Meta–heuristics for Portfolio Optimization: Part I — Review of Meta–heuristics

Erwin, Kyle; Engelbrecht, Andries P.

doi:10.1007/978-3-031-36625-3_35

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Different versions of evolutionary techniques, such as artificial bee colony (ABC) [30], firefly algorithm (FA) [31], genetic algorithm (GA) [32], and particle swarm optimization (PSO) [13], have been found to be effective in managing portfo-lio optimization problems [33][34]. Furthermore, a set-based particle swarm optimization (SBPSO) [34] redefines the domain of the portfolio optimization problem. Few evolutionary techniques, such as GA, suffer from the problem of balance between exploration and exploitation.…”

Section: Related Workmentioning

confidence: 99%

Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem

Gunjan,

Bhattacharyya

2023

Preprint

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Portfolio optimization has always been a challenging proposition and a highly studied problem in finance and management. Portfolio optimization facilitates the selection of the right assets and their distribution according to the set objectives. Often, it has been found that this nonlinear constraint problem cannot be efficiently solved using a traditional approach. In this paper, quantum-inspired incarnations of three evolutionary techniques, viz., (i) genetic algorithm (GA), (ii) differential evolution (DE), and (iii) particle swarm optimization (PSO) are used for the portfolio optimization problem. Experiments have been conducted with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. Several enhancements of the evolutionary algorithms have been proposed in this article, viz., (i) enhanced crossover techniques for the portfolio optimization problem, (ii) regularization function to allocate funds efficiently, and (iii) dynamic parameter tuning using sensitivity analysis.

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