A re‐examination of analysts versus time‐series extrapolation of quarterly earnings: Superiority in accuracy and earning expectation proxy. Issue 4 (11th June 2022)

• Record Type: Journal Article
• Authors: Li, Tao; Yu, Ji; Liu, Zenghui
• Is Part Of: Journal of corporate accounting & finance. Volume 33:Issue 4(2022)
• Page Start: 123
• Page End: 146
• Publication Date: 2022-06-11
• Subjects: analysts' forecasts -- earnings expectation -- PEAD -- seasonal random trend model; Accounting -- Periodicals; Accounting -- Law and legislation -- United States -- Periodicals; Corporations -- United States -- Finance -- Periodicals; Corporations -- United States -- Accounting -- Periodicals; 657
• DOI: 10.1002/jcaf.22571
• Languages: English
• ISSNs: 1044-8136

Abstract

Abstract: We document findings that earning extrapolation based on seasonal random trend (SRT) model is not inferior to analysts' quarterly earnings forecasts, which contradicts the belief that analysts are superior to time‐series models. Our findings suggest that while the frequency of analysts beating SRT extrapolations is greater than 50%, the marginal accuracy improvement is weak. Analysts' forecasts contain larger absolute forecast error and significant pessimistic bias than SRT extrapolation. Prior studies attribute the superiority of analysts' forecasts in proxying for earnings expectation to its higher accuracy. Given that the SRT extrapolations have lower average forecast error, we explore whether market participants use them to develop earnings expectations. Our findings indicate that the market anchors to analysts' forecasts and treats SRT extrapolations as supplemental.