• 지능정보연구
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• 제21권3호
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• pp.79-99
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• 2015

부도 예측은 회계와 재무 분야에서 꾸준히 연구되고 있는 분야이다. 초기에는 주로 다중판별분석(multiple discriminant analysis)와 로짓 분석(logit analysis)과 같은 통계적 방법을 이용하였으나, 1990년대 이후에는 경영 분야의 분류 문제를 위해 많은 연구자들이 인공신경망(back-propagation neural network), 사계기반추론(case-based reasoning), 서포트 벡터 머신(support vector machine) 등과 같은 인공지능을 통한 접근법을 이용하여 통계적 방법보다 분류 성과 측면에서 우수함을 입증해왔다. 기존의 기업의 부도에 관한 연구에서 많은 연구자들이 재무비율을 이용하여 부도 예측 모형을 구축하는 것에 초점을 맞추어왔다. 부도예측에 관한 연구가 꾸준히 진행되고 있는 반면, 부도의 세부적인 유형을 예측하여 제시하는 것에 대한 연구는 미흡한 실정이었다. 따라서 본 연구에서는 수익성, 안정성, 활동성 지표를 중심으로 국내 비외감 건설업 기업들의 부도 여부뿐만 아니라 부도의 세부적인 유형까지 예측 가능한 모형을 개발하고자 한다. 본 연구에서는 부도 유형을 예측하기 위해 두 개의 인공신경망 모형을 결합한 하이브리드 접근법을 제안하였다. 첫 번째 인공신경망 모형은 부도예측을 위한 역전파 인공신경망을 이용한 모형이며, 두 번째 인공신경망 모형은 부도 데이터를 몇 개의 유형으로 분류하는 자기조직화지도(self-organizing map)을 이용한 모형이다. 실험 결과를 통해 정의된 5개의 부도 유형인 심각한 부도(severe bankruptcy), 안정성 부족(lack of stability), 활동성 부족(lack of activity), 수익성 부족(lack of profitability), 회생 가능한 부도(recoverable bankruptcy)는 재무 비율에 따라 유형별로 상이한 특성을 갖는 것을 확인할 수 있었다. 본 연구 결과를 통해 신용 평가 분야의 연구자와 실무자들이 기업의 부도의 유형에 대한 유용한 정보를 얻을 것으로 기대한다.

• 한국IT서비스학회지
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• 제18권1호
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• pp.187-200
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• 2019

Bankruptcy prediction model is an issue that has consistently interested in various fields. Recently, as technology for dealing with unstructured data has been developed, researches applied to business model prediction through text mining have been activated, and studies using this method are also increasing in bankruptcy prediction. Especially, it is actively trying to improve bankruptcy prediction by analyzing news data dealing with the external environment of the corporation. However, there has been a lack of study on which news is effective in bankruptcy prediction in real-time mass-produced news. The purpose of this study was to evaluate the high impact news on bankruptcy prediction. Therefore, we classify news according to type, collection period, and analyzed the impact on bankruptcy prediction based on sentiment analysis. As a result, artificial neural network was most effective among the algorithms used, and commentary news type was most effective in bankruptcy prediction. Column and straight type news were also significant, but photo type news was not significant. In the news by collection period, news for 4 months before the bankruptcy was most effective in bankruptcy prediction. In this study, we propose a news classification methods for sentiment analysis that is effective for bankruptcy prediction model.

• 보건행정학회지
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• 제12권3호
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• pp.54-76
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• 2002

This study investigated how much EVA which evaluate firm's value can explain hospital bankruptcy prediction as a explanatory variable including financial indicators in Korea. In this study, artificial neural network and logit regression which are traditional statistical were used as the model for bankruptcy prediction. Data used in this study were financial and economic value added indicators of 34 bankrupt and -:4 non-bankrupt hospitals from the Database of Korean Health Industry Development Institute. The main results of this study were as follows: First, there was a significant difference between the financial variable model including EVA and the financial variable model excluding EVA in pre-bankruptcy analysis. Second, EVA could forecast bankruptcy hospitals up to 83% by the logistic analysis. Third, the EVA model outperformed the financial model in terms of the predictive power of hospital bankruptcy. Fourth, The predictive power of neural network model of hospital bankruptcy was more powerful than the legit model. After all the result of this study will be useful to future study on EVA to evaluate bankruptcy hospitals forecast.

