Raising the Bar In Target-Date ­Evaluation

“If you challenge conventional wisdom, you will find ways to do things much better than they are currently done.” —Moneyball, by Michael Lewis


Avid baseball fans are likely familiar with the story of Billy Beane, which was popularized over the last decade by a best-selling book and Hollywood movie named Moneyball. For most of baseball’s history, scouts relied on simple statistics and subjective criteria like hits, homeruns, power and physique to evaluate players. As the General Manager for the Oakland Athletics, Billy Beane recognized during the late 1990s that the conventional approach was not highly correlated to wins. To improve his team’s likelihood for success, he broke from tradition by relying on rigorous statistical analysis called Sabermetrics1. His willingness to look at the game from a different perspective and adopt new evaluation techniques has changed baseball forever.

The Moneyball story draws a strong parallel to the evaluation process of target-date funds. As they become the most important investment decision most defined contribution (DC) plans will make, the bar for evaluation is also being raised through new perspectives and techniques.

Tailoring the Lineup

Selecting or designing an appropriate  asset-allocation strategy for investors depends primarily on suitability. Therefore, fiduciaries should ensure that their target-date choice is appropriately matched with the desired outcomes, investment horizon and risk capacity of plan participants, rather than allowing availability, price and performance be the primary drivers of the decision. The Department of Labor’s 2013 “Target Date Tips for ERISA Plan Fiduciaries” supports this approach and suggests that target-date fund decisions should be strongly aligned with plan demographics, which can help fiduciaries reach better conclusions about suitability. At the beginning of any target-date search, fiduciaries should collect and analyze population statistics including savings rates, withdrawal patterns and the distribution of both ages and assets. For example, plans with frequent withdrawals at age 65, the majority of assets near retirement and a strong average savings rate may be best suited for a glide path that maintains a static allocation at retirement and actively seeks to protect from large and unexpected losses.

Fiduciaries can also survey the plan’s committee and a representative sample of participants to best establish the plan’s objectives, investment attitudes and behavioral biases. Evaluating the results around such areas can help fiduciaries further refine the target-date decision beyond demographics. For example, since risk tolerance is one of the most important criteria for assessing suitability, committee members and participants can be surveyed for their maximum loss capacity at different periods along the glide path (i.e. 40, 30, 20, 10, 0 years to retirement)2. Statistical measurements like Value at Risk3 can help identify glide paths that have estimated loss profiles consistent with a plan’s tolerable loss targets. To illustrate this approach, Figure 1 compares a consultant-defined loss capacity for participants with the estimated maximum 12-month loss of two different glide paths.

Together, demographic data and survey results can help fiduciaries prudently design or select glide paths that are a tailored fit for their plan and importantly, remove subjective biases from the decision making process.


*VaR (99%) as of September 30 2014. Chart is provided for illustrative purposes and is not indicative of the past or future performance of any PIMCO product. Source: 2014 PIMCO DC Consultant Survey, PIMCO, MarketGlide.

1 Sabermetrics is a popular term for statistical analysis of baseball players. The term was coined by Bill James who is noted a one of its pioneers and is rooted in the acronym SABR, which stands for Society of American Baseball Research.

2 For a further discussion on this topic, see Loss Capacity Drives 401(k) Investment Default Evaluation, PIMCO, September 2012 (Schaus, Gao)

3 Value at risk (VaR) is a statistical technique used to evaluate the level of risk within an investment portfolio and is measured in three variables: the amount of potential loss, the probability of that amount of loss and the time frame