Monte Carlo Simulation Shows Risk That Cash Settlements May Run Out
How often is a tort claim settlement taken in cash because a financial planner or trust officer, using a hypothetical fixed rate of return, demonstrates how the current lump sum will provide sufficient funds to meet all the future needs of the client? Too often. A technique called Monte Carlo simulation can show that the static spreadsheet model is inadequate to imitate real-life probabilities, and that the investment plan may not be as secure as it is represented to be.
Traditionally, investment advisors have relied on historic average rates of return for a particular investment mix to solve for the lump sum required to generate the cash needed by an individual at appropriate times in the future for such items as income replacement, ongoing medical care, college funds, and retirement. The problem with this assumption is that the timing and uncertainty of an investment portfolio’s future performance will not follow a consistent pattern based on historic averages, and the ability to receive those cash flows is far from being certain. If, for example, a claimant has $1 million to invest after fees, costs, expenses, liens and immediate cash needs are deducted from a settlement, and a 12 percent average annual rate of return is assumed, a fixed-rate projection might show that sufficient funds will be available.
But, that is not how it works in the real world. If probable year-to-year fluctuations in investment return rates are considered, along with other risks such as timing and mortality, a more realistic probability analysis might show that $1 million at an assumed 12 percent return might achieve the desired results only 60 percent of the time. In other words, a fixed-rate spreadsheet model would fail 40 percent of the time to meet the needs of the claimant.
If the full lump sum is invested at the beginning of a downward trend for the investment mix selected, it may take years to recover just to reach the beginning value. If distributions are being made from the invested funds, it would take even longer to recover. So, timing is a critical factor in the probability of an investment plan’s success. If the investment plan assumes that funds will be withdrawn over the person’s normal life expectancy, and that person lives 10 or 20 years longer, the claimant will outlive his or her income. To increase the probability that future cash needs will be met, you need to start with a larger initial lump sum. And, that likely will not be an option.
Monte Carlo simulation is a mathematical technique for solving equations. It uses random numbers and probability statistics to investigate problems. Its methods are used in everything from economics to nuclear physics. In fact, its first notable use was in the Manhattan Project that developed the atomic bomb. This simulation technique was named for Monte Carlo in the principality of Monaco, where the major attractions are its casinos that house various games of chance – roulette wheels, dice, cards and slot machines – all exhibiting random behavior. The Monte Carlo technique selects variable values at random to simulate a model, just as games of chance are made up of several variables within ranges.
For example, when you roll a die, you know the number on top will be one through six. You just do not know which number will come up on a particular roll. With Monte Carlo simulation, you know that a rate of return for a particular year will be within a certain range, and that the probability of achieving a particular rate is different for every percentage. You also know that a person’s life expectancy can range from death today to a finite number of years now somewhat over 100. But, the probability for each additional year of life is different from the rest. A person’s expectation of living to age 75 is much greater than living to age 100. But, anything within the range of probability can happen.
With the use of a spreadsheet, the models are deterministic. That is, the inputs are fixed with one value being assigned to one cell representing one variable. You can see only one solution at a time. If you want to view alternative results, you can change the input values in the model. When there are several variables, the number of possibilities approaches infinity. Monte Carlo simulation is a procedure of using a large number of trials performed on a computer using a set of random variables. The variables are selected in random combinations within the parameters defined by the program. The model incorporates investment risk, timing risk and mortality risk. Each trial simulates a possible outcome. If the computer runs 10,000 trials, it can project the percentage of times that the desired results are achieved or exceeded. It can also project the percentage of times the investment plan will fail. It the computer runs 20,000 trials, the projections will be even more accurate. Some of the popular computer software programs available for Monte Carlo simulation are Crystal Ball by Decisioneering, Money Tree Suite, Cheshire Financial Planning Suite, AASim, @Risk by Palisade, Naviplan and Financial Profiles.
Compare an investment plan using a cash lump sum from a settlement with the use of a guaranteed fixed annuity purchased with the same lump sum to provide a series of payments. This arrangement of several payments over time instead of a single lump sum is called a structured settlement. With the purchase of an annuity, the probability of achieving the financial objective is always 100 percent because the life insurance company issuing the annuity assumes all of the risk. When an annuity’s payment streams are quoted for a specific cost, that is what the annuitant will receive. What you see is what you get—nothing is left to chance. It does not matter if the investment market does not achieve what the life insurance company’s investment managers project, because the company’s reserves will be used to make up any deficiency. If an annuitant is guaranteed a minimum number of payments, there is no need to worry whether these payments will be made. They will. If lifetime payments are promised, perhaps on top of a minimum guarantee period, the payments will be made for the annuitant’s life, even if that person lives 10, 20 or even 30 years beyond a normal life expectancy. Life insurance companies are in the business of assuming risk, whether it is timing, investment or mortality risk, and that relieves the annuitant of the uncertainties caused by it.
If the periodic payments are on account of a personal physical injury or physical sickness within the meaning of section 104(a)(2) of the Internal Revenue Code, they are completely excluded from taxable gross income, including all of the buildup inside the annuity over the years. If the annuity’s guaranteed return seems unimpressive in comparison with an investment advisor’s lure of the prospect of higher annual yields, remember that most investments that have the potential of higher rewards are also subject to having those gains taxed as income. Considering that the prospect of higher rewards also means the downside prospect of lower yields and even loss of principal, the guaranteed tax-free yield of a single premium fixed annuity will look good in comparison if all factors are considered.
Financial advisors who recommend against a structured settlement, saying it is not in their client’s best interest because the client needs liquidity for unforeseen events, says Professor Joseph W. Tombs of Texas Tech University’s Department of Family Financial Planning and a certified financial planner. They say their client can take more risk to get a higher return over a long-time horizon, and that it is impossible to tailor a structure to the client’s unique goals, Tombs adds. But, financial advisors—a term encompassing financial planners, stock brokers, trust officers, CPAs and insurance agents—might really be recommending against structuring for other, unspecified reasons, says Tombs. “Structured settlements are uncomplicated, leaving less need for the financial planner,” Tombs believes, “which reflects insecurity on the part of the advisor.”
Other reasons that financial advisors recommend against structured settlements, according to Tombs, are that they leave less funds for fee-based management or to spend on products for which they will receive a commission. “Financial advisors may also be uneducated and unfamiliar with the advantages of a structure, including its guarantees and unique tax advantages not available to other investors. Sometimes a structure really is not in the best interest of the client,” he adds, “but those instances are rare.”
“Cash is attractive to the claimant,” Tombs explains, “because it gets the client ‘in the game’ with the lure of a higher expected return, a potential to get rich, more flexibility, and the dignity of wealth.” With a structured settlement, the funding asset to provide the future payments cannot be owned by the individual if tax-free growth is to be realized. A cash lump sum is at the disposal of the claimant and often is dissipated due to temptations and bad investments or loans to family members. A structured settlement eliminates those risks.
“The Monte Carlo technique is rapidly maturing as a duty for financial planners,” Tombs says, “and neglecting to present a risk simulation borders on malpractice.”
Monte Carlo simulation may not be offered every time a claimant is considering a cash lump sum. But, in every instance where there is risk of market timing, risk of investment, risk of living past normal life expectancy, and other variable factors, it is virtually certain that the probability of meeting or exceeding financial projections is less than 100 percent. The corollary is always that the remaining percentage represents the probability that there will not be enough money.