A Monte Carlo simulation runs 1,000 hypothetical retirements. Learn what the survival percentage actually measures, where it misleads, and how to read results.
A Monte Carlo simulation for retirement planning runs thousands of hypothetical futures, each with a different sequence of market returns, and reports how many of those futures end with your portfolio intact. The standard approach draws annual returns randomly from a distribution — typically a normal distribution centered on a historical average, with a standard deviation calibrated to historical volatility.
For a 100% equity portfolio, a reasonable parameterization is a 7% mean real return with a 15–17% standard deviation, based on U.S. equity history since 1926. Each simulation runs a complete retirement lifecycle: accumulation, transition to retirement, annual withdrawals, and portfolio depletion or survival.
A 75% Monte Carlo success rate means that in 75% of the 1,000 simulated futures, your portfolio had money remaining at the end of the retirement horizon you specified. It does not mean there is a 75% chance you will be fine. The 25% failure cases do not look the same — some run out at year 28, some at year 15.
The distribution of failure modes matters as much as the failure rate. A plan that fails in 25% of simulations but only fails badly (portfolio exhausted before age 75) in 5% of them is quite different from one where 25% of failures are catastrophic.
Some retirement tools use historical cycles rather than random draws: they apply the actual sequence of returns from every starting year in the historical record. This approach captures the actual correlation structure of returns — the fact that bad years cluster and recoveries are uneven.
The tradeoff: historical cycles are limited to the number of non-overlapping periods in the dataset. With 100 years of data and a 40-year retirement, you have roughly 60 distinct starting points, not 1,000. The FIRE calculator on this site uses a parametric Monte Carlo with 1,000 simulations, which is a reasonable middle ground.
A 90% survival rate looks good. But what happens in the 10% of cases where the plan fails? If the portfolio runs out at age 82 rather than 90, the tail risk is that you live past 82 with no financial buffer. Better questions to ask: in the failure cases, when does the portfolio run out? What is the range of portfolio values at age 85 in the success cases? How does the success rate change if you reduce spending by 10% during downturns?
A simulation success rate of 85–90% is generally considered acceptable for FIRE planning, with spending flexibility providing the buffer for the remaining 10–15%. A rate below 75% suggests the plan needs adjustment. A rate of 95%+ often indicates excessive conservatism, where the plan leaves significant wealth unspent at a high probability.
The simulation’s most useful function is sensitivity analysis: run it with your base case, then with returns 1% lower, then with 10% higher spending. Seeing how the success rate changes across those scenarios tells you which variables your plan is most sensitive to. Use the Monte Carlo retirement calculator on this site to run your own stress test.
If you want to turn the ideas in this article into a concrete plan, try these tools: FIRE Calculator, or the Safe Withdrawal Rate Calculator.
Related reading: The 4% Rule Explained, Safe Withdrawal Rate for 40-Year Retirements.
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