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dc.contributor.authorÖzlem, Şirin
dc.contributor.authorTan, Ömer Faruk
dc.date.accessioned2022-05-26T11:51:12Z
dc.date.available2022-05-26T11:51:12Z
dc.date.issued2022en_US
dc.identifier.citationOzlem, S., & Tan, O. F. (May 2022). Predicting cash holdings using supervised machine learning algorithms. Financial Innovation, 8(1), pp.1-19. https://doi.org/10.1186/s40854-022-00351-8en_US
dc.identifier.issn2199-4730
dc.identifier.urihttps://doi.org/10.1186/s40854-022-00351-8
dc.identifier.urihttps://hdl.handle.net/20.500.11779/1780
dc.description.abstractThis study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCash holdingsen_US
dc.subjectMLNNen_US
dc.subjectMachine learningen_US
dc.subjectTurkeyen_US
dc.subjectXGBoosten_US
dc.titlePredicting cash holdings using supervised machine learning algorithmsen_US
dc.typearticleen_US
dc.relation.journalFinancial Innovationen_US
dc.departmentMühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.authoridŞirin Özlem / 0000-0001-7248-1825en_US
dc.authoridÖmer Faruk Tan / 0000-0002-8875-4696en_US
dc.identifier.volume8en_US
dc.identifier.issue1en_US
dc.identifier.startpage1en_US
dc.identifier.endpage19en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.description.wosidWOS:000796993600001en_US
dc.description.scopusid2-s2.0-85130279042en_US
dc.contributor.institutionauthorÖzlem, Şirin
dc.contributor.institutionauthorTan, Ömer Faruk
dc.description.pubmedid35601748en_US
dc.description.woscitationindexSocial Sciences Citation Indexen_US
dc.identifier.wosqualityQ1en_US
dc.description.WoSDocumentTypeArticleen_US
dc.description.WoSInternationalCollaborationUluslararası işbirliği ile yapılmayan - HAYIRen_US
dc.description.WoSPublishedMonthMayısen_US
dc.description.WoSIndexDate2022en_US
dc.description.WoSYOKperiodYÖK - 2021-22en_US
dc.identifier.doi10.1186/s40854-022-00351-8en_US
dc.description.PublishedMonthMayısen_US
dc.identifier.scopusqualityQ1en_US


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