A new approach to testing credit rating of financial debt issuers
Date
2007
Authors
O'Neill, Terence
Penm, Jack HW
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Publisher
Inderscience Publishers
Abstract
Conventional methods to test for credit ratings of financial debt issuers based on current means of classification are typically undertaken in the framework of applied statistical methods. In this paper, a newly introduced approach, Support Vector Machines (SVMs), has been applied to test a set of Standard & Poor (S&P)'s issuers' credit rating data. The primary purpose of this credit rating analysis is to measure the credit worthiness of credit securities' issuers and thus provide investors valuable information in making financial decisions. To construct our classification model, the ten key financial variables used by S&P's, and a dummy country variable, are used as the input variables. A conventional full-order neural network based classification model is selected as the benchmark. Our findings indicate the superiority of the SVMs approach over the neural network approach.
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Keywords
Keywords: Classification; Credit ratings; Financial services and standards; Learning models
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Source
International Journal of Services and Standards
Type
Journal article
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2037-12-31
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