Mean-Variance Efficient Large Portfolios: A Simple Machine Learning Heuristic Technique based on the Two-Fund Separation Theorem

Michele Costola, Bertrand Maillet, Zhining Yuan and Xiang Zhang
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Bertrand Maillet: EM - EMLyon Business School

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Abstract: We revisit in this article the Two-Fund Separation Theorem as a simple technique for the Mean-Variance optimization of large portfolios. The proposed approach is fast and scalable and provides equivalent results of commonly used ML techniques but, with computing time differences counted in hours (1 minute versus several hours). In the empirical application, we consider three geographic areas (China, US, and French stock markets) and show that the Two-Fund Separation Theorem holds exactly when no constraints are imposed and is approximately true with (realistic) positive constraints on weights. This technique is shown to be of interest to both scholars and practitioners involved in portfolio optimization tasks.

Keywords: Two-Fund Separation Theorem; Machine learning; Robust portfolio; High-dimensional Portfolios; mean-variance efficient portfolios (search for similar items in EconPapers)
Date: 2024-03-01
New Economics Papers: this item is included in nep-cmp
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Published in Annals of Operations Research, 2024, 334 (1-3), 133-155 p

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Journal Article: Mean–variance efficient large portfolios: a simple machine learning heuristic technique based on the two-fund separation theorem (2024)
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