A Hybrid Model for Choosing the Optimal Stock Portfolio under Intuitionistic Fuzzy Sets

Rasoulzadeh, Mehrdad; Edalatpanah, Seyyed Ahmad; Fallah, Mohammad; Najafi, Seyyed Esmaeil

doi:10.22111/ijfs.2024.45118.7968

Document Type : Research Paper

Authors

¹ Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

² Associate Professor; Department of Applied Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran.

³ Department of Industrial Engineering, Islamic Azad University, Central Tehran Branch, Iran

⁴ Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

https://doi.org/10.22111/ijfs.2024.45118.7968

Abstract

In the dynamic world of financial investment, crafting an optimal stock portfolio that judiciously balances risk, return, and efficiency emerges as a critical challenge. Despite the wealth of research on financial portfolio optimization, prevailing methodologies predominantly emphasize either risk minimization or return maximization, often overlooking the imperative for a holistic strategy that simultaneously boosts efficiency and effectiveness. Addressing this gap in the literature, this study introduces an innovative four-objective model that intricately blends risk, return, and efficiency considerations for the strategic selection of stock portfolios. This model ingeniously integrates the foundational principles of Markowitz's mean-variance analysis with the sophisticated network data envelopment analysis (NDEA) techniques, significantly refining the portfolio selection methodology. It further distinguishes itself by incorporating returns represented as trapezoidal intuitionistic fuzzy numbers, adeptly capturing the inherent uncertainties in financial returns. Additionally, the model employs the network data envelopment analysis's cross-efficiency principle, providing a nuanced measure of company performance. To effectively navigate the complexities of this model, we deploy the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a multi-objective genetic algorithm, demonstrating the model's capability to unearth optimal solutions efficiently. The comparative analysis highlights that the proposed model significantly outperforms the efficiency and effectiveness of existing models, marking a substantial advancement in portfolio optimization strategies.

Keywords

Main Subjects

Optimization

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Iranian Journal of Fuzzy Systems

A Hybrid Model for Choosing the Optimal Stock Portfolio under Intuitionistic Fuzzy Sets

References

References

Volume 21, Issue 2
March and April 2024
Pages 161-179

A Hybrid Model for Choosing the Optimal Stock Portfolio under Intuitionistic Fuzzy Sets

References

References

Volume 21, Issue 2March and April 2024Pages 161-179

Volume 21, Issue 2
March and April 2024
Pages 161-179