Financial Planning in Monte Carlo
In my last blog post, I talked about the importance of having a financial plan that gets you from point A to point B. And, at a minimum point B includes your fundamental goals of a desired retirement age and standard of living. A sound plan provides you a high degree of confidence (or, high probability of success) in execution. Developing this confidence is the basis of this post.
First, how do you get from point A to point B in your plan? Generally, you save and invest during your working years to create a portfolio that will fund your living expenses and goals in retirement. A plan assumes a certain level of savings and, just as importantly, a constant investment return that grows those savings. Simple enough, but is a constant rate of return realistic? Not at all, obviously, if you follow the stock market just a little bit.
While returns have historically increased over long time periods, in reality they are not predictable from year-to-year (you can read more about this here). For example, let’s look at the one-year returns for the S&P 500 for three randomly chosen five-year periods:
Years Sequence of Returns (rounded to nearest percentage point)
· 1970 – 1974 +4%, +14%, +19%, -15%, -27%
· 1990 – 1994 -3%, +31%, +8%, +10%, +1%
· 2005 – 2009 +5%, +16%, +6%, -37%, +27%
Source: Dimensional Fund Advisors “Matrix Book 2018”
Not exactly constant returns! Stock market returns are essentially random in any one-year period. The risk posed by this random up-and-down pattern of returns is referred to as sequence of returns risk and can impact your financial plan in various ways (both negatively and positively). Ongoing withdrawals and bad early returns in retirement could result in a depleted portfolio before the end of your life. As advisors, one of our priorities is to focus on understanding the potential likelihood of negative planning outcomes associated with this sequence of returns risk.
How do we do that? When we develop a financial plan, one tool we use is called Monte Carlo simulation which simulates thousands of random sequences of investment returns over the term of the plan. The result of running these simulations is a set of potential projected planning outcomes. From that distribution of outcomes, we can develop an estimated confidence level of a financial plan being successful (i.e., not running out of money). For example, if 10% of the simulations resulted in your plan running out of money, you have a 90% probability of success – high enough to implement the plan.
What if our client’s plan has a low probability of success? In this case, we collaborate to restructure the plan until we achieve a satisfactory probability of success. Options include prioritizing and modifying financial goals, increasing savings and changing the strategic investment allocation. The plan is not a good plan until we have high degree of confidence in it.
Now, it’s important to keep in mind that, as with all financial modeling techniques, Monte Carlo modeling has plenty of limitations. However, if you are aware of those limitations and can interpret the results within the context of those limitations and the overall financial planning model, Monte Carlo modeling can be an indispensable tool in evaluating the strength of your plan.
So, I’ll pose this question to you: what is the probability of success of your financial plan? If you don’t know, let’s begin the conversation.