Deep learning for efficient frontier calculation in finance

Understand

We propose deep neural network algorithms to calculate efficient frontier in some Mean-Variance and Mean-CVaR portfolio optimization problems.

• We show that we are able to deal with such problems when both the dimension of the state and the dimension of the control are high.
• Adding some additional constraints, we compare different formulations and show that a new projected feedforward network is able to deal with some global constraints on the weights of the portfolio while outperforming classical penalization methods.
• All developed formulations are compared in between.