Type-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertainty

Simple item page
dc.contributor.authorJanková, Zuzanacs
dc.contributor.authorDostál, Petrcs
dc.coverage.issue1cs
dc.coverage.volume2021cs
dc.date.issued2021-06-18cs
dc.description.abstractExtensive research results of stock market time series using classical fuzzy sets (type-1) are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty associated with stock market developments due to their limited descriptiveness. This paper fills a scientific gap and focuses on type-2 fuzzy logic applied to stock markets. Type-2 fuzzy sets may include additional uncertainty resulting from unclear, uncertain, or inaccurate financial data through which model inputs are calculated. Here we propose four methods based on type-2 fuzzy logic, which differ in the level of uncertainty contained in fuzzy sets and compared with the type-1 fuzzy model. The case study aims to create a model to support investment decisions in Exchange-Traded Funds (ETFs) listed on international equity markets. The created models of type-2 fuzzy logic are compared with the classic type-1 fuzzy logic model. Based on the results of the comparison, it can be said that type-2 fuzzy logic with dual fuzzy sets is able to better describe data from financial time series and provides more accurate outputs. The results reflect the capability and effectiveness of the approach proposed in this document. However, the performance of type-2 fuzzy logic models decreases with the inclusion of increasing uncertainty in fuzzy sets. For further research, it would be appropriate to examine the different levels of uncertainty in the input parameters themselves and monitor the performance of such a modified model.en
dc.formattextcs
dc.format.extent1-16cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationMATHEMATICAL PROBLEMS IN ENGINEERING. 2021, vol. 2021, issue 1, p. 1-16.en
dc.identifier.doi10.1155/2021/3839071cs
dc.identifier.issn1024-123Xcs
dc.identifier.orcid0000-0003-1798-5275cs
dc.identifier.orcid0000-0002-7871-4789cs
dc.identifier.other171860cs
dc.identifier.researcheridV-3927-2017cs
dc.identifier.urihttp://hdl.handle.net/11012/200866
dc.language.isoencs
dc.publisherHindawics
dc.relation.ispartofMATHEMATICAL PROBLEMS IN ENGINEERINGcs
dc.relation.urihttps://www.hindawi.com/journals/mpe/2021/3839071/cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1024-123X/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectexpert systemen
dc.subjectfuzzy logicen
dc.subjectinvestmenten
dc.subjectIT2FLen
dc.subjecttype-2 fuzzy logicen
dc.titleType-2 Fuzzy Expert System Approach for Decision-Making of Financial Assets and Investing under Different Uncertaintyen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-171860en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:43:27en
sync.item.modts2025.01.17 15:19:17en
thesis.grantorVysoké učení technické v Brně. Fakulta podnikatelská. Ústav informatikycs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
3839071.pdf
Size:
3.38 MB
Format:
Adobe Portable Document Format
Description:
3839071.pdf
Download
Collections
Ăšstav informatiky