Monte Carlo Simulation
Running thousands of randomized market scenarios to estimate the odds a financial plan succeeds — a probability range, not a single prediction.
Instead of assuming one fixed return, a Monte Carlo simulation models many possible sequences of returns and inflation, then reports the share that keep your plan solvent — your “chance of success.” It captures sequence-of-returns risk (the danger of a bad market early in retirement) that a single average return hides. The honest read is a range, not a promise.
Many futures, not one average
A Monte Carlo simulation models a financial plan by running it through thousands of randomized sequences of market returns and inflation, then reporting the share of those runs in which your money lasts — your “probability of success.” Instead of assuming a single fixed return every year, it embraces the reality that markets are volatile and the order of returns matters.
Why sequence-of-returns risk matters
A fixed-return projection can hide the single biggest danger in retirement: a bad market early on, while you're drawing down, can sink a plan that the same returns in a different order would have survived. Monte Carlo captures that sequence risk, which is why its honest output is a range of outcomes and a success probability — not a promise. A 90% success rate means 1 in 10 modeled paths still fell short.
How Formation handles it
Formation's live forecast runs many randomized scenarios against your real balances and lets you stress-test the inputs — spending, retirement date, withdrawal rate — to see how the probability moves. The result is labeled as an estimate and shown as a range, because that's the honest way to present a simulation rather than a single confident line.
A worked example
Two retirees both average 7% returns over 30 years, but one hits a steep market drop in years one and two while drawing income. Despite identical averages, the early-loss retiree can run out while the other thrives — the exact divergence a Monte Carlo simulation surfaces and a single 7% projection completely misses.
Frequently asked
What is a good Monte Carlo success rate?
Many planners look for something in the 80–90%+ range, but higher isn't always better — a 99% success rate can mean you're underspending. The right target balances confidence against the lifestyle you want, and the range of outcomes matters as much as the headline number.
Why is Monte Carlo better than a fixed-return projection?
Because it models the volatility and ordering of returns rather than assuming a smooth average. That captures sequence-of-returns risk — the chance that a poorly timed downturn derails a plan that average returns alone would suggest is safe.
In Formation
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Related terms
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