• 한국정보통신학회논문지
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• 제9권7호
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• pp.1569-1574
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• 2005

In this paper, I propose an intelligent credit rating system using a bankruptcy prediction model based on support vector machines (SVMs). SVMs are promising methods because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study examines the feasibility of applying SVM in Predicting corporate bankruptcies by comparing it with other data mining techniques. In addition. this study presents architecture and prototype of intelligeht credit rating systems based on SVM models.

• 한국정보시스템학회지:정보시스템연구
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• 제14권1호
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• pp.41-65
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• 2005

The credit ration is a significant area of financial management which is of major interest to practitioners, financial and credit analysts. The components of credit rating are identified decision models are developed to assess credit rating an the corresponding creditworthiness of firms an accurately ad possble. Although many early studies demonstrate a priori which of these techniques will be most effective to solve a specific classification problem. Recently, a number of studies have demonstrate that a hybrid model integration artificial intelligence approaches with other feature selection algorthms can be alternative methodologies for business classification problems. In this article, we propose a hybrid approach using rough set theory as an alternative methodology to select appropriate attributes for case-based reasoning. This model uses rough specific interest lies in lthe stable combining of both rough set theory to extract knowledge that can guide dffective retrevals of useful cases. Our specific interest lies in the stable combining of both rough set theory and case-based reasoning in the problem of corporate credit rating. In addition, we summarize backgrounds of applying integrated model in the field of corporate credit rating with a brief description of various credit rating methodologies.

• Journal of Information Technology Applications and Management
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• 제14권2호
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• pp.151-168
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• 2007

The corporate credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this study, the corporate credit rating model employs artificial intelligence methods including Neural Network (NN) and Case-Based Reasoning (CBR). At first we suggest three classification models, as partitioned neural networks, all of which convert multi-group classification problems into two group classification ones: Ordinal Pairwise Partitioning (OPP) model, binary classification model and simple classification model. The experimental results show that the partitioned NN outperformed the conventional NN. In addition, we put to use CBR that is widely used recently as a problem-solving and learning tool both in academic and business areas. With an advantage of the easiness in model design compared to a NN model, the CBR model proves itself to have good classification capability through the highest hit ratio in the corporate credit rating.

• Asia pacific journal of information systems
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• 제25권3호
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• pp.579-596
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• 2015

Many studies have focused on the prediction of corporate credit rating using various data mining techniques. One of the most frequently used algorithms is support vector machines (SVM), and recently, novel techniques such as SVM+ and SVM+MTL have emerged. This paper intends to show the applicability of such new techniques to multi-classification and corporate credit rating and compare them with conventional SVM regarding prediction performance. We solve multi-class SVM+ and SVM+MTL problems by constructing several binary classifiers. Furthermore, to demonstrate the robustness and outstanding performance of SVM+MTL algorithm over other techniques, we utilized four typical multi-class processing methods in our experiments. The results show that SVM+MTL outperforms both conventional SVM and novel SVM+ in predicting corporate credit rating. This study contributes to the literature by showing the applicability of new techniques such as SVM+ and SVM+MTL and the outperformance of SVM+MTL over conventional techniques. Thus, this study enriches solving techniques for addressing multi-class problems such as corporate credit rating prediction.

• Journal of Contemporary Eastern Asia
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• 제20권1호
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• pp.102-119
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• 2021

Social credit rating in China (SCRC) has been criticized as "dystopian" and "Orwellian," an attempt by the Communist Party to hold onto power by exerting ever greater control over its citizens. To explain such measures, value differences are often invoked, that Chinese value stability and cooperation over privacy and freedom. However, these explanations are oversimplifications that result in ethical impasses. This article argues social credit rating should be understood in terms of the commonly human problem of large-scale cooperation. To do so, this paper relies on a cultural evolutionary framework and is an exercise in interpretive pros hen ethical pluralism, attempting to understand how apparently irresolvable cultural differences stem from common human concerns. Wholesale condemnation of SCRC fails to acknowledge the serious, intractable nature of problems resulting from a lack of trust in China. They take for granted the existence of institutions ensuring largescale, anonymous cooperation characteristic of - but somewhat unique to - Western Educated Industrialized Rich and Democratic (WEIRD) cultures. Because of its history and rapid development, China lacks the institutions necessary to ensure such cooperation, and because of anti-social punishment, social credit rating might be one of the few ways to ensure cooperation at this scale. The point is not to defend social credit rating in general, but to raise the possibility of its defense in China and show one way this would be done.

