• Environmental and Resource Economics Review
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• v.21 no.3
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• pp.683-716
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• 2012

As the global concerns focus on the sustainable growth, the green growth has been one of key words for the Korean government's policy trajectory. This study measures the green index for OECD countries in order to gauge and diagnose the current status of the green growth policy for Korea. The empirical results tell us that Korea's green index is relatively small and it needs to benchmark UK for improving her green policy. The contributions of the current study are to use a large set of data for green growth and to result in quantitative empirical results.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.435-441
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• 2003

본 논문에서는 Maximum Energy Dissipation Algorithm(MEDA) 사장교의 MR댐퍼제어에 적용하고자 한다 MR댐퍼의 제어를 위해서 여러 제어 이론들이 제안되었으나, 각각의 특성에도 불구하고 성능면에서는 큰 차이가 없다 MEDA는 Lyapunove 직접법을 바탕으로 군성되는 제어이론으로써, 15년전에 제안되었음에도 실제 토목구조물에는 적용된 바 없어 그 성능 및 장점이 제대로 검증되지 않았다. 따라서 본 논문에서는 벤치마크 사장교 수치예제를 통해서, MEDA의 토목구조물에의 적용성을 성능(performance)과 강인성(robustness) 측면에서 분석하려한다. 수치예제에서 다양한 지진에 대한 층간변위, 가속도, 그리고 상대변위의 각 제어기법에 의한 감소량은 벤치마크문제에 정의된 평가지수(evaluation criteria)를 사용하였다.

• Journal of Korean Society of Water and Wastewater
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• v.29 no.6
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• pp.667-683
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• 2015

Since aged water treatment facilities could threaten the sustainable water supply, asset management system has been adopted for their systematic management. Level of Service(LoS) is one of critical components of asset management and could be quantified through benchmark index(BMI). Water supplier could estimate consumer's satisfaction and their performance through BMI to improve the LoS. We developed BMI for water treatment facilities from customer's satisfaction survey. BMI, represented with the Total Service Score(TSS), was assessed with water quality, water pressure, taste and odor, water rate, and service quality with weighing factors. BMI could, further, be used to assist the analysis of the life cycle cost to increase the unit of LoS.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1777-1783
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• 2006

일반적으로 펀드평가는 절대수익률이나 위험을 조정한 샤프지수 또는 트레이너 지수를 이용하고 있는데, 이러한 방법은 벤치마크 지수를 기준으로 평가하여 펀드규모, 비용 등을 고려한 펀드간의 상대적 성과는 측정하지 못하고 있다. 펀드간의 상대적 성과평가는 펀드의 실질적인 효율성을 측정할 수 있다는 측면에서 펀드평가의 유용한 수단이 될 수 있다. 본 연구에서는 샤프의 스타일 분석을 이용하여 펀드의 유형을 분류하고, DEA를 이용하여 펀드간의 상대적인 성과를 측정한다. 분석자료는 2000년 1월부터 2005년 12월 31일까지의 주식형 펀드의 월간수익률, 수익률 표준편차, 펀드비용, 펀드규모, 운용기간, 샤프지수 등을 이용한다.

• Environmental and Resource Economics Review
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• v.17 no.1
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• pp.51-81
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• 2008

In this paper, economic performance was measured through portfolio analysis for environmentally friendly companies from September 2004 to September 2005. By using portfolio analysis, rate of revenue for environmentally friendly company is twelve to seven teen percent higher than the KOSPI, and KOSPI200 based companies. Except medical and pharmatical industry, environmentally friendly companies had shown low risk and high returns of revenue for banking and financing, chemical and electronic industry. As SRI fund is emerging as a important guideline in recent years, valuation of a cooperate will be very important tool for the financing business area in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.65-82
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• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.

• Journal of KIISE:Computer Systems and Theory
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• v.28 no.1_2
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• pp.110-123
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• 2001

본 논문에서는 자유 선택 신호 전이 그래프와 비동기 유한 상태기로 기술된 회로 명세로부터 직접 속도 독립 회로를 합성하는 시스템에 대해 기술한다. 기존의 상태 그래프 기반의 합성 시스템은 상태의 수가 지수승으로 증가할 수 있기 때문에 큰 규모의 회로에 대해서는 합성에 실패할 수 있다는 문제점을 가지고 있다. 이를 해결하기 위해 여러 직접 합성 방법들이 제안되었는데, 본 논문의 합성 시스템은 마크드 그래프 분할 방법과 임시 전이의 사용을 허용함으로써 합성할 수 있는 회로의 범위를 넓혔다. 기존의 벤치마크 회로에 대한 실험결과 본 합성 시스템은 기존의 상태 그래프 기반의 합성 시스템에 비하여 현저하게 수행 속도를 단축시킬 수 있었고 기존의 직접 합성 시스템에 비하여 보다 확장된 그리고 보다 실용적인 회로 명세를 처리할 수 있었다.

