Retirement income guardrails strategies – i.e., planning strategies that predefine thresholds that would trigger an increase or decrease in retirement spending – have been noted to have some significant communication advantages over probability-of-success approaches for managing retirement spending expectations. For instance, an approach like Guyton-Klinger’s guardrails – perhaps the most popular guardrails strategy of all – can be presented in a manner that not only tells a client when a spending change would occur, but also how much of a spending increase or decrease would result from ‘hitting’ a guardrail. The caveat to such approaches, however, is that guardrails strategies are generally based on portfolio withdrawal rates, which can be rather crude metrics that may not capture a lot of retiree-specific nuances that could warrant the use of higher or lower guardrails in a given retiree’s situation.
By contrast, one of the most significant strengths of Monte Carlo simulations is their ability to incorporate retiree-specific nuance into a simulation. From capturing unique cash flows and goals to varying longevity assumptions and portfolio composition, Monte Carlo simulations shine in their ability to model scenarios that better reflect a given retiree’s goals and preferences in retirement. The caveat to Monte Carlo simulations, however, is that there are a number of weaknesses regarding the communication and presentation inherent in the results these simulations provide. The common focal point for reporting Monte Carlo simulation results is the ‘probability of success’ of a plan, which is not only often misunderstood by retirees (e.g., by assuming ‘failure’, or outcomes that seem more devastating than the ‘adjustment’ that failure scenarios actually imply), but also ignores entire dimensions of planning results (such as the magnitude of spending change) that are crucially important to setting both good short- and long-term retiree expectations regarding retirement income planning.
Fortunately, however, bringing these two approaches together can provide the best of both worlds, as ‘probability-of-success-driven guardrails’ capture both the communication advantages of the guardrails approach and the analytical advantages of Monte Carlo simulation. Essentially, an advisor would define an initial probability of success target (e.g., 95%), an upper probability of success guardrail (e.g., 99%), a lower probability of success guardrail (e.g., 70%), and some spending adjustment rule in the event that a guardrail is hit (e.g., reset to the initial target 95% probability of success). With these parameters in place, an advisor could then solve for dollar values (both portfolio levels and spending levels) that would summarize the guardrails and the changes that would occur in the event that they are hit, and then communicate that to a client, which provides information that is far more insightful to the client than merely telling them what spending level is associated with an X% probability of success.
Furthermore, while software could ideally help speed up the process for advisors and provide some more relevant long-term metrics, the most basic advantages of the probability-of-success-driven guardrails approach involve managing shorter-term expectations, which can be captured using even the most basic Monte Carlo simulation software and without ever needing to utter ‘probability of success’ to a client (unless the client wants the detail, of course). Additionally, advisors could easily substitute historical simulation, regime-based Monte Carlo analyses, or other more advanced modeling methods for traditional Monte Carlo simulation at the modeling level, and still carry out the process of communicating results in a simpler and more effective manner to clients.
Ultimately, the key point is simply to acknowledge that probability-of-success-driven guardrails can bring together both the analytical advantages of Monte Carlo simulation and the communication advantages of guardrails approaches, providing advisors with an opportunity to have better conversations with clients regarding their Monte Carlo results, in a meaningful fashion that will be easy for clients to understand!