• Korean Journal of Construction Engineering and Management
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• v.16 no.1
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• pp.3-14
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• 2015

Since the global financial crisis, the Korean domestic construction market has continuously experienced downturns, and the Korean domain construction firms'profitability has been persistently deteriorated. Domestic construction firms have rapidly advanced to overseas markets exclusively for the construction contract packages. However, the profitability for the construction contracts has been lower compared to engineering or project management contracts. One of the critical issues the Korean firms have faced was project management capability across all phases in project execution. Even though several project management capability assessment tools were introduced, most tools were applicable to a wide variety of industry sectors rather than construction industry. Project management capability assessment tool specifically applicable to domestic CM firms was developed through this research, in order to assess project and program management capabilities and improve the competitiveness in overseas market Also, the correlation between project, programs, and the CM infrastructure were identified. The CM firms were divided into two groups according to the size of the business, and both were evaluated at the project and the program level based for the 9 different criteria. The project management capability assessment tool developed for the CM firms can be used for self-assessment to distinguish the strengths and weaknesses of each company at the project and program level. In addition, the current status of each group can be identified by spotting improvement areas for the management capabilities.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.33-56
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• 2017

As Internet usage is becoming more common worldwide and smartphone become necessity in daily life, technologies and applications related to mobile Internet are developing rapidly. The results of the Internet usage patterns of consumers around the world imply that there are many potential new business opportunities for mobile Internet technologies and applications. The location-based service (LBS) is a service based on the location information of the mobile device. LBS has recently gotten much attention among many mobile applications and various LBSs are rapidly developing in numerous categories. However, even with the development of LBS related technologies and services, there is still a lack of empirical research on the intention to use LBS. The application of previous researches is limited because they focused on the effect of one particular factor and had not shown the direct relationship on the intention to use LBS. Therefore, this study presents a research model of factors that affect the intention to use and actual use of LBS whose market is expected to grow rapidly, and tested it by conducting a questionnaire survey of 330 users. The results of data analysis showed that service customization, service quality, and personal innovativeness have a positive effect on the intention to use LBS and the intention to use LBS has a positive effect on the actual use of LBS. These results implies that LBS providers can enhance the user's intention to use LBS by offering service customization through the provision of various LBSs based on users' needs, improving information service qualities such as accuracy, timeliness, sensitivity, and reliability, and encouraging personal innovativeness. However, privacy concerns in the context of LBS are not significantly affected by service customization and personal innovativeness and privacy concerns do not significantly affect the intention to use LBS. In fact, the information related to users' location collected by LBS is less sensitive when compared with the information that is used to perform financial transactions. Therefore, such outcomes on privacy concern are revealed. In addition, the advantages of using LBS are more important than the sensitivity of privacy protection to the users who use LBS than to the users who use information systems such as electronic commerce that involves financial transactions. Therefore, LBS are recommended to be treated differently from other information systems. This study is significant in the theoretical point of contribution that it proposed factors affecting the intention to use LBS in a multi-faceted perspective, proved the proposed research model empirically, brought new insights on LBS, and broadens understanding of the intention to use and actual use of LBS. Also, the empirical results of the customization of LBS affecting the user's intention to use the LBS suggest that the provision of customized LBS services based on the usage data analysis through utilizing technologies such as artificial intelligence can enhance the user's intention to use. In a practical point of view, the results of this study are expected to help LBS providers to develop a competitive strategy for responding to LBS users effectively and lead to the LBS market grows. We expect that there will be differences in using LBSs depending on some factors such as types of LBS, whether it is free of charge or not, privacy policies related to LBS, the levels of reliability related application and technology, the frequency of use, etc. Therefore, if we can make comparative studies with those factors, it will contribute to the development of the research areas of LBS. We hope this study can inspire many researchers and initiate many great researches in LBS fields.