Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?

Ryan T. Ball () and Eric Ghysels ()
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Ryan T. Ball: Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109
Eric Ghysels: Center for Economic Policy Research (CEPR), Washington, DC 20009; Kenan-Flagler Business School and Department of Economics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599

Management Science, 2018, vol. 64, issue 10, 4936-4952

Abstract: Prior studies attribute analysts’ forecast superiority over time-series forecasting models to their access to a large set of firm, 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 high frequency data to construct forecasts of firm-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 firm 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 firm-level earnings, or other accounting performance measures, on a high-frequency basis.

Keywords: accounting; forecasting; applications; time series (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (12)

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