• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.64-73
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• 2020

The government announced a plan for fund support to the enterprises with high possibility of recovery and early restructuring for the enterprises with low recovery by objectifying credit assessment system. Such announcement of government could be extended to restructuring risk of middle standing enterprises with low financial soundness by establishing the basis to prepare prompt restructuring by reinforcing the basis for restructuring through capital market. This research analyzed financial soundness based on the financial evaluation of bank by selecting 10 middle standing construction companies which focused on housing business in 2019, based on such analysis result, it was confirmed that there was a high possibility of restructuring risk. This research determined that there would be a decrease in growth rate of construction industry on the whole in 2020 due to fall of economic growth rate and reinforced real estate regulation, accordingly, there's a big possibility for middle standing construction companies with paid-in capital ratio due to its low possibility of maintenance of stable credit rating. This research established KCSI assessment model by utilizing the material of a reliable research institute in order for middle standing construction companies to evade restructuring risk, and indicated risk ratio differentiated per each item through a working-level expert survey. Such research result could suggest credit risk reduction method to middle standing construction company management staffs, and prepare a basis to evade restructuring risk.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.213-225
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• 2010

Data mining has empowered the managers who are charge of the tasks in their company to present personalized and differentiated marketing programs to their customers with the rapid growth of information technology. Most studies on customer' response have focused on predicting whether they would respond or not for their marketing promotion as marketing managers have been eager to identify who would respond to their marketing promotion. So many studies utilizing data mining have tried to resolve the binary decision problems such as bankruptcy prediction, network intrusion detection, and fraud detection in credit card usages. The prediction of customer's response has been studied with similar methods mentioned above because the prediction of customer's response is a kind of dichotomous decision problem. In addition, a number of competitive data mining techniques such as neural networks, SVM(support vector machine), decision trees, logit, and genetic algorithms have been applied to the prediction of customer's response for marketing promotion. The marketing managers also have tried to classify their customers with quantitative measures such as recency, frequency, and monetary acquired from their transaction database. The measures mean that their customers came to purchase in recent or old days, how frequent in a period, and how much they spent once. Using segmented customers we proposed an approach that could enable to differentiate customers in the same rating among the segmented customers. Our approach employed support vector regression to forecast the purchase amount of customers for each customer rating. Our study used the sample that included 41,924 customers extracted from DMEF04 Data Set, who purchased at least once in the last two years. We classified customers from first rating to fifth rating based on the purchase amount after giving a marketing promotion. Here, we divided customers into first rating who has a large amount of purchase and fifth rating who are non-respondents for the promotion. Our proposed model forecasted the purchase amount of the customers in the same rating and the marketing managers could make a differentiated and personalized marketing program for each customer even though they were belong to the same rating. In addition, we proposed more efficient learning method by separating the learning samples. We employed two learning methods to compare the performance of proposed learning method with general learning method for SVRs. LMW (Learning Method using Whole data for purchasing customers) is a general learning method for forecasting the purchase amount of customers. And we proposed a method, LMS (Learning Method using Separated data for classification purchasing customers), that makes four different SVR models for each class of customers. To evaluate the performance of models, we calculated MAE (Mean Absolute Error) and MAPE (Mean Absolute Percent Error) for each model to predict the purchase amount of customers. In LMW, the overall performance was 0.670 MAPE and the best performance showed 0.327 MAPE. Generally, the performances of the proposed LMS model were analyzed as more superior compared to the performance of the LMW model. In LMS, we found that the best performance was 0.275 MAPE. The performance of LMS was higher than LMW in each class of customers. After comparing the performance of our proposed method LMS to LMW, our proposed model had more significant performance for forecasting the purchase amount of customers in each class. In addition, our approach will be useful for marketing managers when they need to customers for their promotion. Even if customers were belonging to same class, marketing managers could offer customers a differentiated and personalized marketing promotion.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.31 no.4
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• pp.28-44
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• 2023

