The Derivative - Asset Allocation, AI, and the Alpha Process with Resolve Asset Management

Episode Date: March 4, 2020

We sit down with the three Canadian founders of Resolve Asset Management to talk zebras walking amongst horses, mid-frequency trading (move over HFT), how the Chicago Bears don’t add Alpha, extracting structural market flaws, low carb diets, regulatory and structural arbitrage,  Peru and the Shining Path terrorist group, orthogonal carry, fasting,  winning the content game, and economic reasoning to appease the complex thinker. Resolve Asset Management is a systematic asset manager out of Toronto focusing on unique and advanced ways of implementing global asset allocation. They operate managed account, private funds, and a mutual fund (RDMIX); using varying automated investment and allocation strategies; including flavors and ensembles of trend following, carry, seasonality, skewnewss, behavioral arbitrage (think trading around a big fund needing to rebalance at end of quarter), and AI/machine learning informed “alpha buckets”. Episode Links:  Rodrigo Gordillo Twitter & LinkedIn Adam Butler Twitter & LinkedIn Mike Philbrick Twitter & LinkedIn Gestalt University Podcast Resolve Website Rational/Resolve Adaptive Asset Allocation Fund  And last but not least, don't forget to subscribe to The Derivative, and follow us on Facebook, Twitter, or LinkedIn, and sign-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, visit www.rcmalternatives.com/disclaimer

Thanks for listening to The Derivative. 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 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. Welcome to The Derivative by RCM Alternatives, where we dive into what makes alternative investments go, analyze the strategies of unique hedge fund managers, and chat with interesting guests from across the investment world. Alpha is a process. You're never arrived. It is the constant process of trying to be the right amount ahead of the curve. You can't be too far ahead because then you're just nonsense.

But you can't just be thinking of things from the perspective of where everybody else is thinking of them or you're not going to get different results. You have to be different than that. Welcome to The Derivative by RCM Alternatives, coming to you from sunny Miami, where some of the top investing talent in the world is gathered for the annual Context Conference. I'm your host, Jeff Malek, and I've managed to peel away a couple of talents from their conference duties today. Rodrigo Gordillo, Adam Butler, and Mike Philbrick of Resolve Asset Management. Welcome, guys. Thank you for having us.

Resolve's a Toronto-based asset manager doing all sorts of cool things around asset allocation strategies, from managing mutual funds to private hedge funds, and are one of the more prolific firms in terms of investor education and writing, with white papers, webinars, and podcasts under the Resolve banner. I'll have to admit, we've been known to peek over at your guys' writing from time to time and

borrow some ideas for blog posts and whatnot. Feelings mutual. Got it. So we'll start personal and we'll start with Rodrigo. Give a little background. You're from Peru, right? Born and raised, yeah. So how do you become a hedge fund manager out of peru you you experience a massive economic event for the uh for the nation at an early age and those formative years really make a big impact as to what you do when you're older so in 1989

88 89 the shining path um terrorist group came down upon the city, amongst other economic issues that were happening at the time, high inflation and whatnot. But when they came down and the president decided to renege on his IMF loans, inflation went to 7,000% in six months. My parents lost it all. My neighbor who had his mortgage and was about to be evicted was able to pay down his mortgage with a few U.S. dollars. And a lot of people emigrated out of Peru toward Canada and Australia.

We decided on Canada, and the rest is history. So all of that kind of work. You know, the background is also, you know, we come from a mathematics family, got the quant bug fairly early in the investment career. And then from there, I met Mike and Adam in 2011. We were off to the races. Sounds good. Adam, how about yourself?

Nothing nearly so interesting on a background standpoint. But I didn't really discover that I wanted to be in the investing field until most of the way through university and I entered in a trading competition and ended up doing well and caught the bug and worked on a trading desk and learned some very valuable lessons. And I guess sort of as a third major lesson after the 2008 crisis, decided that I didn't want to run strategies anymore that were vulnerable to that type of outcome again. And that was the catalyst for a move into systematic global macro. And that's kind of where we've been ever since. It's been a neat ride. What'd you place in that trading competition?

I placed first in the first one. It was great. And what was so great was that I was in the psych program and the commerce students, there were three classes of commerce students where all the students also participated in this it was a national contest and and i wanted and none of the other um commerce students did very well so it was a nice sort of psych department versus commerce department rivalry uh point of discussion for a while i love canada calls it

the commerce department that would be like economics at a u. I love Canada calls it the commerce department. That'd be like economics at a U S university. Yeah. Business economics. Yeah. Commerce. And Mike, you might have the most interesting background, uh, some Canadian football league in there. Yeah. Yeah. Um, I actually grew up, uh, on a farm, you know, learned, learned to learn the value of hard work and, um, and then, uh, had the opportunity to play football professionally in Canada for 12 years. Whilst I was doing that, I also was fortunate enough to be hired by a firm who allowed me to do both at the same time. Because in Canada, you can't quite make enough money to just retire forever after.

