Monte Carlo Simulation

Monte Carlo simulation is a technique that runs a financial plan through hundreds or thousands of randomized market scenarios to estimate the probability that it succeeds. Rather than assuming one fixed rate of return, it varies returns, inflation, and sometimes spending across each trial, then reports how often the plan funds its goals — often as a single "probability of success" percentage.

Advisors use it to stress-test retirement income against sequence-of-returns risk: the danger that poor returns early in retirement, combined with withdrawals, deplete a portfolio that an average-return assumption would have called safe.

Advisors care because a probability is easier to explain than a point estimate, and it frames plan changes honestly — spend less, work longer, or save more, and watch the number move.

• Randomizes returns across many trials
• Reports probability of funding goals
• Exposes sequence-of-returns and longevity risk

Nearly all financial planning software includes a Monte Carlo engine, though assumptions differ by vendor. Compare how each tool models it before you rely on the output; the vendor directory is a starting point.