My guest in this episode is Andrew Beer, co-founder of Dynamic Beta Investments.
Andrew has spent the last 15 years trying to pioneer the adoption of hedge fund replication strategies. The core thesis is that several hedge fund categories can be dynamically replicated using just a handful of liquid market exposures and some regression techniques. He argues that if he can deliver the strategy beta while cutting out hundreds of basis points of management fees and trading costs, it would consistently earn him a top decile rank. And all this can be done in a daily liquid vehicle.
The Devil, of course, is in the details. Which categories can be replicated is an important consideration. Whether to perform a bottom-up or top-down replication is another. And, obviously, which factors to incorporate. Andrew stresses that the answer to all these questions comes not from quantitative analysis, but from a qualitative understanding of how hedge fund managers actually operate.
This episode may not be as technical as others, but it certainly had me walking away thinking, “if there’s no points for originality, it certainly seems a lot easier to just copy the work of others. Especially if I can cut out all their fees.”
Please enjoy my episode with Andrew Beer.
Corey Hoffstein 00:00
All right, and are you ready? Yeah. All right. 321 Let’s go. Hello and welcome, everyone. I’m Corey Hoffstein. And this is flirting with models, the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.
Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations, he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions in securities discussed in this podcast for more information is it think newfound.com.
Corey Hoffstein 00:53
If you enjoy this podcast, we’d greatly appreciate it. If you could leave us a rating or review on your favorite podcast platform and check out our sponsor this season. It’s well it’s me. People ask me all the time, Cory, what do you actually do? Well, back in 2008, I co founded newfound research. We’re a quantitative investment and research firm dedicated to helping investors proactively navigate the risks of investing through more holistic diversification. Whether through the funds we manage the Exchange Traded products we power, or the total portfolio solutions we construct like the structural Alpha model portfolio series, we offer a variety of solutions to financial advisors and institutions. Check us out at www dot Tink newfound.com. And now on with the show. My guest in this episode is Andrew beer, co founder of dynamic beta investments. Andrew has spent the last 15 years trying to pioneer the adoption of hedge fund replication strategies. The core thesis is that several hedge fund categories can be dynamically replicated using just a handful of liquid market exposures and some regression techniques. He argues that if he can deliver the strategy beta while cutting out hundreds of basis points of management fees and trading costs, it would consistently earn him a top decile rank. And all this can be done in a daily liquid vehicle. The devil of course is in the details. Which categories can be replicated is an important consideration whether to perform a bottom up or top down replication is another and obviously, which factors are markets to incorporate? Andrew stresses that the answer to all these questions comes not from quantitative analysis. But from a qualitative understanding of how hedge fund managers actually operate. This episode may not be as technical as others, but it’s certainly had me walking away thinking. If there’s no points for originality, it certainly seems a lot easier to just copy the work of others, especially if we can cut out all the fees. Please enjoy my episode with Andrew beer. Major beer, welcome to the podcast excited to have you here going to be talking all things replication on this episode. I did a replication episode a few seasons ago. And I think it’s a topic most people either aren’t aware of, or those who are aware, maybe actually haven’t had that great experience or they’ve got the wrong view of it. So I’m excited about this episode, because I think you’re going to do a great job making sure people aren’t as disillusioned with replication after we walked through this. So again, thank you for joining me.
Andrew Beer 03:41
Well, thank you, Cory. It’s great to be here. Very excited.
Corey Hoffstein 03:44
Let’s dive right in with your background. You are now all in on replication concepts in your career. But I know that wasn’t always the case. Can you walk us through how you got to where you are today?
Andrew Beer 03:58
Sure. Well, I graduated from business school in 1994. And I was on a path to become a private equity guy. That was just my skill set. And one of my professors and my second year of business school said you gotta meet this guy named Seth Klarman. And he’s the smartest guy to come to the school, and so on and so forth. I went for an interview. And it was the hardest interview I had. And I had no idea what a hedge fund was. But the things that they were doing were incredibly weird and esoteric and somebody who was very, very close to going into the doctoral program, and pursuing a career in academia. It was very exciting to be able to look at these things that were truly differentiated and esoteric, and so I went and I joined his firm, which is Belle post, I was an early Portfolio Manager. And for the first few years, I did very, very traditional hedge fund stuff. I did some of the first secondary purchases of limited partnership interest in private equity funds. I did share class arbitrage and Asia, all sorts of different things. And then I left and one of the big things that I took from my time at Bell post was try to get the area right And so within a few years I’d left and by the early 2000s, I’d started a hedge fund one of the earlier hedge funds in the commodity space. And around the same time with a different group of guys, I started one focus on the Greater China region. And so up until 2006, my career was going to a very much more traditional hedge fund hedge fund cedar path. And then I had a chance meeting with a guy with a PhD who told me about factor based replication. And arguably, it’s all been downhill since then. But trying to kind of solve this issue of how do you create index like products and hedge fund land that actually work? And so that’s been the past 15 years and probably the next 20?
Corey Hoffstein 05:41
Well, let’s start there. Because I don’t want to assume all of our listeners know what factor based replication is. So maybe you can start by just simply defining the core goal of replication based strategies. What are you trying to accomplish? And what’s sort of the value add to end investors?
