Filling in the GAAPs in Individual Analysts’ Street Earnings Forecasts
研究发现，预测非GAAP盈利的分析师更准确，且分析师间预测指标差异预示更低的共识GAAP盈利意外和股票回报，是预测分散的重要来源。
Analysts’ street earnings forecasts are sometimes based on GAAP earnings and sometimes based on non-GAAP earnings, which exclude various GAAP earnings components. Therefore, differences in analysts’ street earnings forecasts may capture differences in not only expected performance but also the earnings metric forecasted. We argue that analysts who forecast non-GAAP, rather than GAAP, street earnings are more likely to separately analyze earnings components. Consistent with this argument, we find that analysts who forecast non-GAAP street earnings issue relatively more accurate forecasts. We also argue that excluded earnings components often reflect negative transitory items, and that variation across analysts in the earnings metric forecasted suggests that the negative excluded items are forecasted by only a subset of analysts. Consistent with this assertion, we find that variation across analysts in the earnings metric forecasted is associated with a lower consensus GAAP earnings surprise and lower stock returns around the earnings announcement. Finally, although variation in the earnings metric forecasted is a source of analyst forecast dispersion, we find that it is also incrementally associated with a lower earnings response coefficient, consistent with the existence of transitory items. We therefore find that the variation in the earnings metric forecasted is an important source of analyst forecast dispersion that predicts not only a lower earnings surprise but also a lower earnings response. This paper was accepted by Brian Bushee, accounting. Funding: Financial support from the Gatton College of Business & Economics, the KPMG Fellowship at the Mendoza College of Business, and the Goizueta Business School is gratefully acknowledged. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4553 .