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Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni
Influence Of The Event Rate On Discrimination Abilities Of Bankruptcy Prediction Models, Lili Zhang, Jennifer Priestley, Xuelei Ni
Published and Grey Literature from PhD Candidates
In bankruptcy prediction, the proportion of events is very low, which is often oversampled to eliminate this bias. In this paper, we study the influence of the event rate on discrimination abilities of bankruptcy prediction models. First the statistical association and significance of public records and firmographics indicators with the bankruptcy were explored. Then the event rate was oversampled from 0.12% to 10%, 20%, 30%, 40%, and 50%, respectively. Seven models were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. Under different event rates, models were comprehensively evaluated and compared …
Comparison Of Bankruptcy Prediction Models With Public Records And Firmographics, Lili Zhang, Jennifer Priestley, Xuelei Ni
Comparison Of Bankruptcy Prediction Models With Public Records And Firmographics, Lili Zhang, Jennifer Priestley, Xuelei Ni
Published and Grey Literature from PhD Candidates
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to study the impacts of public records and firmographics and predict the bankruptcy in a 12-month-ahead period with using different classification models and adding values to traditionally used financial ratios. Univariate analysis shows the statistical association and significance of public records and firmographics indicators with the bankruptcy. Further, seven statistical models and machine learning methods were developed, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and Neural Network. The performance of models were evaluated and compared based on classification accuracy, …