Firm-Specific Estimates of Differential Persistence and their Incremental Usefulness for Forecasting and Valuation

• 成果类型: Article
• 署名作者: Call, Andrew C.; Hewitt, Max; Shevlin, Terry; Yohn, Teri Lombardi
• 署名单位: Arizona State University; Arizona State University-Tempe; University of Arizona; University of California System; University of California Irvine; Indiana University System; Indiana University Bloomington
• 刊物名称: ACCOUNTING REVIEW
• ISSN/ISSBN: 0001-4826
• DOI: 10.2308/accr-51233
• 发表日期: 2016
• 页码: 811-833
• 关键词: book-tax differences; Cash flows; Earnings persistence; FUTURE PROFITABILITY; stock-prices; IMPLIED COST; accruals; INFORMATION; models; errors

摘要

Although the differential persistence of accruals and operating cash flows is a firm-specific phenomenon, research seeking to exploit the differential persistence of these earnings components typically employs cross-sectional forecasting models. We find that a model based on firm-specific estimates of the differential persistence of accruals and operating cash flows is incrementally useful for out-of-sample forecasting relative to state-of-the-art cross-sectional models. In doing so, we show that firm-specific estimates of differential persistence are particularly useful when forecasting earnings for more stable firms (e.g., more profitable, lower growth, and less levered firms). We also demonstrate that a trading strategy exploiting investors' fixation on earnings and based on firm-specific estimates of differential persistence earns statistically and economically significant excess returns that are incremental to those generated by trading strategies based on the size of accruals. These results suggest that firm-specific estimates of differential persistence are incrementally informative for forecasting and valuation.