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Showing 3 results for Ghanbari
Clustering of Fuzzy Data Sets Based on Particle Swarm Optimization With Fuzzy Cluster Centers
Seyed Hamid Zahiri, Najme Ghanbari, Hadi Shahraki,
Volume 33, Issue 2 (IJIEPR 2022)
Abstract
In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy numbers, a similarity criterion based on the intersection region of the fuzzy numbers is used.  The performance of the suggested clustering method has been experimented on both benchmark and artificial datasets. These datasets are used in the fuzzy form. The experiential results represent that the suggested clustering method with fuzzy cluster centers can cluster triangular fuzzy datasets like other standard uncertain data clustering methods. Experimental results demonstrate that, in almost all datasets, the proposed clustering method provides better results in accuracy when compared to Uncertain K-Means and Uncertain K-medoids algorithms.
Behavioral Finance biases: A Comprehensive Review on regret approach studies in portfolio optimization
Amirmohammad Larni-Fooeik, Hossein Ghanbari, Seyed Jafar Sadjadi, Emran Mohammadi,
Volume 35, Issue 1 (IJIEPR 2024)
Abstract
In the ever-evolving realm of finance, investors have a myriad of strategies at their disposal to effectively and cleverly allocate their wealth in the expansive financial market. Among these strategies, portfolio optimization emerges as a prominent approach used by individuals seeking to mitigate the inherent risks that accompany investments. Portfolio optimization entails the selection of the optimal combination of securities and their proportions to achieve lower risk and higher return. To delve deeper into the decision-making process of investors and assess the impact of psychology on their choices, behavioral finance biases can be introduced into the portfolio optimization model. One such bias is regret, which refers to the feeling of remorse that can induce hesitation in making significant decisions and avoiding actions that may lead to unfavorable investment outcomes. It is not uncommon for investors to hold onto losing investments for extended periods, reluctant to acknowledge mistakes and accept losses due to this behavioral tendency. Interestingly, in their quest to sidestep regret, investors may inadvertently overlook potential opportunities. This research article aims to undertake an in-depth examination of 41 publications from the past two decades, providing a comprehensive review of the models and applications proposed for the regret approach in portfolio optimization. The study categorizes these methods into accurate and approximate models, scrutinizing their respective timeframes and exploring additional constraints that are considered. Utilizing this article will provide investors with insights into the latest research advancements in the realm of regret, familiarize them with influential authors in the field, and offer a glimpse into the future direction of this area of study.  The extensive review findings indicate a growth in the adoption of the regret approach in the past few years and its advancements in portfolio optimization.

A Fuzzy Approach to Portfolio Optimization in the Light of Credibility Theory and Z-numbers: An Empirical Study of Tehran Stock Exchange
Hossein Ghanbari, Mostafa Shabani, Emran Mohammadi,
Volume 36, Issue 3 (IJIEPR 2025)
Abstract
Portfolio optimization has emerged as a cornerstone of modern financial theory, maintaining its position as one of the field’s most dynamic and extensively studied areas. While numerous optimization models have been developed and implemented, they fundamentally grapple with the persistent challenge of market uncertainty - an inherent and inescapable characteristic of financial markets. This uncertainty necessitates practical quantification methods to improve the reliability of financial projections, among which fuzzy theory has proven particularly valuable. However, despite its advantages over conventional approaches, traditional fuzzy theory contains a fundamental flaw in its underlying assumption: the presumed absolute reliability of fuzzy number estimations. This critical limitation undermines its effectiveness in real-world applications where information quality varies significantly. To address this gap, this paper proposes a novel portfolio optimization framework that integrates Z-number theory with credibilistic Conditional Value-at-Risk (CVaR) to address both the uncertainty and reliability of asset return estimates. Traditional fuzzy portfolio models often overlook the critical dimension of information quality, potentially leading to suboptimal allocations. Our approach overcomes this limitation by incorporating expert reliability assessments as an integral component of the optimization process through Z-numbers, where the first component represents fuzzy return estimates and the second quantifies their reliability. The model incorporates practical constraints, including cardinality limits and position sizing rules, to ensure real-world applicability. Using data from the Tehran Stock Exchange, we demonstrate that the Z-number-enhanced model produces more stable and economically rational portfolios compared to conventional fuzzy approaches. The results show that considering reliability leads to different asset allocations, with improved risk-adjusted performance. A key contribution is the demonstration that information quality measurably impacts portfolio outcomes, establishing reliability assessment as a necessary element in fuzzy portfolio optimization. This framework provides individual investors and portfolio managers with a more applicated tool for decision-making under uncertainty, especially valuable in markets with varying information quality across assets.

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