Monte Carlo simulation is one of the most powerful tools for financial planning, yet it's often misunderstood or misapplied. The technique uses random sampling to model the range of possible outcomes, providing clients with a probability distribution of their financial future rather than a single point estimate.
The Basics of Monte Carlo
The approach is straightforward: generate thousands of possible future scenarios by randomly sampling from assumed return distributions, then calculate the probability of achieving various outcomes. Each simulation run represents one possible future; aggregate them to understand the range of possibilities.
Input Assumptions Matter
The quality of Monte Carlo outputs depends entirely on input assumptions:
- Expected returns should reflect current valuations, not just historical averages
- Volatility should account for regime changes and fat tails
- Correlations should reflect crisis-period behavior, not just calm periods
- Inflation assumptions significantly impact real purchasing power
Communicating Results
Raw probability outputs can be confusing. We've found effective ways to present results:
- Probability of success toward specific goals (rather than abstract percentages)
- Range of outcomes shown as a fan chart over time
- Stress scenarios showing worst-case paths that are still plausible
- Comparison of different strategy choices on the same probability basis
The goal is not to predict the future—it's to help clients make better decisions under uncertainty by understanding the range of possibilities.
Monte Carlo analysis reveals that small changes in savings rate often matter more than portfolio optimization. This insight helps clients focus on what they can control—their savings behavior—rather than obsessing over investment returns.