Odd Lots - How the Hottest Hedge Funds on Wall Street Really Manage Risk

Episode Date: July 25, 2024

Multi-strategy hedge funds, also known as "pod shops," have become the hottest ticket on Wall Street. The business model is supposed to allow hedge funds to operate more efficiently. That includes deploying capital in a more productive manner and better managing risk. But how does risk management at some of the most sophisticated funds on Wall Street actually work? In this episode, we speak with Rich Falk-Wallace, formerly of Citadel and now the founder and CEO of Arcana, which provides risk management and portfolio software for multi-strat funds. We talk about how risk models are impacting investor behavior and wider markets, how multi-strat traders come up with their ideas, and the factors that go into sizing and evaluating their positions.See omnystudio.com/listener for privacy information.

Thanks for listening to OddLots. Follow the show on Amazon Music for more future episodes or just ask Alexa play the podcast, OddLLLots on Amazon Music. Bloomberg Audio Studios. Podcasts Radio News. Hello and welcome to another episode of the Odd Lots podcast. I'm Joe Wisenthall. And I'm Tracy Allaway. Tracy, I still want to learn more about how multi-strategy hedge funds work. I thought you're going to say I still don't know anything about multi-strategy hedge funds. Well, that's true.

I feel like we're slowly getting there and hopefully our listeners don't mind coming along with us for the ride. I feel like every time we have an episode on multi-strategy hedge funds or on the pod shops, as they are sometimes called, we are deepening our understanding and we're sort of getting into more and more detail. And I feel confident that one day after we've done like 50 episodes on this topic, we will get there. I do think it would take about 50. I think that's like an accurate number of what it would actually take to get there. But of course, most recently we had that episode with Giuseppe Paliolo go, Gapi, talking about some of the big ideas and sort of from a high level of how some of these funds actually work. They're very popular.

They've done some of the big ones that people know like the Millenniums, like the Citadel's, have just had incredible runs, really seems to be displacing a lot of the old style quant, disrupting the sort of fund of funds idea that was popular. I have some sense, you know, you have all these managers and you give them very specific mandates and they have to really focus on that. And then if they're not too correlated with each other, you can get above market returns in theory and apparently in practice. But like how that actually works, I still really don't know.

Well, okay, so two things. Number one, everyone should definitely go and check out Gapie's book if you haven't already. advanced portfolio management. A lot of the references that I'm about to throw out on this episode, anything that I say that might sound even remotely impressive or like I know what I'm talking about has come from Gapie's book. And also I will say I read that book going to and from work on the subway. It's pretty short. So I think I did it in like a week. And I have never gotten so many people like talking to me on the subway is when they saw me pull out advanced portfolio management. And they're like, what is that? That is very New York, isn't it? And then secondly, the other thing I will say is

we've been talking about multi-strategy hedge funds. We want to learn more about them because they're this new thing on Wall Street that everyone seems very excited and interested in. But beyond that, there are recent events that make this an even more pressing topic. So we've seen some of the big winners in the market in recent months start to come down. So the big tech names, things like Invidia, we've seen small caps shoot up. A lot of people are talking about whether or not this is a factor rotation and we'll get into what factors actually are. But I think the discussion that we're seeing right now, and I should caveat this with, it is July 18th, so we've seen those big moves in the market very recently. The discussion that's happening now is how much does the, I guess, growth in factor

investing feed into some of these moves? And also, how does the risk models that go alongside this actually impact investor behavior and then also feed into these market moves? So is it the case that everyone's getting out of big tech because their risk models are telling them to? Totally. And this is like a really important element for sort of understanding both how these investment vehicles work and the impact that they have on the market, which is one of the things we know is that the various portfolio managers within these funds have very tight remits. It's like your team is responsible for trading chip stocks and your team is responsible for trading the short end of the Brazilian yield curve and your team is responsible for

international oil players. And then we know that like, and then you're not allowed to take any sector beta and you're not allowed to take any market beta and all these things. And so, you know, factor neutral. Factor neutral. And then, you know, tight risk limits. So if something starts to go down, you don't lose your job and you like get out of position. and that can create interesting moves for the market. Anyway, suffice to say there is much more to learn. Yes. Well, the other thing, just one more thing.

Yeah, the other other thing. The other other thing that I think is kind of funny now is, remember whenever you had weird market moves, like, I guess it would have been 15 years ago or something like that, it was always quant funds, like the quant quake before 2008. And then it became CTAs. And then it was risk parity. And now it's very much the pod shops that people point to. when we start to see sketchiness in the market.

So I think we should talk about, you know, what are the technicalities that are driving that pod shop behavior? Every time there's some big move in the market, someone tweets like, I hear a pod is blowing up. Oh, I hear some pods are blowing up. That's like how to sound like an in guy on a finance tweet. I hear a pod is being up.

Little do they know. The pod that's blowing up is all pots. Good one. If you don't know the pod that's blowing up, you're no. Anyway, we have the perfect guest. I'm very excited. we are going to be speaking with Rich Falk Wallace. He was previously a portfolio manager, who's at Citadel, who's at Viking.

