• International Area Studies Review
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• v.13 no.2
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• pp.311-368
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• 2009

In order to survive the on-going natural resource war, Korea needs various efforts such as enhancing self-exploitation ratio, increasing the supply of new-reuse energy, strengthening cooperation with resource rich countries. Central Asian countries are geometrically far away from Korea. However, Korea should try to develop political, economic and ethnic relationship with those countries into much higher dimension to secure various natural resources. Major countries including U.S., EU. Japan and China are approaching Central Asian countries with long term perspective. Improving country-image through enlargement of ODA is the first concern of those countries. Korea should try to follow their practices. Government should try to improve Korea's image in the first place and lead economic cooperation with very detailed supportive measures to induce Korean firms' investment into the Central Asian countries. In the due process, a lot of information about those countries' political climate, social situation, ethnical composition, major religions, educational system, current state and structure of economies and industries, etc should be made available to Korean firms.

• Journal of Korea Technology Innovation Society
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• v.22 no.3
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• pp.382-415
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• 2019

This study analyzes the impact of exclusive subcontracting on the input, behavior, and output of innovation in manufacturing industries of Korea. Based on the analysis of pooled cross-sectional data of 6,029 small venture firms, this study proved that the exclusive subcontracting between small venture firms and large enterprises are lowering R&D investment of small venture firms. Second, the innovation activities of small venture firms including the ratio of R&D personnel and the scope of cooperation and partnership with external organizations were lower than those of small venture firms that have non-exclusive or no relations with large enterprises. Third, the innovation performance of small venture firms such as the number of patent applications, the ratio of sales by new products, and the cumulative sales growth rate was lower than those of small venture firms that have non-exclusive or no relations with large enterprises. This study verifies that the exclusive subcontracting relationships significantly weaken the innovation process and performance of small venture firms systematically, resulting in a kind of market failure in which small venture firms have almost no incentive to facilitate innovation.

• Knowledge Management Research
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• v.22 no.2
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• pp.247-268
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• 2021

Investors mainly use PER and PBR among financial ratios for valuation and investment decision-making. I conduct an analysis of two basic financial ratios from a statistical perspective. Financial ratios contain key accounting numbers which reflect firm fundamentals and are useful for valuation or risk analysis such as enterprise credit evaluation and default prediction. The distribution of financial data tends to be extremely heavy-tailed, and PER and PBR show exceedingly high level of kurtosis and their extreme cases often contain significant information on financial risk. In this respect, Extreme Value Theory is required to fit its right tail more precisely. I introduce not only GPD but exGPD. GPD is conventionally preferred model in Extreme Value Theory and exGPD is log-transformed distribution of GPD. exGPD has recently proposed as an alternative of GPD(Lee and Kim, 2019). First, I conduct a simulation for comparing performances of the two distributions using the goodness of fit measures and the estimation of 90-99% percentiles. I also conduct an empirical analysis of Information Technology firms in Korea. Finally, exGPD shows better performance especially for PBR, suggesting that exGPD could be an alternative for GPD for the analysis of financial ratios.

• Journal of Korean Society of Forest Science
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• v.83 no.2
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• pp.211-220
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• 1994

The critical need of forest road for enchanting the additional values of various forest products, in addition, giving more recreational opportunity to citizen, has been recognized. In this study the present author aimed to ascertain the most effective construction working plan of forest road being tit to Korean geographic condition. To execute this research program, four locations in national forest of Kangweon-do district and other four locations in private forest in Chollabuk-do district both where forest roads have previously been constructed were selected to analyze the effectiveness basing upon the various factors separately or in combination. The results are summarized as follows ; 1. The investment efficiency in forest road construction showed to increase in the area where terrain factors and district social factors rate is high, and to decrease in the area where forest status factors and forest road structure factors rate is high. So in future the Forest Resource Development Model of forest road should take more importance particularly on those area having terrain factor ratio is low. The extractable value of constructed forest road based on forest status factors rate is expected to increase in case of high considerably. 2. To construct of forest road for increasing multiple use of forests, forest road should be construct with priority on area where obtained total score by evaluation factors is high. And these evaluation factors should take possible determine the position of forest road construction. 3. The following five types of forest road basing upon function performance are suggested with regard to the place where road is constructed. (1) Forest Utilization Model ; where forest status factors and forest road structure factors rate are over 60%. (2) Forest Resource Development Model ; where terrain factors, forest status factors, forest road structure factors and district social factors rate are less than 60%. (3) Community Development Model ; where terrain factors, forest road structure factors and district social factors rate are over 60% but forest status factors rate are less than 60%. (4) Recreation and Health Model ; where terrain factors, forest status factors, forest road structure factors and district social factors rate are over 60%. (5) Multiple Use Model ; where both forest status factors and district social factors rate are over 60%.

