GAAP Earnings Forecast Quality: Implications for Research

• 成果类型: Article; Early Access
• 署名作者: Chen, Xi (Novia); Koester, Allison
• 署名单位: University of Houston System; University of Houston; Georgetown University
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/TAR-2021-0646
• 发表日期: 2025
• 关键词: information-content; analysts treatment; Managers; STREET; complexity; inference; rewards; tests

摘要

We examine the implications of GAAP earnings forecast quality for accounting research. Using a tax law change with an estimable and material GAAP earnings impact, we find that analysts' GAAP forecasts generally fail to incorporate this impact, whereas investors respond promptly, suggesting that GAAP forecasts omit earnings information deemed relevant by investors and are of low quality. Analyzing quarterly GAAP forecasts from 2004-2019 and classifying GAAP forecasts that equal their street counterparts when GAAP and street actuals differ as low quality, we again find widespread low GAAP forecast quality. Low quality GAAP forecasts affect research inferences: they dampen GAAP earnings response coefficient (ERC) estimates, reduce the explanatory power of GAAP surprises for returns, affect inferences regarding market rewards for meeting-or-beating via exclusions, and understate the extent that GAAP forecasts incorporate exclusion components. We propose two strategies to mitigate the adverse effects of low quality GAAP forecasts on research inferences.