Measuring type I and type II errors in an estimation model: an empirical analysis; the case of bankruptcy

The objective of this study is to generate a model that will measure the cost of type I and type II errors in a logistic estimation model. The paper's objective also is to find out how cost behaves when the cut-off point changed in an estimation model which leads to different decisions that should be made by investors and decision-makers. In other words, how will the investors and decision-makers behave when the expectation about the company's future changed which will lead to different costs in regard to making an investment decisions? The results in this study indicate that the best possible cut-off score and the percentage of type I errors fall as the relative cost of type I error increases, reflecting the changing trade-off between type I and type II errors. The results also show that changing in cut-off point lead to different decisions from investors and decision-makers.