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Automated Earnings Forecasts:- Beat Analysts or Combine and Conquer?

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  • Ghysels, Eric
  • Ball, Ryan
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 high frequency 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.

Suggested Citation

  • Ghysels, Eric & Ball, Ryan, 2017. "Automated Earnings Forecasts:- Beat Analysts or Combine and Conquer?," CEPR Discussion Papers 12179, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:12179
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    References listed on IDEAS

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    Cited by:

    1. Vitor Azevedo & Patrick Bielstein & Manuel Gerhart, 2021. "Earnings forecasts: the case for combining analysts’ estimates with a cross-sectional model," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 545-579, February.
    2. Lars Elend & Sebastian A. Tideman & Kerstin Lopatta & Oliver Kramer, 2020. "Earnings Prediction with Deep Learning," Papers 2006.03132, arXiv.org, revised Oct 2020.

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