FN-TOPSIS: fuzzy networks for ranking traded equities

Abdul Yaakob, Antoaneta Serguieva, Alexander Emilov Gegov

Research output: Contribution to journalArticlepeer-review

438 Downloads (Pure)


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 multi criteria 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 optimisation 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 to 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.
Original languageEnglish
Pages (from-to)315-332
JournalIEEE Transactions on Fuzzy Systems
Issue number2
Early online date21 Apr 2016
Publication statusPublished - 1 Apr 2017


  • Fuzzy networks
  • Multi-criteria decision making
  • Portfolio selection
  • Ranking performance
  • Spearman rho correlation
  • Type 1 fuzzy Type 2 fuzzy numbers
  • Z-numbers
  • Type 1 fuzzy numbers


Dive into the research topics of 'FN-TOPSIS: fuzzy networks for ranking traded equities'. Together they form a unique fingerprint.

Cite this