The following is based on “Redefining the Optimal Retirement Income Strategy,” from the Financial Analysts Journal.
Last month, I explored how retirees typically have some ability to adapt their spending to prolong the life of their portfolio. Here, I introduce an approach that incorporates dynamic spending into retirement income projections and provide an example of how it can result in more realistic expectations of potential retirement spending paths.
Evolving Models
Retirement income planning tools largely assume “static” spending: That is, portfolio withdrawals are expected to change over time based on inflation or some other constant factor. This assumption is overly simplistic and inconsistent with the decisions retirees might make when faced with potential portfolio ruin. In reality, retirees cut or increase their spending based on how their situation develops. If their portfolio performance falls below expectations, for example, they may need to tighten their belts, and vice versa.
While research going back decades proposes various methods to adjust portfolio withdrawals over time, these so-called dynamic spending (or withdrawal) rules can be difficult to implement. They may be too computationally complex or otherwise unable to handle nonconstant cash flows, and they may significantly complicate financial planning tools and even “break” more common binary outcome metrics, such as the probability of success. Static spending rules lead to retirement income projections that can differ significantly from the likely choices a household would make in retirement and from the optimal decisions around how that retirement should be funded.
Introducing the Funded Ratio
The funded ratio metric measures the health of pension plans, but it can also estimate the overall financial situation of retiree consumption or any other goal. The funded ratio is the total value of the assets, which includes both current balances and future expected income, divided by the liability, or all current and future expected spending. A funded ratio of 1.0 implies that an individual has just enough assets to fully fund the goal. A funded ratio greater than 1.0 suggests they have a surplus, while one below 1.0 implies a shortfall.
Estimating the funded ratio for each assumed year using a Monte Carlo simulation is one way to adjust expected spending throughout retirement as the retiree’s situation evolves (e.g., based on market returns). The table below provides context around how a certain spending amount could be tweaked based on the funded ratio for the respective goal at the end of the previous year.
Real Spending Adjustment Thresholds by Funding Ratio Level
| Funded Ratio | Needs Goal | Wants Goal |
| 0.00 | -10% | -20% |
| 0.25 | -5% | -15% |
| 0.50 | -3% | -10% |
| 0.75 | 0% | -5% |
| 1.00 | 0% | 0% |
| 1.25 | 0% | 2% |
| 1.50 | 0% | 4% |
| 1.75 | 2% | 8% |
| 2.00 | 4% | 10% |
Based on the above, if the wants spending goal is $50,000 and the funded ratio was 1.40, the amount would increase by 2%, to $51,000, in the subsequent year. Anticipated spending falls as the funded ratio declines, and vice versa.
The changes to the needs and wants spending adjustments vary, with greater adjustments to the latter. These differences reflect how much assumed flexibility is embedded in the two spending goals and the diminishing marginal utility of consumption. We could significantly increase the complexity of the adjustment rules, for example, by considering the remaining duration of retirement, portfolio risk levels, or additional client preferences.
While this dynamic spending model resembles some existing approaches, it is more holistic in how it considers the retiree’s situation. Other common dynamic spending rules, such as variants of how required minimum distributions (RMDs) are determined from qualified accounts, focus entirely on the portfolio balance and cannot incorporate how the role of the portfolio funding retirement could vary over time. Most dynamic spending rules cannot model a scenario in which spouses retire and claim Social Security at different ages and receive future sources of guaranteed income, such as a longevity annuity starting at age 85.
The Impact on Income
Incorporating dynamic spending rules can reveal a very different perspective on the range of potential retirement outcomes than viewing retirement as a static goal. For example, the exhibit below shows how spending could evolve for a retiree with an $80,000 retirement income goal, $1 million in savings, and $40,000 in Social Security benefits for whom 70%, or $56,000, of the total $80,000 goal is classified as needs.
Distribution of Simulation Outcomes
While the probability of success for this simulation is approximately 70% assuming a static retirement income goal based on the key modeling assumptions in the research, overall the retiree does relatively well. The likelihood of missing their retirement income goal, especially the amount they need, is incredibly low.
Conclusion
While financial advisers often say they are dynamically adjusting client spending throughout retirement based on how the retiree’s situation develops, the related decisions are not generally incorporated into the actual plan when it is based on static assumptions. This creates a significant mismatch. Integrating dynamic rules into a retirement income plan can have significant implications on optimal retirement income decisions and must be included in financial planning tools to ensure the modeled outcomes and potential guidance better reflect the realities of retirement.
For more from David Blanchett, PhD, CFA, CPA, don’t miss “Redefining the Optimal Retirement Income Strategy,” from the Financial Analysts Journal.
If you liked this post, don’t forget to subscribe to the Enterprising Investor.
All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
Image credit: ©Getty Images / jacoblund
Professional Learning for CFA Institute Members
CFA Institute members are empowered to self-determine and self-report professional learning (PL) credits earned, including content on Enterprising Investor. Members can record credits easily using their online PL tracker.
David Blanchett, PhD, CFA, CFP
