QIS Market Size: The Quantitative Investment Strategies space was discussed as large and growing, with estimates near $1.3T AUM and a roughly even split between bank and asset manager offerings, though with reporting caveats.
Volatility Carry: The guest argued the volatility selling premium is economically grounded in risk transfer, may compress with competition but should remain positive long term, and warned that overlaying options on already option-based strategies can negate the premium.
Trend Following: 2024 dispersion was tied to trading speed and the April V-shape whipsaw; equities later trended while rates remained choppy and currencies were mixed.
Equity Strength: Equities, including the S&P 500, continued to surprise on the upside, while fixed income faced debate and CTAs saw early-October gains in commodities largely given back.
Client Adoption: QIS usage broadened from asset owners and asset managers to private banks and hedge funds, driven by operational efficiency, technology, and the ability to target specific economic outcomes.
Crowding & Capacity: Commodity role “congestion” premia flattened as markets became more elastic; the focus is on prudent capacity, scalability, and awareness of externalities when many investors hold similar overlays.
Product Design: Most QIS exposure is delivered via delta-1 swaps, with some option wrappers; governance, independent calculation considerations, and benchmark regulation were highlighted.
Research Discipline: Emphasis on resisting data mining, prioritizing explainable underperformance, and evolving products across four pillars: research, client needs, technology, and market liquidity.
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 [music] their product before you make investment decisions. Here's your host, veteran hedge fund manager Neil's Krup Larson. [music] Hello everyone and welcome or welcome back to the systematic investor series on top traders unplucked where each week we have a look at the global markets through the lens of a rules-based investor. My name is Morris Seabirds and today I'm standing in for my friend Neil Castro Larsson who would normally run this show but at this very moment he's on route to Canada and therefore unable to get in front of a mic. Joining me for this episode is Nick Balters from Goldman Sachs, a regular guest on this show, as many of you will surely know. Today, Nick and I will focus on the QAS space at large, discussing questions such as how large is the global QA business, is it growing, who are the main clients, how many indices are out there, what's the outlook for the space, and so on. So, with that as an intro, let me say hello to Nick and welcome him to the show. Hi, Nick. >> Hi, Morit. It's very good to be here. >> Good to have you here. How have you been doing recently? >> I've been doing well. It's been um it's been a busy period, I should say. And um you know, I spent a good amount of time with family over the last couple of days. I had my my son's christening. Um so I had like family and friends flying over from Greece. It was a nice occasion. So that was like a small kind of break. Uh but now back in action, but I'm doing very well. >> Excellent. Congratulations on that questioning. >> Thank you so much. >> Anything else that's been uh top of your mind on your radar? It's the question that Neils likes to bring up. Uh marketwise, yesterday we've had a FET decision. Maybe that has created some turbulence. I don't know. >> Yeah. I think what is on my mind of my mind, I would say this year has been pretty much like a bit of a debate between fragility and resilience, right? You kind of have events that you feel will turn against the overall market and the positive sentiment and the growth and then you have like no results and so on and so forth kind of going against it. and the market seems to be quite resilient um in in in terms of kind of absorbing this information and kind of consuming it. So I would say if there's one thing that you know keeps me still awake um and maybe kind of on my feet um as the days and months go past and we're kind of approaching November, it's this kind of pendulum between a fragile market versus a resilient one. I mean I cannot call the shots but you know if this is one thing that you know probably entering the last phase of the year will be um a memory of the year that would be the one. >> Yes I agree we cannot call the shots and you know personally I'm just you know well we're we're trading in a rulesbased way so we don't really care about equity valuations or anything like that but to see equities performing so strongly again this year is uh is really surprising. I mean, it's just uh nobody's able to beat the S&P 500. The sharp ratio of the S&P 500 has now decoupled from everything else. It's the best investment in the world. [laughter] >> That that is the >> Until it isn't. >> That is true. Until it isn't fair. Exactly. >> Yeah. So maybe with that um as a backdrop, it's a good opportunity to get through a bit of a performance update. These are numbers as of Wednesday uh evening October the 29th. So monthto date and year to date the B top 50 index 1.89% up month to date and now positive 2.35% year-to date. Suction CTA 1.7 for the month up and just minus 1% for the year. So that is a good recovery now in October. The trend index plus 2.15% for this month and around flat for the year. The short-term traders index from Sockchen is up about half a percent this month and still down quite a bit minus 4.6% for the year. And as I've just mentioned with the equities, the MSER World Index plus 2.8% this month, plus 22% this year. Aggregate bond index plus 1.2% this month, plus 7.2 this year. And the S&P 500, the darling of everyone, it seems, plus 3% yet again in October and plus 18% in a bit this year. Isn't this amazing? Who beats the equities? It's It's just tough, huh? It's phenomenal. It is phenomenal. And you know, it's not the first year we've seen that, right? I think what was it 2023? I remember everything was flashing red growth wise and we still had like rallying equity markets. So like to to my earlier point, I think we've been seeing this debate between is the market going to continue going up in the way that it's doing it and and I think maybe the fixed income market is a bit more debated these days and obviously the the place that bonds would hold uh in allocation portfolios but I think when it comes to equities they keep on surprising us in a way. >> Yeah. I mean it's kind of like you say the equity risk premium or you expect to make six seven eight something like that percent per year. Uh that's like the long long longterm historic average, right? >> But for the past 5 years, that's not true. The past 5 years, it's kind of like every year is between plus 15 and plus 20. >> Um >> borrow 2022, I guess. >> Yeah, I don't remember what. So the the co period you mean? >> No, no, the 2022 was basically the inflation um the inflation spike and that was the time that both uh equities and rates sold off. >> And did the S&P have a negative year in 2022? I would think so. Uh actually quite quite negative. >> Yeah. >> Okay. Okay. So maybe that's the the exception. But >> if they're up if the equities are up there like in in my recent memory, it's always kind of like more than 15% in a year. It's uh >> fair. >> Well, that is what it is in in terms of trend following because that is that is a topic that's near and dear to our hearts. Um looking at October, it's been a great start to October. I mean, I don't know the books of other CTAs, but um as far as we're concerned, we've had a great time uh until around the mid-occtober or the 20th October, something like that, with long positions in precious metals and long positions in livestock. And it's just been a great month. And then a lot of give back of open trade profits uh on exactly these markets. Um palladium, gold, silver, platinum, you know, feeder cattle, live cattle, livestock, uh leanhawks, those markets have been reversing and it's kind of like back to around flat um to be quite honest. So really really just U-turning there inconvenient give back of of these open droid profits. But you know that is it is what it is. I'm seeing similar things um on on our side is kind of maybe still on the positive side for the month. Um to your point, most of the return was driven initially by commodities. Now they gave pretty much everything back. Um equities are still kind of holding up there and and rates keep on being the same story for the year. Keep on whipsing, keep on losing on the trend side. Um and main drivers, yes, pretty much equities. Um and then I would say some of the uh kind of long exposures in um in gold in aluminium in copper and good short on the sugar kind of delivering some good performance. >> Yeah, sugar is a recent short entry that um that just continues to go down. Still some of the grain markets I mean they've they still look relatively weak even though they've um recovered a bit from their weakness in in the past say 10 days or so. Uh but I agree with you. The big whips are this year is uh is the fixed income markets. It's like uh on and off all the time. >> We were doing like an attribution analysis somewhat like a straight line down. >> Mhm. I'm not surprised. Not surprised. >> How's it looking for currencies for you guys when you do the attribution? >> Um so I would say for the month is just flat. M >> um pretty much um with the biggest kind of positive contributors being um JPY and maybe on the other side is the Aussie dollar and uh and GBP um year to date it's kind of the second worst past rates you know if there's one asset class that over the year has actually contributed positive it's no surprise again right >> no surprise uh and obviously the question then is how quick or slow the speed is uh to be able to sustain uh an exposure through April So get the recovery and then obviously work from from then on or substantially the gross at that point in time and then missing some of the recovery and therefore building up again the exposure would take some time. So it's a question of speed and and volatility response around the Vshape and I think that's the primary driver I would want to believe not that I have too much data to support it but I think that's the main driver of dispersion that we've seen this year in the trend following space. >> I think that's true. it's uh it's the liberation day or the early April um April period selloff. Um in our case, it's not just been the equities. We got kicked out of some of the equities, but we also got kicked out of some of the currency positions that we had. And >> there were just a lot of positions that uh we had on the books that went to to nothing or even reversed at that point in time. And like you say, it's it's a function most likely of trading speed. You know, do you get back into these markets? Um did you lose your position or not? And that's you know very easily can make a difference of 10% for this year. >> And I guess quite interestingly if you were to be very quick you'd kind of capture some of the Vshape at the time but then the subsequent part of the year wouldn't work at your favor. Uh so I think at the time we're just talking about the short-term traders actually doing well in the Vshape and that's I think part of the reason that you capture some of the first draw down in liberation day and then some of the recovery around um the tariff pose. If you're slower, you'll probably just stomach the whole Vshape and then recover with it and then performing to this day to your earlier numbers, basically capturing the the equity trends. And I think there's some bitter spot in between being too fast and too slow whereby you just deliver at the wrong time that the market is eventually recovering. It actually gave you it gave you a week to also crystallize [laughter] the delivering of the of those exposures. >> There we go. Yes. know if we could only know these type of things before but we don't. Um so we have to stick with our own systems and you know some of them are longer term and some of them are medium or short-term and that's what we do in our case they're more longer term but still you know we did get kicked out of some of these positions and then it really takes a while to get back on. Um I think there were just two or three equity markets if I remember that correctly where we didn't lose our position. I think one was the Hang Sang, I think the share price index, the Aussie index, and the South African equity index. Um, those three we kind of like kept, but all the other ones, you're out. >> Topics, S&P 500, NASDAQ, go down the list. All the European indices, we we just lost all our own positions in April. And then it, you know, probably took until I don't remember, July, August, something like that to get back in. So, yeah, quite some time. And that's basically I guess u some sort of threshold based criterion to keep one of the position right zero flat zero. >> Yeah we have our exits um you know different types of exits some are trailing stops some have other rules but you know we're we're not forcing ourselves to reverse the position into shorts. There is a kind of like neutral um segment or area and that's what we've hit. >> No trade zone. >> Exactly. Exactly. >> Gotcha. So QIS um look this is an interesting business. It's also in a way I would say a little bit opaque to the outside observer. Um like to to most most people really they know okay QIS means quantitative investment strategies. It's a business and an activity that um pretty much all of the larger investment banks engage in these days. Um it's you know focused on risk prima indices. uh advanced beta type of strategies, that type of stuff um designed and then sold to clients, but it's kind of difficult for people to see how large it is uh who the participants are, who the clients are, how these deals are transacted, how the exposure is transferred from a bank to a client and these type of things. So, I thought with with you on the show today that would be a good topic to just speak about the QIS business more from a macro perspective at large. um chat about how large it is. And speaking about the size of this space, there is a survey that was run by Alborne, the alternative investment advisory firm, in the first half year of 2025. So that's a pretty recent survey. And they say that the total notional assets under management, they have hit a new all-time high at about$1,300 billion US. So, $1,300 billion US. And it's a new all-time high, which means this