How to Effectively Use Monte Carlo

How to Effectively Use Monte Carlo
The MoneyGuidePro (MGP) Monte Carlo analysis shows the client how small changes can have a big impact on their chances
of reaching their goals. By varying the annual return for the portfolio (based upon standard deviation), the Monte Carlo
analysis shows the likelihood a client’s Plan would be successful. The Monte Carlo simulation is most useful as a “big picture”
illustration of the probability of success of a Plan.
Monte Carlo simulation results are shown on the Results page and What If Worksheet with, if you choose, the results using
Average Returns and results from Stress Tests. Play Zone also uses the Monte Carlo simulation. If you select SmartCalc as the
calculation method on the What If Worksheet, your results will include Average Returns, Bad Timing (with two years of bad
returns beginning at retirement), and the Monte Carlo simulation results.
Results of the MGP Monte Carlo Analysis
The MGP Monte Carlo analysis uses the advanced “Beyond Monte Carlo ” technique, which provides the equivalent of 10,000
iterations of the Plan. The success percentage displayed is an estimate of the percentage of successful iterations (defined as all
goals funded at the level specified).
Beyond Monte Carlo is a statistical analysis technique using Sensitivity Simulations, which reruns the Plan, making small
changes to the return. The results are analyzed to determine the probability of fully funding all the goals in the Plan.
The MGP Monte Carlo analysis results in a Probability of Success from “Less than 40%” to “99%”, in 1% increments. The highest
Probability of Success is 99%, because there is never a guarantee clients will reach all their financial goals. Because the analysis
counts missing any goal by even one dollar as a failure, small adjustments can have significant effects on a Plan’s Probability of
Monte Carlo results are displayed on a Monte Carlo Meter divided into three segments -- Below Confidence Zone, In
Confidence Zone, and Above Confidence Zone. There is a default set of Confidence Zones. For all plans including a Retirement
Goal, the default Confidence Zones are based on the clients’ ages. The default Confidence Zone for all Plans without a
Retirement Goal is a single range. If you want to change these defaults, you can do so in the Financial Goal Options section of
User Options. If you want to adjust the Confidence Zone for a specific Plan, you can do so on the What If Worksheet.
Even though selecting a more aggressive portfolio (i.e., a portfolio with more stock) will usually increase the Safety Margin
when using Average Returns, it will not necessarily increase the Probability of Success in the Monte Carlo analysis. The
additional volatility associated with the more aggressive portfolio might be more detrimental to the Plan than the benefit
provided by the higher return. You can try different scenarios to see the impact on your clients’ specific Plans.
The Monte Carlo analysis varies the investment return from year to year; it does not vary the Retirement Income amounts.
Clients whose Plan is mostly satisfied each year by using only their annual Retirement Income will not have much (if any)
variation in Monte Carlo results when the investment return is adjusted.
Portfolios Used in Calculations
If you select the Current scenario, MGP uses your current portfolio allocation to calculate the portfolio’s average return and
standard deviation. These are used to generate the simulation results. When using historical returns, if you enter your own
Total Return or Inflation Rate (in the Return and Inflation Override section of the Current Allocation page) or include Return
Adjustments for the Current Portfolio, MGP uses the Standard Deviation, calculated from the Current Portfolio allocation, and
the returns you entered (or selected), including Return Adjustments, to calculate Monte Carlo results.
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How to Effectively Use Monte Carlo
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If you select a What If scenario, and then select a Model Portfolio in the Hypothetical Average Rate of Return section on the
What If Worksheet, MGP uses the data from that portfolio (the average return and standard deviation, incorporating any
Return Adjustments) as the basis for calculations in the simulation.
If you selected “Enter Your Own” return in a What If scenario, MGP generates an unconstrained, optimized portfolio with a real
return equal to the real return calculated in the What If scenario. The average return and standard deviation from that
optimized portfolio are used for the simulation calculations. Because an unconstrained optimized portfolio has the lowest
standard deviation for the specified return, the simulation results when using this portfolio will be more favorable than the
results for other portfolios with the same return.
As an alternative to entering your own returns, you can select a portfolio (or use an Alternative Portfolio) and use the Total
Return Adjustment. You can enter an Alternative Portfolio with your recommended allocation for the client on the Target Band
page in the Asset Allocation section of the Financial Goal Plan. After you create an Alternative Portfolio, you can select it in the
Hypothetical Rate of Return section of the What If Worksheet. This enables the simulation results to reflect the clients’ target
portfolio, rather than reflecting an unconstrained, optimized portfolio.
