The Intersection of Science and Finance with CFM's Jean-Philippe Bouchaud

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On Apple, Spotify, YouTube, or wherever you get your podcasts. Bloomberg Audio Studios. Podcasts, radio, news. This is Masters in Business with Barry Rittholds on Bloomberg Radio. This week on the podcast, yet another extra special guest. John Philip Bouchot is chief scientist, head of research, chairman, and co-founder at CFM. They're a quantitative trend following hedge fund. They run over $20 billion in client money. They've been around for almost 35 years. Put together a very impressive track record. They also run a number of interesting academic research labs and things like that. John Philip has published something like 300 plus academic papers. They are deep into all the things that drive markets from a quantitative perspective. I thought this conversation was fascinating, and I think you will also, with no further ado, my interview of CFM's John Philip Bouchot. So what do people call you? JP, John Philip, Philip, what do you like? There's a lot of deep in France, JP, and Anglo-Saxon countries. JP. All right, it seems a little informal, but I'll go with JP. So JP, let's start with your background. PhD in theoretical physics from ENS. You spent some years at very prestigious research in this institution. As I mentioned, Camindish Labs, what was the original career plan? Yeah, I was planning to be a physicist. But then studying statistical physics, and we can go into that later if you wish, I realized that physics can offer much more than studying physics. And then I was always fascinated by numbers. I've always liked statistics and financial markets spit statistics every day. And I thought, this is a very interesting complex system. There are crisis, crashes, jumps. This system seems to be driven by its own dynamics. Physics have to do something about this. And so-- It sounds very similar to chaos theory. Yeah, exactly. So that was the high days of chaos theory. So I pulled some phrases from some of your papers. One was titled, and I'm going to mangle this, disordered systems in complex phenomena, which can be either physics or finance. Exactly. It sounds like, but what are the dynamics of glassy systems and granular media? That sounds fascinating. Yeah. But the problem is how do interacting elements give rise to something surprising? Granmometer is grains that interact with one another. And then you have these strange phenomena called avalanches, where you drop a grain on the slope. And most of the time, nothing happens. But sometimes there's a big landslide that takes all the grains down. And so this, again, is very remitticent of financial markets, right? I mean, many things happen, nothing much follows. And then sometimes there's a crash. And so this was really intriguing for physicists like me. So I'm-- as you're talking, I'm just thinking of concept and physics that really applies to markets, the three-body problem. When you have those three gravitational masses interacting with each other, it's fairly unpredictable, which kind of seems like markets themselves. Yeah. I mean, there are two ways to be unpredictable. One is that the system is by itself unpredictable, that even with deterministic laws, like the three-body problem, you can't say much after a few seconds, days, or weeks. But there are other kinds of unpredictability where there's a true source of exogenous noise that hits the system and you can't say anything. So that's the traditional way economists think about markets. They're kind of buffeted by things you can't predict because they come from outside. And then I think the physics hunch is that, hey, but there can be self-generated shocks, self-generated randomness that come from large assemblies of individuals, i.e. traders, agents that trade and buy and sell to each other. And this can generate intrinsic randomness that is not of the same kind as the three-body problem, but re-comes from the interaction of a huge number of elements. So I see the parallels between theoretical physics and finance. What led you to begin shifting in the early '90s from studying theoretical physics to becoming fascinated by market microstructure and economic physics? Yeah. So as I said, initially I've always been excited by data and trying to make sense of data. So there was something there anyway. But what really drove the transition was, in a sense, the '97 crash and the Black Sholes theory. So I didn't know anything about that. And then I wrote a paper on what I was working on, which physics systems with large jumps, if you want large crashes, that happened from time to time. And someone who was working in the banking industry called me and said, hey, it's really interesting because it resembles what happens in finance. And in particular, what just happened, the '97 crash. And there's the black-sholes theory that is a theory that only works in a world where there are no crashes. Where all the motions are small and predict. They're random, but they're kind of predictable, even if they're random in some strange way. And I thought, this is really weird. And this guy said, why don't you try to generalize black-sholes to a world where there are crashes? And I thought, well, that's really interesting. So I read black-sholes, and I thought, it can't be right. They must be wrong, these guys. So I kind of redid everything myself, and found something that looked more interesting than black-sholes, because it could be extended to non-gaussian statistics, as they are called, so non-normal distribution, bell curves, and so on. And so it looked to me interesting, and I thought, OK, maybe we can do a software out of that and commercialize it. And so I went and knocked on several doors. And suddenly, the door of Jean-Pierre Guida opened. And Jean-Pierre Guida was someone who had founded actually CFM in '91, that was '94. And I started explaining what I had been doing, and that it was interested in transferring ideas from physics to finance. And he said, why don't we create something together? And so at the time, we created a company called Science and Finance. And this was done in two weeks. It was like amazing the way we met. And there was a fluid that was flowing between us immediately. And so CFM then merged with Science and Finance in 1990. So it's now the same firm. But the idea he had at the time-- he had this small CTA trading firm-- and he thought, I need to beef up my research team. And this guy seems to be interesting, so we just partnered. And that's how it all started. And