Meta-Labeling: Theory and Framework

Posted: 1 Mar 2022

See all articles by Jacques Joubert

Jacques Joubert

Hudson and Thames Quantitative Research

Date Written: February 10, 2022

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 from 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.

Keywords: Machine learning, artificial intelligence, quantamental investing, position sizing, meta-labeling

JEL Classification: G0, G1, G2, G15, G24, E44

Suggested Citation

Joubert, Jacques, Meta-Labeling: Theory and Framework (February 10, 2022). Available at SSRN: https://ssrn.com/abstract=4032018

Jacques Joubert (Contact Author)

Hudson and Thames Quantitative Research ( email )

155 Faraday Road
London, SW19 8PA
United Kingdom

HOME PAGE: http://www.hudsonthames.org

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