Why Trend Following Never Looks the Same Twice | Systematic Investor | Ep.365
Summary
Market Focus: The podcast discusses the current market focus on the Federal Reserve, with a particular interest in the fixed income market, which is seen as a potential catalyst for future trends.
European Political Climate: The political chaos in Europe, particularly in the UK and France, is highlighted as a factor that could impact bond yields due to increased uncertainty.
Technological Innovations: The introduction of Apple's AirPod 3, which offers real-time language translation, is noted as a significant technological advancement with potential implications for the translation industry.
Hedge Fund Brand Awareness: A recent ranking of hedge funds based on brand awareness among consultants and asset owners is discussed, with Man Group and CFM being notable mentions.
Trend Following Performance: The podcast provides an update on trend following strategies, noting a positive start to September, with equities and metals contributing significantly, while fixed income signals remain mixed.
Investment Strategy Insights: The discussion emphasizes the importance of understanding the design choices in trend following strategies, such as speed, market allocation, and the inclusion of alternative markets, which contribute to performance dispersion.
Alternative Markets: The potential benefits of including alternative markets in trend following strategies are explored, with a focus on their diversification benefits and impact on risk-adjusted performance.
Transcript
Imagine spending an hour with the world's greatest traders. Imagine learning from their experiences, their successes, and their failures. Imagine no more. Welcome to Top Traders Unplugged, 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, 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 anything about future performance. Also understand that there's a significant risk of financial loss with all investment strategies and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions. Here's your host, veteran hedge fund manager Neil's Castro Larson. Welcome or welcome back to this week's edition of the systematic investor series with Katie Kaminsky and I Neils Castro Lassen where each week we take the pulse of the global market through the lens of a rule space investor. Katie, it is wonderful to have you back this week. How are you doing? What are what are things going on where you are? >> Things are good. Fall's in full force here. You know, kind of back to school. I'm happy about that. And you know, everybody's focused on the Fed and that's about it. I don't know much else. That's true. You know, um, in some ways the summer kind of seemed pretty quiet in terms of news that really moved the markets, but perhaps we're just getting used to this narrative driven uh news flow, but you never know. The autumn may still present some surprises and maybe even some strong trends. So, uh, we can always hope. Now, what's great about today's conversation um is that we've got some new papers from our friends at Mang Group as well as Quantica um on some of maybe the less discussed areas, if I can put it that way, within the CTA and trend following space. Um but also a few other things that will come up um so it's really relevant to uh to be talking to you today and tackling this. Um, and also, um, before we dive into all of that, I'm always curious to know what's been on your radar, if anything, other than the markets. >> Well, I think the markets I'm just waiting for. I mean, and I got several calls yesterday, both from press and clients. Everybody wants to know what is going to happen in fixed income in the US, but if you look at the trend signals, they haven't moved much. So, I'm kind of thinking that could be a potential catalyst. And it's something that, you know, there's so many themes like inflation, independence, like >> the Fed, like changing like allegations. I mean, it's there's a lot of drama, but not a lot of movement yet. So, that's why I think that's interesting from a trend perspective. >> Yeah. No, very true. Very true. So, on my radar, I put three things. Uh, one is that um, and it is the first one is a little bit related to fixed income and bonds is just the European political chaos we have at the moment. I mean the UK and France um you know it's kind of like a basket case really uh and some of the polls even in the UK now suggest that Nigel Farage party would get the most votes uh if there was an election right now. I mean that's just uh extraordinary to think u that you have like a real contender uh between the two main parties and then of course France uh another prime minister coming in and and uh some of the ways that they describe the financial situation of France obviously should you know lead to a lot of uncertainty about that um and of course uh if there's anything bond yields are very good at reacting to it's uncertainty especially about budgets and and these kind of things so it'll interesting to watch as well as of course what happens in the US at the moment. The next thing I had on my radar, which is a kind of a new one, and this is not an endorsement of anything like that, but last night I did watch uh the uh the Apple event, and I was kind of blown away by one of the products, and this is this new AirPod 3 where essentially, if what we were told yesterday is uh is true, you're now going to have been be able to have a live conversation with someone that doesn't speak your own language, and it'll translate simultaneously ly between the languages if they're both wearing these um AirPods 3 which I find absolutely extraordinary but I'm also a little bit concerned what about all those people who have a job right what if they other thing what if it gets wrong like talking to somebody and like somebody can hack in and change your conversation I don't know I was like wow that's so cool that is really amazing >> I didn't think about that but I did think about all the translators that are hired by the European Parliament or the UN to sit there and translate all this stuff. I mean, they may not have that much of many things to do if everybody's just wearing these uh new AirPods. >> Maybe it'll be like, you know, TV and stuff where your kids go, you actually like had to turn the channel by standing up, you know, and they'll be like, you did and you dialed the phone like that and be like, you had to translate the language. Why don't you just put on your AirPods? >> Yeah. No, I know. It's a crazy new world for sure. And then the final thing, something I picked up from our friend Andrew's uh LinkedIn feed just now. Um because there is apparently a a Q2 2025 brand awareness ranking. I've never heard about this before. Apparently um there is a ranking of how well hedge funds uh do in terms of brand awareness and and they uh distinguish between how well they do among consultants um and how how well they do among asset owners. However we define that um and I will say it's nice to see that some of our friends um are established on the list that the among consultants man Group um has the top rank um Crabel actually comes second that's um a little bit of a surprise to me Winston comes fourth which is great aspect is on the list number 20 AQR number 19 that's a little bit surprising to me I thought awareness wise with all the research they do they would be higher up Um and then there are um in terms of the European based managers you have CFM doing well top 10 systematica top 10 and even Andrew's firm DBI uh a ninth place which is uh you know great great job on on that. Um I imagine it's all his appearances on top traders on block that helps them with all this awareness. I'm sure of course it is. Um among asset owners it's a little bit different. Um actually CFM is doing best in terms of the CTAs we know of. Winon also come in fourth on this one. A man groom a little bit lower number six. Um but yeah it's a fun list. Never seen it before. Um not sure how valuable it is but you know there's nothing bad in being known. uh among investors or consultants, I guess. Um sadly, I didn't see our firms on the list, neither of them. But there we are. We'll we'll stay under the radar uh even if this was on my radar uh this week. Anyway, let's uh elegantly jump to um more familiar waters, namely the trend following update. I mean, it has been a decent start to September after a couple of uh uh good positive months for CTAs and trend followers. Of course, equities, metals continue to do the heavy lifting, I suspect, supported by a few other commodities like sugar. Um, it's been a while since we've seen that. Perhaps even some of the oil products um are helping out. Um, livestock on the other hand is taking a little bit of a breather this month. And one thing I mentioned last week uh in the in the podcast is I'm not entirely sure if managers at the moment agree on whether to be long or short fixed income. It's funny you mentioned fixed income. Um, and I when I see the daily uh changes on on some of the mutual funds and ETFs um that managers um have um it really seems like fixed income plays quite a huge role, but they're not going in the same direction depending on whether bonds are up or bonds are down for the day. So kind of interesting. Always curious to you to hear your observations about um you know the month, the time we're in right now and uh what you see. >> Yeah. So starting with fixed income, it the signals have been very mixed and kind of weak and teetering around between should we be short, should we be long and we've seen a little bit more long signals in the US more recently. Uh UK was definitely a short signal. Um >> true >> and some of the European bonds. But what's interesting to me is if you know someone was was talking about we were talking about sofur yesterday and if you look at sofur sofur is at about 3.4%. Okay. And if you think about that if the rates right now in the US are 42545 you know the market is already decided what's happening. And what we're probably going to see a trend on is whether or not they get that right. Right. So I think why you haven't seen that sort of trend build up is it's been sort of trying to anticipate the Fed and I think where it's going to be interesting is you know if things change in a direction that's much larger than what we've seen. So I think that's what my take on it is the market is always to get trying to get ahead of it figure it out. Um the market has already decided that we're getting cuts. The biggest shocks are going to be if we get a lot more cuts or if we don't get enough. Um, so I think those are the sort of interesting points for me. That's why this next cut is so interesting because if we go for 50, that's kind of like, well, that's what we expected or what people wanted. Uh, but if we get nothing, that could be huge, right? Um, so I think, you know, that's an interesting space, but the catalyst of recent jobs numbers has definitely kind of baked that in. Um, so we'll have to see, but I I do agree with you. It's been teetering around and just, you know, seems like something exciting will happen there, but I just don't know when that's going to happen. >> No, I I completely agree. And let's not forget what happened last time they caught actually bond yields went up. So, I mean, you never know exactly what uh what >> I know cuz there's some shorts. I mean people do have shorts and they think you know you have investors who because I think they're thinking more in terms of trajectory but if people have already shorted I mean if the yields have already gone down you know they could actually go up because somebody said well it's not enough right so that that's why it's kind of an interesting >> conundrum like because futures predict the future rates not >> today's rates right so >> true >> yeah so yeah >> well anyways uh later this month we will know exactly what the Fed decides and um yeah it'll be as you say super interesting. My own trend barometer oddly enough or I shouldn't say oddly enough but it is a little bit on the weak side still has been for a long time but that is because it uses some shorter term uh look back periods and and and we know that short-term models this year are probably the ones on a vol adjusted basis that are struggling the most. So let's stay with that. So the beta 50 index um as of this would be as of the 8th of September since we're recording one day early this week. Um beta 50 is up 91 basis points but down only now 1.93% for the year. So that's a good comeback. Stocken CT index up about 1% uh for the month down now only 5 12% for the year. Stocken trend up one and a quarter down 6 and a half% for the year. And the short-term traders index, nice to see it's having a good month, up 66 and down just shy of 6% so far this year. Of course, compared to traditional markets, MCI world up 90 basis points so far as of yesterday, up 15.1% uh this year having a great year actually. The uh US aggregate bond index up 96 basis points up almost 6% for the year. So not a bad year for bonds uh in that space. And then the S&P 500 total return up 86 basis points up 11.74% so far this year. So you know not a bad year uh all around. Now, we're going to dive into these topics um that I talked about and as we talked or as I also