This study investigates aircraft lessor business models by studying cases and interviewing experts to analyze investors and business strategies of aircraft lessor. The results confirm that there is a wide range of investors including institutional investors, financial institutions, insurance companies, corporations, and wealthy individuals for aircraft lessor. Aircraft lessors can be categorized based on its required rate of return (cost of capital) into bank-investing core, institutional investor-investing value-added, and hedge fund-investing opportunistic. Aircraft lessor decides leasing rate by aircraft purchasing price and lessee's credit rating. Core aircraft lessors invest in new aircrafts for new placement or sale-and-leaseback strategy requiring little technical risk in aircraft, value-added lessors invest in middle-aged aircrafts for re-leasing, opportunistic lessors invest in old aircrafts for freighter conversion or part-out strategy requiring high level of expertise. This study provides insights for future Korean aircraft lessor establishment and investment.

• Journal of Distribution Science
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• v.13 no.9
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• pp.47-57
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• 2015

Purpose - The aim of this paper is to investigate the explanatory power of the Pecking-order theory (the cost of financing increases with asymmetric information) among Korean retail firms from the perspective of debt capacity. According to the Pecking-order theory, a firm's first preference is to use internal funds for its capital needs, its next preference is the issuance of debt, and its last preference is the issuance of equity; this is due to the information asymmetry problem between existing shareholders and investors. However, prior empirical studies, such as Lemmon and Zender (2010), argue that the entire sample test for the Pecking-order theory could be misleading due to the different levels of debt issuance capability of each of the individual firms; in fact, they confirm that the explanatory power of the Pecking-order theory improves after taking into account the differences in debt capacity of the U.S. firms they examined. This paper implements a case study approach among Korean retail firms to examine the relationship between debt capacity and the explanatory power of the Pecking-order theory in Korea. Research design, data, and methodology - This study uses the sample of public retail firms on the Korea Composite Stock Price Index (KOSPI) from the time period of 1990 to 2013. We gather related financial and accounting statements from the financial information firm WISEfn. Credit rating information is provided by the Korea Investor Service. We employ the models of Lemmon and Zender (2010) and Son and Kim (2013) to measure a firm's debt capacity. Their logit models use the rating dummy variable as a dependent variable and incorporate other firm characteristics as independent variables to estimate debt capacity. To test the Pecking-order theory, we adopt variants of the financing deficit model of Shyam-Sunder and Myers (1999). In the test of the Pecking-order theory, we consider all of the changes in total debt obligations, current debt obligations, and long-term debt obligations. Results - Our main contribution to the literature is our confirmation of the predicted relationship between debt capacity and the explanatory power of the Pecking-order theory among Korean retail firms. The coefficients on financing deficits become greater as a firm's debt capacity improves. This is consistent with the results of Lemmon and Zender (2010). The coefficients on the square of the financing deficits are also negative for the firms in the largest debt capacity group, which is also consistent with the predictions in prior literature. Conclusions - This study takes a case study approach by examining Korean retail firms. We confirm that the Pecking-order theory explains the capital structure of retail firms more appropriately, after taking into account the debt capacity of each firm. This result suggests the importance of debt capacity consideration in the testing of the Pecking-order theory. Our result also implies that there has been a potential underestimation of the explanatory power of the Pecking-order theory in existing studies.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.99-121
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• 2009

Recently, there have been many studies that predict corporate bankruptcy using data mining techniques. Although various data mining techniques have been investigated, some researchers have tried to combine the results of each data mining technique in order to improve classification performance. In this study, we classify 4 types of data mining techniques via their characteristics and select representative techniques of each type then combine them using a genetic algorithm. The genetic algorithm may find optimal or near-optimal solution because it is a global optimization technique. This study compares the results of single models, typical combination models, and the proposed integration model using the genetic algorithm.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.7
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• pp.73-84
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• 2020

