Monte Carlo DCF Model
Run thousands of DCF simulations with randomized growth, margins, and discount rates to produce a probability distribution of fair values instead of a single point estimate.
Overview
What is a Monte Carlo DCF?
A Monte Carlo DCF combines traditional discounted cash flow analysis with probability distributions. Instead of a single point estimate, it runs thousands of simulations with randomized inputs to produce a distribution of possible fair values — giving you a probability-weighted view of intrinsic value.
Quantitative analysts and risk managers use Monte Carlo simulation to stress-test valuations. PE firms use it to model downside scenarios. Advanced students use it to stand out in interviews by demonstrating probabilistic thinking.
Features
What you get with this model
10,000 simulation iterations with visual distribution
Stochastic revenue growth, EBITDA margin, WACC, and terminal growth
Percentile analysis (10th, 25th, 50th, 75th, 90th)
Histogram with percentile markers
Reproducible results via seeded random number generator
Use cases
How to use this model
Risk analysis: what's the probability the stock is undervalued?
Stress testing: model worst-case and best-case scenarios
Interview differentiator: show you understand uncertainty in valuation
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