The Derivative - Why Systematic? Why CTA? Why Now? A panel event with Mercer, Campbell, EMC Capital, & Resolve

Episode Date: July 27, 2023

Why Systematic? Why CTA? Why Now? These are common questions on the minds of many professionals in the Managed Futures field. On this episode of the Derivative podcast, host Jeff Malec takes on a unique role as a featured guest on a panel recently hosted by RCM Alternatives in Chicago alongside Cohen & Company and Mercer. While Jeff is usually the one leading the discussions, this time, he gets the opportunity to be a participant alongside other industry experts. Together, they delve deep into the inner workings of various trading models. The main focus of the discussion is to understand how these models function, distinguish between pod-shops and multi-strat data infrastructure, and explore the role of A.I. in the industry. Additionally, they discuss the differences between systematic macro and trend-following approaches.   Joining Jeff on the panel are Joe Kelly from Campbell, Brian Proctor from EMC, and Rodrigo Gordillo from Resolve. The conversation is both insightful and engaging, shedding light on the world of Managed Futures Hedge Funds — SEND IT! Chapters: 00:00-01:42=Intro 01:43-15:41=Approaching CTAs – Why now for systematic macro? Inflation, stacking (yes + this) 15:42-25:57=Current state of rising interest rates & trend following diversification 25:58-40:25=Machine learning: Parameter sets, Data, managing risk, A.I., & no emotions 40:26-48:49=Discretion: When, Why, or do we use it? 48:50-01:11:06=Questions? Let's open it up to the audience Follow along on Twitter with Rodrigo Gordillo @RodGordilloP, Mercer @mercer, & Cohen & Company @CohenCPA for more information! Don't forget to subscribe to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Derivative⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, follow us on Twitter at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@rcmAlts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and our host Jeff at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@AttainCap2⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, or ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ , and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, 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

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. Hello there. Hope you're enjoying the dog days of summer here. We've got a good one for you today to listen to while mowing the lawn or sipping a lemonade or whatever you do. I got to sit in the guest seat during a panel last week, a little bit different for me, but it was fun. And actually, I was really a little bit more of a listener as some of the great pros in the Managed Futures Hedge Fund space were also on the panel and really talked more about their models and let them talk.

The panel was titled, Why Systematic? Why Now? We dove deep into how models work, the difference between pod shops and multistrats, data infrastructure and AI, and finished with an insightful combo from an audience question on how to delineate between systematic macro and trend following. Here we go with Joe Kelly of Campbell, Brian Proctor of EMC, and Rodrigo Gordillo of Resolve. Send it. This episode is brought to you by RCM's Outsource Trading Desk.

Did you know RCM does the clearing and execution for several hedge funds, ETFs, and mutual funds like the one on this panel? Utilizing futures options and more. That's right. Check it out at rcmalts.com. And now, back to the show. We're going to get started. Thank you, everybody, for coming. I want to thank RCM, Matt Bradbard, Cohen, and Mercer for putting this together.

We have a great panel today. Rodrigo from Resolve, Joe Kelly from Campbell, Brian Proctor from EMC, RCM's own Jeff Malek, and David from Mercer, who's going to be hosting and helping everybody channel through the conversation. Thank you very much for coming. Hi, well, thanks again for everyone making it here today. I mean we will have a great panel with these three CTAs that have been around for forever in the industry and Jeff Malik from RCM. I'd just like to start off with kind of getting an idea for the audience, like

how many of all of you have already invested in CTA? Half or so. So of that half, it would actually be good to know the breakdown of how many of you are focused on more trend-following CTAs versus other strategies outside of that. Okay, so definitely a little bit less on that side. So I feel like most people always get the idea of like when they look at CTAs and how Mercer views it as systematic macro as a whole, it's two different buckets. So we kind of view it as there's more trend focus, which is 50% or more in trend or multi-strat

in general. So just kind of to start off with talking to Jeff a little bit more, it'd be good to know like how you guys view the universe and what or how should people be like approaching it for ctas as a whole sure and uh welcome everyone thank you uh i really look at it as we did some research end of last year when ct has been doing well ran some historical testing back to jan of 2000 i think the numbers ended up the compound annual return was about four and a half percent vol 11 draw down 18 that was an average across four indices we put that out on twitter and it got a lot of comments back of like what what the hell that doesn't look so great

that looks terrible coming full circle what we find attractive and what the clients find attractive is that's positive carry that's four and a half percent across that whole period in order to survive and get those pops in the downturns, in the 08, in the 22, 2014. So for us, a little bit less, am I doing trend following? Am I multi-strat? How does that fit in? And more of that return profile. Okay, are you pure trend following? Are you systematic macro? I don't care quite as much if you deliver that return profile that's going to pop and have that positive carry. Yeah, I mean, that makes sense.

We've just seen that there's been numerous environments where during every equity market drawdown that it doesn't necessarily mean that it'll correlate well with each CTA in terms of performance-wise. I mean, some have a better complexity profile versus another. And during the discussion, we'll kind of get more into the specifics on that for each strategy. So then, yeah, so kind of talking about the equity market drawdowns and the different profiles from there. So like, let's kind of more so back up a little bit and just talk about it from a high level of just why now for systematic macro, because you know that certain environments are better for certain profiles, but just looking at it from a

broad perspective is what are the main reasons in this macroeconomic environment that we're in that makes sense for pretty much everyone to be investing in systematic macro. Maybe we'll start with Joe. Yeah, hi, everyone. Joe Kelly. I run the institutional business for Campbell, actually out of Chicago. So if we haven't met, my wife and I moved back to town about a year and a half ago. So the main office is in Baltimore. we run about 4.2 billion across what we consider kind of two multi-strats and about a quarter of the assets in pure trend. You can use, in our mind, multi-strat interchangeably with systematic macro. I caught on a couple, maybe a year ago, year and a half ago, there's always this frustration and this tension between systematic discretionary macro and multi

