• Journal of Korea Port Economic Association
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• v.38 no.2
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• pp.31-47
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• 2022

The purpose of this study is to develop a reliable tool that can classify and measure detailed indicators related to the performance of ESG implementation in the country and verify their applicability. Based on World Bank's data as input data, 67 types of ESG-related detailed indicators measured in a total of 239 countries were tested to derive an optimal model that could group detailed indicators into three categories: environment, society, and governance. As a result of the analysis, it was confirmed that a total of 10 detailed indicators had a statistically significant relationship with the country's ESG performance. In addition, the detailed indicators showed a positive correlation with the primary latent variables E, S, and G, and showed a high overall index in the suitability of the model to secure the validity and reliability of variable input. As a result, this study confirmed that several detailed performance indicators constituting ESG can be classified as latent variables, and it can be said that clear criteria for the selection method and input validity of variables were presented.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.1
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• pp.251-259
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• 2014

Securing publicness and efficiencies are the two core goals for implementing quasi-public bus system. However, researches for evaluating the quasi-public bus system with respect to the two goals have been very limited. This study represents an attempt to evaluate the system performance in these two aspects. To achieve this, a framework for performance evaluation is developed. Utilizing the framework, the 5-year performances of the quasi-public bus system in Busan are evaluated. Acess, agency and public interests are employed as criteria for publicness evaluation. Financial aspects of operators, labor, vehicle utilization, productivity and operational costs are selected as indicators for efficiency evaluation. The results show that the publicness of bus services has been vastly improved, while the efficiency has been also improved in most areas except in cost and vehicle utilization aspects. The results of this study will be of use for planners and policy-makers developing strategies for system improvement. It is hoped that this study can be further refined by adding and adjusting indicators which this study may neglect or ignore.

• The Korean Journal of Financial Management
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• v.26 no.2
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• pp.155-180
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• 2009

This study is to estimate and compare hedging effectiveness in emerging currency and advanced currency futures markets. Emerging currency futures includes Korea won, Mexico peso, and Brazil real and advanced currency futures is Europe euro, British pound, and Japan yen. Hedging effectiveness is measured by comparing hedging performance of the naive hedge model, OLS model, error correction model and constant condintional correlation bivariate GARCH(1, 1) hedge model based on rolling windows. Analysis data is used daily spot and futures rates from January, 2, 2001 to March. 10, 2006. The empirical results are summarized as follows : First, irrespective of hedging period and model, hedging using Korea won/dollar futures reduces spot rate's volatility risk by 97%. Second, Korea won/dollar futures market produces the best hedging performance in emerging and advanced currency futures markets, i.e. Mexico peso, Brazil real, Europe euro, British pound, and Japan yen. Third, there are no difference of hedging effectiveness among hedging models.

• Management & Information Systems Review
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• v.28 no.4
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• pp.229-252
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• 2009

The purpose of this study is to find out the determinants of knowledge management adoption through the analysis which examine structural relationships among knowledge management-based structure, knowledge management activities and business performance using the BSC(Balanced Scorecard) perspective. This study also gives the suggestion for the effective knowledge management implementation in the korean companies. This study has been conducted using the data collected from 91 companies implementing knowledge management. By analyses of the questionnaires, empirical results show that 3 factors of knowledge management-based structure, except organizational structure, have positive effect on knowledge management activities, and the implementation of knowledge management activities has positive effect on business performance with 3 perspectives of BSC, except financial performance. This study showed that more consideration are essential to obtain balanced business performance for companies with knowledge management adoption plan.

• 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.

• The Journal of the Korea Contents Association
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• v.7 no.9
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• pp.156-166
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• 2007

Traditional financial approaches have been failed to satisfy the firms in evaluating the payoffs from information technology(IT) investment. This research developed a processed-oriented model to evaluate the impacts of IT on the primary business activities within the value chain. The model proposed in this research incorporates corporate goals for IT and strategic alignment as key measurements of IT impacts. We found that corporate goals for IT can be classified into one of four categories: unfocused, operations focus, market focus, and dual focus(operations and market focus), using survey data collected from 137 corporate executives in Korea. The empirical analysis confirms that corporate goals for IT are useful indicators of impacts from IT in that executives in firms with more focused goals for IT perceive higher payoffs from IT in the value chain. We also found that strategic alignment contributes to higher perceived levels of IT payoffs.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.186-195
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• 2014

The purpose of this research is to explain what affects the volunteer center's performance on the basis on the program logic model. We assume that internal factors and external factors influence the organizational performance. For the empirical analysis, we use SUR(seemingly unrelated regression). The major findings of this research are as follows: Frist, organizational budget as internal factors are important influential factors. Second, civil society's capacity variable and stakeholder's support variable affect positively volunteer center's performance. Third, the contract out variable's effect as an operation types is not proven statistically. In case of estimating the multi organizational performance, lastly, the SUR method is prefer to the OLS method in the aspect methodology. However, more systemic study is complemented the panel data for organizational performance.