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.51-69
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• 2020

Now, it is a stylized fact that a small number of technology firms such as Apple, Alphabet, Microsoft, Amazon, Facebook and a few others have become larger and dominant players in an industry. Coupled with the rise of these leading firms, we have also observed that a large number of young firms have become an acquisition target in their early IPO stages. This indeed results in a sharp decline in the number of new entries in public exchanges although a series of policy reforms have been promulgated to foster competition through an increase in new entries. Given the observed industry trend in recent decades, a number of studies have reported increased concentration in most developed countries. However, it is less understood as to what caused an increase in industry concentration. In this paper, we uncover the mechanisms by which industries have become concentrated over the last decades by tracing the changes in industry concentration associated with a firm's status change in its early IPO stages. To this end, we put emphasis on the case in which firms are acquired shortly after they went public. Especially, with the transition to digital-based economies, it is imperative for incumbent firms to adapt and keep pace with new ICT and related intelligent systems. For instance, after the acquisition of a young firm equipped with AI-based solutions, an incumbent firm may better respond to a change in customer taste and preference by integrating acquired AI solutions and analytics skills into multiple business processes. Accordingly, it is not unusual for young ICT firms become an attractive acquisition target. To examine the role of M&As involved with young firms in reshaping the level of industry concentration, we identify a firm's status in early post-IPO stages over the sample periods spanning from 1990 to 2016 as follows: i) being delisted, ii) being standalone firms and iii) being acquired. According to our analysis, firms that have conducted IPO since 2000s have been acquired by incumbent firms at a relatively quicker time than those that did IPO in previous generations. We also show a greater acquisition rate for IPO firms in the ICT sector compared with their counterparts in other sectors. Our results based on multinomial logit models suggest that a large number of IPO firms have been acquired in their early post-IPO lives despite their financial soundness. Specifically, we show that IPO firms are likely to be acquired rather than be delisted due to financial distress in early IPO stages when they are more profitable, more mature or less leveraged. For those IPO firms with venture capital backup have also become an acquisition target more frequently. As a larger number of firms are acquired shortly after their IPO, our results show increased concentration. While providing limited evidence on the impact of large incumbent firms in explaining the change in industry concentration, our results show that the large firms' effect on industry concentration are pronounced in the ICT sector. This result possibly captures the current trend that a few tech giants such as Alphabet, Apple and Facebook continue to increase their market share. In addition, compared with the acquisitions of non-ICT firms, the concentration impact of IPO firms in early stages becomes larger when ICT firms are acquired as a target. Our study makes new contributions. To our best knowledge, this is one of a few studies that link a firm's post-IPO status to associated changes in industry concentration. Although some studies have addressed concentration issues, their primary focus was on market power or proprietary software. Contrast to earlier studies, we are able to uncover the mechanism by which industries have become concentrated by placing emphasis on M&As involving young IPO firms. Interestingly, the concentration impact of IPO firm acquisitions are magnified when a large incumbent firms are involved as an acquirer. This leads us to infer the underlying reasons as to why industries have become more concentrated with a favor of large firms in recent decades. Overall, our study sheds new light on the literature by providing a plausible explanation as to why industries have become concentrated.

