Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks

Bankruptcy prediction is one of the major business classification problems. In this paper, we use four different techniques (1) logit model, (2) quadratic interval logit model, (3) backpropagation multi-layer perceptron (i.e., MLP), and (4) radial basis function network (i.e., RBFN) to predict bankrupt and non-bankrupt firms in England. The average hit ratio of four methods range from 91.15% to 77.05%. The original classification accuracy and the validation test results indicate that RBFN outperforms the other models.