What Is a Monte Carlo Retirement Calculator? A Simple Guide

3.9%. In Morningstar's 2025 research, that's the starting withdrawal rate that left money in the account in 90% of simulated outcomes over a 30-year retirement. It wasn't based on an average return. It was based on how often the money held up across thousands of possible futures (Source: Morningstar, The State of Retirement Income 2025, Dec 2025). A Monte Carlo retirement calculator asks the same question about your own numbers. You get back one figure: the share of simulated futures where your savings last.

That's the short answer. The rest of this guide covers what happens inside the calculator, what the percentage actually means, and where you should stay skeptical.

What is a Monte Carlo retirement calculator?

A Monte Carlo retirement calculator estimates the odds that your savings will cover your retirement spending. It doesn't draw one straight line from today to age 95. It runs your plan through many randomized market scenarios: good decades, bad decades, and messy ones in between. Then it counts how many of those scenarios end with money left over.

The name comes from the casino in Monaco, because the method is built on chance. In plain terms, it's a way of rolling the dice thousands of times so you can see the full spread of outcomes, not just one.

What comes out is a success rate. T. Rowe Price describes it as the percentage of scenarios in which your money doesn't run out. A result of 85% means 850 of 1,000 simulated futures stayed funded. The other 150 didn't.

How it works, step by step

Most calculators follow the same basic loop:

1. You enter your inputs. Usually that's your age, your savings, and what you plan to spend. Some tools also ask about retirement age, asset mix, Social Security, and inflation.
2. The engine creates a random future. Each year gets a randomly drawn investment return, and often a random inflation rate as well, taken from a statistical range of possible values.
3. It plays out your retirement year by year. Returns are added, spending is withdrawn, and the balance moves forward to the next year.
4. It repeats. Hundreds or thousands of times, with a different random path each time.
5. It counts. The share of paths that never hit zero becomes your success rate.

That's it. There's no crystal ball involved, just a very large number of "what ifs."

Why not just use an average return?

Plenty of simple calculators assume something like a steady 6% a year. The problem is that markets don't deliver averages in a smooth line. They deliver them in lumps.

Order matters a lot once you start withdrawing. This is called sequence-of-returns risk. Picture two retirees who earn the exact same average return over 30 years:

• Retiree A gets the bad years early. They're selling investments while prices are down to pay for groceries, so less money is left to recover when markets bounce back.
• Retiree B gets the bad years late. By then the portfolio has grown, and the losses hit a bigger cushion.

Same average, very different endings. A single average-return projection can't show you that gap. A Monte Carlo simulation can, because some of its random paths put the crash in year two and others put it in year twenty.

What the success rate actually means

This is where most explainers stop, and where most of the confusion starts.

"Failure" usually means adjusting, not going broke

An 80% success rate sounds like a 20% chance of ending up destitute. It almost never means that. The simulation assumes you keep spending the same inflation-adjusted amount no matter what markets do. Real people don't behave that way. When a portfolio drops sharply, most retirees cut back.

Financial planner Michael Kitces has argued that the result would be better labeled a "probability of adjustment" than a probability of failure. A low number tells you that you may need to change course at some point. It isn't a forecast of running out of money.

It doesn't show how bad the bad outcomes are

A success rate is a yes-or-no count. A scenario that comes up a few thousand dollars short in year 29 counts as a failure, and so does one that's empty by year 12. The CFA Institute notes that probability of success may be the main metric advisers use with Monte Carlo, but it "completely ignores the magnitude of failure."

So treat the percentage as a starting point. Then ask the follow-up question: in the futures that fail, how badly, and how late?

What success rate should you aim for?

No single number is right for everyone. Here's one example framework described by Kitces:

The more interesting finding from that same analysis is that once you allow spending to adjust, targets as far apart as 95% and 50% often lead to surprisingly similar spending paths over time. Kitces also argues that the right target depends on how willing you are to cut back. If you'd hate to trim spending, aim higher. If you can flex easily, you can live with a lower starting number.

Monte Carlo vs. the 4% rule vs. historical backtests

People mix these three up all the time, and some calculators blur the line too.

