Bob Elliott, HFND

Way too much. And what I mean by that is that across the hedge fund industry, the vast majority of the alpha that is generated by hedge funds was being taken by the manager in fees, leaving investors not that much better off than they would be on their own. And so if you think about those two key points, the solution to become a great manager or develop great hedge fund strategies is not to try and be smarter than everybody else, because that's a Sisyphean task. The key strategy to do well, to be the best hedge fund manager you can, is to diversify across managers, to reduce idiosyncrasy, and reduce fees.

And that's what Unlimited is all about, is basically taking that core set of concepts of diversified low-cost indexing, which has totally changed stock of bond investing, and bringing it to the world of hedge funds. Now, of course, doing that, you can't just invest in the funds because they want to get paid, and then you have to pay yourself something, and then you've got fees on top of fees. And so instead, what we've done is we've developed technology that looks over the shoulder of how managers are positioned in real time across each one of the major hedge fund styles. And then we take that understanding, translate into long and short possessions and liquid securities. And from there, we use that understanding to build our hedge fund ETFs. And so

We've been in the market for a few years. HFND, which is really just, you can think about it like SPY for hedge funds, matching hedge fund industry volatility, which is like bond risk, has been in the market for a little over three years, and has demonstrated a track record of tracking hedge fund industry returns. And then this year, we launched a couple of individual hedge fund strategies, HFGM, HFEQ, and HFMF, which are global macro managed futures and equity long short strategies, but done at a 2x target return, one that matches something closer to equity index volatility. And something that a lot of advisors find interesting, because it's more cash efficient, and you get more bang for the buck of the low fees you're paying.

And so because of that positioning is path dependent. And what that means is that when you see an outcome in a given day, we have some daily information, some monthly, some weekly information, that performance, the set of positions that are driving that performance, they have to be a set of positions that are close to what set of positions drove the performance the day before, and the day before that and the day before that. If you think about that, what it means is that there's not that many portfolios of plausible exposures that managers might have on that are adjacent to each other that describe all of these returns. And it's one of the ways that we can actually, one of the nice things about this way of thinking about it is we can actually pick up a lot

More of the tactical alpha, because we don't use sort of like a long-term rolling regression where we're sort of averaging months or years worth of data, we're solving for today's portfolio in the context of those previous portfolios. And look, the theory and the technology, I could describe conceptually to people, but there's also looking at it on the field. And when we look at how well, how those replications have done over the course of the last three plus years since we've been in real time, they're delivering on showing that we can actually track how hedge fund managers are positioned.

Exposure and do it rather efficiently, cheaply and easily would be the spot to look. So why should

Alternative asset and the way I describe it is alternative strategies, not alternative assets, but alternative strategies is like hedge fund strategies. They have the ability to go both long and short, which gives you the flexibility to be able to make money in any type of market environment. So as a simple example, let's take global macro or managed futures. Global macro managed futures both had extraordinary periods in 2022 when all assets went down, stocks went down, bonds went down, private credit and private equity didn't mark as much, but they were definitely under pressure. And at the same time, if we look at the last couple of months, those two assets, those two strategies, global macro managed futures have had some of the best performance they've ever had

During an environment when assets are going up. And that's a perfect example of the type of sort of all weather alpha that these strategies can deliver. The challenge for most investors is if you're investing in a single manager in an LP structure, that's the challenge for most advisors. You don't actually see that sort of outcome because of high single manager idiosyncrasy, which creates a lot of volatility around sort of the indexed alpha. And number two is the manager takes all the alpha away in fees. And so if you can diversify the managers and cut the fees, which we do as much as cutting them by to one 10th of the fees in our 2X products, then you've got a radically different return profile that is one of the most diversifying returns that you can bring

Points for it at a 2X target return. And so those managers are basically adjusting their equity positions through time in order to, they sort of maintain an equity beta, meaning it's like an active equity strategy with a positive beta equities, like 0.8 beta equities, but they're adding alpha on top of it. And it's pretty neat. When I actually started this, I was a little skeptical, honestly, about does it make sense to have an equity long short product, an active equity product. And the amazing thing is, if you'll look at a blog on our website, I actually compare the ability of equity long short managers, what their returns look like relative to the top 100 actively managed equity ETFs in the market on a matched timeframe. And what you see is that equity long short managers

Outperform the vast majority of the top 100 actively managed equity ETFs across the vast majority of different indicators like sharp or information ratio, or just straight out return over the course of, the last on a matched timeframe basis. It's quite remarkable. And maybe that shouldn't be a surprise, like hedge fund managers are smarter than ETF, active ETF managers. That's all there is to it.

