• The Korean Journal of Financial Management
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• v.26 no.1
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• pp.165-188
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• 2009

This paper has suggested the methodology for the frontier portfolios and the optimal portfolio under the mean-VaR framework, not assuming the normal distribution and considering the investor's preferences for the higher moments of return distributions. It suggested the grid and rank approach which did not need an assumption about return distributions to find the frontier portfolios. And the optimal portfolio was selected using the utility function that considered the 3rd and the 4th moments. For the application of the methodology, weekly returns of the developed countries index, the emerging market index and the KOSPI index were used. After the frontier portfolios of the mean-variance framework and the mean-VaR framework were selected, the optimal portfolios of each framework were compared. This application compared not only the difference of the standard deviation but also the difference of the utility level and the certainty equivalent expressed by weekly expected returns. In order to verify statistical significances about the differences between the mean-VaR and the mean-variance, this paper presented the statistics which were obtained by the historical simulation method using the bootstrapping. The results showed that an investor under the mean-VaR framework had a tendency to select the optimal portfolio which has bigger standard deviation, comparing to an investor under the mean-variance framework. In addition, the more risk averse an investor is, the bigger utility level and certainty equivalent he achieves under the mean-VaR framework. However, the difference between the two frameworks were not significant in statistical as well as economic criterion.

• Journal of Service Research and Studies
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• v.9 no.1
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• pp.51-71
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• 2019

The purpose of this research is to analyze the ETF market, which has a large increase in the number of listed shares and the market capitalization, and to identify the investment effects of ETFs. The study procedure and method used to calculate the return and change trend of ETFs for the sample of the transaction information, the transaction amount, and the market capitalization for the period from 2010 to 2018, and performed correlation and regression analysis. As a result, the ETF's total return was 2.11%, the domestic underwriting market ETF yield was 2.39%, and the stock ETF yield was 2.59%, which was lower than the KOSPI 200 index and the KOSPI 200 index. Index ETF was 2.63%, followed by stock ETF and oversea underwriting market ETF. The problem with ETF investment is that the annual return of ETFs and domestic ETFs is as low as 2%, which is not enough for investors to expect more than 5%. The study contributes to the realization of the ETF by analyzing the actual effect of the investment and to establishing considerations when buying ETFs from the viewpoint of investors. The direction of the research is to accumulate more ETF data and present the investment direction precisely.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.109-124
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• 2020

Facing the 4^th Industrial Revolution era, researches on artificial intelligence have become active and attempts have been made to apply machine learning in various fields. In the field of finance, Robo Advisor service, which analyze the market, make investment decisions and allocate assets instead of people, are rapidly expanding. The stock price prediction using the machine learning that has been carried out to date is mainly based on the prediction of the market index such as KOSPI, and utilizes technical data that is fundamental index or price derivative index using financial statement. However, most researches have proceeded without any explicit verification of the prediction rate of the learning data. In this study, we conducted an experiment to determine the degree of market prediction ability of basic indicators, technical indicators, and system risk indicators (AR) used in stock price prediction. First, we set the core parameters for each financial indicator and define the objective function reflecting the return and volatility. Then, an experiment was performed to extract the sample from the distribution of each parameter by the Markov chain Monte Carlo (MCMC) method and to find the optimum value to maximize the objective function. Since Robo Advisor is a commodity that trades financial instruments such as stocks and funds, it can not be utilized only by forecasting the market index. The sample for this experiment is data of 17 years of 1,500 stocks that have been listed in Korea for more than 5 years after listing. As a result of the experiment, it was possible to establish a meaningful trading strategy that exceeds the market return. This study can be utilized as a basis for the development of Robo Advisor products in that it includes a large proportion of listed stocks in Korea, rather than an experiment on a single index, and verifies market predictability of various financial indicators.

• Journal of the Korean Data and Information Science Society
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• v.22 no.1
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• pp.37-47
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• 2011

Stock price index option market has various investment strategies that have been developed. Specially, arbitrage strategies are very important to be efficient in option market. The purpose of this study is to improve profit using rough set and Box spread by using past option trading data. Option trading data was based on an actual stock exchange market tick data ranging from 2001 to 2006. Validation process was carried out by transferring the tick data into one-minute intervals. Box spread arbitrage strategies is low risk but low profit. It can be accomplished by back-testing of the existing strategy of the past data and by using rough set, which limit the time line of dealing. This study can make more stable profits with lower risk if control the strategy that can produces a higher profit module compared to that of the same level of risk.