Andrew Beer 06:00
So the basic idea of hedge fund replication is that what hedge funds do, some of what hedge funds do is not that mysterious? So the question is what’s driving their returns? In the sense if you had to sum it up in a sentence is we think hedge funds are smart, we think they generate alpha, they take too much in fees. And if we can copy it cheaply will do as well or better than they do. What factor based replication is, is basically, it’s based on the premise that most of the performance, and ultimately most of the alpha that hedge funds generate comes from getting factor rotations, right. More so than picking this stock or that stock more so than presciently jumping off the tracks as the train comes down it more so than the illiquidity premium. And so really, in the late 2000s, there was this initial big wave of people saying, Hey, we can copy what hedge funds do cheaply. And then Much to my surprise, this ended up being extraordinarily controversial thing in hedge fund land.
Corey Hoffstein 06:55
Well, let’s walk through some of that history. Because I do think the view in the market is driven by some of the research that was published, we’re gonna get into that later date. But I would love to get into as someone who’s experienced much of the history, walk us through the history of these replication studies and strategies in the marketplace.
Andrew Beer 07:17
So the seminal paper was written in August 2006. And it was written actually by a graduate student of a very famous professor named Andrew Lowe at MIT, her name was just mean a house and Povich. And the paper basically said that we can look at, let’s say, the past 24 months of returns of a very diversified pool of hedge funds. And we can every month we can do what Bill Sharpe said with his style based returns analysis is that we can look at those 24 months of returns, run it through a linear regression model. And if we know what the primary drivers are likely to be. So there’s some exposure to equities, some exposure to read some exposure to other things that we know hedge funds do, then the model on today is very good at telling us really what was driving performance over that period of time. And what the paper did was, by the way, that’s a risk management tools that people use all the time across the industry. What the paper did is it took it one step further and said, Well, what if we were to actually invest in those instruments today, wait a month, until we got new data and see how we did relative to hedge funds. And this research was then repeated by pretty much every investment bank with a decent quant department repeated the research. And again, I’m not a quant, but the guy that I met with in actually it happened to be in August 2006. So the month the paper was published, basically had done some of his own work on it. And so the interesting thing about it was that this wasn’t a surprise at all, to somebody who’d actually been inside hedge funds. This was always how hedge funds made money. When I worked for Seth, he was making money because he had been early into Russian privatization vouchers and to nonperforming real estate after the savings and loan crisis, it doesn’t really do you a lot of good if you do all of this work, and then buy some great investment that max out at 2% of your portfolio. If you like an area and you develop knowledge of an area, you tend to own a lot of it more over time. And so that was really where it started. And there’s been remarkably little of that research was actually published by investment banks and others. And then there were some contradictory papers that were published that ultimately, you know, I have pretty strong feelings about,
Corey Hoffstein 09:34
well walk us through at least some of the history of those contradictory papers, because again, I think the narrative in the industry today is very much influenced by some of those papers. We’ll get into some of your rebuttals perhaps a little later in the conversation, but keep walking that timeline for me.
Andrew Beer 09:51
Sure. So 2008 2009 around that period of time, there were two types of papers that were published. The first was published by me Researchers at edtech, who basically said they had the punchline was they said, we’ve taken Andrew loads models and we have sought to expand on them. And guess what, they don’t work. And then a whole slew of consulting firms and funder funds said, We, too, have done our own analysis. And we believe the results are wrong, and they don’t work. But behind all of this, there was a profound change. Because ultimately, what replication was trying to do was to create an investable index like product for the hedge fund space. And if I can take a step back, why was that really important? In 2000 to 2002, hedge funds made money during the great.com bear market. And that was largely a factor bet. It was being long small cap value and being short, large cap growth, the small cap value stock didn’t go up, they went down 40% or 50%. But the short positions went down 80%. But the performance of hedge funds set off a gold rush, and everybody wanted exposure to hedge funds. And by the way, this is also during the last decade for equities. So allocators were faced with the problems, how do they get into the space. And the view at the time was all hedge funds are good, if only we can get access to it. So people built fund of hedge funds, they built all sorts of different kinds of products to allow more and more people to get access to the hedge fund space. A lot of those products were even less liquid than the underlying hedge funds, or had even more fees built on top. And so there was a parallel effort to say, well, maybe we can create something that’s a little bit more client friendly, a little bit more liquid, but gives you the same kind of return profile. And what replication was was basically so I guess the way people said is that the first Holy Grail of hedge fund investing was you find George Soros in the 1970s, give them all your money and go home? And then you can replace him with your favorite hedge fund manager. But the second was, could you get all the benefits, but with low fees and daily liquidity? And everybody who’s building these other products had basically taken the position? No, you can’t. You must pay 5% in fees and invest in a fund of funds. because how else could you possibly get these benefits. And so during the 2000s, the idea that you could access hedge fund returns cheaply, was enormously threatening to this whole business, this whole industry that had built to facilitate investing in hedge funds. And it was also in the industry had also built its own cannon as to what makes hedge funds great. It was short sighted alpha, it was market timing, it was illiquidity premium, they constructed all of these theories as to what made hedge funds great. And then the way that I’ve described it, as Andrew Lowe comes along with this robot dog. And lo and behold, it looks like the robot dog can do as well as the 5%, illiquid fund of hedge fund products, which would then raise the question is why any sane fiduciary would ever put money into the latter when you could do the former. And so the papers that came out were really built with this idea of how do we disprove it? How do we discourage people from looking at this as a serious, viable option?
