A New Methodology for Estimating Internal Credit Risk and Bankruptcy Prediction under Basel II Regime

TL;DR

This paper introduces a novel multivariate non-linear model for estimating credit risk and bankruptcy prediction, achieving higher accuracy than traditional Altman's $z$ score by utilizing a new financial ratios transformation and distribution fitting.

Contribution

It develops a new non-linear $z$ score model and a credit risk index based on Pearson type-III distribution, improving bankruptcy prediction accuracy over existing methods.

Findings

New $z$ score predicts bankruptcy with 98.6% accuracy.

Transformed financial ratios predict bankruptcy with 93.0% accuracy.

Proposed models outperform Altman's $z$ score in accuracy.

Abstract

Credit estimation and bankruptcy prediction methods have been utilizing Altman's $z$ score method for the last several years. It is reported in many studies that $z$ score is sensitive to changes in accounting figures. Researches have proposed different variations to conventional $z$ score that can improve the prediction accuracy. In this paper we develop a new multivariate non-linear model for computing the $z$ score. In addition we develop a new credit risk index by fitting a Pearson type-III distribution to the transformed financial ratios. The results from our study have shown that the new $z$ score can predict the bankruptcy with an accuracy of $98.6\%$ as compared to $93.5\%$ by the Altman's $z$ score. Also, the discriminate analysis revealed that the new transformed financial ratios could predict the bankruptcy probability with an accuracy of $93.0\%$ as compared to $87.4\%$ using the weights of Altman's $z$ score.