Pod Insights Masters in Business The Intersection of Science and Finance with CFM's Jean-Philippe Bouchaud About this episode Barry speaks with Jean-Philippe Bouchaud, chairman of Capital Fund Management, or CFM. Bouchaud founded 'Science and Finance' in 1994, which merged with CFM in 2000. They discuss some of Bouchaud's research into what drives markets from a quantitative perspective.
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0:38 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.
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2:11 Speaker 02 This is Masters in Business with Barry Ritholtz on Bloomberg Radio.
2:17 Barry Ritholtz? This week on the podcast, yet another extra special guest. Jean-Philippe 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. Jean-Philippe has published something like 300-plus academic papers. They are deep into all the things that drive markets from a quantitative perspective.
3:00 Barry Ritholtz? I thought this conversation was fascinating, and I think you will also. With no further ado, my interview of CFM's Jean-Philippe Bouchot. So what do people call you? JP, Jean-Philippe, Philippe? What do you like?
3:14 Speaker 04 Jean-Philippe in France, JP in Anglo-Saxon countries.
3:17 Barry Ritholtz? 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 institutions. I mentioned Cavendish Labs. What was the original career plan?
3:39 Speaker 04 Yeah, I was planning to be a physicist, but then, you know, 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, you know, was always fascinated by numbers. I've always liked statistics and financial markets, spit statistics every day. And I thought, you know, this is a very interesting complex system. There are crises, crashes.
4:11 Speaker 04 The system seems to be driven by its own dynamics. Physicists have to do something about this.
4:18 Barry Ritholtz? Very, sounds very similar to chaos theory.
4:21 Speaker 04 Yeah, exactly. I mean, that was the high days of chaos theory.
4:24 Barry Ritholtz? So, so I pulled some phrases from some of your papers. One was titled, and I'm going to mangle this. Disordered systems and complex phenomena?
4:35 Speaker 04 Mm hmm.
4:36 Barry Ritholtz? Which can be either physics or finance, it sounds like. But what are the dynamics of glassy systems and granular media? That sounds fascinating.
4:45 Speaker 04 Yeah, but you know, it's, it's all. The, the, the problem is how do interacting elements give rise to something surprising? You know, granular matter is grains that you interact with one another. And then you have these strange phenomena called avalanches. Where you drop a grain on a 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 reminiscent of, of financial markets, right? I mean, many things happen, nothing much follows, and then sometimes there's a crash.
5:24 Barry Ritholtz? Right.
5:25 Speaker 04 And so this was really intriguing for physicists like me.
5:28 Barry Ritholtz? So I'm, I'm, as you're talking, I'm just thinking of a concept in 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.
5:50 Speaker 04 Yeah, I mean, there's, 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 when 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,
6:35 Speaker 04 i.e. traders, you know, agents that trade and buy and sell to each other. And this can generate, you know, intrinsic randomness that is not of the same kind as the three-body problem, but really comes from the interaction of a huge number of elements.
6:54 Barry Ritholtz? 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 econophysics?
7:13 Speaker 04 Yeah, so, as I said, initially I have 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 1987 crash and the Black Scholes 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
7:53 Speaker 04 interesting because it resembles what happens in finance and in particular what just happened, the 1987 crash. And there's a theory, the Black Scholes theory, that, you know, is a theory that only works in a world where there are no crashes, where, you know, all the motions are small and predict, they're random, 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 Scholes to a world where there are crashes? And I thought, well, that's really interesting. So I read Black Scholes 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 Scholes because
8:38 Speaker 04 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, okay, maybe we can do software out of that and commercialize it. And so I went and knocked on several doors and suddenly the door of Jean Pierre Aguilar opened. And Jean Pierre Aguilar was someone who had founded, actually, CFM in ninety-one, that was ninety-four. And I started, you know, explaining what I had been doing and that I 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.
9:23 Speaker 04 And this was done, you know, in in in two weeks. It was like amazing the way we met and the there was a fluid that was flowing between us immediately. And so CFM then merged with Science and Finance in nineteen ninety, so it's now the same firm. But the idea he had at the time, he had this is the 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.
9:58 Barry Ritholtz? And that CTA firm specialized in managed futures, pretty much?
10:03 Speaker 04 Yeah.
10:03 Barry Ritholtz? 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?
