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 STYLE
Qin, Q., Li, L., & Cheng, S. (2014). A novel hybrid algorithm for mean-CVaR portfolio selection with real-world constraints. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8795, pp. 319–327). Springer Verlag. https://doi.org/10.1007/978-3-319-11897-0_38
Mendeley helps you to discover research relevant for your work.