In portfolio analysis, the traditional approach of replacing population moments with sample counterparts may lead to suboptimal portfolio choices. In this paper I show that selecting asset positions to maximize expected quadratic utility is equivalent to a machine learning (ML) problem , where the asset weights are chosen to minimize out of sample mean squared error . It follows that ML specifically targets estimation risk when choosing the asset weights, and that "off-the-shelf" ML algorithms obtain optimal portfolios taking parameter uncertaintyinto account. Linear regression is a special case of the proposed ML framework , equivalent to the traditional approach . Standard results from the machine learning literaturemay be used to derive conditions for when ML algorithms improve upon linear regression . Based on simulation studies and several datasets, I find that ML significantly reduce estimation risk compared to  the traditional approach and several shrinkage approaches proposed in the literature .
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 .
D
In this paper
R
selecting asset positions to maximize expected quadratic utility is equivalent to
optimal portfolio weights can be estimated using
R
problem
framework
R
asset weights are chosen to minimize out of sample mean squared error
outcome to be predicted is a constant and the vector of explanatory variables is the asset returns
R
choosing the asset
estimating portfolio
R
obtain optimal portfolios taking parameter uncertaintyinto account. Linear regression is a special case of the proposed ML framework , equivalent to
can be used to estimate the optimal portfolio in the presence of parameter uncertainty. The framework nests
R
. Standard
and recently proposed shrinkage approaches as special cases. By relying on
R
machine learning literaturemay be used to derive conditions for when ML algorithms improve upon linear regression
ML literature, I derive new insights for existing approaches and propose new estimation methods
R
reduce
reduces
A
both
R
several shrinkage approaches proposed in the literature
the equal weight strategy