The Illusion of Control in Modern Markets ft. Yoav Git | Systematic Investor | Ep.393
Summary
Energy Shock: Extensive discussion of oil price spikes driven by Middle East tensions, with front-of-curve surges and muted long-dated moves due to structural oversupply.
Gold Dynamics: Gold and precious metals showed counterintuitive behavior around the conflict, selling off both on escalation and potential de-escalation, underscoring narrative-driven volatility.
Rates and Correlation: Bonds and equities sold off together, highlighting positive bond-equity correlation in inflationary regimes and the challenge for traditional diversification.
Emerging Markets: EM fixed income saw back-end curve weakness and widening spreads, reflecting risk-off flows and liquidity stress distinct from developed markets.
Flows and Liquidity: Sequenced flows from CTAs, value traders, and noise traders amplified moves, with execution timing and widening spreads materially impacting outcomes.
Private Credit Risks: Signs of strain in private credit surfaced, including cracks and fund gating amid redemptions, suggesting fragility in parts of the Financials ecosystem.
Risk Management: Debate on factor-based hedging versus proportional de-risking, and the importance of market selection and capacity constraints for robust CTA portfolios.
Transcript
Imagine [music] spending an hour with the world's greatest traders. Imagine learning from their experiences, their successes, and their [music] failures. Imagine no more. Welcome to Top Traders Unplugged, [music] the place where you can learn from the best hedge fund managers in the world, so you can take your manager due diligence or investment career to the next level. Before we begin today's conversation, [music] remember to keep two things in mind. All the discussion we will have about investment performance is about the past and past performance does not guarantee or even infer [music] anything about future performance. Also understand that there's a significant risk of financial loss with all [music] investment strategies and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions. Here's your host, veteran [music] hedge fund manager Neil's Krup Larson. Welcome or welcome back to this week's edition of the systematic investor series with Y of Git and I Neils Castro Blasten where each week we take the pulse of the global market through the lens of a rules-based investor. You are it's great to have you back this week. Um I was just going to say I hope you're doing well but I actually know you're battling a cold. So I really really appreciate you persevering through uh through this uh uh conversation today because I I know how it feels when you're not 100%. So really appreciate that. Um but it's really great to have you. So um so welcome back. >> Thank you very much. It's always a pleasure to be back even when we've called. Um >> even when Absolutely. >> But it's a great time because the markets are really exciting. So um it's it's a it's a great time. There's a lot to discuss. Um there's a lot of there's a lot happening. >> There is a lot happening. Uh without a doubt. So let's talk about some of the things that's happening that's catching our attention as we normally do. um what's been on your radar recently? Oh, >> okay. So, so before we get to the to the markets, I'm going to talk about um something called the the EM algorithm. So, this is a a mathematical algorithm which was really the granddaddy of all machine learning. So, that's the idea of uh it's an iterative algorithm um which allows us to two stages one of them calculating expectation and one of uh one of them maximizing the likelihood. And the reason why I want to talk about this algorithm is because the person who invented it is a guy called Arthur Dempster. Uh he was a math professor at Harvard and he came up with that in the late '7s. Um and he passed away uh since we last spoke last month. Um so um not not it's it's been on my radar and you know he's not a very famous person. He's not very significant and within the the war and all things that are happening uh we tend to forget. But actually a lot of machine learning um is predicated and uh by his work. Um so um rest in peace Arthur. >> Yes. Well, you call it the EM algorithm. I you know I'm definitely sort of uh um what you say gravitating towards something with efficient markets. But what what does the EM stand for? >> So so one of them is so EM the E stands for expectation and the M stands for maximization. So it's not emerging market, it's not efficient markets. Um so the idea behind it is if you have suppose suppose you have um you have a lot of observations let's say from two distributions like one of them is red and one of them is blue. Then I ask you to estimate the mean and the variance of each one of them. You would use maximum likelihood estimate to estimates the mean and the variance of of the blues and mean variance of the reds. But what if I didn't tell you which ones are blue and which ones are red? So you have a you have a problem of classification which is exactly what you have in machine learning. You know is this a cat or is this a dog and the way you do that is you have a second stage which says given that you've estimated this distribution you look at each point and say which of these two distribution is it like is it more likely to be a cat is it more likely to be a dog is it more likely to be red is it more likely to be blue and based on that you recalibrate. you do a second round of saying okay now that I've classified each of the points according to assign them with probabilistically to each one of these distributions now we can recalibrate the reds and the blues distributions and then again which point is more likely to be in each distribution and it's an iterative process a little bit like the back propagation process that we have in machine learning these days um and that's actually how you eventually sort of converge to a very accurate estimation of like classification without doing any any uh sort of any explicit determining of which ones which points are the blues and which points are the are the red. >> Got it. Speaking of red and blue, actually what's been on my um radar is something that happened yesterday. It happened in my birth country uh of Denmark. It was election day yesterday and of course we're waking up this morning to one of those uh situations where the voters have basically given them a a very very difficult hand to find any agreement across parties uh reds and blues. Um so um so I'm I'm sure I will be uh following that for the next uh few days to see how it all turns out. What's been interesting about it and why I I find it a little bit uh relevant to even mention um uh in this section is that you know we talk about markets being very narrative driven from time to time and actually in the last few years very narrative driven and you would think something in like Denmark given all the narratives around Greenland in the last 6 months for sure that that would be a narrative that would be like front and center in an election. campaign that started at the end of February. Now, we know, of course, that a few days after that uh election was called, um a war started in the Middle East. That didn't even make it really through to the um to the main topics of the whole election campaign. I'm not going to ask you this unfair question of what do you think became the main topics because I'm pretty sure you wouldn't guess unless you read the news, of course. It ended up being animal welfare and clean drinking water. That were the main two narratives that actually pretty much defined the last two weeks of all the debates between the left and the right and even the prime the candidates to be prime minister. That were the main ones. And I just think it shows you how unpredictable things are u you know even at that level um that you think oh yeah definitely you know uh global you know geopolitics and and and stuff like that will be front and center in a world where you just been under siege uh at least um from a narrative point of view about part of your kingdom. U no didn't really get mentioned whatsoever. So, uh, I think that's a little bit of a lesson for us investors that it's not always that, um, what we expect is what, uh, what happens. Now, of course, I think it's a credit to the Danish people that that was the topic, the the center of the campaign, and I wish all of us worried more about clean water and animal welfare and less about other stuff. >> Yeah. No, I mean, they they are important points. I'm not certainly not dismissing them. Anyways, the other thing that kind of caught my attention and and maybe we'll talk about that um you know now a little bit more is just >> again the unpredictability of markets, right? I think that um just watching what's happened in the last few weeks uh with a market like gold um where it was so bullish uh for the first couple of months of or at least the first month of this year. Um but also to see that how precious metals have reacted through the Iranian war. Um I I it seems like it's selling off when the war began, but it's almost like it's also selling off now that there might be a deescalation of the war. Um, I know this changes every day depending on what uh tweets uh we're what we're reading, but it just it's really it's really interesting to see how how all of this is playing out and how unpredictable it really is. And even though um as we'll come to in a second, for sure uh our beloved industry and CTAs uh overall we haven't profited from it. We've lost a little bit as an industry, nothing too much. Um but there will be a lot of dispersion between managers for sure. How did you navigate these last three weeks? There'll be some real winners, some real losers and lots of uh managers in between. And that's fine in the short term. We know that positioning, initial positioning is so important and um and you can be lucky, you can be unlucky with how you enter a situation like that. But it just keeps on giving us all of these um so to speak uh examples of why um or how difficult it must be to be anything other than a rules-based investor uh in my biased opinion of course uh when you uh when you go through a situation um like this. So um love to get your um sense uh of this and and where you may think some I know you focus on fixed income but where where you may think that some um you know some of the uh how should I say determining factors have been uh in the last uh few weeks. >> So I think I think the points you make are exactly correct um in terms of first of all the narrative in the market has been inflation. So if oil if oil rises in prices um then we're going to see inflation and then we'll see something. Um but I think that's not entirely the story. Um and the reason why you see that is you see for example as you said gold in um behaving in a way that you wouldn't expect gold to behaving in an inflation based. Uh you wouldn't expect bonds um long-term bonds to be affected as much. So if you look at for example the oil curve, the spike in oil is at the front of the curve, right? If actually if you look at Brent 3 years out, it hardly budged because it makes sense. We're going to have supply constraint at the front of the curve. The back of the curve, we're still in a structural situation where we're actually overproducing oil rather than consuming. We're cons we are producing about 105 million barrels a day and consuming about 102. So long-term was affected a lot less than than the crisis. Um but in bonds you see 10-year bonds in even like the guilts or the treasuries. I mean treasuries have moved um from 114 ty moved from 114 all the way to 110. Uh in enormous change that's that's something like you know um that that's something we haven't seen. That's something like a year's like more than a year's worth in in a couple of weeks. Um similarly in EM um we have seen in EM again not a sell off at the front of the curve. We've seen actually a selloff at the back of the curve. Um which is sort of indicative that what this is happening is a bit of a much more of a riskoff event than just a simple narrative of of um sort of inflation impact on the on the country. And that really makes a big difference to um depending on which assets you're trading and which specific assets actually you're trading. So which has more exposure to sort of the Iranian conflict, the Iran Israel conflict. Um and I think the dispersion that you're talking about is is actually quite interesting because the dispersion is not going to be just the dispersion between us and different managers, but actually the way that you're looking at the model. So um you know normally you would do a daily maybe daily or a little bit of intraday trading um and the intraday volatility is is small enough that it actually doesn't matter what time of the day you are executing for example um but in our case um if you look at what we've seen this this month um oil has traded in a range of say 20 $25 a day. So, at the beginning of this week, right, we started off with boots on the ground and oil was spiking all the way to Brent was spiking all the way to near 120 and then, oh no, we've got an agreement with Iran. Um, oh yeah, we're going to trade all the way to 90. Um, so depending on like literally what time of day you've executed, you're executing your trade will make a huge difference to your performance. So your like your model the ability for you to track your own model um this month has been really really difficult. Um so so that's that has been that has been a real a real issue actually. Um and you will see that you will see that I suspect we'll see dispersion. I mean the level of surprise volatility we have seen this month has been staggering. I mean we run a scenario analysis of you know what what historical data all the way back to 98 um is actually more surprise volatility that we have seen and the beginning of of March immediately made it to the top chart to the top of the chart in terms of effect that we have never seen before that is really materially impacting volatility and on a lot of asset classes. So that has been a real that has been a real a real pain point um for us in terms of volatility. Um and I think one of the interesting things if you talk about different asset classes um is the bond equity correlation. I mean we spoke I spoke with uh about the fact that in times of inflation we are going to see correlation between bonds and equity being positive rather than negative. Um, and that's really important for an asset allocator. And I think this this month has been a really strong example because we've seen both equities and bonds selling off. So if you thought one of them was actually acting as a defensive mechanism for you um then think again. I think uh that has been that has you know bonds sold off just as much as equity um if not more actually. So um really interesting dynamic this month. um and making CTAs, different CTAs depending of whether which which asset class you're trading, depending on which which um sort of which speed of model you're trading and which time of the day you're executing. Really different