• 경영과정보연구
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• 제35권4호
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• pp.37-56
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• 2016

본 연구는 K-IFRS 도입이전과 K-IFRS 도입이후의 표본자료를 사용하여 K-IFRS 도입으로 당기순이익과 기타포괄손익이 신용평가에 미치는 영향이 각 기간과 각 변수에 따라 달라졌는지를 분석한다. 연구결과는 다음과 같다. 첫째, K-IFRS 도입이후(2011년-2013년)에 당기손익(NI)이 신용평가에 미치는 영향은 K-IFRS 도입이전(2007년-2010년)보다 증가하였음을 알 수 있었다. 그러나 기타포괄손익(OCI)이 신용평가에 미치는 영향은 K-IFRS 이전과 비교하여 차이가 발생하지 않았다. 둘째, 당기손익은 K-IFRS 도입이후(2011년-2013년)에도 추가적으로 양의 영향을 미치는 것으로 나타났으며 도입이후 보다는 도입 첫해에 증분효과가 더 크다는 것을 알 수 있다. 그러나 IFRS 도입이후에 기타포괄손익이 신용평가에 미치는 증분효과는 미미하거나 없는 것으로 나타났다. 셋째, K-IFRS 도입첫해(2011년)에 기타포괄손익 보다는 당기손익이 신용평가에 미치는 영향이 더 큰 것으로 나타났으나 K-IFRS 도입이후(2012년-2013년)에는 당기손익과 기타포괄손익이 신용평가에 미치는 영향에 차이가 없는 것으로 나타났다. 이를 해석하면 당기손익과 기타포괄손익이 K-IFRS 도입첫해에만 신용평가에 추가적인 영향이 의미 있게 나타나 K-IFRS 도입으로 내재가치와 관계없이 재무비율 변동이 신용평가에 영향을 주었다. 그리고 시간이 지날수록 K-IFRS가 안정적으로 적용되어 도입초기와 같은 추가적인 증분효과는 나타나지 않을 것으로 기대할 수 있다. 본 연구의 의의는 K-IFRS 도입으로 인하여 재무비율 중에 당기손익이 신용평가에 영향을 미치고 있어 K-IFRS 도입전후에 정보이용자들이 신용평가자료를 이용하고자 할 때 K-IFRS의 영향을 고려해야 한다는 것이다.

• The Journal of Asian Finance, Economics and Business
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• 제9권4호
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• pp.1-12
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• 2022

Our study adds to the body of knowledge about the relationship between credit ratings and the capital structure of bond issuers. Using Bloomberg and Datastream databases and employing panel regression models, we study the capital structure changes of Japanese enterprises after credit rating changes by global rating agencies (S&P and Moody's) as well as their local counterparts (R&I and JCR) from 1998 to 2016. We find that after rating downgrades, Japanese enterprises considerably reduce net debt or net debt relative to net equity, similar to the findings of Kisgen (2009), who focused on U.S. industrial firms. They do not, however, make adjustments to their financial structure as a result of rating improvements. In comparison to downgrades by S&P and Moody's, Japanese corporations issue 1.89 percent less net debt and 1.50 percent less net debt relative to net equity after R&I and JCR rating downgrades. To put it another way, Japanese companies consider rating adjustments made by local agencies to be more significant than those made by global rating organizations. Our findings contradict earlier research that suggests S&P and Moody's are more prominent in the investment community than R&I and JCR in Japan.

