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

Qin, Quande, Li, Li and Cheng, Shi (2014) A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints. Lecture Notes in Computer Science, 8795 . pp. 319-327. ISSN 0302-9743

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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.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/735449
Additional Information: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11897-0_38
Keywords: Conditional Value at Risk; CVaR; Hybrid algorithm; Port- folio selection
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > School of Computer Science
Identification Number: https://doi.org/10.1007/978-3-319-11897-0_38
Depositing User: LIN, Zhiren
Date Deposited: 25 Oct 2017 14:18
Last Modified: 04 May 2020 16:53
URI: https://eprints.nottingham.ac.uk/id/eprint/47529

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