Comparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting

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dc.contributor.authorOzcalici, Mehmet
dc.contributor.authorDosdogru, Ayse Tugba
dc.contributor.authorIpek, Asli Boru
dc.contributor.authorGocken, Mustafa
dc.date.accessioned2025-01-06T17:44:33Z
dc.date.available2025-01-06T17:44:33Z
dc.date.issued2022
dc.description.abstractThis study has been conducted on forecasting, as accurately as possible, the next day's stock price using harmony search (HS) and its variants [improved harmony search (IHS), global-best harmony search (GHS), self-adaptive harmony search (SAHS), and intelligent tuned harmony Search (ITHS) together with artificial neural network (ANN)]. The advantage of the proposed models are that the useful information in the original stock data is found by input variable selection and simultaneously the most proper number of hidden neurons in hidden layer is discovered to mitigate overfitting/underfitting problem in ANN. The results have shown that forecasts made by HS-ANN, IHS-ANN, GHS-ANN, SAHS-ANN, and ITHS-ANN demonstrate a tendency to achieve hit rates above 89%, which is considerably better than previously proposed forecasting models in literature. Hence, ANN models provide more valuable forecasting results for investors to hedge against potential risk in stock markets.
dc.identifier.doi10.1504/IJDMMM.2022.10051603
dc.identifier.endpage357
dc.identifier.issn1759-1163
dc.identifier.issn1759-1171
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85142494328
dc.identifier.scopusqualityQ4
dc.identifier.startpage335
dc.identifier.urihttps://doi.org/10.1504/IJDMMM.2022.10051603
dc.identifier.urihttps://hdl.handle.net/20.500.14669/3069
dc.identifier.volume14
dc.identifier.wosWOS:000877706000003
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInderscience Enterprises Ltd
dc.relation.ispartofInternational Journal of Data Mining Modelling and Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectstock price forecasting
dc.subjectartificial neural network
dc.subjectharmony search and its variants
dc.titleComparison of harmony search derivatives for artificial neural network parameter optimisation: stock price forecasting
dc.typeArticle

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