• The Korean Journal of Financial Management
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• v.21 no.2
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• pp.99-123
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• 2004

We focus in this study on incremental financing decisions and estimate a logit model for the probability a firm will choose a private placement over a public bond issue. We hypothesize that information asymmetry, financial risk, agent cost, and proprietary information may affect a firm's choice between public debt and private placements. We find that as the size of firm increases, the probability of choosing a private placement declines significantly. The age of the firm, however, is not a significant factor affecting the firm's choice between public and privately-placed bond. The coefficients on the firm's leverage and non-investment grade dummy are significantly positive, meaning firms with high financial risk and credit risk select private placements. The findings regarding agency-related variables, PER and Tobin's Q, are somewhat complex. We find significant evidence that firms with high PER prefer private placements to public bonds, suggesting that borrowers with options to engage in asset substitution or underinvestment are more likely to choose private placements. The coefficient of Tobin's Q is negative, but not significant, which weakly support the hold-up hypothesis. When we construct an interaction term on the Tobin's Q with a non-investment rating dummy, however, the Tobin's Q interaction term becomes positive and significant. Thus, high Tobin's Q firms with a speculative rating are significantly more likely to choose a private placement, regardless of the potential hold-up problems. The ratio of R&D to sales, proxy for proprietary information, is positively significant. This result can be interpreted as evidence in favor of a role for proprietary information in the debt sourcing decision process for these firms.

• 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.

• Korea Journal of Hospital Management
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• v.8 no.1
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• pp.135-164
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• 2003

The operating room is the major facility that costs the highest investment per unit area in a hospital. It requires commitment of hospital resources such as manpower, equipments and material. The quantity of these resources committed actually differs from one type of operation to another. Because of this, it is not an easy task to allocate the operating cost to individual clinical departments that share the operating room. A practical way to do so may be to collect and add the operating costs incurred by each clinical department and charge the net cost to the account of the corresponding clinical department. It has been customary to allocate the cost of the operating room to the account of each individual department on the basis of the ratio of the number of operations of the department or the total revenue by each operating room. In an attempt to set up more rational cost allocation method than the customary method, this study proposes a new cost allocation method that calls for itemizing the operation cost into its constituent expenses in detail and adding them up for the operating cost incurred by each individual department. For comparison of the new method with the conventional method, the operating room in the main building of hospital A near Seoul is chosen as a study object. It is selected because it is the biggest operating room in hospital A and most of operations in this hospital are conducted in this room. For this study the one-month operation record performed in January 2001 in this operating room is analyzed to allocate the per-month operation cost to six clinical departments that used this operating room; the departments of general surgery, orthopedic surgery, neuro-surgery, dental surgery, urology, and obstetrics & gynecology. In the new method(or method 1), each operation cost is categorized into three major expenses; personnel expense, material expense, and overhead expense and is allocated into the account of the clinical department that used the operating room. The method 1 shows that, among the total one-month operating cost of 814,054 thousand wons in this hospital, 163,714 thousand won is allocated to GS, 335,084 thousand won to as, 202,772 thousand won to NS, 42,265 thousand won to uno, 33,423 thousand won to OB/GY, and 36.796 thousand won to DS. The allocation of the operating cost to six departments by the new method is quite different from that by the conventional method. According to one conventional allocation method based on the ratio of the number of operations of a department to the total number of operations in the operating room(method 2 hereafter), 329,692 thousand won are allocated to GS, 262,125 thousand won to as, 87,104 thousand won to NS, 59,426 thousand won to URO, 51.285 thousand won to OB/GY, and 24,422 thousand won to DS. According to the other conventional allocation method based on the ratio of the revenue of a department(method 3 hereafter), 148,158 thousand won are allocated to GS, 272,708 thousand won to as, 268.638 thousand won to NS, 45,587 thousand won to uno, 51.285 thousand won to OB/GY, and 27.678 thousand won to DS. As can be noted from these results, the cost allocation to six departments by method 1 is strikingly different from those by method 2 and method 3. The operating cost allocated to GS by method 2 is about twice by method 1. Method 3 makes allocations of the operating cost to individual departments very similarly as method 1. However, there are still discrepancies between the two methods. In particular the cost allocations to OB/GY by the two methods have roughly 53.4% discrepancy. The conventional methods 2 and 3 fail to take into account properly the fact that the average time spent for the operation is different and dependent on the clinical department, whether or not to use expensive clinical material dictate the operating cost, and there is difference between the official operating cost and the actual operating cost. This is why the conventional methods turn out to be inappropriate as the operating cost allocation methods. In conclusion, the new method here may be laborious and cause a complexity in bookkeeping because it requires detailed bookkeeping of the operation cost by its constituent expenses and also by individual clinical department, treating each department as an independent accounting unit. But the method is worth adopting because it will allow the concerned hospital to estimate the operating cost as accurately as practicable. The cost data used in this study such as personnel expense, material cost, overhead cost may not be correct ones. Therefore, the operating cost estimated in the main text may not be the same as the actual cost. Also, the study is focused on the case of only hospital A, which is hardly claimed to represent the hospitals across the nation. In spite of these deficiencies, this study is noteworthy from the standpoint that it proposes a practical allocation method of the operating cost to each individual clinical department.

