Deep Reinforcement Learning for Goal-Based Investing Under Regime-Switching

Goal-based investing focuses on helping investors achieve specific financial goals, shifting away from the volatility-based risk paradigm. While numerous methods exist for this type of problem, the majority of them struggle to properly capture the non-stationary dynamics of real-world financial markets. This paper introduces a novel deep reinforcement learning framework for goal-based investing that addresses market non-stationarity through prompt reactions to regime switches. It relies on the integration of regime probability estimates directly into the state space. The experimental results indicate that the proposed method significantly outperforms several benchmarks commonly used in goal-based investing.