• Journal of the Korean Data and Information Science Society
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• v.24 no.1
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• pp.73-83
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• 2013

Among class quantitative tests for the credit rating systems, the credit rating tests for calibration are to test the class homogeneous differences between observed and predicted probabilities. For one time period, binomial test and chi-square test are included, and normal test and extended traffic lights test are also contained for several time peroids. In this work, we consider real data in which there exists correlation among variables, so that these test methods could be applied to the credit rating systems as well as various kinds of the class data such as BWT data and FSI data.

• Information Systems Review
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• v.1 no.1
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• pp.51-61
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• 1999

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.11-20
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• 2024

This study presents TabNet, a novel deep learning method, to enhance corporate credit rating accuracy amidst growing financial market uncertainties due to technological advancements. By analyzing data from major Korean stock markets, the research constructs a credit rating prediction model using TabNet. Comparing it with traditional machine learning, TabNet proves superior, achieving a Precision of 0.884 and an F1 score of 0.895. It notably reduces misclassification of high-risk companies as low-risk, emphasizing its potential as a vital tool for financial institutions in credit risk management and decision-making.

• The Journal of the Korea Contents Association
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• v.16 no.3
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• pp.81-90
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• 2016

This study aims to investigate the association between stock performance and credit ratings, and credit rating changes using a sample of 1,691 KRX firm-years that acquire equity in the form of long-term bonds from 2002 to 2013. Previous U.S. literature is mixed with regard to the relation between credit ratings and stock price. On one hand, there is evidence of a positive relation between credit ratings and stock prices, an anomaly established in U.S. studies. On the other hand, the CAPM model suggests a negative relation between stock prices and credit ratings, implying that investors expect financial rewards for bearing additional risk. To our knowledge, we are the first to examine the relationship between stock price and default risk proxied by credit ratings in period t+1. We find a negative (positive) relation between credit ratings (risk) in period t+1 and stock returns in period t, suggesting that credit rating agencies do not consider stock returns as a metric with the potential to influence default risk. Our results suggest that market participants may prefer firms with higher credit risk because of expected higher returns.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.173-183
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• 2015

At present, it is simple to the electronic commerce credit scoring model, as a brush credit phenomenon in E-commerce has emerged. This phenomenon affects the judgment of consumers and hinders the rapid development of E-commerce. In this paper, that E-commerce credit evaluation model that uses a Gaussian density function is put forward by density test and the analysis for the anomalies of E-commerce credit rating, it can be fond out the abnormal point in credit scoring, these points were calculated by nonlinear credit scoring algorithm, thus it can effectively improve the current E-commerce credit score, and enhance the accuracy of E-commerce credit score.

• The Journal of Information Systems
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• v.30 no.4
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• pp.203-226
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• 2021

Purpose The purpose of this study is to enhance the accuracy score of personal credit scoring using the convolutional neural networks and secure the transparency of the deep learning model using eXplainalbe Artifical Inteligence(XAI) technique. Design/methodology/approach This study built a classification model by using the convolutional neural networks(CNN) and applied a methodology that is transformation of numerical data to imaged data to apply CNN on personal credit data. Then layer-wise relevance propagation(LRP) was applied to model we constructed to find what variables are more influenced to the output value. Findings According to the empirical analysis result, this study confirmed that accuracy score by model using CNN is highest among other models using logistic regression, neural networks, and support vector machines. In addition, With the LRP that is one of the technique of XAI, variables that have a great influence on calculating the output value for each observation could be found.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.122-126
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• 2015

