Earnings Forecasts: The Case for Combining Analysts' Estimates with a Cross-Sectional Model

Review of Quantitative Finance and Accounting, Forthcoming

47 Pages Posted: 20 Jun 2017 Last revised: 10 Jul 2020

See all articles by Vitor Azevedo

Vitor Azevedo

Technische Universität München (TUM), Department of Financial Management and Capital Markets

Patrick Bielstein

Barclays PLC

Manuel Gerhart

Technische Universität München (TUM), Students

Date Written: July 17, 2017

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

JEL Classification: G12, G32, M41

Suggested Citation

Azevedo, Vitor and Bielstein, Patrick and Gerhart, Manuel, Earnings Forecasts: The Case for Combining Analysts' Estimates with a Cross-Sectional Model (July 17, 2017). Review of Quantitative Finance and Accounting, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2988831 or http://dx.doi.org/10.2139/ssrn.2988831

Vitor Azevedo (Contact Author)

Technische Universität München (TUM), Department of Financial Management and Capital Markets ( email )

Munich, 80333
Germany

Patrick Bielstein

Barclays PLC ( email )

1 Churchill Place
London, E14 5HP
United Kingdom

Manuel Gerhart

Technische Universität München (TUM), Students ( email )

Munich, 80333
Germany

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