Prediction and investigation of stock price related to China’s new energy vehicles after the opening of the pandemic based on the LSTM model

Research Article
Open access

Prediction and investigation of stock price related to China’s new energy vehicles after the opening of the pandemic based on the LSTM model

Yanghan Peng 1*
  • 1 Chongqing Nankai Middle School    
  • *corresponding author 631402150230@mails.cqjtu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/22/20231213
ACE Vol.22
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-035-6
ISBN (Online): 978-1-83558-036-3

Abstract

This research examines the repercussions of the COVID-19 pandemic on China's new energy vehicle market through the utilization of machine learning models for stock price prediction. Specifically, the Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) are employed to forecast stock prices and price changes for BYD, Changan Automobile, and Guangzhou Automobile Group. While the LSTM model successfully captures the patterns in the stock price data, it exhibits a lag of one day in its predicted outcomes, indicating its reliance on the previous day's price. However, both models encounter challenges in accurately predicting stock price changes, displaying notable disparities from actual values. The classification task of forecasting whether prices will rise or fall also yields unsatisfactory accuracy scores, highlighting the models' limitations in comprehending the dynamics of the stock market. This study reveals that understanding the impact of the pandemic on the NEV market holds significant importance for informed decision-making and effective navigation of China's automotive industry in the aftermath of the pandemic. Furthermore, further modification for the model is also required to enhance the precision and dependability of stock price forecasts.

Keywords:

stock price prediction, machine learning, LSTM

Peng,Y. (2023). Prediction and investigation of stock price related to China’s new energy vehicles after the opening of the pandemic based on the LSTM model. Applied and Computational Engineering,22,176-182.
Export citation

References

[1]. Klein C Høj J Machlica G 2021 The impacts of the COVID-19 crisis on the automotive sector in Central and Eastern European Countries.

[2]. Černá I Éltető A Folfas P et al 2022 GVCs in Central Europe: A perspective of the automotive sector after COVID-19.

[3]. Behrad F Abadeh M S 2022 An overview of deep learning methods for multimodal medical data mining Expert Systems with Applications 117006.

[4]. Yu Q Wang J Jin Z et al 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control vol 72 103323.

[5]. Chen L Pelger M Zhu J 2023 Deep learning in asset pricing Management Science.

[6]. Obthong M et al 2020 A survey on machine learning for stock price prediction: algorithms and techniques In 2nd International Conference on Finance, Economics, Management and IT Business Vienna House Diplomat Prague, Prague, Czech Republic pp 63-71.

[7]. Nikou M et al 2019 Stock price prediction using deep learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(1) pp 22-42.

[8]. Teo B G 2021 Stock Prices Prediction Using Long Short-Term Memory (LSTM) Model in Python (Medium) Retrieved from https://medium.com/the-handbook-of-coding-in-finance/stock-prices-prediction-using-long-short-term-memory-lstm-model-in-python-734dd1ed6827.

[9]. Krogh A 2008 What are artificial neural networks? Nature biotechnology vol 26(2) pp 195-197.

[10]. Zou J Han Y So S S 2009 Overview of artificial neural networks Artificial neural networks: methods and applications pp 14-22.

Cite this article

Peng,Y. (2023). Prediction and investigation of stock price related to China’s new energy vehicles after the opening of the pandemic based on the LSTM model. Applied and Computational Engineering,22,176-182.

Data availability

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

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.22
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Klein C Høj J Machlica G 2021 The impacts of the COVID-19 crisis on the automotive sector in Central and Eastern European Countries.

[2]. Černá I Éltető A Folfas P et al 2022 GVCs in Central Europe: A perspective of the automotive sector after COVID-19.

[3]. Behrad F Abadeh M S 2022 An overview of deep learning methods for multimodal medical data mining Expert Systems with Applications 117006.

[4]. Yu Q Wang J Jin Z et al 2022 Pose-guided matching based on deep learning for assessing quality of action on rehabilitation training Biomedical Signal Processing and Control vol 72 103323.

[5]. Chen L Pelger M Zhu J 2023 Deep learning in asset pricing Management Science.

[6]. Obthong M et al 2020 A survey on machine learning for stock price prediction: algorithms and techniques In 2nd International Conference on Finance, Economics, Management and IT Business Vienna House Diplomat Prague, Prague, Czech Republic pp 63-71.

[7]. Nikou M et al 2019 Stock price prediction using deep learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(1) pp 22-42.

[8]. Teo B G 2021 Stock Prices Prediction Using Long Short-Term Memory (LSTM) Model in Python (Medium) Retrieved from https://medium.com/the-handbook-of-coding-in-finance/stock-prices-prediction-using-long-short-term-memory-lstm-model-in-python-734dd1ed6827.

[9]. Krogh A 2008 What are artificial neural networks? Nature biotechnology vol 26(2) pp 195-197.

[10]. Zou J Han Y So S S 2009 Overview of artificial neural networks Artificial neural networks: methods and applications pp 14-22.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.