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

Author

Listed:
  • Hui Tian

    (School of Management and Economics, Beijing Institute of Technology, Beijing 100081, People's Republic of China; PBC School of Finance, Tsinghua University, Beijing 100083, People's Republic of China)

  • Andrew Yim

    (Faculty of Finance, Cass Business School, City, University of London, London EC1Y 8TZ, United Kingdom)

  • David P. Newton

    (School of Management, University of Bath, Bath BA2 7AY, United Kingdom)

Abstract

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.

Suggested Citation

  • Hui Tian & Andrew Yim & David P. Newton, 2021. "Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression," Management Science, INFORMS, vol. 67(8), pages 5209-5233, August.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:8:p:5209-5233
    DOI: 10.1287/mnsc.2020.3694
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