Reinforcement Learning with Scenario-Context Rollout in Portfolio Management

When economic structures and market dynamics shift, classic portfolio rebalancing algorithms often suffer from unstable and degraded performance. To improve the return and robustness of portfolio management, we explore reinforcement learning (RL) and propose Scenario-Context Rollout (SCR), a macroeconomics-guided feedback mechanism to produce a distribution of next-day joint returns under potential economic shocks. However, doing so faces new challenges, as history will never tell what would have happened differently. As a result, incorporating scenario-based rewards from rollouts introduces a reward-transition mismatch in temporal-difference (TD) learning, destabilizing RL critic training.