Multi-strategy investing for the changing macro regime featuring Scott Burr

In this episode, Future Standard’s Investment Research team members Alan Flannigan and Andrew Korz sit down with Scott Burr, Senior Portfolio Manager at Future Standard, to discuss how multi-strategy investing may help investors navigate a changing macro regime.

Scott also shares how his liquid alternatives approach blends diversification with conviction, the role of liquid alternatives play alongside private markets, and how strategies go from idea to execution.

For more research insights go to https://futurestandard.com/insights 

Transcript

Alan Flannigan: In our last episode, we discussed the most cliche word in macro, uncertainty, in the realm of policy, geopolitics, and economics.

Today, we're going to talk about how that's showing up in markets. For most investors' working lives, they've operated within a relatively stable framework. Economic cycles followed a familiar rhythm, asset classes usually behaved in predictable ways, and shocks, when they happened, were treated as external disruptions.

It's sort of over here somewhere. Today, what used to feel exogenous increasingly seems embedded within the system itself. The call is coming from inside the house, if you will. A pandemic, inflation, bank failures, two major wars, and an upending of the global trade system all in six years. If once is happenstance, twice is coincidence, and three times is enemy of action...what's the saying for six times? Regime change? That raises a fundamental question for investors. How do you build a portfolio for the less predictable world now at our doorstep, and critically, to avoid falling back on the assumptions of the past?

One answer is diversification, and the idea that relying on the simplistic portfolio construction adages traditionally used will no longer cut it. Another is concentration. Focus your energies on only those few opportunities where you have a true edge. These are opposing, but not necessarily mutually exclusive approaches.

Today, we're going to dig into how those philosophies show up in real portfolios. Scott Burr is a Senior Portfolio Manager here at Future Standard. He runs a liquid alternative strategy that invests across asset classes and merges these two philosophies together, spending each day navigating this environment on behalf of investors. Scott, welcome to the podcast. 

Scott Burr: Thank you for having me. Great to be here. 

Alan Flannigan: And as always, I'm joined by my colleague and partner in podcasting, leader of our investment research efforts here at the firm, Senior Vice President, Andrew Korz.

Andrew Korz: Happy to be here, Alan. Excited for the conversation. 

Alan Flannigan: Yeah, really looking forward to having Scott today. As we talked about in the intro, blending these kind of two approaches, diversification, and then focusing on areas where that informational advantage sort of lies, and then against this macro backdrop of sort of regime shifting, more frequent shocks. It's almost like we're putting out the bat signal for a superhero, and like all superheroes, most have a great origin story and I'm curious to sort of hear yours, Scott. How did you get interested in investing and get started in the business? 

Scott Burr: I don't know about that, it's not quite a Batman origin story, but I was born and raised in New York City and I guess I haven't made it very far because I'm still there. But growing up we used to get the New York Times delivered every morning, and for some reason, I used to love going to the business section. The back pages, they had the list of all the stock tickers, and they were all in tiny font, and I used to comb through them and just be curious about which stocks were going up, which stocks were going down, and why.

And my folks thought it was actually pretty strange because they were never involved in finance or markets but then in middle school, my father put $1,000 in an- E-Trade account for me. And after a lot of analysis, I figured out that computers were gonna be a big deal.

So, I bought CompUSA which is the big computer retailer, my first trade. I think it turned out pretty well. It did end up going bankrupt, but…computers turned out well. Go figure. The rest is history. So, I think E-Trade was definitely part of the origin story. But later in life I went to a very competitive public high school in the city called Stuyvesant, a math and science high school. There's 800 kids in a grade and the average SAT score is 1450 and every year you could only apply to seven colleges, and every year they would publish a list of each college, how many people applied, how many people got in, and they told you the lowest grade point average accepted to that school.

So, it just made super smart kids even more competitive. But that was a really good training ground for a life in markets because once you realize, or you're in an environment where you're not the smartest kid anymore and that's not an edge, you need to really figure out how to operate differently to distinguish yourself. And I think that is the same exact sort of parallel to markets where, you talked about edge…what's the edge? Again, it's really hard in such a competitive space like markets to have a durable edge of just intelligence, making smarter decisions.

I think for our strategy, and in general my investment approach, it's having a variant approach that develops a variant perception that allows you to have a durable, acceptable outcome. But yeah, I would say that definitely my upbringing in terms of my schooling and things like that definitely has, has served me well in terms of the career path that I ultimately took. 

Alan Flannigan: So bring us into your seat here at Future Standard. You operate a multi-strategy hedge fund focused fund and it solves for unique problems that investors have in their portfolios. I'm interested in your view: what is the problem that a multi-strategy hedge fund seeks to solve? And, broadly, not just your strategy and your application of that variant perception which I love as a concept, but sort of the liquid alt space in general. 

