Bob Elliott, HFND
Replicating Hedge Fund Returns at ETF Prices
Bob Elliott spent over a decade at Bridgewater Associates, the world's largest hedge fund, where he served on the investment committee and helped manage the firm's flagship All Weather and Pure Alpha strategies. At Bridgewater, he worked directly with Ray Dalio's team on macro research and portfolio construction across economic environments. He left to co-found Unlimited Funds, and their flagship product HFND aims to replicate the aggregate return of the hedge fund industry in an ETF format at a fraction of the traditional fee.
On this episode, Bob talks with Brad about why most investors' hedge fund exposure underperforms the industry aggregate, how HFND reverse-engineers hedge fund positioning using machine learning, and why he believes this approach solves the biggest structural problems with hedge fund investing.
The Hedge Fund Access Problem
Elliott frames the problem simply: the hedge fund industry as an aggregate generates meaningful alpha over time, but almost nobody gets the aggregate. Instead, investors pick a handful of funds, pay 2-and-20 fees, lock up their capital, and face massive dispersion in outcomes. Even institutional investors with teams of analysts dedicated to manager selection struggle to consistently pick the winners. The data shows that the average hedge fund investor underperforms the industry average because of fee drag, selection bias, and the tendency to invest in funds after they've already had their best performance.
HFND's thesis is that you don't need to pick individual hedge funds if you can replicate what the industry is doing in aggregate. The fund uses machine learning models to analyze publicly available data, including 13F filings, COT reports, and other disclosures, to determine the consensus positioning of the hedge fund industry across equities, fixed income, commodities, and currencies. Then it builds a portfolio of liquid instruments (ETFs, futures, and swaps) that matches that positioning. The result is aggregate hedge fund exposure at an ETF fee rather than 2-and-20.
How the Replication Engine Works
The machine learning system processes regulatory filings from thousands of hedge funds to identify net positioning across asset classes, sectors, geographies, and styles. It looks at both the direction of bets (long vs. short) and the magnitude. The model updates continuously as new filings become available, typically on a quarterly lag for 13F data but with higher-frequency inputs from futures positioning reports.
Elliott acknowledges the lag issue directly. 13F filings are 45 days delayed, and hedge funds can change positions rapidly. His response: at the aggregate level, positioning shifts much more slowly than at the individual fund level. The hedge fund industry as a whole doesn't flip from net long to net short overnight. Aggregate tilts toward value vs. growth, US vs. international, or long vs. short duration tend to persist for quarters or even years. The model captures these slow-moving structural bets effectively, even if it misses the fast-moving tactical trades of any individual fund.
The portfolio typically holds 30-50 positions across asset classes, implemented primarily through liquid ETFs and futures contracts. Turnover is moderate because the underlying hedge fund aggregate changes gradually. Elliott notes that the correlation between HFND and the HFRI Fund Weighted Composite Index (the standard hedge fund benchmark) has been consistently high since launch, validating that the replication engine is working as designed.
Why This Matters for Advisors
Elliott's pitch to advisors is practical: most of your clients can't access hedge funds at all, and those who can are paying enormous fees for uncertain outcomes. HFND gives any advisor the ability to add a hedge fund return stream to a portfolio in a daily-liquid, tax-efficient, low-cost vehicle. He positions it as a replacement for the "alternatives" allocation that many advisory firms struggle to implement. No accreditation requirements, no lock-ups, no capital calls, no K-1s.
He also makes the case that the hedge fund aggregate is a genuinely different return stream from traditional stocks and bonds, which justifies the allocation. Over long periods, the industry has generated returns with significantly lower volatility than equities and with low correlation to both stocks and bonds. For a portfolio construction-focused advisor, that combination is valuable regardless of whether any individual hedge fund is worth its fees.
Key Takeaways
- Elliott spent over a decade at Bridgewater Associates on the investment committee, working on All Weather and Pure Alpha before co-founding Unlimited Funds.
- HFND uses machine learning to reverse-engineer aggregate hedge fund positioning from 13F filings, COT reports, and other public disclosures, then replicates it with liquid instruments.
- At the aggregate level, hedge fund positioning shifts slowly (over quarters, not days), which mitigates the 45-day lag inherent in 13F filing data.
- The fund eliminates the traditional barriers to hedge fund access: no accreditation requirements, no lock-ups, no capital calls, no K-1s, and no 2-and-20 fee structure.
- Correlation between HFND and the HFRI Fund Weighted Composite Index has been consistently high since launch, validating the replication methodology.
