Modelling sovereign credit ratings: Neural networks versus ordered probit

Authors:
Julia A. Bennell;David Crabbe;Stephen Thomas;Owain ap Gwilym
Affiliations:
School of Management, University of Southampton, Highfield, Southampton, SO17 1BJ, UK;School of Management, University of Southampton, Highfield, Southampton, SO17 1BJ, UK;School of Management, University of Southampton, Highfield, Southampton, SO17 1BJ, UK;School of Management and Business, University of Wales, Aberystwyth, Wales, SY23 3DD, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2006

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Abstract

Sovereign credit ratings are becoming increasingly important both within a financial regulatory context and as a necessary prerequisite for the development of emerging capital markets. Using a comprehensive dataset of rating agencies and countries over the period 1989-1999, this paper demonstrates that artificial neural networks (ANN) represent a superior technology for calibrating and predicting sovereign ratings relative to ordered probit modelling, which has been considered by the previous literature to be the most successful econometric approach. ANN have been applied to classification problems with great success over a wide range of applications where there is an absence of a precise theoretical model to underpin the relationships in the data. The results for sovereign credit ratings presented here corroborate other researchers' findings that ANN are highly effective classifiers.