Reframing Monte Carlo Results To Increase Trust In Dynamic Retirement Spending

Reframing Monte Carlo Results To Increase Trust In Dynamic Retirement Spending

Financial advisors often use Monte Carlo simulation in their financial planning process, which (as is commonly found in major financial planning software packages) traditionally presents the results of the projection in terms of probability of success or failure (with ‘success’ being defined as an iteration of the plan where the client doesn’t run out of money, and ‘failure’ signifying the opposite).

However, some commentators have taken issue with this framing, particularly as it concerns the way that Monte Carlo results are presented to clients. Most significantly, the ‘success/failure’ framing fails to capture the reality that retirees, when facing an unlucky sequence-of-returns scenario which could result in their running out of money, can and often do make adjustments to their spending that allow them to avoid that unfortunate outcome.

To better reflect this reality, the phrase “probability of adjustment” has emerged as a commonly suggested alternative to “probability of success”. While representing an improvement over the original, however, “probability of adjustment” itself can be prone to ambiguity and misinterpretation without being clear about what type of adjustment might be needed, and what the outcome might be if that adjustment weren’t made.

A Monte Carlo simulation can tell us, with the benefit of hindsight, exactly which iterations of a plan would have ended with the retiree running out of money. But in reality, retirees do not have the ability to know which iteration (if any) they are on, and in many instances will likely make adjustments in cases where, in hindsight, no adjustment was strictly necessary. As a result, simply replacing “probability of success” with “probability of adjustment” when communicating Monte Carlo results can significantly underestimate the likelihood that a client will actually make an adjustment at some point, since clients (and advisors) do not have the benefit of knowing when an adjustment is ‘truly’ necessary.

Likewise, if an advisor were to recommend a dynamic spending strategy based on Monte Carlo simulations (such as adjusting spending to maintain a constant probability-of-success level), the “probability of adjustment” framing can skew even further from reality, since preserving a consistent probability of success often calls for relatively frequent adjustments in spending. For instance, maintaining a 70% probability of success level – implying only a 30% probability of adjustment – would in reality have required downward spending adjustments in nearly 100% of all historical scenarios, which would understandably have caused confusion for many clients if the advisor had used the standard “probability of success/adjustment” framing!

Ultimately, the key point is that outcomes, not probabilities, are what matter to clients, and any way of communicating Monte Carlo results should be clear about what those results mean in terms of real spending to the client. Though “probability of adjustment” is an improvement over “probability of failure”, it can still greatly underestimate the probability of actual spending adjustments, especially when dynamic spending strategies are involved. In those cases, it may make sense to avoid framing Monte Carlo results in terms of probabilities entirely, but rather to communicate in terms of the actual dollar spending adjustments that would be triggered in specific scenarios – which is what really matters to the client in the end.

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