What Is a Monte Carlo Simulation?
A Monte Carlo simulation replaces fixed assumptions with probability distributions and then recalculates the model over and over, sampling a fresh set of inputs on every run. Instead of asserting that revenue will grow 8%, the analyst specifies that growth is normally distributed around 8% with a standard deviation of 3%, and the simulation explores the consequences across 10,000 or more trials.
The output is a distribution of results, from which the analyst can read the mean, the median, and tail percentiles such as the 5th and 95th. The name comes from the casino district of Monaco, a nod to the randomness at the method's core, and the approach dates to nuclear weapons research at Los Alamos in the 1940s.
How the Simulation Works
The analyst first identifies the uncertain drivers, assigns each a distribution such as normal, lognormal, triangular, or uniform, and specifies correlations between them so that related variables move together, for example commodity prices and input costs. Software then draws a random value for every input, computes the model output, records it, and repeats. With enough iterations the recorded outputs trace the true distribution of possible outcomes.
A simple valuation example: if a DCF's fair value estimate depends on uncertain growth, margins, and discount rate, a simulation might show a mean value of $52 per share with a 90% confidence interval of $31 to $78, and a 22% probability that fair value sits below today's $40 price. That probability statement is something a static model cannot produce.
Where Finance Uses It
Monte Carlo methods price path-dependent derivatives such as Asian and barrier options where closed-form solutions like Black-Scholes fall short, and they underpin value-at-risk calculations on trading desks. Corporate finance teams use them to evaluate large capital projects, actuaries use them for insurance reserves, and wealth managers use them to estimate the probability that a retirement portfolio survives 30 years of withdrawals under volatile returns.
In banking and private equity the technique appears less in day-to-day deal models, which favor scenarios and sensitivities, and more in specialized contexts like contingent consideration valuations and stock-based compensation with market conditions. Understanding when a distribution of outcomes beats a point estimate, and what a 5th-percentile result implies for downside risk, signals real analytical maturity in interviews.
