A Monte Carlo simulation runs your financial plan hundreds or thousands of times, each time using a different randomly generated sequence of investment returns, instead of assuming the same average return every single year. Since real markets deliver good years and bad years in an unpredictable order, this approach captures sequence-of-returns risk, the danger that a few bad years early in retirement can permanently damage a portfolio even if the long-run average return is perfectly fine.

Most financial planners consider a Monte Carlo success rate of 85% to 95% to be a reasonably safe target for early retirement, since even a well-funded plan carries some risk of an unusually bad sequence of returns. A success rate below 70 to 75% generally suggests you should save more, retire later, reduce planned spending, or accept meaningfully more risk of running short of money later in retirement.

The 4% rule is a rule of thumb from the Trinity Study suggesting that withdrawing 4% of your portfolio in the first year of retirement, then adjusting that dollar amount for inflation every year after, has historically had a high probability of lasting at least 30 years. This calculator’s Safe Withdrawal Rate field lets you set your own assumption (4% implies a FIRE number of 25 times annual expenses), and the Monte Carlo simulation tests whether that withdrawal rate actually holds up against your specific inputs and time horizon, rather than relying on the historical rule alone.

Sequence of returns risk refers to the fact that the order in which you experience good and bad investment years matters enormously when you are also withdrawing money, even if the average return over your whole retirement ends up being the same. A severe market decline in the first few years of retirement forces you to sell more shares at depressed prices to cover expenses, permanently reducing the shares left to benefit from the eventual recovery, which is why two retirees with identical average returns can have wildly different outcomes depending purely on when the bad years occurred.

Historically, a diversified portfolio of roughly 60% stocks and 40% bonds has shown an annual standard deviation in the range of 10 to 12%, while an all-equity portfolio has shown 15 to 20% or more depending on the market and time period measured. A higher volatility input widens the range of possible outcomes in the simulation (both better and worse), so it’s worth running the calculator with a couple of different volatility assumptions to see how sensitive your success probability is to this uncertainty.

No, this version treats your portfolio as the sole source of retirement funding covering 100% of your annual expenses. If you expect Social Security, a pension, rental income, or part-time work in retirement, reduce your annual expenses input by that expected income to approximate its effect, since any guaranteed income reduces the amount your portfolio needs to cover and meaningfully improves your true success probability.

Because each Monte Carlo simulation uses freshly generated random numbers for every one of the 1,000 runs, the exact success probability will vary slightly (typically by a percentage point or two) each time you click Run, even with identical inputs. This small amount of variation is normal and expected; with 1,000 runs the results are stable enough for practical planning purposes, though they are not meant to be read as a precise figure to the decimal point.

A Monte Carlo simulation is a planning tool that models a range of statistically plausible futures based on the assumptions you provide, it cannot predict what markets will actually do, account for tax law changes, health shocks, or changes in your own spending needs over a multi-decade retirement. Use this calculator to compare scenarios and stress-test your assumptions, but treat the output as one input among several, alongside guidance from a qualified financial advisor, when making an irreversible decision like leaving your job.