The Derivative - SuperCars, Salad, and Sumo Wrestlers: Inside OneRiver’s Systematic Risk Mitigation Playbook with Patrick Kazley

Episode Date: March 5, 2026

In this episode of The Derivative, Host Jeff Malec and Patrick Kazley of OneRiver explore how long volatility, convexity, trend following, and systematic macro can be combined in a capital‑efficient way to improve equity compounding and protect portfolios from major drawdowns. They discuss crisis “shapes,” why time-based rebalancing often beats intuitive drawdown triggers, how changing volatility microstructure (zero‑DTE, single-name vol, dispersion) creates new opportunities, and why behavioral biases keep most investors under-allocated to positively skewed defensive strategies. Patrick ties it all together with vivid metaphors — from F1 cars and soup vs. salad to sumo wrestlers and the beer boot — and explains how One River’s acquisition of a European alternatives/QIS team fits into their total-portfolio approach..… SEND IT!Chapters:00:00-02:21 = Intro02:22-12:33= From AQR to One River – Patrick’s Background and the Case for Systematic Risk Mitigation12:34-28:07 = Engines and Brakes – Equity Beta, Skew, and the Power of Convexity28:08-43:55= Crisis Types, Trend Following, and Building a Total Portfolio Defense43:56-1:03:04= Visualizing Risk – Crisis Shapes, Rebalancing, and the Math of Convexity1:03:05-1:16:36= Metaphors, Markets, and M&A – From F1 Cars to Das Boot and One River’s Next Phase1:16:37-1:28:37= Soup vs. Salad – Total Portfolio Thinking and the Future of One RiverFrom the Episode:Blog post: A Short History of Market-Moving Middle East ConflictsOneRiver's WhitepapersFollow along with Patrick and OneRiver on LinkedIn and make sure to check out OneRiver's website www.oneriveram.comDon't forget to subscribe to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Derivative⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, follow us on Twitter at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@rcmAlts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and our host Jeff at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@AttainCap2⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, or⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ , and⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠sign-up for our blog digest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.Disclaimer: This podcast is provided for informational purposes only and should not be relied upon as legal, business, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCM Alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations, nor reference past or potential profits. And listeners are reminded that managed futures, commodity trading, and other alternative investments are complex and carry a risk of substantial losses. As such, they are not suitable for all investors. For more information, visit⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.rcmalternatives.com/disclaimer⁠⁠⁠⁠

Welcome to the derivative by RCM Alternatives. Send it. All right, everybody got me here a little bit in the dark for those of you seeing me on YouTube. But welcome back. Hard to believe we've gone and started a war in the few short days between our last episode and this one. But here we are with drones flying and bombs dropping. We actually had the head of a defense tech company, venture capital firm that invests in defense tech. Scheduled to come on the pod later this year.

So went ahead, reached out and going to have him come on next week. talk about modern warfare like drones and anti-drone tech and all the rest so tune in for that one we also put up a new post after seeing the somewhat odd market movement monday after the weekend's conflict began it was a little bit odd oil was up stocks down as you'd expect but bonds weren't up all that much in the beginning and reversed hard by the end of the day with higher rates and bonds down which is not what you typically see in a flight to safety war positioning so is it just by case of buy the rumor, sell the fact, probably, but we wrote a little post on that, so head on over to rcumaltz.com. Check out our short history of Middle East market moves. Okay, onto this episode, we got a good one for you today talking with Patrick Casley of One River. Patrick and I are both card-carrying members of the trend following and long ball clubs. I don't quite have my card here in my desk, but trust me on that one. So it was fun to bounce thoughts off one another. here on this episode.

And also as we talk about risk mitigation strategies are top of mind, given what's happening. So we could have gone at least five hours on this one, but managed to keep it just a little bit long and not crazy long. So grab some soup. Yep, soup. You'll get that reference towards the end as we explore what Patrick and me to a lesser expense have been saying for a while now, which is can you really rely on bonds for

risk mitigation? Maybe it's time to add a few more tools to that toolbox. send it all right everybody we're here with patrick casley of one river patrick how are you i'm good jeff how are you good is this a home office or an office office looks lovely yeah it's this is the home setup got better better lighting better uh audio visual over here but um no yeah it looks a little bit like one of those pre-canned offices but this is actually my house yeah i love it where are you located Well, our offices are in Connecticut, Stanford, Connecticut, and I live a grueling eight-minute commute away from the office,

so not a bad situation. Yeah. Do you get a little, it's like Connecticut hedge fund, it's like very cliche? Do you get a little like, okay, why are we here? It's just like, no, it's cliche because you have a lot of talent and you have a lot of access and all that. Yeah, that's what I mean. We started one river.

We, Eric Peters, started one river in Santa Barbara. So the talent pool was there, actually. We grabbed our current deputy CIO, had a quant from Caltech, Ph.D. Academia, and then he was at Credit Suisse, but joined us. So the West Coast started as a decent talent pool, but as we scaled, all the talent rushed to New York area. And neither Eric nor I are massive fans of living in the city. So we like the trees. We like the open space. And Connecticut's great.

So, yeah, when I was at a, I started my hedge fund career at AQR in Greenwich, but then spent four to five years in Asia. So I had a, at least I got some non-usual geography in there before rushing back to the homeland in Fayfield County. Where in Asia were you? I did a little bit of, so I was in Hong Kong for the better part of four years and then half a year in Japan opened up both those offices. Kind of got to arm wrestle with the great sovereign wealth funds of Asia and learn how they think of. about the world. It's pretty formative. And when I came back to the States with AQR, pretty shortly thereafter, got in touch with Eric Peters at One River, and we started chatting. Saw the world in a very similar way. So then I found myself here.

I'm going to dig into One River's massive footprint, but before that, just any cliff-assness thoughts as he is crazy in person as he seems to be at times online. Loves to pick a fight on Twitter. Yeah, Twitter was crazy in a good way. Yeah, Twitter was a nascent kind of development when I was there, you know, especially towards the end, it started to heat up a little bit and that he was a bit more active. But it was always a marketing tool because, you know, while he's always engaged and at times, I think, charmingly bombastic, he has a pesky tendency to be overwhelmingly right. And so that always, well, it could be viewed as a distraction at some level. It was always fun to see him actually have some pretty cool engagements with meaningful people that probably wouldn't have had the opportunity to have that publicly aired.

Net net ended up being a marketing benefit. But, yeah, he's a fun character. It was a pleasure to work underneath him and the team. But that's kind of where I, you know, that's kind of where I cut my teeth, learned the basics of quant investing as well as the non-basics and then joined a long ball risk mitigation shop totally different mission than place like like aqR right so one river give us the 30,000 foot view and then we'll dig into your smaller purview yeah we've been around since 2013 as I mentioned founded by Eric Peters but I serve as its president head of solutions primarily working with

some of the larger institutional investors globally. But the day in, day out, what we do is primarily systematic risk mitigation focused. The mission of the firm is pretty easy. We want to make their portfolios better. And we do that by acknowledging what the predominant risk in their portfolios is. It's usually some form of equity beta. And then we build building blocks, active strategies that effectively lean against that and protect that pool of capital. So that lends us really well to systematic expressions of long volatility, trend following, increasingly getting into systematic macro. But the heart of what we do and what we're best known for, systematic, long volatility, defensively oriented, highly convex, negatively correlated strategies.

And you would think, right, you view that as unique, right? Of like, hey, we're systematically looking at risk mitigation to make a portfolio better when it seems obvious. I mean, we're both drinking from the same Kool-Aid here, right? So it seems obvious to me. And most of our listeners probably like, of course that makes a portfolio better. What are you talking about? But 12 years ago, whatever, when you started and even now,

like how much of a aha moment is that for institutional investors of like, okay, I actually need to look at that side of the ledger instead of just always trying to make my portfolio better by adding VC, adding real estate, adding lumber, whatever. Yeah, I think it really goes back to those Asian sovereign wealth fund days where, you know, I always call those big. allocators aircraft carrier allocators because they can see something on the horizon but they're way too big and have way too much mass to really turn and avoid it right so

all they have to do pre-planning before they can really change their stripes and so convexity defensive strategies were always really appealing because it was something they could hold on to that would potentially save their tail pun intended if something were to manifest and but but ultimately it does fly in the face of modern portfolio theory in other words if you're buying something that's protective it's It is a form of insurance. Buyers of insurance tend to pay sellers of insurance over the long term. And you see this pesky term.

It's so painful that they call it a bleed, a negative average return that can manifest from defending yourself. We've written a host of white papers over the last couple of years, and I've contributed to a lot of the, I'd say, the industry thought on this, but we challenge that conclusion. In other words, we show that you can effectively hold your equities. You can protect yourself at the same time. And you can have a total portfolio that has a better compounded return. And the metaphor we overuse, as you know, is something like the F1 car with equity beta is a really good engine and convexity is a really good set of brakes.

