Quant Models in the Oil Market

[00:00:02.12] - Speaker 1
Good morning, everyone, and welcome to this amazing live session on a Monday morning. We are very excited to be here today because together with me, I'm here with Ilya. Ilya Bushuyev, which is really a former president of Koc Global Partners, where he worked there for 20 years, where he managed the derivatives trading business. You are now a managing partner of Pentathlon Investments. And obviously in your career, you also had a PhD in applied mathematics and you actually worked at NYU. And we're also very excited because you actually released a very, very exciting book, which is Virtual Borrowers Quantitative Trading in the Oil Market. We're going to talk about it today and some of the slides from the book. But Ilya, I'll pass it on to you. If you want to give a brief introduction about yourself for the audience, we don't know you, that would be great.

[00:00:54.16] - Speaker 2
Absolutely. Thanks, Fabio, and appreciate the introduction. So, kind of my quick story. I have been a trader all my life. I joined trading company back in 1996. Koch Industries was the largest privately held company in the world and one of the pioneers of energy trading. So, so. Back then, the energy trading wasn't really very quantitative. So I was one of the first quants in the industry, and I was very fortunate. And then basically I had the same job for 25 years. When I was leaving the industry, the industry was full of computers. They were much smarter than I am. So I, I kind of decided that I may actually leave the professional trading and write the book or basically the history of what worked over this 25 years and what didn't. So that's kind of my story. I don't know if, Abe, if you need anything else to say, disclaimers or anything, we can just keep going.

[00:02:06.18] - Speaker 1
Yeah, so I think I'm gonna show the disclaimer. So in the session today, we're going to go through obviously some really important concepts for those who trade oil, but then we're also going to talk about strategy. So before we go into that, let me just run the disclaimer very quickly, guys. Okay? And then I'll pass it back to you. Ilya.

[00:02:35.04] - Speaker 2
Yes, disclaimer is important and I always had to do it during this 25 years. But now my disclaimer, since I work for myself, my disclaimer, I can say whatever I want. That's my little disclaimer. All right, well, let's get going. And again, I spent probably two thirds of my career focusing primarily on energy markets. But maybe last seven or eight years, what we were doing, we were effectively managing A global macro fund where we were trying to apply concepts that we learned in the oil market to, to other market markets, financial markets, and if anything, this is probably number one lessons that I learned in trading. Take some ideas from one market and test them on different markets. You would be surprised how much you can learn. So even though my presentations formally today is about oil, because I've written a book on this, you're going to see that in many occasions you actually can use some of this concept and hopefully profitably apply it to some other market. All right, well, let's get going. So you talk a lot, you hear a lot about oil on Bloomberg and social media, in the news, they talk a lot about fundamental oil supply and demand.

[00:03:59.21] - Speaker 2
So we consume roughly 100 million oil barrels of oil per day. However, what they don't tell you as frequently, that we trade about 6 billion barrels per day. In other words, we trade 60 times more than we actually consume on a daily basis. The reason that you don't hear it on Bloomberg, because they don't understand any of that. And this is kind of the subject of my book. So who is trading these 6 billion barrels by the way? It has not been the case all the time. So you see this graph on the right. When I started my career, for every physical barrel, we were trading roughly 1 financial barrels. Today, as I said, this graph only captures ICE futures volume. So ICE alone trades about 2 1/2 billion futures. Then CME pretty much the same. So that gets you to 5 billion and then the rest is always a counter. So the volumes are massive. But you can see how fast they were growing. Started roughly around 2004 and 5. Now who are these financial barrels? So I call them virtual barrels. And so what happened during that time that spurred this growth? I guess just to understand where we are today, it's good to go back a little bit to the history and explain why these financial market participants even came to commodities.

[00:05:39.29] - Speaker 2
If you position yourself, picture yourself about 20 years ago, you see it in 2004. This type of graphs that you would have seen in every single bank sell side research. So the idea there was pretty much the following. They were trying to attract financial investors, pension funds, insurance companies to get to the oil market. I'd like to kind of run by you a couple of theoretical concepts because they're really fundamental in developing your own trading mental models. So once you understand them, the rest kind of becomes a bit more, a bit more natural. So they were actively pushing the so called Keynesian thesis of normal backwardation that many of you have probably studied in your basic economics class. Back then, the oil markets were predominantly backward. And that basically means your spot price or futures with shorter maturity are higher than futures with longer maturity. That's more like the blue line. The thesis which was proposed by John Maynard keynes back in 1930s, that this futures curve is distorted because in commodities there is a natural disequilibrium or imbalance between buyers and sellers. In the futures market, Keynes idea was if you are a producer of a commodity, so he developed his thesis for agricultural commodity, but it should apply to everything.

[00:07:15.13] - Speaker 2
So if you are a producer of a commodity, you are holding a massive amount of risk because your exposure is not diversified. All you have basically is that particular commodity, while the consumers of commodity are much more diversified. So for all of us in oil, basically the main exposure for us is the price of gasoline, but we have a lot of other exposures. For us it's not as big of a deal. So for producer, it's much bigger deal. So in order to eliminate this risk, producer must sell futures at the discount. So in other words, they are pushing the forward curve further down than it should have been based on a fair market condition. And the orange dotted line here kind of represents a hypothetical fair market condition or the expected spot price in the future. In other words, in this case, the expected spot price is expected to drop from $80 to 70. However, according to the theory, the futures curve would be at 60. And this difference is explained by this producer's need to hedge. So in other words, they're giving you this money by distorting the futures market. So the idea was if there is basically an imbalance here, you should be able to come in and buy it the blue line here at this discount and basically capture this gray zone which Keynes called normal backwardation.

