Decision-Analytics-Based Stock Selection: A Fuzzy Aczel–Alsina Ordinal Priority Approach

Abstract In today’s competitive environment, evaluating and selecting stocks for portfolio optimization is a critical challenge for investors, especially under conditions of uncertainty. Traditional approaches often fail to address the complexities of multi-criteria decision-making (MCDM) in real-world investment scenarios. This study introduces a novel fuzzy Ordinal Priority Approach based on Aczel–Alsina weighted evaluation (OPA-AAWE) to tackle the portfolio selection problem. Taking into account seven financial performance criteria, the model was applied to 374 stocks listed on the Istanbul Stock Exchange for a period of 12 months. The findings demonstrate that the proposed methodology effectively handles uncertainty, offers flexibility in decision-making, and identifies the most optimal portfolios. Sensitivity analysis further confirms the robustness and reliability of the model. These results highlight the practical applicability of the fuzzy OPA-AAWE framework in real-world investment decision-making, offering investors a comprehensive tool for improved portfolio selection.