How the Hottest Hedge Funds on Wall Street Really Manage Risk

25:20Well, 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. OK, 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 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.

25:51OK, 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, OK, 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 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, people who are executives, and that can inform that initiation and understanding of the industry as well.

26:26And 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 do they sell? 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.

27:02I have an understanding of unit economics of each player and how each of those is kind of heading. 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.

27:36So 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 analyst 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 the industry.

28:13Convince me, or you don't have to convince me. You could try to convince me or you could agree with 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're 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.

28:49Like, 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.

29:19And 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 spend a lot of time thinking about.

29:49Okay, like how do we mathematize? 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 and we talked about earlier.

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32:04Learn more at chase.com forward slash business card. Chase for Business, make more of what's yours. Accounts subject to credit approval, restrictions and limitations apply. Cards are issued by JPMorgan Chase Bank and a member FDIC. 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 Gappy 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.

32:50Some 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 infrastructure 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.

33:21So 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 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.

33:53And then at the far end, you have funds that have like full software platforms that hand to a 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 video, what does this do to my risk numbers, that IDEO number, factor number?

34:24What 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. OK, I've got a whole universe of stocks. What single stocks would offset this NVIDIA 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 saw, et cetera. But just to go back, one last question on the idea of like actually selecting a stock.

34:51You 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.

35:32So meaning if the market's perception is pick a coffee shop, is going to grow. And people will, the market, 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? So you're waiting for moments in which you believe something is going to emerge that will change the long-term expectation of X or Y. Exactly. That will change the – and there are other situations like tactical things where, hey, it's so heavily shorted and that'll change slightly.

36:10And I'm really looking for a short-term catalyst or, hey, look, everybody's expecting this next alt 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, OK, 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 is headed.

36:38and 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 small today so it actually doesn't hit the headline churn number yeah but that's actually structurally growing faster than every other region.

37:06And so the underlying churn 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 pot. 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.

37:44So does that mean that – does it happen where at the very high end of risk management, they look across and they say, wow, in aggregate, our portfolio managers, 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 exists in various forms, there's sort of a CIO level.

38:27There's a 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 a specific, in any specific factor. So put those limits individually. And then exactly as you said, they roll it up just like, you know, you just add up the line items. Essentially, all these models are structurally linear decomposition.

38:56So 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, will just literally hedge that basket. And there's nuance even there, like, hey, can I build a basket that hedges out that exposure, but doesn't actually basically end up being short the same stocks I'm long underlying the book.

39:32You 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 exposures, 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 BlackRock 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?

40:09So for instance, a mass deleveraging 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.

40:53And, 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, in order 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. But anyway, so like what the model is pushing you to is not any specific stock, right?

41:26It'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 is there's just sort of that risk that it has a big drawdown.

41:59The 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 blow up 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.

42:33So 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 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 is typically the banks basically, that that person for whatever reason needs to pull that leverage or whatever.

43:11And 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 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 decades now is that that model has been proven to be at least have something.

43:51It may not be the only model that's viable to make attractive returns for investors. But at least that sort of result has jumped from the sort of academic theory to realized practice. And yeah. Which also explains why we're seeing some pretty big launches in this. Absolutely. Absolutely. Yeah. It certainly has jumped that gulf. And 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 10 years.

44:22Yeah. 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 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.

44:54So from our angle, what we have seen is just that wide range, as I kind of mentioned earlier, of execution of that. 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?

45:27What 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 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.

45:55They have OK systems. They have a sheet that gives some risk insight. But it doesn't have the sort of detailed input-output experience of, 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 our counter? A little over two years ago. Two years ago. Okay. So what's the difference between what clients ask 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.

46:40Can 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, you know, 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, OK, I see how that was built.

47:15Can I double click on that? What do you mean the portfolio manager isn't necessarily a client of the risk? So at a big multi-manager, you have a risk division, which kind of sits under the CIO almost. Yeah. And you have the portfolio managers. Yeah. 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 CIO who says, you know, 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.

47:46So 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. And so as this becomes a little bit more of an industry or commercial, we do work for them, right? So if, hey, I want this additional feature, 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 of stocks I could cover. All those kinds of tools.

48:15On the 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, OK, 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 this where you can recalibrate the entire model against a benchmark.

48:49And so that's been one example. Rich Falk Wallace, thank you so much for coming on OddLots. 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.

49:19Tracy, 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 Geelen, 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 onto. 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.

50:06Totally. 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 Nvidia, 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.

50:51I can see why then the game is lots of bets over relatively short time periods. 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.

51:40All kinds of interesting stuff there. I want to do more on what I 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 this like 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 there's a lot more that we can. 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 what analysts actually do and stuff like that.

52:26There'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 All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Weisenthal. 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 Armand, Dashiell Bennett at Dashbot, and Kale Brooks at Kale Brooks.

53:04And thank you to our producer, Moses Andam. For more OddLots content, go to bloomberg.com slash OddLots, 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 OddLots Discord, discord.gg slash OddLots. And if you enjoy OddLots, 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 ad-free. All you need to do is connect your Bloomberg subscription to Apple Podcasts.

53:46In order to do that, just find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

54:05Thank you.

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