We’ve always known it’s important to allocate assets among stocks, bonds, etc. in a reasonable manner. But when it comes to implementation, the more we think we understand, the more we realize we don’t understand. So more often than anyone likes to admit, we’re pulling allocations out of folklore, stereotype, gut instinct, etc. So if you decide to go robo, you need to understand how such prototypically human judgments are made.

Focusing on Wealthfront

I’m going to focus today on Wealthfront. They’re one of the big gorillas in this field so that alone makes it reasonable to do so.

Another consideration: They have been far and away the most transparent among the group, having made a detailed and thoughtful white paper available even to those who haven’t registered as clients. This is important. An investment adviser is a fiduciary and that doesn’t change even if the adviser’s brain consists of microchips rather than gooey substance. And when it comes to fiduciary relationships, transparency counts for a lot. So on this very important matter, Wealthfront wins hands down among the large generalist robo firms.

Also, in terms of the kind of portfolio you’re likely to wind up with if you go robo, at least with a big generalists, they are almost carbon copes of one another, so if you analyze one, you’ve pretty much analyzed them all. (I’ll point out little differences as they become relevant to any discussion.)

The Heavy Art and Lite Science of Asset Allocation

Given that the science of asset allocation has a pedigree that includes two Nobel Prizes (Modern Portfolio theory, or MPT and the Capital. Asset Pricing Model or CAPM), you might wonder about my characterization of it as “lite.” Bear in mind that these prizes were in the category of “Economic Sciences,” a field that has often been dubbed “the dismal science.” How dismal? When it comes to asset allocation, we’ll see that it’s dismal on steroids.

We can easily articulate the problems to be solved, and our Nobel Laureates gave us methodologies for solving them. Once upon a time, these algorithms were hogs requiring lots of heavy-duty computing power. But nowadays, like many things I.T., it’s a piece of cake. CAPM can easily be dashed off with a free calculator app and even the complex MPT can be done with Solver, a free add-on that comes with Excel (the existence of which you may or may not be aware). And those who want to work on an Apple or Android tablet, where implementations of Excel can’t readily handle the heavy stuff, it can be done on-line through Google Sheets (it’s created by Frontline Solvers, the same folks that supply the one used in Excel).

The hard part is coming up with credible inputs for the models:

Free shareware implementations of MPT presume you’ll use historical data to generate those inputs, and often don’t even feel a need to allow you to override that approach. So, too, do classroom exercises, where professors can make darn sure they concoct historical data sets that can, indeed, serve as reliable proxies for the expectations regarding the future (i.e. assets whose expected returns should be positive are given histories that are free from inconvenient negative averages, no one asset is allowed to be so far superior to the potters in terms of risk and/or reward that it “dominates” the result set, etc.). But in real life, nobody in his or her right mind would even think of doing anything like that since the only thing that never changes is the fact that things change and since no matter what sample periods you use, there are likely to some assets whose results were pumped up or atypically depressed by factors that are unlikely to be sustained in the future.