And now he is the CEO and co-founder of Arcana, which builds models and software to help investors and hedge funds, etc., actually track all of this stuff and actually track what kind of risks managers are taking and how they're actually performing relative to their benchmark or expectations. So we're going to maybe understand a bit more of the technical aspects of all. this stuff. So Rich, thank you so much for coming on Lodds. Thanks so much for having me. Appreciate it. Why do we start with your background? Obviously, we're going to talk about your software company, Arcana and all that. But you were previously at a couple of these big funds.

What did you do? Yeah, that's right. So I started my career on the by side. It started originally an investment banking out of college, J.P. Morgan, and then worked at Silverpoint, which is like a large credit distressed hedge fund, very value-oriented, none of that sort of risk model framework that gets deployed at the pods. And then after that was at Viking Global, which is, I always describe the tiger cubs in some ways as like a hybrid between the sort of equity, long, short value orientation sort of philosophically and the multi-manager systems. And then finally, most recently it was a portfolio manager at Citadel, manage global materials, natural resources and materials portfolio. I love this because when I think about Silverpoint, I think more sort of traditional value, I guess,

investing. And then you wind up doing. at Citadel, which is a hedge fund that's known for being very quantitatively driven, to better understand the pod shops now. Talk to us about the differences between what you were doing at Silverpoint versus Citadel. Totally, yeah, it's a great question. So the way that any value, super deep value-oriented kind of fund works, like a silver point, is that in the end, you do a ton of very deep research on the company. So you focus on what are the underlying fundamentals, what's the kind of,

track structure out many years in the future. What do the earnings look like? Of course, in the short term, but also in the long term, what's structurally happening competitively. You kind of go way down the rabbit hole. There's a lot more, and we can go into that. And then as you kind of migrate sort of down the time horizon spectrum, at least from what a thesis looks like on a single stock, what you're kind of doing is thinking about where are the catalysts that change the market's perception of that long term? So like, I remember when I joined Viking, I remember asking the question just generally, like, how much do you care about earnings? I think for anybody who's very value-oriented, you kind of are concerned about, like, am I just going to be focused on the next data point, the next earnings, and not sort of able to, you know, see the forest for the trees and sort of care about, you know, what does this data mean for the long term?

But the answer I got back in general, not specifically there, but is in that kind of framework, it's, or the way the question was answered to me was, hey, the long term is a function, a DCS, is a function of years. Years are a function of quarters. And so therefore we care about the quarters. But what that tells you is that like the answer is what about the short-term catalyst changes the perspective about the long-term valuation of the company. And so I think what people sometimes looking from afar don't appreciate is the extent to which there's actually a little bit more of a convergence across styles from the underlying analyst workflow that like even a very long-term investor to some extent is saying even if I'm betting on the long-term, the interim proof points illustrate the view of that long term.

And the short term guy says, well, I may get the number right in the short term, but that only is meaningful to the change in the market's price if it tells you something about that long term. And so there's a little bit of like a... Yeah. And I think that convergence is happening more and more, where people are kind of pushing towards that center, actually, where everybody both cares about the short-term data point and is looking to what that means about the long term. So, but anyway, at the beginning of that process at the silver point or any deep value type place, you're just really focused on that longer term story or less focused on the quarter of the catalyst than trying to understand sometimes things that, and I was a junior analyst when I kind of started there. It was the first job out of banking. And, you know, but you can be looking at like, what does the rail contract look like in 2024 and how does that step up? And you're like, man, does this matter to the stock? It's great training. It's a perfect place to kind of get that, you know, it's almost like private equity like where you just sort of. looking through everything, but that's kind of how that started.

All right. So then at Citadel, you mentioned you covered materials, commodities, stuff like that. I guess two questions. When you come in the door there and you're told like, okay, this is what you do, what are you're told as your constraints and your specific remit? And then also, like, how do you pick a stock? Yeah. Yeah.

So, and I'll talk about this in the general sense, not specific, just at all, but to talk about multi-managers in general. And our client-based today at Arcana is about 50-50 split, I would say, between people who I call like natives who come from the risk model system, either any of the major pods or related. And the other half can be like a deep value fund that says, hey, I don't want to limit myself to this stuff. But I see just like you guys are saying,

this is an increasingly important part of markets, and I want to be deep on it, educated, whatever. So to answer your question on how multi-managers kind of pick stocks run processes and think, not specific to any one place, but that sort of natives group in general. So at any of these places, the core construct is to say the core difference, I guess, is the other way to say it is versus a deep value place. It's about turnover in the end.

It's two things. It's risk limits and it's about how frequently your book turns over. Okay. So at a deep value fund, the goal might be in sort of theory to have more than a year-long average hold period. In practice, it'll be often shorter than that, you know, nine months or whatever as bad idea to cycle out or whatever. But at a multi-manager, those numbers can be anywhere from like 10 to 15 to even higher, meaning the entire book turns over 10 to 15 times in a year. In a year.

