Investors who are nearing retirement all want to know one thing: Will my money last as long as I do? This is the question that causes pre-retirees a lot of sleepless nights. Not knowing the answer to this question also causes many pre-retirees to delay retirement out of fear of possibly running out of money at an age where most people can do little to fix the problem. I don’t think too many folks in their 90s want to go back to work after being retired for a few decades. The reason that this question can be so difficult to answer is due to the fact that there are so many possible random factors that contribute to the final outcome.
Trying to answer the question of whether money will last is what drives many pre-retirees to seek financial advice from professionals. In an attempt to help these pre-retirees, many advisors use software to help determine the likelihood of having a “successful” retirement. While there are literally dozens of different types of software to help assess the viability of your retirement, the most common metric used to gauge success or failure is known as a Monte Carlo simulation. Monte Carlo simulations are certainly not limited to the world of finance, but they can be very helpful in trying to manage all of the possible outcomes relating to market returns and withdrawal rates. The idea is that they can test all of the possible outcomes to give you a percentage likelihood that you will have a successful outcome of your retirement. There are simply too many variables, such as taxes, inflation, market returns, and life expectancy, to try to estimate what the outcome of your retirement might be. This is what Monte Carlo simulations in retirement try to calculate for you. I am simplifying how Monte Carlo works here because if I didn’t, you’d all fall asleep before the end of this article.
A “confidence score” is what the Monte Carlo simulations are trying to predict for a retiree. A retiree might, for example, have a score of 85% confidence that their plan will work out the way they hoped. So far, Monte Carlo simulations sound pretty good, right? Here is where the issues start. While Monte Carlo can account for a whole host of different variables, there are a few major ones that it cannot account for, and they can be so large as to render the data nearly useless.
These limitations include, but are not limited to:
1. Monte Carlo assumes that markets are perfectly efficient.
As we all know, they are not. It assumes an expected return for different asset classes, etc. We all know that these returns are averages, not constants.
2. Investor behavior cannot be accounted for in Monte Carlo simulations.
This is a major issue. Is it realistic to think that an investor will not panic and sell if the markets crash? History tells us that the majority of investors will, if not panic, at least they will reduce risk in the portfolio.
3. Magnitude of failure
While investor behavior is a major issue in relying on Monte Carlo, the biggest issue, in my opinion, is the magnitude of failure. If I told you that you had a 60% chance of having a successful outcome, would you be confident in your plan? Probably not. Most people try to achieve as high a confidence score as possible. In order for most investors to feel confident, they like to see a score well into the 90-95% range. In order to go from 60% to 95%, you would likely be required to make significant changes to your goals. Here is where I feel Monte Carlo falls short, and it is the reason I do not use it in my retirement projections. What if I told you that of the 40% of the time your plan “failed,” it did so by less than $1,000, 90% of the time? To put it another way, let’s say you lived exactly the way you wanted to live in retirement for 30-plus years, but your goal was to have $1,000,000 at the end of your life. However, in reality, you only had $999,000. Would you say that your retirement plan failed? No, of course not. It’s only $1,000 over your long retirement after all. The problem is that any failure, even by $1, is considered a failure under a Monte Carlo simulation.
While Monte Carlo can be designed to account for any number of variables, the reality is that the specific software used to calculate your likelihood of a successful retirement was designed with a specific set of parameters.
This article is intended to just scratch the surface of what Monte Carlo can and cannot do. It’s more important that you take any retirement confidence score with a grain of salt because it is highly likely to be inaccurate.
Securities offered through Kestra Investment Services, LLC (Kestra IS), member FINRA/SIPC. Investment advisory services offered through Kestra Advisory Services, LLC (Kestra AS), an affiliate of Kestra IS. Reich Asset Management, LLC is not affiliated with Kestra IS or Kestra AS. The opinions expressed in this commentary are those of the author and may not necessarily reflect those held by Kestra Investment Services, LLC or Kestra Advisory Services, LLC. This is for general information only and is not intended to provide specific investment advice or recommendations for any individual. It is suggested that you consult your financial professional, attorney, or tax advisor with regard to your individual situation. To view form CRS visit https://bit.ly/KF-Disclosures.