• 디지털융복합연구
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• 제20권5호
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• pp.585-592
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• 2022

본 연구는 경영자의 초과보상이 신용등급에 미치는 영향을 분석하는 것이다. 적정수준을 초과하는 경영자의 초과보상의 크기가 클수록 기업의 미래 성과에 부정적인 영향을 미친다는 선행연구에 근거하여 경영자의 초과보상이 신용등급에도 부정적인 영향을 미칠 수 있다고 예상하였다. 이를 확인하기 위하여, 2014년부터 2019년까지 국내 상장비금융기업들을 대상으로 임원의 평균 보상을 통하여 경영자의 초과보상을 측정한 후, 초과보상의 크기가 차기 신용등급에 영향에 대하여 회귀분석하였다. 분석결과, 초과보상이 양(+)의 값을 가질 때, 즉 적정수준을 초과하여 경영자에게 보상이 지급될 때, 경영자 초과보상과 차기 신용등급이 음(-)의 관계로 나타났다. 또한 중소기업 표본에서 초과보상과 신용등급의 음(-)의 상관관계가 있는 것으로 나타났지만 대기업 표본에서는 상관관계가 없는 것으로 나타났다. 본 연구는 초과보상이 기업의 미래 성과에 미치는 부정적인 영향으로 인하여 신용등급에 영향을 주며, 그러한 영향은 대기업 여부에 따라 달라질 수 있다는 결과를 제시함으로써, 경영자의 초과보상이 기업의 성과에 미치는 부정적인 영향에 대하여 시장의 인지 가능성을 확인하였다는 점에서 공헌점이 있다.

• 응용통계연구
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• 제22권3호
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• pp.443-461
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• 2009

신용전이행렬을 추정함에 있어서 국내의 등급전이자료의 축적이 부족한 점을 극복하기 위하여 외국의 신용평가기관(무디스)의 전이행렬자료와 국내의 신용등급 부여자료를 이용하여 경험적 베이지안 추정방법에 의한 전이행렬을 도출하고, 이 전이행렬을 다른 전이행렬과 비교해보기 위하여 전이행렬의 동적인 요소를 평균전이확률의 개념으로 표시할 수 있는 특성척도를 개발하여 신용전이행렬의 시계열 특성과 통계적 특성을 비교한다. 시계열자료의 척도는 베이지안 추정행렬이 안정적임을 보여주는 반면 국내 행렬은 시간적으로 변화의 폭이 크고 무디스나 베이지안 행렬보다 상대적으로 인접전이의 비율이 높게 나타났다. 붓스트랩 검정을 통하여 세 가지 추정방법이 통계적으로 유의한 차이가 있음을 보이고 베이지안 행렬이 무디스 자료보다는 국내자료에 더 많은 영향을 받았음을 유추할 수 있다. 신용등급 전이에 따른 포트폴리오의 가치변화를 고려하는 몬테칼로 시뮬레이션을 통하여 신용 VaR를 구하여 비교하였다. 국내 전이행렬의 경우에 평균은 가장 크고 신용위험도 가장 큰 값을 보였다. 시뮬레이션에서도 베이지안 추정에 의한 결과가 국내자료에 의한 결과와 더 가깝다는 것을 알 수 있다.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.347-354
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• 1999

The credit rating represents an assessment of the relative level of risk associated with the timely payments required by the debt obligation. In this paper, we present a new approach to credit rating of customers based on the rough set theory. The concept of a rough set appeared to be an effective tool for the analysis of customer information systems representing knowledge gained by experience. The customer information system describes a set of customers by a set of multi-valued attributes, called condition attributes. The customers are classified into groups of risk subject to an expert's opinion, called decision attribute. A natural problem of knowledge analysis consists then in discovering relationships, in terms of decision rules, between description of customers by condition attributes and particular decisions. The rough set approach enables one to discover minimal subsets of condition attributes ensuring an acceptable quality of classification of the customers analyzed and to derive decision rules from the customer information system which can be used to support decisions about rating new customers. Using the rough set approach one analyses only facts hidden in data, it does not need any additional information about data and does not correct inconsistencies manifested in data; instead, rules produced are categorized into certain and possible. A real problem of the evaluation of the evaluation of credit rating by a department store is studied using the rough set approach.