The Derivative - Systematic Commodity Trading (without Trend) with Jae-Min Hyun of NWOne

Episode Date: October 5, 2023

In commodity trading, where volatility reigns supreme, finding an edge can be daunting. In this episode of The Derivative, we switch up our normal Commodity Trading talk with our non-trend following guest, Jae-Min Hyun of NWOne. With extensive experience in commodities trading and a quant background that spans Wall Street firms, hedge funds, and a new talent incubator, Jae-Min brings a wealth of knowledge to the table.  Join us as we explore the intricate intricacies of building a systematic commodity trading strategy using fundamental inputs instead of just price, why inefficiencies equal edge, the importance of risk management, the role of machine learning, and the rest of the challenges of navigating this dynamic market. Jae-Min Hyun's insights shed light on the complexities of this niche within the world of finance, offering valuable perspectives for both seasoned traders and those seeking to understand the nuances of commodity trading — SEND IT! Chapters: 00:00-01:32= Intro 01:33-15:49= NYC is wet, but back! An early start at Morgan Stanley constructing commodities & building Quants 15:50-29:25= Exacting Alpha in inefficiencies, market fundamentals, directional futures & diversification 29:26-43:20= Calendar spreads (delivering exposure) and all models working in concert 43:21-54:04= Why only commodities? Research, systematic strategies & the competition 54:05-01:05:46= Commodity exposure, alpha generation & A.I. in quant trading From the episode: NWOne Diversified Strategy The Predictors - Book Semi-Annual Rankings Whitepaper Follow along with Jae-Min on LinkedIn and for more information visit NWOne's website www.nwone-llc.com 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. It's October, and to quote Mark Twain, this is one of the peculiarly dangerous months to speculate in stocks. The others are July, January, September, April, November, May, March, June, December, August, and February. Love that quote.

And it has been a little iffy here in October so far, with stocks are down about 7.5% from their July highs. So we'll see what happens through the rest of the year. Okay, on to this episode, where we get to dig into one of the best commodity trading programs the past two years, talking with Jamin Hin of NW1, who surprisingly doesn't use a trend-following model, but instead a purely systematic strategy based on fundamental inputs. Hmm, how does that work? Let's find out. Send it. This episode is brought to you by RCM's Managed Futures Group,

who help investors identify and invest in programs like this one. Head over to rcmalts.com slash podcast to see the write-up on this episode or the RCM YouTube channel in this episode's description to find the link to the program's performance and can explore the rest of the RCM database. And now back to the show all right jay man how are you not too bad how are you good am i saying that correctly i believe so right yes uh and what's the uh etymology of the name there well it's a korean name uh behind it but uh i'm not gonna pull your investors well we want to know what what is it uh it's

actually meant to be um it's a korean name so obviously it's the chinese character i think jane means um i think it's meant to mean leader oh perfect apropos there you go thank you yeah uh and where are you in new york i believe right yes but we're based in new york city midtown new york all right you get any of that leftover what was it opheliaelia? The storm leftovers? Indeed. Yeah, it's been a very wet weekend. Actually, right now it's raining outside. And what's your take on New York? Is it back? Is it dying? Is it fine? No, I think New York is pretty much back. It's tough to get reservations at various different restaurants and various different events. So, yes, absolutely. New York is back. It's tough to get reservations at various different restaurants and various different events. So yes, absolutely New York is back. Love it. And we're in New York and Chicago. I can't tell

if that's a siren here in Chicago or in New York. It must be in New York. We got them here all the time too. So let's start. Tell us a little bit about your background. Had some options trading back in the day. So yeah, let us know where you came from. Yes, well, me as a person, I'm Korean. I spent a considerable amount of time in the UK, some time in Indonesia. I had a very brief stint in France. And I've been in the US for close to 12 years now. So that's my personal background. Yeah. So as my professional background, I started my career in Woodward Stanley in London. And I was transferred to New York 12 years ago. After leaving Woodward Stanley, I went to work for

a couple of different hedge funds. Ellington Management was my first place. And then I worked at Tudor's Launchpad Trading before setting up my own company NW1. So what were you doing at Morgan Stanley? Or I'll go back even before that.

Where were you in Indonesia? And what were you doing there? Indonesia, that was like when my family moved to Indonesia Or I'll go back even before that. Where were you in Indonesia? And what were you doing there? Indonesia, that was like when my family moved to Indonesia when I was little. It's my dad's business, so we moved there. But I schooled in the United Kingdom. Got it. Where did you go to school? Eton? No, I went to St. Paul's. I just know it all from that's my that pops up in the crossword every now and then i'm sure it does uh so morgan stanley what what were you doing there here got your first

taste of commodities yes so i did uh started off doing a bunch of different things uh converted bond origination interest rate derivative structuring, equity exotic structuring. But I ended up on the commodities trading desk. So I spent the vast majority of my time trading commodities momentarily between London and New York. And was that, were you kind of banished to the commodities desk or was that a good place to be? Was it a coveted spot or no

