Using a Bayesian model for bankruptcy prediction : a comparative approach

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.

Examining the Learning Effects of Segmented Model Demonstrations on the Motor & Cognitive Learning of the Basketball Jump Shot

The purpose of the present experiment was to determine whether learning is optimized when providing the opportunity to observe either segments, or the whole basketball jump shot. Participants performed 50 jump-shots from the free throw line during acquisition, and returned one day later for a 10 shot retention test and a memory recall test of the jump-shot technique. Shot accuracy was assessed on a 5-point scale and technique assessed on a 7-point scale. The number of components recalled correctly by participants assessed mental representation. Retention results showed superior shot technique and recall success for those participants provided control over the frequency and type of modelled information compared to participants not provided control. Furthermore, participants in the self-condition utilized the part-model information more frequently than whole-model information highlighting the effectiveness of providing the learner control over viewing multiple segments of a skill compared to only watching the whole model.