• Journal of Korean Library and Information Science Society
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• v.20
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• pp.133-153
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• 1993

The purpose of this study is to form a different evaluation method of academic libraries in Korea. In order to this product life cycle and product portfolio matrix have been used. While they are based upon products and services, their general concept is useful for library managers as they help to distinguish which books have potential for growth. Writer tried to transform them into book life cycle and book portfolio matrix as a tool for assessing the quality of stock on the library shelves. With this method, writer a n.0, ppraised practically the Sociology Collection of Pusan National University Library by the state of usage and classified them into four groups that were development books, growth books, maturity books, and declining books.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.123-139
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• 2007

Recently, Super-Super-Market(SSM) is facing more and more difficult situation due to the expansion of hypermarket and target marketing of specialized shop. In this situation, Customer Satisfaction Management(CSM) is emerging as a core business factor to make continuous growth without competitive exclusion. Especially, the first factor in CSM in distribution industry is a Service Quality Satisfaction. In this paper, with a selection of 3-markets as a sample for the research, I have tried to look for necessary Service Quality(SQ) factors in SSM and deduced Service Quality Index(SQI), loyalty and Index of detail factor in SQ through survey. Based on these results, strategic factors required to improve SQ was found and strategic directions for SQ were proposed through matrix portfolio analysis.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1453-1463
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• 2016

The most useful financial risk measure may be VaR (Value at Risk) which estimates the maximum loss amount statistically. The VaR tends to be estimated in many industries by using transformed univariate risk including variance-covariance matrix and a specific portfolio. Hong et al. (2016) are defined the Vector at Risk based on the multivariate quantile vector. When a specific portfolio is given, one point among Vector at Risk is founded as the best VaR which is called as an alternative VaR (AVaR). In this work, AVaRs have been investigated for multivariate normal distributions with many kinds of variance-covariance matrix and various portfolio weight vectors, and compared with VaRs. It has been found that the AVaR has smaller values than VaR. Some properties of AVaR are derived and discussed with these characteristics.

• Proceedings of the Technology Innovation Conference
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• 1996.12a
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• pp.265-295
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• 1996

This paper investigates empirically the relationship between various business portfolio properties (particularly technological properties) and chaebol′s performance using data on the 50 largest chaebols in Korea. In addition to the traditional indexes to measure diversification such as entropy index we calculated inter-industry technological similarity using R&D expenditure data by industry and 1990 Input-output Table in Korea, and obtained chaebol-level technological relatedness and internal transaction proportion from chaebols′business profile, inter-industry technological similarity and 1990 input-output table. We applied factor analysis on 13 business portfolio property indexes and showed that they could be grouped into 3 dimensions, diversification scope, inter-business relatedness and degree of vertical integration. In this paper, using 50 largest chaebols′financial data (1989-1994), we analyzed empirically the effect of business portfolio properties on ROS (Return On Sales) which is conventional index for firm performance and on TFP(Total Factor Productivity) growth which is a pure measure of firm performance. To utilize the advantage of panel data, FEM(Fixed Effect Model) and REM(Random Effect Model) were used. The empirical result shows that the entropy index as a measurement of inter-business relatedness is not significant but technological relatedness index is significant. OLS estimates on pooled data were considerably different from FEM or REM estimates on panel data. By introducing interaction effect among the three variables for business portfolio properties, we obtained three findings. First, only VI (Vertical integration) has a significant positive correlation with ROS. Second, when using TFP growth as an dependent variable, both TR(Technological Relatedness) and f[ are significant and positively related to the deepened variable. Third, the interaction term between TR and VI is significant and negatively affects TFP growth, meaning that TR and VI are substitutes. These results suggest strategic directions on restructuring business portfolio. As VI is increased, chaebols will get more profit. A higher level of either TR or W will increase TFP growth rate. but increase in both TR and VI will have a negative effect on TFP growth. To summarize, certain business portfolio properties such as VI and TR can be considered "resources" themselves since they can affect profit rate and productivity growth. VI and TR have a synergy effect of change in profit rate and productivity growth. VI increases ROS and productivity growth, while TR increases productivity growth representing a technological synergy effect.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.59-71
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• 2014

