Multi-period mean–semivariance portfolio optimization based on uncertain measure

In this paper, we discuss a multi-period portfolio selection problem when security returns are given by experts’ estimations. By considering the security returns as uncertain variables, we propose a multi-period mean–semivariance portfolio optimization model with real-world constraints, in which tra...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 23; číslo 15; s. 6231 - 6247
Hlavní autoři: Chen, Wei, Li, Dandan, Lu, Shan, Liu, Weiyi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Bankruptcy
Computational Intelligence
Constraint modelling
Control
Costs
Engineering
Evolutionary algorithms
Expected values
Fuzzy sets
Linear programming
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Optimization models
Optimization techniques
Random variables
Robotics
Security
Skewness
Multi-period portfolio optimization
Semivariance
Uncertain variable
Imperialist competitive algorithm
Cardinality constraint
ISSN:1432-7643, 1433-7479
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Popis
Shrnutí:In this paper, we discuss a multi-period portfolio selection problem when security returns are given by experts’ estimations. By considering the security returns as uncertain variables, we propose a multi-period mean–semivariance portfolio optimization model with real-world constraints, in which transaction costs, cardinality and bounding constraints are considered. Furthermore, we provide an equivalent deterministic form of mean–semivariance model under the assumption that the security returns are zigzag uncertain variables. After that, a modified imperialist competitive algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.
Bibliografie:ObjectType-Article-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3281-z