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RISS 인기검색어
- 검색키워드
- 저자 : 조희국 ( Hee Gook Cho ) (검색결과 1 건)
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조희국 ( Hee Gook Cho ),김영수 ( Yeong Soo Kim ) 아시아.유럽미래학회 2011 유라시아연구 Vol.8 No.2
To predict the bankruptcy of Saemaeul Kumko (“Kumko”) and objectively determine its bankruptcy, a model of bankruptcy prediction was developed, and the predictive power and utility of the model were verified. For the purpose of the study, 300 sample kumkos were selected. They were then divided into 60 kumkos, which had been classified as bankrupt kumko, and 46 kumkos classified as blue chip, according to the CAMELS rating (assessment of the kumko’s management condition) conducted at the end of 2008. Then, a discriminant analysis was carried out to come up with the prediction model, using the major financial ratios from 2005 to 2008. Using the model, the results of testing the predictive power of the model regarding 194 samples are as follows: First, among the 19 financial variables, 6 were considered useful in bankruptcy prediction, among which such indicators as stability, liquidity, profitability, asset size, and activity were found to be useful in discriminating the bankruptcy of a kumko. Second, while variables based on stability, profitability, and asset size were adopted from the discriminant function, growth-related financial variables were not included even though they used to be selected for an assessment model or a model of bankruptcy prediction. This showed that since the financial crisis, stability, liquidity, and super-sizing have been critical factors in determining whether a kumko is bankrupt or not. Third, As for the predictive power of testing samples in the model, the predictive of power of the Good kumko appeared higher towards the year of prediction (Y): 82.6% in the Y-4 year, 89.1% inY-3, 89.1% in Y-2, and 93.4% in Y-1. The percentage of accurate prediction of bankruptcy also became higher towards the year of prediction with 90.0% in Y-4, 88.3% in Y-3, 95.0% in Y-2, and 95.0% in Y-1. The average prediction of the bankrupt kumkos was higher than that of the Good kumko ones. The predictive power of the blue-chip kumko was higher as the prediction period became shorter, because the kumko’s business results improved during the sampling period, yielding a clear distinction between the bankrupt and Good kumko and, in turn, the consistent results of the predictive power of all the kumkos. Fourth, the entire predictive power of the analysis sample was 92.1%, and that of the average bankruptcy prediction model was 81.0%. Therefore, this model will be a useful indicator for the financial authorities if they look forward to early predictions of bank bankruptcy and sound management. Additionally, this study will help provide theoretical foundations
KCI
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