Listen to the full conversation on Spotify, Apple Podcasts, or YouTube.
Full Transcript
5,180 wordsMachine transcribed from Brad Roth's conversation with Bob Elliott, HFND, with speakers identified automatically. Timestamps link to that moment on YouTube. Lightly cleaned, otherwise unedited.
Welcome to Behind the Ticker. I'm Brad Roth, Chief Investment Officer of Thor Financial Technologies and Portfolio Manager of THLV, the Thor Low Volatility ETF. Behind the Ticker uncovers the inner workings of the ETF industry. We will interview portfolio managers and ETF service providers to dive deep into their work lives and their businesses. We will learn the inner workings of their strategies and what drives them as they continue to grow their company. Many of these individuals are entrepreneurs and will have unique and compelling insights to share as much goes on behind the ticker. Please note, nothing in this show is investment advice and it is meant solely for educational and entertainment purposes only.
Welcome to Behind the Ticker. Today we have on Bob Elliott from Unlimited and we are talking about their multi-strategy return tracker ETF, ticker HFND. It takes all the different facets of hedge funds and combines them into a single index and looks to track their return profiles. They use machine learning in order to build a probabilistic asset allocation that will mirror multi-strategy hedge fund performance and they package it all up in a nice cost-efficient way. So I think you'll find this conversation extremely interesting with Mr. Bob Elliott. Hey Bob, welcome to the show.
Hey, thanks so much for having me.
So before we get started, can you tell everybody a little bit about your background and then what eventually led you to start Unlimited?
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Yeah, for sure. I've been a systematic investor for about 20 years now. I started my career at Bridgewater Associates, which at the time that I left was the world's largest hedge fund, where I was developing, systematic, proprietary systematic investment strategies across all the major asset classes. And after I left, I also spent some time running $125 million venture fund that used big data to identify high potential early stage consumer opportunities. So you can think about it like systematic investing, but the private side of $2 and $20. And increasingly, my later years at Bridgewater, my time in the private side of the world of $2 and $20, increasingly realized that, $2 and $20 businesses are really good for the managers and they're not that great for the investors. And that's because managers generate great returns
And they also charge high fees, which means that investors are not that much better off than they would be otherwise, particularly if they're taxable investors in the U.S. And so that got me to thinking about whether there was a way to bring the concepts of diversified low cost indexing, which has obviously totally changed stock and bond investing for the better, and bring it to the world of $2 and $20. Now, I think there's a lot of people who are trying to do access to alternatives. And the challenge with most of those approaches is that they're often adding to the fee problem rather than increasing access and reducing the fee problem. And so our idea was instead of sort of creating even more niche bespoke products, use our experience
With modern machine learning technology to replicate how those managers are operating and investing and take that understanding and package it into products that are available to a wide variety of investors and that are structured in a way that they're more tax efficient, more liquid, more transparent than typical LP positions. And so that's really the core concept behind Unlimited is that idea of low cost diversified 2 in 20 strategies for every investor.
Well, before we geek out on machine learning and hedge fund replication strategies, I always like to ask, what do you like to do outside the office? Any hobbies, things you like to get into when you're not working?
Well, one of the funny things about my background is actually I'm a trained botanist by academic training. And so while it's not my day to day job, I still keep at it in the garden where I find principles of diversification to be as applicable in the investment management space as they are in the world of gardening. So yesterday was the frost free date here where I live, which means I get to put plants in the ground, which is pretty exciting over the next couple of weeks.
Yeah, I just started our garden last week. So I'm hit or miss. I'm an amateur. You could probably help me and provide some advice there.
Everyone is hit or miss when it comes to gardening, right? Mother nature is fickle. And part of the joy you have to find in gardening is the process of the gardening rather than the particulars of the output. And that's why I've found, at least in my years of gardening, that much, much better to plant a whole bunch of different stuff and see what comes and take joy in that and not stress too much about, hoping to get one particular plant or one particular crop to come out the way you'd like.
Well, that's great. And yeah, I'm hoping to get some peppers this year. We'll see. I'm trying for the
First time, but we'll see. I'm going to do those peppers. I'm going to try my best.
So let's talk about unlimited as a whole, right? I know you guys have the ETF strategy, which we're going to talk about in depth. Do you guys do anything else to kind of service clients or is that your single focus business line?
