Retirement Monte Carlo Simulator (Success Rate & Sequence Risk)

A Monte Carlo simulation runs a retirement plan thousands of times against randomly generated return sequences and reports how often the money lasts. It answers a question a simple average cannot: not what happens on average, but how often the plan fails.

The central insight is that the order of returns matters enormously. Two retirements with identical average returns can end very differently depending on when the bad years arrive. A crash in year two, while the portfolio is large and withdrawals have barely begun, is far more damaging than the same crash in year twenty-five.

The scenarios on this page demonstrate that directly. Holding the 7% average return constant and cutting volatility from 15% to 8% raises the success rate from 75.2% to 97.0%. Nothing about the expected return changed.

Each of 2,000 simulated retirements works as follows:

1. Start with the portfolio value.
2. For each year, draw a random return from a lognormal distribution with your mean and volatility.
3. Withdraw that year's amount, increased from the prior year by the inflation rate.
4. If the balance reaches zero, that run has failed.

The success rate is the share of runs where money remained at the end. The percentile figures show the distribution of ending balances: the 10th percentile is a poor outcome, the 90th a fortunate one.

The generator is seeded, so the same inputs always produce the same result. A retirement projection that changed on every page load would not be worth acting on.

$1,000,000, $40,000 a year, thirty years, 7% return, 15% volatility:

• Success rate: 75.2% -- roughly one run in four exhausts the portfolio
• Median ending balance: $1,585,373.02
• 10th percentile: $0 (a failed run)
• 90th percentile: $7,787,862.48

The spread is the real finding. The same plan can plausibly end with nothing or with nearly eight million dollars. That range, not the median, is what a retirement decision has to accommodate.

Withdrawing $50,000 instead (a 5% rate): success falls to 55.9%. A single extra percentage point of withdrawal turns a workable plan into a coin flip.

Retiring for forty years rather than thirty: 60.0%. Early retirement lengthens the horizon and materially raises the failure rate.

The same 7% return at 8% volatility: 97.0%. This is the sequence risk result, and it is the most important number on the page.

• Why this shows 75% where the 4% rule is often quoted at 95%. Those figures come from historical sequences of US returns, which include mean reversion and specific correlations. This model draws returns independently from a lognormal distribution, which produces harsher tails. Neither is wrong; they answer slightly different questions, and the difference is worth knowing before comparing the two.
• Flexible spending. Real retirees cut back after a bad year. Every model that assumes rigid inflation-adjusted withdrawals overstates failure.
• Taxes, which differ by account type and materially change the sustainable withdrawal.
• Social Security, pensions and annuities, which provide income floors that dramatically raise success rates.
• Fees. Subtract your all-in cost from the expected return before entering it.
• Changing asset allocation over time, including glide paths.
• Long-term care and other lumpy late-life expenses.
• Mean reversion and valuation. Returns are drawn independently, so the model does not know that poor decades often follow expensive markets.
• Mortality. The simulation runs the full term regardless of whether you are alive for it.

• Treating the success rate as a probability of the future. It is the share of runs under one set of assumptions. Change the expected return by a point and the number moves substantially.
• Chasing 100%. A plan with a 100% success rate is usually one that underspends badly. Most planners treat 80% to 90% as sound, given that real retirees adjust spending.
• Ignoring volatility. It is not a secondary input. At the same 7% average return, moving from 15% to 8% volatility changes the success rate by nearly 22 percentage points.
• Using nominal returns with real withdrawals, or vice versa. Here the return is nominal and withdrawals are inflated each year, which is internally consistent. Mixing conventions is not.
• Forgetting fees. A 7% gross return with 1% of fees is a 6% net return, and the difference over thirty years is large.
• Reading the median as the expected outcome. The distribution is wide and right-skewed. The 10th percentile matters far more for planning than the median does.

• Standard Monte Carlo methodology for retirement portfolio sustainability, using lognormal return draws with a specified mean and volatility.
• The simulation uses a seeded Mulberry32 generator for full reproducibility, and runs 2,000 paths per calculation.
• No jurisdictional or statutory data is involved; all inputs are user-supplied assumptions.