Nonparametric Quantile Regression‐Based Classifiers for Bankruptcy Forecasting

Abstract

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 © 2013 John Wiley & Sons, Ltd.

Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

“…;Min and Lee 2005; Wang et al 2005;Zhou and Tian 2007;Altman and Sabato 2007;Mori and Umezawa 2007;Angelini et al 2008;Vasiliauskaite and Cvilikas 2008;Zhang and Härdle 2010;Chen and Du 2009;Lin 2009;Min and Jeong 2009;Ryser and Denzler 2009; Bužius et al 2010;Tseng and Hu 2010; Dan ėnas et al 2011;De Andrés et al 2011c;Pacelli and Azzollini 2011;Mileris 2012;Olson et al 2012;Wu and Hsu 2012;Gurný and Gurný 2013;Lorca et al 2014). As a result, our research group…”

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The role of the AFA coefficient as a new criterion in the long-run liaison between corporate liquidity and bank credit

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