PM shops. And, you know, you tend to have the same conversations with a lot of the same investors. Multi PM has obviously taken in a ton of money. And behind the scenes, what we've been told is ultimately, you know, the track record and the numbers are what they are, but they're better at allocating risk across strategies than a lot of allocators. And so the way we sort of approach systematic and kind of the why now, we have this really cool, what people call, I guess, I call it a periodic table or a quilt chart that basically says like year on year on year, we can't tell you what's going to work. We can't tell you trend or systematic macro or short-term is going to sort of be the dominant strategy, but through risk allocation, you know, we can systematically allocate to what works or deallocate to what doesn't faster in our opinion

than kind of the human brain. And even specific to trend strategies, one of the challenges with institutions is when they need it, they're always too late to the game in terms of board decisions and getting it through the process, and then they buy the top. And so for us, or the message we usually give people is, you know, respectfully, during periods of non-zero interest rates, where there's a lot of dispersion in global markets and you don't know, frankly, where to put your money, we think systematic has a role to play in that. You know, I think, I'm not going to say smarter, you know, faster and in a more tactical way

than any of us are on a discretionary basis. Does that make sense? It makes sense. Brian, do you have anything to add to that? Sure. Brian Proctor from EMC Capital. We've been in the business since the mid-'80s, so traded through a lot of different environments

and obviously have seen a big evolution and changes in the CTA space over the years. So when I think of systematic I always think it's the it's a quantitative approach to trading. So it's very rules-based, you're not making discretionary decisions, you're taking all the human emotion out of trading your own biases fear greed all that stuff goes by the wayside and you look to build trading systems that give you the optimal uh prices to initiate and liquidate trades so that's what we mean by very systematic you're you your losses, you always have a hard stop for every trade you make. So that discipline is good for any investment manager

to have whether it's discretionary or systematic. And then as far as systematic CTAs, we, our strategies are all directional. So we're always looking to capture directional price movement up or down in the markets that we're trading. We're never going to be doing mean reverting or counter trend types of trades. So in evaluating how well we're doing in the current market conditions, if you're, you know, if you're allocating to us, you can look and see what our positions are and basically say, okay, these guys are doing what we expect them to do. And as Joe said, we don't always know where the next good trade is coming,

whether from a specific market or a sector, the currencies or the grains or the energies. So when you get a systematic CTA, what you're really getting is a very diversified opportunity set and very unique markets that were going long or short equally as well. So some years like this year, the best markets tend to are like cocoa and sugar and the Mexican peso. Prior years, it was being short interest rates

or long lumber, palladium. So you, you know, we're in some very esoteric markets that you're not going to get in traditional investments. And by blending us with those um you know it's just a better more balanced portfolio and brian would you say because of the fact that uh every markets have its own kind of regime so to speak that the fact that systematic like trend following ctas performed very well over the past two and a half years minus last like four months or so. So would you say that it really should be used in a portfolio as a full context of it? Absolutely. You know, the question of, oh, should I make the tactical decision to invest now or wait for a drawdown? And believe me, we've had plenty of investors who wait for you to get into a drawdown and they say, I've had we've had plenty of investors

who wait for you to get into a drawdown they say well we're gonna wait to see you come out of it now and it's like so so you make you know it's it's a you know you can make a tactical decision but you know it's more of a strategic decision that you have to make to say we're gonna have CTAs we're gonna have this diversification in our portfolio and we're gonna stick with it and we know the return stream is gonna be different than other assets that we have but in the long run that's gonna help improve the returns and the risk adjusted returns of your portfolio yeah makes sense and then Rodrigo kind of the

same question for you for yeah I think the question was why now? So Rodrigo, president and portfolio manager of Resolve Asset Management. We run just under a billion dollars in a multi-strat CTA. The you know, from the trend side, we've also started a company with Corey Hostein called ReturnstackedETFs.com. And I think one of the reasons why now is because we are in a global macro environment that we really haven't seen in 40 years. For the most part, for our careers and all the instincts that we have garnered from our experiences as advisors or portfolio managers in traditional assets has been what I call a two-dimensional game, almost like balancing on a board in a barrel, right? You're going either right or left, bonds or equities. The Fed's been managing liquidity or taking away liquidity without any care about inflation. Now, as a

Latin American, I can tell you that inflation has been front of mind from the beginning of my life. I emigrated from Peru in 1989 after a 7,200 percent inflationary thrust in six months that left us kind of penniless and emigrating to Canada. So I had front and center my whole life the importance of protecting against inflation. And it turns out that the best thing to do that is to be in some commodities or be able to short some some bonds and nothing offers that directionality in all the categories that we see uh in the alternative space then the cta space whether it's multi-strat or trend or otherwise you have the opportunity to take really interesting and long have the opportunity to take really interesting and long positions in commodities,

to take really interesting and short positions in bonds and equities, of course, and currencies. So that's, you know, most portfolios for the last 40 years have prepared themselves for either growth or disinflation. Now we need to add that third element. And that turns us into a three-dimensional game, right? Because once you introduce inflation, I can tell you that the dynamics change. It's a lot more problematic to really balance a portfolio out, and you don't know when inflation is going to hit or disinflation, aggressive disinflation is going to hit.

We tend to call it inflation volatility is what's here to stay. We've seen an inflationary thrust. Now we're seeing a seen an inflationary thrust. Now we're going to, we're seeing this inflationary thrust. Is it going to end now? Are we going to hit that magic mark at 2%? The truth is we don't know. And I would say timing this thing is incredibly problematic. If we could time CTA's performance, we would have done it. That's our job, right? So I'm putting out a piece in a couple of weeks called Timing the Timer. You really have to think about this from a strategic allocation and put it in as a third leg to your stool that can deal with inflation and bear markets in a way that bonds and equities

can't. The last reason I think it's really exciting now for retail investors, advisors, small pension plans, and so what, is, you know, institutions have always had access to this ability to get the excess return to stack this CTA strategy on top of your traditional strategic asset allocation. But retail investors have been left out. Now, return stack ETFs, for example, has an ETF that is 100% bonds and 100% systematic trend. That's, you know, you give them a dollar, you get $2 where you can, instead of saying this or that and having to make room in your portfolio, you can say yes and. You can actually stack it on top and because of its low correlation,

it doesn't necessarily stack returns. Where one of the products that does this, RDMIX, our other product was a mutual fund, is risk parity plus systematic global macro, right? So we're not the only ones doing this. There's a wide variety of funds that have this stacking capability, but it really allows investors to yes and this, to stack things on top, to not sacrifice their equity and bond returns,

but rather put things on top that can help them and their clients kind of thrive in this third dimensional game that we're going to be playing. Yeah, I mean, over the past 10 years, really, CTA has kind of had a top performance really from probably 2012 to 2019. And how much of that has really been related just to the interest rate environment that we've been in? And like, how will our current regime really be affected from that? Like you kind of hinted at that. I would be curious to hear more from Joe on that. Yeah, there's definitely different opinions over the years, whether CTAs made all their money on rates when, you know, the salad days of the mid