indeed yes uh more sadly i would say you know back in 2000s uh top two commodity trading houses on the street um between moonstone and goldman as you know moonst, we were more involved in physical commodities. As Goldman's, they're much more the paper side with their indices. But yeah, Moonsani was a major player in commodities trading. And so what did that look like? You were helping facilitate moving oil tankers and rail cars full of grain, all that good stuff. Yes, sir. Yeah, absolutely. So, yeah, go ahead. oil tankers and rail cars full of grain all that good stuff yes sir yeah absolutely so

say again we're dominant in physical trading and would so i've always thought was that easy to be on the trade desk if you have the physical to back it up right if you're wrong you can take delivery or vice versa you can did it make it a little bit easier a firm level yes but obviously yeah they'd still count it against your pno yeah yeah i traded paper but i sat across from physical traders so yes i did learn a lot of uh ins and outs of physical trading uh and also market color was helpful but, we obviously, we all had our own books. And then when you say trading paper, you mean futures? Yes. Futures and options.

Those are fighting words here in Chicago. You can't call our beloved futures paper. So then moved out of there and went to, what was the name of the hedge fund? Ellington Management. Ellington. All right. What were you doing there? So I was hired as a portfolio manager at Ellington.

Ellington, predominantly a fixed income derivative house. I set up a global macro fund. So I was hired as a commodity specialist for that fund. And all pure quant? Yes, systematic. And even backing up to Morgan Stanley, were you pure quant then or you were learning the ropes and figured quant was more where you wanted to be? No, at Morgan Stanley, we were all discretionary traders, right? So there were few systematic traders

on the prop side of things, as well as on the agency side of things, but we are on the principal desks. So we are discretionary traders. So we are market making for big institutional flows. So we are all discretionary traders. Ellington Management was where I first learned the ropes of doing systematic trading. And what was your first foray there? Were you kind of trend to a trend following model as most newcomers to quant do? No, I mean, you obviously study various different strategies that are popular. So I did study trend, mini version, carry type strategies when I was at Ellington. But I focused mostly on what I knew, which is bottom up fundamentals trading and and commodities so my role in the team

was to systematize a lot of the offers that I traded and I knew had more insanity love it and then you finally said hey I'm gonna go hang my own shingle so to speak yeah start out on my own how was that nerve-wracking oh absolutely absolutely yeah not an easy journey um by all means um but it has been rewarding and how do you think about that even now as you hire your own people right like does it make you to those funds they fear obviously you're going to go leave and start your own thing, but they also must support it. And they want to train you well enough and teach you enough that you could go do it on your own, right? So how do these firms strike that balance of like, we want to teach you everything we know, but we also don't want you necessarily to leave, but we probably know some percent of all of you are going to leave.

Yeah. So that was the initiative behind launchpad trading, right? So this went after Ellington management. The objective was to identify the next set of start-up traders, give them the infrastructure, the capital and support to make them into qualified money managers right right um yes there are various different institutions places out there that nurture um relatively young traders right

um but those seem to be very few and fine between these days yeah harder and harder it's when what the conversation now probably starts with how how well can you code how well can you do computerized things versus not and then i i buried a little so what was your education and were you a quant by trade in your schooling yes so i started physics uh cambridge bachelor's and master's so i did have a natural inclination to model things right processes mathematical models so that obviously helped um when formulating systematic strategies uh and commodities and do you feel the uh physics background do you still right do you think it's a solvable puzzle or do you think it's always right it may be some far string theory kind of astrophysics of like there's still unknowns and we just have to kind

of do as best we can to get close to the answer absolutely, markets are always evolving, right? The physical world is relatively established, right? Yeah. It's a function of getting the right models, ideas, to fit, to come up with mathematical models and whatnot to fit the world, whereas

markets themselves are constantly evolving. So I think it's a different challenge, right? When trying to fit mathematical models to market behavior, human behavior. So there's most definitely different challenges involved. Right, and I think you see a lot of people come out of I'll use science loosely, right? Or mathematics or science and they come in and they think it's 100% solvable and if they could just measure this or if they could just

measure that or get the right factor they'd solve the machine and be able to print money and all that stuff but sounds like you're saying not not quite that easy no i don't i don't think it is i mean certain areas of i think trading um there are closer linkages, I would say. So if you talk about startup trading, equity longshore startup trading, statistical arbitrage trading, I think you probably see more of those PhD types. But most definitely not in commodities because commodities will require domain knowledge commodities and they don't teach you that you know in schools right right there's a great book old book the predictors um it's about they were out in santa fe i'm gonna forget the uh name of the firm but they basically a bunch of PhDs and they left and tried to model all this.

And there's chapters about how they didn't realize there was slippage and they didn't realize commission costs and exchange. Right. So there's just the lessons they had to learn to get their models close to what they thought they could do were enormous. Yeah. Back in the 70s. We'll put a link to that in the show notes if i can remember um so anything else we need to know the background where did you come up with the uh nw1 name oh yeah um it's named after a postcode in london which is where i used to live so i was inspired by my former boss mike veranos ellington who named ellington after where he grew up. So it's a kind of homage to Mike.

