• ISSN: 2010-023X (Print)
    • Abbreviated Title: Int. J. Trade, Economics and Financ.
    • Frequency: Quaterly
    • DOI: 10.18178/IJTEF
    • Editor-in-Chief: Prof.Tung-Zong (Donald) Chang
    • Managing Editor: Ms. Inez. Chan

    • Abstracting/ Indexing:  Crossref, CNKI, EBSCO

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HOME > Archive > 2025 > Volume 16 Number 1 (2025) >
IJTEF 2025 Vol.16(1): 133-138
DOI: 10.18178/ijtef.2025.16.1.781

Applying Multiple Linear Regression and Neural Network to Predict New Energy Vehicle Manufacturers’ Stock Performances

Yuqing Li*
Department of Finance, School of Economics and Business Administration, Chongqing University, Chongqing, China
Email: liyuqing.only@gmail.com

Manuscript received December 15, 2024; revised January 13, 2025; accepted February 20, 2025

Abstract—The new energy vehicle sector has experienced significant expansion and has become a central focus in global financial markets, propelled by worldwide sustainability and energy transition initiatives. Nevertheless, the sector continues to be exceedingly intricate, influenced by several influences across multiple markets and dimensions. Given the market’s volatility and the increasing demand for accurate financial predictions, sophisticated models are essential to understand these complex processes. This research examines BYD and Tesla as exemplary manufacturers using multiple linear regression followed by backpropagation neural networks to develop a precise predictive model. The model exhibits significant precision in forecasting short-term stock fluctuations, especially for BYD. This study facilitates future investigation into complex market processes.


Keywords—new energy vehicles, customer price index, exchange rate, gold, backpropagation neural network, multiple linear regression


Cite: Yuqing Li, "Applying Multiple Linear Regression and Neural Network to Predict New Energy Vehicle Manufacturers’ Stock Performances," International Journal of Trade, Economics and Finance, vol.16, no.1, pp. 133-138, 2025.


Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).


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