space continues to grow. Um, and the 1,300 billion US, they're split roughly 5050 between bank QIS offerings and asset management QIS offerings. So, asset managers also have quantitative investment strategies. Some of you folks may know, you know, some risk premier usage funds or, you know, there's some asset management firms specializing in risk premier strategies that would kind of like count to what that that 50%. But it's a relatively I would say large number 1,300 billion um and the largest part of that is equities. Equities is more than more than half of all the exposure of all the strategies are linked to equity type of strategies. equity long short you know factor type of exposures um followed by multi-asset class um strategies then commodities and quite surprisingly I was thinking that fixed income would play what would have a much bigger footprint but fixed income is only 50 billion even though fixed income markets are gigantically large markets but in QIS space on a relative basis they're it kind of like seems to me that they're under represented as is FX with totally 27 billion and credit with 13 billion. So that is a backdrop. Um do you think these numbers make make sense to you Nick? >> Yeah. So look, do the numbers make sense? Um there's a reporting convention here that I I I believe different banks are utilizing and maybe asset managers. I think on the asset manager side is a bit easier because that's the actual funded exposure. Um when you look into broker dealers uh the way that QIS products um is is distributed is either via kind of unfunded um swaps um or via sometimes you know call options. Um so the way that we can think of assets here um you know can be debated. Do you do a look through leverage adjustment? Do you do delta adjustment if that is an option? There are different conventions unclear to me whether those conventions are properly utilized and uniformly applied across the reporting banks. Um but I guess in the grand scheme of things themes if you just take at the minimum the brokilla sum which is close to the 600 billion mark um I would say with some confidence of plus or minus 100 I'm just making it up really I don't really have hard data to support it I think it's in the right ballpark. Uh now if you take asset managers into the mix maybe there is some double counting because obviously asset managers are very active in the Q space and some of them do utilize QS products. So in in in the context of reporting um you might end up having both broker dealers reporting numbers for specific strategies which are eventually deployed by asset managers that also report numbers. >> Um so there could be some double counting here. There could be some reporting conventions that are not necessarily uniformly applied on the broker dealer side but broadly speaking it is a business that has been um growing over the years. there have been some winter periods that we can identify maybe around 2018 and 2021 um on on on the reasons that we can potentially discuss later on. To your question on the asset class split um it's interesting to see that the equity long only space is by far dominating the asset manager world and that's more like the smart beta of the world. Um so anything that is quant enhanced uh and delivers an equity exposure that is supposed to outperform uh typical benchmark would come into that pocket. It's less so on the bank side. I think the bank um or the broket dealer asset class contribution is more evenly without that being even but more evenly split between the asset classes. I would say part of the reason why you see less on the fixed income and effects side is that historically liquidity has improved um if you move away from the you know from delta 1 space on the option side uh which typically the one that banks are much more focused on um is something that grew over the years um I think the equity space is more natural that's the equity long short u and that's basically the the genesis of the QS businesses so it has grown over the years I think commodities is the other uh you go back to those rolling futures and how you can indexify commodity exposure both on the beta side as well as on the long short side. >> Multiasset is again kind of a CTA type of a place. Um there is also a good amount of application in um in the retail space when it comes to multiasset. I think effects fixed income are in their growth phase um if we're to say um and even if we're looking into kind of academic activity uh it has been massively more on the equity side and less so on the other asset class. So I think it's a combination I guess of of features of the market um but I would I would I would certainly flag that all asset classes are now becoming part of the toolkit of of uh of brokers. I also now see a lot of multi-QIS uh offerings in the way that you know banks would combine say their commodity carry strategy with their FX carry strategy and an equity carry strategy in order to create a you know crosset carry basket um that's also multiasset but back to the numbers I think just to put this in perspective the numbers could be conservative they could also be aggressive um they could be like there could be um funds out there asset management ers, maybe hedge funds, just you know, people trading risk premium strategies that are not reporting to that survey, right? Think about a hedge fund, right? Maybe that hedge fund is not covered by that survey and they may run whatever like few hundred million in notion exposure in in risk premium type of strategies, but they're not factored into this into the survey. And then it could also be on the other side like you say there could be double counting which could happen if for instance you swap exposure of a strategy to an asset management firm and the asset management firm then on sells it to their client right that you you report it and the asset management firm reports it >> and then what you've mentioned the delta adjustment or the the leverage adjustment is you know if if you're selling a call option linked exposure to one of these strategies it may have an initial delta of 0.5 Um but the question is do you report that delta adjusted exposure or do you report the full notional of the of the option um to the survey right so let's take these numbers with a with a pinch of salt but the I guess um overarching statement here is is that that space is I would say pretty large now and it's growing and there's there's a chart included in the survey where essentially these assets with the exception of what you've called like one or two three winter corals um like you know 3 month or so but they're they're essentially going up. >> Yeah, I mean that's a that's a fair statement. It is a space that has been evolving in a variety of ways. Um maybe it's asset class allocations, maybe it's new markets that are traded, but also the users on the on the on the client side have um have kind of increased over the years. You know, we can we can also discuss that part. Maybe the one thing that I would flag the one last thing that I would flag and then happy to to to go to the to the client side is that there is also an element of substitution to your point. So if today there is a I don't know a um 100 million exposure in a CDA that is now transferred into a QIS product, the market still consumes the same level of liquidity demand. it's just now marked under the QIS kind of badge and therefore would be seen as an increase in the multi asset pocket of a survey as such where in reality just a substitution effect from what otherwise was like knowed exposure on um on a CDA right I'm just making that example whereby a substitution effect could also be present here more so than just genuine growth >> another correct another asterisk >> yes yes there are many asterisk Yes. Um, you've mentioned clients, Nick. Uh, and I think you've mentioned that there's new clients coming in. So, maybe let's talk about that. Who is the who's interested in this? Who is your usual client and how has the spectrum of clients changed over the year? >> Yes. So, the QI space started maybe 15 years ago. You know, we can we can point uh in a timeline where was the first baby QIS trade. I would think of it either coming from the commodity space and those rolling futures indices that were built to deliver um kind of economic exposure into commodities without having to hold physical commodities. So that was one reason why those um QAS teams initially were formed. Um and then the other space uh or the other place if you like that we saw origination of ideas and and and indices um was post GFC and some of the work done by some of the Nordic pensions to replace or maybe enhance um their risk profile by by risk factors and kind of transitioning or repurposing how they think about diversification away from asset class mix into kind of a factor mix. Um and that was a time that some of the banks started working on equity market neutral strategies, some volatility carry strategies, you know, version uh one uh at the time. And in this regard, the primary users in the early days were either asset owners. So we can talk about um large pension funds, so wealth funds across the globe. You know, there is some regional kind of shifts that happened over the years and happy to go through that. and asset managers. These are the primary users. If you do kind of fast forward to today, you can widen the spectrum quite substantially and you can go any from like know private banks to hedge funds. So the utilization of the technology and the utilization of this IP has massively expanded across the clan segment. Um and one could think of a QIS offering nothing more than I guess in a good way um kind of a you know a convenience store in the sense that there are various type of systematic exposures that can deliver performance or various ways of delivering a defensive profile via for example rolling put options and you know being very thoughtful on which tenors and which strikes to utilize whether the delta hedged or not. All I'm just discussing is nothing more than a set of rules. It is >> to a certain extent it's not too dissimilar to saying look if you really want to have the equity market you need to find the 500 larger names and you need on a quarterly basis to reconstitute what those 500 names would be and allocate market cap weights to them. This is nothing more than a set of rules and this is called S&P 500. And once upon a time there came a contract that is called a futures contract that delivers an unfunded exposure to the equities premium. So that in itself facilitated whoever wanted to get equity exposure to avoid going to the market and physically replicate 500 names into a portfolio of what the S&P will represent and basically get on a contract nothing more than the performance of that construct that in itself can be put into a document and be reflected upon um can be reflect upon into an index profile. Right? So if today we claim that volatility is rewarded and whoever is willing to sell insurance should be compensated that in itself can be described with a set of rules by selling some options and you know trying to reduce the spot participation the so-called delta hedging. Now we can go back to the academic roots of the volatility premium. If somebody were to deliver quote unquote a futures contract, you know, in this regard that would be an OTC swap, then this allows the end investor to be exposed to the volatility premium, not too dissimilar to being exposed to the equator premium. And what sits underlying that investment is nothing more than a rules-based index. So I think the way that I think about the QIS space is that it delivers different risk profiles in an accessible format and maybe reduces the operational burden and delivers this operational ease to the end investor and there are other benefits you know cash efficiency and so on and so forth that we can also discuss but the primary reason why we now see this proliferation of this type of investing nobody suddenly thought that systematic is the way but systematic is the pay to deliver into an index format what otherwise could be a cumbersome and tedious process. And the end allocator being a private bank or being an asset manager or a hedge fund would simply have that exposure and utilize their internal models on timing utilize their internal models on sizing focus more on kind of bringing their alpha which is more of a timing element rather than of an implementation element. So that's a quick kind of or maybe not as a quick but that's I think a quick overview as to how I think the space evolving and the utilization across different um different client segments. >> Very good. And and the observation that I personally made and maybe also many others is that tying into what you said you said it's a technology I think that's a good term to use that comes along with operational efficiency. So when you think about the early days of QIS, if you mentioned there's like you know asset owners or insurance companies or large asset managers contracting with you and maybe they wanted to have a new variation of equity value, right? Maybe it's something that they're doing in-house and maybe it's just they want to have another source uh to diversify and and do something like that, but it's not like high frequency, you know, it's it's kind of like, oh, you're running this daily or weekly or monthly or something like that. But then that has changed when most banks came up with hey we now offer intraday V or we offer intraday momentum trading and you know this and which is technology which is a operational thing that not everybody can work with and I guess that's where the hedge fund clients come in where they go like well look >> if if the bank can you know kind of like every five minutes or what it is do like a a vulcar intraday strategy on the S&P 500 then I might be better off just buying it from them as opposed to implementing that internally um myself because it's difficult to do that and and and you're right in this one there's an element of IP that comes with the idea of designing a strategy that delivers some some return profile that can be alpha enhancing that can be defensive but to your point there is a massive um now integration of technology like the way I think about the QI space over the years is as a combination of four main pillars in terms of product development. There's certainly research, industry, academic research that is evolving. You know, how asset prices move, how we think of asset prices and that is definitely a source of