Predicting Credit Rating Migration Employing Neural Network Models

Predicting Credit Rating Migration Employing Neural Network Models

Michael D'Rosario, Calvin Hsieh
Copyright: © 2018 |Pages: 16
DOI: 10.4018/IJSDS.2018100105
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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.
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Literature Review

Over the past few years Latin America has seen a change in how foreign capital is acquired. In the 1960’s the alliance program provided assistance, the 1970’s was when the commercial banks had an influx of funding due to the petroleum boom, which led foreign countries to lend aggressively to developing countries. In recent times these nations have become reliant on the IMF and other international agencies and other private sources (Sinclair, 2003). Apart from private funding, sovereign bonds and debt securities issued by government agencies are increasing in popularity in Latin America. Due to the rise in popularity, investors usually look at the credit ratings of major credit rating agencies, such as Moody’s Investor Services, Standard & Poor’s Rating service to assess the likelihood of default. As credit rating agencies have grown in importance and become a key intermediary between investors and borrowers and are nothing less than guardians of capital assigned to emerging markets (Sinclair, 2005).

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