• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.277-283
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• 2019

Advances in technology have allowed the development and commercialization of various parts; however this has shortened the discontinuation cycle of the components. This means that repair and logistic support of weapon system which is applied to thousands of part components and operated over the long-term is difficult, which is the one of main causes of the decrease in the availability of weapon system. To improve this problem, the United States has created a special organization for this problem, whereas in Korea, commercial tools are used to predict and manage DMSMS. However, there is rarely a method to predict life cycle of parts that are not presented DMSMS information at the commercial tools. In this study, the structured and unstructured data of parts of a commercial tool were gathered, preprocessed, and embedded using neural network algorithm. Then, a method is suggested to predict the life cycle risk (LC Risk) and year to end of life (YTEOL). In addition, to validate the prediction performance of LC Risk and YTEOL, the prediction value is compared with descriptive statistics.

• Korean Journal of Construction Engineering and Management
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• v.18 no.2
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• pp.81-90
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• 2017

In Imjin River basin, three floods occurred between 1996 and 1999, causing many casualties and economic losses of 900 billion won. In Korea, flood damage is expected to increase in the future due to climate change. This study used the climate scenarios to estimate future flood damage costs and suggested a real options-based economic assessment method. Using proposed method, the flood control infrastructures in Imjin River basin were selected as a case study site to analyze the economic feasibility of the investment. Using RCP (Representative Concentration Pathway) climate scenarios, the future flood damage costs were estimated through simulated rainfall data. This study analyzed the flood reduction benefits through investment in the flood control infrastructures. The volatility of flood damage reduction benefits were estimated assuming that the RCP8.5 and RCP4.5 climate scenarios would be realized in the future. In 2071, the project option value would be determined by applying an extension option to invest in an upgrading that would allow the project to adapt to the flood of the 200-year return period. The results of the option values show that the two investment scenarios are economically feasible and the project under RCP8.5 climate scenario has more flood damage reduction benefits than RCP4.5. This study will help government decision makers to consider the uncertainty of climate change in the economic assessment of flood control infrastructures using real options analysis. We also proposed a method to quantify climate risk factors into economic values by using rainfall data provided by climate scenarios.

• Journal of the Korea Organic Resources Recycling Association
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• v.24 no.3
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• pp.83-90
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• 2016

Despite the ability of biochar to enhance soil fertility and to mitigate greenhouse gas, its carbon sequestration and profit analysis with arable land application have been a few evaluated. This study was conducted to estimate carbon sequestration and to evaluate profit of greenhouse gas mitigation during corn cultivation periods. For the experiment, the biochar application rates were consisted of pig compost(non application), 2,600(0.2%), 13,000(1%), and 26,000(2%) kg/ha based on pig compost application. For predicting soil carbon sequestration of biochar application, it was appeared to be linear model of Y = 0.5523X - 742.57 ($r^2=0.939^{**}$). Based on this equation, soil carbon sequestration by 0.2, 1 and 2% biochar application was estimated to be 1,235, 3,978, and 14,794 kg/ha, and their mitigations of $CO_2$-eq. emissions were estimated to be 4.5, 14.6, and 54.2 ton/ha, respectively. Their profits were estimated at $14.6 for lowest and $452 for highest. In Korea Climate Exchange, it was estimated that the market price of $CO_2$ in corn cultivation periods with 0.2, 1 and 2% biochar application was $35.6, $115.3 and $428.2 per hectare, respectively. For the plant growth response, it was observed that plant height and fresh ear yield were not significantly different among the treatments. Therefore, these experimental results might be fundamental data for assuming a carbon trading mechanism exists for biochar soil application in agricultural practices.

• Maritime Security
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• v.1 no.1
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• pp.311-343
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• 2020

Because of the global warming, the Arctic Ocean is expected to be ice-free by the year 2035. When the Arctic Ocean will be opened, a number of national interests will become more salient as experiencing a shortened sailing distance and decreasing navigation expense, possibility of natural resources transport by sea from Arctic Circle, and indirect-profit making by building a herb port in Asia. To secure the national interests and support the free activities of people in this region, R.O.K government is trying to make advanced policies. In order to carry out the naval tasks in the Arctic Ocean, using the operational characteristics(mobility, flexibility, sustainability, presence of capabilities, projection) is necessary. To this end, ROK Navy should analyze the operational environment (O.E.) by its capability(weakness and strength), opportunity, and threat. R.O.K. Navy should make an effort over the following issues to implement the tasks in the Arctic Ocean: first, Navy needs to map out her own plan (Roadmap) under the direction of government policies and makes crews participate in the education·training programs in home and abroad for future polar experts. Third, to develop the forces and materials for the tasks in cold, far operations area, Navy should use domestic well-experienced shipbuilding skills and techniques of the fourth industrial revolution. Next, improving the combined operations capabilities and military trust with other countries in the Arctic region to cover the large area with lack of forces' number and to resolve the ports of call issues. Lastly, preparation in advance to execute a variety of missions against military and non-traditional threats such as epidemics, HA/DR, SOLAS, in the future operation area is required.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.157-176
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• 2018