90s that we haven't seen until now, or, you know, whether we were maybe biased to a longer short rate roll up over 30 years. And we've actually done a white paper on, you know, CTAs performance during periods of rising rates. And, you know, it turns out that for the most part, you know, the folks up here and the investable universe of CTAs that have done their homework, there's no inherent bias towards long or short rates. There is a tailwind because a lot of these portfolios have, you know, minimal margin requirements, 30% margin requirements, so you have 70% in cash earning risk-free. We tend to position our returns on top of that in a very similar way that Rodrigo was talking about, stacking. And so, you know, what we decided, I guess each of us will

kind of tell our approach, what we decided is we don't want to leave trend behind because we believe in the convexity. And frankly, we had had at that point 30 years experience in trend following. But we did have hires along the way that said, look, just because we're systematic doesn't mean we have to be a trend follower. We could also, there was a gentleman named Bruce Cleland who said, you know, we can apply this systematic process to, you know, diversifiers. And, you know, the early diversifier was Cary. We all know that that can be a great trade, but also can have a big left tail. We put in a cash equity infrastructure to try to smooth the ride in the future side by adding equity strategies. And then there's this introduction

of short-term and quad macro along the way. And so, you know, I have worked for some world-class CTAs that have said, like, we want to build our business on trend because it is the great diversifier. We made a decision at Campbell that we want to build that and not leave that behind. We want to smooth the ride for some institutions that maybe wouldn't otherwise invest in systematic

by adding these other strategies. And what that allowed us to do is in the quiet periods for trend is kind of compound on the industry by either introducing RV into the strategies, which I'm sure Brian will have an opinion about, vis-a-vis trend, or things like short-term just to pick up sort of granular moves along the way. And so we have kind of three ways we describe

allocation. Systematically, you need to allocate to sort of a style, a market, and then the third is kind of, is this directional or relative value? And in those quiet periods, relative value really dominated. There are challenges. There are different tail characteristics to relative value versus directional. There's more leverage required. So it's not, you know, this perfect scenario, but it is, every firm has to kind of decide how they want to grow over time. And that's why I describe us as kind of two multi-strats and one trend.

And we've just seen more growth in the multi-strats because it's a little smoother ride. And to be fair, we gave up some convexity last year. So trend was up 35, maybe-ish across the board. And our multi-strats were more like 20, 25. So it's all about the trade-off and what works for the investor. Yeah, Joe, you hit a big point there, just talking about the evolution of funds. All three of your funds have been around for over 20 years and have had different steps along the way of like how you choose to evolve and how some have chosen not to just to keep true to its roots in its own way.

So maybe, Brian, can you kind of discuss a little bit more about like your program and how it has evolved over time? Sure. Well, when we started in the mid 8080s, the risk-free rate was double digits. So our investors would look at us and say, okay, we want at least two, maybe three times the risk-free rate. So when you look at our track records going back that far, you're going to see some big swings, some big drawdowns, and that's just part and parcel of the market environment at that time. Over the years, as rates got into the more moderate level, you know, we adapted our strategies. We used less leverage. We understood the institutional investor was looking for better risk-adjusted returns, drawdowns, you know, 25% was considered kind of the bar you know whereas 15

years prior to that we'd have a 40% drawdown but we'd explain to our investors you have to expect this if you want these kind of returns in multiples of the risk free rate so as far as the evolution of EMC, our flagship program is pretty much stuck to its knitting when it comes to directional trading. So some of our systems we describe as trend following or range dependent systems and some of our systems are momentum based and they all are optimized to a different metric and each one of those metrics is different in nature. And it gives us a way of diversifying how to get in and out of a market.

You know, if one system is long, two, maybe all systems are long. You know, that's when the most risk is on. But we try to diversify how we initiate and liquidate trades along the way and actually some systems longer term systems can be long you know a certain market and the shorter term systems that are getting in and out quicker are actually short so they're offsetting each other so they operate independently of each other and you know it's just a way of diversifying how we do everything. I mean, we look to diversify in terms of markets, global locations of the markets that we're trading, the number of systems. You know, to us, you know, the trend following diversification is what we think an alternative investment looks like

we also have developed products over the last five to ten years that are blending long only stock bond and gold portfolios with active futures trading it's about a 30 70 mix so as joe mentioned that kind of helps smooth out the return streams and his Appeals to other kinds of investors, so we have a couple of different programs that are available out there Okay, and for Rodrigo kind of on the same similar point at least like I would just how would you say? More recently what evolutions and R&D do you think are important that investors should focus on? Well, look, when we first started in the business, there was, like anything, you start with alpha that turns into beta. But then you have to constantly be putting R&D research dollars and manpower in order to continue to add that alpha that turns into beta.

And so when we first started, you know, we thought that, you know, a single factor based momentum or trend was, you know, all we needed. And then you realize that, you know, ensembles is probably better. It's really tough to decipher which trend pattern is going to be best in this decade. So we wrote a paper on ensemble methods and how they have the highest sharp ratio and we can't tell what's better in terms of short-term long-term breakout systems moving averages etc all the while you know luckily our head of research comes from machine learning space and we had a couple other people that have been making a lot of money using machine learning and not in the ai sense that they're trying to predict the market, but rather once you have an ensemble of trend factors, carry factors,

mean reversion, relative value, et cetera, the question is, okay, we have hundreds of thousands of these signals. We assumed in our previous iteration that we don't have a skill in choosing them, so we'll use them all. As we put more man hours on the machine learning side, what we learned is that there are statistical methods using machine learning and proper experimental design, which is just making sure you're sparse the data for testing in the proper way, that'll allow us to really, more than anything, prune out the factors and parameters that are less likely to be real.

