Bond rating: a nonconservative application of neural networks
Dutta, Shekhar · 1988
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.>