Monte Carlo Simulation for Retirement: Why Average Returns Lie
A retirement plan built on “6% average annual returns” will look great on a spreadsheet — and may fail catastrophically in practice. The reason: average returns don’t happen in a straight line. Markets crash, recover, stagnate, and boom in unpredictable sequences. A 30% crash in your first year of retirement does far more damage than a 30% crash in year 20 — even though the “average” return over the full period might be the same.
Monte Carlo simulation solves this by running your retirement plan through thousands of different market scenarios, each with randomized returns, and showing you the probability that your money lasts. Instead of one answer (“you’ll be fine”), you get a distribution: “there’s an 89% chance your money lasts to age 95, a 7% chance it runs out between 85 and 95, and a 4% chance it runs out before 85.”
What Is Monte Carlo Simulation?
Monte Carlo simulation is a statistical technique that uses random sampling to model uncertainty. In retirement planning, it works like this:
- Define your plan: Current savings, annual contributions, spending target, retirement age, account types, tax rules, government benefits
- Set return assumptions: Expected return (e.g., 6%) and volatility (e.g., 15% standard deviation) for each asset class
- Run thousands of scenarios: Each scenario generates a random sequence of annual returns drawn from a probability distribution (typically log-normal)
- Track outcomes: For each scenario, simulate every year of retirement — withdrawals, taxes, benefits, account balances — and record whether the portfolio survives to the end
- Report the distribution: Success rate (% of scenarios where money lasts), percentile bands (5th, 25th, 50th, 75th, 95th), and worst-case outcomes
The name “Monte Carlo” comes from the casino — the technique relies on randomness, just like a roulette wheel.
Why It Matters: Sequence of Returns Risk
The Problem With Averages
Consider two retirees who both experience 6% average returns over 30 years:
Retiree A: Gets +20% in years 1-5, then mixed returns after. Their portfolio grows early, providing a large base that sustains withdrawals easily.
Retiree B: Gets -15% in years 1-3, then strong returns after. Their portfolio drops early while they’re withdrawing, and the remaining balance never recovers — despite the same average return.
Both had “6% average returns.” One ran out of money. This is sequence of returns risk, and it’s the primary reason Monte Carlo simulation exists for retirement planning.
The Math
A $1M portfolio with $40,000 annual withdrawals:
- Good sequence: +15%, +12%, +8% in years 1-3 → portfolio at $1,150,000 after 3 years of withdrawals
- Bad sequence: -20%, -15%, +5% in years 1-3 → portfolio at $630,000 after 3 years of withdrawals
Same 3-year average return (roughly). The bad sequence retiree has 45% less money — and 27 more years of withdrawals ahead.
How to Read Monte Carlo Results
Success Rate
The headline number: “92% of scenarios survived to age 95.” This means that out of 1,000+ simulated market paths, 920 produced enough money to fund your spending through age 95. The remaining 80 ran out.
| Success Rate | Interpretation |
|---|---|
| 95%+ | Very comfortable — plan has significant margin |
| 85–95% | Good — manageable risk with some flexibility needed |
| 70–85% | Moderate risk — consider adjustments |
| Below 70% | High risk — plan likely needs changes |
Percentile Bands
The confidence bands show the range of possible outcomes:
- 95th percentile: Your plan in a bull market — estate could be very large
- 75th percentile: Better-than-average outcome
- 50th percentile (median): The “typical” outcome
- 25th percentile: Below-average — markets underperformed
- 5th percentile: Near-worst-case — you need spending flexibility here
The gap between the 5th and 95th percentile is your uncertainty range. A wider gap means more outcome variability. The 5th percentile is the number that matters most for planning — it tells you what happens if you’re unlucky.
What Monte Carlo Can and Cannot Tell You
What It Does Well
- Quantifies risk: “How likely am I to run out of money?” is the most important question in retirement planning. Monte Carlo gives a probabilistic answer.
- Tests strategies: Compare a Roth conversion ladder versus no conversions, or Guyton-Klinger spending versus fixed spending — see which produces better success rates.
- Reveals tail risk: The 5th percentile outcome shows what happens in the worst ~50 scenarios out of 1,000. If that outcome is tolerable, your plan is robust.
What It Doesn’t Capture
- Fat tails: Real market crashes are more severe and clustered than the log-normal distribution assumes. 2008 was a 4+ standard deviation event — Monte Carlo underestimates the frequency of these.
- Behavioral risk: Monte Carlo assumes you stick to the plan. In practice, people panic-sell in crashes and overspend in bull markets.
- Structural changes: The simulation assumes the future looks statistically like the past. Paradigm shifts (sustained low rates, demographic decline, AI productivity gains) aren’t modeled.
Monte Carlo is a tool for understanding probability, not predicting the future. Use it alongside historical backtesting (which uses actual market data, including Robert Shiller’s S&P 500 dataset dating back to 1871) for a more complete picture.
How to Improve Your Success Rate
If your Monte Carlo success rate is below your comfort level:
- Reduce spending. The most powerful lever. Even $2,000/year less in spending can improve success rates by 5–10 percentage points.
- Delay retirement. Each additional working year adds contributions and reduces the number of withdrawal years. Even 1–2 years can meaningfully shift the curve.
- Delay CPP/Social Security. Higher guaranteed income later means less portfolio dependence in your 70s and 80s — exactly when sequence risk is most dangerous.
- Use adaptive spending strategies. Guyton-Klinger guardrails or VPW allow spending cuts during downturns, dramatically improving survival.
- Optimize the withdrawal sequence. Drawing from the right accounts at the right time reduces tax drag and preserves tax-free growth.
- Reduce portfolio volatility. A higher bond allocation reduces return variability but also reduces expected returns. The optimal allocation depends on your risk tolerance and time horizon.
Monte Carlo vs. Historical Backtesting
| Approach | Method | Strength | Weakness |
|---|---|---|---|
| Monte Carlo | Random returns from a distribution | Tests thousands of paths, including ones that haven’t happened | Assumes returns follow a known distribution |
| Historical backtesting | Real returns from 1871–present | Uses actual market data with real crashes and recoveries | Limited to ~150 years of data; future may differ |
The best approach uses both: Monte Carlo for broad probability assessment, and historical backtesting for grounding in real-world market behavior.
How Cinderfi Helps
Cinderfi runs 1,000+ Monte Carlo simulations in a Web Worker (keeping the UI responsive) with log-normal return distributions and configurable volatility. The results appear as confidence bands on the Monte Carlo chart — showing the 5th through 95th percentile outcomes for your portfolio balance, spending, and estate value over time. The Summary tab shows the headline depletion probability alongside deterministic metrics. Every scenario, withdrawal strategy, and spending approach can be stress-tested through Monte Carlo to see which combination produces the best risk-adjusted outcome.
Stress-test your retirement plan — try Cinderfi free.