difficult. >> Yeah. Um okay, so let's unpack this a little bit. Um because um so first of all, obviously I'm not entirely sure that I that I agree that it's it's difficult to um uh to find out when you should trade. You should of course do like all the people in oil that apparently traded 10 minutes before the announcement. Y you should know that by sure that's like 101 trade before the news and you'll be definitely profitable, right? >> Yeah, I I think we'll get into market manipulation later. But yes, there's been a few instances not just in the oil but also uh in equities um on the Friday. On the Friday, we have seen the S&P um selling off and then selling off again with the boots on the ground and then beginning to rally ahead of Trump's um like it's it was really it was really quite uh quite interesting to behold. Um I I think you're right being rules-based um makes the makes the decision to trade much easier. And um we're going to talk a little bit about flows. So I think it's actually really interesting >> to see. >> We'll we'll come to that. We'll we'll we'll definitely uh cover cover that as well. I just want to sort of stay with the trend following section and just give people also a little uh update on performance and so but um but you you're making uh some really interesting points uh of course. Now just staying with this uh that you bring up about you know correlation um and and um stocks bond correlation of course some people will say well you know CTAs overall didn't give us any protection either this month uh I mean on an index basis okay they're down a little bit less maybe than than some of the bonds. Um uh but of course when you have a quote unquote surprise um like this yeah I don't think CTAs we claim that we will be necessarily uh uh doing better than than anything else. I think what will be interesting from here is really how this manifests in the interest rate sort of area of the portfolio. I mean you're obviously a much more of an expert than I am in that because that's what you're focusing on. Um, do you think this could be the be I mean I mean obviously we're not we say we're rules based so we don't we don't um try to forecast anything and then I'm just about to give you a question about the future which of course is is not um is a bit counterintuitive. Nevertheless, I will I will um I will ask it and that is because some people do uh bring it up and that is could this be the beginning of another 2022 scenario? I think this is what the the concern is among yes institutional investors. Um and and maybe it's the hope of what CTAs are hoping for that this will be another 2022. Um but but how do you see it more from a more from a structural point of view in terms of what's going on uh as you look at what's happening in the fixed income world? Uh maybe you see something that you can comment on that is not pure speculation. So I I think what was interesting is the weakness in the 2-year bond auction recently um in the US government. So that's that's not something that we have seen a long time. Um yeah, from a personal point of view, I think that um a lot of the western countries are in a fiscal um tough spot >> and um inflation actually is a good way for them to um to get rid of that debt. Um, so being being like on a personal level, I mean believing that yields are actually on on on the march up. Um, that's something that I think is is almost inevit inevitable to me. Um, but you know time but timing when is it going to happen uh is exactly exactly the point that we cannot even make this determination. What I can tell you is that the the recent move has been, you know, you go into the month and you're thinking um you're going to be long bonds um and [clears throat] very quick and very quickly you ended the month with actually being um short um because the moves have been very violent. The reversal has been very violent. But we've seen in fixed income actually the the inability to decide for the markets to decide which way it's going um since 2023. like 2022 was a great year. You're going to be short. Um but understanding what's happening what's happening with the US interest rates has been a real struggle and it's been back and forth back and forth. Um and it's been very difficult for CTAs to trade um uh the fixed income portfolio. So um I I wouldn't like to it may be the beginning but it's you know equally equally it may not be. I think what's interesting the other pain points in the industry as a whole uh private credit um is under attack. You've seen cracks. You've seen some funds beginning to gate because redemption you've seen uh uh uh too many redemptions. So some of the funds have started to gate. Um certainly feels a little bit uh the world is feeling a little bit um uh on edge. Let's put it this way. Right. Fragile. I think that's fragile. Yeah. No, that's definitely true. Now, another thing I was thinking of while you were talking, we we obviously talk a lot about price, right? So, higher prices, for example, in in in energy, uh will lead to higher inflation for sure. Um but I think this time around, um again, not being an expert in this, um but I did notice that one European country now has kind of um rationalized oil. So, so for me it's not necessarily just price, it's actually supplies. I mean meaning can we actually get the oil we need or I mean maybe luckily for for for our part of the world um we are heading into to warm warmer weather um uh you know this is this is some you know different from heading into a winter uh in terms of not being able to get uh oil and and and gas and so on and so forth. Um and uh of course in a much bigger picture which will leave to the experts to to to debate. It it shows our continued vulnerability to a lot of things. Um and maybe in particular Europe um you know we thought we were well we knew we were vulnerable when when it came to Russian gas and now it turns out we're obviously um set ourselves up to be um very vulnerable to Middle Eastern gas and and oil and so on and so forth. So it um yeah it's it's an interesting um it's an interesting development uh no doubt. Let me turn to the usual numbers that we talk about. So my trend barometer although numbers will show that it's not necessarily showing up in the performance but but my trend barometer is actually still in a relatively strong level at 52. It means that there is some breath in in the portfolio of the markets. that tracks 44 markets um in terms of quote unquote trending behavior. But as you rightly said, uh the volatility in some of these markets is just incredible. So even just something as time of execution uh may mean that you're not really doing great uh from a performance point of view. But you know um as it stands, that's where we are. Uh now I'm going to because we're recording a day early on Wednesday this week. Um the numbers that I have from the indices are as of Monday evening, but I think yesterday um was a little bit of an update uh for the CTA industry. But anyways, as of Monday evening, Btop 50 was down two and a quarter, still up 6 and a quarter for the year. Um so CTA index down just shy of 2% uh still up 6 and a quarter for the year. And the stock trend index down 2.6% still up almost 6% for the year. And the short-term traders index is no no surprise is doing a little bit better. It's about flat up 10 basis points for the year sorry for the month and up just shy 4% for the year. Now that is in big contrast to what's going on in the traditional market. So Msei World is having a rough time down 6% [snorts] so far in March down 3.23% so far this year. US aggregate bond index as you pointed out down 2% or so and change in uh in March now down about 40 basis points for the year and the S&P 500 total return is down 4.59% as of last night and it's down now uh just shy of 4% so far this year. So interesting developments, interesting dynamics. Um and even though we haven't made money as an industry, uh we certainly lost a little bit less it seems um as an industry at least um for um for the month of March and are also doing significantly better of course for the year as a whole so far. Um now before we jump uh any further, let me just mention to those of you who may not have noticed, but I did recently release the eighth edition of the ultimate guide. Uh we've added another 100 books to that guide. So now you have about 600 plus book titles um that you might uh want to um go through and find something that u will continue your educational journey um in this uh industry of investing. There are two ways you can receive it. You can either sign up to the Sunday emails. I'm sure there's a link somewhere uh in the um uh on the website or you can go to toptradersunplug.com/ultimate and you should be able to get a copy. [music] and it is of course free of charge. [music] Let's stay with the markets a little bit further. You mentioned uh the curve now and you you said this thing that well if you traded oil 3 years out it'll be very different to uh you know trading oil um for the next two months. But as a CTA we don't trade oil 3 years out. Frankly, we trade oil um you know a few months out. Uh so um is there anything you want to add to that or was it just the to to showcase the fact that the action has been somewhat different uh depending on where you are in the current? >> Um so so actually uh some of us do trade oil. >> Yeah, I know you're probably the exception to the rule, I'm sure. >> Indeed. Indeed. So I I think um I think that the the the I'm not responsible for the for the commodity that my my my colleague Tom Babage is is is doing that. I think the idea is uh the way he likes to talk about it is weather versus climate. The idea that one of the nice things that that when you trade the back of the curve is that it is really about fundamental supply and demand. It's not really about it's not really about the the short-term shocks. and and I think this this this month has been uh has been quite instrumental. So you might think he's he's trading a lot of oil but the volatility should be high but actually hasn't seen that much volatility because he's like he's almost away from the the news cycle. Um so me I look at the the curve in terms of trying to understand inflation expectations. So I mean if you look at at oil, oil features uh energy features about 6% into the inflation uh directly as a component into into inflation and then of course there is a secondary effect on fruit prices and and the rest of manufacturing. So there is a there's a significant contribution and then you try to say okay how does the the entire inflation curve sort of rebalances itself um as a result of of of uh a protracted shortage in supply and demand. I think what what is interesting what you said earlier is that in the case of commodities it's not just about money like as you say for the love of money you might not be able to get the oil that you need right and you see countries like um okay in Europe we see that a little bit because we are still very rich but in countries like Egypt they already instituted uh you know energy energy restrictions because they have they have issues and um you know we rely on a supply chain like hormos um and and it's so so so there is a point where it doesn't matter if you have as if you're willing to pay uh it's just not going to be there um so so that that's a very interesting uh that's very interesting dynamic in terms of the supply shocks in the front of the curve which you probably don't see that much in the back >> yeah well let's stay a little bit with trend following then before we move off onto the volatility paper we wanted to talk about. There are two other things that I noticed uh in your notes that you wanted to um kind of flush out a little bit. One was flows, the other one was risk management. Let's let's go for let's go there. >> So, let me tell you a little bit about my experience. So um without let me like broadly um if you trade if you trade rates across globally you would have entered the month probably long bonds but you would have been short in different areas. So like in Japan you would have been short in Taiwanese dollar you would have been short. Then you have uh you might belong in South Africa, you might belong in um in other currencies. Now the shock happens. Now normal CTAs the the way we would normally manage risk is say well the volatility has spiked and we would sell everything. Now what interestingly what you find certainly in emerging markets is that spreads widen. So uh if you look at the yield spreads that we will you were getting quoted is like 10 basis points. It's an enormous it's a it's a it's very it became very expensive to trade >> and in a way the risk that you're experiencing is a directional risk. So you may be long some DV ones, short some DV ones and you're trying to reduce the first factor, the first factor that we are seeing now which is a essentially a bond selloff >> and normally we would manage it um by selling each one of the assets right so we would shrink the entire book uh but that's actually very expensive at the time of crisis now I I actually say I I don't have a model to manage the um this properly but uh it's interesting to me that if I was a discretionary manager and I know you know I've been to breve I've seen I've seen how risk is managed on a on a then what you would do is you would basically sell your receiver you will close the receiver you will sell your long bonds rather than just shrink both the longs and the shorts you would the first the first point of action would be to take the very liquid assets that are very cheap to trade and just close your duration as much as quickly as possible don't do Um and that's a very interesting dynamics that it just brought it to the four. Um the way that um we may be doing something which is which is lacking because if you think about what what discretion you're doing actually makes a lot of sense because what we have is when there is a spike in correlations actually a lot of these assets becomes good substitutes of each other. So hedging with the TY with the US treasuries very quickly is actually as effective as you would do than just shrinking your entire portfolios both the longs and the shorts. It's not something that um that I do actively but it's something that is that when I look at my portfolio and I think okay this is not a behavior that I would have expect to do in terms of crisis and certainly this as a discretionary trader you wouldn't do that but as a CTA this is the rules that we have we take each of the markets we just scale it down so what you end up with is both scaling both the long and the shorts at the same time even though like your short ones might actually hedging your your risk as well. So that was interesting to me. >> Yes. But I let me see if I can add a little dimension I guess is the word I could use here uh to that. So when you talk about that that might be an expensive way of doing it. I'm straight I'm thinking here ah yes but that's because you trade alternative markets for as someone who trades the core markets where it's all futures and it's all super highly liquid. That's exactly what we can do. And this is