A comparative study of ensemble learning algorithms for high-frequency trading

Abstract

High-Frequency Trading utilizes powerful mathematical algorithms to execute transactions at an extremely rapid pace, which makes the use of machine learning techniques for prediction necessary. This paper evaluates the effectiveness of various ensemble learning algorithms, including Boosting (Adaboost and XGBoost), Bagging (Random Forest and Bagging-LSVM), and Stacking, in predicting stock prices using High-Frequency Trading (HFT) data from the Casablanca Stock Exchange. The data contains 311.812 transactions at the millisecond precision level and was obtained under a non-disclosure agreement. Three performance metrics are used: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean of the Squared Errors (MSE) and accuracy of predictions is assessed daily, monthly and annually. The study shows that the Stacking Model outperforms other algorithms in forecasting prices across different periods due to its ability to generate profit from multiple learners and construct a robust model. These results provide valuable insights for improving High-Frequency Trading (HFT) strategies, including the accuracy and robustness of the algorithms in predicting stock prices, which are crucial for effective HFT strategies. The findings highlight how each algorithm characteristics and performance metrics can enhance decision-making in HFT. The novelty of this study lies in its unique combination of a focus on an emerging market, the application of advanced ensemble learning algorithms to high-frequency data, and its comprehensive approach to evaluating algorithm performance. Also, this paper is the first study that focuses on applying ensemble learning algorithms to high-frequency data in Casablanca Stock Exchange and one of the rare that focuses on African stock markets. Thus, this study is pioneering in its comparative analysis of machine learning techniques for HFT in Morocco, introducing sophisticated and more accurate tools for analyzing and predicting Casablanca Stock Exchange trends.