The Effects of Experience and a Decision Aid on the Slope, Scatter, and Bias of Earnings Forecasts

Abstract

This study examines the joint effects of experience and a statistical decision aid on several dimensions of forecasting accuracy. Undergraduate business students, graduate business students, and experienced financial analysts made probabilistic earnings forecasts for 16 firms, either with or without access to a decision aid. Covariance decomposition was used to partition the overall accuracy score into the following components: bias, scatter, and slope. Consistent with prior research, the absolute accuracy of subjects was quite poor, even worse than a uniform forecaster. In contrast to previous results which documented an inverted experience–accuracy effect, experience had a positive impact on accuracy in the current study. This difference in results may be due, at least in part, to the different conditions under which subjects performed the earnings forecasting task. The covariance analysis showed that experienced financial analysts and MBA students had significantly better slope and lower scatter than undergraduate students. The financial analysts were also more biased than either of the student groups, yet still achieved the highest overall accuracy. The decision aid improved forecasting accuracy by increasing the slope of the analysts’ forecasts, while decreasing the scatter in the graduate and undergraduate students’ forecasts.