Monte Carlo Simulation Model
Monte Carlo simulation is used to analyse to what extent the valuation of the chosen company is dependent on the assumptions. Monte Carlo simulation is based on artificially creating a chance process, running it many times and observing the result (Barreto & Howland, 2005). A deterministic model is created on the excel sheet for the calculation of valuation of the share of the company. A set of inputs are identified that are assumed to arrive at the valuation of the company. These inputs are varied and result of the model is evaluated. This way the model is run many times. Results are analysed using statistical methods like histograms, probability distribution, and summary statistics. Monte Carlo simulation takes into account already defined variables with their all possible values and iterate these values thousands of time to analyse all the possible results expected with the change in inputs.
Figure: Monte Carlo Simulation model
So in Monte Carlo simulation instead of fixed inputs a probability distribution is applied to some or all of the inputs which generates a probability distribution of the Output.
Analysis of the result of the Monte Carlo simulation is done to analyse how the valuation of the companies changes with the change in inputs. But this simulation requires that inputs like beta, growth rates are defined by a distribution. Distribution could be normal, triangular, binomial, lognormal, studentt, exponential etc. Triangular distribution is typically used where data is predicted subjectively and it is not possible to collect sufficient data for the population sample. This is based on a minimum, maximum and a subjective guess what is the most likely value the data can take.