Influence of the Event Rate on Discrimination Abilities of Bankruptcy Prediction Models
This research addresses the challenge of low event rates in bankruptcy prediction for financial risk management, but it is incremental as it compares existing models without introducing new methods.
The study investigated how varying event rates affect the discrimination abilities of bankruptcy prediction models, finding that Bayesian Network is the most insensitive to event rate changes while Support Vector Machine is the most sensitive, with performance evaluated using metrics like accuracy and F1 score across rates from 0.12% to 50%.
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 based on Kolmogorov-Smirnov Statistic, accuracy, F1 score, Type I error, Type II error, and ROC curve on the hold-out dataset with their best probability cut-offs. Results show that Bayesian Network is the most insensitive to the event rate, while Support Vector Machine is the most sensitive.