Why Macro Investing Is Becoming More Systematic | Allocator | Ep.35
Summary
Market Outlook: The guest sees a moderately pro-risk macro environment but stresses the need to stay nimble as inflation and oil-driven moves dominate near-term dynamics.
Commodities: Commodities are highlighted as the most effective liquid tool for inflation protection, with energy-led gains underscoring their role; they are a core, often overlooked, component in multi-asset portfolios.
Inflation Hedging: The team actively monitors inflation regimes and adjusts exposures, noting equities hold up below ~4% inflation while commodities and select real assets provide valuable hedging.
Downside Protection: Options-based approaches (e.g., replacing equity with calls and dynamic rebalancing) and defensive rotations are used to cost-effectively add convexity and mitigate drawdowns.
Emerging Markets: EM is flagged as a long-term strategic opportunity after years out of favor, with improving growth prospects and rising institutional interest.
Portfolio Construction: 60/40 isn’t dead but is increasingly complemented by overlays, derivatives, and liquid solutions, especially for institutions crowded in illiquid private assets.
AI and Data: LLMs and NLP drive research for sentiment and theme detection, while the team balances multiple models and transparency to avoid black-box risks; no specific tickers were pitched.
Transcript
You always have to ask yourself, you know, are the assumptions behind the model still valid? Has there been some regime shift in the data? That's the kind of thing we worry about every day when we're using models. We still see like an overall positive macro environment for risk assets. But in many ways, the the US economy has been surprisingly resilient despite all of the challenges that have impacted it. >> [music] >> Welcome to Top Traders Unplugged. In markets, success doesn't come from predicting what happens next. It comes [music] from being prepared for what you can't predict. In each episode, we go deep with some of the world's most thoughtful minds in investing, economics, [music] and beyond to understand how they think, how they prepare, and how they decide, and [music] the experiences that shaped how they see the world. No noise, no shortcuts, [music] just real conversations to help you think better and invest with confidence. Welcome back to Top Traders Unplugged. My name is Alan Dunn and today I'm delighted to be joined by George Patterson. George is a managing director and CIO at Pim Quant Solutions. He oversees all portfolio management and research for Quant equity and multiasset teams. He's in the markets for many years now. Previously was at Axioma uh identifying buyside trends and market opportunities. He was CIO for quant investments at Bank of Montreal Global Asset Management and earlier in his career was co-founder of Mena Capital, a quant equity hedge fund. His background is in physics. He has a PhD in physics and spent time at NASA earlier in his career. So, George, great to have you with us. Great to have a proper rocket scientist to chat to. How are you? >> Uh, very good. Thank you for a great opportunity to be here. >> Good stuff. Well, I gave you I gave our listeners um kind of the highlights from your career, but I mean, as I mentioned, you started off in physics and then you worked at NASA. What got you interested in that? And then how did you make the leap into finance? Um you know I I was always interested in physics from a very young age. It was just an area that I thought brought together you know math and uh you know science and there was a lot of technology involved um in in you know how you do experiments and it just for me it was like the combination of many different fields. I was always very interested. I enjoyed it. However, I knew I was not going to have a traditional academic career. I knew I wanted to do something in industry and I didn't really know what it was. Um, and it's, you know, kind of really by luck, I found some colleagues that that understood my skill set and what I could offer. most, you know, investment firms were not interested in hiring a physics uh PhD, but there were there was a group, it was at Barclay's Global Investors, uh in the mid 90s, and they looked at me and they said, "Yeah, we think we can figure out what to do with you." And um you know, that was really where I cut my teeth. >> Interesting. And you think um I mean, I come from like an economics background, so kind of view markets from that kind of perspective. And then um I guess people with a quant background I mean do do you take a different lens when you're looking at price movement price returns and financial markets. >> So I'm a strong believer in having like a multi-disciplinary team. So you know I think what you know I'd say when I was early in my career I was very focused on the pure quantitative aspects and you know what what did the model say but over the years I've come to realize that it you really have to kind of combine many different views right quantitative is a very useful tool but you also have to pay attention to what's going on in the market. Um so really my goal and particularly with my team is to have people with economics and finance backgrounds but also computer science, mathematics um you know different fields because people bring different insights to the problem. So that's one of the things we try and combine at Pium quantitative solutions. >> Very good. Well give us a sense on PIM quant solutions in terms of the the size and scale. I know there's kind of two elements to it. there's a quant equity and and the multiasset side in terms of kind of assets under management or the types of clients you deal with. >> Sure. So, we're the quantitative solutions group within PJIM. Uh we're actually one of the early pioneers in this space. We've been we just celebrated our 50-y year anniversary and we and actually many of our our track records go back 25 30 even 40 years. So, we really were a pioneer in this space in many ways. The business actually originally started with multiasset and focusing on you know systematic applications of multiasset which obviously has evolved over the years. That's roughly 50% of our $110 billion and then quantitative equity which is much more uh along the lines of you know benchmark relative strategies you know kind of typically with you know tracking error anywhere between two and 4%. Good stuff. And um I mean as part of your role, you're responsible for for portfolio management, research, the the full gamut of of of investment activities, isn't that it? >> Correct. Yes. Yeah. So again, it um again from my perspective, it combines many different skill sets. Uh you know, there's the investment side, obviously, research. Research is one of my passions. um trading again all of these areas have really evolved quite a bit over the past 30 years or so that I've been involved in this industry um and obviously technology plays a very different role right it's you really need to have them embedded in the investment team if you're going to have a quantitative firm I think actually for any firm these days technology really needs to be embedded into the process given the rate of change >> yeah so you mentioned you started off at BGI in the mid '90s and um you know so that's same same time I started off myself but you know it's sobering to think it's uh 30 years ago now or whatever you know lot of change in the market since then as you say in terms of the technology you know market participants market micro structure I mean when you reflect back what what do you think the macro investment landscape how has it changed over that period >> um well there's been a lot of change right I mean when I think about what the main themes of change are is you know one is just data Right. In the in the early days, we were scraping to find data. You know, people didn't really understand the use. They they were not thinking as systematically. And it was also a time when you that was kind of the '9s was really the error of the big macro traders, you know, like Soros and Dereken Miller and Robertson and and really, you know, making big bets. Um but over the years I mean if you if you think about it today we have you know GDP now we have you know web scraping prices we have um you know you can track behavior there's geospatial I mean just the amount of I mean the really the rise of technology has just enabled us to have a much tighter view on what consumers are doing which is obviously critical for understanding where markets are going and I think the consequence of that too is that you know the big opportunities that the the macro concentrated traders have had in the '9s really just don't exist today. You have to be much more nimble opport I mean macro investing has become much more systematic than it than it was in the '9s. >> Yeah. I mean I I don't think you heard too about too many systematic macro traders back then. I think it's fair to say it's a product of the uh of of of more recent decades. Um I mean obviously we we we've you know with that change in micro structure uh and availability of data etc. Have you seen an evolution into strategies over time as well? >> Of course I mean I I would say there's definitely been an evolution of the strategies in in the sense that we're able to get me much more data that we're able to proxy for things. At the end of the day, we're still I I'd say I'd say we're still trying to think about what are the drivers that are going to impact markets, things like inflation, things like growth, >> but we're able to get much better proxies to measure those those characteristics. So, you know, it used to be that you'd rely on government data that came out once a month or once a quarter. Now, with language processing, you can be monitoring news feeds. You can be looking at, you know, papers from across the globe, looking at foreign languages and monitoring convers, you know, news stories about inflation or what are people really focusing on. So, I'd say the overall thesis that drives the investments is similar, but the way that we're able to get data and proxy and measure those is just really exploded. >> Yeah. Um, well, maybe just taking it into the the current day. I mean obviously you're running multiacet solutions and I guess in the multiassid space you know the 6040 has been the you know the benchmark for a very long period of time and worked very well for many decades particularly the 2010s and then as we've come into this decade you know a theme has been the end of 6040 um and you know obviously a shifting macro backdrop how do I mean how do you think about that is it a case of you know people talk about 60 2020 20 or 60 30 whatever it is you know um is that the way or or or what's your thoughts? >> Yeah. So, so people have been talking about the demise of 6040 for years, right? I mean, this is not something new. And there's definitely periods of time when it doesn't work. Like if you look at [clears throat] 94 or if you look at like 2022 where again, you had like uh sudden moves in the bond market that really cause bonds to not work as a defensive instrument. I think 6040 is probably on one hand, it's still probably going to be around for a long period of time. However, there are a lot there are many more ways these days to be able to get access to to diversification that you just couldn't get 30 years ago, right? So, depending on whether you're looking at I mean, if you're an institution, you can become much more involved in overlays um or solutions that are going to give you a little bit of convexity or downside protection or or you know, as you know, return stacking. If you're a retail investor, you know, there's a lot of products that you can get on either on the der I'd say on the the category of derivative income and derivative protection that are really not new strategies, but they've been democratized >> from strategies that were usually only available through private banks. >> Yeah. And and I mean is that you know if a client comes to you with with a blank piece of paper looking to build out a a multi-assid solution is kind of 60/40 still the to the the starting point with some kind of overlay on top of that or how how do you approach the the problem? >> Well it it really depends on the client. I mean most of the time clients are coming to us with a problem. I mean the you know this the solutions part of PM quantitative solutions means that most of our mandates are customized. So most of the time a client typically an institution uh endowment foundation comes to us with a challenge such as you know I have this in my portfolio how do I get back to my strategic alignment? You know I've got a blend of publics and privates. What's the best way to to solve this particular problem? And a lot of times we're really, you know, thinking across the board, not just 6040, but how do we get there? Sometimes with derivatives, sometimes with options, you know, looking to solve those problems really on a much more of a a customized basis. Rarely do we get somebody that just comes in and says, you know, what do you what do you recommend? >> Yeah. Okay, fair enough. Um I [clears throat] mean are you seeing like anecdotally it feels like institutions have you know heavily loaded up in private private markets and maybe are overly exposed there. I mean in terms of the kind of institutions coming into you do you think that's a theme that you're seeing? >> Definitely. So we see a lot of you know a very common request from an institution is I have this portfolio of of you know a large portfolio of privates um maybe concentrated in a particular area and that the you know privates can offer really interesting um long-term return opportunities. However you people forget that once you've got invested in them you largely can't trade them unless you're willing to accept a material discount. So, a lot of the conversations we have are I have this portfolio of privates. I want to get back to my strategic allocation, but it's got to be liquid. I want to have I I'd love to have some alpha in the process, and I want something that's disciplined and systematic. Help me get there. So, those are that's really in the multiasset space. Those are a lot of our conversations. Liquidity is a key component because again, investors have most investors already have relatively mature portfolios. >> Okay. Interesting. So obviously I mean linked to the questions around 6040 is the um I suppose pick up in inflation we've seen in recent years. Obviously we had an inflation spike in 2021 2022. Inflation's come down since then but still above target and it's picked up again recently. Um I mean obviously that now seems to dominate the thought process a little bit more. Um is that what you're seeing as well? And I mean what does that mean in terms of a portfolio construction uh perspective? So if so really since the beginning of the the military actions in the Middle East it's it the market has really it's been you know like there's been one principal component right it's been you know oil's up equity down