Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

Quantum beetle antennae search: a novel technique for the constrained portfolio optimization problem 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). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Science China Information Sciences Springer Journals

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

Loading next page...
 
/lp/springer-journals/quantum-beetle-antennae-search-a-novel-technique-for-the-constrained-05nEVyqMbv

References (61)

Publisher
Springer Journals
Copyright
Copyright © Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
ISSN
1674-733X
eISSN
1869-1919
DOI
10.1007/s11432-020-2894-9
Publisher site
See Article on Publisher Site

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).

Journal

Science China Information SciencesSpringer Journals

Published: May 1, 2021

Keywords: quantum computing; beetle antennae search; portfolio selection; optimization; finance problem

There are no references for this article.