Bond rating: A non-conservative application of neural networks

Soumitra Dutta, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

208 Scopus citations

Abstract

The authors apply neural networks to a generalization problem of predicting the ratings of corporate bonds, where conventional mathematical modeling techniques have yielded poor results and it is difficult to build rule-based artificial-intelligence systems. The results indicate that neural nets are a useful approach to generalization problems in such nonconservative domains, performing much better than mathematical modeling techniques like regression.

Original languageEnglish (US)
Title of host publicationIEEE Int Conf on Neural Networks
PublisherPubl by IEEE
Pages443-450
Number of pages8
StatePublished - Dec 1 1988

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