The Derivative - Trading 80 Synthetic Markets with Trend, Carry & Skew: Jiro Fujisawa, Asset Management One USA

Starting point is 00:00:08 Welcome to the derivative by RCM alternatives. Send it. Hello there. Welcome back. You've found the derivative by RCM alternatives where I'm not going to tell you this time that there's a new website with a lot of cool stuff on it. Won't say a thing. Not even going to say the URL. You'll have to Google it.

Starting point is 00:00:31 Anyway, onto this pod where I sit down with Giro Fujisawa of Asset Management 1, talking his new cross-asset factor alpha strategy. It's a mouthful and we dig in. It's one of the most unique approaches I've heard sitting in this seat for sure. He's got 80 synthetic markets built out of real markets going long and short, run across three models in trend, carry, and skew. What? How do you do that? We're digging in.

Starting point is 00:00:59 Send it. All right, everyone. We're here with Jiro Fujisawa. Did I get that close to right? Hiro. You did. Yeah. You did, Jeff.

Starting point is 00:01:13 How are I'm good, thanks. Are you? Good. Good, thanks. When were we hanging out? In Austin, was it? Yeah, that's right. Talking Hedge.

Starting point is 00:01:24 Uh-huh. It was a good time. And then it looks, I'm in the middle of a, we might even lose power here. There's a huge thunderstorm roaming through Chicago. Okay. So I don't know if we can hear that rain coming down, but we haven't had rain in a while, so it's good. Looks nice and sunny where you are. I can see the darkness.

Starting point is 00:01:40 Yeah, yeah. Today's nice. Apparently it's going to start raining later this evening and the whole week. It looks a little wet. But so far, so good. Hold it up. And you live and work in New York or you live elsewhere? I do.

Starting point is 00:01:53 So right now I'm in the office, which is on Park Avenue, around 47th Street. I live about 10 minutes of a walk, so I can't complain about that. Live, breathe in the city. I sometimes wish I can live outside in the burbs, but I don't think I can handle all the management of a house. I think I've got enough of management on my performance. portfolios. And who would want to do that? You talk to some of these guys

Starting point is 00:02:19 do like hour and a half commute and whatnot. Oh, yeah. Brunel. I think a good number of my colleagues in the office, they live, especially when they have family, like kids, they live out, but very few in the city.

Starting point is 00:02:37 And then are you one of these that tries to get out of the city? Go to the Hamptons or whatnot for the when it gets hot and steamy in the middle of the city? summer yeah or just go abroad you know like I was in Japan just till last week I'm Japanese got my parents my family in in Japan so unfortunately there's no way around getting away from the heat it's pretty humid and really hot over there

Starting point is 00:03:01 so but you know it's it's nice to get out of the city and get refreshed come back and but yeah it's nice to be back as well were you born and raised in Japan or you so this is going to be a long story on its own but I was born in Japan but after six months I started moving around because of my dad's job he just worked for Japanese a securities firm and happened to move a lot so first place was in Jakarta Indonesia for four years and then Budapest Hungary for four years then London which was the longest I spent the better half of my childhood in London but

Starting point is 00:03:45 When I was in my sophomore year of high school, I moved, my parents moved back to Japan. So that was the first real time that I spent time in Japan. Yeah, so spent two years and then went back to London for university. And then thereafter, I started working in Japan. I joined Missouo Bank or Missile Corporate Bank back then in 2013. And then I've been just going back and forth between Tokyo and New York across different functions. and here I am since in New York since 2021 March. Nice.

Starting point is 00:04:20 So when you went back to Japan where you kind of, you were trapped between two cultures, you were kind of considered an outsider, but you were like, hold on, I'm from here. Yeah. So I always, it's, I'm always an alien wherever I go. I'm like, yeah, in a sense, homeless. But I also, you know, I sometimes consider myself homeful, like I'm home wherever I am, as long as, you know, there's some basic infrastructure. and, you know, I can speak the common language.

Starting point is 00:04:48 I'm not really picky. I'm a pretty simple guy in that respect. So, you know, once I go back to Japan, it's like, hey, you're that kind of outsider as Japanese-ish person. But I sometimes sort of, or some people get a surprise by how much I, how much Japanese I speak, despite me not really living there. Yeah. And the opposite, or the same sort of happens here, where I'm kind of, you know, speaking in English, but I'm Japanese, kind of, I guess a lot of people in New York are like that.

