Using a Bayesian Model for Bankruptcy Prediction: A Comparative Approach

39 Pages Posted: 15 Jan 2013

See all articles by Zhongzhi Lawrence He

Zhongzhi Lawrence He

Brock University, Goodman School of Business

Samir Trabelsi

Brock University - Accounting

Date Written: January 15, 2013


The purpose of this study is to examine the impact of the choice of cut-off points, sampling procedures, and the business cycle on the accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to firms, investors and the economy. To test the impact of the choice of cut-off points and sampling procedures, three bankruptcy prediction models are assessed- Bayesian, Hazard and Mixed Logit. A salient feature of the study is that the analysis includes both parametric and nonparametric bankruptcy prediction models. A sample of firms from Lynn M. LoPucki Bankruptcy Research Database in the U. S. was used to evaluate the relative performance of the three models. The choice of a cut-off point and sampling procedures were found to affect the rankings of the various models. In general, the results indicate that the empirical cut-off point estimated from the training sample resulted in the lowest misclassification costs for all three models. When tests were conducted with randomly selected samples and all specifications of Type-I costs over Type-II costs are taken into account, the results show that the Mixed Logit model performs slightly better than the Bayesian model and much better than the Hazard model. However, when tests were conducted with business-cycle samples, the Bayesian model has the best performance and much better predictive power in recent business cycles. This study extends recent research comparing the performance of bankruptcy prediction models by identifying under what conditions a model performs better. It also allays a range of user groups, including auditors, shareholders, employees, suppliers, rating agencies, and creditors’ concerns with respect to assessing failure risk.

Keywords: Basian Network, Bankruptcy Prediction, Performance of Bankruptcy Prediction Models

JEL Classification: M41, G14, G33, C41, G29, M49

Suggested Citation

He, Zhongzhi Lawrence and Trabelsi, Samir, Using a Bayesian Model for Bankruptcy Prediction: A Comparative Approach (January 15, 2013). Available at SSRN: or

Zhongzhi Lawrence He

Brock University, Goodman School of Business ( email )

500 Glenridge Avenue
St. Catherine's, Ontario L2S 3A1

Samir Trabelsi (Contact Author)

Brock University - Accounting ( email )

St. Catharines, Ontario L2S 3A1

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Abstract Views
PlumX Metrics