• 보건행정학회지
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• 제9권2호
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• pp.1-20
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• 1999

This study was conducted to investigate the predictors of hospital bankruptcy in Korea and to examine the predictive power for 3 types of statistical models of hospital bankruptcy. Data on 17 financial and 4 non-financial indicators of 30 bankrupt and 30 profitable hospitals in 1. 2, and 3 years before bankruptcy were obtained from the hospital performance databank of Korea Institute of Health Services Management. Significant variables were identified through mean comparison of each indicator between bankrupt and profitable hospitals, and the predictive power of statistical models of hospital bankruptcy were compared. The major findings are as follows. 1. Nine out of 21 indicators - fixed ratio, quick ratio, operating profit to total assets, operating profit to gross revenue, normal profit to total assets,normal profit to gross revenue, net profit to gross revenue, inventories turnrounds, and added value per adjusted patient - were found to be significantly predictitive variables in Logit and Probit models. 2. The predicdtive power of discriminant model of hospital bankruptcy in 1. 2, and 3 years before bankruptcy were 85.4, 79.0, and 83.8% respectively. With regard to the predictive power of the Logit model of hospital bankruptcy, they were 82.3, 75.8, and 80.6% respectively, and of the Probit model. 87.1. 80.6, and 88.7% respectively. 3. The predictive power of the Probit model of hospital bankruptcy is better than the other two predictive models.

• Asian Journal of Business Environment
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• 제11권2호
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• pp.27-38
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• 2021

Purpose: The study mainly investigates bankruptcy risk and income smoothing tendency of Non-Banking Financial Institutions (NBFIs) in Bangladesh. External parties of NBFIs take investment decisions based on financial reports. Stable and predictable income is one of their preference. On the other hand, poor income is one of the signs of NBFIs having bankruptcy risk. Hence the study tries to find whether the NBFIs having bankruptcy are involved in income smoothing or not. Research design, data and methodology: Data were collected from the annual report of twenty-two listed NBFIs in Bangladesh. Data from 2013 to 2017 were used. Altman's Z score and Eckel's model are used to detecting bankruptcy risk and income smoothing respectively. Results: Result implies that most of the NBFIs which have bankruptcy risk are not involved in income smoothing. Therefore, NBFIs which has bankruptcy risk are involved less with income smoothing. Conclusions: The present study revealed that most of the listed NBFIs in Bangladesh are facing bankruptcy risk. They didn't use any fraudulent technique to show smooth income. The findings will help the investor to take an investment decision on NBFIs in Bangladesh. It will convey signals to the stock market in Bangladesh.

• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.215-219
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• 2022

Bankruptcy is one of the major areas that have attracted the interest of many researchers in the American system, particularly in terms of the laws that oversee it. It provides a plan of reorganization that enables the debtor or the proprietor to discharge liabilities to the creditors through dividing the assets to settle debts. This activity is carried out under supervision to fairly protect the interests of the creditors. Bankruptcy protection systems are dynamic and complex in nature, in line with the economic sector, ensuring the protection of affected individuals from falling into huge losses. Some bankruptcy procedures give the debtor the opportunity to stay in operation or business activity and benefit from revenues until the debt is settled. This law allows some debtors to be relived from any financial burden after the distribution of assets, even if the debt is not paid in full. In light of the above information, this research paper seeks to explore the nature of the complexity of bankruptcy protection laws, their characteristics, and the justice system that regulate them. It also sheds more light on the decision-making powers on bankruptcy cases. There are specialized courts that cover bankruptcy cases located in district courts in every state.