If you attribute time to the brakes at the end of the race, it's going to slow you down. But ultimately, you should be driving faster if you have a good set of brakes. And so the F1 car that wins the race may really have a similar engine to its peers, but a better set of brakes. And so we have systematic breaks that we can install on these client portfolios. we usually help them drive faster too. So sometimes oftentimes we'll put offense and defense into these portfolios. But the objective is actually really simple. We want to generate superior compounded returns. And the reason that mission, as you mentioned, is I'd say unique, is that the hedge fund industry, and I'd say allocators and mass have a natural addiction to average returns and high

frequency average returns, even if they're low magnitude. And that will tend to draw capital towards high average return, but negatively skewed assets. Equities being the prototypical example, but most hedge fund strategies will have a similar profile. Where you make a little bit of money very often, and then when you lose money, you lose way more than you typically would make. But you can still carry positively through that, but it's a negatively skewed endeavor. We're the opposite. We have a very low average return until something that we're betting on manifests at a low frequency. but in extremely high magnitude, right?

Kind of unsurprisingly, A plus B could yield something that is a compound return that's greater than the sum of the parts. And that's what we generally observe. The math there is well studied, well established, and I think even well agreed to. What I really like about the space is that despite consensus on the math and the academics behind it, there's just not a lot of capital pursuing that type of strategy. And a lot of it goes back to behavioral and governance reasons, meaning investors often get paid on their average return outcomes.

And so they tend to issue active ingredients that don't pay frequently. And so that leads. And by average return, are we talking volatility? Like, explain a little bit more of what you mean there. Just like if I have, because if they have an average return of 1% a year or something, maybe that's not exciting. But you're talking a little bit more. We can get into Cliff calling it volatility laundering too.

But sorry. So what do you mean by average return of what they're targeting? Yeah, so if you had a, let's say you found this amazing strategy that that was just a money printing machine and it made 10 basis points a day like clockwork. Okay. Every single day. 2502 trading days a year. It makes 25% if you were to sum that up a little bit more. And the geometric of compound return is is 28, 29% on that return stream. But let's say on the last day of the year, instead of being up 10 basis points, it's down 25%. Well, now your average return is zero, but your compound return is negative five-ish percent. Right. And so what happened there was that even though you had what looked to be a very attractive average return, there was this hidden negative skew factor that when it manifested, just completely destroyed your compound return. And so the siren song is to backtest strategies that have that type of high average return,

meaning they pay you frequently and at a decent clip. In this case, I use an extreme example of something that pays you in a monotonous fashion, 25% per year. But if you're unbounded to the downside and there's a rush to the exits, say it's a levered spread strategy, you could have years of that return wiped out in a moment. And equities directionally follow that in that they tend to lose money reflexibly,

not that extremely. But it's just, So if you can find things that are positively skewed and have the other opposite, negatively correlated return stream to that, you could do really well. Basically, negative skews option selling, positive skew option, buying. There's nuance to that metaphor. But I've sometimes said like, hey, would you, when people are like, hey, the power balls at X, Y, Z? I'm like, would you play it in reverse? Right. If someone's like, you go into the mini mart every day and they give you $5 every day. But some unknown point in the future, and maybe like a thousand years in the future, your heirs owe $6 billion, right? And it wipes out everything you've ever owned

and all your stuff. So would you play that game? Because essentially that's what we're talking about, right? Like they're like collecting, collecting with this unknown time and magnitude event in the future. But I was in that example, they're equal, right? It's like the mathematical expectation is equal. Yeah.

Which nobody gets. Like, oh, I'm just spending this $5 to get this $6 billion. Yeah. Like, yeah, but if you play it over enough thousands of years, you're going to lose $6 billion. So how do you do that of like long term? Are they terminally break even both sides of that equation? Or you think the math works a little better in one way or the other?

Well, I think investors tend, especially allocators, I think you're going to tend to have a better psychological journey if you pursue the high average return route, right? Because the reality is when you fail, you'll fail conventionally. you know so the example would be just buying you know the high growth stock portfolio and you know eventually you don't know but you suspect and you're probably right that you're going to get bitten pretty hard on that it's going to really impact compounded returns for a long period of time but you're in good company because everybody else has been making so that conventional choice makes it a smart choice

because the worst thing you could do is fail unconventionally right so if you take the other side of that bet and let's say you build in sufficient asymmetry you'd end up in the same thing same place. Well, now you have the opposite problem. When everybody else is making money hand over fist, you're massively underperforming because you took the positive skew side of that trade. Now, the question is, can you passively harvest that equity risk premium and then actively defend yourself? And that's what we recommend you do. So that way, in a in the benign years... Don't make the choice. It's a false choice. Yeah, it's a false choice. Yeah. And the reason you can do that is these things are very capital efficient.

You don't have to chew up your whole balance sheet to defend yourself. It's a very small outlay. And that's kind of that use of derivatives and prudent use of leverage is basically a way of keeping up with the Joneses when the average return environment's high. And then when the wheels fall off the bus, you have this hidden source of convexity that manifests and makes you look really good relative to peers.

So that's back to the F1 car. You should drive faster such that whatever that cost of the insurance policy is. you're effectively making up for it in additional equity exposure. Which has been put to the test, right? Like, how far would you take that? So you're saying, like, I can go faster under the curves because I have better breaks, basically, and I won't fly off and wreck, which I love that one. So how far can you take that?

Are you like 98 to, right? Like a universe is going to say put 98% in equities, 2% tail. Sure. And you're good. Some are even going 110 or something like that, right? So how far would you do you or would you take that metaphor of like you, it allows you to increase the offense, increase the speed? What we would say is identify for your given style of defensive investing what the benign market beta is, the benign market beta. So if we're running this long volatility strategy, we would be able to use any number of pretty simple techniques to determine, hey, we have a negative 0.2 beta or a negative 0.3 beta.

And with the understanding that that will really expand in a crisis, but that's a good thing, right? But we say, no, that that theta bleed or that, that negative average return that we typically observe is really just a negative market bet and some residual, okay, long term. Yeah. So let's calculate. Like you sold down some of your position. Yeah, right. And so if you were to just buy that defensive return stream, well, not only do you take on that negative average return, but you also forewent beta. Okay. So what we would say is put on that defensive exposure and put on a requisite amount of long equity exposure to offset the benign market bleed. So if you determine that a given source of long volatility has, say, a minus point two beta, well then put on a 20% exposure to an equity future and put on a full exposure to convexity.

So at the total portfolio level, if you wanted to be maximally, if you really wanted to abide by the F1 car principles, you would say, I'm 120% exposed to the MSCI world, and I'm 100% exposed to this long volatility return stream. The most important thing is that you do those together. Because when you do it together, I mean, inside one pool of capital, now you can rebalance over time. Right. And that's the magic. So if you take something like the Eureka Hedge Longball, we can avoid specific mentioning of products. But we can take the Eureka Hedge Longball, which a pure composite of net a fee convex

returns and you can pluck a constituent out of there that's up 40% on the year of 2020. Okay. And let's say at the end of Q1, it's up 45%. Okay. And then you look at the S&P. It was up 18% at the end of 2020. By the end of Q1, let's call it down 30. Now, let's say you're doing this kind of structure.

You're fully long equities and you're fully long, this long ball manager at the same time. Well, at the end of Q1, you know, sleeve one is down 30. that's the equity. Sleeve 2 is up 45. So you're up net at roughly 15%. Okay. Well, if you did that together in a derivatives portfolio, your nav just went from $100 to $115. But now what you can do is you can resize the sleeves to $115 net asset value. In other words, you can increase your equity exposure to $115 and you can reduce the size of your convexity bet from $145 to $115 as well. taking chips off the table. And then the market recovers really strongly 40 plus percent bounce. Your long wall process almost certainly is going to give some of it back, but hopefully not too

much if it's doing a good job. And at the end of the year, the arithmetic, if you're just summed up the sleeves, you have 40 percent from long ball. You have 18 percent from equities. You would think you'd be at 58 percent. But the compound return of that rebalanced portfolio is closer to 82 percent. And so that difference, and you didn't have to subscribe new capital or come up with new cash, you just leveraged the principles of negative correlation, positive convexity, capital efficiency and rebalancing. And so, you know, what we essentially did a bit of timing, right? Like the rebalancing forced you into what timing advantage there, timing alpha?

Yeah. And that's, we wrote another piece called the Convexie Rebalancing Act. And it's all about how exactly you have to be. gotten through Congress yet. Yeah. You can act. Yeah. That's right.

Yeah. And but kind of much like an act of Congress, it's, it's highly contested as to how you should do it. Right? We always tell people, the irony of buying long ball is that you're no longer stressed about how much money you're losing. Now you're stressed about when you're going to pull the plug. Yeah. Right. And it becomes this.