Yeah. What we've done, if you think about it, is we've built a technology we call our return replication technology, which allows us to look over the shoulder of what asset managers are doing in close to real time and take that understanding and quantify the exposures that they have and translate that into positions in liquid securities. And so, as you mentioned, we have one ETF in the market that folks can see, out in the public. We also use that technology and package it together in other products, some of which are more appropriate for financial institutions, particularly thinking through what's sort of the best way to construct a portfolio.
One of the really neat things about using replication and indexing of alphas is you can start to think about how do those alphas perform at different times in the business cycle in response to different monetary policy conditions, et cetera, and start to build a better and more agile multi-strategy portfolio than what's available for typical allocators. So that's sort of the institutional side
Of the business as well. Yeah, that's great. And so what is it about hedge fund return profiles that investors find appealing, right? We hear a lot about hedge funds and this show is tailored to, financial advisors and RIAs. So what is it about that hedge fund return profile and the risk return of them that makes them appealing or that they should be a part of more of a diversified portfolio?
Well, I think for most people, they can look at, say, net returns indices and they certainly aren't screaming off the chart that that's something that you want in your portfolio. And what I'd emphasize is that the problem there is not the strategies. The problem there is the fees because that performance that you see is net of, hundreds of basis points of fees that the managers are receiving on an annual basis. So if you think about the strategies, not the net return outcomes with the strategies, those strategies are actually quite good on a gross of fee basis. They typically have returns equivalent to are a bit better than the equity market over the last 25 years. They have about half the monthly volatility and about a third of the drawdown profile. So, and they're not,
They're modestly correlated to assets overall. And so you think about that combination of things. And that's actually a pretty good return profile that you probably want in your portfolio. The problem is it's too hard to gain access to it in a way that's tax efficient and low fee. And so that's basically what we're targeting at Unlimited is how do we translate those great strategies that are out there that are generating great alpha? How do we make those available in a way that doesn't have so much decay related to fees, transaction costs, and the tax problems that typical structures have?
Yeah, of course. And I think, I think too, and advisors here, hedge fund or alternative, and they loop that into all one thing, right? So last week we had on Andrew Beer from DBI runs DBMF. Great guy. We had a really good conversation. So can you help, the, can you help the advisor find the distinction between what multi-strategy is, right? Which is what you do and what managed futures are and what Andrew does and how those things can either work together or
How they work differently inside of a portfolio? Well, Andrew has been a great pioneer in the world of low cost hedge fund indexing. So it's great that you, you talked to him right ahead of me coming
On in a lot of ways. It's hedge fund month over here at the podcast. Yeah, exactly. In many ways,
I'm, I'm benefited and many, many products are benefited from his, more than a decade of pushing and bringing these ideas to the, to the forefront for the benefit of investors. Managed futures, you could just think about managed futures, just one hedge fund strategy that exists out there in the market. There's a lot of different hedge fund strategies, equity, long, short, global macro, fixed income strategies, managers that are focused on the emerging markets, maybe event focused, M and a type strategies that are out there. And so whereas, the managed futures approaches focus on one particular strategy, and generally they do a pretty good job of delivering those strategies at a very cost effective way. What we're focused on is,
Is building an overall portfolio of alpha strategies or hedge fund style strategies, which includes all those different strategies. In a lot of ways, you can think about what we're doing is creating an index fund of hedge fund strategies in the same way, there's spy or the cues or things like that. So that, for investors who are, for allocators who are looking for just an efficient way to gain access to that diversified portfolio of different strategies, our idea is this could be one way in which you could do that specifically rather than necessarily picking individual managers or picking individual strategy types, you sort of get the best of the universe in aggregate by using what we're designing.
Yeah, no, and that's, that's great. And it's helpful. So let's talk about your ETF, HFND. So it's your multi-strategy return tracker ETF. So we kind of hit on it a little bit, but why don't you kind of go a little bit deeper about what the strategy is trying to accomplish? We understand it's hedge fund replication, but what can people find in it? How is it constructed? what's the overall strategy behind it?
