Monte Carlo Retirement Calculator
Run a 1,000-trial Monte Carlo simulation of your retirement portfolio. See success probability (the percentage of trials where money lasts), median ending balance, worst-case scenarios, and how spending changes the success rate.
What Is a Monte Carlo Simulation?
A Monte Carlo simulation runs thousands of randomized iterations of a retirement portfolio, drawing each year's investment return from a probability distribution (typically normal or log-normal) defined by an expected return and standard deviation. By sampling 1,000+ random sequences of returns, the simulation produces a probability distribution of outcomes — what percentage of trials end with money still in the portfolio (success), what the median outcome looks like, what the worst 10% of outcomes look like (downside risk), and what the best 10% look like (upside potential). Per SEC investor education on retirement planning, Monte Carlo is the most widely-used method for stress-testing retirement plans because it explicitly accounts for sequence-of-returns risk in a way that average-return calculations cannot. Vanguard, Fidelity, Schwab, and most fee-only advisors use Monte Carlo as their primary planning tool.
How to Read Monte Carlo Results — The 80% Rule
Most planners target a Monte Carlo success probability of 80-90%. A 95-100% success rate suggests you're under-spending and could afford more in retirement; below 70% suggests material risk of running out of money and warrants either reducing withdrawals, working longer, or annuitizing a portion. The exact target depends on flexibility — if you can cut spending in bad years (Guyton-Klinger guardrails) or have other income (Social Security, pension, part-time work), you can target lower success rates because you have a margin to adjust. If your withdrawals are inflexible (fixed expenses, no other income), aim for 90%+ success. Don't aim for 100% success — that requires either ultra-low withdrawals (well below 4% rule) or massively over-saving, both leaving large unspent balances at death rather than enjoying retirement. Per Kitces research, the 100%-success target reflects a behavioral failure to balance "running out of money" risk against "running out of life" risk.
The Limitations of Monte Carlo
Monte Carlo has three main weaknesses to understand: (1) Independence assumption — most simple Monte Carlos assume each year's return is independent of the prior year's, but real markets exhibit serial correlation (mean reversion, momentum) that simple Monte Carlo ignores. More sophisticated implementations use bootstrap sampling from historical data instead. (2) Distribution choice — assuming normal distribution underestimates tail risk; real returns have "fat tails" with more extreme outcomes than normal distribution predicts. The 2008 crash was a 4-standard-deviation event under normal distribution but happened in real life. (3) Static spending — most Monte Carlos assume rigid inflation-adjusted withdrawals, but retirees actually adjust spending in bear markets (Guyton-Klinger guardrails) and respond to portfolio performance. Dynamic withdrawal Monte Carlos produce 5-15 percentage point higher success rates than static ones at the same starting withdrawal rate.
Monte Carlo Retirement Worked Example 2026 — $1M Portfolio at 4%
Here is a concrete Monte Carlo run for a 65-year-old single retiree using current 2026 capital market assumptions. Inputs: $1,000,000 starting balance, $40,000 annual withdrawal (the 4% rule), 30-year horizon, 60/40 portfolio (60% US equities, 40% bonds), expected real return 4.5%, standard deviation 11%. Result (1,000 trials): 87% success rate. Median ending balance: $1.4 million. 10th percentile (downside): $0 at year 28. 90th percentile (upside): $4.2M. Sensitivity: dropping the withdrawal to $35,000 (3.5%) lifts success to 95%; raising it to $50,000 (5%) drops success to 58%. A more conservative 50/50 portfolio with the same $40K withdrawal hits ~82% success but with a tighter range. Per the SSA actuarial life table, a 65-year-old has a median remaining lifespan of ~18 years (male) or ~21 years (female), so a 30-year horizon is conservative for most singles but appropriate for couples (joint life expectancy adds 5-7 years). Updated 2026-06-20.
Monte Carlo vs Historical Simulation vs Safe Withdrawal Rate
Three methods exist for retirement stress-testing: Monte Carlo (random sampling from distribution, 1,000+ trials, produces probability), historical simulation (run the plan against every actual rolling 30-year window from 1926-2026, see how many would have succeeded), and safe withdrawal rate (find the maximum withdrawal that historically would have survived the worst 30-year period, e.g., Bengen's 4% rule). Historical simulation captures real-world serial correlation but is limited to ~30 actual non-overlapping cohorts. Monte Carlo captures more theoretical scenarios but may miss real-world dynamics. Safe withdrawal rate is conservative because it's based on the single worst historical case (1966 retirees). Most rigorous planning uses all three together, looking for consistent recommendations across methods. Last updated May 2026.