Watch the video below to hear directly from Analytic Investors, sub-advisor to the 361 Global Long/Short Equity Fund, on the people, philosophy and process behind the strategy.
When we were thinking about launching a long-short equity fund and we knew we needed a sub-advisor, there was only a few people in the industry that we felt were qualified.
Analytic Investors is a Los Angeles-based quantitative investment management firm. We have a very strong research focus in the way we come up with investment strategies.
We picked Analytic, number one, because of their experience. We were looking for somebody that had managed long-short equity portfolios before.
Dennis and I started the first equity long-short strategy here in 1999, so we've been doing it for quite a while.
I think what's different about our relationship with Analytic versus other sub-advisor relationships is we do treat it as a partnership, and the firms are very similar. We're both boutique firms. We're not focused on managing the most amount of money. We're focused on providing the best returns for our investors.
Analytic has been known historically for being very innovative with respect to investment strategies, and I think the reason for that is really the way we are structured as a firm. The entire firm is involved in the research process. Lots of times people say, "Hey, how does the process work?" and they have this vision that I'm sort of a conductor. But I'm really not. I'm really part of the process, and I'm sort of, you know, basically a member of the jazz band. And everyone kind of has a unique role to play. We all play different instruments, we all do different things, and that's really what makes it work.
We have a lot of diversity, mostly in terms of the way we think. Some people have backgrounds in physics, others come with backgrounds, sometimes in the arts. So we really look for people who have a fundamental commitment to being intellectually curious about what we do.
One of the rewards of working for a small team is that if you come up with a new idea and somebody likes it, it will get incorporated into the strategy very, very quickly. So you really get that thrill of coming up with a new idea, testing it using real data, and then incorporating it in the strategy in a very, very small window, so it really fosters this environment of, "Hey, what can I come up with next that's actually going to help our clients?"
One of the truisms that people have in their head is that if you take more risk, you get more return, but that's not really true if you look within an asset class. So for example, if you look within equities, what you see systematically is that the highest-risk stocks, the stocks with the greatest beta, the stocks with the most volatility, are actually the worst investments. So you don't get rewarded for taking that type of risk. And what we're able to do at Analytic is basically build portfolios that exploit this anomaly.
Why someone typically chooses Analytic is they're looking for a strategy that's very systematic. So the footprint of the strategy is really clear. So we have a lot of transparency into the risk characteristics of the portfolio, how it's going to behave in up markets, down markets, what its correlation is to the market, what its correlation is to other assets. So those are things we manage very actively, and we are very clear about where the source of value is.
So there's lots of long-short strategies that have kind of the stock selection model behind it, the identification of good stocks and bad stocks from a valuation perspective. But that structural difference between the risks of our long book and the risks of our short book is what really makes this strategy stand out relative to its peers.
We are not doing what others do, which is to take a series of factors and build portfolios around factors and have static weights of factors. Ours is much more active. We are basically trying to pick the best factors, and we are varying our factor weights over time, so it's a very dynamic strategy.
So the expectations are that we should be able to significantly protect in down markets. If you look at our downside capture ratio historically, it's only 0.32, so we're providing substantial protection in down markets. Because we're only 70% notionally exposed to the market and have lower risk positioning, we typically lag in sharply rising markets. So our upside capture ratio is only 0.6. So you're getting less of the ups, but a lot less of the downs. And the combination of that smoother ride enables you to actually compound out better over time.
It's the design of the strategy over the long run, the fact that we have a short portfolio that's designed to underperform the market on average and has high correlation with the long portfolio, so it provides a hedge--that's what's going to generate the solid performance we've been able to do in the past.
Analytic has two core capabilities, which is the differentiation of kind of relative performance and the ability to differentiate low-risk stocks from high-risk stocks. And so when we created the global long-short strategy, we specifically designed it to exploit those two capabilities. So we structured it to be 100% long lower and moderate-risk stocks with outperformance potential because of their strong fundamentals, while simultaneously being short 30% higher-risk stocks that we thought could underperform because of their weaker fundamentals. So the portfolio as an aggregate is 70% exposed to the equity market, but because of the low-risk structuring within it, it actually has about half the risk of the overall market.
The long side of the portfolio is structured to be in lower- and moderate-risk stocks, which as an aggregate or as a group have a geometrically compounded return that's superior to the overall market. So as a group, they outperform. And then we're able to identify individual securities that we actually take long positions in that have better fundamentals. They've got superior valuations, better-quality balance sheets, stronger growth potential. And so you have a group of stocks on the long side that outperform the market on average, and then you have individual securities that we actually purchase that outperform that group.
The same thing's true on the short side of our portfolio. So the short side is positioned in the higher-risk stocks that on average geometrically compound out lower than the market, so you're starting in a good position. And then you identify individual securities that have weaker fundamentals, so will actually even underperform that aggregate group. So you're starting with a group that underperforms, and then you find the relative underperformers within that by finding stocks that have excessive valuations, lower-quality balance sheets, and less growth potential.
