Monte Carlo simulations are still being used by financial service people today, despite the fact that so-called “black swan” events have all but discredited them for retirement planning.
What is a Monte Carlo simulation? In, somewhat, simplistic terms, the Monte Carlo simulation, or calculation, is a statistical analysis that determines the probability of certain outcomes. The technique was first developed by Stanislaw Ulam, a mathematician who worked on the Manhattan Project. It has since been used in many fields, including engineering, supply chain, and science, as a tool to model or “guess” the likelihood of various scenarios.
In the financial planning industry, “Monte Carlo methods were first introduced to finance in 1964 by David B. Hertz through his Harvard Business Review article.” (1) The “Monte Carlo simulation is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. A Monte Carlo simulator helps one visualize most or all of the potential outcomes to have a better idea regarding the risk of a decision.” (2)
Monte Carlo simulations are named after the gambling hot-spot in Monaco, since chance and random outcomes are central to the modeling technique, much as they are to games like roulette, dice, and slot machines. (3) Interestingly, the method has even been used to model why the “house always wins” in gambling. (2)
By the late 1990s, some financial service firms, like T. Rowe Price Group, for example, had introduced Monte Carlo retirement planning tools aimed at individuals. By entering information like age, assets, contributions, investments, and allocations, the calculator ran the numbers for hundreds to thousands of potential market scenarios, guided by assumptions of returns, inflation, volatility, and other factors. Once they put in all the needed data, the tool would spit out a probable portfolio “success rate” based on the different market scenarios.
The trouble is, at the end of the day, it’s still just guessing.
The Monte Carlo simulation has turned out to be more of a marketing gimmick than anything else, and it can hurt people if they count on it for retirement.
We believe it’s hard to claim the markets are like a roulette wheel. The market is different. It’s not pure luck. It’s more like black jack where expert card counters are thrown out of casinos for winning too much. There’s a lot more that can go into proper investing if you do your due diligence and follow a strategy, and many people have made their fortunes in the stock market.
Nevertheless, market risk should be reduced to an absolute minimum when it comes to retirement planning. Some of our clients at Decker Retirement Planning don’t have any money in stocks or bonds in their retirement plan. They don’t need to, and there are other ways to do things.
The biggest problem with the Monte Carlo simulation being used for retirement planning became evident in 2008 when the markets experienced a black swan event and crashed. Many retirees were effected, and many lost their retirement funds.
In this case, the black swan event in 2008 was the housing market crash.
What does “black swan” mean? (4)
Financial professional turned writer, Naseem Nicholas Taleb, wrote The Black Swan after the 2008 stock market crash. He pointed out an interesting paradox and problem with statistical modeling in financial planning.
For most of history, swans were large birds known for their striking, white color. In fact, swans were thought of almost synonymously with the color white for centuries. That is, until seafaring explorers discovered black swans in the southern hemisphere of the New World.
Thinking about the discovery of black swans lead Taleb to think of an interesting logic problem—how many white swans would you have to see in order to predict the next one would be black? His answer: Does not compute. You didn’t even know they existed, so you would never predict one, no matter how many swans you saw.
You don’t know what you don’t know, and that was the origin of the black swan concept.
“The idea is something that is viewed as being so improbable as to be impossible, based on all available data and statistics we have. Yet, the truth is, it’s actually quite probable. We just didn’t know because we were only looking at a small subset of the world and not the universe of all possibilities.” (4)
Some “Black Swan” Events that Affected Markets
- The housing market crash of 2008
- The destruction of the Twin Towers in New York in 2001
- The Iraq invasion of Kuwait in 1990
- A new system for program traders that caused Black Monday in 1987
Random Examples of Events that Might Effect Markets but Cannot be Predicted
- The implosion of the internet
- Intercontinental smog, or global cloud cover due to meteor hit or seismic volcanic eruption
- The disappearance of the Arabian oil reserve
- Another Great Depression due to international debt
More Things the Monte Carlo Simulation Can’t Predict
One of the many issues the Monte Carlo doesn’t account for is running out of money in retirement due to longevity, which is an enormous issue. The point of retirement is being able to have the assets you need and want for the rest of your life.
Monte Carlo doesn’t plan anything out. It doesn’t account for retirement cash flow or how long your money might need to last. It guesses about investment risk in a “buy-and-hold” scenario more suitable for young people in accumulation mode.
We believe it gives a false sense of security to the retiree. With the “thousands of iterations” the Monte Carlo produces, it can lull investors into believing they’ve considered all of the possibilities and financial outcomes. This isn’t true. It’s a suggestion of generic outcomes.
If you are in or near retirement—we define “near retirement” as being within seven years of retirement—you must be able to start planning for your cash flow appropriately.
There’s a completely different way to do retirement planning.
“Buy-and-hold” and “diversified asset allocation” (aka stocks and bonds all at risk in the market) are things we hate in retirement. Improving the Monte Carlo simulation isn’t the answer. You’ll never be able to predict a black swan event.
The pie chart and the 4% Rule is a fail for retirement planning. You need a distribution plan for retirement. That’s what we do.
Retirement planning at Decker Retirement Planning is based on math. Not abstract algorithmic guesswork, but solid math that shows you how much money you can spend every month and year, up to age 100, without running out. Our retirement clients sailed through 2008 unaffected because most of their money for their first 20+ years of retirement wasn’t tied up in the market.
The retirement distribution plan takes into account your income streams such as pension, real estate, and Social Security (after we optimize your filing strategy based on your family’s unique situation) and calculates your maximum net income based on your assets. It’s not a one-size-fits-all situation. We take the time to do customized, in-depth distribution plans.
We don’t leave out taxation or planning for RMDs, which is huge. We don’t leave out inflation or cost-of-living increases. You will probably spend more money when you first retire, as we point out and factor in. We take the time to explain and plan for dozens of risks, including healthcare, possible long-term care, liability, and more. We even help you plan your estate, maximizing the tax-advantaged wealth transfer based on your wishes, and work with estate attorneys or your attorney to get the documents prepared.
We advise to stay away from the Monte Carlo, unless you’re on vacation. Don’t spend time or waste energy worrying about black swans; rather, develop a realistic retirement distribution plan with the help of a retirement fiduciary.
Sources
1 “Monte Carlo methods in finance,” Wikipedia.org https://en.wikipeidia.org/wiki/Monte_Carlo_methods_in_finance (accessed December 12, 2018).
2 “The house always wins: Monte Carlo Simulation,” Towardsdatascience.com https://towardsdatascience.com/the-house-always-wins-monte-carlo-simulation-eb82787da23 (accessed December 12, 2018).
3 “Monte Carlo Simulation,” Investopedia.com https://www.investopedia.com/terms/m/montecarlosimulation.asp (accessed December 12, 2018).
4 “Why Monte Carlo Analysis Still Matters And The Risk Of A Retiree Black Swan Is [Probably] Overrated,” Kitces.com https://www.kitces.com/blog/black-swan-explanation-what-is-a-black-swan-event-in-retirement/ (accessed December 12, 2018).