And when you start pruning those systems out, what ends up emerging is just a better outcome. You're not cutting them all to find the one branch or the one parameter that you data mine to have the best performance. You still have a wide variety of parameters, but what you're attempting to do is to really eliminate the ones that are clearly of no value so the machine learning aspect is something that we really took us five years to develop and we really pushed out three years ago on October 1st 2022 and we continue to now use that experimental design to bring in a bunch of multi-strats and can I just say one other thing so that's on the complexity side. On the simplicity side, there's also value in the beta.

And so, you know, on the trend side, which we know is a valuable thing, we decided to make it the classic beta as a stock and trend index. You know, we do a replication strategy for trend and put it into an ETF at a reasonable price so that people can get exposure to that, you know, return stack bond and trend.

So we've kind of gone, we've kind of bookended the whole process. Yeah, on the machine learning topic, I mean, that's been a hot topic really for the past few years at least. So what would you say, Rodrigo, is like been a hot topic really for the past few years at least. So what would you say, Rodrigo, is like one thing that investors should be aware of in terms of like misnomers in the space, which I'm sure there are plenty of, and when investors are looking to like

allocate to funds that are involving machine learning, AI, NLP, and just everything as a whole, how should investors like look at that framework? Look, this is a complex topic, but I think one of the things that we've seen in the last five years is a lot of people come out from non-financial places, like Google. I have a very good example of a professor that came out of Waterloo in Toronto,

in Waterloo, Ontario, Canada, one of the hubs for machine learning, that came out from a place where you have a lot of data, like so much data that, you know, you can get statistically significant results that are unlikely to be data mined, and apply that model to markets. And it turns out, you know, he ran it for a few months, realized that he's making a bunch of money, goes live, lo and behold, it was just a bunch of noise that he was hitting right so i think one of the key things is there's a lot of people with a lot of conviction about machine learning ai that come out of that space and think they have something

but they haven't learned that there's not enough data in financial markets to really attack it from the same problem of big data that google and facebook and Tesla have. We have sparse data. And for that, it requires a much more thorough experimental process of testing the data with holdouts sets and making sure that you're not fooling yourself because you're the easiest one to fool as they say not they what's his name the sign that the anyway it'll come to me in a second but what a scientist once said that you know you don't want to fool yourself and you're the easiest one to fool it's important for people allocating to ask the questions of how do you as far as they did tell me about how much data you have and how it is that you are using your

back test in order to inform what you're doing in live performance i think that's a key question that everybody needs to ask and if they cannot answer it then you might be getting into trouble at the end of the day we're we're not allowing the machine to tell us what to do we're hand crafting the parameters we have a wide variety of things that we know have fundamental reasons to exist from trend to carry and so on and then we're allowing the machine learning process to help us guide the uh the parameters of it yeah joe do you have really any comments on that because i know you've kind of focused more on short term more recently and have some uh interesting things you on on the website uh on your website that kind of go into some pretty intricate details, to say the least, on that space.

Feynman. It was Feynman. Yeah, some of it has, I think that was really well said, around shrinking parameter sets and finding sort of hundreds of parameters instead of millions of parameters, and there's techniques for shrinking them down and then treating sort of hundreds of parameters instead of millions of parameters, and there's techniques for shrinking them down, and then, and then treating sort of the meaningful pieces. I usually say two things. First, I have to talk my team out of saying we've,

we either don't do machine learning, or we've been doing machine learning for a long time, and now we're just calling it machine learning i'm looking at your smile because you're probably have the same conversations the consequences between the gentleman rodrigo is describing coming out of google and applying you know whether you're going to find somebody from your high school class and whether you're going to make somebody enough money to retire are entirely different. I watched AHL sit in front of a state pension eight years ago, and they had a facial recognition story around AI, and they had a center at Cambridge, I believe it was, that they renamed the building the AHL, and none of it had anything to do with the consequences of making or losing money

based on an unsupervised you know piece of machine learning which nobody in the room wanted so the way we kind of describe it is you got to think about the consequences you have to think about your skill set we do do is supervised learning of a limited set of parameters that is being innovated on, but it's not self-adaptive. And so, you know, I haven't met an investor yet, at least that I deal with, that wants me to put things into kind of a black box. And in terms of what people wanted to ask or what people should ask, there was another state pension who, one of our big peers, maybe the biggest peer in our space, salespeople had been in saying, we've got hundreds of years of data, we've traded through inflation, and now we're, you know, 100% AI. And the CIO of this state plan said I already have AI I

have it through this manager and if you went to the CEO of that firm today who is now retired not to give it away too much he would have fired that person immediately it was pure marketing and not blessed by research so I would listen to the researchers. Most of them are going to be very honest around the fact that there is an opportunity in supervised learning, but the consequences of what we do, we take very seriously across this group. And so, you know, applying that to financial markets, I think you just got to have a healthy skepticism around yeah i mean we've talked a lot about just data in general and i mean that's a core component of any systematic macro strategy whether that's going to be price data or fundamental data uh i mean up here we kind of see the full gambit of that so uh maybe rodrigo can you kind of go into

a little bit more about how you're like kind of sourcing your data a little bit and how you're making sure everything's like plain? Because I mean, that's always such an important part, knowing that there are a lot of data sets that are definitely not reliable. Look, data scientist is a sexy name, but it really is spending 90 percent of your time fixing your data set and 10% of the time testing on it. I mean, the amount of effort and energy that has gone and continues to go into creating systems to clean data, you know, futures contracts aren't continuous. You have to stitch them together. You have to make sure you stitch them together in the right way. There's multiple contracts that you can use through your testing environment and so the infrastructure that's necessary to be able to run your data to test on

your data to continuously clean your data and and as you emigrate from an old trading infrastructure where your team is now redesigning a faster you know newer language you have to you know, newer language. So you have to, you know, continuously improve, continuously get faster. All of that process takes years and years of time and a team that knows what they're doing and a backup team that has redundancies

to make sure that you're not screwing things up. So it's this idea that you get a lot of people want to be do-it-yourselfers, you know, I'm just going to go on my own and try it out. It's, you know, in theory, yeah, it's easy. You just follow some trends. In reality, you can get hurt if you're not doing all of that back-end work.