Corey Hoffstein 13:07
When he talks about actually practically, implementing some of these ideas, I typically see people take two paths, there’s those who try to create a process that captures the underlying return drivers. And that might be for example, if we’re looking at a Managed futures strategy, I might say, look, I think 20 over 100, Moving Average crossover represents the way most trend followers invest. So I’m gonna run that process as a representative process of the broad cross section of managed futures managers, and that’s going to be my naive replicator. The second approach is statistical in nature, what you keyed in on initially in our conversation, which is around more of a factor analysis, looking at the historical returns of managed futures funds, using perhaps a bunch of different futures markets, regressing the returns and trying to figure out what those managed futures funds are actually holding at any given time, and then holding them going forward. In your opinion, which of these approaches is ultimately better for replication out of sample? We looked
Andrew Beer 14:19
very hard at the earlier approach, the way that we’ve often framed it is there’s bottom up replication, where you try to copy you say managed futures funds generate most of their returns from trend. And we’re going to build a simple model to capture Trent. And so our first using miniatures as an example, our first approach on miniatures was, that doesn’t sound that hard, maybe we should do it ourselves with the problem we found was that you describe a kind of a simple 220 by 200 day moving average. You get into an enormous number of questions that where you have to make decisions in terms of which assets do you use? How do you size the position? hands. Do you believe that? That’s right? Maybe there are certain periods of time where you want it to be a different parameter. And all of those decisions introduce enormous variability. So if I went into a room and you went into a room and we said, we’re going to come back with a trend model, chances are at the end of the year, we may be 200 basis points apart. But what we found was that it’s really, really easy to be 40 points apart. And in a sense, like, if everybody agreed that there was one simple standard straightforward way to define trend, for instance, then we would all do it the same way. It wouldn’t be that hard. But the reality is, everybody comes up with their own nuances. So bottom up replication, we wrote papers on it, again, not the academic published papers, but rather more practitioners papers, basically saying, yeah, it can work. And it’s a great thing that it’s reducing fees and adding more liquidity with client portfolios and sort of breaking some of the ethos that I need to pay somebody to in 20, to do something like that. But on the other hand, what you’re really marketing is the quarry trend product or the Andrew trend product. And if those are as variable as investing in AQR versus man, HL versus Campbell, there’s somebody else in the managerial space, then we’ve accomplished certain things, but we haven’t captured strategy replication. Because strategy replication, by definition, and what an index and hedge funds mean is very different than what it means in equities. If I say how did equities do yesterday, I may pick the s&p, you may pick NASDAQ, but probably neither one of us is gonna throw out the returns of the Thai equity market. But the idea is that you’re getting broad diversification that roughly captures a space, the only way to do that in hedge fund land is to pick up all of those different nuances of people who are building different models, and average them out somehow, in the same way that nobody says I’m investing in feeling really good about value stocks, and I found one great mid cap EMP company, I’m going to put it all on that. And factor base or top down replication is the only one that gives you broad strategy like returns. And so our whole thesis as a business is that the single manager risk has been consistently underestimated by allocators, particularly in the wealth management space. And we’re huge fans as you know the growth of model portfolios and in a world of model portfolios, single manager risk can absolutely be the bane of a portfolio.
Corey Hoffstein 17:29
Well, that’s certainly true when you look at allocators who are typically using something like we’ll say with managed futures, the sock Jen CTA index, as sort of their target representative index for portfolio allocation, then they try to pick managers. And what you find is, as you mentioned, there is massive, massive dispersion, particularly in the Managed futures space where there’s just a huge breadth of potential choices that one could make and constructing even just a very naive trend strategy that leads to dozens and dozens of percentage points difference per year, let’s stay on this replication topic. And I want to talk specifically, top down, because the most glaring risk to me seems to be that when you’re doing this top down factor analysis, there’s a potential lag embedded in the analysis, you need to see the historical returns to try to figure out what the underlying exposures are. And so you’re going to potentially miss turning points. Curious, how do you think about that risk? Is it something that can be minimized? How do we think about quantifying it? How are we addressing it?
Andrew Beer 18:35
So the question it relates to a couple of different variables. One is the rate at which the underlying portfolios themselves change. And there was one of those myths that I described about hedge funds was there was this, it sounds outdated today, because nobody talks about in these terms anymore. But there was this myth of this Soros disciple sitting in a one room office in Mayfair buying everything on Monday and selling it all on Tuesday and making a killing. The reality is every hedge fund strategy, the portfolios change at different rates, and at different times, I would take a step back and say one thing that is really important in replication land is what not to replicate. And that’s a whole separate discussion. But if you take two examples like managed futures, the portfolio’s change, and they may change relatively significantly in 30 days, they don’t change significantly between Monday and Wednesday. And it’s for the reasons that you just described, if you’re looking at 20 versus 200, and you’re looking at it 10 or 15, or 20 Different instruments at the same time, you’re probably not going to walk in on Tuesday and find that everything has completely reversed on you. If it has, I think we’re buying bottled water and heading into the mountains. On the other hand equity long short, the factor weights that are important move quite slowly. I mean, think about a typical equity Long, short guy. He’s a fundamental guy he’s probably owned. If you look at his portfolio today and six months ago and a year ago, a lot of those same stocks are in it because he bought them thinking Over the next five years, the stock was going to double or triple. And it takes a lot more than a troubling article in the Financial Times over the weekend to cause him to abandon it. And you can see that even with a lot of these tech stocks today that are taking some hedge fund luminaries down with it, I think the 2007 expectation would have been, they would have all seen the warning signs last November and gotten the hell out. But here they are holding on to things with a white knuckle grip. So it’s always an issue that you look at, I think, where we’ve been very different about this as a business is we’re not just quants. And as you can, I mean, if you start to nail me with statistical questions, I will claim static and dodge under my desk. It’s really understanding what hedge funds do and other ways you can test it that are much more qualitative in nature. And you’re always struggling with looking at past data. And the question that we have is, do we think it’s stable enough? And that we’re acting quickly enough that we can actually capture what they’re doing?
