The blind spot in traditional retirement calculators
Open any retirement calculator and you enter three numbers: starting assets, annual spending, investment horizon. Throw in a return rate and it tells you "how much is left after X years."
That number is lying to you.
More precisely: that number is the average-case result, but real markets are never the average case.
An example: same average return, different outcomes
Imagine two retirees, each with NT$15M, spending NT$600K/year (a 4% withdrawal rate), expecting a 20-year average market return of 7%.
Scenario A: good years first, bad years last
| First 5 years | Middle 10 years | Last 5 years |
|---|---|---|
| +15%/yr avg | +7%/yr avg | −2%/yr avg |
20-year average ≈ +7%. Ending assets: NT$18M (withdrawals offset by compounding).
Scenario B: bad years first, good years last
| First 5 years | Middle 10 years | Last 5 years |
|---|---|---|
| −2%/yr avg | +7%/yr avg | +15%/yr avg |
20-year average is still +7%. Ending assets: may be fully depleted.
This is Sequence of Returns Risk — with the same average return, a retiree who hits bad years right after retirement is hammered by withdrawals plus declines, and later gains can't fully undo the damage.
Traditional calculators completely ignore this risk.
What Monte Carlo solves
The Monte Carlo approach:
- Don't assume a fixed return. Use a return distribution (mean, standard deviation)
- For each year, randomly draw a return (like rolling dice)
- Run 1,000 independent retirement paths
- Count how many paths end with non-depleted assets → success rate
This surfaces:
- Median path: the most likely outcome
- Worst 10% paths: the grim case where assets are drained
- Best 10% paths: the smooth-sailing case
Success rate answers the real question better than "how much is left on average": "Will I outlive my money?"
How to read a success rate
Common industry interpretations (not absolute standards):
| Success rate | Interpretation |
|---|---|
| ≥ 95% | Solid; can proceed as planned |
| 85% – 95% | Acceptable, but 1-in-10 chance you'll need to adjust |
| 75% – 85% | Elevated risk; think through contingencies |
| below 75% | Current plan may not hold; lower withdrawals or delay retirement |
The 4% rule from the Trinity Study targets a 95%+ success rate — meaning the 4% rule already incorporates Monte Carlo logic, just using historical back-testing rather than simulation.
What the key inputs mean
Expected annualized return
The long-term market average. Historically, a global stock portfolio returns 7–9% (5–7% after inflation); a balanced stock/bond portfolio 5–7% (3–5% after inflation). Monte Carlo uses this as the sampling mean.
Return standard deviation
This reflects market volatility. Historical reference:
| Portfolio | Annualized std dev |
|---|---|
| All equity (S&P 500) | ~15–18% |
| Balanced (60/40) | ~10–12% |
| Conservative (30/70) | ~7–9% |
| All bonds | ~5–8% |
Higher standard deviation means lower success rate. For the same average return, higher volatility produces more extreme scenarios.
Inflation rate
Withdrawal amounts are adjusted upward each year for inflation. The default is 2%, adjustable based on your view of Taiwan/US inflation.
Limitations of Monte Carlo
This tool isn't magic:
- Assumes normally distributed returns: real markets have "fat tails" — extreme events (2008, 2020 crashes) happen more often than a normal distribution predicts
- Independent yearly sampling: real markets have mean reversion (big drops often followed by rebounds), so simulations may over- or understate streaks
- Constant inflation: the model assumes fixed inflation, but Taiwan hit 3%+ in 2022
- No behavioral factors: in reality many people panic-sell in crashes and deviate from their plan
All of these mean real outcomes may be worse than the simulation. A 90% success rate isn't 100% safety.
How to use this tool
- Start with moderately conservative parameters (e.g., 5% return, 12% std dev, 2% inflation)
- Check the success rate
- Adjust the withdrawal amount and watch success rate move — find the withdrawal that maps to 95% success
- Stress test: drop returns to 3%, raise std dev to 18%, see how bad the worst case gets
- Cross-reference with the single-point result from the 4% withdrawal tool plus the Monte Carlo probabilistic result — two angles on the same question
Disclaimer
Monte Carlo is a probabilistic tool, not a forecast. This site's implementation uses a basic normal distribution with 1,000 simulations. The numbers are for reflection only and do not constitute retirement or investment advice. Consult a qualified professional for actual retirement planning.