10:16 Speaker 04 Yeah, well, that that was exactly Jean Pierre Aguilar's idea. He said, okay, I'm doing trend following, it's good, but it's not, you know, rocket science. Maybe we can do much better. And and so he said, why don't we work on something more beefy than just trend following? And so, you know, that that started the whole thing and the idea of the the main idea is data. You know, physicists are good at looking at data and extracting structures. You know, 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, you know, it all works in physics, that you scrutinize
10:58 Speaker 04 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 were concerned and we are concerned now. It hasn't changed. It's the same process.
11:11 Barry Ritholtz? Huh, really, really quite fascinating. You keep your professorships at E N S 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?
11:30 Speaker 04 Well, first of all, it is me. You know, I feel I'm a researcher at heart and I need to continue. It's like, you know, 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, you know, even physics problems, I can get excited about them or trying new things like the M L revolution. How does M L work? Why do large language models work so well, learn so well? I think it's fascinating. I want to understand.
12:12 Speaker 04 But there's another reason for doing this is that to attract talent, 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 C F M has been attracting talents. And I think part of that, only part of that, of course, it's the 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.
12:51 Barry Ritholtz? So is that the thinking behind establishing the research division at C F M? I mean, you run that as a full academic research department as opposed to, you know, a lot of asset management shops, they have a couple of. CFPs and MBAs and they're CFAs and they're working on their quantitative models. You guys seem like you've taken it to a whole different level.
13:15 Speaker 04 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, you know, make models that work, build portfolios that are robust, model risk, model execution, control costs. All these things are bread and butter for everyday 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, how do markets work? Why are there crashes? Are markets efficient? What about the economy?
13:59 Speaker 04 Do people understand inflation? Do we need new theories to understand inflation, monetary policy and all these things? We, we believe it's, 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, and the reason we're, 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 at CFM, I hope, you know, I don't want to put words in their mouth.
14:35 Barry Ritholtz? Well, you guys opened up a big, or expanded a big New York office. You don't seem to be having much difficulty recruiting people there. What, what's the headcount there now?
14:44 Speaker 04 We have 115 researchers and 15% of them are in New York.
14:50 Barry Ritholtz? Hmm.
14:50 Speaker 04 Yeah.
14:51 Barry Ritholtz? What, what motivated expanding the New York office as much as you have?
14:55 Speaker 04 Well, first of all, a lot of our investors are in the US, so we need to be there and interact with them and, 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 a hub and we should expand there.
15:27 Barry Ritholtz? So you mentioned earlier your co-founder Jean Pierre Aguilar. He passed away in 2009.
15:35 Speaker 04 Yes.
15:35 Barry Ritholtz? What was the impact on the firm? How did you guys manage around? That's a big loss when you lose a founder.
15:42 Speaker 04 Yeah, it was a tragedy because he died in a glider accident. You know, 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. It never occurred to us, which was strange because these things happen. 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, Quants, in a way, what's great about Quants investing is that you don't need star traders. You don't need, you know, PMs that know everything. It's a collective effort.
16:26 Speaker 04 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 was, 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.
17:11 Speaker 04 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 seemed to be the inspiration behind everything. And so we had to. We 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.
17:47 Barry Ritholtz? And the firm carries on as his legacy.
17:50 Speaker 04 Yeah.
17:51 Barry Ritholtz? Coming up, we continue our conversation with Jean-Philippe Pouchot, Head of Research and Chief Scientist at CFM, talking about the growth of capital fund management. I'm Barry Gritholz. You're listening to Masters of Business on Bloomberg Radio.
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18:48 Speaker 01 This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices,
19:32 Speaker 01 but according to recruiters, advisors and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute, brought to you by ChatGPT. Get started at ChatGPT.com.
20:15 Barry Ritholtz? I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Jean-Philippe Bouchot. He is the Head of Research, Chairman and Chief Scientist at Capital Fund Management, a hedge fund managing over $20 billion, a quantitative shop specializing in managed futures and other quant-type funds. So, so let's talk a little bit about the building of the fund. You meet, you co-found Science and Finance in 1994 with, with Jean-Pierre Aguilar. What was the thought process? Did you think you were building a quant fund, a research shop?
20:59 Barry Ritholtz? What, what was the original plan?
21:01 Speaker 04 A quant fund.
21:02 Barry Ritholtz? From day one?
