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.

Name thesis:degree_name

Master of Science in Computer Sciences

Discipline thesis:degree_discipline

Computer Science

Grantor

The University of Texas at Austin

Year dc:date.issued

2024

Author dc:creator

• Barcelona, Jacob

Advisors dc:contributor.advisor

• Plaxton, C. Greg
• Muthuraman, Kumar

• Machine learning
• Stocks
• Curriculum learning
• Prediction

Language dc:language.iso

English

Identifier URI

https://doi.org/10.26153/tsw/54290

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/127754