Monte Carlo Retirement Simulator

The 4% rule tells you the historical worst case. Monte Carlo tells you the full probability distribution — including the 90th percentile outcome, the median, and how often the bad scenarios actually happen. Enter your numbers and run 1,000 simulated retirements.

Why Monte Carlo matters for early retirees

Standard safe withdrawal rate tables (4%, 3.5%, 3.25%) are binary: they show the spending rate that historically survived every 30- or 40-year retirement window in the data set. That's useful but incomplete. It doesn't tell you:

• How much cushion your plan has — a 95% success rate is very different from a 50% success rate, even if both "pass" the 4% threshold.
• What your portfolio looks like in the median case — you probably want to know that too.
• How a small change in spending or allocation shifts the outcome distribution.
• Where your plan sits on a spectrum rather than just pass/fail.

Monte Carlo simulation answers these questions by running hundreds of random return sequences drawn from historical distributions. For a 40- or 50-year early retirement — the planning horizon where sequence-of-returns risk is most severe — this distributional view is essential.

Monte Carlo retirement simulator

Defaults show a typical early retiree: retiring at 52 with $2M saved and $70K/year spending. Adjust to your numbers and click Run Simulation.

How to interpret your success rate

The success rate is the percentage of 1,000 simulated retirements where the portfolio lasted the full horizon without hitting zero. Here's how to think about different thresholds:

Most fee-only retirement planners target 90–95% success rate as the standard threshold for an early retirement plan. Going below 90% requires explicit income flexibility to compensate.

Monte Carlo vs the 4% rule: what's different

The 4% rule originated from Bengen (1994) and works by finding the highest spending rate that survived every rolling historical 30-year window in US market data from 1926 forward. It's a historical worst-case test.

Monte Carlo simulation is different in three ways:

1. It uses a probability distribution of returns, not historical sequences. The simulator draws each year's return randomly from a normal distribution whose parameters match historical equity and bond returns. This means it can generate scenarios that never happened historically — worse bear markets, longer recoveries — giving a richer view of risk.
2. It produces a probability, not a binary pass/fail. Instead of "4% is safe," you get "your plan survives 87% of simulated retirements" — a much more actionable number.
3. It shows the full outcome distribution. The percentile table below your results tells you not just whether your portfolio survives, but what it looks like in the median case (50th percentile) and the good case (90th percentile). Early retirees planning a 45-year horizon want to know the upside too.

How allocation affects success probability

For early retirees with 40–55 year horizons, allocation has a non-intuitive effect on survival probability:

• Too conservative (40/60 bonds-heavy) over 50 years actually increases failure risk because real returns on bonds are too low to maintain purchasing power over a half-century horizon. You die with less money than you started with in real terms.
• 100% equities maximizes median outcomes but increases variance — you get more good scenarios but also more catastrophic early-retirement bad sequences.
• The 60–70% equity range is typically optimal for 40–50 year early retirement horizons — enough equity growth to sustain a long horizon while enough bonds to buffer sequence-of-returns risk in the first decade.

The Kitces-Pfau rising equity glidepath adds a layer of nuance: starting with a more conservative allocation (e.g., 40/60) in the first 5–10 years of retirement — when sequence risk is highest — then gradually increasing equity exposure can improve success rates vs. a static 60/40. See our FIRE portfolio allocation guide for details.

Strategies to improve a below-threshold success rate

If your simulation shows a success rate below 90%, here are the levers in order of impact:

1. Reduce spending (highest impact)

Every dollar less you spend per year directly reduces withdrawal pressure. A 5% spending reduction (e.g., $70K → $66,500) often adds 3–5 percentage points to success rate. This is why the lean FIRE community achieves such robust plans — lower spending shrinks the FI number and raises the margin of safety simultaneously.

2. Add flexible income in the first decade

Sequence-of-returns risk is concentrated in the first 10 years of retirement. Adding even $15,000–$20,000 in annual part-time income during those years (Barista FIRE) dramatically reduces the damage from a bad early sequence, allowing the portfolio to survive intact until it recovers. A part-time income that covers 25% of spending in years 1–7 can add 8–12 points to a 45-year success rate.

3. Optimize Social Security timing

For early retirees who plan to claim Social Security, delaying from age 62 to 70 increases the benefit by 77% — plus any cost-of-living adjustments. At age 70, a $30,000/year SS benefit means $30,000/year less drawn from the portfolio. On a $2M portfolio, that covers 1.5% withdrawal rate. See the SS timing calculator.

4. Implement dynamic spending guardrails

The Guyton-Klinger guardrail strategy allows spending adjustments based on portfolio performance. If your portfolio drops to a threshold, you cut spending by 10% that year. This reduces the failure probability significantly without requiring a structural reduction in "target" spending. Studies show it can improve success rates by 5–15 points while reducing real spending by only 5–7% on average across a retirement.

5. Delay retirement by 1–3 years

One more year of accumulation at high savings rates has outsized effects: more saved, one year shorter horizon, and — if 57 becomes 58 — potentially preserving the Rule of 55 penalty-free access option. The "one more year" instinct that the FIRE community rails against is sometimes mathematically correct.

Limitations of this simulation

Knowing what Monte Carlo doesn't model is as important as knowing what it does:

• Returns are modeled as normal distribution. Actual returns have fat tails (more extreme outcomes than a normal distribution predicts). The real probability of a catastrophic early sequence may be slightly higher than simulated.
• Spending is modeled as flat in real terms. Actual retirement spending typically follows a "smile" pattern — higher early (active years), lower mid-retirement, higher late (healthcare). If your spending will vary, adjust your inputs accordingly or run multiple scenarios.
• Historical return parameters reflect 1926–2024 US data. The next 40 years may look different. Many analysts project 1–2% lower real returns going forward, which would reduce success rates by 5–10 points relative to historical Monte Carlo results.
• This simulation does not model taxes, Social Security income, RMDs, or healthcare costs. A real plan requires integrating all of these. The simulator measures portfolio survival, not total financial plan quality.
• No rebalancing premium is modeled. Regular rebalancing of a mixed portfolio captures a rebalancing premium of roughly 0.5% annually — not included here, so results are slightly conservative for mixed allocations.

Related calculators and guides

• Safe Withdrawal Rate Calculator — historical SWR lookup table for 30–50 year horizons
• Sequence of Returns Risk Simulator — lucky vs. average vs. unlucky scenarios
• 3-Bucket Strategy Calculator — size your cash, bond, and equity buckets for SORR protection
• FIRE Portfolio Allocation + Glidepath Calculator — bond tent, rising equity glidepath, and allocation by FIRE tier
• Tax-Efficient Withdrawal Order — which accounts to draw from and in what sequence