inspiration. Number two is the entire client segment in terms of appetite. You know, it used to be either a hedge fund replacement or compliment >> and it eventually became more of a specific economic outcome focus. No, I want something that is a bit more defensive and I'm happy to take on the negative cost of care or maybe I want something that is enhancing my yield and I should be very focused on what is a spot contribution. Maybe now somebody's focusing on inflation, maybe the economic regime. So that there are different ways of us um delivering product for specific uh economic outcomes. So that's the second pillar how the client utilization of the product has shifted. I think the third one is technology. This there is no order by the way right these are just equally sharing the the load of of inspiration. So technology to your point being able to access markets being able to trade markets being able to trade faster markets. This is a massive enhancement uh that we have in our product offering. And then I would say the fourth um is how markets evolve. Once upon a time you could not trade some of the frontier commodities and now you can. Once upon a time there were no shorted options. Now there are so there's a variety of things that eventually happen around us allowing us to almost do out of sample testing on universes that otherwise would not be able to do so at least back in the days and not net of costs. So this is I think if you were to ask me these are the four pillars that over the years have been contributing to product research technology utilization market market liquidity um evolution. >> Got it. What do you think Nick guesstimate how many different QIS indices are out there or not different total number of QIS indices >> across bank? >> Yeah. Or maybe start with you. I mean it it must certainly thousand thousands of thousands. >> Yeah. It's it's thousands. It's thousands. >> Well, I mean, we can we we can discuss the reason why that can be the case. >> Yeah. >> And there is certainly an element of customization. So, for example, you mentioned equity value. You know, somebody can say, look, >> I don't know what's the best equity value definition. Maybe it's, you know, book to price, maybe it's a dividend yield, maybe it's a combination. So there could be variations of the same product purely to reflect that different design choices could be um um could could suit different investor needs. We can speak about CDAS, you know, somebody would be willing to avoid having equity participation on the upside purely because they want to have more of a defensive profile. That's a different index. So you know the fact that we don't and a bank cannot act as an asset manager and there is no fiduciary element that in itself leads to the need of having a pallet of possibilities and we can talk about an equity investment that is seen from a non- US investor that is exposed to withholding tax. So that has to be a net total return implementation versus a gross total return. So that in itself is two indices. So I think because there's a lot of discussion as to how many indices and you know data mining bias and so on and so forth. I think there is there's an aspect here that should be definitely mentioned and that is purely the fact that any different implementation be it for a specific client ask or a specific need has to be a different index. You know a client doesn't want to have a in their commodities portfolio that's a different index. So there's a lot of customization that is purely driven by a specific need and less so of oh we found a better way of doing it let me just replace it now because I've just done my overfeitting exercise this is in itself has to be addressed right but this is one I guess one way to address >> on on the magnitude right >> why there are so many and they all have their tickers and they all run on the Bloomberg terminal I presume and um you put them into Python code or whatever it is to to run them I guess you need a now these days In Europe, you need an independent calculation agent, benchmark regulation, right? You do you have that internally like Chinese Wald or do you use external parties? >> There's a variety of models as you say. You know, some banks utilize internal um internal calculation agents that obviously are infenced. So there's a lot of governance around this business. Um some others would be using external calculation agents. This is purely a business decision as long as obviously the governance is in place and and >> um and the business overall is um is well run. uh but it's certainly the case >> looking at the types of exposures or products that uh your clients typically request are they is is most of the notional transacted in Delta 1 space you know linked to a swap or node or is is most of it kind of like optionalized and therefore nonlinear index exposure like a people like somebody buying a call option on the index. >> Yeah. So the historically the vast majority has always been in the in the kind of the delta 1 space in the sense that they would do um you know some form of a delta 1 rapper that can be typically a swap that's what you see in the space. Uh obviously there are other rappers like certificates nodes you know depending on the um on the contractual um relationship that a bank would have with a specific institution. um more recently and certainly for the insurance space but also more recently we've seen in the institutional space some interest for um option profiles and just to be clear for I guess for the audience we're not talking about the constituents of an index the constituents can be delta 1 or v you know it can be a volatility carry it can be a trend following strategy we're just talking about the rapper right so your question is really how can I get access to a volatility carry trade or how can I get access to a CTA profile This is typically done as I mentioned on a um on a delta one type of um type of a profile but recently we've had and when I say recently maybe it's the last two three years that we've seen a bit more interest into kind of a fixing a price uh and basically capping the loss so getting a call option on some of those indices um I would say it's a fraction it's a small fraction of um at least of of of the executed trades Um there is a philosophical question here to be to be I guess to be put in place. Um you know let's say you're selling volatility to get a volatility carrier profile and then you want to put an option on that profile that you know it's almost going going in circles right. So, the mere reason why you're supposed to be um benefiting from a premium when you sell options is because you're willing to take on the risk that this thing is going to at times occasionally um experience a V spike. >> So almost writing a call option to it in itself nullifies the existence of the premium in the first place. So why would you have to, you know, get optionality on something that is dropping and still benefit from an upside while the price for it should be the exact premium that you're benefiting from selling it. So almost this vicious circle nullifies the I guess the the value of pricing a call option on some of those kind of negatively skewed profiles. There are still ways that we can think around that, but I guess the high level kind of answer to your question is that it's primarily on the delta 1 space. And when it comes to an option, there are types of uh of transactions maybe more on the underlying delta 1 space and not on the option space. Um, but rarely do we see that as frequently as we do the delta 1 transaction basically, right? when you do the delta 1 transaction, it removes the requirement or the kind of like motivation for you guys to have a vault target or vol cap. I mean, it may still have this as an intrinsic part of the strategy element, but I I reckon when you sell a call option, the underlying index will definitely be volled or targeted to a certain type of level. Um, so there's a lot of that stuff that kind of like slucks through the market as well because, you know, when somebody buys their call option, really the only party that will quote a price is you. You know, they can't go to a well, they could potentially go to a different bank, but they're not going to get a a good bit ask if anything. So you know uh you're the the source of liquidity and therefore you're protecting I guess your book by having this V control in place and selling selling the option at a premium that's uh that that's yeah amenable that that that is fair that is fair it's certainly easier if you think about linear structures underlying it so when we talk about equity momentum equity momentum in itself is volt targeted and that in itself is helping equity momentum because >> negative skew in equity momentum can be moderated by uh by vault targeting. So that in itself to your point almost comes not even for free but for a benefit of the performance while facilitating some option pricing to to to operate on top. Um, so I guess an another way of kind of answering your question is that there are pockets of a QS offering that are more um accommodative for option pricing and some others that can become a bit more nuanced purely by the um I guess the economic principle arounding around the premium but also mechanically if you were to volatility target a strategy that is supposed to be reacting and recovering post the volatility spike you're kind of acting against its ability to recover. So then the whole question is okay fine we can do the volt targeting but then what you're what you're ending up with is a sub-optimal profile for the respective strategy right so all these are important considerations right >> what do you think like uh by and large you would you build your business or the QA business for massive scale like an ETF provider would where like you know theoretically practically theoretically say the AUM of an ETF are you know unlimited it can grow to whatever size it wants to grow. Do you have that same thinking in the QIS space? Um um because you know one thing that I recognize is that for instance the CTA or trend offerings or some of the like uh the more popular offerings they're not exposed to some of the smaller markets. Um you know they're they're they're really lacking uh from these type of indices would more like be the super deep and liquid markets. Is is is is that a fair statement that you're building it for scale? >> Generally we build we build product for scale because that in itself is associated with how we can see the profitability of this business. Um at the same time we are always exploring what are the pockets of the markets that can deliver some good performance even if the scale cannot be at sizes of a much more liquid profile. So you know uh we can spend time talking about frontier commodities or some of the non- kind of benchmark commodities. Obviously they don't have the liquidity that you'd expect to see in some of the larger markets but certainly there could be variations to your earlier point. There could be another index that simply has like a longer or maybe sorry not a longer but a a broader set of assets as underlying with the I guess the the consequence being that um the overall capacity that this profile can support is now a fraction of what a subset of assets could. Um so there is I guess to answer your question in a different way we try to solve for different scales and if there is an argument to be had with regards to premier being more um pronounced uh with lower liquidity or pockets of lower liquidity if there is a liquidity premium to be harvested or illiquidity or whatever it's worth it. um we're always keen to see um what the enhancement can be with the asterisk being that you know we cannot scale it as much as we could with a very liquid universe. So we don't shy away of exploring those pockets of liquidity but what it's probably the most important thing when we design strategy is being very very thoughtful on capacity. So no to put it into an extreme kind of statement nothing is built for infinite scale to be clear right everything has to be done in a very prudent manner very conscious of market liquidity how much we transact how much we consume how much we roll all that lot feeds into the product design >> and that's also because you have the option to at some point say stop if you wanted to stop selling an index you could you could you know close it whereas an ETF cannot right that correct >> and we do right we do I mean at the end of the day you need to protect the investors and you need to protect the market and you need to act um in a in a in a prudent fashion uh when you transact and interact with the market so there are multiple cases whereby a specific design cannot sustain anymore without being market impactful so we're like that that's it for now like we can and I know and I think investors do appreciate that you know there is this scrutiny and there is this transparency and existing investors also see that as um as a good thing in [music] a way that you know we're basically the guardians of their exposures [music] maybe two more things one like this is just an observation um every couple [music] of weeks I receive emails from it's not just banks it's it's all sorts of like people offering product in that space and they come up with something new something that here therefore has not in their view existed. It's kind of like here's a new index, here's a new strategy or a new variation of a strategy. And I sometimes go like h it's kind of like the same that you've had before, just a little bit of a of a new twist to it, but other than that, it's it's pretty much the same thing. So, how do you balance this kind of like being forced to innovate and coming up with new indices um maybe in a in in in an approach or in an effort to stay relevant versus just you know overoptimizing things and you know turnurning out new product for the sake of producing new products? >> Yeah, that's an amazing question actually. Um why it's an amazing question because it's part of human nature to observe and then think and then try to do better uh specifically in that space. Um so if I observe my trend following strategy in April then probably there is a temptation to say oh if only we're a bit slower or if only we're a bit uh I don't know overweight this asset class or the other asset class or if only we're not doing dynamic sizing or we're doing like not static size whatever right so this temptation exists and I think in itself it's quite inherent to have those biases of