The purpose of this research is to develop a detection model for companies designated as administrative issue in KOSDAQ market using financial data. Administration issue designates the companies with high potential for delisting, which gives them time to overcome the reasons for the delisting under certain restrictions of the Korean stock market. It acts as an alarm to inform investors and market participants of which companies are likely to be delisted and warns them to make safe investments. Despite this importance, there are relatively few studies on administration issues prediction model in comparison with the lots of studies on bankruptcy prediction model. Therefore, this study develops and verifies the detection model of the companies designated as administrative issue using financial data of KOSDAQ companies. In this study, logistic regression and decision tree are proposed as the data mining models for detecting administrative issues. According to the results of the analysis, the logistic regression model predicted the companies designated as administrative issue using three variables - ROE(Earnings before tax), Cash flows/Shareholder's equity, and Asset turnover ratio, and its overall accuracy was 86% for the validation dataset. The decision tree (Classification and Regression Trees, CART) model applied the classification rules using Cash flows/Total assets and ROA(Net income), and the overall accuracy reached 87%. Implications of the financial indictors selected in our logistic regression and decision tree models are as follows. First, ROE(Earnings before tax) in the logistic detection model shows the profit and loss of the business segment that will continue without including the revenue and expenses of the discontinued business. Therefore, the weakening of the variable means that the competitiveness of the core business is weakened. If a large part of the profits is generated from one-off profit, it is very likely that the deterioration of business management is further intensified. As the ROE of a KOSDAQ company decreases significantly, it is highly likely that the company can be delisted. Second, cash flows to shareholder's equity represents that the firm's ability to generate cash flow under the condition that the financial condition of the subsidiary company is excluded. In other words, the weakening of the management capacity of the parent company, excluding the subsidiary's competence, can be a main reason for the increase of the possibility of administrative issue designation. Third, low asset turnover ratio means that current assets and non-current assets are ineffectively used by corporation, or that asset investment by corporation is excessive. If the asset turnover ratio of a KOSDAQ-listed company decreases, it is necessary to examine in detail corporate activities from various perspectives such as weakening sales or increasing or decreasing inventories of company. Cash flow / total assets, a variable selected by the decision tree detection model, is a key indicator of the company's cash condition and its ability to generate cash from operating activities. Cash flow indicates whether a firm can perform its main activities(maintaining its operating ability, repaying debts, paying dividends and making new investments) without relying on external financial resources. Therefore, if the index of the variable is negative(-), it indicates the possibility that a company has serious problems in business activities. If the cash flow from operating activities of a specific company is smaller than the net profit, it means that the net profit has not been cashed, indicating that there is a serious problem in managing the trade receivables and inventory assets of the company. Therefore, it can be understood that as the cash flows / total assets decrease, the probability of administrative issue designation and the probability of delisting are increased. In summary, the logistic regression-based detection model in this study was found to be affected by the company's financial activities including ROE(Earnings before tax). However, decision tree-based detection model predicts the designation based on the cash flows of the company.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.25-39
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• 2017

Over 5 million people participate in cycle racing betting and its revenue is more than 2 trillion won. This study predicts the ranking of cycle racing using various statistical analyses and identifies important variables which have influence on ranking. We propose competitive ranking prediction models using various classification and regression methods. Our model can predict rankings with low misclassification rates most of the time. We found that the ranking increases as the grade of a racer decreases and as overall scores increase. Inversely, we can observe that the ranking decreases when the grade of a racer increases, race number four is given, and the ranking of the last race of a racer decreases. We also found that prediction accuracy can be improved when we use centered data per race instead of raw data. However, the real profit from the future data was not high when we applied our prediction model because our model can predict only low-return events well.

• The Korean Journal of Financial Management
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• v.12 no.1
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• pp.167-186
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• 1995