Scott Burr: I think what you touched on in the beginning in your intro is spot on in that what's worked in the past I think is very unlikely to work in the future. And specifically, the 60/40 portfolio really has anchored everyone's portfolio for such a long time because it achieved return but also had balance risk properties because the 40 diversifying the 60 and whatnot. And, yeah, you may not have needed liquid alts or hedge funds when the 60/40 is just kind of delivering it in spades. And I think part of the that environment when the 60/40 was working so well, hedge funds just became levered bets on risk, right? Because that was sort of the trade. But the real premise of hedge funds, the name hedge funds is really to be more all-weather diversifier and offer something different than what the 60 and 40 is, which is equity. Beta is the central risk and then bond beta or duration or interest rate sensitivity.

And I think everyone, if you are a student of portfolios, your goal is to deliver a balanced profile and deliver risk-adjusted returns. And so when you only have the 60 or 40 to choose from, that's limiting. And so there needs to be a third leg of the stool.

I mean, you could say cash is a leg of the stool, but cash is not appealing the vast majority of the time. There's no risk associated with it. There's no real outperformance associated with it. So, there's so much room for this third leg of the stool, this alternative exposure, that the intention of that, in my mind, is to really be a different specie than the 60 or the 40. So what that means is it needs to really load on alternative risks and return. And the reality is, in this space of alternatives, there's still things that are called alternatives that do load principally on equity bond risk. But for our strategy, like we are very focused on this problem and being a solution to that problem, so it's very oriented around limiting bond equity and bond beta. And to really focus on making sure that the majority of our risk and return is coming from an alternative flavor. And, you know, 2022 I think is kind of the canary in the coal mine for...you know, are portfolios resilient to scenarios when the 60 and 40 go down together?

The answer is no. I mean, I can imagine there's so many individual portfolios out there that looked at their statement in 2022 and looked at all their different line items. They thought they were diversified. There's not one line item that's sticking out that's green.

Alan Flannigan: Yeah, where is it? Where's the help? 

Scott Burr: Yeah and so, then you're obviously not balanced across a range of different scenarios. And, you know, it's easy, I think everyone's conditioned by the recent past, and you could define recent. But it's been a great run for equities. I mean, no one can dispute that 10, 15 years...amazing returns, so, you know, it doesn't feel like you need diversification, right? But if you're a student of markets, there's the counterfactual exercise of what happens if this didn't happen or this didn't happen, there's so many markets out there that had lost decades. There's the U.S. equity market, I think from 2001 to 2010 was basically, you know, it eclipsed its all-time high for 10 or 12 years post the dot-com bubble.

So, there’s periods of time where you look at rolling 10 year returns where the equity market doesn't deliver. So, I think, the present moment that you described, the goal in investing is to be forward-looking. A lot of the strategies and positioning and allocations are geared towards what's worked in history. And I think the go forward's going to be a lot different, to be honest with you. 

Andrew Korz: Maybe just to sort of level set for our listeners, we mentioned the fund that you run is a multi-strategy. You invest across different asset classes, different types of investment products.

I think most people hear multi-strategy, right? It's kind of the hot term in hedge funds right now, they probably think of the famous ones, I'm not going to name names, but, pod shops, right? Where you’ve got all these different small teams that invest in a certain area of the market, certain asset class, and you put them all together and you put a risk overlay on top and that's delivered really, really nice performance, risk-adjusted performance. Those platforms are massive now. They represent, I'm not sure what percentage of, float or of trades every day in the market. Yours is a liquid alternative fund. How is it similar or different to some of those platforms?

Are you looking to deliver similar outcomes in a more liquid wrapper? How do you position what you're doing in the liquid alternatives world within the broader investment landscape? 

Scott Burr: It's a great question. As a starting point, I think just structurally, obviously we have more limitations from a leverage perspective, from a fees perspective, we're trying to be a low-fee provider of alpha. You know, these pod shops are kind of notorious for the fee load and they deliver the returns. So, it works for many investors, but they're hiring teams in every market and it's a costly endeavor. And, I think the beta piece as well, I think they're probably more relaxed in terms of their levels of beta than we are. You know, they have a performance fee dimension, we don't. So, we're not just trying to load beta in there because beta does typically deliver the return potential.

We are trying to be a solutions provider to the earlier discussion that we had. So, objectives, I would say they rhyme. They're not exactly the same. They're trying to be all-weather as well but it's tough to be all-weather when you have ample amounts of beta, and you run the math on that, you have a 0.2 equity beta, 0.3 equity beta, the most of your risk is going to come from equity beta if you just scrub the numbers. I think we're playing the same game obviously, and the game of multi-strategy investing, as you alluded to, is a game of risk-return, where you have enough diversification that the risks cancel each other out and you're collecting the return side of the equation.

To win that game, my simple sort of model is, it's a game of breadth and depth. So, breadth is really access to all the markets that exist and that you can source interesting opportunities from. And, also, have a breadth of techniques to generate alpha within those markets.

So, you need to have a wide net and then depth is the skill and expertise and domain knowledge required to extract alpha in a durable way from each of those markets. So, we're playing the same type of game in our strategy. We're solving that equation.

So, I think we need to be a little bit more selective about where we want to lean into in terms of a market, in terms of an alternative strategy. Which hedge fund strategies make the most sense for the present moment, or at least, the medium to long term actually.