I love it. I didn't even know how postal codes work. So NW1, that sounds fancy. It sounds right in the middle. First postcode? That's right, Northwest 1. So it's very close to, well, it includes Regents Park, which is where

I used to live, opposite Regents Park, which is where I used to live before I came to New York. Got it. And how long have you been in New York? New York, 12 years. 12 years. And how long has NW1 been up and running? We've been up and running six and a half years. Six and a half. So what are some of the best parts about running your own firm versus working at a hedge fund and some of the worst parts? I would say working at a...

Obviously having your own company enables you to make decisions that are purely based on your own thinking, right? So not to many people. But with that flexibility comes more responsibility, right? But again, I've worked at different institutions, big institutions, small institutions. And when I started the business, I thought I had a pretty good idea of what a perfect institution should be. And I'm striving to achieve that.

Working at pods, other places, has its own challenges, right? On the upside, you're given a lot of resources on the downside um capital obviously is relatively competitive at those shops and they have very strict risk limits that sometimes move as a function of the market or as a function of the office. So those are the challenges which prompted me to leave that space and start my own shop. But funny enough, it wasn't like you were, well, I don't know what you were like at the shops, but it wasn't like you needed to be more risky, right? You're not very risky now. No, the objective function is maximizing risk-adjusted returns.

So we're not in the business of swinging propensities. So let's dive into the strategy a bit. For those who want to go download our rankings white paper, this has been up there amongst the best over the last couple years so let's just start with kind of a 30 000 foot view what you're trying to accomplish what the strategy does and then we'll dig in from there yeah so our programmed objective is to extract alphas or inefficiencies from commodity markets that are based on individual commodity market fundamentals.

So we mostly focus on market fundamentals as opposed to technical factors or heuristics that a lot of our competitors that specialize in systematic strategies do. So that's our biggest differentiator compared to competitors um and pure commodity too right yeah we only trade commodities so pure systematic commodity and then that was interesting to hear you say that so do you view alphas and inefficiencies interchangeably? Are those the same thing to you?

Yes. But that implies that you have to be able to capture the inefficiency, right? Or profit from it. Absolutely, yes. And again, there are alphas or inefficiencies that are present in the market that you may not be able to capture using systematic frameworks, right? So that's the way I see it. So pure commodities, how many, what are we talking, the global list? The most liquid commodities, how many commodities are we talking? Yes, we trade up to

20 different commodities. We trade in terms of instruments, we trade our features into commodity spreads, calendar spreads. So we're relatively varied in terms of exposure across six different commodity sectors. So all the usual suspects there, oil, grains? Yeah, softs, meats, precious metals, base metals. For base metals, we do not trade LME. I'm not trade LME. No, I've not traded any contracts since leaving more than

standing. Was that so that wasn't a result of their nickel debacle? No, it's just that where they reneged on all the trades. You know, obviously, in retrospect, I think it was the right thing to do but LME contracts are relatively expensive to trade

as far as I'm concerned because you have to trade for three months and then roll to the nearest futures lookalike so you in principle you end up paying transaction costs twice and that was quite a bit frustrating for me so for those reasons i decided not to trade

any contracts in that program got it and so those parts again so you have um directional future strategy and what was the other inter commodity Inter-commodity spreads? Yeah, inter-commodity spreads. That's part of the directional strategy. So that could be long coffee and short cocoa, something like that? Yes, in principle, yes. Okay. And then we spoke before, the future strategy is shorter term? Yeah, my future strategy is shorter term in terms of of holding period, as a holding period around one week. And the calendar spread strategy that has a long,

longer holding period between one to eight weeks. And our last intraday strategy that trades out like futures, and that has a holding period between half an hour to one hour. So let's go through those in order. So the future strategy directional holds up to a week. It's not trend following. It's not mean reversion. It's something in between, something different. So how are you generating, without giving away the secret sauce, how are you generating

those signals? What are you looking at there? Yeah. So for the RI feature strategy and the calendar spread strategy, and to a certain degree the intraday strategy, they're driven by right now nine different classes of models, right? So each class of model model the objective is to identify orthogonal sets of inefficiencies or alphas in the market and the idea is to build a collectively exhaustive uh way of extracting alpha where the individual model classes are orthogonal

from each other so within those nine different classes you, we have different ways of identifying sets of inefficiencies. Could be anything between pure fundamental data set, could be seasonal risk premium, could be related to the black relationships between various variables and asset prices. Some of it could be based on market positioning by the speculators or consumers. All those one type of stuff. It's an easy way to think about that. I know the price of production for oil is $76 and we're trading at $86 or something, right? That relationship between that fundamental piece of data and the market price?

Yeah, that is a component, yes. So we do look at the SMB dynamics for various different commodities, what data is available. We will then run various different statistical tests against those variables and changes in either clap price, calendar spreads, or into commodity spreads. And for those, for the specific set of, say, fundamental data set, if it has a high forecasting power for countless spreads, then it will be considered for inclusion in the final portfolio. But we obviously go through a rigorous in-sample, out-of-sample testing. And after that, we have a period of paper trading before anything can be even considered to be included in the final portfolio. And is the whole idea there is you're using these fundamental factors to kind of assign a value price for the commodity? And then if you're below that, you'll buy.