21:03 Speaker 04 Yeah, 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, you know, again, coming back to the fact that financial markets are complex systems, it's really difficult to, you know, beat the market. We know that. Everybody's 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, 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, we knew already about Renaissance.
21:47 Speaker 04 We knew that these guys at Renaissance, they were very close in spirit and in culture to what we were. And so we thought we're 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.
22:01 Barry Ritholtz? They had a 40-year head start on you guys, so.
22:05 Speaker 04 Yeah.
22:06 Barry Ritholtz? But so it's funny, you mentioned Renaissance Technologies. I think of when I'm doing my research for, for CFM. Kind of reminded of D E Shaw and A Q R and a few other, um, a little bit of Millennium, although they do so much of everything. Um, the, 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, is that the thought process behind it?
22:40 Speaker 04 Yeah.
22:40 Barry Ritholtz? Like we have, we have to do this otherwise, cause it's, uh, all, all of these other quant shops I've mentioned, none of them are quite the academic lab that you've created.
22:52 Speaker 04 A Q R, A Q R is, they're in, I think they're closest to us in that front. I think, you know, Renaissance, they took the initial, the initially the completely different term. 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, you know, life is too short as well. It's not only, we want to make money for ourselves, for our investors, we want to excel, uh, 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, is intellectual as well.
23:30 Barry Ritholtz? Pursuing the truth as to what drives markets and what leads to alpha and returns.
23:35 Speaker 04 Exactly.
23:36 Barry Ritholtz? Uh, you know, every new discovery of alpha eventually gets arbitrage away. Is that, is that the thinking?
23:42 Speaker 04 Yeah, not exactly. I mean, trend following, you know, it's not arbitrage the way at all. And actually, if you think about it, it's very hard to arbitrage, uh, trend following. You know, if, if people trend follow, it's going to lead to more trend following, not to less.
23:58 Barry Ritholtz? Momentum is a, it's not only a pharma French factor, but it takes on its own life.
24:02 Speaker 04 Right. Well, pharma doesn't like momentum, but anyway.
24:05 Barry Ritholtz? But isn't it part, it's not part of the original three factor model, but wasn't it, uh, in the, one of the later models?
24:12 Speaker 04 Reluctantly, I think he had to add it, but it's a disgrace for efficient market theory. So he doesn't like momentum at all.
24:20 Barry Ritholtz? 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, right?
24:29 Speaker 04 I agree, I agree. But, you know, that's again a physicist's 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.
24:53 Barry Ritholtz? I recently had Richard Thaler and Alex Emes 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.
25:09 Speaker 04 I agree.
25:10 Barry Ritholtz? You would think, everything we've learned. So, 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?
25:34 Speaker 04 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. There are many more things that one can do, in particular, reading text. 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, well, and fundamental information about companies. But now, there's so much data that you can use. 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.
26:17 Speaker 04 For example, if you think about microstructure, high-frequency data, you know, there's events happening in the order 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 a Nemele Lab at CFM
27:00 Speaker 04 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. A 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? You know, what is strange is that it works so well, but nobody understands why.
27:45 Speaker 04 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.
28:01 Barry Ritholtz? How much of that is pattern recognition? Because when I think about the work, 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 oversimplifying?
28:31 Speaker 04 Yeah, so why does it work? And can it work in finance too, you know? So 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 relatively simple ideas of statistical prediction of what's going to happen next is enough to generate meaningful sentences? But maybe, maybe that's part of that, the structure of the data, is it the case in finance too?
29:10 Barry Ritholtz? Well, that is literally exactly where I want 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 few 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 math behind that, because there's only so many trillions of combinations of letters, words, sentences.
29:47 Barry Ritholtz? 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?
29:59 Speaker 04 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, 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, stock markets have only existed since, I don't know, 1900 or 1800 if you want.
30:44 Barry Ritholtz? Right, 100 years, small data set.
30:45 Speaker 04 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, so, so there's the problem of the availability of data and the frequency at which you want to predict. So for high frequency, I think there's a lot of structure. For 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.
31:27 Speaker 04 For reading text, we know that there's, there's a lot of structure which corresponds to, to the structure of language. But having said everything you said, there's still something strange about L L M's or, you know, generative A I is that with this process of constructing sentences that are statistic, statistically valid, you can invent new things. And that's the, 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, these pictures and then you ask the machine to generate new ones and it does. And these are pictures that look exactly, I mean, that you look and you think it's a celebrity, but the celebrity doesn't exist.