Like strategically, what are the most attractive strategies? We evolve the portfolio to lean into those strategies that we think have the most durable opportunity set, and I think that's different. I think they're just trying to make sure that they have exposure everywhere and alpha generators, everywhere. And so yeah, ultimately, it's a very similar exercise. 

Andrew Korz: So I am curious, dovetailing off that, you just described a strategy that is very different from, I think when people might watch TV shows or movies and see different types of investors that are represented in those types of mediums. I've known you a long time. You're a thoughtful, circumspect, methodical type of guy. I don't know if you'd disagree with that. I think there's a lot of different ways to succeed at investing. And I think when you meet enough people in this industry, you start to realize that it's not about being one type of person. It's more about understanding your sort of advantages, inherent advantages and also your weaknesses, and molding yourself into the type of role that fits that, right?

So I'm curious, for this specific type of investing, when you realized that your sort of personality fit that mold or if you still question that sometimes? 

Alan Flannigan: It's sort of like the variant perception that Scott mentioned earlier, and Mike Kelly, when we had him on spoke about at length as well, too. It's sort of like that dovetail of, like, what is that edge? 

Scott Burr: No, it's funny that you ask that question. I mean, I'm definitely not the personality that you want to see on TV representing the investor community. It's funny because if I'm at a party of any sort and someone asks me what I do, like- 

Andrew Korz: You go to parties?

Scott Burr: I need to socialize. And, you know, people ask me what I do, and right when I go into what I do, I just see the fear. Like, what did I just get myself into? So, no one wants to talk to me at the dinner party, so I'm definitely not suited for the TV role. In terms of personality, I'll leave you with a few data points. So, I'm the type of person, and I'm curious if you guys are as well, I will always go to fill up at the cheapest gas station. Like, it's just inexcusable. I mean, it's not about the $7. It's probably $10 now, by the way. But it's just about a kind of a principle. And my brother will make fun of me about that. Well, you feel like you've won the transaction at that point. You've extracted some alpha. I mean, it's a risk return situation. The second data point is when we moved from the city to the burbs, and we were looking at houses, I found this tax arb where if you live in a town home, you pay basically half the tax from the same priced freestanding house across the street because of the tax classification. So, when I learned that, I said to Stephanie, "You know what, babe? I think I'm a town home guy." You know, I like being around people. Something about the classic look. I grew up in the city. I just like the community.

It was just such a good trade. And by the way, I think I bottom ticked mortgages at 2.5%. So that's probably my best trade to date. That's a macro trade, though. 

That's alpha. That's alpha. That's good. That's fair. And then the third data point is that I go on vacation, uh, in December. The last week of school, I take my kids out of school for vacation. They're in elementary school, so they're not learning anything, or not much at least anyway. And especially the last week BEcause they were watching movies.

Okay? Yeah. Reading's going out of stock anyway. I'm like, "Stephanie-" They're still teaching cursive these days? Don't worry about handwriting. Take 'em out of school. Don't worry about the handwriting. There you go.

I mean, you do the math. The same vacation a week later, the, the, the flights are three times the, the, the, the, the price. The hotels are double. It's like, I can't- And then you future value those savings, I imagine. I mean, yeah, run the numbers. Run the... It just, it just, it's just the, the, the w- the week before the holidays, y- I mean, the last week of school is the best risk-return trade there is.

So I think to your point on the personality, in my personal life, it’s about making quality financial decisions. My professional life, it's about making quality investment decisions. It's a hard wiring. And sometimes I wish I wasn't like this, to be honest with you.

I'm not saying it's the best way to live, but- From an investment perspective, a lot of that sort of, you know... And it's funny with ChatGPT and AI these days when you're conversing with it, you know, it gets to know you, right? So, you know, it, really draws you in. And whenever I talk to ChatGPT or ask a question, it'll be like, "Well, because you're such an analytical person, this is very you," and I'm like "Who? I don't even know who that is." But no, they're right. They're right. Who are you to say? They're spot on.

So yeah, but I think how that translates into investment decisions…I'm very risk return oriented, as you might imagine, making sure the math computes. You know, philosophically, in the big questions, when you think about big investment philosophies out there, I kind of view it as the Ray Dalio, on one side, which is sort of a believer in diversification. Right. He calls the holy grail as a portfolio of 10 to 15 uncorrelated return streams, and then you have, the Buffetts and that style, which is, you want concentrated conviction.

That's how you make your money. I'm more on the Dalio camp because, you know, the diversification math is just too factual, too powerful. Like, I don't know, are you guys CFAs? 

Alan Flannigan: Sitting in August for level three.

Scott Burr: Okay. Well, you could tell me if it's in level three or level two, but you do the math of…you have two assets in a portfolio, the same volatility. If they're completely uncorrelated to one another, the risk of that portfolio goes down by 30%. You add another asset, right? And completely uncorrelated, the risk goes down by another 15%. Like, that's just math. Did you learn that? 