mentioned um it's great to have you on the show, Katie, because um to to be able to take these deep dives with you, not just on your own papers, but also when other papers come out um is is really wonderful. So, so thank you for doing that and for spending time getting into it. Now, trend following has a reputation of being kind of the simplest strategy in the hedge fund world. You buy strength, you sell weakness, you cut losses, you let winners run. So how different can managers really be? Well, the team over at ManHL has looked into this topic in a recent paper called the dynamics of dispersion. Not because uh of luck, but because of design choices, how fast you chase trends, which market you include, whether you tilt towards bonds or FX, or whether you sneak in a little bit of carry. So dispersion isn't random. uh it is uh the fingerprint of each system um and as I mentioned it is um you know a design choice. So let's dive in. I'm going to let you guide through uh the paper. I'll follow up with a few observations maybe some questions but let's start out with describing uh the paper from from the beginning so to So, I was really excited about this paper because I've actually done some research very similar over the past and we can talk about that later. But one of the key nice aspects of the paper is that they look at four different dimensions of trend. So, different things that you might decide to include in your program and how does that vary over time and how does that impact sort of dispersion. And this is really important for investors because if you look at any given year, um you can have quite a bit of differences between how one manager performs and another based on idiosyncratic and different themes in the markets. And so I think investors struggle with that sometimes because markets are very idiosyncratic. So, it can be a year where it was best just to tie your hands and be slow or it could be a year where um and we'll talk about Quanico's paper later that you happen to be in oil and gas and exotic contracts and they went crazy. Um so that creates a lot of differences which can make things difficult uh for investors to understand sort of why is this manager up and this manager is not. Um, and I think, you know, this is kind of one of the topics that I've spent a lot of time researching because explaining that dispersion helps investors understand the strategies and the pros and the cons. So, what they do look at and they use four key dimensions. Um, they look at speed. So, do you go faster or slower? Um, they also look at allocation. Do you tilt to certain asset classes? And we can think of some simple examples like CTA in the ETF space this year has you know does not use equities and that's been positive for them over the last especially this year. Um and you'll see that dispersion right then there's another they also talk about whether or not they include alternative markets. So additional markets that are perhaps less liquid and maybe less easy to access. And finally they add carry or no carry. Um and Carrie as you know has about a 30 40% correlation with trend. So you know it's another thing you might add into your basket to smooth performance over different horizons. Um so they take these four aspects and they explain how given this variation they can get a much wider range of possibilities of return just like the SG trend index or SGCTA index has over time the top manager and the bottom manager can be quite far apart. um even in an environment where you think trend should be the same. Um there were um one of the things I really liked in this paper is they do focus on crisis alpha. Um I know that's something we we often talk about and they look at which strategies um perform the best during crisis periods versus others. And the thing that stuck out to me is something that I have long known and seen in the literature of for this strategy is that being faster is actually really helpful for really bad environments. And the reason I'm I point this out is that a lot of focus has been on sort of slower strategies like replication and you know long-term uh kind of following the index slow strategies and those have actually worked much better in very very recent periods. Um, but if you look through a history of the CTA index and how the industry's changed, we did this really neat paper called CTA style evolution a couple years ago where we matched different different styles and tried to replicate and this is why you know replicating an index like this is hard, right? Um and what you see is that managers have changed over time. Like today, uh you have a lot more products that I think are on the slow side. And so I think if people are using those strategies for a crisis period, uh they may be a little bit less get a little bit less juice from those strategies based on this analysis and that's something you know I have believed in for quite some time. So just just to point one thing out actually also as you describe the index and some of these challenges. I I I did notice that they put uh on page three that they noticed for example that in 2009 just one constituent stood out with a doubledigit positive territory and the rest was kind of more flat to negative which of course creates dispersion but you have to think you have to know why that you know what it's made up of. And then they point out um that in 2014 I think it was um there were two constituents that delivered more like a zero return while everyone else was doing really great and it turned out that these two outliers um I think were like discretionary managers and they were removed from the index the year after. So, so I know a lot of people might use the when they see the numbers as an argument for saying, "Oh, yes, that's why you should go with replication because then you don't have all this manager risk etc etc." Well, you just need to understand where the dispersion come from and maybe uh not all managers are equal even in an index of relatively few funds. So, you need to really understand the these numbers before you just say, "Oh, that's a big dispersion." I mean, >> and you're right that the challenge of these type of indices are they're not directly investable, right? So, they represent a hypothetical version of if you were a CT investor from this year to this year, what you might have had because those are the larger ones that are included. And so they're just a historical study of different manager returns. Whereas you know today's managers are not the same as you know 2003 managers. And that's why I like that paper because we actually tracked style change style drift within the industry. And you know there can be good reasons for that too right like innovation like coming up with new strategies using machine learning like so my view on the index is you can't get the index today. So you don't buy 2007's CTA, you buy 2025 CTA. So that's why studies like this are so helpful because if you are thinking about crisis performance, you can think like what aspects do I want in a manager uh so that I can be ready for that environment. um you need to think that way because you don't when you invest in a manager today, you don't get the CTA index uh behavior because they're not the same managers as they were. So, it's still a helpful index >> because it was something that someone could have invested in. Um, and that's why, you know, sometimes you'll see criticism for, you know, analysis like this, but this is the only way, the way that man did this is, you know, kind of create a bunch of different trend managers as hypotheticals and aggregate them over time and examine sort of behavioral differences in different environments to compare sort of what could have happened without sort of cherrypicking the best version. Um, which is, you know, what people often do with a back test, right? >> Yeah. Another thing that I just want to ask you before we um continue down your your little story here is how much do you think just simple difference in volatility of the underlying managers? How much do does that play um in an index like this? I I don't remember if they're all more or less the same V or whether there are some real differences between them, but over the years for sure there would have been uh differences I I imagine. >> Yeah. And volatility does matter. I mean it makes sense, right? You can think about oh I love to think of this analysis and we can talk about it later but imagine each of these managers as a random variable. They each have a volatility and mathematically you can think about what the range of differences is going to be. It's going to be affected by the volatility and it's going to be affected by their underlying correlations. Um and so if you imagine people are strategies are changing and correlations are changing and volatilities are changing or different you know th those just naturally mathematically derive some differences in returns um over long time horizons you can actually estimate those and and provide theoretical foundations for that. >> Yeah cool all right well let I don't want to stop you so continue down the path of this paper please. Yeah. So the last thing I mean so there were two things that I thought was interesting in the paper. First you know focusing on showing that faster trend um you also saw that you know faster trend was more crisis alva creative. Um they also demonstrate some outperformance of alternative markets. Um I I think that you know depending on the the time horizon there is some you know question about >> about that but Quanukica really sort of related that and did a really good job talking about that a little later so we can talk about that as well >> but you know there's definitely you know correlation benefits and some interesting attributes of alternative markets that have been desirable and you can see that in this analysis as well. So that that I thought was also interesting. I noticed that uh again our friend Andrew Beer, he made a little comment on LinkedIn the other day when when this art this paper came out from man and he wrote something like my only quipple is that the model numbers seem off well by a lot over 25 years the average sharp ratio uh seems to be around 75. The awkward reality is that the actual sharp ratio of the SG trend index is 36. Is he on to something there or is it just part of this um difficulty in in in replicating the index? >> So if you think about you know when you replic when you use a basket of trend strategies you know a lot of them may be longer term and as I said before some of the managers were discretionary you pointed out some of them were shorter term so the index is not something that's easy to replicate in point in time. So my view is that I'm not surprised that sharp ratios are different because it's a back test, right? So in that case, if I was thinking about replicating the index, you could do it in lots of different ways. And Alex Graaserman and I did this a little bit in our book and we can talk about that later, but you know, you can do a basket of different window lengths and, you know, average them over a period of time. But, you know, we also know today that longer term windows have done better during this history. So if you have more longerterm windows than shorter term windows and you're a little different from the index is not surprising to me that the back test sharp is going to be different. So I think it's more about that's why I focus more on the relative differences um and also like conditional environment differences as something interesting to look at uh as opposed to trying to sort of you know match the actual sharp ratio because I cannot like replicate each of these CTA's point in time over 25 years. So >> true. >> Yeah. >> Okay. No, that makes makes perfect sense. And now you mentioned you brought up uh Alex's name and of course you have uh you know the leading book on on trend following. I think you wrote a lot about return dispersion I think in the book. Um can you take us maybe back to and compare some of what man is doing now and compare that to the work you did uh as well? >> Yes. Oh my gosh. Like we were obsessed with return dispersion. um Alex and I um and we spent a lot of time thinking about it and we have this one chapter and I love it because it's chapter 11. >> Oh, >> so declaring chapter 11 like that's like >> anytime a client or someone talks to me I'm like you got to think about chapter 11 or read chapter 11. Um it is an entire chapter of our book that's just dedicated to analysis of return dispersion. And what's fun about this is this is what led us to and I'll talk about it a little later some of our CTA benchmarking analysis and and style analysis. But what we do in this particular book in chapter 11 um and this is fun to see that man has actually done some of the similar things in their paper. Um we start off by looking at three different aspects of a strategy style. Um ours was equity bias or not. you know, think they added carry or not. Um, we also added, you know, sort of allocation weights. Was it equal risk or market capacity oriented? So, do you focus on having equal risk or do you like kind of focus on just the most liquid markets? >> Um, and then we also talk about speed. So, each of these um dimensions we examine and then