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Multi-objective Beetle Swarmoptimization for Portfolio Selection

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Knowledge Science, Engineering and Management (KSEM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13369))

  • 2020 Accesses

Abstract

The multi-objective portfolio optimization is regarded as an multi-objective optimization problem which is complicated and hard to find a satisfactory solution in a limited time. It is more complex and difficult to use the conventional method to solve this problem. In this paper, we first establish a mean-CVaR-entropy model with transaction costs and investment weight restrictions, and then propose a Multi-objective Optimization Algorithm for BeetleSearch(MOBSO), a meta-heuristic optimization algorithm, and a variant of Beetle Antennae Search (BAS) algorithm, which is applied into portfolio optimization to solve this constraint multi-objective optimization problem. Finally, we use the 20 stocksfrom January 2017 to December 2021 in the US stock market to do case studyand compare the results with other meta-heuristic optimization algorithms. It shows that the MOBSO outperforms swarm algorithms such as the particle swarm optimization (PSO) and the genetic algorithm(GA).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Tan Yan

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  1. Tan Yan
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Correspondence to Tan Yan .

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Editors and Affiliations

  1. Télécom Paris, Paris, France

    Gerard Memmi

  2. Purdue University, West Lafayette, IN, USA

    Baijian Yang

  3. Shanghai Jiao Tong University, Shanghai, Shanghai, China

    Linghe Kong

  4. Nanyang Technological University, Singapore, Singapore

    Tianwei Zhang

  5. Texas A&M University – Commerce, Commerce, TX, USA

    Meikang Qiu

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Yan, T. (2022). Multi-objective Beetle Swarmoptimization for Portfolio Selection. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_49

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  • DOI: https://doi.org/10.1007/978-3-031-10986-7_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10985-0

  • Online ISBN: 978-3-031-10986-7

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