Yes. So, and this is relates back to the paper that Nick and I and Allan and I or have been discussing a couple of times and which we will be discussing with one of the co-authors uh next week actually uh when he joins Katie and me and that is the difference between uh which market universe you put in your um CTA portfolio and and so I'm glad you bring it up in that way because these are some of the periods where in a sense it shows up but it might show up below the radar meaning when we see the final performance for the month of March we may not be able to tell in total who is trading alternatives who's trading core markets etc etc but then once you open the hood and you look down in the engine room these are some of the things that start uh showing up so it's a good example to to talk about that in times of stress [snorts] liquidity is important. >> Very important. >> Absolutely. Yes. >> Absolutely. And and how to manage how to manage the the risk um is different as you said, right? If you are trading if you're trading US treasuries, you just close the position, right? There's no there's nothing to do about it, right? Um but if you're trading um you know the cost of like let's say closing closing the book, the long short book is like 5% of performance. So um that's actually quite expensive. Um, so you have to think about how you do that. Um, and I've got to tell you, I don't have the solution for that. It's part of it's part and parcel of of the way that um, you know, the struggles that we have. But I'm just um, but it it was very interesting to me, you know, when I'm thinking postmortem, what can I learn or what can I do better about managing the portfolio on on on the fixed income side? That was one thing which was very very clear to me in terms of >> but how would you do that from a rulesbased point of view in a sense how could you do that I can understand why you can do it from a discretionary point of view but how would you even be able to program that um without adding too much additional risk that we haven't thought thought about >> so so so it's it's actually it's actually I mean in a way it's about recognizing what is actually the way volatility comes from so in times of crisis you have the first factor uh which has become very um which is which is correlated and its volatility is dominate is dominating everything else >> and the way that you you you manage it is your managers on a factor-based uh on a factor-based risk. So you say that's okay. I just want a lot less of that front risk. And by the way, this is this is just good. It's both good for diversification and both and good for risk management. and he says okay so what you need to do is to have a set up the problem to be able to say I want to ensure that my risk factors are risk managed and at the same times managing the the amount of specific risk to each of the market that you're doing because one of the things that you probably don't want to do is take too much like you can edge everything with the treasury US treasuries but then you're taking on quite a bit of risk in that market right so there's a specific risk that So the idea is can you take off can you balance a a lot of the constraints which is can you um primarily reduce the risk in the first component while the other components may be trading they the volatility on them hasn't increased and secondly making sure that which instrument to use to hedge it in a way that you're actually underexposed you don't expose yourself to too much specific risk in a specific country. It's a mathematical problem. It's an optimization problem. It's not as easy as what we do normally. Certainly >> and certainly in the case of like a liquid CTA probably not worth the hustle. You would just basically down gear everything. >> But that's the like to me when I'm thinking about what improvement I can do in my system, I'm thinking, oh, it's if I look at the cost of trading, right, I say this this month has been very expensive. >> But but and I I get that and I take that on board. I my the initial thought that I have when I hear that is that there's one thing though that you are doing if in doing that whether you do it discretionarily or whether you do it uh systematically and that is you're taking on a huge amount of risk if this turns into a liquidity crisis because in a liquidity crisis yeah correlations may go to one but you can't get out by having put all your by having kept all the quote unquote less liquid more expensive stuff to to trade Um, I guess that would be my initial gut feel that that that could be really that could turn into a a whole different kettle of fish, so to speak. >> So, so you're right. Liquidity is something that that we manage by the way as an industry not just in the alternative market but also in uh also in the liquid market. Um, in terms of the making sure that our footprint is not as big as we think. uh is not is not as big is such that we can actually close um you know in a in a reasonable manner. Um but when you see very violent violent moves certainly there's more stress in the there's certainly a lot more stress in the in the alternative markets 100%. >> Yeah. Well um the other thing I think you mentioned in your notes was something about flow. So I don't know if if that's uh something uh we've already covered or not before we move on to the uh excess volatility paper which um I also agree with you is is extremely relevant uh for what's going on in March. But was there something about flows you wanted to bring up? So I I think it's interesting to see the dynamics of flows this month and I think that will feel into I think let's let's describe what we have seen this month >> and then we can talk about the paper the academic paper and then we can maybe try to bring this two two things together. >> Perfect. So what we've seen is first of all there's a price gap because you know there's an attack and there's an attack on Iran. Um and the market just gaps the you know uh it's not it's not really there's not a lot of trading. It was it was happening over the weekend. It was over the weekend. There was no there was no there was no there was no trading at all in in oil. uh but when the market opens the market opens a lot higher and then you see uh a day a day later you start seeing um or actually on the day itself you start seeing CTA flow. So CTA flows coming in of course volatility as we discussed volatility is gupping. We will start taking risk off and that comes in and I think as you say um the nice thing about about being systematic is that we we basically trade the market the model says it we just we just trade. So that causes a a uh that causes again an an effect um and you're seeing you know equity being sold off. we seeing we are seeing bonds being sold off and then uh as the crisis progressed you actually see other players beginning to close their positions. So as you see uh suppose I'm I'm a value trader and I'm I believe in bonds and I'm holding bonds and I'm holding bonds and bond keeps ticking down keep sticking down keep sticking down at some point you're just being closed out there's not much you can do right so you starting to see flows which are driven by the value traders right who were hoping to hold onto their position and they are just essentially there's a long squeeze here they just can't afford anymore or to to to hold the positions. So, we've seen very interesting flows. On top of it, of course, there's a lot of noise traders, right? So, we're talking about the tweets, okay? We're talking there's a lot of there's a lot of market volatility, but I think the the dynamic of the the liquidity um and who's driving the the the P&L because what you saw at we saw beginning CTAs selling, you saw real money beginning to come in um when it things were beginning to stabilize and then you saw um so the value traders were like actually buying. Then you saw uh an escalation with Israel bombing the South Ps uh upper gas field and then again another bout of selling and then you saw a beginning of a squeeze actually in and you saw that in the guilds market you saw that uh even in the treasury market to an extent where people were selling because I think they were just like being closed out in the value side um and that's really an interesting dynamics um in a crisis. crisis and that's not just in the EM markets. We saw that in in the guilt although you know whether the guilt is an EM market these days is a is open to debate but uh but uh uh you know certainly a market which is traditionally very liquid but we see very interesting dynamics for flows. >> Okay, cool. All right. So the next thing we want to talk about is um an excess volatility paper. Um I'm going to let you completely do this. I opened the paper, I saw all the formulas and I completely tuned out. I got, you know, stone cold when I saw all those all that math. So, um, but I do recognize some of the names. Uh, one of them being previous guests, Jean Philipe Bour from CFM. Um, and there are some other people, Adam Machki or some Majki maybe, and Judah Kurt. Um anyways, before I also manage to um um butcher the the names, I'm going to hand it over to you and talk us through this paper um which is called uh revisiting the excess volatility puzzle through the lens of the and how do you pronounce? >> I think it is Kella um >> Kella model. >> It's an Italian it's an Italian name so I think it's Kella. Um must be but but don't again don't blame me. I can hardly speak English let alone Italian. Um so um I think that's that's it's it's a really interesting um branch of uh finance which is uh understanding flows and understanding how flows um affect prices and it's interesting for example that in all almost all discretionary traders will pay a lot of attention to flows and CTAs in general pay almost next to no attention to flows. So, so and and it's quite it's quite funny because we feature very little in we feature quite prominently in any of such model. Okay. So, I'm going to describe like the generic model and why this model has come to being and a little bit about the efficient market hypothesis and one of the reasons that we have chosen this paper is actually about the it uses the EM algorithm um in order to do cali calibrate. So um so here's a nice example about the way dynamical systems are are are being calibrated using sort of this this this very important algorithm. So here's is is the the efficient market hypothesis. It says well you can't make you can't take money out of the market basically. So uh you know the efficient market hypothesis is actually much stronger. It's it's basically says Sheila came along and says actually the reason why you can't take money out of the market is because the market knows everything. All all the information about the fundamental value has been incorporated and um and like therefore the price is the right price. Okay, if you like in Schiller's universe we are all value traders. We all, you know, if the price is if we think the price is 100 and and the price is actually 90, we would go in and we would buy until there is an auction mechanism and every everything balances it out and everybody's expectations of where the true value lives and that's how we we trade. The problem with this worldview is that markets are a lot noisier than what uh what we would get if everybody would be like that, right? And the reason with this is because we would essentially auction at a certain price at which all the supply and demand actually balance balances out and then there would be no more trading until we expect the value to change again. So so really there's just not enough. You would move very quickly into the equilibrium point and you would not see the volatility that we actually see in the market. [clears throat] >> Okay. Now let me tell you the other uh reason why you can't take money out of the the market which is the completely inefficient market hypothesis which is like I've just invented it so the way that like nobody knows anything suppose like it's completely noise everybody is buying selling like completely randomly we are truly like a martingale we're truly just a brownie in motion of course you can't make any money because like the expectation give of tomorrow we're given today is zero. It's the same is the there's no there is no information at all in the market. Okay. So that that actually would be also like to me an efficient market hypothesis. It's basically you still can't take money out of the market not because everybody knows the true price but because there is no true price. It's like complete complete noise. Okay. Um by the way too long don't read uh the outcome of this paper and actually I can tell you in advance is that the most of the market is noise. So uh the reason is a reason why option pricing and everything works very nicely is because the vast majority of volatility is is sort of noise and this paper uh said okay so let's a few papers said okay so let's let's abandon this idea and the way that we will examine price action in the market is rather we will think about it in terms of flows we will think about who is buying who is selling and how the dynamics of those people who are buying buying and selling will sort of play out in practice. And these models traditionally have three players. Okay. The the player number one is the Schiller player, the fundamental player. If the price is below your your target price, you would buy. If the price is above your target, above your target price, you would sell. >> Uh and you kind of tend to converge, right? You can think of it as like it's gravity pulling you to the center. And then you have the the noise traders which we just described. They just come in and like just introduce the volatility to the market. They don't really they don't really know what we're doing. And the third guys are us, right? The CTAs. And we are the evil people that violate the efficient market hypothesis. We shouldn't exist, right? We shouldn't be making money because like we trade based on like price went up, we will buy up. It's not to do with the value. We don't think there is any there is any certainty. just we just follow that that that um that process and the interesting thing about it is really um what drives the flow from each one of the players. So the value trade value traders it's about the the position it's about the uh is the one that determines whether you are going to flow to create a flow and you're going to create a trade. The trend followers look at the first derivative we look at the returns essentially. Okay. So we we we do things in return space and the noise the noise traders are just there to excite the system to create some action. Okay. So this paper is really nice and he says okay so given that we have these three players let's introduce some uh reasonable assumptions about the way that the response function to position response functions to to trend is. Um and then let's see if we can calibrate the model and at the same time sort of introduce being able to handle genuine drift in the underlying value of the market. So there is a recognition that there is some value the value of things does change and they