Using Participant Data to Improve 401(k) Asset Allocation

Economic theory says that participants in 401(k) plans should gradually rebalance their portfolios away from stocks towards less risky bonds as they approach retirement. The rationale is that at younger ages households hold a substantial portion of their wealth in the expected present value of their remaining lifetime earnings, which are generally viewed as a relatively low risk asset, so they should hold much of their financial assets in high risk/high return stocks. As households approach retirement, the value of the earnings asset declines, so they should compensate by rebalancing their investment portfolios away from stocks and into bonds. Many households fail to rebalance their portfolios as they age, reflecting both inertia and lack of investment skills. In response, 401(k) plans offer life-cycle, or target date, funds which automatically rebalance the household’s portfolio with age. Conventional target date funds take into account only one aspect of an individual – namely, the person’s expected retirement date. In fact, the plan provider knows additional information about the individual, including his earnings, the balance in his 401(k) account, and his saving rate. This brief compares how much a conventional (“one-size-fits-all”) target date fund improves the outcome compared to the asset allocation that individuals would choose on their own and how much taking into account the additional information improves the outcome compared to the one-size fits-all target date fund. This brief, adapted from a new paper, proceeds as follows. The first section establishes the benchmark – expected lifetime utility from an optimal investment strategy – against which each 401(k) investment option is compared. The second section describes the horse race in which the outcomes for each of the three allocation approaches are compared to the benchmark. The third section suggests two additional adjustments – basing portfolios on estimated household characteristics rather than relying solely on participant data and taking into account the riskiness of the participant’s earnings – that would bring outcomes closer to the optimal. The final section concludes that a target date fund is better than leaving the household on its own and that adding information to the one-size-fits-all target date fund can bring the outcome even closer to the optimal for the great majority of households.