Title: Reducing Estimation Risk in Mean-Variance Portfolios with Machine Learning,
Abstract: In portfolio analysis, the traditional approach of replacing population
moments with sample counterparts may lead to suboptimal portfolio choices. I
show that optimal portfolio weights can be estimated using a machine learning
(ML) framework, where the outcome to be predicted is a constant and the vector
of explanatory variables is the asset returns. It follows that ML specifically
targets estimation risk when estimating portfolio weights, and that
"off-the-shelf" ML algorithms can be used to estimate the optimal portfolio in
the presence of parameter uncertainty. The framework nests the traditional
approach and recently proposed shrinkage approaches as special cases. By
relying on results from the ML literature, I derive new insights for existing
approaches and propose new estimation methods. Based on simulation studies and
several datasets, I find that ML significantly reduces estimation risk compared
to both the traditional approach and the equal weight strategy.