Starting point is 00:05:20 So in that sense, this place also feels kind of like home. Yeah. And that was all where you were just there, Missouho's Tokyo, or it's all over, I'm sure, in Japan. But when you go back, you're in Tokyo? Yeah, in Tokyo. That's where the office is, my family. But I do, I did this time around travel a little, went to Osaka, where they had the, Universal Studio. My kids

Starting point is 00:05:45 wanted to see the Super Nintendo world, which was really good. I say my kids, but me included. I actually went to that, the new one in Orlando what was that, three, four months ago. And that was super cool. I remember,

Starting point is 00:06:01 I was a huge fan as a kid. I don't know if you're a New York Times crossword guy, but yes, the Sunday crossword was a Mario had like the tubes and the little question mark where you could jump up. It was a cool little theme. But that was cool.

Starting point is 00:06:15 You feel like you're in the world for a quick second. It was surreal. Like, it was really well built. So, yeah. I didn't know they got built one in Osaka. Yeah, they did. And I think it's like their fifth year. They had a fifth year anniversary when we went there.

Starting point is 00:06:32 Nice. All right. And then where you went to school in London? Or where did you go to school? And university. I went to a university called Imperial College. So I got my master's of engineering degree there. I was a mechanical engineer.

Starting point is 00:06:49 I liked the maths and sciences, especially physics, as a high school student or even middle school. So I thought, you know, I wanted to study more, but not be a scientist, but be a little bit more pragmatic. So the apply application of science, which is engineering. And so that's where I ended up being. But then, you know, when I started off or after I graduated, my dad and also my brother being in the finance industry, I did my internship at a bank. And so I sort of I was interested in the application of sort of the numerical world in or statistical slash numerical. world in finance. And so I wanted to join a firm that offered some sort of quant investment capacity. And I found my job at Mizuho in Boston, actually. They have a huge careers fair once

Starting point is 00:07:52 a year in Boston where they hired Japanese English bilinguals. And that's where I got my job. I want to see your like globe with all the pins on it. A lot of pins. Yeah. It'll be a messy sort of a ball of yarns going back and forth. Yeah. And it always amazes me. I should one day go back of all the podcast guests, like, way over 50% I think have some sort of engineering background, especially with the quant people.

Starting point is 00:08:17 So it's always amazing to me. Like what, and it sounds like you didn't actually know, you were going to be mechanical engineer and then decided to be quant, which seems like what's your theory on that? The people just see the world as a model that can be constructed and deconstructed and put back together. and finance, why not?

Starting point is 00:08:34 It's just another one of those pieces of the world. I mean, I can probably spin a story all in hindsight, but, you know, point in time speaking, I joined mechanical engineering or joined the program thinking I want to be a pilot. I want to build or even build a plane or something. But, you know, as you do thermodynamics and also do some plastics, sort of materials that of studies as well, and you have some stats, you get all these elements that are completely,

Starting point is 00:09:03 sort of different in its own world. They all come together to be mechanical engineering, but there's many features within it. And so I think I wasn't really thinking, oh, you know, I want to be in finance at the point that I started college. If I had, I'd probably go into some, or I may have gone into a different field.

Starting point is 00:09:23 Some quant, something or other. Exactly. But as I sort of, you know, went through college and then the internship that I mentioned, earlier, I thought, okay, maybe this is a little bit more exciting than, you know, building a wind turbine or doing something else. Yeah.

Starting point is 00:09:43 Especially back then, you know, a lot of my classmates, they went to Derby or somewhere a bit more rural in, that's where all the factories are. And, you know, even if some my mates or friends also, they went to the F1 industry, they're an engineer there. Well, that sounds cool, though. That does. Only now I start to like or I watch Formula One. So it's kind of nice to see some or I don't see them on TV obviously, but you know,

Starting point is 00:10:16 or maybe one day I might. But it's nice to sort of see that connection that I, that I have. Yeah. And then did you come to a point or have you? Do you feel like finance in the quant world is a closed system that you can solve? Or did you come to the point where you're like, it's unsolvable and we just want to get as close an approximation and work on the percentages and things like that? That seems to be where some of the engineer quant connection breaks of, is it solvable or not? I think it's not solvable for very many reasons.

Starting point is 00:10:51 But I think in the world of sort of tangible, real physical engineering, you know, you can run experiments in a real. controlled environment. Whereas in finance, you can sort of, or in quant finance, you can sort of simulate in backtest, but that's not the real world that we want to apply into. The real world that we want to apply into is in the future. But, you know, in engineering or in science, you can sort of replicate what you expect to happen in the future in a closed environment. So I think that's a huge difference. And also, I guess, data availability is, different or some sort of homogeneous set of or well-behaving data set is is something that we lack in in finance whereas we do in many cases in in the sort of physical world and that's like any

Starting point is 00:11:47 physics classroom around the world if you put the right ingredients and the right amounts in this experiment it's going to come out the same and any point in time the same students doing something in the markets is going to get a completely different yeah and i i think that way I'm obviously probably really underestimating the sort of complexity and I'm sure people working in, you know, in the engineering sort of sector would probably also argue that, you know, not everything is simulatable or replicable in real life. But, you know, as I see it from where I stand right now, I think it's a little bit more. There's much more unknowns in finance. Yeah. We used to work with a guy who did up.