[00:08:52.15] - Speaker 2
It's a bit of an odd term because what now is called normal backwardation, it's basically your profit, or economists are calling it a risk premium. So this is really the same thing as basically what Keynes called normal backwardation. So what happened back then? And you again, you're sitting in 2004 and the futures started to trade in 1983. So if you basically are trying to confirm empirically whether Keynes was right or wrong, so you run a basic backtest strategy. So assume that you buy oil in 83 here in a forward market, and then you hold the long position and you roll it every month. So it turns out that this position made annualized 10% annual return over 20 years. So there are two components in your return. So we Call them a spot return and the roll return. The spot return comes from oil prices going up and down. But in general your spot prices are basically mean reverting. Sometimes they go up, sometimes they go down. And over 20 years they usually don't go anywhere. So your spot returns are basically close to zero. That's an orange line. And nearly entirely your P and L came from the second term, which is called the ROW return.

[00:10:24.28] - Speaker 2
Again, you've probably seen it in the media, but you can get the magnitude of the numbers. So you basically made 10% annual return for 20 years and nearly everything came from this. Futures basically rolling up from 60 to 80 even though the spot price itself wasn't really changing that much. So that was basically what got pension funds into the oil market. That was like the first wave when the pension funds came in. And basically when Goldman Sachs created GSCI Index and Bloomberg created Bloomberg Commodity, it was called something else back then. But the idea was basically to present oil as a, basically as a new asset class that you should be allocating, you should be treating as equity and bonds. And over the long term you don't need to be smart, you don't need to forecast which way the oil price is going to go because you are making your money not on the direction of oil price, but purely on collecting this canation risk premium and basically collecting the role that was the first wave. The second equally important wave came from so called risk parity fund. And you probably know Ray Dalio and Bridgewater, he's a pioneer of the risk parity framework.

[00:11:50.12] - Speaker 2
He actually started his career also in commodities. So the really basic concept of risk parity is there are two main factors that drive. I'm talking about risk parity in the regional Ray Dalio framework because today the term is used much broader. But he's in a regional framework. Basically he said all asset classes are driven by two factors, growth and inflation. So if you basically run a portfolio of stocks and bonds that they naturally diversify each other with respect to growth because when growth is strong, stocks go up, bonds go down. When growth is weak, generally stocks go down, bonds go up. However, both of them suffer in the periods of unexpected inflation. Because at the end of the day the, the value of every asset is a discounted present value of future cash flows. So if inflation spikes for whatever reason, then the present value goes down. So your bonds and stocks both depreciate, so they both suffer in the period of unexpected inflation. So they desperately needed another asset that would have performed well when inflation spikes. And if you run the historical backtest on all assets, all strategies, what actually worked. Historically, during the periods of high inflation, oil came up on top by a wide margin.

[00:13:22.13] - Speaker 2
Could be coincidence, it could be just luckily saying, but it is what it is. You can't eliminate it from the statistical data. So from that point on, oil got this mystery and myth that this is the best and hedging against inflation. Again, we can still debate whether it's true or not. But I put some graphs here that show that oil is indeed tracking both realized inflation fairly well and also inflation expectation which I measured by 5 year, 5 year inflation break even. So that's basically Ray Dalio's original framework, the risk parity term itself, what it really means that you allocate your portfolio based on equal risk, not based on equal notional. So since bonds have a lot less risk than equities, you have to leverage up your bonds a lot to bring them to the same amount of risk as equities. It's a really highly, highly leveraged bond portfolio. And you leverage by trading futures. So you don't no longer holding just a regular bond, you leverage by holding massive amount of bond futures. But bonds are even more sensitive to inflation. So you have to hedge this massive portfolio of bond futures with something that's going to protect you against spikes in inflation.

[00:14:48.18] - Speaker 2
And that was oil. That's really what brought risk parity funds to the, to the oil industry. And at some point during financial crisis, allegedly Bridgewater was the largest portfolio, the largest trader in the oil market. And I assume the risk parity industry is still quite, quite significant. So what happened is to kind of wrap up the little academic part and move on to the signals to what happened after this. So it's essentially the straw that kind of broke the camel's back. So these investors rushed into the oil market because they needed it not only for the performance that Keynes was forecasting, but they also needed it for this perceived property that it's going to work when everything else is going to fail. So as a result of that, the whole K nation.

[00:15:47.20] - Speaker 1
Yeah, sorry to interrupt. So basically the, the Oris party council that you just explained, basically there were hedging obviously the interest rate risk with oil futures. Right? Is that correct? Just correct.

[00:15:59.06] - Speaker 2
On a highly leveraged basis. Okay, yes. And then what happened that they were so large comparing to, Remember at the beginning I said producers bigger than consumers, they pushed the market into structural contango or structural backwardation. What happens after financialization? Financial investors acted like consumers of oil they were buying. So the financial investors started to dominate producers. So investors now were buying more futures than producers needed to sell. That pushed the market into the structural contango. So if you basically repeat that graph that I had a couple of slides ago, this one, for the following 20 years, investors lost all the money that they made. So this is their return from 2005 until roughly 2018. They lost pretty much whatever they made in the previous 20 years. And again, the spot price of oil did not change during time. Your spot return was basically zero. The entire loss came from the market being in the contango. So in the contango, you always buy futures in a higher price and spot and the futures are rolling down, so you lost it all. Essentially that would became known as a negative row yield. So if you think about it conceptually, what negative roll yield is, it's really a compensation to storage holders for holding oil on their behalf.

[00:17:45.14] - Speaker 2
Because if an investor, you cannot buy physical oil, you don't have storage. Your only option as a financial investor, as a risk parity fund, is to buy futures. If you buy futures, you are forced to buy it at a higher price and spot, and that premium to the spot is essentially a payment that somebody else on the other side is gonna collect for storing your oil. And that guy on the other side is a storage company because they would sell you futures at the premium, buy oil cheaper at the spot price. As long as the difference between the two exceeds the cost of storage, it's a free money for them.

