• 산업경영시스템학회지
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• 제32권4호
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• pp.63-71
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• 2009

The traditional portfolio optimization problem is to find an investment plan for securities with reasonable trade-off between the rate of return and the risk. The seminal work in this field is the mean-variance model by Markowitz, which is a quadratic programming problem. Since it is now computationally practical to solve the model, a number of alternative models to overcome this complexity have been proposed. In this paper, among the alternatives, we focus on the Mean Absolute Deviation (MAD) model. More specifically, we developed an algorithm to obtain an optimal portfolio from the MAD model. We showed mathematically that the algorithm can solve the problem to optimality. We tested it using the real data from the Korean Stock Market. The results coincide with our expectation that the method can solve a variety of problems in a reasonable computational time.

• 충청수학회지
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• 제26권2호
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• pp.343-349
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• 2013

The optimal portfolio selection problem under inflation risk is considered in this paper. There are three assets the economic agent can invest, which are a risk free bond, an index bond and a risky asset. By applying the martingale method, the optimal consumption rate and the optimal portfolios for each asset are obtained explicitly.

• 산업경영시스템학회지
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• 제46권4호
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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.

• 대한수학회보
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• 제60권4호
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• pp.1101-1129
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• 2023

In this study we propose a model of optimal retirement, consumption and portfolio choice of an individual agent, which encompasses a large class of the models in the literature, and provide a methodology to solve the model. Different from the traditional approach, we consider the problems before and after retirement simultaneously and identify the difference in the dual value functions as the utility value of lifetime labor. The utility value has an option nature, namely, it is the maximized value of choosing the retirement time optimally and we discover it by solving a variational inequality. Then, we discover the dual value functions by using the utility value. We discover the value function and optimal policies by establishing a duality between the value function and the dual value function. The model and approach offer a significant advantage for computation of optimal policies for a large class of problems.

• The Journal of Asian Finance, Economics and Business
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• 제8권9호
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• pp.45-52
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• 2021

Stock price fluctuations affect investor returns, particularly, in this pandemic situation that has triggered stock market shocks. As a result of this situation, investors prefer to move their money into a safer portfolio. Therefore, in this study, we approach an efficient portfolio model using smart beta and combining others to obtain a fast method to predict investment stock returns. Smart beta is a method to selects stocks that will enter a portfolio quickly and concisely by considering the level of return and risk that has been set according to the ability of investors. A smart beta portfolio is efficient because it tracks with an underlying index and is optimized using the same techniques that active portfolio managers utilize. Using the logistic regression method and the data of 100 low volatility stocks listed on the Indonesia stock exchange from 2009-2019, an efficient portfolio model was made. It can be concluded that an efficient portfolio is formed by a group of stocks that are aggressive and actively traded to produce optimal returns at a certain level of risk in the long-term period. And also, the portfolio selection model generated using the smart beta, beta, alpha, and stock variants is a simple and fast model in predicting the rate of return with an adjusted risk level so that investors can anticipate risks and minimize errors in stock selection.

• 산업공학
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• 제23권4호
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• pp.300-310
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• 2010

This paper is about an optimal investment portfolio strategy. Financial data of stocks, bonds, and savings from January 2. 2001 through October 30. 2009 were utilized in order to suggest the optimal portfolio strategies. Fundamental analysis and technical analysis were used in stocks-related strategy, whereas passive investment strategy and active investment strategy were used in bond-related strategy. The score is assigned to each stock index according to the suggested strategies and set trading rules are based on the scores. The simulation has been executed about each 29,400-portfolios and we figured out with the simulation result that 26.75% of 7,864 portfolios are more profitable than average stock market profit (22.6%, Annualized). The outcome of this research is summarized in two parts. First, it's the rebalancing strategy of portfolio. The result shows that value-oriented investment(long-term investment) strategy yields much higher than short-term investment strategies of stocks or active investment of bonds. Second, it's about the rebalancing cycle forming the portfolios. The result shows that the rate of return for the portfolio is the best when rebalancing cycle is 12 or 18 months.

• 자원ㆍ환경경제연구
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• 제20권2호
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• pp.335-356
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• 2011

본 연구에서는 Markowitz (1952)의 평균-분산 모형과 지배원리에 입각하여 원유, 석탄, 천연가스로 대표되는 화석에너지원의 최적 소비조합을 구축하려 하였다. 이를 위해 1달러당 열량으로 정의된 화석에너지원들의 편익변동을 동태은닉공통인자 모형을 이용하여 동행부분과 개별 에너지원의 특이적 수급상황에 기초한 변동으로 분해한 후, 그 결과에 기초하여 최적 화석에너지원의 최적 소비조합을 구성하였다. 분석결과, 평균-분산 모형에서 최적 소비조합을 의미하는 효율적 프론티어 선상의 소비조합들에서는 사회적으로 도달 가능한 최저 수준의 원유소비 비중을 유지하면서 석탄보다는 천연가스의 소비비중을 높여야 하는 것으로 나타났다. 이와 같은 결과는 현재 우리나라에서 추구하고 있는 원유 및 석탄의 소비비중 축소전략과도 일치하는 결과라 할 수 있으며, 원유소비의 비중축소가 화석에너지원의 소비로부터 얻을 수 있는 편익향상과 함께 편익변동에 따르는 경제활동의 불안정성을 축소시킬 수 있는 방법임을 지적하는 것이라 할 수 있다.

• East Asian mathematical journal
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• 제34권1호
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• pp.1-16
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• 2018

We study an optimization problem for hyperbolic absolute risk aversion (HARA) utility function under two-factor Heston's stochastic volatility model. It is not possible to obtain an explicit solution because our financial market model is complicated. However, by using asymptotic analysis technique, we find the explicit forms of the approximations of the optimal value function and the optimal strategy for HARA utility function.

• 충청수학회지
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• 제33권2호
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• pp.293-300
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• 2020

In this paper we study an optimal consumption, investment and retirement time choice problem of an investor who receives labor income before her voluntary retirement. And we assume that there is a negative wealth constraint which is a general version of borrowing constraint. Using convex-duality method, we provide the closed-form solutions of the optimization problem.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.