Starting point is 00:12:31 estimated the nuclear contamination in the groundwater of Pigeon Forge, Tennessee, or somewhere like that, where they had tested and built nuclear warheads. So that's right. You can't actually measure it. It's underground. So it was sampling and little pieces. So he would probably argue to that latter point. Yeah, like, even in the physical world, you have to use a lot of estimation and processes. While I mentioned, I was just arguing this with your kids. I don't know if they've done it, but like you have the classroom, hey, we're going to do a stock picking contest or whatnot. They drive me crazy. I'm like, this isn't teaching them anything.

Starting point is 00:13:09 It's just like random luck who picks. Like, first of all, if you want to win it, you should just pick the highest beta stock probably and hope that it goes up, right? So anyway, I went off tangent there. But if you have any thoughts on how to win the school class stock picking contest, let us have it. Design a model. That's a difficult one. If I knew, I would probably do it on my PA.

Starting point is 00:13:29 Exactly, because it's like what's going to happen over the next, and it's such a finite time. It's teaching them bad lessons. So you mentioned Mizzuho. You technically work for asset management one, correct? So tie that whole picture together for us, how that all came to be, and what's happening there? Sure. So it's a little bit of a complicated or a different animal or the whole char system in a Japanese large institution is quite different. from the standard practice over here.

Starting point is 00:14:10 But there's a lot of transfers in Japan. So even if you join a firm, you tend to get rotated. Or at least that was the sort of traditional way of incubating or building in-house capabilities in a Japanese firm. But by your own choosing or they would just take you and say, now you're in this department? Kind of both. Yeah. It's a mixed sort of system.

Starting point is 00:14:36 But when I joined, I specifically joined a under the terms that a stay within the sort of asset management division. And I had my specific goals and things that I wanted to do. And I thought I, and I think that I'm good at, which is kind of in the, in the space that I'm at. So when I actually joined Minnesota Bank, I was first sent to a sort of funds of funds slash gate peak. gatekeeping division. So I was looking at global macro players like the Brevin Howard's of the world and, you know, millennium and usually the blue chip names of hedge funds. And I was advising Japanese institutional investors mainly in the pension space as to how much or what they should invest in and how much, et cetera, et cetera. Were any imposter syndrome of like, why am I in between

Starting point is 00:15:35 these billion dollar transactions. As a gatekeeper, I'm just getting used to this stuff. Since I was kind of like first role, I didn't really, you know, have really hard expectations as to what is the standard. So it was a good experience. Love it. And then just to back up, what's the company line?

Starting point is 00:16:00 The financial group has various arms from banking, It covers retail and also wholesale. It also has securities, kind of the investment bank division. It also has a trust bank. And before, they used to have many small subsidiaries that have been doing asset management, i.e. running external capital and doing money management. Now, because we had multiple firms, we merge all that into one big entity. in 2017 and that's where asset management won.

Starting point is 00:16:39 That was when when it was born. So asset management one where I work for whom I work is a company owned by Missouho Bank and Daichi Life. So we've got two parents, Mizzoho and Daichi Life. When you were allowed to do that as a bank. Yeah, yeah, exactly. And then at that point, we sort of decided that we want to run external capital. and then, you know, 2010-ish risk-premia, one point came about, and then 2.0 later on, and QIS recently.

Starting point is 00:17:12 Whatever, you know, there's a lot of names to what we do. I don't know, just quant. Let's keep it simple. Risk premium. And then so you're actually in there, or over your career, been in there coding, creating various risk premium models, various QIS. So I started this particular role, 20s. 17, I initially started as a training here. I did program back in uni days.

Starting point is 00:17:40 I did Matlab and R, but not in Python. So I to sort of, you know, not relearned, but sort of transfer my sort of language from those languages to Python. Develop my own strategy, get grilled at the investment committee, and, you know, come up with some prototype. You let it run. on no capital. So we typically, once we have a strategy developed,

Starting point is 00:18:09 we tend to have a sort of dry run period of a few months. And then we would put some capital allocation and then gradually increase as if it makes sense. And then are these are all supposed to be or designed to be betas, for lack of a better word, right? They're just known risk premium. Or maybe they're a little bit only known to you risk premium. I think it's a mixed bag of both.

