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.

A Study of role conflict, role ambiguity, locus of control, job satisfaction and participation in decision making among front-line vocational service workers for mentally retarded adults

This study was an investigation of individual and organizational factors, as perceived by front-line vocational service workers from Adult Rehabilitation Centres (ARC Industries) for mentally retarded adults. The specific variables which were measured included role conflict/role ambiguity (role factors), internal/external locus of control (individual differences), job satisfaction with work and supervision (job attitudes) and participation in deci~ion making (organizational factor). The exploration of these constructs was conducted by means of self-report questionnaires which were completed by sixty-nine out of a total of ninety front-line employees. The surveys were distributed in booklet form to nine distinct rehabilitation facilities from St. Catharines, West Lincoln, Greater Niagara, Port Colborne, WeIland, Fort Erie, Hamilton, Guelph and Brantford. The survey data was evaluated by the statisti.cal Package for the Social Sciences (SPSS) which used the Pearson Product Moment Correlation procedure and a compar~son of means test. A comparison of correlation coefficients test was also conducted. This statistical procedure was calculated mathematically. The results obtained from the statistical evaluation confirmed the prediction that self-reported measures of participation in decision making and satisfaction (work and supervision) would be negatively correlated with role conflict and role ambiguity. As well, the speculation that perceived satisfaction (work and supervision) would be positively correlated with participation in decision making was empirically supported. Internal and external locus of control did not contribute to a significant difference in r~sponses to role perceptions (conflict and ambiguity) , satisfaction (work and supervision) or the correlational relationship between participation in decision making and satisfaction (work and supervision). Overall, the findings from this study substantiated the importance of examining employee perceptions in the workplace and the interrelationships among individual and organizational variables. This research was considered a contribution to the general area of occupational stress and to the study of individuals in work organizations.

Management decision-making in nursing

This study developed a new, valid and reliable evaluation instrument to measure the level, type and pattern of management decisions of fifteen nursing students. The management decision score achieved using this instrument was correlated with two psychological determinants of management decision making: creativity and problem-solving ability. The instrument was a written patient management problem in case format, answered by a free form written response. The student responses were classified for type of management decision according to the sub-categories of technical, inter-personal, environmental and unique. Using statistical analysis a significant difference was found in the type of management decisions most frequently selected by the study sample. The students predominantly selected technical type decisions. This preference for one type of management decision may be due to a number of psychological and environmental factors. These factors may program and mold the type of management decisions student nurses make early in their career. Low but positive correlations were found between the total management score and the two psychological tests. This finding supports the authors cited in the literature who state that although creativity augments the type of management decision making, it is not present or encouraged widely in the nursing profession. These factors are worth considering when the profession becomes concerned over ritualization and lack of individuality in patient care. The tool is easy to administer, lends itself to a variety of professional settings and shows promise with further refinement for computer application.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.