overfeitting so maybe Another way of asking the same question is to say how do you control data mining in a way right because frankly if there's a new idea this idea is probably driven by the fact that something must probably have worked better and therefore let me just launch this tweak because that tweak will just make the recent performance look better. So how do you control for it? I mean my personal view is that this is this is genuinely product culture like there's no better way to be conscious of data mining unless you just call it out. So it is part of our at least as as as far as I see it and you know the responsibility I have in the product teams um that I would flag look now we're data mining fine let's look at it let's get a better sense of what the results suggest but you know we should be very conscious of these data mining biases and know as soon as you acknowledge them then it makes I think your brain iterate in a very different fashion because then you see all those new kind of very skilled individuals that get hired from university kind of being exposed to those biases, but as soon as you call them out, then they start ingraining a culture whereby any new tweak they do is not about making the sharp ratio look better anymore. So I don't think there's a right answer to your question in the sense, okay, that's the blueprint. I think it's a combination of acknowledgement, a combination of calling it out, a combination of being very thoughtful and study some of the work that has happened in the academic space to control for data mining biases. I I I think there is no way you can avoid data mining. If I were to show you like 10 back tests, you would basically drop the bottom one just purely because you wouldn't think about it. Um so when it comes to changing a strategy or changing um a design what we try to strive for is I tend to say that quite often explaining the underperformance is more important than you know being lucky on the upside. Like we would love to outperform but if we cannot explain underperformance that's the worst thing that can happen. So at the end of the day, we are only successful because our clients are successful. And you know, we live and die by those performances. I know everyone would say the same thing and asset managers and banks alike, but there is no other way for us to prove to our clients over the years that sustained not necessarily our performance, but explainable underperformance in light of what we initially suggested that we would build um you know, would would bring to us in terms of a kind of confidence and and and reward. Um so I'm not sure whether I answer your question. I don't think there's an easy answer to the question. U that's the process that we kind of go through, right? >> Yeah, I think you did answer it in the sense that you are following a rigorous process as far as research and uh Oh yeah, absolutely. you know, science is is is concerned and you're not just, you know, producing product for the products um provision's sake. Um like you know, there there's no schedule or every week you have to come up with new index which would be ridiculous, right? So you want to come up with something that's >> meaningful um and and different compared to what you had before >> and also no reaction to emotion, right? I mean we could have reacted in SVB, we could have reacted last year in dollar yen unwind. We could have reacted in April when it comes to trend following which just basically sat behind saw what the market were here to to to basically tell us in terms of maybe a new regime maybe not I don't know but reacting on the motion and just making the last draw down outperform is purely the recipe for underperformance. One thing and maybe as a final question um we've mentioned the 1,300 billion and we're not exactly sure whether that number is right or wrong. We've asterisk this and conditioned it on a couple of parameters. But by and large, where do you think if anywhere could the QAS business have become too large? I mean, do you see effects of crowding in some of the markets or in some of the strategies? Let me give you just an example and I'm absolutely not sure whether I'm right or wrong but for instance when you look at commodity commodity indices and liquidity provision around index roles business day 5 6 7 8 and 9 which you know the the GSI become type of role period >> very successful strategy probably 10 to 15 years ago providing liquidity during that role and then reversing the position you know 5 days later I think that strategy has largely decayed um then you know role days have changed So maybe that's just just one example. I'd like to hear your view on this of yeah enough money has crowded into that space so that the effect has essentially gone away and the market is now balanced. Um would you say that's a fair statement and if so are there any other pockets of the the business where you see similar effects? Yeah. So that's both a very um I mean a very good question and a question that comes very often I should say. So how should I answer? Let me just first answer maybe maybe picking up your example. Uh you're referring to commodity congestion I guess uh and the fact that there is >> uh passive exposure to the benchmark roles specifically for institutions that do not want to hold physical commodities and want to be just financially exposed to commodity prices and therefore they need to follow an index of all things because it's easy in this regard. But that index is following a predetermined schedule. So there are days that you need to sell the futures you're holding and buy the new one. So over the course of a few days, there is supply, net supply of the contract you want to move away from and net demand for the contract you want to go into. Now if the market is elastic, you would not expect any price impact from this activity. But lo and behold, for years, because there was this passive requirement for commodity exposure, it had um um kind of an impact, a negative impact from selling at the same time that everyone is selling and buying at the same time that everyone is buying. So there was this kind of strategy called congestion that said, look, I really know when those benchmark grows will operate. So let me just do it a few days before. So I'm going to buy whatever else is going to buy a few days before they do so and I'm going to sell whatever else we're going to be selling a few days before they do so. So then I do like a long short portfolio and I capture what is called the congestion premium which is nothing more than liquidity premium liquidity provision here. Now right so as you said around 2015 16 17 18 this started kind of underperforming in a way and flattening out. Um it's interesting because we wrote a report back then you know I spent some time with the team uh that's 2018 2019 that and we wrote a report on specifically this particular point and there is a very subtle point here on what crowding implications would be. So if this were to be massively crowded, as in QIS investors that do congestion at the margin, you would expect to see very negative performance because now you're the one congesting the market and whoever is doing the benchmark roles is actually benefiting from it. But oddly enough, we saw the strategy flattening out. And the flattening out of the strategy, it's more that the initial demand for this liquidity is no longer there. In other words, whether you pre-roll or post roll or roll together with the benchmark roll, you have no impact whatsoever. The market started becoming elastic again, it started absorbing this net demand and net supply. And the you I guess if you like the the outcome of this analysis suggested that investors became smarter in the way that they all the exposures and they know or at the time were doing more enhanced data exposure in commodities rather than too many investors crowding out the congestion trade and this is very subtle but very important. The strategy became flat not underperforming. Um lo and behold in 2022 congestion trade came back again because obviously there was this kind of need for commodity exposure during the um during the kind of Russia Ukraine kind of conflict. Um so congestion did come back but also we can think of congestion in a nice in in a different fashion now you know do do now do we do we see congestion trades operating in uh in enhanced bet as exposures that could be a question. So that's I guess statement number one. Um but where I want to focus on as a consequence of this discussion is that this demand for commodity roles is not driven by some sort of risk sharing mechanism. It's purely allocation decision. So the premium in itself shouldn't exist in the first place and this is a space that you know if there is too much of crowding it can go away or obviously if there is no demand for those benchmark rows as it happened to be the case there is no premium to be harvested anyway. It's very different when we start looking into maybe volatility selling. So if we sell volatility, there is an underlying risk factor that there is a segment of the market that is not willing to take and we're happy to basically take the other side by selling those options and be paid the premium that whoever is willing to hedge is willing to pay and pay more. Um so can we now observe crowding implications? Maybe the premium falls like I I I I tend to use this you know basic example. If we all become car insurance sellers or car insurance providers, the premium to insure your car is going to drop purely by the by the economics of competition. But that doesn't in itself suggest that the prem is going to go to zero, maybe negative unless somehow we will all become better drivers. So there's a fundamental link here between what is the price to pay for specific insurance and what is the underlying risk sharing mechanism and where the market will clear in terms of pricing that risk. that's the price of the of the of the premium the risk premium if you like but you know suggesting that this can become negative I I think it's a it's it's a very uh bold statement and and the reason why I'm saying so is that there is a barrier to entry who is going to be selling this insurance if it drops below a threshold so the more kind of crowding you end up observing the more subdued the returns would be still positive in the longer term but then the barrier of entry would increase. So there's an equilibrium here that is achieved purely by the fact that there's an underlying risk sharing mechanism and that's different to congestion. And then obviously the last segment of alpha seeking strategies would be more behavioral like know trend following is is I think it's a prime example here. We can debate what crowding will bring to trend following. There's an argument to be had whereby trend following is actually benefiting from early crowding because the more >> to the fire. >> Exactly. Right. The problem here is more risk management. The problem here is how those negative skew events can be moderated. So implications are very different. So where am I going with all this stuff? I think crowding implications have been overstated because they haven't necessarily been looked through the economics and the mechanics of the strategies. But that in itself should not basically put us behind the cart and say look there's no problem at all. So I mean if you if I were to put another example I think intraday momentum is a space that probably some crowding via the QIS wrapper has been there in the I guess in the years of 2021 2022 um when this product became very popular. Um I don't think that the intraday momentum in itself has lost its reactivity or defensiveness profile but maybe the cost of care of maintaining this defensive strategy has kind of increased over the years. Now I think the biggest be careful with my wording but the biggest consideration that banks and QIS desks should have is on the externalities that come with similar products being designed by banks and similar investors holding similar products for specific objectives which are also very similar. So right. So if you hold a 60/40 portfolio and an overlay and I hold a 60/40 and an overlay, both of us will react on a macro signal that would make us deliver the overall portfolio and as a consequence of that, both of us will probably deliver our alternatives. So those components now can expost realize correlations that we haven't seen in a back test. And I think those externalities and contagion impact of orchestrating activity to microshocks is more the concerning factor than how do we design a strategy to make it like you know less crowding um kind of impacted. So yes there's a lot of focus on that. Uh there's a lot of focus together with our trading desk as to how they see and experience the markets. But if there is one pillar of focus when we design those products is to build them for scale to your to your point but for very conscious kind of scaling um rather than let's just you know let's just consume the liquidity of the market however much it can deliver to us and and move on with our lives. Again we are you know we are only assessed by our performance. So all these are very important considerations. Very longwinded answer but I know it's not an easy it's not an easy topic. >> At the end of the day this is what counts right. is the performance. If your indices do not perform, if they do not provide the premium um that you know we suggest or expect there to be, then the volume will dry up. People will leave and uh you'll see reversion of your flows. Um such is life. Look, on that note, I think that's a good wrap. Um let's close this week's conversation. We hope that everybody has enjoyed it as much as we did making the episode for you. I hear from Neil's that next week Allan will be joined by Yoof. So that should be a fun conversation. It's also your chance to have them tackle some of your questions if you like. You can send them as usual by email to info@toptradersunplug.com. Neils will pick them up and do his best to bring them up. So from Nick and me, thanks so much for listening and we look forward to being back with you next week. >> Thanks for listening to Top [music] Traders Unplugged. If you feel you learned something of value from today's episode, the best way to stay updated is to go on over to iTunes and subscribe [music] 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 to [music] 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 podcast. [music] We'll see you next time on Top Traders Unplugged.
