Monte Carlo Insight Transcript
Hello, and welcome to today’s AdviserGo training.
Today, we’re going to review Voyant’s Monte Carlo Insight.
This is a helpful insight for stress testing your client’s plan. It allows you to understand how the plan may perform under both unfavorable and favorable market conditions.
Before running the Monte Carlo Insight, the plan needs to have at least one asset growing through an asset allocation. You can double-check this by clicking the Accounts Affected hyperlink.
This will show you which accounts are being grown through asset allocation and which accounts will be included in the simulation.
The reason asset allocation is needed is because the simulation uses the underlying market assumptions mapped to those asset allocations. These assumptions help provide the range of possible outcomes used in the Monte Carlo simulation.
You can select anywhere from 50 to 1,000 iterations for the simulation.
An iteration is a randomized projection applied to the plan. For example, if you select 50 iterations, the software will run 50 randomized outcomes for each year of the plan, based on the asset allocations outlined in the client’s portfolios.
For this example, we’ll go ahead and click Get Started to review the simulation results.
Now, let’s take a look at the chart at the top of the results screen.
The purple shading in the background of the chart represents the range of possible asset values based on the simulation results. You can compare this range against the asset values shown on the left-hand side of the chart.
The purple dotted line represents the average, or mean, return for each year.
From there, you can review the minimum and maximum projected outcomes. The minimum represents the lowest projected outcome, or worst-case scenario, for that year. The maximum represents the highest projected outcome, or best-case scenario, for that year based on the selected asset and portfolio mix.
As you move through the years, you can see how the range of outcomes changes over time.
If you notice a year where the minimum value drops out, that indicates there is at least one iteration where the plan has run out of assets based on a worst-case outcome.
Next, let’s review the Yearly Probability of Success.
In the early years of the plan, we can see a 100% probability of success. This means that in every simulation iteration, the clients are able to meet their expenses and goals. In other words, there is no projected outcome in those years where the clients do not have enough assets to cover their planned expenses and goals.
As we move into the later years, the probability of success begins to drop below 100%.
This means there are some iterations where the clients may not be able to fully meet their goals in those later years.
When we move all the way to the final year of the plan, we can see the overall probability of success shown here. In this example, the plan has a 48% probability of success.
This means that, based on the simulation, 48% of the projected outcomes successfully meet the client’s expenses and goals throughout the plan without a projected shortfall.
You can save these results so you can return to them later without needing to rerun the simulation.
You can also run a Monte Carlo report, which allows you to create a deliverable that can be presented to your client.
The Monte Carlo Insight is a valuable tool for discussing risk, market uncertainty, and the overall strength of a client’s plan. It can help show where a plan may be more vulnerable and where adjustments may be needed to improve the probability of success.
If you have any questions, or if you would like to walk through this insight one-on-one with a member of our Support team, you can email us at support@planwithvoyant.com.
You can also request support directly from within the client case by selecting the client name in the top-right corner, choosing Request Support, entering your question in the text box, and sharing client access.
Thanks for listening.