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Portfolio optimization using Laplacian biogeography based optimization

Author

Listed:
  • Vanita Garg

    (Galgotias University)

  • Kusum Deep

    (Indian Institute of Technology, Roorkee)

Abstract

Portfolio optimization is defined as the most appropriate allocation of assets so as to maximize returns subject to minimum risk. This constrained nonlinear optimization problem is highly complex due to the presence of a number of local optimas. The objective of this paper is to illustrate the effectiveness of a well-tested and effective Laplacian biogeography based optimization and another variant called blended biogeography based optimization. As an illustration the model and solution methodology is implemented on data taken from Indian National Stock Exchange, Mumbai from 1st April, 2015 to 31st March, 2016. From the analysis of results, it is concluded that as compared to blended BBO, the recently proposed LX-BBO algorithm is an effective tool to solve this complex problem of portfolio optimization with better accuracy and reliability.

Suggested Citation

  • Vanita Garg & Kusum Deep, 2019. "Portfolio optimization using Laplacian biogeography based optimization," OPSEARCH, Springer;Operational Research Society of India, vol. 56(4), pages 1117-1141, December.
    • Handle: RePEc:spr:opsear:v:56:y:2019:i:4:d:10.1007_s12597-019-00400-4
    DOI: 10.1007/s12597-019-00400-4
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    References listed on IDEAS

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    Cited by:

    1. Mousumi Banerjee & Vanita Garg & Kusum Deep, 2023. "Solving structural and reliability optimization problems using efficient mutation strategies embedded in sine cosine algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 307-327, March.

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