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

Quande Qin, Li Li, Shi Cheng

Research output: Journal PublicationArticlepeer-review

6 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)319-327
JournalLecture Notes in Computer Science
Volume8795
DOIs
Publication statusPublished - 23 Sep 2014

Keywords

  • CVaR
  • Conditional Value at Risk
  • Hybrid algorithm
  • Port- folio selection

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