Monte Carlo Simulation in Divorce
Pursuant to divorce, a Monte Carlo simulation can be a powerful tool to empower your clients to make educated decisions around maintenance, asset division, asset allocation, and lifestyle. With the right data, you can evaluate any number of scenarios.
How does a Monte Carlo simulation work?
Here is an excerpt from IBM:
Unlike a normal forecasting model, Monte Carlo simulation predicts a set of outcomes based on an estimated range of values versus a set of fixed input values. In other words, a Monte Carlo simulation builds a model of possible results by leveraging a probability distribution, such as a uniform or normal distribution, for any variable that has inherent uncertainty. It, then, recalculates the results over and over, each time using a different set of random numbers between the minimum and maximum values. In a typical Monte Carlo experiment, this exercise can be repeated thousands of times to produce a large number of likely outcomes.
Monte Carlo simulations are also utilized for long-term predictions due to their accuracy. As the number of inputs increase, the number of forecasts also grows, allowing you to project outcomes farther out in time with more accuracy. When a Monte Carlo simulation is complete, it yields a range of possible outcomes with the probability of each result occurring.
How does a Monte Carlo simulation work with divorce analysis?
A Monte Carlo simulation for divorce projects a set of outcomes based on estimated rates of return on investments to indicate the likely outcome of certain settlement options or client goals. A trial is successful if the client can fund all goals without running out of money before the end of their life. By varying the annual rate of return using standard deviation and normal distribution, the data can stress test more than 1,000 different stock market situations. The result shows the percentage of the 1,000 stock market situations that were successful, or the probability of success. The target is a probability of success between 80-95%, which we refer to as the confidence zone. Different scenarios (changes in data) are often run to analyze the impact on the probability of success.
In divorce, the scenarios often include one or more of the following:
- Varying the amount of maintenance payments.
- Varying the length of maintenance payments.
- Lump sum versus annual maintenance payments.
- Changing which assets are divided, and by how much.
- Life and disability insurance needs if the moneyed spouse passes away or is injured before all maintenance is paid.
- Adjustments to annual spending needs.
- Employment income.
Each situation is different and requires its own thoughtful analysis. While some of the data are assumptions on our part (life expectancy, projected rates of return, and standard deviation), others are facts collected from you or your client. A Monte Carlo simulation can be run on as little or as much data as you want to provide. The more data, and the more accurate the data, the closer the Monte Carlo mirrors real life. Necessary data includes the following information about your client:
- Gender (females tend to live longer than males).
- State of residence.
- If working, their salary and the age they hope to retire.
- Investment assets:
- Type of account (taxable, IRA, ROTH, 529, etc.); and
- Cost basis.
- Personal property that will be sold with proceeds divided.
- Budget or expected monthly or annual expenses.
- Income sources (salary, pension, rental income, etc.).
Case study: Female, age 60, in good health and living in Texas.
- Total assets: $62MM with cost basis of $31MM.
- Business assets: $27MM.
- Income: $2.5MM.
- Annual spend: $4MM.
- Goal: to continue her current lifestyle post-divorce.
Based on the period life table published by the Social Security Administration and the life tables for females contained in the National Vital Statistics Reports, Vol. 70, No. 1, published on March 11, 2021, a woman between the ages of 60 and 61 in the U.S. has an expected remaining lifespan of 25 years. We take a conservative approach and plan for a single female to live to age 91.
Our projected return on the assets is derived from the 10-year projections of leading firms in the industry and includes cash, equities, and fixed income. We use these projections to run Monte Carlo simulations across five asset allocation strategies ranging from income to growth-oriented portfolios. In total, we run 5,000 different market situations.
Since this is a post-settlement Monte Carlo simulation, we not only wanted to help our client understand her plan’s probability of success given her current situation, but also provide her a probability of success across different portfolios of varying risk and return to determine any adjustments that she may need to make to meet her goal.
Scenario 1 shows the Monte Carlo results (probability of success) for our Texas client:
Scenario 1 – Annual Spend of $4MM
|ASSET ALLOCATION||PROBABILITY OF SUCCESS||VALUE AT END OF PLAN|
|Income (most conservative)||57%||$0.00MM – $2.14MM|
|Conservative||76%||$0.89MM – $9.40MM|
|Balanced||87%||$5.96MM – $20.9MM|
|Moderate||90%||$11.9MM – $33.5MM|
|Growth (most aggressive)||92%||$18.2MM – $41.9 MM|
Based on the Monte Carlo simulation, she can feel confident she will be able to maintain her current lifestyle with her investment assets allocated in a balanced portfolio (50% equity and 50% fixed income). In a balanced portfolio, her plan is successful in 87% of trials, and when her plan ends at age 91, she may have between $5.96MM and $20.9MM left.
If she was risk averse, preferring minimal exposure to the stock market, then she may want to move to an income portfolio (20% equity, 80% fixed income). In an income portfolio, her plan is successful in 57% of trials, and when her plan ends at age 91, she may have between $0 and $2.14MM left. The probability of success in the income portfolio falls outside our confidence zone of 80-95%. If she was adamant about an income portfolio, then we would work with her to revise her saving, spending, and/or time until retirement to bring her within the confidence zone.
Knowing these probabilities of success empowers the client to choose a level of portfolio risk that is most comfortable for them, while also thinking ahead toward estate and legacy planning.
As demonstrated above, Monte Carlo simulations aim to remove some of the financial uncertainty of divorce. For a moneyed spouse, it helps them understand if they are still on track to retire or if they need to save more or work longer. For a non-moneyed spouse, it can ease the uncertainty of being able to maintain their lifestyle and address any changes to spending, income, or investment allocation that may be necessary.
In conclusion, it is imperative that the Monte Carlo simulation is utilized throughout the divorce process—not just once it is finalized. Running different scenarios throughout the process can bring to light advantageous ways to structure asset division. In some cases, acquiring income-producing assets will increase the probability of success. In others, lump sum payments may be the better solution. Monte Carlo can help make these differences clear. It will empower you and your clients to make informed financial decisions regarding maintenance, asset division, asset allocation, and lifestyle, and ensure a successful outcome.
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