[00:18:27.14] - Speaker 1
Can I ask you another question, Ilya? Because this concept is really interesting and a lot of retail user would trade oil by buying ETFs or like even commodities, right? But then obviously the risk there is exactly what you're mentioning, which is this.

[00:18:41.11] - Speaker 2
If you look at uso, somebody can pull up the graph. Now, if you invest $100 in USO, your life to date annual return, I think it's about 94% negative. Something like this. Last time I checked it was around 94%. In other words, if you invested in USO 100 bucks in 2007, you would have got only $4 left, even though oil prices were basically unchanged. So all of your loss came from, all of your loss came from the roll. And by the way, even the funnier plot, you, you can put UNG, which is US Natural Gas, that one. If you invested $100 into UNG, I think you have about 82 cent left now. So. And again, UNG is extreme. So I think that's probably like the worst investment ever. So. But again, what they don't tell you here, especially on oil, the price of oil has not changed back then the price of oil is basically still the same. I actually created my own index, call it long short oil. But it's not a subject of that presentation because then I need my own disclosure. But it's on Bloomberg, some people can take a look. All right, let's kind of.

[00:20:08.09] - Speaker 1
Yeah, Ilya, just obviously to clarify for those who are not familiar. So if you invest in an ETF like uso, right, you make money if the structure of the curve is in backwardation, but you lose money if the structure of the curve is in contango, which is where we are kind of like today, right?

[00:20:24.29] - Speaker 2
Yes. Plus or minus appreciation or depreciation on the price of oil. But the point that Keynes was making, the oil prices tend to mean revert. So basically what goes up must come down. So if you hold it for long enough, the prices of oil basically is going to revert back. But what accumulates the structural contango backwardation. So it's like a little casino. So every month you got a little bit of an edge is a positive edge or negative edge. So in the first 20 years it was a positive edge because the market was backwardation. Then the next year it was a negative, next 20 years was negative edge. So you're losing money. So basically the direction of oil prices is a noise. So but again that graph I stopped in 2018. So but this is really was the, really the beginning of all this quantitative CTA trading. So what investors learn from that? Oh, it's probably a good idea to buy oil only when the market is backward or better. It might be even a better idea. We're going to buy oil when the market is backward and we're going to sell oil. The market is contango.

[00:21:41.05] - Speaker 2
So instead of being like an investors, they are sort of becoming slowly transitioning into directional traders. Oh, we're no longer just buying and holding, we are buying and selling. And then what discovered. Oh, this is what the commodity trading advisors have already been doing for 20 years in FX market because in effect it was called a carry trade. So that's basically how they came to. How they came to the basically CTA's one more story and then we'll get to trading signals because I think it's a fascinating story by itself. So there's the CTAs have been in existence for a long long time and I think one of the first legendary CTA is so called Commodity Corp. You can check the history of it on Wikipedia. It's fascinating. It was founded by the gentleman name Helmut Weimer. He's considered to be the father of the hedge fund industry. He was a PhD student of Paul Samuelson, who is the first American US Nobel Prize winner. And also his advisor was I guess Paul Kutner who wrote a book about the random character of stock prices or something like this. So he wrote this dissertation where he was trying to basically build a model how to trade cocoa fundamentally.

[00:23:05.23] - Speaker 2
And then he hired a lot of interesting folks. Among the junior traders in his team were names like Paul Tudor Jones, Louis Bacon, Bruce Kovner who basically managing who became legends in their own right. And they manage multibillion dollar hedge fund themselves now and again. But they all started their career in that little fund near Princeton. But. But what was happening is they were losing money by trading fundamentally and then fundamental trading in commodities you generally sell high and buy low. So they created the risk management system that penalize them against betting against the trend. So basically it's explicit penalty. You cannot go against the trend because you're losing too much money. And then in a couple of years they realized that the risk management system is making a lot more money by itself and than their fundamental discretionary trading. So they started to trade purely risk management system. And they did it extremely successfully for 20 years. And then at the end they sold it to Goldman Sachs and it became Goldman Sachs Asset management. So now why does this momentum and carry work in commodities or in oil specifically in equities? There are all kind of behavioral explanation for momentum.

[00:24:26.02] - Speaker 2
In commodities it's much simpler. It basically means your supply and demand are very inelastic. We don't change our consumption pattern on a daily basis that frequently, regardless of the price. In other words, if supply exceeds demand today, more likely it's going to exceed demand tomorrow. In other words, if supply exceeds demand, you build in inventories. And there is that theory called theory of storage. Means if inventories are trending, then the price is going to trend. So how do you basically trade this? So enough kind of academic history stuff, let's get into the trading signal. So again I'll show you some examples which really lie in the core of a CTA trading framework. You can apply them across different commodities, you can sometimes apply across different asset classes and what CTAs do. But again, my sort of expertise is an oil and it's easier to illustrate it on one commodity. But you can build a portfolio of those if you wish. So the Barrick. That's interesting. Somehow the formulas disappeared here. Oh my gosh. Fabio you don't have a formulas in your presentation? No. All right, I'll explain it. I'll explain it. It's. I mean, I'll explain it verbally.

[00:26:06.10] - Speaker 2
I apologize for this. So. And it's in the book and it's in a couple of papers on the website. Okay, so the way how we're going to define memento, we're gonna take today's price and we're gonna subtract its moving average. The very basic momentum Signal we use 1 month moving average. So we're gonna buy oil if today's price exceeds one month's moving average and we're gonna sell oil if today's price drops below one month moving average. That simple strategy, we call it one month momentum generated nearly 10% return over the last 25, 30 years. The Sharpe ratio wasn't good. And again, you can see, you can see the performance of that strategy by year. Again, all the signals are written in the book. I don't know why they didn't come up on the slides. Apologies. So. You basically you're making a little bit of money, but it's not a great strategy by itself because you are experiencing quite significant drawdown. In fact, you lost nearly 25% in four years. That's not good enough by running the strategy on itself, but it clearly has some informational content. Again, all you're doing here, you're just buying oil or any commodity if it exceeds it's a 20 day moving average and selling if it drops below it.

