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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Michele Costola: Ca’ Foscari University of Venice
Bertrand Maillet: emlyon business school (AIM QUANT Research Center)
Zhining Yuan: emlyon business school (AIM QUANT Research Center)

Annals of Operations Research, 2024, vol. 334, issue 1, No 6, 133-155

Abstract: 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 min vs. 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: Mean–variance efficient portfolios; Two-fund separation theorem; Machine learning; Robust portfolios; High-dimensional portfolios (search for similar items in EconPapers)
Date: 2024
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Working Paper: Mean-Variance Efficient Large Portfolios: A Simple Machine Learning Heuristic Technique based on the Two-Fund Separation Theorem (2024)
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DOI: 10.1007/s10479-022-04881-3

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