Stock market forecasting using a lion-algorithm-optimised hybrid LSTM model

Key Points

• The study aims to improve stock market forecasting accuracy through a novel hybrid LSTM model optimized using a lion algorithm.
• Developed a hybrid LSTM model incorporating optimization techniques.
• Utilized historical stock market data for training and validation.
• Evaluated model performance against traditional forecasting methods.
• Achieved a significant increase in prediction accuracy compared to standard LSTM models.
• Demonstrated a reduction in forecasting error by 25% (p<0.05).
• Provided insights into better decision-making in stock trading.