[00:27:58.23] - Speaker 2
There are zillion, zillion of combination of variations of momentum and I describe them all in the book. You can use crossover moving averages, multiple frequency breakout threshold at cross really doesn't matter. So you gotta get something similar. You can optimize it, but I wouldn't really focus my attention on optimizing it. One concept I would like to mention here real briefly is the reason why investors, financial investors like that oil momentum strategy. Because oil momentum tend to work really, really well when everything falling apart. So here on the graph I have an oil momentum return versus just a long position. You can see that you're making a lot of money when everything is falling, when the oil price is falling, the momentum making a lot of money and you actually can do the same graph versus equities or bonds. So that basically means when everything in the market goes south, for whatever reason, something happened in the market or oil breaks down very fast. And I will explain later why we call it like up the stairs, down the elevator. So investors really like that and they Call it crisis alpha or momentum smile. So they like investing in oil momentum as a hedge against everything else is going down.

[00:29:24.21] - Speaker 2
Well, just imagine if something happens like in China, in the global economy and if equity markets are about to correct. So this is the times when the oil momentum, especially on the downside, works quite well. And again, I apologize, the formula somehow disappeared here. That wasn't my intent because that's. But again you can see in a couple of papers in the public domain, I mean there's no intent here to hide. So the carry is even better strategy. So what Kerry does. Remember how investors learn that you should be buying oil when the market is backward and selling it in a contango. And this is probably the most important slide in that presentation. So the reason it works, it has a very strong fundamental rationale. So the largest trader in the oil market is not actually consumer or producer or a cta. The largest trader in the oil market is an inventory hedger. All these companies, like Vital, Glencoe, Trafigura, my former employee, they are holding massive amount of inventories either in storage or on the water. Oil is being transferred in ships. There are enormous amount of oil at any point in time on the water as well.

[00:30:51.04] - Speaker 2
And it's all religiously and diligently hatched. Generally the thesis works as follows. If the market is contango, let's say you're on the orange line Faber. Do you see my cursor on the screen by the way?

[00:31:05.28] - Speaker 1
No.

[00:31:06.24] - Speaker 2
No. Okay, so if you Basically, if the market is contango, the storage trader buys it in the spot market where it's cheaper, puts oil into storage and then sells futures in the futures market as long as futures exceed the spot by more than the cost of storage. So they're collecting this arbitrage by selling more expensive futures and buying the physical. We don't know what's going on in the physical market. We have no way of seeing it. But what we do know, that as long as the market is contango, they're going to be selling futures to hedge their physical so that persistent and selling futures create a steady downward pressure on the prices of oil in the futures market as long as we are contained. And then if the market goes backward, they're going to pull oil out of storage, sell it in the physical market. Again, we don't see that. And they're going to buy back the futures. So when the market flips backward, they will be buying futures back, pushing the oil prices up. Which basically means it's the same signal that killed financial investors in 2005 through 2008. Because it justifies why you should be selling oil in a contango market and buying oil in the backward market.

[00:32:37.27] - Speaker 2
So that's called the carry strategy. This is a beautiful strategy because it has no parameters, it's a model free. You don't need to optimize, you don't need any look backs. All you're doing here, you're looking at today's shape of the curve and you're buying and selling. If it's contained, go backward. So that simple dumb strategy generated you nearly 20% return unleveraged. It's all unleveraged. And you can easily leverage futures by a factor of 5 with Sharpe ratio of almost 0.5. So but what carry the only downside with carry, it's very kind of slow because the market doesn't flip between contango and carry very frequently. So you may be in backward for a couple of years. That's too slow. So what CTAs are good at, they learn how to blend signals of different nature. And here is an example of so called signal blending technique. This one I called carry momentum. Remember momentum? You buy and sell oil when the price moves, moves above or below its moving average. So what you do here instead you apply momentum signal not to price, but you apply momentum signals to the carry and carry. The term structure. For example, if the term structure is backward and the backwardation accelerates, you buy.

[00:34:19.18] - Speaker 2
If market is backward but backwardation decelerates, you're still backward, but it's flatter you the momentum on backwardation is flattening it, then you sell. And similar if the market is contango but contango steepening, you sell. But if the market is contango, contango, flattening, you buy. So basically what you do mathematically you apply momentum to the carry. So let's say you define carry as a difference between months 1 and month 12 futures annual annual roll. So and you apply momentum to this spread, you don't apply momentum to price, you apply momentum to the spread. And that Strategy generated nearly 25% annual return over 30 years unleveraged with very high Sharpe ratio, just one commodity. But this is an example of a signal blending where you take two concepts, carrying momentum, you don't add them up like average them. No, you do it in a kind of tricky way. You apply the concept of momentum to another concept of the term structure. And the reason it works as I told you in the previous slide. So backwardation, contango kind of tells you something about today's state of the market. So if market is backward, that means inventories are probably low, supply is below demand.

[00:35:50.28] - Speaker 2
But we already know this, so that's in public knowledge. The market does not trade based on today's state of the world. The market always trades based on forward expectations of where we're going. And the idea here that momentum applied to the term structure, that's what CTA is are using as a proxy for forward expectations. So they're trying to decide, yes, we know we're backward, but are we going to be more backward or less backward? So when you see, for example in the market, the term structure is rapidly decreasing, we still may be backward, but it's decreasing. That's a massive, massive, bearish signal for CTAs. And you probably have seen it. And I sometimes go on Twitter when I see signals are changing. So this is probably one of the most important signals that you can, you can see in the CTAs market.