Existing studies disagree over the core predictors of firm-level financial choices in developing countries. The general practice only validates the traditional capital structure model, which leads to inconsistency and a lack of novelty. This study removed overfitting issues among existing factors and presented the most reliable and advanced capital structure model in Pakistani firms. The panel data include 368 Pakistani companies from 19 non-financial sectors over the period 2004 to 2017. We apply Akaike and Bayesian Information Criteria to remove overfitting issues among inconsistent proxies in the capital structure model. The fixed effects regression is used for basic results and the Generalized Method of Moments is applied to control the endogeneity. Besides the conventional proxies, we report that credit rating, distance from bankruptcy, managerial concentration, and institutional quality are the most advanced capital structure determinants in Pakistan. These predictors remain significant across firm size and growth levels. Also, the findings confirm that new predictors are reliable to define capital structure dynamics and improve the speed of adjustment in overall and sub-sample analysis. The major findings suggest that managers and policymakers should consider these advanced predictors to design their financial settings in firms.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.3
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• pp.163-173
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• 1997

Although numerous studies demonstrate that one technique outperforms the others for a given data set, it is hard to tell a priori which of these techniques will be the most effective to solve a specific problem. It has been suggested that the better approach to classification problem might be to integrate several different forecasting techniques by combining their results. The issues of interest are how to integrate different modeling techniques to increase the predictive performance. This paper proposes the post-model integration method, which tries to find the best combination of the results provided by individual techniques. To get the optimal or near optimal combination of different prediction techniques, Genetic Algorithms (GAs) are applied, which are particularly suitable for multi-parameter optimization problems with an object function subject to numerous hard and soft constraints. This study applies three individual classification techniques (Discriminant analysis, Logit model and Neural Networks) as base models for the corporate failure prediction. The results of composite predictions are compared with the individual models. Preliminary results suggests that the use of integrated methods improve the performance of business classification.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.273-279
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• 2014

Recently, it was occurred in the nation's largest Information spill about 140 million cases of credit card customers' personal and credit information. As such, it was rapidly to increase in consumer complaints about the privacy of personal information in accordance with outflow of financial companies increased accident. But it is still not clear precaution. Therefore, in financial customer position, it is possible to confirm and determine in advance whether or not superior to the security company. In addition, It is time to be required institutional device that can be a real effort to equip a good security company. This report is considered a model of "Disclosure of corporate security assessment " of these devices institutional study. And We study in realistic and objective stance about why do we need this policy.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.41
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• pp.51-78
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• 2009

The ISP 98 is developed by the American Institute of International Banking Law & Practice in 1998. The ISP98 are also published as ICC Publication No. 590. A detailed commentary on the rules("The Official Commentary on the International Standby Practice") has been written by Professor James E. Byrnes. Presently there is no compelling reason to revise the rules themselves even if ten years is passed since the issuance of ISP98. Insteadthe American Institute of International Banking Law & Practice will provide Model Forms in the early 2009. Special features of the ISP 98 are as the following. Firstly, the ISP 98 is copyrighted by the Institute of International Banking Law and Practice, Inc., and published by the International Chamber of Commerce. Secondly, the ISP 98 differs from UCP in style and approach because it must receive acceptance not only from bankers and merchants, but also from a broader range of those actively involved in standby law and practice corporate treasurees and credit manager, rating agencies, government agencies and regulators, and indenture trustees as well as their counsel. Because standbys are often intended to be available in the event of disputes or applicant insovency, their texts are subject to a degree of scrutiny not encountered in the commercial letter of credit context. Thirdly, the ISP 98 supplement the UCP if the UCP dose not have the relative rule. Lastly, the ISP 98 has the official commentary. In addition, several provision of the ISP 98 would surprise the commercial parties and/or are rather peculiar, while some of them display a certain bias in favor of the banks.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.159-173
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• 2017

In order to provide technical financial support to small and medium-sized venture companies based on technology, the government implemented the TCB evaluation, which is a kind of technology rating evaluation, from the Kibo and a qualified private TCB. In this paper, we briefly review the current state of TCB evaluation and available indicators related to technology evaluation accumulated in the Korea Credit Information Services (TDB), and then use indicators that have a significant effect on the technology rating score. Multiple regression techniques will be explored. And the relative importance and classification accuracy of the indicators were calculated by applying the key indicators as independent features applied to the generalized boosting model, which is a representative machine learning classifier, as the class influence and the fitness of each model. As a result of the analysis, it was analyzed that the relative importance between the two models was not significantly different. However, GBM model had more weight on the InnoBiz certification, R&D department, patent registration and venture confirmation indicators than regression model.