A comparative analysis of inductive-learning algorithms
International Journal of Intelligent Systems in Accounting and Finance Management - Special issue on neural networks
Neural network credit scoring models
Computers and Operations Research - Neural networks in business
Do transparency and disclosure predict firm performance? Evidence from the Taiwan market
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An improved boosting based on feature selection for corporate bankruptcy prediction
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Past literature on business failure prediction rarely covered international discussions, and hardly any effort had been devoted to the establishment of a failure prediction model using a between-countries approach. In this study, we have proposed a means to collect and determine explanatory variables using a between-countries approach. In addition, we have established a systematic experiment to investigate the influences of techniques for both network architecture selection and variable selection on neural network models' learning and prediction capability. Another issue of the study is to explore the influences on different sample mixture ratios of the training and testing subsets, which relates to the stability and generalizability of models. Four assumptions of cost ratios of Type I and Type II errors (C"I/C"I"I ratios) are also examined in evaluating the model's effectiveness. The results show that the neural network models provide good classification capability in both cross-industry and industry-specific contexts. Moreover, the higher the training sample size and the larger the number of hidden nodes, the higher the classification rates, the lower the Type I error rates, the lower the relative C"I/C"I"I ratios. Among the three variables selection methods, factor analysis is superior to stepwise discriminant analysis (SDA) and ALL in terms of classification accuracy, generalization ability and error costs, while SDA provides the worst performance in all situations.