Right. So our basic goal with, with the HFND ETF is to create a set of positions that over time should reflect the risk and return profile of the aggregate hedge fund industry that the AUM weighted hedge fund industry. And the way that we do that is, is not in the aggregate, but actually what we do is we break it down into the pieces of the major hedge fund styles. So global macro fixed income, equity, long, short managed futures is one of them. And what we do is we build portfolios for each one of those different sub strategies that are intended to match the risk return profile of the overall hedge fund strategy. Now we do that through, as I mentioned, a machine learning approach, which is intended to
Try and get as up to date and understanding of how hedge fund managers are positioned, in close to real time. And so the, the way that we do that, we could sort of talk about it in sort of a, in a complicated black box sort of way, but the underlying intuition is actually very simple, which is that when you look at a manager's returns and you understand what sort of strategies they're pursuing and you see what's happening in the asset markets, you can actually do a pretty good job of inferring how they must be positioned, which securities are, which exposures are they likely overweight or underweight given their sort of normal universe of exposures. And part of the thing that we can do to that makes our ability to match their or understand their positioning more effective is
That because positioning is path is, is continuous, it's also path dependent. And so we actually look at the pattern of returns relative to the plausible exposures those managers might have on at any point in time, and basically back into how they've been positioned through time. And then we take those individual replications and we combine them together. And that is what we use as the core intellectual
Property behind our HFND ETF. So what are the key drivers in that decision-making model? You gave us a little bit of it there, but it would, so you're, you're looking at all the underlying, different hedge fund strategies. You're looking at kind of what the market environment has been going through and what those return profiles were. And so you're basically making, I'm going to simplify this kind of a probabilistic bet on what those likely holdings were in a sense.
That, that, that is in fact, exactly right. We do build probabilistic portfolios because if you think about it, if let's just say I looked at, one particular month's returns or, or, a few weeks of daily returns, there's a lot of different combinations of portfolios that could plausibly explain those returns. So like if you're thinking about a global macro manager, what do they basically have exposure to? It's, stocks, fixed income, credit, commodities, and currencies, right? A mix of those assets. And one of the things that we do in terms of creating that higher probabilistic portfolio is what we do is we look at not just the most recent periods returns and compare that to the market action to solve for what portfolio is the, is the highest probability
One. But we look at it adjacent to the previous period's portfolio. And that's very important because today's outcomes are a function of today's positions. And today's positions are a function of yesterday's positions, which can be observed through yesterday's outcomes. And that's very important because what it means is that probabilistic portfolio narrows over time, narrows because the only positions that can explain the most recent returns have to also an adjacent portfolio, a similar portfolio has to explain the previous periods returns. And so actually that allows us to create a higher probability portfolio. And functionally what we do is we actually go all the way back through time and then all the way back to today and solve for every single portfolio that describes all of the returns back
Through time and today to get that highest probability portfolio. And I think one of the things that's pretty cool about that is that we actually get a higher real-time understanding of the positioning than the managers report themselves and that is aware, that people are aware of in the market. And so we actually published what we call our hedge fund barometer, which gives insight into basically the output of our technology and what that means for hedge fund manager positioning, which folks can look up and take a look at and read. We make that public to folks that can understand how our technology is translating into actual understanding of how hedge funds are positioned in
Real life. Yeah. So this intrigues me. It's very interesting to me. So one more question on this. Could you almost get predictive in a sense if you're understanding prior regimes and market cycles and return streams in those cycles? Could you almost get predictive given as to where the macro economy is and what data is the likely future allocation next day or next week that a manager might start to put on in
Terms of positioning? It's certainly possible, though. The thing you would want to weigh in that is your ability to predict their shifts relative to the quality of interpreting their shifts and the cost of the delay, the delay costs. And so if you have relatively real-time data and there's hedge fund performance information that comes out daily with a couple days lag, very high quality information that comes out monthly with a couple weeks, monthly a couple days into the subsequent month. So you want to weigh your ability to predict relative to the information value that exists there. And what I'd say is hedge fund managers, many people think are like hot shots that are like wildly moving their positions around. But first of all, that is not really true, having sat in the seat of running money for
Institutional quality hedge fund. And that's because you can't move very fast because you start to incur excess transactions costs and that erodes your alpha. So managers, by their nature, if they're managing any significant amount of money, aren't moving that fast. And then when you think about collections of managers, it takes time for them to evolve their positions. And so that's why what we see is there's a lot of information value in just understanding how they're positioned, even with a modest lag, because the quality of that decision-making is generally very good, rather than trying to necessarily predict exactly how they might tweak one way or the other.
Yeah, no, it makes a lot of sense to me. So is the process fully automated or is there some intervention that you guys have to input into the process along the way? Or is it, again, fully run by the machines?
Right. The way I would describe it is it's expert designed and systematic in operation. And what that means is, there's been a lot of efforts at replication approaches in general. And I think one of the challenges with those is that many folks who have pursued those haven't necessarily actually built proprietary strategies and actually run the money to understand how they're constructed. And so there is a real craft. And between my co-founder and I, we have almost 50 years of experience in this business, building proprietary hedge fund strategies. And that shows in how, and that is critically important in terms of how we build the approaches. But from that craft, what we do is we build a systematic process that is running on a day-to-day basis, taking in manager
Performance information, asset return information, allocation information, and basically saying, given that set of information and the sort of decision rules and design that we've created, what is the highest probabilistic portfolio that's describing the returns that we're seeing in the most recent period? And so that part of it is systematic. And what that allows us to do is take the time to ensure that we can continue to do research and improvement of our overall processes.
