Retiring without sufficient assets to maintain a minimally acceptable lifestyle (which each person defines in their unique way) is an unthinkable outcome. That’s why, when investors are planning for retirement, their most important question is usually something like, how much can I plan on withdrawing from my portfolio without having a significant chance of outliving my savings?
The answer generally is expressed in terms of a safe withdrawal rate (SWR)—the percentage of the portfolio you can withdraw the first year with future annual withdrawals adjusted for inflation.
Simulating Retirement Portfolio Returns
While historical returns can provide insight, it’s critical that investors not simply project the past into the future. Current valuation metrics should be used instead. Additionally, investors must address issues involving our limited ability to estimate future returns and the fact that the order of returns matters a great deal. The way to do so is to use a Monte Carlo simulator.
Monte Carlo simulations require a set of assumptions regarding time horizon, initial investment levels, asset allocation, withdrawals, rate of inflation and, very importantly, the distribution of annual returns for various asset classes.
Two numbers determine the expected final wealth distributions in Monte Carlo simulation programs: the average annual return (again, derived from current valuations/yields rather than historical ones) and the standard deviation of the average annual return. The Monte Carlo simulator then randomly selects a return for each year and calculates wealth values over the expected retirement period. This process repeats thousands of times to calculate the likelihood of possible outcomes.
Monte Carlo simulation outputs typically are presented as odds of success. For example, the simulation’s result might show a 90% chance of you not outliving your assets. Said another way, the failure rate, in this case, is an estimated 10%.
Research On Failure Rates
However, while the failure rate has become an essential tool when evaluating SWRs, as Javier Estrada, author of the October 2016 paper “Refining the Failure Rate,” points out: “This variable is silent about how long into the retirement period a strategy failed.”
He continues: “Two strategies that sustained withdrawals for 10 and 25 years of a 30‐year retirement period have both failed, but a retiree would be far from indifferent between them.”
Estrada’s study, which covered 21 countries over the 115-year period from 1900 through 2014, showed that two strategies could have the same failure rate but fail at very different points along the retirement horizon, with one supporting a retiree’s withdrawals for a longer time.
Estrada showed that over the 86 30-year retirement periods he considered, a 4% withdrawal strategy from a global 60/40 portfolio would have failed 20 times, or in 23% of the periods. However, those 20 failures looked very different. In some cases, the plan failed with only two years remaining; in others, it failed with 14 years remaining. Those represent two very different outcomes, with very different consequences. Yet they both count the same way in informing the failure rate.
To overcome this issue, Estrada introduces shortfall years—or the average number of years a strategy fails to support withdrawals over all periods in which the strategy failed—as a complement to failure rate. While adding shortfall years is an improvement upon relying solely on failure rate, it implies the use of two independent variables, which won’t necessarily always agree with each other.
For instance, strategy A could have a lower failure rate than strategy B but a higher shortfall-years metric. This suggests that strategy A will fail less often, but when it does fail, it will fail earlier on average into a retirement period than strategy B. Thus, using both failure rate and shortfall years will lead to trade-off decisions retirees will need to evaluate as opposed to a decision based on a single variable that defines a clear choice between the two strategies.