do a really nice uh well they they pick uh some sort of drift term over time they which basically centralizes the time series to be oscillating around that that value and then using the EM algorithm um they basically calibrate the model to say this is this is the proportion of noise traders. This is a proportion of value traders and so forth. This is a proportion of of trend following traders. Um and and um interestingly enough by the way CTAs or we do not contribute that much to the volatility. So one of the outcomes of this paper which is not surprising and actually I think very true and I could have told them straight away is that we are very small part of the market. So if they look at the the contribution of of volatility uh first order of magnitude by a long way is noise, second order of magnitude are the value traders and third order like you know we just don't feature in the in the equation at all. And I think it's actually very the reason why you can tell this is in advance is because um uh is because uh there's just not enough co autocorrelation in the markets. sort of CTAs would basically create a positive autocorrelation in returns. But we just don't see that in the market and I think it's part of the way that the industry operates. We try to be we talked about trying to be small in the market. We try to make sure that we're not actually the footprint is not that great. So that's that's the paper really exciting. Um I really like the the mathematics the way that they do them. They use dynamical system in a in a very um very intelligent way. um they use the EM algorithm, the calibration is interesting. Um there are a few interesting observations there as well. So uh what is very interesting is that they say oh the market is either spends a lot of the time being underpriced or overpriced um rather than in the center. >> Right? >> Okay. Um now let me be a little bit critical of of the paper. So but before we do that I'm just going to say right let's forget about the maths because all these all these papers all all these models will share a certain feature and I think the way to think about it is actually to leave aside the mathematics and think about the pendulum swinging. So as long as there is a there's a there's a there's a real value you can think about the the dynamics in terms of the distance from the center of the pendulum and you can think about the way that the pendulum swings and then a lot of the results actually in the paper becomes very intuitive. So um so a pendul so what does it mean as as we go through the center through the value the the trend followers the pendulum is swinging fast and the 10 tra the the the trend followers are the dominant ones giving an impulse right as we reach as the pendulum swing upwards then the value traders begin to dominate okay because the trend followers are kind of running out of steam and the value traders saying well you're very far away from where true value is. So I'm going to push start pushing you back. So the the you [clears throat] might get a dynamical system, but it sort of looks the way the the fa the the way that it looks the the phase diagram would look a little bit like a pendulum swinging. And then it becomes actually quite obvious why you would spend time either being overpriced or underpriced. There are two there are two equilibrium points where t trend followers and value traders balance each other on you know on the top of on the top of the the top of the pendulum or the bottom of the p the pendulum sort of on the right on the left of the pendulum case. Um so a lot of the results that you see are actually what you kind of expect from that sort of a dynamics. Um so I I I kind of I kind of like the papers. Um but there are there are a few things which I I I I don't like about the paper which is um that let's let's do the ideological one. The first one is an ideological one. The model I I think that in some sense the the paper sets out to sort of disprove the efficient market hypothesis. Okay. It says here it is. We can calibrate this. We can make this make sense. Um and I don't like going all out to disprove something. I think in reality and what we see in the market is a mixture of flows which are driven by price which are driven by flows and prices which are driven by genuinely a change in the underlying valuation like the the dynamics that like this month what we have seen is the market was fine everything is happy and then suddenly the fundamental value of oil has gone has has changed okay and we don't really And I think that's a dynamics which is really interesting and it's we can clearly see that in in the market where there is a jump and then suddenly the value traders will really need to scramble to start doing some trading and I think it's something that we see in a lot of market events. If you look at non-farm payroll for example ahead of non-farm payrolls you're going to see liquidity actually shrinking like half an hour before non-farm payroll. Nobody wants to trade. Market makers don't really want they don't know what's the economic announcement is going to be. >> They don't want to they don't want to take on the risk. You will see actually a lot less trading >> and then you would see non-farm payroll coming out and the market would gap and the market gaps because instantly the value has changed and and I think that not recognizing that in your model I think is is is a problem, right? you're trying to explain all volatility through trading I think is a mistake. In fact, markets do a lot of the a lot of the the the price change can happen without any trading whatsoever. There's a difference why close price on day one and open price on day two are very different because things have moved, right? Like there's there've been news, right? The weekend has exposed ourselves to an Iran war. So that's that's a dynamics that I think would be very valuable for the paper to recognize and to recognize that that's where a lot of the volatility is coming from and it's not just noise traders. Noise traders are very different to like genuine jumps in the underlying value of the of the of the process. >> Yeah. The other issue that I have there is that again the dynamics in terms of being volatility of volatility and being taken out the value traders being called out again this is a dynamic which is really not recognized in the paper. So I think um I I think that uh you know you want your models to be simple as simple as possible as long as they actually describe reality. And I think the the dynamics that we see in the market in terms of uh I think the assumption that value traders have got a response function which is a cubic which basically goes off to infinity is completely unrealistic. Value traders gets get called out just as much as trend followers in terms of risk management. So that that that response function is to me is completely unrealistic. Inability to understand how volatility and risk management really affects it. Again, not something I would really love to see. To be fair to the paper, the paper deals with a very long time horizon. So it's using is using sort of monthly data all the way back rather than the dynamics that we kind of see in our trading which is the very short-term sort of daily daily cycles that we do. But I think there's I think the industry it would be really useful and it's something which is really important in terms of understanding the way supply and demand affects what we think is the real value of the commodity say or the asset >> and then the dynamics around that I think that would be really useful because the the at the moment the mo these models are not useful for providing any prediction or any way for actually for us to improve the model. >> Sure. No, I appreciate that. Um and um since we gave the title of the paper, people are of course free to go [music] and and visit that um themselves. [music] Anyways, um let's talk uh before we wrap up today >> [music] >> um and before you run out of of any voice uh with your cold, um let's talk a little bit about your um diversification paper that you uh or blog post that you did on on LinkedIn. Um I think together with Tom Babage if I'm not mistaken. Yeah. >> Yes. So Tom Babage Tom Babage did did all of the work. So I'm taking a little bit of credit for uh a little bit of credit for his work as well. So uh I I contributed a little bit but um so um [snorts] but uh it's it's a it's a very interesting question. It's a it's a very interesting question about capacity >> uh and which is >> uh how to manage capacity and is capacity and managing capacity is it actually important? So that's that's a question that we struggle in the alternative markets because capacity is very real and the question is like is it really actually important? Um so the the amount of risk that we like to put into each market is limited precisely for the same discussion that we had that we don't want to trade against ourselves. We don't want to have too much of a footprint and we do want to be able to unwind our our portfolio when there is a crisis like we've seen this month. >> So the amount of risk that we can do is kind of we can put into each market is limited, right? We can put more in the treasuries, we can put less in South African bonds. Now at all do we need to have those small markets in our portfolio right do we want to have them at all and maybe we just should should just trade all these big capacity high capacity markets um you know if you've done if you've done index replication um you know that you know some of our peers might be trading 12 or a dozen or so um assets in their portfolio right and you know this discussion you've had it with Rob you know Rob believes in trading 300 uh 300 futures you I'm sure you guys might be trading 50 okay and the question is like why what's where where's the where's the choice and and the observation that we make is that it really depends on the underlying correlation of your market. So if you have a portfolio where the underlying correlation is high to start with then actually if you have more markets you don't necessarily gain that much of diversity because you there's a plateau which you reach very quickly. So at that point the fact that you you you may be putting a lot of your money in only the very big markets is not that damaging to the overall diversity of the portfolio. I mean the way that we think about CTAs, the way CTAs make money is the diversification times the average market sharp, right? Okay. So uh the more diversified the portfolio in principle, the better the better the better quality your portfolio is, the more resilient it is and so forth. However, if you start with things which are correlated to start with, then the fact that you are taking you're taking a lot of more money and you're pushing a lot of the risk into the big markets or just start by trading the big markets in the first place that's not too damaging to your portfolio. Conversely, if you have a portfolio which is very which has got you've selected it so that it's very low correlation which you're trying to select low correlation market. Now at this point things are becoming important because a large component of your performance comes from that diversity and if you start taking a lot of money in right and you start managing it right you can only put because you can only push that much risk into your smaller market you end up pushing more and more risk into your bigger market and therefore effectively you're reducing the number of of assets that you're trading and that is actually a lot more damaging to So the there's a lot of discussion in in the industry about you know whether 200 futures is good, 50 futures is good or maybe there's 12 futures is good. And I'm saying like it it there's a choice here. If you're looking just for the first factor, the CTA factor, highly correlated market, highly financialized, that's great, right? And in fact, you might think that um this this means that you're very highly correlated to everything, but that's okay because you're doing index replication, high correlation is actually what you're after. Conversely, if you're just saying, I want a high high quality portfolio, then you are handpicking, you want very low correlated market, and then you start having to think about your capacity. So uh we are just trying to be trying to to sort of frame the debate that that that is going on uh in the industry between them. >> Sorry to interrupt you here but the funny part is and I completely agree with you that saying that yeah you theoretically think that the fewer markets you trade should be fine if you want to trying to correlate highly with the index you're trying to replicate. But actually when you look at the numbers that correlation isn't that great. I mean the tracking error is um substantial. But that's another conversation. I just want to throw it in there for the record, >> you know. So, so, so absolutely. Um, I I would say that uh uh trying to replicate the S&P, I mean, that's that's the funny thing. If you're trying to replicate S&P, if you get less than 99% replication, people say that you're not tracking very well. >> Whereas in the CTA universe, replicating the CTA, you have a 80% correlation and people think, "Oh, that's great. you're correlating you know >> right >> so so very it's it's a very I think replicating uh CTAs where there is some opacity to be in terms of what's actually underneath it's a much more difficult question >> yeah anything else you want to uh talk about from your paper >> no I should I really shouldn't be talking about myself too much so I think I think I'm I think I'm good I'm I'm good on that one >> okay well anyways people should go and read it on your blog on your LinkedIn uh anyways um just to uh to get the full flavor of it. Um you I really appreciate you um you know working through your uh your your cough and your cold today. Um yeah, that that means a lot and um you bring up some interesting and important uh topics every time you come on. So uh thank you so much uh for doing that. Um >> always fun. >> Always fun. Now I mentioned that uh next week I will be joined uh or Katie and I will be joined by an extra person. Um we'll keep it um well it's not really a secret. It is one of the authors uh of um one of the best papers I've read recently which is the one about uh how to decide or what is the effect of trading different market universes. Um so it's Harry Moore from AHL. He'll be joining us. Um, and um, I'm sure this will be a really fun, interesting, insightful uh, conversation. The the the topics will of course be much broader. And um, if anyone has a question that they want to bring up with Katie and Harry uh, next week uh, to make the conversation even broader, um, they should definitely email me at infotoptradersunplug.com. Um, the sooner the better and I'll try and do my best to bring it up. If you uh want to show the appreciation to Y of and and all of the co-hosts for um preparing and um and sometimes having to work through a cold um please do so by going to your favorite podcast platform, leave a rating and review because it really does help more people to discover um the uh the podcast and the content uh that we produce um each and every week. With that uh from Y and me, thank you so much for listening. We look forward to being back with you next week. And in the meantime, as always, take care of yourself and take care of each other. Thanks for listening to Top Traders Unplugged. If you [music] feel you learned something of value from today's episode, the best way to stay updated is to go on over to iTunes and [music] subscribe to the show so that you'll be sure to get all the new episodes as they're released. We have some amazing guests lined up for you. And to ensure our show continues [music] to grow, please leave us an honest rating and review in iTunes. It only takes a minute and it's the best way to show us you love the [music] podcast. We'll see you next time on Top Traders Unplugged. [music]