• Advanced Industrial SCIence
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• v.2 no.3
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• pp.22-28
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• 2023

In this study, we conducted research on the automated trading of U.S. stocks through a volatility strategy using the Fear and Greed index. Volatility in the stock market is a common phenomenon that can lead to fluctuations in stock prices. Investors can capitalize on this volatility by implementing a strategy based on it, involving the buying and selling of stocks based on their expected level of volatility. The goal of this thesis is to investigate the effectiveness of the volatility strategy in generating profits in the stock market.This study employs a quantitative research methodology using secondary data from the stock market. The dataset comprises daily stock prices and daily volatility measures for the S&P 500 index stocks. Over a five-year period spanning from 2016 to 2020, the stocks were listed on the New York Stock Exchange (NYSE). The strategy involves purchasing stocks from the low volatility group and selling stocks from the high volatility group. The results indicate that the volatility strategy yields positive returns, with an average annual return of 9.2%, compared to the benchmark return of 7.5% for the sample period. Furthermore, the findings demonstrate that the strategy outperforms the benchmark return in four out of the five years within the sample period. Particularly noteworthy is the strategy's performance during periods of high market volatility, such as the COVID-19 pandemic in 2020, where it generated a return of 14.6%, as opposed to the benchmark return of 5.5%.

• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.189-220
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• 2009

This study tests the significance of momentum and contrarian strategy which challenge the weak efficient market hypothesis (EMH). If momentum and contrarian strategy can make extra return above the market, this can be a significant critics to the weak EMH. By using Monte Carlo simulation we have found that many existing returature, which test the significance of momentum and contrarian strategy, have a significance distortion problem. We test the significance of momentum and contrarian strategy by using reality check test of White(2000) which solve the problem of data snooping bias. The results are following. When we use the KOSPI index as the benchmark portfolio, we can get the best strategy of momentum strategy in the case of mean return. But in the case of Sharp ratio which is the performance measure adjusting risk, we find that the best strategy in the momentum and contrarian strategy can not dominate the performance of benchmark portfolio. Therefore we argue that weak EMH can not be rejected because of superior performance of momentum and contrarian strategy when we consider risk.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.102-105
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• 2013

최근 멀티코어 시스템은 컴퓨터의 성능을 향상시키기 위해 더 많은 수의 코어를 연결시키는 다중코어 시스템으로 발전하고 있다. 그러나 멀티코어 시스템은 사용하는 코어의 아키텍처 구조와 개수에 따라 성능 차이가 발생한다. 이에, 본 논문에서는 코어의 아키텍처 구조와 코어의 개수가 성능에 미치는 영향을 분석하기 위해 Tilera의 다중코어 시스템인 Tile-Gx36, TilePro64와 Intel의 x86-64 멀티코어 시스템인 Core i5의 성능을 비교하였다. 코어의 사용률이 늘어남에 따른 성능차이를 알아보기 위해 벤치마크 프로그램인 SPEC CPU 2006을 이용하여 각 시스템 내 단일코어의 성능을 측정하고, OpenMP 벤치마크 프로그램을 이용하여 시스템의 모든 코어를 사용했을 때의 입력 데이터 크기에 따른 성능을 측정하였다. 실험 결과, 단일코어에서의 성능은 정수형 데이터를 사용하여 측정하였을 경우 Core i5가 Tile-Gx36보다 약 87%, 실수형 데이터를 사용하여 측정하였을 경우 약 94% 더 빠른 것으로 나타났다. 그러나 코어 전체를 이용한 성능 결과에서는 정수형 배열 크기가 이상일 경우 Tile-Gx36 시스템의 처리 속도가 Core i5 시스템 보다 평균적으로 약 7.6배 향상됨을 확인할 수 있었다. 따라서 Tilera의 다중코어 시스템은 클럭 속도와 아키텍처 구조의 영향으로 단일코어의 성능은 떨어지나, 병렬 처리를 이용한 고속연산에서는 성능이 향상된다고 할 수 있다.