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Using a Bayesian model for bankruptcy prediction : a comparative approach

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dc.contributor.author He, Zhanpeng
dc.date.accessioned 2012-03-21T16:07:24Z
dc.date.available 2012-03-21T16:07:24Z
dc.date.issued 2012-03-21
dc.identifier.uri http://hdl.handle.net/10464/3932
dc.description.abstract 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. Although the Hazard and Mixed Logit models resulted in lower costs of misclassification in the randomly selected samples, the Mixed Logit model did not perform as well across varying business-cycles. In general, the Hazard model has the highest predictive power. However, the higher predictive power of the Bayesian model, when the ratio of the cost of Type I errors to the cost of Type II errors is high, is relatively consistent across all sampling methods. Such an advantage of the Bayesian model may make it more attractive in the current economic environment. 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. en_US
dc.subject Bayesian statistical decision theory en_US
dc.subject Business forecasting en_US
dc.subject Bankruptcy -- United States en_US
dc.title Using a Bayesian model for bankruptcy prediction : a comparative approach en_US
dc.degree.name M.Sc. Management en_US
dc.degree.level Masters en_US
dc.contributor.department Faculty of Business Programs en_US
dc.degree.discipline Faculty of Business en_US


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