Multiperiod mean absolute deviation fuzzy portfolio selection model with risk control and cardinality constraints

2014 ◽  
Vol 255 ◽  
pp. 74-91 ◽  
Author(s):  
Peng Zhang ◽  
Wei-Guo Zhang
Keyword(s):  
Portfolio Selection ◽  
Risk Control ◽  
Selection Model ◽  
Mean Absolute Deviation ◽  
Cardinality Constraints ◽  
Absolute Deviation ◽  
Fuzzy Portfolio Selection ◽  
Portfolio Selection Model

scholarly journals Multi-Period Mean-Absolute Deviation Fuzzy Portfolio Selection Model with Entropy Constraints

2016 ◽  
Vol 4 (5) ◽  
pp. 428-443 ◽  
Author(s):  
Peng Zhang ◽  
Heshan Gong ◽  
Weiting Lan
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Path Dependence ◽  
Risk Control ◽  
Selection Model ◽  
Programming Approach ◽  
Absolute Deviation ◽  
Fuzzy Portfolio Selection ◽  
Proposed Model ◽  
Portfolio Selection Model

AbstractThis paper considers a multi-period fuzzy portfolio selection problem maximizing the terminal wealth imposed by risk control, in which the returns of assets are characterized by fuzzy numbers. A fuzzy absolute deviation is originally defined as the risk control of portfolio. Entropy constraints and borrowing constraints are added in the portfolio selection model. Based on the theories of possibility measures, a new multi-period portfolio optimization model with transaction costs is proposed. And then, the proposed model is transformed into a crisp nonlinear programming problem by using fuzzy programming approach. Because of the transaction costs, the multi-period portfolio selection is the dynamic optimization problem with path dependence. Through changing the cost function into a variable, the multi-period portfolio selection is approximately turned into the dynamic programming. Furthermore, the discrete approximate iteration method is designed to obtain the optimal portfolio strategy. Finally, an example is given to illustrate the behavior of the proposed model and the designed algorithm using real data from the Shanghai Stock Exchange.

scholarly journals Fuzzy Portfolio Optimization of Power Generation Assets

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3043
Author(s):  
Barbara Glensk ◽  
Reinhard Madlener
Keyword(s):  
Power Generation ◽  
Decision Making ◽  
Portfolio Selection ◽  
Probabilistic Approach ◽  
Mean Absolute Deviation ◽  
Membership Functions ◽  
Absolute Deviation ◽  
Selection Models ◽  
Fuzzy Portfolio Selection ◽  
Aspiration Levels

Fuzzy theory is proposed as an alternative to the probabilistic approach for assessing portfolios of power plants, in order to capture the complex reality of decision-making processes. This paper presents different fuzzy portfolio selection models, where the rate of returns as well as the investor’s aspiration levels of portfolio return and risk are regarded as fuzzy variables. Furthermore, portfolio risk is defined as a downside risk, which is why a semi-mean-absolute deviation portfolio selection model is introduced. Finally, as an illustration, the models presented are applied to a selection of power generation mixes. The efficient portfolio results show that the fuzzy portfolio selection models with different definitions of membership functions as well as the semi-mean-absolute deviation model perform better than the standard mean-variance approach. Moreover, introducing membership functions for the description of investors’ aspiration levels for the expected return and risk shows how the knowledge of experts, and investors’ subjective opinions, can be better integrated in the decision-making process than with probabilistic approaches.

A novel probabilistic hesitant fuzzy portfolio selection model with value-at-risk and safety level of score

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xue Deng ◽  
Weimin Li
Keyword(s):  
At Risk ◽  
Portfolio Selection ◽  
Value At Risk ◽  
Selection Model ◽  
Selection Models ◽  
Content Type ◽  
Fuzzy Portfolio Selection ◽  
Fuzzy Environment ◽  
Portfolio Selection Model

Purpose This paper aims to propose two portfolio selection models with hesitant value-at-risk (HVaR) – HVaR fuzzy portfolio selection model (HVaR-FPSM) and HVaR-score fuzzy portfolio selection model (HVaR-S-FPSM) – to help investors solve the problem that how bad a portfolio can be under probabilistic hesitant fuzzy environment. Design/methodology/approach It is strictly proved that the higher the probability threshold, the higher the HVaR in HVaR-S-FPSM. Numerical examples and a case study are used to illustrate the steps of building the proposed models and the importance of the HVaR and score constraint. In case study, the authors conduct a sensitivity analysis and compare the proposed models with decision-making models and hesitant fuzzy portfolio models. Findings The score constraint can make sure that the portfolio selected is profitable, but will not cause the HVaR to decrease dramatically. The investment proportions of stocks are mainly affected by their HVaRs, which is consistent with the fact that the stock having good performance is usually desirable in portfolio selection. The HVaR-S-FPSM can find portfolios with higher HVaR than each single stock and has little sacrifice of extreme returns. Originality/value This paper fulfills a need to construct portfolio selection models with HVaR under probabilistic hesitant fuzzy environment. As a downside risk, the HVaR is more consistent with investors’ intuitions about risks. Moreover, the score constraint makes sure that undesirable portfolios will not be selected.

A new bi-objective fuzzy portfolio selection model and its solution through evolutionary algorithms

2018 ◽  
Vol 23 (12) ◽  
pp. 4367-4381 ◽  
Author(s):  
Mohuya B. Kar ◽  
Samarjit Kar ◽  
Sini Guo ◽  
Xiang Li ◽  
Saibal Majumder
Keyword(s):  
Evolutionary Algorithms ◽  
Portfolio Selection ◽  
Selection Model ◽  
Fuzzy Portfolio Selection ◽  
Portfolio Selection Model
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