Monte Carlo Simulation
A computational technique that uses repeated random sampling to model the probability of different outcomes. In finance, it projects the range of possible portfolio values under thousands of simulated market scenarios.
A Monte Carlo simulation generates thousands (or millions) of possible future scenarios by randomly sampling from assumed return distributions. Rather than producing a single projected outcome, it provides a probability distribution of results, helping investors understand the range of what could happen.
How It Works in Investing
A typical Monte Carlo simulation for retirement planning might:
- Assume a distribution of annual returns based on historical data (e.g., mean 8%, standard deviation 15%)
- Simulate 10,000 possible paths of portfolio growth over 30 years
- Account for annual contributions, withdrawals, inflation, and taxes
- Report the probability of meeting a target (e.g., "85% chance your portfolio lasts through age 95")
Monte Carlo analysis is especially valuable because it captures the impact of sequence-of-returns risk—the danger that poor returns early in retirement can deplete a portfolio even if average returns are adequate. For a hands-on walkthrough, read our Monte Carlo simulation guide.