Unpacking QIS: Structure, Scale, and Systematic Investing | Systematic Investor | Ep.372
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 [music] their product before you make investment decisions. Here's your host, veteran hedge fund manager Neil's Krup Larson. [music] Hello everyone and welcome or welcome back to the systematic investor series on top traders unplucked where each week we have a look at the global markets through the lens of a rules-based investor. My name is Morris Seabirds and today I'm standing in for my friend Neil Castro Larsson who would normally run this show but at this very moment he's on route to Canada and therefore unable to get in front of a mic. Joining me for this episode is Nick Balters from Goldman Sachs, a regular guest on this show, as many of you will surely know. Today, Nick and I will focus on the QAS space at large, discussing questions such as how large is the global QA business, is it growing, who are the main clients, how many indices are out there, what's the outlook for the space, and so on. So, with that as an intro, let me say hello to Nick and welcome him to the show. Hi, Nick. >> Hi, Morit. It's very good to be here. >> Good to have you here. How have you been doing recently? >> I've been doing well. It's been um it's been a busy period, I should say. And um you know, I spent a good amount of time with family over the last couple of days. I had my my son's christening. Um so I had like family and friends flying over from Greece. It was a nice occasion. So that was like a small kind of break. Uh but now back in action, but I'm doing very well. >> Excellent. Congratulations on that questioning. >> Thank you so much. >> Anything else that's been uh top of your mind on your radar? It's the question that Neils likes to bring up. Uh marketwise, yesterday we've had a FET decision. Maybe that has created some turbulence. I don't know. >> Yeah. I think what is on my mind of my mind, I would say this year has been pretty much like a bit of a debate between fragility and resilience, right? You kind of have events that you feel will turn against the overall market and the positive sentiment and the growth and then you have like no results and so on and so forth kind of going against it. and the market seems to be quite resilient um in in in terms of kind of absorbing this information and kind of consuming it. So I would say if there's one thing that you know keeps me still awake um and maybe kind of on my feet um as the days and months go past and we're kind of approaching November, it's this kind of pendulum between a fragile market versus a resilient one. I mean I cannot call the shots but you know if this is one thing that you know probably entering the last phase of the year will be um a memory of the year that would be the one. >> Yes I agree we cannot call the shots and you know personally I'm just you know well we're we're trading in a rulesbased way so we don't really care about equity valuations or anything like that but to see equities performing so strongly again this year is uh is really surprising. I mean, it's just uh nobody's able to beat the S&P 500. The sharp ratio of the S&P 500 has now decoupled from everything else. It's the best investment in the world. [laughter] >> That that is the >> Until it isn't. >> That is true. Until it isn't fair. Exactly. >> Yeah. So maybe with that um as a backdrop, it's a good opportunity to get through a bit of a performance update. These are numbers as of Wednesday uh evening October the 29th. So monthto date and year to date the B top 50 index 1.89% up month to date and now positive 2.35% year-to date. Suction CTA 1.7 for the month up and just minus 1% for the year. So that is a good recovery now in October. The trend index plus 2.15% for this month and around flat for the year. The short-term traders index from Sockchen is up about half a percent this month and still down quite a bit minus 4.6% for the year. And as I've just mentioned with the equities, the MSER World Index plus 2.8% this month, plus 22% this year. Aggregate bond index plus 1.2% this month, plus 7.2 this year. And the S&P 500, the darling of everyone, it seems, plus 3% yet again in October and plus 18% in a bit this year. Isn't this amazing? Who beats the equities? It's It's just tough, huh? It's phenomenal. It is phenomenal. And you know, it's not the first year we've seen that, right? I think what was it 2023? I remember everything was flashing red growth wise and we still had like rallying equity markets. So like to to my earlier point, I think we've been seeing this debate between is the market going to continue going up in the way that it's doing it and and I think maybe the fixed income market is a bit more debated these days and obviously the the place that bonds would hold uh in allocation portfolios but I think when it comes to equities they keep on surprising us in a way. >> Yeah. I mean it's kind of like you say the equity risk premium or you expect to make six seven eight something like that percent per year. Uh that's like the long long longterm historic average, right? >> But for the past 5 years, that's not true. The past 5 years, it's kind of like every year is between plus 15 and plus 20. >> Um >> borrow 2022, I guess. >> Yeah, I don't remember what. So the the co period you mean? >> No, no, the 2022 was basically the inflation um the inflation spike and that was the time that both uh equities and rates sold off. >> And did the S&P have a negative year in 2022? I would think so. Uh actually quite quite negative. >> Yeah. >> Okay. Okay. So maybe that's the the exception. But >> if they're up if the equities are up there like in in my recent memory, it's always kind of like more than 15% in a year. It's uh >> fair. >> Well, that is what it is in in terms of trend following because that is that is a topic that's near and dear to our hearts. Um looking at October, it's been a great start to October. I mean, I don't know the books of other CTAs, but um as far as we're concerned, we've had a great time uh until around the mid-occtober or the 20th October, something like that, with long positions in precious metals and long positions in livestock. And it's just been a great month. And then a lot of give back of open trade profits uh on exactly these markets. Um palladium, gold, silver, platinum, you know, feeder cattle, live cattle, livestock, uh leanhawks, those markets have been reversing and it's kind of like back to around flat um to be quite honest. So really really just U-turning there inconvenient give back of of these open droid profits. But you know that is it is what it is. I'm seeing similar things um on on our side is kind of maybe still on the positive side for the month. Um to your point, most of the return was driven initially by commodities. Now they gave pretty much everything back. Um equities are still kind of holding up there and and rates keep on being the same story for the year. Keep on whipsing, keep on losing on the trend side. Um and main drivers, yes, pretty much equities. Um and then I would say some of the uh kind of long exposures in um in gold in aluminium in copper and good short on the sugar kind of delivering some good performance. >> Yeah, sugar is a recent short entry that um that just continues to go down. Still some of the grain markets I mean they've they still look relatively weak even though they've um recovered a bit from their weakness in in the past say 10 days or so. Uh but I agree with you. The big whips are this year is uh is the fixed income markets. It's like uh on and off all the time. >> We were doing like an attribution analysis somewhat like a straight line down. >> Mhm. I'm not surprised. Not surprised. >> How's it looking for currencies for you guys when you do the attribution? >> Um so I would say for the month is just flat. M >> um pretty much um with the biggest kind of positive contributors being um JPY and maybe on the other side is the Aussie dollar and uh and GBP um year to date it's kind of the second worst past rates you know if there's one asset class that over the year has actually contributed positive it's no surprise again right >> no surprise uh and obviously the question then is how quick or slow the speed is uh to be able to sustain uh an exposure through April So get the recovery and then obviously work from from then on or substantially the gross at that point in time and then missing some of the recovery and therefore building up again the exposure would take some time. So it's a question of speed and and volatility response around the Vshape and I think that's the primary driver I would want to believe not that I have too much data to support it but I think that's the main driver of dispersion that we've seen this year in the trend following space. >> I think that's true. it's uh it's the liberation day or the early April um April period selloff. Um in our case, it's not just been the equities. We got kicked out of some of the equities, but we also got kicked out of some of the currency positions that we had. And >> there were just a lot of positions that uh we had on the books that went to to nothing or even reversed at that point in time. And like you say, it's it's a function most likely of trading speed. You know, do you get back into these markets? Um did you lose your position or not? And that's you know very easily can make a difference of 10% for this year. >> And I guess quite interestingly if you were to be very quick you'd kind of capture some of the Vshape at the time but then the subsequent part of the year wouldn't work at your favor. Uh so I think at the time we're just talking about the short-term traders actually doing well in the Vshape and that's I think part of the reason that you capture some of the first draw down in liberation day and then some of the recovery around um the tariff pose. If you're slower, you'll probably just stomach the whole Vshape and then recover with it and then performing to this day to your earlier numbers, basically capturing the the equity trends. And I think there's some bitter spot in between being too fast and too slow whereby you just deliver at the wrong time that the market is eventually recovering. It actually gave you it gave you a week to also crystallize [laughter] the delivering of the of those exposures. >> There we go. Yes. know if we could only know these type of things before but we don't. Um so we have to stick with our own systems and you know some of them are longer term and some of them are medium or short-term and that's what we do in our case they're more longer term but still you know we did get kicked out of some of these positions and then it really takes a while to get back on. Um I think there were just two or three equity markets if I remember that correctly where we didn't lose our position. I think one was the Hang Sang, I think the share price index, the Aussie index, and the South African equity index. Um, those three we kind of like kept, but all the other ones, you're out. >> Topics, S&P 500, NASDAQ, go down the list. All the European indices, we we just lost all our own positions in April. And then it, you know, probably took until I don't remember, July, August, something like that to get back in. So, yeah, quite some time. And that's basically I guess u some sort of threshold based criterion to keep one of the position right zero flat zero. >> Yeah we have our exits um you know different types of exits some are trailing stops some have other rules but you know we're we're not forcing ourselves to reverse the position into shorts. There is a kind of like neutral um segment or area and that's what we've hit. >> No trade zone. >> Exactly. Exactly. >> Gotcha. So QIS um look this is an interesting business. It's also in a way I would say a little bit opaque to the outside observer. Um like to to most most people really they know okay QIS means quantitative investment strategies. It's a business and an activity that um pretty much all of the larger investment banks engage in these days. Um it's you know focused on risk prima indices. uh advanced beta type of strategies, that type of stuff um designed and then sold to clients, but it's kind of difficult for people to see how large it is uh who the participants are, who the clients are, how these deals are transacted, how the exposure is transferred from a bank to a client and these type of things. So, I thought with with you on the show today that would be a good topic to just speak about the QIS business more from a macro perspective at large. um chat about how large it is. And speaking about the size of this space, there is a survey that was run by Alborne, the alternative investment advisory firm, in the first half year of 2025. So that's a pretty recent survey. And they say that the total notional assets under management, they have hit a new all-time high at about$1,300 billion US. So, $1,300 billion US. And it's a new all-time high, which means this space continues to grow. Um, and the 1,300 billion US, they're split roughly 5050 between bank QIS offerings and asset management QIS offerings. So, asset managers also have quantitative investment strategies. Some of you folks may know, you know, some risk premier usage funds or, you know, there's some asset management firms specializing in risk premier strategies that would kind of like count to what that that 50%. But it's a relatively I would say large number 1,300 billion um and the largest part of that is equities. Equities is more than more than half of all the exposure of all the strategies are linked to equity type of strategies. equity long short you know factor type of exposures um followed by multi-asset class um strategies then commodities and quite surprisingly I was thinking that fixed income would play what would have a much bigger footprint but fixed income is only 50 billion even though fixed income markets are gigantically large markets but in QIS space on a relative basis they're it kind of like seems to me that they're under represented as is FX with totally 27 billion and credit with 13 billion. So that is a backdrop. Um do you think these numbers