32:11 Barry Ritholtz? Right.
32:11 Speaker 04 So there's something still weird about this that, as I said, nobody really understands.
32:17 Barry Ritholtz? Huh, really, really kind of interesting.
32:19 Speaker 04 And, and so continuing on that, what we are trying to do is to do the same thing with financial markets. So as I said, hundred years of data is not a lot, but maybe you can use these gen A I models to generate a million years of fictitious financial markets. That's interesting.
32:38 Barry Ritholtz? Huh, very interesting. So let's talk a little bit about trend following and matted futures. It's had a, a few real standout years, in particular, twenty twenty two.
32:50 Barry Ritholtz? Real challenging year and managed futures at a were top of the asset quilt. What does that episode tell us about what strategies work, why they work? And the question I always find with 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 it's almost when you see people giving up, it's just about when the turn occurs.
33:21 Speaker 04 I agree.
33:22 Barry Ritholtz? So so what is it? First of all, why was 2022 such a standout year?
33:27 Speaker 04 I don't know.
33:28 Barry Ritholtz? Just 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 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? How do you think about what environment leads to best results for for trend following?
33:58 Speaker 04 It's very difficult to say because otherwise we would have a meta model that arbitrage and, you know, increases the weight of trend following when it's going to work well. I think it's there probably is more research to do and we're we've been trying. We haven't found anything that's very convincing. But, you know, 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. You know, there are years that are not so good years.
34:40 Speaker 04 But as you say, I mean, it what is striking and 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. So if performance has been bad for a few years, everybody declared. And that was the case in two thousand and fourteen. When we wrote our paper, trend following had been flat for the last five years. And people said, OK, well, trend following is dead now. And we were absolutely convinced that it was not the case that, you know, trend following is such a strong behavioral bias. That performance chasing is so ingrained in every one of us, even rational. We can't help.
35:24 Speaker 04 And so we bet and it was confirmed that trend following would come back. And since two thousand and twenty four is two thousand and fourteen, it's been very good, actually, overall.
35:34 Barry Ritholtz? Well, you had a market that very much was trending mostly in one direction for. I mean, you have Q four of two thousand and eighteen, and I think two thousand and sixteen was so so. But for the past fifteen years, the bias has been pretty much in one direction. If you're on the right side of that, you should do pretty well.
35:51 Speaker 04 Yeah, but I'm not speaking about being long. I mean, I'm really speaking about.
35:54 Barry Ritholtz? Different asset classes and trends up, you're long and short.
35:58 Speaker 04 Yeah, sure.
35:59 Barry Ritholtz? So it doesn't matter as long as the trend is in place, you want to participate in it.
36:03 Speaker 04 Up or down.
36:04 Barry Ritholtz? And for people who are not familiar with managed futures. Uh, there's a decent amount of leverage used in that product. So you have to really manage the risk of the downside. But how do you think about, you know, the potential upside relative to the risk you're taking in in a futures product?
36:22 Speaker 04 What do you mean exactly? I mean, I mean, we we we just think about risk. We want to, risk is a very complex object, actually. You know, there's volatility, but there's also correlation. If you deal with a portfolio of futures that has like hundred and fifty 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.
37:02 Speaker 04 But the way we think about upside risk is the same as the way we think about downside risk is it's just a question of risk.
37:08 Barry Ritholtz? So you mentioned a hundred and fifty different. Of that assets, I'm assuming some of these are commodities.
37:15 Speaker 04 Sure, commodities, bonds.
37:17 Barry Ritholtz? Stocks, bonds, interest rates.
37:18 Speaker 04 Right.
37:19 Barry Ritholtz? What else is in the full list of one hundred and fifty?
37:22 Speaker 04 Well, you know, there's, there's a different maturities, the different countries, futures in China. I mean, if you count everything, it goes up to, you know, hundred, I don't have the exact number, but in the hundred and fifties altogether.
37:37 Barry Ritholtz? Has CFN been looking at prediction markets, things like calcian polymarket?
37:43 Speaker 04 No, we haven't. They're not liquid enough for us.
37:46 Barry Ritholtz? Really? You need size and they can't provide it.
37:48 Speaker 04 Exactly.
37:49 Barry Ritholtz? Really, really quite fascinating. Coming up, we continue our conversation with Jean-Philippe Bouchot, co-founder and chief scientist at Capital Fund Management, talking about how market structures are changing today. I'm Barry Ritholtz, you're listening to Masters in Business on Bloomberg Radio.