The Illusion of Control in Modern Markets ft. Yoav Git | Systematic Investor | Ep.393
Summary
Transcript
Imagine [music] spending an hour with the world's greatest traders. Imagine learning from their experiences, their successes, and their [music] failures. Imagine no more. Welcome to Top Traders Unplugged, [music] the place where you can learn from the best hedge fund managers in the world, so you can take your manager due diligence or investment career to the next level. Before we begin today's conversation, [music] remember to keep two things in mind. All the discussion we will have about investment performance is about the past and past performance does not guarantee or even infer [music] anything about future performance. Also understand that there's a significant risk of financial loss with all [music] investment strategies and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions. Here's your host, veteran [music] hedge fund manager Neil's Krup Larson. Welcome or welcome back to this week's edition of the systematic investor series with Y of Git and I Neils Castro Blasten where each week we take the pulse of the global market through the lens of a rules-based investor. You are it's great to have you back this week. Um I was just going to say I hope you're doing well but I actually know you're battling a cold. So I really really appreciate you persevering through uh through this uh uh conversation today because I I know how it feels when you're not 100%. So really appreciate that. Um but it's really great to have you. So um so welcome back. >> Thank you very much. It's always a pleasure to be back even when we've called. Um >> even when Absolutely. >> But it's a great time because the markets are really exciting. So um it's it's a it's a great time. There's a lot to discuss. Um there's a lot of there's a lot happening. >> There is a lot happening. Uh without a doubt. So let's talk about some of the things that's happening that's catching our attention as we normally do. um what's been on your radar recently? Oh, >> okay. So, so before we get to the to the markets, I'm going to talk about um something called the the EM algorithm. So, this is a a mathematical algorithm which was really the granddaddy of all machine learning. So, that's the idea of uh it's an iterative algorithm um which allows us to two stages one of them calculating expectation and one of uh one of them maximizing the likelihood. And the reason why I want to talk about this algorithm is because the person who invented it is a guy called Arthur Dempster. Uh he was a math professor at Harvard and he came up with that in the late '7s. Um and he passed away uh since we last spoke last month. Um so um not not it's it's been on my radar and you know he's not a very famous person. He's not very significant and within the the war and all things that are happening uh we tend to forget. But actually a lot of machine learning um is predicated and uh by his work. Um so um rest in peace Arthur. >> Yes. Well, you call it the EM algorithm. I you know I'm definitely sort of uh um what you say gravitating towards something with efficient markets. But what what does the EM stand for? >> So so one of them is so EM the E stands for expectation and the M stands for maximization. So it's not emerging market, it's not efficient markets. Um so the idea behind it is if you have suppose suppose you have um you have a lot of observations let's say from two distributions like one of them is red and one of them is blue. Then I ask you to estimate the mean and the variance of each one of them. You would use maximum likelihood estimate to estimates the mean and the variance of of the blues and mean variance of the reds. But what if I didn't tell you which ones are blue and which ones are red? So you have a you have a problem of classification which is exactly what you have in machine learning. You know is this a cat or is this a dog and the way you do that is you have a second stage which says given that you've estimated this distribution you look at each point and say which of these two distribution is it like is it more likely to be a cat is it more likely to be a dog is it more likely to be red is it more likely to be blue and based on that you recalibrate. you do a second round of saying okay now that I've classified each of the points according to assign them with probabilistically to each one of these distributions now we can recalibrate the reds and the blues distributions and then again which point is more likely to be in each distribution and it's an iterative process a little bit like the back propagation process that we have in machine learning these days um and that's actually how you eventually sort of converge to a very accurate estimation of like classification without doing any any uh sort of any explicit determining of which ones which points are the blues and which points are the are the red. >> Got it. Speaking of red and blue, actually what's been on my um radar is something that happened yesterday. It happened in my birth country uh of Denmark. It was election day yesterday and of course we're waking up this morning to one of those uh situations where the voters have basically given them a a very very difficult hand to find any agreement across parties uh reds and blues. Um so um so I'm I'm sure I will be uh following that for the next uh few days to see how it all turns out. What's been interesting about it and why I I find it a little bit uh relevant to even mention um uh in this section is that you know we talk about markets being very narrative driven from time to time and actually in the last few years very narrative driven and you would think something in like Denmark given all the narratives around Greenland in the last 6 months for sure that that would be a narrative that would be like front and center in an election. campaign that started at the end of February. Now, we know, of course, that a few days after that uh election was called, um a war started in the Middle East. That didn't even make it really through to the um to the main topics of the whole election campaign. I'm not going to ask you this unfair question of what do you think became the main topics because I'm pretty sure you wouldn't guess unless you read the news, of course. It ended up being animal welfare and clean drinking water. That were the main two narratives that actually pretty much defined the last two weeks of all the debates between the left and the right and even the prime the candidates to be prime minister. That were the main ones. And I just think it shows you how unpredictable things are u you know even at that level um that you think oh yeah definitely you know uh global you know geopolitics and and and stuff like that will be front and center in a world where you just been under siege uh at least um from a narrative point of view about part of your kingdom. U no didn't really get mentioned whatsoever. So, uh, I think that's a little bit of a lesson for us investors that it's not always that, um, what we expect is what, uh, what happens. Now, of course, I think it's a credit to the Danish people that that was the topic, the the center of the campaign, and I wish all of us worried more about clean water and animal welfare and less about other stuff. >> Yeah. No, I mean, they they are important points. I'm not certainly not dismissing them. Anyways, the other thing that kind of caught my attention and and maybe we'll talk about that um you know now a little bit more is just >> again the unpredictability of markets, right? I think that um just watching what's happened in the last few weeks uh with a market like gold um where it was so bullish uh for the first couple of months of or at least the first month of this year. Um but also to see that how precious metals have reacted through the Iranian war. Um I I it seems like it's selling off when the war began, but it's almost like it's also selling off now that there might be a deescalation of the war. Um, I know this changes every day depending on what uh tweets uh we're what we're reading, but it just it's really it's really interesting to see how how all of this is playing out and how unpredictable it really is. And even though um as we'll come to in a second, for sure uh our beloved industry and CTAs uh overall we haven't profited from it. We've lost a little bit as an industry, nothing too much. Um but there will be a lot of dispersion between managers for sure. How did you navigate these last three weeks? There'll be some real winners, some real losers and lots of uh managers in between. And that's fine in the short term. We know that positioning, initial positioning is so important and um and you can be lucky, you can be unlucky with how you enter a situation like that. But it just keeps on giving us all of these um so to speak uh examples of why um or how difficult it must be to be anything other than a rules-based investor uh in my biased opinion of course uh when you uh when you go through a situation um like this. So um love to get your um sense uh of this and and where you may think some I know you focus on fixed income but where where you may think that some um you know some of the uh how should I say determining factors have been uh in the last uh few weeks. >> So I think I think the points you make are exactly correct um in terms of first of all the narrative in the market has been inflation. So if oil if oil rises in prices um then we're going to see inflation and then we'll see something. Um but I think that's not entirely the story. Um and the reason why you see that is you see for example as you said gold in um behaving in a way that you wouldn't expect gold to behaving in an inflation based. Uh you wouldn't expect bonds um long-term bonds to be affected as much. So if you look at for example the oil curve, the spike in oil is at the front of the curve, right? If actually if you look at Brent 3 years out, it hardly budged because it makes sense. We're going to have supply constraint at the front of the curve. The back of the curve, we're still in a structural situation where we're actually overproducing oil rather than consuming. We're cons we are producing about 105 million barrels a day and consuming about 102. So long-term was affected a lot less than than the crisis. Um but in bonds you see 10-year bonds in even like the guilts or the treasuries. I mean treasuries have moved um from 114 ty moved from 114 all the way to 110. Uh in enormous change that's that's something like you know um that that's something we haven't seen. That's something like a year's like more than a year's worth in in a couple of weeks. Um similarly in EM um we have seen in EM again not a sell off at the front of the curve. We've seen actually a selloff at the back of the curve. Um which is sort of indicative that what this is happening is a bit of a much more of a riskoff event than just a simple narrative of of um sort of inflation impact on the on the country. And that really makes a big difference to um depending on which assets you're trading and which specific assets actually you're trading. So which has more exposure to sort of the Iranian conflict, the Iran Israel conflict. Um and I think the dispersion that you're talking about is is actually quite interesting because the dispersion is not going to be just the dispersion between us and different managers, but actually the way that you're looking at the model. So um you know normally you would do a daily maybe daily or a little bit of intraday trading um and the intraday volatility is is small enough that it actually doesn't matter what time of the day you are executing for example um but in our case um if you look at what we've seen this this month um oil has traded in a range of say 20 $25 a day. So, at the beginning of this week, right, we started off with boots on the ground and oil was spiking all the way to Brent was spiking all the way to near 120 and then, oh no, we've got an agreement with Iran. Um, oh yeah, we're going to trade all the way to 90. Um, so depending on like literally what time of day you've executed, you're executing your trade will make a huge difference to your performance. So your like your model the ability for you to track your own model um this month has been really really difficult. Um so so that's that has been that has been a real a real issue actually. Um and you will see that you will see that I suspect we'll see dispersion. I mean the level of surprise volatility we have seen this month has been staggering. I mean we run a scenario analysis of you know what what historical data all the way back to 98 um is actually more surprise volatility that we have seen and the beginning of of March immediately made it to the top chart to the top of the chart in terms of effect that we have never seen before that is really materially impacting volatility and on a lot of asset classes. So that has been a real that has been a real a real pain point um for us in terms of volatility. Um and I think one of the interesting things if you talk about different asset classes um is the bond equity correlation. I mean we spoke I spoke with uh about the fact that in times of inflation we are going to see correlation between bonds and equity being positive rather than negative. Um, and that's really important for an asset allocator. And I think this this month has been a really strong example because we've seen both equities and bonds selling off. So if you thought one of them was actually acting as a defensive mechanism for you um then think again. I think uh that has been that has you know bonds sold off just as much as equity um if not more actually. So um really interesting dynamic this month. um and making CTAs, different CTAs depending of whether which which asset class you're trading, depending on which which um sort of which speed of model you're trading and which time of the day you're executing. Really different difficult. >> Yeah. Um okay, so let's unpack this a little bit. Um because um so first of all, obviously I'm not entirely sure that I that I agree that it's it's difficult to um uh to find out when you should trade. You should of course do like all the people in oil that apparently traded 10 minutes before the announcement. Y you should know that by sure that's like 101 trade