Abstract
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.
Highlights
Bankruptcy prediction (Altman, 1993; Bellovary et al, 2007; Pate, 2002) is an important and challenging topic for business and corporate institutions
This section illustrates the results obtained towards bankruptcy prediction using Bayesian, Hazard, Mixed Logit and Rough Bayesian models
In this work, a comparative analysis of corporate bankruptcy prediction is made through Bayesian, Hazard, Mixed Logit and Rough Bayesian models based on their empirical performance
Summary
Bankruptcy prediction (Altman, 1993; Bellovary et al, 2007; Pate, 2002) is an important and challenging topic for business and corporate institutions. The methodologies employed have been based mainly on various Statistical and Computational Intelligence models During this distress period, three important Statistical models viz. Bayesian (Sarkar et al, 2001), Hazard (Shumway, 2001) and Mixed Logit (Jones et al, 2004) have been successfully applied to bankruptcy prediction. We build on studies by Jones et al (2004) and Sarkar et al (2001), Shumway (2001) and Hensher et al (2007) who proposed the use of advanced probability modeling in prediction of corporate bankruptcy These studies indicate that Bayesian, Hazard and Mixed Logit models have valuable applications in financial distress research. The basic notion of their research is that besides industry effect, aspects of accounting systems such as going concern and conservatism principle among others limits the performance of any accounting based bankruptcy prediction model Given their results, we study the impact of overall economic business cycle on testing bankruptcy prediction models.
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