This study reports the results of an experiment that examines (1) the effects of forecast horizon on the performance of probability forecasters, and (2) the alleged existence of an inverse expertise effect, i.e., an inverse relationship between expertise and probabilistic forecasting performance. Portfolio managers are used as forecasters with substantive expertise. Performance of this 'expert' group is compared to the performance of a 'semi-expert' group composed of other banking professionals trained in portfolio management. It is found that while both groups attain their best discrimination performances in the four-week forecast horizon, they show their worst calibration and skill performances in the 12-week forecast horizon. Also, while experts perform better in all performance measures for the one-week horizon, semi-experts achieve better calibration for the four-week horizon. It is concluded that these results may signal the existence of an inverse expertise effect that is contingent on the selected forecast horizon.
KEY WORDS Probabilistic forecasting Stock price forecasts
Calibration Inverse expertise effect
Forecasting accuracy of financial variables attracts considerable research attention, with predominantly conflicting findings. Liljeblom (1989) shows expirical evidence displaying the predictive power of financial analysts' forecasts of earnings per share for short-term forecast horizons in the Scandinavian securities market. DeBondt and Thaler (1990), on the other hand, conclude that the earnings forecasts provided by the security analysts are basically overreactive, i.e., when large earnings increases are forecasted actual earnings are lower than predictions and vice versa. Keane and Runkle (1990) use the ASA-NBER survey of economic forecasters and show that the price forecasts for GNP deflator by expert forecasters are rational demonstrating improved performance. Zarnovitz (1985) uses the same data set and rejects the rational expectations hypothesis for inflation forecasts. Summarizing the relevant work on financial forecasting, DeBondt (1991) concludes that 'finance should attempt to model the behavior of representative investors and the nature of their errors' (p. 90). Contradictory results obtained in these studies may be viewed as resulting from data revisions, biases in aggregating data, and the definitions of forecast error that are employed.