What is a Monte Carlo retirement simulation?
A Monte Carlo simulation tests your retirement plan against thousands of possible market futures instead of one smooth average. Each run deals a different random sequence of yearly returns, takes out your withdrawals, and checks whether the money survives.
Do that a thousand times and you get a distribution of outcomes rather than a single guess. The share of runs where the portfolio lasts your full retirement is your success rate. It answers the real question, "what are the odds this works," in a way a single projected number never can.
Why an average return lies
Imagine two retirees who both earn a 7% average return over 30 years. One hits a brutal market in years one through three; the other hits it in years 27 through 29. Same average, wildly different endings. The first retiree sells assets into a downturn to fund early withdrawals and may never recover. The second cruises through the early years and is largely done drawing down before the bad stretch arrives.
This is sequence-of-returns risk, and it is the single biggest reason a plan built on an average can fail. A simple projection assumes the smooth average happens every year. Reality delivers the returns in an order, and the order matters enormously once you are withdrawing. Monte Carlo exists to measure exactly this.
How to read your success rate
A success rate is a probability, not a promise. Here is how the result above maps to a verdict, using the same tiers as the Plan With Clarity app.
| Success rate | What it means |
|---|---|
| 95% and up | Excellent, very robust plan |
| 85 to 94% | Strong, high probability of success |
| 70 to 84% | Moderate, consider adjusting spending |
| Below 70% | At risk, significant chance of running out |
One honest note: chasing 100% is usually a mistake. A plan that never fails across every simulated future almost always means you are spending far less than you safely could, trading years of enjoyment for a cushion you will never use. A high success rate with room to live is the goal, not certainty.
How this calculator works
- It draws a random annual return from a lognormal distribution built on your expected return and volatility, so good years and bad years show up in realistic proportion.
- Each year it subtracts your withdrawal, grown by inflation so your spending power stays level, then applies that year's return to what remains.
- If the balance hits zero before your final year, that run is a failure. If money remains at the end, it is a success.
- It repeats this 1,000 times and reports the percent that succeeded, plus the median and worst-10% ending balances.
This is the 1,000-simulation version. The full Plan With Clarity model runs 10,000 simulations, adds a historical backtest across 98 years of real market data (1928 to 2025), models inflation as its own random variable, and layers in Guyton-Klinger guardrails that adjust your spending up or down as markets move. Those deeper tools are part of the premium app.
What moves your success rate
Spending
The biggest lever you control. Trimming first-year spending even modestly can lift a borderline plan into strong territory, because the cut compounds across every year.
Volatility
Higher volatility widens the range of outcomes and raises the odds of an early bad stretch hurting you. A steadier portfolio can lift the success rate even at the same average return.
Time horizon
A longer retirement is more demanding. Planning to 95 instead of 85 lowers the success rate for the same spending, which is why your assumed age matters as much as your portfolio.
Expected return
It matters, but less than people hope, and it is the input you control least. This is why spending and horizon, the things you actually decide, do most of the work.
Common mistakes
Trusting a single projected number
A plan that shows one tidy ending balance is hiding its own risk. The same inputs can succeed or fail depending on the order of returns, which is the whole point of running a simulation.
Targeting 100% success
Near-certainty almost always means large underspending. A great deal of retirement research finds people retire with more than they ever spend. Aim for a strong rate with room to live, not a perfect one.
Ignoring outside income
Social Security and pensions reduce how much the portfolio has to cover. Leaving them out makes the withdrawal the portfolio faces look larger than it really is and understates your success rate.
Keep going from here
Questions people ask
What is a Monte Carlo retirement simulation?
A method that tests your plan against thousands of possible market futures instead of one average. Each run uses a different random sequence of returns, applies your withdrawals, and checks whether the money lasts. The share that lasts is your success rate.
What is a good Monte Carlo success rate?
85% or higher is generally considered strong, and 95% or higher very robust. Between 70 and 85% is workable but worth strengthening. Below 70% signals a meaningful chance of running out. Targeting a perfect 100% usually means underspending by a wide margin.
Why not just use an average return?
An average hides sequence-of-returns risk. Two retirements with the same average can end very differently depending on whether bad years arrive early or late. Monte Carlo captures that by testing many orderings.
How many simulations should it run?
This free version runs 1,000, which gives a stable estimate. The full Plan With Clarity model runs 10,000 and adds a historical backtest and dynamic spending guardrails.
Does this calculator store my numbers?
No. Every simulation runs in your browser. Nothing you type is sent to a server or tied to an account.
From a probability to a plan
A success rate is one number. Plan With Clarity runs 10,000 simulations plus a 98-year historical backtest, then connects it to the rest of your plan: withdrawal strategy, taxes, Social Security timing, healthcare costs, and Guyton-Klinger guardrails that adjust spending as markets move, so you can see not just the odds but what to do about them.
Open Plan With ClarityNo account linking and no selling of your data. You can run the core models without connecting any outside financial accounts.