How to Most Effectively Use the Monte Carlo Analysis with Clients – General Information
This section provides general information about effective use of the MGP Monte Carlo analysis. The next section discusses
how to most effectively use the Monte Carlo analysis with clients in various situations.
Using SuperSolve
When the Probability of Success is lower than the clients’ comfort level, it is useful to see how adjusting the goal
expense amounts, additional savings, and retirement ages impacts the Monte Carlo Result. There is not a single
Probability of Success percentage right for all clients. It depends on their willingness to accept risk, their age, and the
nature of their goals. The risk of missing some less important goals might not be critical. Generally at younger ages, a
lower Probability of Success might be fine. At older ages and in retirement, clients might only be comfortable with a
higher Probability of Success.
Using SuperSolve, you can specify a Monte Carlo Probability of Success as the “solve target,” and SuperSolve will
attempt to provide a solution that meets the Target. The default Probability of Success is the middle of the Monte
Carlo Confidence Zone for the What If scenario being solved. You can adjust the Solve Target if desired.
SuperSolve displays the calculated Probability of Success, and indicates the solution is less than the Solve Target. Or,
you can use the Advanced Solve features on the SuperSolve Start page to adjust the Goal Values or Extra Savings
Amounts, to see if a solution is possible with lower expenses and/or more savings.
SuperSolve uses the Willingness indicators to determine how much to vary each parameter in relation to all other
parameters. For example, if the clients are only Slightly Willing to retire later, but are Very Willing to save more
money, SuperSolve will increase savings at a greater rate than it increases the retirement ages. Any SuperSolve
solution can be saved as the Recommended scenario.
Using the What If Worksheet to Make Adjustments
In general, it is necessary for the plan to be successful using average returns to be successful in the Monte Carlo
Analysis. Larger Safety Margins using average returns generally mean better results in the Monte Carlo analysis.
Analogously, an unsuccessful plan using average returns will not be successful in the Monte Carlo analysis. If a Plan is
not successful using average returns, the Probability of Success for the Monte Carlo will usually be less than 40%. You
can experiment with the timing and expense amounts for goals, or with additional savings. By creating the clients’
goals in sufficient detail, it is possible to illustrate lower importance goals are the ones at risk of being under-funded
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or unfunded. For example, there is a huge difference in client anxiety over reducing travel expenses or giving fewer
gifts versus running out of money for basic living expenses.
How to Most Effectively Use the Monte Carlo Analysis with Clients – Specific Situations
Employed Clients Many Years from Retirement
When clients have many years until retirement, the Monte Carlo analysis is often useful as an educational tool to
demonstrate how variations in returns can dramatically affect a Plan. Clients who are retiring several years in the
future often have a Plan spanning 40 or 50 years. Even with a substantial Safety Margin when using average returns,
the Monte Carlo Probability of Success might indicate considerable uncertainty of achieving their desired Results,
even when they have a substantial Safety Margin. Showing clients how their Results might change by decreasing their
goal expenses will help to illustrate the real-world options available. At younger ages, the Monte Carlo analysis is
useful to see if the clients are “in the right ballpark.”
Employed Clients Near Retirement OR Clients Who Are Retired
When clients are near retirement or already retired, the effectiveness of the Monte Carlo analysis will be much greater
if you have entered the clients’ financial goals in detail. Rather than combining all their expenses into a single
Retirement Goal, separate their goals into Basic Living Expenses and a series of discretionary goals. Then, you will be
able to demonstrate how a Plan’s Probability of Success is affected by adjusting the amounts for specific discretionary
goals. It is much more meaningful to show which goals are at risk. It also provides the opportunity to review the
importance of the goals. Once you have confirmed the importance of the goals, the Monte Carlo analysis allows you
to show which goals are at risk, and more importantly, the inverse – which goals are not at risk. Not being able to
spend as much on travel, or not having as much money for bequests, is very different than not having enough money
for basic living expenses.
By using multiple What If scenarios, you can compare various solutions, each with its corresponding Probability of
Success. This helps the clients understand their options and to see the impact of their choices.
Plans without a Retirement Goal
When you’ve created a Plan without a Retirement Goal, such as one for funding College, the default for the Monte
Carlo Confidence Zone is a single range. You can adjust the Confidence Zone, if needed, on the What If Worksheet
for any specific Plan.
Other User Guides of Interest
“Configuring MGP – Financial Goal Options” – shows you how to select whether Monte Carlo are enabled, by default,
for new plans.
“What If Worksheet Overview” - shows you how to select whether Monte Carlo is used on the What if Worksheet and
Results page.
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