Monte Carlo Simulation
10,000+ Probabilistic Scenarios — From Point Estimate to Confidence Interval
Monte Carlo Simulation transforms the single point estimate of a DCF or market approach into a probability-weighted distribution of outcomes — revealing the true range of defensible values and the statistical confidence around the central estimate. In Equitest, 10,000+ iterations run across your key valuation assumptions to produce P10, P50, and P90 value ranges.
What Is Monte Carlo Simulation in Valuation?
Monte Carlo Simulation is a computational technique that runs a valuation model thousands of times — each time drawing random values for key assumptions from their specified probability distributions — and aggregates the results into a frequency distribution of possible outcomes. The technique was developed at Los Alamos during the Manhattan Project and has since become a standard tool in quantitative finance, risk management, and professional valuation.
In business valuation, Monte Carlo addresses the fundamental weakness of the single-scenario DCF: the result is only as reliable as the point estimates used for WACC, revenue growth, and terminal value — all of which carry genuine uncertainty. By replacing those point estimates with probability distributions and running 10,000+ iterations, Monte Carlo reveals the full range of outcomes and quantifies how sensitive the valuation conclusion is to assumption uncertainty.
The output is not a single number but a distribution: P10 (the 10th percentile outcome — a pessimistic but plausible result), P50 (the median), and P90 (the optimistic but plausible ceiling). This transforms a point estimate into a defensible, statistically grounded analytical conclusion.
The Output Distribution
10,000+ simulation runs produce a bell-shaped distribution of enterprise values. The shaded region represents the P10–P90 confidence interval.
How Equitest Runs Monte Carlo
Chapter 36 is a full probabilistic simulation engine — not a simple sensitivity table. It runs directly on top of the DCF model built in Chapters 20–24, requiring no re-entry of assumptions. Configure distributions, click run, and receive a complete probability-weighted output in seconds.
Discount Rate as a Probability Distribution
Rather than a single WACC point estimate, the analyst specifies a distribution: distribution type (normal or triangular), the central estimate pre-loaded from Chapter 20, and a standard deviation or min/max reflecting the uncertainty in beta, equity risk premium, and size premium. Each of the 10,000+ iterations draws a fresh WACC from this distribution — so the simulation naturally captures how discount rate uncertainty propagates through to Enterprise Value. The pre-loaded central value is always the Chapter 20 WACC; no re-entry required.
Projection-Period Growth Rate Distribution
The revenue growth rate across the explicit projection period is specified as a triangular distribution (min, most likely, max) or a normal distribution (mean, standard deviation). The central value is pre-loaded from the DCF growth assumptions in Chapter 22–23. Equitest applies the drawn growth rate uniformly across all projection years in each iteration — meaning the full FCF waterfall (revenue → EBITDA → NOPAT → FCF) re-computes for every single simulation run, not just the topline.
Exit Multiple or Terminal Growth Distribution
Terminal value is the single largest source of uncertainty in any DCF — typically 60–80% of total Enterprise Value. Equitest simulates it by drawing the exit EBITDA multiple (or terminal growth rate for Gordon Growth) from a distribution calibrated to the observable range of market multiples for comparable companies in the industry. The distribution bounds are pre-suggested from Equitest's comparable company database and can be overridden by the analyst. Because TV dominates the valuation, its distribution typically has the widest influence on the output histogram.
10,000+ Full DCF Re-Computations Per Run
Each iteration is a complete, independent re-run of the full DCF model: new WACC drawn, new growth rate drawn, new terminal multiple drawn → full FCF projection recomputed → all years discounted → terminal value computed and discounted → Enterprise Value derived → net debt subtracted → Equity Value recorded. 10,000 such iterations run in seconds, producing 10,000 distinct Equity Value outcomes. The three drawn variables are treated as independent in each iteration; Equitest documents this assumption in the compliance narrative.
Full Histogram + P10 / P25 / P50 / P75 / P90
The 10,000 Equity Value outcomes are sorted and aggregated into a frequency histogram rendered in the report. Summary statistics displayed: mean, median (P50), standard deviation, P10 (pessimistic floor), P25, P75, and P90 (optimistic ceiling). The report includes a written interpretation paragraph — auto-generated by Equitest — explaining what the distribution shape, spread, and skew imply about the reliability and defensibility of the central DCF estimate. A tight distribution signals high confidence; a wide or skewed distribution signals assumption sensitivity that warrants disclosure.
P10–P90 Range Feeds the Football Field Chart
The Monte Carlo P10–P90 confidence interval automatically populates the Football Field Chart in Chapter 35 as its own labeled bar — sitting alongside the point-estimate DCF range, market multiples, and comparable transaction ranges. This gives the appraiser and reader an immediate visual comparison: does the probabilistic distribution align with the market approach? If the Monte Carlo range is significantly wider than the market-method ranges, that divergence is analytically meaningful and is documented in the Chapter 35 reconciliation narrative.