• 한국중재학회지:중재연구
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• 제15권1호
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• pp.113-146
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• 2005

Bankruptcy proceedings serve the purpose of the collective satisfaction of the debtor's creditors through the realisation of the debtor's assets and the distribution of the proceeds therefrom. Upon the adjudication bankruptcy, the debtor's right to administer and dispose of the property belonging to the bankruptcy estate shall be vested in the administrator. If a mutual contract was not or not completely fulfilled by the debtor and the other party at the time of the adjudication of bankruptcy, the administrator has right to choose wether to fulfil or terminate the contractual relation. Legal acts that have been conducted prior to the adjudication of bankruptcy and that are detrimental to the debtor's creditors may be contested by the administrator. However, these effects of bankruptcy will have not great influence on the arbitration agreement between the debtor and another party. An arbitration agreement that has been conducted prior to the adjudication of bankruptcy is binding the administrator as an universal legal successor of debtor. Only the arbitration agreement directly disadvantageous to the debtor's creditors may be contested by the administrator. Furthermore, it is not at the discretion of administrator whether or not to submit the dispute to arbitration because an arbitration agreement does not belong under the category of Art. 50 Korean bankruptcy Act which demands a mutual contract. Arbitral proceeding upon the property of the bankruptcy estate and pending for the debtor as plaintiff or against the debtor as defendant at the date of the adjudication of bankruptcy may be taken up at the given status by the administrator. This leads to a change of the party. If a duly summoned party fails to appear in arbitration court, the arbitrator, if satisfied there is no valid excuse, may continue the proceedings and make the award as if all the parties were present. This may be disadvantagious to the debtor's creditors because the arbitral award have the same effects on the participants as the final and conclusive judgement of the court. Even if there is a change of party on side of debtor to the administrator in bankruptcy, the arbitral proceedings will not be automatically postponed or suspended. The matter of how to proceed is at discretion of administrator, when the parties haven't agree on the arbitral proceedings. He can continue the arbitral proceedings without to grant an adjournment of hearing. However, an arbitration award may be challenged by a party dissatisfied and set aside by the court based upon the misconduct that violates the basic rights of the parties to a fair hearing. The arbitrator must treat the parties equally in the arbitral proceedings and give each party a full opportunity to present his case. The arbitrator, therefore, will carefully exercise his discretion in determining whether to continue the arbitral proceedings or to grant a postponing. In the practice, the arbitral proceedings may be usually postponed to grant due process.

• 국제학술발표논문집
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• The 4th International Conference on Construction Engineering and Project Management Organized by the University of New South Wales
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

• Asia pacific journal of information systems
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• 제15권2호
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• pp.51-63
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• 2005

There has been substantial research into the bankruptcy prediction. Many researchers used the statistical method in the problem until the early 1980s. Since the late 1980s, Artificial Intelligence(AI) has been employed in bankruptcy prediction. And many studies have shown that artificial neural network(ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance, it has some problems such as overfitting and poor explanatory power. To overcome these limitations, this paper suggests a relatively new machine learning technique, support vector machine(SVM), to bankruptcy prediction. SVM is simple enough to be analyzed mathematically, and leads to high performances in practical applications. The objective of this paper is to examine the feasibility of SVM in bankruptcy prediction by comparing it with ANN, logistic regression, and multivariate discriminant analysis. The experimental results show that SVM provides a promising alternative to bankruptcy prediction.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.58-65
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• 2023

Bankruptcy is a significant risk for start-up companies, but with the help of cutting-edge artificial intelligence technology, we can now predict bankruptcy with detailed explanations. In this paper, we implemented the Category Boosting algorithm following data cleaning and editing using OpenRefine. We further explained our model using the Shapash library, incorporating domain knowledge. By leveraging the 5C's credit domain knowledge, financial analysts in banks or investors can utilize the detailed results provided by our model to enhance their decision-making processes, even without extensive knowledge about AI. This empowers investors to identify potential bankruptcy risks in their business models, enabling them to make necessary improvements or reconsider their ventures before proceeding. As a result, our model serves as a "glass-box" model, allowing end-users to understand which specific financial indicators contribute to the prediction of bankruptcy. This transparency enhances trust and provides valuable insights for decision-makers in mitigating bankruptcy risks.