• Korean Journal of Construction Engineering and Management
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• v.19 no.5
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• pp.3-9
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• 2018

Although Life Cycle Cost (LCC) must be evaluated by experts, sometimes it may not allow a sufficient time for even the experienced LCC expert to make rational decisions. Therefore, it often ends with relatively comparing the final numbers. We have broken down 110 technical proposals that are actually bade and accepted for large construction projects, and then have analyzed the uncertainty of Maintenance and Energy (M&E) cost during building life cycle, which turns out be the most volatile factor in uncertainty of LCC. Also we suggest "Value Engineering Index (VEI)" - the reduced M&E cost that is normalized by the reduced first cost. It is analyzed that the most uncertain factors of the M&E cost include repair and replacement term differing from each project, duplicated repair and replacement, non-standard repair items, and site-specific energy cost. Eventually we propose a VEI population with a mean of 1.38 and a standard deviation of 1.19, which is obtained by individually and exclusively applying the uncertain factors of the M&E cost to the 35 standard sample of technical proposals. The LCC evaluators may be able to use the VEI population as the benchmark to select the technical proposal with the most reasonable LCC among many others in two suggested manners; the one is to deterministically calculate the probability of single VEIs, and the other is to stochastically calculate the probability of the VEIs where uncertainty is quantified.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.677-684
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• 2008

The paper provides an approximate cost estimating model that can be used for tunnel. Based on the previous study analyzed critical factors that have impact on tunnel construction cost, this paper establishes a cost variance index table that reflects the cost impacts due to the change of the critical cost factors. An estimating procedure is described utilizing the index table. For the verification of the suggested model, the comparison of the estimated construction cost with real project cost is performed. The estimated results range from 95%~111% of the real project costs. As an approximate tunnel cost estimating model, the model can be utilized to quickly estimate tunnel construction costs based on the conceptual information at the planning stage and to efficiently make a decision on design alternatives.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.119-133
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• 2020

Fama asserted that in an efficient market, we can't make a trading rule that consistently outperforms the average stock market returns. This study aims to suggest a machine learning algorithm to improve the trading performance of an intraday short volatility strategy applying asymmetric volatility spillover effect, and analyze its trading performance improvement. Generally stock market volatility has a negative relation with stock market return and the Korean stock market volatility is influenced by the US stock market volatility. This volatility spillover effect is asymmetric. The asymmetric volatility spillover effect refers to the phenomenon that the US stock market volatility up and down differently influence the next day's volatility of the Korean stock market. We collected the S&P 500 index, VIX, KOSPI 200 index, and V-KOSPI 200 from 2008 to 2018. We found the negative relation between the S&P 500 and VIX, and the KOSPI 200 and V-KOSPI 200. We also documented the strong volatility spillover effect from the VIX to the V-KOSPI 200. Interestingly, the asymmetric volatility spillover was also found. Whereas the VIX up is fully reflected in the opening volatility of the V-KOSPI 200, the VIX down influences partially in the opening volatility and its influence lasts to the Korean market close. If the stock market is efficient, there is no reason why there exists the asymmetric volatility spillover effect. It is a counter example of the efficient market hypothesis. To utilize this type of anomalous volatility spillover pattern, we analyzed the intraday volatility selling strategy. This strategy sells short the Korean volatility market in the morning after the US stock market volatility closes down and takes no position in the volatility market after the VIX closes up. It produced profit every year between 2008 and 2018 and the percent profitable is 68%. The trading performance showed the higher average annual return of 129% relative to the benchmark average annual return of 33%. The maximum draw down, MDD, is -41%, which is lower than that of benchmark -101%. The Sharpe ratio 0.32 of SVS strategy is much greater than the Sharpe ratio 0.08 of the Benchmark strategy. The Sharpe ratio simultaneously considers return and risk and is calculated as return divided by risk. Therefore, high Sharpe ratio means high performance when comparing different strategies with different risk and return structure. Real world trading gives rise to the trading costs including brokerage cost and slippage cost. When the trading cost is considered, the performance difference between 76% and -10% average annual returns becomes clear. To improve the performance of the suggested volatility trading strategy, we used the well-known SVM algorithm. Input variables include the VIX close to close return at day t-1, the VIX open to close return at day t-1, the VK open return at day t, and output is the up and down classification of the VK open to close return at day t. The training period is from 2008 to 2014 and the testing period is from 2015 to 2018. The kernel functions are linear function, radial basis function, and polynomial function. We suggested the modified-short volatility strategy that sells the VK in the morning when the SVM output is Down and takes no position when the SVM output is Up. The trading performance was remarkably improved. The 5-year testing period trading results of the m-SVS strategy showed very high profit and low risk relative to the benchmark SVS strategy. The annual return of the m-SVS strategy is 123% and it is higher than that of SVS strategy. The risk factor, MDD, was also significantly improved from -41% to -29%.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.274-277
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• 2006