The benefits of being able to add value from kind of pure stock selection, meaning finding a stock that's good fundamentally versus bad fundamentally, and the benefits of positioning yourself in low-risk stocks versus high-risk stocks are actually uncorrelated with one another. So each of the two things work independently, but really, the best portfolios are formed by combining things that work independently but work in an uncorrelated way.
I think the most important thing we can do with either our long book or our short book is evaluate the merits of each of the two books on a daily basis. Every day we are reevaluating that portfolio, ensuring that it has the composition and the structural desires that we want it to have.
When I think of risk management from an investment standpoint, there's lots of different levels, and lots of times people think about risk management as just loading up a portfolio and looking at its risks from a mathematical standpoint. But I think that's just one dimension, and so there's lots of different ways you need to look at it.
First we start with the fundamental assessment, so we do analyze a stock's size and its style and its sector and its industry and its country, with an understanding that in the long run, that will probably explain a lot of the differences in the way in which stocks behave from a risk perspective. But to capture the differences in risks in those environments where fundamentals don't explain what's going on, we also use a statistical approach to modeling risk. And the statistical approach doesn't look at how a stock looks on paper or the types of risks a stock should experience in theory. It actually looks at the way in which a stock is behaving, so it uses daily returns to analyze the risks that an individual stock has actually experienced. So it's kind of a model that if it walks like a high-risk stock and talks like a high-risk stock, it's a high-risk stock.
But before we make that capital allocation, we also look at additional measures of risk. So we look at implied volatilities that come from the options market so we can understand in the short run how likely and how much a stock might move up or move down by looking at call and put prices. We parse news wire stories from throughout the globe that are done in local language so we can capture the content and the sentiment of those news stories and understand changes in the risk regimes that come from frequency of a lot of negative accounting-related news or a lot of negative regulatory-related news. We even look at environmental, social, and governance metrics with an understanding that there could be additional risks brought on by poor safety practices and weak board oversight.
The term "alpha engine" is simply a term that we use to describe the fact that when we assess the fundamentals of a stock and how much we like it or we don't like it, we aren't thinking of the total return of a stock. We're thinking about relative performance, or alpha. So we assess the merits of an individual stock by looking at 30-plus characteristics that really run the spectrum from measures of valuation, growth, quality, risk and liquidity.
And there's a couple of things that drive the assessment and the value that an individual characteristic might have to us. First is what's called its factor momentum. Factor momentum is just a term used to describe a phenomenon where characteristics that are rewarded by investors over the last few years tend to continue to be rewarded by investors over the next four to six months. So based upon that factor momentum or factor persistency, we understand which types of characteristics we might like and which types of characteristics we might not like.
The only problem with following that factor momentum blindly is it can lead you to a couple of traps. At times things can work, which causes a lot of people to kind of get this herd mentality, where, "X works, so I'm going to start to do it." And that breeds further success to the individual characteristic, and then that breeds further success, and we identify these bubbles by looking at kind of the near-term excess performance of the individual characteristic and compare that to a very, very long-term expectation for the characteristic. And so when that gap starts to materialize, we start to identify it as a bubble. And so our desire is to start to tilt away from that bubble before that herd runs off a cliff.
So portfolio optimization is just really a mathematical technique that enables us to solve a very complex problem accurately and in short order. So if you're thinking about what we're trying to accomplish when we're building a portfolio, we're trying to maximize expected return potential by looking at 30-plus characteristics while simultaneously considering five or six different perspectives on risks, while when we go to build a portfolio, we need to consider the actual transaction or execution costs that might be involved in trading 100 shares or 1,000 shares or 100,000 shares. And so there's all of these things that are going on before we can actually build a portfolio.
So, portfolio optimization enables us to revisit our portfolio decisions on a daily basis. So not only can I examine a portfolio and see whether or not it's meeting my objectives, I can test all of the alternative ways in which I might be able to improve and do that rather quickly. So I can run through almost a scenario analysis where I can look at dozens and dozens of potential improvements that we might be able to make in the portfolio, compare that to the transactions costs associated with making those improvements.
For me personally, I think the big benefit of this fund is that it has three sources of return. You are getting the return from the equity market, you're capturing part of the equity risk premium, you're capturing what we refer to as the beta anomaly, or the low volatility anomaly. And then you're combining those two things to reduce the volatility of the portfolio. And on top of that, you build in analytic stock selection capability, so at any given point you have sort of three things that are potentially going to help you generate a positive return.
You know, in the end, our clients are entrusting very large sums of money to us, and unless we do the research with rigor, it's hard for us to have confidence that we can actually justify their trust. It's really important to remember that the reason people put money in long-short portfolios is they want some comfort that the portfolio will behave in a safe and predictable manner in a crisis environment, and that's one of the reasons we have a very controlled degree of exposure in these portfolios.