• 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.

• 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.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.63-76
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• 2016

Multiperiod portfolio selection problem attracts more and more attentions because it is in accordance with the practical investment decision-making problem. However, the existing literature on this field is almost undertaken by regarding security returns as random variables in the framework of probability theory. Different from these works, we assume that security returns are uncertain variables which may be given by the experts, and take absolute deviation as a risk measure in the framework of uncertainty theory. In this paper, a new multiperiod mean absolute deviation uncertain portfolio selection models is presented by taking transaction costs, borrowing constraints and threshold constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on uncertain theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. To solve the new model in general cases, the forward dynamic programming method is presented. In addition, a numerical example is also presented to illustrate the modeling idea and the effectiveness of the designed algorithm.

• Asian Journal of Business Environment
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• v.10 no.1
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• pp.37-43
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• 2020

Purpose: The purpose of this study was to examine the relationship between portfolio quality and financial sustainability of microfinance institutions in Kenya. Research Design, Data, and Methodology: The analysis was based on a panel dataset of 30 microfinance institutions for the period of 2010 to 2018. Data was obtained from the Microfinance information exchange (MIX) database, and it was analyzed through descriptive and inferential statistics with the aid of STATA. Based on the results of the Hausman test, the study adopted the fixed effect regression model to test the research hypothesis. Results: The study found that portfolio quality had a positive significant effect on financial sustainability of Microfinance institutions in Kenya (β= 0. 211; p-value < 0.05). For the control variables; firm age had a positive effect (β= 0.773; p-value <0.05), while firm size (β= -0. 749; p-value < 0.05) had a negative effect on financial sustainability. Conclusions: The study concluded that portfolio quality has an important influence on the financial sustainability of microfinance institution. The study recommends that managers of microfinance institutions should devise good collection policies to improve portfolio quality while lessening loan default rate. The portfolio quality may improve the overall profitability and enhance investor confidence in their strategic decision-making on refinancing.

• 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.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.10-10
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• 2003

Option pricing theory developed by Black and Sholes depends on an arbitrage opportunity argument. An investor can exactly replicate the returns to any option on that stock by continuously adjusting a portfolio consisting of a stock and a riskless bond. The value of the option equal the value of the replicating portfolio. However, transactions costs invalidate the Black-Sholes arbitrage argument for option pricing, since continuous revision implies infinite trading, Discrete revision using Black-Sholes deltas generates errors which are correlated with the market, and do not approach zero with more frequent revision when transactions costs are included. Stochastic calculus serves as a fundamental tool in the mathematical finance. We closely look at the utility maximization theory which is one of the main option valuation methods. We also see that how the stochastic optimal control problems and their solution methods are applied to the theory.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.972-975
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• 2003

The purpose of this study is to integrate Decision Theory Approach(DTA) and OR Theory Approach(OTA) systemically. and to develop Combination Theory Approach to build an optimal R&D project portfolio by strategies. To Integrate two approaches. Utility theory is introduced. Evaluation Results aye converted into utility values by the utility functions and the values are optimized by 0-1 programming. Scoring method and Integer programming is used to evaluation a correspondence with a goal and to allocation the limiting resources. And utility function is used to reflect the preference of decision makers on the project evaluation.

• 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.

• Asian Journal of Business Environment
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• v.7 no.4
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• pp.17-25
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• 2017

Purpose - The purpose of this study is to present a framework for determining the most appropriate business model for e-learning. Research design, data, and methodology - The Electronics Branch of Azad University has been elected as a case study in this research. This study conducted using a descriptive method. The information was obtained using interviews with experts including managers, faculty and students at the Electronics Branch of Azad University. Results - Three service-product system (product oriented system, use an oriented and result oriented system) approaches determined a framework for the formation of a portfolio. This portfolio is including three types of e-learning business models. Examining the relevant characteristics, correspondence of behaviorism learning theory with a product-oriented approach, correspondence of cognitivism theory with a user-oriented approach and in finally match correspondence of constructivist learning theory with a results-oriented approach which is evident. Conclusions - After reviewing the literature on the fields of e-learning, business model and product - service systems, we have achieved three types of e-learning business models. Then the variables in any of the business models were defined by using business model canvas tool and thus a portfolio consisting of three types of e-learning business model canvas was obtained.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.3
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• pp.362-371
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• 2009

Shipbuilding is a typical 'build to order' industry. It has a business model that generates revenues from building various ships and offshore products in accordance with owner's requirements at each production stage. Under uncertainty in shipping market, it is very essential for the shipbuilder to prepare the fast and competitive decision for product portfolio strategy in order to maximize contribution margin by exploiting production facilities and constraints. In this study, we introduce the unique decision supporting methodology for the optimal product portfolio sets based on Theory of Constraints(TOC). This methodology is established by adopting the concept of Drum Buffer Rope(DBR) in constraints planning and Throughput Accounting (TA) in management accounting of TOC. In addition, Decision Supporting System(DSS) is implemented. This DSS system provides a throughput estimator with reflecting the cost structure of shipbuilding industry and a resource simulator built on heuristic algorithms to operate major constraint-resources in shipyard such as dock, quay and pre-erection area etc. Several examples are presented to show that the proposed methodology and system can effectively support the strategic decision-making process of a global shipbuilding company.