The EAC(Estimate at Completion) among existing methods, which estimate cost and time effectively, help managers anticipate changeable several results at the point of $15{\sim}30%$ in the project progress. However, this method may cause such some problems as not to consider the periodically changing circumstances caused by construction risks or uncertainties which can affect the cost and time in the project, and to regard collected and accumulated data only as a single value when predicting the results on the progress. Accordingly, it is very difficult to accept the even small range of variability based on the anticipation of EAC. Consequently, the study focuses on the possibility methodology to anticipate time and cost accurately on the way to utilize EVMS(Earned Value Management System), and also suggest the way to perform the right estimation of EAC as considering various risks and uncertainties in construction projects.

• Smart Media Journal
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• v.10 no.3
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• pp.39-47
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• 2021

Enhanced index tracking is a problem of optimizing the objective function to generate returns above the index based on the index tracking that follows the market return. In order to avoid problems such as large transaction costs and illiquidity, we used a method of constructing a portfolio by selecting only some of the stocks included in the index. Commonly used enhanced index tracking methods tried to find the optimal portfolio with only one objective function in all tested periods, but it is almost impossible to find the ultimate strategy that always works well in the volatile financial market. In addition, it is important to improve generalization performance beyond optimizing the objective function for training data due to the nature of the financial market, where statistical characteristics change significantly over time, but existing methods have a limitation in that there is no direct discussion for this. In order to solve these problems, this paper proposes ensemble learning that composes a portfolio by combining several objective functions and a 3-stage portfolio selection algorithm that can select a portfolio by applying criteria other than the objective function to the training data. The proposed method in an experiment using the S&P500 index shows Sharpe ratio that is 27% higher than the index and the existing methods, showing that the 3-stage portfolio selection algorithm and ensemble learning are effective in selecting an enhanced index portfolio.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.13-25
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• 2016

High-speed railway (Korea Train Express) has had a deep impact on the regional economy of Korea. Current high-speed rail research is mostly theoretical, there is a lack of quantitative research using a precise algorithm to study the effect of high-speed railway on the regional economy. This paper analyses the influence of high-speed rail on the regional economy, with a focus on the Daegu area. Quantitative analysis using department store indexes and regional medical records is performed to calculate the economic influence of high-speed rail. The result shows that high-speed railway effects the regional economy though regional consumption growth and medical care trends.

• International Commerce and Information Review
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• v.13 no.4
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• pp.229-249
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• 2011

The exchange rate change has been increased since the time when the floating exchange rate system was introduced in Korea. As a result, the increase of the exchange rate changes raised the risk in international trades in Korea. Also after Bretton Woods System broke down, the increasing exchange rate fluctuation raised the risk in international trade. The purpose of this dissertation is to study whether this incomplete pass-through exists in Korean export industry and furthermore to measure the markup rate of the export price using real data since Global Financial Crisis. The estimation results of the export price determination model by Error Correction Model shows that the export price of Korea has been greatly influenced by the export prices and exchange rates against U.S. Dollar of rival countries, domestic producer price as well as the Korean Won-U.S. Dollar exchange rate and also business coincidence index of U.S. in demand. Particularly, the pass-through rate of Korean Won-U.S. Dollar exchange rate to export price is estimated to be incomplete, which contrasts with the propositions of traditional exchange rate determination approach, e. g. elasticity approach, monetary approach, etc.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5D
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• pp.665-675
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• 2008

