Predicting Credit Rating Migration Employing Neural Network Models

Predicting Credit Rating and Credit Rating Changes: A New Approach

In this paper, we propose a hazard rate model for studying credit rating and credit rating changes. Theoretically the hazard rate model is more appropriate than the previous static models. Yet it is difficult to estimate hazard rate model, especially when the covariates are time-varying. This paper extends the results of Shumway(2001) and shows that a multiple-state hazard rate model can be est...

Neural network credit scoring models

This paper investigates the credit scoring accuracy of "ve neural network models: multilayer perceptron, mixture-of-experts, radial basis function, learning vector quantization, and fuzzy adaptive resonance. The neural network credit scoring models are tested using 10-fold crossvalidation with two real world data sets. Results are benchmarked against more traditional methods under consideration...

Municipal credit rating modelling by neural networks

Modelling credit rating by fuzzy adaptive network

Human judgment plays an important role in the rating of enterprise financial conditions. The recently developed fuzzy adaptive network (FAN), which can handle systems whose behaviour is influenced by human judgment, appears to be ideally suited for the modelling of this credit rating problem. In this paper, FAN is used to model the credit rating of small financial enterprises. To illustrate the...

Credit Rating Dynamics and Markov Mixture Models∗

Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on empirical evidence we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit ratin...