Replicating Hedge Funds Using Machine Learning feat. Bob Elliott

If you just take all the people who have taken the same classes and learned the same canonical set of thinking, they'll all basically have the same view of what's likely to transpire. As you look at some of the biggest hedge funds in the world these days, they really are looking for a wide variety of different perspectives which can bring that unique perspective and see things that other people couldn't see.

Totally makes sense. And if you're thinking like everybody else, you're just investing in the market, you're going to get market returns. It sounds so simple, but to get better returns, you have to think differently to find the opportunities other people aren't seeing. And sometimes it requires a different perspective from another area that you can kind of translate into this area, right?

Money hasn't tightened enough to slow the economy. The expansion continues, but we're starting to get the difficult point of that expansion where we're going to have to tighten money more. Either the Fed will have to do more or—

Hello, future billionaires. Welcome back to the Invest Like a Billionaire podcast. We've got a fun episode today. Working with Bob Elliott of Unlimited Funds. So Bob is a quant, he's a macro guy. He comes from the world of hedge funds and actually spent the majority of his career at Bridgewater, which is now the largest hedge fund, and really has brought a really cool perspective to the alternative investment space where most of the hedge funds and a lot of types of alternatives, as you may know, have difficulty being accessed for a lot of investors, and they also lack liquidity many times. So they are using machine learning and different models that they've been running for a long period of time to replicate the returns in the portfolios of hedge funds to be able to bring the alternative investment benefits using liquid public market securities. So you have the ability to still have liquidity. So really, really cool. We get all into the weeds of it here of how it works, how they use this model to work with hedge funds, work with VC firms, also work with more traditional private equity. These are all topics we've talked about and how they're replicating that in their models. Trading publicly traded securities. And then the very end, we talk a lot about macro and kind of where he sees the market going. Very, very smart guy. I think you're really going to enjoy his perspectives here. Definitely aligned in a lot of ways. And I do want to give the caveat, as I always do on any episode where I bring on someone who may be raising capital. This is a disclaimer that you have to do your own due diligence, do your own research. We don't perform due diligence. We bring people on to the show. We bring people on because we're curious about things that they're doing because of expertise, but have not done any due diligence on the track record, what they're offering. So if there is interest, that's on you to go perform that due diligence or talk with advisors, talk with people that know these particular things. So with that, enjoy the show.

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Welcome back to the Invest Like a Billionaire podcast. I'm your host, Ben Fraser. Today we've got an exciting guest to chat, uh, with Bob Elliott. He is the co-founder, CEO, and CIO of Unlimited Funds. He uses machine learning to create low-cost index replications of 2-and-20 alternative investments like hedge funds, venture capital, private equity. So that's a mouthful. For those of us that aren't as smart as you, Bob, give us a little bit of your background and what that means and what you're doing right now.

It's just really interesting.

Yeah, thanks so much for having me. I, I, uh, I've spent a couple decades as a systematic investor in the 2-and-20 space. And when I say 2-and-20, what that often refers to is basically the sort of strategies that charge a 2% fixed fee and a 20% performance fee. So things like hedge funds, venture capital, private equity, etc. Um, and during my time in the 2-and-20 world, increasingly realized that many of those, uh, strategies they're particularly good for the managers and not that great for the investors. And the reason why that is, is they're often structured in a way where the fees are too high for the investor, where liquidity is difficult for the investor to gain access to, and where, particularly for taxable investors, they're put in structures that are not necessarily the most tax-efficient vehicle for the everyday investor. And so, Our idea around Unlimited was to use technology and our decades of experience to basically replicate how those managers are positioned in close to real time and then package that into financial assets, things like ETFs and other structures, which make those returns available to a broad set of investors in a way that's much more liquid, transparent, and tax efficient than what's out there today.

Love it. Okay, super excited to get in. Before we do kind of get into the model and how you've created this, give a little sense of your background because obviously you got some, some big names on your resume, including Harvard, Bridgewater. Talk a little bit about just your journey in this space and really what brought you to this point now. Where did you see an opportunity and, you know, why did you start Unlimited?