Alan Flannigan: I haven't gotten to that section yet. 

Scott Burr: You need to study harder, dude. Come on. 

Andrew Korz: It's the closest thing to a free lunch in finance, I would say. 

Scott Burr: I believe in that principle strongly. And lastly, I'll say just in terms of making quality investment decisions, one of the hardships of managing risk is, especially when you're really analytical, is analysis paralysis.

Like, at some point you need to be decisive and make a call, and I think I struggled with that early in my career for sure. But then I came across Howard Marks and his quote, which is that you can't judge the quality of a decision by its outcome. And that was the most freeing investment philosophy I ever heard, because at the end of the day, in this seat, you're operating in a world of uncertainty. You could have exposures, you do all the analysis, it's a home run, and it doesn't work out. Things change.

But at the end of the day, if you have a process of making quality decisions and you have to make a lot of decisions, quality decisions will compound into good outcomes. Not every decision, but law of large numbers, that works out. And so, the whole approach is just about collecting quality decisions across a breadth and depth model. 

Alan Flannigan: It's a very process-oriented approach, it seems. I mean, folks love, like, when Nick Saban talks about coaching aphorisms and how they think about achieving greatness during his tenure at Alabama, and it was all about, like, "Guys, don't watch the scoreboard." What you're doing right now, do that to the very best, whether you're up 40, down 40, or somewhere in between. That resulted in them not being down 40 very often. Good recruits help. But you know, when you think about the mandate of a multi-strategy hedge fund being absolute return and, when most people think about investing, they think about what does the future look like and trying to predict that.

Like, that's where the informational edge comes from. And, when we think about the past regime of economic cycles, sort of more aggregate demand related, where it was like, "Okay, I know I'm on this continuum. It's a question of what inning of the ball game are we in?" Like how far along in the cycle, and that'll inform some of my positioning.

But you're following a pursuit that is expressly not that. Because we don't know exactly where we are on the continuum. And when you're going through a period like today, maybe it's a different continuum entirely. Maybe it's not the same framework that you've been making decisions on for the past 20 years.

And so, when you're explicitly not trying to forecast the future, or stick to that simple prediction along that aggregate demand continuum, what are you doing? I don't mean to sound like, "What would you say you do here, Scott?" But you know, where does your edge come from, and how is that really applied?

Scott Burr: Yeah, I think you're right, the crystal ball approach to investing is tough. I think some people have skill and really identifying, outcomes, before they occur and it requires a lot of conviction, for sure.

I just, for me...a lot of prognostication, I can really see both sides. I mean, that's part of, the struggle in general is I can create a bull case and bear case for just about anything. And to sort of crystal ball invest you really need to have conviction in one outcome. That's just not how I'm wired frankly. But the beauty of multi-strategy investing is that it's not required because it goes back to the breadth and depth point, is that if you're able to access exposures across markets and instrumentation and, you can organically determine in each of different buckets, what's interesting to do at a given time based on kind of the endogenous variables that kind of guide risk return in those markets. And when you kind of go through different markets and cherry pick what's interesting, a lot of times what's interesting is actually not sort of same-sided exposures, right? So, I'll give you an example.

In the equity market right now, it's obviously AI, all the action, there's a lot of momentum. What's working are the cyclical names that are CapEx beneficiaries, right? So, you might say, "Okay, this is, this is an attractive sort of tailwind to participate in."

You look at a market like volatility and you look and you say, "Well, what's most unusual is just the level of dispersion that exists in the cross-section of markets." There's just so much dispersion. And so, correlation as a parameter that you can take views on is very cheap.

So, you can position for an increase in correlation back to some historical norm. Positioning for correlations to go back to historical norm is extremely cheap because there's this belief that this dispersion environment is just going to persist for so long. And so, you could say, "Okay, it may be interesting to position for correlation, some normalization."

So, those trades naturally offset one another frankly. So, the point being is, and I could have a number of examples of that where you pick a specific variable like the fixed income curve, right? And you could say, "Okay, well, the shape of the curve  informs a lot about macro.”

Like if it's a steep curve or a flat curve, flat curve's recessionary. I could tell you an exposure I love that benefits the most from steepening curves, and I could tell you an expression that's very attractive here that benefits from flattening curves. So, it's really about collecting things that in a vacuum are interesting, but then in the portfolio context making sure that there's enough diversification in that, and that you're kind of neutral to kind of big macro outcomes.

And you're not perfectly neutral. There's leanings. But it's really the multi-strategy approach affords you the opportunity to kind of construct that. If you're just a rates trader, it's pretty hard not to just have a view on whether rates are going higher or lower.

You can't avoid making that call, right? And it's when you've got that extensive breadth, it's almost like there's an unlimited number of trades you could make, and you have different instruments by which to implement the exposure. 

Alan Flannigan: And, you've sort of given a little bit of an example, but I'm curious, like you've got this breadth. How do you decide which rises to the top, and what is like the trade that you want to execute and go about expressing that in the most efficient way? 

Andrew Korz: How do you filter that world down to something that feels actionable? 

