Stock market prediction using ensemble learning methods

Abstract

The stock market is complex and stochastic, which presents a significant challenge to accurately predict movements and prices. This study focuses on leveraging decision tree ensemble methods, specifically Random Forests and Extreme Gradient Boosting (XGBoost), for predicting future stock market movements. The primary focus of this study is to extract valuable stock market information whilst making the necessary changes to establish the best balance between investment risk and portfolio returns. The Random Forest and XGBoost models were trained and evaluated using historical stock data from five leading technology companies, namely Apple, Microsoft, Google, Tesla and Amazon. The results proved that both models were capable of delivering a reliable performance, generating substantial annual returns. The Random Forest model achieved a mean annual return of 47.8%, whilst the XGBoost model showed slightly lower performance, with a mean annual return of 32.0%. However, Random Forests achieved a maximum annual return of 85.2%, whereas XGBoost reached a higher maximum of 101.3%. These findings also revealed that Random Forests produced more consistent and conservative results, while the XGBoost model was more volatile and risky, occasionally achieving higher returns but with less stability. Overall, the results demonstrate that both ensemble models, Random Forests and XGBoost, were able to generalize effectively across unseen data, capturing informative patterns and relationships within the stock market data.