Corey Hoffstein 20:55
I want to go back to something you said before you jumped into your answer, which is the real key question here. First and foremost, is actually thinking about which strategies actually lend themselves to being replicated, and which don’t hoping you could expand on that for me.
Andrew Beer 21:10
Sure. So don’t try to replicate a single fund. That guy, Bob, may get scared by something in the Financial Times and sell half his positions on Monday. And you don’t want to be looking back at the past year of Bob’s numbers to figure out whether how he’s positioned today. But if Bob’s one of 50, guys, the 14, and other guys probably have not moved, and in fact, Fred over there may have been loading up on a buy the dip opportunity, the worst thing you can do is try to take really liquid assets and try to replicate really illiquid things. So if somebody comes to you and says, I have this, if only we could replicate the returns of all of these leverage distressed managers investigate abs and other things and wonder if they have a 2% volatility don’t try. Because the models will look at this artificially smooth return screen, and think they found things that aren’t there. And so you end up increasing risk in the portfolio, we had one experience with a portfolio like that back in the early 2000 10s that we’re doing for a client. And basically, it worked for a while and then it didn’t work, we had a greater diversions that we would have liked. So we decided not to do it again. Other things that are certain strategies that are structurally close to market neutral. Somebody came to me and said, Here’s millennium, Senator Wellington and valley as knee and five other guys like that, again, they’re leveraged, they are structurally market neutral or close to market neutral. They’re not going to swing around on the basis of a value versus growth rotation that you might see in fundamental managers,
Corey Hoffstein 22:42
sort of seems to me like the key question in some of this replication is where’s the Alpha coming from? So for example, I think you mentioned we’re trying to replicate long short equity managers, and we find that the majority of the category alpha is really coming from individual security selection, rather than a regional or factor tilts that might change over time, identifying those individual securities would sort of be a numerical nightmare. Curious, how does the source of alpha within a hedge fund category affect the ability to replicate that category?
Andrew Beer 23:18
It’s interesting. So actual hedge fund managers, their language is about getting factor rotations, right. Stan Druckenmiller shorts treasuries, he doesn’t say, but but the real value was I shorted the 29 year treasury, not the 30 year Treasury because I got a three basis point discount in my position, here and around buying oil. Even Maverick is having a terrible year. But Maverick success back in early 2021, late 2021, was was catching the value rotation early. But back in 2006, when this started, even the terminology of factor rotation was not widely used. And so one of the things I did again, is I went and I talked to actual hedge funds about it. And I went, I spoke to the guy who was the head of risk Abell post at the time. And I said, Look, I’m looking at this thing. And it sounds. I mean, you know, again, I’m not a statistician, but it sounds like this makes a lot of sense to me, if we just conceptually, if we tried to do this with Dow post returns, what do you think we would find? And he said, If you could determine with reasonable accuracy, what our positions were, what our core factor exposures were, of course, you come very close to our numbers over time. But he said, I believe that our factors are going to be a little more esoteric than the factors that you’re going to find. But he said conceptually, our portfolios move slowly. And if we send some guys to Argentina to look at a company, and they come back, and they like the company, first of all, we’re not going to buy 100% of it today. And it’s something we’ll be building up a position. And if we’d like the companies in Argentina, then soon we’ll have guys looking at Brazil and Colombia and every other country in the region. And lo and behold, we might have an EM allocation. That’s significant, but that’ll be in a year or two. And so, for us as a business, we need things that move relatively slowly. I managed futures is different. And I think the value proposition of the Alpha energy futures is much more about just the dispassionate nature of the way that they invest, which is a longer discussion. But they have a huge advantage by not caring about round numbers, not caring about whether their trade has played 80% of the way that they expected it to play out six months ago, whether it’s playing out faster or worse, it’s, but again, the alpha generation, there is factor tilts. But it’s short term factor tilts. It’s shorter term, it’s rotations, it’s things that are moving in the market. But today, interestingly, the language of factors is everywhere. So I talked about this new generation of allocators, it’s very different than the hedge fund guys. And the front of them guys in the 2000s is that I say, sector rotation stripe, hedge fund returns, and everybody nods, knowingly, like of course, back then it was anathema because that wasn’t the short side alpha, buying under followed small cap stocks, all of these things I think serious people always knew was kind of made up.
