RT Journal Article SR Electronic T1 Ensemble Meta-Labeling JF The Journal of Financial Data Science FD Institutional Investor Journals SP 10 OP 26 DO 10.3905/jfds.2022.1.114 VO 5 IS 1 A1 Dennis Thumm A1 Paolo Barucca A1 Jacques Francois Joubert YR 2023 UL https://pm-research.com/content/5/1/10.abstract AB 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.