System trading is becoming more popular among Korean traders recently. System traders use automatic order systems based on the system generated buy and sell signals. These signals are generated from the predetermined entry and exit rules that were coded by system traders. Most researches on system trading have focused on designing profitable entry and exit rules using technical indicators. However, market conditions, strategy characteristics, and money management also have influences on the profitability of the system trading. Unexpected price deviations from the predetermined trading rules can incur large losses to system traders. Therefore, most professional traders use strategy portfolios rather than only one strategy. Building a good strategy portfolio is important because trading performance depends on strategy portfolios. Despite of the importance of designing strategy portfolio, rule of thumb methods have been used to select trading strategies. In this study, we propose a SVM-based strategy portfolio management system. SVM were introduced by Vapnik and is known to be effective for data mining area. It can build good portfolios within a very short period of time. Since SVM minimizes structural risks, it is best suitable for the futures trading market in which prices do not move exactly the same as the past. Our system trading strategies include moving-average cross system, MACD cross system, trend-following system, buy dips and sell rallies system, DMI system, Keltner channel system, Bollinger Bands system, and Fibonacci system. These strategies are well known and frequently being used by many professional traders. We program these strategies for generating automated system signals for entry and exit. We propose SVM-based strategies selection system and portfolio construction and order routing system. Strategies selection system is a portfolio training system. It generates training data and makes SVM model using optimal portfolio. We make $m{\times}n$ data matrix by dividing KOSPI 200 index futures data with a same period. Optimal strategy portfolio is derived from analyzing each strategy performance. SVM model is generated based on this data and optimal strategy portfolio. We use 80% of the data for training and the remaining 20% is used for testing the strategy. For training, we select two strategies which show the highest profit in the next day. Selection method 1 selects two strategies and method 2 selects maximum two strategies which show profit more than 0.1 point. We use one-against-all method which has fast processing time. We analyse the daily data of KOSPI 200 index futures contracts from January 1990 to November 2011. Price change rates for 50 days are used as SVM input data. The training period is from January 1990 to March 2007 and the test period is from March 2007 to November 2011. We suggest three benchmark strategies portfolio. BM1 holds two contracts of KOSPI 200 index futures for testing period. BM2 is constructed as two strategies which show the largest cumulative profit during 30 days before testing starts. BM3 has two strategies which show best profits during testing period. Trading cost include brokerage commission cost and slippage cost. The proposed strategy portfolio management system shows profit more than double of the benchmark portfolios. BM1 shows 103.44 point profit, BM2 shows 488.61 point profit, and BM3 shows 502.41 point profit after deducting trading cost. The best benchmark is the portfolio of the two best profit strategies during the test period. The proposed system 1 shows 706.22 point profit and proposed system 2 shows 768.95 point profit after deducting trading cost. The equity curves for the entire period show stable pattern. With higher profit, this suggests a good trading direction for system traders. We can make more stable and more profitable portfolios if we add money management module to the system.

• Journal of Korean Society of Forest Science
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• v.94 no.1 s.158
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• pp.50-57
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• 2005

This paper examines the relative competitive position of korean forest products market over period of 1999 to 2003 and selects strategic exported goods from its position provide against concluding FTA agreement with China, Japan and ASEAN. The portfolio approach is used to develope competitiveness-market share matrix. The position of major export countries on the competitiveness-market share matrix will be in one of nine cells, with differing implications for their role in korean forest products market. Based the competitiveness-market share matrix, Major export countries are divided into first cell type, third cell type and ninth cell type and the items of ninth cell type are chosen as strategic exportable goods.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.1059-1062
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• 2005

본 논문에서 이론적으로 분석하고자 하는 기술포트폴리오 매트릭스는 기술매력성(계속적 개발잠재성, 응용범위, 호환가능성)과 자원강도(지배범위, 잠재성, 반응속도)에 기초한 기술투자 분석 또는 전략적 기술관리를 위한 유용한 의사결정 수단이다. 즉, 기술포트폴리오는 국내기업뿐만 아니라 현지투자기업의 연구개발과 관련된 의사결정을 위하여 활용될 수 있다. 특히, 본 연구에서는 글로벌 기업에 있어서의 본사 및/또는 현지자회사간의 기술포트폴리오 매트릭스를 분석함으로써 글로벌 기업의 기술포트폴리오 관리를 위한 방법을 제시하고자 한다.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.424-428
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• 2005

As the application of NPM in waste administration branch this paper proposes a new waste management method in order to increase the efficiency of resources and reduce the quantity of waste. First the matrix method is suggested which comprehensively consider and integrate the proposals of different government departments. Moreover the inhabitant's attitude toward the new waste management measures was investigated. Based on the investigation the evaluation function was made, which include three elements: necessary budget, the effect of cost decrease and the environmental burden decrease. The optimal method of budget allocation to maximize social welfare is proposed under the condition of limited budget by the evaluation function. By applying this system further local governments will be able to determine their adequate service level and budget size.

• Journal of Navigation and Port Research
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• v.32 no.8
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• pp.653-658
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• 2008

The demand of international intermodal transportation is continuously increasing in accordance with a changing environment on international logistics, Under this circumstance, the Sea&Air intermodal transportation, combined by sea-based and air-based transport, has a potential growth in the future. After analyzing routes for Origin/Destination and implementing portfolio analysis, finally, this research aims to propose alternatives to create additional customers(or cargoes) for the Sea&Air transport. As a result of the analyses, China appeared to be a major customer of the Sea&Air transport in Korea because some of the Chinese areas - i.e. Qingdao, Shanghai, Weihai and Yantai - account for 88.1% of the total throughput. In general, this indicates that it would be more efficient to establish specific strategies targeting those major areas. Excluding the four areas, most of the other area, have much less demands and are relatively unstable. The demands, growth rates and market shares especially in Vladivostok, Dandong and Tianjinxingang are on the decrease, and therefore, stable strategies seems to be appropriate than aggressive strategies for these areas.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1449-1466
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• 2014

In this paper, we consider a hedge portfolio based on futures of underlying asset. A classical way to estimate a hedge ratio for a hedge portfolio of a spot and futures is a regression analysis. However, a regression analysis is not capable of reflecting long-run equilibrium between a spot and futures and volatility clustering in the conditional variance of financial time series. In order to overcome such defects, we analyzed KOSPI200 index and futures using VECM-CC-GARCH model and computed a hedge ratio from the estimated conditional covariance-variance matrix. In real data analysis, we compared a regression and VECM-CC-GARCH models in terms of hedge effectiveness based on variance, value at risk and expected shortfall of log-returns of hedge portfolio. The empirical results show that the multivariate GARCH models significantly outperform a regression analysis and improve hedging effectiveness in the period of high volatility.