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

David Newton, Hui Tian, Andrew Yim

Research output: Contribution to journalArticle

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, 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 flows 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
JournalManagement Science
DOIs
Publication statusAcceptance date - 7 May 2020

Keywords

  • Heavy tails, distributional shape, profitability forecast, quantile regression

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