기업인수합병(M&A) 시장의 활성화에 따라 적대적 공개매수를 방어하기 위한 반인수조치(Antitakeover Techniques)들에 관한 관심도 고조되고 있다. 지금까지 널리 알려져 있는 대표적인 반인수 조치들은 Fair Price Amendment(FPA), Classified Board Amendment(CBA)와 Poison Pills(PP) 등이다. 이들 대표적 세 반인수조치들 중에서 FPA와 CBA 채택의 경우는 주주들의 사전 승인이 요구되는데 반하여 PP는 주주들의 사전 승인없이 채택이 가능한 반인수조치이다. 이처럼 상이한 반인수조치들의 채택은 채택기업의 가치에 상이한 부의 효과를 미치는데, 이 분야의 많은 실증적연구 결과들이 보고되고 있다. 본 연구에서는 표본기업으로 현재까지 상호개별적으로 연구되어 왔던 두가지 반인수조치(FTA, CBA)에 PP와 비채택기업도 포함시키고 있다. 지금까지의 반인수조치 채택에 따른 기업가치에 미치는 부의 효과에 관한 연구결과를 확인해보고, 반인수조치 채택에 관한 경영자의 의사결정과 경영자의 부 사이에 체계적인 관계가 존재하는지를 실증분석하고자 한다. 여기서 경영자의 부는 기업내부자 지분율과 기업내 경영자를 위한 Golden Parachute의 존재 유무로 측정한다. 본 연구에서는 3개의 가설을 설정하였다. 가설1: 만일 경영자가 주주의 이익을 희생하면서 자신의 이익을 위한 반인수조치를 채택한다면, 반인수조치 채택의 공표는 평균적으로 기업가치에 부(-)의 효과를 보일 것이다. 가설2: 경영자의 내부지분율이 낮을때 경영자들은 주주에게 가장 해로운 반인수조치를 선택할 것이다. 가설3: Golden Parachute가 존재하지 않을때 경영자들은 주주에게 가장 해로운 반인수조치를 채택할 것이다. 본 연구의 대상기업들중에서 반인수조치 채택 기업들은 IRRC 1990년도판에서 수집되었고, 대칭표본 기업으로 반인수조치를 채택하지 않은 기업들은 CRSP 파일에서 기업규모, SIC 코드를 대응시켜 선정하였다. 임원, 관계이사들과 친인척을 포함하는 내부자의 지분과 Golden Parachute 존재 여부는 이 연구의 표본기업들의 Proxy Statement에서 수집하였다. 최종 표본기업은 FPA 채택기업, CBA 채택기업, PP채택기업, 그리고 비채택기업으로 4개의 상호 배타적인 기업 그룹으로 구성되었다. 본 연구는 Event Study와 Multinomial Logistic Regession의 두가지 실증분석 방법을 사용하였다. Event Study방법론은 반인수조치 채택 공표시 초과수익률을 조사하기 위해 사용하였다. Multinomial Logistic Regession은 선택된 반인수조치 종류와 설명 변수들(내부자 지분율, Golden Parachute)간에 체계적인 관계가 존재하는지를 검증하기 위해 사용되었다. 반인수조치들을 채택하는 기업들은 반인수조치를 채택하고 있지 않은 기업들에 비해 내부자 지분율이 낮게 나타났으며, 반인수조치 중 PP를 채택한 기업에서 가장 낮은 내부지분율을 보이고 있다. GP 채택을 보면 PP를 선택한 기업의 50%가 GP를 채택하였다. 본 연구에서 반인수조치 채택 발표일 하루 전후의 초과수익률을 조사한 결과는 반인수조치 미채택기업, CBA, FPA 채택기업들의 초과수익률은 통계적으로 의미가 없었으나, PP채택에 따른 초과수익률은 의미 있는 부(-)의 값을 나타냈다. 이와같이 CBA와 FPA채택기업들은 주주의 부를 감소시키지 않았으나 PP채택기업들은 주주의 부를 감소시켰다. 따라서 경영자는 주주의 이익을 희생시키면서 자신의 이익을 위해 PP를 선택하고 있음을 보여 주고 있다. 연구결과는 내부자 지분율의 크기가 경영자와 주주간의 이해를 효과적으로 일치시키고 있음을 제시하고 있다. 즉, 내부자 지분율이 큰 기업일수록 반인수조치를 채택하지 않거나 반인수조치 채택시에 주주의 이익에 반하지 않은 반인수조치를 선택하는 경향이 높다. Golden Parachute이 존재하는 기업은 FPA를 채택하거나 반인수조치를 채택하지 않는 것보다 PP나 CBA를 채택하는 경향이 더 높다. 한편 기업에서의 GP의 존재가 경영자의 가장 해로운 반인수조치 선택을 억제하지 못함을 보여주고 있는데, 이는 GP가 비효과적인 계약메카니즘임을 제기한다. GP가 경영자와 주주간의 이해를 일치시키도록하는 계약이라기 보다는 차라리 기업방어전략이 비효과적일때 경영자 자신의 안전판으로 제공되고 있음을 보여준다. 이 논문의 주요공헌은 기업내부자 지분율의 크기와 GP의 존재가 반인수조치 선택에 체계적인 영향을 미치고 있음을 보여준 것이다. 여기서 사용된 Multinomial Logistic모델은 내부지분을 크기와 GP의 존재가 PP또는 CBA가 채택될 것인지를 예측할 수 있게 한다.