And I want to be challenged. I like, I like the, um, the nature of our business, uh, in, in the way in which it allows you to be, uh, competitive and that competition is something that is enticing. And I like to play a game every day where, you know, the score is. is yeah it is very similar to athletics right there's a score there's a winner there's a loser each and every day and in some case it you know sports is the ultimate zero-sum game the super bowl is is going to happen on sunday and there was 256 games played in the nfl this year and there always is and there's a winner and loser to every one and you have a winner at the end, and teams take different approaches to add alpha and value and win.

At the end of the day, there can only be one winner, so there's some analogous. My Bears didn't add a lot of alpha this year. I do like the Bears, though. They're coming around. We'll see. Who do you like in the Super Bowl? I would say, so probabilistically thinking that it's probably

underestimated the opportunity the Niners have I think that it's probably the Chiefs but I think you and I were talking about this last night I think there's a lot of strange opportunities within the potential futures that will go on in the next few days that would

maybe favor the Niners a little bit more than the odds makers are suggesting. I like it. So we'll throw it back out to any of you. How did the three of you come together and found resolve? Well, when there's, you know, three zebras walking amongst, you know, a herd of horses, you find each other pretty quickly. And Adam and I, as you, as Adam alluded to, 2008 was, was, you know, sort of informative to us as to how we might think about the investment problem. And we ran into Rodrigo who was managing his assets all from an alternative perspective and who had prospered very, very well in 2008. So when we

met each other, it was, it was kind of obvious that this is something that, um, should come together. And, and it's, um, it really is hard to find great talent to work with people that are talented, but also mindful and, um, you know, uh, constructive to all situations. And so it's been a really good... And diverse skill sets, right? Yeah, it was. Absolutely. I had a few partnerships before that, and it was always about,

okay, we all do the same thing really well. Let's get together and try to do something. And there was always an ego trip, right? Who's doing better at the one thing that we're all good at? When I met Mike and Adam, we all had very diverse skill sets. Adam on the writing side and the quant and analytics side, Mike has been leading

men and women his whole career through both bank system and in sports. And I was, you know, pretty good business development person in quant. So a little bit of a Venn diagram there, but enough diversity that it made it easy for us to just trust each other on what we're good at and say all right everybody do their own thing in their own area and let's build this up and the the content size that you mentioned is that in a conscious effort to educate or did that just come out of your research process and wanting to uh figure out things for your own and then sharing what you found out? That publishing effort was a catharsis after the 2008 crash. Honestly,

it was sort of an effort to find ourselves. You know, it was sort of Mike and I had recently teamed up. We had this very interesting experience, um, trying to navigate around how to emerge from it. Um, not just sort of learning lessons and then moving on, but learning lessons and making changes. And, um, so it was just sort of an effort to document that journey and hold us accountable to that thinking. And it was, it ended up being this incredible feedback mechanism where you sort of put yourself out there. You don't fully understand the problem in the beginning, even though you don't really know enough to know what you don't know. So at each stage along the journey, you think, you know, a lot more than you do, but you know, then people sort of surrounding

you, they're, they're increasingly reading the material, they're giving you feedback, you're learning about how you might want to shift a little bit, how you think about the problem. And, you know, that was beginning of 09, we're in the beginning of 2020. So that's a 10 year journey. And I think we've all learned an incredible amount about the quant community and how to think about the problem. And I know our evolution or thinking has evolved very substantially in that time. Do you ever look back at some of the 09 and 10, 11 stuff and be like, Oh my God, we didn't know what we didn't know? Oh, absolutely. Yeah. Um, I mean really stuff prior to, to 2012 is complete nonsense and, and stuff prior to 2014 mostly needs a complete rewrite.

But you know, that's, that's part of the journey. Is it still out there? Yeah. Yeah, absolutely. They're still valid.

Like they're still good. Even though it's, it seems like a little bit Mickey Mouse to us now at the time, it was a massive leap in our thinking. So, um, yeah, um, yeah, definitely,

definitely worthwhile reading. If you want to go back down that far. I mean, I think it's a, an, an excellent example that, um, alpha is a process,

right? You, you're never arrived. It is the constant process of trying to be, um, the right amount ahead of the curve. You can't be too far ahead because then you're just nonsense. But you can't just be thinking of things from the perspective

of where everybody else is thinking of them or you're not going to get different results. You have to be different enough. And if you go back through the evolution of the thinking and i would i would argue yeah you go back it's kind of funny 12 11 9 last year absolutely so so our you know our process is one of understanding it's a process yeah well said tell me a little bit more about the overall firm so there's you three how many others but what 15 or 16 people in total um an operations team that's been trading futures for almost 20 years now 16 years and um um, um, a growing research team,

