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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.

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

Journal Article Type Article
Acceptance Date Jan 1, 2014
Publication Date Sep 23, 2014
Deposit Date Oct 25, 2017
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
DOI https://doi.org/10.1007/978-3-319-11897-0_38
Keywords Conditional Value at Risk; CVaR; Hybrid algorithm; Port- folio selection
Public URL http://eprints.nottingham.ac.uk/id/eprint/47529
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.nottingham.ac.uk/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