Earnings forecasts: the case for combining analysts’ estimates with a cross-sectional model

Artikel

We propose a novel method to forecast corporate earnings, which combines the accuracy of analysts’ forecasts with the unbiasedness of a cross-sectional model. We build on recent insights from the earnings forecasts literature to improve analysts’ forecasts in two ways: reducing their sluggishness with respect to information in recent stock price movements and improving their long-term performance. Our model outperforms the most popular methods from the literature in terms of forecast accuracy, bias, and earnings response coefficient. Furthermore, using our estimates in the implied cost of capital calculation leads to a substantially stronger correlation with realized returns compared to earnings estimates from extant cross-sectional models.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: Review of Quantitative Finance and Accounting ; ISSN: 1573-7179 ; Volume: 56 ; Year: 2020 ; Issue: 2 ; Pages: 545-579 ; New York, NY: Springer US

Classification and Topics

• Classification: Wirtschaft ; Asset Pricing; Trading Volume; Bond Interest Rates ; Financial Institutions and Services: Other ; Accounting
• Subject: Earnings forecasts ; Analysts’ forecasts ; Forecast evaluation ; Implied cost of capital ; Expected returns

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Azevedo, Vitor ; Bielstein, Patrick ; Gerhart, Manuel
• Event: Veröffentlichung
• (who): Springer US
• (where): New York, NY
• (when): 2020

Further information

• DOI: doi:10.1007/s11156-020-00902-z
• Last update: 10.03.2025, 11:43 AM CET