Beyond the 4% Rule: Creating Retirement Spending Guardrails That Really Work

Monte Carlo simulations have become the dominant method for conducting financial planning analyses for clients, and they represent an important advance over previous planning frameworks with less predictive power, such as the ubiquitous 4% withdrawal rule.

However, such simulations ultimately capture what one planning expert calls an “outrageous and potentially misleading” spectrum of outcomes, and clients often have trouble accurately interpreting the “probability of success” metrics such analyses generate.

As such, traditional Monte Carlo reports may not really be the best way for advisors to help their clients manage their spending in retirement. Instead, as the retirement researcher and financial advisor Derek Tharp argues, a spending framework based on dynamic, risk-based guardrails can deliver both better outcomes and clearer communication with clients.

According to Tharp, the key to understanding what makes risk-based spending guardrails different from traditional Monte Carlo methods (and other guardrail-based strategies) is the appreciation of the difference between setting spending based on a one-time projection versus ongoing projections.

Simply put, when one conducts ongoing planning and regularly reviews and readjusts the spending level based on recalculated probabilities of success, a very different spending approach emerges — one that gives clients more exact expectations in real dollar terms about how their future spending might need to be adjusted, up or down, to keep their retirement prospects on track.

Tharp, who among other roles is an assistant professor of finance at the University of Southern Maine and the lead researcher at Kitces.com, made this case during a recent Kitces.com webinar. During the presentation, Tharp detailed the four key levers that can be adjusted in setting proper (i.e., risk-based) guardrails for retirement income, and he offered insights about how such guardrails can be communicated to clients.

While not as simple as plugging client information into a Monte Carlo simulator and reading off the results, Tharp says, this new way of planning is superior both analytically and from a simplicity of communication perspective.

How Risk-Based Guardrails Work

To help demonstrate how an advisor and client might use the risk-based spending framework, Tharp gave the example of a client starting with a target initial Monte Carlo probability of success of 90%.

If their portfolio experiences strong growth and the success probability reaches 99%, under this methodology, the client could comfortably increase spending to a level that would again leave them with a 90% forward-looking probability of success.

If they experienced tough markets early in the retirement period or they ended up spending more than anticipated and the recalculated probability of success fell to 70%, the client could then decrease spending back to a level that would give them a 90% probability of success.

Tharp gave an example of a client who plans to start their retirement spending $9,000 per month based on a $1 million portfolio and other guaranteed income sources such as Social Security. Using this approach, this client could increase spending to $9,500 per month if the portfolio grows to $1.1 million, while they would need to decrease spending to $8,500 per month if the portfolio declines to $700,000.

Tharp says clients really appreciate the fact that the advisor in this planning scenario can give them exact dollar figures that speak to when spending changes would have to happen and how large they would have to be. This is much different than what a traditional Monte Carlo simulation provides, he notes.

Tharp further suggested that the risk-based guardrails approach offers more levers to pull with respect to adjusting the plan on a regular basis. He says the four main levers are the initial withdrawal rate, the potential adjustment thresholds, an optional spending ceiling and an optional spending floor.

Ultimately, Tharp argues, advisors should give thought specifically to what probability of success level best balances the trade-off between income and legacy for a client.

Failure: Not as Scary as It Sounds

“The reality is that, when reporting Monte Carlo results to a client framed around probability of success, anything less than 100% can sound scary,” Tharp explains. “Consider a 50% probability of success. ‘Failing’ one out of every two times when failure implies running out of money in retirement simply does not sound acceptable.