Corey Hoffstein 25:59
So let’s rewind back to the sort of 2007 2009 era and address a topic we sort of skirted around earlier, which is that there are some well known papers with major critiques. As you mentioned, Ed Tech, in particular wrote a series of papers largely dismissing both bottom up and top down approaches to hedge fund replication, arguing from both a theoretical and empirical basis. And their work is probably the most highly cited as to why replication doesn’t work. I don’t think there’s been much else after their work coming out, because I think it was sort of seen as the nail in the coffin for the space by I know that you have a serious bone to pick here. So I just want to give you maybe here’s the soapbox floor is yours. Where do you think the author’s went wrong in these studies,
Andrew Beer 26:51
everybody gets things wrong in papers, and lo wrote the seminal paper that I’m hugely supportive of, but he made two mistakes in it. One was that the data pool that they use for hedge funds had a huge selection bias issue. So his big conclusion was you can capture something like 70% of the returns, but 70%, of overstated returns, because of the selection bias issue. The right conclusion was actually my God, you can capture 100% of the returns, right? It was actually even more significant than than what he said. The other mistake that he made was, and I think this is a problem when you talk about people citing yet tech papers, and a pretty dim view of financial academics, particularly when they start citing 20 year old papers. And in the case of Lowe’s paper, they weren’t using emerging markets, because in the very early 2000s, other academics had said, we think that these are the primary drivers of hedge fund returns. But a few years after they wrote the paper, all hedge funds could talk about what’s emerging markets, and it was 35% of their exposure, and it’s driving all of their alpha. So there’s always this kind of disconnect. The edtech case was special in that the EdTech was reportedly funded by a very large fund to funds business. And that edX papers were believed to be a hit piece. And when you look at how they constructed, how they described what they were trying to do in the papers, and then how they actually constructed the experiments around it, there was a huge disconnect. And so they basically said, we’re seeking to expand upon this five factor or simple five factor model that Andrew Lowe had did to had employed to, to individual underlying strategies. And then when you looked at what they actually did is they actually took a five factor model and turned it into a one factor model or a two factor model and apply it to different sub strategies. That to me, was the equivalent of punching a hole in a boat, and then claiming it was Nazi worthy in the first place. No one thought a one or two factor model could reasonably replicate these underlying strategies. And my critique of it was basically I don’t see how somebody who was actually trying to come up with a reasonable research conclusion would have constructed things like that. And I noted that it wasn’t lost on me that this was hugely controversial, for high cost investors in hedge funds, even Goldman Sachs or competitor, when they announced some guy went on the record and said, we may have found a way to deliver as good or better returns in fund of funds at 1/5 of the fees. And my friends at Goldman Sachs said you could almost hear the calls going up to the CEOs office and then back down. And so they flipped out when I wrote this. And they basically challenged me for questioning the objectivity of it somehow disparage the Western canon. And then I got a call a couple of months later from a guy who tried to recreate the results and he said they blew it. And he said they used fixed income returns based on prices, not total return. He informed them over that they didn’t correct the record. So to me, when people take these academic papers really, really seriously, academics should put it disclosure on the front, in the same way that we should require people who are writing papers in the 1950s saying that smoking is good for you, who was funding them. And there should be some sort of an obligation. And I think Ken French has set the standard for this, you write a paper, you publish the results, keep publishing them, you know, if you think they’re not going to work, if you think the results are valid, continue to show them update the papers, like what Reinhardt had to do is their studies. And the other papers were written by consulting firms, which were one consulting firm, Hewitt wrote a paper that looked at the performance of replication based strategies for the crisis. And when you look at the charts, you’re like, actually, they kind of did what they were supposed to do. But they kind of get through all this analysis of it. And they say, but by the way, sure, they may do an okay job of replicating the broad hedge fund space, but we think only 7% of hedge funds are worth investing in, in the first place. And we’re gonna help you pick those funds. The bone to pick that I have is like I’m in the business of making money. I believe in replication, you can argue against what we do, but my cards are on the table. The fact that people are still citing these ad hoc papers today is I just think is hugely problematic.
Corey Hoffstein 31:08
Well, let’s talk maybe about away from theoretical results and more towards empirical results, because there have now been several firms that have tried to launch replication based products. And the results have been largely mediocre, in my opinion. And I’m curious as to your thoughts, why you think that is the case, your experience aside, not discussing the funds you manage here, but other funds that have been launched, haven’t had great track records and actually trying to replicate the underlying indices that they’re tracking? There are
Andrew Beer 31:45
two parts to that answer. One is good, they did mediocre performance during a time when hedge funds themselves were mediocre. Is that a success. And in replication land, that actually is a success. Because high cost Fund of Funds were some mediocre liquid alternative versions of mutual funds and use its funds that we’re picking individual managers to beat the index. We’re even worse than that. The general problem though, and it goes back to the point that I was making about top down versus bottom down, is if you’re going to do replication, right, you kind of got to try to get yourself as the allocator out of the way as much as possible. If we went to Vanguard and said, We want to buy your low cost s&p 500 ETF, and there was some guy there saying, You know what, I decided to get rid of stocks 470 through 499 today, and I’m loading up with these other stocks, because I think that’s better than what we were doing yesterday. So the big problem tends to be the two problems tend to be over engineering and adding in bells and whistles. So in the US, we owe a debt to index IQ for having launched replication based ETFs. I’ve been in this space for 15 years, I still can’t tell you what they do. They’re complicated. They try to replicate a lot of individual underlying strategies, they have some other model that figures out how to roll it up. And then you look at what they’re invested in, and you got hundreds and hundreds of underlying positions. That’s not replication. That’s something else. The other two examples early with us were Goldman Sachs and alpha simplex. And both of them couldn’t resist changing it. And so in the early 2000 10s, alpha simplex started layering all of these single manager strategies actually is basically saying, let’s add in a whole bunch of the bottom up stuff, the bottom up stuff did terribly. That fund has now lost 95% of its assets. So they took a replication based product that was working well and turns it into a single manager macro fund. And then Goldman to a lesser extent did the same thing with theirs, which was the industry leading product. So the hard thing with quants, simpler and efficient, stable tends to work better in replication. And there are three shining examples of people who have kept it relatively simple. Two of our competitors have kept it in Europe and kept it simple for over 10 years. And it just works unbelievably well.