or oil up inflation up equities down or vice versa and it's that's really been the dominating risk factor that's driven the news. You just kind of have to look at one asset in the morning and you can figure out what everything else is doing. um you know inflation and again again we what we rely on is a number of like advanced language models to kind of see how people are talking about this in the marketplace as well as monitoring flows in the marketplace and it's clear that inflation's become a risk factor. It's it's not quite at the level where oh this is going to cause a recession or this is going to cause a minor a major downturn. It is definitely going to weigh on the economy and and shave a bit off of GDP, but I I think you know t typically the rule of thumb is around 4%. If you get you know if inflation's under 4% you can still do okay with equities. Obviously commodities become more important. Um if it starts getting above 4% that's really when we start seeing you know kind of material impact you know either both in terms of fiscal policies but also in terms of markets. And it doesn't seem like we're going to get there just yet in in the near term, but again, this is something that we we monitor on a daily basis. >> And then from I I guess from a portfolio construction perspective, do presumably then you have more clients looking to actively hedge inflation risk or are cognizant of that risk. Um, yeah. Is that fair? >> So, we're definitely, so we're definitely, so the way I would say we think about it is is like there's there's kind of a mid horizon view and then there's a a much shorter term horizon view. And the mid- horizon view is much more tied to fundamentals. So there, you know, we're looking at the, you know, we're looking at the many the multitude of data that you can get, you know, forward interest rates, you know, GDP, corporate profitability, you know, employment, jobs. There's a there's a whole host of numbers that that we use to then try and figure out what regime are we in in the middle in kind of like the mid horizon. And then we combine that with a shorter term tactical view that again is going to basically try and pivot between, you know, cons risks about inflation. Obviously, you know, having some risk about inflation will mean that we're a little bit more, you know, we're still pro- risk in general, but the the the the risk concerns around inflation have caused us to kind of shrink some of our active exposures just because of the of the really the tactical overlay on top of that. Um, but you know, if you look at the other economic data, we're still generally in a benign pro-risk environment. If you look at any of the macro indicators, you know, any of the the forward rates, I mean, people are people are expecting this to, you know, continue for a short period of time, but yeah, >> 3 to 6 months, end of the year, we expect, you know, things will be they're not going to go back to where they were, but they're not they're probably not going to get worse. >> Yeah, fair enough. But just going back to the inflation bit, I mean obviously you know the kind of traditional way people might hedge or have um exposures to assets that would benefit from inflation or maybe real assets, property, infrastructure, etc. And then obviously in the liquid space is it via gold I guess or commodity futures or how would you kind of construct a basket or a portfolio or is it more on the equity side more energy stocks etc or how do you think about kind of creating some kind of liquid inflation edge? >> So it's really a combination um it's really a combination most of the time we implement we think about how to hedge against inflation using positions as you said. So again probably the best tool is commodities to to be able to hedge and obviously if you look at the return you know the return of energy has been something you know it's been you know kind of huge returns since basically the overyear to date just given the overall geo geopolitical risk but again commodities are still one of the best liquid tools right um you know real estate infrastructure etc those all give you some long-term uh in you know hedging against inflation equities tend to do well up to a certain threshold to hold. >> Um so again we do we have been and actually you know some of our commodity uh we were already >> um you know kind of we always include some commodities given the the um the the inflation hedging nature of them. Commodities if you ask me are I I kind of call them the the forgotten stepchild of multi-asset. Everybody thinks about stocks and bonds and real estate, but again, commodities often times give you a a good amount of inflation protection and and really what you got to worry about is the inflation shocks and that that's when commodities really can perform in the short term. >> Yeah, fair enough. you mentioned there about regimes and identifying regimes and I know that's something you've done a lot of research and and work on and I mean it feels like the macro regime has undergone a fairly significant shift from you know um you know from if you look back to the 2010s obviously we had secular stagnation we had low inflation low growth um now obviously since co we've obviously gone we went into a higher nominal GDP environment for a while, but now at least uh a little bit stronger growth and certainly more higher and more volatile inflation. So what are the ways you kind of categorize the the the economic and market regimes? >> So we still so so for us again we we're relying on a number of of techniques to use to to categorize the regimes. The one that we're using most recently is just to throw in a little bit of math, throw in a little bit of mathematics as kind of a a Gaussian mixture model that basically views the world, views the economy as some sort of unobservable state and then we use all of the different data points to kind of classify and say what type of what type of regime are we in. And again, you know, that tends to be a I'd say a medium-term view. we don't use any like market-based data there because markets can move much faster than the overall economy. So again, that view tends to put us in a in a moderately pro-risk environment. This is something that what I would say is when you're in the in the world of multi-asset, you really only have so many instruments that you can that you can use. So again, it's useful to have a model, but it's always useful. I mean, one of the challenges that you have when using models is you always have to ask yourself, you know, are the assumptions behind the model still valid? Has there been a regime shift in the data? That's the kind of thing we worry about every day when we're using models. We still see like an overall positive macro environment for risk assets. Definitely risks on the horizon. There's no question. But in many ways the the US economy has been surprisingly resilient despite all of the challenges that have impacted it. >> I mean people talk about regimes and also people talk about different um you know I suppose there's different terminology. Some people call it different quads you know uh or people talk about the different stages you know u disinflationary boom or inflationary boom and then stagflation or recession etc. I mean do you have those kind of do you kind of categorize markets in that way and then try and map that to asset performance or obviously you're talking about this um Gaussian approach which sounds much more I guess statistical in nature is is that it >> so that so we have multiple approaches so yes we do have kind of more traditional you know you know where are we in inflation is inflation you know inflation high and rising you know high and falling again we have those traditional and then we also have more statistical methods. >> Yeah. >> Rarely rarely do you find that one model is the best model, right? Often times what you really want to do is you want to combine multiple models together. So behind the scenes, rarely is there one model that says, you know, here's the, you know, here's the one scenario that we think is the highest likelihood. Oftent times there's really multiple models running in the background. And again, you want to com I mean, just like different data sources, we're looking to, you know, we're looking to, you know, we don't have a portfolio that's just a growth portfolio or just an inflation portfolio. We're trying to balance all of those things together. You know, we think about relative valuation. We think about growth, but at the same time, it's the same thing with models, you know, like rarely do you want to have one model oftentimes. And and again if you look at like um if you look at these um you know I love to look at this website Kaggle that has these um you know kind of model building competitions. If you look at the the winners of that particular of those projects that they have um they're all multimodel the winners are usually like a multimodel approach. Um so rarely is there one approach that is that is like okay this is the one answer. World is the world is just not that clean. No, no. I mean it's absolutely I mean that that's it's a segue to the next question which is I mean if you look at today and maybe look at from a regime perspective or a market perspective and parallels with the past. I mean have you observations because as you say there's always observations with lots of different periods but not one exactly or are your models pointing to any kind of parallels? I think people do get a little too attached to like okay my you know my particular analysis says that you know we're in a regime we're in a regime just like a particular year in the past and and again I think this is probably I'd say the bias of human of of like human pattern matching what I would say is we spend a lot of time doing the research building the models and then we largely use the models in the real world right and again you're always going to have models that have differing views Rarely do we build things that are everything's going to point in the same direction. But really the the effort and the thinking about all of that happen at the model construction stage. And once the once the models are being once the model output is being produced and recommending a position, we're always asking ourselves are the assumptions correct? But if the assumptions are correct, we're going to use the model. Right now, my job and the hardest part of my job is making sure my team sticks to their research and sticks to their process. And that's this is the hardest part of investing is the market's working against you. You have a process and you know you're getting nervous and you know it's not working. You're losing money. People are picking up the phone. What's going on? That's the time that that people tend to panic. And the the hardest part of this job is being patient and sticking with your guns and you know if you have faith in your process, sticking with your process that that is the single hardest part of this job, particularly when everyone's questioning you. >> Absolutely. Fair enough. Um at the same time, you did touch on kind of regime changes or structural shifts and all of that. I mean, and which for any model builder, I guess that's always top of your mind. Um I mean are the are the models all kind of fun fun fundamental or do you you know respect the market trend or the market price action or are there any kind of pure price price uh inputs uh into the models kind of more technical in nature >> so there there are some you know I would say most of the model probably 80% is really derived on fundamentals right looking at fundamental data and combining it in in unique ways that give us our give us our our our recommended positioning. So, this kind of goes back to exactly what I was saying before. If you have a purely statistical model and it tells you to go long, you know, go long Europe and short the US and it's just a purely statistical model, there's going to be a period of time when that doesn't work. >> Yeah. >> And when it doesn't work, you have to ask yourself, what do I do? And if you don't understand it, if it's a purely black if purely statistical blackbox model, that's when you're going to get nervous and you're going to unplug it because you don't understand it. If you have a model that has fundamental data and you've checked the insights as you're building the model and as you build the model, you're thinking through like does this make sense? Does this type of a, you know, how should what do I expect my priors to be? And is the model coming out in the way that does that? So it's not just like, hey, I run a regression and I take the coefficients and I throw them in the new model. As you're building the model, you need to really think about what is it that you're trying to tease out here and how does this align with your insights. And this is really true with the macro side because the degrees of freedom is very very very few. >> So when you're doing that, that that is what allows you to stick with your model when times are tough, right? >> Okay. >> We do have some small statistical components um just because again you need to pay attention to those. they tend to be much shorter term. >> Okay. And have there been periods where you know obviously we've had big dislocations maybe co is the obvious example where we had big moves in markets seemingly you know um divorced from economic fundamentals because obviously there was a pandemic out there that didn't immediately get reflected in economic data. Was that a challenging period or did that prompt you to have to intervene or how did how did you deal with that as a fundamental macro modeler? >> Yeah. So, so again there were situations where like co was very challenging. So on the macro side I would say some of the ch the biggest challenge was that certain data were not released on time right I mean everything was shut down. So certain time series were not being updated and that is where you have to ask yourself okay you know here's my model it was built with a certain set of assumptions how would the model work today if you know based on what we would might estimate this data to be and this is really th those are the periods of time that where yes you have to think a bit beyond just okay what what does the model say based on some historical data that may not be accurate that's where un I mean what I always tell my portfolio managers at the end of the day is we're implementing a certain philosophy. The model is a tool that enables us to be much more efficient in doing that. But at the same time, the the investment philosophy comes first. And it's periods of time like co that you have to be a bit more hands-on and say wait a minute what you know what is really happening in the market here. The model's not updated quickly and we might need to intervene. That was definitely true on the quant equity side where you know you saw cruise ship you know again cruise ships airlines literally you know couldn't couldn't dock you know landed on the tarmac and weren't taken off again there you had to kind of