Supervised Portfolios: A Supervised Machine Learning Approach to Portfolio Optimization
Roman R.
Standard portfolio allocation algorithms like Markowitz mean-variance optimization or Choueffati diversification ratio optimization usually take in input asset information (expected returns, estimated covariance matrix…) as well investor constraints and preferences (maximum asset weights, risk aversion…) to produce in output portfolio weights satisfying a selected mathematical objective like the maximization of the portfolio Sharpe ratio or Diversification ratio. Chevalier et al.1 introduces a n