Author(s)

Bianmengyan Ji ¹, Xiyuan Tong ¹, Xiaohan Wang ¹

Affiliation(s)

¹ School of Mathematics and Physics, Xi'an Jiaotong-Liverpool University, Ren Ai Street, Suzhou, China

Corresponding Author

ABSTRACT

The increasing attention towards low-carbon initiatives has led to a surge in interest for products and services that contribute to a sustainable future. As a result, cap-and-trade policies, green bonds, and low-carbon stocks have emerged as significant areas of investigation. This study aims to explore the predictability of low-carbon stock prices, using BYD (Build Your Dream), a prominent new energy vehicle brand, as a case study. To effectively analyze and forecast BYD stock closing prices, we have evaluated various models and determined that Long Short-Term Memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models exhibit superior performance in comparison to other alternatives. Employing a range of evaluation metrics, such as Standard Deviation (STD), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), we demonstrate that the selected models exhibit a satisfactory level of fit. In future research endeavors, we aspire to expand the scope of our investigation to encompass additional facets of low-carbon stocks and their potential impact on the broader financial landscape.

KEYWORDS

CITE THIS PAPER

Bianmengyan Ji, Xiyuan Tong, Xiaohan Wang, The stock price of BYD prediction using LSTM and ARIMA. Financial Engineering and Risk Management (2023) Vol. 6: 73-79. DOI: http://dx.doi.org/10.23977/ferm.2023.060509.

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