Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/289151 
Year of Publication: 
2020
Citation: 
[Journal:] Review of Quantitative Finance and Accounting [ISSN:] 1573-7179 [Volume:] 56 [Issue:] 2 [Publisher:] Springer US [Place:] New York, NY [Year:] 2020 [Pages:] 545-579
Publisher: 
Springer US, New York, NY
Abstract: 
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.
Subjects: 
Earnings forecasts
Analysts’ forecasts
Forecast evaluation
Implied cost of capital
Expected returns
JEL: 
G12
G29
M41
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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