Evidence map›Paper›PMID 39341855›Full record

ArticleScientific reports2024

An adapted Black Widow Optimization Algorithm for Financial Portfolio Optimization Problem with cardinalty and budget constraints.

Rahenda Khodier, Ahmed Radi, Basel Ayman, Mohamed Gheith

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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3 · Its place in the literature

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Rahenda KhodierDepartment of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt.
Ahmed RadiDepartment of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt.
Basel AymanDepartment of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt.
Mohamed GheithDepartment of Industrial and Manufacturing Engineering, Egypt-Japan University of Science and Technology, Alexandria, 21934, Egypt. mohamed.gheith@ejust.edu.eg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz's 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version. New features are introduced for the BWAPO to adapt better to the problem, including (1) mating attraction and (2) differential evolution mutation strategy. The proposed BWAPO is evaluated against other metaheuristic approaches used in portfolio optimization from literature, and its performance demonstrates its effectiveness through comparative studies on benchmark datasets using multiple performance metrics, particularly in the unconstrained Mean-Variance portfolio optimization version. Additionally, when encountering cardinality constraint, the proposed approach yields competitive results, especially noticeable with smaller datasets. This leads to a focused examination of the outcomes arising from equality versus inequality cardinality constraints, intending to determine which constraint type is more effective in producing portfolios with higher returns. The paper also presents a comprehensive mathematical model that integrates real-world constraints such as transaction costs, transaction lots, and a dollar-denominated budget, in addition to cardinality and bounding constraints. The model assesses both equality/inequality cardinality constraint versions of the problem, revealing that the inequality constraint tends to offer a wider range of feasible solutions with increased return potential.

Indexed as

Black Widow Algorithm for Portfolio OptimizationMean-varianceMeta-heuristicPortfolio optimization

Identifiers

PMID39341855
PMCPMC11439028

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