19 Aralık 2024 Dergi makalesi Açık Erişim

Gülmez, Burak

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/274157</identifier>
  <creators>
    <creator>
      <creatorName>Gülmez, Burak</creatorName>
      <givenName>Burak</givenName>
      <familyName>Gülmez</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6870-6558</nameIdentifier>
      <affiliation>Mudanya University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Stock Price Prediction Using The Sand Cat Swarm Optimization And An Improved Deep Long Short Term Memory Network</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2024</publicationYear>
  <dates>
    <date dateType="Issued">2024-12-19</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/274157</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.bir.2024.12.002</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nd/4.0/">Creative Commons Attribution-NoDerivatives</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Stock 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&amp;#39;s superior performance across all evaluation metrics. The model achieved an annualized return of 66.25% compared to the DAX index&amp;#39;s 47.45%, with a Sharpe ratio of 2.9091. The integration of technical indicators and macroeconomic variables enhanced the model&amp;#39;s predictive capabilities. These findings establish LSTM-SCSO as an effective tool for stock price prediction, offering practical value for investment decision-making.&lt;/p&gt;</description>
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