Your Monte Carlo Says 80%. Should You Retire Anyway?

Quick Answer

Probably yes, if you have spending flexibility. An 80% success rate means that in 200 out of 1,000 simulated scenarios, your portfolio ran out of money. But those 200 "failures" include return sequences worse than anything in recorded market history. Many of them fail at age 89 or 92, not age 65. And the simulation assumes you never adjust your spending, never earn another dollar, and never collect Social Security at a different age. Real retirees are not that rigid. An 80% Monte Carlo result with a willingness to cut spending by 15% in bad years is safer than a 95% result with zero flexibility.

What Monte Carlo Actually Does

A Monte Carlo retirement simulation generates 1,000 or more random sequences of annual investment returns, then tests whether your portfolio survives all of them.

Each run picks a different combination of annual returns from a statistical distribution based on historical stock and bond performance. Some runs get lucky: great returns early, bad ones later when the portfolio is large enough to absorb them. Other runs get unlucky: a crash in year one, right when the portfolio is smallest and withdrawals hurt the most.

After running all 1,000 scenarios, the simulation counts how many survived your entire retirement period. If 820 out of 1,000 made it, your success rate is 82%.

The key thing to understand: these are not forecasts. Nobody is predicting that you have an 82% chance of success. The simulation is saying that across 1,000 randomly generated futures, 82% of them worked. The other 18% include scenarios that are mathematically possible but historically unprecedented.

What "Failure" Actually Looks Like

The word "failure" in Monte Carlo analysis scares people. It shouldn't, at least not the way most people interpret it.

A "failed" scenario does not mean you are broke at 62 and living under a bridge. In most cases, failure means the portfolio hit zero sometime between year 25 and year 35 of a 30 to 40-year simulation. Many of the failed runs last until the retiree is in their late 80s or early 90s.

Consider a 60-year-old who retires with an 80% success rate over 35 years. The 20% failure scenarios might look like this:

• 5% of scenarios: portfolio runs out between ages 85 and 90
• 8% of scenarios: portfolio runs out between ages 90 and 95
• 5% of scenarios: portfolio runs out between ages 80 and 85
• 2% of scenarios: portfolio runs out before age 80

The catastrophic outcomes, running out before 80, are rare even within the failure set. And every one of those scenarios assumes the retiree never made a single adjustment over 20+ years. No spending cuts. No part-time work. No downsizing. That is not how people behave.

Why 100% Is the Wrong Goal

Many people refuse to retire until their Monte Carlo hits 95% or 100%. This sounds prudent. It is often wasteful.

Achieving 100% success in a Monte Carlo simulation means your plan survives every scenario the model can generate, including return sequences worse than the Great Depression combined with 1970s inflation. To survive those scenarios, you need to save so much that in the median outcome, you die with three or four times what you started with.

Think about what that means in human terms. A person who saves until they hit 100% success at age 55 might have worked five extra years compared to someone who retired at 50 with an 85% success rate. Those five years of working are gone forever. The extra money, in the median case, goes to their estate.

Munger put it well: "All I want to know is where I'm going to die, so I'll never go there." The Monte Carlo equivalent is knowing which scenarios kill your plan, then building the ability to adapt if you see them coming. You do not need to pre-fund for the apocalypse. You need a plan that bends without breaking.

The Sweet Spot: 80% to 95% With Flexibility

Most retirement researchers converge on the same range. An 80% to 95% success rate is the planning sweet spot, if you pair it with spending flexibility.

Here is why. Wade Pfau and Michael Kitces have both shown that the difference between a rigid 80% plan and a flexible 95% plan is not the starting portfolio size. It is the retiree's behavior.

What Flexibility Buys You

An 80% success rate with all four adjustments available is a 95%+ success rate in practice. The simulation does not model these behaviors because they are hard to quantify. But they are real, and most retirees use at least two of them naturally.

What Really Moves the Needle

People focus on the wrong levers. They think they need to save another $200,000 to move from 80% to 90%. In most cases, spending flexibility does more than extra savings.

Saving More vs. Spending Less in Downturns

Adding $200,000 to a $1 million portfolio (a 20% increase in savings) typically improves your Monte Carlo success rate by 5 to 8 percentage points.

A commitment to cut spending by 15% during any year the portfolio drops 20% or more improves your success rate by 8 to 12 percentage points. And it costs you nothing upfront.

The math is straightforward. The worst Monte Carlo scenarios are driven by sequence of returns risk, a bad market in the first few years of retirement. If you reduce withdrawals during those bad years, you preserve more shares for the recovery. The recovery then compounds on a larger base.

Think of it as the inverse of dollar-cost averaging. In accumulation, buying during dips helps you. In retirement, not selling during dips helps you. Same principle, different direction.

Social Security Timing

Delaying Social Security from age 62 to 67 increases your annual benefit by roughly 30%. Delaying to 70 increases it by roughly 77% compared to claiming at 62. This is the single best annuity you can buy, guaranteed and inflation-adjusted.

For every year you delay, your portfolio carries more weight early but less weight later. In Monte Carlo terms, this front-loads the risk but massively reduces tail risk in your 80s and 90s, exactly when most "failure" scenarios occur.

