Filling in the GAAPs in Individual Analysts' Street Earnings Forecasts

• 成果类型: Article
• 署名作者: Bratten, Brian; Larocque, Stephannie; Yohn, Teri Lombardi
• 署名单位: University of Kentucky; University of Notre Dame; Emory University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.4553
• 发表日期: 2023
• 页码: 4790-4809
• 关键词: Analysts; forecasts; street vs. GAAP; exclusions

摘要

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 forecastsmay 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 earningsmetric forecasted is associatedwith 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.