Monte Carlo retirement planning, explained with a free simulator

A Monte Carlo retirement calculator runs thousands of simulated futures for your portfolio, each with a different random sequence of market returns, and tells you the probability your money lasts through retirement. Instead of a single "you'll have $X at age 85" answer based on an average return, you get a distribution showing best case, worst case, and everything in between. This reveals sequence-of-returns risk, the most important blind spot in simple retirement projections.

Why a Single Average Return Misleads

Most retirement calculators assume you earn the same return every year. Assume 7% annually for 30 years, compound it forward, and report whether the money runs out. The problem is that this ignores volatility and the order of returns. Two retirees who both earn an average of 7% over 30 years can have wildly different outcomes depending on when good and bad years fall.

A retiree who hits a bear market in years 1-3 while withdrawing may deplete the portfolio decades earlier than one who hits the same bear market in years 25-27. The average return is identical. The outcome is not. This is sequence-of-returns risk, and a spreadsheet that uses a fixed return cannot see it at all.

How a Monte Carlo Simulation Works

Instead of assuming the same return every year, the tool draws a random annual return each year from a bell-curve distribution centered on your expected return, with spread set by your volatility. Each year it applies that return to your balance and then adds your contribution. For each simulated future, and for each year in it, the tool draws a random annual return from a normal distribution centered on your expected return with a spread set by your volatility.

The formula is simple:

next balance = balance × (1 + random return) + annual contribution

It repeats this for the number of years you set, records the whole path, and runs the entire experiment hundreds or thousands of times. Sorting the results by year produces the percentile bands: the 10th percentile is a pessimistic path, the 50th is the median, and the 90th is an optimistic one.

For example, the MinMaxDoc simulator allows you to set your current age, savings, expected annual return, volatility, and how many years you plan to save. It then generates your "fan chart", a widening cone of possible outcomes over time. The median line is the middle outcome, half of simulations did better, half did worse. The shaded bands show the range: the wide band spans the 10th to 90th percentile, the inner band the 25th to 75th. A wider fan means more uncertainty, driven mostly by higher volatility and a longer time horizon.

Reading the Output: Success Rates and Percentiles

A Monte Carlo retirement calculator aggregates the results. The key output is your success rate, the percentage of simulations where your money lasted the full horizon. You also get percentile bands showing the range of portfolio values over time.

A 75% Monte Carlo success rate means that in 75% of the 1,000 simulated futures, your portfolio had money remaining at the end of the retirement horizon you specified. It does not mean there is a 75% chance you will be fine. A success rate is not a guarantee. An 85% success rate means that in 15% of simulated scenarios, you ran out of money.

The distribution matters. A plan that fails in 25% of simulations but only fails badly (portfolio exhausted before age 75) in 5% of them is quite different from one where 25% of failures are catastrophic.

What Success Rate Benchmarks Mean

A simulation success rate of 85-90% is generally considered acceptable for FIRE planning, with spending flexibility providing the buffer for the remaining 10-15%. A rate below 75% suggests the plan needs adjustment. A rate of 95%+ often indicates excessive conservatism, where the plan leaves significant wealth unspent at a high probability. Whether that risk is acceptable depends on your personal situation, how flexible your spending is, whether you have fallback income, and your tolerance for uncertainty.

How Many Simulations Matter

More iterations mean more stable estimates. At 100 iterations, your success rate might bounce around by several percentage points between runs. At 1,000, it stabilizes. At 10,000-50,000, the percentile bands converge and you can trust the numbers.

What Monte Carlo Cannot Do

Garbage in, garbage out. If your return assumptions are wrong, say you assume 10% equity returns when the next decade delivers 4%, no amount of simulation will save the projection. Monte Carlo quantifies uncertainty within your assumptions, not the uncertainty of your assumptions.

Returns are drawn independently each year from a normal distribution. Real returns have fatter tails and some autocorrelation, so treat the extremes as illustrative. A calculator that uses a Student's t-distribution or models black swan events as discrete shocks will give you a more honest picture of downside risk.

Results are nominal. For today's dollars, subtract your inflation assumption from the expected return. Taxes, fees, and withdrawals are not modeled in basic simulators. This is an accumulation (saving) projection, not a withdrawal (spend-down) plan.

Using Monte Carlo as a Stress Test

The right way to use Monte Carlo is as a stress-testing tool, not a fortune-telling one. It answers "how would my plan hold up under a wide range of market conditions?" rather than "will my plan work?"

The simulation's most useful function is sensitivity analysis: run it with your base case, then with returns 1% lower, then with 10% higher spending. Seeing how the success rate changes across those scenarios tells you which variables your plan is most sensitive to. Each iteration is not a prediction. No single run tells you what will happen. The power is in the aggregate, the shape of the distribution tells you how robust your plan really is.

MinMaxDoc's simulator is deterministic: nothing is saved server-side, everything runs in your browser, and the same link always reproduces the same chart. This means you can test different scenarios, save the URLs, and compare them side by side. You can also use "Copy shareable link" to share your projection with a financial advisor or colleague.

Monte Carlo retirement simulation shifts the question from "what will happen?" to "how robust is my plan?" It does not eliminate uncertainty, but it makes uncertainty visible. By running your plan through thousands of possible market paths instead of one misleading average, you can see which sequences of returns threaten your retirement and whether your plan has enough margin of safety to survive them.

FAQ

What is the difference between a 75% success rate and an 85% success rate?

A 75% success rate means you ran out of money in 25% of simulated futures. An 85% success rate means you ran out in 15%. Whether the extra 10 percentage points matters depends on your spending flexibility and other sources of income. Someone with a pension or part-time work in retirement can tolerate lower success rates; someone with no backup may want 90%+.

Why do the percentile bands widen over time?

A wider fan means more uncertainty, driven mostly by higher volatility and a longer time horizon. As more years pass, more possible sequences of returns can occur, so the range between pessimistic and optimistic outcomes expands. If you have 30 years of compounding, the 10th and 90th percentiles are much further apart than after 5 years.

Does Monte Carlo account for inflation?

Results are nominal. Most basic simulators do not adjust returns for inflation automatically. You either enter real (inflation-adjusted) returns and real spending, or nominal returns and nominal spending. Read the tool's documentation to see which convention it uses.

Can I use Monte Carlo to predict next year's stock market?

No. Each iteration is not a prediction. No single run tells you what will happen. Monte Carlo is useful for testing whether a long-term plan survives a wide range of market conditions. It is not a market timing tool and cannot forecast specific returns in specific years.

Disclaimer: This content is for educational and informational purposes only and does not constitute financial, investment, or tax advice. The information presented reflects the author's opinions and analysis at the time of writing and may not be suitable for your individual circumstances. Always consult with a qualified financial advisor before making investment decisions. Past performance is not indicative of future results. MinMaxDoc and its authors are not registered investment advisors.