Yeah, we've talked a lot about kind of more alpha sources throughout this conversation, but we really haven't talked too much on risk management. Kind of within this space, it's definitely more set up differently. The fact that it's a systematic program compared to most funds within just the hedge fund universe as a whole.

So maybe Brian, if you could talk a little bit more about like how you view risk management within your fund. Well, I would say risk management is a number one priority of any CTA. Properly managing and thinking about risk is why I think CTAs are able to last 10, 20 years or more. Trading systems can come and go. Ideas, you ideas, using machine learning to, we always said it was unsupervised learning and we would open our parameter sets and let the algorithms determine what the optimal parameter set was. We used to sit at the table and say, here's the optimal parameter set for each one of the parameters.

And the parameters are, how do you initiate a trade? What kind of price action? How do you liquidate a trade? And then maybe a couple of filters in there that say, here's when you wanna stay out of the market when this kind of volatility is happening or whatever. So every system will have maybe anywhere from eight to 11, 12 parameters.

The fewer, the better. But in terms of managing risk, we do it at every level of the portfolio. So I think one of the things that our firm does that might be unique, I'm not quite sure what other CTAs are doing, but we have a very short look-back window when determining the volatility for each market in our program. So we basically are looking at the last two weeks of trading data and we're looking at the average true range of all those markets. And on our trading platform, we have a risk page that'll show the volatility and how it's changing in each market from day to day it could be good it could be shrinking or it could be expanding so that's the first thing you have to take into

account because when you're sizing positions like we do you have to normalize the risk across the markets you might take 50 corn contracts but only eight Japanese bond contracts because you want to risk the same amount of capital per trade in each market that a signal is being generated in. So looking at individual market volatility at a very short look back window is important to us. We feel that's a better way to respond to quick changes in volatility in the markets that we're trading. You know, a global macro event unforeseen happens overnight. And obviously, you have to be able to monitor that.

And any new positions, you could have existing positions on and vol is increasing. And that's good vol. I mean, we always think of volatility kind of as a two-edged sword good volatility and bad volatility. So when when you're positioned correctly and volatility is expanding those are the trades that in the long run create the returns for CTAs and if it's bad vol well you're liquidating that system pretty quickly. I have a quick question for you. Sure.

How do you protect against a historically volatile market being very non-volatile in those two weeks? Well, there are times when a market, say like the euro-yen, when the price of the euro yen was flat lining we would not allow the contract size to get above a certain level because we recognize inherently that something could happen to make that market move 20 standard deviations overnight so in that in those rare instances you know we put a limit on the number of contracts we're going to trade, take. But as far as protecting yourself from unforeseen market moves, I can think of one in particular when the Swiss franc decoupled from the euro, and it was a 26 standard deviation move in like one second. And, you know, so if you don't limit the amount of risk that you take in each market and each sector that you're trading you'll obviously open yourself up to concentration

risk so i think we lost four and a half percent on the short swiss franc position we had that day which was somewhat offset by a long euro sw position. So natural diversification happening there. But so you really, you know, you look at the volatility, you look at each individual market and say, here's the maximum amount of risk that we're gonna take when we're initiating the trade. And if vol's expanding and you're making money, great. I think another thing that we do a little bit different

in the way we manage risk is When we get to certain profit Objectives were up 10 15 20 25 percent during a good run We have a mechanism in our programs that actually starts scaling existing positions taking small profits Gradually over time so that it helps us improve the drawdown from peak equity, it helps our standard deviation, and usually those periods where we're making those outlier profits,

vol is expanding, so we're getting our positions kind of back to where the current vol is instead of the vol when we put the positions on. So those are some of the things that we're doing kind of unique in the risk management space. But any CTA, the first thing they should talk about when you're talking to them is how they manage risk and how they think about risk at all levels of the portfolio. And David, I'll just jump in on an allocator perspective, right? I'm looking at all three of these guys to allocate to.

I want to manage the risk across them. I don't want EMC to be driving the portfolio when Campbell has less vol. So what we'll do there is look at your max drawdown, your annualized vol and your margin usage, basically get a risk score out of those. If you're twice as high as him, I'm gonna be doing twice as much of him so that i'm vol weighted across risk weighted across those three managers uh and then

also that uh you were saying ensembles of your we like to get ensembles of ensembles right so you have an ensemble you have multiple time frames you have multiple strategies to us the best method is to blend all three of those together in an intelligent way, in a risk-weighted way, and then we can get an ensemble of ensembles and have even better performance. Yeah, Mercer kind of takes the same approach as that, not necessarily just within systematic macro, but within our hedge fund as a whole through our OCIO groups. One last question from me and then we'll kind of open it up to the audience in a quick one. Discretion is sometimes used across certain CTAs.

Some feel it's more appropriate than others. Some just prefer to let the systems itself always kind of correct for the situation so just kind of quickly does your program use discretion at all and like how frequently if if ever yeah so um discretion for us is almost exclusively at the risk management level so we do allow our systems that have been you know again all the risk management all the work has been done upfront but things like the yen where you know it starts to flatline you know that we number one we've already thought about what maximum level of exposure we're gonna get to each

sector so that's predetermined but for us when we looked at that we said even that's too risky so from a for us, when we looked at that, we said even that's too risky. So from a risk management perspective, we said we're only gonna, we're gonna take it off for now as we look at the global macro space and see if that's gonna blow off or not. What is taking off that contract due to a Sharpe ratio

going back? Is it a big impact? No, it's not a big impact. What's the impact if it goes against us, even with those limits, it's big. Why don't we take it off until such time as they de-peg that situation. Other things are when we look at

the historical risk characteristic of our strategy, and when there's certain days that are above a certain standard deviation of the history of our strategy, we immediately put the risk management team together to sit down, get the whole team to figure out what's going on. How are we doing against our peers? Is it a resolved thing? Is it a CTA thing we're all struggling through? Okay, so those are the kind of checklists that we go through in order to decide whether we want to take some risk off the table. So if we find that we're offside and nobody else is, we're going to go deep into the system to understand whether we screwed up somewhere

and we're going to take risk off the table before we go in to do the work. We do the work, we find something, this has never happened, this is all hypothetical, but it's in the checklist. Then we will fix it and then take the risk back up. If everybody else is struggling with it then you know there's really nothing we can do the system's automatically designed to take risk off the table so there's been two or three occasions where in the meeting we're like okay we're gonna take 30 risk off and the system's already doing that the next day so we do have discretion but at the risk level joe, how about you? Same question.

