Financial planning software comes in a variety of packages. Some are more granular or detailed than others and some are more comprehensive. Also, most plans today utilize Monte Carlo simulation (MCS) to account for the uncertainty inherent in many assumptions. In this post, we’ll discuss the two assumptions on which it is most important to incorporate MCS.

The Most Important Assumptions

As any planner will agree, a thorough and comprehensive financial plan requires a number of inputs. Some inputs also have a greater influence on the results than others but all are necessary to create a thorough analysis of a client’s financial situation. However, amid the myriad of assumptions required to create the plan, there are two assumptions which are most significant. In other words, it’s more important to use MCS on these two assumptions than it is on others. They are investment returns and expenses. 

Before proceeding we should define these terms: risk and uncertainty. We’ll define risk as the possibility of an undesirable outcome, while uncertainty is that something unknown may happen, good or bad. Where the first is a negative outcome, the second is simply not knowing if the outcome will be good or bad. When I first set out to determine the assumptions on which incorporating MCS is most important, I adhered to the following process: 

Step 1: Input all assumptions 

Step 2:  Add MCS on all assumptions which contain uncertainty

Step 3:  Run the simulation 

Step 4:  Save the results 

Yes, this is pretty simple, but it is the basis for making this determination. Next, I removed MCS from one assumption, ran another simulation and saved the results. I then added MCS back to that assumption, removed the MCS from a different assumption, ran another simulation and saved the results.

After following this process for each assumption which contained MCS, I ended up with multiple scenarios and could compare the impact that MCS (and its absence) had on the analysis. This exercise confirmed that not only are some assumptions more significant than others, but that utilizing MCS on certain assumptions was more meaningful than it was on others. 

Next week we’ll continue this discussion with a detailed list of the most common assumptions I use in the planning process. From there we’ll discuss the role of Monte Carlo simulation in the planning process, how many trials are needed to create the most accurate result and a host of other issues. Ironically, some of these other issues lead planners to convey an overly optimistic picture to the client by understating risk and, sadly, the planner doesn’t even realize it! 

Until then, have a great week and thanks for reading!