• Journal of Intelligence and Information Systems
• /
• v.17 no.3
• /
• pp.25-41
• /
• 2011

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved them more powerful than traditional artificial neural networks (ANNs) (Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al., 2005; Kim, 2003).The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is so cost-sensitive particularly in financial classification problems such as the credit ratings that if the credit ratings are misclassified, a terrible economic loss for investors or financial decision makers may happen. Therefore, it is necessary to convert the outputs of the classifier into wellcalibrated posterior probabilities-based multiclass credit ratings according to the bankruptcy probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create the probabilities (Platt, 1999; Drish, 2001). This paper applied AdaBoost algorithm-based support vector machines (SVMs) into a bankruptcy prediction as a binary classification problem for the IT companies in Korea and then performed the multi-class credit ratings of the companies by making a normal distribution shape of posterior bankruptcy probabilities from the loss functions extracted from the SVMs. Our proposed approach also showed that their methods can minimize the misclassification problems by adjusting the credit grade interval ranges on condition that each credit grade for credit loan borrowers has its own credit risk, i.e. bankruptcy probability.

• The Journal of Information Systems
• /
• v.18 no.3
• /
• pp.375-399
• /
• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Journal of applied mathematics & informatics
• /
• v.18 no.1_2
• /
• pp.145-158
• /
• 2005

We formulate a stochastic control problem on proportional reinsurance that includes impulse and regular control strategies. For the first time we combine impulse control with regular control, and derive the expected total discount pay-out (return function) from present to bankruptcy. By relying on both stochastic calculus and the classical theory of impulse and regular controls, we state a set of sufficient conditions for its solution in terms of optimal return function. Moreover, we also derive its explicit form and corresponding impulse and regular control strategies.

• The Journal of Society for e-Business Studies
• /
• v.24 no.4
• /
• pp.135-149
• /
• 2019

Accounts receivable insurance is a system in which small and medium-sized enterprises insure the accounts receivables acquired by the purchasing company, and the insurance company pays when the purchaser fails to pay the debts. Accounts receivable insurance is a very effective means of eliminating the risk of loss due to the counterparty default, and it is economically effective to protect the domestic industry by preventing the bankruptcy of one company leading to a chain bankruptcy of other companies. In this study, we constructed a business model of the accounts receivable insurance, by building an infrastructure based on a private blockchain in activating the accounts receivable insurance accounts. The accounts receivable insurance platform using these blockchain technologies not only addressed the problem of document and reliability verification for insurance, but also sought ways to facilitate accounts receivable insurance by small businesses through rapid transaction rates, easy network expansion and access management based on private blockchain.

• Journal of Internet Computing and Services
• /
• v.17 no.2
• /
• pp.49-57
• /
• 2016

Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving prediction accuracy. Bagging is one of the most popular ensemble learning techniques. Bagging has been known to be successful in increasing the accuracy of prediction of the individual classifiers. Bagging draws bootstrap samples from the training sample, applies the classifier to each bootstrap sample, and then combines the predictions of these classifiers to get the final classification result. Bootstrap samples are simple random samples selected from the original training data, so not all bootstrap samples are equally informative, due to the randomness. In this study, we proposed a new method for improving the performance of the standard bagging ensemble by optimizing bootstrap samples. A genetic algorithm is used to optimize bootstrap samples of the ensemble for improving prediction accuracy of the ensemble model. The proposed model is applied to a bankruptcy prediction problem using a real dataset from Korean companies. The experimental results showed the effectiveness of the proposed model.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.24 no.3
• /
• pp.49-62
• /
• 1999

Artificial Neural Networks(ANN) have been successfully applied to various kinds of business and engineering problems, especially those involved in pattern classification. However, because of the lack of design standard or guidelines, the structure of specific ANN depends on the designer's own experiments or choices. In other words, even though we could construct a better ANN, we often steeled down with just a satisfactory ANN. The purpose of this research is to apply the Genetic Algorithm(GA) to design a structure of ANN that yields better performance compared to the existing test results. For a bankruptcy prediction problem. an exiting research using ANN which consists of 22 input processing elements(PEs) for financial ratios and 5 hidden PEs showed 70% hit ratio. In our research, the input financial ratios and the number of hidden PEs are determined by GA. The best ANN, which consists of 8 input PEs and 6 hidden PEs, shows 78.03% hit ratio. In addition, we compare the performance of two types of reproduction schemes, i.e., generational reproduction and steady-state reproduction.

• KDI Journal of Economic Policy
• /
• v.23 no.1_2
• /
• pp.149-191
• /
• 2001

In this paper, we propose a new bankruptcy algorithm. The proposed algorithm is comprised of four tasks. Task A is the procedure of soliciting bids, Task B is the procedure of allocating claims, Task C is the procedure of trading claims, and Task D is the procedure of exercising options and holding shareholders' meeting. Tasks A, B, and D are based on Bebchuk(1988) and Aghion, Hart, ad Moore(1992). This paper adds Task C, the procedure of trading claims. Claims are in the form of options which are written on the new shares of the bankrupt firm. Trading options expedites the process of finding the value of the bankrupt firm, and also it mitigates the problem of incomplete capital market by expanding the pool of new investors.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.327-328
• /
• 2011

마이크로그리드는 주로 신재생 전원으로 구성되는 소규모 전력시스템으로 그 관심이 고조되고 있다. 최근 멀티에이전트 기반의 마이크로그리드의 운용 및 제어 기술에 대한 연구가 진행되고 있다. 전력시스템과 연계되지 않는 독립운전의 경우는 상용주파수를 유지하기 위해서 전력의 공급과 부하의 균형을 유지시켜야 하며, 특히 전력공급이 부족한 경우는 강제적으로 전력의 부하를 차단하여야 한다. 본 논문에서는 멀티에이전트 시스템 기반의 독립운전을 하는 마이크로그리드 운용을 위한 강제적인 부하 차단을 위해서 파산문제(bankruptcy problem)와 CEA(constrained equal awards) 규칙에 근거하여 부하 차단의 기법을 제안하고 이에 대해서 그 활용 가능성을 검토하고자 한다.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.10
• /
• pp.1-8
• /
• 2020

The objective of the study is to investigate the effect of growth opportunities on capital structure and dividend policy in Indonesia. The study employs panel data of companies listed on Indonesia Stock Exchange that distribute dividends from 2007 to 2017. Fixed and random effect regression models are used. Findings based on growth opportunities on capital structure and dividend policy in Indonesia are in line with the existing theory (i.e., contracting theory). Growth opportunities have a significant negative correlation with debt ratio and dividend yield, which suggests that firms with high growth opportunities are discouraged to generate debt to resolve underinvestment and asset-substitution problem. Firms with more investment opportunities tend to adopt a low dividend payout policy because the cash flows will be used up for investment. The positive impact of firm size on leverage is due to the low bankruptcy risk and cost of a large company. Profitability has a positive impact on the dividend policy because profitable companies can reserve larger free cash flows and, thus, pay higher dividends. The positive influence of ownership on leverage is interpreted by the unwillingness of majority stockholders to commit to equity financing in order to avoid reducing the ownership and preserve control of the company.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.509-515
• /
• 2005

Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.