A single projected return is the most comfortable number in finance. It's also the least useful one for planning. The analysis runs 3,000 complete market sequences and tells you where your plan lands across all of them.
The traditional approach assumes a fixed expected return — say, 7% — and calculates from there. That tells you where you end up in an average market. It says nothing about whether you survive 2001, or 2008, or a four-year drawdown starting the week you retire.
Monte Carlo simulation takes a different approach. The model generates 3,000 complete market scenarios — each one a year-by-year simulation from today through your planning horizon. In one scenario, markets return 18% in the first year of retirement and −31% in the third. In another, a slow five-year grind starts the moment you stop working. In most, something in between. Each path is a plausible market history, drawn from a calibrated return distribution.
The headline output — "this plan projects at 85% confidence" — means that in 85 of those 3,000 simulated sequences, the model shows the portfolio surviving to the horizon. Not the average. Not the median. All of them, in sequence, with spending obligations met each year along the way.
The problem with average returns is that no one retires into an average market. They retire into this year's market — whatever it happens to be.
That probability is a more honest number than any single projection. It carries the distribution of outcomes with it. It has already priced in the bad sequences — not by assuming they won't happen, but by running them.
The simulation isn't trying to predict what markets will do. It's mapping what your plan can absorb.
In 1987, stock markets fell 22% in a single day. Under a standard bell curve, that's an event so improbable it shouldn't happen once in the lifetime of the universe. It happened on a Tuesday in October.[1]
The bell curve doesn't handle markets well. It treats extreme years as nearly impossible — but −30% years and +40% years occur more often than any normal distribution predicts, and they cluster in ways that compound on each other. Two bad years in a row don't feel twice as hard as one. They feel ten times harder, because you're spending down assets at a discount while waiting for a recovery that hasn't arrived.
The model uses a Student's t-distribution with 4 degrees of freedom — a heavier-tailed shape that treats bad years as less of a surprise.[2] At df=4, the math still holds (variance is finite), but the tails are fat enough to generate the kind of years that actually happen: a −31% shock early in retirement, a five-year grind that starts the week you stop working, a single bad year followed by several decent ones that still don't fully undo the damage.
The bell curve was wrong about the past. That's a poor foundation for planning the future.
A plan that survives 85% of fat-tailed sequences has been tested against conditions the bell curve barely considers. A plan modeled under normal returns might show a similar probability — but it hasn't been run against the same set of histories.
Using fat tails makes the projections more conservative in the scenarios that matter. That's the point.
The analysis frames retirement planning as three distinct questions. Each uses the same Monte Carlo engine — same return distribution, same simulation paths, same confidence threshold — but searches over a different variable.
The first question: what can this plan support? Given your current savings, contributions, retirement age, and planning horizon, the model finds the highest monthly spend that survives at the target confidence. It does this not by formula, but by running the simulation at progressively higher spend levels and halving the search space eight times until it converges on an answer.
The second question: what does your goal require? Given your target spend, the model finds the starting nest egg needed at retirement for the plan to meet the threshold. Same engine, same confidence level — the search variable is capital required, not spend supported.
The third question: when does work become optional? Given your target spend and current savings trajectory, the model finds the earliest retirement age at which accumulated capital is sufficient for the plan to succeed at the threshold. Earlier retirement means less accumulation and a longer drawdown horizon; the model searches for the crossover point.
Three questions. One answer, expressed three ways. If they're inconsistent with each other, the inputs are the culprit — not the model.
The internal relationship is a useful integrity check: the required nest egg from the second question, used as starting capital in the first, should reproduce the target confidence level. A gap between them usually signals a mismatch in inputs, not an engine error.
The three scenarios aren't three tools. They're three views of the same underlying plan.
The model defaults to 85% — meaning the plan survives in 85 of 100 simulated market sequences through the planning horizon. The analysis also computes 75% and 90% so you can see how much the answer shifts between positions.
Choosing a threshold is not a math question. It's a question about how much tail risk you're willing to carry. Planning at 90% typically requires more capital, a lower spend, or a later start than the same plan at 75%. The model prices the difference — it doesn't make the call for you.
What the threshold does not mean: a 15% non-survival rate is not a 15% probability that the portfolio is depleted. It's a 15% rate across the specific simulated sequences the model generates. Actual outcomes depend on spending adjustments, labor income flexibility, inheritance, and other factors the model doesn't see. Most people don't hold spending perfectly fixed through a long drawdown — they adapt. The model can't capture that adaptation.
The threshold is also not a statement about which sequences are likely. The model treats all simulated paths as equally plausible. Some of the 15% non-surviving paths represent conditions that may be extremely unlikely; others represent conditions that have already occurred in living memory. The model doesn't distinguish.
One calibration note worth understanding before comparing results across tools: because this model uses fat-tailed returns rather than a normal distribution, its probability estimates are more conservative than those from most consumer planning calculators. Two tools can show the same 80% headline and be testing against materially different conditions. A plan that survives 80% of fat-tailed sequences has cleared a higher bar than a plan that survives 80% of normally-distributed ones. If a number in this model looks lower than you expected — lower than what another tool showed, or lower than what your own advisor is using — that gap is worth examining rather than assuming one is wrong.
Eighty-five percent is a starting position. The plan you build around it is where the real choices are.
Every number in the output traces back to a number you entered. The model doesn't adjust for optimism, recency bias, or the return figure you heard somewhere. It runs the simulation and reports what it found.