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39:47 Barry Ritholtz? I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week, Jean-Philippe 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.
40:25 Speaker 04 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 volatility of tomorrow, the volatility of our portfolio tomorrow. And we're pretty good at that. So of course, you know, we know financial markets are difficult beasts. And even if you have the best model in the world, you can still have unexpected events that blow up your portfolio. That's, that's something that we can't say will never happen. And but in a way, we're, you know, if you don't want to make, take any risk, you shouldn't be in financial markets. You shouldn't be in that business. So we accept that there might be,
41:09 Speaker 04 I don't know, a completely unexpected event that, you know, breaks the whole financial markets everywhere in the world. And, you know, everything is going to fail and there's nothing to do about that. So that can happen. But by During these extreme events, we think we're pretty good at predicting what's going to happen. And over the last thirty five years of the existence of CFM, we're actually on our anniversary is this year. We're celebrating our thirty fifth anniversary in June in Paris, very proud of that.
41:44 Speaker 04 So, you know, it it kind of resisted these thirty five 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 thirty five years, we decided that our risk model couldn't know about things that we humans knew, like, you know, I don't know, the Brexit votes or and in these cases.
42:15 Barry Ritholtz? So let's talk about that, because this year, just the past twelve 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 what I'm hearing is the humans have to do what humans do and sometimes override the machines.
42:40 Speaker 04 Sometimes, yes.
42:42 Barry Ritholtz? I mean, when when unexpected geopolitical events disrupt the world,
42:50 Barry Ritholtz? are models just not built to really work their way through that?
42:54 Speaker 04 You're right. Some events are okay. And like the war in Iran, for the moment is not, you know, something that our risk models are completely blind to.
43:06 Barry Ritholtz? Right.
43:06 Speaker 04 It doesn't mean that they've predicted at all. It just means that, you know, we're comfortable with the risk that our model have predicted and they've adapted sufficiently fast to what to the events so that we're comfortable with the risk level, no human intervention. On the other hand, in some cases, it is completely unexpected, like tariffs and liberation day. This created havoc, although strangely enough, liberation day was announced.
43:32 Speaker 04 You know, everybody knew what was going to be said. And still, everybody was surprised.
43:37 Barry Ritholtz? Of it. I don't believe, despite I'm Tariff Man, it's the most beautiful word in 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.
43:59 Barry Ritholtz? And then a week later, you know, the little bit of a taco trade where let's just put a pin in this for 90 days. Again, back to the volatility, how do you deal?
44:09 Speaker 04 I agree.
44:09 Barry Ritholtz? Oil is trending upwards and then you have a tweet, the world, the war is 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?
44:36 Speaker 04 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 judgment 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, you know, periods of the world where things like this happened. But, you know, looking back, I've been in the markets for 35 years.
45:19 Speaker 04 And everything, every year, there seems to be something unexpected that happens.
45:23 Barry Ritholtz? Just not every day.
45:24 Speaker 04 Not every day, but every year.
45:26 Barry Ritholtz? Seems like a lot.
45:27 Speaker 04 Right. But in a way, every day means that it becomes a new normal.
45:31 Barry Ritholtz? I guess.
45:32 Speaker 04 And so it's not that bad, if it's every day. But really, you know, 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.
45:52 Barry Ritholtz? So let's stay with the idea of modeling. You've been kind of skeptical of certain applications of deep learning in finance. There's overfitting. You know, no one's ever seen a bad back test because 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?
46:20 Speaker 04 Yeah, well, for example, this, 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 use judgment again to say, this is plausible, this is not plausible, we can't believe that. We kind of replace the trader that trades every day is signals or his beliefs to a higher level where we are traders of models. We, you know, 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.
47:04 Speaker 04 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?
47:27 Barry Ritholtz? 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 these 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 portfolios.
47:57 Speaker 04 Sure, it's something that we, you know, every day we think about this. We were in the quant quake in two thousand, two thousand and seven.
48:03 Barry Ritholtz? Mm hmm.
48:04 Speaker 04 And actually, we were fortunate enough to be out of the markets or to, you know, have deleveraged already two weeks before the worst day of the quant quake.
48:17 Barry Ritholtz? Was that an external signal or one of your model signals or what led you to?
