Monte Carlo simulation (MCS) wasn’t always a common financial planning software tool. When it was included in the past, it was often limited to one or two variables. Today, even if the software applies MCS to a few more assumptions, it’s usually locked down which prevents the user from modifying existing parameters or applying MCS to other variables in the plan. This can be a major flaw, but if the output looks sleek, it is a flaw which receives very little discussion. In this post, we’ll discuss the use of MCS distributions in financial planning applications.
Monte Carlo simulation may or may not be the ultimate or optimal solution to financial forecasting. However, it is the best option I know about and is certainly more advantageous than deterministic or linear analysis. What makes MCS so beneficial is the ability to place a distribution on an uncertain input to model its variance. If this is unfamiliar territory, consider this. Assume you have an investment account with an expected return of 10% and a standard deviation of 20%. Using MCS on this assumption, the return will fluctuate by the degree of its standard deviation.
For example, the mean or average return is 10%, as indicated by the vertical line in the center of the distribution. The lines to the right and left of the mean represent one, two and three standard deviations on each side of the average. Therefore, the expectation is that two-thirds of the returns will fall between negative 10% and positive 30% (i.e., one standard deviation). Two standard deviations would include 95% of the expected returns (negative 30% to positive 50%). Three standard deviations encompass 99% of the expected results. One question should emerge immediately. Is this the best distribution to use? If not, is there a better distribution and how do you determine it?
When a client engages an advisor to create a financial plan, the planner needs the flexibility to select the best type of distribution for each assumption. There are several additional MCS distributions which can be used in a financial plan, including Triangular, Uniform, Lognormal, Beta, BetaPERT and Gamma.
Next week we’ll discuss distribution selection in financial planning and the situations where they might apply.
Until then, thanks for reading and have a nice week!
See Mike Patton’s previous blog on this topic, Using Monte Carlo in Financial Planning: The Assumptions