• The Journal of Fisheries Business Administration
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• v.15 no.1
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• pp.131-153
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• 1984

Fishery resources are still abundant compared with other resources and the possibility of exploitation is probably great. The Korean fishery industry has grown remarkably since 1957, and Korea is ranked as one of the major fishery countries. Its of fishery products reached the 9th in the world and the value of exports was 5th in 1982. But recently a growth rate has slowed down, due to the enlargement of territorial seas by the declaration of the 200 mile, Exclusive Economic Zone, the tendency to develop fishery resources strate-gically in international bargaining, the change in function of the international organizations, the expansion of regulated waters, the illegal arrest of our fishing boats, the rapid rise in oil prices, and the fall in fish prices, the development of fishery resources as a symbol of nationalism, the fishing boats decreptitude, the rise of crew wages, regulations on fishing methods, fish species, fishing season, size of fish, and mesh size, fishing quotas and the demand of excessive fishing royalties. Besides the the obligation of coastal countries, employing crews of their host countries is also an example of the change in the international environment which causes the aggravation of foreign profit of fishing firms. To ameliorate the situation, our Korean fishery firms must prepare efficient plans and study systematically to internationalize themselves because such existing methods as conventional fishing entry and licence fishing entry are likely to be unable to cope with international environmental change. Thus, after the systematic analysis of the problem, some new combined alternatives might be proposed. These are some of the new schemes to support this plan showing the orientation of our national policy: 1. Most of the coastal states, to cope with rapid international environmental change and to survive in the new era of ocean order, have rationalized their higher governmental structure concerning the fishery industries. And the coastal countries which are the objectives of our expecting entry, demand excessive economic and technical aid, limit the number of fishing boats’entry and the use of our foreign fishing bases, and regulate the membership of the international fishery commissions. Especially, most of the coastal or island countries are recently independent states, which are poorer in national budget, depend largely on fishing royalties and licence entry fees as their main resources of national finance. 2. Alternatives to our entry to deep sea fishing, as internationalization strategies, are by direct foreign investment method. About 30 firms have already invested approximately US $ 8 million in 9 coastal countries. Areas of investment comprise the southern part of the Atlantic Ocean, the Moroccan sea and five other sea areas. Trawling, tuna purse seining and five other fields are covered by the investment. Joint-venture is the most prominent method of this direct investment. If we consider the number of entry firms, the host countries, the number of seas available and the size of investment, this method of cooperation is perhaps insufficient so far. Our fishery firms suffer from a weakness in international competitive ability, an insufficiency of information, of short funds, incompetency in the market, the unfriendliness of host coastal countries, the incapability of partners in joint-ventures and the political instability of the host countries. To enlarge our foreign fishing grounds, we are to actively adopt the direct investment entry method and to diversity our collaboraboration with partner countries. Consequently, besides proper fishing, we might utilize forward integration strategies, including the processing fied. a. The enterprise emigration method is likely to be successful in Argentina. It includes the development of Argentinian fishing grounds which are still not exploited in spite of abundant resources. Besides, Arentina could also be developed as a base for the exploitation of the krill resources and for further entries into collaboration with other Latin American countries. b. The co-business contract fishing method works in American territorial seas where American fishermen sell their fishery products to our factory ships at sea. This method contributes greatly to obtaining more fishing quotas and in innovation bottom fishing operation. Therefore we may apply this method to other countres to diffuse our foreign fishing entry. c. The new fishing ground development method was begun in 1957 by tuna long-line experimental fishing in the Indian Ocean. It has five fields, trawling, skipjack pole fishing and shrimp trawling, and so on. Recently, Korean fisheries were successful in the development of the Antarctic Ocean krill and tuna purse seining. 3. The acceleration of the internationalization of deep sea fishing; a. Intense information exchange activities and commission participation are likely to be continues as our contributions to the international fishery organizations. We should try to enter international fishery commissions in which we are not so far participating. And we have to reform adequately to meet the changes of the function of the international commissions. With our partner countries, we ought to conclude bilateral fishery agreements, thus enlarging our collaboration. b. Our government should offer economic and technical aids to host countries to facilitate our firms’fishery entry and activities. c. To accelerate technical innovation, our fishery firms must invest greater amount in technical innovation, at the same time be more discriminatory in importing exogeneous fishery technologies. As for fishing methods; expanded use of multi-purpose fishing boats and introduction of automation should be encuraged to prevent seasonal fluctuations in fishery outputs. d. The government should increases financial and tax aid to Korean firms in order to elevate already weak financial structure of Korean fishery firms. e. Finally, the government ought to revise foreign exchange regulations being applied to deep sea fishery firms. Furthermore, dutes levied on foreign purchaed equipments and supplies used by our deep sea fishing boats thould be reduced or exempted. when the fish caught by Korean partner of joint-venture firms is sold at the home port, pusan, import duty should be exempted.

