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