Stress-test your withdrawal plan against multiple scenarios

Run your plan against the worst historical periods — not just the average — before retiring.

Why it works

Most people intuitively plan for average outcomes. But retirement sustainability is a tail-risk problem: the average return scenario is fine; it is the low-return, high-inflation decades (like the 1966-1982 period) that kill fixed-withdrawal plans. Deliberately modeling worst-case scenarios forces the brain to engage the real distribution of outcomes rather than anchoring on the favorable average, which is how good plans are stress-tested.

How to do it

1. Identify the three worst 30-year market periods in US history (roughly 1929, 1937, 1966 starts).
2. Model your withdrawal plan against each: what is your portfolio balance at year 20 and 30?
3. Define what specific spending cuts you would make if year-5 portfolio is down 30% from day one.
4. Write the contingency plan before you retire so it is not improvised under emotional pressure.

Evidence

Scenario analysis and Monte Carlo simulation are standard tools in retirement planning; their value is well-established in demonstrating that averages are misleading for long-horizon sequential-withdrawal problems. (mechanistic)

Historical scenarios do not capture unprecedented future conditions (e.g., prolonged deflation, currency crisis). Monte Carlo relies on assumed return distributions that may understate tail risk.

Common mistake

Running only the Monte Carlo average case and reading "95% success rate" as personal safety — the 5% failure scenarios are specific historical periods, not random noise.