Updated deep long short-term memory with Namib beetle Henry optimisation for sentiment-based stock market prediction

Stock price prediction is a challenging and promising area of research due to the volatile nature of stock markets influenced by factors like investor sentiment and market rumours. Developing accurate prediction models is difficult, given the complexity of stock data. Long short-term memory (LSTM) models have proven effective in uncovering hidden patterns, enabling precise predictions. Therefore, in this research work, an innovative approach called updated deep LSTM (UDLSTM) combined with Namib beetle Henry optimisation (BH-UDLSTM) is proposed and applied to historical stock market and sentiment analysis data. The UDLSTM model enhances prediction performance, offering stability during training and increased data accuracy. By incorporating Namib beetle and Henry gas algorithms, BH-UDLSTM further improves prediction accuracy by striking a balance between exploration and exploitation. The evaluation against existing methods demonstrates that the proposed approach achieves a higher accuracy rate (92.45%) in stock price prediction compared to state-of-the-art techniques. © 2024 Inderscience Enterprises Ltd.