Yeah, my, my, uh, my background, uh, academically is, uh, probably pretty pretty unusual relative to most of the people you talk to. Uh, I was a botanist, uh, by training, uh, academically, uh, thought maybe that that's what I would do with my career. Um, and the, uh, and the, the pure sciences I think were a pretty unique way of thinking that was— that's always been useful in, in applying it to the world of markets and macroeconomies and investing. You know, because in many ways, if you think about the macroeconomy, it's just one large complex system that's where lots of different pieces are interacting with each other all at the same time. And to understand the macro outcome, one case growing a plant and the other, you know, the macroeconomy, you have to understand all those underlying systems and how they interact with each other. And so that is a particularly, I think, unique lens into the world. And when you think about investing in general, you have to have a unique perspective, a non-consensus perspective in order to generate differentiated returns over time. And then I, you know, started after I left college, started to get interested in macroeconomics for a variety of reasons and spent almost 15 years at Bridgewater Associates, which transitioned while I was there from being a small challenger organization in the woods of Connecticut to being the world's largest hedge fund. And it was particularly focused on, you know, systematic macro understanding.

That's so cool. Well, I'm a little bit of a fan of Bridgewater and Dalio. And so those who are listening, don't know those names, you need to go do some research. Ray Dalio started Bridgewater, some partners, and it's so cool. I guess you hear you say your background in botany, you know, might be unique, but Ray was kind of famous for this, right? Where he kind of skewed the normal models of you gotta go to Ivy League get the MBA and the finance degree and, you know, and go through the system and then come over here. He wanted people that thought differently and could bring disciplines from other areas and apply them into investing. Because as you kind of said, investing mimics a lot of different areas of life, of science, of the world, and understanding these things and the components of it. You don't have to just learn in, you know, an Accounting 201 class.

Right, right. Well, I think if you think about how do you generate alpha in markets, and alpha meaning how do you generate returns that are differentiated from, you know, passively investing, you have to— the way you do that is by having a unique perspective, a non-consensus perspective, on what's going to happen, you know, to the economy, to markets, you know, to various investment assets depending on what you're looking at. And if you just take all the people who have taken the same classes and learned the same canonical set of thinking, they'll all basically have the same view of what's likely to transpire in the world. And so part of the edge that, you know, Bridgewater certainly focused on this early, but As you look at some of the biggest hedge funds in the world these days, they really are looking for a wide variety of different perspectives, backgrounds, you know, academic experiences, et cetera, which can, you know, bring that unique perspective and see things that other people couldn't see.

Yeah, no, totally makes sense. And you kind of alluded to it, but the idea of alpha or generating above-market returns, if you're thinking like everybody else, you're just investing in the market, you're going to get market returns, right? So it sounds so simple, but to get better returns, you have to think differently to find the opportunities other people aren't seeing. And sometimes it requires a different perspective from another area that you could kind of translate into this area, right? And so I, one, it's just, I love that I kind of nerd out on the background there because I think it's such a cool experience. Talk a little bit about, so working for the world's largest hedge fund, you know, from our research, our perspective, and this whole kind of goal of the show, educating around alternative investments, there's some distinct advantages, right? Where you see a lot of the ultra-wealthy investors, you know, the endowments, the pensions, the family offices, institutional investors are investing in these types of assets and have been for a long time. And, you know, with great success and sometimes not, but having a more diversified portfolio, having alternatives as part of the portfolio, you know, that's become kind of a standard in a lot of ways, right? Where maybe 30, 40 years ago it wasn't. But there are some downsides, right? The— probably the biggest one being liquidity. And so talk a little bit about, you know, your time spent at Bridgewater. What did you see that worked well? What did you see that was— how did you guys grow into the biggest hedge fund?

Right.

Number one. And then number two, what led you to leave and do your own thing where you maybe saw an opportunity that wasn't being addressed?

