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 will my money 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 it 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 back 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, life expectancy, etc. 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 near useless. These limitations include but are not limited to
- Monte Carlo assumes that markets are perfectly efficient, which 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.
- 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. If you introduce new variables such as a different risk level into the equation, you need to re-run the simulations.
- While investor behavior is a major issue in relying on Monte Carlo, the biggest issue in my opinion is 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 of 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. After all, a 95% chance of success is a lot more comforting than 60% right? In order to go from 60% to 95% you would likely be required you 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+ years but your goal was to have $1,000,000 at the end of your life but 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. The is known as magnitude of failure. The inability to distinguish between failing by $1 or $1,000,000 is the biggest limitation of Monte Carlo simulation confidence scores.
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. One way to improve your likelihood of success is to re-run the simulation every year, but that is of little help in determining whether or not your plan is likely to succeed from day 1. This article is intended to just scratch the surface with 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.
Frequently Asked Questions About Monte Carlo
1.What is a Monte Carlo simulation in retirement planning?
A Monte Carlo simulation is a financial modeling tool that runs thousands of potential market scenarios to estimate the likelihood that your retirement savings will last throughout your lifetime. It considers variables such as investment returns, inflation, taxes, life expectancy, and withdrawal rates to generate a probability of success.
2. Why shouldn't investors rely solely on Monte Carlo simulations?
While Monte Carlo simulations can provide valuable insights, they are based on assumptions and averages that may not reflect real-world conditions. They cannot accurately predict future market behavior, changes in investor decision-making, or unexpected life events that may impact a retirement plan.
3. What does a retirement plan "failure" mean in a Monte Carlo simulation?
In a Monte Carlo simulation, any scenario where a retiree falls short of a stated goal, even by a small amount, is considered a failure. The simulation does not distinguish between missing a goal by $1,000 versus $1 million, which can make results seem more concerning than they actually are. Reviewing the magnitude of potential shortfalls is just as important as looking at the overall confidence score.