Corey Hoffstein 34:09
So when we talk about this top down approach, I want to go back to maybe some of the nitty gritty here. It strikes me as a bit of a small data problem, where you look at something like let’s say we’re trying to replicate the sock Jen CTA index, you’re getting daily returns at best, and yet in certain categories, like manage futures, positions can flip on a dime. I know even if you look at a cross section of CTAs all their trend blanks will be different, but they do get crowded into positions today. Most are very long the dollar they’re very short bonds. If you saw a rally in bonds, I think the vast majority would quickly flip overnight to being long bonds. Maybe I’m wrong there. But I am curious again to push you on that navigate Seeing that potentially small data issue when there are times that you can get a category, making fairly sizable changes potentially fairly rapidly.
Andrew Beer 35:10
One of the things that all hedge fund data sucks. And people have tried to apply increasingly sophisticated analytical tools, people will pull out Kalman filters, and they’ll pull out all sorts of other things to try to tease more and more information out of what is fundamentally not great data. Take even the SOC Gen CTA index, which is very good by the standards of hedge fund data. When do they set the price for the day? What if they’ve got funds that are in Europe, they’ve got funds in the US? What if a fund in the US closes happens book on us time, but it’s also trading contracts in Asia? When do those get priced in the replication in hedge fund land is getting the broad themes right now, getting the broad themes, right, have a diversified pool of hedge funds tends to be a lot less risky than picking one of those guys and hoping that they’re the broad themes that they’re investing in do better than everybody else’s. The big thing that we really haven’t talked about is, our business would be a lot less interesting if hedge funds, charged low fees, and are probably big pivot, we’ve made very few pivots. And we’ve only done three products in 15 years. So shows you we are not a what Ben Johnson would call a spaghetti cannon. But we started to focus on preferred returns. And the idea was it was incredibly simple. But it was like my God, if there’s 500 basis points of trading costs and fees and other things associated with investing in the 20 funds in the sock gent CTA index. If we aim 500 basis points higher than what they’re doing. And we capture 400 of it, and charge a lot less. Going back to your point about Alpha. It’s not just what else was there and how its generated. It’s also who it goes to. And so this has kind of turned me into this kind of thorn in the side of the hedge fund industry where my friends Joe could apply car for breaks down in Greenwich, Connecticut, to lock the doors, it’s a trap, call hostage rescue or something. So you really are trying to use what data you have to tease enough information out of it, that you’re pointing in the right direction. It’s not perfect. And it’ll never be perfect. But back to your point, what we haven’t seen, at least in the managers future side is circumstances where and by the way, we just rebalance once a week. So we know the deterioration of quality of information that we have does go down, it doesn’t go down that much from Monday to Monday. But that also reduces our trading costs. So that’s a trade off we make, we could go to trading daily, it doesn’t really help our returns, maybe it would make us feel a little better that we’re keeping up with it, then maybe if we get very large, we’ll do more like that. On the other hand, if we traded today, and then went away for four weeks and came back, we’d have a very different portfolio on our hands. And so a lot of it is really just the judgment calls that goes into it as how efficiently Can you figure out what they’re doing? How close can you get. And there plenty of times he missed things plenty times it feels like maybe we’re moving a little bit more slowly, but it tends to self correct over time. And when you have that much of a cushion between pre fee, and that if he returns, going back to my elbow stays, that’s a positively asymmetrical trade for us.
Corey Hoffstein 38:16
Let’s talk a little bit about being a thorn in the side of the hedge fund industry, because in many ways you are through replication, trying to almost Vanguard hedge funds, a lot of the argument is if they can deliver value, and you can deliver similar enough value, but cut the fees dramatically. That’s an improvement for the end investor. But you are relying on the existence of those hedge funds. And I think I don’t want to put words in your mouth, you would actually root for those hedge funds to be generating alpha, right? So how do you weigh this dichotomy of you need them and you want them to do well, and at the same time, you’re trying to beat them at their own game. So
Andrew Beer 38:55
our products are boring in index like they’re designed to be that they’re designed to be for an allocator, who wants 5% 10% in one of these strategies, is sensitive to costs, and really wants to be able to make the allocation and not have any noise in the next five or 10 years. Because from cutting out fees if equity long short hedge funds do six and we do eight Netta fees but with daily liquidity, etc. That’s a huge win. If that arguments following that same line of reasoning, the whole act of stock picking business should be gone by now. But it doesn’t people like the excitement people like to pick people like there are people whose careers are built up around picking and selecting managers and deciding who goes in these portfolios. I have never viewed us as a full replacement for what hedge funds do. A going back to your point if you believe that short term trend is going to be the way to make money and you can find some guy who was whipping his portfolio between Monday and Wednesday and Friday. You want that to be product portfolio. We are not going to be able to replicate that guy. You If you have a client that doesn’t care about liquidity that you can put into millennium, and tie up their money for the next five or 10 years and get a 2.4 Sharpe ratio for doing it, and being a hero for getting them access to one of the greatest investment products of the past 30 years, by all means do that. But what’s been lacking for hedge fund allocators is a robust toolset, where it’s basically that was the high cost hedge fund argument, it’s either you either have to pay full fees, and have terrible terms and all these other horrible things, and everybody makes money but you or don’t even bother. And on the thorn in the side, I think it’s also a little bit of a thorn in the side and LiquidSpace. Because we’re known really for two things. We’re known as the only guys with daily look at low cost products who consistently outperform hedge funds in every product we’ve done over the past 15 years. But we also know what not to do. So also, we’re a bit of a thorn in the side. And that we often argue that 95% of the products that are out there on their own, have no diversification benefits. Nobody takes a single stock and adds it to a 6040 portfolio and says, haha, I’ve improved my Sharpe ratio, but that’s what they do all the time in the liquid space.