48:21 Speaker 04 It was, it was something in the performance already in July, the quant quake, the really bad day happened, maybe ninth of August, I don't remember exactly, but early August.
48:31 Barry Ritholtz? Dog days of summer, for sure.
48:32 Speaker 04 Right, but starting like tenth of July already, there was something really very strange in our portfolio and we started reflecting and what was going on and decided someone was deleveraging and hitting us by shorting our longs and buying our shorts and, you know, this thought process of imagining that even if a fund that was like ten percent correlated with ours, not a lot, but ten percent, and having every day a kind of systematic deleveraging 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
49:15 Speaker 04 are going to, you know, suffer, suffer, and at one point they're going to.
49:19 Barry Ritholtz? Cascades.
49:20 Speaker 04 Cascade and so on. And so that was the rationale for getting out. So in some cases you have, 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.
49:34 Barry Ritholtz? That's really, that's really fascinating. So you mentioned almost thirty five years of doing this. What do you think that, I'm gonna say that again. So you are now thirty five, almost thirty five years into being a market quant. What's the most important thing about markets that the mainstream funds still get wrong?
50:02 Speaker 04 Well, I think it's this question of what is price doing? Why, what are moving prices? And a lot of people still believe that there's something like a fundamental value and that the price is really moving because fundamentals are moving. Whereas we believe, and this is going, you know, this touches very recent academic papers that Gabix and Koetgen, two economists have put forward. They've called it the inefficient, inelastic market hypothesis. 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.
50:46 Speaker 04 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 is going to move prices. It's not going to move prices on a short time scale 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 parcolate and convince more and more people looking forward. But, you know, having this change of tack is really important because in one case, what you need to do to make money is to predict fundamentals.
51:31 Speaker 04 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 there's crowding, it's easier to predict what the crowd 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 and 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.
52:08 Barry Ritholtz? So this sounds a little bit like the Ben Graham line, I think it's Graham. Uh, in the short run, markets are voting machines, in the long run, they're weighing machines. Is that, is that the balance between flows and fundamentals?
52:22 Speaker 04 I, I, yeah, I think it's an old idea. I mean, you know, it's, there's a Keynes also said things like that, you know. But in the long run, we're all dead, right? Keynes is dead.
52:31 Barry Ritholtz? Right.
52:31 Speaker 04 Was saying that. So it's really a question of whether you're going to be, uh, solvable. I mean.
52:43 Speaker 04 Ooh, I'm getting tired. I'll, I'll take that again. What's the word?
52:49 Barry Ritholtz? Was that, by the way, Keynes or Graham? I initially thought it was Keynes and then I checked myself.
52:54 Speaker 04 I, I'm not sure, but what Keynes said is that you, you, uh, what was his quote?
53:04 Speaker 04 Markets can remain irrational longer than you can remain solvent.
53:07 Barry Ritholtz? So he never said that, but it's always attributed to him.
53:10 Speaker 04 Really?
53:11 Barry Ritholtz? There's a great website called Quote Investigator.
53:14 Speaker 04 I see.
53:14 Barry Ritholtz? And you give them a quote, cause people as a sort of a, a authority, you know, appeal to authority. They'll put somebody sophisticated as the source. Einstein never said compounding is the most powerful force in the universe, but they always attribute it to.
53:32 Speaker 04 I didn't know that one.
53:32 Barry Ritholtz? And you would be, you would be, I, I spend 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?
53:46 Speaker 04 I don't think it's Keynes, by the way.
53:47 Barry Ritholtz? No, I don't remember if it was Keynes or Graham, but I thought it was one or the other. And it'll tell me, yeah, Benjamin Graham. But the first thing that came to mind was Keynes or Galbraith. I have so many favorite quotes from both.
54:01 Speaker 04 He said many relevant things even today.
54:04 Barry Ritholtz? Yes, absolutely.
54:05 Speaker 04 But actually, we have models that predict exactly that, that, you know, on the short run, you can have trends and irrational behavior, and on the long run, it revolves back to fundamentals. But the long run, from our estimate, is like five, 10 years. It's a pretty long time scale.
54:20 Barry Ritholtz? I'm trying to remember. It might have been French, a farmer 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?
54:58 Speaker 04 Well, it really depends what you mean by efficient. If you mean that they are very close to unpredictable, then you're right. But I think it's a very dumbed-down version of EMH. The question is whether prices reflect something fundamental that is in principle knowable, 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
55:38 Speaker 04 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.