that CTA firm specialized in in managed futures. Yeah, exactly. Yeah. Now, I know most of the futures traders, I know, they all seem to be trend followers. How do you think about applying quantitative research and theoretical physics to dealing with futures? Yeah, well, that was exactly Jean-Pierre Haigilard's idea. He said, "Okay, I'm doing trend following. It's good, but it's not rocket science. Maybe we can do much better." And so he said, "Why don't we work on something more beefy than just trend following?" And so that started the whole thing. And the idea of the main idea is data. Physicians are good at looking at data and extracting structures, looking at data and imagining that from that data you can build theories, you can identify what's important and what's not. And that's, I think, the way it all works in physics, that you scrutinize data. And then there's a flash and you think, "Okay, I can model that." And it's really the same process in finance, at least as far as we work on some and we are concerned now. It hasn't changed. It's the same process. Huh, really, really quite fast. And you keep your professorships at ENS and you've maintained a foot in academia, even as you're building and running an asset management firm, tell us about that. You're still publishing papers. What keeps you interested in the academic side of finance? Well, first of all, it is me. I feel I'm a researcher at heart and I need to continue. It's like people running the marathon. They are doing something else in life and then there's an urge to run the marathon. For me, there's an urge to understand what I'm doing and understand also things that I'm not doing, even now. Even physics problems I can get excited about them or trying new things like the ML revolution, how does ML work? Why do large language models work so well, learn so well? I think it's fascinating. I want to understand. But there's another reason for doing this, is that to attract talents, you need to identify them, you need to attract them, you need to be their professor at one point. And I think a lot of the success of CFM has been attracting talents. And I think part of that, only part of that, of course, it's a teamwork, is due to the fact that I'm still very connected in academic circles. And young students, they've listened to me, giving talks, lecturing, they've read my papers. And so they feel, let's go and work for that firm because it seems that they're really doing cool stuff. So is that the thinking behind establishing the research division at CFM? I mean, you run that as a full academic research department. As opposed to a lot of asset management shops, they have a couple of CFPs and MBAs and their CFAs and they're working on their quantitative models, you guys seem like you've taken it to a whole different level. Yeah, I mean, most maybe even all our researchers have a PhD. It doesn't mean that we're an academic lab. We're really working on concrete stuff. We're really there to make models that work, build portfolios that are robust, model risk, model execution, control costs. All these things are bread and butter for every day work. But at the same time, we feel that when we find something that is beyond the kind of daily work and that can be published because it brings something to the academic debate or to the public debate. You know, why do markets work? Why are there crashes? Are markets efficient? What about the economy? Do people understand inflation? We need new theories to understand inflation, monetary policy and all these things. We believe that it's our role also because we have access to so much data and we're privileged. You know, academics, they don't have access to so much data. And so we have to give back in a way. And the reason we're doing this is, as I said, it's not only because we're driven to do that, but also because it creates an atmosphere where people are happy to work. And CFM, I hope, you know, don't want to put in words in their mouth. Well, you guys opened up an expanded, a big New York office that you don't seem to be having much difficulty recording people there. What's the head count there now? We have 115 researchers and 15% of them are in New York. Yeah. What motivated expanding the New York office as much as you have? Well, first of all, a lot of our investors are in the US, so we need to be there and interact with them. And we need to have a presence, if only for investor relation, but also because there's a lot of talents in the US that we want to grab and attract. There's a lot of data, a lot of brokers, so it makes a lot of sense. So we've been in New York for 20 years. And, you know, it's obvious that it is hub and we should expand there. So you mentioned earlier your co-founders, Jean-Pierre, Aguilar. He passed away in 2009. Yes. What was the impact on the firm? How did you guys manage around? That's a big loss when you lose a founder. Yeah, it was a tragedy because he died in a glider accident. We knew that he was gliding. We knew that gliding was dangerous. But in a sense, and it really means bad risk management, right? We never thought that he could crash. You never occurred to us, which was strange because these things happened. And so it was tragic because we were not prepared. And it was tragic because he was not only a friend, but he was, you know, the public figure of CFM. He was not involved in constructing models. I mean, in a way what's great about quant investing is that you don't need star traders, you don't need, you know, PMs that know everything. It's a collective effort. And so when someone disappears or resigns or dies, it's not a tragedy. But in the case of Jean-Pierre, it was even that he was not really involved in the construction of models. He was just a very inspiring, generous, and he was really great. He had a vision, you know, when we met and he thought, okay, with that guy, we can build something, build something great. It's amazing, you know, to think that he was so enthusiastic about creating what we created together. And so we owe him a lot. So when he passed away, it was really difficult to, well, you know, there were several issues. One is that he had 57% of the company, so we had to negotiate with the estate to get back control. That was pretty difficult, but we went through that. And also we needed to reassure our investors, you know, Jean-Pierre was, he seemed to be the public figure. He was a public figure and he seemed to be the inspiration behind everything. And so we had to, you know, communicate that we were at the helm and that we would navigate that and it