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A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints

Qin, Quande; Li, Li; Cheng, Shi

Authors

Quande Qin

LI LI li.li@nottingham.ac.uk
Senior Research Fellow

Shi Cheng



Abstract

In this paper, we employ the Conditional Value at Risk (CVaR) to measure the portfolio risk, and propose a mean-CVaR portfolio selection model. In addition, some real-world constraints are considered. The constructed model is a non-linear discrete optimization problem and difficult to solve by the classic optimization techniques. A novel hybrid algorithm based particle swarm optimization (PSO) and artificial bee colony (ABC) is designed for this problem. The hybrid algorithm introduces the ABC operator into PSO. A numerical example is given to illustrate the modeling idea of the paper and the effectiveness of the proposed hybrid algorithm.

Journal Article Type Article
Publication Date Sep 23, 2014
Journal Lecture Notes in Computer Science
Electronic ISSN 0302-9743
Publisher Humana Press
Peer Reviewed Peer Reviewed
Volume 8795
Book Title Advances in Swarm Intelligence
APA6 Citation Qin, Q., Li, L., & Cheng, S. (2014). A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints. Lecture Notes in Artificial Intelligence, 8795, doi:10.1007/978-3-319-11897-0_38
DOI https://doi.org/10.1007/978-3-319-11897-0_38
Keywords Conditional Value at Risk; CVaR; Hybrid algorithm; Port- folio selection
Publisher URL https://link.springer.com/chapter/10.1007%2F978-3-319-11897-0_38
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11897-0_38
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