scholarly journals Uncertain Portfolio Selection with Background Risk and Liquidity Constraint

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Zhai ◽  
Manying Bai
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Risk Model ◽  
Background Risk ◽  
Asset Return ◽  
Uncertain Variables ◽  
Asset Liquidity ◽  
Security Returns ◽  
The Impact ◽  
Mean Risk

This paper discusses an uncertain portfolio selection problem with consideration of background risk and asset liquidity. In addition, the transaction costs are also considered. The security returns, background asset return, and asset liquidity are estimated by experienced experts instead of historical data. Regarding them as uncertain variables, a mean-risk model with background risk, liquidity, and transaction costs is proposed for portfolio selection and the crisp forms of the model are provided when security returns obey different uncertainty distributions. Moreover, for better understanding of the impact of background risk and liquidity on portfolio selection, some important theorems are proved. Finally, numerical experiments are presented to illustrate the modeling idea.

scholarly journals An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Chen
Keyword(s):  
Portfolio Selection ◽  
Artificial Bee Colony ◽  
Mean Value ◽  
Investment Risk ◽  
Entropy Model ◽  
Uncertain Variables ◽  
Bee Colony ◽  
Proposed Model ◽  
Security Returns ◽  
Mean Variance

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

Multiperiod portfolio selection models under uncertain measure and with multiple criteria

2021 ◽  
pp. 1-16
Author(s):  
Jia Zhai ◽  
Haitao Zheng ◽  
Manying Bai ◽  
Yunyun Jiang
Keyword(s):  
Portfolio Optimization ◽  
Portfolio Selection ◽  
Background Risk ◽  
Multiple Criteria ◽  
Turnover Rates ◽  
Uncertain Measure ◽  
Selection Models ◽  
Uncertain Variables ◽  
Portfolio Optimization Problem ◽  
Mean Variance Portfolio

This paper explores a multiperiod portfolio optimization problem under uncertain measure involving background risk, liquidity constraints and V-shaped transaction costs. Unlike traditional studies, we establish multiperiod mean-variance portfolio optimization models with multiple criteria in which security returns, background asset returns and turnover rates are assumed to be uncertain variables that can be estimated by experienced experts. When the returns of the securities and background assets follow normal uncertainty distributions, we use the deterministic forms of the multiperiod portfolio optimization model. The uncertain multiperiod portfolio selection models are practical but complicated. Therefore, the models are solved by employing a genetic algorithm. The uncertain multiperiod model with multiple criteria is compared with an uncertain multiperiod model without background risk and an uncertain multiperiod model without liquidity constraint, we discuss how background risk and liquidity affect optimal terminal wealth. Finally, we give two numerical examples to demonstrate the effectiveness of the proposed approach and models.

Sign in / Sign up

Export Citation Format

Share Document