Problem
Research questions and friction points this paper is trying to address.
portfolio choice
predictable returns
convex constraints
Self-Consistent Adjoint Policy Iteration for Constrained Dynamic Portfolio Choice
🤖 AI Summary
研究通过自洽伴随策略迭代方法解决了带约束的动态投资组合选择问题，适用于可预测回报和凸约束情况。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Self-Consistent Adjoint Policy Iteration
Constrained Dynamic Portfolio Choice
Shifted-Adjoint Cancellation
HJB Hamiltonian-Gradient Discrepancy
CRRA Portfolios
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