FN-TOPSIS: Fuzzy Networks for Ranking Traded Equities

Fuzzy systems consisting of networked rule bases, called fuzzy networks, capture various types of imprecision inherent in financial data and in the decision-making processes on them. This paper introduces a novel extension of the technique for ordering of preference by similarity to ideal solution (TOPSIS) method and uses fuzzy networks to solve multicriteria decision-making problems where both benefit and cost criteria are presented as subsystems. Thus, the decision maker evaluates the performance of each alternative for portfolio optimization and further observes the performance for both benefit and cost criteria. This approach improves significantly the transparency of the TOPSIS methods, while ensuring high effectiveness in comparison with established approaches. The proposed method is further tested to solve the problem of selection/ranking of traded equity covering developed and emergent financial markets. The ranking produced by the method is validated using Spearman rho rank correlation. Based on the case study, the proposed method outperforms the existing TOPSIS approaches in terms of ranking performance.