Theoretical and Empirical Research on the Application of LSTM and xLSTM to Stock Price Prediction

Cuicui Yang; Min Zhu

doi:10.2991/978-94-6463-742-7_51

<Previous Article In Volume

Theoretical and Empirical Research on the Application of LSTM and xLSTM to Stock Price Prediction

Authors

Cuicui Yang¹, Min Zhu²^{, *}

¹School of Artificial Intelligence, Shanghai Normal University Tianhua College, Shanghai, China, 201815

²School of Data Science & Engineering, East China Normal University, Shanghai, China, 200062

^*Corresponding author. Email: mzhu@cc.ecnu.edu.cn

Corresponding Author

Min Zhu

Available Online 31 May 2025.

DOI: 10.2991/978-94-6463-742-7_51 How to use a DOI?
Keywords: xLSTM model; LSTM model; Stock price prediction; Deep learning; Model evaluation
Abstract: Under the background of global economic integration and financial market deepening, stock price forecasting has become a hot spot in the financial field. Traditional methods struggle to accurately predict stock prices affected by complex factors. This study explores the application of LSTM and xLSTM models in stock price prediction by leveraging big data and artificial intelligence. Historical data from the Shanghai Stock Exchange 510050 was collected through the Tushare library. The data was preprocessed, normalized, and segmented into training, validation, and test sets. LSTM and bidirectional xLSTM models were implemented and trained using PyTorch. The experimental results show that the xLSTM model significantly outperforms the standard LSTM model, achieving an R² score of 0.7542 compared to 0.4590 for LSTM. The results highlight the potential of deep learning models in financial forecasting, providing valuable references for investors and decision-makers. Future improvements can focus on hyperparameter optimization and integrating additional financial indicators.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume

Volume Title: Proceedings of the 2025 4th International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2025)
Series: Advances in Intelligent Systems Research
Publication Date: 31 May 2025
ISBN: 978-94-6463-742-7
ISSN: 1951-6851
DOI: 10.2991/978-94-6463-742-7_51 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Cuicui Yang
AU  - Min Zhu
PY  - 2025
DA  - 2025/05/31
TI  - Theoretical and Empirical Research on the Application of LSTM and xLSTM to Stock Price Prediction
BT  - Proceedings of the 2025 4th International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2025)
PB  - Atlantis Press
SP  - 539
EP  - 548
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-742-7_51
DO  - 10.2991/978-94-6463-742-7_51
ID  - Yang2025
ER  -

download .riscopy to clipboard