A Novel Hybrid Algorithm for Mean-CVaR Portfolio Selection with Real-World Constraints

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• 作者：Quande Qin (18) (19)
  Li Li (18)
  Shi Cheng (20) (21)
  
• 关键词：Conditional Value at Risk ; CVaR ; Hybrid algorithm ; Portfolio selection
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8795
• 期：1
• 页码：319-327
• 全文大小：192 KB
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• 作者单位：Quande Qin (18) (19)
  Li Li (18)
  Shi Cheng (20) (21)
  
  18. Department of Management Science, Shenzhen University, Shenzhen, China
  19. Research Institute of Business Analytics & Supply Chain Management, Shenzhen University, Shenzhen, China
  20. Division of Computer Science, University of Nottingham Ningbo, China
  21. International Doctoral Innovation Centre, University of Nottingham Ningbo, China
  
• ISSN：1611-3349

文摘

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.