If you're above that, you'll sell. Yes and no. So we, in terms of our model outputs, we try to, we focus on continuous outputs, right? So we never have a single step function type model output. So all our models generate a continuous spectrum, minus one, most bearish, plus one, most bullish. Got it. And everywhere in between. Everywhere in between. And then by having continuous distribution, it's much easier to combine different signal sets and to minimize any data mining, right? For example, if you have a step function, you could always tweak your threshold.

Narrative will fit the most recent historical data but we avoid that by using continuous spectrum that's interesting so so across all those models we'll stick with uh oil or we'll get off oil because we're going to talk about that next but cotton you trade cotton yeah it's a challenging market. So we'll talk cotton. So model one is saying 26% long or something like that? Yeah. Model two is saying 16% short. Model three is saying...

And then you'll get the net of all those and say, okay, we want to be 32% long. Absolutely, yes. So we apply a different weighting function across different model outputs and then the resulting aggregate of those individual model outputs will be applied to either cotton flat price, calendar spreads. We don't do intercommodity spreads in cotton.

But the idea there is not that, so you're into commodity spreads in cotton. Got it, yeah. But the idea there is not that, so you're not necessarily saying cotton's underpriced versus its real value, we want it to go up. You're just saying over the next week, we think it's going to go, the aggregate of the models believe the price is going to rise based on those factors. We don't care why.

No, we do care. Individual models will then will have their own reasons to say why it should be going up, right? For example, one of the models could be based on fundamentals that say, okay, fine, in terms of S&D balances, you know, latest USDA balances, the model is bullish, according to spreads. on the flip side it may say you know the currency dynamics between these dollars versus various different other foreign currencies make the tapes bearish right i like that so that's the directional futures and then it could be in theory could it be 100 long right one on all of those or there's limits within within each sector and within each market yeah

there are limits so at the individual model level will produce a model signal from minus one to plus one okay news will then get aggregated based on our proprietary algorithm and they will then get passed through to our portfolio construction engine. So at the portfolio construction engine, we place constraints, right? So we have constraints at various different levels. So one of the constraints we have is at the sector level, let's say no single sector can take up more than 50% of the portfolio risk. And within the sector, no single commodity can take up more than 50 percent of the risk. That ensures our portfolio is diversified at any given time, right? Because we trade across up to 20 different commodities, we rely on diversification, right?

So therefore, our portfolio has to be diversified in terms of exposure at any given time. What would you say some trend followers that trade 120 markets would say that's not enough diversification, only 20 markets isn't enough. Of course, a lot of those that they're mentioning were probably fixed income and currencies and bonds. But what's your view on how many markets is enough versus diminishing returns of adding more markets? How do you view that? Yeah. trying to extract this unique set of alphas that stem from either cognitive biases in

the individual market participants or inefficiencies in information transmission mechanism. What they're trying to do is they're trying to extract that small piece of alpha that are present in every single market. And the only way you can consistently do that is by deploying this strategy across many, many different markets. So they need diversification to extract a relatively small amount of alpha that's present in each of the markets. Whereas our approach is quite

different, right? So we don't look for top-down, heuristic-based alphas. We study the individual markets, right? Say, okay, what are the fundamental drivers of crude oil, of corn, soy milk, right? So we build understanding around the visual commodity market, fit relevant models, rigorously test them, and then we build portfolio from bottom up. So our portfolio is very much bottom up. Therefore, by construction, you can say that our exposures are less correlated, right? Because in trend following, we've seen this many many times right if the trend breaks in one segment market uh that tends to translate to the other segments of markets right because people unwind certain set of strategies um as a knock-on effect whereas

for us we treat the division market as his own own thing. Do we get affected by some of these flows from trend followers? Of course we do. But again, the way we construct our portfolios, until we're robust enough to withstand any of those flows. Is that an input even? Is that one of the data pieces in a way of those flows? Yeah, I mean we do monitor flows. We monitor flows of speculative community as well as hedging community.

We have models that contribute signals from those types of alphas or inefficiencies. So it's the futures, next was what? Calendar spreads or inter-commodity spreads? Or you use those interchangeably? The calendar spreads. So inter-commodity spreads falls into the bucket of outright futures trading. Okay, you're basically just creating a new futures market? Yes. Yeah. All right. Will that get really crazy or there has to be some link?