before the news and you'll be definitely profitable, right? >> Yeah, I I think we'll get into market manipulation later. But yes, there's been a few instances not just in the oil but also uh in equities um on the Friday. On the Friday, we have seen the S&P um selling off and then selling off again with the boots on the ground and then beginning to rally ahead of Trump's um like it's it was really it was really quite uh quite interesting to behold. Um I I think you're right being rules-based um makes the makes the decision to trade much easier. And um we're going to talk a little bit about flows. So I think it's actually really interesting >> to see. >> We'll we'll come to that. We'll we'll we'll definitely uh cover cover that as well. I just want to sort of stay with the trend following section and just give people also a little uh update on performance and so but um but you you're making uh some really interesting points uh of course. Now just staying with this uh that you bring up about you know correlation um and and um stocks bond correlation of course some people will say well you know CTAs overall didn't give us any protection either this month uh I mean on an index basis okay they're down a little bit less maybe than than some of the bonds. Um uh but of course when you have a quote unquote surprise um like this yeah I don't think CTAs we claim that we will be necessarily uh uh doing better than than anything else. I think what will be interesting from here is really how this manifests in the interest rate sort of area of the portfolio. I mean you're obviously a much more of an expert than I am in that because that's what you're focusing on. Um, do you think this could be the be I mean I mean obviously we're not we say we're rules based so we don't we don't um try to forecast anything and then I'm just about to give you a question about the future which of course is is not um is a bit counterintuitive. Nevertheless, I will I will um I will ask it and that is because some people do uh bring it up and that is could this be the beginning of another 2022 scenario? I think this is what the the concern is among yes institutional investors. Um and and maybe it's the hope of what CTAs are hoping for that this will be another 2022. Um but but how do you see it more from a more from a structural point of view in terms of what's going on uh as you look at what's happening in the fixed income world? Uh maybe you see something that you can comment on that is not pure speculation. So I I think what was interesting is the weakness in the 2-year bond auction recently um in the US government. So that's that's not something that we have seen a long time. Um yeah, from a personal point of view, I think that um a lot of the western countries are in a fiscal um tough spot >> and um inflation actually is a good way for them to um to get rid of that debt. Um, so being being like on a personal level, I mean believing that yields are actually on on on the march up. Um, that's something that I think is is almost inevit inevitable to me. Um, but you know time but timing when is it going to happen uh is exactly exactly the point that we cannot even make this determination. What I can tell you is that the the recent move has been, you know, you go into the month and you're thinking um you're going to be long bonds um and [clears throat] very quick and very quickly you ended the month with actually being um short um because the moves have been very violent. The reversal has been very violent. But we've seen in fixed income actually the the inability to decide for the markets to decide which way it's going um since 2023. like 2022 was a great year. You're going to be short. Um but understanding what's happening what's happening with the US interest rates has been a real struggle and it's been back and forth back and forth. Um and it's been very difficult for CTAs to trade um uh the fixed income portfolio. So um I I wouldn't like to it may be the beginning but it's you know equally equally it may not be. I think what's interesting the other pain points in the industry as a whole uh private credit um is under attack. You've seen cracks. You've seen some funds beginning to gate because redemption you've seen uh uh uh too many redemptions. So some of the funds have started to gate. Um certainly feels a little bit uh the world is feeling a little bit um uh on edge. Let's put it this way. Right. Fragile. I think that's fragile. Yeah. No, that's definitely true. Now, another thing I was thinking of while you were talking, we we obviously talk a lot about price, right? So, higher prices, for example, in in in energy, uh will lead to higher inflation for sure. Um but I think this time around, um again, not being an expert in this, um but I did notice that one European country now has kind of um rationalized oil. So, so for me it's not necessarily just price, it's actually supplies. I mean meaning can we actually get the oil we need or I mean maybe luckily for for for our part of the world um we are heading into to warm warmer weather um uh you know this is this is some you know different from heading into a winter uh in terms of not being able to get uh oil and and and gas and so on and so forth. Um and uh of course in a much bigger picture which will leave to the experts to to to debate. It it shows our continued vulnerability to a lot of things. Um and maybe in particular Europe um you know we thought we were well we knew we were vulnerable when when it came to Russian gas and now it turns out we're obviously um set ourselves up to be um very vulnerable to Middle Eastern gas and and oil and so on and so forth. So it um yeah it's it's an interesting um it's an interesting development uh no doubt. Let me turn to the usual numbers that we talk about. So my trend barometer although numbers will show that it's not necessarily showing up in the performance but but my trend barometer is actually still in a relatively strong level at 52. It means that there is some breath in in the portfolio of the markets. that tracks 44 markets um in terms of quote unquote trending behavior. But as you rightly said, uh the volatility in some of these markets is just incredible. So even just something as time of execution uh may mean that you're not really doing great uh from a performance point of view. But you know um as it stands, that's where we are. Uh now I'm going to because we're recording a day early on Wednesday this week. Um the numbers that I have from the indices are as of Monday evening, but I think yesterday um was a little bit of an update uh for the CTA industry. But anyways, as of Monday evening, Btop 50 was down two and a quarter, still up 6 and a quarter for the year. Um so CTA index down just shy of 2% uh still up 6 and a quarter for the year. And the stock trend index down 2.6% still up almost 6% for the year. And the short-term traders index is no no surprise is doing a little bit better. It's about flat up 10 basis points for the year sorry for the month and up just shy 4% for the year. Now that is in big contrast to what's going on in the traditional market. So Msei World is having a rough time down 6% [snorts] so far in March down 3.23% so far this year. US aggregate bond index as you pointed out down 2% or so and change in uh in March now down about 40 basis points for the year and the S&P 500 total return is down 4.59% as of last night and it's down now uh just shy of 4% so far this year. So interesting developments, interesting dynamics. Um and even though we haven't made money as an industry, uh we certainly lost a little bit less it seems um as an industry at least um for um for the month of March and are also doing significantly better of course for the year as a whole so far. Um now before we jump uh any further, let me just mention to those of you who may not have noticed, but I did recently release the eighth edition of the ultimate guide. Uh we've added another 100 books to that guide. So now you have about 600 plus book titles um that you might uh want to um go through and find something that u will continue your educational journey um in this uh industry of investing. There are two ways you can receive it. You can either sign up to the Sunday emails. I'm sure there's a link somewhere uh in the um uh on the website or you can go to toptradersunplug.com/ultimate and you should be able to get a copy. [music] and it is of course free of charge. [music] Let's stay with the markets a little bit further. You mentioned uh the curve now and you you said this thing that well if you traded oil 3 years out it'll be very different to uh you know trading oil um for the next two months. But as a CTA we don't trade oil 3 years out. Frankly, we trade oil um you know a few months out. Uh so um is there anything you want to add to that or was it just the to to showcase the fact that the action has been somewhat different uh depending on where you are in the current? >> Um so so actually uh some of us do trade oil. >> Yeah, I know you're probably the exception to the rule, I'm sure. >> Indeed. Indeed. So I I think um I think that the the the I'm not responsible for the for the commodity that my my my colleague Tom Babage is is is doing that. I think the idea is uh the way he likes to talk about it is weather versus climate. The idea that one of the nice things that that when you trade the back of the curve is that it is really about fundamental supply and demand. It's not really about it's not really about the the short-term shocks. and and I think this this this month has been uh has been quite instrumental. So you might think he's he's trading a lot of oil but the volatility should be high but actually hasn't seen that much volatility because he's like he's almost away from the the news cycle. Um so me I look at the the curve in terms of trying to understand inflation expectations. So I mean if you look at at oil, oil features uh energy features about 6% into the inflation uh directly as a component into into inflation and then of course there is a secondary effect on fruit prices and and the rest of manufacturing. So there is a there's a significant contribution and then you try to say okay how does the the entire inflation curve sort of rebalances itself um as a result of of of uh a protracted shortage in supply and demand. I think what what is interesting what you said earlier is that in the case of commodities it's not just about money like as you say for the love of money you might not be able to get the oil that you need right and you see countries like um okay in Europe we see that a little bit because we are still very rich but in countries like Egypt they already instituted uh you know energy energy restrictions because they have they have issues and um you know we rely on a supply chain like hormos um and and it's so so so there is a point where it doesn't matter if you have as if you're willing to pay uh it's just not going to be there um so so that that's a very interesting uh that's very interesting dynamic in terms of the supply shocks in the front of the curve which you probably don't see that much in the back >> yeah well let's stay a little bit with trend following then before we move off onto the volatility paper we wanted to talk about. There are two other things that I noticed uh in your notes that you wanted to um kind of flush out a little bit. One was flows, the other one was risk management. Let's let's go for let's go there. >> So, let me tell you a little bit about my experience. So um without let me like broadly um if you trade if you trade rates across globally you would have entered the month probably long bonds but you would have been short in different areas. So like in Japan you would have been short in Taiwanese dollar you would have been short. Then you have uh you might belong in South Africa, you might belong in um in other currencies. Now the shock happens. Now normal CTAs the the way we would normally manage risk is say well the volatility has spiked and we would sell everything. Now what interestingly what you find certainly in emerging markets is that spreads widen. So uh if you look at the yield spreads that we will you were getting quoted is like 10 basis points. It's an enormous it's a it's a it's very it became very expensive to trade >> and in a way the risk that you're experiencing is a directional risk. So you may be long some DV ones, short some DV ones and you're trying to reduce the first factor, the first factor that we are seeing now which is a essentially a bond selloff >> and normally we would manage it um by selling each one of the assets right so we would shrink the entire book uh but that's actually very expensive at the time of crisis now I I actually say I I don't have a model to manage the um this properly but uh it's interesting to me that if I was a discretionary manager and I know you know I've been to breve I've seen I've seen how risk is managed on a on a then what you would do is you would basically sell your receiver you will close the receiver you will sell your long bonds rather than just shrink both the longs and the shorts you would the first the first point of action would be to take the very liquid assets that are very cheap to trade and just close your duration as much as quickly as possible don't do Um and that's a very interesting dynamics that it just brought it to the four. Um the way that um we may be doing something which is which is lacking because if you think about what what discretion you're doing actually makes a lot of sense because what we have is when there is a spike in correlations actually a lot of these assets becomes good substitutes of each other. So hedging with the TY with the US treasuries very quickly is actually as effective as you would do than just shrinking your entire portfolios both the longs and the shorts. It's not something that um that I do actively but it's something that is that when I look at my portfolio and I think okay this is not a behavior that I would have expect to do in terms of crisis and certainly this as a discretionary trader you wouldn't do that but as a CTA this is the rules that we have we take each of the markets we just scale it down so what you end up with is both scaling both the long and the shorts at the same time even though like your short ones might actually hedging your your risk as well. So that was interesting to me. >> Yes. But I let me see if I can add a little dimension I guess is the word I could use here uh to that. So when you talk about that that might be an expensive way of doing it. I'm straight I'm thinking here ah yes but that's because you trade alternative markets for as someone who trades the core markets where it's all futures and it's all super highly liquid. That's exactly what we can do. And this is Yes. So, and this is relates back to the paper that Nick and I and Allan and I or have been discussing a couple of times and which we will be discussing with one of the co-authors uh next week actually uh when he joins Katie and me and that is the difference between uh which market universe you put in your um CTA portfolio and and so I'm glad you bring it up in that way because