The Problem with Average Returns
Every basic retirement calculator asks the same question: "What annual return do you expect?" You type 7%, hit calculate, and get a single number. That number is almost certainly wrong.
The issue isn't with 7% as a long-term average — historically, it's reasonable. The problem is that markets don't deliver 7% every year. They deliver +22% one year, -35% the next, +15% after that. The order of those returns matters enormously, especially in the years right before and after you retire.
This is called sequence-of-returns risk, and it's the single biggest blind spot in traditional retirement calculators. A 20% drop in year one of retirement is catastrophically different from a 20% drop in year twenty — even if the average over 30 years is identical.
What Monte Carlo Actually Does
Monte Carlo simulation solves this by running your retirement plan through hundreds or thousands of possible market histories. Each simulation randomly generates a sequence of annual returns based on statistical parameters — mean return, standard deviation (volatility), and correlation patterns.
In Sagery, we offer four distinct Monte Carlo models: (1) Standard Normal — classic bell curve distribution; (2) Fat-Tailed — accounts for extreme events that happen more often than normal statistics predict; (3) Regime-Switching — models distinct bull and bear market periods with different statistics for each; (4) Historical Bootstrap — resamples from actual historical market data.
Each model runs 1,000 simulations. That means 1,000 different possible futures for your money — some great, some terrible, most somewhere in between.
Reading the Results
The key output is your success rate: the percentage of simulations where you don't run out of money before your life expectancy. But raw success rate is just the start.
We also show you the distribution of outcomes — best case, worst case, median, 25th percentile, and 75th percentile. This gives you a range: "In most scenarios, your monthly income will be between $3,200 and $5,800 after taxes."
That range is infinitely more useful than a single number. It lets you plan for realistic outcomes, not just the average case.
Why This Changes Your Plan
When you see that your plan has an 87% success rate, you can make informed decisions. Is 87% enough? Some people want 95%+ and will save more or work longer. Others are comfortable at 80% with a flexible spending strategy.
Monte Carlo also reveals which levers matter most. Sometimes an extra $200/month in savings barely moves the needle. Other times, retiring one year later adds 15% to your success rate. You can't see these trade-offs with a single-number calculator.
This is exactly what Sagery's What-If Analysis is built for — adjust the sliders and watch your Monte Carlo results update in real time.
📌 Key Takeaways
- 1A single average return ignores sequence-of-returns risk — the #1 retirement planning blind spot
- 2Monte Carlo runs 1,000+ simulations to show a range of possible outcomes, not just one
- 3Success rate tells you the probability of not running out of money
- 4Different Monte Carlo models capture different types of market behavior
- 5The results help you see which changes to your plan matter most
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