get inter you had to intervene in the model because the updates the inputs were just not updated. >> Yeah. Okay. Um so in terms of kind of integrating that into traditional multi-asset is it so it's effectively you know you're taking client portfolios which are generally you know have a lot of either privates or equity risk or something like a 60/40 and then running effectively a quant macro or a systematic macro as an overlay using derivatives. Is that the kind of most common >> part of it? So multiassid is a is is just is is extremely varied. So there's some there's parts of it that are just long only as you mentioned. >> Yeah. >> Uh you know kind of 6040 7030 type benchmarks you know you know using active management on the different sleeves. >> There are other components that are you know long short you know more of a global macro and then there there are overlays. Um there are also a number of optionbased strategies that are designed to either give protection or give convexity during periods of market stress. So the multiasset team really has this large um mandate across many different types of asset classes typically much more on the pro-risk side I would say of the equation. >> Yeah. Very good. And I mean in terms of that kind of downside protection is that kind of um I suppose uh buying protection or going long volatility or you know just thinking of you know people often point to you know obviously long V did very well in co so that was fantastic but then you contrast that with 2022 when the equity market went down but volatility was moderately high without ever spiking very high and longv kind of strategies didn't provide an enormous amount of convexity in in that period. So, you know, if somebody is looking for downside protection, how do you think about that from an options perspective? >> So, there's a couple ways we do it. So, just go just going out and buying puts is expensive. Replacing um some equity exposure with kind of effectively some call exposure typically on the long term. And what you'll do is you'll get more protection on the downside. And it's really how you rebalance that allows you to get the downside protection. and do it in a very cost-effective manner. That's the challenge is always like how do you pay for the downside protection. There are also other dynamic ways that you can effectively do it with with either kind of more defensive equity strategies or you know sector rotation that kind of gives you a bit more downside protection as the market moves down. But it's very expensive to just go out and buy a call [music] or go out and buy a put. [music] you talked about the evolution that we've seen in markets in in [music] data in technology etc. Obviously um everybody's talking about um AI at the moment um and and obviously in in from a quant perspective people have been using uh machine learning already for for for quite a long time. Give us a sense on that evolution of the use of u machine learning and and AI in in your research process. >> Sure. So, so I I this is an area where again the language has changed so much over the uh over the years and you know what you know what exactly is machine learning you can argue people have been doing machine learning for decades um you know if you estimate a regression and you update the parameters with some sort of a rule is that machine learning well that's basic machine learning >> um so we've been involved in in various aspects of that for you know really you know for for many years. Um for us, I would say most of the focus in the current environment is around language models. That's just been a very rich source of data and a rich source of alpha that we found. And this is both for quant equity and for multiasset. So again, you know, in the early days it was like counting positive words versus negative words, which is called the bag of words method. Um then we moved on to like BERT and Finnbert, you know, which were which measured sentiment based on news. And now we've moved on to using LLMs to to basically ascertain, you know, what types of themes are playing out in the markets. >> And you know, again, with everything, the goal is for this to be as transparent as possible. you give up a little transparency when you start using some of these methods because they are more statistical. But we're always looking to make sure that we can trace from what the recommended positioning is all the way down to the to the raw underlying data just for transparency. >> Okay. And I mean in terms of the use of LLMs is that you know obviously um people have been using it say to read a lot of say Fed speakers or earnings releases or things like that. [clears throat] I mean there I guess obvious uh applications you know beyond that without sharing the the secret sauce any other kind of um obvious ways that you you're using it or you're hearing people are using it? Well, I we're just it's an area of focus for us because if you think about it, so much of human knowledge is encoded in te in text, right? I mean, there's a certain you know, you can get compet data on quarterly earnings and you can get you can get uh >> earnings estimates from various vendors and you can measure, you know, kind of web traffic. But again, there's a huge if you think about it like most of the world focuses around written text. So, you know, whether it's an analyst report, whether it's a company website that is talking about product descriptions, whether it's, you know, a 10K or or a um a product release, just the amount of information in there is very rich and the ability to extract things both on the alpha side, but more importantly on the risk side have become have just really advanced by leaps and bounds. And so that's really I would say the area where we see the most um the most relevant research in the current environment. I would there's lots of applications obviously just you know accessing research summarizing you know kind of consolidating data in a in a timely manner. Obviously the software developers are big fans um accelerating a lot of their process. >> Yeah. Now obviously you mentioned simple tasks like counting um number you know counting frequency or whatever and then positives to negative. So I mean do you think LLMs are at the point now of being good at at evaluating things or you know say for example the tone of a fed speaker or something like that >> for things yes I would say for certain tasks yes keep in mind this is a statistical model right sure so if you have like all of these models are only as good as the data they've been trained on and you know you have to remember that all of these LLMs are basically trying to predict the next token that's all they're doing you know uh they've got these tokens that they've seen in the past and they can predict the next token. So for things like human speech, yes, you know, you can they're very good at saying how does this really what you're saying is how does this compare with the past, you know, how what is the you know, what is the pattern of speech and does that tend to be positive or negative? So for things like that where you're trying to evaluate something, yes, LLMs are are good. Actually, Bird is surprisingly good or Finnbert's surprisingly good in terms of extracting sentiment. So, so there's a number of areas like that where, you know, yes, LLMs have gotten to the point where you can really use them as effective tools in analyzing either speech or text documents and really probably has become the state-of-the-art. >> Okay. I mean, you do hear a lot of people, I mean, talking about the use of bots and how bots are maybe will overreact to something and you'll see this uh, you know, short-term spike or, you know, sell off in the market because the bots read something which proved to be incorrect. I mean, is that all are they valid observations? Do you see that would or or not? >> You know, it's hard to know because we just don't know exactly who's doing what. I would suspect that yes, there are, you know, automated strategies that are that are probably um, you know, in need of uh in in need of, you know, continual refinement and maybe driving prices in the short term. I mean, we, you know, again, this is this is no different than somebody who fat fingers a trade, you know, and accidentally moves a market one, you know, kind of in an extreme manner. you know, somebody's programmed a bot to do something and and you know, drives the price in one, you know, in an extreme manner. So, is it happening? Yes, it's likely. Uh, it's a little hard to like show evidence that it is indeed the case. At Egim Quantitative Solutions, it's still, you know, we use a lot of these advanced tools, but it's still overseen. There's still what we call a human in the loop. It's still portfolio manager reviewing trades and ensuring it the the portfolio is in the same spirit as the overall investment philosophy. It's not it's not kind of unconstrained robo trading. >> Yeah. And I mean a big debate at the moment is the economic impact of all of this. I mean do you see you know it making presumably it's making your your researchers more efficient, more productive. Does that mean less hiring need over time or is that an oversimplification or how do you read that? >> So I I I don't view this as like a a net destructor of d of jobs. I view it like like with every techn technology change and I get think if you go back and look at historical patterns you know around the introduction of the steam engine and you know the you know the the automobile and the and light bulbs and things of this or you know kind of machine automation. There have been lots of times that it's basically said it's going to put everyone out of work and what's happened is yes certain types of jobs may get eliminated or reduced but other jobs are created. Don't forget building these models is not something that is done easily, right? It costs, you know, hundreds of thousands or if not millions of dollars of time to calibrate these models and to to you know, I I don't know if anyone's really making money in this space yet, right? There's there's a lot of people that have very high hopes of of commercializing this. So, I think it's going to make people more productive. It's probably going to eliminate some positions, but it's going to create new positions. I mean again if you think back to you know the you know like the introduction of the internet I don't think anybody thought of the types of things that were going to come out of the internet and you know in the early days it's always a little rough you know you think oh yeah this is kind of hokey it's not good enough you know if you think about like the first you know the first you know cell phones that were bulky or you think about the first iPhone and again it really was kind of like yeah we can't see this thing taking over the world we still need dig digital cameras. Well, guess what? Nowadays, we don't um because the technology has gotten continues to get better. I think that's the trend we're going to see. >> Yeah. Yeah. Um interesting. Um may maybe moving into, you know, current markets a little bit more. Um just curious to see uh or get a sense on what you're seeing in in markets. Obviously, um you know, we've seen quite a number of shifts on the rate side in the last number of months. You know, if you go back to February, we had, you know, all of the talk about the Catrini report and yields went down. Um, and then obviously as we went into March, we had the strike in Iran and and we had a big reversal in rates and um we've had market going from pricing and rate cuts to to possible rate hikes. I mean, how has that um you know, impacted performance positioning? Um what are your thoughts on that? So, you know, it it's again this is this the challenge is is and I think this is just the way the modern world is. You have to be very nimble, right? You can't you you know, I don't think that there's such a thing as a uh you know, a forecast, you know, a year out that isn't going to get revised along the way. And if you look at like again if you look at if you're looking at future interest rate you know uh moves as predicted by forward curves you know you can go back and look at that historically and rarely is it accurate. I mean it often times they're forecasting hikes which then never materialize or they're forecasting cuts that never really materialize. So again this is just where we are right now. We are very data dependent. So yes inflation is elevated. It's not at the level that it's again it's not at the level that we see it causing like a major downturn or a recession. It is a risk factor. I think the other challenge too that's probably going to pay out over a longer term is just the overall fiscal situation. You know you know where are we in terms of debt to GDP where you know what are the long-term policies? There are a number of issues that probably need to be reser resolved and it doesn't seem like really either party is is tackling them but at some point they are going to become painful enough. We are going to you know hit the so-called tipping point. The hard part the hard question is >> where is that and when does it happen? It's it's not easy to there's no magic formula for that which is I think why you just need to be very nimble in this world. And is that something you can model or or not? Or I mean [snorts] something like the the the term premium has went from being significant to being negative at now seems to be growing again and that would kind of encapsulate those deficit concerns. Would is that something you would specifically look at? >> Yes, it's something you can I mean listen with everything you can attempt to model it. The question is how good is the model? Um, you know, people forget that in the world of quantitative investing, our models are are our models are are, you know, you know, I like to say that, you know, we have a little bit of an edge. We don't have a huge edge. You know, we want to be right a little bit more than 50% of the time. And you know, I think it goes back to the old uh the old uh you know, baseball analogy, you know, kind of the Billy Bean uh money ball analogy of like getting people on first. So like our goal really is to be right and get, you know, kind of be right on average a little bit more often than not. And over time that adds up, right? And it's also a much more smoother ride because you're not taking these big bets along the way. So again, do we try and model these things? Yes. it it's very hard for us to come we're not we don't rarely do we come out with a big call that's like yes this is grossly overvalued or this is grossly undervalued >> and I mean there's a bit of a debate in markets amongst quant researchers maybe about six months ago I think it was around you know simplicity versus complexity and you know for a long time the you know the the mantra was always parsimmonious models you know fewer um you know um inputs uh keep it simple more robust etc. And then there's a research piece came out saying no that's not correct actually that maybe there is a case uh for more