56:00 Barry Ritholtz? Right.
56:00 Speaker 04 And there's no trend, no mean reversion, ever.
56:05 Barry Ritholtz? 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, in quotes, price.
56:22 Speaker 04 But that's the assumption of a 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 overpriced, 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.
57:05 Speaker 04 So instead of having independent guys doing random stuff, they're kind of, one guy who's doing only one thing, which is a fictitious body that aggregates everybody in the same way.
57:21 Barry Ritholtz? 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 your career.
57:30 Speaker 04 That's an easy one. I have, you know, several mentors, but two of them are really close to my heart. One is Benoit Mandelbrot.
57:38 Barry Ritholtz? Of course.
57:39 Speaker 04 You know, the fractal guy. I knew him personally.
57:42 Barry Ritholtz? Oh, really?
57:43 Speaker 04 Yes, and he did a lot of things in physics as well.
57:47 Barry Ritholtz? Sure.
57:48 Speaker 04 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, you know, when you break a material, what emerges from... From the fracture is a kind of very rough landscape that is fractal. And Mandelbrot had worked on that and there was a lot of infraction. Fractal at a molecular level or at a larger level? Well, fractal from the kind of very fine structure to macroscopic length scales. And so Mandelbrot also did his work on financial markets. And for me, it was really a revelation.
58:30 Speaker 04 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, you know, 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 Yeah, yeah, sure. 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 Dejean, who was a Nobel Prize in Physics, a French physicist who was so fantastic.
59:10 Speaker 04 And, you know, both these two and also Phil Anderson, 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 understanding 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 de-inhibited me in the sense that I thought, okay, maybe I'm not legitimate to speak about finance because I'm a physicist.
59:54 Speaker 04 But, you know, 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. That's really a very broad 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.
1:00:28 Barry Ritholtz? What's the name of it?
1:00:29 Speaker 04 John and Paul.
1:00:30 Barry Ritholtz? Uh huh. Is it John and Paul, a love story? Am I remembering it correctly? I'm a big Beatles fan and that's in my queue.
1:00:36 Speaker 04 Oh, you should read it. Very emotional.
1:00:39 Barry Ritholtz? 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 love this. What else besides John and Paul?
1:01:06 Speaker 04 Yeah, I'm reading someone, something that I actually have read for years, Mrs. Dalloway, Virginia Woolf. Yeah, I'm really a great admirer of Virginia Woolf.
1:01:17 Barry Ritholtz? Really, really interesting.
1:01:22 Barry Ritholtz? Do you do much streaming, TV, podcasts, anything like that? Or we can skip over that?
1:01:26 Speaker 04 Podcasts, a bit, but they're kind of French podcasts.
1:01:29 Barry Ritholtz? So let's, you know, people are always looking for stuff to, I'm gonna ask that.
1:01:34 Barry Ritholtz? So what are you streaming today? What sort of podcasts are you listening to?
1:01:37 Speaker 04 Yeah, you know, in France, we are very fortunate. We have something called France Culture. It's a radio where there's an 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, you know, movies, politics, and so.
1:01:57 Barry Ritholtz? We have NPR here. It's very similar. You can, it's a rabbit hole. You could fall down.
1:02:01 Speaker 04 Yeah, okay, so life of big people, of major celebrities in, in culture, in, in cinema, theater, all these things. So I'm really a big fan of France Culture.
1:02:15 Barry Ritholtz? 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?
1:02:29 Speaker 04 Well, study theoretical physics and, and study everything that's related to data. You pay attention to data and, and.
1:02:39 Speaker 04 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.
1:02:51 Barry Ritholtz? Really, really good answer. And our final question, what do you know about the world of investing today? Might have been useful 35 years or so ago when you were first getting started.
1:03:03 Speaker 04 That is extremely competitive, much more than we thought.
1:03:06 Barry Ritholtz? Really?
1:03:07 Speaker 04 Yeah.
1:03:08 Barry Ritholtz? 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 can find those at Apple Podcasts, Spotify, YouTube, Bloomberg, wherever you find your favorite podcasts. 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. Anna-Luc is my podcast producer. Sean Russo is my head of research. I'm Barry
1:03:52 Barry Ritholtz? Ritholtz. You've been listening to Masters in Business on Bloomberg Radio.
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