You Have a Point – But a Point Is Not Enough: The Case for Distributional Forecasts of Earnings

60 Pages Posted: 27 Aug 2023 Last revised: 27 Nov 2023

See all articles by Ilia D. Dichev

Ilia D. Dichev

Emory University - Department of Accounting

Xinyi Huang

Emory University - Goizueta Business School

Donald K.K. Lee

Emory University - Goizueta Business School; Emory University - Dept of Biostatistics & Bioinformatics

Jianxin (Donny) Zhao

Emory University

Date Written: August 21, 2023

Abstract

Existing forecasts of earnings are typically expressed as point estimates. The future earnings number is ex-ante uncertain, however, and is statistically represented by a probability distribution over all possible earnings outcomes. We use recent advances in statistical machine learning to estimate the 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 way too narrow, 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 extreme decile probabilities of beating (missing) the consensus produces hedge returns of about 60 basis points over the three-day earnings announcement window.

Keywords: Earnings distribution, Statistical machine learning, Analyst forecasts, Stock returns

JEL Classification: G14, G15, M41

Suggested Citation

Dichev, Ilia D. and Huang, Xinyi and Lee, Donald K.K. and Lee, Donald K.K. and Zhao, Jianxin (Donny), You Have a Point – But a Point Is Not Enough: The Case for Distributional Forecasts of Earnings (August 21, 2023). Available at SSRN: https://ssrn.com/abstract=4547337 or http://dx.doi.org/10.2139/ssrn.4547337

Ilia D. Dichev (Contact Author)

Emory University - Department of Accounting ( email )

1300 Clifton Road
Atlanta, GA 30322-2722
United States

Xinyi Huang

Emory University - Goizueta Business School ( email )

1300 Clifton Road
Atlanta, GA 30322-2722
United States

Donald K.K. Lee

Emory University - Goizueta Business School ( email )

1300 Clifton Road
Atlanta, GA 30322-2722
United States

Emory University - Dept of Biostatistics & Bioinformatics ( email )

Atlanta, GA 30322
United States

Jianxin (Donny) Zhao

Emory University ( email )

464 Goizueta Business School
Atlanta, GA 30322
United States

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