• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.153-163
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• 2009

Unlike the mean-variance approach, the stochastic dominance approach is to form a portfolio that stochastically dominates a predetermined benchmark portfolio such as KOSPI. This study is to search a set of portfolio weights for the first-order stochastic dominance with maximum expected return by managing the constraint set and the objective function separately. A nonlinear programming algorithm was developed and tested with promising results against Korean stock market data sets.

• Journal of Korean Society for Quality Management
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• v.31 no.3
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• pp.160-171
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• 2003

Criteria for the comparison of quality of asset portfolio management are risk and return. In this paper a method to use structural time-series model to determine an optimal portfolio for the improvement of quality of asset portfolio management is suggested. In traditional mean variance analysis expected return is assumed to be time-invariant. However, it is more realistic to assume that expected return is temporally dynamic and structural time-series model can be used to reflect time-varying nature of return. A data set from an insurance company was used to show validity of suggested method.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.134-137
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• 2007

Unlike the mean-variance approach, the stochastic dominance approach is to form a portfolio that stochastically dominates a predetermined benchmark portfolio such as KOSPI. This study is to search a set of portfolio weights for the first degree stochastic dominance with maximum expected return by managing the constraint set and the objective function separately. An algorithm was developed and tested with promising results against Korean stock market data sets.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.75-93
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• 2013

In applying Markowitz's portfolio selection model to the stock market, we developed a comprehensive investment decision-making framework including key inputs for portfolio theory (i.e., individual stocks' expected rate of return and covariance) and minimum required expected return. For estimating the key inputs of our decision-making framework, we utilized an exponentially weighted moving average (EWMA) which places more emphasis on recent data than the conventional simple moving average (SMA). We empirically analyzed the investment results of the decision-making framework with the same 15 stocks in Samsung Group Funds found in the Korean stock market between 2007 and 2011. This five-year investment horizon is marked by global financial crises including the U.S. subprime mortgage crisis, the collapse of Lehman Brothers, and the European sovereign-debt crisis. We measure portfolio performance in terms of rate of return, standard deviation of returns, and Sharpe ratio. Results are compared with the following benchmarks : 1) KOSPI, 2) Samsung Group Funds, 3) Talmudic portfolio based on the na$\ddot{i}$ve 1/N rule, and 4) Markowitz's model with SMA. We performed sensitivity analyses on all the input parameters that are necessary for designing an investment decision-making framework : smoothing constant for EWMA, minimum required expected return for the portfolio, and portfolio rebalancing period. In conclusion, appropriate use of the comprehensive investment decision-making framework based on the Markowitz's model integrated with EWMA proves to achieve outstanding performance compared to the benchmarks.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.35-52
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• 2013

Markowitz's portfolio selection model is used to construct an optimal portfolio which has minimum variance, while satisfying a minimum required expected return. The model uses estimators based on analysis of historical data to estimate the returns, standard deviations, and correlation coefficients of individual stocks being considered for investment. However, due to the inaccuracies involved in estimations, the true optimality of a portfolio constructed using the model is questionable. To investigate the effect of estimation inaccuracy on actual portfolio performance, we study the changes in a portfolio's realized return and standard deviation as the accuracy of the estimations for each stock's return, standard deviation, and correlation coefficient is increased. Furthermore, we empirically analyze the portfolio's performance by comparing it with the performance of active mutual funds that are being traded in the Korean stock market and the KOSPI benchmark index, in terms of portfolio returns, standard deviations of returns, and Sharpe ratios. Our results suggest that, among the three input parameters, the accuracy of the estimated returns of individual stocks has the largest effect on performance, while the accuracy of the estimates of the standard deviation of each stock's returns and the correlation coefficient between different stocks have smaller effects. In addition, it is shown that even a small increase in the accuracy of the estimated return of individual stocks improves the portfolio's performance substantially, suggesting that Markowitz's model can be more effectively applied in real-life investments with just an incremental effort to increase estimation accuracy.