complexity um which kicked off a whole debate I can't say I followed all of it but I was aware of it and as somebody more in the weeds on all of this stuff what's your perspective on that >> yeah so generally what I say is we want just enough complexity to to do like the to basically do what we need to do so like I I tend to go more on the simplistic side. However, you know, you have to you have to do a good job of representing the world and modeling the situation and and thinking about that when you're building the strategy. Again, you're not just running regressions and I mean the thing you you know like there's a lot of people that say, "Hey, let's just go run a back test." Well, that's interesting, but like I'm not that that's a great way of solving the insample problem. The problem the real problem we're trying to solve is what happens next. And the the the absolute worst thing you can do is is optimize in sample performance, right? You want to when you're building the model, you need to be thinking about what happened in this sample, what you know, what is it, what is the environment, what did this capture, is that likely something that's going to persist. If you take that approach, you're much more likely to be successful in the future. And again, you need to have enough complexity to represent the the world, but or or attempt to we're never really representing the full world, but we're but we want to have enough complexity to to uh to to represent the richness of what's out there, but you don't want to overdo it because again, that's just like a a loaded weapon and someone's going to blow their toe off. >> And I mean, from a research process perspective, how do you guard against that? Presumably you have you know a whole bunch of researchers who are motivated to try and come up with something to present to the investment committee and obviously they're going to be influenced by their experience in markets effectively uh solving in samples. So what are the guard rails? >> Yeah. So listen, this is this is again one of the challenges and again you typically have a lot of uh you know young members of the team that are looking to make a name and you know they're basically saying well how do I how do I turn this from a you know an IR of one to an IR of 1.5 and and and you know how do I make this a little bit better? What I keep my question to them is always what do you have to do to kill this research? Like what assumptions do you have to make in order for this to go away? Like give me the scenario that this fails. Um, so we always like to have somebody that's, you know, thinking about like what's the what's the argument against this? And we have we have a relatively healthy debate internally. Again, it's we're it's it's a fine line. Like on one hand, having a systematic process is great. And at the same time, you have to be careful not to overoptimize for in sample. And it's an it's it's I can't tell you how many times I've seen this where somebody's built a strategy, optimized it to fit a particular period of time. The truth of the matter is is there's so much noise in the data in the real world that you can find, you know, this this has always been my challenge with economics is you can always find some period to prove some thesis that you want, but it may not work all the time. And this is the this is kind of both the the curse and the the interesting. This is what makes markets interesting, right? Is that they don't stay the same and that, you know, what people are doing today is very different than what they were doing 20 and 30 years ago. >> As you say, markets evolve. I mean, um, apart from having some complexity in there, are there other mechanisms to build in adaptiveness in into in into models? Is that is that just kind of looking back on parameters that have worked and then updating for that or how do you think about kind of adaptivity? Yeah. So, we're always looking. So, we're always monitoring what's working, you know, what's you know, you know, I I I have this very it's a very humbling exercise that I force my team to do, which is that, you know, we we've done research and we basically say, okay, when we did the research, you know, how did the signal work? What were the parameters during that period of development? And then if you if you freeze it at that point once we've gone live what has been the outof sample performance and it's not it's it's tough right I mean again there is this bias to optimize in sample and it's a very humbling meeting because there's lots of signals that have been like positive positive positive and then you know you rarely does it go negative exactly it has happened but usually it's like positive but not as positive as we thought it was. Um but we're always you know my view is is you again you can't solve the in sample problem and you know whenever we look at parameters and we say hey something decayed over 3 months in sample over this simulation I'm always pushing people to say well if that's the case today's parameter should probably be somewhat shorter right you can't be assuming that what was in sample is going to work just because you calibrated that in sample you have to extrapolate forward So again there's lots of things like if you look at policy response policy response is much faster now you know if markets are again it's it's extreme with this administration in terms of like markets are panicking and people will start tweeting um you know it used to be that trends the policy response took time and and they did not move fast but really if you think about it you know GFC I mean liquidity you know GFC kind of like slowly got worse over time and then really accelerated did um you know and instigated policy response to kind of unlock liquidity in the market. If you think about you know Silicon Valley Bank I mean we had a bank run that occurred overnight. This really never happened before. And again similarly with COVID we had you know we had you know kind of a a market move in in just really a record short period of time. If you if you got if you panicked at the bottom of COVID you really missed out on the rebound. So we're always trying to think how we've done research, it's worked in sample, what parameters do we need to adjust or do we need to kind of think about ways of shutting something off because the current environment is much faster. So it's not it's something that you need to build in. We don't wait we try not to wait until something has failed because of that reason. We try and design it in from the beginning. I mean another change we've seen in the last while has been more retail participation in the stock market. I mean I mean I don't know does that impact kind of macro markets? Presumably it may impact your long short equity equity market neutral stuff. Um again is that something now you're actively thinking about more and more? >> We do. Yes, exactly. you know, I'd say probably a bit more on the equity side just given that that in the US you tend to have these meme stocks and again the you know in the long run the models work but again you can have periods of time you know and again this is not a new insight you know you can be right in the long run but you just can't afford to maintain the position in the short run so yeah again particularly in the US where you get you know I like to say that you know in the US you know you get these little pockets of hopes and dreams right where you know companies can be losing money and have no near-term forecasted you know uh profitability but people get excited about the story and invest and and again this is this is one of the features and not a this is actually one of the features right because if you think about other markets where you know if you think you know again I often talk to you know people say like oh we've got these irrational investors and you know they're chasing things in the short term and I'm like that's a that's a feature that's not a problem because if you Think about the world of bonds. The world of bond trading is largely an institutional market, right? And again there you have much you don't have as large you know extreme moves driven by kind of speculation maybe apart from you know bonds that are in you know default from Venezuela or Argentina or something like that. But you know it it's you know kind of it's it's much more of one institutional investor trading against another. Whereas in the equity market, you have retail investors and that's really creates a lot of opportunities. Um the trick is obviously staying in staying out away from that steamroller. >> Just I mean we we we've talked a little bit about kind of the shifts in in markets of late particularly on the rate side. Um and [snorts] you know you mentioned your kind of the the perspective that the overall kind of macro environment is still kind of pro- risk based on all of you know all of the parameters and and models you look at on average but I mean would you say is there anywhere where where investors are underappreciating risk in markets at the moment or overlooking opportunity anything that's standing out or any signals you're getting uh from that side? Obviously, we're in an environment where we're seeing this kind of rolling wave of exuberance in in the stock market. Um, you know, what you think that's a warning or is is just reflecting structural trends? >> There are definitely some pockets of of excess. I mean, the thing that about the US in particular is that earnings have been solid. I mean, you know, if you look at what is interesting, again, I hear lots of commentary against kind of the mega cap names, but you know, if I think back to the tech bubble, the tech bubble was all about names that were not profitable, right? That were all, you know, I mean, we were talking about valuing companies based on clicks and eyeballs. The the mega cap names that are doing well today all have cash flows, right? All these are all profitable businesses. So listen, I think there is a debate about, you know, is the current capex cycle going to be worth it? And I think that to be honest, that's something we're just we're just going to have to see like does does this capex get rewarded? Does AI turn out to be a bit of a you know, turn out to be a boom. I think it is going to help some some areas, you know, where's there opportunity? Um again, I think people have probably ignored emerging markets for a period of time. um you know for for many years you know emerging market emerging markets was always a story of like oh it's the next best thing but the earnings never mater materialized but I think we are seeing changes in emerging markets and they've been out of favor for so long we do see like I would say for institutions most of the incoming questions we get are about emerging markets because they also see the long if you look think about long-term GDP growth you know long-term profitability that and and the fact that the asset class has been ignored for some time. We see that as a as a long-term strategic area of growth. >> Okay. Um I mean how do you see quant investing evolve from here? Obviously as you say it's evolved so much since the 1990s. Um you know obviously in terms of data um now we've then the internet and then you know obviously now we've got um machine learning and now the LLMs. Um is quantum computing the next thing or what you know what will it look like in 10 years time? Yeah, it listen um yeah I quantum computing I don't know I mean there's you know again I I um people tell me that things like fusion are really just around the corner but as we know that's been the case for you know for for the past 50 years it's been just around the corner or and quantum computing again look I I think some of these technologies are very interesting what I would say is there's a big difference between making something work in the lab and and scaling it up in the real world, right? Like to making something work in the real world, it has to be pretty robust, you know? It can't be a it can't be like a scientific curiosity. It's got to be something where, you know, it's robust enough to work in the real world. They've figured out I mean, why have semiconductors been so successful? It's because these semiconductor fab companies have figured out how to do like extreme clean room automation, you know, all of the many processes of making chips and and again it's become a huge industry and there's a lot of technology that has gone into like the the commercialization of this and you know again it took years to go from the the early chips that we saw you know in the days of you know the the, you know, 6502 processor to where we are today. And it's going to take time for some of these technologies e even if it works in the lab today, it's going to take time for it to become commercially viable. >> Okay, good. So, just before we wrap up, we always like to ask our guests um for some reflections on career and you know, maybe advice for people looking to build a career in quant. I mean any things you've read or done that have been influential on you? >> So what I would say so my my general advice to to younger folks interested I in this particular field or interested really a lot of this applies I'd say any technical field is a you know learn how to communicate learn how to write and learn how to speak. I personally underappreciated these skills early in my career, but what I've what I've seen is that being able to communicate, whether it's a podcast or writing is something that with, you know, is going to be a huge determinant of your, you know, of your um success over time. You like being able to communicate simply, effectively, being able to read the room, understand the audience, you know, what type of what you know what how do you interact with and how do you interact? The second thing I would say is build a network, right? I none of my jobs have come from like a blind application. You know, never once I think I I applied blindly to graduate school, but I think after that going to NASA, going to Barclays Global Investors, they were always connections that I had with individuals that ultimately led to a role. So the trick about a network is you need to build it when you don't need it. And because by the time you need it, it's too late to build it. And I think the last thing I'd say is just continual learning. Whatever we're doing today, we didn't I wasn't taught in grad school or or undergrad. I mean, I I didn't, you know, I didn't study economics, but you can learn it, right? So, you have to just be committed to evolving, you know, whether it's using new tools, whether it's picking up ideas, you have to be committed to learning and pushing yourself. And look, it's it's uh you know, it's it's not an easy process, but I think that's you know, that that's how you stay relevant in this in this in in in industries in general. Good stuff. Well, thanks very much for coming on, George. It's been uh great to get your insights on the whole world of quant and quant multiasset and quant macro and all that goes with it. So, we'll be fascinating to see how that whole world evolves in the next while. Um, people can obviously follow you and Py and Pim Quan Solutions to read and hear more about your work and uh certainly encourage everybody to do so because it's a it's a fascinating field. Uh, but from all of us here at Top Traders Unplugged, thanks for tuning in and we'll be back soon with more content. Thank you very much. [music] 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 your favorite podcast [music] platform and follow the show so that you'll be sure to get all the new episodes as they're released. [music] We have some amazing guests lined up for you. 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Why Macro Investing Is Becoming More Systematic | Allocator | Ep.35
