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Q: Where should correlation fit in portfolio design? Correlation is the unloved redheaded stepchild of mean variance optimization. Traditional models for large, sophisticated, institutional asset owners have evolved to focus on expected return, expected volatility, and correlation.
What do we think the equity markets will return over the next ten year period? Or the bond markets? Or inflation? Or cash? Everybody has an opinion on expected return. Some people will have an opinion on expected risk as well. But correlation is a key determinant of not just the portfolios that you structure but the way that you define the assumptions in your model and the internal consistency therein.
Q: How do things look different using risk factors? For example, inflation is very hard to decompose further, versus a bond, which is sensitive to numerous risk and return factors that are macroeconomic in nature—what happens with GDP, real interest rates, and inflation along with asset class-specific things like duration, convexity, and spread. Investors looking to maximize wealth are interested in holding compensated premia.
It needs to make sense from a first principles economic standpoint. Is this a compensated premium? We can group factors into different buckets. The macroeconomic bucket has things like exposure to GDP growth and productivity, real rates, inflation, and volatility. A regional bucket includes things like currency, emerging or developed market, and sovereign exposure. The equity-specific bucket includes size, value, and momentum.
For fixed income there is risk of default, where this bond is in the capital structure of an organization, and, as I mentioned, duration, convexity, and credit spreads. In the traditional model, when I hold a U. In a risk factor context, I can put factors together in such a way that I explicitly capture that crosstalk and understand where the overlaps are and where the gaps are. In theory, I could do this at a very granular level with every possible risk factor.
So I have to create factor-mimicking portfolios typically using long-short spread exposures. If I want to replicate something like size, I would be long a global small-cap index and short a global large-cap index to get exposure to that particular factor premium. Could you explain here how factor-based investing moves from theory to practice? The first real step is to define the parsimonious set of factors.
How many do you need to cover the investable universe? Do you have any gaps? Did you have any overlaps? Do you have things that are truly uncorrelated? Once we decide which factors or premia to look at, the next challenge is trying to come up with ex-ante predictions for risk, return, and correlation for each of those factors. For instance, what do I think is going to happen with real interest rates? That may take some of the measurement error out of the estimation. So in a mean variance optimization model, I need to make a judgment on what the expected returns, and the risk, and the correlations of the different pieces of my portfolio are going to be before I put them together.
I think that there is a power in focusing on the smaller, granular units used for risk factors. Now, we also have less experience doing that. Q: How often do you expect to be questioning ex-ante assumptions? This is a great question, because it also applies not just to factors but to asset classes as well. So for something to change, it needs to be not just a marginal change. It needs to be something real like a change in market structure or a long-term secular trend that is going to impact a particular factor or asset class.
His work with industry was recognized by aiCIO naming him one of the top 10 most influential academics in the institutional investing world. Ang used to be a Fellow of the Institute of Actuaries of Australia. I view myself as a conductor. I deliver investment outcomes in terms of factors — broad, persistent drivers of returns.
I think factors are going to be transformative but in order for them to be very wide-spread, we need data and technology. We need efficient investment vehicles. The way that I see data and technology is very similar to my phone, my smartphone. And I love my smartphone. So we can deliver those types of intuitive investment styles across the world. We can trade them with ruthless efficiency and we can help them to achieve investment outcomes. Risk management has always been part of the DNA of this firm and factors have been central to that.
Reducing risk or enhancing returns. Increased diversification.
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