The 4% rule came out of historical backtesting. William Bengen's 1994 work and the 1998 Trinity Study replayed actual return history. Bengen found that portfolios of roughly 60% stocks and 40% bonds lasted about 35 years or more at that withdrawal rate (Financial Planning Research Journal, 2024). Monte Carlo goes a step further by generating futures that never actually happened, which is both its power and its weak spot.

Where the model can be wrong

A Monte Carlo result looks precise. It isn't. Three things to keep in mind:

• Bell curves are too polite. Many models assume returns follow a normal distribution, which produces fewer extreme crash years than real markets have delivered. Baird lays out this and related limitations.
• One set of assumptions adds forecast error. In Income Lab research reported by Kitces, traditional Monte Carlo built on a single set of return assumptions was among the more error-prone methods when compared against real-world outcomes.
• Garbage in, garbage out. If your spending estimate is a guess, your success rate is a guess built on that guess.

None of this makes Monte Carlo useless. It means the answer is a probability, not a promise, and you should treat it that way.

The inputs that matter most

The math is the easy part. Your inputs are what make or break the result.

Spending. This is usually the biggest lever. Divide your first-year withdrawal by your savings to get your withdrawal rate. Taking $39,000 from $1,000,000 is 3.9%, right at Morningstar's 90% figure. Spending more pushes the rate up and the success rate down. Yet EBRI's 2025 Retirement Confidence Survey found that only 52% of workers have estimated the monthly income they'll need in retirement (Source: EBRI/Greenwald Retirement Confidence Survey 2025, Fact Sheet 3). The other 48% are guessing.

How long the plan runs. About one in three 65-year-olds may live to at least 90, according to the Social Security Administration (Source: SSA, Retirement Information for Medicare Beneficiaries, 2026). A simulation that stops at 85 quietly bets against a third of retirees. Check where your calculator ends.

Health care. Fidelity estimates that a 65-year-old retiring in 2025 will need $172,500 for Medicare premiums, deductibles, and prescriptions over retirement, and that figure excludes long-term care (Source: Fidelity Investments, 2025 Retiree Health Care Cost Estimate, Jul 2025). That's a line item, not a rounding error.

How to use a Monte Carlo calculator well

• Run it more than once. Change one input at a time, such as spending, retirement age, or savings, and watch how the success rate moves.
• Test the plan to an old age. Use 95, not 85.
• Look past the headline number. Ask what the failing scenarios look like.
• Revisit it regularly. Kitces makes the case that Monte Carlo works best as an ongoing check with guardrails, not a one-time verdict.

Run your own three numbers

The plan you build at Monte Climb starts with three numbers: your age, what you've saved, and what you spend each month. We run them through 1,000 simulated futures and return one clear answer, the share that stay funded. Plans run to age 95 by default, and you can push that further. The engine charges Medicare premiums and income-based surcharges year by year instead of using a flat guess.

It runs in your browser, and nothing is saved until you say so. If the number raises questions, like "Am I on track?" or "Can I retire at 62?", you can ask William, our AI planner. Talk to him the way you'd talk to a friend. It's planning software, not advice, but it gives you something real to work with.

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Frequently asked questions

What is a Monte Carlo retirement calculator?

It's a tool that runs your savings, spending, and timeline through many randomized market scenarios and reports the percentage of those scenarios in which your money lasts. That percentage is called your success rate.

What is a good Monte Carlo success rate for retirement?

There's no universal answer. One framework described by Michael Kitces starts at a 95% target, with an upper guardrail at 99% and a lower guardrail at 70%, and adjusts spending when either guardrail is hit. The right target depends on how willing you are to cut spending if markets disappoint.

Does a 20% failure rate mean I'll run out of money?

Usually not. The simulation assumes you never change your spending. In real life most retirees cut back after a bad stretch, which is why some planners call the result a 'probability of adjustment' instead of a probability of failure.

How is Monte Carlo different from the 4% rule?

The 4% rule came from historical backtests (Bengen 1994, the Trinity Study 1998) that replayed real past returns. Monte Carlo draws random returns from a statistical distribution and tests your own numbers across thousands of possible futures, including futures that never actually happened.

Can a Monte Carlo retirement calculator be wrong?

Yes. Its results depend on its assumptions. Many models use bell-curve returns, which produce fewer extreme crash years than real markets have had, and a single set of return assumptions adds forecast error. Read the result as a probability, not a promise, and rerun it over time.