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

Tae Kyung Sung, Namsik Chang, Gunhee Lee

Research output: Contribution to journalArticlepeer-review

153 Scopus citations


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 that the major variables in predicting bankruptcy were "cash flow to total assets" and "productivity of capital" under normal conditions and "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 percent and 81.0 percent, respectively. When the normal model was applied in crisis situations, prediction accuracy dropped significantly in the case of bankruptcy classification (from 66.7 percent to 36.7 percent) to the level of a blind guess (35.71 percent). Therefore, the need for a different model in crisis economic conditions is justified.

Original languageEnglish
Pages (from-to)63-85
Number of pages23
JournalJournal of Management Information Systems
Issue number1
StatePublished - 1999


  • Bankruptcy prediction
  • Crisis management
  • Data mining
  • Dynamics of modeling


Dive into the research topics of 'Dynamics of Modeling in Data Mining: Interpretive Approach to Bankruptcy Prediction'. Together they form a unique fingerprint.

Cite this