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 |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221484502400156X |
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| author | Burak Gülmez |
| author_facet | Burak Gülmez |
| author_sort | Burak Gülmez |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-2c11158f3a8e462ca6bd4db03932eb14 |
| institution | Kabale University |
| issn | 2214-8450 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Borsa Istanbul Review |
| spelling | doaj-art-2c11158f3a8e462ca6bd4db03932eb142025-01-22T05:42:32ZengElsevierBorsa Istanbul Review2214-84502024-12-01243246Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory networkBurak Gülmez0Department of Industrial Engineering, Mudanya University, 16940, Mudanya, Bursa, Türkiye; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, Netherlands; Corresponding author. Department of Industrial Engineering, Mudanya University, 16940, Mudanya, Bursa, Türkiye.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.http://www.sciencedirect.com/science/article/pii/S221484502400156XStock price predictionSand Cat swarm optimizationLSTMDeep learningArtificial intelligence in financeFinancial forecasting |
| spellingShingle | Burak Gülmez Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network Borsa Istanbul Review Stock price prediction Sand Cat swarm optimization LSTM Deep learning Artificial intelligence in finance Financial forecasting |
| title | Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network |
| title_full | Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network |
| title_fullStr | Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network |
| title_full_unstemmed | Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network |
| title_short | Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network |
| title_sort | stock price prediction using the sand cat swarm optimization and an improved deep long short term memory network |
| topic | Stock price prediction Sand Cat swarm optimization LSTM Deep learning Artificial intelligence in finance Financial forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S221484502400156X |
| work_keys_str_mv | AT burakgulmez stockpricepredictionusingthesandcatswarmoptimizationandanimproveddeeplongshorttermmemorynetwork |