Journal Article

Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy

Zeng, Xiaohua, Yang, Qian, Cai, Jieping, Liang, Changzhou, Wang, Fei

2025

Overview

Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance. PSO-LSTM model leveraging PSO’s efficient swarm intelligence and strong optimization capabilities is proposed in this article. The experimental results on six global stock indices demonstrate that PSO-LSTM effectively fits real data, achieving high prediction accuracy. Moreover, increasing PSO iterations lead to gradual loss reduction, which indicates PSO-LSTM’s good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. Furthermore, the impact of different retrospective periods on prediction accuracy and finding consistent results across varying time spans are. Conducted in the experiments.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Accuracy / Algorithms / Artificial intelligence / Automation / Biology and Life Sciences / Comparative analysis / Computer and Information Sciences / Correlation analysis / Data mining / Deep learning / Dow Jones averages / Efficient markets / Electronic trading systems / Emergency communications systems / Forecast accuracy / Forecast improvement / Forecasting - methods / Humans / Hydrology / Impact analysis / Impact prediction / Investments - economics / Landslides & mudslides / Long short-term memory / Loss reduction / Machine Learning / Mean square errors / Methods / Models, Economic / Neural networks / Neural Networks, Computer / Optimization techniques / Physical Sciences / Predictions / Research and Analysis Methods / Securities markets / Stock exchanges / Stock price forecasting / Stock prices / Swarm intelligence / Time dependence / Time series / Time signals / Trends / Wavelet transforms