Publication:
Prediction of Turkish mutual funds' net asset value using the fund portfolio distribution

dc.contributor.authorYılmaz, Ümit
dc.contributor.authorOrbak, Ali Yurdun
dc.contributor.buuauthorORBAK, ALİ YURDUN
dc.contributor.departmentBursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü
dc.contributor.researcheridM-9216-2014
dc.date.accessioned2024-10-01T08:01:20Z
dc.date.available2024-10-01T08:01:20Z
dc.date.issued2023-06-11
dc.description.abstractAccurate prediction of mutual funds' net asset value (NAV) has become increasingly important for investors. Mutual fund investors will be significantly supported by the development of models that accurately predict the future performances of mutual funds. Using these models will facilitate the selection of suitable mutual funds for investors who want to invest in the medium and long term. The aim of this study, using artificial neural networks and nonlinear autoregressive networks with exogenous inputs (NARX) methods and Levenberg-Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient training algorithms, is to predict the NAV of two Turkish mutual funds, which are Deniz Asset Management First Variable Fund (DBP) and Istanbul Asset Management Short-Term Bonds and Bills Fund, with the funds' their portfolio distributions. For this purpose, prediction models were developed with these methods, training algorithms, and some specific hyperparameters and applied to the datasets of the funds examined in the study. The performances of the developed models were compared according to the method and training algorithm pairs for each fund. For performance evaluation, mean squared error, mean absolute percent error, and coefficient of correlation statistical measures are used. From the result, it can be clearly suggested that the NARX-BR pair outperforms other models for DBP, and the NARX-LM pair outperforms other models for IST.
dc.identifier.doi10.1007/s00521-023-08716-5
dc.identifier.eissn1433-3058
dc.identifier.endpage18890
dc.identifier.issn0941-0643
dc.identifier.issue26
dc.identifier.startpage18873
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08716-5
dc.identifier.urihttps://link.springer.com/article/10.1007/s00521-023-08716-5
dc.identifier.urihttps://hdl.handle.net/11452/45574
dc.identifier.volume35
dc.identifier.wos001004463500002
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherSpringer
dc.relation.journalNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectNeural-network
dc.subjectArtificial neural networks
dc.subjectNarx
dc.subjectMutual fund
dc.subjectNav
dc.subjectPrediction
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectComputer science, artificial intelligence
dc.titlePrediction of Turkish mutual funds' net asset value using the fund portfolio distribution
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublicationae18a8e7-a6d4-448a-95fb-81b6a41b0577
relation.isAuthorOfPublication.latestForDiscoveryae18a8e7-a6d4-448a-95fb-81b6a41b0577

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