Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

In a previous work, we presented Agora, a stock recommendation system based on sentiment analysis. One of the potential areas for improvement we recognized was the accuracy of the supervised machine learning model. We had previously employed a logistical regression machine learning model to generate our predictions. This paper aims to detail the improvements in accuracy we made by training and deploying ensemble models for our application. We detail our improved methodology in training Random Forest and XGBoost Classifier models on similar datasets from our original publication. We performed a comparison of the accuracies between the two models to show how our improved models lead to better results in stock market prediction. We have also provided sufficient context along the way to help a reader understand what we are attempting to achieve with this paper. Our Random Forest Classifier model outperformed the logistic regression model by 10.4%, which marked a significant improvement. Detailing how we beat our original accuracy is the major takeaway of this paper.