How a Monte Carlo analysis could help improve your retirement plan

When it comes to market performance over a retirement that could last 30 years or more, it is impossible to know exactly what the future will hold. When financial advisors seek answers to these kinds of forward‑looking questions, they often turn to planning tools that use probability modeling, such as a Monte Carlo analysis. This type of analysis is particularly helpful when it comes to individuals, as well, for retirement planning.

A Monte Carlo analysis is a technique that simulates a range of possible outcomes for an uncertain event. In the context of financial planning, the analysis helps to test an individual retirement plan’s viability against a range of market environments and investment outcomes. The results of Monte Carlo simulations are then expressed as a percentage of scenarios (from 0 to 99) where there was money remaining at the end of the retirement horizon. For instance, a Monte Carlo score of 80 means that 80% of the test simulations resulted in $1 or more at the end of the period, while 20% of the simulations ran out of money.

By comparison, other types of retirement planning might assume a static annual rate of return (for example, 7%) for the entire duration of the planning period. While an average rate of return is an important assumption, that approach assumes that market returns go up every year and does not consider a period of poor market performance, for example.

While investors may assume that the highest success rate from a Monte Carlo analysis is best, that’s not always the case. These investors may be surprised when an advisor suggests that they can spend more in retirement, retire earlier, or both. After decades focused on saving, some investors feel reluctance to spend their own money. However, they may be in a position to enjoy retirement more than they think.

T. Rowe Price retirement advisors typically recommend aiming for a good score, rather than a perfect one. For example, many advisors, including the T. Rowe Price Retirement Advisory Service, counsel investors to aim for a Confidence Zone that ranges from 80% to 95%.

At the same time, a lower score, or seeing a dip in the score, may not mean it’s time to panic, either. The score is a helpful barometer to understand how much attention you may need to give to your retirement plan at a particular point in time.

The power of the Monte Carlo analysis is not just limited to the initial results. Most tools allow you to make adjustments and test how the changes could affect your long‑term financial success. For instance, minor adjustments in the following factors can lead to large changes in your score:

What’s more, a Monte Carlo analysis as part of an advisor‑led planning service can help you craft your vision of retirement, personalizing your plan for your unique situation. An advisor can guide you through the process and help you prioritize what adjustments or actions will benefit your plan the most. (See “The benefit of our Retirement Advisory Service.”)

A Monte Carlo analysis is not a prediction of the future. It is merely a way to test various assumptions against an unpredictable future. The results of the analysis are heavily influenced by the quality and accuracy of those assumptions and inputs. Keep in mind, if you’re planning for retirement with a spouse or partner, you should also include their information for a more complete picture.

Ultimately, a Monte Carlo score is a data‑driven way to approach emotional decisions in a rational and informed way—and to strategically plan for a successful retirement. While the results of a Monte Carlo analysis may seem black and white or present as success or failure, they’re more of an indication that you may have to take some action to make your retirement plan sustainable. The good news is—most of those actions are in your control.

The projections in this article were calculated using the Retirement Income Calculator. The calculator uses Monte Carlo analysis to generate 1,000 hypothetical scenarios based on inputs such as, but not limited to, performance of various asset classes; saving and spending assumptions; a client’s time horizon, life expectancy, income, and expenses; and other variables. The Monte Carlo analysis provides ranges of potential future outcomes based on a probability model. The Monte Carlo simulation runs the user’s scenario 1,000 times. So, for example, if 600 of those runs are successful (i.e., all goals are funded and the user has at least $1 of assets remaining at the end), then the probability of success would be 60% and the probability of failure would be 40%. For purposes of the illustration: Hypothetical Investor No. 1 Assumptions (Fig. 2): is assumed female, single, and residing in Colorado with a date of birth of August 1, 1964. Ninety percent of the assets are in qualified retirement accounts. Expected annual living expenses in retirement are initially assumed $52,493. Those expenses are assumed to increase to $63,000 (about 20% from initial) and to $57,000 (about 10% from initial). To assess a decline of 20%, the starting asset value of $1,000,000 was reduced to $800,000. Hypothetical Investor No. 2 Assumptions (Fig. 2): is assumed male, single, and residing in Colorado with a date of birth of August 1, 1974. Ninety percent of the assets are in qualified retirement accounts. Expected annual living expenses in retirement are initially assumed $75,000. Those expenses are assumed to decline to $70,000 (about 6% from initial). Both illustrations assume the use of an aggressive allocation (90% stocks, 10% bonds) and a moderate allocation (60% stocks, 40% bonds). These allocations are assumed to be rebalanced annually to remain consistent. For details on this and other assumptions, please read our Methodology and Assumptions (PDF).  

The information provided in this tool is for general and educational purposes only and is not intended to provide legal, tax, or investment advice. The assumptions and methodology are not tailored to the needs of any specific investor. Results are intended as an aid, are not guaranteed, and should not be your only source of information when making financial decisions. Other T. Rowe Price educational tools or advice services use different assumptions and methods and may yield different results.

IMPORTANT: The projections or other information generated by the Retirement Income Calculator regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results, and are not guarantees of future results. The simulations are based on assumptions. There can be no assurance that the projected or simulated results will be achieved or sustained. Actual results will vary with each use and over time, and such results may be better or worse than the simulated scenarios. Clients should be aware that the potential for loss (or gain) may be greater than demonstrated in the simulations.