Think about it simply like the average idea stays on for a month is the way to put it in the book. And so as you step into any of these places, to your point, A, you have a, you know, there's a structure. There's an analyst and then a portfolio manager. And the analyst generally has a single industry focus. So it's like, hey, I am, as you said, chip stocks or, you know, somebody else might have software or it's a sort of defined single universe and then a portfolio manager will have a set of analysts below them who have typically very related coverage universes and will feed up into the portfolio manager. So that's like kind of the structure. Stock picking kind of ends up being what we were talking about earlier. In the end, it's the analyst job to have a detailed model,

of course, to have a view on earnings across their coverage universe. And that coverage universe for that analyst, by the way, can be, and it varies by multi-manager, but it can be any we're from like 30 names at the low end to like 80 names at the high end by analyst. So there's a lot of process there. I'm June Grasso, inviting you to join me for the Bloomberg Law podcast. Every weekday, we help you make sense of the legal stories that shape the nation and the world. Listen for complete analysis of the biggest court cases, the latest actions from Congress and regulators, and the legal moves driving the markets.

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There are many differences between a retail investor and a multi-strategy fund, but one of the key ones, I think, is maybe position sizing. So if you're a retail investor and you have a single stock thesis, I don't know, you want to buy Nvidia or something. You buy Nvidia and you're probably making that decision based on how much cash you have in your Robin Hood. account or something like that. But if you're at a multi-strap fund, it seems like a much more sophisticated process. So I guess I'm curious, if you're at a pod shop, how do you know how much to buy? How do you know how much to allocate to a single stock? And I guess another way of saying it is you're looking at that single stock on a risk-adjusted basis, right? Like, that's what you want to get right, the risk-adjusted performance, not just the single-stock

performance. That's right. That's right. So there are two or three ways that gets implemented. So the first is constraints. So step one is dollar neutrality. I'm long as many dollars as I'm short. That's a simple limit. Sort of one level higher is beta neutrality relative to the overall market. Am I long or short on a beta adjusted basis?

Right. Sort of the third level is factor neutrality. I'm balanced against all of the sort of, if you maybe simplify it just slightly, the sub-components of beta. So instead of like, hey, I have a beta to the market. I actually have a beta to the basket of size, large companies. I have a beta to the basket of companies with momentum. I have a basket to the beta of... Oh, I see.

So you decompose beta into factors? Essentially, it's a decomposition. Essentially, when people talk about factors and factor neutrality, it's a decomposition of beta into its constituent parts. There's a lot of statistics that goes under the hood to make that orthogonal and precise and how it's built. That's a good word. Orthogonal. But at the sort of functional level, at the level that people at the...

stock picking level at multi-managers interact with the model. Yeah. It's essentially just a decomposition of betas. And then you add up those exposures on each side and you are limited essentially by the percent of your bets in a book in aggregate that are betting basically on factor type bets as compared to the percentage of your bets that are betting on the remainder term, the non-factor component of any stock. So as you look at any stock, it fits within that broader portfolio that you're putting together.

Okay. And then the second thing that you talked about earlier is this idea of turnover. And so just to press on this point, how much do trading costs factor into investment decisions and also position sizing? Because as you just stated, you could theoretically size or arrange all of your positions to be factor neutral or neutral in terms of systematic risk, I guess. But I imagine in order to do that, you would have to be trading pretty much constantly, right, which would add to your execution costs. So does that come into play as well? Yeah, it does. In practice, the stock picker, portfolio manager and analyst doesn't flow in a complicated set of formulas to their decision around sort of how do I optimize trading costs. The engines operating at the multi-managers do think a ton about how do I take the stock picks that a single portfolio does and then execute. them in a optimal way, A, crossing, some firms do and some firms don't cross each other's orders within the pod level, and think about all of that. And then, so the first level of how do

people get limited is the constraints on what percent of my bets are in factor type bets versus non-factor type bets. There are also a bunch of like single position limits. So that's like one version is basically limiting the portfolio manager to have to sort of live pick stocks under this constraint. The other framework of how do you size positions to your sort of earlier question, which comes around to this trading cost question is there are tools that are called optimizers that basically look at the expected return that each portfolio manager thinks they have in their book of stocks and tries to solve for the optimal balance of the expected return against the volatility of those stocks and the volatility of the factor bets in the book. And it'll spit out an answer

for you. That answer may not be exactly where you want to land. But in the most sophisticated places, that answer that the optimizer is spitting out is including how much trading costs impacts the book. So it's sort of flowing that mathematically into a, you know, machine-driven optimal book. But again, that's sort of in the more science bucket. Of course, there's art even underneath that statistics, but basically that's in the more sort of science bucket. Then the portfolio manager has to say, okay, the machine sort of took my expected returns, took the variance of those pieces and the trading costs into account and gave me an answer. Does that actually still fit with my, you know, fundamental bottoms at work back to, hey, the contract of this company changes in

2026, the earnings are going to be this. Here's the positioning and setup and crowding of other you know, playing the game you mentioned earlier. So there's then the sort of second level of art that goes on top of that. Yeah. So I want to talk more about the speed of turnover because, okay, let's say you're like bullish on Nvidia. And Vindia's had this big run. And you're like, all right, but I don't want to have size exposure because it's going to be

correlated to big caps. I don't want to have general market beta because probably if the market goes up and Vida is going to go up. And I don't have chip beta and all this stuff. So what you're trying to identify is just the invidia-specific idiosyncratic. That's exactly right. But why does that inherently lend itself when you're thinking about that? I mean, I feel like there must be some connection.