make make sense to you Nick? >> Yeah. So look, do the numbers make sense? Um there's a reporting convention here that I I I believe different banks are utilizing and maybe asset managers. I think on the asset manager side is a bit easier because that's the actual funded exposure. Um when you look into broker dealers uh the way that QIS products um is is distributed is either via kind of unfunded um swaps um or via sometimes you know call options. Um so the way that we can think of assets here um you know can be debated. Do you do a look through leverage adjustment? Do you do delta adjustment if that is an option? There are different conventions unclear to me whether those conventions are properly utilized and uniformly applied across the reporting banks. Um but I guess in the grand scheme of things themes if you just take at the minimum the brokilla sum which is close to the 600 billion mark um I would say with some confidence of plus or minus 100 I'm just making it up really I don't really have hard data to support it I think it's in the right ballpark. Uh now if you take asset managers into the mix maybe there is some double counting because obviously asset managers are very active in the Q space and some of them do utilize QS products. So in in in the context of reporting um you might end up having both broker dealers reporting numbers for specific strategies which are eventually deployed by asset managers that also report numbers. >> Um so there could be some double counting here. There could be some reporting conventions that are not necessarily uniformly applied on the broker dealer side but broadly speaking it is a business that has been um growing over the years. there have been some winter periods that we can identify maybe around 2018 and 2021 um on on on the reasons that we can potentially discuss later on. To your question on the asset class split um it's interesting to see that the equity long only space is by far dominating the asset manager world and that's more like the smart beta of the world. Um so anything that is quant enhanced uh and delivers an equity exposure that is supposed to outperform uh typical benchmark would come into that pocket. It's less so on the bank side. I think the bank um or the broket dealer asset class contribution is more evenly without that being even but more evenly split between the asset classes. I would say part of the reason why you see less on the fixed income and effects side is that historically liquidity has improved um if you move away from the you know from delta 1 space on the option side uh which typically the one that banks are much more focused on um is something that grew over the years um I think the equity space is more natural that's the equity long short u and that's basically the the genesis of the QS businesses so it has grown over the years I think commodities is the other uh you go back to those rolling futures and how you can indexify commodity exposure both on the beta side as well as on the long short side. >> Multiasset is again kind of a CTA type of a place. Um there is also a good amount of application in um in the retail space when it comes to multiasset. I think effects fixed income are in their growth phase um if we're to say um and even if we're looking into kind of academic activity uh it has been massively more on the equity side and less so on the other asset class. So I think it's a combination I guess of of features of the market um but I would I would I would certainly flag that all asset classes are now becoming part of the toolkit of of uh of brokers. I also now see a lot of multi-QIS uh offerings in the way that you know banks would combine say their commodity carry strategy with their FX carry strategy and an equity carry strategy in order to create a you know crosset carry basket um that's also multiasset but back to the numbers I think just to put this in perspective the numbers could be conservative they could also be aggressive um they could be like there could be um funds out there asset management ers, maybe hedge funds, just you know, people trading risk premium strategies that are not reporting to that survey, right? Think about a hedge fund, right? Maybe that hedge fund is not covered by that survey and they may run whatever like few hundred million in notion exposure in in risk premium type of strategies, but they're not factored into this into the survey. And then it could also be on the other side like you say there could be double counting which could happen if for instance you swap exposure of a strategy to an asset management firm and the asset management firm then on sells it to their client right that you you report it and the asset management firm reports it >> and then what you've mentioned the delta adjustment or the the leverage adjustment is you know if if you're selling a call option linked exposure to one of these strategies it may have an initial delta of 0.5 Um but the question is do you report that delta adjusted exposure or do you report the full notional of the of the option um to the survey right so let's take these numbers with a with a pinch of salt but the I guess um overarching statement here is is that that space is I would say pretty large now and it's growing and there's there's a chart included in the survey where essentially these assets with the exception of what you've called like one or two three winter corals um like you know 3 month or so but they're they're essentially going up. >> Yeah, I mean that's a that's a fair statement. It is a space that has been evolving in a variety of ways. Um maybe it's asset class allocations, maybe it's new markets that are traded, but also the users on the on the on the client side have um have kind of increased over the years. You know, we can we can also discuss that part. Maybe the one thing that I would flag the one last thing that I would flag and then happy to to to go to the to the client side is that there is also an element of substitution to your point. So if today there is a I don't know a um 100 million exposure in a CDA that is now transferred into a QIS product, the market still consumes the same level of liquidity demand. it's just now marked under the QIS kind of badge and therefore would be seen as an increase in the multi asset pocket of a survey as such where in reality just a substitution effect from what otherwise was like knowed exposure on um on a CDA right I'm just making that example whereby a substitution effect could also be present here more so than just genuine growth >> another correct another asterisk >> yes yes there are many asterisk Yes. Um, you've mentioned clients, Nick. Uh, and I think you've mentioned that there's new clients coming in. So, maybe let's talk about that. Who is the who's interested in this? Who is your usual client and how has the spectrum of clients changed over the year? >> Yes. So, the QI space started maybe 15 years ago. You know, we can we can point uh in a timeline where was the first baby QIS trade. I would think of it either coming from the commodity space and those rolling futures indices that were built to deliver um kind of economic exposure into commodities without having to hold physical commodities. So that was one reason why those um QAS teams initially were formed. Um and then the other space uh or the other place if you like that we saw origination of ideas and and and indices um was post GFC and some of the work done by some of the Nordic pensions to replace or maybe enhance um their risk profile by by risk factors and kind of transitioning or repurposing how they think about diversification away from asset class mix into kind of a factor mix. Um and that was a time that some of the banks started working on equity market neutral strategies, some volatility carry strategies, you know, version uh one uh at the time. And in this regard, the primary users in the early days were either asset owners. So we can talk about um large pension funds, so wealth funds across the globe. You know, there is some regional kind of shifts that happened over the years and happy to go through that. and asset managers. These are the primary users. If you do kind of fast forward to today, you can widen the spectrum quite substantially and you can go any from like know private banks to hedge funds. So the utilization of the technology and the utilization of this IP has massively expanded across the clan segment. Um and one could think of a QIS offering nothing more than I guess in a good way um kind of a you know a convenience store in the sense that there are various type of systematic exposures that can deliver performance or various ways of delivering a defensive profile via for example rolling put options and you know being very thoughtful on which tenors and which strikes to utilize whether the delta hedged or not. All I'm just discussing is nothing more than a set of rules. It is >> to a certain extent it's not too dissimilar to saying look if you really want to have the equity market you need to find the 500 larger names and you need on a quarterly basis to reconstitute what those 500 names would be and allocate market cap weights to them. This is nothing more than a set of rules and this is called S&P 500. And once upon a time there came a contract that is called a futures contract that delivers an unfunded exposure to the equities premium. So that in itself facilitated whoever wanted to get equity exposure to avoid going to the market and physically replicate 500 names into a portfolio of what the S&P will represent and basically get on a contract nothing more than the performance of that construct that in itself can be put into a document and be reflected upon um can be reflect upon into an index profile. Right? So if today we claim that volatility is rewarded and whoever is willing to sell insurance should be compensated that in itself can be described with a set of rules by selling some options and you know trying to reduce the spot participation the so-called delta hedging. Now we can go back to the academic roots of the volatility premium. If somebody were to deliver quote unquote a futures contract, you know, in this regard that would be an OTC swap, then this allows the end investor to be exposed to the volatility premium, not too dissimilar to being exposed to the equator premium. And what sits underlying that investment is nothing more than a rules-based index. So I think the way that I think about the QIS space is that it delivers different risk profiles in an accessible format and maybe reduces the operational burden and delivers this operational ease to the end investor and there are other benefits you know cash efficiency and so on and so forth that we can also discuss but the primary reason why we now see this proliferation of this type of investing nobody suddenly thought that systematic is the way but systematic is the pay to deliver into an index format what otherwise could be a cumbersome and tedious process. And the end allocator being a private bank or being an asset manager or a hedge fund would simply have that exposure and utilize their internal models on timing utilize their internal models on sizing focus more on kind of bringing their alpha which is more of a timing element rather than of an implementation element. So that's a quick kind of or maybe not as a quick but that's I think a quick overview as to how I think the space evolving and the utilization across different um different client segments. >> Very good. And and the observation that I personally made and maybe also many others is that tying into what you said you said it's a technology I think that's a good term to use that comes along with operational efficiency. So when you think about the early days of QIS, if you mentioned there's like you know asset owners or insurance companies or large asset managers contracting with you and maybe they wanted to have a new variation of equity value, right? Maybe it's something that they're doing in-house and maybe it's just they want to have another source uh to diversify and and do something like that, but it's not like high frequency, you know, it's it's kind of like, oh, you're running this daily or weekly or monthly or something like that. But then that has changed when most banks came up with hey we now offer intraday V or we offer intraday momentum trading and you know this and which is technology which is a operational thing that not everybody can work with and I guess that's where the hedge fund clients come in where they go like well look >> if if the bank can you know kind of like every five minutes or what it is do like a a vulcar intraday strategy on the S&P 500 then I might be better off just buying it from them as opposed to implementing that internally um myself because it's difficult to do that and and and you're right in this one there's an element of IP that comes with the idea of designing a strategy that delivers some some return profile that can be alpha enhancing that can be defensive but to your point there is a massive um now integration of technology like the way I think about the QI space over the years is as a combination of four main pillars in terms of product development. There's certainly research, industry, academic research that is evolving. You know, how asset prices move, how we think of asset prices and that is definitely a source of inspiration. Number two is the entire client segment in terms of appetite. You know, it used to be either a hedge fund replacement or compliment >> and it eventually became more of a specific economic outcome focus. No, I want something that is a bit more defensive and I'm happy to take on the negative cost of care or maybe I want something that is enhancing my yield and I should be very focused on what is a spot contribution. Maybe now somebody's focusing on inflation, maybe the economic regime. So that there are different ways of us um delivering product for specific uh economic outcomes. So that's the second pillar how the client utilization of the product has shifted. I think the third one is technology. This there is no order by the way right these are just equally sharing