Summary
Transcript
You always have to ask yourself, you know, are the assumptions behind the model still valid? Has there been some regime shift in the data? That's the kind of thing we worry about every day when we're using models. We still see like an overall positive macro environment for risk assets. But in many ways, the the US economy has been surprisingly resilient despite all of the challenges that have impacted it. >> [music] >> Welcome to Top Traders Unplugged. In markets, success doesn't come from predicting what happens next. It comes [music] from being prepared for what you can't predict. In each episode, we go deep with some of the world's most thoughtful minds in investing, economics, [music] and beyond to understand how they think, how they prepare, and how they decide, and [music] the experiences that shaped how they see the world. No noise, no shortcuts, [music] just real conversations to help you think better and invest with confidence. Welcome back to Top Traders Unplugged. My name is Alan Dunn and today I'm delighted to be joined by George Patterson. George is a managing director and CIO at Pim Quant Solutions. He oversees all portfolio management and research for Quant equity and multiasset teams. He's in the markets for many years now. Previously was at Axioma uh identifying buyside trends and market opportunities. He was CIO for quant investments at Bank of Montreal Global Asset Management and earlier in his career was co-founder of Mena Capital, a quant equity hedge fund. His background is in physics. He has a PhD in physics and spent time at NASA earlier in his career. So, George, great to have you with us. Great to have a proper rocket scientist to chat to. How are you? >> Uh, very good. Thank you for a great opportunity to be here. >> Good stuff. Well, I gave you I gave our listeners um kind of the highlights from your career, but I mean, as I mentioned, you started off in physics and then you worked at NASA. What got you interested in that? And then how did you make the leap into finance? Um you know I I was always interested in physics from a very young age. It was just an area that I thought brought together you know math and uh you know science and there was a lot of technology involved um in in you know how you do experiments and it just for me it was like the combination of many different fields. I was always very interested. I enjoyed it. However, I knew I was not going to have a traditional academic career. I knew I wanted to do something in industry and I didn't really know what it was. Um, and it's, you know, kind of really by luck, I found some colleagues that that understood my skill set and what I could offer. most, you know, investment firms were not interested in hiring a physics uh PhD, but there were there was a group, it was at Barclay's Global Investors, uh in the mid 90s, and they looked at me and they said, "Yeah, we think we can figure out what to do with you." And um you know, that was really where I cut my teeth. >> Interesting. And you think um I mean, I come from like an economics background, so kind of view markets from that kind of perspective. And then um I guess people with a quant background I mean do do you take a different lens when you're looking at price movement price returns and financial markets. >> So I'm a strong believer in having like a multi-disciplinary team. So you know I think what you know I'd say when I was early in my career I was very focused on the pure quantitative aspects and you know what what did the model say but over the years I've come to realize that it you really have to kind of combine many different views right quantitative is a very useful tool but you also have to pay attention to what's going on in the market. Um so really my goal and particularly with my team is to have people with economics and finance backgrounds but also computer science, mathematics um you know different fields because people bring different insights to the problem. So that's one of the things we try and combine at Pium quantitative solutions. >> Very good. Well give us a sense on PIM quant solutions in terms of the the size and scale. I know there's kind of two elements to it. there's a quant equity and and the multiasset side in terms of kind of assets under management or the types of clients you deal with. >> Sure. So, we're the quantitative solutions group within PJIM. Uh we're actually one of the early pioneers in this space. We've been we just celebrated our 50-y year anniversary and we and actually many of our our track records go back 25 30 even 40 years. So, we really were a pioneer in this space in many ways. The business actually originally started with multiasset and focusing on you know systematic applications of multiasset which obviously has evolved over the years. That's roughly 50% of our $110 billion and then quantitative equity which is much more uh along the lines of you know benchmark relative strategies you know kind of typically with you know tracking error anywhere between two and 4%. Good stuff. And um I mean as part of your role, you're responsible for for portfolio management, research, the the full gamut of of of investment activities, isn't that it? >> Correct. Yes. Yeah. So again, it um again from my perspective, it combines many different skill sets. Uh you know, there's the investment side, obviously, research. Research is one of my passions. um trading again all of these areas have really evolved quite a bit over the past 30 years or so that I've been involved in this industry um and obviously technology plays a very different role right it's you really need to have them embedded in the investment team if you're going to have a quantitative firm I think actually for any firm these days technology really needs to be embedded into the process given the rate of change >> yeah so you mentioned you started off at BGI in the mid '90s and um you know so that's same same time I started off myself but you know it's sobering to think it's uh 30 years ago now or whatever you know lot of change in the market since then as you say in terms of the technology you know market participants market micro structure I mean when you reflect back what what do you think the macro investment landscape how has it changed over that period >> um well there's been a lot of change right I mean when I think about what the main themes of change are is you know one is just data Right. In the in the early days, we were scraping to find data. You know, people didn't really understand the use. They they were not thinking as systematically. And it was also a time when you that was kind of the '9s was really the error of the big macro traders, you know, like Soros and Dereken Miller and Robertson and and really, you know, making big bets. Um but over the years I mean if you if you think about it today we have you know GDP now we have you know web scraping prices we have um you know you can track behavior there's geospatial I mean just the amount of I mean the really the rise of technology has just enabled us to have a much tighter view on what consumers are doing which is obviously critical for understanding where markets are going and I think the consequence of that too is that you know the big opportunities that the the macro concentrated traders have had in the '9s really just don't exist today. You have to be much more nimble opport I mean macro investing has become much more systematic than it than it was in the '9s. >> Yeah. I mean I I don't think you heard too about too many systematic macro traders back then. I think it's fair to say it's a product of the uh of of of more recent decades. Um I mean obviously we we we've you know with that change in micro structure uh and availability of data etc. Have you seen an evolution into strategies over time as well? >> Of course I mean I I would say there's definitely been an evolution of the strategies in in the sense that we're able to get me much more data that we're able to proxy for things. At the end of the day, we're still I I'd say I'd say we're still trying to think about what are the drivers that are going to impact markets, things like inflation, things like growth, >> but we're able to get much better proxies to measure those those characteristics. So, you know, it used to be that you'd rely on government data that came out once a month or once a quarter. Now, with language processing, you can be monitoring news feeds. You can be looking at, you know, papers from across the globe, looking at foreign languages and monitoring convers, you know, news stories about inflation or what are people really focusing on. So, I'd say the overall thesis that drives the investments is similar, but the way that we're able to get data and proxy and measure those is just really exploded. >> Yeah. Um, well, maybe just taking it into the the current day. I mean obviously you're running multiacet solutions and I guess in the multiassid space you know the 6040 has been the you know the benchmark for a very long period of time and worked very well for many decades particularly the 2010s and then as we've come into this decade you know a theme has been the end of 6040 um and you know obviously a shifting macro backdrop how do I mean how do you think about that is it a case of you know people talk about 60 2020 20 or 60 30 whatever it is you know um is that the way or or or what's your thoughts? >> Yeah. So, so people have been talking about the demise of 6040 for years, right? I mean, this is not something new. And there's definitely periods of time when it doesn't work. Like if you look at [clears throat] 94 or if you look at like 2022 where again, you had like uh sudden moves in the bond market that really cause bonds to not work as a defensive instrument. I think 6040 is probably on one hand, it's still probably going to be around for a long period of time. However, there are a lot there are many more ways these days to be able to get access to to diversification that you just couldn't get 30 years ago, right? So, depending on whether you're looking at I mean, if you're an institution, you can become much more involved in overlays um or solutions that are going to give you a little bit of convexity or downside protection or or you know, as you know, return stacking. If you're a retail investor, you know, there's a lot of products that you can get on either on the der I'd say on the the category of derivative income and derivative protection that are really not new strategies, but they've been democratized >> from strategies that were usually only available through private banks. >> Yeah. And and I mean is that you know if a client comes to you with with a blank piece of paper looking to build out a a multi-assid solution is kind of 60/40 still the to the the starting point with some kind of overlay on top of that or how how do you approach the the problem? >> Well it it really depends on the client. I mean most of the time clients are coming to us with a problem. I mean the you know this the solutions part of PM quantitative solutions means that most of our mandates are customized. So most of the time a client typically an institution uh endowment foundation comes to us with a challenge such as you know I have this in my portfolio how do I get back to my strategic alignment? You know I've got a blend of publics and privates. What's the best way to to solve this particular problem? And a lot of times we're really, you know, thinking across the board, not just 6040, but how do we get there? Sometimes with derivatives, sometimes with options, you know, looking to solve those problems really on a much more of a a customized basis. Rarely do we get somebody that just comes in and says, you know, what do you what do you recommend? >> Yeah. Okay, fair enough. Um I [clears throat] mean are you seeing like anecdotally it feels like institutions have you know heavily loaded up in private private markets and maybe are overly exposed there. I mean in terms of the kind of institutions coming into you do you think that's a theme that you're seeing? >> Definitely. So we see a lot of you know a very common request from an institution is I have this portfolio of of you know a large portfolio of privates um maybe concentrated in a particular area and that the you know privates can offer really interesting um long-term return opportunities. However you people forget that once you've got invested in them you largely can't trade them unless you're willing to accept a material discount. So, a lot of the conversations we have are I have this portfolio of privates. I want to get back to my strategic allocation, but it's got to be liquid. I want to have I I'd love to have some alpha in the process, and I want something that's disciplined and systematic. Help me get there. So, those are that's really in the multiasset space. Those are a lot of our conversations. Liquidity is a key component because again, investors have most investors already have relatively mature portfolios. >> Okay. Interesting. So obviously I mean linked to the questions around 6040 is the um I suppose pick up in inflation we've seen in recent years. Obviously we had an inflation spike in 2021 2022. Inflation's come down since then but still above target and it's picked up again recently. Um I mean obviously that now seems to dominate the thought process a little bit more. Um is that what you're seeing as well? And I mean what does that mean in terms of a portfolio construction uh perspective? So if so really since the beginning of the the military actions in the Middle East it's it the market has really it's been you know like there's been one principal component right it's been you know oil's up equity down or oil up inflation up equities down or vice versa and it's that's really been the dominating risk factor that's driven the news. You just kind of have to look at one asset in the morning and you can figure out what everything else is doing. um you know inflation and again again we what we rely on is a number of like advanced language models to kind of see how people are talking about this in the marketplace as well as monitoring flows in the marketplace and it's clear that inflation's become a risk factor. It's it's not quite at the level where oh this is going to cause a recession or this is going to cause a minor a major downturn. It is definitely going