Skip to main content

Research Repository

Advanced Search

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

Qin, Quande; Li, Li; Cheng, Shi


Quande Qin

Senior Research Fellow

Shi Cheng


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.


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
Keywords Conditional Value at Risk; CVaR; Hybrid algorithm; Port- folio selection
Public URL
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address:
Additional Information The final publication is available at Springer via