the the load of of inspiration. So technology to your point being able to access markets being able to trade markets being able to trade faster markets. This is a massive enhancement uh that we have in our product offering. And then I would say the fourth um is how markets evolve. Once upon a time you could not trade some of the frontier commodities and now you can. Once upon a time there were no shorted options. Now there are so there's a variety of things that eventually happen around us allowing us to almost do out of sample testing on universes that otherwise would not be able to do so at least back in the days and not net of costs. So this is I think if you were to ask me these are the four pillars that over the years have been contributing to product research technology utilization market market liquidity um evolution. >> Got it. What do you think Nick guesstimate how many different QIS indices are out there or not different total number of QIS indices >> across bank? >> Yeah. Or maybe start with you. I mean it it must certainly thousand thousands of thousands. >> Yeah. It's it's thousands. It's thousands. >> Well, I mean, we can we we can discuss the reason why that can be the case. >> Yeah. >> And there is certainly an element of customization. So, for example, you mentioned equity value. You know, somebody can say, look, >> I don't know what's the best equity value definition. Maybe it's, you know, book to price, maybe it's a dividend yield, maybe it's a combination. So there could be variations of the same product purely to reflect that different design choices could be um um could could suit different investor needs. We can speak about CDAS, you know, somebody would be willing to avoid having equity participation on the upside purely because they want to have more of a defensive profile. That's a different index. So you know the fact that we don't and a bank cannot act as an asset manager and there is no fiduciary element that in itself leads to the need of having a pallet of possibilities and we can talk about an equity investment that is seen from a non- US investor that is exposed to withholding tax. So that has to be a net total return implementation versus a gross total return. So that in itself is two indices. So I think because there's a lot of discussion as to how many indices and you know data mining bias and so on and so forth. I think there is there's an aspect here that should be definitely mentioned and that is purely the fact that any different implementation be it for a specific client ask or a specific need has to be a different index. You know a client doesn't want to have a in their commodities portfolio that's a different index. So there's a lot of customization that is purely driven by a specific need and less so of oh we found a better way of doing it let me just replace it now because I've just done my overfeitting exercise this is in itself has to be addressed right but this is one I guess one way to address >> on on the magnitude right >> why there are so many and they all have their tickers and they all run on the Bloomberg terminal I presume and um you put them into Python code or whatever it is to to run them I guess you need a now these days In Europe, you need an independent calculation agent, benchmark regulation, right? You do you have that internally like Chinese Wald or do you use external parties? >> There's a variety of models as you say. You know, some banks utilize internal um internal calculation agents that obviously are infenced. So there's a lot of governance around this business. Um some others would be using external calculation agents. This is purely a business decision as long as obviously the governance is in place and and >> um and the business overall is um is well run. uh but it's certainly the case >> looking at the types of exposures or products that uh your clients typically request are they is is most of the notional transacted in Delta 1 space you know linked to a swap or node or is is most of it kind of like optionalized and therefore nonlinear index exposure like a people like somebody buying a call option on the index. >> Yeah. So the historically the vast majority has always been in the in the kind of the delta 1 space in the sense that they would do um you know some form of a delta 1 rapper that can be typically a swap that's what you see in the space. Uh obviously there are other rappers like certificates nodes you know depending on the um on the contractual um relationship that a bank would have with a specific institution. um more recently and certainly for the insurance space but also more recently we've seen in the institutional space some interest for um option profiles and just to be clear for I guess for the audience we're not talking about the constituents of an index the constituents can be delta 1 or v you know it can be a volatility carry it can be a trend following strategy we're just talking about the rapper right so your question is really how can I get access to a volatility carry trade or how can I get access to a CTA profile This is typically done as I mentioned on a um on a delta one type of um type of a profile but recently we've had and when I say recently maybe it's the last two three years that we've seen a bit more interest into kind of a fixing a price uh and basically capping the loss so getting a call option on some of those indices um I would say it's a fraction it's a small fraction of um at least of of of the executed trades Um there is a philosophical question here to be to be I guess to be put in place. Um you know let's say you're selling volatility to get a volatility carrier profile and then you want to put an option on that profile that you know it's almost going going in circles right. So, the mere reason why you're supposed to be um benefiting from a premium when you sell options is because you're willing to take on the risk that this thing is going to at times occasionally um experience a V spike. >> So almost writing a call option to it in itself nullifies the existence of the premium in the first place. So why would you have to, you know, get optionality on something that is dropping and still benefit from an upside while the price for it should be the exact premium that you're benefiting from selling it. So almost this vicious circle nullifies the I guess the the value of pricing a call option on some of those kind of negatively skewed profiles. There are still ways that we can think around that, but I guess the high level kind of answer to your question is that it's primarily on the delta 1 space. And when it comes to an option, there are types of uh of transactions maybe more on the underlying delta 1 space and not on the option space. Um, but rarely do we see that as frequently as we do the delta 1 transaction basically, right? when you do the delta 1 transaction, it removes the requirement or the kind of like motivation for you guys to have a vault target or vol cap. I mean, it may still have this as an intrinsic part of the strategy element, but I I reckon when you sell a call option, the underlying index will definitely be volled or targeted to a certain type of level. Um, so there's a lot of that stuff that kind of like slucks through the market as well because, you know, when somebody buys their call option, really the only party that will quote a price is you. You know, they can't go to a well, they could potentially go to a different bank, but they're not going to get a a good bit ask if anything. So you know uh you're the the source of liquidity and therefore you're protecting I guess your book by having this V control in place and selling selling the option at a premium that's uh that that's yeah amenable that that that is fair that is fair it's certainly easier if you think about linear structures underlying it so when we talk about equity momentum equity momentum in itself is volt targeted and that in itself is helping equity momentum because >> negative skew in equity momentum can be moderated by uh by vault targeting. So that in itself to your point almost comes not even for free but for a benefit of the performance while facilitating some option pricing to to to operate on top. Um, so I guess an another way of kind of answering your question is that there are pockets of a QS offering that are more um accommodative for option pricing and some others that can become a bit more nuanced purely by the um I guess the economic principle arounding around the premium but also mechanically if you were to volatility target a strategy that is supposed to be reacting and recovering post the volatility spike you're kind of acting against its ability to recover. So then the whole question is okay fine we can do the volt targeting but then what you're what you're ending up with is a sub-optimal profile for the respective strategy right so all these are important considerations right >> what do you think like uh by and large you would you build your business or the QA business for massive scale like an ETF provider would where like you know theoretically practically theoretically say the AUM of an ETF are you know unlimited it can grow to whatever size it wants to grow. Do you have that same thinking in the QIS space? Um um because you know one thing that I recognize is that for instance the CTA or trend offerings or some of the like uh the more popular offerings they're not exposed to some of the smaller markets. Um you know they're they're they're really lacking uh from these type of indices would more like be the super deep and liquid markets. Is is is is that a fair statement that you're building it for scale? >> Generally we build we build product for scale because that in itself is associated with how we can see the profitability of this business. Um at the same time we are always exploring what are the pockets of the markets that can deliver some good performance even if the scale cannot be at sizes of a much more liquid profile. So you know uh we can spend time talking about frontier commodities or some of the non- kind of benchmark commodities. Obviously they don't have the liquidity that you'd expect to see in some of the larger markets but certainly there could be variations to your earlier point. There could be another index that simply has like a longer or maybe sorry not a longer but a a broader set of assets as underlying with the I guess the the consequence being that um the overall capacity that this profile can support is now a fraction of what a subset of assets could. Um so there is I guess to answer your question in a different way we try to solve for different scales and if there is an argument to be had with regards to premier being more um pronounced uh with lower liquidity or pockets of lower liquidity if there is a liquidity premium to be harvested or illiquidity or whatever it's worth it. um we're always keen to see um what the enhancement can be with the asterisk being that you know we cannot scale it as much as we could with a very liquid universe. So we don't shy away of exploring those pockets of liquidity but what it's probably the most important thing when we design strategy is being very very thoughtful on capacity. So no to put it into an extreme kind of statement nothing is built for infinite scale to be clear right everything has to be done in a very prudent manner very conscious of market liquidity how much we transact how much we consume how much we roll all that lot feeds into the product design >> and that's also because you have the option to at some point say stop if you wanted to stop selling an index you could you could you know close it whereas an ETF cannot right that correct >> and we do right we do I mean at the end of the day you need to protect the investors and you need to protect the market and you need to act um in a in a in a prudent fashion uh when you transact and interact with the market so there are multiple cases whereby a specific design cannot sustain anymore without being market impactful so we're like that that's it for now like we can and I know and I think investors do appreciate that you know there is this scrutiny and there is this transparency and existing investors also see that as um as a good thing in [music] a way that you know we're basically the guardians of their exposures [music] maybe two more things one like this is just an observation um every couple [music] of weeks I receive emails from it's not just banks it's it's all sorts of like people offering product in that space and they come up with something new something that here therefore has not in their view existed. It's kind of like here's a new index, here's a new strategy or a new variation of a strategy. And I sometimes go like h it's kind of like the same that you've had before, just a little bit of a of a new twist to it, but other than that, it's it's pretty much the same thing. So, how do you balance this kind of like being forced to innovate and coming up with new indices um maybe in a in in in an approach or in an effort to stay relevant versus just you know overoptimizing things and you know turnurning out new product for the sake of producing new products? >> Yeah, that's an amazing question actually. Um why it's an amazing question because it's part of human nature to observe and then think and then try to do better uh specifically in that space. Um so if I observe my trend following strategy in April then probably there is a temptation to say oh if only we're a bit slower or if only we're a bit uh I don't know overweight this asset class or the other asset class or if only we're not doing dynamic sizing or we're doing like not static size whatever right so this temptation exists and I think in itself it's quite inherent to have those biases of overfeitting so maybe Another way of asking the same question is to say how do you control data mining in a way right because frankly if there's a new idea this idea is probably driven by the fact that something must probably have worked better and therefore let me just launch this tweak because that tweak will just make the recent performance look better. So how do you control for it? I mean