A hybrid financial analysis model for business failure prediction
Expert Systems with Applications: An International Journal
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming
Expert Systems with Applications: An International Journal
Using rough set and worst practice DEA in business failure prediction
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Expert Systems with Applications: An International Journal
Enhanced default risk models with SVM+
Expert Systems with Applications: An International Journal
Multi-threaded support vector machines for pattern recognition
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
| Hi-index | 12.05 |
Business failures are depressing events which not only decimate the benefits of stakeholders but also affect the continuing development of economy and society. In order to reduce the impact of business failure, various models of business failure prediction have been developed. Although failure prediction models currently achieve a collective average accuracy of more than 85%, few persons can bear a risk of less than 100% accuracy under the present conditions of economic crisis. It is of particular interest that current failure prediction models have tended to adopt the technique of matching up failed and non-failed firms. This method, however, seems to have merely led to further complications. This paper proposes a method which directly explores the features of failed firms rather than researching pairs of failed and non-failed firms. To this end, automatic clustering techniques and feature selection techniques are employed for this study.