And so if you can get ahead on the price, particularly, you can do it in a way that is, is quite disciplined, which, these systematic approaches do, then you can generate pretty good alpha over time. What we're doing are sort of, we have a slightly different replication approach than, say, what the folks at Return Stack are doing, or what Andrew Beer is doing, over at TBI. But no one would really know the difference from an eye, in terms of the difference, and they have great products, and you should absolutely be looking at those as well. But what we're doing is we're targeting a 2x target return. So one of the challenges in the managed futures space is that it's a pretty episodic set of returns, particularly ones that are defensive. And so you

Can have long periods of relatively modest performance, and then some periods of relatively extraordinary performance that are particularly diversifying to a 60-40 portfolio. If it's a big cash line item in your portfolio, as an advisor, you've got to defend the periods where the returns are relatively modest. But if you can get a more cash efficient implementation of it, which is what we're offering, then essentially it becomes a smaller and smaller, less relevant line item for advisors, so you don't have to talk about it. And you get that nice convexity in defensive properties when it's

And you can replicate what they're doing, right? You can say, well, look, I basically know how they're generally positioned. I can replicate it. That sort of rolling regression-based approach really served as the foundation of how most hedge fund replications were implemented really from lows period all the way up to relatively recently. And in general, found very poor traction in areas like global macro where there's a wide set of assets and where, you don't, let's say, if you have a 30 asset portfolio that's plausible at any point in time, you have to have at least 30 months of data in order to do your regression. And even then you probably should really have 60 or more than that in order to get an unoptimized regression. And so that doesn't really work because,

Hedge funds change their positions more than once every five years. It did work in the managed futures space. And the reason why that is, is there's high frequency data around performance. And very importantly, the positioning is relatively constrained, meaning like if the yen's been falling, managed futures managers are going to be short the yen. And so you have an ability to constrain that regression in a way that's a little easier than something as open-ended as global macro, 30 positions, could be long, could be short, who knows what they're doing. And so that's why I think it really caught on quite favorably. And for good reason, it's done a very good job in the managed futures space to replicate how those managers are positioned. The challenge is in a lot of other places,

We needed more powerful statistical techniques than just rolling regressions in order to pick up that tactical alpha. And frankly, we've come a long way. Like as a person who's been a systematic investor from the early 2000s to today, I have gone from running rolling regressions as being novel, or at least standard to now machine learning techniques being the, the cutting edge approach. And so my partner and I, we developed a proprietary machine learning approach of a Bayesian model, which does that probabilistic modeling and inference based upon timely returns. And the great thing about that is it allows us for a lot of these different strategies to understand what the portfolio of positions is describing the returns as close to today as we can in a way that leverages and

Contextualizes that set of positions in the past, but it doesn't average them in. And so it's a perfect sweet spot of using that past historical information, but getting that tactical alpha positioning that we're inferring. And, it's stuff that in reality was not like commercially viable five or 10 years ago. And, with the, uh, the advent of, uh, of cheaper and cheaper compute, you know,

Something that, that a lot of people don't realize. Like when you run a systematic, whether you run a proprietary systematic process or you run, uh, what we're doing, which is a, which is a replication, a proprietary replication process, like 95% of your explorations fail in terms of improving the process. Um, and that's, uh, that can be a little bit, uh, uh, demoralizing to some, but actually when I look at that, I say, no, that's actually really good because it means that you haven't over, over optimized your thing. And so you're, the process that you've used. And so, three years later, you want to totally revamp it. And so, um, we're constantly on that cutting edge, looking at, marginal to major improvements and we'll keep working on it.

How do I create something that will in the moment may not look as good as it could, but will in the future look better than how it could look in the moment. And so that sort of training and thinking that's the sort of stuff that comes from having run money for decades and seen the problems with trying to be overly precise. Uh, and it's sort of core at the, at the way in which we have developed our, our processes. And part of that's because, we've been doing this between the two of us for 50 years. Yeah. Yeah. You bring up a great, a good point. I'm going

Money, understands asset management and deploying strategies versus somebody who is a technologist or an academic that is, is putting, theoretical ideas on paper that look good, but in reality they break, uh, when you start to run them in real life. Yeah. I think a good, a good example is

Perfect combination, which will, for instance, here's a very simple example, which would have excluded silver from trading in your portfolio. Actually, Corey wrote about this recently, which I thought was, was pretty funny. Um, you would have never included silver in your managed futures, uh, universe, uh, because it was kind of mediocre for, I don't know, 30 years, something like that. And obviously it's gone up a lot recently. Um, uh, another approach is to basically say, look, I'm not gonna, I'm not gonna, I've tested a bunch of combinations. It basically shows that it's pretty good across a wide range of different plausible combinations. I'm not going to get too worried about that. And so what I'm going to do is I'm just going to write down the top