worked. And so it was, it was very stressful, but it was very rewarding as well to go through that. And the firm carries on as his legacy. Yeah. Coming up, we continue our conversation with John Philip Peshau, head of research and chief scientist at CFM, talking about the growth of capital fund management. I'm Barry Grittholtz, you're listening to Masters in Business on Bloomberg Radio. The Vanguard lineup includes over 80 bond funds actively managed by a 200 person global squad of sector specialists, analysts and traders. Lots of firms love to highlight their star portfolio managers like it's all about that one brilliant mind that makes the magic happen. They believe the best active strategy shouldn't be one person. All investing is subject to risk Vanguard marketing corporation distributor. The news doesn't stop on the weekends. Contacts changes constantly and now Bloomberg is the place to stay on top of it all. Hi, I'm David Gurra. Join us every Saturday and Sunday for the new Bloomberg this weekend. I'm Christina Raffini. We'll bring you the latest headlines in-depth analysis and big interviews. All the stories that hit home on your days off. And I'm Lisa Mateo. Watch and listen to Bloomberg this weekend for thoughtful, enlightening conversations about business, lifestyle, people and culture. On Saturday mornings, we put the past week's events into context, examining what happened in the markets and the world. That on Sundays we speak with journalists, columnists and key political figures to prepare you for the week ahead. Join us as soon as you wake up and bring us with you wherever your weekend plans take you. Watch us on Bloomberg Television. Listen on Bloomberg Radio, stream the show live on the Bloomberg Business app, or listen to the podcast. That's Bloomberg this weekend, Saturdays and Sundays starting at 7am Eastern. Make us part of your weekend routine on Bloomberg Television, Radio, and wherever you get your podcasts. I'm Barry Rittultz, you're listening to Masters in Business on Bloomberg Radio. My extra special guest this week is John Philip Bechow. He is the head of research, chairman and chief scientist at Capital Fund Management, hedge fund managing over $20 billion, a quantitative shop specializing in managed futures and other quant type funds. So let's talk a little bit about the building of the fund. You co-fined science and finance in 1994 with John Pierre, Aguilar, what was the thought process? Did you think you were building a quant fund, a research shop? What was the original plan? A quant fund from day one. From day one, a quant fund. But from day one, we knew that we wanted to be strongly associated with academia. We knew that the only way to innovate, again, coming back to the fact that financial markets are complex systems, it's really difficult to beat the market. We know that, everybody is trying to beat the market. If we want to have something else to say and not follow the crowd, we have to innovate. And innovating is hard. You have to spend time, you have to have new ideas that nobody else has. And so this means investing heavily in research. So the two are not contradictory. We really wanted to be a quant fund. We had already, we knew already about Renaissance. We knew that these guys at Renaissance, they were very close in spirit and in culture, what we were. And so we thought we were going to try to emulate them. Of course, they're so great that there's no way to emulate them. But anyway, this was the aim. They had a 40-year-old start on you guys, so. Yeah. But so it's funny. You mentioned Renaissance technologies. I think of when I'm doing my research for CFM, kind of reminded of D.E. Shaw and AQR and a few other, a little bit of millennium, although they do so much of everything. The thought process behind being a quant shop, when there's so many other quant shops, if we don't create our own models, if we don't create our own findings and innovations, we're just an also run. Is that ran? Is that the thought process behind it? Yeah. We have to do this otherwise. Because it's all of these other quant shop that I've mentioned, none of them are quite the academic lab that you create. AQR is. AQR is. I think that closest to us in that form. I think, you know, Renaissance, they took the initially, the completely different time. They thought we have to be completely secretive about everything and be a kind of black hole where everything goes in but nothing goes out. And that was not our philosophy. We thought that life is too short as well. It's not only we want to make money for ourselves, for our investors, we want to excel, but not at any cost. We think that there's something else in life, that there's a legacy that we want to leave. And this legacy is intellectual as well. It's pursuing the truth as to what drives markets and what leads to alpha and returns. Exactly. You know, every new discovery of alpha eventually gets arbitrage the way. Is that the thinking? Yeah, not exactly. I mean, transferring, you know, it's not arbitrage the way at all. And actually, if you think about it, it's very hard to arbitrage a transferring. You know, if people, if people, if it's going to lead to more transferring, not momentum is a, it's not only a pharma French factor, but it takes on its own life. Right. Well, pharma doesn't like momentum, but anyway. But is it part, it's not part of the original three factor model, but wasn't it one of the later models that we like, essentially, I think he had to add it. But it's a disgrace for efficient market theory. So he doesn't like momentum at all. Listen, you know, if the math is there, it doesn't matter if you like it. If it works, if it's a valid factor, it's a valid factor. I agree. I agree. But, you know, that's again, the physicist point of view, experiments is about everything else. But sometimes when you talk to economists, they have a strange view that theorems and axioms are supersede any empirical observation. I was told that by an economist. And so there's a very strong difference in perception. I recently had Richard Thaler and Alex Eamis in the studio. And I was shocked to learn from them. They still aren't teaching behavioral finance in economics course at a college level, which is kind of shocking. I agree. I think everything we've learned. So let's talk about another technology. There have been over the past decade, but especially the past few years, huge advances in artificial intelligence and machine learning to say nothing about large language models. How are you guys thinking about real world investing driven by AI and