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Quantum beetle antennae search: a novel technique for the constrained portfolio optimization problem

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Abstract

In this paper, we have formulated quantum beetle antennae search (QBAS), a meta-heuristic optimization algorithm, and a variant of beetle antennae search (BAS). We apply it to portfolio selection, a well-known finance problem. Quantum computing is gaining immense popularity among the scientific community as it outsmarts the conventional computing in efficiency and speed. All the traditional computing algorithms are not directly compatible with quantum computers, for that we need to formulate their variants using the principles of quantum mechanics. In the portfolio optimization problem, we need to find the set of optimal stock such that it minimizes the risk factor and maximizes the mean-return of the portfolio. To the best of our knowledge, no quantum meta-heuristic algorithm has been applied to address this problem yet. We apply QBAS on real-world stock market data and compare the results with other meta-heuristic optimization algorithms. The results obtained show that the QBAS outperforms swarm algorithms such as the particle swarm optimization (PSO) and the genetic algorithm (GA).

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Authors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, Hong Kong, 999077, China

    Ameer Tamoor Khan

  2. School of Management, Shanghai University, Shanghai, 201900, China

    Xinwei Cao

  3. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Shuai Li & Bin Hu

  4. Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Athens, 15772, Greece

    Vasilios N. Katsikis

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  1. Ameer Tamoor Khan
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  2. Xinwei Cao
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  3. Shuai Li
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  4. Bin Hu
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  5. Vasilios N. Katsikis
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Correspondence to Xinwei Cao.

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Khan, A.T., Cao, X., Li, S. et al. Quantum beetle antennae search: a novel technique for the constrained portfolio optimization problem. Sci. China Inf. Sci. 64, 152204 (2021). https://doi.org/10.1007/s11432-020-2894-9

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  • DOI: https://doi.org/10.1007/s11432-020-2894-9

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