Pedal to the Metal or Slow and Steady?
Just like planning a road-trip, investing requires you to consider several things in order to get where you want to go:
- Where you’re starting from
- Where you want to go
- When you want to get there
- How fast you can safely drive
Obviously, driving faster increases your chances of arriving on time. Unfortunately, it also increases your chances of crashing. Your planning has to balance these competing risks. The same is true in investing. The product of this balancing act is your asset allocation.
Seeing is Believing
Your asset allocation should balance your investment goals with your tolerance for investment risk. Generally, investment risk is expressed in terms of volatility of return or, in statistical terms, standard deviation. Well, this is great if you’re a computer, but not very meaningful for most human beings. That’s why Compass employs Monte-Carlo techniques in order to help our clients visualize the risks inherent in their asset allocation choices.
Beware the Optimizers
At this point, most consultants offer to perform a portfolio “optimization.” This process involves changing your asset allocation so that
under a specific set of assumptions it has the highest expected return for the lowest level of expected risk.
Unfortunately, this optimal asset allocation is usually anything but. Although the under-lying theory of portfolio optimization
is sound, many consultants seed their optimization models with improper assumptions. Fortunately, they also saddle them with
allocation constraints that render the whole expensive process relatively harmless, except for your wasted time. The end
result is an allocation that looks remarkably similar to the portfolio next door. For a more complete discussion of portfolio optimization
see our research paper An Empirical Test of Mean-Variance Portfolio
Optimization.
Throwing Off Constraint
When optimization is properly applied, it can be very helpful. What most people fail to realize is that the constraints, or allocation limits, imposed on the optimization process by consultants are artificially derived. Where do these constraints come from? Consultants use either overall market allocations or what everyone else is doing. The truth is that constraints are really just compensating for bad assumptions. An optimization seeded with appropriately derived assumptions generally will not need them. Unfortunately, this might result in your allocation looking different from those of your peers. But then, maybe that's not such a bad thing after all.
