361 Global Long/Short Equity Fund Q&A

 

George Matthews, CFA, Head of Consultant Relations
Analytic Investors, Sub-Advisor to 361 Global Long/Short Equity

 

 

 

Q: What makes the thesis surrounding the 361 Global Long/Short Equity Fund so interesting?

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A: The global long-short strategy is pretty interesting, because what we’ve learned from low-volatility equity investing is that there really isn’t a very strong relationship between risk and return within equities. What I mean by that is lower-beta stocks tend to keep up with, moderate-beta stocks. And in fact, high-beta stocks actually have a little bit of a headwind. They tend to have a lower expected return than more moderate-beta securities.

And so the low volatility, the long-short equity strategy is pretty interesting in the sense that we’re not necessarily exploiting the low-vol anomaly from the long perspective. We’re actually exploiting it from the short perspective. So we’re shorting very high-beta stocks. And what’s nice about shorting high-beta stocks that tend to have a lower expected return is that you don’t have to short a lot of securities in order to really take the risk down for the portfolio.


Q: What differentiates the 361 Global Long/Short Equity Fund from its peers?

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A: I think what really differentiates our strategies from some of our peers is the fact that we’ve got an incredible amount of risk management.

We’re taking a lot of the technology that we’ve learned, being one of the pioneers in low-volatility equity, and using that risk management framework, pairing it with 20-plus years of an alpha engine in one package in this particular strategy.

What’s interesting is the portfolio is 100 long, 30 short, but a beta all the way down at 0.5. So we still have a net 70 exposure to equity securities versus our peers, that typically, if they’ve got a beta of 0.5, tend to be 100 long, 50 short, or some combination of that.

At the end of the day our research suggests is that you’re not getting paid for taking beta risk. So just by your equity exposure alone, we’ve got 70% exposed to equities. So in the long run, we should capture 70% of the equity return. However, our beta’s at 0.5, so we tend to have about half the volatility of the market—very different from peers, that in the long run, they’ve got about 50% market exposure. And as a result, just from their equity, stands, they’re capturing 50% of the equity return. So they’ve got a little bit of making up to do. And generally, what happens is our peers tend to have to really crank up their alpha to just get good returns. And that can be a detriment, especially when the market turns, because it’s likely that their alpha engine is weaker in that environment, and as a result, they’re more likely to underperform and not protect like they’d want to.

Our portfolio is structured in such a way that it’s long, $100 of more moderate sort of defensive stocks. So the beta on the long portfolio tends to be around 0.9. The beta on the short portfolio tends to be around 1.5, and it’s only about $30 that we’re shorting.

So when there is a large sell-off—and yes, our alpha engine is likely more weaker in that environment as well—we’re structured well, and we’ve got a lot of confidence in the risks that we feel that we have in the portfolio because we’re relying on all of the academic work and all of the risk modeling techniques and technology that we’ve implemented into these strategies from our from our low-volatility equity experience.


Q: Describe the risk modeling techniques used?

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A: Some of the things that we do from the risk management side that I think are quite robust compared to peers when we’re valuating stocks we want to have really good confidence in terms of what to expect from those securities. So we use two different risk modeling techniques. We use things like implied volatility to help us better understand if the risk profile of the stock has suddenly changed. We use a news-based algorithm to tease out if there’s something going on with a stock, and basically, we use that news-based algorithm to create synthetic implied volatility. Not all stocks have implied volatilities. We even incorporate ESG into our process. From our perspective, everything else may indicate that a stock is low risk. However, maybe they’ve got a hidden risk from an ESG perspective. They’ve got poor safety practice… they’ve got poor environmental practice. These are left-tail risks that we are just not willing to take. And so as a stock starts indicating that it’s got a lower ESG score, we’ll start limiting our confidence in that security.


Q: How is the portfolio constructed?

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A: The way that we build this portfolio, it’s a combination of buying stocks that have beta say, less than 1, so it’s not necessarily a low-vol portfolio on the long side. It’s more of a defensive portfolio. The long book has, call it beta 0.8, 0.9. We’re trying to buy stocks that we really like in that space. So from an alpha perspective, that’s where we’re trying to add value from stocks that we like that have slightly lower beta. And then we’re shorting stocks that from our alpha perspective, we dislike, but they also have very high beta. So it’s a combination of alpha as well as this beta play. So really taking advantage of what we’ve learned, in the sense that high-beta stocks tend to have a lower expected return than stocks with beta 1 or less. And so we’re using those stocks to short, very efficient place to take risk off the table, because they tend to have a lower expected return. And for every dollar you short, you get a lot of risk out of the portfolio, coupled with picking stocks that we don’t like, the ones we expect to have very low performance versus peers. So that combination seems to work really well, through a market cycle.


Q: Can you explain the alpha engine?

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A: From an alpha perspective, we use 30 different factors to help us pick securities. The way that we do it is we determine which factors have worked well over the last 36 months, and we try and tilt towards those factors. The weakness of that is, if a factor’s done really well, you may start overweighting that factor, or maybe that factor’s been neglected. What we do to complement just looking at the 36 months is we pair that with the long-run payoff to the factor. And we use that as an anchor point.

So if a factor starts getting really far away in terms of its expected return versus its long-run payoff, we’re going to get less confident in that factor. And so we’ll certainly probably have an overweight to that factor, but we’ll have—we will be the ones sort of hovering around the door as opposed to the back of the room. So when it does mean revert, it’s going to have less impact on our portfolio.