We try as hard as possible to remove discretion from the whole process. There's outliers. You know, this last year, we removed the Russian ruble just before, you know, the invasion, just to take that out of the entire process and portfolio, not necessarily from a political stance

perspective, but from a liquidity perspective. Before that, you know, when the tsunami hit Japan, we removed sort of all the Japanese markets because we didn't know when the exchange were going to open or for how long they would be closed. So those are easy ones. Those are ones where, you know, it's liquidity driven. It's always taking risk off the table. It's kind of the known unknowns like election risk and unknown unknowns, you know, like COVID that drive us probably all kind of crazy around, you know, the temptation to use discretion. And throughout those periods, we have sort of been dogmatic about things like vol floors around not loading up on contracts that have flatlined from a notional perspective. We have this wonderful head of risk named Grace Lowe,

who's co-head of IC, and then that IC meets every single day. And if there are events, as Rodrigo was saying, that may look like outliers either to Campbell or to our piece of the industry, Grace is always in there trying to generate some systematic approach to that event or environment. And a good example is something like using

implied volatility of the pound going into Brexit instead of realized volatility allows you to systematically kind of cut your position because implied vol is reflecting the uncertainty of the event better than realized. And then the event happens, implied volatility collapses to realize and you normalize your

position again. It's just a way of systematically taking risk down by using a different input going into an unknown event. And so we do use discretion. It's kind of a five-person IC committee, but it is extremely infrequent and always around taking that liquidity risk out of the portfolio. Well, and I'll chime in on that. First of all, when I think of discretion in the research process, there's always some discretion going on. You're figuring out how much risk waiting to give a market or the discretion, what kind of trading system parameters do we want that will affect each of the systems that we're trading. So in that sense, there's a regular amount of discretion, but it's all in the building

block process. Once you have the system set, then you don't use discretion on what signals to take or whatever. Second of all, I agree with Joe. There are times when we use discretion only to reduce risk in the portfolio. So there's election risk or whatever it is. We or in 2008 when you came in on Monday and, you know, banks were out of business and we were up, you know, a substantial amount on that day.

We knew the volatility across all the markets was probably 20 or 25 percent higher than the previous day. So we'll make a decision to say, okay, we're going to cut all the positions that we have in the portfolio by 20, 25%, just because we know that even the short look back window we have for the volatility of the markets that we're trading, you know, today is 20 or 25% more volatile than it was yesterday. So that would be a very rare instance of when we decide, okay, we should just cut some of the risk because it's so elevated right now. And then finally, the last place that we use discretion fairly regularly is in spreading commodities from the commodity markets from the front month to the next month when expiration comes because 50% of our classic program is essentially in commodity

markets and not financial markets. So we're constantly monitoring the spreads in the markets that we're trading and we can look and know if the spread is working in our favor, we'll hold a position a little bit longer before we spread. You know, research always has us spreading on a certain day, X amount of days before first notice day and things like that. You mean rolling? Yeah, rolling.

But, you know, in reality, sometimes you can hold on to a position because there might be a near-term squeeze because of some kind of shortage or something. And if you're positioned correctly You don't want to just roll the market just because the calendar says well you should be rolling this soon You know you can watch that and and benefit from that so we do use some discretion When spread markets between commodities get a little bit? Stretched and from my standpoint

We don't want any discretion. Like if you're saying, oh, right, because it invalidates the back test, you don't know when those discretionary moves were made in each of those events and how much would you have made in October of 08 if you left those on. So, right, the more discretion that's used, the less I can trust the track record.

Yeah, I mean, it makes sense. I mean, even with the rolling aspect, it's like WTI spreads going negative in certain times. So it's like you have to be very cognizant of what month and how you're handling your portfolio. Well, I could pretty much ask questions all day. This is pretty much what I do with CTAs. So I'll go ahead and open it up to the floor for anyone that has questions.

Do you guys want us to turn the light show back on? Yeah. All right. So Brian, you made a point 20, 30 years ago. Rates being so high, you guys have a target of 30% return. So what are you starting to see now with rates going back up, this is what, 60%? That now investors are saying, why don't you guys come back

at 18, 25, 30? Because as you guys all know, since I've been in this institutionalization of trend buying, another 30% guys down to 10, that's 2010, around there. Now it seems like investors are saying they want to be worried. We haven't seen that yet, but it could be around the corner. If rates, you know, they're fairly stable now. Actually, here in the U.S US I should preface that.

I mean in the UK they just did kind of a surprise 50 basis point hike. So no, we haven't had investors coming back to us saying hey the risk free rate is 6% now, we want you guys to shoot for higher returns. But you have to be cognizant of that. If you have a strategy that's only making single digit, you know, 8%, 9%, and the risk-free rate is 6%, then, you know, then you have to have a conversation with your investors about possibly increasing leverage or those kind of things.

