A Nonlinear Interval Portfolio Selection Model and Its Application in Banks

Yan, Dawen; Hu, Yaxing; Lai, Kin Keung

doi:10.1007/s11424-017-6070-3

Cited by 10 publications

(3 citation statements)

References 46 publications

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“…For example, Zhang and Li (2009) [10] proposed interval-valued mean semi-variance model. Yan et al (2017) [11] established mean downside semi-variance portfolio selection model. Zhang and Zhang (2022) [12] considered portfolio selection problems based on interval-valued CVaR.…”

Section: Introductionmentioning

confidence: 99%

Portfolio Selection Optimization with Hierarchical Fuzzy Conditional Value-at-Risk

Zhang

2022

Frontiers in Artificial Intelligence and Applications

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Quantitative risk management (QRM, for short) is very important for investors or financial institutions. This paper discusses portfolio selection in fuzzy environments by means of stochastic and fuzzy methods. Two risk measures called hierarchical fuzzy value-at-risk (HFVaR, for short) and hierarchical fuzzy conditional value-at-risk (HFCVaR, for short) are proposed. And then fuzzy portfolio selection models are established based on the risk measure HFCVaR.

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Section: Introductionmentioning

confidence: 99%

Portfolio Selection Optimization with Hierarchical Fuzzy Conditional Value-at-Risk

Zhang

2022

Frontiers in Artificial Intelligence and Applications

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“…Multi-objective programming problems are sometimes called multi-objective optimization problems, and they mainly focus on the simultaneous optimization of multiple numerical objectives under various constraints. Multi-objective programming has been applied in many important fields, such as portfolio [22], energy [23], engineering design [24], and information resource planning [25]. Our research aims to solve the multi-objective planning problems that are characteristic of insurance community underwriting, mainly from the perspective of the alliance's overall operational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Cost Sharing in Insurance Communities: A Hybrid Approach Based on Multiple-Choice Objective Programming and Cooperative Games

Cao

et al. 2022

Sustainability

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At present, utilizing the insurance community is a common method to deal with investment risks along the Belt and Road; however, there is no clear method or mechanism to deal with the decision-making optimization and cost allocation of the insurance community participants. We propose a hybrid approach to solve this problem. First, we construct an underwriting decision optimization model for the insurance community using the multi-choice goal programming method, which generates the cost characteristic function based on a cooperative alliance. Second, we use the cooperative game method combined with the modified Shapley value method to take risk factors into consideration, which allows us to optimize the cost allocation among members of the insurance community. Finally, our simulation analysis results show that the multi-choice goal programming method can optimize the insurance community’s underwriting decisions. Specifically, the total underwriting cost is lower than the sum of the underwriting costs under the insurance company’s single-action strategy, and the total underwriting scale is as large as possible. Compared with the classical Shapley value method, the modified Shapley value method can better reflect differences in the underwriting risks of different regions, encouraging governments to take measures to reduce underwriting risks. To conclude, we propose some suggestions based on our research findings. The possible contributions of this paper are as follows: our research provides a hybrid optimization method based on multiple-choice objective programming and cooperative games to solve the cost allocation problem facing the insurance community, and it has some reference value for improving the cost-sharing system of the insurance community.

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“…Thus, other studies also used different risk measures in the portfolio optimization model and then examined the model under interval uncertainty. For example, mean-Value-at-Risk (VAR) portfolio optimization model [25], Mean-Variance-Skewness (MVS) portfolio selection model [26], and mean-semi-absolute deviation portfolio selection model [27–29] are some of the examples which have examined the portfolio optimization under interval uncertainty.…”

Section: Introductionmentioning

confidence: 99%

A portfolio selection model based on the knapsack problem under uncertainty

2019

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One of the primary concerns in investment planning is to determine the number of shares for asset with relatively high net value of share such as Berkshire Hathaway on Stock market. Traditional asset allocation methods like Markowitz theorem gives the solution as a percentage and this ratio may suggest allocation of half of a share on the market, which is impractical. Thus, it is necessary to propose a method to determine the number of shares for each asset. This paper presents a knapsack based portfolio selection model where the expected returns, prices, and budget are characterized by interval values. The study determines the priority and importance of each share in the proposed model by extracting the interval weights from an interval comparison matrix. The resulted model is converted into a parametric linear programming model in which the decision maker is able to determine the optimism threshold. Finally, a discrete firefly algorithm is designed to find the near optional solutions in large dimensions. The proposed study is implemented for some data from the US stock exchange.

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A Nonlinear Interval Portfolio Selection Model and Its Application in Banks

Cited by 10 publications

References 46 publications

Portfolio Selection Optimization with Hierarchical Fuzzy Conditional Value-at-Risk

Portfolio Selection Optimization with Hierarchical Fuzzy Conditional Value-at-Risk

Cost Sharing in Insurance Communities: A Hybrid Approach Based on Multiple-Choice Objective Programming and Cooperative Games

A portfolio selection model based on the knapsack problem under uncertainty

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