Joining Advicent as a partner support specialist in 2013, Alex provided high-quality support for partners within NaviPlan and Profiles. In 2015, he took that product knowledge to the learning development team as a technical writer, creating detailed documentation for partners with a practical focus on what they need to know. Alex received a BSBA from Drake University in Marketing/Advertising Creative in 2013.
When building a financial plan there can be concerns about the accuracy of the outputs if you are using static return rates. Plans with static return rates always assume the same percentage of growth year after year and could be viewed as unrealistic. To support this issue, NaviPlan provides advisors access to Monte Carlo sensitivity analysis.
What is Monte Carlo?
Monte Carlo analysis is a simulation tool that you can use to determine the effect of market and longevity risks on a completed plan. This analysis allows you to take your current plan's static returns, apply standard deviation, and see how likely it is that your plan will meet its needs.
If the Monte Carlo module is active, Monte Carlo analysis can be analyzed within the Quick Actions drop-down.
How Monte Carlo works
Monte Carlo analysis works by taking established static return rates for each account and factoring in their standard deviations. Monte Carlo analysis will assign each account a random return rate within the standard deviation of each account and then repeats this process for every year of the plan. Any return rate change will be accounted for and factored in for calculation.
Once complete, the run will look at the static need that was created in the plan. If the random return rates yield a net surplus, the plan is a success. If the plan yields a net loss, the plan is a failure. This calculation is then repeated for as many iterations as you have requested the Monte Carlo analysis to run (the default is 500).
After all of the trials are completed, the Monte Carlo analysis will then give a percentage of how likely it is that the plan will succeed. High percentages of success mean a higher likelihood of meeting retirement or other goal needs.
Potential concerns with Monte Carlo
As Monte Carlo is a probability simulation there may be some questions about the analysis. Common issues may be why results are zero despite high or 100% coverage in the main plan, or why the analysis isn't 100% despite having more than enough funds in the static plan. To assess this, a few things must be considered.
First, the base plans should have goal coverages of 100% in order for Monte Carlo analysis to give any result above 0%. This is due to the fact that while the main plan may be working, that is only because it is in a static environment. The results are not flexible enough to absorb any variance that the Monte Carlo puts on it, resulting in unsuccessful Monte Carlo simulations.
Secondly, a plan does not need a perfect Monte Carlo score of 100% in order to be considered successful. A plan with a 90 to 95% success rate may just be as effective. Monte Carlo merely presents the likelihood of meeting, at a minimum, 100% of goal needs. It does not show a percentage of how much a goal can be covered if variance has been placed on it.