And everybody thinks ex ante that the smart thing to do is going to be when I'm at my peak pain point, I'll pull the plug in my convexy and buy cheap equities. I'll do the contrarian. thing and I'll do it aggressively. And it feels great. But the reality is people's pain points famously don't come at the right time, meaning they're pulling the plugs, say, at the end of February 2020 or versus mid-March 2020. And what that does is basically if you have a, we call it a threshold-based rebounds where you're picking the threshold, equities are down this, or my hedges up with this, and I'm going to pull a plug. When you do that, you create a very

path-dependent outcome. Now you want that based on, you watching the news or you're emotions. Yeah. Right. Or are you saying like an actual percent down? Yeah, we've had clients do, hey, when when markets are down, you know, a rolling three sigma event over a two-year horizon, if they're being more sophisticated, or if they're being simplistic, they're saying equity down 20, cut the hedge in half, equity down 40, you know, empty the hedge. Okay. Yeah. Because maybe the equities are going down to 55 in a GFC, but it's good enough. Okay. We actually don't recommend doing that. We don't recommend doing that because it ends up doing really well in some crises and really poorly in others. And oftentimes, the worst thing that can happen is you race right up to your

threshold and you don't hit it. Yeah. And then you end up having a really bad compounded return when you could have had a really great one. So what we do instead is a time-based rebalancing. And a little bit all the time works way better than any of these threshold-based programs. Now, to be clear, in any one crisis, the best threshold will be the best option. But the reality is the relationship between... Right, down 49.76%. Like, how come you didn't have that in your model? Right.

And the relationship... In Quant parlance, the relationship between parameter selection and outcomes is random. In other words, it's a really hard model to get right. But if you do a time-based three balancing, you don't have to get at that right. In other words, you know, you could do it every month. every quarter, you know, we do something that we just want to maximize path independence. So, you know, in production, we'll do something like every week we'll rebalance a quarter of the portfolio, such that every month we've done a full rebalance, but now we're not overly reliant on certain

calendar effects. And we run those two sleeves together, equities and long ball, and we let the compounding do the work. So for us, then we remove the stress of when to pull the plug, and we're focused more on just doing well in that long ball trade. And that's singular, that stands on its own. So it's not like a, if you're 100x, your premium you paid or something, like, is that built into some of the models as well? Yeah, the way that we structure it is more of a traditional alternative line item. So instead of having it be some sort of premium spend approach, the issue with those types

of approaches is that, well, it feels good as an investor to shuffle up premium like an insurance policy. The geometric return, the compounded return of that line item tends to not. favor very well. You have these prints that look really great. Oh, you're up 3,000 percent. And if you were to normalize our returns using outstanding premium, you'd get similar returns. But if you were to actually look at the... We'd be down 100 percent. Compounder return, it would be way less impressive. So we actually present our returns in the accurate format, I would say, in that we have a compounded return that can be compared to any hedge fund or any holding in your portfolio. So, yeah, investors would

subscribe into an AUM or buy an SMA with a trading level. And yes, that fund could hold the equities and the long ball. Many clients just hire us to do the long ball. They handle the equities on their own. Have you guys done work? Like part of the rebalancing, right? All the math, all the papers looks great. In lived experience, you go through these periods where equities have these steady,

consistent runs, and it feels terrible, right? Because you're taking those chips off the table. You're ruining your equity compounding. in a way. And so you need those like periodic episodes to be able to get that rebalancing and be able to compound higher. So do you guys have math or just how do you think about it generally of when rebalancing doesn't work? Yeah, rebalancing will have its toughest episodes in the scenario you mentioned if it only if you haven't properly neutralized that negative carry. So the example you mentioned like a 2017. Equities were up every single month. Really tough times.

to be long ball, no hiccups. But if you look at a, if you've accurately determined what the bleed of your portfolio should be, well, then you will have the requisite amount of extra equity exposure to make up for that. And so, so if you've reached construction nirvana here, you really shouldn't care where the return comes from, the extra equity exposure or the insurance policy. If you are overly concerned about that, then yes, you're going to set yourself up for frustrating evaluations because it's entirely possible over a multi-year period. In fact, it's probable over a multi-year period that one side of the other will dominate. It's very unlikely you're in an equal contribution to return from both. Yeah, definitely. Or instead, in my view,

of like, it's very unlikely you're going to have that perfectly timed period where the rebouncing worked out. Yeah, 2020 is an exception to that where you're like, well, equities are up 18 and the tail hedge is up 40 and everybody looks smart. That's kind of, I mean, if 20, 17 is a tail hedger's nightmare. Then 2020 is, you know, a dream. And so the worst it is for the world, generally the better it is for the strategy. That's by construction.

But ultimately, we try to put our portfolios in a place where we and our clients do not care where the return comes from. As long as it out compounds equities over the long term. And then the two, 17, 20, also 22, was a risk responders dream slash nightmarine, mirror, right? So the long ball components weren't working. Equities were also down. Yeah. So that's the flip side of that equation. Right. If you're 120, 100, that's very painful.

It is. And that's, you mentioned risk responders. That's what we call this framework where you combine a tail hedge or long ball approach with something like a systematic macro, a trend following process. What we really like about trend following is it's longer flexibility. So if you get this kind of orderly rush to the exits, so not like a panicked rush to the exits where you get an expansion of vol and long vol approaches should compensate you. You get this kind of orderly march to the exits, you know, kind of famously like a rolling put portfolio in 22 lost something like 18 percent is basically along with the markets. Now, our long ball process was down far less than that, but trend had its best year in a decade. I don't know more than a decade. And so the rationale,

and trend is a category, including our trend, right? The rationale for why trend does well there is the exact reason for why it's such a tough period for equity vol. So that's why long vol in trend. So to summarize, I think convexity is a bit like the fast twitch muscle to respond concurrently to a crisis as it's unfolding. Trend is a slow twitch muscle that by the nature of how those algorithms work, it's going to do better over prolonged declines. So if you do both, you can have that immediate response and that slow response.

And you can actually have a much more robust kind of fish net of very. risk mitigation. You can lean both of those against equities. We do that. In fact, it's such a holistic solution. We literally call it the total portfolio strategy because you have the total portfolio of the equity risk. You have this fast-twitch muscle of convexity and the slow-twitch muscle of trend. And that one-two-three punch kind of leads to, again, if we do that well, you shouldn't really care where the return comes from. You just have a pretty high degree of confidence, very long-term. You're going to out-compound equities. in that scenario, like we can't cover every scenario, right?

Like, okay, do you have CDS and you should have crypto in case all the currencies go away and yada, yada, yada, right? You can think of a thousand different pieces of convexity to add to the portfolio. So kind of how do you draw the line and be like, we've got enough here, we're good? Yeah, you generally have to pick your battles here. So we try to protect something. We picked equities because it's the predominant risk in any institutional large pool of capital, which is where our clients are focused. So whether you have a 60% weight or 90% weight, you probably have a risk contribution coming from beta that is somewhere between 70 to 95% if you're a institutional investor. That's a typical contribution to risk coming from equity beta.

So the predominant risk in the portfolio is that equities don't compound well. So that's different than saying equities don't return well. I was saying that they forego compounding. So if you are to forego compounding, you need to either A, have a big drop and stay down, or B, you need to spend a really long period of time not going up. The kind of crises that we care the most about are the massive reflexive declines like 87 GFC COVID. the prolonged declines like 2022 or the tech bubble. And then the mini chaotic declines like August 2015, February 2018, those are impactful too because or the U.S. debt downgrade.

Those are kind of the gut punches along the way where you don't get that immediate bounce back to high watermark and you stay low for a while. And you don't know in real time whether it's the start of a GFC or it, right? Right. And you also don't know if it's going to, it might just. immediately resolve itself. And that's the fourth type of crisis that we care less about. So the V-shaped decline. So to your question about picking battles, the battle that we pick is the type of crises where you forego the most long-term compounding, which means that we tend to focus a bit less on the type of crises where you don't forego compounding. So a V-shaped decline is something like a tariff tantrum

of this year. That was very short order. Re-Sol markets could be about 17% below their high mark on the month of April up until that eighth or ninth, which ninth was the policy capitulation day. We saw the taco trade kind of manifested. But before that, if you were to look at a long ball process like ours, we were doing exactly what you would have thought. You know, we were up more than the market was down and that convexity factor was growing at a growing rate. And so the risk reward was really attractive. You know, we were saying, hey, the market's down 10% in the month. We're up 12% in the month. And we're realistically, we're risking 9% to make 30, 40. And I really like that risk reward. Now, of course, what happened on the ninth is that the left tail for our

process manifested, the right tail for equities manifested. And so we gave up the majority of that accrued return. But that's okay in the context of what our mission is, which is to because equities were down 80 bips on the month and we were back through high watermark for equities in very short order. So there was no foregone compounding. And so our process preserved capital, we made a little bit, but we're happy to run the risk of giving back accrued gains if it means that we can stay in the fight if that were to turn into something much worse.

So, yeah, you have to pick your battles. I'd say deprioritizing V-shaped declines without giving up on them. Like there are ways to capture those and we're getting better at that as a matter of research. But if you try to do too much of the V-shape, then what's going to happen is you get that big reflexive crisis and markets are down 50% and you're only up 20.

