• Strategy21
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• s.42
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• pp.5-52
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• 2017

Debates about introducing nuclear submarines have been a main issue in Korea. The highest officials and the government has started to think seriously about the issue. Yet there were no certain decision to this issue or any agreements with US but it is still necessary to review about introducing nuclear submarines, the technologies and about the business. The reason for such issues are the highest officials of Korea to build nuclear submarine, nK's nuclear development and SLBM launching. ROKN's nuclear submarine's necessity will be to attack(capacity to revenge), defend(anti-SSBN Operation) and to respond against neighboring nation's threat(Russia, Japan, China). Among these nations, US, Russia (Soviet Union), Britain, France had built their submarines in a short term of time due to their industrial foundation regarding with nuclear propulsion submarines. However China and India have started their business without their industrial foundation prepared and took a long time to build their submarines. Current technology level of Korea have reached almost up to US, Russia, Britain and France when they first built their nuclear propulsion submarines since we have almost completed the business for the Changbogo-I,II and almost up to complete building the Changbogo-III which Korea have self designed/developed. Furthermore Korea have reached the level where we can self design large nuclear reactors and the integrated SMART reactor which we can call ourselves a nation with worldwide technologies. If introducing the nuclear submarine to the Korea gets decided, first of all we would have to review the technological problems and also introduce the foreign technologies when needed. The methods for the introduction will be developments after loans from the foreign, productions with technological cooperations, and individual production. The most significant thing will be that changes are continuous and new instances are keep showing up so that it is important to only have a simple reference to a current instances and have a review on every methods with many possibilities. Also developing all of the technologies for the nuclear propulsion submarines may be not possible and give financial damages so there may be a need to partially introduce foreign technologies. For the introduction of nuclear propulsion submarines, there must be a resolution of the international regulations together with the international/domestics resistances and the technological problems to work out for. Also there may be problem for the requirement fees to solve for and other tough problems to solve for. However nuclear submarines are powerful weapon system to risk everything above. This is an international/domestically a serious agenda. Therefore rather than having debates based on false facts, there must be a need to have an investigations and debates regarding the nation's benefits and national security.

• Journal of Industrial Convergence
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• v.21 no.1
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• pp.111-122
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• 2023

The purpose of this study is to verify the factors affecting survival time by estimating survival rate and survival time using non-financial information of social enterprises using credit guarantee in credit guarantee institutions, and provide information to stakeholders to improve survival rate and employ to contribute to maintaining and expanding the As a research method, survival analysis was performed using a non-parametric analysis method, Kaplan-Meier Analysis. As a sample, 621 companies (577 normal companies, 44 insolvent companies) established between 2009 and 2018 were selected as the target companies. As a result of examining the factors affecting survival time by classifying social enterprise representative information and corporate information, representative credit rating, representative home ownership, credit transaction period, and corporate credit rating were derived as significant variables affecting survival time. In the future, financial institutions will be able to induce corporate soundness by reflecting factors that affect survival when examining loans for social enterprises, contributing to job retention and reduction of social costs. Supporting organizations such as the government and private organizations will be able to use it in various ways, such as policy establishment and education and training for the growth and sustainability of social enterprises. With this study as an opportunity, I hope that research will continue with more interest in the factors influencing social enterprise performance as well as corporate insolvency.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.4
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• pp.167-178
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• 2021

The purpose of this study is to analyze the direct investment decision factors in the Kaesong Industrial Complex and Vietnam, and to contribute to the creation of domestic jobs and the revitalization of the inter-Korean economy. According to the analysis, most of the Kaesong Industrial Complex and Vietnamese investment companies are entering the complex for the purpose of utilizing cheap labor, cheap factory locations, sales/development of local markets, and bypass export production bases in third countries. This can be divided into production-efficient investors using differences in production price such as labor costs and market-oriented investors to sell and expand the local market, which seems to be consistent with global direct investment patterns such as Nike, Apple, and Amazon. However, even if the North Korea-U.S. denuclearization talks ease or lift sanctions, Vietnamese investors' willingness to invest in the North Korea has been most burdened by the possibility of closing special economic zones due to political risks. Last but not least, it is important to note that those willing to invest in North Korea are mostly smaller enterprises in textiles, sewing, footwear and leather industries-those that benefit from low-cost labor. Since their size is small, they need policy support in financing, especially in the early stages of their business. Even after they grow past the early stages, those without collateral would still need state guarantee letters to get financing. Thus, it is worth considering to use the Inter-Korean Cooperation Fund to compensate commercial banks for bad loan loss or for low-interest loans for smaller SMEs. The interviews with SMEs found that red-tape is one of the biggest difficulties they face. Thus, it is recommended that a one-stop service agency should be established to cover all processes and issues related to inter-Korean economic cooperation to eliminate redundancy and expediate government support for SMEs.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.83-102
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• 2021

