Earnings Forecasts: The Case for Combining Analysts' Estimates with a Cross-Sectional Model
45 Pages Posted: 20 Jun 2017 Last revised: 9 Feb 2019
Date Written: July 17, 2017
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
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