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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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 23; H. 15; S. 6231 - 6247
Hauptverfasser: Chen, Wei, Li, Dandan, Lu, Shan, Liu, Weiyi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019
Springer Nature B.V
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3281-z