• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.635-646
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• 2016

This paper proposes a housing business model, where the presale and Chonsei housing are supplied under a presale system at the same time based on the characteristic correlation between the housing presale market and Chonsei market in Korea. Markowitz portfolio theory was used to review the risk diversification effects from the changes in the ratio between the presale housing supply and the Chonsei housing supply. The housing sale price indicator was used as a proxy variable to determine the presale housing supply. The housing Chonsei price indicator was used as a proxy variable to determine the Chonsei housing supply. The proposed housing business model was applied to major areas in Korea to examine the risk diversification effect. Comparisons of the regional portfolio analyses showed that the flexibility of the proposed housing business model can be quite effective because each regional housing market exhibits different characteristics. Market participants, such as developers, construction companies, consumers, and government, can expect various effects through the proposed housing business model. Nevertheless, policy support is necessary for practical applications of the proposed housing business model. In particular, public funds from the government need to be introduced.

• The Korean Journal of Applied Statistics
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• v.35 no.2
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• pp.229-250
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• 2022

Since the publication of Markowitz's (1952) mean-variance portfolio model, research on portfolio optimization has been conducted in many fields. The existing mean-variance portfolio model forms a nonlinear convex problem. Applying Dantzig's linear programming method, it was converted to a linear form, which can effectively reduce the algorithm computation time. In this paper, we proposed a Dantzig perturbation portfolio model that can reduce management costs and transaction costs by constructing a portfolio with stable and small (sparse) assets. The average return and risk were adjusted according to the purpose by applying a perturbation method in which a certain part is invested in the existing benchmark and the rest is invested in the assets proposed as a portfolio optimization model. For a covariance estimation, we proposed a Gaussian kernel weight covariance that considers time-dependent weights by reflecting time-series data characteristics. The performance of the proposed model was evaluated by comparing it with the benchmark portfolio with 5 real data sets. Empirical results show that the proposed portfolios provide higher expected returns or lower risks than the benchmark. Further, sparse and stable asset selection was obtained in the proposed portfolios.

• Management Science and Financial Engineering
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• v.10 no.1
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• pp.25-41
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• 2004

There have been many studies to build a model that can help investors construct optimal portfolio. Most of the previous models, however, are based upon the path-breaking Markowitz model (1959) which is a quantitative model. One of the most important problems with that kind of quantitative model is that, in reality, most of the investors use not only quantitative, but also qualitative information when they select their optimal portfolio. Since collecting both types of information from the markets are time consuming and expensive, making a set of target assets smaller, without suffering heavy loss in the rate of return, would attract investors. To extract only desired assets among all available assets, we need knowledge that identifies investors' preference for the risk of the assets. This study suggests two-layer decision-making rules capable of identifying an investor's risk preference and an architecture applying them to a quantitative portfolio model based on risk and expected return. Our knowledge-based portfolio system is to build an investor's preference-oriented portfolio. The empirical tests using the data from Korean capital markets show the results that our model contributes significantly to the construction of a better portfolio in the perspective of an investor's benefit/cost ratio than that produced by the existing portfolio models.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.637-649
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• 2022

Investors aim to increase their profitability by investing in the stock market. An adroit strategy for minimizing related risk lies through diversifying portfolio operationalization. In this paper, we propose a six-step stocks portfolio selection model. This model is based on data mining clustering techniques that reflect the ensuing impact of the political, economic, legal, and corporate governance in Mongolia. As a dataset, we have selected stock exchange trading price, financial statements, and operational reports of top-20 highly capitalized stocks that were traded at the Mongolian Stock Exchange from 2013 to 2017. In order to cluster the stock returns and risks, we have used k-means clustering techniques. We have combined both k-means clustering with Markowitz's portfolio theory to create an optimal and efficient portfolio. We constructed an efficient frontier, creating 15 portfolios, and computed the weight of stocks in each portfolio. From these portfolio options, the investor is given a choice to choose any one option.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.235-237
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• 2017

The AKARI NEP Deep Field Survey is an international multiwavelength survey over $0.4deg^2$ of the sky. This is the deepest survey made by the InfraRed Camera (IRC) of the infrared astronomical satellite AKARI with 9 filters continuously covering the $2-25{\mu}m$ range, including three filters in the Spitzer gap between the IRAC and MIPS coverages. This enabled us to make sensitive MIR detection of AGN candidates at z~ 1, based on hot dust emission in the AGN torus. It is also efficient in detecting highly obscured Compton-thick AGN population. In this article, we report the first results of X-ray observations on this field. The field was covered by 15 overlapping Chandra ACIS-I observations with a total exposure of ~300 ks, detecting ${\approx}450$ X-ray sources. We utilize rest-frame stacking analysis of the MIR AGN candidates that are not detected individually. Our preliminary analysis shows a marginal detection of the rest-frame stacked Fe $k{\alpha}$ line from our strong Compton-thick candidates.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.48-58
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• 2013

