forecasts errors versus the discriminant scores. Earnings that are predicted as negative in the test period are then adjusted using the adjustment factor. Test period results indicate that this methodology provides forecasts that outperform security analysts' consensus forecasts. Mean square error before adjustment is greatly reduced in all but one test year.

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