19 Aralık 2024 Dergi makalesi Açık Erişim

Gülmez, Burak

Citation Style Language JSON

{
  "DOI": "10.1016/j.bir.2024.12.002", 
  "abstract": "<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>", 
  "author": [
    {
      "family": "G\u00fclmez", 
      "given": " Burak"
    }
  ], 
  "container_title": "Borsa Istanbul Review", 
  "id": "274157", 
  "issued": {
    "date-parts": [
      [
        2024, 
        12, 
        19
      ]
    ]
  }, 
  "title": "Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network", 
  "type": "article-journal"
}