• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.211-222
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• 2020

This study examines simultaneous relationships between regulatory capital, risk, and cost-inefficiency for a sample of 30 commercial banks in Bangladesh from 2006 to 2018. To conduct the analysis, we used the Generalized Methods of Moments (GMM) in an unbalanced panel data framework. The empirical results show that there is a negative and significant relationship between capital regulation and credit, and overall risk. It is also evident from the results that the capital adequacy ratio is positively and significantly related to default risk and liquidity risk. Therefore, higher capitalized banks take an effort to prevent more credit risk and promote financial stability by reducing liquidity risk. Results also report that banks have been characterized as inefficient, less capitalized, and high risk. On the other hand, efficient banks are more stable but have a high level of liquidity risk. Besides, from the size of the bank, large banks are defined as having lower regulatory capital, are more risk seekers but stable with higher cost-efficiency. Notably, higher capitalized banks are more profitable and cost-efficient by reducing risk. Finally, this study also provides some insightful policy suggestions to the stakeholders.

• 무역상무연구
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• 제76권
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• pp.103-123
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• 2017

If additional condition in letter of credit is used in malicious way, it affects the international trade transaction in jeopardy. Therefore, it's significant to identify whether additional conditions are malicious or ordinary in the transaction with letter of credit. In normal cases, thanks to lots of useful features as an international payment method, such as security of payment, legal protection, and versatility, a letter of credit is widely used in international trade. However, even with these advantageous features, a letter of credit is complicate and costly to use, compared to other payment methods. Furthermore, due to its principle of independence from underlying contract, a use of letter of credit creates another type of concern for proper handling and needs significant caution upon field use. At some points, malicious additional conditions are used for buyer's advantage in deal making and fraud instance in worst situation. In addition, some countries request malicious conditions against sellers as a non-tariff barrier. Therefore it's extremely important to recognize whether malicious additional condition exists in letter of credit and, if so, how to deal with it. This study delivers the information to distinguish and categorize the malicious conditions in various cases and to figure out how to deal with them for safer trade with less risk.

• 아태비즈니스연구
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• 제11권3호
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• pp.199-215
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• 2020

Purpose - This article tries to test if the conditional consumption capital asset pricing model (CCAPM) with bank credit for household as a conditional variable can explain the cross-sectional variation of stock returns in Korea. The performance of conditional CCAPM is compared to that of multifactor asset pricing models based on Arbitrage Pricing Theory. Design/methodology/approach - This paper extends the simple CCAPM to the conditional version of CCAPM by using bank credit for household as conditioning information. By employing KOSPI and KOSDAQ stocks as test assets from the second quarter of 2003 to the first quarter of 2018, this paper estimates risk premiums of conditional CCAPM and a variety of multifactor linear models such as Fama-French three and five-factor models. The significance of risk factors and the adjusted coefficient of determination are the basis for the comparison in models' performances. Findings - First, the paper finds that conditional CCAPM with bank credit performs as well as the multifactor linear models from Arbitrage Pricing theory on 25 test assets sorted by size and book-to-market. When using long-term consumption growth, the conditional CCAPM explains the cross-sectional variation of stock returns far better than multifactor models. Not only that, although the performances of multifactor models decrease on 75 test assets, conditional CCAPM's performance is well maintained. Research implications or Originality - This paper proposes bank credit for household as a conditional variable for CCAPM. This enables CCAPM, one of the most famous economic asset pricing models, to conform with the empirical data. In light of this, we can now explain the cross-sectional variation of stock returns from an economic perspective: Asset's riskiness is determined by its correlation with consumption growth conditional on bank credit for household.

• Asia pacific journal of information systems
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• 제19권2호
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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.

