We were visiting a hedge fund some years back when we had our first taste of the problem with mean-variance optimization—the tool advisors use to balance risk and reward in client portfolios. We loaded a portfolio’s positions into an optimizer, pressed the button, and discovered 25% of the portfolio should be in General Mills. You’ve probably experienced the same sort of weird behavior from your perplexing optimization tools, which sometimes act like impenetrable black boxes.
What is going on? If we can’t trust this tool, should we throw up our hands and revert to making adjustments by hand?
Computers Versus The World
As far as the computer is concerned, the optimization tool is doing exactly what it’s supposed to do. The problem for us stems from the computer’s insistence that the key data, the variance-covariance matrix that encapsulates the relationships among the variables, is the literal truth, and will be for all of the future. For General Mills, there were two instances where the stock moved in the opposite direction of the market in a big way, giving the company a strong diversification value—but only if we have a replay of the past. That’s not likely.
If the relationship among equities is fully reflected through that matrix (which it isn’t), if that relationship never changes (which it will), and if the optimized portfolio is held unswervingly forever (which it won’t be), the computer is getting it right.
Of course we know the variance-covariance matrix is only an estimate based on what has happened in recent history. We also know that the world will change.
So even if we got it 100% right, it would not work going forward. Also, we are not holding the portfolio long enough for the most optimal performance to show through. We will be changing the portfolio according to the client’s objectives and the changes in portfolio value, so if we “set it and forget it” with the current optimal portfolio, we’re ignoring the ever-changing world.
Portfolios Versus People
Our most recent run-in with mean-variance optimization was in trying out a software program designed specifically for financial advisors. True to form, it occasionally gave funny results, though these were not as bad as other outcomes because there was a secondary logic filter to keep its results from getting too out there. But its results missed the question being asked. Advisors are not fishing for alpha; they are not trying to maximize return for a specified portfolio volatility. They are trying to create the best portfolio for their client. And that means designing a portfolio that meets the client’s objectives, something that goes beyond returns and volatility risk. The objectives are multifaceted and vary over the client’s lifetime.
If we don’t look at the client’s objectives, we risk using an optimization program that resonates with a portfolio manager and falls flat for a client.
For one take on these objectives, Ashvin Chhabra, the president and CIO of Euclidean Capital, looks at three types of client objectives, requiring wealth for three different buckets: Clients want a baseline of financial security, they want to maintain their lifestyle, and they have aspirations they want to fulfill. Each of these buckets demands different portfolios, ranging from something with low risk and high liquidity for financial security to something with high risk for the clients’ pursuit of their aspirations.
The clients’ objectives will vary over time and in a somewhat predictable way, and will require more from the advisor than the simple minimizing of data variance for each bucket. A single client in his late 20s with marketable job skills needs less in financial security than someone in his mid-30s who is married with three children in tow, and his needs are different still from an empty-nester in his 60s with amassed wealth. How can a mean-variance optimization speak to this? It can’t.
Most importantly, advisors cannot set it and forget it because the path of a client’s life is subject to twists and turns. We want to design the portfolio the way we might design a guided missile. When we are far away and see the target veering to the right, we don’t keep going straight ahead, but we also don’t move to direct ourselves to its current location either, because we know its current location has a cloud of uncertainty around it; there is zigging and zagging to come. Given the sensitivities of standard, mean-variance optimization to estimates of returns and correlations between assets, in times of uncertainty this optimization approach might tell us we need to make big changes to a client’s portfolio, but that leads to substantial costs with ultimately little value to a client’s needs.