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Nonparametric Quantile Regression-Based Classifiers for Bankruptcy Forecasting
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DOI：10.1002/for.2280.png)
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Abstract
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An improved classification device for bankruptcy forecasting is proposed. The proposed approach relies on mainstream classifiers whose inputs are obtained from a so-called multinorm analysis, instead of traditional indicators such as the ROA ratio and other accounting ratios. A battery of industry norms (computed by using nonparametric quantile regressions) is obtained, and the deviations of each firm from this multinorm system are used as inputs for the classifiers. The approach is applied to predict bankruptcy on a representative sample of Spanish manufacturing firms. Results indicate that our proposal may significantly enhance predictive accuracy, both in linear and nonlinear classifiers. Copyright (c) 2013 John Wiley & Sons, Ltd.
Keywords:
bankruptcy forecasting
classifiers
nonparametric methods
quantile regression
accounting ratios
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