• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4881-4894
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• 2014

This study selected 83 manufacturing firms that had been delisted from the KOSDAQ market from 2009 to 2012 and the sample firms for the two-paired sampling method were compared with 83 normal firms running businesses with same items or in same industry. The 75 financial ratios for five years immediately before delisting were used for Mean Difference Analysis with those of normal firms. Fifteen variables assumed to be significant variables for five consecutive years out of the analysis were used to in the Dichotomous Classification Technique, Logistic Regression Analysis and Discriminant Analysis. As a result of those three analyses, the Logistic Regression Analysis model was found to show the greatest discrimination. This study is differentiated from previous studies as it assumed that the firm's failure proceeded slowly over long period of time and it tried to predict the firm's failure earlier using the five years' historical data immediately before failure, whereas previous studies predicted it using three years' data only. This study is also differentiated from the proceeding comparative studies by its statistically complex Multi-Variate Analysis and Dichotomous Classification Analysis, which general stakeholders can easily approach.

• The Korean Journal of Financial Management
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• v.19 no.2
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• pp.111-134
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• 2002

본 연구에서는 건설업종에 특화된 신용평가 모형을 개발하여 건설업종에 대한 부도 예측력를 제고하고자 하였다. 건설업은 여타 업종과는 다른 재무적 특성을 지니고 있다. 특히, 재무적 안정성이 취약하고 자산의 대부분이 매출채권, 재고자산으로 구성되어 유동성이 극히 낮은 실정이다. 본 연구는 이러한 건설업종의 특성을 충분히 감안한 신용평가 모형을 개발하고자 한것이다. 신용평가 모형 중 그 현실적 유용성이 높아 많이 이용되어 오던 신용평점 모형을 개발하였다. 총 2,475개 건설업체를 대상으로 모형구조 및 각종 계량지표 및 비계량지표에 대한 분석을 주로 평균차이 검증과 로짓분석에 의거 선정하였다. 그 결과 새로운 신용평점 모형은 매출액 경상이익률, 총 현금흐름 대 차입금 비율 등 9개의 재무지표와 5분류의 비재무지표로 구성되었다. 이 모형을 기존의 신용평점모형과 비교한 결과 신규모형의 변별력이 높은 것으로 나타났다. 본 연구가 제시한 신용평점모형과 그 개발 방법이 향후 금융기관들의 부실을 줄이고 결과적으로 수익성을 개선하는데 일조하리라 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.872-875
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• 2009

In a bankruptcy prediction model, the accuracy is one of crucial performance measures due to its significant economic impacts. Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. In this paper, we analyze the performance of boosted neural networks for improving the performance of traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the boosted neural networks showed the improved performance over traditional neural networks.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1068-1071
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• 2005

최근 들어 기술을 담보로 하는 신용금융의 역할이 증대되면서 자금지원 대상기업의 기술평가 시스템 구축이 중요한 과제가 되고 있다. 국내에서는 기업 보유의 기술경영성과를 측정하여 한정된 자원의 효율적 배분을 위한 민간 투, 융자를 위한 기술신용평가모형'이 제시되었다 본 연구에서는 기술신용평가모델의 평가항목 타당성을 실증 분석한다. 모형의 항목 분류가 적절하게 되었는지를 검증하기 위하여 구조적 타당성을 평가하며 통계적 유의성을 검증하여 신뢰성을 평가한다. 구조적 타당성 검정을 위해 확인 요인분석을 수행하며 평가모형의 신뢰성을 검증하기 위해서는 다변량 통계방법 중의 하나인 판별분석을 수행한다. 본 연구는 기술개발 성공 및 부실발생의 예측력을 갖는 기술신용평가 시스템 구축을 위한 기초 자료로 활용될 수 있을 것이다.

• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.1-13
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• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.25-41
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• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• Korean Journal of Construction Engineering and Management
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• v.11 no.1
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• pp.101-112
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• 2010

The Korean real estate market currently is experiencing a slowdown due to the global economic crisis which has resulted from subprime mortgage lending practices. In response, the Korean government has enforced various policies, based on intend to deregulate real estate speculation, such as increasing the Loan to value ratio (LTV) in order to stimulate housing supply, demand and accompanying housing transactions. However, these policies have appeared to result in deep confusion in the Korean housing market. Furthermore, analyses for housing market forecasting particularly those which examine the impact of the international financial crisis on the Korean real estate market have been partial and fragmentary. Therefore, a comprehensive and systematical approach is required to analyze the real estate financial market and the causal nexus between market determining factors. Thus, with an integrated perspective and applying a system dynamics methodology, this paper proposes Korean Real Estate and Mortgage Market dynamics models based on the fundamental principles of housing markets, which are determined by supply and demand. As well, the potential effects of the Korean government's deregulation policies are considered by focusing on the main factor of these policies: the mortgage loan.

• Management & Information Systems Review
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• v.28 no.3
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• pp.1-23
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• 2009

Prior bankruptcy studies have established that bankrupt firm's pre-filing financial ratios are different from those of healthy firms or of randomly selected going concerns. However, they may not be sufficiently different from the financial ratios of other firms in financial distress to allow the development of a ratio-based model that predicts bankruptcy with reasonable accuracy. As the result, in the multiple discriminant model, independent variables divided firms into bankrupt firms and healthy firms are retained earnings to total asset, receivable turnover, net income to sales, financial expenses, inventory turnover, owner's equity to total asset, cash flow to current liability, and current asset to current liability. Moreover four variables Retained earnings to total asset, net income to sales, total asset turnover, owner's equity to total asset indicate that these valuables classify bankrupt firms and distress firms. On the other hand, Owner's Equity to borrowed capital, Ordinary income to Net Sales, Operating Income to Total Asset, Total Asset Turnover and Inventory Turnover are selected to predict bankruptcy possibility in the Logistic regression model.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.135-147
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• 2001

This study proposes bankruptcy prediction model using fuzzy neural networks. Neural networks offer preeminent learning ability but they are often confronted with the inconsistent and unpredictable performance for noisy financial data. The existence of continuous data and large amounts of records may pose a challenging task to explicit concepts extraction from the raw data due to the huge data space determined by continuous input variables. The attempt to solve this problem is to transform each input variable in a way which may make it easier fur neural network to develop a predictive relationship. One of the methods selected for this is to map each continuous input variable to a series of overlapping fuzzy sets. Appropriately transforming each of the inputs into overlapping fuzzy membership sets provides an isomorphic mapping of the data to properly constructed membership values, and as such, no information is lost. In addition, it is easier far neural network to identify and model high-order interactions when the data is transformed in this way. Experimental results show that fuzzy neural network outperforms conventional neural network for the prediction of corporate bankruptcy.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.173-190
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• 2015

The purpose of this study is to improve bankruptcy prediction models by using a novel hybrid under-sampling approach. Most prior studies have tried to enhance the accuracy of bankruptcy prediction models by improving the classification methods involved. In contrast, we focus on appropriate data preprocessing as a means of enhancing accuracy. In particular, we aim to develop an effective sampling approach for bankruptcy prediction, since most prediction models suffer from class imbalance problems. The approach proposed in this study is a hybrid under-sampling method that combines the k-Reverse Nearest Neighbor (k-RNN) and one-class support vector machine (OCSVM) approaches. k-RNN can effectively eliminate outliers, while OCSVM contributes to the selection of informative training samples from majority class data. To validate our proposed approach, we have applied it to data from H Bank's non-external auditing companies in Korea, and compared the performances of the classifiers with the proposed under-sampling and random sampling data. The empirical results show that the proposed under-sampling approach generally improves the accuracy of classifiers, such as logistic regression, discriminant analysis, decision tree, and support vector machines. They also show that the proposed under-sampling approach reduces the risk of false negative errors, which lead to higher misclassification costs.