my personal view is that this is this is genuinely product culture like there's no better way to be conscious of data mining unless you just call it out. So it is part of our at least as as as far as I see it and you know the responsibility I have in the product teams um that I would flag look now we're data mining fine let's look at it let's get a better sense of what the results suggest but you know we should be very conscious of these data mining biases and know as soon as you acknowledge them then it makes I think your brain iterate in a very different fashion because then you see all those new kind of very skilled individuals that get hired from university kind of being exposed to those biases, but as soon as you call them out, then they start ingraining a culture whereby any new tweak they do is not about making the sharp ratio look better anymore. So I don't think there's a right answer to your question in the sense, okay, that's the blueprint. I think it's a combination of acknowledgement, a combination of calling it out, a combination of being very thoughtful and study some of the work that has happened in the academic space to control for data mining biases. I I I think there is no way you can avoid data mining. If I were to show you like 10 back tests, you would basically drop the bottom one just purely because you wouldn't think about it. Um so when it comes to changing a strategy or changing um a design what we try to strive for is I tend to say that quite often explaining the underperformance is more important than you know being lucky on the upside. Like we would love to outperform but if we cannot explain underperformance that's the worst thing that can happen. So at the end of the day, we are only successful because our clients are successful. And you know, we live and die by those performances. I know everyone would say the same thing and asset managers and banks alike, but there is no other way for us to prove to our clients over the years that sustained not necessarily our performance, but explainable underperformance in light of what we initially suggested that we would build um you know, would would bring to us in terms of a kind of confidence and and and reward. Um so I'm not sure whether I answer your question. I don't think there's an easy answer to the question. U that's the process that we kind of go through, right? >> Yeah, I think you did answer it in the sense that you are following a rigorous process as far as research and uh Oh yeah, absolutely. you know, science is is is concerned and you're not just, you know, producing product for the products um provision's sake. Um like you know, there there's no schedule or every week you have to come up with new index which would be ridiculous, right? So you want to come up with something that's >> meaningful um and and different compared to what you had before >> and also no reaction to emotion, right? I mean we could have reacted in SVB, we could have reacted last year in dollar yen unwind. We could have reacted in April when it comes to trend following which just basically sat behind saw what the market were here to to to basically tell us in terms of maybe a new regime maybe not I don't know but reacting on the motion and just making the last draw down outperform is purely the recipe for underperformance. One thing and maybe as a final question um we've mentioned the 1,300 billion and we're not exactly sure whether that number is right or wrong. We've asterisk this and conditioned it on a couple of parameters. But by and large, where do you think if anywhere could the QAS business have become too large? I mean, do you see effects of crowding in some of the markets or in some of the strategies? Let me give you just an example and I'm absolutely not sure whether I'm right or wrong but for instance when you look at commodity commodity indices and liquidity provision around index roles business day 5 6 7 8 and 9 which you know the the GSI become type of role period >> very successful strategy probably 10 to 15 years ago providing liquidity during that role and then reversing the position you know 5 days later I think that strategy has largely decayed um then you know role days have changed So maybe that's just just one example. I'd like to hear your view on this of yeah enough money has crowded into that space so that the effect has essentially gone away and the market is now balanced. Um would you say that's a fair statement and if so are there any other pockets of the the business where you see similar effects? Yeah. So that's both a very um I mean a very good question and a question that comes very often I should say. So how should I answer? Let me just first answer maybe maybe picking up your example. Uh you're referring to commodity congestion I guess uh and the fact that there is >> uh passive exposure to the benchmark roles specifically for institutions that do not want to hold physical commodities and want to be just financially exposed to commodity prices and therefore they need to follow an index of all things because it's easy in this regard. But that index is following a predetermined schedule. So there are days that you need to sell the futures you're holding and buy the new one. So over the course of a few days, there is supply, net supply of the contract you want to move away from and net demand for the contract you want to go into. Now if the market is elastic, you would not expect any price impact from this activity. But lo and behold, for years, because there was this passive requirement for commodity exposure, it had um um kind of an impact, a negative impact from selling at the same time that everyone is selling and buying at the same time that everyone is buying. So there was this kind of strategy called congestion that said, look, I really know when those benchmark grows will operate. So let me just do it a few days before. So I'm going to buy whatever else is going to buy a few days before they do so and I'm going to sell whatever else we're going to be selling a few days before they do so. So then I do like a long short portfolio and I capture what is called the congestion premium which is nothing more than liquidity premium liquidity provision here. Now right so as you said around 2015 16 17 18 this started kind of underperforming in a way and flattening out. Um it's interesting because we wrote a report back then you know I spent some time with the team uh that's 2018 2019 that and we wrote a report on specifically this particular point and there is a very subtle point here on what crowding implications would be. So if this were to be massively crowded, as in QIS investors that do congestion at the margin, you would expect to see very negative performance because now you're the one congesting the market and whoever is doing the benchmark roles is actually benefiting from it. But oddly enough, we saw the strategy flattening out. And the flattening out of the strategy, it's more that the initial demand for this liquidity is no longer there. In other words, whether you pre-roll or post roll or roll together with the benchmark roll, you have no impact whatsoever. The market started becoming elastic again, it started absorbing this net demand and net supply. And the you I guess if you like the the outcome of this analysis suggested that investors became smarter in the way that they all the exposures and they know or at the time were doing more enhanced data exposure in commodities rather than too many investors crowding out the congestion trade and this is very subtle but very important. The strategy became flat not underperforming. Um lo and behold in 2022 congestion trade came back again because obviously there was this kind of need for commodity exposure during the um during the kind of Russia Ukraine kind of conflict. Um so congestion did come back but also we can think of congestion in a nice in in a different fashion now you know do do now do we do we see congestion trades operating in uh in enhanced bet as exposures that could be a question. So that's I guess statement number one. Um but where I want to focus on as a consequence of this discussion is that this demand for commodity roles is not driven by some sort of risk sharing mechanism. It's purely allocation decision. So the premium in itself shouldn't exist in the first place and this is a space that you know if there is too much of crowding it can go away or obviously if there is no demand for those benchmark rows as it happened to be the case there is no premium to be harvested anyway. It's very different when we start looking into maybe volatility selling. So if we sell volatility, there is an underlying risk factor that there is a segment of the market that is not willing to take and we're happy to basically take the other side by selling those options and be paid the premium that whoever is willing to hedge is willing to pay and pay more. Um so can we now observe crowding implications? Maybe the premium falls like I I I I tend to use this you know basic example. If we all become car insurance sellers or car insurance providers, the premium to insure your car is going to drop purely by the by the economics of competition. But that doesn't in itself suggest that the prem is going to go to zero, maybe negative unless somehow we will all become better drivers. So there's a fundamental link here between what is the price to pay for specific insurance and what is the underlying risk sharing mechanism and where the market will clear in terms of pricing that risk. that's the price of the of the of the premium the risk premium if you like but you know suggesting that this can become negative I I think it's a it's it's a very uh bold statement and and the reason why I'm saying so is that there is a barrier to entry who is going to be selling this insurance if it drops below a threshold so the more kind of crowding you end up observing the more subdued the returns would be still positive in the longer term but then the barrier of entry would increase. So there's an equilibrium here that is achieved purely by the fact that there's an underlying risk sharing mechanism and that's different to congestion. And then obviously the last segment of alpha seeking strategies would be more behavioral like know trend following is is I think it's a prime example here. We can debate what crowding will bring to trend following. There's an argument to be had whereby trend following is actually benefiting from early crowding because the more >> to the fire. >> Exactly. Right. The problem here is more risk management. The problem here is how those negative skew events can be moderated. So implications are very different. So where am I going with all this stuff? I think crowding implications have been overstated because they haven't necessarily been looked through the economics and the mechanics of the strategies. But that in itself should not basically put us behind the cart and say look there's no problem at all. So I mean if you if I were to put another example I think intraday momentum is a space that probably some crowding via the QIS wrapper has been there in the I guess in the years of 2021 2022 um when this product became very popular. Um I don't think that the intraday momentum in itself has lost its reactivity or defensiveness profile but maybe the cost of care of maintaining this defensive strategy has kind of increased over the years. Now I think the biggest be careful with my wording but the biggest consideration that banks and QIS desks should have is on the externalities that come with similar products being designed by banks and similar investors holding similar products for specific objectives which are also very similar. So right. So if you hold a 60/40 portfolio and an overlay and I hold a 60/40 and an overlay, both of us will react on a macro signal that would make us deliver the overall portfolio and as a consequence of that, both of us will probably deliver our alternatives. So those components now can expost realize correlations that we haven't seen in a back test. And I think those externalities and contagion impact of orchestrating activity to microshocks is more the concerning factor than how do we design a strategy to make it like you know less crowding um kind of impacted. So yes there's a lot of focus on that. Uh there's a lot of focus together with our trading desk as to how they see and experience the markets. But if there is one pillar of focus when we design those products is to build them for scale to your to your point but for very conscious kind of scaling um rather than let's just you know let's just consume the liquidity of the market however much it can deliver to us and and move on with our lives. Again we are you know we are only assessed by our performance. So all these are very important considerations. Very longwinded answer but I know it's not an easy it's not an easy topic. >> At the end of the day this is what counts right. is the performance. If your indices do not perform, if they do not provide the premium um that you know we suggest or expect there to be, then the volume will dry up. People will leave and uh you'll see reversion of your flows. Um such is life. Look, on that note, I think that's a good wrap. Um let's close this week's conversation. We hope that everybody has enjoyed it as much as we did making the episode for you. I hear from Neil's that next week Allan will be joined by Yoof. So that should be a fun conversation. It's also your chance to have them tackle some of your questions if you like. You can send them as usual by email to info@toptradersunplug.com. Neils will pick them up and do his best to bring them up. So from Nick and me, thanks so much for listening and we look forward to being back with you next week. >> Thanks for listening to Top [music] Traders Unplugged. If you feel you learned something of value from today's episode, the best way to stay updated is to go on over to iTunes and subscribe [music] 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 to [music] 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 podcast. [music] We'll see you next time on Top Traders Unplugged.