to weigh on the economy and and shave a bit off of GDP, but I I think you know t typically the rule of thumb is around 4%. If you get you know if inflation's under 4% you can still do okay with equities. Obviously commodities become more important. Um if it starts getting above 4% that's really when we start seeing you know kind of material impact you know either both in terms of fiscal policies but also in terms of markets. And it doesn't seem like we're going to get there just yet in in the near term, but again, this is something that we we monitor on a daily basis. >> And then from I I guess from a portfolio construction perspective, do presumably then you have more clients looking to actively hedge inflation risk or are cognizant of that risk. Um, yeah. Is that fair? >> So, we're definitely, so we're definitely, so the way I would say we think about it is is like there's there's kind of a mid horizon view and then there's a a much shorter term horizon view. And the mid- horizon view is much more tied to fundamentals. So there, you know, we're looking at the, you know, we're looking at the many the multitude of data that you can get, you know, forward interest rates, you know, GDP, corporate profitability, you know, employment, jobs. There's a there's a whole host of numbers that that we use to then try and figure out what regime are we in in the middle in kind of like the mid horizon. And then we combine that with a shorter term tactical view that again is going to basically try and pivot between, you know, cons risks about inflation. Obviously, you know, having some risk about inflation will mean that we're a little bit more, you know, we're still pro- risk in general, but the the the the risk concerns around inflation have caused us to kind of shrink some of our active exposures just because of the of the really the tactical overlay on top of that. Um, but you know, if you look at the other economic data, we're still generally in a benign pro-risk environment. If you look at any of the macro indicators, you know, any of the the forward rates, I mean, people are people are expecting this to, you know, continue for a short period of time, but yeah, >> 3 to 6 months, end of the year, we expect, you know, things will be they're not going to go back to where they were, but they're not they're probably not going to get worse. >> Yeah, fair enough. But just going back to the inflation bit, I mean obviously you know the kind of traditional way people might hedge or have um exposures to assets that would benefit from inflation or maybe real assets, property, infrastructure, etc. And then obviously in the liquid space is it via gold I guess or commodity futures or how would you kind of construct a basket or a portfolio or is it more on the equity side more energy stocks etc or how do you think about kind of creating some kind of liquid inflation edge? >> So it's really a combination um it's really a combination most of the time we implement we think about how to hedge against inflation using positions as you said. So again probably the best tool is commodities to to be able to hedge and obviously if you look at the return you know the return of energy has been something you know it's been you know kind of huge returns since basically the overyear to date just given the overall geo geopolitical risk but again commodities are still one of the best liquid tools right um you know real estate infrastructure etc those all give you some long-term uh in you know hedging against inflation equities tend to do well up to a certain threshold to hold. >> Um so again we do we have been and actually you know some of our commodity uh we were already >> um you know kind of we always include some commodities given the the um the the inflation hedging nature of them. Commodities if you ask me are I I kind of call them the the forgotten stepchild of multi-asset. Everybody thinks about stocks and bonds and real estate, but again, commodities often times give you a a good amount of inflation protection and and really what you got to worry about is the inflation shocks and that that's when commodities really can perform in the short term. >> Yeah, fair enough. you mentioned there about regimes and identifying regimes and I know that's something you've done a lot of research and and work on and I mean it feels like the macro regime has undergone a fairly significant shift from you know um you know from if you look back to the 2010s obviously we had secular stagnation we had low inflation low growth um now obviously since co we've obviously gone we went into a higher nominal GDP environment for a while, but now at least uh a little bit stronger growth and certainly more higher and more volatile inflation. So what are the ways you kind of categorize the the the economic and market regimes? >> So we still so so for us again we we're relying on a number of of techniques to use to to categorize the regimes. The one that we're using most recently is just to throw in a little bit of math, throw in a little bit of mathematics as kind of a a Gaussian mixture model that basically views the world, views the economy as some sort of unobservable state and then we use all of the different data points to kind of classify and say what type of what type of regime are we in. And again, you know, that tends to be a I'd say a medium-term view. we don't use any like market-based data there because markets can move much faster than the overall economy. So again, that view tends to put us in a in a moderately pro-risk environment. This is something that what I would say is when you're in the in the world of multi-asset, you really only have so many instruments that you can that you can use. So again, it's useful to have a model, but it's always useful. I mean, one of the challenges that you have when using models is you always have to ask yourself, you know, are the assumptions behind the model still valid? Has there been a regime shift in the data? That's the kind of thing we worry about every day when we're using models. We still see like an overall positive macro environment for risk assets. Definitely risks on the horizon. There's no question. But in many ways the the US economy has been surprisingly resilient despite all of the challenges that have impacted it. >> I mean people talk about regimes and also people talk about different um you know I suppose there's different terminology. Some people call it different quads you know uh or people talk about the different stages you know u disinflationary boom or inflationary boom and then stagflation or recession etc. I mean do you have those kind of do you kind of categorize markets in that way and then try and map that to asset performance or obviously you're talking about this um Gaussian approach which sounds much more I guess statistical in nature is is that it >> so that so we have multiple approaches so yes we do have kind of more traditional you know you know where are we in inflation is inflation you know inflation high and rising you know high and falling again we have those traditional and then we also have more statistical methods. >> Yeah. >> Rarely rarely do you find that one model is the best model, right? Often times what you really want to do is you want to combine multiple models together. So behind the scenes, rarely is there one model that says, you know, here's the, you know, here's the one scenario that we think is the highest likelihood. Oftent times there's really multiple models running in the background. And again, you want to com I mean, just like different data sources, we're looking to, you know, we're looking to, you know, we don't have a portfolio that's just a growth portfolio or just an inflation portfolio. We're trying to balance all of those things together. You know, we think about relative valuation. We think about growth, but at the same time, it's the same thing with models, you know, like rarely do you want to have one model oftentimes. And and again if you look at like um if you look at these um you know I love to look at this website Kaggle that has these um you know kind of model building competitions. If you look at the the winners of that particular of those projects that they have um they're all multimodel the winners are usually like a multimodel approach. Um so rarely is there one approach that is that is like okay this is the one answer. World is the world is just not that clean. No, no. I mean it's absolutely I mean that that's it's a segue to the next question which is I mean if you look at today and maybe look at from a regime perspective or a market perspective and parallels with the past. I mean have you observations because as you say there's always observations with lots of different periods but not one exactly or are your models pointing to any kind of parallels? I think people do get a little too attached to like okay my you know my particular analysis says that you know we're in a regime we're in a regime just like a particular year in the past and and again I think this is probably I'd say the bias of human of of like human pattern matching what I would say is we spend a lot of time doing the research building the models and then we largely use the models in the real world right and again you're always going to have models that have differing views Rarely do we build things that are everything's going to point in the same direction. But really the the effort and the thinking about all of that happen at the model construction stage. And once the once the models are being once the model output is being produced and recommending a position, we're always asking ourselves are the assumptions correct? But if the assumptions are correct, we're going to use the model. Right now, my job and the hardest part of my job is making sure my team sticks to their research and sticks to their process. And that's this is the hardest part of investing is the market's working against you. You have a process and you know you're getting nervous and you know it's not working. You're losing money. People are picking up the phone. What's going on? That's the time that that people tend to panic. And the the hardest part of this job is being patient and sticking with your guns and you know if you have faith in your process, sticking with your process that that is the single hardest part of this job, particularly when everyone's questioning you. >> Absolutely. Fair enough. Um at the same time, you did touch on kind of regime changes or structural shifts and all of that. I mean, and which for any model builder, I guess that's always top of your mind. Um I mean are the are the models all kind of fun fun fundamental or do you you know respect the market trend or the market price action or are there any kind of pure price price uh inputs uh into the models kind of more technical in nature >> so there there are some you know I would say most of the model probably 80% is really derived on fundamentals right looking at fundamental data and combining it in in unique ways that give us our give us our our our recommended positioning. So, this kind of goes back to exactly what I was saying before. If you have a purely statistical model and it tells you to go long, you know, go long Europe and short the US and it's just a purely statistical model, there's going to be a period of time when that doesn't work. >> Yeah. >> And when it doesn't work, you have to ask yourself, what do I do? And if you don't understand it, if it's a purely black if purely statistical blackbox model, that's when you're going to get nervous and you're going to unplug it because you don't understand it. If you have a model that has fundamental data and you've checked the insights as you're building the model and as you build the model, you're thinking through like does this make sense? Does this type of a, you know, how should what do I expect my priors to be? And is the model coming out in the way that does that? So it's not just like, hey, I run a regression and I take the coefficients and I throw them in the new model. As you're building the model, you need to really think about what is it that you're trying to tease out here and how does this align with your insights. And this is really true with the macro side because the degrees of freedom is very very very few. >> So when you're doing that, that that is what allows you to stick with your model when times are tough, right? >> Okay. >> We do have some small statistical components um just because again you need to pay attention to those. they tend to be much shorter term. >> Okay. And have there been periods where you know obviously we've had big dislocations maybe co is the obvious example where we had big moves in markets seemingly you know um divorced from economic fundamentals because obviously there was a pandemic out there that didn't immediately get reflected in economic data. Was that a challenging period or did that prompt you to have to intervene or how did how did you deal with that as a fundamental macro modeler? >> Yeah. So, so again there were situations where like co was very challenging. So on the macro side I would say some of the ch the biggest challenge was that certain data were not released on time right I mean everything was shut down. So certain time series were not being updated and that is where you have to ask yourself okay you know here's my model it was built with a certain set of assumptions how would the model work today if you know based on what we would might estimate this data to be and this is really th those are the periods of time that where yes you have to think a bit beyond just okay what what does the model say based on some historical data that may not be accurate that's where un I mean what I always tell my portfolio managers at the end of the day is we're implementing a certain philosophy. The model is a tool that enables us to be much more efficient in doing that. But at the same time, the the investment philosophy comes first. And it's periods of time like co that you have to be a bit more hands-on and say wait a minute what you know what is really happening in the market here. The model's not updated quickly and we might need to intervene. That was definitely true on the quant equity side where you know you saw cruise ship you know again cruise ships airlines literally you know couldn't couldn't dock you know landed on the tarmac and weren't taken off again there you had to kind of get inter you had to intervene in the model because the updates the inputs were just not updated. >> Yeah. Okay. Um so in terms of kind of integrating that into traditional multi-asset is it so it's effectively you know you're taking client portfolios which are generally you know have a lot of either privates or equity risk or something like a 60/40 and then running effectively a quant macro or a systematic macro as an overlay using derivatives. Is that the kind of most common >> part of it? So multiassid is a is is just is is extremely varied. So there's some there's parts of it that are just long only as you mentioned. >> Yeah. >> Uh you know kind of 