Corey Hoffstein 41:11
To go back to, again, the details, I feel like I keep yo yoing you high level, low level high level to throw you off. But another and this is something you brought up earlier. Another potential problem with this top down factor based replication is choosing the actual spanning set of factors or markets that you want to select. I really have two questions here. And the first is, you mentioned earlier on the idea that those factors that are important can change over time. So in the early 2000s, there was a significant adoption of emerging markets among equity, long short managers, that if you were picking a set of factors, say in 1999, and then sticking with those factors forever, you would miss in your replication. How do you address that sort of issue that the factors or baskets that these managers can be interested in can actually evolve and change?
Andrew Beer 42:09
So I guess our starting philosophical point is that people often confuse a number of positions. For diversification. There was a whole wave of multi asset products in Europe, where people thought they were diversified, because you’d have five or 600 underlying positions, all of which had one overlapping investment team around it. And a lot of the hedge fund industry and liquid ultiworld is sold off of bells and whistles. Part of what people consider radical about what we do is when we try to replicate equity long short, it’s between five and 10. Futures contracts, we’re not even buying individual stocks, when we replicate managed futures, it’s roughly 10 futures contracts. So over long periods of time, we know that the principal drivers of performance are not going to change that much. And by that I mean, take managed futures, returns are going to come from rates, currencies, commodities and equities. Crypto, people will say that crypto is soon going to be the fifth leg of it a number of years ago, people said, oh, VIX, volatility, instruments, etc. But none of those are material today. But we watch it. And we say the difference because we’re practitioners, is we pay a lot of attention to what hedge funds are actually doing. And so that kind of gives us an early read if the factor set is changing. I think one thing that surprises people about it, though, is that I think people assume that our results would be very, very sensitive to exactly which instruments we use. But if you think about it, let’s say, for whatever reason, we didn’t use the 10 year treasury, we only had the 30 year treasury, what would the model do in the window that it’s looking at, it would say it’s sniffing around for the 10 year Treasury return. But instead, it’s got this somewhat more volatile 30 year Treasury return that is going up and down almost in exactly the same form, maybe with slight differences, but a little bit more volatile. So it’ll just take a little bit less of that. And I think one of the things we found early on was that as long as you have the major areas covered, the models are pretty good at adapting what we have, to what they’re trying to the sources of return that they’re trying to identify. We don’t always get it perfectly. So in September of last year, I wish we’d had natural gas in our Managed futures portfolio, why natural gas went up 34%. And if we’d been geniuses and decided in August to add natural gases and 11 factor, we would have a 7% position. But then we also would have given it back over the next two months. So it’s not to say that we don’t have judgment calls and what we do but what we’re trying to do are basically make rational calls on ultimately, laser focused on the end objective, which is let’s invest in the deepest, most liquid most efficient things that we can use because we want to be efficient. illiquid, and not try to get really cute and fancy to get from capturing 93% of their preferred returns to 95%. And going back to your question about how other people have made mistakes in the space, I think it’s very, very hard for quants not to do anything. I think the culture of particularly in investment banks and a lot of asset management firms is if you’re not constantly adding bells and whistles and changing parameters, you’re not doing your job. I think the great quants, people that I know who’ve gone from investment banks to places like a D, Shaw, will tell you that actually, stability is rewarded in the latter. And I’ve been very lucky to work with quants who have always believed that.
Corey Hoffstein 45:42
So I want to dive in a little bit deeper on this idea that and let’s use managed futures as an example, the basis of instruments that you choose, may not actually be massively influential in your ability to replicate accurately. And I’m sure there are managed futures managers who are listening that were shocked when you said you only use 10 to 15 futures markets to replicate futures returns when so many managers emphasize how many different unique markets they actually trade 40 to 60 different markets and how much beneficial that diversification brings them. You’re sort of saying, hey, look as a category, not only do all those different markets not matter, but actually you could probably make some substitutions within the basket of futures that you do trade, and it probably wouldn’t even matter all that much. I know you guys have actually explicitly studied this as a topic. I’d be curious if maybe if you can just expand on what you found and how influential that basket really is.
