The Best Approach To Adjustable Retirement Withdrawals

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The Best Approach To Adjustable Retirement Withdrawals

The Best Approach To Adjustable Retirement Withdrawals

May 12, 2015

by Joe Tomlinson

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A great deal of recent research has focused on strategies that adjust withdrawals in retirement depending on investment experience. But such strategies disrupt retirement plans by causing withdrawals to vary a lot from year to year. I’ll examine the prominent approaches for determining what will work best for clients.

A simple example of an adjustable strategy would be to a set percentage of the current portfolio to withdraw each year.  This approach contrasts with the classic 4% rule where withdrawals are set at 4% of the initial portfolio and increased by inflation each year, regardless of performance of the underlying portfolio. A problem with basing withdrawals on current portfolio values is that, if the portfolio drops in value from one year to the next, withdrawals will drop by the same percentage. For example, with a $1 million portfolio and a 5% withdrawal rate, a 20% drop in the portfolio will reduce annual withdrawals from $50,000 to $40,000.

Researchers have recognized the need to smooth withdrawals when employing adjustable strategies, and have come up with a variety of different approaches. In this paper, Wade Pfau analyzed 10 different methods to generate retirement withdrawals, and noted the particular smoothing techniques they employ. The best-known adjustable approach Pfau examined was developed by Jonathan Guyton and William Klinger and described in their Journal of Financial Planning article.  They started with inflation-adjusted withdrawals, akin to the 4% rule, and then prescribed decision rules to move withdrawals above or below this inflation-adjusted path as a function of how the underlying portfolio performs. Their objective was to modify the approach used in the 4% rule to make retirements more sustainable, but without too much year-to-year fluctuation in withdrawals. To some extent their approach achieves smoothing by taking money from good investment years to support withdrawals when investments don’t perform as well.

Economist Laurence Siegel has expressed concern about smoothing approaches that attempt to spread investment impacts forward instead of applying full recognition in the current year. He presented his argument in this Financial Analysts Journal article co-authored with M. Barton Waring, which Siegel summarized in this Advisor Perspectives article. These authors contended that withdrawals should adjust immediately for changes in the underlying portfolio and should not be smoothed out or deferred. They pointed out that a series of bad investment years and keeping withdrawals too high can permanently impair a retirement plan. If a smoother pattern of withdrawals is desired, they argue for solving the problem by lowering the stock allocation in the underlying portfolio.

Waring and Siegel named their recommended adjustable approach ARVA, which stands for annually recalculated virtual annuity. They determined each year’s withdrawal rate by calculating a payment factor based on the combination of current portfolio value, conservatively estimated remaining longevity and expected investment returns. They applied this payment factor to the current value of the portfolio, so any changes in portfolio value have a full impact on withdrawal amounts. There is no smoothing.

Testing the methods

Smoothing is the crucial element in many proposed retirement strategies, so let’s examine all sides of the “smoothing debate.” In the following sections, I model retirement outcomes by first applying Waring and Siegel’s ARVA approach. I then compare that to two ARVA variations that utilize smoothing – the Waring/Siegel recommendation of lowering the stock allocation and a modification I make to ARVA where I limit the amount of year-to-year change in withdrawals. I then model the Guyton/Klinger decision rules and compare outcomes with the ARVA-related approaches. Details of the modeling and assumptions are presented in the appendix.

My expectation before doing the modeling was that Waring and Siegel’s no-smoothing approach would produce the best outcomes. Their ARVA method was published earlier this year and has been scrutinized by other researchers; it is firmly grounded in life-cycle economics principles that have developed over the past 90 years. I expected that applying smoothing techniques would give rise to the types of problems Waring and Siegel warned about. But sometimes research produces surprises.

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