Integrating metaheuristics and Artificial Neural Networks for improved stock price prediction

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dc.authoridDosdogru, Ayse Tugba/0000-0002-1548-5237
dc.authoridGOCKEN, Mustafa/0000-0002-1256-2305
dc.authoridOZCALICI, Mehmet/0000-0003-0384-6872
dc.contributor.authorGocken, Mustafa
dc.contributor.authorOzcalici, Mehmet
dc.contributor.authorBoru, Asli
dc.contributor.authorDosdogru, Ayse Tugba
dc.date.accessioned2025-01-06T17:36:28Z
dc.date.available2025-01-06T17:36:28Z
dc.date.issued2016
dc.description.abstractStock market price is one of the most important indicators of a country's economic growth. That's why determining the exact movements of stock market price is considerably regarded. However, complex and uncertain behaviors of stock market make exact determination impossible and hence strong forecasting models are deeply desirable for investors' financial decision making process. This study aims at evaluating the effectiveness of using technical indicators, such as simple moving average of close price, momentum close price, etc. in Turkish stock market. To capture the relationship between the technical indicators and the stock market for the period under investigation, hybrid Artificial Neural Network (ANN) models, which consist in exploiting capabilities of Harmony Search (HS) and Genetic Algorithm (GA), are used for selecting the most relevant technical indicators. In addition, this study simultaneously searches the most appropriate number of hidden neurons in hidden layer and in this respect; proposed models mitigate well-known problem of overfitting/underfitting of ANN. The comparison for each proposed model is done in four viewpoints: loss functions, return from investment analysis, buy and hold analysis, and graphical analysis. According to the statistical and financial performance of these models, HS based ANN model is found as a dominant model for stock market forecasting. (C) 2015 Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.eswa.2015.09.029
dc.identifier.endpage331
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-84944097250
dc.identifier.scopusqualityQ1
dc.identifier.startpage320
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2015.09.029
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1891
dc.identifier.volume44
dc.identifier.wosWOS:000365051500027
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArtificial Neural Network
dc.subjectGenetic Algorithm
dc.subjectHarmony Search Algorithm
dc.subjectStock market price
dc.titleIntegrating metaheuristics and Artificial Neural Networks for improved stock price prediction
dc.typeArticle

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