Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network
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,...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Borsa Istanbul Review |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S221484502400156X |
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| Summary: | 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. |
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| ISSN: | 2214-8450 |