Stock markets are one of the most intricate and dynamic sectors globally. The need for predicting the future course of business trends and investing wisely has never been more critical. Machine Learning has provided a solution with its sophisticated and deep algorithms that are capable of gleaning through historical data to find patterns and predict the future. Among the different models, the ensemble models are much more attractive since they aim to increase the accuracy of the predictions. Ensemble models combine several predictive models to make a joint prediction, and the underlying assumption is that a group of models can predict more accurately than a single model . There are a few ensemble methods, namely, the Bagging or Bootstrap Aggregating, Boosting or Adaptive Boosting , and Random Forest . Bagging, also known as Bootstrap Aggregating, puts a group of models and feeds randomly chosen bootstrap samples of the data. Bagging generates the final prediction by taking an average or voting each of the predictions made by the models. Boosting, with the underlying principle being creating a bunch of models in a sequence, trains a model, creates a next model that corrects the errors of the previous one, and so on until the model does not produce errors. Random Forest aggregates on bootstraps samples of the data and generates a multitude of decision trees. Each tree is given the full features available and results in the final prediction being averaged from all the decision trees.

In summary, ensemble methods have the following advantages when applied to stock market prediction: Key benefits: improved accuracy by combining multiple models to eliminate overfitting and underfitting; enhanced robustness through lower susceptibility to noise and outliers; reduced variance by averaging or voting outcomes across multiple models. Most importantly, such methods should be implemented with caution and several factors should be considered: model selection and diversity: the models within the ensemble should be as diverse as possible; hyperparameter tuning of each model, as well as the hyperparameters within the ensemble; data preprocessing, starting with the quality and preprocessing of the raw data. Since ensemble methods, particularly random forests, have already demonstrated themselves as powerful tools for high-performance computing, the application described in the investment market is noteworthy for data-driven decision-making in this turbulent sector.