Comparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market

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dc.authoridDosdogru, Ayse Tugba/0000-0002-1548-5237
dc.contributor.authorDosdogru, Ayse Tugba
dc.date.accessioned2025-01-06T17:45:19Z
dc.date.available2025-01-06T17:45:19Z
dc.date.issued2019
dc.description.abstractIn this study, a new methodology is proposed to automatically determine six parameters of artificial neural network using population-based metaheuristics. We considered following three issues: What is the effect of used metaheuristic on performance? Which parameters are mostly selected? Is there a difference between the forecasting results when using stationary or nonstationary dataset that are selected according to the augmented Dickey-Fuller test statistics? Based upon results of performance measures, proposed method leads to significant opportunities to forecast stock market more effectively. We also expect proposed methodology can provide remarkable advantages for other complex, dynamic, and nonlinear forecasting problems.
dc.identifier.doi10.1002/mde.3016
dc.identifier.endpage471
dc.identifier.issn0143-6570
dc.identifier.issn1099-1468
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85062939573
dc.identifier.scopusqualityQ2
dc.identifier.startpage460
dc.identifier.urihttps://doi.org/10.1002/mde.3016
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3371
dc.identifier.volume40
dc.identifier.wosWOS:000465851100010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherJohn Wiley & Sons Ltd
dc.relation.ispartofManagerial and Decision Economics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.titleComparative study of hybrid artificial neural network methods under stationary and nonstationary data in stock market
dc.typeArticle

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