19 Aralık 2024 Dergi makalesi Açık Erişim

Gülmez, Burak

Dublin Core

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  <dc:creator>Gülmez, Burak</dc:creator>
  <dc:date>2024-12-19</dc:date>
  <dc:description>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'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:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/274157</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:274157</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nd/4.0/</dc:rights>
  <dc:source>Borsa Istanbul Review</dc:source>
  <dc:title>Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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