And that's... You monetized too quickly, essentially? Yeah, preemptively. Right. And then how do you write our, my friend Jason Buck and the Cochran, he's always talking about nobody's thinking about the second leg down? Yeah. Like all the most risk responders, everything's just put in place for the first leg down.

Yeah. So how do you guys think of that? It seems like you're on board with that was what we're just saying. Like, hey, we're not trying to protect against that 10% move. It's a 40% move. Yeah, we actually wrote a paper. It was good trends come to those who wait. And it was exactly that. We examined the responsiveness of both convexity and trend in the first 10% of a decline and then looked at it 10 afterwards and very different charts. In fact, something like a trend falling program is a little bit better than a coin flip historically in a 10% decline in terms of being up or down. And then in the next 10% percent, or even any percentage beyond 10% decline for equities, it has an incredibly high hit rate. And so, yes, it may take a while for the chameleon to change its stripes, but in the time of

greatest need, it's very reliable. And that's how we model our process. So, by the way, easier said than done. Because at the end of April, if you're an allocator who bought a tail hedge or a trend following portfolio for defensive reasons, and everybody feels like the world just went through this big tumultuous event and you look at the returns, for the long vault, you're up a little. And for a trend portfolio, the category was down a lot, right? Very frustrating. And you can walk through everything I just said and everybody can understand it. But then it gets to the next layer of the board and it may not meet favorable scrutiny, you know, just because it's, it is a nuanced story. This is all part of the behavioral bias that I think makes these returns, makes the margin for

these returns very wide, meaning there's just not a lot of capital. that's going to be competing to express these types of strategies because of the frustration with the frequency of returns. And talk a little bit. Like that lived experience in April for the trend followers was brutal and thereafter, right? It lasted through June or July. What does that do to the math of like, okay, this was a non-event. It was this V-shaped that we're not trying to cover, but actually we experienced these big losses

on that piece of it. So just talk through that a little bit. Like that seems like more you're realizing negative carry in that scenario. Yeah, and I think we wrote about that as well in terms of when we observe the largest drawdowns for trend, and then we examine the subsequent periods following those largest drawdowns, we tend to see that the average and median outcomes following the biggest drawdowns are well above the full sample, average and median outcomes. In other words, trend tends to bounce. Okay, so why does trend as a category tend to bounce?

It's not like trend gets cheap. It's not a stock, you know, so there's no immediately obvious risk. based rationale for why a trend following strategy should bounce. But, you know, I would propose the following for why trend tends to bounce. If you have a period. Call on me. Yeah, go ahead. I'll let you start. The, uh, for me, it's, it's, when you look at the worst case scenario for trend, it would be something like in April. And if you look at what April was, it was, you know, it was in a manufactured period of whipsaw, meaning there was a series of policy. choices and prescriptions that if their intention was to be anti-trend, they couldn't have done a

much better job. And so when you get that, it's a distortion on prices, meaning it's an anti-trend, hypermean reversionary force. And when you get distorted forces like that in markets, and they achieve their goal of maybe foregoing some sort of acute liquidity crisis, et cetera, and that gets removed. You go back to quote-unquote normal dynamics. Well, then the market has to kind of snap back to what it was planning on doing the whole time, right? And that's across all assets. And so basically, any sort of really acute period of losses for a trend falling program tend to be followed by really strong periods because that distortive force is eventually lifted. And then it tends to, you get the snap back in the trends that were already forming.

And trend tends to be, by definition, pointing in the right direction for that heading into it. That's exactly what we've seen, by the way. We've seen a really strong bounce for trend is for our version, but also in the SG trend index. So the year for the index wasn't terrible, actually, round trip, even though it was a really tough print through April. So to me, it's the more you distort trend to kind of create these bad outcomes. The good news is, if you stick with it, you actually tend to earn back that foregone compounding way faster than you'd expect

than if you were just applying the average returns for the category. And it's been well, well more used. That doesn't sound like good English, but right, people have loved it as a risk responder way more than longball over the years because it's carried positively. Yeah. Right. It can have in an up year, it's up four, five, six percent or something. So they're like, cool. Yeah.

But forgetting every now and then you're going to have one of these nasty periods. I would debate you that. I think it's also things change. Like it didn't just snap back. It also changed the structure of what's happening. So in April, right, foreign non-U.S. companies and everything, we're like, hey, we need to pull stuff in-house. We need to do a lot more in-country, and you started to see those markets rise. We need to secure commodities outside of the

U.S. structure, and you saw a commodity market start to rise. So part of me is, like, what caused the drawdown can also push you into that new environment where the new trend, right, the drawdown itself caused a new trending environment of like the rules changed overnight. And now there's the new rules which we need to adapt to. Yeah, kicked off a new regime. Both of these theories are nice because they're untestable, which is, you know, my favorite kind of theory. But the results are there, and it's not just isolated to this trend drawdown. If you pick the 10 worst trend drawdowns over the last decade, more or less ubiquitously, you saw a strong balance. So there's something to it. Yeah. And my non-scientific, non-testable thesis is trend works because it's so frustrating.

Sure. Right? Annual premium. Yeah. If it wasn't so frustrating and everyone was in it, maybe the trends wouldn't get in. But because it shakes so many people out, that it continues to work. Yeah. And I think what you just said there is is doubly true for Longbowl. There is, I mean, it's a pretty masochistic life calling to pursue Long Ball as a business, right? You're telling clients, hey, I'm going to generate what will appear to be a very frustrating return. But it's going to make your portfolio better. And they're going to say, well, when? I don't know. I really don't know. when it's going to help you, but if you look at the very long term, it helps you not a little, but a lot. And we're going to do that every single day in a systematic way, waking up, probably saying, you know, the portfolio is probably going to be down today. But when we're up, the magnitude of when we're going to be up is going to be so great that it's going to more than compensate for that whole period, and you're actually going to beat somebody who didn't do it at all. And you're

going to have this insurance policy. And, you know, here's a load of evidence for it, but it's going to be very frustrating to hold. You know, it's how about no pain, no premium. You know, the long-val premium is a paper that we wrote, which basically shows that for whatever reason, the right tail of convex instruments is more convex than is the left tail of, say, equities. And for us, that's a mispricing, right? Because you could, if you're a quant like myself, you could beta-neutralize your long-ball bet and make money. And that's true. You can. You can do that with a rolling put index.

You really don't even need to be good at it. If you are good at it, it pays. But let's say you're bad at it, and you just want to buy 5% out of the money puts. Yeah, you can pair that with an equity bet and make money. That's pretty remarkable that that can be. There's an index that's been increasingly hard to beat, actually. Yeah, yeah.

Right, the P put in it. Yeah, the P put in it. Right. So these kind of silly passive indices that are protective, they look frustrating to hold. But once you realize that the reason they're frustrating is because you're leaning against the market, well, that's pretty easy.

You can passively lean into the market and do that. And so that simple aha moment in construction, and by the way, we preach this gospel day in and day out, and we don't get ubiquitous implementation of it. So I think there's a lot of capacity left in this type of trading, and there's a pretty strong and resilient premium. We know who's on the other side of the trade, which is great, and they're not going away, meaning there's a lot of people who will be taking advantage of these dynamics from the short ball side. and they have mechanisms to try to make money as well. So there's a world in which they can make money in average return space, and we can make money for total portfolios, and that equation clears. A bunch of stuff I got to unpack there.

So one, who led me right into the question of who's on the other side of that trade? Yeah. So when we buy a long ball instrument, if we're buying a VIX future, or if we're buying a call option on the VIX or an equity straddle, the obvious other side of that trade is going to be a vol risk premium harvester, somebody who's making money from the tendency of implied balls to be higher than realized balls. So if we buy, say, a call option on the front month of the VIX, there's going to be a plethora of capital that is rushing in to sell spot VIX

because this implied realized phenomenon, the penny in front of the steam roller strategy. And then there's going to be a lot of people who are non-economically buying the front month VIX, to hedge their portfolios. we could be part of that, right? And there's going to be people who want to play the difference between those two. So the people on the other side, and by the way, they're happy to do that because they get that positive average return. Now, by the way, that positive average return has a ton of equity beta. It just so it turns out. It's a very positively, it's a pro cyclical strategy. And what I would propose is that if you were to beta adjust that Vol-Ris premium strategy,

you might see a negative residual for many implementations of it. But they're okay with that because they might say, I'm going to sell VAL here. I'm going to sell an option. I'm going to sell the front month VIX or buy a put on the front month VIX. And when that strategy works against me, I'm going to have, I'm going to be able to, A, get out of the way or I have enough kind of protective stuff to compensate me. You know, that's strategy. For us, we're long, only VAL. So we're on the exact opposite side of that. They're going to be there a full market cycle because they're collecting a risk premium. It's a you only make money in a risk premium strategy. If you're not too selective for when you're in it. Conversely, we will be long that we'll have a few different

levers we can pull to mitigate the negative carry without giving up convexity. That's our skill. But ultimately, we know the other side of the trade there is not going away because they're making money. And the way that we make money too is that we take that negative return or what appears to be a negative return and we neutralize it using a different market, which is not the ball market, but the actual underlying equity market that you're protecting. So take the thing, the 120, 100. Yeah, the linear. So we take this non-linear right tail.