what sort of opportunities does this open up? Well, you know, AI is really an advanced form of data analysis. And in a way, we've been doing machine learning forever. The thing is that techniques have evolved. It's now much more efficient. For many years, we were just using numbers. And actually for many years, we were just using prices and volumes and not anything else. And for the mental information about companies. But now there's so much data that even used. There's new data set every day that we're presented by data vendors. And so there's a need to handle that data to read sometimes huge data files. For example, if you think about microstructure, high frequency data, you know, there's events happening in the other book of major exchanges at the millisecond level or even faster. This generates a huge amount of information that has to be dealt with, analyzed, and machine learning helps you very much doing that. Reading texts that no human would be able to read and extracting information, statistical information from that text. So for us, it is, I wouldn't say a revolution, but it's an acceleration of things that we were trying to do before. And obviously we're much in tune with that. We've actually created an email lab at CFM to help transferring technology from what ML people are constructing and what researchers at CFM may be using. But also to try to understand how these things work, right? Because we're very uncomfortable with the idea of black boxes. Black box is something that can improve the research process. But when you think about implementing that in production and having models trading with these models, you really want to be sure that the machine has done something that makes sense. And so understanding what machine learning is actually doing, why are these things working to start with? What is strange is that it works so well, but nobody understands why. When you're driving a car, the car works really well, but we know exactly why it works, how it works. Machine learning, nobody really understands what's the magic. And I think it's a huge intellectual challenge and we want to be part of that. How much of that is pattern recognition? Because when I think about the work I whenever I read about LLMs, it's really just statistically what makes the most sense for the next letter, the next word, the next sentence. It's kind of hard to just think of crafting a document based on probabilities of the most likely word if you have these few words beginning. But apparently that's a big part of how they work. Or am I grossly oversteer? Why does it work? And can it work in finance too? Is it because the language or images have such a strong structure that there's an internal logic to language or to pictures or to other things that the model is able to capture and using these breathively simple ideas of statistical prediction of what's going to happen next is enough to generate meaningful sentences. But maybe this part of that, the structure of the data, is it the case in finance too? Well, that is literally exactly where I'm to go. If you're training an LLM on billions and billions of documents, pages, books, whatever, and it's now has a giant data source of when you get these first two words or these first few sentences, here's what's most common, here's what's second most common. And here's a reference check for you to say, how does this compare grammatically structurally to the giant corpus we have? I can see the myth behind that, because there's only so many trillions of combinations of letters where it's sentences. But when you now apply it to markets, which seem to be so random, tick to tick day to day, can you apply the same sort of logic to invest in? Well, there are two problems. One is fundamental. Are there structures that you can extract? And we believe that there are. Because otherwise, we wouldn't be there. I mean, you know, trend following is a structure. It's a pretty trivial one, but it is a structure. Now, many other types of structures in the data that we've extracted without using ML or using ML now or recovering with ML or even more complicated one with MLs. But the major difference between finance and languages or pictures is one, the amount of data. Because in the end, you know, stock markets have only existed since 1900 or 1800 years. Small data set. Small data set. Except if you go to high frequency, as I said, you know, if you go to tick by tick, all the book data, there's huge amounts of data. And there, you can think that there's more to, but what was I saying? Yeah. So there's the problem of the availability of data and the frequency at which you want to predict. So for a high frequency, I think there's a lot of structure. For a lower frequency, it's not clear yet that it is going to be useful, you know, used as a kind of technical model with only looks at prices without reading text. For reading text, we know that there's a lot of structure which corresponds to the structure of language. But having said everything you said, there's still something strange about LLMs or, you know, generative AI is that with this process of constructing sentences that are statistically valid, you can invent new things. And that's the thing that is really strange, right? I mean, you can learn pictures. For example, you know, this celebrity database where you have, you make the machine learn these pictures. And then you ask the machine to generate new ones. And it does. And these are pictures that are look exactly, I mean, that you look and you think it's a celebrity, but the celebrity doesn't exist. So there's something still weird about this that, as I said, nobody really understands. Really, really kind of interesting. And so continuing on that, what we are trying to do is to do the same thing with financial markets. So as I said, 100 years of data is not a lot, but maybe you can use these Gen AI models to generate a million years of fictitious financial markets. That's interesting. Huh. Very interesting. So let's talk a little bit about trend following and managed futures. It's had a few real standout years in particular, 2022, real challenging year and managed futures at a top of the asset quilt. What does that episode tell us about what strategies work, why they work? The question I always find with trend following, why do so few investors tend to stay with them? They all seem to get nervous and tap out right before things. It's almost when you see people giving up, it's just about when the turn occurs. I agree. So what is it? Well, first of all, why was 2022 such a standout year? I don't