• Journal of Korea Port Economic Association
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• v.37 no.4
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• pp.127-144
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• 2021

The purpose of this study is to derive service factors based on the "Rail Statistical Yearbook" data of railroad service providers from 1990 to 2019, and to analyze the effect of the service factors on the operating profit ratio(OPR), a representative management performance variable of railroad transport service providers. In particular, it has academic significance in terms of empirical research to evaluate whether the management innovation of the KoRail has changed in line with the purpose of establishing the corporation by dividing the research period into the first period (1990-2003) and the latter (2004-2019). The contents of this study investigated previous studies on the quality of railway passenger transportation service and analyzed the contents of government presentation data related to the management performance evaluation of the KoRail. As an empirical analysis model, a research model was constructed using OPR as a dependent variable and service factor variables of infrastructure, economy, safety, connectivity, and business diversity as explanatory variables based on the operation and management activity information during the analysis period 30 years. On the results of research analysis, OPR is that the infrastructure factor is improved by structural reform or efficiency improvement. And economic factors are the fact that operating profit ratio improves by reducing costs. The safety factor did not reveal the significant explanatory power of the regression coefficient, but the sign of influence was the same as the prediction. Connectivity factor reveals a influence on differences between first period and latter, but OPR impact direction is changed from negative in before to positive in late. This is an evironment in which connectivity is actually realized in later period. On diversity factor, there is no effect of investment share in subsidiaries and government subsidies on OPR.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.19-32
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• 2010

Volatility plays a central role in both academic and practical applications, especially in pricing financial derivative products and trading volatility strategies. This study presents a novel mechanism based on generalized autoregressive conditional heteroskedasticity (GARCH) models that is able to enhance the performance of intelligent volatility trading systems by predicting Korean stock market volatility more accurately. In particular, we embedded the concept of the volatility asymmetry documented widely in the literature into our model. The newly developed Korean stock market volatility index of KOSPI 200, VKOSPI, is used as a volatility proxy. It is the price of a linear portfolio of the KOSPI 200 index options and measures the effect of the expectations of dealers and option traders on stock market volatility for 30 calendar days. The KOSPI 200 index options market started in 1997 and has become the most actively traded market in the world. Its trading volume is more than 10 million contracts a day and records the highest of all the stock index option markets. Therefore, analyzing the VKOSPI has great importance in understanding volatility inherent in option prices and can afford some trading ideas for futures and option dealers. Use of the VKOSPI as volatility proxy avoids statistical estimation problems associated with other measures of volatility since the VKOSPI is model-free expected volatility of market participants calculated directly from the transacted option prices. This study estimates the symmetric and asymmetric GARCH models for the KOSPI 200 index from January 2003 to December 2006 by the maximum likelihood procedure. Asymmetric GARCH models include GJR-GARCH model of Glosten, Jagannathan and Runke, exponential GARCH model of Nelson and power autoregressive conditional heteroskedasticity (ARCH) of Ding, Granger and Engle. Symmetric GARCH model indicates basic GARCH (1, 1). Tomorrow's forecasted value and change direction of stock market volatility are obtained by recursive GARCH specifications from January 2007 to December 2009 and are compared with the VKOSPI. Empirical results indicate that negative unanticipated returns increase volatility more than positive return shocks of equal magnitude decrease volatility, indicating the existence of volatility asymmetry in the Korean stock market. The point value and change direction of tomorrow VKOSPI are estimated and forecasted by GARCH models. Volatility trading system is developed using the forecasted change direction of the VKOSPI, that is, if tomorrow VKOSPI is expected to rise, a long straddle or strangle position is established. A short straddle or strangle position is taken if VKOSPI is expected to fall tomorrow. Total profit is calculated as the cumulative sum of the VKOSPI percentage change. If forecasted direction is correct, the absolute value of the VKOSPI percentage changes is added to trading profit. It is subtracted from the trading profit if forecasted direction is not correct. For the in-sample period, the power ARCH model best fits in a statistical metric, Mean Squared Prediction Error (MSPE), and the exponential GARCH model shows the highest Mean Correct Prediction (MCP). The power ARCH model best fits also for the out-of-sample period and provides the highest probability for the VKOSPI change direction tomorrow. Generally, the power ARCH model shows the best fit for the VKOSPI. All the GARCH models provide trading profits for volatility trading system and the exponential GARCH model shows the best performance, annual profit of 197.56%, during the in-sample period. The GARCH models present trading profits during the out-of-sample period except for the exponential GARCH model. During the out-of-sample period, the power ARCH model shows the largest annual trading profit of 38%. The volatility clustering and asymmetry found in this research are the reflection of volatility non-linearity. This further suggests that combining the asymmetric GARCH models and artificial neural networks can significantly enhance the performance of the suggested volatility trading system, since artificial neural networks have been shown to effectively model nonlinear relationships.