• Health Policy and Management
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• v.27 no.4
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• pp.273-275
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• 2017

It has been forty years since the implementation of National Health Insurance (NHI) in South Korea. Following the 1977 legislature mandating medical insurance for employees and dependents in firms with more than 500 employees, South Korea expanded its health insurance to urban residents in 1989. Resultantly, total expenses of the National Health Insurance Service (NHIS) have greatly increased from 4.5 billion won in 1977 to 50.89 trillion won in 2016. With multiple insurers merging into the NHI system in 2000, a single-payer healthcare system emerged, along with separation policy of prescribing and dispensing. Following such reform, an emerging financial crisis required injections from the National Health Promotion Fund. Forty years following the introduction of the NHI system, both praise and criticism have been drawn. In just 12 years, the NHI achieved the fastest health population coverage in the world. Current medical expenditure is not high relative to the rest of the Organization for Economic Cooperation and Development. The quality of acute care in Korea is one of the best in the world. There is no sign of delayed diagnosis and/or treatment for most diseases. However, the NHI has been under-insured, requiring high-levels of out-of-pocket money from patients and often causing catastrophic medical expenses. Furthermore, the current environmental circumstances of the NHI are threatening its sustainability. Low birth rate decline, as well as slow economic growth, will make sustainment of the current healthcare system difficult in the near future. An aging population will increase the amount of medical expenditure required, especially with the baby-boomer generation of those born between 1955 and 1965. Meanwhile, there is always the problem of unification for the Korean Peninsula, and what role the health insurance system will have to play when it occurs. In the presidential election, health insurance is a main issue; however, there is greater focus on expansion and expenditure than revenue. Many aspects of Korea's NHI system (1977) were modeled after the German (1883) and Japanese (1922) systems. Such systems were created during an era where infections disease control was most urgent and thus, in the current non-communicable disease (NCD) era, must be redesigned. The Korean system, which is already forty years old, must be redesigned completely. Although health insurance benefit expansion is necessary, financial measures, as well as moral hazard control measures, must also be considered. Ultimately, there are three aspects that we must consider when attempting redesign of the system. First, the health security system must be reformed. NHI and Medical Aid must be amalgamated into one system for increased effectiveness and efficiency of the system. Within the single insurer system of the NHI must be an internal market for maximum efficiency. The NHIS must be separated into regions so that regional organizers have greater responsibility over their actions. Although insurance must continue to be imposed nationally, risk-adjustment must be distributed regionally and assessed by different regional systems. Second, as a solution for the decreasing flow of insurance revenue, low