these are some of the periods where in a sense it shows up but it might show up below the radar meaning when we see the final performance for the month of March we may not be able to tell in total who is trading alternatives who's trading core markets etc etc but then once you open the hood and you look down in the engine room these are some of the things that start uh showing up so it's a good example to to talk about that in times of stress [snorts] liquidity is important. >> Very important. >> Absolutely. Yes. >> Absolutely. And and how to manage how to manage the the risk um is different as you said, right? If you are trading if you're trading US treasuries, you just close the position, right? There's no there's nothing to do about it, right? Um but if you're trading um you know the cost of like let's say closing closing the book, the long short book is like 5% of performance. So um that's actually quite expensive. Um, so you have to think about how you do that. Um, and I've got to tell you, I don't have the solution for that. It's part of it's part and parcel of of the way that um, you know, the struggles that we have. But I'm just um, but it it was very interesting to me, you know, when I'm thinking postmortem, what can I learn or what can I do better about managing the portfolio on on on the fixed income side? That was one thing which was very very clear to me in terms of >> but how would you do that from a rulesbased point of view in a sense how could you do that I can understand why you can do it from a discretionary point of view but how would you even be able to program that um without adding too much additional risk that we haven't thought thought about >> so so so it's it's actually it's actually I mean in a way it's about recognizing what is actually the way volatility comes from so in times of crisis you have the first factor uh which has become very um which is which is correlated and its volatility is dominate is dominating everything else >> and the way that you you you manage it is your managers on a factor-based uh on a factor-based risk. So you say that's okay. I just want a lot less of that front risk. And by the way, this is this is just good. It's both good for diversification and both and good for risk management. and he says okay so what you need to do is to have a set up the problem to be able to say I want to ensure that my risk factors are risk managed and at the same times managing the the amount of specific risk to each of the market that you're doing because one of the things that you probably don't want to do is take too much like you can edge everything with the treasury US treasuries but then you're taking on quite a bit of risk in that market right so there's a specific risk that So the idea is can you take off can you balance a a lot of the constraints which is can you um primarily reduce the risk in the first component while the other components may be trading they the volatility on them hasn't increased and secondly making sure that which instrument to use to hedge it in a way that you're actually underexposed you don't expose yourself to too much specific risk in a specific country. It's a mathematical problem. It's an optimization problem. It's not as easy as what we do normally. Certainly >> and certainly in the case of like a liquid CTA probably not worth the hustle. You would just basically down gear everything. >> But that's the like to me when I'm thinking about what improvement I can do in my system, I'm thinking, oh, it's if I look at the cost of trading, right, I say this this month has been very expensive. >> But but and I I get that and I take that on board. I my the initial thought that I have when I hear that is that there's one thing though that you are doing if in doing that whether you do it discretionarily or whether you do it uh systematically and that is you're taking on a huge amount of risk if this turns into a liquidity crisis because in a liquidity crisis yeah correlations may go to one but you can't get out by having put all your by having kept all the quote unquote less liquid more expensive stuff to to trade Um, I guess that would be my initial gut feel that that that could be really that could turn into a a whole different kettle of fish, so to speak. >> So, so you're right. Liquidity is something that that we manage by the way as an industry not just in the alternative market but also in uh also in the liquid market. Um, in terms of the making sure that our footprint is not as big as we think. uh is not is not as big is such that we can actually close um you know in a in a reasonable manner. Um but when you see very violent violent moves certainly there's more stress in the there's certainly a lot more stress in the in the alternative markets 100%. >> Yeah. Well um the other thing I think you mentioned in your notes was something about flow. So I don't know if if that's uh something uh we've already covered or not before we move on to the uh excess volatility paper which um I also agree with you is is extremely relevant uh for what's going on in March. But was there something about flows you wanted to bring up? So I I think it's interesting to see the dynamics of flows this month and I think that will feel into I think let's let's describe what we have seen this month >> and then we can talk about the paper the academic paper and then we can maybe try to bring this two two things together. >> Perfect. So what we've seen is first of all there's a price gap because you know there's an attack and there's an attack on Iran. Um and the market just gaps the you know uh it's not it's not really there's not a lot of trading. It was it was happening over the weekend. It was over the weekend. There was no there was no there was no there was no trading at all in in oil. uh but when the market opens the market opens a lot higher and then you see uh a day a day later you start seeing um or actually on the day itself you start seeing CTA flow. So CTA flows coming in of course volatility as we discussed volatility is gupping. We will start taking risk off and that comes in and I think as you say um the nice thing about about being systematic is that we we basically trade the market the model says it we just we just trade. So that causes a a uh that causes again an an effect um and you're seeing you know equity being sold off. we seeing we are seeing bonds being sold off and then uh as the crisis progressed you actually see other players beginning to close their positions. So as you see uh suppose I'm I'm a value trader and I'm I believe in bonds and I'm holding bonds and I'm holding bonds and bond keeps ticking down keep sticking down keep sticking down at some point you're just being closed out there's not much you can do right so you starting to see flows which are driven by the value traders right who were hoping to hold onto their position and they are just essentially there's a long squeeze here they just can't afford anymore or to to to hold the positions. So, we've seen very interesting flows. On top of it, of course, there's a lot of noise traders, right? So, we're talking about the tweets, okay? We're talking there's a lot of there's a lot of market volatility, but I think the the dynamic of the the liquidity um and who's driving the the the P&L because what you saw at we saw beginning CTAs selling, you saw real money beginning to come in um when it things were beginning to stabilize and then you saw um so the value traders were like actually buying. Then you saw uh an escalation with Israel bombing the South Ps uh upper gas field and then again another bout of selling and then you saw a beginning of a squeeze actually in and you saw that in the guilds market you saw that uh even in the treasury market to an extent where people were selling because I think they were just like being closed out in the value side um and that's really an interesting dynamics um in a crisis. crisis and that's not just in the EM markets. We saw that in in the guilt although you know whether the guilt is an EM market these days is a is open to debate but uh but uh uh you know certainly a market which is traditionally very liquid but we see very interesting dynamics for flows. >> Okay, cool. All right. So the next thing we want to talk about is um an excess volatility paper. Um I'm going to let you completely do this. I opened the paper, I saw all the formulas and I completely tuned out. I got, you know, stone cold when I saw all those all that math. So, um, but I do recognize some of the names. Uh, one of them being previous guests, Jean Philipe Bour from CFM. Um, and there are some other people, Adam Machki or some Majki maybe, and Judah Kurt. Um anyways, before I also manage to um um butcher the the names, I'm going to hand it over to you and talk us through this paper um which is called uh revisiting the excess volatility puzzle through the lens of the and how do you pronounce? >> I think it is Kella um >> Kella model. >> It's an Italian it's an Italian name so I think it's Kella. Um must be but but don't again don't blame me. I can hardly speak English let alone Italian. Um so um I think that's that's it's it's a really interesting um branch of uh finance which is uh understanding flows and understanding how flows um affect prices and it's interesting for example that in all almost all discretionary traders will pay a lot of attention to flows and CTAs in general pay almost next to no attention to flows. So, so and and it's quite it's quite funny because we feature very little in we feature quite prominently in any of such model. Okay. So, I'm going to describe like the generic model and why this model has come to being and a little bit about the efficient market hypothesis and one of the reasons that we have chosen this paper is actually about the it uses the EM algorithm um in order to do cali calibrate. So um so here's a nice example about the way dynamical systems are are are being calibrated using sort of this this this very important algorithm. So here's is is the the efficient market hypothesis. It says well you can't make you can't take money out of the market basically. So uh you know the efficient market hypothesis is actually much stronger. It's it's basically says Sheila came along and says actually the reason why you can't take money out of the market is because the market knows everything. All all the information about the fundamental value has been incorporated and um and like therefore the price is the right price. Okay, if you like in Schiller's universe we are all value traders. We all, you know, if the price is if we think the price is 100 and and the price is actually 90, we would go in and we would buy until there is an auction mechanism and every everything balances it out and everybody's expectations of where the true value lives and that's how we we trade. The problem with this worldview is that markets are a lot noisier than what uh what we would get if everybody would be like that, right? And the reason with this is because we would essentially auction at a certain price at which all the supply and demand actually balance balances out and then there would be no more trading until we expect the value to change again. So so really there's just not enough. You would move very quickly into the equilibrium point and you would not see the volatility that we actually see in the market. [clears throat] >> Okay. Now let me tell you the other uh reason why you can't take money out of the the market which is the completely inefficient market hypothesis which is like I've just invented it so the way that like nobody knows anything suppose like it's completely noise everybody is buying selling like completely randomly we are truly like a martingale we're truly just a brownie in motion of course you can't make any money because like the expectation give of tomorrow we're given today is zero. It's the same is the there's no there is no information at all in the market. Okay. So that that actually would be also like to me an efficient market hypothesis. It's basically you still can't take money out of the market not because everybody knows the true price but because there is no true price. It's like complete complete noise. Okay. Um by the way too long don't read uh the outcome of this paper and actually I can tell you in advance is that the most of the market is noise. So uh the reason is a reason why option pricing and everything works very nicely is because the vast majority of volatility is is sort of noise and this paper uh said okay so let's a few papers said okay so let's let's abandon this idea and the way that we will examine price action in the market is rather we will think about it in terms of flows we will think about who is buying who is selling and how the dynamics of those people who are buying buying and selling will sort of play out in practice. And these models traditionally have three players. Okay. The the player number one is the Schiller player, the fundamental player. If the price is below your your target price, you would buy. If the price is above your target, above your target price, you would sell. >> Uh and you kind of tend to converge, right? You can think of it as like it's gravity pulling you to the center. And then you have the the noise traders which we just described. They just come in and like just introduce the volatility to the market. They don't really they don't really know what we're doing. And the third guys are us, right? The CTAs. And we are the evil people that violate the efficient market hypothesis. We shouldn't exist, right? We shouldn't be making money because like we trade based on like price went up, we will buy up. It's not to do with the value. We don't think there is any there is any certainty. just we just follow that that that um that process and the interesting thing about it is really um what drives the flow from each one of the players. So the value trade value traders it's about the the position it's about the uh is the one that determines whether you are going to flow to create a flow and you're going to create a trade. The trend followers look at the first derivative we look at the returns essentially. Okay. So we we we do things in return space and the noise the noise traders are just there to excite the system to create some action. Okay. So this paper is really nice and he says okay so given that we have these three players let's introduce some uh reasonable assumptions about the way that the response function to position response functions to to trend is. Um and then let's see if we can calibrate the model and at the same time sort of introduce being able to handle genuine drift in the underlying value of the market. So there is a recognition that there is some value the value of things does change and they do a really nice uh well they they pick uh some sort of drift term over time they which basically centralizes the time series to be oscillating around that that value and then using the EM algorithm um they basically calibrate the model to say this is this is the proportion of noise traders. This is a proportion of value traders and so forth. This is a proportion of of trend