Deep Reinforcement Learning for Robust Goal-Based Wealth Management

TL;DR

This paper introduces a deep reinforcement learning approach for goal-based wealth management, demonstrating its effectiveness in optimizing investment strategies to achieve specific financial goals using both simulated and real market data.

Contribution

It presents a novel deep reinforcement learning framework tailored for robust goal-based wealth management, outperforming existing benchmarks.

Findings

Superiority over benchmark strategies in simulations

Effective on both simulated and historical market data

Robustness in achieving financial goals

Abstract

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.