PT - JOURNAL ARTICLE AU - Michael Meyer AU - Jacques Francois Joubert AU - Mesias Alfeus TI - Meta-Labeling Architecture AID - 10.3905/jfds.2022.1.108 DP - 2022 Oct 31 TA - The Journal of Financial Data Science PG - 10--24 VI - 4 IP - 4 4099 - https://pm-research.com/content/4/4/10.short 4100 - https://pm-research.com/content/4/4/10.full AB - Separating the side and size of a position allows for sophisticated strategy structures to be developed. Modeling the size component can be done through a meta-labeling approach. This article establishes several heterogeneous architectures to account for key aspects of meta-labeling. They serve as a guide for practitioners in the model development process, as well as for researchers to further build on these ideas. An architecture can be developed through the lens of feature- and/or strategy-driven approaches. The feature-driven approach exploits the way the information in the data is structured and how the selected models use that information, whereas a strategy-driven approach specifically aims to incorporate unique characteristics of the underlying trading strategy. Furthermore, the concept of inverse meta-labeling is introduced as a technique to improve the quantity and quality of the side forecasts.