Scott Burr: In our strategy we actually have to make a bunch of different types of investment decisions and one of them is just what, like I mentioned with our balance sheet, we allocate our balance sheet to a number of external managers and their specific hedge fund strategies like convertible arbitrage or merger arbitrage or long-short credit. And so there, you really have to understand what are the drivers of return for those strategy? What are the conditions where those strategies have above average returns? Convertible arbitrage for example, the issuance of converts is a big source of return for that type of strategy. Obviously, the amount of risk capital in the space, deal activity is important for merger arb.

And so, for cashflow strategies, obviously the interest rate environment's very important. So those, you need to understand the broad tailwinds for hedge fund strategies when you're allocating to hedge fund strategies. In terms of more opportunistic trades, which is what I spend a lot of time on and what we do in-house, like you said, it's kind of drinking from a fire hose a bit because the idea generation, there's so many ideas. The question is how do you filter through and determine the quality idea, the quality risk return. And there's a few things, and you touched on one of them, Alan, but the first one is what I call signal agreement which is that, you're looking for things where, there's a sort of a lot of reasons why, you know, you're building a case for why this idea should work.

And so, you're identifying things that drive return outcomes, Whether it's fundamentals, whether it's positioning, whether it's macro environment or catalyst, you're looking for signal agreement across the dimensions or the drivers of return. So multiple things that are hypothetically independent of each other, but that point towards the same potential outcome for this given exposure. These variables are skewed. It's a checklist. Skewed positive. Like, how many checks can an idea get is a simple way to think about it. And there's trades like in 2022 equity value themes.

Scott Burr: We had a trade where we were long kind of reflation, stocks versus non-profitable tech, because it's kind of like a value expression because '21 there was just this sort of growth investing was all the rage. These sort of, the Cathie Wood kind of names were flying, huge valuations, and then you had this inflation shock, higher rate.

Everyone thought rates were pinned at zero forever and that sort of derated all the growth companies, so you looked at that and it was sort of value spreads that when you looked at high versus low value were so extreme. Positioning was so extreme, and then you had this macro catalyst of a higher rate shock, right?

Like, a lot of alignment of, of signaling. And, you could say that about the mortgage basis when the Fed switched from QE to QT. Like, the home run trades, when you look back and do a diagnostic, it's about this signal agreement. 

And then the second piece, which you mentioned, is implementation alpha. And I think that's where, frankly, I would say that's my wheelhouse the most discriminating against different ways to express an idea. And if you have a broad toolbox of markets as well as instrumentation in terms of derivatives and options and all the different types of structures out there to sculpt risk and return, there's so many, that's where a lot of times you can add a lot of value just from how you structure an idea and implement something. And, just as a simple trade on betting that the curve, the fixed income, the treasury curve will steepen, right?

You can just buy the vanilla implementation is like buy the front end, sell the back end. Or you can harvest dynamics from the vol and skew market and say, actually what makes a lot more sense is let me sell a payer on the front end and buy a payer on the back end and I have better carry and I have better convexity. It's like there's a lot there which is smarter ways to sort of construct risk-return. And so, when you have an idea that checks a lot, has signal agreement, you have an implementation alpha as well, like those are great trades. And then also, there's not all return is equal in terms of how dependable or deterministic the return is, right? Like there's stuff that's very deterministic like a merger-arb deal. You know what the price is if the deal is completed.

You largely know. You have a pretty good idea of downside. Like those numbers are pretty reliable versus there are returns that are fuzzier math where you think this will happen or that happens or... And so, in terms of quality trades, the more tangible the risk-return, the more that you have a handle on risk-return certainty, those are also better trades. So, the quality of your risk-return estimate is the third piece. And so that's a simple framework, I would say, to how I filter those types of opportunities. 

Andrew Korz: So maybe now we can take that framework and let's bring it to today's market. I'll give another plug. We released our Q2 economic outlook about a month ago. Did a podcast, go back and listen to our last podcast if you want the rundown on that. But our fundamental thesis is basically that we're entering this macro regime, and this is what we focused on because, early April we had no idea what was going to happen with the war.

I think we're sort of still in that environment, but it felt disingenuous to try to predict what was going to be growth in Q2 or whatever. So we focused on our broader macro framework and how today's market environment fits into that, and I think the bottom line thesis is, you've got a U.S. economy that's probably more dynamic, more resilient, less cyclical than it's been in a long, long time, right?

And I think it's proven that through all of these shocks that Alan mentioned in his intro, growth has stayed pretty steady through all of those. You look at earnings right now, right? Earnings in Q1 were absolutely incredible, so the economy looks to be doing okay. But, we're operating in this world where, external and unexpected shocks, in many cases supply-side shocks, are becoming more and more common.

And I would argue these are not coincidences, these are what happens when you go into a less certain type of global environment, more competitive, driven, less efficiency driven like the one we've been in the past few decades, in that type of world where you do have these more sort of truly unknowable things happening, whether it's a war, whether it's a bank failure, whether it's central bank policy, whatever it might be, it feels to me like that might open up more opportunities on your end and more sort of mispricings that you might be able to take advantage of more deviations from historical norms.

