Self-Consistent Adjoint Policy Iteration for Constrained Dynamic Portfolio Choice
Jeonggyu Huh, Yeoneung Kim, Seungwon Jeong
Abstract
We develop simulation-based policy iteration for continuous-time portfolio choice with predictable returns and convex constraints. Each outer step re-evaluates a fixed-latent OL-BPTT adjoint after deployment and solves the constrained update. Shifted-adjoint cancellation controls the adjoint--HJB Hamiltonian-gradient discrepancy by the policy-improvement residual. For CRRA portfolios, exact HJB policy iteration identifies the optimal reduced value factor, while population OL-BPTT iteration converges globally under an occupation-measure relative-error condition. A theorem-matched audit yields a maximal 95% upper endpoint of 0.074 against the required 0.75 threshold. In a three-factor, fifty-asset design, current-policy re-evaluation outperforms matched pooled refinement under both evaluation laws.
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