We combine it with something that's linear and has a known left tail relative. So basically, we're buying the relative distribution of we're long convexity, which means that fat right tail, and we're accepting on behalf of our clients these known left tail of equities. And we're betting that the residual of that right tail and left tail is going to be positive. And historically, it's been robustly positive. And clients can do that with us where we buy the equities and the long ball. And most of our clients just say, you know what, give me the most capital efficient expression of that long ball.

And we'll do the extra equity buying ourselves. So to back to my lotto philosophical experiment, like you're saying buying it is better. Right? You're not when they come calling and like, hey, you've been getting these $5. You now have to pay it back in your research. the buying has a premium. Yeah, well, I would say that because most people would prefer to get the daily payment, it makes it much more competitive premium.

It's going to be way harder to outperform in the high average return negative skew strategy because that attracts way more capital. So we'd rather, meaning that a passive approach is much more likely to be the most efficient approach. Conversely, because the low frequency of payment positive skewer strategy is so far. frustrating to hold. We have a much, we have much wider lanes to compete and be exceptional. So we can help. It's so frustrating like it's on sale. Yeah. People aren't going to pay up for it. Yeah. And so because long volatility is such a frustrating distribution and frequency of return, we call it a

frequency versus the magnitude problem. You know, it's people want high frequency and they're willing to accept low bag, you know, they're willing to accept high frequency of returns and low magnitudes. And they're unwilling to be to wait for returns that have a low frequency of returns but a high magnitude. And so we have that systematic discipline to just harvest that tendency for people to issue those type of returns. We can be positively skewed. And again, if you've done that construction really well, you really don't care when you get paid. You just know that when the wheels fall off the bus, it's going to work. I think we had a blog post, which was taller heads and fatter tails, basically they want taller heads.

They want more of the smaller thing in exchange for these fat or tan. I wanted you to say, oh, Warren Buffett's on the other side of that trade or like X, Y, Z. But a lot of these big insurance companies and all that are doing that all day, every day, right? That's like just a line item for them of like,

we make 7% a year selling this volatility. And if it crashes, so be it, we have this super long time horizon and what that. I think when you see things like the covered call phenomenon and the structured product writing, that that's clearly somebody who's on the other side as well if we're kind of naming, not naming names, but naming classes of people who would be on the other side.

Those are clearly distortive to this vol dynamic and we're very happy to be on the other side of that. As you mentioned that, do you think that's a blow-up problem? You think can that blow up or is it just going to peter out because people will eventually be like, I keep doing these structured products and the market's up 20 and I only make eight. Or the market was down 30 and I lost 30, right?

is the bigger problem with some of the buffered notes and whatnot. But just quick aside, what are your thoughts on all that stuff? Predicting a blow-ups hard. You need a few factors that to happen at the same time. But I think it's good dry tender for sure. You know, there's a lot of frustration. It begins with frustration. And then it begins with smaller returns.

And then that leads to smaller returns because people learn about this, you know, what appears to be a free money trap. And then there's a negative skew event and that creates fear, which leads to more capital being withdrawn from those strategies, which leads to, you know, this reflexive cycle of people getting stopped out of the trade. But that's one way it could end. And that's the quote unquote blow up. That's like in Valmageddon.

That's what we saw with the short Vicks exposure. That was February of 2018. There was a rush to the exits. That's great for our process, by the way. But it could just as easily end with a very long and frustrating period for something that used to work very well. And people rebalance, you know, calmly away from it. It doesn't always have to crescendo into a, if it did have to crescendo into a blowup, then what we do would be much easier, you know, because more blowups, less whimpers.

Things, things don't have to blow up. There has to be the right combination of leverage and overreliance on an outcome. And then you get that kind of sweet spot of a rush to the exits. It seems like people think of that the same way on that side, right? I'm like, hey, if I'm earning this income and reducing my beta, by doing it, let's increase both. Yes. Yeah, which seems like a problem to me, but I have trouble thinking of that philosophic of like if the problem's just you, everyone gets called out. Yeah.

Right. You're not going to have like a cascading liquidity cascade issue. And I think that's right. We have a chart that we use a lot, which is just showing rolling returns in equity markets, excess of cash going back a century. And right now we're in the 99th percentile of that observation. And you look at that and you go, wow, I should fade equities, right? Not necessarily, right? because we've been at this level of excess return.

So we look at 10, 15, 20-year periods. We've been at this kind of level of excess of cash returns over those kind of periods twice in the last century. Once was a tech bubble, okay, yeah, in that case, the correct policy would have been to run away from equities and probably hide under your desk. What we do would have been very great for that period. Conversely, the other time was a post-World War II reconstruction period,

and markets spent a decade crashing through all-time highs, you know, more or less without fail, without break. And so with a sample of two, there was exact opposite protocol was called for. So, you know, even the tech bubble, famously people say like it was overpriced in 94 and Aztec still went up 300% until 99 or whatever. Right, right.

And yeah, if you called the 1929 crash, well, 1928 was, you know, forget what that, you know, you had something like an 80% run in the 18, 15 months before the crash. And so all of these, you know, obvious rear view mirror points to jump off are not so obvious in the moment. But I think it goes beyond that. What it is is that just because things are at an extreme level or you've identified a pocket of leverage that you think is unsustainable, I think the U.S. fiscal situation has been deemed unsustainable for the better part of this market

cycle. And here we are. And so rather than take a directional bet, To us, it's about combining pro-cyclical and defensive bets with enough asymmetry where you can do both at the same time all the time. And then you're totally agnostic to whether you get a post-World War II or a tech bubble in this 99% observation. You shouldn't care. Right. I was go back. Like, how much value can we put in 100 years ago or even 20 years ago? And like to me, today the world, right, who was it, Eisenhower said, beware of the military industrial complex.

Like, beware of the financial industrial complex, right? All their job is night and day is to keep this thing moving, right? Like, there's trillions and trillions of dollars on that side to keep this thing moving versus a handful of us who benefit if and when there's a crash. So don't fight the tape. You know, there's tons of adages there. It's nearly impossible to predict these themes. I mean, I think January of 21 was a real watershed moment where we realized, you know, this theme of degeneracy actually has weight. You know, this GameStop phenomenon.

Oh, yeah, yeah. It's funny to muse on, but if you actually look at quant portfolios that favor, say, heavily shorted companies, there's been a structural change in the performance of those portfolios versus the previous 20 years. Like that event had a systemic change in the market. And then we've seen a manifestation of that through betting markets and Apollo market. Now they call them prediction markets. They're not even betting markets anymore. They serve up social utility.

We predict popes and game outcomes and Fed chairs through betting markets. And the volume that goes through these markets is non-trivial, right? And so now we have to incorporate into our models. What about a whole class of investors who are intentionally making uninformed bets. How do you model that in to a market cycle? I don't really know, but I don't inform, right? People are using prediction market data in their models that are informed bets. So yeah, you're using uninformed bets to inform bets. Right. Square that. Yeah. And so, you know, you can think through that and make a bet on a side of the equation. I don't, by the way of my bias is that

doesn't make the world less fragile. But ultimately, that's now a meaningful portion. That's a distortive force in the market that didn't used to be there. Will you guys look at using those for certain things? Like there's certain convexity plays in there, I think. Yeah, I think so. I've been doing to that, but I'd say the closest analog I can find in our markets would be something like a zero DTE, like a zero-day option. The volume there can be very different in its composition to say like a front month

VIX future. You don't really see too much retail flow in front-month-vix future, but you'd see a lot of more retail flow and something like a zero-day option. So we've actually, we have, we aren't necessarily trading that instrument. It'd be interesting if they're like, make me a market on,

there'll be a crash in the next one, two, three, five, whatever. Like you'll have a whole stretch of years. Yeah. And people, well, and then you see banks making markets and hedging those trades through the instrument they have at their disposal. So what we've seen is a lot more opportunities in short-dated equity. You know, three to five days. You don't have to be zero days. But there's these feedback loops through more gamma oriented trades, which is great. If we can own cheaper gamma, that's more mispriced. That's great for our process. So, you know, some of our more innovative sleeves in our portfolio are taking advantage of those phenomena through, you know, fixed downside long gamma trades. And that's great. You know, so. And how do you give you that the portfolio is like always building, right? Especially a long ball book is building.

So it's not just like, oh, we put on this trade every Tuesday. You can grab stuff at a discount. You can warehouse risks. How do you think about that and how does that work? Yeah, for a good long-vall process, first off, when you build a long-ball portfolio, you kind of have to be building a portfolio for things that have happened and haven't happened. That's a unique feature. Every other manager, I think, has the excuse in a major market event.

