TY - JOUR T1 - Meta-Labeling: Theory and Framework JF - The Journal of Financial Data Science SP - 31 LP - 44 DO - 10.3905/jfds.2022.1.098 VL - 4 IS - 3 AU - Jacques Francois Joubert Y1 - 2022/07/31 UR - https://pm-research.com/content/4/3/31.abstract N2 - 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. ER -