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.
Highlights
Earnings forecasts are a critical input in many academic studies in finance and accounting as well as in practical applications
When analyzing two-year-ahead forecasts, we note that the Combined Model (CM) shows a higher Earnings Response Coefficients (ERC) coefficient than the Cross-Sectional Analysts’ Forecasts (CSAF), HVZ, Earnings Persistence (EP), Residual Income (RI), models and a higher adjusted R-squared than the CSAF, HVZ and RI models at a statistically significant margin
Panel A presents univariate OLS regressions of ex-post excess realized returns on Implied Cost of Capital (ICC) premium based on five proxies of earnings forecasts: Combined Model (CM), Analysts’ Forecasts (AF), Cross-Sectional Analysts’ Forecasts (CSAF), Hou et al (2012) (HVZ), Earnings Persistence (EP), and Residual Income (RI)
Summary
Earnings forecasts are a critical input in many academic studies in finance and accounting as well as in practical applications. Ball and Ghysels (2017) develop a model based on mixed data sampling regression methods (MIDAS), which combines various high-frequency time-series data to forecast earnings Their model outperforms raw analysts’ forecasts in some cases and can be combined with analysts’ forecasts to improve forecast accuracy. Combining analysts’ forecasts with a cross-sectional model has one important disadvantage compared to regression-based models: the coverage is limited to firms with an analyst following. This reduces the sample of firms compared to models that only use financial data.
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