Yeah, I mean, I think for the world of institutional investing, if you just look at it, you know, most institutional investors these days put about half of their capital into alternatives. And there's a lot of good reasons why they do that combination of, you know, higher risk-adjusted returns, greater diversification, and really access to outcomes that wouldn't otherwise be available, you know, to just purely in the public markets. And so I think when you think about that space, the real advantage that hedge funds bring is the ability to tactically move, you know, long and short between assets. That's something that a lot of different strategies, you know, whether you're passively invested in stocks or bonds or, you know, more structural long alternative assets like private equity and venture capital, you don't have that opportunity to navigate and switch your positioning depending on the market. Now, in general, being long assets and, and, and being invested is, is the right strategy to do in general over time. So it's not to say you want to, you know, put all your money into a long and short strategy, but there are certain times when those strategies can be complementary to an overall portfolio being long assets. And that's really what hedge funds bring to the table is that ability to bring, using long and short positions in liquid markets, to be able to bring that agility to your portfolio that you otherwise wouldn't be able to do. And frankly, part of the reason why it's, you know, they're so successful is they're able to hire some of the, you know, the smartest minds in the world to be able to generate that alpha to generate those strategies and, um, you know, are essentially hired to do that by institutional investors. And the outcome is, as you say, which is that institutional investors, in part because of their allocations to things like hedge funds, uh, you know, typically experience meaningfully better risk-adjusted returns than, than the everyday investor.

Yeah, talk a little bit about just how hedge funds work. We've, we've talked about this in previous episodes, but it hasn't been the primary focus for us, just because it does kind of seem, you know, set aside for the big investors and not as accessible to more everyday retail, you know, credit type investors. But talk about the idea of long and short, right? Because if you're investing in real estate, you're buying an asset, you are inherently in a long position, meaning you're bullish on the future value of that being higher than it is now for whatever reason. But in hedge funds, you're generally trading in public markets, liquid markets, and you can create hedges with different positions, long versus short, betting on, you know, different things going on. Talk about where that kind of fits in a portfolio from your perspective and explain what that means a little bit more.

Yeah, I mean, most hedge fund strategies in, in are typically taking are trading some of the largest liquid markets in the world. So, you know, things that you see covered regularly on CNBC, stocks, bonds, currencies, commodities, you know, fixed income, credit, et cetera. So, you know, maybe 100 or 200 of the world's largest liquid markets. And one of the nice things about typical hedge fund structures is that they have the flexibility to go long and short. So, you know, they're in general, as an example, you want to be long stocks because stocks, there's good reason to believe that stocks will go up over time, say relative to holding your money in the bank in cash. But there are certain macroeconomic circumstances where stocks go down. And I think one of the challenges for many long-only investors and, and really the challenges for any passive investor is that if you're just simply staying long through time, you can experience meaningful drawdowns. For instance, in stocks, you can experience 60%, 70% drawdowns in your equity market portfolio. And so what hedge funds are really focused on doing in general is trying to identify those circumstances where, you know, one or many of those major asset classes have pressures on them to either go you know, up or down more substantially than what's expected in the market, and then build a portfolio of trades, which are really just bets on those markets. And so one of the benefits of being in a hedge fund structure is that flexibility, going long, going short, not being locked into a particular view and being able to respond to what is likely to occur in the macro economy.

Right. So if values go down, if you have short positions, then you actually make money, right? Versus losing money. And so you have that flexibility to, to, to be— go back and forth and be agile, as you said. So what was really the driver for you to, to leave Bridgewater? You know, it's, uh, you know, I'm sure it's got its pros and cons, but obviously, um, sure, great experience. And what, what led you to start Unlimited?

Yeah, I mean, it's definitely a great place to start, uh, my career and, and also, you know, help be— help, uh, build something that went from that sort of challenger world, which is kind of hard to believe given where it ended up. But, but that's— it was, it was that way when I started to being the incumbent. I think, you know, naturally companies as they move through that life cycle to incumbency start to slow down. The desire to innovate starts to moderate as well. And the frank reality is I've always enjoyed smaller, leaner startup-type environments. And so I wanted to get back to something that was, you know, was the new challenger of the market. And so what, uh, my co-founder Bruce McDevitt and I saw was that there really wasn't, uh, anyone in the market trying to bring 2-and-20-style strategies to make them more accessible but also cheaper. Uh, you know, there's a lot of work to try and increase accessibility, you know, to increase accessibility, but in certain circumstances those may— those, those strategies may end up adding to the fee problem or adding to the illiquidity problem rather than reducing fees, increasing accessibility, and increasing liquidity. And so we saw an opportunity to build technology based upon our 50 years of experience between the two of us to be able to build replications of those strategies. And because we're building, you know, replications, using liquid public market securities, we could package those things into liquid securities like ETF structures or other structures. And because we're using technology, we could do it at a much lower fee point. And so our basic idea, basically premise behind Unlimited is that idea of diversified low-cost indexing in the same way that has changed and totally revolutionized stock and bond investing, bringing that to the world of 2 and 20.

