Why Monte Carlo Simulations are Misleading
Monte Carlo (MC) simulations have been a tool used in the financial services industry for decades. Because I think MC testing is often used in a misleading manner….that means for decades consumers have more than likely been misled by advisors who use MC simulations in their sales process.
What is MC simulating? MC simulates the future investment return of the asset that is being tested (stock, mutual fund, EFT, etc.). For a full explanation of how MC simulations work, click here to read my 49-page Investment Risk White Paper. A user chooses the number of times the MC simulation will run. Most programs run at least 5,000 simulations. If you plotted them on a chart it might look something like the following:
After the simulations are completed, a software program creates one smooth looking line to illustrate what the “most likely outcome” would be for an investor based on the data from the 5,000 simulations. The smooth line in an MC simulation is typically the “95% probability” line.
Why I don’t like MC simulations-advisors love to use the smooth looking MC simulated line and then tell clients it’s the “95% probability” line. What the smooth line ignores is that many of the 5,000 simulations had bad outcomes.
When we put together the OnPointe Risk Analyzer, we use MC simulations in their traditional manner, but we also selectively pick out one positive and one negative (above and below the smooth 95% probability line) simulation out of the 5,000 run so advisors can show clients what could happened in the “real world.”
Let’s look at an example from the OnPointe Risk Analyzer Software.
Assume the following:
-Initial amount to invest: $10,000
-Annual contributions to investment account: $10,000
-Age to begin withdrawals: 65
-Amount to withdraw annually: $75,000
I am not going to take into account taxes (income or capital gain), money management fees, or just about any other variable you can think of. I’m going to run a simple example using a 60/40 mix of stocks and bonds for comparison.
Let’s first look at the classic smooth lines. I say lines because OnPointe has three lines, not one.
-The blue line illustrates the 50th percentile value.
-The green line is the 75th percentile value.
-The red line is the 25th percentile value.
While most of the industry only shows clients the blue line, with OnPointe we wanted to remind advisors and their clients that they could get any one of the colored lines.
While we think the OnPointe Risk Analyzer with the three smooth lines is already doing more than any other program in the market to show what can happen in the real world, it’s the next chart that I really like.
The next chart not only has three different lines, but you’ll instantly notice that they are not smooth. Why not smooth? Because money does not grow or decline in smooth lines. Most investments don’t grow or decline at a linear rate of return. They go up and down in very unpredictable ways.
Every time you run the MC simulator using the OnPointe software, it will randomly illustrate three different lines.
Why show the squiggly lines in outputs that will be given to clients? To illustrate that no advisor can predict what will happen and that clients not only needs to understand this, but they need to plan for randomness (especially negative randomness like the 2000-2002 crash, the 2007-2009 crash, and what happened at the end of 2018).
Summary on Monte Carlo
Monte Carlo simulations can be a useful tool in the financial services industry. The problem is that the results may not always be used correctly by advisors. Selling off one smooth 95% probability line may be the easiest way to make a sale, but it’s setting the advisor up for issues if and when the market doesn’t perform similar to the line.
When you use a software program that has not only the 95% probability line but also at least a lower line showing the likelihood that things might not turn out as well as planned, it’s helpful to temper the client’s expectations.
Additionally, when you use software that also shows a squiggly line showing a few individual Monte Carlo simulations, you are furthering the point that money doesn’t grow in a straight line (and that the client may want to plan for a less than 95% probability return over time).
If you would like to learn more about the OnPointe Risk Analyzer program and how you can use it’s unique MC simulator (which can be done on a client’s actual portfolio), click on the following link: https://onpointeriskanalyzer.com.
Roccy DeFrancesco, JD