Asset-Mix Strategy: Needed For Variable Products
By John M. Bragg
To serve the public, the insurance industry needs a long-term asset-mix strategy. The strategy must be workable. And heres the hard part: It must be salable to the public.
By an asset-mix strategy, I mean a mix of three-month Treasury bills, long-term Treasury bonds and common stocks. This mix needs to be adjusted regularly according to not only the customers risk tolerance and needs, but also according to the changes in the business cycle.
Many insurers now offer asset allocation and automatic rebalancing programs with their variable insurance policyholders, of course. Some also offer life-cycle investment strategies. But such modeling does not go far enough because many clients end up putting their money in a group of funds and then leaving it in there, no matter what happens in the economy.
There needs to be a pre-established review process for clients, so that their variable investments choices are continually updated. This pre-established plan would lay out what to do if certain economic conditions should appear.
If the industry is successful, it can help its customers avoid some of the extremes of the yield roller coaster, such as has occurred in recent times (see Chart I for an example).
This asset-mix strategy will help the variable policies cut across the key sectors in the convergence market–insurance, securities and banking–and its implementation will help even out performance for customers.
Sometimes, it seems that real convergence is about impossible. To illustrate, see Chart II, which shows the historical attitudes of players in the convergence scene. They are quite divergent.
In general, insurers believe in being guarantors and in offering safety and year-to- year stability. Given that, the industry also wants to maximize yield.
So lets talk a little bit about common stocks, which are supposed to maximize yield. Between 1993 and 2002, their average yield was very good (11.2%). But year-to-year stability was terrible; their yield in 2002, for example, was a killer (-22.1%). (Statisticians would say that their standard deviation, 20.7, was too high!)