The government recently announced various policies for developing big-data and artificial intelligence fields to provide a great opportunity to the public with respect to disclosure of high-quality data within public institutions. KSURE(Korea Trade Insurance Corporation) is a major public institution for financial policy in Korea, and thus the company is strongly committed to backing export companies with various systems. Nevertheless, there are still fewer cases of realized business model based on big-data analyses. In this situation, this paper aims to develop a new business model which can be applied to an ex-ante prediction for the likelihood of the insurance accident of credit guarantee. We utilize internal data from KSURE which supports export companies in Korea and apply machine learning models. Then, we conduct performance comparison among the predictive models including Logistic Regression, Random Forest, XGBoost, LightGBM, and DNN(Deep Neural Network). For decades, many researchers have tried to find better models which can help to predict bankruptcy since the ex-ante prediction is crucial for corporate managers, investors, creditors, and other stakeholders. The development of the prediction for financial distress or bankruptcy was originated from Smith(1930), Fitzpatrick(1932), or Merwin(1942). One of the most famous models is the Altman's Z-score model(Altman, 1968) which was based on the multiple discriminant analysis. This model is widely used in both research and practice by this time. The author suggests the score model that utilizes five key financial ratios to predict the probability of bankruptcy in the next two years. Ohlson(1980) introduces logit model to complement some limitations of previous models. Furthermore, Elmer and Borowski(1988) develop and examine a rule-based, automated system which conducts the financial analysis of savings and loans. Since the 1980s, researchers in Korea have started to examine analyses on the prediction of financial distress or bankruptcy. Kim(1987) analyzes financial ratios and develops the prediction model. Also, Han et al.(1995, 1996, 1997, 2003, 2005, 2006) construct the prediction model using various techniques including artificial neural network. Yang(1996) introduces multiple discriminant analysis and logit model. Besides, Kim and Kim(2001) utilize artificial neural network techniques for ex-ante prediction of insolvent enterprises. After that, many scholars have been trying to predict financial distress or bankruptcy more precisely based on diverse models such as Random Forest or SVM. One major distinction of our research from the previous research is that we focus on examining the predicted probability of default for each sample case, not only on investigating the classification accuracy of each model for the entire sample. Most predictive models in this paper show that the level of the accuracy of classification is about 70% based on the entire sample. To be specific, LightGBM model shows the highest accuracy of 71.1% and Logit model indicates the lowest accuracy of 69%. However, we confirm that there are open to multiple interpretations. In the context of the business, we have to put more emphasis on efforts to minimize type 2 error which causes more harmful operating losses for the guaranty company. Thus, we also compare the classification accuracy by splitting predicted probability of the default into ten equal intervals. When we examine the classification accuracy for each interval, Logit model has the highest accuracy of 100% for 0~10% of the predicted probability of the default, however, Logit model has a relatively lower accuracy of 61.5% for 90~100% of the predicted probability of the default. On the other hand, Random Forest, XGBoost, LightGBM, and DNN indicate more desirable results since they indicate a higher level of accuracy for both 0~10% and 90~100% of the predicted probability of the default but have a lower level of accuracy around 50% of the predicted probability of the default. When it comes to the distribution of samples for each predicted probability of the default, both LightGBM and XGBoost models have a relatively large number of samples for both 0~10% and 90~100% of the predicted probability of the default. Although Random Forest model has an advantage with regard to the perspective of classification accuracy with small number of cases, LightGBM or XGBoost could become a more desirable model since they classify large number of cases into the two extreme intervals of the predicted probability of the default, even allowing for their relatively low classification accuracy. Considering the importance of type 2 error and total prediction accuracy, XGBoost and DNN show superior performance. Next, Random Forest and LightGBM show good results, but logistic regression shows the worst performance. However, each predictive model has a comparative advantage in terms of various evaluation standards. For instance, Random Forest model shows almost 100% accuracy for samples which are expected to have a high level of the probability of default. Collectively, we can construct more comprehensive ensemble models which contain multiple classification machine learning models and conduct majority voting for maximizing its overall performance.