• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2993-3002
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• 2018

This case study introduces the process of developing the technology rating evaluation model for investment. The technology evaluation rating model for investment is a project that the Financial Services Commission and the Ministry of Commerce, Industry and Energy collaborated to expand the scope of technology finance from loan to investment. The technology evaluation model for investment was developed with the aim of predicting high growth companies. The model consists of a statistical model and an expert model. Here, statistical models were modeled by using logistic regression analysis. Expert models gathered opinions of experts and identified the weight of each evaluation item and set the model. The rating system of the model is composed of 10 grades. The distribution of the model was consistent with KTRS grade distribution. Interestingly, the emphasis is on technology and marketability. In the technology valuation grade model for the goddess, there is a considerable difference from the emphasis on managerial competence or business performance.

• Journal of Digital Convergence
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• v.15 no.1
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• pp.199-208
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• 2017

This study investigates the components and rating system of reliable technology credit information for a technology finance donor who is a consumer of the information and aims to create an effective and optimal technology credit appraisal system to enlarge technology finance supply. Firstly, we calculate the optimal TCAR which becomes the maximum AUROC through the combination of ratio change, verify the substitution possibility between TAR and CR through the existing CR and system gap simulation, and propose a rating system by which financial institutes can utilize the TCAR as a credit rating. As a result, 70% : 30% is the most suitable as the weighted combination ratio of credit rating : technology rating. As a result of this study, we confirmed the possibility that the technical credit rating information could be substituted by the credit rating or the technology appraisal rating. Furthermore, it also suggests that sophisticated risk management is possible through using technology credit rating that are combined with credit and technology appraisal rating.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.191-211
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• 2022

As the value of highly sustainable companies increases, ESG(Environmental, Social, and Governance) has emerged as the biggest topic of discussion for companies around the world. In addition, as domestically, more research is being done on ESG in line with global trends, it is necessary to examine ESG research trends. Accordingly, ESG academic papers that have been published for the past 10 years were collected for each year, and frequency analysis was conducted using text mining techniques regarding key themes and thesis titles. This paper analyzed the number of selected publications by year and the cumulated number of studies through bibliometric analysis. The findings suggested that the number of ESG papers is increasing each year and that academic interest in ESG-related issues continues to abound. Next, according to the results of frequency analysis of the keywords and titles of the research papers, the words- "ESG", "company", "society", "responsibility", "management", "investment", and "sustainability"- were extracted. This analysis identified the research fields and keywords that have been relevant to ESG in the past 10 years. As a result of comparing the major ESG issues presented in recent overseas studies and the common factors of the ESG key keywords presented in this study, it was confirmed that the environment is the focus of recent studies compared to previous studies. Third, it was found that the data used by domestic ESG studies mainly include the KEJI index, the KRX index, and the KCGS ESG evaluation index. After identifying the main research subjects of ESG papers, research found that 8 out of 152 domestic ESG studies were focused on SMEs. Through this study, it was possible to confirm the ESG research trend and increase in research, and future researchers divided the research topics and research keywords and presented basic data for selecting more diverse research topics. Based on both, the arguments of previous ESG studies conducted on SMEs and the results of this study, there is a lack of studies on guidelines for ESG practice and their application to SMEs, and more ESG research regarding SMEs will need to be conducted in the future.

• KDI Journal of Economic Policy
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• v.13 no.1
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• pp.35-63
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• 1991

Currently, Korea's banking industry holds a sizable amount of non-performing loans which stem from the government-led bailout of many troubled firms in the 1980s. Although this burden was somewhat relieved with the aid of banks' recapitalization in the booming securities market between 1986-88, the insolvent credits still resulted in low profitability in the banking sector and have been detrimental to the progress of financial liberalization and internationalization. This paper surveys the corporate bailout experiences of major advanced countries and Korea in the past and derives a rationale for readjustment measures against non-performing loans, in which rescue plans depend on the nature of the financial system. Considering the features of Korea's financial system and the banking sector's recent performance, it discusses possible means of liquidation in keeping with the rationale. The conflict of interests among parties involved in non-performing loans is widely known as one of the major constraints in writing off the loans. Specifically, in the case of Korea, the government's excessive intervention in allocating credits has preempted the legitimate role of the banking sector, which now only passively manages its past loans, and has implicitly confused private with public risk. This paper argues that to minimize the incidence of insolvent loan readjustment, the government's role should be reduced and that the correspondent banks should be more active in the liquidation process, through the market mechanism, reflecting their access to detailed information on the troubled firms. One solution is that banks, after classifying the insolvent loans by the lateness or possibility of repayment, would swap the relatively sound loans for preferred stock and gradually write off the bad ones by expanding the banks' retained earnings and revaluing the banks' assets. Specifically, the debt-equity swap can benefit both creditors and debtors in the sense that it raises the liquidity and profitability of bank assets and strengthens the debtor's financial structure by easing the debt service burden. Such a creditor-led or market-led solution improves the financial strength and autonomy of the banking sector, thereby fostering more efficient resource allocation and risk sharing.

• 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.

• KDI Journal of Economic Policy
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• v.14 no.1
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• pp.3-36
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• 1992

"Corporate control by owners" characterizes the current structure of ownership, control and management of big business groups in Korea. It has become an ever more serious obstacle for the Korean economy to end its distinctive "personal capitalism" and to transform the current system into people's capitalism. The current issue, the deconcentration of ownership, through the course of heated debates should be treated from an integrated perspective. That is, the debate should center on the concentration of economic power and it effects on national economy, instead of sticking to the issue of ownership-control issue per se. This paper, by referring to the historical experiences and development paths which advanced countries have already traveled, analyzes the respective aspects of the concentration issue in a rather descriptive and taxonomist manner - market concentration, business diversification, ownership concentration, integrated management of conglomerates, i.e., managing in groups' unit, and the roles of financial institutions. The government policies against the concentration of economic power have so far focused on the size of big business groups and their diversification activities. The two major policy measures are restrictions on cross-ownership and excess capital investment by big business groups, and controls on their credit deals. This paper strongly suggests that the government should change its current priorities in targeting its policies against concentration. The government should reduce the regulations on size and diversification, and focus its policies on substantial dispersion of corporate ownership. The efficacy of government intervention in the management and control of business enterprises seems quite dubious and even anachronistic given the extent of maturity of Korean firms. Therefore, it should be noted that the current regulation-oriented stance taken by the government against the management style of big business groups should be suppressed, as it has assumed some a priori and typical pattern in advance in directing big business groups, such as independent and specialized management in respective firms' unit. This paper, also, raises the need for introducing new regulations on inter-sectoral diversifications between finance, industry and the press.