Ensemble Meta-Labeling

Abstract

This study systematically investigates different ensemble methods for meta-labeling in finance and presents a framework to facilitate the selection of ensemble learning models for this purpose. Experiments were conducted on the components of information advantage and modeling for false positives to discover whether ensembles were better at extracting and detecting regimes and whether they increased model efficiency. The authors demonstrate that ensembles are especially beneficial when the underlying data consist of multiple regimes and are nonlinear in nature. The authors’ framework serves as a starting point for further research. They suggest that the use of different fusion strategies may foster model selection. Finally, the authors elaborate on how additional applications, such as position sizing, may benefit from their framework.