Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression

David Newton, Hui Tian, Andrew Yim

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3 Citations (SciVal)
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We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry.

Original languageEnglish
Pages (from-to)5209–5233
Number of pages25
JournalManagement Science
Issue number8
Early online date7 Dec 2020
Publication statusPublished - 1 Aug 2021

Bibliographical note

Publisher Copyright:
© 2020 INFORMS.


  • Distributional shape
  • Heavy tails
  • Profitability forecast
  • Quantile regression

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research


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