Independent advisors who think they are doing a good job for their clients ought to know about Mark Kritzman’s rigorous approach to asset allocation. Kritzman’s firm, Windham Capital Management Boston LLC, advises large institutional investors, managing over $6 billion in currency strategies while also providing asset allocation advice to large pension funds, endowments, foundations, and public institutions. Kritzman is also research director of the Research Foundation of the CFA Institute, formerly called the Association for Investment Management and Research, and has written dozens of articles in scholarly journals on portfolio management. It is because Kritzman’s methods are not widely applied among independent advisors that he should be heard. In the interview that follows, I focused my questions on the use of optimizers, an investment tool that Kritzman is expert on and which advisors generally have moved away from.
What do you think of advisors who are not using optimizers? Let’s say there are two advisors and they agree on a forecast of returns, standard deviations, and correlations. And let’s say their forecasts are reasonably good, and that one of them uses an optimizer. If they each have clients who want to select the portfolio that has no more than a 5% chance of depreciating by 20% in any given year, and you are advising that client ad hoc, then how can you answer that question without using an optimizer to figure out which portfolio is going to give me the highest return for the amount of risk I’m going to have these clients take? You can’t do that unless you have an optimizer. You can’t figure out that calculation in your head. Rules of thumb are not going to help. Second, without using mathematics, how do you estimate the likelihood of a loss? How do you know in your head what that probability ought to be? It is like preparing your taxes without a calculator. I think there are advisors who don’t really understand what the contribution of an optimizer is. Like a lot of things you don’t understand, they tend to be rejected, rather than admit you do not understand them. I guess if you are an advisor to individuals and you don’t understand the technology out there, you may not admit that. You are just going to say it doesn’t work. I’m not saying that this is what everybody does, but I suspect that is what is going on to a large extent.
Optimizers were popular in the 1990s among high-end RIAs working with the mass affluent, but came to be viewed as a rear-view mirror tool for building portfolios. You want to come up with a portfolio that is efficient. For a particular level of risk, you want to identify a combination of assets that is going to give you the highest expected return. Or, for a particular expected return, you want to find the portfolio that has the least amount of risk. The optimizer will isolate all of those portfolios for different levels of risk off of the highest expected return. The optimizer’s solution is based on estimates you make for each asset’s expected return and standard deviation, and then how their returns are correlated with one another. Sure, there are going to be mistakes in your inputs.
Let’s talk about what you call garbage inputs. Garbage inputs are na?ve, hard to defend, or simple-minded. An optimizer is just a sophisticated calculator. Not using an optimizer just compounds problems. If you get the inputs right, the optimizer will give you the right answer. If you get the inputs wrong, the optimizer will give you the wrong answer. If you get the inputs right and you don’t use the optimizer–in other words, you come up with the correct estimates for expected returns, volatilities, and correlations and then through some kind of seat-of-the- pants approach try to figure out what the best portfolio is–you have little hope of getting the right answer. So optimization is a necessary condition for coming up with the right portfolio. But it is not sufficient. To be sufficient, you would also need to have pretty good estimates. However, you have little hope of coming up with the right answer unless you do some kind of optimization process.
Is using historical returns in your optimizer a garbage input? It does depend on how long a history you have, but if you are using returns over the last five or 10 years, then I would say those are garbage inputs. More data is better. There are longer time series back to 1926 or even longer. However, if conditions are much different today than they were 50 or 100 years ago, then the distant past is less relevant.
But blindly relying on history is a major mistake, right? Yes. That is one of the reasons that optimizers have been criticized. People will put in historical numbers that make no sense and then they will do the optimization. It will give them some answer that either looks very strange or turns out to be not very good, and then they say optimization does not work. Well, that is not what happened. What happened is that they put in garbage for inputs.
And Harry Markowitz never intended it to be that way, right? If you read Markowitz’s classic article, “Portfolio Selection,” he says right at the outset that there are two steps in the portfolio selection process: forming beliefs, and how you take those beliefs and construct portfolios from them. His article, this seminal work for which he won the Nobel Prize, addresses the second step; how you take beliefs and then go forward, rather than how you form those beliefs. So the problem with the industry is that they form bad beliefs and then criticize the optimization processes as a consequence. That is just silly.
What is your approach to coming up with your own inputs? My approach is based on theory and history. The capital asset pricing model says that an asset’s return should be proportional to its contribution to the broad market’s systematic risk. There are two kinds of risks in the market, systematic risk, which is a function of broad, pervasive economic factors that affects all assets, and specific risk, which is based on factors specific to the individual securities or asset classes. The model Bill Sharpe won the Nobel Prize for says you can divide risk into systematic risk and specific risk, and then Sharpe points out that specific risk can be diversified away by holding a broad market portfolio. Since it can be diversified away, you should not get any compensation for bearing that specific risk.
So how does that get you to your return inputs? The expected returns of different assets should be proportional to how much they contribute to the broad market’s systematic risk. That’s another way of saying that returns ought to be proportional to their beta with respect to the broad market. If assets are fairly priced and if markets are reasonably integrated, that means you can trade to correct your perception of misvaluations.