A Machine Learning Efficient Frontier

We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the mean-variance efficient frontier out-of-sample. Our results, therefore, shed important light on the appeal of machine learning into portfolio selection under estimation risk

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Year of publication:	2020
Authors:	Clark, Brian J.
Other Persons:	Feinstein, Zachary (contributor) ; Simaan, Majeed (contributor)
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Künstliche Intelligenz \| Artificial intelligence \| Technische Effizienz \| Technical efficiency \| Portfolio-Management \| Portfolio selection \| Data-Envelopment-Analyse \| Data envelopment analysis \| Lernprozess \| Learning process

Extent:	1 Online-Ressource (14 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Operations Research Letters, DOI: https://doi.org/10.1016/j.orl.2020.07.016 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments May 21, 2020 erstellt
Other identifiers:	10.2139/ssrn.3541387 [DOI]
Classification:	C4 - Econometric and Statistical Methods: Special Topics ; C6 - Mathematical Methods and Programming ; C13 - Estimation ; D8 - Information and Uncertainty ; G1 - General Financial Markets
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10012841156