Another Way to Calculate How Much Clients Can Spend in Retirement

Analysis February 28, 2023 at 05:28 PM
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Chance are that pretty much any financial advisor working with pre-retirees and retired clients will be very familiar with the use of Monte Carlo simulations, which are commonly used to assess an individual's probability of running out of money once their income from work ceases.

According to retirement planning expert and researcher Derek Tharp, however, many financial advisors and their clients fail to appreciate the limitations of Monte Carlo simulations — especially the limitations associated with running just one analysis at the start of the client's retirement period and using this as the basis for all future spending decisions.

Most significantly, Tharp says, the traditional "success versus failure" framing fails to capture the reality that retirees, when facing an unlucky sequence-of-returns scenario that could result in their running out of money, can and often do make adjustments to their spending that allow them to avoid that unfortunate outcome.

Ultimately, Tharp says, the superior way to help retired clients avoid both over-spending and under-spending is to constantly update and revisit their spending plan. Doing so will mean leveraging many ongoing Monte Carlo simulations that can help a given individual set upper and lower guardrails on spending and risk-taking.

While this approach will demand more effort from the advisor, it will almost certainly result in superior outcomes for clients, Tharp says.

Tharp makes this case on the latest episode of Morningstar's The Long View podcast, hosted by Christine Benz and Jeff Ptak. During a nearly hour-long discussion with the Morningstar experts, Tharp also explains why he is drawn to the retirement income planning topic, both as a practicing financial advisor and as a researcher.

Simply put, Tharp says, the effort of seeing a client (or a research subject) through the retirement period is one of the most intellectually challenging and rewarding parts of his job. As such, he encourages fellow industry professionals to consider their own planning process and whether they can improve their approach to the "decumulation challenge."

The Problem With Single-Simulation Planning

According to Tharp, financial advisors working with retirement clients very often use Monte Carlo simulations in their financial planning process. Typically, they utilize financial planning software packages that generate projections in terms of probability of success or failure.

Broadly speaking, success is defined as an iteration of the plan where the client doesn't run out of money, Tharp explains, while failure naturally signifies the opposite. Often, plans are considered acceptable if the probability of success is near 90%.

Tharp says these analyses are an incredibly important part of the planning process, but they are also at significant risk of being misinterpreted. This is especially the case when a financial advisor simply runs one Monte Carlo analysis at the beginning of the retirement period and fails to consider whether ongoing reviews of the spending plan are in order.

Most significantly, the single success-or-failure framing fails to capture the reality that retirees, when facing an unlucky market scenario, can and often do make adjustments to their spending that allow them to avoid financial failure.

According to Tharp, to better reflect this reality, the phrase "probability of adjustment" has emerged as a commonly suggested alternative to "probability of success." While representing an improvement over the original, Tharp says, "probability of adjustment" itself can be prone to ambiguity and misinterpretation without being clear about what type of adjustment might be needed — and what the outcome might be if that adjustment weren't made.

In addition to discussing this problem with Morningstar, Tharp has also written in depth on the topic on Kitces.com. Ultimately, he says, the key point is that outcomes, not probabilities, are what matter to clients, and any way of communicating Monte Carlo results should be clear about what those results mean in terms of real spending to the client over time.

In some cases, it may even make sense to avoid framing Monte Carlo results in terms of probabilities entirely, and to instead communicate results in terms of the actual dollar spending adjustments that would be triggered in specific scenarios.

The Guardrails Approach

Tharp says he likes to explain this approach using "guardrails" terminology, as that seems to resonate with clients.

He encourages advisors to utilize ongoing Monte Carlo simulations as a means of tracking the client's probability of success as an ongoing issue, and to put pre-defined guardrails in place that will trigger specific spending changes as the probability of success rises and falls over time.

"Advisors can use withdrawal-rate guardrails, which are guidelines to increase or decrease spending when portfolio withdrawal rates reach certain levels," he says. For example, if an initial 4% withdrawal rate calls for $5,000 in monthly spending, the spending amount could be adjusted higher if it reaches 2% of the portfolio value or lower if it hits 6%.

Of course, even withdrawal-rate guardrails can be flawed, Tharp warns, because the relatively steady withdrawal rate patterns that are often assumed in the underlying Monte Carlo simulations do not necessarily align with how retirees actually pull distributions from a portfolio in retirement.

In reality, Tharp says, what is more commonly seen is a "retirement distribution hatchet" in which the initial retirement distribution rates from a portfolio are highest early in retirement, and then they significantly decline when deferred Social Security is claimed as late as age 70.

Spending tends to fall even further later in life, Tharp says, as older retirees tend to spend less on discretionary items like travel. Another factor to consider is that there are often other sources of income in retirement, such as pensions or rental income, that are not directly factored into the Monte Carlo simulations.

To compensate for these issues, Tharp says, advisors should consider using holistic risk-based guardrails, which reflect current longevity expectations, expected future cash flows, expected future (real) income changes and other factors.

With this approach, probability of success via traditional Monte Carlo analysis can serve as the risk metric to guide the implementation of risk-based guardrails. According to Tharp, there is still a possibility of causing anxiety for clients if the risk is presented in terms of the success or failure of their plan as a whole, but advisors can instead use the language of "income risk," which may be less stress-inducing.

Ultimately, Tharp says, the key point is that a risk-based guardrails model can provide clients with a more accurate picture of how much they can sustainably spend than can models based on static withdrawal rates or withdrawal-rate guardrails. While Tharp says risk-based guardrails can be less efficient to calculate manually than withdrawal-rate guardrails because of the many factors considered in the risk-based model, when properly assisted by technology, risk-based guardrails can be implemented and maintained as efficiently as withdrawal-rate guardrails.

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