• Korean Management Science Review
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• v.28 no.2
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• pp.1-16
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• 2011

In this study we suggested two optimization models to answer a question from an investor standpoint : how many convertible bonds should one convert, and how many keep? One model minimizes certain risk to the minimum required expected return, the other maximizes the expected return subject to the maximum acceptable risk. In comparison with Markowitz portfolio models, which use the variance of return, our models used Conditional Value-at-Risk(CVaR) for risk measurement. As a coherent measurement, CVaR overcomes the shortcomings of Value-at-Risk(VaR). But there are still difficulties in solving CVaR including optimization models. For this reason, we adopted Rockafellar and Uryasev's[18, 19] approach. Then we could approximate the models as linear programming problems with scenarios. We also suggested to extend the models with credit risk, and applied examples of our models to Hynix 207CB, a convertible bond issued by the global semiconductor company Hynix.

• 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 Korean Society of Forest Science
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• v.104 no.2
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• pp.248-253
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• 2015

This study applied the portfolio approach as a means to provide decision-making information for the establishment of the optimal production plan for non-timber products. The target items of non-timber forest product were Chestnut, Jujube, Walnut and Astringent Persimmon. The data used in this study were the annual report of forestry production cost survey which contains the annual production, annual gross income, and annual product cost from 2008 to 2013. These data were used to calculate the expected return of non-timber forest product. The objective function in the portfolio models was to minimize the expected return volatility, called risk and the constrain was to achieve the minimum expected return rate. Results indicated that the production ratio of the nuts and fruits in 2013 was 7% for Chestnut, 20% for Jujube, 5% for Walnut and 68% for Astringent Persimmon. Furthermore, portfolio presented that the production ratio was 10% for Chestnut, 9% for Jujube, 3% for Walnut and 78% for Astringent Persimmon in the near future. The cause was analyzed due to maintain stable production and income of Astringent Persimmon and Chestnut. Meanwhile, the revenue of Walnuts and Jujube was in great variation with relatively higher revenues.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.123-138
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• 2022

Purpose - This study compares the performances of dynamic asset allocation strategies using Korean stocks and U.S. dollar, which have been negatively correlated for a long time, to examine the diversification effects in the portfolios of them. Design/methodology/approach - In the current study, we use KOSPI200 index, as a proxy of the aggregated portfolio of Korean stocks, and USDKRW foreign exchange rate to implement various portfolio management strategies. We consider the equally-weighted, risk-parity, minimum variance, most diversified, and growth optimal portfolios for comparison. Findings - We first find the enhancement of risk adjusted returns due to risk reduction rather than return increasement for all the portfolios of consideration. Second, the enhancement is more pronounced for the trading strategies using correlations as well as volatilities compared to those using volatilities only. Third, the diversification effect has become stronger after the global financial crisis in 2008. Lastly, we find that the performance of the growth optimal portfolio can be improved by utilizing the well-known momentum phenomenon in stock markets to select the length of the sample period to estimate the expected return. Research implications or Originality - This study shows the potential benefits of adding the U.S. dollar to the portfolios of Korean stocks. The current study is the first to investigate the portfolio of Korean stocks and U.S. dollar from investment perspective.

• Asia-Pacific Journal of Business
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• v.11 no.4
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• pp.243-254
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• 2020

Purpose - This paper explores whether overnight returns measured from the last closing price to today's opening price explain the cross-section of stock returns. Design/methodology/approach - This study is conducted using the Korean stock market data from 1998 to 2018, obtained from DataGuide database. The analysis begins with portfolio-level tests, followed by firm-level cross-sectional regressions. Findings - First, when decile portfolios sorted on the daily average of overnight returns in the previous months, the highest decile portfolio exhibits a significant negative risk-adjusted return. This suggests that stocks with higher average overnight returns are temporarily overvalued due to buying pressure from investors. Second, at least 6 months of persistence exists in average overnight returns, which is in line with the results reported by Barber, Odean and Zhu (2009) that investor sentiment persists over several weeks. Finally, Fama-MacBeth cross-sectional regression of expected returns after controlling for a variety of firm characteristic variables such as firm size, book-to-market ratio, market beta, momentum, liquidity, short-term reversal, the slope coefficient for overnight returns remains negative and statistically significant. Research implications or Originality - Overall, the evidence consistently suggests that overnight return is considered as a new priced factor in the cross-section of expected returns. The findings of this paper not only adds to finance literature, but also could be useful to practitioners in making stock investment decision.