Yeah, makes sense. So talk a little bit about just practically how you do that, because what you're saying is, hey, there's obviously some clear advantages of these alternative investments. Otherwise the big money, the smart money wouldn't be doing that. But the limitations are usually accessibility. A lot of the models that have created accessibility either might be feeder funds that have other layers of fees Or they're just limited to accredited or qualified investors that have to meet certain minimum financial thresholds to even have access to them. Some funds I know you can't even access without an advisor and, you know, certain levels of advisory. And so what you're saying is, hey, what if we can replicate the benefits of, you know, some of these models that the 2 and 20, but actually make it lower cost and using liquid investments to also reduce the, um, the illiquidity, uh, kind of downside of this. Am I hearing that right?

That's exactly right. And so, I mean, the way the, the, the, the key to being able to do that is, um, is through our, uh, return replication technology. And it's, and it's, you know, what we do is, um, we, we can look at manager returns, compare those returns, which we see in close to real time, and we compare those returns to what's happening in financial markets and basically back out, infer how managers must have been positioned to generate the types of returns that we're seeing. It's the sort of thing you must do, you know, everyone does when they're looking at a particular manager's returns, right? Say, you know, you know a manager, you see how they did in a let's say, a given month or a given period, you know what happened in the markets and you kind of say, well, look, I kind of know they must have been overweight tech or, you know, underweight bonds or overweight stocks to get the kind of outcomes that they're doing. All we're doing is just taking that core concept and just applying it, you know, using machine learning, which is really just a more computationally rigorous way of approaching that problem than, you know, than just kind of eyeballing it and guessing.

Interesting. So are you using correlation analysis? So, you know, at this time period they were up this percent and, you know, the tech was up similar. And or if you back out that, then, you know, where's the alpha coming from? What's kind of process statistically that you're kind of replicating the returns of these broad kind of managers?

Yeah, I mean, a lot— there's, there's been a, you know, almost 30-year story around hedge fund replication, and a lot of the original work, uh, was built on, on that, that exact intuition, which is you could basically use regressions of returns of managers against asset returns and back out how they must have been positioned over the course of, you know, a multi-year period. I think one of the things that's really exciting is that there's been a real advance in our ability to use certain computational techniques to be able to, instead of having to use that long lookback window, really get a much more tangible sense of how managers are positioned close to today that we see when we see their returns. And the way that we do that, it really stems from a basic insight, which is that because positioning is continuous. There's path dependency in positioning. So the outcomes that you're seeing today in terms of returns are a function of today's positions, and today's positions are a function of yesterday's positions, which you can observe through yesterday's returns. And so functionally, what we do is we, we essentially run a large Monte Carlo simulation to create probabilistic portfolios that best describe the returns that we're seeing. And th— those are constrained by the portfolios that must have described the full series of returns, not which are adjacent to each other, not just a single point in time or a long lookback window. And this is a sort of techniques, particularly in machine learning or statistical learning, depending on how you think about it, that really weren't sort of commercially viable even 5 or 10 years ago. And it's actually approaches techniques that we're using today, sort of going back to the previous part of the conversation, that are drawn from things like weather forecasting, techniques drawn from other areas of forecasting that we're bringing to the table to use in this context.

Yeah, so interesting. So you're saying it wasn't cost-effective or the barriers were too high a decade ago. Is that due to the speed at which computers could make these calculations? It was, we've progressed enough to where you can run more simulations faster and get more insights quicker. Is that the breakthrough that's happened in the past 10 years or what's the difference between now and 10 years ago?

Yeah, I mean, there's always this interesting, as a systematic investor for several decades, I've, you know, used a wide variety of different tools. Um, and, and approaches. And there's always this interesting interplay in terms of technological advancement between, uh, essentially commercially viable computational horsepower and technique availability, right? And those things play into each other because the more computational horsepower is available, the more emergent techniques exist. And the more that those emergent techniques exist, the more that they require computational horsepower in order to be effective at them. And so, you know, there's, there's been this, this is a constantly evolving, you know, world in terms of the techniques that are available. What was, you know, running regressions 30 years ago on, you know, 3,000 managers' returns was pretty sophisticated. And it was sort of the basis of the sort of underlying fundamental ideas of replication, manager replication. But today we can, you know, today we can run 5 million simulations, you know, in, in a very short period of time. And that, you know, 5 million simulations for every month for the last 30 years, we could run, you know, in the matter of minutes and hours rather than essentially an impossible task 10 or 15 years ago.

