Mean-variance efficient large portfolios: a simple machine learning heuristic technique based on the two-fund separation theorem

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.