Publisher's Synopsis
Excerpt from Individual Forecasting Ability
The criterion I apply to evaluate forecasting ability is average accuracy on the portfolio of predictions made by an individual, through time. It is reasonable to presume that some firms' earnings are harder to forecast than others'. Since analysts may, to some extent, choose to predict earnings for some firms and not others, this endogenous selection is important to the specification of statistical tests. The results reported here remove the effects differential predictability across industries in comparing analysts.
Evaluation of average portfolio accuracy through time is important in earnings forecast data. Each year, there is a relatively large component of unanticipated information which is common across firms, probably because of unanticipated economy-wide or industry-wide events. This common information is evident in the large, statistically significant year-by-year differences in (cross-sectional) average forecast errors. This general feature of earnings forecast data makes it difficult to distinguish between a lucky guess on a macro event and true forecasting ability at the micro level, when only short time-series are available.5 Current databases are rich in cross-sectional observations, but the number of years over which forecasts are available is still relatively small. This issue, and my treatment of it, is discussed further in section 3.
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