It has to be soybean and bean meal or oil and that gas. You wouldn't do something totally crazy. No, we only look at pairs or spreads that are actively traded by, say, pedgers. Okay, got it. So it's like a logical fundamental link as well as a quantitative link. Correct. Yeah. Okay, so then the calendar spreads, what's going on with those? Yeah, calendar spreads is obviously inter-delivery exposure, cost of

commodities. And one nice thing about calendar spreads is that you're hedged from currency exposure point of view, right? So people who don't have, say, a cotton contract,

so you're long cotton and you're short years dollars, right? Whereas in calendar spreads, if you have the long and short across different parts of the curve, the whole thing will move up or down. Yeah, mostly. So that's nice characteristic of calendar spreads. And also, calendar calendar spreads obviously are linked to storage dynamics for various different commodities. And storage obviously is mostly driven by fundamentals. So trading calendar spreads is a pure way of expressing or extracting any alphas that are related to fundamentals

Do you view that as trying to earn a yield sort of? Almost like a carry strategy? No, not necessarily I mean there are certain spreads that exhibit either positive or negative beta, right? Where you want to express it. But our goal is not to extract those sets of premiums. If it's there, fine, you know, we will extract it. But our goal is not to exclusively extract those sets of themes. It'd be more of, hey, based on these fundamental factors that we've brought in as data, we think it's going to steepen or it's going to flatten. Correct.

Yes. Do you ever get curious and run that on bonds just to see where we're going to go, where the yield curve is gonna go uh no unfortunately no i try to focus on the things that are that i know exactly yeah right uh and so the futures was days to weeks the calendar spreads weeks to months that makes sense yeah weeks in a couple of months um and as you know know, calendar spreads exhibit lower dollar volatility. So therefore, you have to have a relatively long holding period to justify transaction costs as well as the volatility of those contracts. But in the past, some people have been kind of a widowmaker trade too, right?

Because it has such low volatility until it doesn't, until the spread totally, you know, who are the NatGas guys famously, but Amaranth. So how do you avoid that of like, okay, I know it's a low volatility trade, I'm going to hold it months, but it has the ability to get totally out of whack, right? Hurricane Katrina, those, all the production was shut shut down in New Orleans. So there's outside events that can make those spreads go pretty crazy. Yeah, I mean, that's a definite concern. So we mitigate that by, number one, using relatively conservative estimates for volatility, for sizing trades. And number two, we're commodity specialists right so we don't wear out those sets of spreads that can blow out or have these risk reward profiles uh and we position our

strategies or or trades to be on the long convexity side of things, right? So those spreads you talk about, a lot of these black-ass spreads, especially some gasoline spreads, some of the seasonal spec changes, they have a – it's like being long carry, right? Yeah, where it's like a negative skew, right? I'm collecting, collecting, collecting until now. So we tend to build our portfolio to avoid those. We want to build a portfolio of complex expected returns, right?

Positively skewed. So often, you know, we take the other side of that. So often, you know, some of our positions end up paying away a small amount of decay with expected relatively big returns. And so we have a lot of long volatility people on this pod. Do you view it as kind of a long volatility program? Do you believe it'll do better in a way to March of 2020, those kind of periods when volatility is spiking?

That's kind of difficult to say because we don't construct our portfolio or our signals with those specific characteristics in mind. So by construction, we try to be agnostic. But do we do better in those volatile periods of high elevated levels of macro volatility? Probably not, because our strategies and our models are based on individual market fundamentals, right we want we need an environment where individual market fundamentals can play out if there's an elevated amount of macro noise then our signal to noise ratio each model is expected to be lower right right you cannot expect our portfolio to perform you know when all hell's breaking loose right but then you have long ball players out there, you have trend followers who are supposed to take advantage of those conditions. So we don't aim to offer... We don't aim to be like...

Stig Brodersen Yeah, yeah. But it was interesting to hear you say you are cognizant of, well, we putting on at least in the spread trades right these huge negative skew negative convexity trades um but yeah that's it makes me think of back during covid right and they're on the news saying we've never seen the meat market like this and there's we have to kill all you know call this herd and do all this stuff so if that kind of talk is going on it it's generally not fitting with your models, right? Because it's outside the norm. Yeah, that's right. COVID wasn't particularly friendly for us.

Huge demand destruction across all commodities, right? So those kind of idiosyncratic events have an overwhelming effect across all different markets. You can't expect us to perform. We haven't designed our portfolio or our strategies with those events in mind. Right. But your risk control, you're not going to blow up in those type of events.

You're just going to mainly be on the sideline or be lower conviction. And then the most interesting piece of these three to me at least is this intraday oil trade so what's going on there even though you said you can't tell me too much about it we'll try and pull something out of you yeah so the intraday trading in the world the idea is to model behavior of physical traders who are active during certain periods in the day. Our objective is to trade around them. So we anticipate what the physical hedges are going to do and we trade around them.

So fair to say flow-based, for lack of a better word, or demand-buy-side-based, something like that? Yeah, it's flow that are derived from hedges. And as you know, hedges, they tend to be less price-sensitive. Yeah. And who are we talking about they're like airlines and uh manufacturers whatever any bona fide oil hedger or even the majors who are trading in and out on both sides oh yeah everybody okay so not just hedgers but well they're hedging on

both sides got it i was thinking just the long side. Great. All right. So then those three are always working in concert. Is there a set budget, like a third of capital to each? Or they're just each model's independent bottom up, you're saying, with the risk controls on top? Yeah, so we do try to limit risk exposures across different strategies. So each strategy, the three strategies, the outright futures, calendar spreads, and intraday strategies, they're always competing for risk capital. So we do a monthly review of risk allocation across three different strategies.