growing research. We've got, um, well you, yeah. Walk through the research team. That's a great. Yeah. Well, I mean, our, um, our product line has evolved. You know, we started out running, um, systematic ETF strategies, kind of global asset allocation. And, um, then there was demand from new clients who were really enamored with how we thought about thefully. And that was several years ago now. And so our research needs have changed. The products we run right now are orders of magnitude more sophisticated than the ones that we ran five or six years ago. But while the thinking has evolved,

some of the major themes that inform how we think about markets and think about the problem really haven't changed that much. But, you know, we've added people to the team with backgrounds in high frequency trading, backgrounds in applied math one of the members of the team did some of the founding research on neural nets in the 1980s and then went on to found and build a software company and now is back doing work in machine learning you know it's it's it's a diverse team with complementary skill sets a lot like the way that the partners, the founding partners, kind of came together. And it's been really neat to see all that reinforce. Do you feel like it's a bit of an arms race that you need to spend and hire in order to stay relevant and keep up with your peers and competitors?

A little bit, but you've also got to know where you fit. I mean, we're not going to compete with Rentech. We're not going to compete at the microsecond scale. That really is an arms race. At least not right now. We may evolve to get higher and higher frequency. There's mathematical reasons why higher frequency has higher expectancy, all things equal. But, um, you know, I think you've got to know who you are and who you're competing against in the, who you're competing against in the market and where are there gaps, um, that you might be able to fill with your expertise. And I think we're uniquely positioned to fill some sort of mid frequency gaps, um, that are pretty exciting in the next sort of six to 12 months. And in the meantime,

just bringing on new blood who've been very successful thinking about the problem in a very different way has inspired us to think about the problem in very different ways. And just the evolution in our thinking on the research side over the last year, it has been pretty incredible to see. Yeah, I think it's a function of strategy, structure, execution, and behavior. So you have a strategy. What structure is it going to be in? Because that will have limitations on how you might be able to execute it. And are you going to be able to behaviorally stick with it so then how do you fit in to the marketplace

in order to extract some error or some mistakes in the marketplace that are being made on a regular basis where you're going to be able to reliably extract those opportunities for excess return. And I think that we think very, very carefully about that in order to manifest the strategy that's going to provide for some excess return that's reliable. And then having a number of those edges and then assembling those edges in a way that's thoughtful and different. I think that the way we approach that is quite unique and the things that we are willing to do that others aren't is largely where we're going to gather some excess return from.

Which that's a good lead into getting in diving into the strategies a bit. We kind of buried the lead a little here. But maybe Rodrigo, give me the 30-second elevator pitch on what you guys do and what you're good at from a strategy standpoint. Well, as I think we've alluded to, we're 100% systematic. And one of the things that Mike mentioned is the structure and where we want this business to really grow. And so we consider ourselves to be institutional quality research and product. And in order to do that, you need to accommodate large AUM. You need to be institutional quality research and product and in order to do that you need to accommodate large AUM you need to be able to bring it in so you can't go down on the microscope like take that data level you have to do in a day in order to be able to accommodate the institutional

interest that's coming our way while still being different enough to uh to get paid what an alpha manager should get paid and so the way the our evolution futures program is designed it is not it is all futures but it's a different way of managing futures it's not the traditional trend it is a multi-strat with different alpha buckets that include some of the basic fundamental understanding that you see in the style premium space on quality, value, and so on, except we're trying to extract other behavioral flaws that are less popular and still provide a robust alpha as we see the traditional style premium really collapse. And so the Evolution Futures Program is designed to fill that gap,

to provide a series of non-correlated alpha strategies like seasonality, skewness, mean reversion, certain types of carry strategies in ensembles. So each one of these little alpha buckets has not just one or two ways of trying to extract that particular signal, but thousands of different ways. And we've written a ton on the values of ensemble. And then the other side of the equation,

beyond just trying to find better alpha buckets that allow us to choose across 80 different futures contracts for better or for worse, is the weighting scheme, which I think is ignored by a lot of the alternative space. So we focus too much on trying to find an edge and too little on then once the edge being what are the winners what am i going to go long what am i going to short very little time spent on how you weight those and we spent an equal amount of time on that side of the equation which has the ability to increase sharp ratios as much as 50 percent of times so that is i think where our niche is very different portfolio construction We're working in an area that is yet to be harvested globally

in a very aggressive way. And that means that our correlation for this strategy to traditional risk premiums, CTAs, and whatnot is nearly zero. And take me one level up, though. So the evolution strategy is just one of your products? Yeah, it's our lead hedge fund product, long, short futures product. Okay, and then there's how many others? run a 40 act fund as well um that is uh run by rational funds so we sub advise for them and that one is a kind of coming in from our some of our legacy programs

which were completion portfolios right so you have it's a long flat strategy using similar kind of alpha signals in order to decide what the weighting is going to be in the same type of optimization at the back end. But it's designed to be a bit more transparent and approachable for the retail space where you have a 60-40 portfolio. This sleeve can represent 10% and give you exposures to things like commodities, real estate, global equities, German bonds. And that's sort of your guy's DNA of asset allocation, right? Not just I'm focusing on one trade that makes money, but these different pieces, as you called them, the ensemble and how do the different assets mix and work together? That's right. Well, even thinking about not just the assets,