we talk about how equal risk outperformed historically in our analysis. Again, a similar analysis of that approach. um in the man article. So that was kind of fun to see that they kind of use some similar methodologies that we did although they added also alternative markets which is which was fun to see. Um we then moved and looked at you know how do you position size and the different types of signals which is something that man didn't discuss. So do you use channel breakouts or moving averages? And we even had these sort of random signal studies too just to understand sort of how much of that is driven by the sort of allocation and how much of it is driven by signals themselves. So kind of went a little deeper into some signal analysis. >> What do you remember what you found? Because obviously position sizing is something that comes up often in in my conversations. Um, >> so we weren't looking at, you know, trying to performance. We were looking at sort of how much dispersion you get cuz that wasn't the goal of this particular chapter. The goal of this chapter was to demonstrate how tilting asset classes, changing position sizing approaches, how that naturally leads to dispersion, >> okay? >> And how much dispersion. And so I think our point was more you know look at these different aspects that all are kind of similar but a little different that create return dispersion over history. Uh we did one thing that did stick out we studied things like you know market volatility environments too. Do you have more dispersion in a 2008 versus others? And we did find that 2008 given the size of the moves regardless of you know which type of methodologies you use you had a bigger range just because you had much bigger moves um in that year and this is an old analysis but you know it kind of makes sense. You saw the same in 2022 as well. >> Everyone was up but the range of up varied a lot. >> Okay. And um I think there were some other things you looked into in your paper as far as I recall. >> Yeah. So I have two other aspects that was kind of fun and I I'll talk about those two and one of them is like kind of nerdy so I kind of like that one. Um >> so we also tried to think about it from the perspective of an investor in this chapter where we looked at sort of how much return dispersion you had as you add more managers together. And this was important because it's a hypothetical you know my original background was a CTA allocator many many years ago and so I was very concerned about this question right so as you add more managers together how much return dispersion sort of dissipates um and what you see is that you adding at least two maybe three together gets you pretty far in terms of adjusting some of that return dispersion we didn't go into the specifics of which manager we kind of used a random sample analys is we randomly sampled them and then aggregated that over time. But this is helpful because the number that's kind of sticks out is 3 to 4%. So the return dispersion actually went down quite a bit, but it it's not, you know, drastically different from like return dispersion you might find in a replication approach as well. So replication approach, we wrote a paper on this last year or earlier this year on sort of replicating and there's usually 500 to 600 basis points of slippage expected. So that's normal for an index that's volatile um that isn't, you know, directly investable. >> And so it kind of shows there's a lot of ways where investors can mix different things to try and reduce some of that return dispersion. But guess what? You always have it, right? You can't just buy the index in the space. So I think this analysis helps investors understand even if you have three managers, you still have, you know, you could still have some return dispersion that that is for the whole aggregate portfolio versus the index. And that's important if you're an investor with two or three CTAs and you think, oh, I'm 400 basis points or 500 basis points behind the index. That's that's not that can happen by chance with two random variables that have those correlations. >> Yeah. Well, we've certainly seen tracking errors between uh even those who try to replicate a uh you know a benchmark. Uh so for sure now I don't know if this is in the paper but so maybe it's just more your um um insights that I I'm looking for. Um, of these four kind of um, areas that drives dispersion, are there any one of those four you would say are more important sort of the main driver of dispersion? >> So, we kind of showed that they can vary over time, right? So, in an in an individual year, one or the other could be very different. So asset allocation for example, if it's a year like 2019 where the only trend was fixed income, you know, that could be the driver of that year's main dispersion. We did see some differences in terms of risk allocation over longtime horizons, but that was a relative performance choice. I think any of these particular ones will kind of stick out in a given year depending on what's happening. And I think that's why it's hard because you can't just pick one like oh well if I you know think about this one aspect like alternative markets so that's why the quantico paper is useful well if I just have alternative markets I don't have any return dispersion it's better that's not true right so it really depends on you know each market environment is different and over time each of these factors can contribute to that and that's why they look at so many um because each one of them have a different environment where hey it wasn't good to be fast this year or man if I had a tilt. I mean I think the you know example in the ETF space is even bigger where you know if you'd avoid equities this year like that was fantastic right um >> so it it really can create any of them can do it and that's why we study so many right >> yeah and plus it makes it actually pretty difficult for investors as you say to uh um you know to decide but but very helpful to know also that you get a lot of that dispersion disappearing if you just allocate two or three managers. Uh, which kind of makes sense. Um, we'll come to the Quantica paper shortly because I actually do find it to be a really great paper and the first paper that I've seen that dives deep into this discussion we've had on and off about alternative markets and so on and so forth. Um, but there are other papers out there. Um, many of them you've been uh writing about. Let's talk a little bit about some of the other things u that you've worked on and and that you found that you think is relevant for for this particular conversation. >> So this is you know what's really interesting is this obsession with return dispersion caused us to do some very interesting research. Um it's over 10 years ago that we started looking at this but basically we when we wrote our book Alex and I we realized like we're very irritated that there's no benchmark. So we started sort of explaining how you could design a benchmark and then we created style factors similar to equities. So in if you're thinking about equity world right it's so great because you have the benchmark and then you have your style factors and then you do your regression and ooh you have too much small cap you know and you you just know like what drives relative performance much easier. So our goal was at least to like tackle that type of thing. Um and what we did was design and I've built some other factors and we track them um at Alpha Simplex too because when we look at a given year like you asked before there's never it's very idiosyncratic. One year it's speed that matters another year smaller markets correlated markets. Um and so by tracking those factors you can disentangle the aggregate performance of the benchmark versus these factors. So you can kind of try and explain some of the relative dispersion across different managers by looking at their style factor loadings. Um so that was you know it's a very interesting area. Um it's something I continue to look at and and use as a tool when I'm examining performance over over different horizons and looking at different managers trying to understand thematically like why they have differences. You know, it's funny um as you're talking and I don't remember the year, but I do remember I think it's one of the first live talks I've seen you do. It was an event in London at one of these fancy uh hotels. Um uh and I even forget the the guy who who ran that um series of of events. Um >> Battle of the Quants. I remember that. >> Battle of the Quans. That's exactly what it was. >> It was you, me and London. I remember that. >> Exactly. And uh I mean this is a long time ago and I remember >> 15 years ago I think probably a long time ago. I I agree and it's um but it was interesting in in the way you kind of were able to describe uh describe different managers and and what they were um tilted towards in terms of uh different factors. Um so >> that's awesome. You were at the CGA style factors analysis the fir first paper on that. >> Yeah. Yeah. Absolutely. Had to learn about it of course. Yeah. Absolutely. What other papers have you been um or have you delved into other papers uh >> for returning dispersion? Yeah. >> Yeah. Or similar things. >> One that's really not as well known, but it is so nerdy and so cool. So, I want to talk about that one. >> Oh, yeah. By all means, >> it's it's called quantifying turbulence in CTAs. And what this paper does and this is going to get a little nerdy and I hope that's okay but basically we use something called the malhanobus distance. So you look at sort of the distance between different you know random variables and you try to measure how much turbulence so movement across a group of things. Um, it's really neat. It's used a lot in the research by Mark Critzman, who writes a lot of stuff for the FJ and stuff like that. But basically, you use the Malhanobis distance to talk about how much movement there is. And what's cool about it is it's a point in time measurement of correlate of of the movement because usually when we talk about correlation, we have to use a window of data. So you're kind of messing up like so if something big happens on one day you need like a year of information and you're kind of like averaging everything when you look at correlation. What this does is it looks at point in time differences and it actually measures an instantaneous effect of movement across a group of things. So, it's super neat and it's kind of you looking at it in in a distance metric and you kind of can here's where it gets even nerdier and I'm going to explain it. I'm sorry. >> Okay. Yeah. >> So, by taking this turbulence metric um you can decompose the movement across managers between something they call a magnitude surprise and a correlation surprise. So magnitude surprise is sort of like boom a big shift that is not a change in correlation >> right >> and correlation surprise is where your big shifts across the managers is is actually a um shift in their underlying behavior. >> Yeah. >> Okay. So magnitude is boom something happens and everything reacts. correlation is things are changing and like you know returns are moving and trends are different across the managers so they actually don't look as similar as they they normally do. So this paper is really cool and why I like it is you can actually do point in time analysis on a day and so we did this analysis for CTAs and if you look at magnitude surprises there's a history of them and we plot them there's a few of them that are really big and purely magnitude. So you can guess which ones they are. Brexit, you know, SVB, Black Friday, like >> Liberation Day. It's not in the data set, but I'm pretty sure it's a magnitude surprise. Um, yeah. So that's cool because we also showed that CTAs do not do as well on magnitude surprise days, >> right? >> Which you're not surprised by, I think. >> No. >> But that correlation surprise was more sort of an inflection point. So periods where you know trends are changing and what you do may create differences in performance as there sort of rotations in those trend signals and they kind of move around. So that is a good way to kind of think about it might be a good metric to understand you know what's driving different behavior across your returns and and understand that return dispersion. Is this driven by just a shock or is it driven by sort of actual changes in their their returns? So that's super nerdy. I told you. >> But it's really cool. Like the pictures are really neat because >> never heard about this kind of analysis. I'm sure there's a few of our listeners that are >> It's also fun to say the Malanobus distance like it like really sounds very >> sounds very >> geeky, right? >> Exactly. >> You know, >> now before we dive into the last paper, the Quantica paper, um let me ask you this. So we get all these kind of tools now, analysis tools, and people can get really uh detailed in trying to quote unquote explain differences between managers and between models, etc., etc. And for the purpose of people who want to invest uh in uh trend following and and again picking one manager is probably not the right way. Picking 10 is probably unnecessary as well. But my concern a little bit and I know