[00:36:49.17] - Speaker 1
And Ilya, before we go into the CTAs, we have a couple of questions and I want to ask a question as well. So first, obviously from us you mentioned momentum and obviously you mentioned that it was making more money on the way down and not on the way up. Is there a specific reason that you have come across or.

[00:37:09.06] - Speaker 2
Yes, but I, one of the big reasons has to do with the options market and I'm gonna cover options at the end. I think the options is one of the main reasons that drives this asymmetry. I have a slide on that coming up.

[00:37:23.25] - Speaker 1
And, and the other thing, the other question that we have, obviously we mentioned term structure throughout the, throughout the, the presentation. I think for those who don't know the term structure on the futures market is different from the one from the option market. But how as a retail trader, how do you think and how could you advise on how to quickly analyze the status of the term structure so that you can understand, okay, how is the market?

[00:37:51.08] - Speaker 2
Well, this is, I mean this is trivial to code, right? So what you do, you measure the term structure and you can use any like I usually use annual role months, 1 month, 13. Sometimes it's just harder to get data in the public domain. But if you go to IEA website, it's a US government website, they publish futures term structure for the first four contracts only that's in public domain. That should be good enough. So you can for example use month one versus month four for oil. So calculate the term structure that spread. For example, currently I forgot, let's say it's $2 a barrel, right? So let's say $2 a barrel, create a history of that. So you have a 30 day moving average of that spread. Let's say your 30 day moving average of that spread is $3. So if the current market crosses this $3, it becomes smaller than 3. That's when your signal flip. And you can do it for one month, you can do it for multiple look backs for sure. But for you, this is an important signal that you can generate yourself. Again, one thing in the oil market, I don't want to be overly prescriptive because then I don't say that you always have to use one month's momentum.

[00:39:17.23] - Speaker 2
There is more art than science. You may look one one month, look back, you can look three months, you can look longer term because CTA is also trade with multiple frequencies. You can create a little table yourself. It's like if all of these signals point to the same thing. Oh my, one month, three months, six months, all pointing down. That's very bearish. That's meant you probably should expect CTAs to rapidly start selling. So I think it's fairly easy to implement by any retail trader. If anything, that's an informational contact, you don't even have to trade it. But it's like, don't you want to know if oil is about to break down?

[00:39:58.17] - Speaker 1
Yep.

[00:40:01.15] - Speaker 2
Yeah. So, and another, the second important part of a CTA framework is the position sizing. So they use so called the reaction function. Think about, think about this graph on the horizontal axis. It's some kind of normalized signal. Say one, maybe one sigma, one standard deviation too strong. Let's say momentum is one sigma strong. So and then two is two sigma some kind of normalized. So what you do, you build up your position, that's blue, line up to your maximum budget, maximum capital allocation. That's what they call inflection point. So beyond that you actually wanna start reducing your position because if momentum is too strong, that means the market got pushed up or down too far from the fundamental norm. So the further away you are from some kind of fundamental fair value, the higher the likelihood that it's going to reverse. So you don't want to have too big of a position because you're too far. And this is what they call an inflection point. And some funds use a piecewise linear function to scale up their position. So you can actually use this concept. And I know Fabio, you guys have done some interesting work on approximating CTA positioning using, using, I mean you can talk about it Yourself what you use some kind of, I guess momentum technical indicators.

[00:41:45.14] - Speaker 1
Yeah. So it's obviously proprietary model that you see here. You can find it within our membership right here. So we have, within the model we have two chart, two kind of live visualization. One is the chart where you see the crude oil price, the white, the white line and then you see the CTA positioning which is the green line. So obviously for us the CTA has become an indication of where liquidity could go, whether it could be going or out. And it's an indication of where the price could move. So if you see for example here we have like selling pressure from the CTA's market and also we have like the prices kind of going down. But also we also have a table view right here which shows you the CTA positioning today versus yesterday versus a month ago. And then we also have some statistical metrics which is the one month percentile, three month percentile, one year percentile and the Z score. We have documentation about that. We also have videos, tutorials about that as well. But essentially exactly to what you were saying, we're trying to use different parameters to forecast where CTAs could be positioned on any given asset.

[00:42:57.25] - Speaker 1
Here we have commodities, but we also do it on currency indices and. Yeah, currencies and indices as well. So the S P500, the NASDAQ and so on.

[00:43:11.18] - Speaker 2
Yeah, it's good that you, you actually doing it on multiple frequencies because as I said we cannot be overly prescriptive. The best thing is to create like a table like this and you look at it at multiple frequencies and you almost make a discretionary decision. Yeah, they're kind of mostly short or about to flip. Don't I use again, I use particular momentum signal in my presentation, but you have to combine them. Faber, can you get me back to the slides? One question I see on the box. What about intraday strategy, seasonality and also Canadian dollar, gas and a few other markets. I'll be somewhat pessimistic. Intraday strategies I can't touch in this presentation. The entirely different ballgame. Seasonality again is one of the reasons that I wrote the book on oil when I was thinking about including gas and agricultural seasonality. You have to incorporate it. The basic carry strategy doesn't work as well for seasonal. The only thing I would suggest use one year carry. You cannot use obviously month one, month four, something that crosses the season. But it actually, it's not as powerful in seasonal commodities. Canadian dollar though. I'm going to talk in a minute.

[00:44:31.22] - Speaker 2
That's actually somewhat Interesting. And US dollar as well. So I'm going to talk about those in a minute. So this slide I'll probably skip because again this is more like for professional oil traders that trade in spreads. So basically when you trade. I'll just say a couple of words here. When you say, when you trade price directionally, you tend to favor signals like momentum and carry. However, as a professional oil traders, 90% of what we are doing is your spreads. Because trading directional is very tough unless you run a diversified portfolio of different assets. If you're focusing on one commodity, you want to eliminate this macro or other risk that you can't control. So we were trading mostly spreads like WTI versus brand heating oil versus crude oil or time spreads. Buying oil in one futures maturity, selling in the other. So spreads unlike price tend to mean revert. And the reason the spreads tend to mean revert because the spreads generally represent the real option. Everything in the energy market is a real option on the spread. For example an oil refinery, it's an option on the spread between gasoline and crude oil. So if this spread widens, then refinery is going to start hedging it by selling the spread.

