DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction

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dc.authoridAri, Davut/0000-0001-6439-7957
dc.authoridAlagoz, Baris Baykant/0000-0001-5238-6433
dc.authorwosidAri, Davut/GPX-1182-2022
dc.authorwosidAlagoz, Baris Baykant/ABG-8526-2020
dc.contributor.authorAri, Davut
dc.contributor.authorAlagoz, Baris Baykant
dc.date.accessioned2024-08-04T20:53:04Z
dc.date.available2024-08-04T20:53:04Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractStock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87% average accuracy in buy-sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8% more average income compared to the average income of a long-term investment strategy.en_US
dc.identifier.doi10.1007/s00500-022-07571-1
dc.identifier.endpage2574en_US
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85140070955en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2553en_US
dc.identifier.urihttps://doi.org/10.1007/s00500-022-07571-1
dc.identifier.urihttps://hdl.handle.net/11616/100949
dc.identifier.volume27en_US
dc.identifier.wosWOS:000868468400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofSoft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectGenetic programmingen_US
dc.subjectStock market predictionen_US
dc.subjectStock priceen_US
dc.subjectHyperparameter optimizationen_US
dc.subjectTrend predictionen_US
dc.titleDEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend predictionen_US
dc.typeArticleen_US

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