Part-Time Income

Earning $12,000 per year in the first five years of retirement is equivalent to reducing your withdrawal rate by about 1.2 percentage points on a $1 million portfolio. That one adjustment can move a plan from 80% to 88% success.

Most early retirees earn something. Consulting, freelancing, teaching, seasonal work. This is not about grinding at a job you hate. It is about recognizing that even modest income in the early years, when sequence risk is highest, has an outsized impact on portfolio survival.

How to Read Your Monte Carlo Results

When you run a Monte Carlo simulation, look beyond the headline success rate. Here is what matters:

The Failure Distribution

When do the failures happen? If most failures occur after age 90, that is a different problem than failures at age 75. Late failures can be absorbed by Social Security increases, spending reductions, or home equity. Early failures require a fundamentally different plan.

The Median Outcome

In most Monte Carlo runs, the median ending portfolio is far larger than zero. A plan with an 85% success rate often has a median ending balance of 1.5x to 2x the starting value. That means half the time, you die with more money than you started with. The "failures" are in the long tail of bad luck.

The Sensitivity

Run the simulation twice: once with your planned spending, once with spending cut by 15%. If the first run shows 80% and the second shows 93%, your plan is highly sensitive to spending levels. That is good news, because spending is the one variable you control completely. If cutting spending barely moves the needle, you may have a structural shortfall that flexibility cannot fix.

When 80% Is Not Enough

Flexibility is a powerful tool, but it has limits. An 80% success rate might not be adequate if:

• Your spending is mostly fixed. If 80% of your budget is mortgage, healthcare, and property taxes, you cannot cut 15% without defaulting on something. Build more cushion.
• You have no Social Security coming. Without that floor of guaranteed income, your portfolio carries 100% of the risk. Target 90%+ in the simulation.
• You are retiring at 40. A 50-year retirement has more time for things to go wrong. The compounding effect of a bad early sequence over 50 years is worse than over 30 years. Add a few percentage points to your target.
• You are risk-averse by temperament. An 80% success rate might be mathematically fine, but if it keeps you up at night, the stress cost is real. Peace of mind has financial value.

The Paradox of Precision

Monte Carlo simulations generate precise numbers. 82.3% success. $1,247,000 median ending balance. This precision creates false confidence. The model's assumptions, return distributions, inflation rates, correlation between stocks and bonds, are all approximations. Changing the assumed average return by 0.5% can swing the success rate by 5 to 8 percentage points.

The right way to use Monte Carlo is as a stress test, not a crystal ball. It answers: "How does my plan hold up under a wide range of conditions?" It does not answer: "What will happen?"

Bogle said to never think you know more than the market. The retirement version: never think you know more than the future. Build a plan that works across many futures, not one that is optimized for a single prediction.

Frequently Asked Questions

What success rate do financial planners recommend?

Most planners target 85% to 95%. Below 80% raises flags. Above 95% usually means the client is oversaving. The right number depends on spending flexibility, other income sources, and risk tolerance. A flexible retiree with Social Security can be comfortable at 80%. A rigid spender with no backup income should target 90%+.

How many simulations should a Monte Carlo run?

At least 1,000. Most tools run 5,000 to 10,000. Beyond 10,000, the results barely change. The precision of a 10,000-run simulation is roughly plus or minus 1 percentage point, which is meaningless given the uncertainty in the underlying assumptions.

Is Monte Carlo better than historical backtesting?

They answer different questions. Historical backtesting uses real return sequences, which preserves the correlation structure of actual markets. Monte Carlo generates random sequences, which can produce scenarios worse (or better) than history. Neither is "right." Use both. If your plan passes historical backtesting and scores 80%+ in Monte Carlo, you are in solid shape. Pfau's research suggests that Monte Carlo tends to be slightly more conservative than historical testing for US data, because it can generate sequences worse than anything we have actually seen.

What if my Monte Carlo is below 70%?

Below 70% means that in nearly one-third of scenarios, your plan fails. Flexibility alone probably cannot bridge that gap. You likely need to either reduce planned spending, delay retirement, or increase savings. Consider running the simulation at a lower spending level to see what spending target gets you into the 85%+ range, then evaluate whether that lifestyle is acceptable.

Sources

• Bengen, William P. "Determining Withdrawal Rates Using Historical Data." Journal of Financial Planning, October 1994.
• Pfau, Wade D. "An Efficient Frontier for Retirement Income." Journal of Financial Planning, 2012. Comparison of Monte Carlo vs. historical simulation methodologies.
• Kitces, Michael. "Understanding Sequence of Return Risk: Safe Withdrawal Rates, Bear Market Crashes, and Bad Decades." Nerd's Eye View, 2022.
• Guyton, Jonathan T. and William J. Klinger. "Decision Rules and Maximum Initial Withdrawal Rates." Journal of Financial Planning, 2006. Guardrails approach to dynamic spending.
• Blanchett, David. "Estimating the True Cost of Retirement." Morningstar Research, 2013. Analysis of actual spending patterns and the "retirement spending smile."
• Social Security Administration. "Effect of Early or Delayed Retirement on Retirement Benefits." 2024 benefit calculation methodology.