Abstract

AbstractThe 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). KeywordsMulti-objective portfolio optimizationBeetle Antennae Search (BAS)Particle Swarm Optimization (PSO)Meta-heuristic optimizationFinance problem

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