A genetic algorithm application in bankruptcy prediction modeling
Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness of NNs in classification studies, there exists a major drawback in building and using the model. That is, the user cannot readily comprehend the final rules that the NN models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to bankruptcy prediction modeling. An advantage of present approach using GAs is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.
genetic algorithms bankruptcy prediction rule extraction