know. Because I mean, aside from the fact that we had big fed rate hikes and fixed income and equities, both got shellac double digits. Kind of rare occurs the same year. I think you have to go back about 40, 41 years to see both of them down significantly. How do you think about what environment leads to the best results for trend following? It's very difficult to say, otherwise we would have a meta model that arbitrage and increases the weight of trend following when it's going to work. I think there probably is more research to do and we've been trying. We haven't found anything that's very convincing. But beginning of 2026 is also a very good period for trend following. Actually, since we wrote a paper in 2014 called 200 years of trend following and we were reporting on the fact that since 1800, if you paper trade a very simple trend following strategy, you make money every decade with ups and downs. There are years that are not so good years. But as you say, I mean, what is striking in about the very point you made about people getting out of trend following just before it gets back on is I think it's ingrained in people's behavior to chase performance. If performance has been bad for a few years, everybody declares and that was the case in 2014. When we wrote our paper, trend following had been flat for the last five years and people said, "Okay, well, trend following is dead now." We were actually convinced that it was not the case that trend following is such a strong behavior or bias that performance chasing is so ingrained in every one of us, even rational. We can't help. We bet and it was confirmed that trend following would come back. Since 2024, 2014, it's been very good, actually, overall. You had a market that very much was trending mostly in one direction for, I mean, you have Q4 of 2018 and I think 2016 was so-so. For the past 15 years, the bias has been pretty much in one direction. If you're on the right side of that, you should do pretty well. I'm not speaking about being long. I'm really speaking about different asset classes and trends up your long and short. Yeah, sure. So it doesn't matter. As long as the trend is in place, you want to part the trend. And for people who are not familiar with managed futures, there's a decent amount of leverage used in that product. You have to really manage the risk to the downside. But how do you think about the potential upside relative to the risk you're taking in a futures product? What do you mean exactly? I mean, we just think about risk. We want to. Risk is a very complex object, actually, you know this, but I tell you what you see, but there's also correlation. If you deal with a portfolio of futures that has 150 futures, there's a very subtle correlation structure between all the assets that you have in your portfolio. So if you think about risk, you really have to think about how all these products interact with one another, talk to one another. And so it's not only a question of volatility that goes up and down that you have to control, but also a question of how these assets co-move together or anti-co-move together. But the way we think about upside risk is the same as the way we think about downside risk. It's just a question of risk. So you mention 150 different assets, I'm assuming some of these are commodities, which are a lot of tees, bonds, stock, bonds, interest rates. But what else is in the full list of 150? Well, there's different. The matureities, the different countries, futures in China. I mean, if you count everything, it goes up to, you know, 100, I don't have the exact number, but in the 150s altogether. Does CFN been looking at prediction markets, things like calcium and polymarket? No, we haven't. They're not liquid enough for us. Really? You need size and they can't provide it. Exactly. Really, really quite fascinating. Coming up, we continue our conversation with John Philippe Puchot, co-founder and chief scientist at Capital Fund Management, talking about how market structures are changing today. I'm Barry Rittholz, you're listening to Matthews Business on Bloomberg Radio. This is Tom Keane, inviting you to join us for the Bloomberg Surveillance Podcast. It's about making you smarter every business day. I'm Paul Swini, we bring you complete coverage of the US market open. We cover stocks, bonds, commodities, even crypto, all the information you need to excel. And I'm Alexis Christofferis. Bloomberg Surveillance also brings you the analysis behind the headlines. We do that through conversations with the smartest names in economics, finance, investment and international relations. We do all this live each and every weekday that bring you the best analysis in our daily podcast. Search for Bloomberg Surveillance on Apple, Spotify, YouTube or anywhere else you listen. On the East Coast, listen at lunch. And on the West Coast, listen as soon as you wake up. That's the Bloomberg Surveillance Podcast with Tom Keane, Paul Swini and me, Alexis Christofferis. Subscribe today wherever you get your podcasts. Bloomberg Surveillance essential listening each and every business day. I'm Barry Rittholz, you're listening to Masters in Business on Bloomberg Radio, my extra special guest this week, John Philip Bouchot, chief scientist and chairman at CFM, a quantitative hedge fund managing over $20 billion in assets. So let's just talk a little bit about risk management. I know that when you're dealing with leveraged or long short or futures, there's a very robust thought process around risk management, tell us a little bit about how you think about correlation and risk. - Yeah, we have a discipline and systematic approach, not only to alpha signals to building prediction, but also to risk management. We have a pretty sophisticated tool to predict the validity of tomorrow, the validity of our portfolio tomorrow. And we're pretty good at that. So of course, we know in financial markets are difficult beasts. And even if you have the best model in the world, you can still have an expected event that blow up your portfolio. That's something that we can't say will never happen. And but in a way, if you don't want to take any risk, you shouldn't be in financial markets. You shouldn't be in that business. So we accept that there might be, I don't know, a completely unexpected event that breaks the whole financial markets everywhere in the world. And everything is going to fail and there's nothing to do about. So that can happen. But borrowing these extreme events, we think we're pretty good at predicting what's going to happen. And over the last 35 years of the existence of CFM, we're actually on anniversary is this year. We're celebrating our 35th anniversary in June in Paris. Very proud of that. So it kind of resisted these 35 years, although we've become much better with time. But having said that, there's always an element that you have to be ready to intervene, even if you're a quant shop. And so on several occasions in the past 35 years, we decided that our risk model couldn't know about things that we humans knew, like, I don't know, the Brexit votes or in these cases. So let's talk about that. 