Modeling Portfolio Optimization Problem by Probability-Credibility Equilibrium Risk Criterion

Wang, Ye; Chen, Yanju; Liu, Yankui

doi:10.1155/2016/9461021

Cited by 9 publications

(8 citation statements)

References 33 publications

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“…is assumed to be a linear uncertain variable, where b i � a i + r i , a i is generated randomly from the uniform distribution U(− 0.5, 0.5) and r i is generated randomly from the uniform distribution U(0.1, 0.3). According to models (24)- (26) and eorem 3, we can solve the problems by using MATLAB 2017. e experiment results are listed in Table 8, where "objective value 1" represents the objective value of the initial chance-mean model, "objective value 2" represents the objective value of the chance-mean model without loans, and "objective value 3" represents the objective value of the chance-mean model without background risk. For each (w, v, u, α), Table 8 shows that the portfolio return with loans is greater than that without loans.…”

Section: Large-scale Experimentsmentioning

confidence: 99%

“…Bhattacharyya et al [25] proposed a fuzzy meanvariance-skewness model by considering transaction cost. Wang et al [26] investigated a portfolio selection problem with random fuzzy variables by defining a new equilibrium risk value. Kar et al [27] considered the Sharpe ratio and the value-at-risk ratio and then proposed a biobjective fuzzy portfolio selection model.…”

Section: Introductionmentioning

confidence: 99%

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Uncertain Portfolio Selection with Borrowing Constraint and Background Risk

Zhang

Peng

et al. 2020

Mathematical Problems in Engineering

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Due to the complexity of financial markets, there exist situations where security returns and background factor returns are available mainly based on experts’ subjective beliefs, such as in the case of lack of historical data. To deal with such indeterminate quantities, uncertain variables are introduced. Based on uncertainty theory, this paper discusses the distribution function of the optimal portfolio return. Two types of new uncertain programming models, namely, the chance-mean model and the measure-mean model, are proposed to make an optimal portfolio selection decision in an uncertain environment. It is proved that there exists an equivalent relation between the chance-mean model and a deterministic linear programming model, which leads to an approach to obtain the optimal solutions of the proposed models. Finally, some numerical examples are illustrated to show the modelling ideas and the effectiveness of the models.

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Section: Large-scale Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertain Portfolio Selection with Borrowing Constraint and Background Risk

Zhang

Peng

et al. 2020

Mathematical Problems in Engineering

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“…Suppose also that one investor has a utility function that squares shaped like the following [16], [22] The function of risk aversion ( ) W for someone of such investors can be specified as: …”

Section: Utility Function Of Investormentioning

confidence: 99%

Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic

Sidi¹,

Bon²,

Supian³

2017

AIP Conference Proceedings

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Estimation of value at risk in currency exchange rate portfolio using asymmetric GJR-GARCH Copula AIP Conference Proceedings 1827, 020006 (2017) Abstract. The problems of investing in financial assets are to choose a combination of weighting a portfolio can be maximized return expectations and minimizing the risk. This paper discusses the modeling of Mean-VaR portfolio optimization by risk tolerance, when square-shaped utility functions. It is assumed that the asset return has a certain distribution, and the risk of the portfolio is measured using the Value-at-Risk (VaR). So, the process of optimization of the portfolio is done based on the model of Mean-VaR portfolio optimization model for the Mean-VaR done using matrix algebra approach, and the Lagrange multiplier method, as well as Khun-Tucker. The results of the modeling portfolio optimization is in the form of a weighting vector equations depends on the vector mean return vector assets, identities, and matrix covariance between return of assets, as well as a factor in risk tolerance. As an illustration of numeric, analyzed five shares traded on the stock market in Indonesia. Based on analysis of five stocks return data gained the vector of weight composition and graphics of efficient surface of portfolio. Vector composition weighting weights and efficient surface charts can be used as a guide for investors in decisions to invest.

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“…Konak and Bagei [10] applied fuzzy linear programming for portfolio optimization. Wang et al [11] introduced a new risk index variable called equilibrium risk value (ERV) of the random fuzzy expected value (EV) and the EV-ERV model was used for portfolio selection.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Risk and Return Using Fuzzy Multiobjective Linear Programming

Panwar

Jha

Srivastava

2018

Advances in Fuzzy Systems

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Stock selection poses a challenge for both the investor and the finance researcher. In this paper, a hybrid approach is proposed for asset allocation, offering a combination of several methodologies for portfolio selection, such as investor topology, cluster analysis, and the analytical hierarchy process (AHP) to facilitate ranking the assets and fuzzy multiobjective linear programming (FMOLP). This paper considers some important factors of stock, like relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), and price to earnings growth ratio (PEG ratio), apart from the risk and return and stocks which are included within these same factors. Employing fuzzy multiobjective linear programming, optimization is performed using seven objective functions viz., return, risk, relative strength index (RSI), coefficient of variation (CV), earnings yield (EY), price to earnings growth ratio (PEG ratio), and AHP weighted score. The FMOLP transforms the multiobjective problem to a single objective problem using the “weighted adaptive approach” in which the weights are calculated by AHP or choices by the investors. The FMOLP model permits choices in solution.

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Modeling Portfolio Optimization Problem by Probability-Credibility Equilibrium Risk Criterion

Cited by 9 publications

References 33 publications

Uncertain Portfolio Selection with Borrowing Constraint and Background Risk

Uncertain Portfolio Selection with Borrowing Constraint and Background Risk

Modeling of Mean-VaR portfolio optimization by risk tolerance when the utility function is quadratic

Optimization of Risk and Return Using Fuzzy Multiobjective Linear Programming

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