Identifying Factor Risk In Smart Beta ETFs

June 05, 2018

Sebastian CeriaSebastian Ceria is founder and CEO of Axioma, a company that’s one of the leaders in risk modeling and risk management. He will be talking about the many facets of risk in smart-beta ETFs at the upcoming Inside Smart Beta & Active ETFs conference in New York City this week. Here Ceria offers a preview. I’d think it’d be difficult today to model risk given so many variables, so much volatility, countries so reactionary to everything. Governments typically signal things, and there's some predictability about the outcome of that signaling, but today we don’t seem to have that type of clarity. Some say it's easier to forecast risk than returns, but do investors have the right tools to forecast risk today?

Sebastian Ceria: It’s definitely easier to forecast risk than return. Nevertheless, there are periods in time when it’s easier to forecast risk than others. And that has to do with market behavior and structure relative to the past. Ultimately, to predict risk, we have to use the past to learn about the future.

When we think about predicting risk, we tend to think about at least two very different scenarios. One is when things are “normal”; in which case, history tells us a lot, and there are a lot of tools we’ve developed to help us get a pretty good grasp of what risk is going to be in those times.

Second is times of stress. There, obviously, predicting risk ends up being more of an art than a science. You have to use history to inform yourself, but you also have to be able to build some sort of scenarios you can use for stress-testing your portfolios in those times.

For example, in the context of the Italian elections or euro—the euro crisis we're seeing right now—we're helped by the fact that there are times in the past where similar scenarios have taken place. True, it was not Italy, but we do understand what happens when the euro is in distress, because there are times in the past where the euro has been in distress. We can learn from those events and try to link what a euro distress means to other factors and how they drive the risk.

That’s what risk management is about. If you're in normal times, it's about understanding from past history how things behave. If we're in an abnormal time, we use our ability to do macro analysis to figure out which macro factors will be affected, and then use history and our learnings from the past to try to see how those macro effects that we think are going to happen are going to affect the other factors that drive the risk. In these times of stress, do investors see index investing as a way to manage risk, or as providing a layer of safety—no need to try and figure out the markets; just own them?

Ceria: I don't think the move to passive investing comes from situations like this. For example, you’d never say, “Let's buy a passive European index because I'm afraid about Italy, because ultimately, European equities are going to suffer because Italy is going to suffer.” So, indexing isn’t necessarily the refuge for situations like this.

I think indexing is much more of a wider phenomenon, which is less related to the risk environment and more related to the fact that investors are not getting from active managers the net-of-fee returns they’d expect relative to a passive vehicle. The move to passive is more a return-driven phenomenon than a risk-driven phenomenon. You're speaking at a smart-beta ETF conference, so let’s talk ETFs and risk. There are two basic models for looking at factor risk: one being through regression analysis of a portfolio relative to an index; the other by looking at individual holdings. Which way is better?

Ceria: The way we look at factor risk is by building, first and foremost, factor models that are independent of the portfolio. Those factor models are informed by a combination of the literature that’s out there, and then applying those factor models to the ETF and looking at the actual holdings. That gives us a true understanding of how those individual securities are going to drive the risk of the portfolio.

Contrary to that, regression analysis has many issues. First of all, you have to figure out what you’re regressing against—what’s the “factor exposure” you're going to be regressing against and how do you define that? Second is that those betas you find for those regressions can be quite unstable. They're not very consistent over time, which means they're not going to give you a great sense of risk.

The factor model approach—which looks at holdings and then applies the factor model to get to a risk composition of the ETF—is much more accurate in terms of telling you which factor risks are driving the risk of your portfolio.

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