You're trying to strip out all of these different factors that you don't want to have exposure to. You're trying to find the idiosyncratic drivers of a specific name. What is it about that process that sort of inherently lends itself to short hold periods? That's a great question. And a sort of deep one. And you might get different answers to that question from a few different people. I'll give you mine. The essential reality is that in order for this entire model to work, you have to have a great deal of diversification across idiosyncratic bets, meaning the non-factor bets.

And the way to think about that is the core reason a lot of these models work is that the residual return or idiosyncratic return is approximately normally distributed across a certain window, meaning it's sort of. of like flipping a coin basically. And the intuition is if you flip 1,000 coins, obviously you'll center around whatever your hit rate is on that coin. If the coin is loaded 52% versus 50, as you flip three coins, it could be the mean, the expected value of that is going to be, who knows, right?

But as you flip 1,000 coins or 10,000 coins, you will center around that mean. 52%. And so, and that variance is effectively, if you think about things from a return standpoint, the sharp ratio, right? Is it returned about the variance? of the volatility of that return.

And so as you have more and more bets, you shrink the variance relative to the return you're generating. And the more and more your bets are in idiosyncratic bets, which are normally distributed, unlike market bets, you know, which can be wild, right? Wait, can you actually just explain that point? Because that's a great answer. You're basically, you have some assumption about returns,

but there's going to be a lot of variance. So you want to make a lot of bets basically in order to achieve that. Why is it that idiosyncratic returns are normally distributed, such as you described? Totally. Yeah. So what you're actually solving for as you go down the factor model building rabbit hole is cross-sectionally normally distributed, meaning across the universe of stocks within a period of time. Okay.

So that's kind of also what the model solves for. And it sort of solves for a combination effectively of what's the highest R squared, meaning how much of the model explains what's happening across stock movements? across different stocks in the market. And then the output of any regression within its period is going to produce that result of a normally distributed kind of residual term. But the key way that this model works

is that it's normally distributed not across time, but across stocks within a given period. And so what that means is you're going to have, you know, as many stocks that are on a residual basis that are outperforming in a period as that are underperforming on this residual basis. Whereas, of course, if you just bet on semis, right, in a month,

and you just were long semis. Yeah. Within a period, within a month, right, that's not going to be normally distributed, of course, right? It's just if you're managing to a model that is cross-sectionally, approximately, normally distributed within a month, let's say, you're going to get winners and losers. And you're going to center around that hit rate, basically. Okay.

I get that. You keep mentioning a month. What is, like, a normal or a reasonable time horizon that these models, like, typically operate in? Yeah, they're sort of calibrated to. So technically the model, the regression. runs daily, actually. But when you are building any of these models, people calibrate them to sort of optimize for, like, the average whole period of a discretionary stock pickers, not a day,

obviously. And so you try to calibrate the bias of these models to say, and people actually, you can run multiple models, say, hey, we're going to run one that's calibrated for a one-month horizon or a six-month horizon or whatever. And so you're trying to pick the calibration horizon that matches the investor that we're talking about. So I mentioned a month because a lot of the multi-managers, let's say the average whole period ends up around a month. You know, that's 12 times turns a year. But it could be higher. It could be 17 turns. It could be eight turns. There are managers who are in that range. Just to go back to the question of idea generation, you're going to hold a stock for a month, maybe, maybe a few weeks, maybe a little longer. Some analysts who's like monitoring

all this stuff. What goes into it? Someone says to you, okay, like, you're doing materials and or commodities. and they say, suddenly you have a bullish view on Exxon or something or some small shale player. Right. What happened before that? Totally. That led to that idea. Not just that they like the stock, but that they like the stock in a very short period of time. Yeah.

So, and this is not always true, but as a sort of simplified rule of thumb, typically the winners are going to be on longer than that month. Okay. And, you know, you realize you were wrong about something. And then you cut that fast. And there's trading turnover as well. That's not pure idea turnover, if that's that's. makes sense, which is idea generation. So that's going to be a little slower too. But anyway,

with those caveats, to your question, yeah, so there's, in an ideal world, you do a, you sort of separate the idea generation process into two steps. Okay. The first is initiation, where you sort of learn about the stock, if that makes sense. And in that process, you basically do all the things I mentioned that a core value-oriented fund does in terms of thinking about, okay, what's the long-term of this? What's the secular trend? Within companies, who's gaining, who's losing share? In order to do that, you do all the classic Warren Buffett stuff, meaning you understand, you look at industry reports. The earnings. Earnings and industry reports and filings and all of those kinds of things.

You talk to experts also as part of that process. That could be any of the expert network calls, somebody who were people who were executives, and that can inform that initiation and understanding of the industry as well. And some people spend, you know, some analysts spend the majority of their time doing sort of initiation type work. That sort of build a deep financial model that tries to build not just from like the high, level revenue, but to unit economics. Like, okay. And by that, I mean like, you know, if you're looking at a coffee shop, like, okay, how many cups of coffee to this hell? What's the price? How much is that going to change? What are the inputs to a cup of coffee? And just trying to get to that

level of granularity on unit economics. Yes. And so that's kind of like the initiation process. And then ongoing coverage is a little bit more of, hey, I have a view from that initiation work on sort of long-term relative winners and losers in a space. I have an understanding of unit economics of each player and how each of those is kind of heading. And And then the ongoing maintenance process is a lot to do with what data sets, what data points, what conversations from an industry conference standpoint or whatever, can I do to understand more granularly how each of those unit economics points is changing? And then finally also, like there's this question of crowding and positioning and understanding

