A Tax-Efficient Model Predictive Control Policy for Retirement Funding

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 (such as Social Security payments), 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. Because this problem is convex, it can be solved quickly and reliably. We leverage this planning method to form a retirement funding policy, i.e., an algorithm 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, with realistic statistical models of investment returns and inflation. By simulating thousands of trajectories of realized investment returns and inflation, and the retiree’s lifetime, we show that the MPC retirement policy works well, delivering a higher bequest than the standard 4% rule.

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Publication Details

Journal
The Journal of Retirement
Published
2025-10-16
DOI
https://doi.org/10.3905/jor.2025.1.195
Primary Topic
Retirement, Disability, and Employment
Type
article
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article

A Tax-Efficient Model Predictive Control Policy for Retirement Funding

Kasper Johansson, Stephen T. P. Boyd
The Journal of Retirement
Retirement, Disability, and Employment
article

A Tax-Efficient Model Predictive Control Policy for Retirement Funding

Kasper Johansson, Stephen T. P. Boyd
article en

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 (such as Social Security payments), 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. Because this problem is convex, it can be solved quickly and reliably. We leverage this planning method to form a retirement funding policy, i.e., an algorithm 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, with realistic statistical models of investment returns and inflation. By simulating thousands of trajectories of realized investment returns and inflation, and the retiree’s lifetime, we show that the MPC retirement policy works well, delivering a higher bequest than the standard 4% rule.

The Journal of RetirementVol. 13(2)
Stanford Medicine (US), Stanford University (US)
Openalex Percentile: Top 48%
Retirement, Disability, and Employment
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