Summary
Defined Contribution (DC) plan participants are haunted by an invisible risk called sequence risk (sometimes called sequence-of-returns or path dependency risk), that is, getting the “right” returns but in the “wrong” order. Sequence risk in the retirement phase has been studied extensively. Sadly, not as much attention has been paid to sequence risk during the accumulation phase, but it is equally important. Sequence risk rears its head in this way: Even if an individual employee does everything “right” – participates in the plan, defers income religiously, takes full advantage of the company match, and even gets his exact expected return from his investments – he can still fall victim to disappointing final wealth outcomes if the order of those returns works against him. Current models of asset allocation – the most popular being static, or predetermined, target date glidepaths – “know” that sequence risk exists, but behave as if there is nothing that can be done to mitigate it. Valuation-based dynamic allocation, on the other hand, can help soften the bite.
Here’s a riddle for you: Who ate almost $300,000 of Joe’s retirement money?
Meet some pretend employees, Joe and Jane. They worked for Acme, Inc., and were identical in essentially all aspects of their job, their salary, and their participation in their company’s defined contribution retirement plan (a “typical” 401(k)). Here are some key dimensions to consider:
- Identical length of time working: Both started working at 25, and both worked for 40 years.
- Identical starting salary and salary growth.
- Identical deferral rates and identical company match2 for a combined 9% annual contribution.
- Identical investments: Both invested in a typical target date fund (TDF), which started out with a 90% equity allocation, and glided down to below 50% by retirement age.
- Identical returns: This is a key point. During their 40 years of investing, they both earned exactly 5% annualized real (above inflation, or roughly 8% nominal) after fees.
Identical in virtually every way. At the end of their 40-year careers, however, Jane had $880,000 in her account, while Joe had $590,000. How is this possible? Who ate $290,000 of Joe’s retirement money? How do you explain a 50% gap between these two employees?
The answer? Sequence risk.
[drizzle]Here’s what we didn’t tell you. Joe started his career in 1954, while Jane started in 1967, 13 years later. So, even though Joe earned the same annualized return as Jane, he earned it in a slightly different sequence, and that made all the difference. Sequence risk – an insidious risk in all DC plans – took a sizeable bite out of his potential retirement nest egg.
Sequence risk has been in the shadows for some time. One reason is that sequence risk is typically not a major concern for traditional Defined Benefit (DB) plans. (See sidebar discussion regarding DB plans on page 11.) Second, when it has been discussed in the academic or investment community, the focus has traditionally been on the withdrawal phase of retirement, when cash flows are large. The main thrust of those studies demonstrated that the returns a retiree experiences in the first few years of his/her retirement are extremely important. A significant loss early on, even if it is recouped later, dramatically increases the risk that a retiree will run out of money. Sequence risk is undeniably important in the retirement phase, but most analyses simply start with an assumption that the retiree begins with some large lump sum. But this glosses over the fact that in a DC environment, it takes about 40 years of contributions, matches, and market returns to get to that final lump sum. Sequence risk rears its ugly head wherever cash flows matter – and we know cash flows matter both in the retirement and accumulation phases.
This paper tries to demonstrate the importance of sequence risk during the accumulation phase. The basic message is this: Even employees, like Joe, who apparently do everything “right” by traditional playbooks – stay in the plan, defer their income religiously, take full advantage of their company match, and even get the exact expected return from their investments – can still suffer from the effects of sequence-of-returns risk. That is, they get good returns, but they get them in a bad order (or, more specifically, they get good returns early in their career, and bad returns later when their account balance is higher).
Analysis: quantifying the significance of sequence risk
The Jane and Joe example above is interesting, but it only represents one “run” of history. Another method for measuring the impact is to simulate multiple runs, even thousands, through a stochastic process (akin to Monte Carlo simulations). We can artificially create 20,000 simulations of 40-year runs of history. Before we begin, however, let’s remind ourselves that as it relates to returns, employees confront both investment risk and sequence risk. Investment risk, the variability and distribution of possible returns around the mean, or expected return, is easily observable. Sequence risk, on the other hand, is much more insidious and harder to observe. We need to isolate it in order to see its significance.
We’ll start with a simple case, as above, by constructing a very typical target date allocation structure. We ran 20,000 simulations,5 and the output (see Exhibit 2) is a distribution of both returns (investment risk) and wealth outcomes (a function of both investment risk and sequence risk). The fact that there is a wide distribution of returns and possible wealth outcomes should not be surprising to anybody. Investment risk is well understood.
Many, however, might be surprised by the magnitude of sequence risk, which we can isolate by focusing on those simulated histories where the realized returns are identical. In our 20,000 simulations, for example, there were close to 1,400 instances where the realized returns were 5% real.6 Fourteen hundred Joes and Janes, if you will. Yet even if we control for a given realized return, wealth outcomes are still widely dispersed (see the dotted orange box in Exhibit 2). This is the effect of sequence risk. Each of the 1,400 runs occurred in a unique sequence, some advantageous, some less so. The “luckiest” employee’s 5% return netted her over $1,000,000, while her unluckiest colleague, with the identical 5% return, netted $314,000, a massive, almost unimaginable discrepancy.
See full PDF below.
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