'Cause this year, just the past 12 months, between the tariffs and Venezuela and now the ongoing war in Iran, how does global market volatility around all these geopolitical events, how does a quant shop deal with that? What I'm hearing is the humans have to do what humans do and sometimes override the machines. Sometimes. Yes. I mean, when unexpected geopolitical events disrupt the world, our models just not built to really work their way through that. You're right. Some events are okay. And like the war in Iran, for the moment, is not something that our risk models are completely blind to. It doesn't mean that they've predicted at all. It just means that we're comfortable with the risk that our models have predicted and they've adapted sufficiently fast to the events so that we're comfortable with the risk level, no human intervention. On the other hand, in some cases, it's completely unexpected, like tariffs and liberation day. This created havoc. Although, strangely enough, liberation day was announced. Everybody knew what was going to be said. And still, everybody was impressed of it. I don't believe, despite I'm tariff man, it's the most beautiful and the dictionary, I think the 100, 150% tariffs on specific countries later found to be completely unconstitutional. But at the time, I think people were genuinely shocked by this. And then a week later, the little bit of a taco trade, where let's just put a pen in this for 90 days. Again, back to the volatility, how do you deal? - I agree. - Oil is trending upwards, and then you have a tweet, the world, the war's over. And then it resumes, and then you have a tweet, I think we've got a deal. And then the other side says, we're not even negotiating. I don't recall a period in history where the president of the United States just constantly disrupted the normal flow of market activity. How disruptive is this to a quant model? - Well, as I said, the two cases seem to be pretty different. Liberation day was really a surprise, and we had to manually intervene. There was something in our models that was completely blind to these things, and we had to make a judge on the call. I think the idea really is that humans should use their best judgment in these cases. And decide whether it's reasonable that the risk model knows something about what's going on or not. In some cases, it does. In some cases, it doesn't. I think the trick, the tricky part is not to overreact, because you said you don't remember periods of the world where things like this happen. But looking back, I've been in the markets for 35 years, and everything, every year, there seems to be something unexpected that happens. - Just not every day. - Not every day, but every year. - Every day seems like a lot. - Right. But in a way, every day means that it becomes a new normal. - I guess. - And so it's not that bad. It's if it's every day. But really, this idea that this time is different is something that's strange. If you look at the world and the history of financial markets, it's really being normal, that's not normal. And we've become used to that. So let's stay with the idea of modeling. You've been kind of skeptical of certain applications of deep learning and finance. There's overfitting. No one's ever seen a bad back test, 'cause they all seem to work perfectly in the past. You have a lot of signal and noise issues. What are some of the problems with models that you're focusing on improving? - Well, for example, this is exactly what you just said. Can you have indicators that tell you whether your back test is overfitted or not? And for many years, we struggled with that and we used judgment again to say, this is plausible, this is not plausible. We can believe that. We kind of replace the trader that trades every day his signals or his beliefs to a higher level where we are traders of models. We kind of judge the models, we say, this model is good enough to go in production, this model is not convincing enough. But it would be great to have something more systematic. And over the years, we've been struggling and I think with some success to have meta models that predict whether your back test is really fudged or if it's decent enough to go in production. So we are kind of industrializing this process of selecting models that will go into production. Does that make sense? - Yeah, no, that makes perfect sense. You're a quant and we've seen some issues with a lot of quant shops in the US where crowding became a structural risk. You have all the systematic strategies and math is math. So essentially, you end up with a crowded trade. We had what people called the Quant Quake way back. When, how do you think about that? How do you manage that problem when you're constructing portfolio? - Sure, it's something that every day we think about this. We were in the Quant Quake in 2007. And actually, we were fortunate enough to be out of the markets or to have delaffaged already two weeks before the worst day of the Quant Quake. Was that an external signal or one of your model signals? Or what led to this? - It was something in the performance already in July. The Quant Quake, the really bad day happened, maybe 9th of August. I don't remember exactly, but it was early August. - Early, or two days of summer. - Right, but starting like 10th of July already, there was something really very strange now portfolio. And we started reflecting and what was going on. And decided someone was leveraging and hitting us by shorting our longs and buying our shorts. And this thought process of imagining that even if a fund that was like 10% correlated with ours, not a lot, but 10%. And having every day a kind of systematic the leveraging policy, it would create exactly the kind of signals that we were seeing in our portfolio. So we thought, okay, this is maybe going to lead to a crash because people are going to suffer, suffer, and at one point they're going to-- - A cascade, and so on. - And so that was the rationale for getting out. So in some cases you're