But again, similar risk management framework is in place so no single strategy can take up majority of the risk capital um majority in the strategy level sense is 60 so no single strategy is expected to take up more than 60 of risk capital portfolio level that ensures that you know ensures that in terms of our exposure at any given time or different groups of offers, it's diversified. 50% max sector and market inside each strategy sleeve and then each strategy sleeve is 60% max?

No, the sector exposure that's done on the portfolio level, yes. Okay. Right, because then you could have 50 of 60, 30, 30, 30. You could have 90 in one market if you did it my way. Stick with your way. And so that is quite unique. And why just in oil for that intraday trade? Do you do any other markets?

Or no, for now it's just in oil? No, it's just oil. There's a phenomenon in oil that's driven by fundamentals that induces hedgers to behave in a certain way. That's why it's currently only trades oil markets, but we are always researching different markets for interday strategies. And let's touch on that for a second,

just what your whole research process looks like, how long it took you to get to where you are now what this might look like in two years will be the exact same or always progressing what's what's the research that look like yeah so we're always progressing um the nine different class models are currently in line you know i began with one class of models, right? We've added on many, many models. We've retired many models. The research process is always ongoing,

but it's always incremental, right? You can't expect us to make any big changes because we need enough out of sample data to justify model inclusion or model retirement. So you'd expect the makeup of our models to expand overall as we have more resources to deploy, as there are more data sets, as the market evolves, we'll be adding on more and more models, more strategies. At the same time, we'll continue to retire any strategies or models that are not working.

As markets have structurally changed. So yeah, it's an ongoing process. And back to that other question, the 20 markets, is there some limiting factors or point of diminishing returns? Right. Could you eventually have 500 models, 5000 models? Yeah, absolutely. There are there is the element of diminishing returns. Right. Because there is there's so much inefficiencies out there right yeah the efficiency is obviously we try to extract using different models often could be overlapping uh to a degree um so if that's the case then we'll look to enter new markets right so we'd love to be able to trade chinese commodities onshore and

offshore q3 commodities that's in the cards. I also like to be able to trade FX contracts and also interest rate contracts. But that's, you know, much, much further down the line. Well, I know a guy who can help you get into China. Zooming back out a little bit, we touched on a lot of this but i just want to follow up with uh the why commodities question right why and touches on you want to get into interest rates you want to get into that stuff but why why is it just commodities for now well number one you know my background has been in commodities trading back to mid-2000s. But another interesting aspect of commodity markets is that you have this one group of market participants, namely hedgers, who are willing to pay away hedging premium to the market.

So you have a large group of participants that are paying away hedging premium to the market. Now, from Speculate's point of view, if you have relatively good models to model the price process, model the fundamentals, you can pick up those hedging premiums that are being paid away by producers and consumers. That's a unique aspect. So I'd rather participate in a market where a group of participants are willing to pay

away X amount of value to the market, as opposed to having to fight with other like-minded speculators for a relatively limited amount of inefficiency available market right so right i think that's why i like i think commodities markets is pretty unique from that point of view um but also commodity market is relatively inefficient compared to the markets right because if you talk about commodities you know you can talk about different grades of commodities number one number two where you know geographical uh attributes associated with commodities right is it soybeans growing in the us is it brazil right um so it's not just like one single asset talking about the spectrum.

So that leads to more inefficiencies in the market that can be extracted, right? If you're an informed speculator. So this is why I like trading commodities. You're preaching to the choir. And how do you view the traders at the big commodity houses who have billions of dollars? They have the same ideas, they have the same thoughts, and they have way more money. So how do you view that kind of competition of why don't they do what you do? Yeah, it's a very good question, actually.

So some of these big commodity trading houses, their primary business is in physical commodities, right? So transporting these physical commodities from region A to region B, storing those commodities, and then obviously hedging those exposures, sending them ahead. So they're pretty much involved in trading physical commodities. And they use paper slash futures market to hedge their positions, right? And we are exclusively focused in trading futures. So, in fact, a lot of the activity, a lot of these trading activities that are done by physical commodity trading houses are useful for us, right? We use information, the hedging activity of these physical traders to infer, to augment the current fundamental market state of various different commodities, right?

So I don't particularly view them as competitors per se, right? They play a very important role, obviously, in the global trade flows. And if anything, their hedging activity tells us more about the current fundamental market state. And as to why are they not involved in trading futures in the way that we do. I'm sure they do. But, you know, give an example of, you know,

a car company, for example, right? So, why is Mercedes not involved in running taxi business or Uber,

for example, right? Their primary focus is physical commodities, right? And our primary focus is physical commodities, right? And our primary focus

is futures trading. Obviously, there are areas that overlap for the most part. They stick to what they know, and we stick to what we know. But ultimately, sure, in 10, 15 years' time, there will be some sort of vertical integration, right? Or the car industry, that's what Elon Musk is trying, right? He's trying to integrate, have robo cars running around. And commodity markets, maybe in the near future, that will come.

