Working Paper: CEPR ID: DP10186
Authors: Ryan Ball; Eric Ghysels; Huan Zhou
Abstract: Can we design statistical models to predict corporate earnings which either perform as well as, or even better than analysts? If we can, then we might consider automating the process, and notably apply it to small and international firms which typically have either sparse or no analyst coverage. There are at least two challenges: (1) analysts use real-time data whereas statistical models often rely on stale data and (2) analysts use potentially large set of observations whereas models often are frugal with data series. In this paper we introduce newly-developed mixed frequency regression methods that are able to synthesize rich real-time data and predict earnings out-of-sample. Our forecasts are shown to be systematically more accurate than analysts' consensus forecasts, reducing their forecast errors by 15% to 30% on average, depending on forecast horizon.
Keywords: forecast combination; MIDAS regression; real-time data
JEL Codes: C53; M40; M41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| mixed data sampling (MIDAS) regressions (C32) | earnings forecasts (G17) |
| macroeconomic, equity market, and firm-specific financial variables (E44) | earnings forecasts (G17) |
| MIDAS regressions (C29) | outperform analysts' consensus forecasts (G17) |
| forecasting performance (C53) | more pronounced in cyclical industries (E32) |
| earnings movements (F29) | captured by macroeconomic influences (E39) |