Andrew Beer 46:47
I wouldn’t be very surprised if seasoned miniatures guys have the reaction that you say, I think part of what led us down this line I spoke to, again, we’re very unusual in quant land and doing stuff like this. Here’s a little story. I was talking to a JPMorgan launched a bunch of replication based ETFs. And I was talking to a friend of mine. And he said when we’ve got the smartest quants in the world, and they’re doing this and they say they can do it this other way. And I said, but they’re telling you that they’re replicating what hedge funds, do you guys have 40 or $50 billion invested in hedge funds? How many times have they actually gone and sat down with the hedge funds, and said, This is how we’re modeling what we think you’re doing. And he looked at me and he said, oxygen, you’re right. It’s almost his two different worlds. In our case, we believe in stability, simplicity, etc. So one of the studies that we did early on, is we said, Let’s randomly pick factors. And what you find is sort of shadows of the edX studies is if we tried to replicate managed futures with only the s&p 500, it doesn’t work. If we try to use only the s&p 500 and a 10. Year Treasury, it works a little bit better, but really not. But so when you kind of when you when you end up needing as you need exposure to what we know are the four drivers of their returns those four major categories. And then within that, it’s better to have a couple of instruments than one. But one of the studies that we did was basically what if we just randomly picked two commodity instruments and randomly threw in two rates, instruments, etc. This goes back to kind of the economic proposition that the spread between pre PHY I mean, some clearly worked better than others. And we ended up selecting them that we thought were much more rational and representative. But every single one, even with bizarre factor sets did better than the net if he returns to the index, because that 500 basis points of fees and expenses was so significant, that even if with some weird factors that were only replicating 65% of their preferred returns, not 98%, we still did better on a net a few basis. So we try to keep part of our philosophies to stay in the deepest, most liquid markets, I always live in fear that there’s another Lehman moment around the corner. And the last thing I want is some swap that I can’t unwind some derivative contract that’s traded in some esoteric market, that’s all of a sudden frozen overnight. So we try to stay in the deepest, most liquid markets. So when that happens, we have more liquidity than the
Corey Hoffstein 49:18
next guy. You’re trying to avoid nickel, nickel. So one of the areas I’ve been really focused on this year is this idea of strategy versus structure. I know that you helped manage UCITS in Europe and ETF here in the United States. Curious how those different structures impact your ability to implement replication based strategies.
Andrew Beer 49:43
Given that we start with the idea that we want to use a relatively small number of highly liquid futures contracts, in everything that we’re doing, it is about 10 times easier for us to manage within the constraints of those entities that And most hedge fund like strategies, each one has their own idiosyncratic constraints. The big issue between Europe and the US, so we use its funds versus Rick’s in the US, is it’s harder to invest in commodities in Europe. And so when we have built, for instance, our Managed futures strategy that we built in Europe, works around it. And this kind of goes back to the point that you were asking by basically saying, Alright, we’re not going to invest in commodities. So what would we have instead? So if the model is sniffing around for long crude oil exposure in the first quarter, it’s going to find things that have been over that window, highly correlated with it. So it might own more short positions in bonds, etc. That’s a bigger point of variability than whether the 10 instruments that we have in the US should be 11 instruments by including natural gas in September of last year, there are people who have approached it by saying, well, we can use swaps or notes or other things. To get exposure to it, we have just been, I guess, we’ve taken a very dogmatic position in that. I’ve always said to our investors, I can pick up the phone and call you and say, look, the replication models didn’t move quite as fast as we would have liked, it seems to picked up some spurious correlation here or there, we underperformed the target by 200 basis points, I cannot pick up the phone and call somebody and say and by the way, you invested in a fund with t plus one liquidity. And we have a small problem called we have nickel contracts that we can’t price. So no money’s coming in at our for the next three weeks. But by and large, they both work very well.
Corey Hoffstein 51:39
I’ve had some fun over the last couple of months building out position based replication strategies of my own in the Managed futures space trying to estimate if markets move in a certain direction, how managed futures managers flows will change. And then I have been using the hosted holdings of your ETF to track live on average how I think the sock Jen, on average index positions did change over that period. And one of the very frustrating aspects of that exercise is, when you have an ETF like yours, you have this big chunk of a Cayman entity. And this I think, is totally out of Queensland at this point. But this is more maybe just fun structure education, I get people asking me about what these Cayman entities are, why are they in managed futures funds, I was hoping maybe you could just explain that for the record. So I’m
Andrew Beer 52:34
gonna put in big flashing red lights. I know this is a podcast, but I’m not a tax advisor, and not a tax lawyer etc. But basically ETFs can invest directly in commodities. Legally, the problem is that the tax characteristics of commodity futures contracts are different than the tax characteristics of, say, a Treasury 10 year Treasury futures contract. And most of the time, it probably doesn’t matter. But if you’re ever on the wrong side of it, it destroys the whole structure, basically. So nobody ever wants to take that risk. So instead, what you do is you take some of the money that’s sitting on the balance sheet, you basically create a new company offshore, a Cayman fund, that is 100%, owned by the ETF and you put some money down into it. And so for, in our case, golden oil are traded down in the Cayman sub. One of the things that’s slightly irritating about it is that the gains that are made in those contracts when they come back up into the ETF come back at a disadvantageous tax rate. So the trade off would be you can do it without investing in commodities. And you wouldn’t have to have the Cayman sub. But I think we and other people think that it’s worth the trade off, even though it’s a little bit more operationally complex. The last
Corey Hoffstein 53:53
question I’m asking everyone this season is to reflect back on their career and think about what was the luckiest break over your career?
Andrew Beer 54:03
Oh, that’s a good question. I guess I would say, probably getting interviewed by Seth Klarman, learning about the hedge fund industry. It was an area that I knew nothing about. I think even when I left bow post, I still didn’t really understand the industry. But I love what I do. It sent me down this path of working with incredibly smart people, always having new things to focus on every year, and having new opportunities. And I’ve been very lucky having my own firms for much of the past 25 years. And it’s given me an enormous flexibility in terms of where I work and how I work. And I don’t think I would have had that if I had gone into a more traditional area like private equity.
Corey Hoffstein 54:44
Well, Andrew, this has really been phenomenal. Thank you for joining me.
Andrew Beer 54:47