Deep Reinforcement Learning for Robust Goal-Based Wealth Management
This work addresses the challenge of robust investment strategy optimization for wealth managers and investors, representing an incremental improvement in applying reinforcement learning to financial domains.
The paper tackled the problem of optimizing goal-based wealth management by proposing a deep reinforcement learning approach, achieving superior performance over existing benchmarks on simulated and historical market data.
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.