Modeling Daily Financial Market Data by Using Tree-Based Genetic Programming

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dc.authorscopusid57207456092
dc.authorscopusid57221078940
dc.contributor.authorAri D.
dc.contributor.authorAlagoz B.B.
dc.date.accessioned2024-08-04T20:03:56Z
dc.date.available2024-08-04T20:03:56Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.descriptionUmniah and UWalleten_US
dc.description2021 International Conference on Information Technology, ICIT 2021 -- 14 July 2021 through 15 July 2021 -- 170653en_US
dc.description.abstractA behavioral modeling of financial markets based on daily data is not an easy problem for machine learning algorithms. The social and physiological factors can take effect on market data and result in significant uncertainty in data. This study demonstrates an implementation of tree-based genetic programming (GP) to develop a mathematical model of stock market from the daily stock data of other stock markets to observe relations between global market trends and to consider this effect in market prediction problems. To obtain a prediction model of Istanbul Stock Exchange 100 Index (ISE100), numerical data from ISE100 and seven other international stock market indices are used to produce GP models that can estimate daily price changes in ISE100 according to daily change in other international stock market indices. To reduce negative effects of the data uncertainty on the GP modeling, ensemble average GP modeling performances are investigated and the results are reported for future research direction suggestions. © 2021 IEEE.en_US
dc.identifier.doi10.1109/ICIT52682.2021.9491652
dc.identifier.endpage386en_US
dc.identifier.isbn9781665428705
dc.identifier.scopus2-s2.0-85112189932en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage382en_US
dc.identifier.urihttps://doi.org/10.1109/ICIT52682.2021.9491652
dc.identifier.urihttps://hdl.handle.net/11616/92219
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2021 International Conference on Information Technology, ICIT 2021 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectaverage ensemble learningen_US
dc.subjectfinancial market data modelingen_US
dc.subjectGenetic programmingen_US
dc.subjectstock marketsen_US
dc.subjecttree-based genetic programmingen_US
dc.titleModeling Daily Financial Market Data by Using Tree-Based Genetic Programmingen_US
dc.typeConference Objecten_US

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