Credit enhancement is absolutely essential for financing Independent Power Producer (IPP) projects in Asia particularly for countries whose sovereign credit rating is on non-investment grade and foreign investment is difficult to achieve. Due to nexus of agreements among varies parties in IPP project, it is hard to clearly visualize the roles of these agreements. Examples are: What credit enhancement factors are most influential to minimize the associated risks of IPP projects? Why are they powerful? What are their roles? Who are less powerful and what are the obstacles that causes them less powerful? A research is conducted to identify the credit enhancement factors for IPP projects in Asia. IPP professionals validated 27 out of 28 identified credit enhancement factors, and five factor groupings were made through factor analysis. Afterwards, network theory is applied to find the unanswered questions, which by graphical and mathematical representations show that the host government's credit enhancement, MDBs, ECAs and other parties' credit enhancement are prominent and of great importance to handle the associated risks of IPP projects in Asia

• The Journal of Fisheries Business Administration
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• v.47 no.2
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• pp.43-59
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• 2016

This study investigated the difference of the effects of public loan programs in fishery industry on management performance from a balanced score card (BSC) perspective depending on the type of loan, scale of fund, period of support and business category, using the financial data of fisheries firms having the balance of loan at the end of 2014. The key factors influencing credit rating change were also analyzed after public loan support. From a integrative perspective, results show that the firms supported by working fund have higher management performance than the firms supported by facility fund. The firms received large scale fund showed higher management performance than the firms received small scale fund. While management performance was decreasing or slowing down over time after financial support, management performance of the firms supported by facility fund improved over time. From a non-financial perspective, the firms received facility fund invested more in education and growing perspective than the firms received working fund. As the size of fund increased, the investment in education, growing, internal process and customer increased. Personnel expenses and employee benefits for education and growing has increased over time. However, the firms with facility fund restricted the expenses of education, personnel expenses and employee benefits as time goes by. Because the effects of public loan on credit rating of fisheries corporations have no statistical significance, it has become known that the financial support of public loan program has no influence on the change of credit rating of fisheries corporations. This study attempted performance analysis from a BSC perspective which combine factors of non-financial perspective with factors of financial perspective. Findings from this study suggest the direction of microscopic performance analysis of public loan in fishery industry.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.3
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• pp.5-15
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• 2016

This study examines whether Korean rating agencies such as Korea Investors Service (KIS), National Information & Credit Evaluation (NICE), and Korea Ratings Corporation (KR), incorporate corporate governance into their corporate bond ratings in Korea. We find that the Korean rating agencies assign higher ratings to the bonds issued by Chaebol (Korean business group) affiliated firms. Our results also indicate that those rating agencies give higher ratings to the bonds with greater foreign investor share ownership. Moreover, if the rating agencies value corporate governance, higher rated firms should issue bonds at lower yield to maturity. We discover that Chaebol affiliation is counted favorably by the rating agencies. We find that investors are willing to pay lower risk premium for bonds with higher institutional ownership, but higher risk premium to bonds with greater equity ownership in the form of depository receipts. Therefore, even if the rating agencies and investors in Korea consider corporate governance (Chaebol affiliation and ownership structure) an important determinant in bond ratings and the yields to maturity, they have opposite views on institutional ownership and share ownership in the form of depository receipts.

• Journal of Families and Better Life
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• v.27 no.6
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• pp.95-109
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• 2009

This study focused on the development of a scale to measure the ability of consumers to use credit cards. The purposes of this study were to develop a tool which would be able to measure consumer knowledge, consumer skills and consumer attitudes. Data were collected from 313 credit card using consumers and were analyzed by employing a goodness of fit test, principal component analysis & confirmatory factor analysis(Amos 5.0), multiple regression. The results from this study were as follows: 1) Six factors of consumer knowledge(16-items) were identified: damage salvation; credit delinquency; personal credit information; credit provision period; credit & credit card issuance; credit delinquent striking out a record & credit rating. The total variance was 55.86%. 2) Three factors of consumer skills(17-items) were identified: credit delinquency & over-consumption; credit card management; and loss & damage salvation. The total variance was 62.90%. 3) Three factors of consumer attitudes(16-items) were identified: credit delinquency & credit; credit card issuance & use; and credit card management. The total variance was 58.75%.