it's kind of a weird thing to say that I'm concerned that we're almost giving too many tools and we there's a risk of investors who are not in the engine room um making assumptions from their own analysis that may not be true. They don't know what's going on in terms of potential changes in a manager and so on and so forth. other kind of simpler way to think about uh these things uh where people don't go too overboard in terms of the the deep dive. Do you know what I mean? >> Yeah. I mean I think my view is you know you start simple. When I talk to investors in the space, I say pick two or three, at least two or three managers that you like, that you think are a little different, you know, and you can do some simple analysis of their returns. >> But the the real truth is I do believe CTAs is one of the few strategies that has nice accretive properties compared to equities. So I try to think about the big picture is that there are few tools out there in the alternative space that don't just have hidden equity beta. Um and so you know having something different is good. So start simple. Don't over analyze. Don't get analysis paralysis, right? >> Um and you know, kind of combine things that are different, right? I mean, and you know, that goes for things like even replication, right? Like so maybe you can have some of that and you can have >> a manager that's going to be faster because you think, I like the crisis alpha and that one's slow, so let's combine them. um or you have another manager that's diversified that has you know but the point is is really sort of not over because I think if you get too complex you have too high expectations of what your results will be and that as we know what you can never predict right so every year is different >> and every style factor that you thought you picked there's no magic factor um they'll change so it's about diversification which is you know kind of the simple answer for me that's what I would say. >> Okay. Well, let's dive in um to the last uh paper, the Quantica paper. What I really liked about this paper um and and all the papers they do is pretty detailed and they tackle an issue that um I think we've talked about over the years on the podcast and it deals specifically about kind of this battle between managers who stays with very liquid developed market portfolios and those who let's call it 10 years ago started to uh move into alternative markets. some of them maybe with the claim that they were trending better so there would be better opportunities certainly for a while performance was very competitive uh without a doubt now we've had a couple of years where it's kind of been the reverse um so it's an interesting it's really an interesting topic uh and I like the way they kind of uh very uh methodical in terms of the way they try and see it from different uh angles So, why don't you again um put your um teacher hat on here and um and tell us a little bit about what what they were trying to do and some of their findings and whether you agree or not agree. >> Um I really liked how they set it up. So, they set it up as having sort of a traditional market portfolio with 50 markets and an alternative market portfolio with 120 across a wide range of sectors, some of which traditionals don't invest in. things like credit, gas and power, um more exotic as and you know livestock contracts. So kind of that like you know hodgepodge of like stuff that's a little bit less easy to access for sort of futures markets. >> And what's fun about this paper is you know it goes back to the man paper. What's the essential difference here? like one of them has different sectors and different sector compositions and during different periods in time those different sectors may contribute poorly or not to trend following and what they do show which I think will really help a lot of clients out there that have both of these things. Um I think there was a sense that you know alt markets there is a case that you know correlation being lower has benefits over longer time horizons but given the extreme outperformance in the recent period um it could have maybe gotten people's expectations a little high um and then when your expectations are high and it doesn't work for the last 3 years um people start questioning the thesis and what this paper did which is so nice is it just kind of said Hey, you know there has been an extreme outperformance in some certain sectors and it happens to be during this time horizon alt markets were in those sectors for example gas and power um credit some things that worked really well and I really resonate with this narrative because I remember many years ago there was big discussions about metals should not be traded in trend following. I've looked at the back test. They're terrible. And you can't think that way as a trend follower. You have to think like every asset is an opportunity for trend. >> I just don't know which one is going to work, right? And so I think, you know, if you imagine any particular asset that maybe never trended sometimes can start to trend. So like palladium or platinum used to not be trending. Now they trended a lot recently. Gold has been a huge trend recently. nickel like and all these things were things that you would have not in you would have like had you know questions about 15 years ago like who ever cares about copper but you know now we all care about copper so they kind of highlighted this particular uh theme that actually showed up to be a very strong performer in alt markets that's not in traditional markets and I think that was helpful because it can kind of explain to a client who may be thinking about alt markets is not like a is it broken or not kind of question. It's like well this was a very favorable environment for these assets and recently those assets have been very rangebound um after the big shock of co. So I think that's very interesting and it it it was a a very well done analysis because it explained that asset class tilt and why that was important in relative performance over the recent periods um which I think will be helpful for those that that really think about alt markets. You mentioned that there was this one particular sector that really yeah I think it was like 40 or 50% of the outperformance came from uh gas and power and of course we know that uh at least also from memory I think some of it was also related to the Ukraine situation where these markets really took off and so on and so forth. Well, there's an example like they may not have trended it at all and then suddenly you have the situation in the world where that asset turns a lot and >> you know you you can't replicate that it's going to be what's that asset class going to be now I don't I don't know yet and that's what trend waits until it finds what's the next >> big trend. Yeah. I I guess my question was how much can we extract from the fact that we've only got 10 years worth of data where there are really are some alternative um market managers around. So the the period we're comparing is quite short in in in many ways. uh since you are the since you're the quant of the two of us uh I mean how how much does that play into you in the back of your mind saying yeah I mean it's a great for this period of time but do I really think that this will be any kind of guide um for the for the future >> well this is why I like the paper so much because they actually address that issue right they said let's look at sharp per market on each individual trend instead of thinking like I know which trends or this period was better. They kind of explain relative adjustments for that period and then they say you know if you think about trend more from a theoretical perspective right you have assets in your trend portfolio the P&L streams have a certain sharp ratio the sharp ratio of those P&L streams and they plotted it as well. What's the correlation over time of a trend and and Alex and I did this even in our trend book. So you think about each asset is a unit of trend right and the P&L streams have a specific sharp you need to have an assumption for that then when you build a portfolio of those assets the correlations matter right and so their argument and this is a mathematical argument is that on aggregate the more lowly correlated P&L streams that you can add the little bit higher your sharp is and I think there is definitely you know some mathematical credence to that argument. I think where they they didn't clarify this a ton, but they do address it is, you know, not all of these assets are exactly the same because they're not as tradable, right? So, for example, trading, you know, exotic power contracts may have some more cost associated with it. So, that reduces what seems to be a higher sharp closer to the traditional sharp, right? Um, but their argument is that they're similar or maybe a little bit, you know, so they they don't argue that alt markets trend better per se. They argue that they trend, but you know, maybe if they trend better, they cost some too. So the point is they do provide some diversification. So that's a mathematical argument that when you combine a lot of things that are different, you can sometimes increase your overall risk adjusted performance. And so, you know, I think that that was very well done because yeah, it's hard like you said, it's really hard to statistically prove those, but there are mathematical foundations to like diversification improves risk adjusted sharp. The question is you need to figure out what the sharp inputs are and the correlation inputs are. So, that's hard. >> Absolut absolutely. And there's one other thing I think they do mention it from memory. I think they do mention it but I do think it's relevant and that is of course that >> because of this relative outperformance that the alternative market managers did for a few years a lot of money went into those actually and I wonder if the last couple of years worth of underperformance to some extent not saying all of it but to some extent is also influenced by the fact that now we actually have maybe too much money tracing these less liquid markets. um adding even more so to the transaction cost etc. Now I don't have the answer but I think it's something people should be thinking about. >> I think that would be a great analysis. I would love to see that because crowding and I've always been so excited by crowding is an interesting factor. But what's also it's it's it's a two-edged sword, right? Because on one side trend falling still works in some of the most crowded markets we know. But you know, you start to wonder a little bit when you get the things with a little bit more capacity constraints. Um, and so I think you know what what one could argue is maybe when you look at a back test when less people were in those you get a more normal sharp >> uh for those positions. So it's just really about diversification and less about like these are better or worse. >> Um, and that's kind of the read I get from their paper. But I think a more detailed analysis of crowding and changes in those markets. Even a case study of one market could be super interesting. So I'd love to see someone, you know, actually do that analysis and work so that we could have a better sense because that question came in my head as well. >> Yeah. Okay, cool. Anyways, the paper is the uh Q3 uh 2025 paper from Quantica that you can find on their website. So definitely go and check it out for sure. So we've talked a lot about um papers. Um, is there anything that you've been thinking about uh writing about maybe for our next conversation, Katie? Anything that has piqued your interest lately that you think, "Yeah, maybe I should do a paper on this." >> Yeah, I've been thinking a lot about macro and systematic macro and some interesting connections between the two strategies, both systematic macro and trend. So, you know, we I'm hoping to have a paper. We'll see. I'm working on some stuff with some colleagues, but um I'm very interested in macro. That's something that I've been kind of fascinated by and it's done a lot better during liberation day. So, it's kind of some of the themes like how much of economic trends versus, you know, technical trends. So, that's kind of a neat area of thought and research right now. >> Yeah. No, I think that sounds very relevant and very interesting. Absolutely. Anything else you want to sort of close out with today before we wrap up um our conversation? Katie? >> No, I think you know return dispersion is such a fun topic. Um hopefully one day I'll come back with another paper on style factors and we can talk about that as well. Um we talked about turbulence. I mean we really got the gamut. We had return dispersion in our topics on return dispersion. So that's cool. >> Yeah, >> definitely a deep dive for sure. Well, as I said, I can't um thank you enough. We really appreciate all the uh the work that you put into uh these conversations. And of course, if the audience listening to this feels the same, why don't you go to your favorite podcast platform and leave a very nice rating and review for Katie? Um it really does help uh more people find the show and uh be able to listen to these uh nuggets from her. Um next week I'll be joined by Rob Carver. So uh of course another uh fan favorite. So, if you have any questions for him, uh, feel free to suggest some topics or some direct questions and I'll try and do my best to make sure we get them discussed. As always, the email address is info@ toptradersonplot.com. Um, so that will be um, for next week. From today, Katie and I would like to say thank you for listening and we look forward to be back with you next week. And until next time, as usual, take care of yourself and take care of each other. Thanks for listening to Top 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 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 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. We'll see you next time on Top Traders Unplugged. [Music]
