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

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

PLOS ONE, 2025, vol. 20, issue 1, 1-31

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0310296

DOI: 10.1371/journal.pone.0310296

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