Dynamics of Modeling in Data Mining: Interpretative Approach to Bankruptcy Prediction

다국어 초록 (Multilingual Abstract)

This paper uses a data mining approach to develop
bankruptcy prediction models suitable for normal and crisis
economic conditions. It observes the dynamics of model
change from normal to crisis conditions and provides
interpretation of bankruptcy classifications. The bankruptcy
prediction model revealed the major variables in predi cting
bankruptcy to be ‘cash flow to total assets’ and ‘productivity
of capital’ under normal conditions while ‘cash flow to
liabilities’, ‘productivity of capital’, and ‘fixed assets to
stockholders equity and long -term liabilities’ under crisis
conditions. The accuracy rates of final prediction models in
normal conditions and in crisis conditions were found to be
83.3%, and 81.0%, respectively. When the normal model was
applied in crisis situations, prediction accuracy dropped
significantly in the case of bankruptcy classification (from
66.7% to 36.7%) at the level of a blind guess (35.71%).
Therefore, the need for a different model in crisis economic
conditions is justified.