In a controlled lifecycle benchmark, per-date backward neural policies beat single and two-regime networks on welfare and Bellman residuals, and a shape penalty fixes negative MPC violations.
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Simulation-Based Neural Policies for Portfolio Choice: Architecture, Training, and Interpretability
In a controlled lifecycle benchmark, per-date backward neural policies beat single and two-regime networks on welfare and Bellman residuals, and a shape penalty fixes negative MPC violations.