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Modelling sovereign credit ratings: neural networks versus ordered profit

Modelling sovereign credit ratings: neural networks versus ordered profit
Modelling sovereign credit ratings: neural networks versus ordered profit
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.
0957-4174
415-425
Bennell, Julia A.
38d924bc-c870-4641-9448-1ac8dd663a30
Crabbe, David
4cf76723-878b-41fe-8667-31b13241144c
Thomas, Stephen
3ebf2346-25f1-4f19-b854-7a7da0cee9ca
Ap Gwilym, Owain
dbd356d9-b22d-420b-a980-7341f6d52f34
Bennell, Julia A.
38d924bc-c870-4641-9448-1ac8dd663a30
Crabbe, David
4cf76723-878b-41fe-8667-31b13241144c
Thomas, Stephen
3ebf2346-25f1-4f19-b854-7a7da0cee9ca
Ap Gwilym, Owain
dbd356d9-b22d-420b-a980-7341f6d52f34

Bennell, Julia A., Crabbe, David, Thomas, Stephen and Ap Gwilym, Owain (2006) Modelling sovereign credit ratings: neural networks versus ordered profit. Expert Systems with Applications, 30 (3), 415-425. (doi:10.1016/j.eswa.2005.10.002).

Record type: Article

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.

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Published date: 2006

Identifiers

Local EPrints ID: 35761
URI: http://eprints.soton.ac.uk/id/eprint/35761
ISSN: 0957-4174
PURE UUID: 6fd049a8-1a55-4fa0-80be-39be1fc03356

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Date deposited: 22 May 2006
Last modified: 15 Jul 2019 19:05

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