20 assets that matter. Let's just say by, by magnitude or, or, by, I'm going to have, five equities, five currencies, five fixed income, five commodities or something like that. You just kind of like in a blank sheet of paper, write the things down. And then you say, Hey, look, that's what we're going to use. and of course you can back test that and make sure it's not something that's totally insane, but that is, that's a very robust strategy because you're not reliant. Your opportunity set isn't really reliant on any particular empirical evidence. It's relied about intuitive evidence, understanding that these are the major exposures that the managers are taking and knowing that over time, that'll basically get

You what you need. And so that's actually how we approach all of our opportunities, whether it's futures or equity long short, um, or global macro. I like to describe it as, um, we are, uh, comprehensive in our approach, but parsimonious. And that is a very, no academic would solve a problem as comprehensive and parsimonious because it would, cause it fails all the, cause it, it inevitably will under, it will look worse relative to all the traditional academic outputs, even though it's a much better way to manage money. Couldn't agree more. Let me get back on the rails

In downside markets. And, obviously no strategy is perfect. That doesn't mean they will always do that. But what it does mean is that, um, having that potential is important. So it's why for us, a real focus on these two X target strategies is really important because the, the biggest indictment of, of the sort of alts and particularly the liquid alt space over the last couple of decades has been like, you don't get a lot of bang for your buck. And that is absolutely right. The volatility is too low and the fees are too high. And so, but it doesn't have to be that way. There's no reason why you have to take, the BlackRock strategy that has, de minimis returns over time and low volatility. You don't have to do that, right? You could simply

Target a higher return, something akin to something like equity index volatility, which is, we all experience equity index volatility. that, that is the basis for basically everyone's portfolio. There's no reason why you can't run these strategies at that level and cut the fees. And when you do that, you start to see things that create high diversification potential. And at the same time are not sort of dragging on that long-term expected return, uh, the way many previous rounds of liquid alts have.

Challenging. It doesn't mean they can't make money in other environments, but that's predominantly a defensive portfolio. You might think about it with other defensive strategies, like say a chaos ETF or something like that, which has got, um, which is protecting and downside environments. And then really for us, what we found is that the global macro strategy, which in many ways I described as sort of an all weather alpha, meaning it can go anywhere like globally, uh, long or short, it can go across different markets and therefore it can generate alpha positive, strong alpha in a wide range of different market environments. And the reality is when you think about sort of the balancing of, I want diversification, I don't want necessarily too much tracking area. I want higher potential return in, in a wide range

Of different market environments and a little flavor of defensiveness. That's really where global macro sits. And so the reality is most people for their first moving into our products are looking at the global macro strategy. it's why we've been able to get a hundred million in it, um, bit by bit over the course of the last nine months. Um, uh, because they see the real potential of it. It also doesn't hurt in terms of, how it's, uh, how it's delivered on its

Is not going to be easily replaced by BlackRock Vanguard or whatever, because, that would put a real cap on your business. And so that that's, so you start with that and you have something that's compelling along that dimension. And then the, the, the reality is from our perspective, you can't, we can't, we can't beat state street on number of reps in the field, quality number, uh, dollar value of sponsorship at exchange, number of billboards.

Competitive product or related product, right? Or might be just someone who's shown interest in looking at cutting edge ETFs, ones that don't have three-year track records or, or are from the Indies. That's all information that we can see live. And, and we go through a process of, uh, of leveraging that to, to, to do it. And, I, I like to say we have like a $0 marketing budget. obviously we pay, we have some employees, but, um, but we're not spending money beyond leveraging that content. Now, look, that's going to work for, that works for a period of time. And then there'll be a day when it will make sense to scale up our sales and distribution. But the biggest thing, I guess this is a little bit for like the industry people, the, the startup

ETF industry people, the biggest risk you have as a startup ETF, if you have a product that's differentiated and an investment mode is that you blow yourself up spending too much money on sales and distribution. That literally, that's what it is that you will, you will die. And we have seen one company after another blow through tens of millions of dollars, uh, without much to show for it, doing it right off the bat. And so the key thing is stay small, uh, stay focused. And if you do that and you have a good product and you stick around long enough, you can really differentiate and it doesn't take much, once the ball gets rolling, every day those creates come in and you're like, here we go. Yeah. It's funny. I think I, yeah,

About unlimited? Where can people get information on all your funds? And I know you're pretty active, uh, on social, which is always a good follow. So why don't you give people a little bit of information about that as well? Yeah, yeah, for sure. Uh, anyone who's interested in our products,