Ask a roomful of retirees what keeps them up at night, and you’ll hear the same answer from many of them: running out of money. Not death, not illness, not nothing to keep them busy—money. The technical term for this is “longevity risk,” but a less technical, more common term for some is FORO—”fear of running out”—which is similar to “fear of missing out” (FOMO) but scarier to most.
Still, FORO is a legitimate concern. Some haven’t saved as much as they wanted. Retirements that once lasted ten or twelve years now routinely stretch to twenty-five or thirty. Inflation quietly erodes purchasing power. Markets don’t always cooperate and can be downright cantankerous at times. And unlike your working years, when a bad month could be offset by next month’s paycheck, a bad sequence of returns in early retirement can do permanent damage to a portfolio. This is why financial planners invented a tool with a name that makes it sound considerably more glamorous than it actually is: the “Monte Carlo Simulation” (apparently, everyone in Monte Carlo wears tuxes and evening gowns, at least in the movies).
The Monte Carlo simulation may sound like it came from a James Bond film, but it’s actually just a very busy calculator running thousands of hypothetical scenarios at once.[1] This method, named after the famous Monaco gambling destination—which should tell us something—works by running thousands of randomized scenarios on your retirement numbers to see how many come out favorably. It’s what smart financial advisors do when they finally admit that predicting the future is impossible, but that systematically guessing at it is at least better than guessing at it casually. As baseball catcher and philosopher Yogi Berra once said, “It’s hard to make predictions, especially about the future.”
The difference between Monte Carlo and just winging it is roughly the difference between playing darts with a very expensive dartboard and no dartboard at all. The dart throws are still random. The board is just better labeled than the wall around it that you keep hitting. (My brother and I had a dartboard in our bedroom growing up, and the walls were made of plaster. As you might imagine, we made quite a mess—it looked like a wild woodpecker had gotten loose in our room.)
Here’s the basic idea behind running simulations: rather than assuming your portfolio will earn a steady average return every year—which it never does, not even close, not once—a Monte Carlo simulation runs hundreds or thousands of randomized sequences of market returns, inflation rates, and spending patterns. At the end, it tells you something like: Based on these assumptions, your plan succeeds in 87% of scenarios. Which sounds enormously reassuring until you spend a few seconds thinking about the other 13%—as Dot did. Then you’re no longer as reassured.
This is part of financial planning that you can do and that many advisors do for their clients. So, you can run it again with different inputs, and the probability of success may or may not change. Still, the really fun thing is that you can make your inputs more optimistic, which can improve the outputs and may even make you feel better, even if it’s way off. Stock returns that average 12% a year and inflation averaging 1% a year—sure, why not?
Actually, this is the most useful thing Monte Carlo does. It doesn’t promise everything will be fine, but it forces you to consider all the variables involved in the calculations. (By the way, they’re called “variables” for a reason: they can vary because they are unpredictable, so you have to make “informed guesses.”)
In a way, it answers what if? questions. What if you retire the year before a major market downturn? What if inflation runs hotter than expected? What if you live to 95? What if you live to 137? What if bread goes to $47 a loaf, or $47 a slice? What if you retire early, the market crashes and never comes back, you live to be 100, and they don’t make bread anymore (I don’t think there’s a bread-baking variable in the models).
The simulation probably won’t cover all of these, but the good ones will cover most of them, and the exercise of asking the questions is genuinely valuable even when the answers are uncomfortable. The simulation doesn’t predict the future; it maps the territory of possible futures and asks how prepared you are for the range of them. Some think of it as a may make you worry app, which can be bad, but at least it’s organized.
I’m being serious now, really I am. Used well, it’s a genuinely helpful planning tool. Used poorly—or trusted absolutely—it gives false precision to an inherently uncertain exercise. The inputs matter enormously: your assumed rate of return, your spending level, your time horizon. Change any of them, and the results shift dramatically. Garbage in, garbage out, as they say, regardless of how impressive the output looks, and Monte Carlo output always looks impressively precise, which is part of the problem. A number like 94.3% suggests a level of certainty that the actual future may not mirror.
That’s why the wisest response to a Monte Carlo result—whether it says 94% or 74%—is neither panic nor false comfort, but something closer to what the Proverbs writer had in mind: “The heart of man plans his way, but the Lord establishes his steps“ (Prov. 16:9). We plan, model, and stress-test and then stress-out (just kidding—don’t do that). We adjust our spending, diversify our holdings, and build in a margin of safety for the unexpected. And then we hold those plans loosely, trusting our Heavenly Father, whose knowledge of the future is not statistically probabilistic but sovereign and certain; and who, notably, has never once needed to run a simulation. (If God had run a “creation simulator,” He may have changed his mind.)
Earl loves his simulator and runs it every quarter—assuming he can clear all the input errors and cached values that keep mysteriously returning. The answer is almost always the same, but he runs it again anyway—but that’s something for another day (or cartoon). Dot remains skeptical of anything that requires a computer and involves percentage-based prognostication—think weather reports. And Tippy would like to point out that he has never once thought about sequence-of-returns risk, has no idea what a Monte Carlo simulation is, and sleeps twelve hours a day without interruption.
That correlation is hard to ignore.
[1] Suggestion: try https://www.honestmath.com/model for a good, easy-to-use, and reliable simulator. (Be sure to read the documentation to fully understand the model before you use it. Especially why your input—the variables—matter so much.)