Retirement Planning: Monte Carlo vs. Linear Planning – The Edwardsville Intelligencer

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, 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).

The Monte Carlo simulation will usually show a summary of 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.

Linear Planning

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.

We like to meet in the middle by choosing a small subset of linear projections, most of which use conservative assumptions to depict a less-than-favorable environment. This mixes the clarity of linear planning with the range of outcomes shown by a Monte Carlo analysis.

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.

For example, we might choose to forego using historical rates of return in lieu of a lower future projected return. One reason is to hedge against the uncertainty of future returns.

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.

Most importantly, you’ll want to consider living expenses. Creating a budget can be helpful, but it’s also too easy to forget the expenses that don’t occur every month. Underestimating expenses, even by a little bit, can have a significant impact on the actual success of your retirement plan, so it’s best to round up.

Also, when looking at expenses, don’t forget about inflation. To hedge against inflation uncertainty, you may decide to overestimate, or at least using an average from the last 40 years (which is higher) instead of an average from the last 10 years (as mentioned above regarding Social Security).

Bottom Line

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. 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.

Investing involves the risk of loss and investors should be prepared to bear potential losses. Past performance may not be indicative of future results. No portion of the article shall be construed as a solicitation to buy or sell any specific security, investment product, or particular investment strategy. In addition, this article shall not constitute personalized investment, tax or legal advice. Information contained in this article may have been derived from third-party sources that CAWM believes to be reliable; however CAWM does not control such information and does not guarantee the accuracy or timeliness of such information and disclaims all liability for damages resulting from such sources