But I guess on the opposite end of that, like if we truly are entering a new regime and these cross-asset relationships truly are changing durably, then maybe like betting on mean reversion is less reliable, right? So, on one hand, I could see this opening up more opportunities for you.

On the other end, how do you manage this risk, these relationships that we've all relied on, are no longer reliable. Like, how do you kind of fit those two things together? 

Scott Burr: It's a great question. Listen, there's a lot to contend with in terms of market headlines, market shifts, right? You mentioned shocks, geopolitical, obviously AI policy, the mid elections, AI, a transformational technology, we have a sovereign debt bubble globally, right? There's just so many things that can impact markets, derail markets, and markets also get sort of fixated on one topic and then move on and you need to have a hierarchy of what things are driving markets, across asset, markets, you know, principally the most and that shifts over time, so you need to have that in your model. But to your point, you certainly need to be aware of…like, I'm open-mind, that's why you can't be dogmatic. You can't be a one-trick pony and say "I'm going to just bet on exposures that appear to be dislocated and play mean reversion." Stylistically, you need to be diversified in how you're making money, right?

You need to have a portfolio with deterministic sources of return like merger arb, right? That regardless as to those types of questions, they're going to have alpha opportunities. So that's where the breadth and depth of styles and techniques is really important in this environment because it's unknowable, to your point, how a lot of these market realities will play out at what frequency and what type…

Andrew Korz: But when they do happen, like you know, the SaaSpocalypse maybe, for example, earlier this year, do you wake up jazzed when that happens?

Scott Burr: No, well there's...yeah, I mean, it's odd having a strategy that sort of benefits from tumult fear and irrationality. I mean, I love blood in the streets. No. The worst environments are, like the 2017 low vol, sleepy, nothing's happening. Certainly the environment where there's all these different crosscurrents, is one where I think professional active management, kind of proves its value. When beta's ripping higher, like, what do you need to pay an active manager for?

It's just ride the beta train. I think the other thing that I would say along those lines is when there's shocks like Liberation Day or the war, I mean, the one benefit of having a low beta portfolio as well that is not risk on is that when there are those types of shocks, you get to be offensive. You're not licking wounds. You're not risk managing. You're not cutting risk. Those are opportunities to position for a bounce if you believe in a bounce, and you can do that because you're in a good posture.

Andrew Korz: So I would say, I think there's a lot of certainly unknowns, but the more things that the market has to grapple with, in general, I would say, it's sort of a tailwind for this type of investing. There's no question about it. So I'm curious, you mentioned AI.

I'm curious your thoughts specifically here. Probably argue that's a positive supply-side shock. We've been talking about negative supply-side shocks here. But in general, I’m not going to say anything new here, but I think if you look at the past four or five years of equity market performance, even just zoom into this year, if you look at the drawdown we had in Q1, I think the Mag Seven were responsible for almost 100% of that.

If you took them out, the market would've been flat. The bounce since then, which I think we're up, like, 12 off the bottom now, the Mag Seven's responsible for, like, half of that if you add in, like, Broadcom and AMD and even Intel. How about Intel getting in the action? That would be almost the entire contribution to that 12% rebound, right? So, you have this market that's just absolutely dominated by AI. We've done some work to sort of add up the different areas of the market that are, like, really levered to the AI-theme, and it's close to half of the S&P 500, right? It's basically beta now is now AI beta, right?

So, everything we've talked about here I think would suggest you have no interest in adding more beta to that theme in investor portfolios, but given that it is sort of dominating, the markets we've seen semis this year just absolutely rip, as you mentioned, what are the sort of ways that you can play this since it is all-consuming?

I assume you have to sort of take positions within the theme, what are some of the things that you're doing that are interesting that don't add more beta, but maybe like, take advantage of some of the things that are going on? 

Scott Burr: Yeah, I mean, I love the AI theme. We don't have interest in AI, in beta anything, to be honest with you, but, you know, I think your premise, I don't know if I fully subscribe to the sort of beta is AI beta idea because you're right that beta has been driven by the S&P 500 has been in return been driven by the earnings growth associated with the CapEx spend, and there's a narrow group of names that have driven the bulk of the returns. I think it's 40 or 50 real CapEx beneficiary names. But on the flip side, maybe this changed this week, but the median S&P name is down on the year.

And so AI, at this stage is a game of, I think, there's winners or there's beneficiaries at least around the CapEx cycle, and then there's losers and those businesses that are being disrupted. And so yeah, the beta is up, S&P is up six or seven maybe. But you look at the cross-section of- semis versus software year-to-date, that spread is 60%. And software used to be the biggest GICS sector. Now it's semis. And so it's kind of like the Bezos, your margin is my opportunity where Amazon just consumed everyone else's market cap.