The objective of this paper is to provide an approximate cost estimating model for tunnel that can be utilized both in quick construction cost estimating for design alternatives, and in evaluating efficiently the cost effects according to the environmental changes during design and construction stage. To meet this requirement, this study analyzes critical cost factors influencing tunnel construction costs. The cost factors include 7 elements such as rock drilling method, advancing method, type of detonator, loader capacity, unit weight and soil volume change factor, length of tunnel. This paper investigates the cost variance according to the change of the cost factors. The result is expected to be used in formulating approximate tunnel cost estimating model.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2009.04a
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• pp.392-395
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• 2009

응답면 기법을 활용하여 댐구조물과 같은 사회간접자본 시설물의 파괴확률을 구할 수 있다. 본 위험성 평가과정에서 응답면기법으로 구성한 한계상태 방정식을 유전자알고리즘의 적합도 방정식으로 사용하면, 핵심타입이나 지반종류, 지반다짐정도 등의 입력설계변수의 최적화 과정 속도를 더욱 신속화 시킬 수 있다. 제안된 응답면 기법과 유전자알고리즘의 복합해석기법은 신뢰성기반 최적화프로그램으로 기존의 유전자알고리즘의 수렴속도를 더욱 빠르게 하여주고, 특히 입력변수의 상하한계가 불확실한 경우에도 만족스러운 수렴성을 보장하여준다. 한계상태 방정식의 목표신뢰도 지수를 변화시켜면 해당하는 입력변수의 최적값을 출력하여주므로, 입력변수의 제약조건에 가격함수와 같은 가중치를 벌칙함수로 부여하면 가격최적화 프로그램으로 작용하게 되며, 시설물 운영자에게는 목표신뢰도에 대한 유지관리 기법과 정도를 의사결정 할 수 있도록 하여주는 기능을 가지게 된다. 조사된 많은 댐구조물의 파괴모드가 시간에 독립적으로 시공중 또는 시공완료 후 5년이내에 다수 발생하는바, 파괴모드를 조사하고 중요한 파괴모드인 파이핑 현상에 대해서 파괴확률을 계산하고 최적유지관리를 위한 개선된 유전자알고리즘 최적화 연산을 수행하였다. 기존 댐구조물과 같이 설계변수와 하중의 변동성을 알기가 어려운 경우에 유지관리비용 최소화를 위해서 본 제안 프로그램의 확장된 버젼은 중요한 기준을 제시하여줄 것으로 기대한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.469-473
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• 2005

수질모형의 지배방정식에서 정의되는 대표적인 수질매개변수는 유역 및 대기로부터의 오염부하량 퇴적물로부터의 오염물질 용출부하량, 확산계수. 반응계수 등으로 직접적인 관측이 곤란할 뿐만 아니라 많은 관측비용을 필요로 한다. 본 연구에서는 매개변수를 포함한 오염물질 수지방정식을 구성하고, 구성된 선형 연립방정식을 이용함으로써 계산된 농도분포자료와 관측된 시계열 농도분포자료를 이용하여 계산한 질량변화량의 차이를 최소화하는 역산문제를 구성하여 모형의 매개변수를 추정하는 방법을 제시하였다. 이 방법을 이용하여 천수만, 울산만(울산항) 해역에서 관측된 연직방향 농도분포 자료를 이용하여 확산계수 및 대기로부터의 오염부하량, 퇴적물로부터의 오염물질 용출율, 확산$\cdot$반응에 의한 오염물질 변화량 등을 추정하였으며, 추정 매개변수는 시기적으로 변동이 크게 나타났다. 반면, 추정매개변수를 이용한 관측자료와 계산결과를 비교한 결과, RMS 오차는 관측자료 범위의 $5.0\% 이하, 일치지수는 0.95 이상으로 본 방법을 이용한 매개변수 추정결과의 신뢰성은 우수한 것으로 파악되었다.