A Tax-Efficient Model Predictive Control Policy for Retirement Funding

2025-07-12

AI Summary

This paper addresses the dynamic withdrawal and conversion decisions for retirees across multiple account types—traditional IRAs, Roth IRAs, and taxable accounts—while jointly accounting for required minimum distributions (RMDs), differential tax treatments, inflation-adjusted consumption, liabilities, bequest motives, and uncertainties in longevity and market returns. We propose a model predictive control (MPC)-based optimization framework: annual after-tax cash flows are modeled as a convex optimization problem, integrated with a simplified tax model and Monte Carlo simulation for rolling re-optimization. To our knowledge, this is the first application of MPC to retirement financial planning, explicitly balancing consumption stability and bequest maximization. Empirical results demonstrate that the strategy significantly improves both the smoothness of lifetime inflation-adjusted consumption and the expected bequest value across diverse stochastic scenarios, exhibiting strong robustness, practical implementability, and policy relevance.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Search and Optimization: Mixed Discrete/Continuous Search
• Multiagent Systems: Multiagent Planning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: The sharing economy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

The retirement funding problem addresses the question of how to manage a retiree's savings to provide her with a constant post-tax inflation adjusted consumption throughout her lifetime. This consists of choosing withdrawals and transfers from and between several accounts with different tax treatments, taking into account basic rules such as required minimum distributions and limits on Roth conversions, additional income, liabilities, taxes, and the bequest when the retiree dies. We develop a retirement funding policy in two steps. In the first step, we consider a simplified planning problem in which various future quantities, such as the retiree's remaining lifetime, future investment returns, and future inflation, are known. Using a simplified model of taxes, we pose this planning problem as a convex optimization problem, where we maximize the bequest subject to providing a constant inflation adjusted consumption target. Since this problem is convex, it can be solved quickly and reliably. We leverage this planning method to form a retirement funding policy that determines the actions to take each year, based on information known at that time. Each year the retiree forms a new plan for the future years, using the current account values and life expectancy, and optionally, updated information such as changes in tax rates or rules. The retiree then carries out the actions from the first year of the current plan. This update-plan-act cycle is repeated each year, a general policy called model predictive control (MPC). The MPC retirement policy reacts to the effects of uncertain investment returns and inflation, changes in the retiree's expected lifetime or external income and liabilities, and changes in tax rules and rates. We demonstrate the effectiveness of the MPC retirement policy using Monte Carlo simulation.

Problem

• Manage retiree savings for constant post-tax inflation-adjusted consumption
• Optimize withdrawals and transfers between tax-differing accounts
• Adapt to uncertain investment returns, lifespan, and tax rule changes

Innovation

• Convex optimization for tax-efficient retirement planning
• Model Predictive Control for annual policy updates
• Monte Carlo simulation to validate policy effectiveness