• East Asian Economic Review
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• v.19 no.4
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• pp.357-379
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• 2015

We investigate interconnectedness and the contagion effect of default risk in Asian sovereign CDS markets since the global financial crisis. Using dynamic conditional correlation analysis, we find that there are significant co-movements in Asian sovereign CDS markets; that such co-movements tend to be larger between developing countries than between developed and developing countries; and that in the co-movements intra-regional nature is stronger than inter-regional nature. With the Spillover Index model, we measure contagion probabilities of sovereign default risk in CDS markets of seven Asian countries and find evidence of contagion effects among six of them; Japan is the exception. In addition, we find that these six countries are affected more by cross-market spillovers than by their own-market spillovers. Furthermore, a rolling-sample analysis reveals that contagion in the Asian sovereign CDS markets expands during episodes of extreme economic and financial distress, such as the Lehman Brothers bankruptcy, the European financial crisis, and the US-credit downgrade.

• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.63-71
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• 2016

This paper is about applying efficient data mining method which improves the score calculation and proper building performance of credit ranking score system. The main idea of this data mining technique is accomplishing such objectives by applying Correlation based Feature Selection which could also be used to verify the properness of existing rank scores quickly. This study selected 2047 manufacturing companies on KOSPI market during the period of 2009 to 2013, which have their own credit rank scores given by NICE information service agency. Regarding the relevant financial variables, total 80 variables were collected from KIS-Value and DART (Data Analysis, Retrieval and Transfer System). If correlation based feature selection could select more important variables, then required information and cost would be reduced significantly. Through analysis, this study show that the proposed correlation based feature selection method improves selection and classification process of credit rank system so that the accuracy and credibility would be increased while the cost for building system would be decreased.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.373-385
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• 2021

The objective of the study is to empirically investigate the impact of bank capital on the lending behavior of Vietnamese commercial banks from 2007 to 2019. Lending behavior is captured by two dimensions, including the quantity (loan growth) and quality (credit risk) of loans. Instead of investigating loan growth and credit risk separately, we combine these two aspects in our study and further develop the interaction term between capital buffers and credit risk to capture the asymmetric impact. We apply the dynamic model (regressed by the generalized method of moments) and the static models (regressed using the fixed effects, random effects, and the pooled regression approach) to perform regressions. The results show that banks with higher capital ratios tend to expand lending more, while the risk of credit portfolios is controlled at lower levels at these banks. Further analysis reveals that credit risk mitigates some aspects of the relationship between bank capital and loan expansion. The patterns remain robust across alternative measures and econometric techniques. The study provides insightful policy implications for bank managers and regulators in the process of upgrading capital resources to ensure the safety and soundness of the banking industry in an emerging country.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.469-477
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• 2021

In this paper, the relationship between the profitability level of an enterprise and the credit policy adopted by an enterprise was measured. A sample of industrial firms listed on the stock exchanges of Iraq, Jordan, and Kuwait was analyzed. Five industrial firms were randomly selected from each exchange with a condition of having at least 5 year-activity. The total sample size was 15 industrial firms. The study financial data was imported from the sample firms' websites. The financial data was for the financial year 2017. The Regression Analysis was adopted to measure the impact of trade credit on the profitability of an enterprise using the SPSS software. It was found that the receivable accounts have a proportional relationship with the turnover property rights rate. Similarly, the statistical results showed that the turnover property rights rate increased with an increase in the turnover receivable accounts rate and the percentage of investment in receivable accounts. The influence of trade credit on the enterprise profitability percentage in the Iraq stock exchange, Amman stock exchange, and Boursa Kuwait were 0.938, 0.200, and 0.089, respectively. The results showed that the three secondary assumptions were incorrect, while the zeroth assumption, i.e., trade credit has no influence on profitability, was correct.

