• 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 the Korea Academia-Industrial cooperation Society
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• v.13 no.4
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• pp.1551-1556
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• 2012

This study proposes an efficient portfolio selection methodology for the listed corporations in KOSPI with consideration of managerial efficiency. For each industrial sector classified by KRX(Korea Exchange), the proposed method carries out an efficiency analysis using DEA (Data envelopment analysis) approach and for the efficient corporations filtered by DEA, construct portfolio using Markowitz's Model. In order to show the effectiveness of the proposed method, we constructed annually portfolios for 4 years (2007-2010) out of 600 listed corporations in KOSPI and KOSDAQ, and proved that our portfolios are superior to benchmark portfolios in terms of rate of returns.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.899-904
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• 2013

This paper proposes an efficient portfolio selection methodology for the listed construction corporations in KOSPI and KOSDAQ. For the construction industrial sector classified by KRX(Korea Exchange), the proposed method carries out an efficiency analysis using DEA (Data envelopment analysis) approach and for the efficient corporations filtered by DEA, construct portfolio using Markowitz's Model. In order to show the effectiveness of the proposed method, we constructed annually portfolios for 5 years (2007-2011) out of 53 listed corporations in KOSPI and KOSDAQ, and proved that our portfolios are superior to benchmark portfolios in terms of rate of returns.

• Korean Management Science Review
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• v.26 no.2
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• pp.19-35
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• 2009

This paper investigated performance of the Markowitz's portfolio selection model with applications to Korean stock market. We chose Samsung-Group-Funds and KOSPI index for performance comparison with the Markowitz's portfolio selection model. For the most recent one and a half year period between March 2007 and September 2008, KOSPI index almost remained the same with only 0.1% change, Samsung-Group-Funds showed 20.54% return, and Markowitz's model, which is composed of the same 17 Samsung group stocks, achieved 52% return. We performed sensitivity analysis on the duration of financial data and the frequency of portfolio change in order to maximize the return of portfolio. In conclusion, according to our empirical research results with Samsung-Group-Funds, investment by Markowitz's model, which periodically changes portfolio by using nonlinear programming with only financial data, outperformed investment by the fund managers who possess rich experiences on stock trading and actively change portfolio by the minute-by-minute market news and business information.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.76-94
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• 2008

This paper investigates performance of the Markowitz's portfolio selection model with applications to Korean stock market. We choose Samsung-Group-Funds and KOSPI index for performance comparison with the Markowitz's portfolio selection model. For the most recent one and a half year period between March 2007 and September 2008, KOSPI index almost remains the same with only 0.1% change, Samsung-Group-Funds shows 20.54% return, and Markowitz's model, which is composed of the same 17 Samsung group stocks, reaches 52% return. We perform sensitivity analysis on the duration of financial data and the period of portfolio change in order to maximize the return of portfolio. In conclusion, according to our empirical research results with Samsung-Group-Funds, investment by Markowitz's model, which periodically changes portfolio by using nonlinear programming with only financial data, outperforms investment by the fund manager who possesses rich experiences on stock trading and actively changes portfolio based on minute-by-minute market news and business information.

• 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 Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.878-884
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• 2012

This study presents a portfolio selection strategy focusing on construction corporations by taking into accounts managerial efficiency and growth potential of a company. Data envelopment analysis(DEA) methodology and dividend scoring table are adopted for evaluating the managerial efficiency and growth potential of a company respectively. In order to show the effectiveness of the portfolios selected by the strategies proposed in this study, we constructed 3 portfolios for every 4 years (2007-2010) out of 56 listed construction corporations in KOSPI and KOSDAQ, and proved that our portfolios are superior to benchmark portfolios in terms of portfolio evaluation measures.

• 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.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.45-63
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• 2019

Until recently, most people have invested in a traditional portfolio consisting of stocks, bonds and real estates based on the three-division method of properties in Korea. However, this study analyzed the impact of the composition of a portfolio combining representative real estate indirect investment products such as Reits and real estate funds on the investment performance. For this purpose, the empirical analysis using the mean variance model, which is the most appropriate method for the portfolio composition, was used. For variables used in this study, mixed asset portfolios were classified into Portfolio A through Portfolio G depending on the composition of assets, and the price indices selected as Kospi, Krx bond, Reits Trus Y7, Hanwha-Lasal fund, and Office (Seoul). The results are as follows; first Portfolio D, which combined bonds, stocks, Reits and Real Estate funds, and Portfolio G, which added the office, the actual real estate, were shown to have the lowest risk. second, Portfolio B composed of bonds, stocks and Reits and Portfolio D with added real estate funds had the lowest risk while Portfolio F composed of bonds, stocks, offices and real estate funds, and Portfolio G with added Reits were the most profitable. As a result, it has been analyzed that it was more effective to compose a portfolio including Reits and real estate funds, which were real estate indirect investment products that eliminated the illiquidity limitation of real estates than real estates, the traditional three-division method of properties. Therefore, it is possible to minimize the risk of investors and reduce the cost of ownership of the real estate by solving the illiquidity problem that is the biggest disadvantage of the direct investment, In addition, it is considered that it is more necessary to reinvigorate the real estate indirect investment market where small amounts can be invested.

• 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.