Mean-Variance Efficient Large Portfolios : A Simple Machine Learning Heuristic Technique based on the Two-Fund Separation Theorem
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1 mars 2024
- handle: 10670/1.cpklfl
- hal: hal-04514343
info:eu-repo/semantics/OpenAccess
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Two-Fund Separation Theorem Machine learning Robust portfolio High-dimensional Portfolios mean-variance efficient portfoliosSujets proches
Scrip Investment securities Securities law Capitalization (Finance) Blue sky laws Securities--Law and legislation Portfolio Underwriting Learning, Machine Investing Investment management PortfolioCiter ce document
Michele Costola et al., « Mean-Variance Efficient Large Portfolios : A Simple Machine Learning Heuristic Technique based on the Two-Fund Separation Theorem », HAL-SHS : droit et gestion, ID : 10670/1.cpklfl
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Résumé
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.