how do the different resulting strategies that you're running on various assets manifest in a return stream that you would then optimize within a portfolio? But if you guys found one thing that worked just really well on oil, even if it's an ensemble approach and many different, you probably wouldn't trade that

because you wanted more broad-based asset exposure? I'm not going to say anything about that. Actually, that's, that's funny, but it's also an interesting point because I think, um, you know, it used to be the point when we were running strategies that were informed primarily by trend or momentum, um, that we shared all of our research. There wasn't anything behind the veil, you know. And as the strategies have become more sophisticated and we've realized just how quickly other investors, including, you know, commercial investors,

will steal your IP and, you IP and use it for themselves. And we've developed IP that is, I think, legitimately different and represents genuine alpha. We're a lot less inclined to publish all of that research. We still publish lots of research, but there's now a lot of stuff that goes on behind the scenes that we're a little bit less willing to share the details about. You do see that with some of the biggest pensions and endowments out there bringing strategies in-house. Okay, I've watched this for five years.

Absolutely. You guys have shown me the ropes.. Okay, I've watched this for five years. Absolutely. You guys have shown me the ropes. I'm going to hire some PM for less money and try it myself, which I personally think is a dangerous road for them. It absolutely is. If you didn't develop the strategy, you didn't build the strategy, we have heard of several scenarios where adaptive asset allocation has been that paper that was written back in 2011 how that was adopted by some major pension plans with catastrophic results

mainly because they just didn't quite fully understand the depth of what was required always want to add your own tilt and um they go sideways so it it is again this whole this whole idea of alpha is a process you you need you know it's the it's what is it the red queen syndrome from um um the mad hatter right you need to be running just to keep up yeah and there's so what mike is speaking of is the difficulty operationally and trying to replicate a bunch of white papers that are trying to extract whatever premium so the adaptive asset allocation framework is what we run in the 40 act fund but it can it has evolved every year as we continue to do the research. But I think there's also the issue of publishing and what the impact of that once it's widely accepted.

And I think we can see that in the factor space. As we speak to institution after institution, they're all rationalizing. They're firing their external managers that are charging many fees and trying to bring the factors in-house. And we actually just did a podcast. We have a podcast of our own called Gestalt University, and we just did an interview with the, what was his position at the time?

Global tactical asset allocation, and then eventually he developed. Global style premia or something, yeah. So he developed a style premia approach from first principles back in 2004 before anybody was doing it. And the Sharpe ratio was above above one like 1.2 and then he said that in 2010 when all the papers came out and everybody finally accepted it he saw 100x aum go into the product and his sharp get cut by 30 and so what's interesting now this year is that i'm seeing and hearing more and more and more institutions saying we're bringing we're going to do style premia we're going to do factors we're

going to bring them all in-house so you can imagine this is this is already a trend that's bad it's going to get even worse and so the even if they replicate it poorly what that does mean is for the people who are getting paid for that they're probably either going to get fired or they're just not going to be any return to speak of. So the key for us is to make sure that we're attacking that space from an acute angle and providing something completely different. And are you seeing those, so they're bringing it in-house or they're going to a bank platform or whatnot that's offering those different risk premiums? I'm seeing it more in-house. I think the sophisticated guys that can pull it off, because they're hiring quants internally. Yeah. And those quants recognize that,

that, you know, simple, the 50 basis points swap that they can get from the banks are not, you know, they're going to try to do a little bit better, but at the end of the day, I think a lot of them have been burned on those ultra simplistic bank oriented

factor strategies. And I mean, what the banks do of course is they create a strategy and they let it run. They call it something and then it doesn't work out and they invent something, you know, pretty well similar and then they let it run and it doesn't work out. And it's,

you know, they like trend one, trend two, trend three, trend four. I guess it's absurd. They can just keep manufacturing these things. Trend without short energy.

Trend with... Yeah, yeah, yeah, exactly. Coming back to the weightings, you were saying it's really important. Half or a good part of your research and your process, or process, as Mike would say. I do that don't i it's you're canadian it's good um so if i'm bringing that in-house that's got to be a big

issue right like i may know how to work the model but the weightings the risk control actually that's a really good point and and the current situation is a really good example. So if you look across a lot of the trend funds in the early part of 2020, you can tell that the vast majority of the returns have come because they're all, it's like a trampoline, right? You get all your guys moving over to the same point on the trampoline. You're all jumping up and down together. You can go really high right but you're also going to all go down together and so you can you can see all the equity index based exposure did really well for a lot of these guys um for most of the month and now they've a lot of them over the last couple of days have really taken a beating whereas we our strategies are designed to um acknowledge the fact that you need to have very strong confidence in your signals and certain signals against the opportunity to

