Ordinal pairwise partitioning (OPP) approach to neural networks training in bond rating

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Statistical classification methods such as multivariate discriminant analysis have been widely ued in bond rating classificaion in spite of the limitations of the methodology. Recently, neural networks have emerged as new methods for business classification. This approach to neural networks training is to categorize a new instance as one of the predefined bond classes. Such a conventional approach has limitations in dealing with the ordinal nature of bond rating. In addition, most of the prior studies have used sample data which are evenly divided among the classes. However, the natural population in real application is usually unevenly divided among the classes. Under such circumstances, it is hard to achieve good predictive performance. As the number of classes to be recognized increases, the predictive performance decreases. In this article, to increase the predictive performance in real-world bond rating, we propose the ordinal pairwise partitioning (OPP) approach to backpropagation neural networks training. The main idea of the OPP approach is to partition the data set in the ordinal and pairwise manner according to the output classes. Then, each backpropagation neural networks model is trained by using each partitioned data set and is separately used for classificaion. Experimental results show that the predictive performance of the proposed OPP approach can be significantly enhanced, when compared to the conventional neural networks modeling approach as well as multivariate discriminant analysis. The OPP approach has two computation methods, and we discuss under which circum-stances one method performs better than the other. We also show the generaliz-ability of the OPP approach.
Publisher
John Wiley & Sons, Ltd.
Issue Date
1998-12
Language
English
Citation

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE AND MANAGEMENT, v.6, pp.23 - 40

ISSN
1099-1174
URI
http://hdl.handle.net/10203/3781
Appears in Collection
MT-Journal Papers(저널논문)
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