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

An episode of [Masters in Business](https://invisiblenetwork.ai/podcast/730188152), 17 Apr 2026, 59 min.

Audio: https://podtrac.com/pts/redirect.mp3/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/4e4cd910-40a1-4619-a5f3-ae2b0012ffff/a686c4f7-4858-4cba-a5fc-b42f01600aa1/audio.mp3?utm_source=Podcast&in_playlist=5873a3cb-298f-40bc-b71f-ae2b0013000d

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. See omnystudio.com/listener for privacy information.

## Transcript

[0:02] Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Masters in
[0:12] Speaker 1: Business with Barry Ritholt on Bloomberg Radio.
[0:16] Speaker 2: This week on the podcast, yet another extra special guest.
[0:21] Speaker 2: John Philip Buschow is chief Scientist, head of research, chairman
[0:25] Speaker 2: and co founder at CFM. They're a quantitative trend following
[0:31] Speaker 2: Hedge Fund. They run over twenty billion dollars in client money.
[0:35] Speaker 2: They've been around for almost thirty five years, put together
[0:38] Speaker 2: a very impressive track record. They also run a number
[0:42] Speaker 2: of interesting academic research labs and things like that. Jean
[0:48] Speaker 2: Philippe has published something like three hundred plus academic papers.
[0:53] Speaker 2: They are deep into all the things that drive markets
[0:56] Speaker 2: from a quantitative perspective. I thought this conversation was fascinating
[1:00] Speaker 2: and I think you will also with no further ado,
[1:04] Speaker 2: My interview of CFMS Jean Philippe the show. So what
[1:09] Speaker 2: do people call you? JP? John Phillip Philippe, What what
[1:13] Speaker 2: do you?
[1:13] Speaker 3: Philippe? In France JP and Anglo Saxon Countries JP.
[1:17] Speaker 2: All right, it seems a little informal, but I'll go
[1:20] Speaker 2: with JP. So, so JP, let's start with your background
[1:25] Speaker 2: PhD and theoretical physics from NS. You spent some years
[1:29] Speaker 2: at very prestigious research in the institutions I mentioned Cavendish Labs.
[1:35] Speaker 2: What was the original career plan.
[1:38] Speaker 3: Yeah, I was planning to be a physicist, but then,
[1:40] Speaker 3: you know, studying statistical physics, and we can go into
[1:43] Speaker 3: that later if you wish. I realized that physics can
[1:47] Speaker 3: offer much more than studying physics, and and then I,
[1:51] Speaker 3: you know, was always fascinating, fascinated by numbers. I've always
[1:57] Speaker 3: liked statistics and financial markets spit statistics every day, and
[2:03] Speaker 3: I thought, you know, this is a very interesting complex system.
[2:07] Speaker 3: There are crisis, crashes, jumps, this system seems to be
[2:12] Speaker 3: driven by its own dynamics. Physicists have to do something
[2:16] Speaker 3: about this, and so.
[2:17] Speaker 2: The sounds very similar to chaos theory.
[2:20] Speaker 3: Yeah, exactly, mean that was the high days of chaos theory.
[2:23] Speaker 2: So I pulled some phrases from some of your papers.
[2:29] Speaker 2: One was titled, and I'm going to mangle this disordered
[2:32] Speaker 2: systems and complex phenomena which can be either physics or finance.
[2:37] Speaker 2: That sounds like, but what are the dynamics of glassy
[2:40] Speaker 2: systems and granular media?
[2:42] Speaker 4: That sounds fascinating, Yeah, but you know it's all the
[2:49] Speaker 4: problem is how to interacting elements give rise to something surprising.
[2:55] Speaker 3: You know. Granuar matter is grains that you know, interract
[3:00] Speaker 3: with one another, and then you have these strange phenomena
[3:02] Speaker 3: called avalanches where you drop a grain on the slope
[3:06] Speaker 3: and most of the time nothing happens, but sometimes there's
[3:09] Speaker 3: a big landslide that takes all the grains down. And
[3:13] Speaker 3: so this again is very reminiscent of financial markets, right,
[3:18] Speaker 3: I mean, many things happen, nothing much follows, and then
[3:22] Speaker 3: sometimes there's a crash, right, And so this was really
[3:25] Speaker 3: intriguing for physicists like me.
[3:27] Speaker 2: So I'm as you're talking, I'm just thinking of a
[3:30] Speaker 2: concept in physics that really applies to markets, the three
[3:35] Speaker 2: body problem. When you have those three gravitational masses interacting
[3:42] Speaker 2: with each other, it's fairly unpredictable, which kind of seems
[3:47] Speaker 2: like markets themselves.
[3:49] Speaker 3: Yeah, I mean, there's there are two ways to be unpredictable.
[3:52] Speaker 3: One is that the system is by itself unpredictable, that
[3:55] Speaker 3: even with deterministics laws like the three body problem, you
[3:59] Speaker 3: can't see much after a few seconds, days or weeks.