Starting point is 00:18:38 I think arguably it's very rare to find any sort of groundbreaking, revolutionary idea that prints money and is different from everyone else. I think there's various reasons as to why that is a case. But firstly, by definition of a quant, it has to be statistically significant. So whatever phenomena that you're trying to capture, you need it to be, it needs to be significant. You can't just, you know, try to capture a really short-term thing in size and scale.

Starting point is 00:19:15 So I think a lot of the times we do source our ideas, or at least more on the sort of traditional factors from academia world. So it's much more well-covered, discovered. usually that comes with some intuition, which is something we always like to have. There's a paper of buy on Tuesday morning, sell on Wednesday evening. And that just was the whole concept. You'd be like, well, what's why? Yeah.

Starting point is 00:19:46 So you're against that. You want to know why. Yeah. Sometimes it's a little easier to explain than some other times. It may be a little bit more heavy on the empirical evidence as opposed to more fundamental. until, oh, you know, these kind of flows are driven by these kind of retail flows or whatever. It's not always fully explainable, but it at least needs to be intuitive. We can't just be looking at data and trying to data mine it and come up with a beautiful

Starting point is 00:20:18 back test of a Sharma 3 or whatever. And then give us like five examples. Carry. Carry value. trend maybe not really screaming but like what we should have some defensive strategies it's shortfall I guess that's kind of an extension of

Starting point is 00:20:39 carry but different from like a curve carry but yeah those would be the typical sort of traditional factors but then that we do also sorry go ahead I was going to say we also do have a little bit more of a niche or if you like a bucket which we call market in balance, which is a sort of omnibus category for various things that we, the premium that we try to harvest by providing liquidity to the markets and getting that premium out of it. It can be like a, like a really well discovered, covered one is, you know, congestion. There's certain flow patterns

Starting point is 00:21:15 or dynamics in markets that we try to provide liquidity and gain money out of it. And is that as the bank can provide that liquidity or any investor could access that. premium and provide that liquid. I think any investor can access it. It's just whomever can provide that liquidity, at least theoretically gets that premium. So it is a, it is risk premium in that sense. If that flow or if that thesis doesn't hold, then you, you, that is the risk that you hold. And then talk for a minute. There's been some papers. I mention this all the time. I should go actually get the actual name of the paper, but write that one. risk premium is published, its efficacy declines quite a bit, right? Once it's like out there in

Starting point is 00:22:12 the public and all the investors are using it, have you guys seen that in practice? Do you have a number on it? Like, what, is it still good enough, even though we know it's not going to be like what was in the paper? Like, how do you think about all that? I think it differs from factor to factor. Some factors that rely on, especially on certain flows where, you know, maybe if everyone piles in on that flow, then you might have certain market concentrations that sort of essentially killed that premium that used to be available. But in other cases where, you know, maybe in certain commodities markets where you know that the impact of that isn't as large as the underlying sort of flow mechanism then the impact is marginal and shouldn't impact the the efficacy of

Starting point is 00:23:07 that factor and I think it's difficult to say that okay because a lot of players are piling on the same bed risk premium has a sort of life expectancy of X number of years. I think the really fundamental risk-pringia strategies should work in the long run, but I think it's also about parameterization. You don't want to be sort of always tweaking it, but I think the design of a strategy should be able to take an account of these adaptations in the market. So I think that's what we really value in our research process. So sort of tying that into what we talked about earlier, there's very little

Starting point is 00:23:55 groundbreaking ideas. But I think the importance or a lot of value comes into the detail of the construction. We just call them implementation in our shop, but I think that's the key in

Starting point is 00:24:11 delivering a good strategy within the risk premium space. And something as simple as like execution algos or speed of execution, things like that could be part of the game. Agreed. Agreed. And then, so it's always funny to me, we talk about, well, there's five, ten, but then how many are actually on the platform?

Starting point is 00:24:31 Like hundreds? Is that just tweaks or different parameter sets of known factors? If I had to put like a number on it, I'd say maybe 40, 50, purely different ideas. And then there'll be hundreds of different variations for different purposes. I think that's the sort of ballpark. range of things. If I run like a PCA analysis on that, I'm going to come up with like 50 unique bet, maybe. Maybe. So the, I think the sort of ex ante and ex post worlds are two separate things. Conceptually, one risk premium strategy may be completely different from another,

Starting point is 00:25:14 but at the end of the day, they kind of look similar. If you, I don't know, run a correlation on on those strategies versus the SalkGen CTA index, you might get like 0.6, 0.7 sort of correlation. So in that sort of long run, maybe the correlation structure may be quite similar, but that's kind of looking at the longer-term trend or the longer-term behavior and the similarities. But I think when you're looking at your PNL day-to-day

Starting point is 00:25:43 and the positions, in certain markets, the slight difference could make a huge difference in the sort of end return of if you compare different strategies over the same period. And then do you feel like investors, to me it's like what happened to Cable, right? Cable, we had all the channels bundled together. I'm getting one nice product. And then everyone's like, no, I don't want the Hallmark channel. I don't want this.