what everybody else thinks, that sort of weighing machine versus voting machine, Ben Graham, classic analogy. But you sort of separate that process, have that secular view, and then you're trying to understand what data sets. So that could be like all data sets. It could be industry conferences. It could be talking to people in the industry through the supply chain. It could be, you know, people always should be doing this, but don't always actually in practice doing it. But your analysts should understand if they're covering an auto company, they should understand the auto suppliers. And they should understand the downstream of that. So each of those sort of up and down the value chain,

that's like a big, I'd say in reality, a differentiator among analysts is how deep into the value chains, you're seeing what's happening to inform you about the changing trends in those unit economics that you had a baseline view about at the beginning of the sort of initiation, understanding of the industry. Convince me or you don't have to convince me. You could try to convince me. Try to convince me. I don't know.

Convince me that this isn't just momentum trading with some added maths and maybe efficiencies coming from like centralized risk management and capital management systems. Okay. So on the convincing part, so momentum itself is a factor in every essentially commercial factor model. And so you're actually, therefore, because you were limited, constrained on your factor bets, you're constrained on how much like just momentum you can be long ever. So you're limited in your ability to be long and momentum can have nuance. Like do you calculate momentum over a six month window, a nine month window? And what are the inputs to that? But in aggregate, you're actually limited in your ability to be long or short momentum at all. It's actually one of the most focused on factors within commercial factor models that everybody asks about all the time. So that's like point one to mention on momentum. The other is the way you described at the beginning was interesting too because there's the concept of factor investing where you're betting on the factor, meaning you're finding cheap ways to be long momentum or cheap ways to be long the value factor or other pieces.

And that's a kind of growing, and that ties into the whole sort of growth of passive and all those things. What these risk models actually do in the multi-managers essentially are the elimination of factor bet, meaning it's the opposite. It's kind of a mirror image of that where you're sort of eliminating the factor bets entirely and trying to find just the performance in the residual. That then leads to this question of like what factors exist inside the residual term that are not momentum. And that's where you get the concepts that you mentioned earlier, like a pod's blowing up and what's positioning and crowding and nuances there, which is something we spent a lot of time thinking about, okay, like how do we mathematicize, how do we characterize that? And what gives information incrementally beyond, okay, you've eliminated this sort of straightforward momentum topics.

you've eliminated value, what within that residual can give you more and more insight beyond just like the core research work. We talked about earlier. You can get the news whenever you want it with Bloomberg News Now. I'm Amy Morris. And I'm Karen Moscow here to tell you about our new on-demand news report delivered right to your podcast feed. Bloomberg News Now is a short five-minute audio report on the day's top stories.

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the world. Listen to the latest from Bloomberg News Now on Apple, Spotify, or anywhere you listen. I'm glad you mentioned the sort of off-the-shelf commercial factor models, because this is something that came up in our conversation with GAPE as well. So in order to be factor neutral, you have to be able to identify the factors in the first place. And my understanding is that most of the pods will just purchase those models from a company like yours. Yeah, so what people do is there's kind of a full spectrum of the way people implement a factor awareness or factor neutrality strategy. Some will buy a single model and sort of view that and then integrate that in whatever way they do. And at the other end of the spectrum, there are funds sort of the most heavily infrastructureed funds that'll buy several factor models and pick and choose different, hey, I think this factor is constructed appropriately here, this factor is less well constructed by this model and kind of put them together.

And then there's sort of also a spectrum in terms of people's software tooling that they, how far down they hand into the organization, a sort of sophisticated tool to let portfolio managers see, what are my factor exposures? So like some places, there's a total separation almost of church and state of, you know, stock picking and risk management. And that is partly a function. There could be a philosophy element to that. And there could also just be a constraint.

I mean, it takes engineers and time and money and focus to build all this stuff. So some places will have nothing in terms of tooling. And they'll just have a risk team that kind of looks at books and helps people understand their risks on a sort of shorter cycle, meaning longer cycle. Like it'll take once a week, once a month, or whatever, they'll get a report on their risks or they'll check in, et cetera, et cetera. And then at the far end, you have funds that have like full software platforms that hand two-way portfolio manager. Like, okay, if you change this, what happens to that? If you want to sort of see what the optimization math does for you instantly, can you see that? And so that's kind of the spectrum of what things do.

And we sort of provide that software toolkit, everything from the risk model, as you mentioned, like the core underlying factors all the way up to the software infrastructure that lets you just play with it. Okay, if I had a billion dollars in Vidae, what does this do to my risk numbers? That idio number, factor number? What does it do to each of my factor exposures? And then how does that change dynamically? And it'll also sort of like find hedges for you. Like what single stocks would optimally hedge this book in this way?

Now, of course, it's still on you to pick stocks. But it'll sort of, it'll source, okay, I've got a whole universe of stocks. What single stocks would offset this, InViti, or these five single stocks would offset that? Just to go back, and then I want to talk more about the software and what you sell, et cetera. But just to go back, one last question on the idea of, like, actually selecting a stock. You know, you mentioned maintenance, and the analyst really builds out a coverage universe, and then they really get to know the unit economics of the coffee shop or the company that makes, you know, something for a car or whatever. But then what do they see to say, and now we should buy it?

