TY - JOUR
T1 - Dynamics of Modeling in Data Mining
T2 - Interpretive Approach to Bankruptcy Prediction
AU - Sung, Tae Kyung
AU - Chang, Namsik
AU - Lee, Gunhee
PY - 1999
Y1 - 1999
N2 - 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.
AB - 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.
KW - Bankruptcy prediction
KW - Crisis management
KW - Data mining
KW - Dynamics of modeling
UR - http://www.scopus.com/inward/record.url?scp=0033277431&partnerID=8YFLogxK
U2 - 10.1080/07421222.1999.11518234
DO - 10.1080/07421222.1999.11518234
M3 - Article
AN - SCOPUS:0033277431
SN - 0742-1222
VL - 16
SP - 63
EP - 85
JO - Journal of Management Information Systems
JF - Journal of Management Information Systems
IS - 1
ER -