• International Area Studies Review
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• v.21 no.1
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• pp.3-24
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• 2017

The effects of strategic partnership can be positive or negative. This paper intends to explain why the partnership produces opposite results. This paper incorporates strategic behavioral direction and perceived values for relational performance. The conceptual model as well as empirical analysis was made and the results are as follows. First, this study uses bi-directional decomposition of two research constructs. The first construct of perceived values is classified into benefits and risks. Also, the behavioral directions are either active or passive behaviors. The critical path is found that the perceived benefits make significant effects on active behavioral direction which does on commitment. Another path is that the perceived risks make passive behavioral direction which results in opportunistic behaviors between strategically partnered firms.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.1727-1731
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• 2008

수자원의 효율적인 관리를 위해서는 신뢰성 있는 유량자료의 획득이 대단히 중요하다. 우리나라는 양질의 유량자료를 획득하기 위해 매년 많은 시간과 돈을 투자하고 있으나 자료의 질적인 면에서 만족할 만한 성과를 얻지 못하고 있다. 현재까지 우리나라의 유량자료는 댐의 수문자료와 수량관리 부처인 건교부에서 운영하는 수위표 지점의 수위-유량곡선에서 산출된 자료에 의존하고 있다. 그러나 수위-유량 관계식을 보정하기 위한 유량측정사업이 지속적이지 못하며, 이 관계식은 유량이 적은 저수기 및 갈수기에는 부정확하다는 한계가 있다. 또한, 국립환경과학원 낙동강물환경연구소에서 오염총량관리를 위한 낙동강수계 유량측정사업을 실시하고 있지만, 목적은 낙동강수계의 오염총량관리 단위유역 말단 47개 지점에서 유량측정을 효율적으로 실시하여 수질정책의 기초자료를 제공하는데 있다. 이 자료 역시 오염총량관리를 위하여 유량측정을 실시하여 수자원의 효율적인 관리를 위한 일 유량을 알 수가 없는 한계점을 가지고 있다. 따라서 저수기 및 갈수기에 수질정책의 기초자료를 제공하기 위해서 하천을 포함한 유역의 정확한 강우-유출특성의 파악이 필요하다. 그러나 강우-유출특성 또한 유역 내 강우의 시 공간적 분포가 다르며 그 자가 비선형성이 강하고 여러 변동성을 포함하므로, 강우로부터 하천의 유출량의 정확한 해석이 불가능하다. 그러나 최근 인공지능 분야에서 신호처리, 지능제어 및 패턴인식 등의 수단으로 사용되고 있는 신경망은 학습이라는 최적화 과정을 통해 입력과 출력으로 구성되는 하나의 시스템을 비선형적으로 구축할 수 있으며 이러한 이점을 활용하여 수자원 분야에서 다양하게 적용되고 있다. 본 연구의 목적은 강우-유출자료 및 댐 방류량 자료의 비선형적인 특정을 가장 잘 반영할 수 있는 신경망모형을 적용하여 수질정책의 기초자료를 제공하기 위하여 신뢰성 있는 유량자료를 산정하는 모형을 개발하는 것이다. 이를 위해서 낙동강물환경연구소에서 오염총량관리를 위한 낙동강수계 유량측정 지점 상류의 댐 방류량의 일 방류량자료와 강우자료를 입력 자료로 하여 유량을 예측할 수 있는 유량예측 신경망 모형 FFBN(Flow Forecasting By Neural)을 개발하였다. 그리고 입력 자료로서 장기유출모형인 SWAT의 모의결과를 입력 자료로 추가한 FFBNS(Flow Forecasting By Neural and SWAT)을 개발하였다. 신경망 모형의 구조는 입력층과 출력층 사이에 하나의 은닉층이 존재하는 다층 신경망으로 구성하였으며, 학습단계에서는 오류 역전파 알고리듬 학습방법 중 모멘텀법을 사용하였다. 예측된 유출량을 실측치와의 비교를 위하여 낙본D지점과 낙본 E지점에 대하여 $2005{\sim}2006$년까지의 모의 결과를 낙동 수위측정지점과 구미 수위측정지점의 실측치 통하여 복잡한 비선형성을 가지는 유출 시계열 자료에 대한 효과적인 최적의 신경망모델을 개발하여 유량을 예측하고 적용 가능성을 검토하고자 한다. 모의 결과는 수질정책의 기초자료 제공에 기여할 수 있을 것으로 판단된다.

