Abstract
This study employs the XGBoost algorithm, enhanced through hyperparameter optimization with Optuna, to forecast the closing stock prices of CATL and develop effective trading strategies. The XGBoost model achieved superior performance, with an R² score of 0.9201, a Mean Absolute Error (MAE) of 0.1982, and a Mean Squared Error (MSE) of 0.0825, outperforming benchmark models such as ElasticNet and Decision Tree. The resulting trading strategy generated actionable buy, sell, and hold recommendations over a 30-day horizon, aiding investors in profit optimization. The findings highlight the high prediction accuracy and stability of the XGBoost model, particularly when optimized with Optuna, thereby enhancing decision-making efficiency in stock trading. Nonetheless, the study underscores the potential for further improvement through the integration of advanced deep learning models, alternative optimization techniques, or ensemble learning approaches. Future research could also explore incorporating unstructured data to refine forecasting accuracy and expand the applicability of trading strategies.
