Monte Carlo Simulation Calculator

A Monte Carlo simulation runs hundreds or thousands of randomized scenarios using your inputs (expected return, volatility, withdrawals) to model a range of possible futures. Instead of a single deterministic forecast, it shows you a distribution of outcomes — helping you understand the probability that your portfolio survives your retirement horizon.

Probability of success is the percentage of simulated scenarios in which your portfolio balance stayed above zero for the entire time horizon. A 90% success rate means 90 out of 100 random simulations ended with money remaining. Most financial planners target a 85–95% success rate for retirement planning.

A commonly used baseline for a diversified stock portfolio is a 7% mean return and 15% standard deviation (volatility). A 60/40 stock-bond blend might use around 5–6% return and 10–12% volatility. Conservative portfolios might use 4% return and 8% volatility. Adjust based on your actual asset allocation.

Each year, your annual withdrawal amount is increased by a randomly sampled inflation rate (drawn from a normal distribution around your specified mean and volatility). This models real-world purchasing power erosion and ensures your withdrawals in future years reflect actual living costs, not a fixed nominal figure.

For most purposes, 500 simulations gives a stable and reliable estimate of your success probability. Running 1,000–2,000 simulations reduces variance in the output further but takes longer to compute. 100 simulations is fast but can produce noisy results that vary significantly between runs.

The widely cited '4% rule' suggests withdrawing 4% of your initial portfolio per year, inflation-adjusted, gives a high probability of lasting 30 years. However, this rule was derived from historical US market data. With lower expected returns or longer horizons (e.g. 40+ years), a 3–3.5% withdrawal rate may be more appropriate.

This simplified simulator focuses on the core Monte Carlo mechanics — return randomness, inflation, and withdrawals. It does not model taxes, Social Security income, required minimum distributions, or variable spending strategies. For a comprehensive retirement plan, consult a financial advisor or use a specialized planning tool.

Monte Carlo simulations use pseudo-random number generation to model return and inflation variability. Each run samples a fresh set of random scenarios, so results will vary slightly between calculations — especially with fewer simulations. This variability is normal and reflects genuine uncertainty in future outcomes.