Starting balance and contributions. The model grows your current savings at the pre-retirement return, adds contributions each year until retirement, then stops. Contributions are a fixed annual dollar amount — not inflating. For couples where both partners work, the working spouse's half continues as a portfolio inflow after the primary partner retires, reducing what the portfolio has to fund on its own.
One thing worth being explicit about: the confidence number the model shows is a conditional result. It answers the question "given that your savings reach the projected balance at retirement, how does the plan fare from there?" The 3,000 simulated sequences cover the drawdown phase — retirement through the planning horizon — not the accumulation phase that gets you there. Pre-retirement markets can run ahead of or behind the modeled pace. If you arrive at retirement with a larger or smaller nest egg than projected, the plan's odds shift accordingly. This is the model's second-largest simplification, after taxes. It's one reason the output is most useful as a range of scenarios to stress-test, not a single forecast to optimize toward.
Expected return and volatility. Return runs at three levels: Conservative (5%), Default (7%), Aggressive (9%) — all nominal, pre-retirement. Volatility is held fixed at 12% and isn't user-adjustable. At retirement, the model lowers the expected return by 1.2 points and volatility by 2.0 points, reflecting the shift toward a less aggressive allocation most investors make in drawdown. A bit over a percentage point of return, compounded across thirty years, isn't a rounding error. Run all three return settings before anchoring on any single output.
Inflation. The rate at which spending grows: Conservative 4%, Default 3%, Aggressive 2%. Applied uniformly to gross spending, Social Security, and retirement income each year. The spread between 2% and 4% over thirty years of retirement isn't a different variant of the same plan. It's a different plan.
Target spend. Monthly spending in today's dollars, after tax — the take-home lifestyle cost you actually live on, not the pre-tax portfolio draw. The model grosses this up by an assumed 15% blended tax to get the pre-tax withdrawal it simulates, then shows results back in after-tax terms. Social Security and retirement income are modeled as inflows that reduce what the portfolio funds each year. The model doesn't have a view on whether the number is achievable. It reports what the simulation found.
Planning horizon. The age the portfolio is modeled through. Paths that exhaust before this age count as failures; paths that survive count as successes. The model has no view on longevity — it runs to the boundary either way. What changes with a longer horizon is the fraction of paths that make it.
Social Security. Monthly benefit in today's dollars at your elected claiming age. The model inflates to nominal at the claim date and applies SSA delay factors: 70% of the full retirement age benefit at 62, 100% at 67, 124% at 70.[3] Waiting until 70 is one of the largest single-decision effects available in the model — and one of the few that doesn't require saving more.
Retirement income. Other contractual monthly income beyond Social Security — pension, annuity income, rental income. Modeled as an inflation-adjusted inflow each year. Even a modest fixed income stream changes what the portfolio has to do.
One more thing worth knowing: the same inputs always produce the same outputs. The simulation uses a deterministic random number generator seeded from your inputs. There's no randomness between sessions — only between simulated paths.
The model is only as honest as the assumptions behind it. The assumptions are the plan.
A model that claims no limits is the most dangerous kind. The following are things this model does not capture — areas where a complete plan goes beyond the simulation.
Taxes. The model's only tax handling is a single 15% blended estimate — the rate it uses to convert your after-tax target into a pre-tax draw, and to show results back after-tax. Beyond that one number, it doesn't model brackets, the difference between ordinary income and capital gains, account types (taxable, traditional, Roth), or required minimum distributions. A $10,000 monthly draw from a traditional IRA and the same draw from a Roth account are not the same in reality — the model treats them identically. This is its largest simplification.
Investment fees. Expense ratios and advisor fees reduce effective returns without appearing in the simulation. A 1% annual fee compounded over 30 years can reduce ending portfolio value by 25% or more. If your expected return is 7% gross and your actual costs are 0.75%, the effective input the model would use is closer to 6.25%.
Behavioral risk. The model assumes you hold through every simulated sequence, including the ones that lose 35% in year two of retirement. In practice, most investors don't hold perfectly through extended drawdowns. The gap between modeled returns and actual investor returns — caused by timing decisions, panic selling, and drift — has historically been material.[4] The model cannot quantify your own behavioral risk.
Healthcare and long-term care. Medical cost inflation has consistently exceeded general CPI. Long-term care costs — home care, assisted living, memory care — are among the largest unmodeled risks in a retirement plan and can restructure a portfolio in ways the simulation doesn't see. The model applies one uniform inflation rate to all spending.
Variable spending. The model assumes real spending is flat across the planning horizon. Research suggests most people spend more in early retirement and less in later years as physical activity declines — a pattern sometimes called go-go, slow-go, no-go. Flat spending is a conservative assumption early in retirement and a generous one later. The paid report models this pattern explicitly.
Longevity and survivorship. Every path runs to the same fixed planning horizon, with both people in a couple assumed alive the whole way through. The model doesn't draw on mortality tables, doesn't model the chance one spouse outlives the other, and doesn't step Social Security down to a survivor benefit. A real plan has to weigh how long the money needs to last against how long it's likely to be needed.
One asset, not a portfolio. The simulation uses a single aggregate return and volatility — not a mix of asset classes with their own correlations, drift, and rebalancing. Allocation enters only as the small return-and-volatility reduction the model applies at retirement. Two portfolios with the same expected return but different compositions behave identically here; in practice they don't.
The model holds the assumptions steady. The plan has to account for everything the model can't see.
Understanding the limits isn't pessimism. It's the only way to use the output correctly.