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

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Advances in Swarm Intelligence (ICSI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8795))

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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.

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Author information

Authors and Affiliations

  1. Department of Management Science, Shenzhen University, Shenzhen, China

    Quande Qin & Li Li

  2. Research Institute of Business Analytics & Supply Chain Management, Shenzhen University, Shenzhen, China

    Quande Qin

  3. Division of Computer Science, University of Nottingham Ningbo, China

    Shi Cheng

  4. International Doctoral Innovation Centre, University of Nottingham Ningbo, China

    Shi Cheng

Authors
  1. Quande Qin
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  2. Li Li
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  3. Shi Cheng
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Computer Science Department, CINVESTAV-IPN, Mexico

    Carlos A. Coello Coello

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© 2014 Springer International Publishing Switzerland

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Qin, Q., Li, L., Cheng, S. (2014). A Novel Hybrid Algorithm for Mean-CVaR Portfolio Selection with Real-World Constraints. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_38

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  • DOI: https://doi.org/10.1007/978-3-319-11897-0_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11896-3

  • Online ISBN: 978-3-319-11897-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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