Robust Optimization in Portfolio Selection by m-MAD Model Approach

Ghahtarani, Alireza; Najafi, Amir Abbas

doi:10.24818/18423264/52.1.18.17

Cited by 5 publications

(2 citation statements)

References 7 publications

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“…Erdas [15] has investigated the MAD model for the Borsa Istanbul 30 Index. Ghahtarani and Najafi [16] have studied the MAD model in portfolio optimization for investments. Kasenbacher et al [17] have generated the optimal portfolio of stocks with the MAD model.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model

Siew

Jaaman

2021

Entropy

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Investors wish to obtain the best trade-off between the return and risk. In portfolio optimization, the mean-absolute deviation model has been used to achieve the target rate of return and minimize the risk. However, the maximization of entropy is not considered in the mean-absolute deviation model according to past studies. In fact, higher entropy values give higher portfolio diversifications, which can reduce portfolio risk. Therefore, this paper aims to propose a multi-objective optimization model, namely a mean-absolute deviation-entropy model for portfolio optimization by incorporating the maximization of entropy. In addition, the proposed model incorporates the optimal value of each objective function using a goal-programming approach. The objective functions of the proposed model are to maximize the mean return, minimize the absolute deviation and maximize the entropy of the portfolio. The proposed model is illustrated using returns of stocks of the Dow Jones Industrial Average that are listed in the New York Stock Exchange. This study will be of significant impact to investors because the results show that the proposed model outperforms the mean-absolute deviation model and the naive diversification strategy by giving higher a performance ratio. Furthermore, the proposed model generates higher portfolio mean returns than the MAD model and the naive diversification strategy. Investors will be able to generate a well-diversified portfolio in order to minimize unsystematic risk with the proposed model.

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Section: Introductionmentioning

confidence: 99%

Portfolio Optimization with a Mean–Absolute Deviation–Entropy Multi-Objective Model

Siew

Jaaman

2021

Entropy

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“…However, they did not consider the fundamental factors in their analysis. Many researchers have worked on developing financial risk measures [9][10][11][12][13][14][15] But, these researches have gap in considering fundamental factors and bubble condition.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling for a new portfolio selection problem in bubble condition, using a new risk measure

2019

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A portfolio selection model is developed in this study, using a new risk measure. The proposed risk measure is based on the fundamental value of stocks. For this purpose, a mathematical model is developed and transformed into an integer linear programming. In order to analyze the model's efficiency, the actual data of the Tehran Stock Exchange market are used in 12 scenarios to solve the proposed model. In order to evaluate the scenarios, data mining approaches are employed. Data mining methods which are used in this paper include ANFIS, decision tree, random forest, FDA, and GEP. The best method for scenario evaluation is GEP based on numerical results. Hence, the market values are evaluated by this algorithm. Software packages like MATLAB, GEP xpero tools, and LINGO are used to solve the model. Different trends of market value and fundamental value volatility in the optimum stock portfolio are determined. It is possible to examine the optimum portfolio profitability in different scenarios. By using realworld data, trends are extracted and analyzed. Results show that the developed model can be effectively applied in bubble situations.

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