Monte Carlo Simulation (FI Planning)

A statistical method that runs thousands of simulated retirement scenarios with random return sequences to estimate the probability your plan succeeds.

What it actually means

In the FI/retirement planning context, Monte Carlo simulation runs your portfolio through 5,000-10,000 random return sequences (sampled from historical market data or modeled distributions) to compute the percentage of those sequences where you do not run out of money. A "90% success rate" means 9,000 of 10,000 simulated paths kept the portfolio above zero through your plan duration. The method captures sequence-of-returns risk that simple "expected return" calculators miss.

Distinguishing it from look-alikes

Monte Carlo success rates are NOT real-world probabilities. They're probabilities under the model's assumptions (return distribution, correlation, inflation, fees). Five places it diverges from reality: tail-risk underestimation, regime-change ignoring, behavioral abandonment of the plan, fee/tax modeling simplifications, and Social Security policy uncertainty. P10 (10th percentile) outcome is more decision-relevant than the median (P50).

Examples

"90% success rate" Monte Carlo result

9,000 of 10,000 simulations kept portfolio > $0 across the plan horizon

P10 vs P50

P10 = bottom 10% outcome ($X portfolio at end). P50 = median. P10 should drive risk-tolerance decisions.

HELM's Monte Carlo

10K simulations using Box-Muller normal sampling, with sequence-of-returns risk explicitly modeled