lower the overall systematic portfolio risk by using diversification. So that really is the guts of how we think about the optimization. I think that a lot of more traditional systematic firms spend a lot of time thinking about their indicators and their signals and their execution. But maybe the portfolio construction is a bit of an orphan. That, I think, is a real strength of ours. Yeah, and I've been banging that drum for a while here. I think in order to survive,

a lot of classic managed futures programs said, hey, we got to add long bias. We got to add more equity exposure or else we're going to be flat to down four percent for eight years right and then the assets leave so it was either you change or die so they've either explicitly or not added some of that exposure and it comes back to bite them uh from time time. So how did you guys manage that last, because you're a long ball type program, right? Would you agree with that or no? I mean, we've got sort of 40% of the risk budget

is designed to be positive convexity and maybe 60% is sort of, you know, have zero correlation to equity beta strategically. So I wouldn't say we're necessarily long vol biased. I think we're probably vol neutral. Okay. So that was part of how you differentiated and were different over those, what has been a painful eight years for classic trend followers, classic volatility breakout type strategies that we're struggling with vol just getting sucked out of not just equities,

but across all the asset classes. Yeah, yeah. I mean, vol is sort of omnipresent, right? I mean, you can't kind of escape it and it is a source of premium, but one of the objectives is how do you kind of diversify away from just you can't you can't totally get completely away from it and also expect to generate a premium but but you can diversify into other risk exposures and structural

inefficiencies that um so you're not entirely relying on vol to deliver your performance. That did really well over the last 10 years. If you're depending purely on trend and you require that vol and extension of a trend in order to make money beyond a couple of weeks, then you're going to have had a tough time over the last 10 years. And so the approach when, when using all these other factors that we use is that, you know, yes,

we've a portion of the portfolio may be suffering because trend continues to suffer. But if you have these other very non orthogonal, non correlated alpha streams that didn't suffer the same fate as trend in the futures arena, then you have a product that um that you're not necessarily going to get sold out and so let's unpack what are those different alpha generators as much as you can share that you don't want to the uh all the ip out there well i mean i think we were

we our presentations and stuff share the fact that we've got some trend ensembles some carry ensembles and we think about carry i think in a some trend ensembles, some carry ensembles, and we think about carry. And I think some of our carry indicators are a little more traditional and others I think are very different. And the ones that are very different are just as powerful as the more traditional ones, but they're orthogonal. So they are really complimentary in the portfolio. And some of the other, and I don't want to go too down this path, but there are other arbitrage opportunities. They're not pure arbitrage, but it's sort of regulatory arbitrage and structural arbitrage factors that have been extraordinarily steady, profitable over many, many, many years. And that we, if you sort of look at simplistic versions of strategies that try to harness those inefficiencies,

they work, but they're not attractive enough to really generate much attention, which is what we really love. We sort of try to target anomalies or inefficiencies or edges that there have been some papers published on them, but not very many because the original papers weren't very exciting. But if you just think about them in a slightly different way and you apply ensemble type thinking, then what emerges is actually this incredibly powerful signal that you can't capture using traditional thinking.

But if you use the type of thinking that we bring to bear is fabulously profitable, a very high Sharpe ratio, and orthogonal to the other things in the portfolio. So I think that type of thinking is also a really important part of our edge. It's almost like Google AdWords platform. You don't want to just compete on the highest ad. You want to find the really long-tail keyword that not a lot of people are using that delivers to your business.

Exactly. And you're looking for large, sustainable, structural, sometimes they're regulatorily related, willing losers. Those who are driven to make transactions that are not necessarily driven by sort of simple or pure wealth maximization. There is some other driver that's driving the behavior that forces them to

make mistakes. Therefore, the mistakes are consistent and more reliable. And they're happy to do it. So this is an interesting topic that Chris Schindler brought up where the other side is making a rational decision to give you money. Yeah. So I'm going to have that as for some example. We have some sort of an example. Sure. Let's take a simple example. I'm an advisor and someone gives me an order for an ETF and the order is before 9.30 in the morning. And I'm obliged by the regulators to make sure that market order is put in the market by 9.30 in the morning and I'm obliged by the regulators to make sure that market order is put in the market by 9.30 a.m. and that may not be the best time for that order to get executed. And so you see all kinds of hairy bars on certain different types of each as a simple,

simple example. So you can take that example of that simple RIA, which would be a small edge, and then think about that at an institutional level, when institutions are driving transactions, where their main objective is not optimizing the transaction, but it's the speed of the transaction. Or an investment board who has to manage a number of investment committee is managing a portfolio and they're making decisions based on that portfolio that aren't necessarily 100% wealth maximizing. Or the requirement to hedge risk out of the portfolio and you're paying an insurance premium by buying puts, protecting the portfolio in certain times. This is driven by a committee.