Yeah. So talking more about the underlying holdings and how it's all constituted, is the strategy, easy question, is the strategy go anywhere? Is there any kind of guardrails? I know you're using some futures and some ETFs, but can you really go anywhere you want?
Well, functionally, if you think about there's $5 trillion of assets in hedge funds, basically what that means is that hedge funds are invested in or exposed to the 70-ish largest liquid markets in the world. And so our opportunity set is essentially the 70-ish largest liquid markets in the world. And that may adjust over time as certain markets emerge and others no longer are as large. But in general, that opportunity set exists through time. And then we just express those views in what we believe to be the most efficient security possible, whether it's in ETFs or cash equities or futures in many cases. And on prospectus, we have the ability to also use things like vanilla or custom swaps as well. So we're basically just using whatever is the most efficient way to express
The view. And excuse me for not knowing this, I probably, is the portfolio long only or can you
Get, can the portfolio go net short? No, the portfolio includes positions that are both long and short. And we do have aggregate risk controls in place that are inside the margins that exist from a regulatory perspective to ensure that we don't take undue risks or risks that would be unexpected to us over time. Although we don't really find ourselves in that position frequently and haven't since we've
Launched the strategy. So staying on those guardrails, do you have any guardrails in positioning in terms of sector or regional exposure? are there kind of max and mins built in there so you're not getting the portfolio maybe overexposed to, to, I don't know, home builders, right?
Right. So, first of all, the thing I would say is by design and craft of the replication technology, it's intended to be what we describe as a parsimonious yet comprehensive set of opportunities. And so just functionally as a result of that, there is not, there's limits to concentration that can exist. For instance, if you're an equity long, if you're, you're trading equity long short, there's regional, sector indices, regional indices, factors, sizes, sectors, et cetera, that all of these different pieces that go together to create, an opportunity set of, let's say, 30 different exposures just for that one type of replication. And so by sort of by, by design, we ensure that it is, it is not overweight one
Particular outcome. And then of course, at an aggregate level, one of the things people often can forget is that there are significant restrictions and constraints on, on portfolio concentration, regulatory constraints. We're all more well within those regulatory constraints, but those are sort of like the ultimate backstop to ensure that there is sufficient diversification. Functionally what you see is, roughly 50 positions, and a whole wide variety of
Different exposures that are in the portfolio. So how often are you kind of doing a look? Is it a daily look and you're readjusting? Is it weekly, monthly? Like, is there a normal cadence of rebalancing or is it just fully active? Yeah. What we're doing is we're getting market
Performance information and manager performance information essentially in essentially every day and, and reflecting and understand, reflecting that understanding on a daily basis. Now it doesn't necessarily mean we trade or adjust the positions every day. Part of running the money also is ensuring that you're trading in a way that doesn't, create undue churn or chop in the process. And so you want to have significant enough shifts in position to be trading them. So it doesn't necessarily trade every day at all, but, but we're sort of reevaluating on a continuous basis. And so if, and when things change meaningfully, we are in a position to be able to adjust the portfolio
Accordingly, within that same day. That's great. So if you're sitting down with, an RIA group, that's got an existing model portfolio that they've constructed, where are you would kind of steering them to find an allocation for this ETF and kind of, I would assume you're going to put it in maybe an alt bucket or maybe adjacent to their equity exposure maybe, but, and also kind of talk about how you would look at allocation size for this
Particular ETF. Yeah. Part of the inspiration actually of this whole approach was looking at what big institutional investors do with their allocations and to, to, to simplify the picture, essentially what, if you're, if you're, hundreds of billions of dollars in size sovereign wealth fund, you go invest in dozens of managers and you beat them down on fees to get essentially a low cost index hedge fund index problem. And so our kind of idea was to create the, the, the same style of product and just make it available, whether you got, 20, 20 bucks or 20 million bucks. And so that's the kind of idea. If you look at those institutional allocator, the most sophisticated ones are typically allocating about 20 ish percent of their portfolio to
