Success rate is the percentage of Monte Carlo scenarios where your portfolio survives the full retirement period. 80-90% is the sweet spot — below 75% signals real risk, while 100% usually means you're spending too conservatively. It's the single most important output of a retirement simulation.
Success rate — also called probability of success or probability of ruin (its inverse) — is the percentage of Monte Carlo simulation iterations in which the retirement portfolio does not run out of money before the end of the planned period. It is the single most referenced output of any retirement simulation and the primary metric retirees use to evaluate whether their plan is viable.
How It Works
In a Monte Carlo simulation with 10,000 iterations:
- Each iteration simulates a unique sequence of market returns, withdrawals, income, and inflation
- At the end of each iteration, the portfolio either has money remaining (success) or was depleted before the target age (failure)
- Success rate = successful iterations ÷ total iterations × 100
For example, if 8,700 of 10,000 iterations end with a positive portfolio balance, the success rate is 87%.
What the numbers mean:
| Success Rate | Interpretation |
|---|---|
| 95–100% | Very conservative — likely over-saving or under-spending |
| 85–95% | Strong plan — comfortable margin of safety |
| 75–85% | Reasonable if you have spending flexibility |
| 60–75% | Elevated risk — consider adjustments |
| Below 60% | High risk of portfolio depletion |
Why It Matters for Retirement Planning
Success rate condenses thousands of scenarios into a single, actionable number. But interpreting it requires nuance:
- It's not a guarantee: an 85% success rate doesn't mean your plan will work. It means that given the model assumptions, 15% of plausible market scenarios result in failure
- Small changes, big impact: adjusting the withdrawal rate from 4% to 3.5% can shift the success rate by 10+ percentage points
- Distribution assumptions matter: the same plan can show 92% success with normal distributions and only 83% with fat-tail distributions — a meaningful difference that highlights the importance of realistic modeling
- It doesn't measure severity: a plan that fails in year 29 of a 30-year retirement and one that fails in year 10 both count equally as "failures"
Interactive chart: success-rate-by-withdrawal
Success rate vs. withdrawal rate across different distribution assumptions
Coming soon
Improving Your Success Rate
The most effective levers for increasing success rate, roughly in order of impact:
- Lower the withdrawal rate: the most direct lever — each 0.5% reduction typically adds 5-10 points of success
- Add guaranteed income: Social Security, pensions, and annuities reduce portfolio dependence
- Use dynamic spending: Guyton-Klinger or floor & ceiling strategies adapt to downturns
- Delay retirement: even 2-3 extra years of work shortens the drawdown period and grows the portfolio
- Optimize asset allocation: the right stock/bond mix balances growth against sequence risk
Success Rate in Retirement Lab
Retirement Lab calculates the success rate across all simulation iterations and displays it prominently alongside percentile bands showing the 10th through 90th percentile portfolio trajectories. With fat-tail modeling enabled (pro tier), the success rate reflects a more realistic distribution of extreme market events — giving you an honest assessment rather than a falsely reassuring one.
Frequently Asked Questions
- Is a 100% success rate necessary for retirement?
- No — and targeting 100% usually means spending far too conservatively. A 100% rate in a Monte Carlo simulation means your plan survived even the most extreme worst-case scenarios, which implies significant over-saving. Most financial planners target 80-90%, especially if you have flexibility to cut spending in bad years.
- What is a good success rate for retirement planning?
- Most planners consider 80-90% to be a reasonable target. Above 95% suggests you could likely spend more. Below 75% indicates meaningful risk. The right target depends on your spending flexibility — if you can cut expenses by 15-20% in a downturn, you can tolerate a lower baseline success rate.
- Why does my success rate change when I switch from normal to fat-tail distributions?
- Fat-tail distributions model extreme market events (crashes and booms) more frequently than a normal bell curve. Since retirement plans are more sensitive to crashes than booms (due to sequence-of-returns risk and withdrawals), adding fat tails typically reduces the success rate by 5-10 percentage points. This more realistic modeling reveals risk that a normal distribution hides.