They're making a conscious decision that helps them. I thought you were going to say it's driven by a comedian. I was going to... Yeah, well, kind of. And then the other side is able to provide the other side of what their needs are. And so it is an ecosystem of, you know, winners and losers in different areas. But at the end of the day, they're there in a positive way. I'll give you one concrete example. Imagine, due to banking regulations, banks are required to square their duration book, square their treasury book, and monitor a variety

of other VAR-related measures into the end of reporting season every month and every quarter. Now, if you don't think that that impacts bond returns around those dates, then you're probably missing something, right? So there's one simple example of how the regulations impose behavior that is structural, it's not going away, and is systematic and is profitable for those who are able to identify it and harvest it systematically. Well, just the carry trade is a structural example also, right? Sure, I mean, yeah. And carry, it's been argued,

depending on the asset class, carries can be kind of risk-based because it hurts when it hurts to hurt, right? It's sort of pro-cyclical, especially on the currency side. But if you put it together like traditional carry into a diversified portfolio, you find it's not nearly so risk-based and it becomes more of a sort of inefficiency. So yeah, I mean, it's hard to try and ascribe cause effect to a lot of these things is, um, I think really challenging, but you can sort of point to enough examples of, uh, where structural effects are identifiable that you can say, okay, if these structural, if I can identify these ones through some kind of narrative

or some sort of cause effect, then there must be a wide variety of other ones that I can't see, but I can kind of harvest in the same way. And that comes back into some game theory and who's doing what they need to do, why do they need to do it. Do they care if they're giving up the edge? Exactly, yeah. And so are you guys consciously pressing on that game theory? Are you identifying the players, identifying their motives, or you're just see behavior in the price action.

And then you go and sort of poke and prod, and there's something there. And then if you apply some of the sort of boosting, bagging, ensemble-type thinking to it, then something meaningful emerges from it. But sometimes it emerges from the data. Sometimes it emerges from a thesis. But we're happy to come at it from either direction.

The research team is actively trying to find these theses. Yeah, absolutely. Theses. Thesai. Thesai. Thesai. Is there any machine learning or AI involved? I think we've talked about that at some points before, but where do you guys stand on that?

Yeah, what's funny is, I mean, talk about the evolution of thinking, right? I mean, I think we had an associate who worked with us about five years ago, and I remember him, he was very enthusiastic about machine learning, and I remember him saying or asking, you know, are you going to start thinking about using some machine learning and I remember him saying or asking you know when are you gonna start thinking about using some machine learning and I said to him we will never be using machine learning as a major source of information in our strategies and I think our thinking on that has well our understanding of what is meant by machine learning in air quotes has evolved very substantially. And, um,

yeah, my big caveat always, whenever I asked someone to use AI with the caveat that almost anyone who says they do, they're really just talking about automation and not pure black box, the machines doing all the everything. Yeah, exactly.

Um, and I think certainly are the, the direction of motion for us is in, is in the direction of using the tools that fall out of the machine learning space to do a better job of finding and refining strategies and portfolios. And from a pure manpower, you can just process way more data points? Yeah, there's that side of it. And it's just, again, without going too far into it, it's just a shift in thinking about what's possible with a different tool set.

Like if you want to build a house and all you've got is a rock, um, axe, you know, you're going to build a different house than if you've got a bunch of modern tools. And I think it's, uh, that's really the difference. We cover this extensively in a podcast we did. It was a machine learning Pandora's black box or something like that. And we go through the different alluding to adam's tool analogy so there are many different tools in the machine learning toolbox that affect and can improve the portfolio construction process understanding covariances cl, and so on that have nothing to do with picking better futures. It's used in a completely different area. And that tool set we have been using for years.

But everybody thinks of machine learning as can you stick it on the market and it'll come back and find this magical equity line. And what we show is that that's not the case. It really does continue to come down to the imagination of the individual or individuals who are working on the problem, understanding that all the machine learning is doing when trying to find trends or trying to find some alpha is finding patterns. And the vast majority of those patterns are overfit and garbage and aren't going to actually work out a sample so the big human aspect to all of this is do you have a filtering

or validation system that truly works to only let in the patterns identified by the process that are going to work out a sample and that's really where a couple of our quants came from that space have been profiting in a very substantial way using these tools, this quantum mental approach of using the machine learning to find better edges, but making sure that the filtering process is tight. And that's really where the IP comes in in the machine learning space. And then it sounds like you would always, no matter what comes out of even that research process, would come in front of a committee or have to be like, okay, is there a fundamental reason behind this before we sick the computers on it?

I think our thinking on that has evolved. We've got lots of stuff that we've got fundamental backing behind, and I think we're being a little bit more flexible in our thinking. Yeah. How deep, how deep do you want to go on that? Yeah. Well,

totally. Are we going to get there? Exactly. Because every economic reasoning is simply a story. Yeah. And the stories get more and more complex to appease the complex thinker, which again, then comes then comes into the AI scenario where you're going to talk to an investment committee

and you're going to say, we are running some AI in your CalPERS pension fund. Why did it do what it did? We don't know. And we can't tell you whether it does good or bad. So you tell me how many people are going to be behaviorally able to jump on that bandwagon. Now, eventually, when everybody's doing it, they probably will.

