Both the Monte Carlo and the Historic insights are designed to stress test plans using variable future returns. Both bring variability to future returns based on the upside-downside range of possible returns set when you choose an asset allocation for an account and both simulate future ups and downs in the market. However, while the Historic simulation uses a selected period of past market data to model a recurring market cycle of your own choosing and shows the resulting plan when this cycle is applied to future returns, the Monte Carlo simulation completely randomizes future returns over a set number of iterations and shows as a percentage the number of times the plan was “successful” – i.e. the number of runs (iterations) in which the client never ran out of money.
The Monte Carlo insight includes an Asset Chart view, which illustrates the range of randomized returns that were applied when running the insight.
The mouseover chart details show additional yearly figures for the Monte Carlo insight’s iterative runs including the yearly average total value of all invested assets (illiquid assets and trusts are excluded), maximum and minimum totals of invested assets during these runs, and average cumulative account withdrawals and contributions up to and including the selected year of the plan.
Use the drop down menu top-right to switch between the Assets view and the Yearly Probability of Success view, which shows, year by year, the percentage of times a given year was found to have no shortfalls when running this randomized insight.
Since AdviserGo also offers the concept of planning around goals, the Monte Carlo simulation shows not only the overall plan’s percentage chance of success but also the percentage of runs in which the various goals in the plan were achieved. Some goals may have a greater likelihood of being realized based on their timing while other often longer term goals may have a lower percentage chance of success due to iterations in which the assets in the plan were depleted before the goal could be fully achieved. The percentages shown for goals indicate the number of iteration in which the client could fully afford the goal.
The Monte Carlo simulations allows an adjustable number of iterations to be run, from 50 up to 1000 iterations.
Asset Allocations Must be Used to Run the Simulation
To randomize returns, the Monte Carlo insight (as well as the Historic simulation) must have a range of returns to work within. This range is only established when you set an account to be grown using a portfolio - i.e. an asset allocation.
Investment and retirement/pension accounts (and savings accounts, if desired) can be grown using either simple, 'fixed' growth rates, or asset allocations (model portfolios). Only accounts to which an asset allocation is being applied will come into play when running the Monte Carlo or Historic insights.
To run either insight, a plan must have at least one investment or pension-retirement account using an asset allocation to determine the rate of investment growth. Why? Because the assumptions that underpin the asset allocation allow for a range of possible returns, in accordance with the 'probability distribution'. The assumed investment return for any given asset class will range along a spectrum from the extremely good to the extremely poor.
If a large proportion of the accounts have been assigned a pre-determined 'fixed growth' rate, the output from the Monte Carlo will tend towards either 0%, or 100% depending on whether the client’s goals can be met at this fixed growth rate. In using fixed growth rates, we have nullified the element of chance that was the reason for utilizing the Monte Carlo simulation in the first place.
Key Takeaway: The Monte Carlo is intended for use in situations where most (or all) of one’s investment, and/or pension accounts are utilizing an asset allocation.
The Monte Carlo and Historic insights both have a convenient link that shows the number of accounts in the plan that are set to be grown using asset allocations. Click this link to view which accounts will be tested by the insight.
On this screen you can select additional accounts, applying asset allocation to them and bringing them into the purview of the Monte Carlo simulation. Additional options are available at the bottom of the screen to apply asset allocation to all investment-retirement accounts and even to apply a 100% cash allocation to all cash accounts (savings). Cash accounts are usually excluded from the Monte Carlo simulation.
If all the accounts in a plan are set to be grown using fixed growth rates, you will be prompted with this screen to activate asset allocation for at least one account before running the Monte Carlo or Historic simulation.
Please Note – Activating asset allocation for an account will activate this method for determining growth rate not only for the run of the insight but in the plan itself. If you want to temporarily select asset allocation for an account but later switch back to a fixed growth rate (not that this would be a valid test of the plan considering its growth assumptions are normally set to assume otherwise), be sure to visit the Accounts Affected link and deselect the account once you are finished running the insight. Deselecting the account will revert its growth settings to fixed growth rates.
Also, if you selected either of the “Grow all accounts using asset allocation” options at the bottom of this screen, toggling this setting off will revert all accounts to their original settings, either using fixed growth rates or asset allocations.
Read more about the Monte Carlo simulation here.
The Monte Carlo simulation will now incorporate variable inflation if the assumptions for inflation are added to the market assumptions.
The Inflation class allows the simulation to model a variable inflation rate that can be used to grow the EXPENSES in a Monte Carlo simulation in a variable manner.
If an expense is set to grow at the preference inflation rate, then in a Monte Carlo simulation with the INFLATION asset class, then these expenses will grow in a given year based on the return rate of the INFLATION class.
For more information on how to customize your assumptions.