Stock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method

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dc.authoridBoru Ipek, Asli/0000-0001-6403-5307
dc.contributor.authorBoru Ipek, Asli
dc.date.accessioned2025-01-06T17:37:48Z
dc.date.available2025-01-06T17:37:48Z
dc.date.issued2023
dc.description.abstractPurpose Coronavirus disease (Covid-19) has created uncertainty in all countries around the world, resulting in enormous human suffering and global recession. Because the economic impact of this pandemic is still unknown, it would be intriguing to study the incorporation of the Covid-19 period into stock price prediction. The goal of this study is to use an improved extreme learning machine (ELM), whose parameters are optimized by four meta-heuristics: harmony search (HS), social spider algorithm (SSA), artificial bee colony algorithm (ABCA) and particle swarm optimization (PSO) for stock price prediction. Design/methodology/approach In this study, the activation functions and hidden layer neurons of the ELM were optimized using four different meta-heuristics. The proposed method is tested in five sectors. Analysis of variance (ANOVA) and Duncan's multiple range test were used to compare the prediction methods. First, ANOVA was applied to the test data for verification and validation of the proposed methods. Duncan's multiple range test was used to identify a suitable method based on the ANOVA results. Findings The main finding of this study is that the hybrid methodology can improve the prediction accuracy during the pre and post Covid-19 period for stock price prediction. The mean absolute percent error value of each method showed that the prediction errors of the proposed methods were all under 0.13106 in the worst case, which appears to be a remarkable outcome for such a difficult prediction task. Originality/value The novelty of this study is the use of four hybrid ELM methods to evaluate the automotive, technology, food, construction and energy sectors during the pre and post Covid-19 period. Additionally, an appropriate method was determined for each sector.
dc.identifier.doi10.1108/K-12-2021-1252
dc.identifier.endpage4109
dc.identifier.issn0368-492X
dc.identifier.issn1758-7883
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85132663321
dc.identifier.scopusqualityQ1
dc.identifier.startpage4081
dc.identifier.urihttps://doi.org/10.1108/K-12-2021-1252
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2369
dc.identifier.volume52
dc.identifier.wosWOS:000787535100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherEmerald Group Publishing Ltd
dc.relation.ispartofKybernetes
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectExtreme learning machine
dc.subjectHarmony search
dc.subjectSocial spider algorithm
dc.subjectArtificial bee colony algorithm
dc.subjectParticle swarm optimization
dc.titleStock price prediction using improved extreme learning machine methods during the Covid-19 pandemic and selection of appropriate prediction method
dc.typeArticle

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