But at the beginning, there's likely going to be, for thoughtful practitioners, there's probably going to be an opportunity there to provide some excess returns that's overlooked by others. And it really then, from a business perspective, becomes how much, like Michael was saying earlier, how far into the future you want to go, not just on the strategy development.

If it works, it works. But is it going to sell? Are you actually going to raise assets in a pure machine learning space? So the truth is that from the institutional space, they're not ready for a full-on AI deep, you know. There's no narrative. There's nothing there.

But you can use the tools in the other areas of portfolio construction, and also you can apply machine learning to these factors to identify better ways of combining them, right? Rather than just saying I'm going to grab the same universe and do, right? Rather than just saying, I'm going to grab the same universe and do a bunch of mean reversion strategies and I'm going to do the same thing and just focus on carry or whatever.

You actually put all of those into a bag and let the machine combine them in ways that you get a pretty decent outcome. So at the end of the day, you're still based on fundamental understanding and that persistence of those factors, but you're combining them in interesting ways the other side of things and where we may actually launch a completely separate product is the we're just going to find patterns i don't i don't when you look under the hood you don't you don't even want to know what

the indicators are but it works really well and it's a zero correlation to everything else. And there is an audience for that. It generally tends to be really advanced quant based family offices. Yeah. And so that'll probably be the beginning of that side of the machine learning evolution. And you would have risk controls and whatnot on it. It's not just going to be like, oh, we're going a hundred percent. The portfolio is a capacity importantly as well. That's right.

So that's the future? That's the evolution of... Evolution of evolution. No, and we see on our side of the world a heavy interest in AI. Again, air quotes AI, investment strategies. They don't understand it. They think it's going to make everything better.

There was an interesting blog post ben hunt you guys know him he was saying the market's a bonfire and it's basically like you can't model a bonfire even the most sophisticated computer you couldn't recreate the uh so everyone thinks they can just get the the holy grail and get the key to the clock or whatever but there's no such thing so if i think as you're saying if you can get your ai mind to say hey i'm not trying to figure out the the secret quote unquote secret i'm just trying to pull little pieces out of the puzzle it's just it's a i thought your analogy on a better tool set i mean you know you have a set of tools you're

gonna get you're gonna get results you have a different set of tools you have the potential for better results different results part of the tool set is is um finding tools to help explain what what's going on beneath the hood you know i mean it's i subscribe to the affect theory of decision making so the limbic system drives behavior and the prefrontal cortex defends it, right? So, you know, we all think about cause effect as we take in information, the prefrontal cortex processes it like a computer, and then we take some logical action from what falls out of that logical processing. In reality, if you look at how the actual brain operates, drives the decision or drives the behavior, information comes in, the limbic system reacts far more quickly, you act, and then the cerebral cortex kicks in, the neocortex kicks in and says,

this is why you did this, right? It explains the behavior. Biological coverups, nonstop. Exactly. Go create a story on why we did that. Yeah. And so I think we, you know, you're going to create tools, things are going to work, other things are not going to work. You're maybe not going to have a precise cause effect connection there, but you can tease out enough of a probabilistic cause effect narrative so that we can get comfortable and client can get comfortable, but different clients are going to get comfortable with different levels of abstraction.

Okay. So putting this, all this package together, things have gone pretty well for the firm, for the programs. I think assets are close to all time highs. Yeah, pretty much. Yeah. And you were nominated for some awards recently. Yeah, no, we were lucky enough to, to be nominated for four to the 15 HFM Kwan awards recently, one for a 40 act fund and the other ones for the Evolution Futures program. So we're pumped.

We'll see. Fingers crossed. They'll announce it next month. RCM's nominated for a few things as well. So we'll see you there, hopefully. Yeah. Fantastic. We'll celebrate.

Time for our favorites. We'll do a little quick fire. And I also wanted to mention we're all low-carb proponents here. Not on this trip. Not in Miami, but in our real lives. I'm carbo-loading. That's right.

Carb cycling. How did you guys get to that place? I like it because it kind of shows a discipline that also is needed in the marketplace. Just two seconds on your low carb aspirations. I started it years ago. It's been more, more than, yeah, it just, my body agreed with it when I started reading up on it. Then, um, halfway through I found keto, uh, the ketogenic diet and you know, that's cutting out even more. And it's a very precise,

you have to thread that needle really precisely for it to work for you. But I just found your cognitive abilities go way up. You don't have that lull in the middle of the day. And, you know, body composition gets better. Everything just basically improved for me. And we were able to spread the love in the workplace and try to get our partners to join us

with a lot of resistance. Well, Mike and Rodrigo are evangelists for things that they are passionate about and both have a real sort of coaching dimension to them. And so I think when they cotton onto something, it's infectious. And it took me a while to embrace it and I fought it for a while and eventually thought a while to uh embrace it and i fought it

for a while and and eventually thought i'd try it and it was just magical so we're mixing in a little fasting now too you've had some great results with the uh intermittent fasting yep yep five days seven day fasts yeah those are pretty fun it's too long yeah well so i i think that so many years ago someone said something to me that that sort of stuck which is um here's a diet you should try and personal say well i can't do that or it's not going to work it's not this or that it's like no hold on a second um you do it and then tell me if it works. Do not listen to me. Don't listen to me. Get off your ass and do it, and then you tell me what impact it had on you.

