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You have a point - but a point is not enough: The case for distributional forecasts of earnings

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  • Dichev, Ilia
  • Huang, Xinyi
  • Lee, Donald K.K
  • Zhao, Jianxin

Abstract

Existing forecasts of earnings are typically expressed as point estimates. However, ex-ante, the future earnings number is unknown, and is statistically represented by a probability distribution over all possible earnings outcomes. We use recent advances in statistical machine learning to estimate the ex-ante distributions of future earnings right before earnings announcements, and investigate how these distributions can help managers, analysts, and investors make better decisions along three directions. First, we show that our distributional forecasts are well calibrated to actual earnings realizations. Second, we document that management and financial analyst forecasts are substantially miscalibrated, severely underestimating the variability of future earnings. Critically, since our distributional estimates are available ex-ante at the firm-quarter level, they can be proactively used to identify and correct such miscalibration. Third, we use our distributional estimates to model the probability of beating or missing the consensus analyst forecasts. Going long (short) on stocks in the top (bottom) decile probabilities of beating (missing) the consensus produces hedge returns of about 60 basis points over the three-day earnings announcement window.

Suggested Citation

  • Dichev, Ilia & Huang, Xinyi & Lee, Donald K.K & Zhao, Jianxin, 2023. "You have a point - but a point is not enough: The case for distributional forecasts of earnings," SocArXiv 4b2y8, Center for Open Science.
    • Handle: RePEc:osf:socarx:4b2y8
    DOI: 10.31219/osf.io/4b2y8
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