[4:04] Speaker 3: But there are other kinds of unpredictability. When there's a
[4:07] Speaker 3: true source of exognous noise that hits the system and
[4:12] Speaker 3: you can't say anything. So that's the traditional way economists
[4:15] Speaker 3: think about markets. They are kind of buffeted by things
[4:18] Speaker 3: you can't predict because they come from outside. And then
[4:21] Speaker 3: I think the physics hunch is that, hey, but there
[4:24] Speaker 3: can be self generated shocks, self generated randomness that come
[4:30] Speaker 3: from large assemblies of individuals, i e. Traders, you know,
[4:36] Speaker 3: agents that trade and buy and sell to each other,
[4:39] Speaker 3: and this can generate you know, intrinsic randomness that is
[4:45] Speaker 3: not of the same kind as the three body problem,
[4:48] Speaker 3: but really comes from the interaction of a huge number
[4:51] Speaker 3: of elements.
[4:53] Speaker 2: So I see the parallels between theoretical physics and finance.
[4:59] Speaker 2: What I led you to begin shifting in the early
[5:02] Speaker 2: nineties from studying theoretical physics to becoming fascinated by market
[5:10] Speaker 2: microstructure and econophysics.
[5:13] Speaker 3: Yeah, so, as I said initially, I've always been excited
[5:17] Speaker 3: by data and trying to make sense of data. So
[5:20] Speaker 3: there was something there anyway, But what really drove the
[5:25] Speaker 3: transition was, in a sense, the nineteen eighty seven crash
[5:30] Speaker 3: and the Black Shoal theory. So I didn't know anything
[5:33] Speaker 3: about that, and then I wrote a paper on what
[5:37] Speaker 3: I was working on, physics systems with large jumps, if
[5:42] Speaker 3: you want large crashes that happened from time to time.
[5:47] Speaker 3: And someone who was working in the banking industry called
[5:51] Speaker 3: me and said, hey, it's really interesting because it resembles
[5:54] Speaker 3: what happens in finance, and in particular what just happened
[5:57] Speaker 3: the nineteen eighty seven crash. And there's this theory, the
[6:00] Speaker 3: black steals theory, that is a theory that only works
[6:04] Speaker 3: in a world where there are no crashes, where you know,
[6:08] Speaker 3: all the motions are small and predict they're random, but
[6:12] Speaker 3: they're kind of predictable even if they're random in some
[6:15] Speaker 3: strange way. And I thought this is really weird. And
[6:18] Speaker 3: this guy said, why don't you try to generalize black
[6:21] Speaker 3: seals to a world where where there are crashes? And
[6:24] Speaker 3: I thought, well, that's that's really interesting. So I read
[6:27] Speaker 3: black Shoals and I thought it can't be right. They
[6:29] Speaker 3: must be wrong, these guys. So I kind of redid
[6:32] Speaker 3: everything myself and found something that looked more interesting than
[6:36] Speaker 3: baccheors because it could be extended to non Gaussian statistics,
[6:40] Speaker 3: as they are called non normal distribution Bell curves and
[6:44] Speaker 3: so on, and so it looked to me interesting and
[6:47] Speaker 3: I thought, okay, maybe we can do software out of
[6:51] Speaker 3: that and commercialize it. And so I went and knocked
[6:55] Speaker 3: on several doors and suddenly the door of Jean Pillard opened,
[6:59] Speaker 3: and was someone who had founded actually CFM in ninety one,
[7:05] Speaker 3: that was ninety four. And I started, you know, explaining
[7:09] Speaker 3: what I had been doing and that I was interested
[7:12] Speaker 3: in transferring ideas from physics to finance, and he said,
[7:15] Speaker 3: why don't we create something together, and so at the
[7:18] Speaker 3: time we created a company called Science and Finance and
[7:22] Speaker 3: this was done, you know, in in two weeks. It
[7:25] Speaker 3: was like amazing the way we met, and there was
[7:30] Speaker 3: a fluid that was flowing between us immediately. And so
[7:36] Speaker 3: CFM then merged with Science and Finance in nineteen ninety
[7:40] Speaker 3: so it's now the same firm. But the idea he
[7:43] Speaker 3: had at the time, he had this this small CTA
[7:48] Speaker 3: trading firm, and he thought, I need to beef up
[7:50] Speaker 3: my research team, and this guy seems to be interesting.
[7:53] Speaker 3: So we just partnered and that's how it all started.
[7:57] Speaker 2: And that CTA firm specialized in manage future Yeah, exactly much.
[8:02] Speaker 2: Now I know most of the future straders. I know,
[8:05] Speaker 2: they all seem to be trend followers. How do you
[8:08] Speaker 2: think about applying quantitative research and theoretical physics to dealing
[8:14] Speaker 2: with futures?
[8:15] Speaker 3: Yeah, well that that was exactly Jean Pierre Haguilar's idea.