Very interesting. Okay, so let me, let me throw a few kind of harder questions to ask you because from my perspective, and I don't have the numbers in front of me, but in looking at the most successful VC firms, the most successful hedge funds, generally, from the numbers I've seen, maybe you've seen other numbers, it's a very small subset that has the most outperformance. So the, you know, average returns may not be that much better than the market, but the top 1% of the top decile, the amount of outperformance in that relative to the rest of the group is so significant that it makes you wonder what are they doing differently than others, right? And you, you apply that to maybe VC where they're trying to select what's going to be the next unicorn business and you see consistent success with certain groups versus others, right? So, How do you take that into play? Right? Because there might be an element of, okay, if you have enough, if you're a big enough sample size of data, you can kind of get a sense of what the portfolio is. But is the alpha being driven by portfolio selection or is it being driven by some unique aspect of a philosophy, a niche, a looking for some, you know, unquantifiable X factor in the founder, right? How do these things kind of play into that model and, might be limited understanding that I'm bringing to this question, but help me, help me work through that.

Yeah, well, I think it's probably worth first starting in the hedge fund space, and I think it's, it's, it's a very interesting space because what you see is the aggregate of the hedge fund industry generates a fair amount of alpha, meaning they generate better-than-market returns relative to the risk that they're taking. Particularly if you take away those extraordinarily high fees, right? So that's an important component, right? So the aggregate industry without the fees is quite good on its own. You know, hedge fund returns gross of fees are over the last 20, 25 years have been a bit better than stocks with about half the monthly volatility, about a third of the drawdown. So definitely quite a compelling return. When you go under the hood and look at individual manager performance, one of the important insights that, uh, that we've discovered is that there is no single manager outperformance persistence. So the odds that any one manager will be, say, in the top 50% of performance for consecutive periods is about equivalent to random. Of course, there has to be some manager, right? If you randomly select managers. Of course, some manager will do better and some manager will do worse, and there'll be some persistence in that because, you know, naturally, uh, you know, due to sheer randomness, some will be better and some will be worse. But that's a function of luck, not a function of skill. And so if there is no strategy outperformance persistence or single manager outperformance persistence, You're much better off building a diversified portfolio of their views and lowering the costs than you are trying to select a manager that you think is top decile, maybe top decile for one year, but not necessarily— but, but essentially impossible to identify top decile for an extended period of time.

Very interesting. So what you're saying is the persistence of being in that top decile is pretty rare, if not random. And if that's the case, then it does have more to do with luck. And hey, we just had the best positioning in the market given all the circumstances going on, you know, that happened to work out, right? But maybe next quarter or next year it's the opposite. How do you apply that to maybe VC? Is that a different approach in your mind?

Well, There has been some academic literature which suggests that on a pro forma basis, not on an ex post basis, but on a pro forma basis, it's actually pretty hard to identify which funds are likely to be persistent outperformers. But even if you acknowledge that there are structural reasons why certain funds might have advantages over time because they get better access to better deal flow, etc., you still have to answer the question around fees and taxes and illiquidity. So as a simple example, I mean, actually venture replication is a lot easier than hedge fund replication because venture in general is long only. And the fees are very high. A typical, you know, 2 and 20, or maybe it's 2.5 or 3 and 20 venture fund might charge on average between 5 and 8% in annualized fees a year. Well, if you can, you know, if you could offer a product that has under 1% fees, right, that's a lot of what we call fee alpha that exists there. And when you take into consideration the fee alpha that's available, um, to, you know, through the process of replication, you actually can compete, be on par with or better than, you know, those funds that are persistently in the top deciles of performance.

