Is Time Segmentation a Superior Strategy?

In part 1, I provided the case for time segmentation strategies used by their advocates, as well as how it fits into the spectrum of retirement-income approaches. In part 2, I examined the potential for time segmentation by considering three different ways to implement it. Now, we reach the heart of the matter: Is time segmentation a superior investment strategy for retirees relative to total-return investing? To examine this, we need to disentangle the effects of the dynamic asset allocation created by time segmentation from whether holding individual bonds to maturity helps to manage sequence-of-returns risk.

Whether time segmentation is a superior investing strategy is a controversial issue, though the general consensus is that time segmentation is not a uniquely better way to invest. Under some restrictive assumptions my findings confirm this consensus view.

To be a better investment strategy, time segmentation needs to reduce sequence risk relative to a total-return portfolio with the same asset allocation. The difficulty is how to compare two different strategies while controlling for asset allocation. A time segmentation strategy necessitates a dynamic (variable) asset allocation with a potentially higher average stock holding, and it cannot easily be compared to a total-return investing strategy with a static allocation.

Whether time segmentation is a better strategy depends on three interrelated issues:

  • Because asset allocation is allowed to fluctuate, a time segmentation approach can have a very different asset allocation glide path than a total-return approach. Is this dynamic asset allocation acceptable to the retiree if he or she believes that a more static or slowly changing allocation is the right way to invest?
  • Time segmentation approaches require varying degrees of effort to avoid selling stocks at inopportune times.
  • Because time segmentation requires holding individual bonds to maturity, fixed income assets do not need to be sold at a loss to support retirement income.

Figure 1 provides the ongoing probabilities of success for seven different strategies to support a retirement spending goal of $40,000 initially with 2% cost-of-living adjustments in subsequent years for up to 40 years of retirement. The client has $1 million available to support this goal. The analysis is based on 10,000 Monte Carlo simulations starting from today’s low bond yields with the capacity for rates to increase over time (this is the Monte Carlo simulation approach I have been using recently for a number of peer-reviewed research articles).

Three of the strategies are the time segmentation rolling ladder approaches previously described in part 2 with bond ladder lengths targeted at 10 years. Ladders are extended either automatically, just in years after positive market returns, or when remaining wealth exceeds the critical path level for that year of retirement.