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

Vitor Azevedo (), Patrick Bielstein and Manuel Gerhart
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Vitor Azevedo: Technical University of Munich
Patrick Bielstein: Barclays Bank PLC
Manuel Gerhart: Information Services Group Germany GmbH

Review of Quantitative Finance and Accounting, 2021, vol. 56, issue 2, No 6, 545-579

Abstract: 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.

Keywords: Earnings forecasts; Analysts’ forecasts; Forecast evaluation; Implied cost of capital; Expected returns (search for similar items in EconPapers)
JEL-codes: G12 G29 M41 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11156-020-00902-z

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