Why Trend Following Never Looks the Same Twice | Systematic Investor | Ep.365
Summary
Transcript
Imagine spending an hour with the world's greatest traders. Imagine learning from their experiences, their successes, and their failures. Imagine no more. Welcome to Top Traders Unplugged, 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, 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 anything about future performance. Also understand that there's a significant risk of financial loss with all investment strategies and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions. Here's your host, veteran hedge fund manager Neil's Castro Larson. Welcome or welcome back to this week's edition of the systematic investor series with Katie Kaminsky and I Neils Castro Lassen where each week we take the pulse of the global market through the lens of a rule space investor. Katie, it is wonderful to have you back this week. How are you doing? What are what are things going on where you are? >> Things are good. Fall's in full force here. You know, kind of back to school. I'm happy about that. And you know, everybody's focused on the Fed and that's about it. I don't know much else. That's true. You know, um, in some ways the summer kind of seemed pretty quiet in terms of news that really moved the markets, but perhaps we're just getting used to this narrative driven uh news flow, but you never know. The autumn may still present some surprises and maybe even some strong trends. So, uh, we can always hope. Now, what's great about today's conversation um is that we've got some new papers from our friends at Mang Group as well as Quantica um on some of maybe the less discussed areas, if I can put it that way, within the CTA and trend following space. Um but also a few other things that will come up um so it's really relevant to uh to be talking to you today and tackling this. Um, and also, um, before we dive into all of that, I'm always curious to know what's been on your radar, if anything, other than the markets. >> Well, I think the markets I'm just waiting for. I mean, and I got several calls yesterday, both from press and clients. Everybody wants to know what is going to happen in fixed income in the US, but if you look at the trend signals, they haven't moved much. So, I'm kind of thinking that could be a potential catalyst. And it's something that, you know, there's so many themes like inflation, independence, like >> the Fed, like changing like allegations. I mean, it's there's a lot of drama, but not a lot of movement yet. So, that's why I think that's interesting from a trend perspective. >> Yeah. No, very true. Very true. So, on my radar, I put three things. Uh, one is that um, and it is the first one is a little bit related to fixed income and bonds is just the European political chaos we have at the moment. I mean the UK and France um you know it's kind of like a basket case really uh and some of the polls even in the UK now suggest that Nigel Farage party would get the most votes uh if there was an election right now. I mean that's just uh extraordinary to think u that you have like a real contender uh between the two main parties and then of course France uh another prime minister coming in and and uh some of the ways that they describe the financial situation of France obviously should you know lead to a lot of uncertainty about that um and of course uh if there's anything bond yields are very good at reacting to it's uncertainty especially about budgets and and these kind of things so it'll interesting to watch as well as of course what happens in the US at the moment. The next thing I had on my radar, which is a kind of a new one, and this is not an endorsement of anything like that, but last night I did watch uh the uh the Apple event, and I was kind of blown away by one of the products, and this is this new AirPod 3 where essentially, if what we were told yesterday is uh is true, you're now going to have been be able to have a live conversation with someone that doesn't speak your own language, and it'll translate simultaneously ly between the languages if they're both wearing these um AirPods 3 which I find absolutely extraordinary but I'm also a little bit concerned what about all those people who have a job right what if they other thing what if it gets wrong like talking to somebody and like somebody can hack in and change your conversation I don't know I was like wow that's so cool that is really amazing >> I didn't think about that but I did think about all the translators that are hired by the European Parliament or the UN to sit there and translate all this stuff. I mean, they may not have that much of many things to do if everybody's just wearing these uh new AirPods. >> Maybe it'll be like, you know, TV and stuff where your kids go, you actually like had to turn the channel by standing up, you know, and they'll be like, you did and you dialed the phone like that and be like, you had to translate the language. Why don't you just put on your AirPods? >> Yeah. No, I know. It's a crazy new world for sure. And then the final thing, something I picked up from our friend Andrew's uh LinkedIn feed just now. Um because there is apparently a a Q2 2025 brand awareness ranking. I've never heard about this before. Apparently um there is a ranking of how well hedge funds uh do in terms of brand awareness and and they uh distinguish between how well they do among consultants um and how how well they do among asset owners. However we define that um and I will say it's nice to see that some of our friends um are established on the list that the among consultants man Group um has the top rank um Crabel actually comes second that's um a little bit of a surprise to me Winston comes fourth which is great aspect is on the list number 20 AQR number 19 that's a little bit surprising to me I thought awareness wise with all the research they do they would be higher up Um and then there are um in terms of the European based managers you have CFM doing well top 10 systematica top 10 and even Andrew's firm DBI uh a ninth place which is uh you know great great job on on that. Um I imagine it's all his appearances on top traders on block that helps them with all this awareness. I'm sure of course it is. Um among asset owners it's a little bit different. Um actually CFM is doing best in terms of the CTAs we know of. Winon also come in fourth on this one. A man groom a little bit lower number six. Um but yeah it's a fun list. Never seen it before. Um not sure how valuable it is but you know there's nothing bad in being known. uh among investors or consultants, I guess. Um sadly, I didn't see our firms on the list, neither of them. But there we are. We'll we'll stay under the radar uh even if this was on my radar uh this week. Anyway, let's uh elegantly jump to um more familiar waters, namely the trend following update. I mean, it has been a decent start to September after a couple of uh uh good positive months for CTAs and trend followers. Of course, equities, metals continue to do the heavy lifting, I suspect, supported by a few other commodities like sugar. Um, it's been a while since we've seen that. Perhaps even some of the oil products um are helping out. Um, livestock on the other hand is taking a little bit of a breather this month. And one thing I mentioned last week uh in the in the podcast is I'm not entirely sure if managers at the moment agree on whether to be long or short fixed income. It's funny you mentioned fixed income. Um, and I when I see the daily uh changes on on some of the mutual funds and ETFs um that managers um have um it really seems like fixed income plays quite a huge role, but they're not going in the same direction depending on whether bonds are up or bonds are down for the day. So kind of interesting. Always curious to you to hear your observations about um you know the month, the time we're in right now and uh what you see. >> Yeah. So starting with fixed income, it the signals have been very mixed and kind of weak and teetering around between should we be short, should we be long and we've seen a little bit more long signals in the US more recently. Uh UK was definitely a short signal. Um >> true >> and some of the European bonds. But what's interesting to me is if you know someone was was talking about we were talking about sofur yesterday and if you look at sofur sofur is at about 3.4%. Okay. And if you think about that if the rates right now in the US are 42545 you know the market is already decided what's happening. And what we're probably going to see a trend on is whether or not they get that right. Right. So I think why you haven't seen that sort of trend build up is it's been sort of trying to anticipate the Fed and I think where it's going to be interesting is you know if things change in a direction that's much larger than what we've seen. So I think that's what my take on it is the market is always to get trying to get ahead of it figure it out. Um the market has already decided that we're getting cuts. The biggest shocks are going to be if we get a lot more cuts or if we don't get enough. Um, so I think those are the sort of interesting points for me. That's why this next cut is so interesting because if we go for 50, that's kind of like, well, that's what we expected or what people wanted. Uh, but if we get nothing, that could be huge, right? Um, so I think, you know, that's an interesting space, but the catalyst of recent jobs numbers has definitely kind of baked that in. Um, so we'll have to see, but I I do agree with you. It's been teetering around and just, you know, seems like something exciting will happen there, but I just don't know when that's going to happen. >> No, I I completely agree. And let's not forget what happened last time they caught actually bond yields went up. So, I mean, you never know exactly what uh what >> I know cuz there's some shorts. I mean people do have shorts and they think you know you have investors who because I think they're thinking more in terms of trajectory but if people have already shorted I mean if the yields have already gone down you know they could actually go up because somebody said well it's not enough right so that that's why it's kind of an interesting >> conundrum like because futures predict the future rates not >> today's rates right so >> true >> yeah so yeah >> well anyways uh later this month we will know exactly what the Fed decides and um yeah it'll be as you say super interesting. My own trend barometer oddly enough or I shouldn't say oddly enough but it is a little bit on the weak side still has been for a long time but that is because it uses some shorter term uh look back periods and and and we know that short-term models this year are probably the ones on a vol adjusted basis that are struggling the most. So let's stay with that. So the beta 50 index um as of this would be as of the 8th of September since we're recording one day early this week. Um beta 50 is up 91 basis points but down only now 1.93% for the year. So that's a good comeback. Stocken CT index up about 1% uh for the month down now only 5 12% for the year. Stocken trend up one and a quarter down 6 and a half% for the year. And the short-term traders index, nice to see it's having a good month, up 66 and down just shy of 6% so far this year. Of course, compared to traditional markets, MCI world up 90 basis points so far as of yesterday, up 15.1% uh this year having a great year actually. The uh US aggregate bond index up 96 basis points up almost 6% for the year. So not a bad year for bonds uh in that space. And then the S&P 500 total return up 86 basis points up 11.74% so far this year. So you know not a bad year uh all around. Now, we're going to dive into these topics um that I talked about and as we talked or as I also mentioned um it's great to have you on the show, Katie, because um to to be able to take these deep dives with you, not just on your own papers, but also when other papers come out um is is really wonderful. So, so thank you for doing that and for spending time getting into it. Now, trend following has a reputation of being kind of the simplest strategy in the hedge fund world. You buy strength, you sell weakness, you cut losses, you let winners run. So how different can managers really be? Well, the team over at ManHL has looked into this topic in a recent paper called the dynamics of dispersion. Not because uh of luck, but because of design choices, how fast you chase trends, which market you include, whether you tilt towards