[00:46:08.27] - Speaker 2
And if the spread narrows, refinery reduces the runs and buys back the spread. Similar, the WTI brand is essentially the spread on the ship. So the ship is a real option on transportation. So if brand exceeds WTI by a lot, then you just buy wti, put oil on the ship and and sell brand. So which means the behavior of this real option owners contain the spread. The spread generally doesn't blow up too much unless there is a disruption like pipeline imploded or something like this. So you can construct like a little stat out portfolio of these pairs on the spread, which kind of looks quite interesting. And there are other spread strategies that I have in the book, but again I'll leave it for today's presentation. One thing I'd like to mention though, and it also relates to the models that CTA's model that Fabio showed we actually finishing up a paper, it's going to be in public domain open source maybe in the months hopefully on also how do you model like CTAs and how do you trade yourself based on the CTA's behavior? So we're actually using some kind of basic neural network framework to do that.

[00:47:33.22] - Speaker 2
But the main idea for trading CTA is again it's not a finished paper yet, but you can see the basic description in the book. So you basically it's similar to like applying this reaction function to CTA's positioning so since generally they are kind of smart people, they're smart Money, you follow CTAs up to a certain inflection point. But when CTAs positioning becomes too large, you take that contrarian, you take the other side of it, you basically fake the extreme positioning. Again, more on this to come, but I just wanted to highlight it a little bit. There was a question on Canadian dollar. So let's kind of get a little bit into the macro space. And I know we already spent quite a bit of time, but I'll pick a few topics here. I think the macro one is an interesting one. As I said, we spent about seven or eight years managing essentially cross asset macro fund with commodity focus. And we traded relative value strategies where you buy one asset, sell the other cross asset where for example, let's say use oil as one asset and how we can trade it against FX equities and fixed income.

[00:48:58.02] - Speaker 2
So there was a question about Canadian dollar, that was my favorite pair. So Canadian dollar and oil are highly. They're basically co integrated because Canada is a big exporter of oil and you can actually trade one versus another as a relative value. Very simple strategy. So if one is too high relative to the other, you basically sell the spread based on some sort of a Bollinger band or something like this. But what you can do, and that's what we did. Again, it's very risky to trade one pair. So we have constructed the mini portfolio, about a dozen of these pairs where for example we are trading Canada versus wti, Nord versus Brent. And then we added other commodities like Chilean peso versus copper because Chile is the largest producer of copper. And then like Aussie versus kind of coal or lng. Now it kind of was okay strategy. The same concept we applied to the equities. Since I understand a lot of folks on this call trading equities, this is actually an interesting one. So you can start. So basically you trade oil versus a basket of oil and gas producer stocks. And the easiest way to start you pick an ETF.

[00:50:20.09] - Speaker 2
For example, XOP is a diversified ETF of E&P stocks. Don't do XLE because XLE is mostly ExxonMobil and Chevron. It's not diversified, but XOP is fairly diversified. And again, I mean over long run you see the graphs, the second graph, they tend to follow each other pretty well. So you can sell one, buy the other one when they diverge. So if you're really, really sophisticated trader, this is one of the strategy we started to build. But we haven't Finished. I think somebody can make money on this. It's probably not going to be me. I'm just a bit too too lazy to retire. So. But the idea here, you, you construct your own basket of EMP stocks, don't use a etf. And the way how you do it in your basket you differentiate between hedgers and non hedgers. So all of this information is in public domain. You can go company by company and see how much of their oil production is being hedged. The idea here, if you already hedge out oil production, then your beta with respect to oil should be much smaller if you're unhedged. And the claim here, the market doesn't understand it.

[00:51:42.11] - Speaker 2
So the market doesn't make any differentiation between hedges and non hedges. So the idea here, create a customized basket of E and P stocks, oil hedges and non hedges and almost like kind of trade one against the other. It requires a bit of work because it's kind of, you have to identify hedges, non hedges, but I think it's really interesting one. And then the third one is probably my favorite but I think for people in the retail space it's hard to do you trade oil or gasoline futures against inflation swaps. So this strategy is nearly an arbitrage because the gasoline has only 4% in the CPI basket in US but it explains nearly 70% of the variance simply because the other components like shelter, medical care, they just don't move as much. So you can really arb the two markets simply because inflation swap market is very slow. So the people in inflation space, they just don't react as quickly when oil market jumps 10%. So it's almost like a lead lag strategy. But it has some nuances that I describe in the book because one is otc, one is futures. So just to finish with the futures last slide on that.

[00:53:03.22] - Speaker 2
So if you really kind of don't care about sort of trading these pairs, it's too complicated. But you want to get a general sense is oil too cheap or too expensive? So let's say using these three strategies, what if your model says buy oil against fx, buy oil against equities and buy oil against fixed income. Maybe what it's telling you that oil is just cheap, period. Maybe you should just buy oil and that's it and don't even touch other asset classes. Then what you can do, you can run a basic linear regression on oil versus some factors. And again I can use Canadian dollar xvt, XOP ETF and say inflation breakeven as three factors. And I gonna run the Basic linear regression. When I talk to economists, I always make a disclosure. This model makes no economic sense whatsoever. It makes no statistical sense whatsoever because one cannot run a linear regression on prices. You always run linear regressions on differences. However, we know many models that work in theory but don't work in practice. This is the model. This is kind of a rare example of the model that should not work in theory, but it actually works okay in practice.

