How a Monte Carlo retirement simulation works
A Monte Carlo retirement simulation tests a financial plan against thousands of possible futures rather than a single average. Instead of assuming a fixed annual return, it randomly samples returns, inflation, and sometimes lifespan from probability distributions and runs the plan forward year by year. Running this many times, often 10,000 paths, produces a spread of outcomes that captures sequence-of-returns risk: the danger that poor returns early in retirement deplete a portfolio faster than the same returns later.
Each simulated path either sustains spending for the full horizon or runs out of money before it ends.
Reading the success rate
The headline output is a success rate: the percentage of simulated paths in which the money lasted the target horizon, commonly 30 years or more. A 90 percent success rate means the plan survived in 9 out of 10 simulated futures. The result is sensitive to assumptions about expected returns, volatility, withdrawal rate, and fees, so it is a probabilistic guide rather than a guarantee. This is general educational information and not financial advice.