Starting point is 00:26:11 And unbundled. Now I can get all these different pieces. So it's basically what QIS or risk premium is, right? The investors can get just the pieces they want. they don't need the Hallmark channel. Or I'll flip that the people who love the Hallmark channel. You don't have to have ESPN, but I'm assuming most of our listeners want ESPN

Starting point is 00:26:27 and don't want the Hallmark channel, which might be put a note in the comments. Let me know if you want that Hallmark Channel. Anyway, in the cable space, now we've seen it kind of re-bundling. Like, people got too much choice and they didn't want to have to manage all these different streams. And now they're like, can't you just put this back together to me?

Starting point is 00:26:44 So any risk of, or maybe an opportunity there, of like, are people trying to manage this or how do they manage all those choices? Are they starting to say, like, can you just bundle these ones I want for me? Yeah. I think the key is in having the flexibility to provide both. So they can choose to choose or not to choose.

Starting point is 00:27:07 Typically, when we have conversations with certain investors, we would sort of start out by, you know, having a sort of default basket of, strategies, one sort of portfolio, if you like. And then certain investors, we would sort of go into the details of what goes into that portfolio. And then maybe some investors may already have trend so that they don't want any more trend.

Starting point is 00:27:37 So if I weed that out, then, okay, that directional component within the portfolio is gone. So how do I sort of make that basket a little bit more well-rounded between the different kinds of strategies. So I think if certain investors may want to just have, just end the conversation there, but others who want to have that sort of piecewise offering, we can, you know, sort of have a conversation that goes more into detail of the specific factors or strategies that go into it. And then do you see investors more, are they doing risk premium instead of a multi-strat hedge fund

Starting point is 00:28:17 or whatnot? in concert with. I'm like, okay, right, the multistrats are hiring a bunch of different PMs. They're doing a bunch of different risk premia. There's a center book. They're weighing it all. Or a pod shop, if you want to call it a pod shop. But they're basically using all those risk premium and balancing it internally,

Starting point is 00:28:33 giving you one return. What are these investors after? That same thing. They want to build their own multistrat, essentially? I see both. I think some people might just want the sort of, you know, sudden the multi-strategies have already some stat arb or some sort of quant element into it. And then they actually want some additional exposure that's complementary to what they already have,

Starting point is 00:29:05 which is where the QIS slash risk premium stuff comes in. We're sort of, we're not a hedge fund that sort of is set up in such a way that we have multiple PMs separated by, you know, separate different capital allocations and sort of competing PM by PM to get the best single portfolio where the approach we take is a little more collegial and more sort of institutional approach where you know we manage one book one book different ideas different factors or strategies that go into one single portfolio but it's more homogeneous if you like yeah We use the same building block, exactly.

Starting point is 00:29:50 Same building block, but different mix and match for different clients. And then my last question, maybe, but I'll say it's my last question on the risk premium. Say I wanted to build these all myself

Starting point is 00:30:02 using Claude code or whatnot. Where does that lead me? You think it is achievable or I end up with just an implementation problem? Right. Like I feel like that's the people are going to start to say, well, I don't even need these banks now.

Starting point is 00:30:16 I can just build this risk premium myself. Right. It does a good job in trying to get the overall gist of things, but I think kind of repeating what I mentioned earlier, the devil's in the detail. Those details Claude may not, or any sort of AI bought may not be able to capture. And that's where exactly things blow up in your face when things don't go right. So it becomes an issue of accountability. Do you, want to risk your capital in doing it in taking that approach that's probably where where the cost of relying relying on on a AI agent yeah what's a bet on is the public knowledge of everything that could go wrong public or the secrets of what can go wrong internal to a group like you right that hasn't been published out there but has been fixed and corrected and that and right you know where the pitfalls are. And maybe didn't publicize all that.

Starting point is 00:31:21 Right. Interesting to think about. All right, we're going to rename the pod, Barry the lead, because you started a new strategy, and now we're just getting to it 30 minutes later. Inside Asset Management 1, Cross Factor Asset Strategy, the new strategy, give us the 30,000-foot view of what you're doing with that, and then we'll dig in some more.