Okay, interesting. So going back to the venture side of things, you know, from, you know, things I've learned in school and things that I've, you know, been, been taught is, you know, part of the value of VC is one, identifying the right founders, business ideas, markets, etc. But when they go IPO, we see this huge usually delta in, you know, now liquidity premium and kind of the private market discount, right, of having liquidity. And a lot of times you get obviously these big pops in value once you can be publicly traded. And so there's an element of, hey, if you can get to that point to then go IPO or be sold, you know, to a publicly traded firm, that creates an additional alpha. Is that something you can replicate the model, or— I was trying to understand, how do you replicate VC in the model, or is it mostly just hedge funds?

Yeah, so I mean, when you're thinking about venture, um, something like 85% of the returns of the overall venture investing industry are concentrated in just a couple of dozen private companies. So it's a very small— and that's because those companies are so large, you know, Right. Can you replicate million-dollar checks, seed checks, to random companies? Of course you can't, right, using public markets. But when you start to become a company that's as big as, you know, say a SpaceX, that the question is, um, when— or, or an Instacart as a simple example— when you're that big, the, you know, you are, you are knocking on the door of IPOing. And so investors in those businesses start to think about those businesses in the context of publicly traded competitive companies. So if you're an Instacart, I mean, what is Instacart? I mean, it's basically just like a stripped-down version of Uber Eats or DoorDash, right? And those are publicly traded companies. And so you can go ahead and— there's an arbitrage, a natural arbitrage between those two cohorts, between the privately list— you know, the privately in private company, in the, in the venture space, and the valuation of the publicly traded comparables. And so in order, you know, since there's only— there's not thousands of these companies that drive the returns, there's dozens of these companies— you can start to think about what is the portfolio of publicly traded companies that, uh, effectively relates to the valuation undulations that will likely occur with those private companies. Through time as they move through that transition of being a growth stage venture company to then being a company that is publicly traded in the IPO market. And that's basically what drives that— the dollar value of venture returns rather than the sort of percent changes, which mostly comes from like, you know, random $1 million checks into seed deals.

Right, right. Okay. What about more traditional private equity, right, where you know, firms going and buying businesses, doing leveraged buyouts, maybe doing a roll-up strategy. So they're trying to aggregate certain types of businesses in an industry to create, you know, multiple expansion because of size and, you know, sophistication. Is that something you can replicate in the model or not really?

Yeah. I mean, when you think about private equity and particularly when you start to think about something like buyout private equity, What are they doing? They're basically buying high free cash flow businesses that are cheaply valued and leveraging. And then they may or may not be adding value beyond what the public markets can add in terms of the corporate structuring or efficiencies and things like that. And so if you just think about that case, well, there are publicly traded comparables that are either direct targets, for, for buyout private equity, right, in the sense that they are targets of actually being bought by those funds, or they're essentially very similar in attributes to the types of companies that those funds are purchasing in the private market. And so as a simple example, if you were to simply take the Russell 2K Value, uh, index and you were to lever at 1.75 times, you would have a top decile private equity fund return that is, you know, 90% correlated to the accounting matched returns of the buyout private equity index over the last 25 years. Now, what we're doing is a little more sophisticated than that because there's changes in sectors which are non-trivial in terms of what drives those returns, like the shift from more industrial— traditional industrials 20 years ago to more tech and healthcare today. Are, are an influence in terms of the overall returns. But the core basic idea of go buy cheap cash-flowing businesses and lever them, and that is what generates your returns, like that can definitely be put together in the private markets.

Wow. Very, very cool. So talk a little bit about how you're structured. Is your fund, is it also a private fund? Is it traded? Talk a little bit about how how you guys run your fund.

Yeah, so we, we have created, you know, our first, our first products are around the hedge fund replication approach, and we use that technology basically as the foundation of, of products that we build both for what I'd call advisor-directed retail investors, you know, so advisors who are looking for alternatives in their portfolios for their clients who may, may not have clients that are qualified purchasers or accredited investors and are looking for an alternative, typically through, you know, through like a 40 Act product, an ETF product, which is for most investors, for us small-scale investors, ETFs are the most efficient way in which they can gain access to various strategies because they're tax efficient and low cost and very liquid. And then we also have used that technology to create a product more targeted for institutional investors that can switch between different hedge fund style strategies at a time when, you know, the macroeconomic conditions align with certain strategies, say global macro or fixed income strategies or equity long-short strategies, you know, shift between strategies when those underlying hedge fund strategies are likely to do better or shift away from them when they're likely to do worse. Based upon macroeconomic conditions.

