Selection of Optimal Stock for Business Investment Using Pythagorean Fuzzy Aczel-Alsina CoCoSo-Method

Amal Kumar Adak; Stefan Jovcic; Darko Bozanic; Dragan Marinkovic

Authors

Amal Kumar Adak Department of Mathematics, Ganesh Dutt College, Begusarai, India Author https://orcid.org/0000-0002-3644-782X
Stefan Jovcic Department of Transport Management, Marketing and Logistics, University of Pardubice, Pardubice, Czech Republic Author https://orcid.org/0000-0002-9162-2133
Darko Bozanic Military academy, University of Defence in Belgrade, Belgrade, Serbia Author https://orcid.org/0000-0002-9657-0889
Dragan Marinkovic 1) Department of Structural Analysis, Technische Universität Berlin, Berlin, Germany; 2) Institute of Mechanical Science, Vilnius Gediminas Technical University, Lithuania; 3) University College, Korea University, Seoul, Republic of Korea Author https://orcid.org/0000-0002-3583-9434

Keywords:

Pythagorean Fuzzy Sets, Aczel Alsina aggregation operator, Unknown weight of Criteria, CoCoSo Method, Stock investment, MCDM

Abstract

Selecting an optimal stock for business investment is a complex multi-attribute decision-making (MADM) problem often characterized by vague, inconsistent, and uncertain information. This article introduces a novel decision framework to address this challenge under Pythagorean fuzzy environments, offering greater flexibility than intuitionistic fuzzy sets. To aggregate uncertain evaluation information, Pythagorean fuzzy Aczel-Alsina weighted aggregation operators are developed, leveraging the parameterized family of Aczel-Alsina t-norms and t-conorms to capture the full range of decision-makers’ risk attitudes. A modified score function is first proposed to effectively rank aggregated values. Subsequently, the combined compromise solution (CoCoSo) method is extended within the Pythagorean fuzzy Aczel-Alsina context to evaluate and rank investment alternatives under conditions of unknown attribute weights. The viability and procedural clarity of the proposed methodology are demonstrated through a numerical example involving stock selection in the financial market with ten alternatives and eight criteria. Finally, a comprehensive comparative analysis using different aggregation operators and score functions is conducted, confirming that the proposed method yields robust, consistent, and discriminative results, thereby offering a superior tool for strategic investment decision-making under complex uncertainty.

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