Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)

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Application of LSTM and Attention Mechanism for Stock Price Prediction and Analysis

Authors
Yingbing Li1, Xue Zhang2, Xueyan Zhu3, *
1School of Economics, Qingdao University, Qingdao, 266071, China
2College of Biological Science and Biotechnology, Beijing Forestry University, Beijing, 100083, China
3School of Technology, Beijing Forestry University, Beijing, 100083, China
*Corresponding author. Email: xueyan0111@bjfu.edu.cn
Corresponding Author
Xueyan Zhu
Available Online 28 August 2023.
DOI
10.2991/978-94-6463-222-4_60How to use a DOI?
Keywords
stock price; CNN; LSTM; attention mechanisms; CBAM
Abstract

The relationship between stock prices and economic development is well-established, and the study of stock price prediction methods is crucial for gaining insights into the economy. This research aims to enhance the accuracy of stock price prediction by leveraging a combination of convolutional neural network (CNN), attention mechanisms, and long short-term memory (LSTM). Specifically, the stock data of Banco Bilbao Vizcaya Argentina (BBVA) was selected as the research object, and the stock price prediction results after incorporating efficient channel attention (ECA), channel attention module (CAM), spatial attention module (SAM), and convolutional block attention module (CBAM) in CNN + LSTM were analyzed. The findings demonstrate that the incorporation of attention mechanisms in CNN + LSTM has a positive impact on stock price prediction accuracy. Notably, the model that integrates CBAM attention mechanism in CNN + LSTM yields the best prediction results, with MAE, MSE, RMSE, and R2 metrics achieving 58.68 × 10–4, 7.94 × 10–5, 8.91 × 10–3, and 0.9673, respectively. These results have implications for improving the accuracy of stock price prediction and provide valuable insights for future research in this area.

Copyright
© 2023 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.

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Volume Title
Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
28 August 2023
ISBN
978-94-6463-222-4
ISSN
2589-4919
DOI
10.2991/978-94-6463-222-4_60How to use a DOI?
Copyright
© 2023 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.

Cite this article

risenwbib
TY  - CONF
AU  - Yingbing Li
AU  - Xue Zhang
AU  - Xueyan Zhu
PY  - 2023
DA  - 2023/08/28
TI  - Application of LSTM and Attention Mechanism for Stock Price Prediction and Analysis
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 553
EP  - 561
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_60
DO  - 10.2991/978-94-6463-222-4_60
ID  - Li2023
ER  -