Monte Carlo retirement simulations: what the odds mean

The short answer

A Monte Carlo simulation tests your plan against many randomly generated futures and reports the share in which it works. A 50% success rate at your coast date is what the math produces: the coast date is based on the middle outcome, so half of the simulated markets do better and half do worse. Odds above 50% have to be built in, by saving past the coast date or planning with a more cautious return.

The number is most useful for comparing choices. In our default example, saving for three more years lifts the odds from 50% to 57%, and saving until retirement lifts them to 78%. Below, we explain exactly how our simulation works, what moves the odds, and what the model can't tell you.

How a Monte Carlo simulation works

A normal retirement calculator assumes one return, say 7%, every single year. Real markets never do that. Some years are up 25%, some are down 20%, and the average only shows up over long periods.

A Monte Carlo simulation handles this by playing out your plan many times with random returns:

1. Pick a return for each year by drawing from a range of possible returns, like rolling weighted dice.
2. Run your plan through that string of years: your starting balance, your contributions and the random returns.
3. Note whether the plan worked, for example whether you reached your target by your retirement age.
4. Repeat hundreds or thousands of times, then count the share of runs that worked. That share is the success rate.

The method is used widely in science, engineering and finance whenever the outcome depends on chance. It doesn't predict the future. It shows the range of outcomes that the assumptions you fed in would produce.

How our simulation works

The Coast FIRE calculator shows the odds of success next to your coast date. Here's exactly what it does, as described on our methodology page:

• 1,000 simulated markets. Each one runs from your current age to your retirement age, one year at a time.
• Random yearly returns. Each year's return is drawn from a log-normal distribution. Its median, the middle outcome, is your expected return after inflation and fees. With our defaults (7% return, 2.5% inflation, no fees) that's 4.39% a year.
• 15% yearly volatility. That's the standard deviation of the yearly swings, close to what a mix of 75% US stocks and 25% Treasury bonds showed from 1928 to 2025.
• Saving stops at the coast date. You contribute each year until the month you stop, then the portfolio grows on its own. The year's contributions are added at the end of that year.
• Success means reaching your FIRE number by your retirement age. A market that ends a dollar short counts as a miss, and one that ends double the target counts the same as one that just makes it.
• A fixed seed. The random numbers always come out the same, so the same inputs always give the same odds.

What does 15% volatility look like? Across the 28,000 simulated years in our default run, 39% lose money after inflation, 16% lose more than 10%, and about 4% lose more than 20%. Because big gains compound more than big losses, the simple average of those years is about 5.6%, higher than the 4.39% median. That's normal for a log-normal model, and it's why we anchor the median, not the average, to your expected return.

How to read a success rate

A success rate squeezes a whole range of outcomes into one number, so it helps to look at the range behind it. These are the balances at 60 across our 1,000 simulated markets for our standard example, Alex: 32 years old, $185,000 invested, $1,500 a month, $40,000 a year of spending at a 4% withdrawal rate, so a FIRE number of $1,000,000.

Three things to take from this:

• A miss is usually not a disaster. When Alex stops at 39, a third of the markets end below $750,000 and about one in five end below $500,000. Many misses are near misses that a few more years of saving or work would cover.
• The spread is wide. Thirty years of 15% swings produce everything from $376,000 to $2.8 million between the 10th and 90th percentiles. Any single projected number is a guess inside that range.
• The rate is a snapshot. It assumes you set the plan today and never adjust it. In real life you'd see a bad decade coming at your yearly check-ins and respond, which is exactly what our guide to life after Coast FIRE covers.

Why your coast date shows about 50%

Your coast date is calculated assuming the expected return every year. In our simulation, that expected return is the median of each year's draws, so the plain projection lands in the middle of the simulated outcomes. Stop saving on the coast date and roughly half of markets beat it.

This holds whatever your numbers are. We tried three versions of Alex, and each one lands near 50% on its own coast date:

The result also wobbles slightly with the random numbers. Using other seeds, Alex's odds at the coast date range from 49.0% to 51.6%, and with 10,000 markets instead of 1,000 they come out at 50.5%. Treat differences of a point or two as noise.

What changes the odds

Saving longer, and how volatile the market is

We ran Alex's plan with different stopping points and three volatility levels: 10%, our default of 15%, and 20%. In general, portfolios with more bonds swing less and portfolios that are all stocks swing more.

Volatility pulls the odds toward 50% from both sides. If Alex stopped today, a wilder market makes a lucky outcome more likely, so the odds rise. If Alex saves to 60, a wilder market makes an unlucky outcome more likely, so the odds fall. At the coast date itself it barely matters. Each extra year of saving helps, but the gains shrink: with 15% volatility, getting to 60% means saving until about 12 years 4 months from now, 70% means 19 years 2 months, and even saving all the way to 60 stops short of 80%.

