Monte Carlo Simulation Explained for Non-Quants
Published 2026-05-07
Monte Carlo simulation is a method for modeling uncertainty by running a large number of randomized scenarios and observing the distribution of outcomes, rather than relying on one fixed assumption. The name originates from the randomness inherent in casino games at Monte Carlo, referenced by physicists who pioneered the technique in the 1940s for modeling unpredictable physical processes.
Applied to investing, a Monte Carlo simulation doesn't ask 'what will my portfolio be worth if it grows at exactly 8% every year' — a static, unrealistic assumption. Instead, it asks the more honest question: 'if returns vary randomly year to year in a way consistent with historical volatility, what range of outcomes is plausible, and what fraction of those outcomes succeed at my goal?'
Each individual simulation run represents one possible future — one specific, randomly generated sequence of good years and bad years. Run enough of these (the Crypto Runway Calculator uses 1,000), and patterns emerge: a success rate (the percentage of runs that met the goal), a median outcome (the middle result), and percentile bands showing the range between likely best-case and worst-case scenarios.
The value of this approach over a single projected number is that it makes uncertainty visible and quantifiable, rather than hidden behind one falsely precise average. A financial plan that looks fine under an average-return assumption can reveal meaningful risk of failure once the same plan is tested against a realistic range of possible return sequences.
Frequently Asked Questions
Is Monte Carlo simulation guaranteed to be accurate?
No simulation predicts the future with certainty. Its value lies in illustrating a realistic range of possible outcomes based on historical or assumed volatility patterns, not in producing a guaranteed forecast.
Why 1,000 simulations specifically?
1,000 runs is generally enough to produce stable percentile estimates for this kind of planning tool while still computing quickly in a browser; larger institutional tools sometimes run 10,000 or more for additional statistical precision.