Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network

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dc.authorid0000-0002-6870-6558
dc.contributor.authorGülmez, Burak
dc.contributor.authorid222298
dc.date.accessioned2025-04-17T13:51:31Z
dc.date.available2025-04-17T13:51:31Z
dc.date.issued2025-01
dc.departmentFakülteler, Mühendislik, Mimarlık ve Tasarım Fakültesi, Endüstri Mühendisliği Bölümü
dc.descriptionSocial Sciences Citation Index (SSCI)
dc.description.abstractStock price prediction remains a complex challenge in financial markets. This study introduces a novel Long Short-Term Memory (LSTM) model optimized by Sand Cat Swarm Optimization (SCSO) for stock price prediction. The research evaluates multiple algorithms including ANN, LSTM variants, Auto-ARIMA, Gradient Boosted Trees, DeepAR, N-BEATS, N-HITS, and the proposed LSTM-SCSO using DAX index data from 2018 to 2023. Model performance was assessed through Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and out-of-sample R2 metrics. Statistical significance was validated using Model Confidence Set analysis with 5000 bootstrap replications. Results demonstrate LSTM-SCSO's superior performance across all evaluation metrics. The model achieved an annualized return of 66.25% compared to the DAX index's 47.45%, with a Sharpe ratio of 2.9091. The integration of technical indicators and macroeconomic variables enhanced the model's predictive capabilities. These findings establish LSTM-SCSO as an effective tool for stock price prediction, offering practical value for investment decision-making.
dc.identifier.citationGülmez, B. (2024). Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network. Borsa Istanbul Review. https://doi.org/10.1016/j.bir.2024.12.002
dc.identifier.doi10.1016/j.bir.2024.12.002
dc.identifier.eissn2214-8469
dc.identifier.endpage46
dc.identifier.issn2214-8450
dc.identifier.startpage32
dc.identifier.urihttps://dspace.mudanya.edu.tr/handle/20.500.14362/314
dc.identifier.volume24
dc.identifier.wosWOS:001438872500002
dc.identifier.wosqualityQ1
dc.institutionauthorGülmez, Burak
dc.language.isoen
dc.publisherElsevier
dc.relation.journalBorsa Istanbul Review
dc.relation.publicationcategoryMakale- Uluslararası- Hakemli Dergi- Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectStock price prediction
dc.subjectSand Cat swarm optimization
dc.subjectLSTM
dc.subjectDeep learning
dc.subjectArtificial intelligence in finance
dc.subjectFinancial forecasting
dc.titleStock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network
dc.typeMakale
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