A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints
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 c...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Springer Verlag
2014
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/47529/ |
| _version_ | 1848797568353435648 |
|---|---|
| author | Qin, Quande Li, Li Cheng, Shi |
| author_facet | Qin, Quande Li, Li Cheng, Shi |
| author_sort | Qin, Quande |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:05:57Z |
| format | Article |
| id | nottingham-47529 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:05:57Z |
| publishDate | 2014 |
| publisher | Springer Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-475292020-05-04T16:53:37Z https://eprints.nottingham.ac.uk/47529/ A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints Qin, Quande Li, Li Cheng, Shi 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. Springer Verlag 2014-09-23 Article PeerReviewed 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 Conditional Value at Risk; CVaR; Hybrid algorithm; Port- folio selection https://link.springer.com/chapter/10.1007%2F978-3-319-11897-0_38 doi:10.1007/978-3-319-11897-0_38 doi:10.1007/978-3-319-11897-0_38 |
| spellingShingle | Conditional Value at Risk; CVaR; Hybrid algorithm; Port- folio selection Qin, Quande Li, Li Cheng, Shi A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints |
| title | A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints |
| title_full | A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints |
| title_fullStr | A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints |
| title_full_unstemmed | A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints |
| title_short | A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints |
| title_sort | novel hybrid algorithm for mean-cvar portfolio selection with real-world constraints |
| topic | Conditional Value at Risk; CVaR; Hybrid algorithm; Port- folio selection |
| url | https://eprints.nottingham.ac.uk/47529/ https://eprints.nottingham.ac.uk/47529/ https://eprints.nottingham.ac.uk/47529/ |