premium level must be increased to an appropriate level. Likewise, the national reserve fund (No. 36, National Health Insurance Act) must be enlarged for re-unification preparation. Third, there must be revolutionary reform of benefit package. The current system built a focus on communicable diseases which is inappropriate in this NCD era. Medical benefits must not be one-time events but provide chronic disease management. Chronic care models, accountable care organization, patient-centered medical homes, and other systems that introduce various benefit packages for beneficiaries must be implemented. The reimbursement system of medical costs should be introduced to various systems for different types of care, as is the case with part C (Medicare Advantage Program) of America's Medicare system that substitutes part A and part B. Pay for performance must be expanded so that there is not only improvement in quality of care but also medical costs. Moreover, beneficiaries of the NHI system must be aware of the amount of their expenditure through a deductible payment system so that spending can be profiled and monitored. The Moon Jae-in Government has announced its plans to expand the NHI system; however, it is important that a discussion forum is created so that more accurate analysis of the NHI, its environments, and current status of health care system, can take place for reforming NHI.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.417-428
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• 2005

Today, IT organizations perform projects with vision related to marketing and financial profit. The objective of realizing the vision is to improve the project performing ability in terms of QCD. Organizations have made a lot of efforts to achieve this objective through process improvement. Large companies such as IBM, Ford, and GE have made over $80\%$ of success through business process re-engineering using information technology instead of business improvement effect by computers. It is important to collect, analyze and manage the data on performed projects to achieve the objective, but quantitative measurement is difficult as software is invisible and the effect and efficiency caused by process change are not visibly identified. Therefore, it is not easy to extract the strategy of improvement. This paper measures and analyzes the project performance, focusing on organizations' external effectiveness and internal efficiency (Qualify, Delivery, Cycle time, and Waste). Based on the measured project performance scores, an OT (Opportunity Tree) model was designed for optimizing the project performance. The process of design is as follows. First, meta data are derived from projects and analyzed by quantitative GQM(Goal-Question-Metric) questionnaire. Then, the project performance model is designed with the data obtained from the quantitative GQM questionnaire and organization's performance score for each area is calculated. The value is revised by integrating the measured scores by area vision weights from all stakeholders (CEO, middle-class managers, developer, investor, and custom). Through this, routes for improvement are presented and an optimized improvement method is suggested. Existing methods to improve software process have been highly effective in division of processes' but somewhat unsatisfactory in structural function to develop and systemically manage strategies by applying