Close Close
ThinkAdvisor
Jamie Hopkins, SVP and Director of Private Wealth at Bryn Mawr Trust

Retirement Planning > Spending in Retirement > Income Planning

Jamie Hopkins: The ‘Fun’ New Retirement Planning Metric You Should Know

X
Your article was successfully shared with the contacts you provided.

While sustainable retirement-income planning has always received both academic and industry-driven analysis, a veritable groundswell of innovative research is being published on the subject.

One such example is a new paper published in the fall edition of the Journal of Financial Planning by Javier Estrada, a financial advisor and professor of finance at the IESE Business School in Barcelona, Spain. The paper seeks to answer a seemingly simple question: “In retirement planning, is one number enough?”

Specifically, Estrada is referring to the “incidence of failure” metric that dominates many advisors’ Monte Carlo-based income planning efforts. Although the paper includes some in-depth analysis of the math and assumptions that underpin this style of income planning, Estrada’s answer can be summed up with a simple “no.” He goes on to offer his own key metric that he calls the “risk-adjusted coverage ratio.”

The recently published paper is generating some buzz among U.S. financial advisors and retirement industry thought leaders. This includes Bryn Mawr Trust’s Jamie Hopkins.

The risk-adjusted coverage ratio is a “really fun” metric to look into, he said this week in a video posted to the social media platform X, formerly Twitter.

How Monte Carlo Falls Short

As Hopkins explained, Estrada’s paper shows how financial planners can do better for their clients by helping them to optimize and regularly update their spending plan. One powerful means of doing so is to introduce new metrics that help clients to understand the “magnitude of failure” concept that is often overlooked in traditional Monte Carlo simulations.

Estrada is asking an important question, Hopkins says, and is pointing out that advisors have had too much focus on one number when it comes to deciding what retirement strategy makes sense — the failure rate of a portfolio in a traditional Monte Carlo simulation.

As Hopkins has explained in prior videos and in discussion with ThinkAdvisor, when reporting binary Monte Carlo results to a client framed around probability of success, anything less than 100% can sound scary. For example, for a client with a 75% probability of success at a given starting spending amount, failing one out of every four times simply does not sound acceptable to many people.

It is crucial, however, to think carefully about what a 75% success result in a Monte Carlo simulation actually suggests. While this metric does project that one in four retirement scenarios will “fail,” the metric alone actually tells a client nothing about how severe that failure is.

“Now here’s the thing,” Hopkins said. “Retirement is not binary. It is not success or failure. People adjust their spending, they adjust their lifestyles, when [the] plan starts to go off course.”

So, as Estrada is asking, why would advisors only make decisions about what the retirement strategy should be based on that outdated, binary notion?

Building a Better Income Approach

In the paper, Estrada pushes on the idea that the failure rate taken alone has two big flaws. The first is that it doesn’t speak to the timing of failure.

“Did your portfolio run out of money super early in retirement, like in year 15, which you would find unacceptable?” Hopkins asked. “Or did it run out of money in year 29 [of the 30-year projection period]?”

These are two very different levels of failure. The other question is the magnitude of failure, which relates to the timing but is also a distinct consideration. How far short did the client run at that time? Would it be a devastating failure or a minor inconvenience?

The other key consideration is to ask whether it is really a “successful” retirement if clients are petrified of spending and end up following a very conservative plan with a 100% success projection. This could mean they end up leaving a large bequest — either to a spouse, children or the government via estate taxes.

“Is that a good thing? Is that even what you’re looking for?” Hopkins asked. 

What Is the Risk Adjusted Coverage Ratio?

In the paper, Estrada defines this concept using a few terms.

Essentially, the advisor is taking the projected number of years of inflation-adjusted annual withdrawals that could likely be sustained in a given situation, and then they are dividing that number by the anticipated length of retirement for the individual client, given their health status and longevity expectations, Hopkins explained.

As a simple baseline, if clients expect to make it 30 years in retirement, and their withdrawal plan is projected to cover all 30 years without leaving any leftovers, that would provide a “1” value for their coverage ratio.

On the other hand, if they assume that their retirement goes 36 years and that their portfolio in the simulation would cover just 30 years, that would give them a ratio of 1.2.

Obviously, the more this ratio increases, the more cautious a client would want to be about that plan and the more they may need to consider making lifestyle adjustments along the way to protect themselves from running out, Hopkins said. 

The idea is that, as one navigates retirement, they will see their coverage ratio move up and down according to their actual spending, market conditions and other factors.

Alternatively, if clients assume a 24-year retirement period and their portfolio could easily sustain 30 years of withdrawals, that gives them a starting coverage ratio of 0.8, which may signal that they could expect to spend more annually early in retirement — especially if they don’t have big legacy giving goals.

The Bottom Line

Estrada argues that advisors and clients can use this framework to help make ongoing adjustments according to the perceived likelihood of these different events occurring.

“Now remember, you should pick a strategy not based just off of this research but one that resonates with you and that you can understand,” Hopkins said. “You should also not get too overly focused on one number. Remember, success isn’t binary and neither is your retirement.”

Estrada summarizes his findings in a similar way.

“When selecting an optimal retirement strategy, a retiree may aim to maximize the coverage ratio, a novel metric superior to the failure rate,” he wrote. “This article suggests focusing on the whole distribution of coverage ratios instead, or at least on some percentiles that may be of particular interest to a retiree.”

Although such an approach may not be as neat as making decisions based on optimizing a single variable, it does enable consideration of the relevant trade-offs a retiree needs to evaluate in order to find an ideal retirement strategy.

Pictured: Jamie Hopkins 


NOT FOR REPRINT

© 2024 ALM Global, LLC, All Rights Reserved. Request academic re-use from www.copyright.com. All other uses, submit a request to [email protected]. For more information visit Asset & Logo Licensing.