Monte Carlo: Take The Gamble Out Of Divorce Settlements

Your first thought when hearing the words “Monte Carlo” might be of the lively Las Vegas strip, a swift roll of dice, or colorful slot machines.

While that is one meaning (and albeit, maybe a more exciting one), “Monte Carlo” is also a tool frequently used in the financial industry. Specifically, the Monte Carlo we are talking about here is a statistical model that evaluates the impact that different inputs have on the result of a financial projection–i.e., how decisions you make today impact your finances decades into the future.

Robert Powell, CFP^®, Retirement Columnist on MarketWatch and entrepreneur, once notably said “Monte Carlo offers you the opportunity to be approximately right, rather than exactly wrong.” When the power of Monte Carlo is used effectively, it can be an invaluable tool in divorce. Given how little control anyone has in a divorce, almost everything often feels unpredictable so getting things directionally right in divorce is a huge success. Using Monte Carlo in divorce can give you peace of mind that you’ve negotiated a great settlement or confidence that the benefits of continuing to negotiate might outweigh the total price–the time, energy, and “life” costs in addition to the money paid.

Monte Carlo In General Financial Planning

If we break it down further, Monte Carlo in financial planning is a barometer which tells us how likely someone is to be able to achieve their goals without having to make adjustments.

To start, one must make some assumptions. The standard inputs include:

• Current age and estimated mortality
• State of residence for income tax purposes
• Desired retirement age
• Desired spending in retirement
• Investable assets and cost basis of taxable assets
• Current and future income

By way of example, let’s assume the following is true: a 55-year-old has $3 million of investable assets today, wants to spend $200,000 annually in retirement, and is inquiring about at what age they can retire and not run out of money.

You can run a “straight-line projection” where you assume that the investment portfolio will provide the same average rate of return each year and evaluate how many years of spending can be accomplished before running out of money. You can then back into the successful retirement age.

However, while a straight-line projection is a helpful tool, it has its pitfalls.

With a straight-line projection model, one must assume the market returns the average long-term rate of return each year. Any investor experiencing the market in 2022 knows that is not very reflective of how the market works. Some years your portfolio makes money and others it loses money—at least on paper.

Additionally, it is rare for an investor’s return to match the average. For instance, if we assume the average long-term rate of return for a portfolio comprised of 60% stocks and 40% bonds is 6%, the most likely scenario is that an investor would experience a variety of annual returns scattered around 6%, without seeing an annual return of 6% very often. It’s kind of like the weather. While the actual temperature on a given day is typically within a reasonable range of the historical average, it’s rare that the actual temperature is the historical average for that specific day, and there are years when the temperature is 20 degrees above or below the average.

Instead of assuming an average rate of return each year, like in the straight-line projection, Monte Carlo uses randomized annual returns based on a range of historical returns and risk to add in the reality of market volatility. That means when Monte Carlo is used, some years the portfolio return will exceed the annual average, and in some years, it will be less than the market average.

So, the straight-line projection might assume a 6% return every year for 35 years while the Monte Carlo will randomly generate different rates of return each year. For our 55-year-old, the first five years of the projection could look like this:

Straight Line:

Year 1: 6%

Year 2: 6%

Year 3: 6%

Year 4: 6%

Year 5: 6%

Monte Carlo:

Year 1: 11%

Year 2: –15%

Year 3: 7%

Year 4: 18%

Year 5: –3%

Another layer to Monte Carlo is that the projection is run many times (typically, one thousand) with randomized market returns and different timing of those returns included in each scenario.

This provides a “stress test” of the projection and the Monte Carlo simulation then produces a probability of success. Success can be defined differently, but most commonly success means having an 80% chance or higher that no adjustments would be needed to current spending or saving assumptions from now until estimated mortality.

Monte Carlo In Divorce Decision Making

In addition to Monte Carlo being a critical tool in retirement planning, it is also immensely helpful in evaluating divorce settlement proposals.

As anyone who has experience with divorce knows, the process is extremely difficult. In fact, the Holmes-Rahe Stress Scale names divorce the second highest stressor for humans, second only to the death of a spouse—and during a divorce, many people wish their spouse would die!

Throughout the process there are so many decisions that need to be made, it can be challenging for those going through it to feel like they are not making any missteps. Hence, when weighing crucial financial decisions that will impact the rest of their lives, most people have a hard time seeing the forest from the trees.

We refer to Monte Carlo simulations for those going through divorce as our SettleSmart™ analysis. SettleSmart™ can help provide clarity on what life can look like post-divorce. There are various ways to model scenarios, however, common complexities explored are:

• The financial impact of a proposed property split.
• Comparing a buyout versus annual support payments.
• Options in settlement of significant non-marital assets.
• Keeping the marital home.
• Affordability of purchasing or keeping a second home.
• Ability to provide discretionary support to young adult children.
• Determining a travel budget.
• Funding of college for a child or grandchild.
• Necessity for growth and risk in the investment portfolio.
• Most tax-efficient strategies for philanthropy and legacy goals.

The most common concern with Monte Carlo simulations is being able to verify or model realistic inputs. This ranges from reasonable portfolio returns to inputting realistic spending assumptions. Inputs can be subjective (which is okay!), however, it is important to use accurate, defensible assumptions to ensure the simulation is modeled as closely to reality as possible. Otherwise, as they say – garbage in, garbage out. It’s critical to work with a family law attorney and financial professional experienced in the use of Monte Carlo for divorce to ensure the assumptions used are reasonable in your case as well as in your future.

If you or someone you know is going through a divorce and would like to learn more about how our SettleSmart™ Monte Carlo analysis can help the decision making process, reach out to us at [email protected].

How will you make smart decisions with Monte Carlo to have confidence in settling or continuing your case?

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