following traders. Um and and um interestingly enough by the way CTAs or we do not contribute that much to the volatility. So one of the outcomes of this paper which is not surprising and actually I think very true and I could have told them straight away is that we are very small part of the market. So if they look at the the contribution of of volatility uh first order of magnitude by a long way is noise, second order of magnitude are the value traders and third order like you know we just don't feature in the in the equation at all. And I think it's actually very the reason why you can tell this is in advance is because um uh is because uh there's just not enough co autocorrelation in the markets. sort of CTAs would basically create a positive autocorrelation in returns. But we just don't see that in the market and I think it's part of the way that the industry operates. We try to be we talked about trying to be small in the market. We try to make sure that we're not actually the footprint is not that great. So that's that's the paper really exciting. Um I really like the the mathematics the way that they do them. They use dynamical system in a in a very um very intelligent way. um they use the EM algorithm, the calibration is interesting. Um there are a few interesting observations there as well. So uh what is very interesting is that they say oh the market is either spends a lot of the time being underpriced or overpriced um rather than in the center. >> Right? >> Okay. Um now let me be a little bit critical of of the paper. So but before we do that I'm just going to say right let's forget about the maths because all these all these papers all all these models will share a certain feature and I think the way to think about it is actually to leave aside the mathematics and think about the pendulum swinging. So as long as there is a there's a there's a there's a real value you can think about the the dynamics in terms of the distance from the center of the pendulum and you can think about the way that the pendulum swings and then a lot of the results actually in the paper becomes very intuitive. So um so a pendul so what does it mean as as we go through the center through the value the the trend followers the pendulum is swinging fast and the 10 tra the the the trend followers are the dominant ones giving an impulse right as we reach as the pendulum swing upwards then the value traders begin to dominate okay because the trend followers are kind of running out of steam and the value traders saying well you're very far away from where true value is. So I'm going to push start pushing you back. So the the you [clears throat] might get a dynamical system, but it sort of looks the way the the fa the the way that it looks the the phase diagram would look a little bit like a pendulum swinging. And then it becomes actually quite obvious why you would spend time either being overpriced or underpriced. There are two there are two equilibrium points where t trend followers and value traders balance each other on you know on the top of on the top of the the top of the pendulum or the bottom of the p the pendulum sort of on the right on the left of the pendulum case. Um so a lot of the results that you see are actually what you kind of expect from that sort of a dynamics. Um so I I I kind of I kind of like the papers. Um but there are there are a few things which I I I I don't like about the paper which is um that let's let's do the ideological one. The first one is an ideological one. The model I I think that in some sense the the paper sets out to sort of disprove the efficient market hypothesis. Okay. It says here it is. We can calibrate this. We can make this make sense. Um and I don't like going all out to disprove something. I think in reality and what we see in the market is a mixture of flows which are driven by price which are driven by flows and prices which are driven by genuinely a change in the underlying valuation like the the dynamics that like this month what we have seen is the market was fine everything is happy and then suddenly the fundamental value of oil has gone has has changed okay and we don't really And I think that's a dynamics which is really interesting and it's we can clearly see that in in the market where there is a jump and then suddenly the value traders will really need to scramble to start doing some trading and I think it's something that we see in a lot of market events. If you look at non-farm payroll for example ahead of non-farm payrolls you're going to see liquidity actually shrinking like half an hour before non-farm payroll. Nobody wants to trade. Market makers don't really want they don't know what's the economic announcement is going to be. >> They don't want to they don't want to take on the risk. You will see actually a lot less trading >> and then you would see non-farm payroll coming out and the market would gap and the market gaps because instantly the value has changed and and I think that not recognizing that in your model I think is is is a problem, right? you're trying to explain all volatility through trading I think is a mistake. In fact, markets do a lot of the a lot of the the the price change can happen without any trading whatsoever. There's a difference why close price on day one and open price on day two are very different because things have moved, right? Like there's there've been news, right? The weekend has exposed ourselves to an Iran war. So that's that's a dynamics that I think would be very valuable for the paper to recognize and to recognize that that's where a lot of the volatility is coming from and it's not just noise traders. Noise traders are very different to like genuine jumps in the underlying value of the of the of the process. >> Yeah. The other issue that I have there is that again the dynamics in terms of being volatility of volatility and being taken out the value traders being called out again this is a dynamic which is really not recognized in the paper. So I think um I I think that uh you know you want your models to be simple as simple as possible as long as they actually describe reality. And I think the the dynamics that we see in the market in terms of uh I think the assumption that value traders have got a response function which is a cubic which basically goes off to infinity is completely unrealistic. Value traders gets get called out just as much as trend followers in terms of risk management. So that that that response function is to me is completely unrealistic. Inability to understand how volatility and risk management really affects it. Again, not something I would really love to see. To be fair to the paper, the paper deals with a very long time horizon. So it's using is using sort of monthly data all the way back rather than the dynamics that we kind of see in our trading which is the very short-term sort of daily daily cycles that we do. But I think there's I think the industry it would be really useful and it's something which is really important in terms of understanding the way supply and demand affects what we think is the real value of the commodity say or the asset >> and then the dynamics around that I think that would be really useful because the the at the moment the mo these models are not useful for providing any prediction or any way for actually for us to improve the model. >> Sure. No, I appreciate that. Um and um since we gave the title of the paper, people are of course free to go [music] and and visit that um themselves. [music] Anyways, um let's talk uh before we wrap up today >> [music] >> um and before you run out of of any voice uh with your cold, um let's talk a little bit about your um diversification paper that you uh or blog post that you did on on LinkedIn. Um I think together with Tom Babage if I'm not mistaken. Yeah. >> Yes. So Tom Babage Tom Babage did did all of the work. So I'm taking a little bit of credit for uh a little bit of credit for his work as well. So uh I I contributed a little bit but um so um [snorts] but uh it's it's a it's a very interesting question. It's a it's a very interesting question about capacity >> uh and which is >> uh how to manage capacity and is capacity and managing capacity is it actually important? So that's that's a question that we struggle in the alternative markets because capacity is very real and the question is like is it really actually important? Um so the the amount of risk that we like to put into each market is limited precisely for the same discussion that we had that we don't want to trade against ourselves. We don't want to have too much of a footprint and we do want to be able to unwind our our portfolio when there is a crisis like we've seen this month. >> So the amount of risk that we can do is kind of we can put into each market is limited, right? We can put more in the treasuries, we can put less in South African bonds. Now at all do we need to have those small markets in our portfolio right do we want to have them at all and maybe we just should should just trade all these big capacity high capacity markets um you know if you've done if you've done index replication um you know that you know some of our peers might be trading 12 or a dozen or so um assets in their portfolio right and you know this discussion you've had it with Rob you know Rob believes in trading 300 uh 300 futures you I'm sure you guys might be trading 50 okay and the question is like why what's where where's the where's the choice and and the observation that we make is that it really depends on the underlying correlation of your market. So if you have a portfolio where the underlying correlation is high to start with then actually if you have more markets you don't necessarily gain that much of diversity because you there's a plateau which you reach very quickly. So at that point the fact that you you you may be putting a lot of your money in only the very big markets is not that damaging to the overall diversity of the portfolio. I mean the way that we think about CTAs, the way CTAs make money is the diversification times the average market sharp, right? Okay. So uh the more diversified the portfolio in principle, the better the better the better quality your portfolio is, the more resilient it is and so forth. However, if you start with things which are correlated to start with, then the fact that you are taking you're taking a lot of more money and you're pushing a lot of the risk into the big markets or just start by trading the big markets in the first place that's not too damaging to your portfolio. Conversely, if you have a portfolio which is very which has got you've selected it so that it's very low correlation which you're trying to select low correlation market. Now at this point things are becoming important because a large component of your performance comes from that diversity and if you start taking a lot of money in right and you start managing it right you can only put because you can only push that much risk into your smaller market you end up pushing more and more risk into your bigger market and therefore effectively you're reducing the number of of assets that you're trading and that is actually a lot more damaging to So the there's a lot of discussion in in the industry about you know whether 200 futures is good, 50 futures is good or maybe there's 12 futures is good. And I'm saying like it it there's a choice here. If you're looking just for the first factor, the CTA factor, highly correlated market, highly financialized, that's great, right? And in fact, you might think that um this this means that you're very highly correlated to everything, but that's okay because you're doing index replication, high correlation is actually what you're after. Conversely, if you're just saying, I want a high high quality portfolio, then you are handpicking, you want very low correlated market, and then you start having to think about your capacity. So uh we are just trying to be trying to to sort of frame the debate that that that is going on uh in the industry between them. >> Sorry to interrupt you here but the funny part is and I completely agree with you that saying that yeah you theoretically think that the fewer markets you trade should be fine if you want to trying to correlate highly with the index you're trying to replicate. But actually when you look at the numbers that correlation isn't that great. I mean the tracking error is um substantial. But that's another conversation. I just want to throw it in there for the record, >> you know. So, so, so absolutely. Um, I I would say that uh uh trying to replicate the S&P, I mean, that's that's the funny thing. If you're trying to replicate S&P, if you get less than 99% replication, people say that you're not tracking very well. >> Whereas in the CTA universe, replicating the CTA, you have a 80% correlation and people think, "Oh, that's great. you're correlating you know >> right >> so so very it's it's a very I think replicating uh CTAs where there is some opacity to be in terms of what's actually underneath it's a much more difficult question >> yeah anything else you want to uh talk about from your paper >> no I should I really shouldn't be talking about myself too much so I think I think I'm I think I'm good I'm I'm good on that one >> okay well anyways people should go and read it on your blog on your LinkedIn uh anyways um just to uh to get the full flavor of it. Um you I really appreciate you um you know working through your uh your your cough and your cold today. Um yeah, that that means a lot and um you bring up some interesting and important uh topics every time you come on. So uh thank you so much uh for doing that. Um >> always fun. >> Always fun. Now I mentioned that uh next week I will be joined uh or Katie and I will be joined by an extra person. Um we'll keep it um well it's not really a secret. It is one of the authors uh of um one of the best papers I've read recently which is the one about uh how to decide or what is the effect of trading different market universes. Um so it's Harry Moore from AHL. He'll be joining us. Um, and um, I'm sure this will be a really fun, interesting, insightful uh, conversation. The the the topics will of course be much broader. And um, if anyone has a question that they want to bring up with Katie and Harry uh, next week uh, to make the conversation even broader, um, they should definitely email me at infotoptradersunplug.com. Um, the sooner the better and I'll try and do my best to bring it up. If you uh want to show the appreciation to Y of and and all of the co-hosts for um preparing and um and sometimes having to work through a cold um please do so by going to your favorite podcast platform, leave a rating and review because it really does help more people to discover um the uh the podcast and the content uh that we produce um each and every week. With that uh from Y and me, thank you so much for listening. We look forward to being back with you next week. And in the meantime, as always, take care of yourself and take care of each other. Thanks for listening to Top Traders Unplugged. 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