Monte Carlo Simulation Retirement Calculator

How to read your result

The big number at the top is a success rate. It is the share of the simulated paths in which the money lasted to your plan-to age. Nothing more. Take a hypothetical example: if a tile reads 80%, that is a count, with 80 of every 100 invented histories, built from the assumptions printed under the tile, ending with a balance above zero. It is not a statement about your own odds. Change the assumptions and the count changes. A score is what the model produced from the inputs shown, and the inputs are your guesses plus a slice of market history.

The example plan that loads with the page is age 45, retiring at 65, planning to 95; $300,000 saved, $10,000 saved a year, $65,000 a year of spending in today's dollars, $24,000 a year of Social Security from 67, 60% stocks and 40% bonds. Under the default assumptions (real returns from 1971 to 2025, which means returns after inflation), that example plan lasted to the plan-to age in 4,367 of 5,000 simulated paths, a success rate of 87%. Swap in the longer 1928 to 2025 data and the same plan scores 83%. Both figures are right, in the sense that the arithmetic is the same; they differ because the two stretches of history behaved differently. The good Monte Carlo success rate page goes through why scores move around like this and what the numbers can and cannot tell you.

What this number leaves out. It comes from a model with the assumptions shown, in annual steps and after-inflation returns. It does not model taxes, health or long-term care costs, inflation that differs from the model, or how you would act in a bad year. See what a Monte Carlo simulation cannot see.

Below the tile you will find a fan chart. The middle line is the median path, meaning half the simulated paths ended above it and half below. The shaded bands show where the 25th to 75th and the 10th to 90th percentiles sat. In the example plan, the median balance at retirement was $1,235,000 in today's dollars, while the 10th percentile was $730,000 and the 90th was $2,132,000. By the plan-to age the spread was far wider: $0 at the 10th percentile, $2,507,000 in the middle, and $10,354,000 at the 90th. A wide fan is the most honest part of the picture. It says that the same plan, in the same model, led to very different places.

If the bottom of the fan touches zero before the plan-to age, that is where the failed paths live. The fan does not tell you which year of history will resemble your own, and neither does any other chart. What it can do is let you see how much of the outcome rests on ordinary luck, as opposed to the choices you control. Try moving one input at a time, and watch both the tile and the width of the fan. A change that nudges the score by a point but leaves the fan unchanged is a different kind of change from one that lifts the whole fan.

When paths fail, the tool also reports when. In the example, the median age at which the money ran out among the failing paths was 86, and 2% of the failures began within the first ten years of retirement. That detail matters because a plan that runs short at 86 and a plan that runs short at 70 are different problems, even if they carry the same score.

What moves the score most? In the examples we ran, the retirement age and whether Social Security income was counted moved it far more than small changes to market returns. Retiring five years earlier took the example plan to 57%, retiring five years later took it to 98%, and removing the Social Security income took it to 64%. Raising yearly spending by one fifth in the same example gave 74%. Lowering the assumed stock return by one point a year gave 80%. All of these come from the default example inputs under the 1971 to 2025 assumptions, and none is a forecast of what would happen to you. If you want to see how each change plays out, the what a Monte Carlo simulation cannot see page lays them side by side.

CLAIM FREE GOLD IRA KIT

Two habits make the tool more useful. First, write down the score before you change anything, so that you can see the size of each change rather than guessing at it. Second, press the run-again button a few times with the same inputs. If the score moves by a point or two, you are looking at random noise in the draw, not at news about your plan. Only changes much larger than that tell you something about the inputs.

A note on the gold slider

The calculator has a slider that moves some of the mix into gold, and a card that compares the result with and without it. We built it as a way to see what the dataset says, not as a suggestion. The card states the difference as a fact, whichever way it falls, and the answer depends on what you assume gold earns after inflation.

Here is what the same example plan gave. With 10% of the mix in gold and gold earning what the 1971 to 2025 data shows, the success rate was 93%, a change of +6.0 points against 87% with no gold. If gold instead earned nothing after inflation, the same 10% slice gave 85%, a change of -2.5 points. Same plan, same random draws, opposite directions. That is the whole lesson of the slider: the result depends on a number nobody knows in advance. The gold in a retirement portfolio page sets out what gold did in dated periods, including the stretch when it lost most of its value after inflation, and the Augusta Precious Metals gold IRA page explains what one company says about its free information kit.

Augusta is the only company we mention. The button in the box above requests a free information kit; the page it opens carries consent text about phone, text and autodialed contact. Augusta states a standard minimum initial purchase of $50,000, read on 2026-11-04, so for many readers the honest answer is that this is not a fit, and you can ignore the box and keep using the calculator.

Frequently asked questions