Why One Projection Can Mislead You in Retirement

A retirement plan built on an average return is usually wrong in the way that matters most: it ignores the order of gains and losses.

The problem with averages

The stock market has historically returned around 7-10% annually after inflation. But it doesn't return that every year. Some years it's up 30%. Some years it's down 40%. The average smooths all of that out.

Here's why that matters: imagine two scenarios, both with identical average returns.

Scenario A: You retire with $1 million. Markets drop 30% in year one. You withdraw $40,000 to live on. You now have $660,000 trying to recover.

Scenario B: You retire with $1 million. Markets rise 20% in year one. You withdraw $40,000. You have $1.16 million working for you.

Same average return over 30 years. Completely different outcomes. The total return over time could be identical in both cases, yet the outcomes diverge because withdrawals magnify early losses.

This is called sequence of returns risk, and it's one of the biggest threats to a retirement plan.

The range behind one number

Instead of assuming your portfolio grows at a steady rate, Monte Carlo simulations run your plan thousands of times with randomized returns. Some runs include strong early growth and weak later years. Others begin with a crash followed by recovery. Some are uneventful until a late-life downturn.

After thousands of runs, you can see what percentage of scenarios resulted in your money lasting. A 90% success rate means 1 in 10 paths fail, which is why it can still feel terrifying.

What the odds can and cannot tell you

Success rate is a summary, not a guarantee. Whether it's acceptable depends on your flexibility. Can you cut spending if markets tank? Work part-time? Delay Social Security?

The number alone isn't enough. Look at when failures happen and why. If most failures occur because of a crash in the first few years of retirement, that's a different risk than failures from living to 100.

The limits of simulation

Monte Carlo is not prediction. It is stress-testing. The simulations typically assume future market behavior will resemble historical patterns. That's a reasonable assumption, but not a guarantee.

What simulations do well:

• Show how sensitive your plan is to bad timing
• Reveal whether you have enough buffer for volatility
• Help you compare different strategies (more bonds vs. more stocks, different withdrawal rates)

What they don't do:

• Predict what will actually happen
• Account for truly unprecedented events
• Replace judgment about your own risk tolerance

Using this in practice

When reviewing a Monte Carlo analysis:

1. Don't fixate on the exact percentage. The difference between 89% and 92% success is rarely meaningful. The difference between 70% and 95% is.

2. Look at the failure scenarios. Do they involve circumstances you could adapt to? Or are they catastrophic?

3. Test your assumptions. What happens if you reduce spending by 10%? Delay retirement by 2 years? The flexibility in your plan often matters more than the starting success rate.

4. Rerun periodically. Your success probability changes as markets move and your timeline shortens. A plan that was 90% successful five years ago might look different now.

Pathwyze is built to show this with your own data. You can stress test a withdrawal rate and see the full spread of outcomes, not just a single projection.

See what this looks like with your numbers; start your plan.