Despite a larger number of approaches
developed for predicting bankruptcy over the past three decades, rare research
has considered the effects of misclassification cost and group size. Uneven
cost of misclassification results from Type I (misclassify a healthy company as a failure) and
Type II errors (misclassify
a failed company as healthy), which are seldom considered. Without accounting
for unevenness in misclassification cost, the classifier is developed based on
minimizing total misclassification errors to improve the hit-ratio for classification
performance. This not only results in poor decision capability, but also causes bias towards the larger group. This
paper explores the issues of uneven misclassification costs and imbalanced
group size by applying an asymmetric-stratified data envelopment analysis to
bankruptcy prediction. The results show a tradeoff between hit-ratio and
misclassification cost when Type II error cost is ten times over that of Type I, that is, the higher the hit-ratio
is, the greater the resulting misclassification costs are. By incorporating
different proportions of Type II error costs to Type I into the classification
procedures, the proposed approach provides greater flexibility to decision
makers for credit evaluation or bankruptcy prediction based on different risk
attitudes and situations.
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