Like, there's a lot of that going on, right? Which is that the CapEx beneficiaries are stealing the market cap of the businesses that are thought to become obsolete type deal. The opportunity in AI is in the cross section of returns. And I think the questions of the day really are around the go forward CapEx spend and have taken a view on sort of whether that spend is the estimates around future CapEx are too high or too low because the Mag 7, the hyperscalers at least have been funding all this CapEx into high highly cyclical businesses with huge operating leverage so that's why they're generating all that earnings growth. But the CapEx spigot needs to continue for that growth to continue. But you look at the estimates of hyperscaler CapEx next year, there's a flat lining, right?

And there's a kind of an assumption, and that's why their ROICs are still pretty elevated and their valuations are still pretty elevated because there's an idea that they can stop this extraordinary CapEx and then start harvesting the return from the investment. But now we're in a theme of kind of compute scarcity.

Like, there's less fears of there not being enough demand and AI doesn't work and it's more around we can't service and meet that demand. 

Andrew Korz: Well, that's the interesting thing, right? It's like on one hand if AI does become as ubiquitous as we think then that's great for these guys, because they own the infrastructure. But that means we're going to need to spend a whole bunch more on inference to provide the compute necessary. 

Scott Burr: Well, that's why the hyperscalers is interesting, right? Because compute scarcity is good for them because they own the compute and there's a little bit of a RV between, you know, for every subscription to a foundational model or lab, how much are they paying the hyperscalers for the compute? And what percent? And so who has the leverage there? The people providing the compute.

And so compute scarcity is good for the hyperscalers, but the real question is, this CapEx spend and how much they need to spend to make sure that compute is available? For me, the hyperscalers are tough buy until they show that stabilization of CapEx. You see Meta keep on getting punished by the CapEx spend exceeding expectations. You need to sort of see that level off, or you need to see the return on investment start to be generating. So for me, the AI trade is an evolution, right? And it's in the cross section of returns where you have to think about the AI cake of the different segments of the market, right?

You have the energy producers, you have the chips, you have the infrastructure, you have the models and then you have the applications. Like, over time, there's different opportunities in each layer, but you have to be selective, and it's not a set it and forget it. I want to be long AI beta.

Andrew Korz: There's no AI trade. 

Scott Burr: There's no AI beta. It's in the cross section. And then, you know, there's also other expressions too, right? Like, we are long the credit side of the digital infrastructure build-out because in the equity world you'd argue a lot of these names are expensive and really levered to this AI outcome going down without a hitch in the fixed income and the credit market, there's huge financing need. And when there's a lot of supply and new issuance in credit, like spreads widen out because you need to incentivize demand. So, like data center credits in the ABS and CMBS market, they have wide spreads to the same rated paper in the corporate bond market and so like there's real value in playing AI infrastructure and credit. I don't think you can make that case in the equity market. So there's a lot of different expressions in AI. You can even go to macro asset classes to have a view of AI because, you have to have a view on labor displacement or productivity and all. Like, AI is ubiquitous and really impacting, of course, commodities. There's different formats of the AI trade of course. 

Andrew Korz: What’s interesting too is, a final point I'll make on this is like 2010s were like the decade of like the consumer subsidy. I feel like we're now in like the decade of the enterprise subsidy because like tokens are just so cheap right now for companies, right? These companies are like losing money hand over fist, the model companies, because they're just subsidizing all the usage by these enterprises. I don't know how long that'll go on. I think for a while longer, probably. And what we see in like the middle market, for example, as we talked about, is if it's cheap to implement AI and it's really as good as we think it is, and you can really, glean the productivity gains like that becomes part of the AI trade too. So that's another sort of level to this. 

Alan Flannigan: I was sitting here kind of smirking because I'm sitting here thinking about like, the solution to the problem Scott just described as it relates to the AI hyperscalers is just start billing people for the compute usage. I can't wait to get my phone bill, then my electric bill, now my compute bill on top of that. 

Andrew Korz: Token efficiency is gonna be the name of the game.  

Alan Flannigan: I mean, there's something to be said for pay for what you use, right? I'm a fan of a toll bridge. 

Andrew Korz: Sam Altman did say it was gonna become a utility. 

Scott Burr: Love a contrarian like that. The toll bridge fan. Nice contrarian right there. 

Andrew Korz: Alright, so last couple questions here, Scott. Last one from me. As you know, Future Standard, private markets centric firm, I would say. We're not all private markets obviously. That's why we have you in the seat today. This is a private market centric podcast. I'm curious from your seat, the way that you see, the strategy you run, but liquid alternatives more generally fitting in with the growing private markets ecosystem within a broader portfolio, right?

Obviously, there's the liquidity, there's the daily liquidity in a mutual fund versus the more periodic liquidity in many of the private markets, evergreen or a drawdown, which there's no liquidity, but how do you think about how they fit together? Like, is there tension there? Is there actually some symbiosis? Like how do you feel like those two sort of worlds fit together? 

Scott Burr: I think definitely they go hand in hand. Very complementary. You know, I think when you think about why folks go do alternatives, it goes back to what we discussed. There's a deficiency in the beta and so you're going to alternatives for a solution, right? So, the solutions can be growth opportunities, right? Middle market access, things you can't find directly in public markets. There's income-type solutions.