Like what would be the signal that they're looking for in the market that say, you know, again, on some short-term period, this is really, I've gotten to really know this company, but there's something about X right now that makes it a compelling buy for a short-term period. Totally. The core idea is that you're looking for differential insight, meaning something that changes the perception of everybody else about the value of this company in a long-term sense. So meaning, I see. If the market's perception is pick a coffee shop, is going to grow. And people will, the market, it's, you know, whatever the market means. But typically the market is who is the marginal price setter of a stock, basically. And there's a perception there implicit in the price at a minimum about, okay, how many units of coffee and what's the price of those coffee cups going to be?

And what's the underlying cost of the beans? You're waiting for moments in which you believe something is going to emerge that will change the long-term expectations. That will change the, you know, and there are other situations like tactical things where, hey, it's so heavily shorted. Yeah. That'll change slightly. And I'm really looking for a short-term catalyst or, hey, look, everybody's expecting this next all data print to mean something specific and they're all positioned on one side. That's where crowding positioning comes into the equation. And everybody's positioned this way and I think it's going to go the other way.

And I've got a very tactical thing. That is a part of the equation. But a much larger part of the equation are still catalyst driven. like, okay, there's a data point that comes out, but it's a data point that indicates something about the overall perception of where this company's headed. And so, like, classic ones in software can be changes in churn direction. And, like, where people can get smart on that is often, like, okay, there's an overall headline churn number. But then there's like, like, if it's an internet company or something like that or subscriber company, and then you can go down the line,

like, okay, if somebody's looking at churn by region and has some forward look on something that gives them insight to, like, okay, churn is changing in this region, and this region's small today. So it actually doesn't hit the headline turn number. But that's actually structurally growing faster than every other region. And so the underlying turn rate that looks like it's this level is going to step up structurally because this smaller region is going to be a bigger part of the overall path. That's the kind of thing. At some point, by the way, we really need to do another, I'm sure we've done one in the past, a deep episode on Alt Data because, yeah, Walmart satellites of Walmart parking lots and like credit cards, I've heard about it. But it's like, I know

there's more to it and there's, you know, it's important. You mentioned the different shops have different software infrastructure and the level at which it's on the manager is different sometimes and the different in which it's at the umbrella level. So like, does that mean that like does it happen where at the very high end of risk management, they look across and they say, wow, you know, in aggregate, our portfolio managers, even maybe, perhaps unintentionally or even within their remit have built up a lot of implied exposure to momentum or implied exposure to rates or implied exposure to value. And then what do they do?

Like, tap people on the shoulder and say, like, what happens then? Yeah, absolutely. So, again, there's sort of a spectrum of people's technology and factor awareness risk systems. But at the sort of platonic ideal of that that, you know, exists in various forms, there's sort of a CIO level. There's a, you know, CIO and Risk team level. there's the PM level, there's even an analyst level, that sort of is monitoring each level of that. So like you'll put limits, as we talked about, on the portfolio level, right, on a aggregate risk basis.

And then on an individual factor, you'll say, okay, you can't have more than blank percent of your variance in your book in any specific factor. So put those limits individually. And then exactly, as you said, they roll it up, just like you, you know, you just add up the line items. Essentially all these models are structurally linear decomposition. So they add up actually linearly. So like John's momentum exposure in dollar terms here, Jill's exposure is there, and they add up. So you do see aggregate level, CIO level,

kind of, hey, we're net long blank or whatever. At that level, and it depends how teams structure their limits and how tightly they limit exposures at the portfolio level, but you will see aggregate exposures. And then there are ways to take like an ETF or a basket or a custom basket that will just limit out, we'll just literally hedge that basket. And there's nuance even there, like, hey, do I,

can I build a basket? basket that hedges out that exposure but doesn't actually basically end up being short the same stocks I'm long underlying the book. You can see how that can get into a whole rabbit hole of like sort of technical behind the scenes execution detail. But at the high level, you sort of roll up the exposure as you add them up and you say, am I long or short one or two or three or all the factors? And let me balance those out at an aggregate level. So one thing that often comes up in discussions of risk management software that's been popularized on Wall Street. And I'm thinking, especially you hear this a lot about Black Rock and Aladdin, but this idea that if everyone's using the same

risk management software, then is there a risk that you could get everyone like doing the same thing at the same time? So for instance, a mass de-leveraging event because everyone's software is like based on a particular model and one thing happens and the model spits out and says everyone needs to sell right now. Is that a risk? Is that like a realistic risk? Or is it the case? that all of this off-the-shelf risk management software is so customizable, I guess, and there's still that discretionary factor for the PMs, that you don't really get that hurting behavior. So I'd say yes and no.

I think in the no camp, the fact is that you're kind of eliminating those sources of exposure that are common. So you're kind of trying to focus people on residual bets. And, you know, for example, that could be oversimplifying, but that could be long Coke and short Pepsi or long Pepsi and short Coke. And that would equivalently neutralize factors, let's assume they're kind of proxies for each other. And so kind of what the model lets you do is kind of instead of having to be perfect pairs in the Alfred Sloan original hedge fund concept, where you have to be factor neutral, you just have to find perfect comps. It kind of lets you pick non-perfect comps, but end up in a risk place that is similar to that, where your only bet is on a single stock. So what the model is pushing you to is not any specific stock, right?