Starting point is 00:31:52 Sure. So it's actually called Cross-Asset Factor Alpha. You missed EA at the end. Yes. We'll call it CAFA. CAFA. So sort of rewining, setting the background, as a management one, we've been running a suite of products that invest in risk factors. What I mean by that is instead of investing in ES or TY or G.C. or specific markets, we sort of try to decompose

Starting point is 00:32:25 the investment universe into the different components of risks. And those are called risk factors. So each risk factor is a basket of assets. It's a long, short combination. And it changes over time. Not every day. We try to stabilize it, but it's essentially a basket of assets. So we try to trade those baskets. And we've been doing this for a long time. But there were certain constraints in some of the existing products where, you know, they, these funds had to be long biased so that we're not overly short at any point in time just by the sort of nature of the client demand at that point in time. Now, while that was, while we have been doing that on the other side of the pond in New York, we've been sort of doing the, you know, quant CTA type strategies where, you know,

Starting point is 00:33:22 we're long and short, we try to be market neutral. And so we sort of combine the two worlds of this risk factor investment and the sort of long, short, CTA type approach and came up with the new concept, which is what CAFA is. So we look at an investment universe of about 80 different futures market. we tried to decompose that investment universe into a finite set of risk factors, and then we invest in these risk factors in a long, short manner. How do we do that? We look at various risk premiums, risk premium ranging from trend, looking at skewness, and also carry. So what we try to achieve in doing this is firstly we get orthogonal bets by the fact that we're

Starting point is 00:34:20 investing in these synthetic assets which are by construction orthogonal to each other when we do the risk factor extraction we design in such a way that we're extracting orthogonal factors and then the second layer of diversification benefit that we get is that we're taking multi-factor approach. We're not just doing a trend play or just doing a carry play. We're taking multiple approaches. All right, a lot to unpack. I'll start with the risk factor.

Starting point is 00:34:58 Do you have examples of some of the risk factors? Like liquidity or give me a couple examples of different risk factors. We try not to put names to it. We try to stay a little bit more statistical at that point. we don't want to sort of inject our own view as to what is driving markets, especially when it comes to a sort of wide range of vases that, you know, the 80 different markets, we look into ranges from equities to bond futures to effects and commodities.

Starting point is 00:35:28 And especially commodities, you know, there's a whole wide range of things. So we want to keep, we want to remove some, any sort of bias. So typically, you know, PC1 would be like the growth. sort of factor if you look at general investments. And how many are there again? Risk factors?

Starting point is 00:35:49 So we would go up to about 80. Same as the breadth of the markets. Obviously, you got to change that because that really confused me in Austin and we'll see it. I see. It'd be better if we had

Starting point is 00:36:01 100 risk factors in 60 markets. Then we could keep those. But it's so it's 80 risk factors, 80 market. Yeah. Which I think that's a little bit more one to one right it's yeah yeah and so that risk fact call it growth and I'm just for simplicity

Starting point is 00:36:19 sake and to call one liquidity but you're saying it's just a statistical like we were talking about before I'm going to run my analysis and there's 80 distinct factors here and that's before putting them into the synthetic market so that's after the synthetic market so these are the synthetic asset. So let's say the first factor, risk factor, is growth. Growth is, you know, maybe it's long equities and short bonds and many, it'll have 80 different positions across the different markets. That is the synthetic asset, synthetic asset number one. So each risk factor is a synthetic acid. Ah, that's why it's 80.

Starting point is 00:37:01 They're the same thing. Just kind of viewed separate. Correct. Viewed at a different angle. Okay. So then you have all 80 are in each bucket, all 80 markets? Yep.

Starting point is 00:37:14 That's crazy. And so, God, so in bucket one for growth, maybe I'm long stock, short bonds, short gold, yada, yada, yada, long crude oil, something like that. Then factor 71, I might be a different, mix of those, or it will be a different mix of those,

Starting point is 00:37:31 right? Short equities. So at the end of the day, is this all 80 are working together? Is it weighted amongst the 80 or are they just all offset in some way and whatever you're left with the net is the position of the portfolio?

Starting point is 00:37:44 So when I look at the portfolio, there's three different buckets, three major different, three different major buckets, a trend carry in skunis. So if I just start with a trend, the trend bucket looks at the synthetic assets and says which one should I buy and which one should I sell and how much. So

Starting point is 00:38:06 trend may say okay synthetic synthetic acid number one looks trendy in the positive way. So I'll buy that and then you know synthetic asset number three is trending down so I'm going to short that and do the same for all 80 synthetic acids and then you have that trend bucket. The trend bucket, you know, technically has a little bit of a different sort of trend measure within it, but we'll sort of leave that aside for now. And so that different question. So each of the synthetic markets, you're building a record of its price movement? Correct.