That breaks my brain thinking about that. if you were inside one of these big places and had all their information and their flow and we're trading against it right would it basically eliminate the inefficiency because then their other side of the business would be like oh jay min's telling us we're overpaying here basically by his pnl so we're gonna we're gonna lower that side and your pnl is gonna come down right so the it's kind of hard to think about them existing in the same place because it would kind of cannibalize one or the other, would even each other out. But you'd think like a Citadel or some of these big firms would say, hey, and they do. Right.

They have commodity trading. They have oil trading. So I'm sure they're looking at many of the much of the same similar stuff. But. Yeah, definitely. Yeah. many of the much of the same similar stuff but yeah it was definitely you know yeah but you you've got your own flavor that does what does what it does just coming back to how do you view right if you started as discretionary on that Morgan Stanley desk and now totally systematic or there were times when you're like oh I wish I I don't want to put on that position that doesn't make any sense or if i was discretionary i'd do it separately like how's that journey been of coming from a discretionary trader to a purely quant systematic yeah it's um it's an ongoing journey

so um i think it's a journey that not many people take, maybe for obvious reasons, but I've certainly been on it. It's been interesting, right? Are there times in which I wanted to overwrite some of my signals? Absolutely. Have I done it before, early in my career? Yes, I have. But the beauty of

systematic strategies is that you're looking for sets of signals or phenomenon that are repeatable, right? And are relatively persistent, right? Whereas if you're discretionary trader, you look for one of these, you look for one off relatively big opportunities that come by maybe two to three times a year, right? You're limited to that. You're systematic, you're able to extract relatively difficult to obtain alphas, but because you're systematic, you do it in a systematic manner and you do it across many different strategies, you're able to extract those sets of outputs. So I think it's a much more sustainable way of extracting outputs from the market.

But that is not to say, you know, there are plenty of successful discretionary traders out there, right? Less and less these days, but yeah. I much prefer this approach compared to discretionary trading. Right. And how do you view, right, the USDA report is notoriously error prone, lots of revisions. China has been known to manipulate their orders and whatnot to get better prices for themselves. Right. So you feel like a discretionary trader could better see through those.

That bad data, so to speak. So yeah. How do you view that of like, okay, I need to make sure this data is what I'm really seeing. Yeah. So what we do here is we augment the pure fundamental data set with some of other market derived signals, right? the pure fundamental data set with some of other market-derived signals. So if those two are contradictory, ultimately, our aggregate model signals tend to be lower, because fundamental model will say X and the market-derived model will say why, right? So then exposure will be less. So we have

strategies and models that mitigate some of those types of nuances you just described. But you know, more often than not, these things are relatively short term in nature, right? So USDA, they may get it wrong. One or two crop cycles. Overall, they do a pretty good job. But for a short term trader, that could be all the difference in the world. One or two cycles.

That's true. But that just shows to me like it's hard, right? We can sit here and talk about, oh, we just throw in this fundamental data and get these price picture and can trade this stuff but especially in commodities that data is harder right it's harder to come by or no like do you use any alt data sets like satellite imagery or things like that or is it all rather standard stuff that comes out of bloomberg yeah so we have studied some of the alternative datasets in the past and there are many

providers of alternative datasets but because often when we go through model formulation and model validation some of these alternative datasets do not meet our standards. So we currently do not

use any alternative datasets in production, but we keep track of many different data's, alternate data out there. So for the models that are in production, we predominantly use data coming out from government agencies or well-recognized trade bodies. And I'll come back one bit on commodities. So do you have a mandate to have commodity exposure?

Like would someone switch out their 5% long commodities at an institutional portfolio for your strategy, or they're not necessarily going to get the commodity exposure, but not the correlation to upside in commodities? Yeah, our program, we don't have a long bias in the commodity markets. So if you're looking specifically for price-related inflation protection, you cannot expect that from our program. So our program is designed to extract inefficiencies or alphas that are driven by market fundamentals across different sets of commodities, right? So we are more absolute return focused as opposed to offering beta with respect to a given commodity market. Got it. So throw it in your alts absolute return bucket. And then as you've been on both sides of

this, I wanted to ask the question of kind of how do you view and coming back to your strategy design the pros and cons of like okay i've developed 20 models say in-house that i'm implementing they're non-correlated i'm getting different alphas versus i'm a pod shop and i've hired 20 different alpha sources so kind of right what are the what are the pros and cons of hiring it versus building it yeah i mean i can't speak much for hot shops because of you know because i've never i mean i sort of worked at a pot shop um but i can speak from fund the funds perspective right so you can conceivably replicate um a lot of the outputs that we look for by hiring different sector specialists right so you can hire someone who specializes in oil not gas grains soft metals meats but then by doing that, you effectively, you know, is a much more costly way of doing things, right? Because you have to pay, if you're talking about the funds,

you've got to pay management fees and incentive fees to individual manager, right? And after that, you don't get in any effects yeah group of traders may make money another set of traders may lose money right but then you still have to pay fees management fees regardless or you have to pay incentive fees on the managers who make money right you don't get any callback from managers who lose money right so by trying to replicate by you know a diversified set of commodity outputs in the way that we do by hiring discretionary traders is a much, much more expensive way of doing things. Whereas for us, we deploy systematic strategies in place for discretionary traders, right?

