In the retirement planning part of my practice, I use a lot of different tools to model retirement recommendations and the “success” of various paths. Ultimately, the best that any of these tools can provide is a test of how reasonable the plan and path are based on the assumptions used and cash flows known at the time of analysis. As a peer likes to say, “Please do not confuse precision with accuracy.”
In financial planning, we can be accurate (i.e., “This is a prudent plan”) even when we can’t be precise (i.e., “You will have $2,027,497.54 in your portfolio at age 65” or “You will be able to leave a legacy valued at $4,872,399.09 at your passing”).
We just can’t have precise results when most of the inputs are estimates of the unknown and may be long into the future.
Since retirement decisions - timing and cash flows – are so important to clients, the trend has been to find better ways to quantify the results of certain decisions. Monte Carlo analysis is now the most common method to determine a client’s “probability of success.” More often than not, we – professionals and clients – tend to fixate on something simple to boil down all the analysis.
We have tended to rely on this statistical approach and interpret results much the way we would our grades in high school. So, if something in the 90% range is an A, and something in the 80% range is a B, then anything with a C grade or less must be worse. And a 50% probability of success which implies running out of money in retirement one-out-of-two times must certainly mean failure.
The reality is much different and more nuanced.
If a client has various guaranteed income sources such as Social Security and pension benefits or supplemented by rent or reverse mortgage proceeds or annuity payouts or even a possible inheritance, the amount of cash flow needed from the portfolio can and will be less.
So, when the probability of success is less than 100% (even 50% less than 100%), the measure itself doesn’t provide detail on how much is the “failure” gap. A household with 90% of retirement lifestyle needs being met from guaranteed income sources may not need a lot from the retirement nest egg.
Retirees may be walking on a tight rope above a pool. But if the plan’s probability “fails” in 10%, 20% or even 50% of the time, does it mean that the client will fall off and drown or just get a little wet? In other words, clients who “simply see a probability of success – and an implied risk of failure of falling off the rope – still may not know whether they are walking a tightrope six inches or sixty feet off of the ground.” Will they fall into the deep end or the shallow end? The difference may make it easier to recover.
In reality, retirees will tend to make adjustments to their spending plans over time. When faced with concerns or new information about spending needs, clients make choices such as decreasing discretionary spending or making substitutions in purchasing. Experience has shown that sometimes very small adjustments can get clients back on track.
Life is dynamic. And we cannot assume that the path taken at the beginning of the retirement journey will unfold without changes. But the limitation of many software tools is that they rarely include the ability to dynamically model human behavior. But my experience and other anecdotal evidence supports the reality that people do make adjustments.
Monte Carlo probability is sensitive to a number of factors such as investment returns, longevity, stability of any guaranteed income sources, and the rate of inflation and taxation, among other things. Some of these factors we may have control over. Others, not so much.
Aside from the allocation of investment assets, the biggest factors may be how much a client needs to spend versus how much they want to spend. Another important factor can be the amount of “terminal wealth” desired to leave as a legacy to family or other beneficiaries like charities. Depending on a client’s views, a client may choose to adjust the terminal wealth goal when faced with a lower-than-desired probability of success so that retirement spending may be increased. Other clients may make sacrifices in retirement lifestyle to maintain or increase that terminal wealth goal.
Based on my experience with clients, I will note that in many instances a high probability of success indicates that the client may have reduced their quality of life in retirement – sometimes significantly – in order to maintain a Monte Carlo probability of success near 100%. In other words, clients could have spent more.
This is one of the downsides of one-time financial planning. By fixating on a certain number at the outset without accounting for ongoing review and changes, one may be on a path that reduces opportunities. And isn’t the goal of accumulated wealth to have opportunities?
One of the benefits of a lower Monte Carlo probability of success is that it is an opportunity to do more planning, retest cash flow assumptions, and make adjustments from time to time. The other is that by seeing a perceived “higher risk” Monte Carlo result (let’s say at or under 50%), clients may have higher initial spending amount upon retirement. Then, later on, when on-going planning reviews are done, the client and advisor may be able to identify ways to reduce spending. For some, this may not be practical and possible. So, for them, it's better to show a higher probability of success at the outset for a lower spending level.
Another benefit of a lower initial Monte Carlo result is that it helps advisors frame the conversation with clients. Instead of focusing on “failure rates,” the goal is to focus on planning opportunities and “adjustments.” For instance, a client seeing a probability of success outside of a preferred “comfort zone” may be asked to consider delaying retirement, changing the claiming age for Social Security benefits, reallocating an investment portfolio to a higher equity weight, or discussing reductions in spending.
In one recent client example, a couple indicated that they want to retire within a couple of years and wished to purchase a second home on Cape Cod. After seeing the initial Monte Carlo probability of success results, they agreed to delay their retirement date by a year or two and also chose not to use their limited capital to buy that vacation property.
Like people, every retirement plan is different. And the key takeaway is that even if using sophisticated financial modeling software, there is no one-size-fits-all rule. The “comfort zone” that a client may seek may or may not be appropriate. And for clients who are willing and able to make course corrections along the way, they will benefit from on-going guidance from advisors who can interpret the results and provide clarity about the options to be discussed – even if there is a 50% or lower Monte Carlo probability of success.
