| This research examined corporate continuation through the forecasting of business survival and failure. The purpose of this exploratory research was to determine the suitability and reliability of a bankruptcy forecasting model for alerting management to deteriorating corporate financial health, which in turn may reduce the social burden of bankruptcy.; A backpropagation artificial neural network (BANN) was evaluated as a tool to predict corporate failure and nonfailure. Fifty-four BANN configurations were tested with respect to efficiency, consistency, and accuracy for forecasting bankruptcy; the configurations were compared using a multiattribute decision model. The predictive accuracy of the BANN selected as superior was examined and the results compared to Altman's Z-score (i.e., discriminant analysis methodology), which is considered the benchmark for bankruptcy prediction.; One year prior to bankruptcy (failure) or nonbankruptcy (survival) the BANN's accuracy was 93.69% compared to 72.97% for Altman's Z-score, a 28.39% increase. For the 5 years prior to bankruptcy or nonbankruptcy the BANN classified 80.91% to Z-score's 62.05%. Overall, the neural network was 30.39% more accurate than the discriminant analysis formula. |
