19 Aralık 2024 Dergi makalesi Açık Erişim

Gülmez, Burak

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{
  "@context": "https://schema.org/", 
  "@id": 274157, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0002-6870-6558", 
      "@type": "Person", 
      "affiliation": "Mudanya University", 
      "name": "G\u00fclmez, Burak"
    }
  ], 
  "datePublished": "2024-12-19", 
  "description": "<p>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&#39;s superior performance across all evaluation metrics. The model achieved an annualized return of 66.25% compared to the DAX index&#39;s 47.45%, with a Sharpe ratio of 2.9091. The integration of technical indicators and macroeconomic variables enhanced the model&#39;s predictive capabilities. These findings establish LSTM-SCSO as an effective tool for stock price prediction, offering practical value for investment decision-making.</p>", 
  "headline": "Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network", 
  "identifier": 274157, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "https://creativecommons.org/licenses/by-nd/4.0/", 
  "name": "Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network", 
  "url": "https://aperta.ulakbim.gov.tr/record/274157"
}