Exploring the specific capacity of different multi criteria decision making approaches under uncertainty using data from financial markets

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dc.contributor.authorBaydas, Mahmut
dc.contributor.authorElma, Orhan Emre
dc.contributor.authorPamucar, Dragan
dc.date.accessioned2024-02-23T14:02:53Z
dc.date.available2024-02-23T14:02:53Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractEven if the MCDM methods produce statistically significant and similar rankings in a given problem, they can present the best alternatives in a different order. Random selection of the best alternative can create a complexity for the decision maker in reaching the most suitable outcome in a scenario. It is extremely challenging to oversee what the capacity or capability strengths of the more than 100 MCDM methods are, based on the results they produce. This issue is still regarded as a paradox, as there is no approved criterion to compare MCDM methods under uncertainty, in the literature. This study is aimed to determine the capacity of MCDM methods by outputs rather than inputs, unlike the previous literature. Discussions in the recent literature points out that the capacity of a MCDM method that better fits real life problems can be higher. In this respect, share returns were regarded as a reference in comparing MCDM methods objectively by financial performance of companies in this study. A multi-criteria approach that consistently produced significantly higher correlations with share returns compared to other methods has been accepted as the most appropriate MCDM method in the framework of this research. The study was conducted on 23 companies in the BIST30 index, which lists the largest companies in Borsa Istanbul. 10 MCDM methods were compared according to their significance in producing a higher relationship with share returns. As a result, PROMETHEE and FUCA methods clearly shared the first place as the most efficient compared to other methods, which are TOPSIS, GRA, S-, WSA, SAW, COPRAS, MOORA and LINMAP.en_US
dc.identifier.doi10.1016/j.eswa.2022.116755
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85125647365en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2022.116755
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11874
dc.identifier.volume197en_US
dc.identifier.wosWOS:000792298400004en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulti Criteria Analysisen_US
dc.subjectShare Returnen_US
dc.subjectFinancial Performanceen_US
dc.subjectSpearman 'S Correlation Coefficienten_US
dc.titleExploring the specific capacity of different multi criteria decision making approaches under uncertainty using data from financial marketsen_US
dc.typeArticleen_US

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