Definition
Monte Carlo simulation runs thousands of iterations with randomly sampled inputs from defined probability distributions. Instead of a single point estimate, it produces a range of outcomes with associated probabilities. This is valuable for complex valuations, risk assessment, and portfolio analysis where multiple uncertain variables interact.
lightbulb Example
Running 10,000 DCF iterations with uncertain revenue growth (normally distributed, mean 8%, std 3%), margins, and WACC. Results show: 5th percentile = $35/share, median = $52/share, 95th percentile = $72/share. There's a 70% probability the stock is undervalued at $45.
verified_user Key Points
- Produces probability distribution of outcomes
- Accounts for multiple interacting uncertainties
- More sophisticated than deterministic sensitivity analysis
- Requires defined probability distributions for inputs