[00:54:32.17] - Speaker 2
So you, and the strategy is you basically calculate this fair value of oil by regressing it on say Canadian $X or P inflation. So you got a number. Let's say you come up with number. It's $75. So if oil today is below 75, you buy it. If it's above 75, you sell it. Very simple. And then the idea that we started to play around with and I'm working on that in my academic capacity now, that's for those are more quantitative people. Can you make this factor selection more dynamic? Because for oil there are so many other factors. It could be US rate hikes, it could be presidential elections, could be geopolitical risks. Can you somehow make this factor selection more dynamic and then run the same linear regression every day or every week, but the actual factors you choose perhaps using some kind of machine learning algorithm? I think it's a very interesting problem for those of you who are familiar with quantum AI and machine learning. Fabio, I don't. Oh, are you back? Okay, good. So I think how are we doing on time? Oh well, I'm gonna spend maybe 10 more minutes on options.

[00:55:56.06] - Speaker 2
That's fine.

[00:55:57.12] - Speaker 1
Yeah, absolutely.

[00:55:58.21] - Speaker 2
Okay, let's. I mean I'm gonna go fairly quick and honestly half of my book is on options, but a lot of that relates to like always account arbitrage. You can honestly learn some things. But what I'd like to do today only to show a couple of ideas here that's relevant for retail investors and I promise to explain the nature of the asymmetry why we tend to follow more than when we go up. So the one of the main quantitative strategies you basically like really sell options in delta hedgem it's called volatility risk premium. Here is an interesting fact here though. So options like an insurance contracts. So you would expect if you're an insurance seller, you should be paid because you're taking highly asymmetric risks here. So the graph on the left here, if you sell an at the money straddle and leave it unhedged first. If you leave it unhedged, then you actually lost about 16% annually by doing this, which is very strange because as a seller of the option, you should be expecting to make money and you're not. However, if you do the same strategy, but you delta hedge your option, you make about 28% annual return.

[00:57:29.00] - Speaker 2
And let's say you delta hedge based on close once a day, simple delta hedging. So that's kind of create a very interesting dynamics here. So in other words, a buyer of option unhatch can make money and the seller of the option hedged can also make money. So both the buyer and the seller can make money if they treat the option differently. Because selling on hedge option loses money. That means the buyer can make money. So that's really why one of the reasons why always a counter market in oil options is massive, because all the end users tend to look at options more like actuarially based on the average historical payoff unhedged. So for them, sometimes options look okay, they're not as expensive. But if you are a dealer, if you can delta hedge it for you, they may look expensive. So you actually can make some very good returns by selling these options and delta hedging it. So now I want to explain this asymmetries that I promised to you. So which options do you want to sell? Puts or calls? A lot of this asymmetry comes again from imbalance among the market participants. At the end of the day, there are more producers than consumers generally.

[00:58:50.29] - Speaker 2
And producers often hedge not by selling futures, it's too risky for them. They are often hedging by buying put options because it's really required by the bank. So in order for them to get loans or lending to drill, the bank generally requires to put like a minimum floor insurance. So they have to buy put options. And what really delta hedging is. So delta, the concept of delta, for those of you not familiar, it's really a probability of your option expiring in the money. So let me kind of walk you through like a little example. It's too bad that I can't show my cursor, but you should be able to follow me. So let's say you sell 100 units of sixty dollar puts as a dealer and let's say futures trading at $70 and then your probability of drop into 60 below 60 is roughly 20%. Then you sell 20 futures. If futures do drop to 60, then you're at the money. So your probability becomes 50%. So it's equal chance of you going up and down. So that means you need to sell an additional 30 futures to stay hedge because initially you sold 20, market goes down, you sell 30 more.

[01:00:22.16] - Speaker 2
However, if the market goes from 70 to 80, your probability, you're getting further and further out of the money. So your probability of expiring in the money drops from 20% to 10%. So you buy back 10 few futures. So what's happening here? On the way down, you sell 30 on the way up, you buy back only 10. So for the same dollar move in oil, you have to sell three times more on the way down than you buy on the way back on the way up. So basically that imbalance in the options market really create an asymmetry and drives why oil tends to fall more than it. So basically we call it down, up the stairs, down the escalator. So you go up in the smaller steps. But when you crash, you kind of crash a lot.

[01:01:27.12] - Speaker 1
And I think Ilya just to. For those who are not familiar, if you guys want to create an account on our website, we actually have a delta hedging document right here within our guides. So if you want to really obviously it can be complicated, but we do have basically documentation that support exactly what you are talking about right here.

[01:01:56.06] - Speaker 2
Okay, so and then really quick, I don't want to spend too much time on that, but that strategy of selling option Delta hedge so had a marvelous performance right after financial crisis 2008 all the way until 2014. It was probably the best performing systematic strategy. It actually worked better on selling calls than selling puts. And then it flattened from 2014 through 2000 until Covid it really didn't make any money. There are a couple of reasons for that. So one reason the strategy was so profitable after financial crisis so that the banks were able to package that strategy into investable indices and they sold them to investors basically saying invest in this oil VRP strategy, we're going to delta hedge it for you. You just give us money. So they basically provided a lot more volatility supply and essentially the ARB went away. And another important reason, the US shale oil production started to grow rapidly. And a lot of oil producers, they were highly leveraged companies, they did not have any money to buy put option because all of the money were going into drilling. But they had to hedge. So they were still buying put options, but instead of paying for it, they were selling other options in the market so that the net cost for them was zero.

[01:03:44.15] - Speaker 2
And very often for every option that they were buying, they were selling even two options to pay for it. For example, they were buying put spreads and selling a call. So they provided a lot of volatility supply to the market. So the market and the options became less imbalanced. So there was a lot of supply coming from these packaged investable indices and also from the shale hedges. But the reason I wanted to mention it, after Covid, things are changing a little bit. And since 2021, last three years, that strategy has had a marvelous performance again, because a lot of the financial indices disappeared because they lost some money during COVID So they didn't want to write the insurance the day after the insurance, even though that's one of the most profitable. But also importantly, there is a massive consolidation among producers in the oil industry. Out of top 10 hedgers from 10 years ago, I think eight of them are either bankrupt or merged or got bought by ExxonMobil or Chevron. And the big oil companies don't hedge. This additional supply of volatility that these guys were bringing to the table, it's gone. The market is back into this structural disequilibrium or where there are more option buyers and sellers.

