Retirement Planning: Monte Carlo vs. Linear Planning
Retirement planning often includes making assumptions on future unknowns. To avoid negative outcomes down the road, it’s best to understand a range of possible outcomes using either Monte Carlo simulations or a series of linear projections.
Planning for retirement is both an art and a science. There are many factors that need to be considered throughout the process, but the biggest and most impactful of them all is time. If you are too optimistic on certain assumptions, the effect of compounding and time can create a significant shortfall between what you thought your portfolio would look like and what it actually becomes.
That is why it is crucial to “under promise and overdeliver” when creating your retirement plan.
This can be done by using a Monte Carlo simulation or a series of linear projections. Both have pros and cons, so let’s explore the differences.
Are Monte Carlo Simulations Best?
A common tool used by financial planners for the purposes of retirement planning is a Monte Carlo simulation. A Monte Carlo simulation, as defined by Investopedia.com, is a model used to predict the probability of different outcomes when the intervention of random variables is present (i.e. variables like rate of return, life expectancy, inflation, and others relevant to retirement planning).
Monte Carlo simulations are like taking a trip back to statistics class with means (averages), standard deviations, and probabilities. They rely on historical data to produce a normal distribution curve, which is used to provide retiree hopefuls with a range of possible retirement outcomes. How many outcomes can a Monte Carlo provide? Typically, it’s around 10,000 possible scenarios!
The Monte Carlo simulation will usually show the summary of these 10,000 scenarios in the form of a normal distribution curve, but some financial planning software programs will translate the results into a probability score. For example, the analysis may show there is a 75% probability of success, meaning that 25% of the scenarios failed. In the retirement planning world, this means that 25% of scenarios resulted in the prospective retiree running out of money while still living.
Because of the vast amount of information presented, and because the average person is not an expert in statistics, Monte Carlo simulations can be difficult to translate data into real, simple, and understandable terms. This doesn’t mean that Monte Carlo simulations are not effective, but just that they can be overly complex.
An alternative approach to Monte Carlo simulation is to simply show a linear simulation. This means that instead of providing a range of assumptions (as with a Monte Carlo), there is one set of assumptions used. Instead of a range of outcomes shown, there is one outcome.
The simplicity of a linear projection makes the results much easier to understand. However, the challenge is that it’s highly unlikely the assumptions used will end up being accurate. Therefore, the criticism of the linear projection is that it will almost certainly be “wrong.”
Which One is Better? Neither
Personally, I like that a Monte Carlo simulation gives a range of outcomes, but I also like the clarity that a linear projection provides. People appreciate and value clarity and direction on their financial plan. They don’t value and appreciate hearing that they have a 75% chance of success in retirement. It’s not comforting or too helpful to know they could either live to age 100 with $4 million or could run out of money at age 90 (an example of a Monte Carlo output).
I get it. The argument for Monte Carlo simulations would be that both scenarios mentioned above are possible, and that may be true. But, I think a better way to provide a range of outcomes is to look at a handful of linear scenarios instead of 10,000 Monte Carlo scenarios.
When using linear projections, we believe in using conservative assumptions to offset some of the uncertainties of the future. If a Monte Carlo shows 10,000 scenarios, we believe in choosing one of the less desirable scenarios as the “base plan.” Why? Because if the plan “works” in an undesirable environment, we know it will work in an average or above average environment.
Using Conservative Assumptions for Linear Planning
When choosing assumptions, you also must be careful about going too conservative, which could end up hindering spending ability or willingness to spend in retirement. We look at historical data to help us choose a conservative, but not quite “apocalyptic,” assumption.
For example, we might choose to forego using historical rates of return in lieu of a lower projected return. One reason is to hedge against the uncertainty of future returns. Another reason is that we actually expect returns to be lower in the future due to the current and expected future interest rate environment. If historical returns for a particular allocation mix show 7%, we might use 5%.
For Social Security, the average cost-of-living-adjustment (COLA) increase from 1975-2021 has been 3.7%. However, the average over the last ten years (2012-2021) was only 1.9%. Instead of assuming 3.7% or even 1.9%, one might use a 1% or 1.5% COLA assumption for Social Security.
We also get the question “Will Social Security be there at all in the future?” According to the most recent Social Security Trustees Report summary, the Old-Age and Survivors Insurance (OASI) Trust Fund will be able to pay scheduled benefits until 2033, at which time the fund’s reserves will become depleted. Tax income after 2033 would only be sufficient to pay 76% of scheduled benefits. So, you could also assume only 75% of your scheduled benefit to account for that possible shortfall.
Life expectancy is another major factor to consider. Prior to the COVID-19 pandemic, the average life expectancy for an American was 78.8 years. Now, it sits at 77.3 years. However, Americans who have attained age 65 can expect to live much longer (83 for males and 85 for females), according to statista.com.
So how long should you plan to live for financial reasons? The answer varies for each person because health should be a major consideration. For a healthy individual, age 100 might be a conservative life expectancy to use. After all, if you plan for your portfolio to last until age 100, then you’ll be okay financially if you only live to age 90.
Last, but not least, are living expenses. Estimating an accurate amount for living expenses might be the most crucial part of retirement planning. Creating a budget can be helpful, but it’s also to easy to forget expenses that don’t occur every month. I usually recommend “budgeting backwards,” meaning to account for each dollar spent for the last 12 months. This should provide an accurate estimate of what your current lifestyle costs per year.
Otherwise, it can be helpful to take what you think you’ll spend and round up. Also, don’t forget about inflation. To hedge against inflation uncertainty, you may decide to overdo it, or at least using an average from the last 40 years (which is higher) instead of an average from the last 10 years.
The best data in the world is worthless if it cannot be understood by those that need to understand it. Monte Carlo simulations provide good data, but they are based on a range of assumptions and they provide a range of outcomes. Linear projections narrow in on one set of each. Using a handful of linear assumptions can help bridge the gap between the 10,000 scenarios produced by Monte Carlo simulations and the one scenario produced by a linear projection.
The most important thing to remember in either case is that assumptions are best implemented in a conservative fashion. It can be exciting to see what your portfolio might be worth if you obtain a 10% annual return, but what happens if you only earn 6%? Plan for the worst and hope for the best. It is also recommended to speak with a CERTIFIED FINANCIAL PLANNER™ professional before making any major retirement decisions.