Comparative Analysis of Weighted Average and Ordered Weighted Average Methods for Stock Portfolio Optimization

Abstract

In light of the escalating market fluctuations and intricate investor inclinations, the identification of optimal equities

necessitates analytical paradigms that integrate diverse decision-making criteria. This research employs multicriteria decision- making frameworks to assess and rank six publicly listed corporations according to six principal investment parameters: expected

return, risk profile, liquidity, volatility, and alignment with market trends ESG scores. By employing both the weighted sum model and ordered weighted averaging aggregation techniques, we develop a decision matrix and extract comparative scores for each equity. The weights assigned to the criteria were determined based on two different approaches with a particular focus on return and risk. The findings reveal that although both WSM and OWA yield similar rankings for high-performing equities, subtle

distinctions underscore the influence of operator sensitivity to score distribution. This research enriches the financial decision- making corpus by demonstrating how computationally straightforward yet analytically robust models can facilitate systematic

portfolio selection.