Got it. Okay. And then how is your fee structure different than maybe your traditional hedge fund?

Yeah, so when you're thinking about, uh, you know, from, uh, from the perspective of an ETF, um, you know, those fees are typically, uh, those are fixed fees, fixed management fees, uh, that are typically associated with an ETF. And, you know, the vast majority of ETFs are priced under 1% in terms of the annual fee. And so that's considerably lower than you might see with a typical 2-and-20 structure, which is, you know, maybe something like 400 basis points a year or 4% a year. And then the other thing important to recognize for taxable investors, ETFs are also more tax efficient because typically if you buy an ETF and hold it for more than a year, it gets taxed just like a stock. From the perspective of being charged capital gains at the point of sale, which is very different from traditional LP investments, which typically generate K-1 income that you have to take annually regardless of whether or not you sell out of the position and you're taxed on those gains and have to pay the taxes on an annual basis.

Yeah. Okay. Very interesting. So it sounds like you said advisor-related retail, or advisor-sourced retail investors. So investors need to go through their advisors to access the funds, I'm assuming, or do you guys have direct-to-retail, direct-to-consumer access?

Most retail platforms that folks are trading on, you know, our, our product is, is accessible on there. But the reality is that this is a sort of product that's not going to become the next meme stock. You know, it's the sort of thing that frankly advisors who are trying to build robust institutional quality portfolios, but instead of using direct investments into managers, what they're using are, you know, ETF model portfolios. This is the, the sort of product or strategy that fits into that alt bucket of your ETF model portfolio in the same way you might have, you know, an equity allocation or a bond allocation.

Got it. Makes sense. Okay, I'd be remiss if I didn't ask you, because I know we nerded about— out about this, uh, before we, uh, on our last call. But talk a little bit about macro. Just what are you seeing in the kind of bigger picture? Obviously you're intaking a lot of data, you're looking at a lot of regressions, correlations, things that are working, things that aren't working. You know, that's all historical data, right? How are you positioning for the future? What are you thinking about? What are the big kind of knobs in your, your head that are impacting the investment themes and, and thesis that you're, you're using right now?

Yeah, I mean, I think in many ways we're experiencing an economic cycle or an economic period that is very traditional in the sense of an income-led economic expansion. Which is creating inflationary, you know, inflationary pressures, which are being responded to by the central bank, uh, the Fed in this case, uh, which tightens monetary policy in response in order to slow the economy and bring inflation down. Uh, that cycle is actually a bit unusual for most people because most people in their professional careers have basically lived through the 2000 cycle, which was like a an asset bubble and bust. The housing crisis, the housing cycle of '08, which was an asset bubble fueled by debt and then bust. Or COVID, which was just a totally unusual circumstance, you know, that hadn't happened in 100 years. And in all those cycles, there was basically, you know, a rapid buildup of unsustainable activity and then an immediate, quick, sharp reversal And then, and it was very abrupt in, in part because there was so much debt in the system or money creation in the system that was driving the economic activity. So when that stopped, then the economic activity stopped. What's happening right now is totally different from that, which is that, as I like to describe, we have an income-led expansion. So people are earning, you know, increasing salaries 5 or 6% a year. They're taking that and they're spending it, and that spending is becoming someone else's income. And that's a positive virtuous cycle. That's super sustainable. And the reason why it's sustainable is because if I spend money and it becomes somebody else's income, and then they spend money and become somebody else's income, there's no weight the way debt creation creates. Debt is something you have to pay back. Income— you don't have to pay income back. So if you spend out of your income, you don't have to pay it back. And so that's created a durability of this recovery, of this expansion, that has surprised most people because most people expect expected to see, you know, as interest rates rose, the economy falter. But that only occurs when you're borrowing a lot of money, and that's not what's going on in the economy. And so the result is that the economic expansion has been longer and stronger than what most people had expected, including the Fed. And it means that those inflationary pressures have remained more persistent than certainly desirable by the Fed, which is essentially creating several rounds here of recognition that that's what's going on. We're sort of in one of the latest rounds where the expectations of the Fed policy for 2024 have moved from cutting, you know, at the start of the year they were expected to cut between 6 and 7 times over the course of the year, and that's basically close to 0 times right now. But we haven't quite had enough of a tightening environment in order to actually slow the economic conditions in the economy. And so inflation is still a little too hot. Money hasn't tightened enough to slow the economy. And so the expansion continues, but we're starting to get the difficult point of that expansion where we're going to have to tighten money more. Either the Fed will have to do more or we'll have a, a market-based tightening with interest rates, long-end interest rates rising, which will create a tightening of the economy, which will create a hit to asset prices. And so that's kind of where we're at right now is sort of that classic late-cycle moment of the economy's too hot, too sustainable. We need tighter monetary policy, and it's going to come one way or the other, either from the Fed or from a market-based tightening.

