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Machine Learning for Electric Vehicle Stock Price Prediction: Analyzing Artificial Neural Network and Random Forest Performance |
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| รหัสดีโอไอ | |
| Creator | Montira Jarudecha |
| Title | Machine Learning for Electric Vehicle Stock Price Prediction: Analyzing Artificial Neural Network and Random Forest Performance |
| Contributor | Sanya Khruahong |
| Publisher | Faculty of Informatics, Mahasarakham University |
| Publication Year | 2569 |
| Journal Title | Journal of Applied Informatics and Technology |
| Journal Vol. | 8 |
| Journal No. | 2 |
| Page no. | 260666 |
| Keyword | Artificial Neural Network, Electric Vehicles, Machine Learning, Random Forest, Stock Market |
| URL Website | https://ph01.tci-thaijo.org/index.php/jait |
| Website title | Journal of Applied Informatics and Technology |
| ISSN | 3088-1803 |
| Abstract | Forecasting the stock prices of electric vehicle (EV) companies presents acomplex challenge due to market volatility and constantly changing ex-ternal factors. This study aims to address a research gap in the literature,where comparative analyses of multiple machine learning models acrossseveral EV companies remain limited. Specifically, the study evaluatesand compares the predictive performance of Artificial Neural Networks(ANN) and Random Forest (RF) in forecasting the stock prices of Tesla,BYD, Volkswagen, Geely, and GM using data from January 2018 to June2023. The dataset comprises key stock market indicators—opening price,highest price, lowest price, volume, and closing price—augmented withCOVID-19 pandemic data to reflect external influences on market be-havior. Prior to analysis, missing values were handled using mean im-putation, and data were normalized using Min-Max scaling to optimizemodel training. Performance was assessed using Root Mean Square Error(RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Er-ror (MBE). The results indicate that RF generally outperforms ANN inforecasting stock prices across most companies, particularly GM (RMSE= 0.3760, MAPE = 0.8238, MBE = 0.0537) and Volkswagen (RMSE =1.0437, MAPE = 0.6868, MBE = 0.0584). In contrast, ANN performedbest for Geely (RMSE = 0.2240, MAPE = 1.4160, MBE = -0.0271), sug-gesting that ANN may be better suited for datasets with more consistentor specific characteristics, while RF delivered more stable performanceacross companies. A t-test revealed statistically significant differences inperformance between RF and ANN for Volkswagen (p = 0.0050) and GM(p < 0.001), while no significant differences were found for Tesla, BYD,and Geely (p > 0.05), indicating that model selection should considerthe specific data characteristics. This research contributes a novel ap-proach by conducting cross-company ML model comparisons in the EVsector while incorporating external variables such as COVID-19, whichare rarely addressed in prior work. The findings offer practical insightsfor investors, analysts, and market intelligence systems, emphasizing theimportance of tailoring model selection to the characteristics of individ-ual stock data and supporting the use of AI for more accurate investmentdecisions.1. IntroductionStock market prediction has gained significant attentionin recent years, as more individuals seek profitable in-vestment opportunities. Beyond serving as a secondaryincome source, stock market investments provide analternative to traditional employment, with two mainstrategies: long-term investments, where stocks are heldfor extended periods to generate annual returns, andshort-term trading (day trading), where stocks are fre-ISSN 3088-1803 | Copyright© 2026. Published by the Faculty of Informatics, Mahasarakham University. All rights reserved.This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). |
