Selective machine learning for stock market prediction

Applying state-of-the-art machine learning models to predicting stock returns has been a common focus of research for practitioners. However, these models often face challenges due to the inclusion of a wide universe of stocks, leading to performance degradation caused by significant noise. In this study, we apply two mechanisms from Selective Machine Learning and Curriculum Learning to enhance the robustness of our model to noise and improve overall performance. Additionally, we explore several avenues for future research in selective machine learning within the domain of finance.