This research investigates the efficacy of various ensemble methods in financial forecasting by
comparing Random Forests, Gradient Boosting, XGBoost, and AdaBoost. With the advent of big
data analytics, accurate financial predictions are crucial for informed decision-making in volatile
markets. This study evaluates the performance of these ensemble techniques using a range of metrics
including accuracy, precision, recall, F1 Score, Mean Absolute Error (MAE), and Root Mean Squared
Error (RMSE). Experimental results reveal that XGBoost outperforms other methods with the highest
accuracy (89%) and lowest error metrics, demonstrating its superior capability in handling complex
financial data. Gradient Boosting follows closely with robust performance and balanced metrics, while
Random Forests and AdaBoost, though less effective, still provide valuable predictive insights. The
findings underscore the significant advancements that ensemble methods bring to financial forecasting
and highlight XGBoost as the most reliable approach for achieving accurate and precise predictions in
dynamic financial environments.
