Machine Learning for Electric Vehicle Stock Price Prediction: Analyzing Artificial Neural Network and Random Forest Performance - Jarudecha, Montira; Khruahong, Sanya (Journal of Applied Informatics and Technology, 2025)
Forecasting the stock prices of electric vehicle (EV) companies presents a complex challenge due to market volatility and constantly changing external factors. This study aims to address a research gap in the literature, where comparative analyses of multiple machine learning models across several EV companies remain limited. Specifically, the study evaluates and 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 June 2023. The dataset comprises key stock market indicators—opening price, highest price, lowest price, volume, and closing price—augmented with COVID-19 pandemic data to reflect external influences on market behavior. Prior to analysis, missing values were handled using mean imputation, and data were normalized using Min-Max scaling to optimize model training. Performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE). The results indicate that RF generally outperforms ANN in forecasting 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 performed best for Geely (RMSE = 0.2240, MAPE = 1.4160, MBE = -0.0271), suggesting that ANN may be better suited for datasets with more consistent or specific characteristics, while RF delivered more stable performance across companies. A t-test revealed statistically significant differences in performance 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 consider the specific data characteristics. This research contributes a novel approach by conducting cross-company ML model comparisons in the EV sector while incorporating external variables such as COVID-19, which are rarely addressed in prior work. The findings offer practical insights for investors, analysts, and market intelligence systems, emphasizing the importance of tailoring model selection to the characteristics of individual stock data and supporting the use of AI for more accurate investment decisions.