Predicting Credit Rating Migration Employing Neural Network Models

Michael D'Rosario and Calvin Hsieh
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Michael D'Rosario: Deakin University, Burwood, Australia
Calvin Hsieh: Deakin University, Burwood, Australia

International Journal of Strategic Decision Sciences (IJSDS), 2018, vol. 9, issue 4, 70-85

Abstract: Credit rating migration ranks amongst the most pertinent issues concerning institutional lenders and investors alike. There are a number of studies that have employed both parametric and non-parametric methodologies to forecast credit rating migration, employing machine learning methods; and notably, artificial intelligence methods becoming increasingly popular. The present study extends upon research within the extant literature employing a novel estimation method, a neural network modelling technique, herewith the MPANN (multi-layer neural network). Consistent with the extant literature, the present article identifies that the legal framework and system of taxation enacted within a polity are pertinent to predicting rating migration. However, extending upon traditional estimation techniques the study identifies that a number of different model calibrations achieve greater predictive accuracy than traditional parametric regression. Notably, the method is able to achieve superior goodness of fit and predictive accuracy in determining credit rating migration than models employed within the extant literature.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsds00:v:9:y:2018:i:4:p:70-85

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