• East Asian Journal of Business Economics (EAJBE)
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• v.5 no.1
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• pp.33-49
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• 2017

This article can see the analysis of the monetary market of Russia at the present stage, its main segments. An assessment is given to the regulation of mechanism by liquidity, the transactions of the Bank of Russia on the provision of liquidity and on absorption of liquidity, the transaction of fixed action and the transaction in the public market are analyzed. To determine the tendency of development of the monetary market and its segments. In the work using the methods of multivariate statistics, the tools of financial mathematics. To be analyzed the amount of data from the 2015 -2016 year, the 2013 year. (daily data). Hypothesis 1. The dynamics of the money market of Russia at the present stage of development of domestic economy is rather stable. Hypothesis 2.The many transactions of regulation to decrease the liquidity of by monetary movement, the control function. Also in the article consider the contour of the financial transaction. This article reveals the theoretical bases of analysis of profitability of credit operations.

• Journal of the Korean Society of Clothing and Textiles
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• v.23 no.2
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• pp.250-261
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• 1999

This study is designed to classify consumer groups based on their perception toward department store credit cards and the behavior they exhibit during the purchase of clothing. This classification is based on the study of factors taken into consideration during shopping and disparities in credit cared usage., The specific goals of this study are the following : First it is to classify female consumers over age 20 into "shopping orientation" types and "clothing purchase behavior" types according to their perception towards department store credit care usage. Second it is to discover the degree of perceived utility of department store credit card in clothing purchases. Third finally it is to assist a department store credit card market researcher establish a marketing strategy to best address consumers; needs and wants in credit card purchases The study methodology utilized and the results found were that : 1. The division of consumers into positive and negative groups based on factor analysis with the positive group found to have favorable attitudes towards department store credit card usage. 2. Classification of female consumers into three " shopping orientations" : fashion purchasing economic value purchasing and convenience purchasing. The positive group were predominantly fashion convenience purchasers who valued low cost and convenience over "fashionability" 3. The three classes of "purchase behavior" used were impulse buying planned buying and unplanned buying. The positive group those who had favorable attitudes toward department store credit cards. made mostly impulse and unplanned purchases while the negative group made largely planned purchasee the negative group made largely planned purchase.

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

• Knowledge Management Research
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• v.21 no.3
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• pp.229-247
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• 2020

In the remarkable growth of P2P financial platform in the field of knowledge management, only companies with big data and machine learning technologies are surviving in fierce competition. The ability to analyze borrowers' credit is most important, and platform companies are also recognizing this capability as the most important business asset, so they are building a credit evaluation system based on artificial intelligence. Nonetheless, online P2P platform providers that offer related services only act as intermediaries to apply for investors and borrowers, and all the risks associated with the investments are attributable to investors. For investors, the only way to verify the safety of investment products depends on the reputation of P2P companies from newspaper and online website. Time series information such as delinquency rate is not enough to evaluate the early stage of Korean P2P makers' credit analysis capability. This study examines the credit analysis procedure of P2P loan platform using artificial intelligence through the case analysis method for well known the top three companies that are focusing on the credit lending market and the kinds of information data to use. Through this, we will improve the understanding of credit analysis techniques through artificial intelligence, and try to examine limitations of credit analysis methods through artificial intelligence.

• East Asian Economic Review
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• v.25 no.3
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• pp.273-309
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• 2021

The development of regulatory systems varies between transition and non-transition economies. This suggests that they provide different incentives for entrepreneurial development and could have varied effects on the economy because they have different methods to deal with market failure. However, limited empirical evidence exists to prove the assumption of dichotomy. Using comprehensive data for institutional quality, labor market and financial market development, this research sought to analyze their effect on employment growth at micro level. The results show that the quality of institutions in transition economies are poorer relative to those in non-transition economies, but their financial and labor markets are more developed than the latter. Further analysis for the transition sample shows that the three variables are individually positively related with employment growth. For the non-transition sample, institutional quality and labor market flexibility bear a positive and significant effect on employment. Financial market development enters the model with a negative coefficient when regressed alone, but a joint test of significance finds that all the variables have a positive effect on employment growth. This result could imply that there is interdependence between institutional quality, labor flexibility and financial market development in firm-employment-growth relationship, or complementarity between regulations and the quality of institutions. Alternatively, this finding suggests that a stringently regulated credit market in non-transition economies have a selection effect-allocating credit only to entrepreneurs who already demonstrate strong growth potential. In sum, despite differences in the evolution of regulatory environment between the two samples, both of them complement employment growth at firm level. The overall implication of these findings is that less rigid regulations and coherent policies that are enforced with impartiality provide incentives for firms to expand.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.