Yaakob, Abdul Malek and Serguieva, Antoaneta and Gegov, Alexander (2016) FN-TOPSIS: fuzzy networks for ranking traded equities. IEEE Transactions on Fuzzy Systems, 25 (2). pp. 315-332. ISSN 1063-6706

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Abstract

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 here on portfolio selection problems 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 term of ranking performance.

Item Type:	Article
Uncontrolled Keywords:	FN-TOPSIS, traded equity ranking, fuzzy systems, networked rule bases, fuzzy networks, financial data, technique-for-ordering-of-preference-by-similarity-to-ideal solution, multicriteria decision-making problems, benefit criteria, cost criteria, portfolio optimization, traded equity selection, financial markets, Spearman rho rank correlation
Subjects:	Q Science > QA Mathematics
Divisions:	School of Quantitative Sciences
Depositing User:	Mr. Abdul Malek Yaakob
Date Deposited:	18 Sep 2017 00:24
Last Modified:	18 Sep 2017 00:24
URI:	https://repo.uum.edu.my/id/eprint/22925

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