Well, that's never happened before. You can't really say that as a long-ball manager. You know, if 1987 happened tomorrow. Yeah, we're there for that at end, right? Yeah. So in that respect, we have to have a portion of the portfolio that's always on, and that's the hardest part of the portfolio because it tends to be the most expensive, meaning where rain or shine, we are protected, and that's a big part of our risk.

That's where most of the bleed's going to come from, the bleed that you'd observe. Then we have dynamic parts of the portfolio that are brave enough to market time. The only reason we have that bravery is because we have the portion that's always on. So you're going to have the dynamic. You can afford to be wrong, basically. Yeah. And then if you do those two things well, if you have a good long-only market timing mechanism, you have a good always-on mechanism. We believe we do. That's pretty minimal negative carry. Well, then you can do really exciting research where you're like, well, can we buy really short-dated gamma? That's super explosive. And do we have a signal or alpha approach to buying that at a cheap level at the market really underappreciates?

you know, and some of these signals that we've seen have been really fascinating. So we've been able to hold positive expectancy gamma, which is kind of the holy grail of hedging. I'll give you an example, you know, when these banks make the market for options, they have to sell options to the street. So if there's an exogenous catalyst, it could be a bad economic print or anything. And there's an overwhelming bid from the street to go buy options. Well, the banks will make that market. That's their job.

and they're going to sell those options. They may take on a net short profile to market, a negative gamma profile. They have to hedge that out pretty quickly. If you have a good mousetrap to identify when they take on that risk positioning, you can actually basically take advantage of a behavior you know they're going to have to do. Right. Now, doing that requires back to the GameStop thing.

If the market makers had to keep buying GameStap because they kept selling long out of the money calls to right, punters. Right. So if you have a good, if you have the right feeds and you have the right way to process that data, you can actually, with a really good degree of accuracy, predict when that's going to happen before it happens or as it's happening. And so you can actually get in front of that trade. And so that would be kind of, you know, you're saying how do we think about growing the portfolio is can we find

sub strategies like that that'll give us really punchy convexity? So we'll go in right there and buy a three days to expiry straddle. By the way, buying a straddle that expires in three days, usually not a good idea because it's going to be very expensive and the crisis usually doesn't happen. But if you do it with that kind of filter, then all of a sudden it turns into you get your money back 90% of the time and you had that gamma. And you're like, well, great. And now it's a really good trade. Sign me out. Yeah. So that's what, you know, when we do exciting research, it's really first off making those, I'd see the lettuce of our salad just better and better, meaning having the always on having that market timing piece good. And then, yeah, if we can overlay these kind of

really explosive gamma strategies or, you know, protecting ourselves against de-leveraging risk, that's where we get better and better. So every single day, we'll review, you know, series of different research projects that are looking at, I mean, just amazing techniques to try to win that game. It's fun. And that's still rooted in S&P exposure, U.S. stock market exposure? Like, could you be like, there's huge convexity by buying this uranium mine or something, right? Something like that. We're equity-centric, but we're not dogmatically equity only. So dynamic convexity as it exists today happens to be just equities, but that's not a risk constraint, meaning that some of our research is pulling us into things like CDS and may eventually

pull us into some rates and commodity fall. We're open-minded to it. I would be shocked if in a year or two years from now, we had zero non-equity long ball in there because there are just some really attractive lead lags. What we need to be mindful of, though, is not forgetting the mission. The mission here isn't to make money. The mission here is to protect an equity book. So we need to make sure that what we're adding isn't going to add pesky bleed to a phenomenon that isn't tied to equity markets. Because now the mission is to protect equity books. We can't say, oh, your equity insurance policy lost more money than we thought because we were, you know, trying to pursue convexity in commodities. Well, that's not an acceptable form of bleed for a portfolio.

We're not willing to take that basis risk that investor. So if we're going to do it, it has to be something that really has a direct economic loop to pro-cyclicality and being on the other side of that asymmetrically. But there are opportunities that I think will meet that criteria, and I expect that we'll grow into those areas. Selectively. From my seat, seeing a lot of these cross-asset ball, that's where people have done well over the last couple years instead of equity ball. Equity ball has been to flip that on its head of a lot of your, I don't know, maybe the institutional investors

aren't as concentrated, right? But a lot of portfolios have become more and more concentrated on AI stocks, NVIDIA, whatever. Like, how do you work that? And in theory, Navidia crashes the whole market's coming down. But that's also a basis, right? It could sell off and the S&P holds up rather well, or you have like a, right, your investors are kind of long or short the dispersion trade.

They kind of have this dispersion trade on they don't know and you're not necessarily hedging against that. So do you guys think about that or you're just saying, hey, we're looking to hedge a broad market to climb? But yeah, single name ball is a really interesting area of opportunity for us because, as you mentioned, there can be these idiosyncratic dumps in individual names where the implied ball really explodes there, but at the S&P level, it's just tame, right? And year to date is a decent example. I think some of these high flying names are down in the realm of 10 to 17 percent. You have a relatively flat index return, right? Yeah. And so it's a really good opportunity. It's dispersion

trades. It's great for dispersion trades. And we do that elsewhere in our in our platform. We do have some, we have a dispersion alpha portfolio. So we acknowledge that as, but I wouldn't call that a form of explicit long ball. Within our long ball program, we would be looking just alongside of that trade. Can we own some of these single names? I think the answer is yes. And what's really changed over the last few years, that wasn't true a few years ago, is just the amount of volume flowing through these single name balls. And so we can really do things. We can apply a lot of the models that we have successfully applied at the index level at the single stock level. And now, because it's a single stock, a lot of the fundamental indicators that we

have become much more relevant. So we can actually have a much more specific way to apply fundamental insights to whether or not a vol is cheap. So yeah, it's it's a it's an area of extremely active research for us and the increased volume just means that now we can do that at greater scale and greater impact to the portfolio. So I think single name is, you're not necessarily making it a dispersion. It seems like everyone else in the vol space is like oh, if I need single name, I need to pair it with short index vol, right, and have this dispersion trade on. It's like, well, Well, yeah, that's the siren song of long ball investing is that you can sell enough vol somewhere to mitigate your bleed.

And the issue with that is that it's, that's exactly the end. That's what we're trying to take advantage of is the belief that you can sell all the time and not lose money when the wheels fall off. And back to my thesis. Like at some point you just are on both sides of your own trade. Yeah. You're just trading against each other. Yeah, it's countermandate to do that.

So for us, we don't, we aren't really tempted. We're a long only fall in our long ball program for that reason. And the overwhelming temptation, the siren song that people give into to do that is part of what feeds the premium. Every single crisis, there will always be a long ball manager who loses a lot of money. One of them. It's like a truism of markets. And invariably, they had some sort of curve trade or dispersion-esque trade that didn't go their way. And it's exactly the dynamics you're talking about where, you know, I'm going to sell the sixth month.

fall contract. I'm going to buy the first month. I'm doing a ratio. That's always worked. Look at the back test. Yeah. Lo and behold, there's a snap election, six months in the future, and that contract moves way more than it should. And lo and behold, your convexity programs down when it needs, what it needed to be up. Yeah. C, Amaranth and natural gas spreads.

Right? Which is just, a lot of people just got hit on that this week. I want to finish with two different things. visuals and your Mount Rushmore of metaphor. So let's do the visuals first. So you guys and three things, and a little news bulletin at the end. So I've been to a few conferences with you,

seen you speak, seen you have some great slides. We don't really like to do slideshows on here, but if you have the ability there, just pull up a couple of your favorites. Sure. And we'll talk to them real quick. Show them to those on YouTube. if you're listening on Spotify, head on over to YouTube if you want to see him or we'll try and explain them as well.

So A, while you're pulling this up, who does this? You got a whole team there? Or like, how does it go from idea to looking like a cool chart? I do it old school. I write a picture down on Post-it and my team is amazing at this. I have a particularly talented kind of squad of two of us, and we crank these out. So it's a little factory. But I do it very old school.

I have an idea. Literally draw it. And then we get the data and then we create the slide. So it's a, but we'll make a couple of these. We got to get you some AI tools to spice up that drawing component. Exactly. But yeah, this is one of the, this is one of the more popular ones that I mentioned earlier. You know, you're just looking at basically periods that defying people's investing careers, 10, 15, 20 year horizons.

And right now we're at the 99th percentile of these rolling observations. And this is excess of cash, right? And this is the point that I was making earlier. You see in the tech bubble, we were here-ish. And we spent 15 years below high watermark and having a really tough time. Conversely, we got here in the late 50s as well, and we stayed there. for a decade plus, right? And the point of this slide is just to say, we're in a two-tailed distribution, and just because valuations are stretched, you know, and just because we're a long

ball manager doesn't mean that we're saying it's a really good time to embrace what we do and run away from the thing that's made you money to this point. It could very well be the case that we have a decade of exceptional equity returns. So we really recommend that people find a way to embrace the left and right tail at the same time. It could be argued, right? late 90s, Vol was increasing with equity prices too, right? Like at some point

it gets so crazy that Ball's increasing you got spot up ball up. Yeah, that's right. Yeah, and just going down the visual of Rushmore. Well, actually Rushmore

is a different question of yours, but... Yeah, give me the next one. This one is... Oh, yeah, I love this one. Make a lot of kind of, I'd say, mathematically-oriented slides. Sometimes just squiggles.