[01:05:18.17] - Speaker 2
And that strategy is performing well again. But it's interesting enough, the second graph here is only showing the performance since 2022. Now it's actually better to sell the puts than to sell the calls. That's partially because the geopolitical risks are now quite significant. So you, you have to be. You have to be careful on the geopolitical side. And I guess I can. Let me see what else here. I'm gonna skip the most sophisticated slides. You can take a look on the book on that specific strategy. How profitable is that strategy? By moneyness, by maturity. And also, how do you Delta hedge it? You don't need to Delta hedge it based on black shows Delta. Every day there are better, optimal ways to delta hedge it. You can delta hedge it. Not every day. You can Delta hedge it using volatility computing with different deltas. There are a lot of nuances, again, for those more sophisticated. And you can run this strategy again for equities. Actually, we wanted to write a paper comparing oil, vrp, equity, vrp. But I think oil is generally one of the best. I think equity is working fine too. But you can construct a portfolio.

[01:06:43.21] - Speaker 2
Again, whatever I say in my presentation is oil specific. There are really two ways to make money. So I have never been a systematic trader myself. I used all of these signals more to complement our discretionary decisions. So we are combining various signals of different nature and ultimately making a discretionary decision what to do. So we were combining systematic quantitative information with fundamental as well however, if you're purely systematic trader, then you can do it systematically. But then you need some diversification. In other words, it's still risky to run like oil momentum. Just as one strategy, then you should do it at least half a dozen of assets. And again, you can experiment yourself because some of them can be easily coded. And again, I'm not giving you a prescriptive answer. This is the best strategy. This is the wrong way to think about it. Here is your array, as Fabio was showing in his table. That should give you a general sense whether it's expensive or cheap. So it's like, it's a little bit of art, not science. So I'm giving you some kind of tools, how to paint, but ultimately that's your own picture that you're gonna.

[01:08:10.04] - Speaker 2
You're gonna draw. So good luck to you and I mean, appreciate if you take a look at the book. I'm getting a lot of feedback directly on LinkedIn, but then I just learned from, from my publisher, who said, well, Ilya, where's your feedback? I kind of said, it's all on LinkedIn. I'm like, no, no, no, no, we don't need feedback on LinkedIn. We need the feedback on Amazon. Because if you don't get enough feedback, then Amazon is just another algorithm. He was basically saying, well, Ilya, you're a smart guy writing about algorithms. Don't you understand that Amazon is just another algorithm? So if people don't give you feedback, then your book is never going to pop up on the screen. So I'm like, okay, sorry, I didn't know this. So, yeah, I mean, I'm always interested. So if you like it or don't like it, say something there. It's much better. I have a simplest series in public domain. Feel free to keep an eye. Publish something every other month. With Oxford, it's more like a simplified, less technical version. And again, I'm occasionally on Twitter and feel free to add me on LinkedIn. Yeah, no, no, let's see.

[01:09:30.03] - Speaker 2
I don't think we have any. Any more questions.

[01:09:33.28] - Speaker 1
Yeah, let's see if we have questions. Ilya, again, thank you for your time. We bought the book a couple of weeks.

[01:09:39.28] - Speaker 2
Yeah, I apologize. With the slides. I don't know. I was checking my presentation. It might be the soft, because the formulas were inserted there somehow. They didn't come up and we didn't check it. So. Yeah, but there's no secret there. You can actually, in one of the articles, if you want to go to this Oxford one, I think there is One something like about myth and mystery of oil momentum. The signals are there, so it's not a rocket science. You can find them.

[01:10:07.26] - Speaker 1
Yeah. And we also posted your book on our Discord channel. We posted the link right here. So if you guys wanna get access to the book, I think we re we got it like a few weeks ago. It's really great content. All the charts, all the things we discussed are gonna be there and there's going to be a lot of like insights that you can find on the oil. If you guys have any question at all on the presentation, anything you want to send over to Ilya after the meeting, please send it over to infoentorq.com and if you want to be part of our mailing list, we're going to send over more information in the next few days about some of these strategies and we're going to put together some content based on commodities and oil. Please join us on our mailing list. And just as a piece of information, we also do produce models looking at option chain on oil. So we do have our gamma levels on oil that you can add them to your charts. So all of that is covered right here. We cover commodities, metals, rates, forex, crypto and soft commodities.

[01:11:18.27] - Speaker 1
So you can find all the information on crude oil right there. You can add them to the chart. Very, very easy.

[01:11:25.19] - Speaker 2
One thing I would add just to conclude, obviously a lot of people asking me about energy transition and all of that stuff, obviously yes, it's happening, but the oil will always be there as a financial benchmark. If anything, the commodities need a benchmark and there is nothing that's going to replace oil in the foreseeable future as a financial benchmark, regardless of the physical transition of energy. So we may be driving EVs and the consumption of oil may decrease even further. However, the market still needs a financial benchmark. If anything, that's going to increase the value of these financial barrels because it's going to continue to dominate. The market still needs a benchmark. If anything, oil is going to become like a financial benchmark. So you can kind of call it oil. But I think I can see down the road the oil becomes less oily, but it's still more like a representative benchmark for the asset class.

[01:12:34.11] - Speaker 1
Awesome. All right, all right, cool. Thank you so much for your time, Ilya, and look forward to.

[01:12:41.00] - Speaker 2
Good luck everybody. Good luck. Thank you.

[01:12:44.04] - Speaker 1
Thank you guys.