Predicting Stock Prices in the China A-share Market Using Long Short-Term Memory (LSTM) Neural Networks
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Predicting Stock Prices in the China A-share Market Using Long Short-Term Memory (LSTM) Neural Networks

Wentao Ding 1* Yanci Chen 2, Dumou Zhang 3
1 University of Minnesota
2 University of Sydney
3 Guangzhou Tianxing Experimental School
*Corresponding author: ooorangecatt@gmail.com
Published on 11 July 2025
Volume Cover
AEMPS Vol.202
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-263-8
ISBN (Online): 978-1-80590-264-5
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Abstract

Stock price prediction in the volatile and evolving China A-share market faces both a challenge and an opportunity. In this paper, an attempt is going to be made to implement Long Short-Term Memory (LSTM) neural networks in predicting stock price movements of several large banks in China. The database comprises daily information on stocks of eight banks, split into training and testing sets. Development and Optimization of an LSTM Model Using MSE and the Adam Optimizer. The model exhibited quite good predictive capability over the training data but weak generalization over the unseen data. However, the study found that LSTM-based predictions could support profitable trading strategies, thus providing valuable insight for investors and policymakers. This could be further investigated by enhancing the robustness of the model and deploying across different markets and time frames.

Keywords:

China A Shares, LSTM Model, Stock Prediction

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Ding,W.;Chen,Y.;Zhang,D. (2025). Predicting Stock Prices in the China A-share Market Using Long Short-Term Memory (LSTM) Neural Networks. Advances in Economics, Management and Political Sciences,202,1-14.

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Cite this article

Ding,W.;Chen,Y.;Zhang,D. (2025). Predicting Stock Prices in the China A-share Market Using Long Short-Term Memory (LSTM) Neural Networks. Advances in Economics, Management and Political Sciences,202,1-14.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis

ISBN: 978-1-80590-263-8(Print) / 978-1-80590-264-5(Online)
Editor: Ursula Faura-Martínez
Conference website: https://2024.icftba.org/
Conference date: 13 June 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.202
ISSN: 2754-1169(Print) / 2754-1177(Online)