This one was genuinely a drawing, talking about the origin, but the different ways equities can lose money. And the different way you forego compounding, scenario one is the least frequent, but the most important to protect against the 87, the GFC, the COVID. This is really what we're primarily built for, because if you don't have something to save your tail here, your equity portfolio can forego, you know, decades of compounding.

Yeah, like lost decade, all that sort of thing. talk is what happens. Yeah, that's when it happens. The second most, like you're institutional investors, but if you're an individual or a family office and that's when you need the cash for something, right? That's a bigger problem, too. Yeah. So that's the most

important to protect against. It's the least frequent though. So you have to have this discipline. You have to have a way to do that all the time. The second one is the second most frustrating and impactful and that's going to be the slow bleed, the erosion of portfolio value. And this is where this slow

to which muscle that we talked about comes through. So direction. 22. Following 22 tech bubble. And then the mini crisis is one that very few people talk about. They don't talk about it because it's the hardest to protect against. We do, if I may say so, I think a uniquely good job of doing this, but this is the type of event where you gap down really quickly and you just don't come back right away.

And August of 2015 was this pesky Chinese deval that structurally changed the market. We gap down 10 and 12 percent stayed there. The U.S. debt downgrade is another one. You know, that was a big, you know, we ended up recovering in both cases. In the case of 2018, we actually saw another decline in Q4. Now nobody cares. We'd just be like government shut down for four months. Right.

No downgrade. Right. So now, you know, those three events are the most impactful. And then you have these V shapes. And the V shapes are the ones like the August 24, like the April of 2025, the tariff tantrum. And you're kind of ordered in frequency here and how often they happen? Yeah.

Well, in frequency, but also in terms of what's the most painful. Painful and important. Yeah. So this is going to be the V shape here is going to be the most psychologically damaging, but the least impactful alongside choppy markets, right? Because by definition, you've gotten back your value. So this is the type of thing where we're happy to run a process that, you know, has accrued meaningful gains through the trough of this chart.

But if it's going to sacrifice some returns in an immediate recovery, so in the case of the tariff tantrum, we literally had a 12% upside intraday reversal on the S&P. We're going to give back some gains. There's no world that we don't. Right. Now, you could have just, if you emptied the coffers every single day, meaning if you just monetized your hedging benefit every single day,

you don't have that phenomenon, but you also aren't protected in scenario one, right? Yeah. because you just have to keep rebuying at its prevailing level. It gets very expensive late in a crisis. So we make a choice, and that's our choice. And then these choppy range bond markets, this is where I'd say, you know,

systematic macro trend can do well here, depends on how it manifests. If it's too choppy, then it does poorly, but if it's kind of ebbs and flows, then it does great. But for us, I'd say, systematic macro, risk premium, alt-risk premium are the main ingredient here. So you have this kind of combination of strategies that tend to work well together,

convexity, directional macro and trend following, and then this kind of risk premium or systematic macro, that's the one, two, three punch of our platform for that reason. Because basically, our mission is pretty easy. Identify the periods in which equities disappoint you and build active risks that won't disappoint you. And if you do that well enough, then you should end up with a portfolio that compound better than equities by themselves. And do you think it's over, like probably I've said it before on this, probably, like there's an infinite number of paths to some S&P drawdown, but maybe not, right? They're going to somewhat resemble one of these.

Yeah, of course, the reason these are squiggles on a line and not specific historical events is because we don't overfit. We just try to say, well, if this shape of Christ happens, what do the off ramps look like and what gets elevated? You know, in the case of a chaotic decline, it makes sense that, you know, things like option premium would expand, so implied vault is a good place to be. In the case of a slow decline, it makes sense that you want an algorithm that will identify that trend and join it. So if you can break these down into their core parts, then you can build an engine to extract the other side of that. And realizing there'll be hundreds of paths within each of these looks, right? This is the... Yeah.

Yeah. And what's nice is no part of our process do we say, well, geez, what's the frequency of that? You know, we don't, we don't, there's no normative lens as to how often these things should happen or even how big they should be. It's more about building ingredients that do well if they occur. Yeah. And so markets just want to dance up into the right odd infinitum. We're at peace with that. We actually are because most of our clients are doing that too. Yeah. Yeah, exactly. So for us, you know, it's, it's really about building something that's a bar. that's not going to cost you while you're waiting for that to happen in the total. And the Stanford economy is booming because all those guys are long. Yeah. All right. And yeah. And then, you know, this chart is basically the top three things that you should be worried about if you're trying to maximize compounding, which is maximize diversification, which,

you know, uncorrelated, it's great. It's nice to perform randomly relative to equities. It's better to perform inversely relative to equities in terms of what you're adding to the portfolio. you have to do that in a capital-efficient way, going back to our earlier discussion. If you can't stack this on top of equities or if you need to chew up a lot of balance sheet to defend yourself, then it's just probably not going to be the right. You're going to forego too much beta to make the defense worth it.

So you need you very capital-efficient. And then if you do those two things well and only if, then you can rebalance along that journey. And I also like this paper because it's a little bit of an opportunity for people. to hop on, you know, read our white papers. But yeah, that's, you know, there's a lot more slides behind this, as you know. Yeah. Do you have the rebalancing one handy or no? We can skip that.

That's inside the convexity rebouncing. Yeah, the convexity rebalancing act. So, and this one was, this one was really fun to do. I mean, you can see the plethora of slides here that we go through with clients. It's, it could be a little dizzying, but let me just, here we go. So I'm sure you're going to. Okay. Yeah, there I do.

We just got there. So yeah, this is the Convexity Rebalancing Act, as you mentioned, which is we talked about calendar-based, which is time-based rebalancing, more path independent. And then we have threshold-based, which is the tempting one. Turns out to be much worse on average and even in the extremes. And then we combined the two, which is kind of interesting as well. But in the paper, we looked at basically thousands of permeate. of different types of programs categorized into these broad groups. And lo and behold, calendar-based was way more path independent.

The average outcome was way higher than it was for this chunkier threshold-based rebalancing. And I think most interestingly, higher floor. Yeah, the max of one is basically the minute of the other. You're pretty close to that. But also, if you look within the paper, we also go into all the different trials that we ran. And it turns out that whether you rebalance monthly, every two weeks, every four weeks, it didn't make a big difference. So you didn't have to get it exactly. It's always nice when you don't have to get it exactly right.

It usually means you have a pretty good model. Conversely, with threshold-based, it would be you would change one parameter a little bit, imperceptibly. It would have a massive consequence. Because you just missed the August, April 7th. Right. You were at 82x versus 83x. Right. And the purple actually has a funny story here.

So the purple was we presented the green and blue to a client of our. as a sovereign wealth client. And they said, this is great. We're still going to do threshold. And I said, okay, I'm only moderately offended. Why would you do that given this evidence? And their response was actually pretty insightful. They said, well, if the market's down 50 and you're up 80,

I don't care what data you show me. I'm doing it. I'm going to pull the plug. So I said, that makes a lot of sense, actually. So instead, let's do this. If you're going to send, send a little to borrow from my colleagues at then let's do a calendar-based program where every you know say every month or every you know quarter will knock these two things back into shape and then if you as a client want to pull

the plug just do that through redemption you know and so the purple combines both calendar and threshold programs um the bad news is you still are hyper path dependent the good news is your average outcome now looks a lot like the calendar yeah so now you can actually sin a little and you can actually have a decent likelihood that you'll do better than just doing the calendar base program if you nail it. So yeah, now you can appease the CIO and the board while adhering to some sort of, yeah, I'd say rigor in terms of the empirics. Love it. We'll go off the screen and I'll get you, thank you for those. But it's interesting. So it starts with the research. You're not coming up with the drawing and then make that and then

fitting the research, you're doing the research, and then what's a good visual to relay that information? Yeah, a lot of the times it's something that, you know, I think a lot of paper writing goes like this, you're trying to get across an idea in a meeting, and yet something that you know is true because you've done the, you've done the quantitative work behind it, you've done the, you've done the actual empirics behind it, and then you find it really difficult to express that in a way that gets across in the meeting. You're like, well, I never want that to happen again. Yeah, I need a better video. So instead of having a bad meeting, I'm going to have this exhibit that summarizes all this work.

