• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.12 no.1
• /
• pp.133-144
• /
• 2017

This study investigates firm-specific financial variables that determine investment or speculative grades from the viewpoint of firms, which are one of the major stakeholders related to the credit rating. We employ an ordered probit model for our analysis with the sample data from 1999 to 2015 for listed firms in the Korean stock markets. For investment grades, operating margin, sales, market-to-book, dividend payment, capital expenditure ratio, and tangible asset ratio have a significantly positive impact on credit ratings. In the subsample for speculative grades, the coefficients of the dividend payment, retained earnings ratio, and capital expenditure ratio are significantly positive while short-term debt ratio and R&D expenditures have a significantly negative impact on credit ratings. For the analysis before and after 2009, when the Credit Information Use and Protection Act was strengthened after the global financial crisis, the coefficients of the capital expenditure ratio, cash ratio, and tangible asset ratio are significantly positive in the subsample for investment grades before 2009, but not significant after 2010. The coefficient of the long-term debt ratio is more significantly negative than that of the short-term debt ratio before 2009, for speculative grades, but short-term debt ratio has a more negative effect on ratings than long-term debt ratio after 2010. Surprisingly, the coefficient of the R&D expenditures is significantly negative in both investment and speculative grades since 2010. Our findings are inconsistent with the conjecture that the increase in R&D expenditures enhances the possibility of creating cash-flow by raising the investment growth opportunity, and thus affects positively the credit rating.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.4
• /
• pp.1-12
• /
• 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.

• Information Systems Review
• /
• v.4 no.2
• /
• pp.361-376
• /
• 2002

This paper proposes a framework for integrated credit risk management system in domestic bank financial institutions. Credit evaluation system, loan processing system, credit monitoring system, and credit risk management system are integrated for efficient and effective risk-adjusted performance management in this framework. Risk exposures, not only for each credit, but also for bank's whole credit portfolio need to be measured and analyzed through the concept of Value-at-Risk (VaR). The effects of changes in credit ratings of individual loaners on bank's credit risk exposure are also considered. We tried to model this integrated credit risk management system by using object-oriented modeling language, UML.

• KDI Journal of Economic Policy
• /
• v.43 no.3
• /
• pp.1-32
• /
• 2021

With CB data in South Korea, this study examines whether the credit risk of borrowers changes when the regulation on bank mortgage supply is relaxed. We analyze the effect of deregulation on LTV and DTI limits in the Seoul-metropolitan area in August 2014 with a difference-in-difference approach. We find that the probability of delinquency is lower in the Seoul metropolitan area after the deregulation than in other urban areas. The effect is noticeable among low-income and low-credit borrowers. We also find that borrowers change their debt structure to reduce the interest costs utilizing their improved access to bank mortgages. The findings suggest the necessity to consider the burden of the high interest costs of unsecured loans for debtors with low incomes and low credit ratings in designing housing finance regulations.

• Asia pacific journal of information systems
• /
• v.21 no.2
• /
• pp.43-58
• /
• 2011

Support vector machines (SVMs), a machine learning technique, has been applied to not only binary classification problems such as bankruptcy prediction but also multi-class problems such as corporate credit ratings. However, in general, the performance of SVMs can be easily worse than the best alternative model to SVMs according to the selection of predictors, even though SVMs has the distinguishing feature of successfully classifying and predicting in a lot of dichotomous or multi-class problems. For overcoming the weakness of SVMs, this study has proposed an approach for selecting features for multi-class SVMs that utilize the impurity measures of classification trees. For the selection of the input features, we employed the C4.5 and CART algorithms, including the stepwise method of discriminant analysis, which is a well-known method for selecting features. We have built a multi-class SVMs model for credit rating using the above method and presented experimental results with data regarding S&P 500 companies.