And of course, a diversification solution like we talked about today. So, I think private liquid and liquid alts, you have your menu of objectives or solutions across the spectrum of liquid and less liquid alts. And of course, liquidity is a very important variable and constraint that you need to solve for.

So, when you combine liquid alts with private markets, you can really optimize around in your portfolio, what your objectives are, what your liquidity needs are. And then in general, just from an asset allocation perspective, it's important to have liquidity in a portfolio so that you can utilize that liquidity to generate a better outcome, right?

Because that's what asset allocators should do. They need levers to pull. Everything is strategic set-and-forget allocations I don't subscribe to. If you do the work, there's always better and worse opportunities for certain investment strategies, approaches, exposures.

And so it's important to pair liquidity with illiquidity so that when it's opportunistic to load into some less liquid exposures, you can fund that from more liquid exposures. Conversely, there's less liquid exposures that can go through periods where they're less attractive.

You can source that liquidity over time, and then where do you want to park that? In a liquid exposure. So, the idea that an optimal portfolio is all liquid or all illiquid, it's tough to make that case. It needs to be a hybrid alternative allocation that combines runs across the full spectrum of liquidity.

Alan Flannigan: So, to close this out, like a constant theme throughout the episode's been diversification, but also then, the targeting element and like how do you bring that together and the conversation around that's been terrific and we're so thrilled to have asked you to join the show today because I think your perspectives align with what we're seeing in the market, the level of uncertainty that we've been talking about for a couple episodes.

We've had some really hard macro-focused episodes over the past few weeks. And, this is almost like a philosophical question, which is, when you think about true diversification, what is it really that you're trying to provide? I'll make sure to get this right on the exam, which is, two fully uncorrelated assets, 30% risk reduction. Add a third, an additional 15%...

Scott Burr: I can't wait for you to get this question on the test. 

Alan Flannigan: If I miss it, that would be a sad day. But, you know, hopefully quick learner here. But, when you think about a truly uncorrelated source of return, like what is that? Like diversification's a catch all. Like sure, it can be quantified in the ways we just did, but, what are the tangible outcomes it could provide for investors? It's obviously the compounding at a less volatile rate of return, but, it also gives you real options within a portfolio with what you're looking to implement. 

Scott Burr: Yeah, I mean the tangible outcome is risk return. And I think the problem with diversification is a lot of the stats that people talk about, they don't really resonate. Like Sharpe ratio. Like volatility isn't necessarily a bad thing.

So, solving for return over vol, it's not... Is that tangible? That's just kind of, quant finance talk. When I think about risk, I don't think about vol. I think about losing money. And going back to quality decision making, it's any investment decision anywhere is about how much can I make, how much can I lose? How much can I lose is not volatility. I mean, it rhymes with volatility, but it's not that. It's drawdown. 

How much loss do I have to stomach mark-to-market loss. And so, the objective adding diversifiers to your portfolio is to enhance return and reduce loss and change that skew. And part of that's from the product itself delivering return when the other things aren't, but also having that lever you can pull in an asset allocation perspective that allows you to take advantage of opportunities on the beta side and that also is additive from a return perspective. So, there's a lot of ways to manufacture excess return when you have a balanced portfolio. But more practically, it's about curtailing that drawdown. And when you look at equity markets, great run, but you've had to live through a lot of tumult in the past, six years. I mean, the emotional side of investing, you wake up and your portfolio's down 10, 15, 20. Like that's really tough to stomach. You can make bad decisions.

And so ultimately, when you have a balanced portfolio, it's peace of mind, you sleep better at night because you're not fearful that one day, you have to stomach a double digit drawdown in your portfolio. And I'll leave you with a funny story about when I first joined Future Standard, I guess this is nine years ago, and I was kind of talking to some of the distribution folks about the strategy and diversification. And diversification was kind of a new solution for us. And I was going through, sort of these CFA laws of diversification and correlations and betas and like I looked around the room, it was like the dinner party story I'd told. It was like- 

Alan: Trainers, Tortinos, Omegas. They're running through my head like a ticker right now.

Scott: So glazed eyes. And one of the guys looks at me and goes, "So are you saying that it's going be there for you when you, when you need it to be?"

And I was like, "Yeah. That's it, there when you need it, you know?" And, and, and there's some, there's some truth to that which is that, you know, diversification, there's certain times, right, when beta's ripping where you're kind of like, "Oh man, if I only just didn't have it and didn't have this diversifier."

Like, but then when it shows up at you know, the 2022s when you have that line item in your portfolio that's up and everything else is down, it's a powerful thing. And again, in the go forward environment, you look at the PE ratio, there's a famous chart about like what's the 10-year return when PEs are 22, 23? Like, in those environments, I think again, what strategic allocations are there out there for investors? Not a lot. So I think these types of strategies are certainly paramount, in the go forward, if that makes sense.

Alan Flannigan: Be there when you need it. I think that's the takeaway. 

Alan Flannigan: Scott Burr, thanks for joining the show.