What Is a Monte Carlo Retirement Simulation?

A Monte Carlo retirement simulation answers a different question from a normal retirement projection. A projection says "if you earn 7% every year, here is what you will have". A simulation says "if markets deliver a different sequence of returns every year, in how many futures does the money last?". It runs the same plan many times with randomly drawn annual returns and counts the runs that survive, producing a success rate rather than a single balance.

The distinction is called sequence risk, and it is why simulations exist. Two paths can average exactly 7% a year over 30 years and end with completely different balances if the poor returns arrive in the first decade, when there is a large balance to lose and no new contributions coming in. Estimating that risk with one average return hides the danger entirely.

How It Works

The calculator builds two phases. During accumulation, each simulated year multiplies the balance by a return drawn from a normal distribution with the mean and volatility you supply, then adds the annual contribution. During retirement, the balance continues to earn the drawn return while spending is withdrawn and raised with inflation each year. A run fails if the balance reaches zero before the plan horizon.

The random draws come from a seeded generator, so the simulation is deterministic: the same inputs and seed always produce the same result. That is important for a planning tool, because it means the success rate cannot flicker between renders and the output can be reproduced. Changing the seed draws a different set of futures, which is a useful stress test in itself — if the success rate swings widely with the seed, the number of paths is too small or the plan sits near a knife edge.

The model deliberately ignores fees, taxes, Social Security and spending flexibility. Each of those omissions is conservative in one direction and optimistic in another: fees and taxes make the result worse, while the ability to trim spending in a bad year — something almost every real retiree does — makes it better. Treat the output as the pure market-risk picture.

Understanding Your Results

The primary card shows the success rate: the share of simulated futures that still had money at the plan horizon. The three statistics beside it give the median ending balance, the 10th percentile — the outcome one in ten futures falls below — and the median expressed in today's purchasing power, which is the number that actually describes the lifestyle the plan supports.

The table lays out six percentiles from the 5th to the 90th, each with the ending balance, its real value and whether that path funded retirement. Summary Statistics adds the failure rate, the accumulation and retirement lengths, the average ending balance, the age of the median shortfall when a shortfall exists, and how many years of spending the median ending balance represents. A common experiment is to raise spending by $5,000 and watch the success rate fall — the drop tells you how much margin the plan really has.

Data vintage: this is a simplified model of market risk, not a forecast. Return and volatility assumptions are yours to set; historical US equity returns have averaged roughly 7% real with annual volatility near 15%, but the simulation makes no claim about future returns.