The stock market is constantly changing and sometimes a slump or a sudden rising in stocks happens without any special reason. So the stock market is recognized as a complex system and it is hard to predict the change on stock prices. In this paper we consider the stock market to a network consisting of stocks. We analyzed the dynamics of the Korean stock market network and evaluated the changing of the correlation between shares consisting of the time series data of 137 companies belong to KOSPI200. Our analysis shows that the stock prices tend to plummet when the correlation between stocks is very high. We propose a method for recommending the stock portfolio based on the analysis of the stock market network. To show the effectiveness of the recommended portfolio, we conducted the simulated stock investment and compared the recommended portfolio with the efficient portfolio proposed Markowitz. According to the experiment results, the rate of return of the portfolio is about 10.6% which is about 3.7% and 5.6% higher than the average rate of return of the efficient portfolio and KOSPI200 respectively.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1479-1494
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• 2015

Since the Markowitz's mean-variance framework for portfolio analysis, the topic of portfolio optimization has been an important topic in finance. Traditional approaches focus on maximizing the expected return of the portfolio while minimizing its variance, assuming that risky asset returns are normally distributed. The normality assumption however has widely been criticized as actual stock price distributions exhibit much heavier tails as well as asymmetry. To this extent, in this paper we employ the genetic algorithm to find the optimal portfolio under the Value-at-Risk (VaR) constraint, where the tail of risky assets are modeled with the generalized Pareto distribution (GPD), the standard distribution for exceedances in extreme value theory. An empirical study using Korean stock prices shows that the performance of the proposed method is efficient and better than alternative methods.

• Archives of Plastic Surgery
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• v.37 no.4
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• pp.496-498
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• 2010

Purpose: Lacerations requiring formal wound closure compose a significant number of all childhood injuries presenting to the emergency department. The problem with conventional suture technique are that suture removal is quite cumbersome, especially in children. Unwanted soft tissue damage can result in the process of suture removal, which calls for sedation, stressful for both medical personnel and child. The purpose of this study is to introduce the convenient suture technique for pediatric facial lacerations. Methods: Children under the age of four, presenting to the emergency department with facial lacerations were enrolled in the study. From March 2008 to June 2009, 63 patients (41 males and 22 females) with an average age of 1.4 years were treated with our convenient suture technique using utilized a loop suspended above a double, flat tie. Clean, tension free wounds were treated with our technique, wounds with significant skin defect and concomitant fractures were excluded. Results: The Patients were followed-up in 1, 3 and 5 days postoperatively. On the third hospital visit, suture removal was done by simply cutting the loop suspended above the wound margin and gently pulling the thread with forceps. There were no significant differences in the rates of infection and dehiscence compared with conventional suture technique. Conclusion: The use of our technique was to be simple with similar operative time compared with conventional suture technique. Removal of suture materials were easy without unwanted injuries to the surrounding tissue which resulted in less discomfort for the patient and greater parental satisfaction, minimized the complications. It can be considered as a viable alternative in the repair of pediatric facial lacerations.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.10
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• pp.441-450
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• 2014

The stock market is constantly changing and sometimes the stock prices unaccountably plummet or surge. So, the stock market is recognized as a complex system and the change on the stock prices is unpredictable. Recently, many researchers try to understand the stock market as the network among individual stocks and to find a clue about the change of the stock prices from big data being created in real time from Internet. We focus on the correlation between the stock prices and the human interactions in Internet especially in the stock message boards. To uncover this correlation, we collected and investigated the articles concerning with 57 target companies, members of KOSPI200. From the analysis result, we found that there is no significant correlation between the stock prices and the article volume, but the strength of correlation between the article volume and the stock prices is relevant to the stock return. We propose a new method for recommending stock portfolio base on the result of our analysis. According to the simulated investment test using the article data from the stock message boards in 'Daum' portal site, the returns of our portfolio is about 1.55% per month, which is about 0.72% and 1.21% higher than that of the Markowitz's efficient portfolio and that of the KOSPI average respectively. Also, the case using the data from 'Naver' portal site, the stock returns of our proposed portfolio is about 0.90%, which is 0.35%, 0.40%, and 0.58% higher than those of our previous portfolio, Markowitz's efficient portfolio, and KOSPI average respectively. This study presents that collective human behavior on Internet stock message board can be much helpful to understand the stock market and the correlation between the stock price and the collective human behavior can be used to invest in stocks.