• Journal of Preventive Medicine and Public Health
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• v.24 no.4 s.36
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• pp.531-548
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• 1991

To assess the adequacy of medical manpower and medical fee for the newborn nursery care, the author visited 20 out of 24 hospitals with the pediatric training program in Youngnam area between July 29 and August 14, 1991. Total number of newborn, both normal and sick, admission and discharge in 1-30 June 1991 was obtained from the logbook of nursery. Head nurse and staff pediatrician of the nursery were interviewed to get the current staffing for the nursery and their subjective opinion on the adequacy of nursery manpower and the difficulties in recruiting manpower. Average medical fee charged for the maternity and normal newborn nursery care was obtained from the division of self-audit of medical insurance claim of each hospital. Average minimum requirement of nursing care time for one normal newborn per day was 179.5 (${\pm}58.6$) minutes; 202.3(${\pm}50.7$) minutes for the university hospitals and 164.2(${\pm}60.5$) minutes for the general hospitals. The ratio of minimum requirement of nursing care time and available nursing time was 1.42 on the average. Taking the additional requirement of nursing care for the sick newborns into consideration, the ratio was 2.06. The numbers of R. N. and A. N. in the nurserys of study hospitals were 31%, and 17%, respectively, of the nursing manpower for the nursery recommended by the American Academy of Pediatrics. These findings indicate that the nursing manpower in newborn nursery is in severe shortage. Ninety percent of the head nurses and 85% of the staff pediatrician stated that the newborn nursery is short of R.N. and 75% of them said that the nurse's aide is also short. Major reason for not recruiting R.N. was the financial constraint of hospital. For the recruitment of nurse's aide, short supply was the second most important reason next to the financial constraint. However, limit of quarter in T.O. was the mar reason for the national university hospitals. Average total medical fee for the maternity and newborn nursery cares of a normal vaginal delivery who stayed two nights and three days at hospital was 219,430won. Out of the total medical fee, 20,323won(9.3%) was for the newborn nursery care. In case of C-section delivery who stayed six nights and seven days, total medical fee was 732,578won and out of the total fee 76,937won (12.0%) was for the newborn care. Cost for a newborn care per day by cost accounting was 16,141won for the tertiary care hospitals and 14,576won for the all other hopitals. The ratio of cost and the fee schedule of the medical insurance for a newborn care per day was 5.0 for the tertiary care hospitals and 4.9 for the all other hospitals. Considering the current wage level of the medical personnel, capital investment for the hospital facilities and equipments, and the cost for hospital maintenance, it is hard to expect adequate quality care in the newborn nursery under the current medical insurance fee schedule.

• The Korean Journal of Financial Management
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• v.26 no.3
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• pp.171-198
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• 2009

The primary purpose of this study is to examine the impact of new BIS regulation, which is the preparations to incorporate not only credit risk but also market and operation risk, on the bank behaviors. As methodology, SUR(seemingly unrelated regression) and pool unit test are used in the empirical analysis of banks survived in Korea. It is employed that quarterly data of BIS capital ratio, ratio of standard and below loans to total loans, ratio of liquid assets to liquid liabilities, allowances for credit losses, real GDP, yields of corporate bonds(3years, AA) covering the period of 2000Q1~2009Q1. As a result, it could be indicated that effectiveness and promoting improvements of BIS capital regulation policy as follows; First, it is explicitly seen that weight of lending had decreased and specific gravity of international investment had increased until before BIS regulation is built up a step for revised agreement in late 2001. Second, after more strengthening of BIS standard in late 2002, banks had a tendency to decrease the adjustment of assets weighted risk through issuing of national loan that is comparatively low profitability. Also, it is implicitly sought that BIS regulation is a bit of a factor to bring about credit crunch and then has become a bit of a factor of economic stagnation. Third, as the BIS regulation became hard, it let have a effort to raise the soundness of a credit loan because of selecting good debtor based on its credit ratings. Fourth, it should be arranged that the market disciplines, the effective superintendence system and the sound environment to be able to raise enormous bank capital easily, against the credit stringency and reinforce the soundness of banks etc. in Korea capital market.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.