Before revision:  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 .
After revision:  In this paper I show that selecting asset positions to maximize expected quadratic utility is equivalent to a machine learning (ML) problem , where the outcome to be predicted is a constant and the vector of explanatory variables is the asset returns .