Скачать или смотреть Ensemble Meta-Labeling

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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. We demonstrate that ensembles are especially beneficial when the underlying data consists of multiple regimes and is non-linear. Our framework serves as a starting point for further research. We suggest that the use of different fusion strategies may foster model selection. Finally, we elaborate on how additional applications, such as position sizing, may benefit from our framework.