@article {Joubertjfds.2022.1.098, author = {Jacques Francois Joubert}, title = {Meta-Labeling: Theory and Framework}, elocation-id = {jfds.2022.1.098}, year = {2022}, doi = {10.3905/jfds.2022.1.098}, publisher = {Institutional Investor Journals Umbrella}, abstract = {Meta-labeling is a machine learning (ML) layer that sits on top of a base primary strategy to help size positions, filter out false-positive signals, and improve metrics such as the Sharpe ratio and maximum drawdown. This article consolidates the knowledge of several publications into a single work, providing practitioners with a clear framework to support the application of meta-labeling to investment strategies. The relationships between binary classification metrics and strategy performance are explained, alongside answers to many frequently asked questions regarding the technique. The author also deconstructs meta-labeling into three components, using a controlled experiment to show how each component helps to improve strategy metrics and what types of features should be considered in the model specification phase.}, issn = {2640-3943}, URL = {https://jfds.pm-research.com/content/early/2022/06/23/jfds.2022.1.098}, eprint = {https://jfds.pm-research.com/content/early/2022/06/23/jfds.2022.1.098.full.pdf}, journal = {The Journal of Financial Data Science} }