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Bond rating: a nonconservative application of neural networks

213 citations0/0 files storedIEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks

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Credit Risk and Financial Regulations Neural Networks and Applications Financial Distress and Bankruptcy Prediction

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.>

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1973A MULTIVARIATE ANALYSIS OF INDUSTRIAL BOND RATINGS 1966The Determination of Long-Term Credit Standing with Financial Ratios 1969What's in a Bond Rating

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1995A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks 2000Forecasting exchange rates using general regression neural networks 1997COST ESTIMATION PREDICTIVE MODELING: REGRESSION VERSUS NEURAL NETWORK 2002Predicting stock price performance: a neural network approach 2008Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul Stock Exchange 2007Nonlinear neural network forecasting model for stock index option price: Hybrid GJR–GARCH approach 1993A neural network model for estimating option prices 2013Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings

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