lucky enough to have strong enough signals that tell you that your standard risk model is wrong and you should do something else. - That's really fascinating. So you mentioned almost 35 years of doing this, what do you think that, I'm going to say that again. So you are now almost 35 years into being a market quant. What's the most important thing about markets that the mainstream funds still get wrong? - Well, I think it's this question of what is price doing? Why are moving prices? And a lot of people still believe that there's something like a fundamental value. that the price is really moving because fundamentals are moving. Whereas we believe, and this is going, you know, it touches very recent academic papers that Gabek's encouraging to economists have put forward, they've called it the inefficient, in, in the elastic market hypothesis. And, and we've contributed to that debate as well. And the idea is really that markets are not driven by fundamentals, or at least they are, they are to some extent driven by fundamentals. But this is a small, long-term effect. On short run, short run meaning from one day to one year, which is pretty long already, it's really flows that matter. That is, people buying or selling stuff. Whatever the reason they buy or sell, it's going to move prices. It's not going to move prices on a short timescale and then disappear. It's really going to leave a trace in markets. And this is really a fundamental change of point of view that I think that, you know, is going to calculate and, and, and convince more and more people looking forward. But, you know, having this change of tag is really important because in one case, what you need to do to make money is to predict fundamentals. In the other case, you need to predict what people are going to do. And so, in a sense, crowding can be a good thing because if they're crowding, it's easier to predict what the crow is going to do. And so, if whatever the reason people do things, they move prices, and you're able to predict what people are going to do because you have, you know, behavioral models and structural models that tell you, you know, everything else being equal, people are more likely to do this than that, then you can build models. And I think that's the reason why we've been successful is this change of philosophy. We were not kind of anchored to fundamentals. We're anchored to flows. So, this sounds a little bit like the Ben Graham line. I think it's Graham, in the short run markets, or voting machines in the long run, their weighing machines, is that, is that the balance between flows and fundamentals? Yeah, I think it's an all-dye idea. I mean, you know, it says, "Cains also say things like that." But in the long run, we're all dead, right? The gains that were saying that. So, it's really a question of whether you're going to be solvable. I mean, I'm getting tired. I'll take that again. What's the word? Was that kept by the way, "Cains or Graeme?" I initially thought it was Canes and then I checked myself. I'm not sure. But what Canes said is that what was his quote, "Markets can remain irrational longer than you can remain solvable." So, he never said that, but it's always attributed to him. Really? There's a great website called "Quote Investigator." I see. And you give them a quote, because people, as a sort of authority, appealed with authority, they'll put somebody's sophisticated as the source. Einstein never said compounding is the most powerful force in the universe, but they always attributed it to him. I didn't know that. And you would be, you would be, I spent way too much time perusing quotes on the site, but you would be shocked. Who said, "Markets are a voting machine in the short run, but a weighing machine in the long run?" I don't think it's Canes, by the way. No, I don't remember if it was Canes or Graeme, but I thought it was one or the other. And it'll tell me, Benjamin Graham. But the first thing that came to mind was Canes, because, or Galbraith, they have so many favorite quotes from me. He said many relevant things each of the day. Yes, absolutely. But actually, we have models that predict exactly that. That on the short run, you can have trends in the irrational behavior. And on the long run, it revolves back to fundamentals. But the long run from RSMIT is like five, 10 years. I'm trying a long time skill. I'm trying to remember, it might have been French of I'm a French, who said, "You can't really tell if a manager is skilled until you have 20 years of data, because it could just be good luck over five or 10 years," which is kind of fascinating. I want to stay with the efficient market or the inelastic market hypothesis. I'm curious as to your thoughts on EMH, I've always thought markets were kind of sort of eventually efficient, but not very efficient in the short run. What's your criticism of EMH? Well, it really depends what you mean by efficient. If you mean that they're very close to unpredictable, then you're right. But I think it's a very dumb, downed version of EMH. The question is, whether prices reflect something from their mental, that is in principle, noble, that reflects reality or not at all. And one, I think, smoking gun of that is, do you have long-term mean reversion? That is, can prices do random things in particular trending, which is really completely against EMH, on the short run, that is, from a week to six months. Markets are trending over six months one year. And then, on the longer time scale, they kind of hover around some long-term trend. And I think this is true. But this is really at odds with efficient market, which tells you that every day markets are around the correct price. And there's no trend, no mean reversion ever. Which is, which, you know, that's not exactly what your day-to-day experience is if you're in the markets. It seems, it's not magic that the collective votes of all market participants don't magically bring you to the correct end quote. But that's price as your assumption of efficient market. Or actually, even not even an assumption, it's the argument that collectively, you know, if you have, that's the difference between having rational investors that all take decisions based on noisy observation, but independent from one another. Then because they're independent, they realize the mean. I mean, some over-priced, some underpriced, and then it's a voting machine and the vote comes out right because there's enough investors and they're uncorrelated to one another. But the problem with markets is that it's not the way it works. The people are influenced