Learning the Efficient Frontier
Abstract
The efficient frontier (EF) is a fundamental resource allocation problem where one has to find an optimal portfolio maximizing a reward at a given level of risk. This optimal solution is traditionally found by solving a convex optimization problem. In this paper, we introduce NeuralEF: a fast neural approximation framework that robustly forecasts the result of the EF convex optimization problem with respect to heterogeneous linear constraints and variable number of optimization inputs. By reformulating an optimization problem as a sequence to sequence problem, we show that NeuralEF is a viable solution to accelerate large-scale simulation while handling discontinuous behavior.
Cite
@article{arxiv.2309.15775,
title = {Learning the Efficient Frontier},
author = {Philippe Chatigny and Ivan Sergienko and Ryan Ferguson and Jordan Weir and Maxime Bergeron},
journal= {arXiv preprint arXiv:2309.15775},
year = {2023}
}
Comments
Accepted at the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)