Starting point is 00:38:47 Yeah. So then all three of those models, trend carry, skew are going on top of the synthetic price action. And then I can see, okay, this synthetic one is trending up, synthetic three is trending down. That's right. And so sticking with the trend, well, can it be 80 long trend? Zero short or just in practice? No, it's always going to be somewhere around 50-50. So we do apply a constraint at the end to make sure that we're not overly betting on any synthetic acid.

Starting point is 00:39:19 Or also right at the end, we also apply a market-level constraint where, you know, we sort of transpose from the risk factor. or synthetic asset space into back in the real world specific contract base, we also don't want to pile on to any sort of single any single market. So there's multiple levels of

Starting point is 00:39:43 caps that we apply, but even at the sort of synthetic asset level, we try not to take on too much directionality, but specifically for trend because it's a directional strategy by nature we do. We could be long biased,

Starting point is 00:39:59 or short bias in any point of time. Yeah. And depending on each of those models. And then is it hard, it's hard to explain, right? It's like how to say, not even how it works, but I understand how it works,

Starting point is 00:40:14 but now moving forward of like, okay, what am I rooting for? Like, it's hard, a trend is hard enough to explain what you're rooting for to people. But now you're like, well, it's, the synthetic is long short,

Starting point is 00:40:24 these different things, and we're rooting on that. Yeah, I think that's what sort of, it's difficult to explain, but because it's difficult to explain, it sort of captures a whole new dimension of price, price actions. And we're not trying to overcomplicate things to confuse people, obviously, or to sort of just for the sake of making something new. Because statistically, or at least each process that we apply goes back to our philosophy that has to be

Starting point is 00:40:56 intuitive. So as a whole, it may be complicated or a little bit difficult to explain, but every decision that we make ultimately leads to us, or we believe that it allows us to achieve much better orthognality and stability in the long run. Right. And that's for my friend George, orthogonal is just basically non-correlating, right? Exactly. Yeah. Yeah, a bunch of unique bets. But so it's really diversification on steroids, right? Because each of these you've completely eliminated single markets. So

Starting point is 00:41:33 now these synthetic markets, each of that is a more diversified version of the underlying what's inside of it, right? So each of those buckets have less volatility than for sure, right, than the sum of the markets inside of it? The volatility of each component,

Starting point is 00:41:51 it's uniform. I, obviously within the portfolio, you can allocate more vol or more risk than others. But each unit, when we look at these, each... Steve Ball, weight them to be equal. Or when we look at each individual

Starting point is 00:42:09 synthetic acid, we sort of compare it on a sort of equal level. But when we, how much we include it, really it's driven by the appeal appealingness from each, you know, trend, carry

Starting point is 00:42:25 or skew lens. And then those trend carries skewer are equal third exposure and then each of the 80 synthetics is equal so that would vary risk weighted equally

Starting point is 00:42:43 or if you're like model 17 has been killing it for the last year we're going to overweight model 17 or synthetic 17 so if I take the trend model it would look at synthetic asset one two

Starting point is 00:42:59 say 80 the trend model will allocate more to asset number one synthetic asset number one versus synthetic asset number two so it won't always have the same vol or same allocation

Starting point is 00:43:14 it's more trendy it's agnostic exactly okay and we don't have any so it's like prior view and then so we mentioned these so the

Starting point is 00:43:24 the 80 synthetics change over time over time it does yeah How often is that? So we observe every day and we adapt. We trade on it every day. But it doesn't mean that we're flipping around the positions every single day.

Starting point is 00:43:43 It's gradual. We look at a long enough period, not too long, but not too short set of time horizon to be adaptive, but also not be too sticky. So, you know, if there's a change. in the market structure, we would be able to capture that, but we would incrementally change that day by day. And something like, I'm

Starting point is 00:44:10 thinking back to like Swiss Frank, deep pegged or something, if you had that in the portfolio, that would be something that would, like now that VAL is 10x what it used to be. Right. There's time for a change. So that's when, you know, those kind of scenarios,

Starting point is 00:44:22 I think it's very difficult for a statistical model to adapt to. And that's where I think our job as a portfolio manager step in to make that assessment. We're fully systematic. We don't want to impose any of our judgments within our portfolio. Obviously, there are at the stage of designing a portfolio or even a strategy. There's a human being is building it.

