🎰Monte Carlo Retirement Simulation
Run 500 simulated retirements using randomly drawn annual returns to find the probability your portfolio survives. Unlike a single-scenario projection, Monte Carlo captures the uncertainty that comes with real market volatility.
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What Is Monte Carlo Retirement Simulation?
Every deterministic retirement calculator gives you a single answer: 'At 7% returns, your money lasts 34 years.' But markets don't return exactly 7% every year. They might return +30% in one year and -20% the next, with an average of 7% over time. The specific sequence of those ups and downs in the first decade of retirement, when the balance is largest and withdrawals begin, can permanently determine whether the portfolio survives or fails. A single-scenario projection cannot capture this.
Monte Carlo simulation addresses this by running hundreds of separate retirement scenarios, each with a different randomly drawn sequence of annual returns (using a normal distribution centered on your expected mean return, with a spread defined by historical volatility). The result is a probability: what percentage of realistic market environments would your plan have survived? A 90%+ success rate is generally considered robust; below 75% suggests the plan is meaningfully fragile.
A practical way to use this tool is to run it three times with different assumptions and compare the results. First run it with a 7% mean return and 17% standard deviation (a rough approximation of US equity historical statistics). Then run it with 5% mean and 12% standard deviation (a more conservative mixed portfolio). Then run it with your actual planned withdrawal reduced by 10% (modeling a flexible spending rule where you cut spending modestly in down years). The third scenario almost always shows a dramatically higher success rate than the first two, because flexible spending breaks the mechanical link between bad sequence returns and forced share sales at depressed prices. If your success rate at a 4% withdrawal is 82%, but rises to 94% with a 3.6% withdrawal, the difference in annual spending may be $2,400 on a $1.5 million portfolio. That is a small lifestyle adjustment with a 12 percentage point improvement in survival probability. Seeing that comparison concretely, rather than as an abstract planning rule, is what makes Monte Carlo simulation genuinely useful rather than just academically interesting.
How This Calculator Works
Each of 500 simulations draws a sequence of annual returns from a normal distribution with the specified mean and standard deviation. In each year, the balance is multiplied by (1 + that year's return), then the inflation-adjusted withdrawal is subtracted. If the balance reaches zero before the end of the specified period, that simulation is counted as a failure. The success rate is the percentage of simulations that survived all years. Percentile bands (10th, 25th, 50th, 75th, 90th) are computed from all simulation final balances to generate the fan chart.
Personal Considerations
The single most useful thing Monte Carlo does psychologically is make failure explicit and countable. A plan with a 78% success rate isn't bad — it means 22 out of 100 realistic scenarios resulted in the money running out. Whether that's acceptable depends entirely on your risk tolerance and your backup options. Someone with Social Security providing a meaningful income floor might accept 80% without concern; someone with no other income sources might want 95%+.
The fan chart also addresses a specific anxiety pattern: the fear that 'anything bad could happen.' Seeing the 10th-percentile scenario (the worst 10% of outcomes) in concrete dollar terms is often less frightening than an undefined worst-case. The bad scenarios are real and possible, but they are countable and the plan's resilience to them is now measurable.
Overconfidence shows up in how people calibrate the return and volatility inputs. Most people gravitate toward the mean return assumption that produces the success rate they want, and toward lower volatility than historical data supports, because lower volatility makes the fan chart look narrower and less scary. Confirmation bias shows up afterward: a 90% success rate feels like permission to proceed without interrogating what happens in the 10% of scenarios that fail. A healthier use of this tool is to deliberately enter parameters that make you uncomfortable, a lower mean return and higher volatility, and check whether the success rate is still acceptable. If the plan only works under optimistic assumptions, that is important information. If it holds up under pessimistic ones, confidence in it is justified.
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Frequently Asked Questions
Most financial planners cite 85-95% as the target range. At 90%, your plan survives 9 out of 10 randomly generated market histories. At 95%, it survives 19 out of 20. Targeting higher than 95% usually requires either a very low withdrawal rate or so much cushion that you're likely to die with far more than you needed. Below 80% suggests a meaningful probability of running out, and the plan should be stress-tested for what happens in failure scenarios.
Because each run uses freshly generated random returns. This is intentional — it reflects real uncertainty. The success rate itself is stable (the law of large numbers means 500 simulations gives a reliable estimate), but individual percentile band paths will vary slightly. The broad shape — how wide the fan is, where the median lands — is consistent across runs.
The Sequence of Returns Calculator shows two specific scenarios: bad years early vs. bad years late, with the same stylized return pattern. Monte Carlo generates 500 unique random sequences and reports statistical outcomes across all of them. Monte Carlo gives you a probability; the sequence calculator shows you the mechanism of why early bad returns are damaging.
No. The simulation models pre-tax returns and withdrawals. In practice, taxes reduce the effective return and increase the effective withdrawal burden depending on your account mix (Traditional IRA vs. Roth vs. taxable brokerage). For a post-tax approximation, reduce the mean return by 0.5-1% to account for taxes on gains.
Normal distributions are a reasonable approximation for annual returns, but real market returns have 'fat tails' — extreme events (crashes, booms) happen more frequently than a pure normal distribution predicts. Some advanced Monte Carlo tools use historical resampling instead of a parametric distribution. For planning purposes, a normal distribution is a good first approximation, and the results should be interpreted with that caveat.