6040 7030 type benchmarks you know you know using active management on the different sleeves. >> There are other components that are you know long short you know more of a global macro and then there there are overlays. Um there are also a number of optionbased strategies that are designed to either give protection or give convexity during periods of market stress. So the multiasset team really has this large um mandate across many different types of asset classes typically much more on the pro-risk side I would say of the equation. >> Yeah. Very good. And I mean in terms of that kind of downside protection is that kind of um I suppose uh buying protection or going long volatility or you know just thinking of you know people often point to you know obviously long V did very well in co so that was fantastic but then you contrast that with 2022 when the equity market went down but volatility was moderately high without ever spiking very high and longv kind of strategies didn't provide an enormous amount of convexity in in that period. So, you know, if somebody is looking for downside protection, how do you think about that from an options perspective? >> So, there's a couple ways we do it. So, just go just going out and buying puts is expensive. Replacing um some equity exposure with kind of effectively some call exposure typically on the long term. And what you'll do is you'll get more protection on the downside. And it's really how you rebalance that allows you to get the downside protection. and do it in a very cost-effective manner. That's the challenge is always like how do you pay for the downside protection. There are also other dynamic ways that you can effectively do it with with either kind of more defensive equity strategies or you know sector rotation that kind of gives you a bit more downside protection as the market moves down. But it's very expensive to just go out and buy a call [music] or go out and buy a put. [music] you talked about the evolution that we've seen in markets in in [music] data in technology etc. Obviously um everybody's talking about um AI at the moment um and and obviously in in from a quant perspective people have been using uh machine learning already for for for quite a long time. Give us a sense on that evolution of the use of u machine learning and and AI in in your research process. >> Sure. So, so I I this is an area where again the language has changed so much over the uh over the years and you know what you know what exactly is machine learning you can argue people have been doing machine learning for decades um you know if you estimate a regression and you update the parameters with some sort of a rule is that machine learning well that's basic machine learning >> um so we've been involved in in various aspects of that for you know really you know for for many years. Um for us, I would say most of the focus in the current environment is around language models. That's just been a very rich source of data and a rich source of alpha that we found. And this is both for quant equity and for multiasset. So again, you know, in the early days it was like counting positive words versus negative words, which is called the bag of words method. Um then we moved on to like BERT and Finnbert, you know, which were which measured sentiment based on news. And now we've moved on to using LLMs to to basically ascertain, you know, what types of themes are playing out in the markets. >> And you know, again, with everything, the goal is for this to be as transparent as possible. you give up a little transparency when you start using some of these methods because they are more statistical. But we're always looking to make sure that we can trace from what the recommended positioning is all the way down to the to the raw underlying data just for transparency. >> Okay. And I mean in terms of the use of LLMs is that you know obviously um people have been using it say to read a lot of say Fed speakers or earnings releases or things like that. [clears throat] I mean there I guess obvious uh applications you know beyond that without sharing the the secret sauce any other kind of um obvious ways that you you're using it or you're hearing people are using it? Well, I we're just it's an area of focus for us because if you think about it, so much of human knowledge is encoded in te in text, right? I mean, there's a certain you know, you can get compet data on quarterly earnings and you can get you can get uh >> earnings estimates from various vendors and you can measure, you know, kind of web traffic. But again, there's a huge if you think about it like most of the world focuses around written text. So, you know, whether it's an analyst report, whether it's a company website that is talking about product descriptions, whether it's, you know, a 10K or or a um a product release, just the amount of information in there is very rich and the ability to extract things both on the alpha side, but more importantly on the risk side have become have just really advanced by leaps and bounds. And so that's really I would say the area where we see the most um the most relevant research in the current environment. I would there's lots of applications obviously just you know accessing research summarizing you know kind of consolidating data in a in a timely manner. Obviously the software developers are big fans um accelerating a lot of their process. >> Yeah. Now obviously you mentioned simple tasks like counting um number you know counting frequency or whatever and then positives to negative. So I mean do you think LLMs are at the point now of being good at at evaluating things or you know say for example the tone of a fed speaker or something like that >> for things yes I would say for certain tasks yes keep in mind this is a statistical model right sure so if you have like all of these models are only as good as the data they've been trained on and you know you have to remember that all of these LLMs are basically trying to predict the next token that's all they're doing you know uh they've got these tokens that they've seen in the past and they can predict the next token. So for things like human speech, yes, you know, you can they're very good at saying how does this really what you're saying is how does this compare with the past, you know, how what is the you know, what is the pattern of speech and does that tend to be positive or negative? So for things like that where you're trying to evaluate something, yes, LLMs are are good. Actually, Bird is surprisingly good or Finnbert's surprisingly good in terms of extracting sentiment. So, so there's a number of areas like that where, you know, yes, LLMs have gotten to the point where you can really use them as effective tools in analyzing either speech or text documents and really probably has become the state-of-the-art. >> Okay. I mean, you do hear a lot of people, I mean, talking about the use of bots and how bots are maybe will overreact to something and you'll see this uh, you know, short-term spike or, you know, sell off in the market because the bots read something which proved to be incorrect. I mean, is that all are they valid observations? Do you see that would or or not? >> You know, it's hard to know because we just don't know exactly who's doing what. I would suspect that yes, there are, you know, automated strategies that are that are probably um, you know, in need of uh in in need of, you know, continual refinement and maybe driving prices in the short term. I mean, we, you know, again, this is this is no different than somebody who fat fingers a trade, you know, and accidentally moves a market one, you know, kind of in an extreme manner. you know, somebody's programmed a bot to do something and and you know, drives the price in one, you know, in an extreme manner. So, is it happening? Yes, it's likely. Uh, it's a little hard to like show evidence that it is indeed the case. At Egim Quantitative Solutions, it's still, you know, we use a lot of these advanced tools, but it's still overseen. There's still what we call a human in the loop. It's still portfolio manager reviewing trades and ensuring it the the portfolio is in the same spirit as the overall investment philosophy. It's not it's not kind of unconstrained robo trading. >> Yeah. And I mean a big debate at the moment is the economic impact of all of this. I mean do you see you know it making presumably it's making your your researchers more efficient, more productive. Does that mean less hiring need over time or is that an oversimplification or how do you read that? >> So I I I don't view this as like a a net destructor of d of jobs. I view it like like with every techn technology change and I get think if you go back and look at historical patterns you know around the introduction of the steam engine and you know the you know the the automobile and the and light bulbs and things of this or you know kind of machine automation. There have been lots of times that it's basically said it's going to put everyone out of work and what's happened is yes certain types of jobs may get eliminated or reduced but other jobs are created. Don't forget building these models is not something that is done easily, right? It costs, you know, hundreds of thousands or if not millions of dollars of time to calibrate these models and to to you know, I I don't know if anyone's really making money in this space yet, right? There's there's a lot of people that have very high hopes of of commercializing this. So, I think it's going to make people more productive. It's probably going to eliminate some positions, but it's going to create new positions. I mean again if you think back to you know the you know like the introduction of the internet I don't think anybody thought of the types of things that were going to come out of the internet and you know in the early days it's always a little rough you know you think oh yeah this is kind of hokey it's not good enough you know if you think about like the first you know the first you know cell phones that were bulky or you think about the first iPhone and again it really was kind of like yeah we can't see this thing taking over the world we still need dig digital cameras. Well, guess what? Nowadays, we don't um because the technology has gotten continues to get better. I think that's the trend we're going to see. >> Yeah. Yeah. Um interesting. Um may maybe moving into, you know, current markets a little bit more. Um just curious to see uh or get a sense on what you're seeing in in markets. Obviously, um you know, we've seen quite a number of shifts on the rate side in the last number of months. You know, if you go back to February, we had, you know, all of the talk about the Catrini report and yields went down. Um, and then obviously as we went into March, we had the strike in Iran and and we had a big reversal in rates and um we've had market going from pricing and rate cuts to to possible rate hikes. I mean, how has that um you know, impacted performance positioning? Um what are your thoughts on that? So, you know, it it's again this is this the challenge is is and I think this is just the way the modern world is. You have to be very nimble, right? You can't you you know, I don't think that there's such a thing as a uh you know, a forecast, you know, a year out that isn't going to get revised along the way. And if you look at like again if you look at if you're looking at future interest rate you know uh moves as predicted by forward curves you know you can go back and look at that historically and rarely is it accurate. I mean it often times they're forecasting hikes which then never materialize or they're forecasting cuts that never really materialize. So again this is just where we are right now. We are very data dependent. So yes inflation is elevated. It's not at the level that it's again it's not at the level that we see it causing like a major downturn or a recession. It is a risk factor. I think the other challenge too that's probably going to pay out over a longer term is just the overall fiscal situation. You know you know where are we in terms of debt to GDP where you know what are the long-term policies? There are a number of issues that probably need to be reser resolved and it doesn't seem like really either party is is tackling them but at some point they are going to become painful enough. We are going to you know hit the so-called tipping point. The hard part the hard question is >> where is that and when does it happen? It's it's not easy to there's no magic formula for that which is I think why you just need to be very nimble in this world. And is that something you can model or or not? Or I mean [snorts] something like the the the term premium has went from being significant to being negative at now seems to be growing again and that would kind of encapsulate those deficit concerns. Would is that something you would specifically look at? >> Yes, it's something you can I mean listen with everything you can attempt to model it. The question is how good is the model? Um, you know, people forget that in the world of quantitative investing, our models are are our models are are, you know, you know, I like to say that, you know, we have a little bit of an edge. We don't have a huge edge. You know, we want to be right a little bit more than 50% of the time. And you know, I think it goes back to the old uh the old uh you know, baseball analogy, you know, kind of the Billy Bean uh money ball analogy of like getting people on first. So like our goal really is to be right and get, you know, kind of be right on average a little bit more often than not. And over time that adds up, right? And it's also a much more smoother ride because you're not taking these big bets along the way. So again, do we try and model these things? Yes. it it's very hard for us to come we're not we don't rarely do we come out with a big call that's like yes this is grossly overvalued or this is grossly undervalued >> and I mean there's a bit of a debate in markets amongst quant researchers maybe about six months ago I think it was around you know simplicity versus complexity and you know for a long time the you know the the mantra was always parsimmonious models you know fewer um you know um inputs uh keep it simple more robust etc. And then there's a research piece came out saying no that's not correct actually that maybe there is a case uh for more complexity um which kicked off a whole debate I can't say I followed all of it but I was aware of it and as somebody more in the weeds on all of this stuff what's your perspective on that >> yeah so generally what I say is we want just enough complexity to to do like the to basically do what we need to do so like I I tend to go more on the simplistic side. However, you know, you have to you have to do a good job of representing the world and modeling the situation and and thinking about that when you're building the strategy. Again, you're not just running regressions and I mean the thing you you know like there's a lot of people that say, "Hey, let's just