Asset portfolio optimization using fuzzy mathematical programming

Gupta, Pankaj; Mehlawat, Mukesh Kumar; Saxena, Anand

doi:10.1016/j.ins.2007.10.025

Cited by 159 publications

(75 citation statements)

References 38 publications

(39 reference statements)

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“…After the introduction of the mean-variance model by Markowitz which is the basis for single-period investment portfolio selection models, many developments were performed on the model (see, for example, Xia et al, 2000;Giove et al, 2006: Gupta et al, 2008Yu & Lee, 2011).…”

Section: Single-period Investment Portfoliomentioning

confidence: 99%

Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm

Barati¹,

Mohammadi²,

Naderi³

2016

10.5267/j.ijiec

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Multi-period models of portfolio selection have been developed in the literature with respect to certain assumptions. In this study, for the first time, the portfolio selection problem has been modeled based on mean-semi variance with transaction cost and minimum transaction lots considering functional constraints and fuzzy parameters. Functional constraints such as transaction cost and minimum transaction lots were included. In addition, the returns on assets parameters were considered as trapezoidal fuzzy numbers. An efficient genetic algorithm (GA) was designed, results were analyzed using numerical instances and sensitivity analysis were executed. In the numerical study, the problem was solved based on the presence or absence of each mode of constraints including transaction costs and minimum transaction lots. In addition, with the use of sensitivity analysis, the results of the model were presented with the variations of minimum expected rate of programming periods.

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Section: Single-period Investment Portfoliomentioning

confidence: 99%

Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm

Barati¹,

Mohammadi²,

Naderi³

2016

10.5267/j.ijiec

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“…Yield estimates were obtained using only historical data, while the risk is assessed as in previous work. Gupta et al (2008) used fuzzy methodology in the assessment of the expected returns, liquidity and risk.…”

Section: Fuzzy Optimization In Literaturementioning

confidence: 99%

10.5937/industrija42-5731 = Fuzzy models in measuring competitiveness of agroindustry

Adžić¹,

Stojić²

2014

Industrija

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“…Considering the complexity of the security market in the real world, the non-uniqueness of randomness as a kind of uncertainty and the lack of enough historical data to reflect the future performances of security returns in some real life cases, many scholars began to regard security returns as fuzzy variables which rely on experienced experts' evaluations instead of historical data. Thus, fuzzy portfolio optimization theory is developed and has been mainly studied based on following three methods: (i) Fuzzy set theory [5]; (ii) Possibility measure [6,7]; (iii) Credibility measure [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

A Risk-Free Protection Index Model for Portfolio Selection with Entropy Constraint under an Uncertainty Framework

Gao

Huicheng

2017

Entropy

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This paper aims to develop a risk-free protection index model for portfolio selection based on the uncertain theory. First, the returns of risk assets are assumed as uncertain variables and subject to reputable experts' evaluations. Second, under this assumption, combining with the risk-free interest rate we define a risk-free protection index (RFPI), which can measure the protection degree when the loss of risk assets happens. Third, note that the proportion entropy serves as a complementary means to reduce the risk by the preset diversification requirement. We put forward a risk-free protection index model with an entropy constraint under an uncertainty framework by applying the RFPI, Huang's risk index model (RIM), and mean-variance-entropy model (MVEM). Furthermore, to solve our portfolio model, an algorithm is given to estimate the uncertain expected return and standard deviation of different risk assets by applying the Delphi method. Finally, an example is provided to show that the risk-free protection index model performs better than the traditional MVEM and RIM.

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Asset portfolio optimization using fuzzy mathematical programming

Cited by 159 publications

References 38 publications

Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm

Multi-period fuzzy mean-semi variance portfolio selection problem with transaction cost and minimum transaction lots using genetic algorithm

10.5937/industrija42-5731 = Fuzzy models in measuring competitiveness of agroindustry

A Risk-Free Protection Index Model for Portfolio Selection with Entropy Constraint under an Uncertainty Framework

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