• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.33-42
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• 2020

The paper investigates the mechanism through which corporate credit ratings affect dividend payments by decomposing the mean difference of dividends into a part that is explained by the determinants of dividends and a residual part that is contributed by the pure credit group effect, in the framework of the traditional dividend model of Fama and French (2001). Historically, better credit rated firms have shown consistently higher propensity to pay dividends especially during the economic crisis period. According to the counter-factual decomposition technique of Jann (2008), better rated firms are more responsive to the firm characteristics that have positive impact on dividends and poor rated firms are more responsive to the negative dividend predictors. As a result, good (bad) credit ratings make corporate managers become more bold (timid) in their dividend payments and they tend to pay more (less) dividends than what their firm characteristics prescribe. The degree of information asymmetry increases for the poor group firms during crisis periods and they attempt to reserve more cash in preparation for future investments. The decomposition results suggest that the credit group effect can potentially exceed the effect of firm characteristics because firms of different credit ratings can respond to the very same firm characteristics in a different manner.

• The Journal of Information Systems
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• v.18 no.4
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• pp.41-57
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• 2009

Although financial information is a great influence upon determining of the group which use them, detection of management fraud and earning manipulation is a difficult task using normal audit procedures and corporate credit evaluation processes, due to the shortage of knowledge concerning the characteristics of management fraud, and the limitation of time and cost. These limitations suggest the need of systemic process for !he effective risk of earning manipulation for credit evaluators, external auditors, financial analysts, and regulators. Moot researches on management fraud have examined how various characteristics of the company's management features affect the occurrence of corporate fraud. This study examines financial characteristics of companies engaged in fraudulent financial reporting and suggests a model and system for detecting GAAP violations to improve reliability of accounting information and transparency of their management. Since the detection of management fraud has limited proven theory, this study used the detecting method of outlier(upper, and lower bound) financial ratio, as a real-field application. The strength of outlier detecting method is its use of easiness and understandability. In the suggested model, 14 variables of the 7 useful variable categories among the 76 financial ratio variables are examined through the distribution analysis as possible indicators of fraudulent financial statements accounts. The developed model from these variables show a 80.82% of hit ratio for the holdout sample. This model was developed as a financial outlier detecting system for a financial institution. External auditors, financial analysts, regulators, and other users of financial statements might use this model to pre-screen potential earnings manipulators in the credit evaluation system. Especially, this model will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings and to improve the quality of financial statements.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.121-130
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• 2006

In next generation wireless networks, an application must be capable of rating service information in real-time and prior to initiation of the service it is necessary to check whether the end user's account provides coverage for the requested service. However, to provide prepaid services effectively, credit-control should have minimal latency. In an endeavor to support real-time credit-control for Mobile IPv6 (MIPv6), we design an implementation architecture model of credit-control authorization. The proposed integrated model combines a typical credit-control authorization procedure into the MIPv6 authentication procedure. We implement it on a single server for minimal latency. Thus, the server can perform credit-control authorization and MIPv6 authentication simultaneously. Implementation details are described as software blocks and units. In order to verify the feasibility of the proposed model. latency of credit-control authorization is measured according to various Extensible Authentication Protocol (EAP) authentication mechanisms. The performance results indicate that the proposed approach has considerably low latency compared with the existing separated models, in which credit-control authorization is separated from the MIPv6 authentication.

• Korean Management Science Review
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• v.21 no.2
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• pp.273-289
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• 2004

Although the Internet is useful for transferring information, Internet auction environments make fraud more attractive to offenders, because the chance of detection and punishment is decreased. One of these frauds is the phantom transaction, which is a colluding transaction by the buyer and seller to commit the illegal discounting of a credit card. They pretend to fulfill the transaction paid by credit card, without actually selling products, and the seller receives cash from the credit card corporations. Then the seller lends it out with quite a high interest rate to the buyer, whose credit rating is so poor that he cannot borrow money from anywhere else. The purpose of this study is to empirically investigate the factors necessary to detect phantom transactions in an online auction. Based upon studies that have explored the behaviors of buyers and sellers in online auctions, the following have been suggested as independent variables: bidding numbers, bid increments, sellers' credit, auction lengths, and starting bids. In this study. we developed Internet-based data collection software and collected data on transactions of notebook computers, each of which had a winning bid of over W one million. Data analysis with a logistic regression model revealed that starting bids, sellers' credit, and auction length were significant in detecting the phantom transactions.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.2
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• pp.43-52
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• 2020