go run a back test." Well, that's interesting, but like I'm not that that's a great way of solving the insample problem. The problem the real problem we're trying to solve is what happens next. And the the the absolute worst thing you can do is is optimize in sample performance, right? You want to when you're building the model, you need to be thinking about what happened in this sample, what you know, what is it, what is the environment, what did this capture, is that likely something that's going to persist. If you take that approach, you're much more likely to be successful in the future. And again, you need to have enough complexity to represent the the world, but or or attempt to we're never really representing the full world, but we're but we want to have enough complexity to to uh to to represent the richness of what's out there, but you don't want to overdo it because again, that's just like a a loaded weapon and someone's going to blow their toe off. >> And I mean, from a research process perspective, how do you guard against that? Presumably you have you know a whole bunch of researchers who are motivated to try and come up with something to present to the investment committee and obviously they're going to be influenced by their experience in markets effectively uh solving in samples. So what are the guard rails? >> Yeah. So listen, this is this is again one of the challenges and again you typically have a lot of uh you know young members of the team that are looking to make a name and you know they're basically saying well how do I how do I turn this from a you know an IR of one to an IR of 1.5 and and and you know how do I make this a little bit better? What I keep my question to them is always what do you have to do to kill this research? Like what assumptions do you have to make in order for this to go away? Like give me the scenario that this fails. Um, so we always like to have somebody that's, you know, thinking about like what's the what's the argument against this? And we have we have a relatively healthy debate internally. Again, it's we're it's it's a fine line. Like on one hand, having a systematic process is great. And at the same time, you have to be careful not to overoptimize for in sample. And it's an it's it's I can't tell you how many times I've seen this where somebody's built a strategy, optimized it to fit a particular period of time. The truth of the matter is is there's so much noise in the data in the real world that you can find, you know, this this has always been my challenge with economics is you can always find some period to prove some thesis that you want, but it may not work all the time. And this is the this is kind of both the the curse and the the interesting. This is what makes markets interesting, right? Is that they don't stay the same and that, you know, what people are doing today is very different than what they were doing 20 and 30 years ago. >> As you say, markets evolve. I mean, um, apart from having some complexity in there, are there other mechanisms to build in adaptiveness in into in into models? Is that is that just kind of looking back on parameters that have worked and then updating for that or how do you think about kind of adaptivity? Yeah. So, we're always looking. So, we're always monitoring what's working, you know, what's you know, you know, I I I have this very it's a very humbling exercise that I force my team to do, which is that, you know, we we've done research and we basically say, okay, when we did the research, you know, how did the signal work? What were the parameters during that period of development? And then if you if you freeze it at that point once we've gone live what has been the outof sample performance and it's not it's it's tough right I mean again there is this bias to optimize in sample and it's a very humbling meeting because there's lots of signals that have been like positive positive positive and then you know you rarely does it go negative exactly it has happened but usually it's like positive but not as positive as we thought it was. Um but we're always you know my view is is you again you can't solve the in sample problem and you know whenever we look at parameters and we say hey something decayed over 3 months in sample over this simulation I'm always pushing people to say well if that's the case today's parameter should probably be somewhat shorter right you can't be assuming that what was in sample is going to work just because you calibrated that in sample you have to extrapolate forward So again there's lots of things like if you look at policy response policy response is much faster now you know if markets are again it's it's extreme with this administration in terms of like markets are panicking and people will start tweeting um you know it used to be that trends the policy response took time and and they did not move fast but really if you think about it you know GFC I mean liquidity you know GFC kind of like slowly got worse over time and then really accelerated did um you know and instigated policy response to kind of unlock liquidity in the market. If you think about you know Silicon Valley Bank I mean we had a bank run that occurred overnight. This really never happened before. And again similarly with COVID we had you know we had you know kind of a a market move in in just really a record short period of time. If you if you got if you panicked at the bottom of COVID you really missed out on the rebound. So we're always trying to think how we've done research, it's worked in sample, what parameters do we need to adjust or do we need to kind of think about ways of shutting something off because the current environment is much faster. So it's not it's something that you need to build in. We don't wait we try not to wait until something has failed because of that reason. We try and design it in from the beginning. I mean another change we've seen in the last while has been more retail participation in the stock market. I mean I mean I don't know does that impact kind of macro markets? Presumably it may impact your long short equity equity market neutral stuff. Um again is that something now you're actively thinking about more and more? >> We do. Yes, exactly. you know, I'd say probably a bit more on the equity side just given that that in the US you tend to have these meme stocks and again the you know in the long run the models work but again you can have periods of time you know and again this is not a new insight you know you can be right in the long run but you just can't afford to maintain the position in the short run so yeah again particularly in the US where you get you know I like to say that you know in the US you know you get these little pockets of hopes and dreams right where you know companies can be losing money and have no near-term forecasted you know uh profitability but people get excited about the story and invest and and again this is this is one of the features and not a this is actually one of the features right because if you think about other markets where you know if you think you know again I often talk to you know people say like oh we've got these irrational investors and you know they're chasing things in the short term and I'm like that's a that's a feature that's not a problem because if you Think about the world of bonds. The world of bond trading is largely an institutional market, right? And again there you have much you don't have as large you know extreme moves driven by kind of speculation maybe apart from you know bonds that are in you know default from Venezuela or Argentina or something like that. But you know it it's you know kind of it's it's much more of one institutional investor trading against another. Whereas in the equity market, you have retail investors and that's really creates a lot of opportunities. Um the trick is obviously staying in staying out away from that steamroller. >> Just I mean we we we've talked a little bit about kind of the shifts in in markets of late particularly on the rate side. Um and [snorts] you know you mentioned your kind of the the perspective that the overall kind of macro environment is still kind of pro- risk based on all of you know all of the parameters and and models you look at on average but I mean would you say is there anywhere where where investors are underappreciating risk in markets at the moment or overlooking opportunity anything that's standing out or any signals you're getting uh from that side? Obviously, we're in an environment where we're seeing this kind of rolling wave of exuberance in in the stock market. Um, you know, what you think that's a warning or is is just reflecting structural trends? >> There are definitely some pockets of of excess. I mean, the thing that about the US in particular is that earnings have been solid. I mean, you know, if you look at what is interesting, again, I hear lots of commentary against kind of the mega cap names, but you know, if I think back to the tech bubble, the tech bubble was all about names that were not profitable, right? That were all, you know, I mean, we were talking about valuing companies based on clicks and eyeballs. The the mega cap names that are doing well today all have cash flows, right? All these are all profitable businesses. So listen, I think there is a debate about, you know, is the current capex cycle going to be worth it? And I think that to be honest, that's something we're just we're just going to have to see like does does this capex get rewarded? Does AI turn out to be a bit of a you know, turn out to be a boom. I think it is going to help some some areas, you know, where's there opportunity? Um again, I think people have probably ignored emerging markets for a period of time. um you know for for many years you know emerging market emerging markets was always a story of like oh it's the next best thing but the earnings never mater materialized but I think we are seeing changes in emerging markets and they've been out of favor for so long we do see like I would say for institutions most of the incoming questions we get are about emerging markets because they also see the long if you look think about long-term GDP growth you know long-term profitability that and and the fact that the asset class has been ignored for some time. We see that as a as a long-term strategic area of growth. >> Okay. Um I mean how do you see quant investing evolve from here? Obviously as you say it's evolved so much since the 1990s. Um you know obviously in terms of data um now we've then the internet and then you know obviously now we've got um machine learning and now the LLMs. Um is quantum computing the next thing or what you know what will it look like in 10 years time? Yeah, it listen um yeah I quantum computing I don't know I mean there's you know again I I um people tell me that things like fusion are really just around the corner but as we know that's been the case for you know for for the past 50 years it's been just around the corner or and quantum computing again look I I think some of these technologies are very interesting what I would say is there's a big difference between making something work in the lab and and scaling it up in the real world, right? Like to making something work in the real world, it has to be pretty robust, you know? It can't be a it can't be like a scientific curiosity. It's got to be something where, you know, it's robust enough to work in the real world. They've figured out I mean, why have semiconductors been so successful? It's because these semiconductor fab companies have figured out how to do like extreme clean room automation, you know, all of the many processes of making chips and and again it's become a huge industry and there's a lot of technology that has gone into like the the commercialization of this and you know again it took years to go from the the early chips that we saw you know in the days of you know the the, you know, 6502 processor to where we are today. And it's going to take time for some of these technologies e even if it works in the lab today, it's going to take time for it to become commercially viable. >> Okay, good. So, just before we wrap up, we always like to ask our guests um for some reflections on career and you know, maybe advice for people looking to build a career in quant. I mean any things you've read or done that have been influential on you? >> So what I would say so my my general advice to to younger folks interested I in this particular field or interested really a lot of this applies I'd say any technical field is a you know learn how to communicate learn how to write and learn how to speak. I personally underappreciated these skills early in my career, but what I've what I've seen is that being able to communicate, whether it's a podcast or writing is something that with, you know, is going to be a huge determinant of your, you know, of your um success over time. You like being able to communicate simply, effectively, being able to read the room, understand the audience, you know, what type of what you know what how do you interact with and how do you interact? The second thing I would say is build a network, right? I none of my jobs have come from like a blind application. You know, never once I think I I applied blindly to graduate school, but I think after that going to NASA, going to Barclays Global Investors, they were always connections that I had with individuals that ultimately led to a role. So the trick about a network is you need to build it when you don't need it. And because by the time you need it, it's too late to build it. And I think the last thing I'd say is just continual learning. Whatever we're doing today, we didn't I wasn't taught in grad school or or undergrad. I mean, I I didn't, you know, I didn't study economics, but you can learn it, right? So, you have to just be committed to evolving, you know, whether it's using new tools, whether it's picking up ideas, you have to be committed to learning and pushing yourself. And look, it's it's uh you know, it's it's not an easy process, but I think that's you know, that that's how you stay relevant in this in this in in in industries in general. Good stuff. Well, thanks very much for coming on, George. It's been uh great to get your insights on the whole world of quant and quant multiasset and quant macro and all that goes with it. So, we'll be fascinating to see how that whole world evolves in the next while. Um, people can obviously follow you and Py and Pim Quan Solutions to read and hear more about your work and uh certainly encourage everybody to do so because it's a it's a fascinating field. Uh, but from all of us here at Top Traders Unplugged, thanks for tuning in and we'll be back soon with more content. Thank you very much. [music] 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 your favorite podcast [music] platform and follow the show so that you'll be sure to get all the new episodes as they're released. [music] We have some amazing guests lined up for you. 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