It's telling you to pick which one of the stocks that don't have comparable factor exposures is more attractive. So that's one level. The second is there is leverage. And the leverage you're putting on is not leverage against beta. That's the distinction that I think people often allied is that when you think of like LTCM or maybe forgetting even LTCM, but any fund that takes very high leverage on a beta, a directional bet, that's beta on a factor. And the issue with that is many issues with that. If you're taking lots of leverage on a beta,

there's just sort of that risk that it has a big drawdown. The hope, I guess, or the sort of mathematical reality, as you kind of pointed out, that's actually been executed on, is that when you're levering alpha, it's again, it's sort of the quant fund world works this way too, is that what you're levering is just that residual term. You're getting back to that coin flipping, and you're finding a source of return that is normally distributed across stocks. And therefore, if there is a big blowup in markets, actually typically the factors become more and more statistically significant.

And so if you're neutral against those factors, the residual return remains cross-sectionally normally distributed. So there's obviously a lot of detail under the hood. But the basic answer is that you're trying to find a type of return and a diversified source type of return that doesn't have that risk in a blowup. So you're kind of levering alpha. That's the key kind of point versus beta. And the final yes answer to your question is that you are. are still levered. So notwithstanding everything you can do to sort of solve the mathematical piece of this equation, you still have some risk that the person providing you the leverage has a

business problem or somebody who, like whoever is providing that leverage to you, which typically the banks basically, that that person for whatever reason needs to pull that leverage or whatever and that it's almost a little bit even in the category of business risk that exists intrinsically with leverage. So that's kind of the yes portion of the answer, I'd say. This type of software, these models, they exist, they've existed for a while. That's right. When you started your company, Arcana, what was the theory that there was a need for more? Yeah, and it's what you mentioned at the beginning too, which is the sort of the theoretical

beauty of these models and how it all works and the normally distributed residuals and the sort of diversification of alpha and the levering of alpha. But what really has happened over, you know, decades now is that that model has been proven to be, at least have something. It may not be the only model that's viable to make attractive returns for investors, but at least that sort of, you know, result has jumped from the sort of academic theory to realized practice. And, yeah, and as- Charlesson explains why we're seeing some pretty big launches in this. Absolutely. Absolutely. Yeah, it certainly has jumped that gulf. And, you know, look, in the quantitative world, it made that jump

long ago. Yeah. It's in the fundamental stock picking world that it made that, and again, a few firms had been doing it for a long time, but it sort of made the most convincing leap over the last whatever, five to ten years where it just sort of decisively generated very attractive risk-adjusted returns for investors and kind of proved that sort of synthesis, which is really what's happening between the sort of quant view of the world of factorization and finding idiosyncratic or residual performance within inside what's left over after the factors, synthesize that risk and sort of quant perspective with that Warren Buffett style fundamental type research and analysis and work. That synthesis was, you know, implemented by a few firms, and now it's sort of proven

itself to work in a lot of different ways. So that's what's happening. So from our angle, like, what we have seen is just that wide range, as I kind of mentioned earlier, of execution of that. Like, how easy is it to actually have a system in place for a portfolio manager or analyst or CIO? How sort of, not only user-friendly, but sort of functional, how efficiently can it source new hedge ideas that balance out a specific factor exposure? How efficiently does it connect that risk perspective of where I'm long and short to a topic you mentioned earlier, performance attribution, like where am I generating returns, what are my hit rates, what are my hit rates on residual versus on factor, what are my hit rates on earning season versus

outside of earning season and on a residual basis? And how does that connect to my risk and my portfolio construction? And all of that is a lot of work. You know, it's a lot of painful kind of putting together the software and the risk and all the different elements together. And as I mentioned, what we see is some funds have done this, you know, at a level that is really excellent and some funds, most funds, because they have to do the very hard work of stock picking. That's a very challenging job. You have to have incredible IQ allocated to that problem and effort. They have OK systems. They have a sheet that gives some risk insight, but it doesn't have the sort of detailed input, output experience. Let me tweak this. Let me see what happens there.

Let me understand how it connects. And so putting all those elements together is what we kind of hope to do. When did you actually found Arcana? A little over two years ago. Two years ago. Okay. So what's the difference between what clients ask for? for now versus what they were asking for two years ago, because this is a rapidly evolving space. You know, I think in that sort of split that I mentioned of our client base that is kind of native to that risk world and the group that is sort of newer to it, I think the native group has this constant sort of question set of how do I make this, again, more functional, see more analysis, more quickly, how everything relates to each piece. Can I see insights on crowding

and how that relates to my book? And can I see all those different pieces? So that's kind of like a steady escalation in thoughtfulness, I would say, as you hand the portfolio manager tools on this factor, and you also sort of empower them. Because again, a lot of these organizations are set up where there's a risk side and a portfolio management function. And the portfolio manager isn't necessarily the client of the risk in-house at these places, but as there becomes this industry of people like us who are providing these tools, in a way, we have to be a little more responsive to the portfolio manager who says, okay, I see how

that was built. Can I double click on that? portfolio manager isn't necessarily a client. So at a big multi-manager, you have a risk division, which kind of sits under the CIA almost, and you have the portfolio managers. And the portfolio managers aren't the client of the risk people. The risk people kind of work, if you want to put it that way, for the CIA who says, you know, and sort of, it's kind of a limiter in some ways.

