For the last year, I've had a nagging feeling that the investment universe was changing in fundamentally significant ways that could have a profound impact on our clients' lives. I'm now convinced that it has, and it will. Although a long-term proponent of the application of mathematics to our work as financial planners, I think we've succumbed to sexy but simpleminded, psuedosophisticated analysis. In the process, we devote endless hours to touting (and inaccurately extolling) secondary issues and techniques.
At the same time, we ignore fundamental issues critical to our clients' well-being. The classic example of "rearranging the deck chairs on the Titanic" is our current fascination with Monte Carlo simulation, while we ignore the "iceberg" on the horizon. The real threat to our clients' well-being is not point-estimation; it's the current "assumption set" (e.g., expected return estimates for bonds and stocks) we use in the investment- planning process.
We have three choices: Continue to rearrange the chairs. Emulate the voyagers on the Titanic by heading for the lifeboats. Or, figure out how to deal with the iceberg.
It's high time for professionals to reconsider their assumption set (AS) and, if changes are necessary, develop strategies for dealing with the implications for our clients' futures. At the risk of disappointing the reader, a brief warning is in order. Although I do believe we need to make changes that will fundamentally affect our clients, I don't yet have a comfort level with what those changes should be. My goal in writing this piece is to refocus the profession's interest from the chairs to the iceberg, so that together we can reduce ambiguity and more effectively manage risk.
Monte Carlo Simulation
I don't pick on Monte Carlo simulation because I think it's a bad tool. It's a wonderful tool. Monte Carlo simulation is an effective way of educating people regarding the uncertainty of risks. Unfortunately, it's not nearly the panacea that is suggested by some commentators. Rather than reducing uncertainty, Monte Carlo simulation increases the guesswork manyfold. A point estimate requires a single guess. Monte Carlo requires three additional estimates for each point estimate: the shape of the distribution (a normal bell distribution is not a given) and the high and the low range for the distribution.
The problem is the confusion of risk with uncertainty. Risk assumes knowledge of the distribution of future outcomes (i.e., the input to the Monte Carlo simulation). Uncertainty or ambiguity describes a world (our world) in which the shape and location of the distribution is open to question. Contrary to academic orthodoxy, the distribution of U.S. stock market returns is far from normal. We need to recognize that in planning our clients' future, we're dealing with ambiguity, not risk. That leads me to believe our current AS is an iceberg of monumental proportions.
The fundamental investment recommendation a financial advisor makes is the allocation between bonds and stocks. This allocation drives recommendations regarding savings and spending. In fact, it is one of the most significant influences on the quality of our clients' financial future. Whether we arrive at our recommendation for allocation by using a mathematical optimizer, capital-needs programs, a 12-C and/or darts, practitioners begin with an AS.
A practitioner's AS, at a minimum, includes estimates regarding the expected returns on bonds and stocks. They may be based on a total return or real-return estimates and may vary depending on the time frame of the analysis. In our practice, we use different forward-looking, real-return assumption sets for policy development than we do for capital-needs analysis (see sidebar).