And that's usually the impetus for every good chart that we've made. Moving on. You've used a couple of them already. The F1, I think you mentioned some salad earlier. So give me your Mount Rushmore of your favorite metaphors you use to describe the risk responders or basically everything you do. I think the F1 card is. to it, the best. Yes, you want to not just add good brakes, but you need to drive faster as well. That's got to be on the Mount Rushmore. I would say, yeah, you mentioned soup or salad. We wrote a paper in January of, I think it was 23, called convexy correlation and compounding. And within, we talked about the total portfolio approach, which is now ubiquitously discussed. And at the time, it was a bit more, I'd say niche. You know, the Enzi Super was doing it, Future Fund was doing it, but it wasn't really a tidal wave quite yet. Now I'd say it really is. But at the time, it was hard to explain to people what the total portfolio approach was. And so we use this metaphor of soup, portfolio soup versus portfolio salad.

And in a traditional strategic asset allocation, you effectively have this portfolio salad where you have a salad bowl, it's full, you have the lettuce of your salad, probably your equity beta, and you have all these accoutrement that you mix in. And every single bite you get is a vertical cross-section of that salad. And if you want to add something, you have to remove something else. and you try to get the right ratios to have the best tasting salad and being nutritious and you have all these goals. Great.

That's the side of growth. Conversely, the total portfolio approach is like a soup with a really big cauldron and this kind of base of liquid and all these ingredients. Well, the nice thing about soup is that you have this big bowl. You can add more liquid, like leverage. You can add different ingredients, which would be diversifying strategies or things that make that liquid taste better. But everything you add to the soup, it touches everything else.

So you only add things that make the whole thing taste better. But convexities like salt, you know, so there's two metaphors here, two metaphors for one. Convexities like salt, it doesn't taste very good on its own, but it makes everything else taste better. And so if you have the portfolio soup, you're much more likely to embrace salt because you're just going to sprinkle it in the soup. It's funny, one of my colleagues said that he puts salt in a salad, and I was like ready to report them to the FBI. But what we say is, yes, if you really think about it in the total portfolio context where you have one big cauldron of soup that you're constantly mixing, and you're adding some things. But ultimately, you know, when you add something, you don't have to take anything out. You know, it's this integration of exposures.

It's this stacking of capabilities. When you add salt to a soup, you do it because it makes everything else taste better. And how much is the right amount? Well, just keep adding until you get it right. You know, and so that metaphor helps us because I genuinely think the people who will embrace what we do the best come at it with a total portfolio mindset. One slurp. To me, you actually can go above this gets into leverage. Mixing metaphors, but in the salad, I can go above the bowl's edge.

The soup I can only get to the bowl's edge. But I get you. Some soup. Jim, right. All right. Stu, what else you got? So yeah, I'd say those two like the sumo wrestling metaphor is a common one as well that we used in recent paper, but it's just a reference to effectively difference between kinetic energy and potential energy in markets. And oftentimes, and this is just an non-over reliance on back tests when it comes to convexity, right?

You create a back test that looks unattractive, but then at the same time, you say you're trying to hedge something that hasn't happened yet. Right. And so you can't overly penalize what looks like a tough back test when you're really trying to protect something that hasn't happened in the future. And so the metaphor we use is that it's like two big sumo wrestlers deadlocked in a ring. And if you're an unknowledgeable observer, you might say, well, there's nothing going on. Right. If you're an knowledgeable observer, you say, well, this is two massive forces deadlocked in the doyo, which is the ring. They're deadlocked in this doyo. And I know eventually, because it's exhausting, to wrestle that it's going to break. I don't know how it's going to break. I don't know who's going to win, but I know there's going to be, it's going to be chaotic when it does. Okay, so I'm going to make a little bet

that that happens all the time. And by the way, most of the time it won't happen from second to second the match will, but eventually it will. I know it will. And it will reset into a new match. So the sumo wrestled, the metaphor is markets, right?

You should always be betting on the end of the match, even from the first second of the match, because sometimes the matches can last half a second. Sometimes they can last a couple of minutes. Did you go to some when you were in Japan? I did. Yeah, that was the impetus for the paper from my perspective. I actually had gone to a few doyos.

It's a really interesting experience. And you mentioned fast twitch, slow twitch. So we won't touch that one. What's your last one? You were recently in Germany, came up with the new one? Oh, yeah, that was great. That was a CBO conference in Munich. And it was Beer Fest.

So there was a classic 2006 movie called Beer Fest. and in it there's this famed beerstein, which is the glass boot, or Das Boot, is it called? Das Boot. Yeah. And the game is simply. You chug a beer full of boot as fast. A boot full of beer as quickly as you can.

And, well, the thing is, as you chug the boot, the boot's a weird shape, you get this bubble, this abscess of air forming in the toe. And then eventually that air bubble needs to transfer from the toe. to the main funnel. When it does, it creates this gurgling effect. You tend to get overwhelmed. Okay. That's a decent metaphor for markets, okay, or equity of all pricing, where people like now, you know, they'll see something in markets I think is overvalued or potentially bubbly. They see the bubble at the end of the boot. Yeah. And so they're chugging and seeing the bubble forming

and they could do a host of things to prepare today for that bubble to eventually reach the main funnel of the boot, but instead, their policies to just react when that bubble does manifest as a problem. The issue is, is that it may manifest in a way that's too overwhelming to react in the moment. And so if you don't do that preparatory work, if you don't have a plan, it's unlikely you're going to navigate that bubble very well. And beer all over your face? What's the outcome? Yeah, the outcome is, yeah, you fail to boot chug.

You end up underwater, in this case, quite literally, under beer. And you end up looking back on that period saying, well, geez, if I had done some sort of hedging activity, if I had a plan for the bubble, I probably would have had a better time, even if it slowed down my chug. Even if it was just drinking a little bit slower at that point in time, right? The angle of the boot? I got to try it now. Love it. Those are great. Keep coming up with some new ones.

You're probably going to get strained on finding new ones. But that's fun. Yeah, I mean, I think traveling helps because you just, you go. somewhere new in the world and you see some sort of interesting cultural unique item like sumo wrestling or like F1 cars right and you kind of see that these unique trade-offs they make when somebody's really exceptional at a craft or or something they usually make some really they make a choice that appears unintuitive and I think that we have a parallels to that in the long ball space it appears pretty unintuitive what we're doing day in and day out but when you see why

we do it it all of a sudden makes sense and so you try to find those moments in nature, and then they make great metaphors. Yeah, I've talked to advisors before, and they're like, oh, well, you don't see people just walking around with parachutes on at the airport. If the plane's going down, they hand out the parachutes and you're fine. Like, you know when the plane's going down. Like, A, no, they don't hand out the parachutes on the plane. I don't, yeah.

I hope they don't have to learn that one in real life, yeah. Yeah. Or they say the life best, right? You're like, not everyone's not walking around downtown waiting for a flood with a life fest on. Yeah. Well, what I would say is that, you know, you should listen to the announcement at the beginning of the flight because everybody puts on their own mask first. Yes.

Yeah. Love it. We'll finish. You shared some, you guys got some news to share. Yeah. It's fellow long ball space. Yeah, we put out an announcement in December and just confirmed this week, you know, kind of final signed agreement. But we're moving forward with acquisition.

of a European alternatives firm. The firm is part of LGT, which is kind of one of the premier Switzerland-based private equity firms. They have had a fantastic internal QIS hedge fund team that runs really similar strategies to the ones we run. They run a systematic long ball and a systematic risk premium or systematic macro strategy as well. So those two strategies, they just fit perfectly

into our view of the world. They're negatively or lowly correlated, very capital efficient, and they are very liquid to be rebalanced alongside what we want to do. So going back to all those exhibits, we think they're just going to make us better at all those things in a way that we can acquire a decade plus of experience and some great strategies, start finding ways to integrate that inside what we do. So, you know, a lot of what we do, just very simple is just like being a chef and you're reaching the pantry, the better ingredients you have, the better dishes you can make. If you have a good framework, the better soups you can make. You're not making dishes. You're making soups. And yeah, for us, it's, it really comes down to ingredients at the end of the day.

You know, for us, we're always on the lookout for new strategies, new research. But sometimes it makes sense to kind of make, I'd say, a more nonlinear jump. Bivers build. Yeah. Yeah. And to defining these ingredients that are well established, well proven, and overlaying them on top of what we do. Yeah, the main... Yeah, go on.

Will that open you up to outside hedge funds as well, inside of their portfolio? Or it's just their internal stuff? Just their internal stuff. So, yeah, we're going to continue to be 100% the investment manager for what we do across our platform. So no external hedge funds. And we're hiring that team, actually,

as full-time employees, we're even bringing on some partners to One River through it. Love it. Awesome. We know those guys. So that's a good fit. All right. We've taken up way too much of your time.

Thank you, Patrick. No, thank you for having me. Appreciate it. Keep up the good work. Keep trying to convince everyone this is a good idea because we believe it over on our side. One day, one day we'll be proven right. Amen.

Thank you so much for you. And if not, we've got the equity side. We're fine. There you go. Awesome. Thanks so much, Jeff. Appreciate it. You've been listening to the derivative.

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