Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection

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dc.authoridGOCKEN, Mustafa/0000-0002-1256-2305
dc.contributor.authorGocken, Mustafa
dc.contributor.authorOzcalici, Mehmet
dc.contributor.authorBoru, Asli
dc.contributor.authorDosdogru, Ayse Tugba
dc.date.accessioned2025-01-06T17:43:23Z
dc.date.available2025-01-06T17:43:23Z
dc.date.issued2019
dc.description.abstractOver the years, high-dimensional, noisy, and time-varying natures of the stock markets are analyzed to carry out accurate prediction. Particularly, speculators and investors are understandably eager to accurately predict stock price since millions of dollars flow through the stock markets. At this point, soft computing models have empowered them to capture the data patterns and characteristics of stock markets. However, one of the open problems in soft computing models is how to systematically determine architecture of models for given applications. In this study, Harmony Search is utilized to optimize the architecture of Neural Network, Jordan Recurrent Neural Network, Extreme Learning Machine, Recurrent Extreme Learning Machine, Generalized Linear Model, Regression Tree, and Gaussian Process Regression for 1-, 2-, 3-, 5-, 7-, and 10-day-ahead stock price prediction. The experimental results show worthy findings of stock market behavior over different prediction terms and stocks. This study also helps researchers understand which prediction model performed the best and how different conditions affect the prediction accuracy of the models. Proposed hybrid models can be successfully used by speculators and investors to make the investment or to hedge against potential risk in stock markets.
dc.identifier.doi10.1007/s00521-017-3089-2
dc.identifier.endpage592
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85021824862
dc.identifier.scopusqualityQ1
dc.identifier.startpage577
dc.identifier.urihttps://doi.org/10.1007/s00521-017-3089-2
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2650
dc.identifier.volume31
dc.identifier.wosWOS:000460171000018
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London Ltd
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectJordan Recurrent Neural Network
dc.subjectRecurrent Extreme Learning Machine
dc.subjectGeneralized Linear Model
dc.subjectRegression Tree
dc.subjectGaussian Process Regression
dc.subjectStock price prediction
dc.titleStock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection
dc.typeArticle

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