by what other people are doing and what other people are saying. So instead of having independent guys doing random stuff, they're kind of a one guy who's doing only one thing, which is a fictitious body that aggregates everybody in the same way. Let's jump to our favorite questions that we ask all of our guests, starting with, tell us about your mentors who helped shape the direction of that career. That's an easy one. I have several mentors, but two of them are really close to my heart. One is Benoam Mandelbrolt. Of course. The fractal guy. I knew him personally. Oh really? Yes. And he did a lot of things in physics as well. Sure. And so he influenced a lot my wife, my wife, was a physicist before turning a playwright now. And she worked on fracture surfaces the way when you break a material, what emerges from the fracture is a very rough landscape that is fractal. Mandelbrolt had worked on that and there was a lot of interaction. And fractal at a molecular level or at a larger level? Well, fractal from the very fine structure to macroscopic length scales. And so Mandelbrolt also did his work on financial markets. And for me, it was really a revelation. It was something very influencing and out of the dogma of Brownian statistics and Gaussian phenomena and so on. And so it was also very close to what I was doing myself in physics. So it was clear that he influenced me enormously on that. Didn't he write a book on market crashes and how there's a fractal nature within those. The misbehavior of markets. That's right. There you go. I'm actually quoted in that book. Oh, get out. Oh, that's fascinating. And then Pierre-Gilles de Jean, who was a Nobel Prize in Physics of French Physicists, who was so fantastic. And both these two and also Philanthuson, who was a Nobel Prize in Physics as well in the US, these three people, they convinced me that you shouldn't be stuck to your own field. You should broaden your scope and what you learn from one field can be very useful on signing another field. The three of them, they're really kind of hovered around and not got tied to their specific initial field. And I think this creates, well, at least for me, this created, this, this, this, the inhibited me in the sense that I thought, okay, maybe I'm not legitimate to speak about finance because I'm a physicist, but you know, no, it doesn't matter. If I have things that I strongly believe in, I should better say them and go to the end of them. So I think they were really influential in that way. Since we mentioned the misbehavior of markets, let's talk about some books. What are some of your favorites? What are you reading right now? Wow, so many. Let's read you a very poor question. So my last book is a book on John and Paul by Ian Leslie John Lennon and Paul McCartney. It's a beautiful book. I really loved it. What's the name of it John and Paul? Uh-huh. Is it John and Paul a love story? Yeah, I'm a big Beatles fan. Yeah, that's in my queue. Oh, you should read it I'm very emotional. I'm gonna make a recommendation to you for a YouTube channel called You Can't Unhear This. They take apart Beatles songs in ways that just little things that were done in the recording process that in a million years you never would have noticed and then once you hear it you just can't unhear it and if you're a Beatles fan it's a rabbit hole. You'll you'll love this. What else besides John and Paul? Yeah, I'm reading someone that something that I actually read for years that misses the way of Virginia Woolf. I'm re-agreated Myra of Virginia Woolf. Really interesting. Do you do much streaming, TV podcasts and anything like that? We can skip over that. Podcasts, a bit of kind of French podcasts. So let's you know people are always looking for stuff to I'm gonna ask that. So what are you streaming today? What sort of podcast are you listening to? Yeah, in France we are very fortunate. We have something called "Hoskultur". It's a radio where there's a enormous. I mean you could stay tuned all day if you wanted. There's so many interesting things going on about everything cultural literature but also movies, politics and so we have NPR here. It's very similar. It's a rabbit hole. You could fall down. Yeah, okay. So life of big people of major celebrities in culture, in cinema, theater, all these things. So I'm really a big fan of "Hoskultur". And our final two questions. First, what sort of advice would you give to a recent college grad interested in a career in either quantitative investing or theoretical physics? Well, study theoretical physics and study everything that's related to data. Pay attention to data. And think about something that you strongly believe in and that you feel has not been investigated. And it doesn't matter if it's big or small. Make the effort of building something you strongly believe in. Really good answer. And our final question. What do you know about the world of investing today? It might have been useful 35 years or so ago when you were first getting started. That is extremely competitive. Much more than we thought. Really? Wow, that's really fascinating. Jean-Philippe, thank you so much for being so generous with your time. We have been speaking with Jean Philippe Bouchot, CFM's co-founder, chairman and chief scientist. If you enjoy this conversation, well, check out any of the 600 plus we've done over the past 12 years. You could find those at Apple Podcasts, Spotify, YouTube, Bloomberg, wherever you find your favorite podcast. I would be remiss if I didn't thank the crack team that helps me put these conversations together each week. Meredith Frank is my video producer and Alouk is my podcast producer. Sean Russo is that my head of research. I'm Barry Rittalts. You've been listening to Masters in Business on Bloomberg Radio. Hello, I'm Michelle Hussein and for more than 20 years, I was at the BBC. But all the time I was delivering the headlines, I wanted to go further than the news of the day, to spend more time with the people shaping our world. And that's what I'm doing here on this podcast. Speaking to people from Nigel Farage. This will love you trying it. To tech journalist, Karra Swisher. And the tech industry is running wild. You know, they've gotten what they wanted and they've seen a huge run up in their stock prices. This will be a place where every weekend you can count on one essential conversation to help make sense of the world. So please join me, listen and subscribe to the Michelle Hussein show from Bloomberg Weekend. Wherever you get your podcast. You certainly ask interesting questions.