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

• KDI Journal of Economic Policy
• /
• v.28 no.1
• /
• pp.1-47
• /
• 2006

Even after significant changes in the financial market due to the financial crisis the corporate debt markets have seen created turmoil caused such as by Daewoo, Hyundai, and credit card companies in the financial system. These lagging improvements of corporate debt markets are mainly due to inadequate market infrastructure. Specifically, the credit rating agencies have not been successful in providing proper and timely information on the loan repayment abilities of debtors. This study analyzes past performance of credit rating agencies in Korea and tries to develop policy implications to improve the role of credit rating agencies based on the recent discussions on credit rating agencies by academics and the SEC. In addition, this study focuses on unique operation environments of Korean credit rating agencies, which have kept credit rating agencies from providing fair, timely, and useful information. To warrant proper operation of credit rating agencies, it is essential to cope with unique problems in Korean credit rating agencies. We classify the unique problems of Korean credit rating agencies into ownership and governance structure, conflict of interests due to ancillary fee-based business, legal recognition of credit rating in the court, and code of conduct problem, etc. and propose policy directions to improve the quality and credibility of credit ratings.

• Korean Journal of Construction Engineering and Management
• /
• v.19 no.1
• /
• pp.90-102
• /
• 2018

In preparation of the increase in the domestic debts and the rise in the US interest rate, the Korean government has started to strengthen the regulation on the property market since 2017. So, it is likely that the sales in the domestic construction market would be decreased. Even in the overseas plant projects market, as there has been the continuous increase in the cost and the resulting increase in the losses, it looks hard for the large construction companies to keep their credit ratings as they are now. This study is designed to check Korean government's property policy and any possible problems caused by the overseas and domestic economic environment, which include the property market policy, interest rate, rise in the property price and lackluster sales in housing market. It showed the change in the credit ratings by finding out the sales, work capability, sales in non-governmental projects, operating profits and PF contingency liabilities. For this study, the questionnaires were sent to 30 practical experts to analyze the effect of the risk factor on the outside credit rating of large construction companies.

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.3
• /
• pp.605-615
• /
• 2017

Recently, financial supervisory authority of Korea and international credit rating agencies have been concerned about a stand-alone rating that is calculated without incorporating guaranteed support of parent companies. Guaranteed by parent companies, most foreign subsidiaries keeps good credit rate in spite of weak financial status. However, what if the parent companies stop supporting the foreign subsidiaries, they could have a probability to go bankrupt. In this paper, we have validated a credit rating model through statistical measurers such as performance, calibration, and stability for Korean companies owning foreign subsidiaries.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3627-3641
• /
• 2021

Fintech, which stands for financial technology, is growing fast globally since the economic crisis hit the United States in 2008. Fintech companies are striving to secure a competitive advantage over existing financial services by providing efficient financial services utilizing the latest technologies. Fintech companies can be classified into several areas according to their business solutions. Among the Fintech sector, peer-to-peer (P2P) lending companies are leading the domestic Fintech industry. P2P lending is a method of lending funds directly to individuals or businesses without an official financial institution participating as an intermediary in the transaction. The rapid growth of P2P lending companies has now reached a level that threatens secondary financial markets. However, as the growth rate increases, so does the potential risk factor. In addition to government laws to protect and regulate P2P lending, further measures to reduce the risk of P2P lending accidents have yet to keep up with the pace of market growth. Since most P2P lenders do not implement their own credit rating system, they rely on personal credit scores provided by credit rating agencies such as the NICE credit information service in Korea. However, it is hard for P2P lending companies to figure out the intentional loan default of the borrower since most borrowers' credit scores are not excellent. This study analyzed the voices of telephone conversation between the loan consultant and the borrower in order to verify if it is applicable to determine the personal credit score. Experimental results show that the change in pitch frequency and change in voice pitch frequency can be reliably identified, and this difference can be used to predict the loan defaults or use it to determine the underlying default risk. It has also been shown that parameters extracted from sample voice data can be used as a determinant for classifying the level of personal credit ratings.