Fine-tuned support vector regression model for stock predictions

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dc.authoridNguyen, Tu N./0000-0001-7184-4102
dc.authoridSharma, Aditi/0000-0002-5364-8420
dc.authoridDash, Ranjan Kumar/0000-0003-3482-465X
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authorwosidNguyen, Tu N./Q-9119-2016
dc.authorwosidSharma, Aditi/ABA-1791-2021
dc.authorwosidSharma, Aditi/HLQ-6616-2023
dc.authorwosidDash, Ranjan Kumar/ABA-8593-2020
dc.authorwosidCengiz, Korhan/HTN-8060-2023
dc.authorwosidCengiz, Korhan/ABD-5559-2020
dc.contributor.authorDash, Ranjan Kumar
dc.contributor.authorNguyen, Tu N.
dc.contributor.authorCengiz, Korhan
dc.contributor.authorSharma, Aditi
dc.date.accessioned2024-06-12T11:02:50Z
dc.date.available2024-06-12T11:02:50Z
dc.date.issued2023
dc.departmentTrakya Üniversitesien_US
dc.description.abstractIn this paper, a new machine learning (ML) technique is proposed that uses the fine-tuned version of support vector regression for stock forecasting of time series data. Grid search technique is applied over training dataset to select the best kernel function and to optimize its parameters. The optimized parameters are validated through validation dataset. Thus, the tuning of this parameters to their optimized value not only increases model's overall accuracy but also requires less time and memory. Further, this also minimizes the model from being data overfitted. The proposed method is used to analysis different performance parameters of stock market like up-to-daily and up-to-monthly return, cumulative monthly return, its volatility nature and the risk associated with it. Eight different large-sized datasets are chosen from different domain, and stock is predicted for each case by using the proposed method. A comparison is carried out among the proposed method and some similar methods of same interest in terms of computed root mean square error and the mean absolute percentage error. The comparison reveals the proposed method to be more accurate in predicting the stocks for the chosen datasets. Further, the proposed method requires much less time than its counterpart methods.en_US
dc.identifier.doi10.1007/s00521-021-05842-w
dc.identifier.endpage23309en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue32en_US
dc.identifier.scopus2-s2.0-85102768494en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage23295en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-05842-w
dc.identifier.urihttps://hdl.handle.net/20.500.14551/21437
dc.identifier.volume35en_US
dc.identifier.wosWOS:000629103500005en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGrid Searchen_US
dc.subjectMachine Learningen_US
dc.subjectRoot Mean Square Erroren_US
dc.subjectMean Absolute Percentage Erroren_US
dc.subjectSupport Vector Regressionen_US
dc.subjectVolatilityen_US
dc.subjectNeural-Networken_US
dc.titleFine-tuned support vector regression model for stock predictionsen_US
dc.typeArticleen_US

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