Fuzzy portfolio selection model with real features and different decision behaviors

Liu, Yong Jun; Zhang, Weiguo

doi:10.1007/s10700-017-9274-z

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…where e * tt is the optimal efficiency value of the t asset (DMU t ), x it and x ir have m kinds of inputs of assets, y jt and y jr have n ′ kinds of the objective statistical data output of assets, y bt , y br are n ″ kinds of the subjective fuzzy data output of assets, α i is input weights, and β j and β b are output weights. rough model (13) (15)…”

Section: Definition 7 Given An Arbitrary Probabilistic Hesitant Fuzzymentioning

confidence: 99%

“…Second, the investment portfolio model proposed is based on the nonstatistical model, which requires relatively little data. Nonstatistical methods used in portfolio models include neural networks [10], CEDEA [11], genetic algorithms [12], and fuzzy theory [13,14]. ird, some qualitative portfolio models were developed based on fuzzy environments, such as Wang and Zhu [15], Chiarawongse et al [16], and Zhou and Xu [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Zhang

Zhou

Luo

et al. 2021

Mathematical Problems in Engineering

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In the case of insufficient quantitative data, qualitative evaluation information is very important for investment decision-making. However, if it is completely based on qualitative evaluation information, the results may be subjective. In response to this problem, this paper proposes a method, namely, probabilistic hesitant fuzzy cross-efficiency evaluation (PHFCEE) method, based on probabilistic hesitant fuzzy theory, cross-efficiency data envelopment analysis (CEDEA). This method uses probabilistic hesitant fuzzy sets to collect qualitative evaluation information and then uses the cross-efficiency DEA model to fuse quantitative data and qualitative information. And finally, an investment portfolio is built based on the cross-efficiency value and its variance. In addition, this article gives the specific operating steps of the PHFCEE method and uses the construction of a portfolio of 10 stocks in the China CSI 300 Index as an example to illustrate the effectiveness of this method.

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Section: Definition 7 Given An Arbitrary Probabilistic Hesitant Fuzzymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Zhang

Zhou

Luo

et al. 2021

Mathematical Problems in Engineering

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“…s e (r it ) and s e (r b ) in the proposed model are defined by Equations (18) and (19), respectively.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…With the introduction of fuzzy set theory [14], an increasing number of scholars began to investigate the portfolio selection problems in the fuzzy environment. Assuming that the returns are fuzzy, there exist numerous papers employing possibility theory for fuzzy portfolio selections; see, for example, Vercher et al [15], Chen [16], Jana et al [17], Chen and Tsaur [18], Liu and Zhang [19], and Chen and Xu [20]. Although possibility theory is widely used, it has limitations.…”

Section: Introductionmentioning

confidence: 99%

Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk

Zhang

2019

Entropy

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In financial markets, investors will face not only portfolio risk but also background risk. This paper proposes a credibilistic multi-objective mean-semi-entropy model with background risk for multi-period portfolio selection. In addition, realistic constraints such as liquidity, cardinality constraints, transaction costs, and buy-in thresholds are considered. For solving the proposed multi-objective problem efficiently, a novel hybrid algorithm named Hybrid Dragonfly Algorithm-Genetic Algorithm (HDA-GA) is designed by combining the advantages of the dragonfly algorithm (DA) and non-dominated sorting genetic algorithm II (NSGA II). Moreover, in the hybrid algorithm, parameter optimization, constraints handling, and external archive approaches are used to improve the ability of finding accurate approximations of Pareto optimal solutions with high diversity and coverage. Finally, we provide several empirical studies to show the validity of the proposed approaches.

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“…At the same time, application of fuzzy sequencing, fuzzy set operation, sensitivity analysis, duality theory and fuzzy optimization in production practice also become important research contents of fuzzy optimization theory and method [9].…”

Section: 4mentioning

confidence: 99%

Application of Interval Valued Fuzzy Linear Programming for Stock Portfolio Optimization

Yin¹

2018

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In this paper, based on existing results, decision making about portfolio investment schemes is discussed, ordering method of fuzzy numbers of interval value is shown, corresponding auxiliary models are established and solutions are provided with theories of fuzzy mathematics, optimization theory and numerical calculation, etc. Then it applies software programming to solve the portfolio investment situation between investors in savings and four securities according to the established models. The result shows that investors can choose the risk coefficient that they can bear to reach the maximum value of expected returns. The greater the risk coefficient, the greater the income, the smaller the risk coefficient and the smaller the income. Investors can determine their own portfolio strategy according to their own conditions in order to meet their own interests.

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Fuzzy portfolio selection model with real features and different decision behaviors

Cited by 18 publications

References 20 publications

Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

Credibilistic Mean-Semi-Entropy Model for Multi-Period Portfolio Selection with Background Risk

Application of Interval Valued Fuzzy Linear Programming for Stock Portfolio Optimization

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