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<p>Bankruptcy prediction has been a topic of active research for business and corporate institutions in recent times. The problem has been tackled using various models viz. Statistical, Market Based and Computational Intelligence in the past. In this work, we analyze bankruptcy using both parametric and nonparametric prediction techniques. This investigation concentrates on the impact of choice of cut off points, sampling procedures and business cycle on accuracy of bankruptcy prediction models. Misclassification can result in erroneous predictions leading to prohibitive costs to investors and economy. To test the impact of choice of cut off points and sampling procedures, four bankruptcy prediction models are examined viz. Bayesian, Hazard, Mixed Logit and Rough Bayesian techniques. To evaluate the relative performance of models, a sample of firms from Lynn M. LoPucki Bankruptcy Research Database in US is used. The choice of cut off point and sampling procedures are found to affect rankings of various models. The results indicate that empirical cut off point estimated from training sample resulted in lowest misclassification costs for all the models. Although Hazard and Mixed Logit models resulted in lower costs of misclassification in randomly selected samples, Mixed Logit model did not perform well across varying business cycles. Hazard model has highest predictive power. However, higher predictive power of Rough Bayesian and Bayesian modes when ratio of cost of Type I to cost of Type II errors is high is relatively consistent across all sampling methods. This advantage of Bayesian models may make them more attractive in current economic environment. This study also compares the performance of bankruptcy prediction models by identifying conditions under which a model performs better. It applies to a varied range of user groups including auditors, shareholders, employees, suppliers, rating agencies and creditors' concerns with respect to assessing failure risk.</p

Bankruptcy Prediction Using Bayesian, Hazard, Mixed Logit and Rough Bayesian Models: A Comparative Analysis