Working Paper: CEPR ID: DP12179
Authors: Ryan Ball; Eric Ghysels
Abstract: Prior studies attribute analysts' forecast superiority over time-series forecasting models to their access to a large set of rm, industry, and macroeconomic information (an information advantage), which they use to update their forecasts on a daily, weekly or monthly basis (a timing advantage). This study leverages recently developed mixed data sampling (MIDAS) regression methods to synthesize a broad spectrum of highfrequency data to construct forecasts of rm-level earnings. We compare the accuracy of these forecasts to those of analysts at short horizons of one quarter or less. We find that our MIDAS forecasts are more accurate and have forecast errors that are smaller than analysts' when forecast dispersion is high and when the rm size is smaller. In addition, we find that combining our MIDAS forecasts with analysts' forecasts systematically outperforms analysts alone, which indicates that our MIDAS models provide information orthogonal to analysts. Our results provide preliminary support for the potential to automate the process of forecasting rm-level earnings, or other accounting performance measures, on a high-frequency basis.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| high-frequency data (C55) | improved forecasting performance (C53) |
| richer timeseries forecasting models (C22) | improved forecasting performance (C53) |
| MIDAS combination forecasts (C53) | lower forecast errors (C53) |
| MIDAS combination forecasts (C53) | outperform analysts' forecasts (G17) |
| combining MIDAS forecasts with analysts' forecasts (C53) | reduction in forecast error (C53) |