Goal-based investing is an approach to wealth management that prioritizes achieving specific financial goals. It is naturally formulated as a sequential decision-making problem as it requires choosing the appropriate investment until a goal is achieved. Consequently, reinforcement learning, a machine learning technique appropriate for sequential decision-making, offers a promising path for optimizing these investment strategies. In this paper, a novel approach for robust goal-based wealth management based on deep reinforcement learning is proposed. The experimental results indicate its superiority over several goal-based wealth management benchmarks on both simulated and historical market data.
@article{arxiv.2307.13501,
title = {Deep Reinforcement Learning for Robust Goal-Based Wealth Management},
author = {Tessa Bauman and Bruno Gašperov and Stjepan Begušić and Zvonko Kostanjčar},
journal= {arXiv preprint arXiv:2307.13501},
year = {2023}
}