It's kind of a constraint in a lot of cases. And the best places are doing it where it's completely synergistic, where you're using the risk tools and this factor awareness and all of the things you can do with that on offense, not just defense. So that is happening at a few places. But there's a whole other group of places where it's kind of, hey, this limits me. This isn't working with or for me. Yeah.

And so as this becomes a little bit more, you know, of an industry or commercial, like we do work for them. Right. So we're like, hey, I want this additional feature that, you know, they're the client, right? So there's that piece. But there's sort of this constantly escalating sort of demand for tooling and incremental insight. And okay, let me click this. Let me understand this across my whole universe, across the entire universe stocks. I could cover all those kinds of tools.

sort of the people who don't come necessarily out of the pod systems. The interesting thing is the extent to which people want to focus on, okay, let me, how do I frame that system, that factor awareness, instead of in a market neutral context, but hey, I'm a long only, or I'm a sort of directionally oriented value fund or whatever, how do I reorient the model, shift it to be sort of true comparative to my benchmark? And so that's been an interesting evolution is the types of investors who are not structurally market neutral, but still want all the insights from where you can recalibrate the entire model against a benchmark. And so that's the one example.

Rich Falk Wallace, thank you so much for coming on Odd Lots. That was fantastic, learned a lot, and now have like 10 ideas for further episodes we have to do, which is always, as we say, the test of whether we had a good conversation. Absolutely, absolutely. Thanks so much for having me. I really appreciate it. Tracy, I thought that was great. I really do have like, there's like 10 more episodes that we have to do now.

But that was very illuminating on multiple levels, particularly about like what the job of the PM or the analyst actually is in these contexts. Yeah, absolutely. And also I was thinking it kind of dovetailed interestingly enough with the conversation we had recently about thematic investing with James Van Geelan, where he was talking about like, okay, price is obviously a factor. But also you kind of want to identify the story that everyone's going to latch on to. And then Rich was talking about how when you're coming up with investment ideas, you're sort of trying to identify something that will change everyone's perception of the trajectory of a particular stock or investment. Totally. And I thought it was just like really interesting this idea that like, okay, like no one knows what's going to happen tomorrow. Some major event could take place that causes the, you know, the whole market to crash. I guess big events don't usually happen that cause the whole market to surge.

unfortunately, it's always the other way around. Like, nobody knows what interest rates are going to do, and we know, you know, a lot of stocks are tied to interest rates, and no one knows maybe some chip company will come out tomorrow that beats in video, whatever. No one knows any of that stuff. And then this idea that if you can then strip out all of this and then identify the idiosyncratic drivers of a stock, and then those idiosyncratic drivers of stock, almost inherently, some will be winners and some will be losers.

Yeah. I can see why then the game is lots of bets over relatively short time periods. Like that really clicked to me in this conversation. Yes, that's the other thing that stood out to me, like the idea of diversification across those different bets. Yeah. Yeah, I hadn't really, I guess like when you think about hedge funds still, even though multi-strategy funds are sort of where it's at nowadays. Yeah, I still think about like that classic. I don't know, Bill Ackman type thing where you make one big bet on something and that's your source of alpha.

But again, the thing that's coming through in this conversation is really like the diversification aspect, the desire to be factor neutral and to lever the alpha instead of the beta. All kinds of interesting stuff there. I want to do more on, what don't want to do more? Well, I definitely want to do more on alt data because I feel like usually when that gets discussed, it's like, it's like very, very, like sort of tired cliche ways. Like, I know everyone has a credit card data. But I want to understand more about that. I don't know.

There's a lot more that we can add. Also, just like the different models. Like, I'm sort of fascinated that like there's all of these different multistrad funds that exist. And the fact that they're not all the same is interesting to me. And the fact that like where the risk manager sits and the amount of tools and what they build in house and what they don't and the degree of flexibility that pods get and, you know, what analysts actually do and stuff like that, there's much more to do.

I really want to do an episode on differences in compensation models at the pod shops. I think that would be really interesting because that would also feed into, I assume, investor behavior. All right. Well, now that we've come out of that with like ideas for 10 more episodes, shall we leave it there? Let's leave it there. This has been another episode of the Alld Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Allaway. And I'm Joe Wisenthall. You can follow me at the stalwart. Follow our guest, Rich Falk Wallace. He's at Rich Falk Wallace. Follow our producers. Carmen Rodriguez at Carmen, Arman. Dashel Bennett at Dashbot and Kelbrooks. And thank you to our producer, Moses Andam. For more Odd Lots content, go to Bloomberg.com slash Odd Lots, where we have transcripts, a blog, and a newsletter. And if you want to talk about all of these topics, including investing and markets, you can do that 24-7 in the Odd Lots. Discord.org.g.g.s.org. And if you enjoy all thoughts, if you like our ongoing attempt to understand multi-strategy funds,

then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes. Absolutely add-free. All you need to do is connect your Bloomberg subscription to Apple Podcasts. In order to do that, just find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening. A lot of short daily news podcasts focus on just one story. But right now, you probably need more.

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