• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.109-126
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• 2006

An effective customer classification has been essential for the successful customer relationship management. The typical customer rating is carried out by the proportionally allocating the customers into classes in terms of their life time values. However, since this method does not accurately reflect the homogeneity within a class along with the heterogeneity between classes, there would be many problems incurred due to the misclassification. This paper suggests a new method of rating customer using Holistic profit technique, and validates the new method using the customer data provided by an insurance company. Holistic profit is one of the methods used for deciding the cutoff score in screening the loan application. By rating customers using the proposed techniques, insurance companies could effectively perform customer relationship management and diverse marketing activities.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.147-162
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• 2020

The purpose of this study is to present practical improvement plans for policy fund support in national policy banks through an analysis of the efficiency of policy fund support. It targets small and medium-sized enterprises(SMEs) that received policy funding from national policy banks in '17 and '18 consecutively. As for the analysis method, characteristic analysis and corresponding sample T-test was performed. The analysis results are as follows. First, as a result of analyzing the characteristics of small and medium-sized enterprises, most of the financial funds were concentrated on the manufacturing industry. By region, the western region of Gyeonggi Province, by credit rating, was A grade, technology grade was T5, and the use of funds was mostly concentrated on facility funds. Second, as a result of efficiency analysis, profitability had a positive effect on total capital return, stability had a positive effect on interest compensation ratio, and activity had a positive effect on total capital turnover. In conclusion, it is expected to provide practical improvement plans to support policy funds to influence the growth and distribution of funds appropriate to the needs of SMEs.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.77-88
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• 2023

The purpose of this study is to confirm whether the current technology appraisal model for investment, which is designed to identify high-growth SMEs in sales, which is one of the characteristics of gazelle companies, has the possibility of expanding employment effects. For SMEs classified as technology investment adequate firms(TI1-TI6) through technology appraisal for investment between 2016 and 2018 were targeted. At this time, the employment effect was analyzed by dividing the absolute employment effect and the relative employment effect. As a result of the analysis, it was confirmed that the technology appraisal items for investment defined as innovation characteristics did not have significant explanatory power for the absolute employment effect. However, for the relative employment effect, among innovation characteristics, technicality(TC) was found to have significant explanatory power, and this is because the item appraised based on future growth potential. In particular, the relative employment effect is meaningful in terms of the actual employment effect, and the conclusion is drawn that the current technology appraisal model for investment is an appraisal model with the possibility of expansion in terms of employment effect.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.115-125
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• 2024

This study defined the appraisal items of technology appraisal for investment as innovation characteristics and derived the determining factors for predicting high-growth companies. Through this, we presented a direction for improving the technology appraisal model for investment. High-growth companies were classified into high-growth companies in sales, high-growth companies in operating profit, and high-growth companies in both sales and operating profit. At this time, the concept of a gazelle company was applied and defined as a company with an average growth rate of 20% or more over three years after the appraisal year. As for the analysis results, in terms of technicality (appraisal items), it was significant in predicting high-growth companies in sales and high-growth companies in sales and operating profit. Therefore, it will be possible to increase the discrimination power of predictions by strengthening the technicality (appraisal items). On the other hand, the business feasibility (appraisal items) was significant in predicting high-growth companies in sales and high-growth companies in sales and operating profit, but in a negative direction. This is due to the composition and criteria of the business feasibility (appraisal items), and it was concluded that changes to the composition and criteria for the relevant items are necessary for future model improvement.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.1-29
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• 2009

Since convertible debt has both characteristics of stocks and bonds, its issuance can be related to both interests of stockholders and bondholders. Nevertheless, the existing studies focused mainly on the wealth effect on stockholders. In this paper we revisit the hypotheses on the issue of convertible debt especially from the viewpoint of a firm's growth, by making an additional investigation into bondholders' wealth effects. We find that stockholders' wealth increases with bondholders' wealth in the firm whose book-to-market ratio is low and thus is considered a growth firm. This finding seems consistent with the hypothesis in which the issue of convertible debt mitigates the agency cost of debt in the high-growth firm.

• The Korean Journal of Financial Management
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• v.22 no.1
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• pp.93-118
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• 2005

This paper examines credit spreads in Korea corporate market using one of structural models, the mean reverting leverage ratio model (Collin-Dufresne and Goldstein (2001)). Compared to the actual credit spreads, we show that the credit spreads induced by the model are overpredicted. We also investigate the systematic errors that cause the over-pre-diction of credit spreads using the t-test. We show that the systematic errors are affected by the current leverage ratio and asset volatility.

• Asia Marketing Journal
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• v.9 no.3
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• pp.233-255
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• 2007

본 사례는 외환위기 및 대우 그룹 해체로 인해 존폐의 위기에 처했던 대우증권이 선도업체 위상을 탈환하기까지의 재건 과정에서 실행했던 마케팅 전략 및 영업활동을 분석하고 있다. 이를 통해 경영위기를 효과적으로 극복하고 새롭게 도약하기 위해 활용 가능한 마케팅 및 영업 전략의 일례를 소개하고자 한다. 끝으로 대우증권의 향후 마케팅 과제와 관련된 토의 주제가 제시된다. 국내 증권업계 선두주자였던 대우증권은 1999년 '대우 사태'로 인해 한때 업계 5위까지 추락하고 1조 2,000억원의 적자를 기록하였으며 신용등급은 투자부적격 단계인 CCC+까지 하락하는 등 위기를 맞이하게 되었다. 대우증권이 회생과 재도약을 위해 채택한 마케팅 전략은 '선택과 집중'이었다. 즉, 최대 수익원이자 경쟁력이 있는 사업 분야인 위탁매매(brokerage)에 역량을 우선 집중하여 안정적으로 수익 기반을 다진 뒤 이를 바탕으로 IB(Investment Bank)와 자산 관리(Wealth Management) 등 여타 분야에서의 역량도 점진적으로 강화하는 전략이다. 전략의 실행 방향은 높은 효율성 확보 및 자원의 확충을 통해 시장 지배력과 수익성을 창출하는 것이었다. 전략적 공감대, 오프라인 영업 강화, 현장 및 고객 중시를 위한 기업 자원의 확충 배치를 바탕으로 영업 프로세스, 영업 인프라, 영업 관리, 고객 서비스, 영업 문화의 혁신을 실행한 결과, 대우증권은 증권명가의 옛 명성과 위치를 성공적으로 탈환하게 되었다. 대우증권은 2004년 위탁매매 영업 부문에서 1위 위상을 되찾은 이래 전 사업 분야에서 성장을 지속하였다. 2006년 증권업종 시가총액 1위를 회복하는 등 각종 경영지표는 큰 폭으로 개선되었고, 신용등급은 AA-로 상향 조정되었다. 나아가 자본시장통합법 시대에 걸맞는 선도 글로벌 투자은행으로서 진화한다는 계획을 추진하고 있다.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.857-871
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• 2013

Export credit insurance is a policy tool for export growth. In the era of free trade under the governance of WTO, export credit insurance is still allowed as one of the few instruments to increase exports. This paper, using data on short-term export insurance contracts issued to foreign subsidiaries of Korean companies, calculates the expected loss per exposure by combining the effect of risk factors (credit rate of foreign importers, size of mother company, and payment period) on loss frequency and loss severity in different levels. We, applying generalized linear models (GLM), first fit loss frequency and loss severity to negative binomial and lognormal distribution, respectively, and then estimate the loss frequency rate per contract and the ratio of loss severity to coverage amount. Finally, we calculate the expected loss per exposure for each level of risk factors by combining these two rates. Based on the result of statistical analysis, we present the implication for the current premium rate of export insurance.