But we haven't seen that yet. Yeah, I don't know if you guys have had a resurgence of fund-to-fund interest, but when you have a fund-to-fund netting vol down to three, giving you a one sharp versus risk-free, that's a tough business. And so we've seen an uptick, family office, fund-to-fund, endowment foundation that frankly we're very happy taking all the equity risk in the world over the past 10 years and now they're massively underweighted to our space mercer you know is a great partner and you know in that consultant space a great group to sort of carry the torch but it takes time it takes time for them to come back to what they didn't believe in for ten years and so I think we're all

probably fascinated by a two-year sales cycle with risk-free rate at six even if you're putting up ten twelve you're far outstripping their low vol portfolios that are netting down even with a good sharp. So we're seeing at least a resurgence if not of people asking for more return, the people that used to be in our space coming back to the space. Who's adjusted the risk-free rate in their sharps and their decks? Anyone? Male Speaker 1 in audience 2 in audience 3 in audience 4 in audience 5 in audience 6 in audience 6 in audience 7 in audience 8 in audience 9 in audience 10 in audience 11 in audience 12 in audience 13 in audience 14 in audience 14 in audience 15 in audience 16 in audience 17 in audience 18 in audience 19 in audience 20 in audience 21 in audience 22 in audience 22 in audience 23 in audience 24 in audience 24 in audience 25 in audience 26 in audience 26 in audience 27 in audience 28 in audience 28 in audience 29 in audience 30 in audience 31 in audience 31 in audience 32 in audience 33 in audience 33 in audience 34 in audience 34 in audience 35 in audience 36 in audience 37 in audience 37 in audience 38 in audience 39 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 in audience 40 You went from zero. Yeah. Yeah. Ed. So just for definitions, right, because you're talking about systematic and trend following.

Look, what point does a course over from trend following with rule sets in there and then it becomes systematic in definition, right? Is that more products? And I mean, what makes the difference in trend following multiple products I think you mean becoming systematic macro? Yeah, I mean, I would say for the most part how Mercer would define it is anyone that's in systematic macro is a hundred percent

systematic with a few exceptions that they were talking about in terms of typically reducing risk there's almost no exceptions to that and Mercer's universe of how we're defining what systematic macro is and we used to call it CTAs beforehand managed futures all three of them are basically the same thing but systematic macro is kind of used because it's a little bit more all-encompassing for a few outliers so to speak and that's when with within our universe and we have our two subsets one being kind of trend oriented for more higher convexity profile more often than not in higher skewness versus the multi-strat style

i'll take a shot at that so these these guys are all registered CTAs, Commodity Trading Advisors. That's their registration status. The kind of consultant label is Managed Futures, which encompasses all of it. Trend following is the primary strategy within Managed Futures. I'm just, I know you guys do more than pure,

but I'm going to use you guys as the example there of EMC, more pure trend following. Now when I get over into Campbell's area and I'm doing more carry, more multi-strat stuff, now I'm starting to morph into a systematic macro manager that's doing more than just trend following. I'm doing some long equity, I'm doing some carry, I'm doing some other things. So I think to me, those lines have really blurred in the last three to five years of historically trend followers who've now morphed into doing way more strategies, multi-strat versus more discretionary macro guys coming down into the systematic space. And it's kind of

blending in the middle there yeah it's a good point we're trying to go upstream and systemize fundamental ideas so the way we think about it is it's non-price data so g20 economic release data is a big thing for us point in time for 30 years when you're building economic surprise indices that either you can trade directionally or mean reverting based on revised data, that's all non-price, right? And so that is where we really see things like systematic macro be a better definition. There might be price used in trend, I think universally with some conditioning factors around the environment, flip that on its head and use fundamental inputs

of which there are like, you know, hundreds, if not thousands, if not hundreds of thousands of more parameter sets you can use that are non-price to determine a relationship. And maybe a good example is Russia invades Ukraine. Generally, you would go long dollar and short pretty much everything else on a risk-off event

directionally as a trend follower. Our macro systems saw the commodity squeeze from the grain complex and realized that EMFX exporters were really going to go bid in the face of a risk-off environment and EMFX exporters were really going to go bid in the face of a but actually went long of those EMFX exporters, short the importers because of the grain squeeze and what that was going to mean to the macro environment. And so it's just a, it's another different set of inputs that are

non-price. For us, it's a combination of directional and relative value. And really, ultimately, the way we try to describe it in front of investors is I should be able to explain to you every single idea in this portfolio the way a discretionary macro trader could, and we just use computing power to systemize it. Rodrigo, I know you have something to say about that too. I think let's talk about the evolution of CTAs.

Back in the day, it was you had a chart in front of you and you were basically looking at the charts and as that line crossed over, you were calling and picking up the phone and making your trades. Well, you can expect, but as time has gone by, that has become less of a charting thing and more of a grabbing a computer, putting that data into zeros and ones, and this is what we do, we zero and one the situation so that we don't have to look at any screens,

the triggers are just happening in the background. So everything that you said with regard to looking at factors that are fundamental, price driven, and then codifying them so that we can, and we can have a lot more of them in executing i think what differentiates this category of systematic macro versus fundamental macro is that yes a lot of these guys that are fundamental macro are you are ctas they are trading similar contracts but they're more forward looking They're listening to the Fed notes and deciding how to position their trades. They worry less about that equal risk contribution and have more conviction weightings than they

do equal risk contribution weightings. So there's some people that are really good at that. We're not. They're trying to sparse out what's going to happen based on language and data and grabbing all those parameters and trying to see the future, whereas what we're doing is we're understanding what has happened in the past, given the certain parameters, certain patterns that we're seeing across all of our multi-factors and then executing that religiously.

So I think there's room for both. If you find the right, strong, fundamental manager, I don't have any skill set in finding that manager and I was bad at it, so I decided to codify it with my team. But does it create a better clarification saying it's all based on price data, price data and fundamentals, both systematic, putting just on on price data, and price data and fundamentals is supposed to be systematic.

Just put in on a price database, your stock losses and your trend breaking and all that, and that could all be non-discretionary to get a pattern to trade. But Brian talked about what state, the Mexican basis, right? You have that range from 18 to 25, then the sell-off, right?

How does a systematic pickup any different than a trend follow-on? Wouldn't it be nice to once that trend started, either way? I mean, it's got the beauty of like, okay, it's got the cavity, it's got those things. Wouldn't both of those trades pick it up? Well, in the peso as an example, we have in our flagship program, we have four systems that are operating. And like I said, each one of them has a different way of getting in and out of trade. So some of them, we design the system to try to get into a trade and stay in no matter what the volatility is. So to capture, the most efficient way to capture a trend

from point A to point B. So that system is much more accepting of volatility over time. And then we have other systems that look to complement that that are optimized to a different metric, and then we have other systems that look to complement that that have That are optimized to a different metric to a sharp metric instead of an absolute return metric So that system is designed to get in and out back in and back out and regularly try to be nimble. So You know we look at each system

independently and You know each one has kind of its own unique way of positioning itself in a market that's trending consolidating possibly the end of the trend is beginning to appear but we could still be long our longest term system even though it's it might be at a two-month low and you know other CTAs are not sure at that point so I think that the systematic macro would use the rate differential or a GDP print or something to trigger the same trade and totally agree yeah they're probably both going to be in the same trade which is why they get lumped together by the investors right of like hey these tend to kind of end up in the same place.