bonds or FX, or whether you sneak in a little bit of carry. So dispersion isn't random. uh it is uh the fingerprint of each system um and as I mentioned it is um you know a design choice. So let's dive in. I'm going to let you guide through uh the paper. I'll follow up with a few observations maybe some questions but let's start out with describing uh the paper from from the beginning so to So, I was really excited about this paper because I've actually done some research very similar over the past and we can talk about that later. But one of the key nice aspects of the paper is that they look at four different dimensions of trend. So, different things that you might decide to include in your program and how does that vary over time and how does that impact sort of dispersion. And this is really important for investors because if you look at any given year, um you can have quite a bit of differences between how one manager performs and another based on idiosyncratic and different themes in the markets. And so I think investors struggle with that sometimes because markets are very idiosyncratic. So, it can be a year where it was best just to tie your hands and be slow or it could be a year where um and we'll talk about Quanico's paper later that you happen to be in oil and gas and exotic contracts and they went crazy. Um so that creates a lot of differences which can make things difficult uh for investors to understand sort of why is this manager up and this manager is not. Um, and I think, you know, this is kind of one of the topics that I've spent a lot of time researching because explaining that dispersion helps investors understand the strategies and the pros and the cons. So, what they do look at and they use four key dimensions. Um, they look at speed. So, do you go faster or slower? Um, they also look at allocation. Do you tilt to certain asset classes? And we can think of some simple examples like CTA in the ETF space this year has you know does not use equities and that's been positive for them over the last especially this year. Um and you'll see that dispersion right then there's another they also talk about whether or not they include alternative markets. So additional markets that are perhaps less liquid and maybe less easy to access. And finally they add carry or no carry. Um and Carrie as you know has about a 30 40% correlation with trend. So you know it's another thing you might add into your basket to smooth performance over different horizons. Um so they take these four aspects and they explain how given this variation they can get a much wider range of possibilities of return just like the SG trend index or SGCTA index has over time the top manager and the bottom manager can be quite far apart. um even in an environment where you think trend should be the same. Um there were um one of the things I really liked in this paper is they do focus on crisis alpha. Um I know that's something we we often talk about and they look at which strategies um perform the best during crisis periods versus others. And the thing that stuck out to me is something that I have long known and seen in the literature of for this strategy is that being faster is actually really helpful for really bad environments. And the reason I'm I point this out is that a lot of focus has been on sort of slower strategies like replication and you know long-term uh kind of following the index slow strategies and those have actually worked much better in very very recent periods. Um, but if you look through a history of the CTA index and how the industry's changed, we did this really neat paper called CTA style evolution a couple years ago where we matched different different styles and tried to replicate and this is why you know replicating an index like this is hard, right? Um and what you see is that managers have changed over time. Like today, uh you have a lot more products that I think are on the slow side. And so I think if people are using those strategies for a crisis period, uh they may be a little bit less get a little bit less juice from those strategies based on this analysis and that's something you know I have believed in for quite some time. So just just to point one thing out actually also as you describe the index and some of these challenges. I I I did notice that they put uh on page three that they noticed for example that in 2009 just one constituent stood out with a doubledigit positive territory and the rest was kind of more flat to negative which of course creates dispersion but you have to think you have to know why that you know what it's made up of. And then they point out um that in 2014 I think it was um there were two constituents that delivered more like a zero return while everyone else was doing really great and it turned out that these two outliers um I think were like discretionary managers and they were removed from the index the year after. So, so I know a lot of people might use the when they see the numbers as an argument for saying, "Oh, yes, that's why you should go with replication because then you don't have all this manager risk etc etc." Well, you just need to understand where the dispersion come from and maybe uh not all managers are equal even in an index of relatively few funds. So, you need to really understand the these numbers before you just say, "Oh, that's a big dispersion." I mean, >> and you're right that the challenge of these type of indices are they're not directly investable, right? So, they represent a hypothetical version of if you were a CT investor from this year to this year, what you might have had because those are the larger ones that are included. And so they're just a historical study of different manager returns. Whereas you know today's managers are not the same as you know 2003 managers. And that's why I like that paper because we actually tracked style change style drift within the industry. And you know there can be good reasons for that too right like innovation like coming up with new strategies using machine learning like so my view on the index is you can't get the index today. So you don't buy 2007's CTA, you buy 2025 CTA. So that's why studies like this are so helpful because if you are thinking about crisis performance, you can think like what aspects do I want in a manager uh so that I can be ready for that environment. um you need to think that way because you don't when you invest in a manager today, you don't get the CTA index uh behavior because they're not the same managers as they were. So, it's still a helpful index >> because it was something that someone could have invested in. Um, and that's why, you know, sometimes you'll see criticism for, you know, analysis like this, but this is the only way, the way that man did this is, you know, kind of create a bunch of different trend managers as hypotheticals and aggregate them over time and examine sort of behavioral differences in different environments to compare sort of what could have happened without sort of cherrypicking the best version. Um, which is, you know, what people often do with a back test, right? >> Yeah. Another thing that I just want to ask you before we um continue down your your little story here is how much do you think just simple difference in volatility of the underlying managers? How much do does that play um in an index like this? I I don't remember if they're all more or less the same V or whether there are some real differences between them, but over the years for sure there would have been uh differences I I imagine. >> Yeah. And volatility does matter. I mean it makes sense, right? You can think about oh I love to think of this analysis and we can talk about it later but imagine each of these managers as a random variable. They each have a volatility and mathematically you can think about what the range of differences is going to be. It's going to be affected by the volatility and it's going to be affected by their underlying correlations. Um and so if you imagine people are strategies are changing and correlations are changing and volatilities are changing or different you know th those just naturally mathematically derive some differences in returns um over long time horizons you can actually estimate those and and provide theoretical foundations for that. >> Yeah cool all right well let I don't want to stop you so continue down the path of this paper please. Yeah. So the last thing I mean so there were two things that I thought was interesting in the paper. First you know focusing on showing that faster trend um you also saw that you know faster trend was more crisis alva creative. Um they also demonstrate some outperformance of alternative markets. Um I I think that you know depending on the the time horizon there is some you know question about >> about that but Quanukica really sort of related that and did a really good job talking about that a little later so we can talk about that as well >> but you know there's definitely you know correlation benefits and some interesting attributes of alternative markets that have been desirable and you can see that in this analysis as well. So that that I thought was also interesting. I noticed that uh again our friend Andrew Beer, he made a little comment on LinkedIn the other day when when this art this paper came out from man and he wrote something like my only quipple is that the model numbers seem off well by a lot over 25 years the average sharp ratio uh seems to be around 75. The awkward reality is that the actual sharp ratio of the SG trend index is 36. Is he on to something there or is it just part of this um difficulty in in in replicating the index? >> So if you think about you know when you replic when you use a basket of trend strategies you know a lot of them may be longer term and as I said before some of the managers were discretionary you pointed out some of them were shorter term so the index is not something that's easy to replicate in point in time. So my view is that I'm not surprised that sharp ratios are different because it's a back test, right? So in that case, if I was thinking about replicating the index, you could do it in lots of different ways. And Alex Graaserman and I did this a little bit in our book and we can talk about that later, but you know, you can do a basket of different window lengths and, you know, average them over a period of time. But, you know, we also know today that longer term windows have done better during this history. So if you have more longerterm windows than shorter term windows and you're a little different from the index is not surprising to me that the back test sharp is going to be different. So I think it's more about that's why I focus more on the relative differences um and also like conditional environment differences as something interesting to look at uh as opposed to trying to sort of you know match the actual sharp ratio because I cannot like replicate each of these CTA's point in time over 25 years. So >> true. >> Yeah. >> Okay. No, that makes makes perfect sense. And now you mentioned you brought up uh Alex's name and of course you have uh you know the leading book on on trend following. I think you wrote a lot about return dispersion I think in the book. Um can you take us maybe back to and compare some of what man is doing now and compare that to the work you did uh as well? >> Yes. Oh my gosh. Like we were obsessed with return dispersion. um Alex and I um and we spent a lot of time thinking about it and we have this one chapter and I love it because it's chapter 11. >> Oh, >> so declaring chapter 11 like that's like >> anytime a client or someone talks to me I'm like you got to think about chapter 11 or read chapter 11. Um it is an entire chapter of our book that's just dedicated to analysis of return dispersion. And what's fun about this is this is what led us to and I'll talk about it a little later some of our CTA benchmarking analysis and and style analysis. But what we do in this particular book in chapter 11 um and this is fun to see that man has actually done some of the similar things in their paper. Um we start off by looking at three different aspects of a strategy style. Um ours was equity bias or not. you know, think they added carry or not. Um, we also added, you know, sort of allocation weights. Was it equal risk or market capacity oriented? So, do you focus on having equal risk or do you like kind of focus on just the most liquid markets? >> Um, and then we also talk about speed. So, each of these um dimensions we examine and then we talk about how equal risk outperformed historically in our analysis. Again, a similar analysis of that approach. um in the man article. So that was kind of fun to see that they kind of use some similar methodologies that we did although they added also alternative markets which is which was fun to see. Um we then moved and looked at you know how do you position size and the different types of signals which is something that man didn't discuss. So do you use channel breakouts or moving averages? And we even had these sort of random signal studies too just to understand sort of how much of that is driven by the sort of allocation and how much of it is driven by signals themselves. So kind of went a little deeper into some