Abstract
Allen et al (ALS) (2019) claim that a CAPM based theoretical framework for Markowitz (1952) mean-variance (MV) efficiency and a small level of forecast information (IC) can beat equal weighted portfolios. A portfolio optimization procedure worse than equal weighting would have little practical investment value or interest. They challenge the 1/N empirical results in DeMiguel et al (DGU) (2009) and, implicitly, the “error maximization” characterization of MV optimization in Michaud (1989). However, their conclusions are inconsistent with canonical Monte Carlo simulation studies of estimation error in MV optimization. This is because their theoretical CAPM-like framework ignores the bulk of estimation error – model error and covariance matrix estimation – by assumption. Our extension of classic Monte Carlo studies indicates that many times the level of forecast information assumed in ALS is likely required to outperform equal weight in theoretical budget-constrained MV optimized portfolios in practice.
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