Liquid market alpha strategies. So like hedge fund style strategies. And so, we could, we could probably talk all day about whether 20% is the right number or is it 10 or is it 50? I think, there's a good, good reason why they've sort of landed on roughly 20, given the balance of the portfolio improvement, the probabilistic success of alpha and the goodness of other return opportunities. And so you're thinking about, Hey, look, I, I have a, a, a, a standard ETF portfolio and I'm thinking about, how big should all to be in my portfolio? I think in that context, like roughly 20% is the right number. And, and within that context, what I'd say is, in the same way, uh, and your, your liquid equity portfolio,
You might have spy or the equivalent, the Vanguard equivalent, and then have, maybe some sector options or sector allocations or, um, thematic allocations that seem compelling at the time. So too, in your alts allocation, could you use, uh, HFND as a product that is, sort of your, your standard index for there, and then allocate some of that capital to, uh, probabilistic opportunities that you think, uh, could be, uh, compelling. Uh, one of the things actually, which compare this, uh, this conversation with the previous one, uh, if you go look at, uh, how to create the sort of best alts portfolio, you can, one of the things that, that has worked out really well through time is actually pairing diversified alpha, which is essentially what we're
Trying to do with, with HFND with, uh, managed futures and like a 50, 50 allocation between those two actually creates a, uh, at least back through time, uh, would have created a relatively, a particularly consistent, uh, uh, return stream, uh, combining those two pieces for that alts bucket. So, that's, that's a, that would be a compelling way, uh, to put the
Pieces together as well. Yeah, no, I agree. It's so your fund launched about the same time as ours. So you're almost what a year and a half, maybe a year and a half in, we know it's kind of a, it's kind of a, uh, a patient's game in terms of distribution. So how are you thinking about as you're getting closer to that, three-year number, um, thinking about distribution kind of going forward and, and starting to really, um, get more people involved, uh, in, in the fund and in this space in general, because I, I agree with, with your assessment. I think everybody, I think managed futures and multi-strategy deserves a spot in kind of every diversified portfolio. Um, and so, how are you thinking about getting the word out
Marketing and distribution as you kind of get a little bit closer to your three-year numbers?
Yeah. as I'm sure you appreciate in the, in the early stage ETF market, primarily, uh, the investors that are coming to those products, the early movers in that market are your independent, uh, RIAs. And so, um, we've had, we've built great relationships with dozens of, uh, independent RIA, uh, managers around the country. Um, many of whom are trying to bring their clients, diversified low cost ETF model portfolios, uh, and pair that sort of asset management with, planning activities that in many ways are need to be much more tailored to the individual investor. Um, and so that's mostly who we're focused on. I think probably the sort of critical way in which we've done that we're still a very small team is, um, is through, a, a, a,
A content oriented strategy where, um, a lot of what I do, uh, is connect with both the advisors, uh, directly as well as, um, across social media, Twitter, YouTube, et cetera. Um, and as part of that process, really bringing that sort of institutional quality research and perspective, uh, that had existed when I, when I worked at Bridgewater and bringing that, uh, to every, to every, uh, RIA that's out there in the world, it's kind of amazing how underserved those folks are with getting high quality research because they're typically too small for the banks to really pay attention to. And they're, obviously not getting access to big institutional allocator, uh, manner, research that's being, that's being created on ongoing basis. And so
In a lot of ways, when I was at Bridgewater, I wrote, I used to write the daily observations, which was very well-regarded institutional research piece there came out daily. And today I basically have taken that concept and just translated it on Twitter. And that's been great. Um, it's been an incredible environment and you would be amazed at how many advisors are out there getting meaningful information from Twitter, from YouTube, from all of these sources, because they don't have great
Other ways of getting that understanding of what's going on. Yeah. Well, Bob, I really, really appreciate your time before I let you go. Um, where can people learn more about you? You have your, you have your Twitter handle right here on here. Um, learn more about unlimited and learn more about
HFND. Yeah, for sure. If you want to, um, get into the flow of those sort of ongoing macro takes, definitely check out Twitter at this handle or, um, YouTube under the same handle, which I think, uh, where we have monthly, uh, macro outlooks published there as well as clips from my ongoing media appearances. So that I think people find that very, uh, very interesting bite-sized ways of, uh, getting some perspective on what's going on. Uh, I also write about the industry as a whole on my LinkedIn under my name. Um, and if you want to learn more about what we're doing at unlimited, definitely check out our advisor site, unlimitedfunds.com, which has a lot more information about our technology, our products, uh, and backgrounds about, what we're doing.
Well, again, Bob, thank you so much for your time. Thanks for joining us. Thanks so much for having me. Bye.
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