• Proceedings of the Korean Vacuum Society Conference
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• 1999.07a
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• pp.44-44
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• 1999

저진공(1 kPa~ 100 kPa)은 대기압 측정, 비행고도, 기체의 온도 측정, 질량의 부력 보정, 레이저의 굴절률 측정등에 사용되는 영역으로 과학적 중요성을 갖고 있다. 또한 대기압 이상의 압력 측정과 고진공 측정의 경계적 역할도 수행하고 있어 압력 표준기의 국제 비교에 필수적으로 권장되는 역역이다. 이 영역에 주로 사용되는 압력 표준기는 수은 압력계(Mercury manometer)와 분동식 압력계(Deadweight piston gauge or Pressure)가 있다. 이들은 이동이 불편하거나 불가능하므로 표준기의 국제 비교에 사용되는 전달 표준기로는 보다 이동이 간편한 탄성 압력계인 CDG(Capacitance diaphragm Gauge)가 있다. 이 게이지는 반도체 산업의 공정 제어용으로도 많이 사용되고 있다. 그러나 게이지와 함께 사용되는 컨트롤러의 부피가 크고 무거우며 영점 이동이 커서 측정때 마다 재조정하여야 하는 단점이 있다. 본 논문에서는 이 같은 단점을 극복하기 위해 수정빔 진동형 진공 센서를 잔달 표준기로 사용하는 것에 대한 연구를 수행하였다. 수정빔 진동형 압력 센서는 수정빔으 공진주파수가 스트레인에 비례하는 것을 이용하여 제작된 센서로 주로 대기압 이상의 고압 측정에 많이 사용되고 있다. 먼저 수정빔의 압력과 주파수간의 관계를 측정하고 또한 내장된 수정 온도센서의 공진 주파수를 측정하여 온도 보상을 위한 자료로 사용하였다. 규격에 나와 있는 수정빔의 기하학적 형상으로부터 거동에 관한 이론 모델식을 구하고 압력교정 자료로부터 얻어진 데이터를 이 식과 비교 분석하여 적합한 특성식과 인자를 구하였으며 게이지의 불확도를 추정하였다.모델은 길이가 유한한 0-차원 실린더 모델로 가정하였고, 이에 대한 기하학적 성질 및 열역학적 성질은 유효계수를 고려하여 산출하였다. 진공용기 이중 벽 내부로 흐르는 질소가스의 유량과 온도의 계산은 진공용기 내벽과 외벽을 각각 독립적인 열전달 요소로 가정하여 구성한 모델을 이용하였다. 전체 해석에서 각 열전달 요소의 비열 값은 온도에 따라 변화하는 비열의 특성을 반영하였으며. 진공용기와 플라즈마 대향 부품의 방사율(emissivity)은 앞서 가정했던 각 온도 상승 곡선에 대해서 각각 0.1, 0.2, 1.3의 경우를 가정하여 계산하였다. 직선적으로 증가하는 온도 상승 곡선중 2$0^{\circ}C$/hr의 온도상승율을 갖는 경우가 다른 베이킹 시나리오 모델에 비해 효과적이라 생각되며 초대 필요 공급열량은 200kW 정도로 산출되었다. 실질적인 수치를 얻기 위해 보다 고차원 모델로의 해석이 필요하리라 생각된다. 끝으로 장기적인 관점에서 KSTAR 장치의 베이킹 계획도 살펴본다.습파라미터와 더불어, 본 연구에서 새롭게 제시된 주기분할층의 파라미터들이 모형의 학습성과를 높이기 위해 함께 고려된다. 한편, 이러한 학습과정에서 추가적으로 고려해야 할 파라미터 갯수가 증가함에 따라서, 본 모델의 학습성과가 local minimum에 빠지는 문제점이 발생될 수 있다. 즉, 웨이블릿분석과 인공신경망모형을 모두 전역적으로 최적화시켜야 하는 문제가 발생한다. 본 연구에서는 이 문제를 해결하기 위해서, 최근 local minimum의 가능성을 최소화하여 전역적인 학습성과를 높여 주는 인공지능기법으로서 유전자알고리즘기법을 본 연구이 통합모델에 반영하였다. 이에 대한 실