Monte Carlo Simulation for Retirement Portfolio Planning

Morningstar's safe starting withdrawal rate is 3.9%. That number doesn't come from an average return. It comes from asking which withdrawal rate still leaves money in the account in 90% of 1,000 simulated market paths. That's what a Monte Carlo simulation for a retirement portfolio does. It skips the single forecast, tests your plan against many possible futures and tells you how often it survives. The answer is a probability, not a promise. Below we cover how it works, how to read the score, and where it can mislead you.

The short answer: what Monte Carlo does

Most retirement projections draw one line. They assume something like a fixed yearly return and show your balance rising or falling smoothly to the end. Markets don't move that way.

Monte Carlo takes its name from the casino. It gives you a spread of outcomes instead of one line:

1. Start with your inputs. Your age, savings and spending, plus assumptions about returns, inflation and how long the plan runs.
2. Generate a random market path. Each simulated year gets a return drawn from a range of plausible outcomes. Some years are great, some are terrible, in no fixed order.
3. Run your withdrawals through that path. Take out spending each year and track the balance until the plan ends.
4. Repeat. Usually somewhere between 1,000 and 10,000 times.
5. Count the survivors. If 870 of 1,000 paths still have money at the end, your success rate is 87%.

Morningstar works this way. Its 2026 research uses forward-looking return and inflation assumptions, tests them across 1,000 simulated paths, and defines the safe rate as the highest starting withdrawal that leaves money at the end in at least 90% of paths. The base case is a 30-year retirement, inflation-adjusted spending and a 40% stock / 60% bond portfolio, with market assumptions as of Sept. 30, 2025. (Morningstar, What's a Safe Retirement Withdrawal Rate for 2026?)

The year before, the figure was 3.7%. Morningstar put that down to high stock valuations and lower bond yields. (Morningstar, What's a Safe Retirement Spending Rate for 2025?) The safe number moves when the market's starting point moves. A plan built on a single average return can't show you that.

Why an average return can mislead you

Picture two retirees who earn the same average return over 30 years. One has a bad crash in year two. The other has it in year twenty-five.

On paper they look identical. In practice they aren't. The retiree hit early is selling investments at low prices to cover spending, which leaves less money to recover when markets rebound. This is called sequence-of-returns risk: the order of returns matters once you start withdrawing, not only their average.

A single-line projection can't see sequence risk. Monte Carlo can, because it shuffles good and bad years into thousands of different orders. (Acadifi; MSCI, Retire in Monte Carlo?)

How to read your success rate

Here's where most beginner guides stop: "93% is good." There's more to it than that.

"Failure" usually means "adjust," not "broke"

A failed path assumes you kept spending exactly as planned, every year, all the way down to zero. Real people don't do that. If markets go badly, you trim travel, delay a big purchase, or pick up some income.

Planning researcher Michael Kitces makes this point in his work on success-rate "guardrails." In one example, a plan starts at a 95% success target, raises spending if the score climbs above 99%, and cuts spending if it falls below 70%. (Kitces, Probability-of-Success-Driven Guardrails) In that framework a 70% score is a cue to adjust, not a disaster.

100% has a cost too

A 100% success rate sounds ideal. But a plan that survives every simulated future is almost always a plan that underspends in the typical one. You might die with money you could have used. Kitces explores the trade-off between a higher success target and a higher spending level in his work on customizing probability of success.

A rough way to read the score

This is a frame for thinking, not a rule. Your comfort with adjusting matters as much as the number.

Is Monte Carlo too optimistic or too pessimistic?

Experts disagree, and it's worth knowing both sides.

The "too pessimistic" case. Kitces compared Monte Carlo results with rolling historical periods. He found that with annual returns, which is how planners usually model them, the extreme-crash concern mostly disappears. A 93.5% Monte Carlo score roughly matched a 100% success rate across actual historical periods. In other words, the worst simulated paths can be worse than anything in the historical record. (Kitces, Monte Carlo Analysis Risk: Fat Tails vs. Safe Withdrawal Rates)

The "too optimistic" case. Critics point to two blind spots:

• Fat tails. Real markets have extreme moves more often than a simple bell curve predicts.
• Clustering. Simple random draws treat each year as independent, but bad years often come in runs. Resampling real historical sequences can capture that better. (Calcifer, Monte Carlo explainer)

What that means for you. The method matters, but your assumptions matter more. A simulation is only as good as the returns, inflation and spending you feed it. That's why academic reviews of the method stress testing your inputs, not just running more trials. (Using Monte Carlo Methods for Retirement Simulations, arXiv)

The inputs that move your number most

You'll never control the market. You do control, or at least can plan around, the inputs.