• Korean Business Review
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• v.19 no.1
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• pp.81-92
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• 2006

This study investigates the relation between institutional investor's net purchase and the volatility of KOSPI. Some portion of volatility in stock prices comes from noise trading of irrational traders. Observed volatility may be defined as the sum of the portion caused by information arrival, fundamental volatility, and the portion caused by noise trading, transitory volatility. This study decomposes the observed volatility into fundamental volatility and transitory volatility using Kalman filtering method. Most studies investigates the effect on the observed volatility. In contrast to other studies, this study investigates the effect on the fundamental volatility and transitory volatility individually. Estimation results show that institutional investor's net purchase was not significantly related to all kinds of volatility(observed volatility, fundamental volatility and transitory volatility). This means that institutional investor's net purchase did not increase noise trading.

• Convergence Security Journal
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• v.15 no.3_2
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• pp.83-93
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• 2015

In this research, we used KOSPI, KOSDAQ and a stock price of Information Security related Company - S1, Ahnlab, Suprema, Raonscure and Igloosecurity. From August 2010 to July 2014, that is during 208 weeks(4 years), we had grasped index and stock price trend. Also we had attempted various Empirical analysis - Basic statistics of Security related Stock, Analysis of variance, Correlation analysis and Weekly Rate of Rise trends. The first purpose of this research is to see correlation between Security related Company and KOSPI, KOSDAQ. The second purpose of this research is to analyze whether stock items have investment value or not while watching features of flow of stock price per item. We expect possibility and merit of investment when we suppose Security industry's high potential to grow. It seems that Security related Company deserves to be invested. We expect investment for Security related Company that has high possibility of growing will create high yields compared to Market yields.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.823-834
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• 2010

Stock markets perpetually accumulate information. During trading hours the price instantaneously reacts to new information, but accumulated overnight information reacts simultaneously on the opening price. This can create opening price uctuations. This study explores the overnight information e ects on intra-da stock market volatility. GARCH models and the VKOSPI model are provided. Empirical data includes daily opening and closing prices of the KOSPI 200 index and the VKOSPI from March $3^{rd}$ 2008 to June $22^{th}$ 2010. Empirical results show that the VKOSPI signi cantly decrease during trading time when positiv overnight information moves the Korean stock upward. This study provides useful information to investors since the Korea Exchange plans to introduce a futures market for the VKOSPI soon.

• Korean Business Review
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• v.18 no.1
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• pp.43-57
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• 2005

This paper investigate to test the validity of the basic assumptions of strategic trading models with the broker and informed trader using daily closing data of KOSPI 200 stock index futures for the year 2001-2003. Major results are summarized as follows: (i) For these years, while foreign investors and brokerage companies traded for the directions consistent with the model, brokerage companies and individual investors traded for inconsistent directions. (ii) Cross correlation function (CCF) analysis shows no systematic dependency in the trading between all three participants(foreign investor, brokerage companies and individual investors) for these years. (iii) Chi-square validity test for the 30 days of the largest unexpected trading volume shows some systematic dependency in the trading between three participants for these years. Finally, some limitations of this paper and direction for further research were suggested.

• Management & Information Systems Review
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• v.31 no.3
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• pp.21-46
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• 2012

This paper attempts to offer an effective strategy of hedge fund based on trade probability control in the futures market. By using various technical indicators, we create an association rule and transforms it into a trading rule to be used as an investment strategy. Association rules are made by the combination of various technical indicators and the range of individual indicator value. Adjustments of trade probabilities are performed by depending on the rule combinations and it can be utilized to establish an effective investment strategy onto the risk management. In order to demonstrate the superiority of the investment strategy proposed, we analyzed a profitability using the futures index based on KOSPI200. Experiments results show that our proposed strategy could effectively manage and response the dynamics investment risks.

• Journal of Service Research and Studies
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• v.5 no.2
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• pp.35-49
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• 2015

The purpose of this paper is to see what the problem is and what the direction of the strategy of asset management after this study has analyzed asset management status of nonlife insurance companies according to interest rate trends, analyzing in time series asset state, management asset lists, asset distribution state, securities list and total asset yield of Nonlife insurance companies during year 2009~2014. As the study result, nonlife insurance companies have managed assets in stability than profitability according to safety asset was increased, but risky asset was decreased. Performance rate of total asset was dropped according to interest rate declined trends. Trend between stock index and performance rates of total asset was not accord. The correlation coefficient between interest rate and performance rates of total asset was highly plus, but the correlation coefficient of KOSPI and performance rate of total asset showed minus.