They tend to both be positive skew and have those pops in bad periods for traditional investments. So investors tend to lump them together, even though they're doing different things behind the scenes. Yeah, we're definitely seeing more of that in just the overall CTA space, systematic macro as a whole, just more people using systematic data. Sometimes it's more forward-looking versus backward-looking, depending on what data

and how you're using it as well. So sometimes, oh, maybe half or plus I've seen in some funds, at least, where they always do correlate. But then there's definitely others where might have the opposite signal, which is reducing their positions in times where other CTAs that are full price- based trend might be doing better without looking at additional factors so it's just another way to like diversify your type of trade instead of just being more focused toward one or the other and there's not necessarily a right answer to that that's right, but what I am describing, the volatility in those two, it's still price data that you're looking at, right? I would think that the real systematic, if we're going to talk about the fundamentals, it would be more like China decoupling and that's where they're onshore. And that's, how do you pick that up? Like, say, Campbell or Amico, you know, for picking up that, that's completely different. That's a price-based, I look at a volatility, right?

You're going to have this complete shift that you haven't seen in a while. Yeah, they might be trading copper outright. And we might be trading copper based on movement in the Chilean peso. Or we might be trading the Chilean peso based on the move in copper. And so I guess my point is that you can treat each market individually. Or the way we think about macro is, and this is both directional and relative value, the effect of one market on possibly a completely different market. So not curve trading, the way people used to think about kind of macro, but how

does a given interest rate regime affect the equity markets of a given country? And we'll look at one as the input to trade another. And so, as I said, it's not just the price of that individual market. The input might be something related economically that's driving the behavior, even though we might have the exact same position. I think that's, as Jeff said, the hard part is you guys all made money last year. Who do I value more?

How do I distill the quality of those returns? And I think that's hard to do as an investor. I'll give you an example of from a systematic macro, it doesn't necessarily need to be fundamental macro data. We trade seasonality, right? So seasonality is looking at historical price data, look at the seasons and the patterns, day of the week, week of the month,

month of the year, et cetera. And oftentimes that makes a trade that's complete opposite of what trend is doing. Trend might be saying, hey, go long gold right now, it's an amazing trend. And our other system, seasonality, is saying that's a terrible period for gold. You should be shorting two units of gold at this point.

Plus we have a mean reversion thing that's saying that that's in a six standard deviation event. So there's a wide variety of, it's kind of like systematic macros also, you can say multi-strat, but I think the category has been used as systematic macro to mean things in the CTA space that are outside of just pure trend. And some of them are fundamentally driven. Some of them are just different ways of looking at data. And just our evolution of like how Mercer's even called it has even changed in really five, six years just because of the evolution in the space as a whole. More fundamental data is being

used, more different versions of that. STATARB is sort of becoming part of Systematic Macro is how we define it. There's just so much evolution where even we cut out like how we viewed diversified trend because we used to have a different category of like 50 to 90 percent trend was one thing then 90 plus was pure trend and now it's kind of like how do you define that if you're looking at price versus fundamental data that's still for that's still looking at trends or something else and it just became more for us of looking for one profile or another in terms of risk return profile and all the characteristics around that i got one more you guys want to go drink just to your point rodrigo you have the gold trend you have the seasonality short and joe if

you have all these multi-strats how do you protect against just becoming the risk-free rate right of all these different models doing all these different things and you're just you know the no no no the opposite of like you have so much diversification that you just end up with the risk-free rate yeah when i when i came in in 2016 there was a little bit of a an attitude towards anything that's diversifying is good in the portfolio and I would say that there were some lesson learned around you know there used to be this thing around model count in the industry if you had more models somehow you were better. And I never believed in that. And so over time, we've sort of set a bar around saying like, look, in some models, a 0.5 sharp is outstanding, especially if it's lowly correlated to the rest of the portfolio.

More recently, in some of the short-term research we've done that David referenced on the website, we're finding negatively correlated models to other short-term models that are north of a 0.5 sharp. And so, as an allocator, you have some of the same challenges in terms of how to put a portfolio together and achieve a risk target and a certain return. For us, we just upped the bar on a minimum requirement for a model to have, you know, sharp characteristics, correlation characteristics. You know, some models come up to the investment committee and they're better implementations of what we're already doing so that can be a good

thing we try to kick out the ones that aren't additive earlier in that model development and peer review process and so it it's been a learning curve and we've definitely to be fair you know at times fell into well it's it's you know uncorrelated but its return is expected to be fair, you know, at times fell into, well, it's, you know, uncorrelated, but its return is expected to be a 0.0 sharp. And yes, it's uncorrelated, but that's like saying, you know, CTAs are uncorrelated in 2016, and we're all down 10. And, you know, you're buying an uncorrelated strategy, but it's not a good strategy at the time, given the market environment. So setting the bar higher around return expectations, correlation expectations with the current portfolio, and not, you know, we put in four or five new models a year. We kick out two or three. So, you know, we're not in an arms race. And on on the back of that we have to do a lot of work around

cost modeling that brian referenced when he does a two-week look back on volatility that has different metrics and then you got to think about infrastructure and all the things i mean it is a it takes you know it takes an army and it's not just those pure researchers you've got to have people around them that evolve the process to reflect that. And so, you know, when you think about evolving your belief systems, it's not small. Those lessons have to filter through pretty quickly if you're going to adjust over time. What he said, That was great. Anyone else?

Thanks again for everyone for coming today. Appreciate your time and hope you all learned a lot. Okay, that's it for the show. Thanks to Cohen & Co., RCM, and Mercer for sponsoring the event. Thanks to David for moderating

and acting as the de facto pod host here. Thanks to Jeff Berger for producing. Tune in next week. 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 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. 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.