And so I think for us, it's, well, the carnivore diet, we've cycled in and out of that, keto, intermittent fasting. It really is a function of, I'm going to do some eating, and that can have some positive effects. It can have some sideways effects. Why don't I just mindfully observe how I feel in different ways that I might eat and then think about how I would like to feel and then understand that what I consume from a food perspective will have implications on that. So I should think about that and do some stuff. And so we just try a bunch of stuff and say,

wow, the carnivore diet has some really interesting manifestations in your body that are amazing. It's really difficult, socially difficult. I was talking to a couple people at the table last night about how fasting can be difficult socially. How do you build it into your your daily life but the point is just try a protocol and see what it does for you it's the old n equals one you're one biological system sometimes these things work for people sometimes they don't

but don't tell me just go and try it i like it ties back to your quant kind of roots right of like hey we're gonna test we're gonna try we're going to test. We're going to try. We're going to see what the results are. Then we'll make a decision. And we kind of love when people say you're crazy. That sounds crazy. I'm like, it's crazy. I'll probably do it immediately.

I love how like keto is now very popular. But when we started it, there was no literature on it. There was no food. There was nothing. There was no food. Now you can actually do it and get cookies and stuff. But at the time, everybody thought we were nuts. I still want McDonald's to just sell like a bag of peanuts or something.

Because if you have kids, you're going to end up at McDonald's and there's nothing low-carb. You've got to prepare for sure. I wrote a personal blog once on this because I lost like 30 pounds going low-carb. And I think I had like 10 points how to do this. And number two is like, you're going to get made fun of. Yeah. Because you're at a Cubs game and you're like eating a hot dog without the bun. And people are like, what are you doing?

Yeah. But once you get past that, it's. That's probably one of my superpowers to be able to be made fun of and not really think much about it. You invite it. Irreverent. Yeah. I think that's it. You guys want to leave listeners with any last thoughts or we'll put in the

show notes, uh, links to the pod and your white papers and whatnot. You can follow us on Twitter. We're Mike and Adam are pretty active on Twitter. So maybe you give it your Twitter handles out. I'm at gestalt. U G E S T A L T U.

I'm looking mine up. And I'm Rod Gordiop. Gordio. At Rod Gordiop. Yeah, you guys are active on Twitter and all the, maybe one day they'll do a case study of like, this is how you grow assets and become a professional. I think it's been a great forum for discussion. And I know that probably

Adam takes advantage of that more than any of us, but it is just a great way to have a conversation about, you know, what's refreshing. There's way too many managers who are what you mentioned of like trying to hide their IP so much that they don't want to give up anything or even engage in a conversation. It's kind of a bit of a galaxy brain too you know like i came up with a just a kernel which we don't need to get into an idea and i i drew it out i didn't want to bother my quant team with it and i said can you um anyone give me a function that maps to this shape and i had 15 people write and say yeah this function maps to that shape. And I had 15 people write and say, yeah,

this function maps to that kernel, this function, you could use this step function or this polynomial or whatever. And it's just stuff that you, it's kind of wacky. There's always an expert that if you just put it out there, that they're happy to kind of dig in and. You also have to give in order to get.

Absolutely. A lot of people just get on there and they're like, how do I do this? Yeah. We're getting, we're getting a lot of traction. We did a mini-series podcast, 12 episodes, 50 minutes apiece, on a wide variety of important topics on portfolio construction and whatnot.

So practitioners love that. And allocators that are getting to know us actually like listening to those because you can double-click into it and get the content behind it. I would say that's called The Resolves 12 Days of Investment Wisdom. You can have it in any podcast. I think it's a pretty neat collection of thoughts. And if you listen to it at 2x speed, you can get through it a lot. That's right. It's like Game of Thrones.

Once you do episode one, you can't stop. I sound way better at 2x. I do 2x. I know. You know when content is thick and that's how I know the content is really good. I'm like, ooh, two, two and a half. This is not, I'm not, I can't follow. Supposedly Netflix is experimenting on watching,

being able to speed up the watching. I watch all my YouTube videos at 2x. I would totally watch it in an hour. All right. Thanks, guys. That's it. Thanks, Jeff. Thank you.

Thanks. All right. Thanks, guys. That's it. Thanks, Jeff. Thank you. 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 RCM Alts and visit our website to read our blog or subscribe to our newsletter at rcmalts.com. If you liked our show, introduce a friend and show them how to subscribe. And be sure to leave comments. We'd love to hear from you.