Executive Summary
One of the primary virtues of using Monte Carlo analysis for evaluating a retirement plan is that it frames the conversation in terms of the probability of success and the risk of failure, rather than simply looking at how much wealth is left at the end of the plan. As a result, the focus of planning shifts from maximizing wealth, to maximizing the likelihood of success and minimizing the risk of failure.
Yet the reality is that while "failure" from the Monte Carlo perspective means the client ran out of money before the end of the time horizon, in truth most clients will not simply continue to spend on an unsustainable path right to the bitter end. Instead, if the plan is clearly heading for ruin, clients begin to make adjustments. Some failures may be more severe than others, and consequently some plans may require more severe adjustments than others.
But the bottom line is that a "risk of failure" is probably better termed a "risk of adjustment" instead. However, when viewed from that perspective, it turns out that the plan with the lowest risk of adjustment may not be the ideal plan for the client to choose!
The inspiration for today's blog post is some analysis I did on the uses and applications of Monte Carlo for the February issue of The Kitces Report. In the process of digging into how we typically frame "probability of success" or "probability of failure" in the plan - naturally trying to maximize success and minimize failure - I realized that there is an inherent danger in doing so blindly, because of the simple fact that not all "failures" are the same, nor do they all require the same adjustments to get back on track.
For instance, imagine a 65-year-old client couple looking at two plan options. Plan A uses a relatively equity-centric portfolio that has a 90% probability of success that the plan will sustain for 30 years. Plan B uses a more conservative allocation, but because it generates a lower average rate of return that more frequently fails to keep pace with inflation-adjusted spending requirements, it has only an 85% probability of success. Measured by probability of success alone, the path seems clear: Plan A fails only 10% of the time, while Plan B fails 15% of the time, so Plan A is the winner, as shown in the table below.
|
Probability of Success |
|
|
Plan A |
90% |
|
Plan B |
85% |
In a world where all "failures" are the same, choosing Plan A over Plan B would be a prudent decision. But the reality is that not all failures are the same, and not all failure scenarios require the same adjustment in order to get back on track. For instance, what happens if we look at one of the worst case scenarios, such as a -2 standard deviation result? According to the normal distribution, an end result that is -2 standard deviations or worse should only happen about 2.15% of the time (we'll round off and assume it's equivalent to the 2nd percentile), so this represents a relatively rare, but certainly not impossible, outcome.
So what happens when we look at the 2nd percentile of results under Plan A? It's pretty ugly. Due to the heavy exposure to equities, a severe bear market is a rather destructive event. If we look at this very negative scenario, the hypothetical client actually runs out of money in year 20. In other words, while the client may succeed in 90% of the scenarios and only fail in 10% of them, a whopping 20% of the failures (2% out of the 10%) are rather catastrophic, as the client runs out of money a whole decade early! If that rare but possible adverse event happens early in the client's plan, getting back on track could require some very draconian cuts to the client's standard of living, if the client is trying to recover from what would otherwise be a 10 year shortfall in the plan. In this case, failure may be relatively uncommon, but if it does occur, it requires a big adjustment.
On the other hand, Plan B has far less exposure to equities and far less overall volatility. As a result, an unfavorable sequence can't be all that unfavorable, and a below average series of returns can't be all that far below average. Consequently, when we look at the 2nd percentile in this case, the client doesn't run out of money until year 27, which in turn means that the adjustments necessary to get back on track are relatively mild. In other words, while the odds are higher that this client will have to make some adjustments (i.e., it is a higher failure rate), the failures themselves are not very severe, and the adjustments necessary to mitigate tough scenarios are far more manageable.
|
Probability of Success |
2nd percentile failure year |
|
|
Plan A |
90% |
Year 20 |
|
Plan B |
85% |
Year 27 |
Suddenly, the optimal retirement decision is far less clear. While Plan A has a higher probability of success at 90% - and thus "only" a 10% risk of adjustment - the consequences of those required adjustments can be very harsh to shore up a 10-year shortfall. Plan B may have a lower probability of success, and therefore a higher probability of adjustment (i.e., a "failure"), but the magnitude of the adjustment will be relatively minor, as the client only has to eke out an extra 3 years of retirement income over the 30 year time horizon even in a highly adverse scenario. In other words, it may be better to follow the plan that leads to a slow failure - which can be easily fixed with mid-course adjustments - than a fast failure, even if the slow failure scenario is somewhat more likely to require at least some modest adjustments. On the other hand, as was recently highlighted on this blog, it appears that most clients tend to slow down their spending in later years anyway - at least, unless they have so much extra wealth they just start giving it away when they no longer spend it - which means plans that cause slow failures and require slow, modest adjustments may simply move in sync with an aging client's lifestyle anyway.
The bottom line is that while the benefit of Monte Carlo is to focus on the probability of success and risk of failure (as opposed to just the average final wealth projected with zero-volatility straight-line growth), when we take a deeper look at what those failure scenarios look like, the reality emerges that the highest probability of success and lowest probability of failure may not always be the most desirable plan. Instead, we have to look at both the risk of adjustment, and the potential magnitude of adjustment, to get a clear picture of the risks and opportunities involved.
So what do you think? Is probability of success or risk of failure better framed as the risk of adjustment? Do you ever discuss the magnitude of changes that would be required with an adjustment, in addition to the probability? Would this help your clients make better retirement decisions? Would it change the path any of them are currently taking?
(Editor's Note: This article was featured in the Carnival of Retirement #9 on Sense to Save.)
