What is a Monte Carlo retirement simulation?

A Monte Carlo simulation plays out your savings plan thousands of times, each with a different randomly generated sequence of market returns. Instead of one answer, you get a range: the median outcome, the unlucky and lucky cases, and the probability of reaching your goal.

Why isn't the average return enough for retirement planning?

The same average return can hide wildly different sequences. A crash early in your career barely matters because you keep buying cheap, but the same crash near retirement can be devastating. This sequence-of-returns risk is invisible to calculators that use a single average number.

What do the 10th and 90th percentiles mean?

In 10% of the simulated market histories you end up below the 10th percentile, and in 10% you end up above the 90th percentile. The band between them is the middle 80% of outcomes — the range your plan most likely lands in.

Are Monte Carlo results guaranteed?

No. The model draws returns from a normal distribution, which understates real market crashes; your return and volatility inputs are estimates; and results are shown in nominal dollars ignoring fees, taxes, and inflation. It is a hypothetical illustration, not financial advice or a prediction.

Monte Carlo Retirement Simulator

A single "average return" hides how bumpy the road really is. This tool plays out your savings plan across thousands of randomly generated market histories — so you see the median outcome, the unlucky cases, and your real odds of hitting your goal.

Your inputs

Current portfolio value

Annual contribution

Years to simulate

Quick presets

Expected annual return As a % — e.g. 7 for 7%

Annual volatility How bumpy the ride is — e.g. 12 for 12%

Savings goal

Number of simulations More runs = smoother results. 1,000 runs in a blink.

Median outcome

—

half of histories beat this

Chance of goal

—

of simulations

Likely range

—

10th–90th percentile

Range of outcomes over time

Median path Middle 80% of histories Your goal

Distribution of final outcomes

Median outcome Your goal

What is a Monte Carlo simulation?

A Monte Carlo simulation stress-tests your plan against randomness. Instead of assuming your portfolio earns a smooth 7% every year — which never happens in real life — it plays out your exact savings plan 1,000 times (or up to 5,000), each run drawing a different random sequence of monthly market returns from your expected return and volatility. Some simulated histories are lucky, some are brutal, most land somewhere in between. The result isn't one answer but a whole picture: the median outcome, the bad-luck and good-luck cases, and the probability you actually reach your goal.

Why the average return lies

Here's the trap most calculators set: a "7% average return" can mean +30% one year and −20% the next, or a calm +7% every single year. The average is identical — the outcomes are not. When you're young and adding money, an early crash barely hurts (you're buying shares on sale). When you're near the finish line, the same crash with no time to recover can wreck the plan. This is called sequence-of-returns risk, and it's completely invisible to any calculator that uses a single average number. Watch what happens with $100,000 starting, $12,000/year saved, 20 years at a 7% average return with 12% volatility: the median lands near $940,000, but the unlucky 10% end up around $560,000 while the lucky 10% clear $1.7 million. Same average — over a million dollars apart.

Worked example

Start with $100,000, contribute $12,000 a year, assume 7% average annual return with 12% volatility (a typical stock-heavy mix), over 20 years, targeting $1,000,000. Run 1,000 simulations: the median final value comes out near $940,000, the middle 80% of outcomes fall between roughly $560,000 and $1.7 million, and about 46% of runs reach the $1 million goal. Now nudge the return to 8% or add $2,000 more per year and watch that probability climb — that sensitivity analysis is the real power of this tool. Small, boring changes compound into dramatically better odds.

Honest caveats

Markets aren't perfectly normal. This model draws monthly returns from a bell curve, but real markets have "fat tails" — crashes bigger than the bell curve predicts. The simulation likely understates the truly extreme outcomes on both ends.
Your inputs are guesses. Expected return and volatility are estimates, not measurements. A model fed with optimistic guesses returns optimistic odds — treat results as a range to explore, not a promise.
Nominal dollars, no fees or taxes. Like our other calculators, this shows pre-tax, pre-fee dollars and ignores inflation. A 1% annual fee or 3% inflation quietly reshapes every number on the chart.
A model, not a prediction. Monte Carlo shows what could happen given your assumptions. It cannot tell you what will happen — no tool can.

Methodology note: geometric Brownian motion with monthly steps — each month's return is drawn from a normal distribution scaled to your annual volatility, the industry-standard approach to retirement Monte Carlo modeling. With volatility set to zero, the median path equals the standard compound-growth formula exactly.

Educational purposes only — not financial advice. Projections are hypothetical illustrations based on random sampling, not predictions of actual investment performance.