Monte Carlo Simulation (Valuation)
Monte Carlo Simulation applies random sampling across probability distributions for key input variables to generate thousands of possible valuation outcomes, producing a full probability distribution of enterprise value or share price rather than a single point estimate.
Named after the famous Monaco casino, Monte Carlo Simulation replaces fixed inputs with probability distributions and then runs the model thousands or tens of thousands of times, each time randomly drawing values from those distributions. The aggregated outputs form a distribution showing not just the range of possible values but the probability of each outcome — far richer information than a standard sensitivity table.
In a valuation context, the analyst first identifies the key uncertain inputs: revenue growth rates, EBITDA margins, WACC, terminal growth rate, and capital intensity. Rather than assigning each input a single value, the analyst assigns a distribution — for example, revenue growth is normally distributed with a mean of 5% and a standard deviation of 3%; EBITDA margin is triangularly distributed with a minimum of 18%, most likely 22%, and maximum of 26%. The simulation engine draws randomly from each distribution, computes the DCF value, records the result, and repeats thousands of times.
The output is a histogram and cumulative probability curve for enterprise value or per-share price. An analyst might report: 'Under our Monte Carlo simulation, there is a 50% probability that the intrinsic value falls between $45 and $65 per share, and a 10% probability that it exceeds $80.' This language is more intellectually honest than stating 'our DCF implies a price target of $55.'
Monte Carlo is particularly valuable for US companies with high uncertainty — development-stage biotechs, early-stage technology companies, commodity producers facing volatile prices, or leveraged businesses sensitive to the business cycle. It forces analysts to be explicit about their uncertainty rather than burying it in a single base-case assumption. Financial technology tools like Crystal Ball and @Risk have made Monte Carlo accessible in Microsoft Excel, and Python libraries like NumPy make large-scale simulations trivial.
One caution: the output quality depends entirely on the quality of the input distributions. If the assumed correlations between variables are wrong — for example, ignoring that in a recession both revenue falls and WACC rises simultaneously — the simulation will produce an incorrect picture of risk. Correlation matrices should be specified for variables that are economically linked, particularly in macro-sensitive sectors like US banking, energy, or homebuilding.