Valuing Equity in Start-ups 2: Using Monte Carlo Simulation

Monte Carlo simulation in traditional capital budgeting use repeated random sampling from probability distributions of crucial primary variables underlying cash flows to arrive at output distributions or risk profiles of probable cash flows in the project for a given management strategy. Simulation attempts to imitate a real world decision setting by using a mathematical model (consisting of operating equations or identities) to capture the important functioning characteristics of the project as it evolves through time encountering random events, conditional on management's preset operating strategy.

A Monte Carlo simulation usually follows these steps :
Modeling the project through a set of mathematical equations and identities for all the important primary variables, including a description of interdependencies among different variables and across different time periods.

Specifying probability distributions for each of the crucial variables, either subjectively or from past empirical data.

A random sample is then drawn (using a computer random number generator) from the probability distribution of each of the important primary variables enabling (with the help of the modelling equations and identities) the calculation of net cash flows for each period.

The process is repeated many times, each time storing the resulting cash flow sample observations so that finally a probability distribution for the project's cash flows can be generated (along with its expected value, standard deviation and other statistics).

Thus simulation in capital budgeting is useful in assessing the probability distribution of cash flows, from which the expected value of cash flows and the appropriate risk adjusted discount rates can be determined and used to derive a single value expected valuation.