About the Monte Carlo Retirement Simulator
This Monte Carlo retirement simulator tests your plan against hundreds of possible futures instead of one average return. Each simulation draws a different random return for every year — some years up, some sharply down — and tracks whether your savings last to your planning age. The share of simulations that never run out is your probability of success.
It is built for people within a few decades of retirement who want to stress-test a plan: how much volatility, a bad sequence of early returns or higher spending does to the odds. Enter your savings, yearly contributions until retirement, planned spending in today’s dollars, and any Social Security or pension income; spending and other income rise with inflation each year.
Returns are drawn from a normal distribution with the mean and standard deviation you choose. The seed makes results repeatable — change it to see a fresh set of random paths. Results are estimates of risk, not guarantees; real markets have fatter tails than a normal curve.
With the default inputs, the probability of success is 76.5%. Change any value above to recalculate instantly.
How to use the monte carlo retirement simulator
- 1Enter your age, planned retirement age and the age your money must last to.
- 2Enter current savings and what you will add each year until retirement.
- 3Enter yearly retirement spending and Social Security or pension income in today’s dollars.
- 4Set the average return, volatility and inflation for your portfolio.
- 5Run the simulation and read the probability of success; change the seed to test a new set of markets.
Formula and method
Each simulation walks year by year from your current age to the plan age. Every year a return Rₜ is drawn from a normal distribution with mean μ and standard deviation σ (using the Box–Muller transform on a seeded random-number generator). Before retirement the balance grows and the yearly contribution C is added at year end. From retirement, the withdrawal Wₜ — spending minus Social Security or pension, both inflated from today’s dollars — is taken at the start of the year and the rest is invested.
A simulation fails if the balance cannot cover a withdrawal. The probability of success is the share of simulations that never fail; percentile balances are read across all simulations for each age. Returns below −95% are capped, and fees and taxes should be netted out of the average return you enter.
- μ
- Average annual return
- σ
- Volatility (standard deviation of annual returns)
- Zₜ
- Standard normal random draw for year t
- C
- Yearly contribution before retirement
- Wₜ
- Inflation-adjusted withdrawal in year t
- Bₜ
- Portfolio balance at the start of year t
Worked examples
45-year-old with $300k, spending $60k in retirement
With seed 2026, about 76.5% of 1,000 simulated markets last to 95. The $36,000 gap between spending and Social Security becomes about $58,990 by 65 after 2.5% inflation. At a steady 7% the plan would end with about $5.78 million, showing how much volatility and bad timing lower the odds.
No volatility: every path is identical
With 0% volatility every simulation earns exactly 7% a year, so all paths match the steady-return projection: about $1.78 million at 65 and $5.78 million at 95, and the plan succeeds 100% of the time.
Overspending plan fails even with steady returns
Spending $90,000 with $24,000 of Social Security needs $66,000 a year in today’s money — about $108,149 in the first retirement year. Even at a steady 7%, the money runs out at 83, so every simulation fails.
Retiring now at 60 with $1 million and a 5% withdrawal
Withdrawing $50,000 (5%) rising 3% a year from a 6%-return portfolio lasts until about 89 at steady returns, but random returns mean only about 36% of 500 simulations reach 90 — a classic sequence-of-returns risk.
Frequently asked questions
What is a Monte Carlo retirement simulation?+
It runs your retirement plan through hundreds or thousands of randomly generated market histories. Instead of assuming a steady average return, each path has good and bad years in a different order, which shows how likely the plan is to survive real-world volatility.
What is a good probability of success?+
Many financial planners target 80% to 90%. Below about 70% the plan relies heavily on good markets; near 100% you may be under-spending. Treat the number as a guide to adjust spending, not a guarantee.
Why is the success rate lower than a steady-return projection suggests?+
Volatility reduces compound growth (a 50% loss needs a 100% gain to recover), and withdrawing money during early bear markets locks in losses. This sequence-of-returns risk is exactly what a Monte Carlo simulation captures.
What volatility should I use?+
US stocks have historically had annual standard deviations of roughly 15%–20%, a 60/40 stock-and-bond portfolio roughly 10%–12%, and high-quality bonds around 4%–7%. Use a higher figure to be conservative.
Why does the result change when I change the seed?+
The seed controls the random numbers. A different seed produces a different set of simulated markets, so the success rate moves by a percentage point or two. Large swings suggest the plan is on a knife-edge.
Results are estimates for educational purposes and are not financial advice. Rates, fees and terms vary — confirm with your lender or a licensed advisor before making decisions.