The paper investigates the mechanism through which corporate credit ratings affect dividend payments by decomposing the mean difference of dividends into a part that is explained by the determinants of dividends and a residual part that is contributed by the pure credit group effect, in the framework of the traditional dividend model of Fama and French (2001). Historically, better credit rated firms have shown consistently higher propensity to pay dividends especially during the economic crisis period. According to the counter-factual decomposition technique of Jann (2008), better rated firms are more responsive to the firm characteristics that have positive impact on dividends and poor rated firms are more responsive to the negative dividend predictors. As a result, good (bad) credit ratings make corporate managers become more bold (timid) in their dividend payments and they tend to pay more (less) dividends than what their firm characteristics prescribe. The degree of information asymmetry increases for the poor group firms during crisis periods and they attempt to reserve more cash in preparation for future investments. The decomposition results suggest that the credit group effect can potentially exceed the effect of firm characteristics because firms of different credit ratings can respond to the very same firm characteristics in a different manner.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.669-680
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• 2008

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.423-440
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• 2007

Although various models have been developed to establish the enterprise credit scoring, no model has utilized the enterprise human resource so far. The purpose of this study was to build an enterprise credit scoring model using enterprise human resource factors. The data to measure the enterprise credit score were made by the first-year research material of HCCP was used to investigate the enterprise human resource and 2004 Credit Rating Score generated from KIS-Credit Scoring Model. The independent variables were chosen among questionnaires of HCCP based on Mclagan(1989)'s HR wheel model, and the credit score of Korean Information Service was used for the dependent variables. The statistical method used for data analysis was logistic regression. As a result of constructing a model, 22 variables were selected. To see these specifically by each large area, 6 variables in human resource development(HRD) area, 15 in human resource management(HRM) area, and 1 in the other area were chosen. As a consequence of 10 fold cross validation, misclassification rate and G-mean were 30.81 and 68.27 respectively. Decile having the highest response rate was bigger than the one having the lowest response rate by 6.08 times, and had a tendency to decrease. Therefore, the result of study showed that the proposed model was appropriate to measure enterprise credit score using enterprise human resource variables.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.12 no.1
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• pp.133-144
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• 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 Fisheries Business Administration
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• v.45 no.3
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• pp.55-69
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• 2014

We try to test the pecking order theory of Korean fisheries firm's capital structure using debt capacity. At first, we estimate the debt capacity as the probability of assigning corporate bond rating from credit-rating agencies. We use logit regression model to estimate this probability as a proxy of debt capacity. The major results of this study are as follows. Firstly, we can confirm the fisheries firm's financing behaviour which issues new debt securities for financial deficit. Empirical test of SSM model indicates that the higher probability of assigning corporate bond rating, the higher the coefficient of financial deficit. Especially, high probability group follows this result exactly. Therefore, the pecking order theory of fisheries firm's capital structure applies well for high probability group which means high debt capacity. It also applies for medium and low probability group, but their significances are not good. Secondly, the most of fisheries firms in high probability group issue new debt securities for their financial deficit. Low probability group's fisheries firms also issue new debt securities for their financial deficit within the limit of their debt capacity, but beyond debt capacity they use equity financing for financial deficit. Therefore, the pecking order theory on debt capacity come into existence well in high probability group.

• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.239-347
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• 2009

This paper presents Kolmogorov-Smirnov, mean difference, AUROC and AR, four well known statistics that have been widely used for evaluating the discriminatory power of credit rating models. Criteria for these statistics are determined by the value of mean difference under the assumption of normality and equal standard deviation. Alternative criteria are proposed through the simulations according to various sample sizes, type II error rates, and the ratio of bads, also we suggest the meaning of statistic on the basis of discriminatory power. Finally we make a comparative study of the currently used guidelines and simulated results.