Spending

This is usually the biggest lever. Every extra dollar you withdraw each year compounds against you across every simulated path. That's why Morningstar frames its research as a withdrawal rate: spending relative to savings is the core question. It has also written about simple ways to boost your safe withdrawal rate, including being flexible with spending.

How long the plan runs

A longer horizon means more years of withdrawals and more chances for a bad stretch. Morningstar's research on withdrawing safely if you retire early shows safe rates falling as retirements stretch past 30 years.

And retirements do stretch. The Social Security Administration's planning guidance says about one in three 65-year-olds may live to at least 90 (SSA, Retirement Information for Medicare Beneficiaries, 2026). A plan that ends at 85 quietly bets against a third of retirees. That's why the plan you build with Monte Climb runs to age 95 by default, and you can extend it.

Health care

Fidelity's 2025 estimate puts health care costs for a single 65-year-old retiring this year at $172,500 over retirement. That covers Medicare premiums, deductibles and prescriptions, and leaves out long-term care entirely (Fidelity Investments, 2025 Retiree Health Care Cost Estimate, July 2025). It's a real line item, not a rounding error. Our engine charges Medicare premiums and income-based surcharges year by year instead of using one flat guess.

Stock/bond mix and return assumptions

More stocks raise the potential upside and widen the spread of outcomes. Forward-looking assumptions, like Morningstar's, can produce different results from purely historical ones. If a tool doesn't tell you what it assumes, treat its score with caution.

What a failing path usually looks like

Pages rarely show this, but it's worth picturing. Failed paths tend to share a few features:

• An early downturn, while withdrawals are already running.
• Stretches of high inflation, which push up inflation-adjusted spending while real returns lag.
• Spending that never changes, even as the balance shrinks.

Most failing paths don't hit zero suddenly. The balance wears down over years, and you'd usually see it coming. That's the case for checking in regularly.

Rerun it every year

A Monte Carlo score is a snapshot. It reflects your savings, spending and market conditions on the day you run it. Kitces argues for treating it as an ongoing process instead of a one-time verdict. Rerun the plan, see whether the score has moved, and make small changes early. (Kitces, Monte Carlo: One-Time vs. Ongoing Retirement Planning)

A simple annual routine:

1. Update your balance and actual spending.
2. Rerun the simulation.
3. If the score has risen well above your target, you may have room to spend more.
4. If it has dropped, decide what you'd adjust: spending, timing or income.

The first step most people skip

The EBRI/Greenwald 2025 Retirement Confidence Survey found that only 52% of workers have estimated the monthly income they'll need in retirement. The other 48% are guessing.

You don't need a spreadsheet to stop guessing. Three numbers will do: your age, what you've saved and what you spend each month. Monte Climb's retirement calculator runs them through 1,000 simulated futures and gives you a success rate. It runs in your browser, and nothing is saved until you say so. If you want to go further, ask William, our AI planner, questions like "Am I on track?" or "Can I retire at 62?"

It's planning software, not advice. A probability, not a promise. But it's a far better starting point than one average-return line.

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Frequently asked questions

What is a Monte Carlo simulation for a retirement portfolio?

It's a method that runs your savings, spending and time horizon through many randomly generated market paths, often 1,000 or more, and counts how many paths still have money at the end. The share that stay funded is your probability of success.

What is a good Monte Carlo success rate for retirement?

There's no single right number. Morningstar uses 90% to define its safe starting withdrawal rate. Some planners treat scores around 70%–85% as workable if you're willing to cut spending when markets disappoint. A score near 100% can mean you're underspending.

How many simulations does a retirement Monte Carlo analysis need?

Most tools run somewhere between 1,000 and 10,000 paths. Morningstar's safe withdrawal research uses 1,000. Beyond a certain point, the quality of your assumptions matters more than the number of runs.

Is Monte Carlo better than the 4% rule?

The two answer related questions. The 4% rule is a single rule of thumb. Monte Carlo tests your own numbers across many possible futures and captures sequence-of-returns risk. Morningstar's latest Monte Carlo-based research puts the safe starting rate at 3.9% for a 30-year retirement.

What are the limitations of Monte Carlo retirement simulations?

Results depend on the return, inflation and spending assumptions you feed in. Simple random draws may miss fat tails and the way bad years tend to cluster, while some research suggests the worst simulated paths can be harsher than history. Treat the score as a guide to rerun regularly, not a guarantee.