• 무역상무연구
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• 제48권
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• pp.127-155
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• 2010

The international transactions of capital goods such as industrial plant exports, overseas constructions, and shipbuilding exports, are so huge that tremendous amount of funds are required, and that most of the loans are long-term credits of over five years. In the export of huge capital goods, financing is more crucial than technology itself. Some of the importing countries are developing ones that are politically and economically unstable. Therefore the financing mechanism for these transactions is conclusive in winning these projects. Global financial market instability caused by US sub-prime mortgage financial crisis expanded all over the world, and the international transactions have been decreased due to global credit crisis. This indicates how much influential the financing market is in international transactions. The financing schemes are classified into supplier credit and buyer credit by who provides the financing. A supplier credit is a credit extended by an exporter(seller) to an importer(buyer) as part of an export contract. Cover for this transaction may be extended by an export credit agency('ECA') to the exporter. In a sales contract a seller shall provide fund required to manufacture goods, and in a construction contract a contractor shall provide fund required to complete a construction. A buyer credit is an arrangement in which an exporter enters into a contract with an importer, which is financed by means of a loan agreement A Comparative Study on a Supplier Credit and a Buyer Credit in International Transactions of Capital Goods 155 where the borrower is the importer. In a sales contract a buyer shall provide fund required to manufacture and procure the goods, and in a construction contract an owner shall provide fund required to complete a construction. Therefore an exporter is paid on progressive payment method. A supplier credit and a buyer credit have their own advantages and disadvantages in the respect of the parties respectively. These two financing methods are selectively used considering financing conditions such as funding cost, importer's and/or exporter's financial conditions, importing country's political risk.

• Asia pacific journal of information systems
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• 제10권2호
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• pp.149-176
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• 2000

As companies become more dependent upon information systems(IS), the potential losses of IS resources become critical. IS management must assume the increasing responsibility for protection of IS resources as the IS and business environments become more vulnerable to various threats. The major issues facing management, when attempting to manage risks, include the assessment of the impact of risks on business objectives and the design of security safeguards to reduce the unacceptable risks to an acceptable level. This paper provides a case study of the risk management for IS. A Korean credit card company which has the high sensitivity for customers security was selected as a case. The risk management procedure using a powerful tool, CRAMM(the Central Computer and Telecommunications Agencys Risk Analysis and Management Method) was applied for this company.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2005년도 추계학술대회 발표 논문집
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• pp.334-346
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• 2005

The purpose of this paper is to introduce the concept of worst practice DEA, which aims at identifying worst performers by placing them on the efficient frontier. This is particularly relevant for our application to credit risk evaluation, but this also has general relevance since the worst performers are where the largest improvement potential can be found. The paper also proposes to use a layering technique instead of the traditional cut-off point approach, since this enables incorporation of risk attitudes and risk-based pricing. Finally, it is shown how the use of a combination of normal and worst practice DEA models enable detection of self-identifiers. The results of the empirical application on credit risk evaluation validate the method which is proposed in this paper.

• 경영과학
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• 제15권1호
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• pp.137-148
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• 1998

We proposed a framework for computer-supported credit evaluation systems for the effective management of credit risks in Korean commercial banks. Especially for medium and small sized companies, credit evaluators used to depend much on past experience rather than formalized principles and rules. Therefore, we applied case-based reasoning. The credit grade of a company is roughly determined by searching for alreadygraded similar companies in terms of usually accepted evaluation items. And then the grade is refined and adjusted by considering additional information about exceptional facts or by reflecting other evaluation results from different methods or techniques. Booch's object-oriented analysis and design method, Visual Basic 5.0 and MS Access 97 are used for the development of this prototype system.

• 응용통계연구
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• 제24권4호
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• pp.587-595
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• 2011

Credit scoring is an objective and automatic system to assess the credit risk of each customer. The logistic regression model is one of the popular methods of credit scoring to predict the default probability; however, it may not detect possible nonlinear features of predictors despite the advantages of interpretability and low computation cost. In this paper, we propose to use a generalized partially linear model as an alternative to logistic regression. We also introduce modern ensemble technologies such as bagging, boosting and random forests. We compare these methods via a simulation study and illustrate them through a German credit dataset.

• 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.