Does Monte Carlo Overstate Retirement Success?

Yes, Monte Carlo can overstate retirement success. It usually does it in one of two ways: the market model is too gentle, or the household model is too obedient. Both produce a success rate that looks like a measurement and behaves like a sales pitch. The fix is not to throw the method out. The fix is to name the lies, measure how far they move the number, and stop quoting a 90% from an engine that cannot fail the way a life can fail.

This page uses one already-published household — the June Monte Carlo study, 5,000 paths, seed 20260622 — so the comparison is not a new run dressed up as news. Same person, same $3 million goal, four ways of telling the story. The line falls as the story gets less polite. That drop is the overstatement.

Does Monte Carlo overstate retirement success?

Does Monte Carlo overstate retirement success? It can, and on a typical retail implementation it does. “Monte Carlo” is not a single test. It is a family of generators. An engine that draws each year independently from a fixed normal, ignores fees, and assumes you never change spending will converge on a high success rate. An engine that clusters crashes, lets inflation have a memory, and lets you panic will converge on a lower one. Both will print a clean percentage. Only one of those percentages is trying not to flatter you.

The phrase monte carlo bias retirement researchers actually worry about is this one: the generator’s assumptions are more pleasant than the world, so the success rate is too kind, and the kindness is invisible because it is inside the machinery rather than in a footnote. A 90% from that engine is not comparable to a 90% from a regime-switching engine or from historical start years. Comparing them as if they were the same coin is how a household ends up believing a number no one computed on their life.

Monte Carlo bias retirement: the gentle market

Independent draws from a single normal (or lognormal) distribution do not cluster. A 40% year is followed by a typical year. Real bear markets arrive in streaks. Correlations that were supposed to hedge you break on the same Tuesday. Inflation does not reset to 2% because the calendar flipped. The Monte Carlo article ran the June household both ways. The naive, independent-draw engine left 37% of worlds below a $3 million goal — a 63% success rate. The regime-aware engine left 51% below — 49%. Same contributions, same allocation, same seed family. Twelve points of “success” were an assumption about how risk arrives.

That is the first overstatement. It is also the one a lot of calculators still ship, because independent draws are easy and fast and produce a fan chart that looks like work. We do not use that generator in the product. The methodology page is explicit about the five regimes. If another tool prints 90% on a plan that prints 75% here, start by asking whether their years are allowed to come in streaks.

iid returns retirement versus clustered risk

An iid returns retirement engine treats each year as an independent draw from one distribution. “I.i.d.” means independent and identically distributed: no memory, no regime, no hangover from last year’s crash. It is a beautiful assumption for a problem set. It is a kind assumption for a 25-year accumulation or a 40-year decumulation, because the damage in those lives is not a single bad draw. It is a cluster of them, arriving while you are contributing or withdrawing through the same ugly weather.

Sequence of returns risk is what iid quietly deletes. Two lives with the same average return and the same volatility can finish a retirement apart, depending on whether the bad years landed first. An independent-draw engine still has sequences — randomly — but it under-produces the long, correlated ones that actually break plans. The dashed 90% line on the chart is a typical quoted success. The iid point sits at 63%. The regime point sits at 49%. The gap is the cluster the iid model would rather not run.

The obedient household

Most engines assume the household spends the rule, every year, with no job-loss, no healthcare shock, no helpful aunt, and no 2 a.m. login after a 20% drop. That can cut both ways. A rigid 4% with no mid-course change understates how often real people cut spending and survive. A success rate that assumes they never cut, and then reports 99%, is selling a lifestyle they will not live. A success rate that assumes they never panic, and then reports 80%, is selling a temperament they may not have.

The June study modeled the temperament. Same 5,000 market worlds, two investors: one who stays invested, one who dumps to cash for six months after any 20% drop. The panic-seller’s chance of missing the $3 million goal jumped from 51% to 86% — success fell from 49% to 14%. That is not a market-model problem. It is a household-model problem. Fees, taxes, and a spending floor you cannot cross do the same kind of damage, in the other ledger. An engine that ignores them will overstate success even if every return draw is honest. We do not yet model federal tax or household sharing in the public engine; the methodology page says so. A 87% that names those omissions is more usable than a 97% that does not.

When the success rate too high is a modeling choice

A success rate too high is usually not a bug in the random number generator. It is a list of things the authors decided not to model. Independent years. Constant inflation. No fees. No taxes. A household that never retires into a crash and never sells. A 30-year horizon sold to a 40-year-old. Each of those choices lifts the print. Stack three of them and you can manufacture a 90% out of a plan that is a coin flip in a harsher engine. The chart’s left-most point — 100% — is the extreme version: a constant-return projection that always lands on $4.3 million and therefore always “succeeds” at $3 million. It is not a Monte Carlo at all. It is the number a lot of people still get from a calculator.

The honest move is to publish the engine, the seed, the path count, and the meaning of the success rate. Then publish what the engine refuses to see. We would rather a 87% that names its lies than a 97% that does not. The sample-size piece is the other half: more paths will not fix a gentle model. They will only shrink the error bars around the overstatement.

What we publish so you can see the lie

The product default is 1,000 seeded regime-aware paths. Research tables that need a tighter comparison use 5,000. Historical backtests start in 1928 and are reported as window counts, not as a fake probability — see Monte Carlo vs historical backtesting. Free calculators that still use a lognormal teaching engine say so on the page, and the age-and-horizon table is one of them. None of that makes the number “true.” It makes the number checkable.

Fees deserve their own sentence because they are the quietest lift. A 1% advisory fee is not a rounding error on a 40-year withdrawal. It is a second spending rule that never flexes. An engine that compounds “the market” without that drag will print a success rate the account cannot earn. Taxes are the same kind of omission, only lumpy. We would rather leave them out in the open than bake in a pretend net return. The fee-drag study is the accumulation version of this point.

A spending floor works the other direction. Guardrails look almost unbreakable on this site’s teaching tables because they are allowed to cut. If the mortgage, the insulin, and the property tax cannot cut, you do not own those tables. You own the rigid column. Quoting the flexible success rate for a floor-bound household is another way a success rate gets too high — not because the generator was kind, but because the household in the model is more flexible than the household in the kitchen.

If you take one thing from this article, take the shape of the line, not a single percent. As the model stops being gentle and the household stops being a saint, the success rate falls. A tool that cannot show you that fall — that only ever prints a high, stable, reassuring number — is not being careful. It is being kind. Kindness is a poor substitute for a plan you can revise.

Notes on the figures. The four success rates are from the June 22 2026 Monte Carlo study on one hypothetical household (age 40, retiring at 65, $250,000 invested, $2,000/month contributions, 80/15/5 stocks/bonds/cash, 5,000 paths, seed 20260622). “Below a $3M goal” shares were 0% (constant-return $4.3M projection), 37% (i.i.d. Monte Carlo), 51% (regime-aware), and 86% (regime-aware plus a panic-seller who moves to cash for six months after a 20% drop). Success is one minus those shares. No new engine run was performed for this article. This article is educational analysis, not investment advice, and does not recommend any security or strategy.