signal analysis. >> What do you remember what you found? Because obviously position sizing is something that comes up often in in my conversations. Um, >> so we weren't looking at, you know, trying to performance. We were looking at sort of how much dispersion you get cuz that wasn't the goal of this particular chapter. The goal of this chapter was to demonstrate how tilting asset classes, changing position sizing approaches, how that naturally leads to dispersion, >> okay? >> And how much dispersion. And so I think our point was more you know look at these different aspects that all are kind of similar but a little different that create return dispersion over history. Uh we did one thing that did stick out we studied things like you know market volatility environments too. Do you have more dispersion in a 2008 versus others? And we did find that 2008 given the size of the moves regardless of you know which type of methodologies you use you had a bigger range just because you had much bigger moves um in that year and this is an old analysis but you know it kind of makes sense. You saw the same in 2022 as well. >> Everyone was up but the range of up varied a lot. >> Okay. And um I think there were some other things you looked into in your paper as far as I recall. >> Yeah. So I have two other aspects that was kind of fun and I I'll talk about those two and one of them is like kind of nerdy so I kind of like that one. Um >> so we also tried to think about it from the perspective of an investor in this chapter where we looked at sort of how much return dispersion you had as you add more managers together. And this was important because it's a hypothetical you know my original background was a CTA allocator many many years ago and so I was very concerned about this question right so as you add more managers together how much return dispersion sort of dissipates um and what you see is that you adding at least two maybe three together gets you pretty far in terms of adjusting some of that return dispersion we didn't go into the specifics of which manager we kind of used a random sample analys is we randomly sampled them and then aggregated that over time. But this is helpful because the number that's kind of sticks out is 3 to 4%. So the return dispersion actually went down quite a bit, but it it's not, you know, drastically different from like return dispersion you might find in a replication approach as well. So replication approach, we wrote a paper on this last year or earlier this year on sort of replicating and there's usually 500 to 600 basis points of slippage expected. So that's normal for an index that's volatile um that isn't, you know, directly investable. >> And so it kind of shows there's a lot of ways where investors can mix different things to try and reduce some of that return dispersion. But guess what? You always have it, right? You can't just buy the index in the space. So I think this analysis helps investors understand even if you have three managers, you still have, you know, you could still have some return dispersion that that is for the whole aggregate portfolio versus the index. And that's important if you're an investor with two or three CTAs and you think, oh, I'm 400 basis points or 500 basis points behind the index. That's that's not that can happen by chance with two random variables that have those correlations. >> Yeah. Well, we've certainly seen tracking errors between uh even those who try to replicate a uh you know a benchmark. Uh so for sure now I don't know if this is in the paper but so maybe it's just more your um um insights that I I'm looking for. Um, of these four kind of um, areas that drives dispersion, are there any one of those four you would say are more important sort of the main driver of dispersion? >> So, we kind of showed that they can vary over time, right? So, in an in an individual year, one or the other could be very different. So asset allocation for example, if it's a year like 2019 where the only trend was fixed income, you know, that could be the driver of that year's main dispersion. We did see some differences in terms of risk allocation over longtime horizons, but that was a relative performance choice. I think any of these particular ones will kind of stick out in a given year depending on what's happening. And I think that's why it's hard because you can't just pick one like oh well if I you know think about this one aspect like alternative markets so that's why the quantico paper is useful well if I just have alternative markets I don't have any return dispersion it's better that's not true right so it really depends on you know each market environment is different and over time each of these factors can contribute to that and that's why they look at so many um because each one of them have a different environment where hey it wasn't good to be fast this year or man if I had a tilt. I mean I think the you know example in the ETF space is even bigger where you know if you'd avoid equities this year like that was fantastic right um >> so it it really can create any of them can do it and that's why we study so many right >> yeah and plus it makes it actually pretty difficult for investors as you say to uh um you know to decide but but very helpful to know also that you get a lot of that dispersion disappearing if you just allocate two or three managers. Uh, which kind of makes sense. Um, we'll come to the Quantica paper shortly because I actually do find it to be a really great paper and the first paper that I've seen that dives deep into this discussion we've had on and off about alternative markets and so on and so forth. Um, but there are other papers out there. Um, many of them you've been uh writing about. Let's talk a little bit about some of the other things u that you've worked on and and that you found that you think is relevant for for this particular conversation. >> So this is you know what's really interesting is this obsession with return dispersion caused us to do some very interesting research. Um it's over 10 years ago that we started looking at this but basically we when we wrote our book Alex and I we realized like we're very irritated that there's no benchmark. So we started sort of explaining how you could design a benchmark and then we created style factors similar to equities. So in if you're thinking about equity world right it's so great because you have the benchmark and then you have your style factors and then you do your regression and ooh you have too much small cap you know and you you just know like what drives relative performance much easier. So our goal was at least to like tackle that type of thing. Um and what we did was design and I've built some other factors and we track them um at Alpha Simplex too because when we look at a given year like you asked before there's never it's very idiosyncratic. One year it's speed that matters another year smaller markets correlated markets. Um and so by tracking those factors you can disentangle the aggregate performance of the benchmark versus these factors. So you can kind of try and explain some of the relative dispersion across different managers by looking at their style factor loadings. Um so that was you know it's a very interesting area. Um it's something I continue to look at and and use as a tool when I'm examining performance over over different horizons and looking at different managers trying to understand thematically like why they have differences. You know, it's funny um as you're talking and I don't remember the year, but I do remember I think it's one of the first live talks I've seen you do. It was an event in London at one of these fancy uh hotels. Um uh and I even forget the the guy who who ran that um series of of events. Um >> Battle of the Quants. I remember that. >> Battle of the Quans. That's exactly what it was. >> It was you, me and London. I remember that. >> Exactly. And uh I mean this is a long time ago and I remember >> 15 years ago I think probably a long time ago. I I agree and it's um but it was interesting in in the way you kind of were able to describe uh describe different managers and and what they were um tilted towards in terms of uh different factors. Um so >> that's awesome. You were at the CGA style factors analysis the fir first paper on that. >> Yeah. Yeah. Absolutely. Had to learn about it of course. Yeah. Absolutely. What other papers have you been um or have you delved into other papers uh >> for returning dispersion? Yeah. >> Yeah. Or similar things. >> One that's really not as well known, but it is so nerdy and so cool. So, I want to talk about that one. >> Oh, yeah. By all means, >> it's it's called quantifying turbulence in CTAs. And what this paper does and this is going to get a little nerdy and I hope that's okay but basically we use something called the malhanobus distance. So you look at sort of the distance between different you know random variables and you try to measure how much turbulence so movement across a group of things. Um, it's really neat. It's used a lot in the research by Mark Critzman, who writes a lot of stuff for the FJ and stuff like that. But basically, you use the Malhanobis distance to talk about how much movement there is. And what's cool about it is it's a point in time measurement of correlate of of the movement because usually when we talk about correlation, we have to use a window of data. So you're kind of messing up like so if something big happens on one day you need like a year of information and you're kind of like averaging everything when you look at correlation. What this does is it looks at point in time differences and it actually measures an instantaneous effect of movement across a group of things. So, it's super neat and it's kind of you looking at it in in a distance metric and you kind of can here's where it gets even nerdier and I'm going to explain it. I'm sorry. >> Okay. Yeah. >> So, by taking this turbulence metric um you can decompose the movement across managers between something they call a magnitude surprise and a correlation surprise. So magnitude surprise is sort of like boom a big shift that is not a change in correlation >> right >> and correlation surprise is where your big shifts across the managers is is actually a um shift in their underlying behavior. >> Yeah. >> Okay. So magnitude is boom something happens and everything reacts. correlation is things are changing and like you know returns are moving and trends are different across the managers so they actually don't look as similar as they they normally do. So this paper is really cool and why I like it is you can actually do point in time analysis on a day and so we did this analysis for CTAs and if you look at magnitude surprises there's a history of them and we plot them there's a few of them that are really big and purely magnitude. So you can guess which ones they are. Brexit, you know, SVB, Black Friday, like >> Liberation Day. It's not in the data set, but I'm pretty sure it's a magnitude surprise. Um, yeah. So that's cool because we also showed that CTAs do not do as well on magnitude surprise days, >> right? >> Which you're not surprised by, I think. >> No. >> But that correlation surprise was more sort of an inflection point. So periods where you know trends are changing and what you do may create differences in performance as there sort of rotations in those trend signals and they kind of move around. So that is a good way to kind of think about it might be a good metric to understand you know what's driving different behavior across your returns and and understand that return dispersion. Is this driven by just a shock or is it driven by sort of actual changes in their their returns? So that's super nerdy. I told you. >> But it's really cool. Like the pictures are really neat because >> never heard about this kind of analysis. I'm sure there's a few of our listeners that are >> It's also fun to say the Malanobus distance like it like really sounds very >> sounds very >> geeky, right? >> Exactly. >> You know, >> now before we dive into the last paper, the Quantica paper, um let me ask you this. So we get all these kind of tools now, analysis tools, and people can get really uh detailed in trying to quote unquote explain differences between managers and between models, etc., etc. And for the purpose of people who want to invest uh in uh trend following and and again picking one manager is probably not the right way. Picking 10 is probably unnecessary as well. But my concern a little bit and I know it's kind of a weird thing to say that I'm concerned that we're almost giving too many tools and we there's a risk of investors who are not in the engine room um making assumptions from their own analysis that may not be true. They don't know what's going on in terms of potential changes in a manager and so on and so forth. other kind of simpler way to think about uh these things uh where people don't go too overboard in terms of the the deep dive. Do you know what I mean? >> Yeah. I mean I think my view is you know you start simple. When I talk to investors in the space, I say pick two or three, at least two or three managers that you like, that you think are a little different, you know, and you can do some simple analysis