Starting point is 00:44:51 So there's definitely some human input. But once we built a model, we don't override any decision unless, there's some extreme market event and we have to step in from purely from a risk management standpoint. And I think that sort of, you know, Swiss franc deep pecking or even a little bit more recent, the rubble sort of liquidity drying up, that those kind of events we would intervene. And we don't want to be stuck with the positions that we can't get out of or can't get out with a reasonable sort of spread. So only in those times we would intervene and step in and make changes. But we don't tell you lightly.

Starting point is 00:45:37 And it's not a simple exercise, especially when you have a lot of models that contribute to whatever positions that you have. If you just blindly just rip out that position, you may be exposed. If you're doing some sort of relative value play on that position versus another, you're sort of susceptible or expose yourself. to a certain beta risk or some direction. It essentially changes all 80 synthetic markets, all 80. But to me, you've built the perfect thing to not tinker or have opinions on that, because I don't even know what, right, if synthetic number 14 goes long, like, I don't know which markets are actually in there, so I don't have to have the normal systematic manager thing

Starting point is 00:46:20 of like, why are we going long oil here? This is just going to reverse as soon as they make a fake piece deal or whatever. So you've solved that problem. What's next? Will you go to 100 synthetic or what's in the research pipeline? No, I think just creating more new, I think one obvious avenue of research is, are there more factors, not risk factor, but, you know, alongside Transkew, carry. Is there anything else that's sound that we can sort of add into that mix if we were to specifically talk about Kafa? but outside of the Kaffa world, there are other initiatives, other research topics. But I think for Kafa, we definitely don't want to sort of increase more synthetic assets.

Starting point is 00:47:08 If you start trying, if you start trying to expand on that more, you sort of incur this instability. And also you're sort of adding on a lot more noise than value. So I think we're happy with where we are in the synthetic assets. And then how did, which did we cover before? Like, how did you arrive on the 80 in the first place? Like that was, you ran the component analysis and came out to 80. So it's more to do more a sort of mathematical exercise of how much breadth can you get with n number of assets. usually that sort of

Starting point is 00:47:49 if you go way beyond so if we have 80 different markets if you try to extract 200 or you know something ridiculous then you're picking up a lot of noise and you know just trying not really extracting anything

Starting point is 00:48:05 law of diminishing returns essentially yeah so I think 80 was its limit but you also don't want to just pile on to like two or three different things I think I'll see you here in Chicago in October right you're coming to the conference

Starting point is 00:48:31 I believe so I'll have to check with with Mark on that but it'll be nice to do a trip there for sure any last thoughts want to leave us no I think you know we've sort of it was a great pleasure to be on this thanks again Jeff for having me we're I think Quant in general has had a a tough beating over the few years. Finally, it's becoming a sort of field where people have started to revisit. And even though the stock market has been very bullish, despite what it's all, what's going on, you know, I think it's people sort of started to look at if and when markets turn around. How do we sort of diversify our portfolio out of sort of the PEs and the MaxSel?

Starting point is 00:49:24 or AI stocks. So I think it's an exciting time for us. We didn't cover that. Like this is supposed to be an absolute return vehicle, right? Doesn't care what the market's doing, doesn't care what the economy's doing. It's going to do what it does regardless, right? Would you put it in that bucket? It's more of an absolute return versus a crisis period performer or something like that.

Starting point is 00:49:44 Yeah. Love it. Well, it's cool. I think you've nailed it, right? It's like a systematic multistrat, systematic quant multistrat. We'll come up with some cool. words for it. All right, Gerald, thanks so much. We'll see you soon. Okay, that's it for the pod. Thanks to Giro for coming on. Thanks to Jeff Berger for producing. Thanks to RCM for sponsoring.

Starting point is 00:50:08 Drop us a comment, drop us a note, invest at rcm.com. Check out that new website and we'll see you next week. I'm not sure who we'll have. Maybe Jerry Parker, trend royalty, maybe a newer commodity manager. One of those too. Peace. You've been listening to The Derivative. Links from this episode will be in the episode description of this channel. Follow us on Twitter at RCMaltz and visit our website to read our blog or subscribe to our newsletter at RCMaltz.com. If you liked our show, introduce a friend and show them how to subscribe. And be sure to leave comments.

Starting point is 00:50:45 We'd love to hear from you. This podcast is provided for informational purposes only and should not be relied upon as legal, business, investment, or tax advice. All opinions expressed by podcast participants are solely their own opinions and do not necessarily reflect the opinions of RCMLT's. alternatives, their affiliates, or companies featured. Due to industry regulations, participants on this podcast are instructed not to make specific trade recommendations nor reference pass their potential profits, and listeners are reminded that manage 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.

The Derivative - Trading 80 Synthetic Markets with Trend, Carry & Skew: Jiro Fujisawa, Asset Management One USA

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