What Monte Carlo simulation does
Monte Carlo simulation generates a large number — often thousands — of randomized hypothetical return sequences, based on assumed statistical properties (average return, volatility) of an asset class, then reports what share of those random sequences succeeded. Its strength is exploring return sequences that never actually occurred historically, including scenarios worse or better than anything in the record.
As a hypothetical illustration: a Monte Carlo run might assume a 7% average annual return with 15% volatility, then generate 10,000 random 30-year paths consistent with those two numbers, and report that a given withdrawal plan succeeded in, say, 90% of them. None of those 10,000 paths is a real market history — they're statistically plausible variations built from the assumed inputs.
What historical backtesting does
Historical backtesting replays sequences of returns that actually happened, starting from every real historical month the available data supports. Its strength is being grounded in real, documented market behavior — including real crashes, real recoveries, and real correlations between asset classes, which Monte Carlo models often simplify or misstate, especially during crises when correlations tend to shift.
The same withdrawal plan tested historically instead reports something different: not “90% of random paths succeeded,” but “this plan survived starting from 44 of the 50 real historical months tested, and specifically failed starting from 2000, 2007, and 2008.” That's a smaller, less smooth set of outcomes, but every one of them is a real, nameable period.
Where each falls short
Monte Carlo's output is only as good as its assumed inputs — mean return, volatility, correlation. If those are miscalibrated, thousands of simulated paths just repeat the same bias thousands of times. Many implementations also assume returns follow a normal distribution, when real markets tend to have “fatter tails” — more frequent extreme moves than a bell curve suggests.
Historical backtesting has its own limits: it's confined to whatever periods actually occurred in the available data, so it can't show a crash worse than history's worst, and a relatively short data history for many individual tickers means a limited number of truly independent multi-decade windows to test.
Which one should inform a real plan
Neither is definitively better — many professional planning approaches use both. Historical backtesting is useful for grounding a plan in specific, real past periods and understanding exactly what happened during them. Monte Carlo is useful for exploring a wider hypothetical range, including scenarios that haven't occurred historically.
This tool implements the historical approach specifically — a deliberate design choice to test plans against real, documented market behavior, not a claim that Monte Carlo simulation is inferior.
Trying the historical approach here
Running a scenario takes no setup: pick a time period, build a portfolio from over 14,000 stocks and ETFs (or start from a preset), and set a withdrawal or contribution plan. Results update live, and the historical outcomes chart shows every tested starting month at once rather than a single simulated line.