Set-Based Particle Swarm Optimization for Portfolio Optimization

Erwin, Kyle; Engelbrecht, Andries P.

doi:10.1007/978-3-030-60376-2_28

Cited by 14 publications

(5 citation statements)

References 127 publications

(385 reference statements)

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“…Erwin and Engelbrecht [95] used SBPSO as part of a portfolio optimisation approach to maximise return and minimise risk. The resultant algorithm solved portfolio optimisation by interleaving SBPSO, to select assets, and PSO, to optimise the weights of selected assets.…”

Section: Portfolio Optimisation Using Set-based Particle Swarm Optimi...mentioning

confidence: 99%

“…The resultant algorithm solved portfolio optimisation by interleaving SBPSO, to select assets, and PSO, to optimise the weights of selected assets. To select an optimal combination of financial products, the PSO algorithm is interleaved with SBPSO into a bi-level optimisation process to find optimal ratios of assets [95]. The two stages of the bi-level optimisation process are solved by SBPSO and PSO.…”

Section: Portfolio Optimisation Using Set-based Particle Swarm Optimi...mentioning

confidence: 99%

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Set-Based Particle Swarm Optimisation: A Review

Zyl

Engelbrecht

2023

Mathematics

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The set-based particle swarm optimisation algorithm is a swarm-based meta-heuristic that has gained popularity in recent years. In contrast to the original particle swarm optimisation algorithm, the set-based particle swarm optimisation algorithm is used to solve discrete and combinatorial optimisation problems. The main objective of this paper is to review the set-based particle swarm optimisation algorithm and to provide an overview of the problems to which the algorithm has been applied. This paper starts with an examination of previous attempts to create a set-based particle swarm optimisation algorithm and discusses the shortcomings of the existing attempts. The set-based particle swarm optimisation algorithm is established as the only suitable particle swarm variant that is both based on true set theory and does not require problem-specific modifications. In-depth explanations are given regarding the general position and velocity update equations, the mechanisms used to control the exploration–exploitation trade-off, and the quantifiers of swarm diversity. After the various existing applications of set-based particle swarm optimisation are presented, this paper concludes with a discussion on potential future research.

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Section: Portfolio Optimisation Using Set-based Particle Swarm Optimi...mentioning

confidence: 99%

Section: Portfolio Optimisation Using Set-based Particle Swarm Optimi...mentioning

confidence: 99%

Set-Based Particle Swarm Optimisation: A Review

Zyl

Engelbrecht

2023

Mathematics

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“…The MOEA/D algorithms were successfully applied to different application areas such as PO [11], [26], image segmentation [27], and network routing [28].…”

Section: Introductionmentioning

confidence: 99%

Bitcoin Optimized Signal Allocation Strategies using Decomposition

Omran,

El-Behaidy,

Youssif

2023

IJACSA

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Bitcoin is the first and most famous cryptocurrency. It is a virtual currency that is operated in a decentralized form using cryptographic strategies called blockchains. Although it has experienced significant market acceptance by traders and investors in recent years, it also suffers from volatility and riskiness. Technical analysis is one of the most powerful tools used for trading signals' allocation using some algorithmic strategies called technical indicators. In this research, a newly proposed multi-objectives decomposition-based particle swarm optimization algorithm is used to find the best parameter values for some technical indicators, which in turn generates the best trading signals for Bitcoin trading. In this context, three conflicting objectives have been used, i.e., the return on investment, the Sortino-ratio, and the number of trades. The proposed algorithm is compared to the original MOEA/D algorithm as well as the indicators using their original parameters. Results showed the superiority of the proposed algorithm during the training and testing periods over the other benchmarks.

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“…Since then, researchers have applied PSO to a variety of contexts. For instance, PSO has been used for efficient frontier estimation [18], portfolio optimization [19][20][21][22][23][24], interest rate modeling [25], earnings forecasting [26], and inventory management [27].…”

Section: Introductionmentioning

confidence: 99%

Search on an NK Landscape with Swarm Intelligence: Limitations and Future Research Opportunities

Chen,

Miller,

Toh

2023

Algorithms

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Swarm intelligence has promising applications for firm search and decision-choice problems and is particularly well suited for examining how other firms influence the focal firm’s search. To evaluate search performance, researchers examining firm search through simulation models typically build a performance landscape. The NK model is the leading tool used for this purpose in the management science literature. We assess the usefulness of the NK landscape for simulated swarm search. We find that the strength of the swarm model for examining firm search and decision-choice problems—the ability to model the influence of other firms on the focal firm—is limited to the NK landscape. Researchers will need alternative ways to create a performance landscape in order to use our full swarm model in simulations. We also identify multiple opportunities—endogenous landscapes, agent-specific landscapes, incomplete information, and costly movements—that future researchers can include in landscape development to gain the maximum insights from swarm-based firm search simulations.

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Set-Based Particle Swarm Optimization for Portfolio Optimization

Cited by 14 publications

References 127 publications

Set-Based Particle Swarm Optimisation: A Review

Set-Based Particle Swarm Optimisation: A Review

Bitcoin Optimized Signal Allocation Strategies using Decomposition

Search on an NK Landscape with Swarm Intelligence: Limitations and Future Research Opportunities

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