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Motivated by research from the late 1990s and early 2000s, researchers often examine analyst performance as a function of individual analyst characteristics using variables representing analysts' experience, busyness, and resources. Studies make different choices for variable construction and fixed effects, sometimes with varying results, suggesting there is no standard approach to evaluating the effect of analyst characteristics. We reproduce analyses from two early studies (Clement (1999) and Clement and Tse (2003)) and extend these analyses to a more recent sample period and across different methodological approaches and alternative analyst performance measures. We provide robust evidence that analyst experience is positively associated with earnings forecast accuracy but that associations for analyst resources and busyness are sensitive to variable measurement and fixed effect choices. Our results can inform accounting and finance scholars examining analyst performance as they consider which analyst characteristics to include and how to test the robustness of their findings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]