• Medientyp: E-Book
  • Titel: Ensembles of Portfolio Rules
  • Beteiligte: Nardari, Federico [VerfasserIn]; Schüssler, Rainer Alexander [VerfasserIn]
  • Erschienen: [S.l.]: SSRN, 2022
  • Umfang: 1 Online-Ressource (44 p)
  • Sprache: Englisch
  • DOI: 10.2139/ssrn.4217088
  • Identifikator:
  • Schlagwörter: Portfolio choice ; Combination of estimators ; Ensemble learning ; Estimation risk
  • Entstehung:
  • Anmerkungen: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments November 1, 2022 erstellt
  • Beschreibung: We propose a framework for combining portfolio rules while mitigating the impact of estimation error. Our main goal is to integrate heterogeneous rules that previously proposed combination methods are unable to accommodate, enabling researchers and investors to leverage established and ongoing advances in portfolio choice. The proposed framework relies on the (pseudo) out-of-sample returns of the considered rules, and the optimal combination is determined using an ensemble approach that maximizes the discounted utility generated jointly by the candidate rules. Based on out-of-sample evaluations of over forty years, we document substantial utility gains for our approach
  • Zugangsstatus: Freier Zugang