Behind the scenes, Monte Carlo calculations are quite complex. They’re based on inputting a number of factors that reflect your unique circumstances
If there’s one question on every investor’s mind, it goes something like this: “How am I doing so far?” This universal query is especially relevant when you’re making plans to retire in style, or pursuing other substantial financial goals.
To arrive at relatively reliable answers, we have Monte Carlo Analysis. Don’t let the casino-inspired name fool you. We employ this serious software to calculate the odds that any given financial plan will playout as hoped for, based on the likely range of plausible outcomes, from awful to amazing.
How Monte Carlo Analysis Works
Behind the scenes, Monte Carlo calculations are quite complex. They’re based on inputting a number of factors that reflect your unique circumstances, along with a few broad assumptions. We typically identify and input at least the following particulars:
· Net worth
· Sources of income
· Investment profile (expected rates of return, given your portfolio build)
· Debt load
· Spending goals
· Inflation rates
· Life expectancy
· Legacy goals
The software then runs hundreds or thousands of iterations that generate a broad range of the best, worst, and mostly in-between outcomes you might experience over the next few decades, given your circumstances and depending on how things play out in real life.
In other words, we cannot know what the future holds. But we can generate relatively reliable expectations for questions such as:
· What are the odds you’ll still be okay, even if worse comes to worst?
· How much better off will you be if you receive every lucky break that heads your way?
· How can you expect to fare as you encounter the usual mixed bag of good news and bad?
The results of your personalized Monte Carlo Analysis should provide you with a realistic picture to consider.
A Simple Monte Carlo Illustration
Let’s say you’re a single, 55-year-old male. Here’s where you’re at:
· You’re in good health, with at least an average life expectancy.
· You’ve got a $1 million investment portfolio in a “typical” 60/40 stock/bond mix.
· You’ve also got a 401(k) account at work that’s worth$100,000 from a former employer’s rollover. You plan to continue contributing$1,500/month, plus a 3% employer match.
· The rest of your $110,000 annual salary goes to living expenses and discretionary spending.
· You’ve got a home mortgage, with 10 years left before you pay it off.
Here’s what you’re considering (in today’s dollars, without adjusting for inflation):
· You’d like to retire at age 65.
· By then, you estimate your portfolio will be worth just over $1.5 million.
· You’re expecting to spend around $95,000/year in retirement – $27,000 from Social Security, and the rest from your investment portfolio and 401(k) account.
· By the time you pass away, you’d like to have a balance of around $100,000 left, to serve as a safety net and/or to allow for an inheritance.
After we input these and other assumptions, and let the Monte Carlo software run its iterations, we can present results that may look something like this (although, again, your own analysis outcome may vary widely from this general illustration):
If Your Life Expectancy Is …
If Your Portfolio(Stocks/Bonds) Is a …
Each table shows different odds of “succeeding” with any given financial plan. In this particular illustration, if we stick with the initial assumptions of living to around age 80 and maintaining a 60%/40% stock bond mix in your investment portfolio, you would have about an 87%chance of having at least$100,000 left at the end, as desired. This means there’s a 13% chance you won’t.
These tables can also give you an idea of what might happen if you spend more or less than planned, live a longer or shorter life, or invest more or less aggressively in your portfolio. For example, if you spend10% more and live to age 90, your success rate would drop to just 45%.
Are you comfortable with these odds? If so, we can plan accordingly. If not – perhaps your family has sturdy genes – we continue to tweak the assumptions until we’ve arrived at acceptable odds. For example, we could see what your analysis might look like if you decided to:
(1) Work longer before retiring.
(2) Spend less, now or in retirement.
(3) Alter your investment portfolio mix.
In an actual Monte Carlo Analysis, we cover a lot more ground – plus, we’re often running the analysis for a couple rather than an individual. Either way, beyond presenting a single “success or failure” percentage point, the analysis also provides a dollar range you’re expected to fall into. In our illustration above, “success” might be anywhere between $100,000 to several million dollars, with decreasing odds the further out you go. Likewise, “failure” doesn’t necessarily mean you’ll have nothing. It means you could be left with anywhere from $99,999 to $0. These insights can further contribute to creating a realistic financial plan for you and your goals
The Caveats: No Guarantees
As you might imagine, Monte Carlo Analysis depends heavily on the data. In other words, if we put garbage in – such as unrealistic spending goals or inaccurate portfolio balances – that’s exactly what we’ll get back out.
Time matters too. The further out we go, the more likely you’ll experience lucky breaks and bad outcomes not yet factored into the equation. Your own life may change, for better or worse. So might the markets, or inflation, or tax laws, or … you get the idea.
As such, Monte Carlo Analysis becomes ineffective if we try to peer more than 20–30 years out. Especially as you approach retirement, your analysis should be re-run regularly, to ensure your numbers remain realistic. Bottom line: The more time you have to adjust your plans if needed, the more likely you’ll be able to continue spending comfortably before and in retirement. without losing too much sleep over that all-important question – How am I doing so far?
 FORILLUSTRATION ONLY. To keep things simple here, we’ve omitted many important details we’d use in an actual Monte Carlo Analysis. Do NOT assume these are realistic results for your own analysis, even if your broad circumstances seem similar to those presented. Assumptions made may differ dramatically from your own.