of their returns. >> But the the real truth is I do believe CTAs is one of the few strategies that has nice accretive properties compared to equities. So I try to think about the big picture is that there are few tools out there in the alternative space that don't just have hidden equity beta. Um and so you know having something different is good. So start simple. Don't over analyze. Don't get analysis paralysis, right? >> Um and you know, kind of combine things that are different, right? I mean, and you know, that goes for things like even replication, right? Like so maybe you can have some of that and you can have >> a manager that's going to be faster because you think, I like the crisis alpha and that one's slow, so let's combine them. um or you have another manager that's diversified that has you know but the point is is really sort of not over because I think if you get too complex you have too high expectations of what your results will be and that as we know what you can never predict right so every year is different >> and every style factor that you thought you picked there's no magic factor um they'll change so it's about diversification which is you know kind of the simple answer for me that's what I would say. >> Okay. Well, let's dive in um to the last uh paper, the Quantica paper. What I really liked about this paper um and and all the papers they do is pretty detailed and they tackle an issue that um I think we've talked about over the years on the podcast and it deals specifically about kind of this battle between managers who stays with very liquid developed market portfolios and those who let's call it 10 years ago started to uh move into alternative markets. some of them maybe with the claim that they were trending better so there would be better opportunities certainly for a while performance was very competitive uh without a doubt now we've had a couple of years where it's kind of been the reverse um so it's an interesting it's really an interesting topic uh and I like the way they kind of uh very uh methodical in terms of the way they try and see it from different uh angles So, why don't you again um put your um teacher hat on here and um and tell us a little bit about what what they were trying to do and some of their findings and whether you agree or not agree. >> Um I really liked how they set it up. So, they set it up as having sort of a traditional market portfolio with 50 markets and an alternative market portfolio with 120 across a wide range of sectors, some of which traditionals don't invest in. things like credit, gas and power, um more exotic as and you know livestock contracts. So kind of that like you know hodgepodge of like stuff that's a little bit less easy to access for sort of futures markets. >> And what's fun about this paper is you know it goes back to the man paper. What's the essential difference here? like one of them has different sectors and different sector compositions and during different periods in time those different sectors may contribute poorly or not to trend following and what they do show which I think will really help a lot of clients out there that have both of these things. Um I think there was a sense that you know alt markets there is a case that you know correlation being lower has benefits over longer time horizons but given the extreme outperformance in the recent period um it could have maybe gotten people's expectations a little high um and then when your expectations are high and it doesn't work for the last 3 years um people start questioning the thesis and what this paper did which is so nice is it just kind of said Hey, you know there has been an extreme outperformance in some certain sectors and it happens to be during this time horizon alt markets were in those sectors for example gas and power um credit some things that worked really well and I really resonate with this narrative because I remember many years ago there was big discussions about metals should not be traded in trend following. I've looked at the back test. They're terrible. And you can't think that way as a trend follower. You have to think like every asset is an opportunity for trend. >> I just don't know which one is going to work, right? And so I think, you know, if you imagine any particular asset that maybe never trended sometimes can start to trend. So like palladium or platinum used to not be trending. Now they trended a lot recently. Gold has been a huge trend recently. nickel like and all these things were things that you would have not in you would have like had you know questions about 15 years ago like who ever cares about copper but you know now we all care about copper so they kind of highlighted this particular uh theme that actually showed up to be a very strong performer in alt markets that's not in traditional markets and I think that was helpful because it can kind of explain to a client who may be thinking about alt markets is not like a is it broken or not kind of question. It's like well this was a very favorable environment for these assets and recently those assets have been very rangebound um after the big shock of co. So I think that's very interesting and it it it was a a very well done analysis because it explained that asset class tilt and why that was important in relative performance over the recent periods um which I think will be helpful for those that that really think about alt markets. You mentioned that there was this one particular sector that really yeah I think it was like 40 or 50% of the outperformance came from uh gas and power and of course we know that uh at least also from memory I think some of it was also related to the Ukraine situation where these markets really took off and so on and so forth. Well, there's an example like they may not have trended it at all and then suddenly you have the situation in the world where that asset turns a lot and >> you know you you can't replicate that it's going to be what's that asset class going to be now I don't I don't know yet and that's what trend waits until it finds what's the next >> big trend. Yeah. I I guess my question was how much can we extract from the fact that we've only got 10 years worth of data where there are really are some alternative um market managers around. So the the period we're comparing is quite short in in in many ways. uh since you are the since you're the quant of the two of us uh I mean how how much does that play into you in the back of your mind saying yeah I mean it's a great for this period of time but do I really think that this will be any kind of guide um for the for the future >> well this is why I like the paper so much because they actually address that issue right they said let's look at sharp per market on each individual trend instead of thinking like I know which trends or this period was better. They kind of explain relative adjustments for that period and then they say you know if you think about trend more from a theoretical perspective right you have assets in your trend portfolio the P&L streams have a certain sharp ratio the sharp ratio of those P&L streams and they plotted it as well. What's the correlation over time of a trend and and Alex and I did this even in our trend book. So you think about each asset is a unit of trend right and the P&L streams have a specific sharp you need to have an assumption for that then when you build a portfolio of those assets the correlations matter right and so their argument and this is a mathematical argument is that on aggregate the more lowly correlated P&L streams that you can add the little bit higher your sharp is and I think there is definitely you know some mathematical credence to that argument. I think where they they didn't clarify this a ton, but they do address it is, you know, not all of these assets are exactly the same because they're not as tradable, right? So, for example, trading, you know, exotic power contracts may have some more cost associated with it. So, that reduces what seems to be a higher sharp closer to the traditional sharp, right? Um, but their argument is that they're similar or maybe a little bit, you know, so they they don't argue that alt markets trend better per se. They argue that they trend, but you know, maybe if they trend better, they cost some too. So the point is they do provide some diversification. So that's a mathematical argument that when you combine a lot of things that are different, you can sometimes increase your overall risk adjusted performance. And so, you know, I think that that was very well done because yeah, it's hard like you said, it's really hard to statistically prove those, but there are mathematical foundations to like diversification improves risk adjusted sharp. The question is you need to figure out what the sharp inputs are and the correlation inputs are. So, that's hard. >> Absolut absolutely. And there's one other thing I think they do mention it from memory. I think they do mention it but I do think it's relevant and that is of course that >> because of this relative outperformance that the alternative market managers did for a few years a lot of money went into those actually and I wonder if the last couple of years worth of underperformance to some extent not saying all of it but to some extent is also influenced by the fact that now we actually have maybe too much money tracing these less liquid markets. um adding even more so to the transaction cost etc. Now I don't have the answer but I think it's something people should be thinking about. >> I think that would be a great analysis. I would love to see that because crowding and I've always been so excited by crowding is an interesting factor. But what's also it's it's it's a two-edged sword, right? Because on one side trend falling still works in some of the most crowded markets we know. But you know, you start to wonder a little bit when you get the things with a little bit more capacity constraints. Um, and so I think you know what what one could argue is maybe when you look at a back test when less people were in those you get a more normal sharp >> uh for those positions. So it's just really about diversification and less about like these are better or worse. >> Um, and that's kind of the read I get from their paper. But I think a more detailed analysis of crowding and changes in those markets. Even a case study of one market could be super interesting. So I'd love to see someone, you know, actually do that analysis and work so that we could have a better sense because that question came in my head as well. >> Yeah. Okay, cool. Anyways, the paper is the uh Q3 uh 2025 paper from Quantica that you can find on their website. So definitely go and check it out for sure. So we've talked a lot about um papers. Um, is there anything that you've been thinking about uh writing about maybe for our next conversation, Katie? Anything that has piqued your interest lately that you think, "Yeah, maybe I should do a paper on this." >> Yeah, I've been thinking a lot about macro and systematic macro and some interesting connections between the two strategies, both systematic macro and trend. So, you know, we I'm hoping to have a paper. We'll see. I'm working on some stuff with some colleagues, but um I'm very interested in macro. That's something that I've been kind of fascinated by and it's done a lot better during liberation day. So, it's kind of some of the themes like how much of economic trends versus, you know, technical trends. So, that's kind of a neat area of thought and research right now. >> Yeah. No, I think that sounds very relevant and very interesting. Absolutely. Anything else you want to sort of close out with today before we wrap up um our conversation? Katie? >> No, I think you know return dispersion is such a fun topic. Um hopefully one day I'll come back with another paper on style factors and we can talk about that as well. Um we talked about turbulence. I mean we really got the gamut. We had return dispersion in our topics on return dispersion. So that's cool. >> Yeah, >> definitely a deep dive for sure. Well, as I said, I can't um thank you enough. We really appreciate all the uh the work that you put into uh these conversations. And of course, if the audience listening to this feels the same, why don't you go to your favorite podcast platform and leave a very nice rating and review for Katie? Um it really does help uh more people find the show and uh be able to listen to these uh nuggets from her. Um next week I'll be joined by Rob Carver. So uh of course another uh fan favorite. So, if you have any questions for him, uh, feel free to suggest some topics or some direct questions and I'll try and do my best to make sure we get them discussed. As always, the email address is info@ toptradersonplot.com. Um, so that will be um, for next week. From today, Katie and I would like to say thank you for listening and we look forward to be back with you next week. And until next time, as usual, take care of yourself and take care of each other. Thanks for listening to Top 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 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 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. We'll see you next time on Top Traders Unplugged. [Music]