Monte Carlo Retirement Planning: How Simulation Actually Works

Historical backtesting replays your plan from every actual start year: what if you retired in 1929, in 1966, in 2000? It is concrete and interpretable, and it is how the 4% rule was originally derived. But 150 years of history contains only about five non-overlapping 30-year retirements. The overlapping windows are highly correlated — the 1965 and 1966 retirees lived through nearly identical markets — so the sample of truly independent historical experiments is tiny.

Backtesting also cannot answer the question you actually care about: what if the future recombines historical ingredients in a new order? A 1970s-style inflation shock arriving after a 2000s-style lost decade never happened in the US record — but nothing rules it out. Simulation exists to explore those recombinations systematically.

The most common Monte Carlo implementation draws each year's return independently from a normal distribution (or from the historical pool one year at a time). This destroys the multi-year structure of real markets: bear markets that persist, recoveries that follow crashes, inflation regimes that last a decade. Real market history has momentum over short horizons and mean-reversion over long ones.

For retirement planning this is not a technicality. Independent draws understate the probability of the exact thing that kills retirements — several bad years arriving together early on (sequence-of-returns risk). A simulator using independent annual draws will tell you a plan is safer than the historical record suggests, which is the wrong direction to be wrong in.

Block bootstrap fixes this by sampling multi-year blocks of actual history instead of single years. Our simulator draws blocks of 5 to 15 consecutive years (averaging around 10) from the historical record and chains them together into a synthetic future. Within each block, everything that happened together stays together: the crash and its recovery, the inflation and the rate response, the correlation between stocks and bonds in that regime.

The result is thousands of futures that never literally happened but are built entirely from market dynamics that did. We validated the block length choice against the variance structure of long-horizon historical returns — short blocks destroy too much persistence, very long blocks reduce the diversity of sampled futures — and the 5-15 year range preserves the multi-year risk structure that retirement outcomes depend on.

Even a perfect resampling method inherits the biases of its source data. US-only data encodes the survivorship of the single most successful market of the modern era. Our simulator therefore also offers pooled sampling from 16 developed countries (the Jordà-Schularick-Taylor macrohistory dataset, 1870 onward), drawing blocks from each country's history with equal probability. Pooled results are consistently more pessimistic than US-only results — that gap is a measure of how much a US-only plan is betting on American exceptionalism continuing.

We have also cross-validated the simulator itself with walk-forward testing: calibrate on data up to some cutoff year, simulate forward, and compare against what actually happened. Monte Carlo with block bootstrap comes out modestly conservative — its success-rate estimates ran a few percentage points below realized outcomes — while naive historical backtesting was overoptimistic in turbulent eras. For a retirement plan, a tool that errs slightly toward caution is the right default.

The headline number is the success rate: the share of simulated futures in which the portfolio outlasted the horizon. Treat it as a planning gauge, not a prophecy — 90% does not mean a guarantee, it means that in one simulated future out of ten, this plan needed a correction it didn't have. Look next at the percentile fan chart: the 10th-percentile trajectory shows what the bad-but-plausible version of your retirement looks like, and it is far more informative than the median.

Finally, watch how results respond to your inputs rather than fixating on any single run. If success collapses when the withdrawal rate rises by half a percent, your plan is on a cliff edge; if it barely moves, you have real margin. That sensitivity — not the point estimate — is what simulation is for.