the processes to Projects. The proposed OT model provides a solution to this problem. The OT model is useful to provide an optimal improvement method in line with organization's goals and can reduce risks which may occur in the course of improving process if it is applied with proposed methods. In addition, satisfaction about the improvement strategy can be improved by obtaining input about vision weight from all stakeholders through the qualitative questionnaire and by reflecting it to the calculation. The OT is also useful to optimize the expansion of market and financial performance by controlling the ability of Quality, Delivery, Cycle time, and Waste.

• The Korean Journal of Financial Management
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• v.12 no.2
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• pp.273-298
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• 1995

1960년대, 각국의 환율이 미국의 달러화에 연동(pegging)된 고정환율제도를 근간으로 하는 브레튼우즈(Bretton Woods)체제하에서 각국의 중앙은행은 환율을 일정한 범위 내로 유지하기 위한 정책수단으로 외환시장개입을 적극 활용하였다. 1973년 브레튼우즈체제하의 고정환율제도가 붕괴되고 변동환율제도가 채택된 이후에도 각국의 외환시장개입정책은 계속되었다. 1980년대에 레이건 행정부의 재정팽창정책과 미연방준비은행의 긴축통화정책으로 금리의 지속적인 상승과 미달러화의 큰폭의 절상이 이루어 졌다. 이에 국제무역의 위기를 우려한 미국, 독일, 프랑스, 영국, 일본 등 선진 5개국(Group-5, G5)은 1985년 9월 22일 미 달러화의 절하를 위해 외환시장에 공동으로 개입할 것을 주내용으로 한 플라자합의(Plaza Agreement)를 발표하였다. 그후에도 1987년 2월 23일 열린 루브르협정(Louvre Accord, G-6 Communique)에서 환율을 현수준으로 유지시키기 위한 목표환율대(Target zone)를 설정하고 외환시장개입을 통해 이를 유지하기로 합의한 바 있다. 이후의 구미각국은 환율의 관리를 위하여 국가가 공동으로 외환시장에 개입하곤 했다. 본 논문은 1987년 루브르협정 이후 미국, 독일 및 일본의 중앙은행의 외환시장 개입 정책이 소기의 목적을 달성했는지의 여부를 규명해 보고자 한다. 즉, Federal Reserve, Bundesbank 및 Bank of Japan의 외환시장개입이 현물환율시장(spot market)에서 각각의 변동성을 감소 시켰는지의 여부를 독일의 마르크화 및 일본의 엔화를 중심으로 규명해 보고자 한다. 1981년 루브르협정이후, 미국, 독일 및 일본의 중앙은행은 미국 달러화에 대한 마르크 및 엔화의 환율을 안정시키기 위해 꾸준히 외환시장에 개입해 왔다. 외환시장의 개입유형은 크게 태화외환시장개입(non-sterilized intervention)과 불태화외환시장개입(sterilized intervention)으로 구분할 수 있는데, 전자는 외환당국이 민간부문과 외화채권을 거래함으로써 본원통화의 크기가 변하는 개입형태를 의미하는 반면에 후자는 외환당국의 순외화자산의 크기변화가 본원통화의 변화를 초래하지 않는 경우이다. 즉, 불태화외환시장개입은 순외화자산의 증감이 순국내자산의 증감과 반비례해서 이루어지기 때문에 본원통화의 크기에는 변함이 없다. 외환시장개입이란 외환당국이 은행간 시장에서 민간시잔 참가자들과 행하는 적극적인 외환거래를 의미한다. 반면, 넓은 의미에서의 외환시장개입에는 수동적 외환시장개입이라고 불리는 고객거래가 포함된다. 후자의 거래는 국내통화 및 외화표시 자산의 상대적 공급규모를 변화시킨다는 의미에서 전통적외환시장개입과 동일한 효과를 갖기 때문에 광의의 외환시장 개입으로 분류된다. 외환시장의 개입목적은 크게 세 가지로 분류할 수 있다. 첫째, 환율의 안정적 운영이다. 환율수준이 자유롭게 변화되는 변동환율제도하에서 환율의 지나친 변동으로 인한 실물경제로의 부정적인 영향을 최소화하기 위해서 환율의 지나친 변동으로 인한 실물경제로의 부정적인 영향을 최소화하기 위해서 환율의 안정을 정책 목표로 설정하는 경우와 고정환율제도하에서 환율을 일정수준으로 유지시키기 위해서 외환당국이 외환시장에 개입하는 경우가 여기에 해당된다고 볼 수 있다. 둘째, 환율수준의 균형수준으로의 조정이다. 이때 야기될 수 있는 문제점으로는 환율균형 수준을 어떻게 정의, 추정할 것이냐 하는 점과 목표환율정책이 다른 정책목표와 상충될 수 있다는 점이다. 셋째, 외환당국이 공적외환보유액이나 구성을 변화시킬 목적으로 외환시장에 개입하는 경우이다. 이때의 외환시장개입은 현재의 환율수준이 개입으로 인하여 과도하게 이탈하는 문제가 발생하지 않을 것을 전제로 한다. 본고에서는 현물환율에 영향을 미치는 요소로 미국, 독일 및 일본의 중앙은행의 개입효과, 요일효과, 통화의 공급량(M1), 무역적자의 폭, 산업의 생산량, 생산가격지수(PPI), 소비자물가지수(CPI), 실업률, 옵션의 내재적 변동성 등을 고려한다. 환율의 변동성을 추정하는 식은 GARCH 모델이 사용된다. 본 추정모델은 Dominguez(1993)의 확장이다. Dominguez (1993)의 논문은 GARCH 모델을 써서 미국, 독일 및 일본의 중앙은행의 시장개입효과를 분석했으나, 거시변수를 고려대상에서 제외시켰다. 본 논문은 위의 방법에 거시변수를 삽입하고 모델을 변형시켜서 더 확실한 시장개입효과와 거시변수효과를 밝혔다. 또한 옵션의 내제적 변동성을 구하는 과정에서 American option model을 사용하는 대신, Bourtha & Courtadon (1987)등이 밝힌 바와 같이 American style option이라 할지라도, European Model을 쓰면 더욱더 간편하고, 예측력도 American Model에 뒤지지 않음을 이용하여, European Model을 써서 내재적 변동성을 구한 다음 이것을 독립변수로 이용하였다. 본 모델의 추정 결과는 3국의 시장개입정책이 현물환율과 옵션의 내재적 변동성을 증가시켜서 Louvre 협정이후 각국은 시장개입의 목적을 달성하지 못한 것으로 나타났다.