Retirement Calculator
Monte Carlo Retirement Simulator: What It Calculates and How to Read the Result
Quick answer
A Monte Carlo simulation runs thousands of randomized market sequences against your retirement plan to estimate the probability your portfolio survives a 30-year retirement. A success rate of 80–90% is typically targeted, but the more important number is what happens in the worst 10–20% of outcomes, because that's where retirement plans actually fail.
If yes, retirement start age locks to current age.
Reduces withdrawals after claim age.
Used when COLA is set to Override.
Optional income stream.
Rental / part-time work.
Used when tax mode is Simple.
Optional “shock year” overlay (simple).
Quick presets (use these, then save snapshots to compare):
- Success probability: 0%
- Ruin probability: 0.0%
- A coordinated plan can reduce risk and improve efficiency.
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| Age | Start | Gross wd | Taxes | Net spend | SS | Pension | Other | Return | Fees | End | Note |
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This simulator is for educational purposes only and is not investment, tax, or legal advice.
Results are hypothetical. Future returns may differ materially from assumptions and historical patterns.
Success probability is model-dependent and sensitive to assumptions (inflation, fees, taxes, spending behavior, and sequence risk).
If you’d like a plan tailored to your household and tax situation, consider scheduling a consultation.
What This Calculator Actually Answers
Monte Carlo replaces a single deterministic retirement projection (which always shows steady growth and produces false confidence) with thousands of randomized market sequences drawn from historical or modeled return distributions. The output is a probability distribution: in what percentage of trials did your plan survive the full retirement horizon? In what percentage did you run out of money before age 95?
The strength of Monte Carlo is that it explicitly models sequence-of-returns risk: the danger of bad returns early in retirement that can permanently damage portfolio durability even if average lifetime returns are fine. A deterministic projection cannot show you that risk; Monte Carlo can.
How to Read the Result
Do not focus exclusively on the headline success rate. An 85% success rate sounds reassuring, but the 15% of trials that fail are the ones that matter. Look at the failure distribution: at what age do failing trials run out of money? If the median failure happens at age 88, that's a different planning problem than failures concentrated at age 75.
The other critical output is the ending balance distribution. A plan with 90% success but a 10th-percentile ending balance near zero is a fragile plan. A plan with 80% success but a 10th-percentile ending balance of $1M is more durable than the headline number suggests. The full distribution tells you the truth that a single success percentage hides.
Common Mistakes
- Targeting 100% success rate. Plans that score 100% in Monte Carlo are almost always over-saving or under-spending, leaving consumption on the table for the sake of false confidence.
- Using historical bootstrap returns without acknowledging that future return distributions may differ from the last 30 years (which were unusually generous to equity holders).
- Ignoring the year-by-year withdrawal flexibility that real retirees actually use. A static 4% inflation-adjusted withdrawal underestimates durability; most retirees cut spending modestly when markets fall.
- Not modeling Social Security and pension income as separate non-portfolio income streams. They reduce the load on the portfolio and materially change the success rate.
- Forgetting that Monte Carlo doesn't model black-swan risks (hyperinflation, currency collapse, multi-year war) that fall outside the modeled distribution.
When This Calculator Is Not the Right Tool
Monte Carlo is a portfolio-durability tool, not a tax-strategy tool. It tells you whether your assets will last, not how to draw from them tax-efficiently. For tax-aware planning, such as Roth conversion sizing, IRMAA management, or withdrawal sequencing, use the dedicated tools and pair them with the Monte Carlo output once your withdrawal strategy is set.
Frequently Asked Questions
What success rate should I target?
Most planners target 80–90% across a 30-year horizon. Higher than 90% usually means you're under-spending; lower than 75% means the plan is fragile to bad return sequences. The right target depends on your flexibility: a retiree willing and able to cut spending in down years can live comfortably at a lower headline success rate than one with fixed expenses.
Are historical returns or modeled returns more accurate?
Neither is more accurate: they answer different questions. Historical bootstrap returns capture the actual variability the U.S. market produced over a defined period. Modeled returns (with chosen mean and variance) let you stress-test different forward assumptions. Most rigorous analyses run both and compare the spread.
How does Monte Carlo handle Social Security and pensions?
These are typically modeled as separate income streams that reduce required portfolio withdrawals each year. A retiree with $50,000 of Social Security plus a $20,000 pension is drawing materially less from the portfolio than someone with no fixed income, and the simulation reflects that. Modeling them correctly is one of the biggest swing factors in success rate.
Why do some plans show 95% success but feel fragile?
Because success-rate is binary (run out or didn't); it doesn't distinguish a plan that ends with $5M from one that ends with $5,000. Two plans with the same success rate can have very different ending balance distributions. Always look at the 10th-percentile ending balance, not just the success percentage.