Robust Utility Optimization via a GAN Approach

arXiv:2403.15243v1 Announce Type: cross
Abstract: Robust utility optimization enables an investor to deal with market uncertainty in a structured way, with the goal of maximizing the worst-case outcome. In this work, we propose a generative adversarial network (GAN) approach to (approximately) solve robust utility optimization problems in general and realistic settings. In particular, we model both the investor and the market by neural networks (NN) and train them in a mini-max zero-sum game. This approach is applicable for any continuous …

abstract adversarial arxiv case cs.lg deal gan general generative generative adversarial network investor market network optimization q-fin.cp q-fin.mf q-fin.pm robust solve type uncertainty utility via work