A lower return than you planned on

The simulation trusts your expected return. If you plan on 7% but the true median turns out to be 6%, stopping on the 7% coast date succeeds in only 36.8% of markets. A 1% yearly fee does exactly the same damage, because it lowers your real return by the same amount (see how fees delay FIRE).

It works the other way too. Plan with a cautious 6% and you'd coast after 14 years 6 months instead of 7 years 7 months. If markets then deliver 7%, that later stop has a 64.6% chance of success. A cautious return is a buffer you build in up front. Our guide to real vs nominal returns covers how to pick one.

What the simulation leaves out

Every model simplifies. Ours does so in ways worth knowing:

• Each year is independent. A bad year doesn't make the next one better or worse. Real markets sometimes rebound after crashes and sometimes stay weak for years.
• No fat tails. A bell-shaped model rarely produces extreme years. In NYU Stern's data, US stocks lost more than 35% in three years between 1928 and 2025: 1931, 1937 and 2008. In our default run, 15 of the 28,000 simulated years were worse than −36.55%, the 2008 figure, about one in 1,900. The model's theoretical odds are about one in 2,200. Part of that gap is because the data is for stocks alone and before inflation, while our model is for a stock-and-bond mix after inflation, but real markets do have fatter tails than a bell curve.
• Fixed assumptions. The expected return and volatility stay the same for decades. In reality, you don't know either one.
• No withdrawal phase. We test whether you reach your FIRE number, not whether it lasts. How long it lasts depends on your withdrawal rate, covered in our 4% rule guide, and on the order of returns once you start withdrawing.
• No life events. Job loss, raises, taxes, a pause in saving or a change of plan aren't modeled.

Another approach: historical backtesting

Instead of random returns, a backtest runs your plan through actual past periods: retire in 1929, in 1966, in 2000, and so on. The research behind the 4% rule, from William Bengen and the Trinity study, worked this way. Its strength is realism: real crashes, real recoveries, real inflation. Its weakness is a small sample. There are only so many separate 30-year periods in a century of data, and they overlap heavily. Some forecasters, such as Morningstar, also use forward-looking assumptions about returns rather than history alone. No method is definitive, which is why looking at more than one can be reassuring.

What success rate to aim for

There's no correct answer. It's a judgment call that depends on how flexible you can be. For comparison, Morningstar's safe withdrawal research defines "safe" as a 90% chance of money lasting 30 years. That's a different question from ours, though: it's about running out of money in retirement, when your options are fewer.

A few things to weigh:

• How easily could you save again? If you're coasting in a job you like and could restart contributions, a plan near 50% with yearly check-ins means accepting a real chance of saving again for a while. If restarting would be hard, you need a bigger buffer up front.
• How fixed is your retirement age? If working a year or two longer is acceptable, you need less certainty now.
• How much would a shortfall hurt? Missing by 10% means trimming spending a little. Missing by 50% means a different retirement.

Very high targets have a cost. For Alex, even saving all the way to 60 gives 78%, not 95%. Chasing near-certainty often means saving much more than you'll ever need. Flexibility, such as being willing to save again, work a little longer or spend a little less, buys you more safety per dollar than another decade of saving.

Sources

All odds and balances in this guide come from our own calculator engine with the default assumptions, explained on our methodology page. Data and research referred to:

1. Historical returns on stocks, bonds and bills (annual data from 1928). Aswath Damodaran, NYU Stern School of Business, updated January 2026. Our calculations from this data: from 1928 to 2025, US stocks (the S&P 500 with dividends) returned about 10% a year, and a mix of 75% stocks and 25% 10-year Treasury bonds returned about 9% a year with yearly swings (standard deviation) of about 15%.
2. Determining Withdrawal Rates Using Historical Data. William P. Bengen, Journal of Financial Planning, 1994. The original 4% rule: a 4% first-year withdrawal, raised each year with inflation, lasted at least 30 years in every historical US period studied.
3. Retirement Savings: Choosing a Withdrawal Rate That Is Sustainable. Philip L. Cooley, Carl M. Hubbard and Daniel T. Walz, AAII Journal, 1998. The “Trinity study”: withdrawals of 3% to 4% rarely ran out of money over periods of up to 30 years with a mix of stocks and bonds.
4. What’s a Safe Retirement Withdrawal Rate for 2026?. Morningstar. Puts a safe starting withdrawal rate at 3.9% for a 30-year retirement, with a 90% chance of success.