Evaluating Bayesian Classifiers and Rough Sets for Corporate Bankruptcy Prediction

• Main Author: Fitzpatrick, Margo L.
• Published: NSUWorks 2004
• Subjects: Computer Sciences
• Online Access: http://nsuworks.nova.edu/gscis_etd/517

Corporate failure or bankruptcy is costly to investors as well as to society in general. Given the high costs of corporate failure, there is much interest in improved methods for bankruptcy prediction. A promising approach to solve this problem is to provide auditors with a tool that aids in estimating the likelihood of bankruptcy. Recent studies indicate that some success has been achieved in identifying a model and good predictive variables, but the research has been limited to narrow industry segments or small samples. This research evaluated and contrasted two approaches for predicting corporate bankruptcy that were relatively successful in prior studies with narrow or small samples of corporations. The first approach used a Bayesian belief network that incorporated a naive Bayesian classification mechanism. The second approach used an expert system that incorporated rough sets. The contribution of this study is two-fold. First, this comparative evaluation extends the research by providing insights into relative advantages of Bayesian classifiers and rough sets as tools for predicting corporate bankruptcy. One or more such tools could be useful to auditors and others concerned with forecasting the likely bankruptcy of corporations. Second, this research contributes to the literature by identifying a single set of predictor variables that have broad applicability to corporations and that can be used in both the rough sets and naive Bayesian models. Employing a single set of predictor variables in both models is essential for comparing the relative effectiveness of the models. The result of this study offer a set of predictor variables and a determination of which model has greater general applicability and effectiveness for forecasting corporate bankruptcies.