Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network
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Date
2025-01
Authors
Gülmez, Burak
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
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.
Description
Social Sciences Citation Index (SSCI)
Keywords
Stock price prediction , Sand Cat swarm optimization , LSTM , Deep learning , Artificial intelligence in finance , Financial forecasting
Citation
Gülmez, B. (2024). Stock price prediction using the Sand Cat Swarm Optimization and an improved deep Long Short Term Memory network. Borsa Istanbul Review. https://doi.org/10.1016/j.bir.2024.12.002
