What Is a Monte Carlo Retirement Simulation and Why Does It Matter

Most retirement calculators give you a single number. Enter your savings, your expected return, your retirement age, and the tool tells you: "You'll have $1.4 million at 65."

Quick answer: A Monte Carlo retirement simulation runs 25,000 different market scenarios against your actual savings, spending, and timeline — showing you the probability your money lasts, not just a single optimistic projection.

That number feels reassuring. It is also nearly useless.

Real markets do not return a smooth 7% every year. Some years they return 28%. Others they fall 35%. The order of those returns — the sequence — matters enormously, especially in the first ten years of retirement when you are withdrawing to live on. Two people with identical savings and identical average returns can have radically different outcomes depending on whether the bad years come early or late.

A Monte Carlo simulation accounts for this. Instead of assuming one smooth path, it runs thousands of scenarios — each with a different randomized sequence of market returns — and tells you the probability that your money lasts across all of them.

How it actually works

The simulation starts with your current portfolio, your planned retirement age, your expected spending, and your income sources — Social Security, pension, annuity, part-time work. It then models the years between now and the end of your planning horizon, randomizing annual returns using historical market data.

Each run through that model is one trial. At the end of each trial, the simulation records a simple outcome: did the portfolio survive to the end of the plan, or did it run out of money?

Run that 25,000 times. Count how many trials succeeded. Divide by 25,000.

That percentage — your success rate — is the most honest number in retirement planning. It does not tell you what will happen. It tells you the probability of different outcomes across a realistic range of futures.

What the results actually mean

A 100% success rate means that in every one of the 25,000 simulated futures, the portfolio lasted the full planning horizon. That does not mean nothing can go wrong — it means the plan has enough margin to absorb the range of market outcomes that history suggests are plausible.

A 75% success rate means 25% of simulated futures ran out of money. That might be acceptable depending on your circumstances, your flexibility to adjust spending, and what other resources you have. Or it might not be. The simulation gives you the information to have that conversation with yourself clearly.

Most financial planners target 85% to 95% for clients with no fallback options. If you have other assets, flexibility to reduce spending, or a pension that covers baseline expenses, you can accept a lower probability on the discretionary portion of your plan.

Why sequence of returns is the critical risk

Imagine two retirees. Both have $1.5 million. Both withdraw $60,000 per year. Both average 7% returns over 30 years.

Retiree A gets good returns in the first ten years, then bad returns later. The early growth builds a large base before withdrawals make a dent. The portfolio survives comfortably.

Retiree B gets the bad years first — a 35% decline in year two, another 20% drop in year five. They are selling shares at depressed prices to fund withdrawals. When the recovery comes, they own far fewer shares to benefit from it. The portfolio may not recover.

Same average return. Dramatically different outcomes. This is sequence of returns risk, and it is the primary reason a single-number retirement calculator is not adequate for real retirement planning.

Monte Carlo addresses this directly. By randomizing the sequence across 25,000 trials, it shows you the full distribution of outcomes — not just the average.

What a good simulation includes

Not all Monte Carlo tools are equal. The quality of a simulation depends on what it models.

A basic simulation handles portfolio value, withdrawals, and randomized returns. A more complete one includes Social Security timing and spousal benefits, state income taxes on withdrawals, Required Minimum Distributions starting at age 73, Roth conversion strategies and their tax impact, healthcare costs including IRMAA surcharges, and what happens if one spouse passes earlier than expected.

Each of these factors can shift the outcome by tens of thousands of dollars over a 30-year retirement. Leaving them out produces a cleaner-looking answer that is less true.

How to use the results

The simulation output is not a prediction. It is a stress test. Use it to answer the questions that actually matter:

Can I afford to retire at 65, or should I work two more years? What happens to my plan if I claim Social Security at 62 versus 70? How much does my Roth conversion strategy improve my long-term outcome? If my spouse passes at 78, does the survivor plan hold?

Run the simulation. See the number. Change one variable. Run it again. The value is not in any single result — it is in understanding how sensitive your plan is to the decisions you still have time to make.

That is what a Monte Carlo simulation is for. Not a prediction. A map of the territory you are about to cross.