Yeah, makes a lot of sense and definitely aligned in a lot of ways. You know, it's interesting because I think, you know, rewind the clock 9, even 12 months ago, everyone was worried about recession, right? This is the fastest rise of interest rates that we've seen in in, in history. And oh, they're going to shock the system. This is going to cause massive, you know, fall-off-a-cliff scenario like we've seen in the past. But we haven't seen that. And to your point, we've seen very strong wage growth. We've seen strong consumer spending and, you know, lower household debt service, etc. All these kind of things that are creating the foundation of a strong economy. Are you seeing, you know, what type of recession, if any, are you seeing? Do we hit a soft landing or does this kind of just get prolonged a little bit as we're kind of in this weird space of economy's still growing, but, you know, asset prices are coming down maybe at a slower rate than kind of this rapid fall kind of domino effect scenario. And do we kind of just slowly hit the bottom and then, you know, things kind of tick up from there? Or do you, do you see there has to be a reset in a certain part of the economy or asset prices for this to kind of kick back into the next— the game of the next economic cycle?

Yeah, well, I think the core, um, the core challenge with the current economic environment is that inflation's too high, and it's too high. And I think, you know, we can all focus on all sorts of, uh, nitpicky details about exactly how CPI is measured and this or that, but I think, uh, certainly if you talk to anyone on Main Street, they'd say inflation is too high, um, and is, you know, a persistent consideration and influence on activity. And that's a problem for the Fed. And it means that in order to address that problem, you know, they've had a lot of benefit from supply chains healing, actually from a fair amount of labor supply occurring, both from people in the, you know, domestic residents basically coming into the labor force, as well as an inflow of immigration. But we've kind of exhausted those, I'd call, sort of easy ways to deal with inflation, the inflation problem. And now we get to the tougher points to deal with the inflation problem, which is basically this trade-off between, you know, are we going to accept an environment where measured inflation is 3 to 4% relative to the Fed's target of 2%, um, or is the Fed going to do something, or is the, frankly, the market going to force a set of actions by raising long-term interest rates? Are we going to have bond vigilantes basically come back to basically create a market-based tightening if the Fed won't do it to bring that inflation down? I think that's the key question. If that happens, it can— we could either have a, a, an okay moderation, um, of economic conditions to bring inflation down, but it's— that's a very risky moment because it could also lead to a circumstance where we get a self-reinforcing negative environment and that could create a more meaningful downturn that's going to be challenging for the Fed to reverse.

Right, right, man. Okay. Well, I have to have you back on and talk just about macro because I could do that all day long, but kind of coming to the end of the time here. So, Bob, thanks so much for coming on. Really, really fun episode. I enjoyed this. And, you know, what's the best way for people to learn more about Unlimited and kind of the models that you guys are working on?

Yeah, if you want to learn more about Unlimited and our products, check out unlimitedfunds.com where we have more information and a relatively regular blog where we're talking about alternative assets and some of the considerations, the portfolio construction considerations there. And if you want an ongoing flow of takes around the macro economy and markets, definitely check me out either on Twitter @bobbyunlimited or on YouTube where we have clips of regular appearances as well. Also @BobFraser.

All right, thanks so much. We'll put some of those links in the show notes. I appreciate it, Bob. It was really fun.

Thanks so much for having me.

This is the Invest Like a Billionaire podcast where we uncover the alternative investments and strategies that billionaires use to grow wealth. The tools and The topics you'll learn from this podcast will make you a better investor and help you build legacy wealth. Join us as we dive into the world of alternative investments, uncover strategies of the ultra-wealthy, discuss economics, and interview successful investors.