And we pass on all the netting effects to the investor, right? Right. So that is a very cost-efficient way of getting exposure to alphas that are related to individual commercial markets. But how would you view, right, it's like a bet on, and I wasn't necessarily saying a pod shop trying to do exactly what you're doing, but just in general, like, right. I've because you're kind of creating the same thing, all these different return drivers, right.

An ensemble. And they're doing the same thing, creating an ensemble. They're not correlated. Let's generate the return. So it's kind of just the question of the hive brain versus your brain, right? Like you're it's all coming out of your brain and your research and your team, but versus each of those in theory comes not only with their own model, but their own way of thinking about it. And although I would suspect after being in there for a while, they kind of get some group think and approach problems the same way. But so I don't

know if there's a question in there, but it's just interesting to me, right? Of the, the two paths of, Hey, you can do this all with one paths of hey you can do this all with one brain or you can do this all with kind of a galaxy brain of these different people look you know kind of leads leads on to a nice segue you know we are expanding you know it's not just gonna be one brain right so i am we're actively recruiting uh multiple uh quantilists or contractors multiple quant analysts or quant traders who are going to be value additive to the organization. So I don't think it's about our approach

versus their approach. I think the approach is it's about dedicating resources to extract inefficiencies from the market. And we are actively looking for more brain power for our team. Which leads me into

AI. So do you use AI to augment that already? And just being a physicist, being a quant, what's your views on the AI buzz recently? Yeah, I mean, it's been fantastic, right? So the AI and machine learning investments into those areas have definitely been helpful for us. So we use,

all the architecture we use in-house is developed from the open source packages and tools available in the market. So it has been very helpful to us. the open source packages and tools available in the market. So has been very helpful to us. So in terms of using AI tools, it's less relevant for us because in AI, you typically need very, very large sets of data, right?

To train your models, right? To formulate your models, to formulate your models, formulate slash train and validate your models. Because we do bottom-up fundamentals-driven trading, so we treat each individual market as its own entity, there simply isn't enough data available for a given market to train a relatively sophisticated AI model. So those are the reasons that prevent us from fully adopting AI. But we are using tools that are machine learning based.

We're actually using those tools for our research as well as portfolio generation. Explain that for a minute because Korn goes back, I think there's 100 years of data on Korn futures and whatnot, but you're saying there's not 100 years of USDA reports and some of the other fundamental data pieces? Yeah, so this goes back to, you know, question of why aren't other people doing what we're doing, right? Yeah. The typical, you know, systematic strategies or systematic trading, as I mentioned earlier, you see it in long-shared equities or start-up, right?

So in start-up space, you typically trade 2,000, 3,000 stocks on the long side and 2,000, 3,000 stocks on the short side, right? So every day, you have up to 3,000 data points, right? Right. That's a data points, right? Right. That's a long short, right? And if you were to backtest those strategies across many, many years, right? 10 years, there you have enough data point

for you to formulate as well as validate any models that extract inefficiencies that are present in every single dose, every single stocks, right? Whereas for us, we don't have that luxury, right? So we only get to observe, if it's end of day data, only one data point per commodity, right? So it's very, the challenge is, you know, adopting some of these off the shelf models and frameworks into our space due to lack of data, right? So

the way we get around that is by augmenting fundamental knowledge, right? So we can really zone in and say, okay, a lot of these feature sets or potential data points are not really relevant for determining the price process for a given commodity, right? Because I know, my employees know, because we studied the market from the bottom of our fundamentals point of view. So we're able to bridge the gap. And the gap that's that's missing due to that data points. Yeah, that's, that's, you know, our philosophy. And

that's the way we approach this matter trading in our space. Right. That's a cool way to look at. All right, I think we're about to leave it there. You got any other last bits for us? Anything we missed? No, I think we covered pretty much everything. Tell everyone where they can find you. What's the website? Website is www.nwone-llc.com. You can also reach us on LinkedIn. Like I said earlier, we're actively looking to hire

quant analysts, quant traders, quant researchers. So please get in touch with us. We've got a lot of young student level people listening to this, so go give them a call. What does that market look like? Has it been tight? Are you competing with a lot of other firms with that talent yeah i mean there is

there is a lot of competition um but the kind of people we look for are relatively different to what what these pots look for so yeah yeah Or versus like going to work at Google or Amazon or something too, right? Awesome. Well, thank you, J-Man. Thanks for being here and best of luck moving forward. We'll come see you next time

we're in New York. Will do, sir. Thank you, Jeff. Thank you. Okay, that's it for the podcast. Thanks to J-Man. Thanks to RCM for sponsoring. Thanks to Jeff Berger for producing. We'll be back next week with, I don't know yet, but tune in to find out. Peace.

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