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.