What is a Monte Carlo simulation in investing?

A method that runs your plan through thousands of random return sequences to estimate the range of possible outcomes and the probability your money lasts — instead of relying on a single average projection.

How accurate are Monte Carlo simulations?

They're only as good as their assumptions, and real markets have extreme events simple models understate. Use them to understand the range of outcomes and stress-test a plan, not to predict a specific result.

How many simulations are enough?

Typically thousands (often 10,000) — enough that the probability estimates stabilize. More runs reduce noise but won't fix unrealistic assumptions.

Monte Carlo Simulation, Explained: Planning for a Range of Futures

6 min read · Updated 2026-06-15

Most retirement projections draw a single smooth line: “assume 7% a year and here's your balance in 2050.” The problem is that markets never deliver the average in a straight line — and that single line hides all the risk.

A Monte Carlo simulation fixes this by running your plan through thousands of different random return paths and showing you the range of outcomes. Here's what it does, how to read the results, and where it can mislead.

Run a Monte Carlo simulation →

Why a single average projection misleads

Two retirements can average the same return and end completely differently, because the order of returns matters when you're adding or withdrawing money (sequence risk). A straight-line projection assumes a calm, average world that never actually happens — so it tends to make plans look safer than they are.

What a Monte Carlo simulation does

Instead of one path, it generates thousands — each a different plausible sequence of good and bad years drawn from assumptions about return, volatility, and how assets move together. Run 10,000 of them and you get a distribution of where you might end up, not a single guess.

How to read the results

The output is about odds and ranges, not a single number:

•Probability of success — the share of simulated paths where your money lasted (e.g. “87% of scenarios didn't run out”).
•Percentile bands — a range like the 10th-to-90th percentile shows the good-case and bad-case spread, not just the middle.
•The median path — a more honest “typical” outcome than the average, which a few extreme runs can skew.

Garbage in, garbage out

A Monte Carlo is only as good as its assumptions. Real markets have fat tails (extreme events happen more often than simple models expect) and regime shifts that basic simulations understate. Treat the output as a well-reasoned range of scenarios to stress-test your plan — not a forecast.

How to run one

Enter your portfolio, contributions or withdrawals, time horizon, and assumptions, then run the simulation and read the probability of success and the percentile range. Change one variable at a time (save more, retire later, shift your mix) to see what actually moves the odds.

Try it yourself

Monte Carlo SimulationRun thousands of scenarios on your portfolio.Open tool →Retirement Withdrawal LabSee how long your savings last across many paths.Open tool →Financial Goals PlannerCheck the odds you hit a savings target.Open tool →

FAQ

What is a Monte Carlo simulation in investing?: A method that runs your plan through thousands of random return sequences to estimate the range of possible outcomes and the probability your money lasts — instead of relying on a single average projection.
How accurate are Monte Carlo simulations?: They're only as good as their assumptions, and real markets have extreme events simple models understate. Use them to understand the range of outcomes and stress-test a plan, not to predict a specific result.
How many simulations are enough?: Typically thousands (often 10,000) — enough that the probability estimates stabilize. More runs reduce noise but won't fix unrealistic assumptions.

Key terms in this guide

Plain-English definitions in the Learning Hub.

Monte Carlo simulation →Probability of success →Percentile →Sequence-of-returns risk →

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