Retirement, like life, is inherently uncertain. That’s why we need to provide clients with more context about what missing out on their retirement income goals might look like, and do it in a thoughtful way.
In my two previous articles, I explored how retirees tend to have more flexibility in their retirement spending than conventional models imply and have discussed a frame to dynamically adjust their spending. Here, I examine how imperfect commonly used financial planning measures—probability of success, in particular—are, and why we should consider other outcome measures that can offer additional and better insights into income situations. customer retirement.
The rise of Monte-Carlo
Financial advisors often use Monte Carlo projections to demonstrate the uncertainty associated with funding retirement income and other retirement goals. The element of luck, or randomness, is the primary differentiator of Monte Carlo projections from time value of money calculations and other methodologies.
While it’s important to show the likelihood of a goal not being met, it’s also important to describe the range of potential scenarios. Probability of success is the most common outcome measure in Monte Carlo tools and refers to the number of runs, or trials, in which the goal is fully achieved in a given simulation. For example, if a retiree wants an annual income of $50,000 for 30 years and this goal is achieved 487 times in 1,000 runs, there is about a 48.7% chance of success.
However, measures related to success treat the outcome as binary and do not describe the magnitude of failure or how far the individual has traveled to achieve the goal. According to these measures, it does not matter if the retiree fails in the 10th or 30th year or by $1 or $1 million. All failures are treated the same. Thus, a retiree may have a relatively small shortfall, but also a low probability of success, especially when their retirement income goal is primarily funded by guaranteed income and for an assumed relatively long period, say 30 years.
But a financial goal is not a discrete set of successes or failures. It’s a range of possibilities. This is why it is so important to add context to the degree of potential failure. Percentage of goal achieved is a critical metric. The chart below illustrates this effect with an assumed goal of $100 per year for 10 years.
Percentage chance that the goal of $100 per year for 10 years will be reached
In rounds 1 to 5, for example, the objective is only partially achieved. The percentage varies across the five simulations, but each run constitutes a “failure” based on success metrics. Other measures tell a different story. Using average goal achievement, 90% of the goal is covered, on average, while success rates indicate a 50% chance of success. Although based on identical data, these two measures provide very different perspectives on the safety of spending at the target level.
The relatively low success rate suggests that achieving the goal is far from assured. But the goal completion score offers a much more positive picture. This is especially important with long term goals like retirement where “failure” is more likely in the later years of the simulation.
Diminishing Marginal Utility
While goal completion percentages demonstrate a more colorful perspective on the results of Monte Carlo simulations, they also don’t account for how the disutility, or pain, associated with missing a goal can vary. . For example, not funding essential expenses like housing or health care is likely to cause more dissatisfaction than cutting travel or other flexible items.
The concept of diminishing marginal utility describes this relationship: the pleasure of consuming or financing something generally increases, but at a decreasing rate. This may explain why people buy insurance even though it reduces their wealth on average. They guarantee that they will be able to finance a minimum level of consumption.
Goal completion percentages can be further modified to incorporate diminishing marginal utility, so that the implicit satisfaction associated with reaching a given level of consumption changes, particularly depending on whether the consumption is discretionary or non-discretionary. I have developed a framework to make these adjustments based on prospect theory. These values can be aggregated over multiple years within a given run and across all runs. This gives a measure of goal completion score that may require very different guidance and guidance than modeling based on probability of success rates.
Work with what we have
Our industry needs to deploy better results indicators in financial plans. These metrics should consider goal achievement and incorporate utility theory more directly. Admittedly, relatively few instruments achieve this today, so financial advisors may need to offer enhanced advice using the current toolset.
Financial advisors who continue to rely on success rates should lower their targets a bit. According to my research, 80% is probably the right target. This may sound low: who wants a 20% chance of failure? But the lower value reflects the fact that “failure” in these situations is rarely as cataclysmic as the metric implies.
Customers also need more context about what exactly a bad result entails. As financial advisers, we can explain how much revenue is generated in unsuccessful trials. How bad are the worst case scenarios? Will the client need to generate $90,000 at age 95? It’s much more meaningful than a success rate and shows how badly things could go if they don’t go right.
The probability of success may be the primary outcome measure for advisors using Monte Carlo projections, but it completely ignores the magnitude of failure. Success rates can be particularly problematic for retirees with higher levels of protected or guaranteed income for longevity and for those with more spending flexibility. Alternative outcome measures can help us fill the void and ensure that we are providing reasonable and accurate information to customers to help them make the best possible financial decisions.
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All posts are the opinion of the author. As such, they should not be construed as investment advice, and the opinions expressed do not necessarily reflect the views of the CFA Institute or the author’s employer.
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