[8:19] Speaker 3: He said, Okay, I'm doing trend following is good, but
[8:22] Speaker 3: it's not you know, rocket science. Maybe we can do
[8:25] Speaker 3: much better. And so he said, why don't we work
[8:28] Speaker 3: on something more beefy than just trend following, And so
[8:33] Speaker 3: you know that that started the whole thing. And the
[8:35] Speaker 3: idea of the main idea is data. You know, physicists
[8:39] Speaker 3: are good at looking at data and extracting structures, you know,
[8:42] Speaker 3: looking at data and imagining that from that data you
[8:46] Speaker 3: can build theories. You can identify what's important and what's not.
[8:51] Speaker 3: And that's I think the way you know it all
[8:54] Speaker 3: works in physics that you you you scrutinize data and
[8:58] Speaker 3: then there's a flash and you think I can model that.
[9:01] Speaker 3: And it's really the same process in finance, at least
[9:04] Speaker 3: as far as we wear concern, and we are concerned now,
[9:08] Speaker 3: it hasn't changed. It's the same process. Huh.
[9:11] Speaker 2: Really really quite fascinating you keep your professorships at NS
[9:16] Speaker 2: and you've maintained a foot in academia even as you're
[9:20] Speaker 2: building and running an asset management firm. Tell us about that,
[9:23] Speaker 2: you're still publishing papers. What keeps you interested in the
[9:27] Speaker 2: academic side of finance.
[9:29] Speaker 3: Well, first of all, it is me. You know, I
[9:32] Speaker 3: feel I'm a researcher's researcher at heart, and I need
[9:36] Speaker 3: to continue. It's like you know, people running the marathon.
[9:40] Speaker 3: They are doing something else in life and then there's
[9:43] Speaker 3: an urge to run the marathon. For me, there's an
[9:45] Speaker 3: urge to understand what I'm doing, and understand also things
[9:50] Speaker 3: that I'm not doing even now, you know, even physics problems,
[9:54] Speaker 3: I can get excited about them, or trying new things
[9:57] Speaker 3: like the mL revolution and how does mL work? Why
[10:03] Speaker 3: do large language models work so well? Learn so well?
[10:08] Speaker 3: I think it's fascinating. I want to understand. But there's
[10:11] Speaker 3: another reason for doing this is that to attract talents,
[10:16] Speaker 3: you need to identify them, you need to attract them,
[10:20] Speaker 3: you need to be their professor at one point. And
[10:23] Speaker 3: I think a lot of the success of CFM has
[10:26] Speaker 3: been attracting talents, and I think part of that only
[10:31] Speaker 3: part of that. Of course, it's a teamwork is due
[10:33] Speaker 3: to the fact that I'm still very connected in academic
[10:36] Speaker 3: circles and young students. They've listened to me giving talks, lecturing,
[10:42] Speaker 3: They've read my papers, and so they feel let's go
[10:47] Speaker 3: and work for that firm because it seems that they're
[10:49] Speaker 3: really doing cool stuff.
[10:50] Speaker 2: So is that the thinking behind establishing the research division
[10:54] Speaker 2: at CFM. I mean, you run that as a full
[10:57] Speaker 2: academic research department as oppose to you know, a lot
[11:01] Speaker 2: of asset management shops. They have a couple of cfps
[11:06] Speaker 2: and MBAs and their CFAs and they're working on their
[11:10] Speaker 2: quantitative models. You guys seem like you've taken it to
[11:13] Speaker 2: a whole different level.
[11:14] Speaker 5: Yeah.
[11:15] Speaker 3: I mean most, maybe even all, our researchers have a PhD.
[11:21] Speaker 3: It doesn't mean that we're an academic lab. We're really
[11:24] Speaker 3: working on concrete stuff. We're really there to, you know,
[11:27] Speaker 3: make models that work, build portfolios that are robust, model risk,
[11:32] Speaker 3: model execution, control costs. All these things are bread and
[11:36] Speaker 3: butter for everyday work. But at the same time, we
[11:40] Speaker 3: feel that when we find something that is beyond the
[11:43] Speaker 3: kind of daily work and that can be published because
[11:46] Speaker 3: it brings something to the academic debate or to do
[11:49] Speaker 3: public debate. You know, why how do markets work? Why
[11:53] Speaker 3: are there crashes? Are market sufficients? What about the economy?
[11:58] Speaker 3: Do people understand invation? We need new theories to understand inflation,
[12:03] Speaker 3: monetary policy and all these things. We believe it's our role.
[12:08] Speaker 3: Also because we have access to so much data, and
[12:11] Speaker 3: we're privileged, you know, academics they don't have access to
[12:14] Speaker 3: so much data, and so we have to give back
[12:17] Speaker 3: in a way. And the reason we're doing this, as
[12:20] Speaker 3: I said, it's not only because we're driven to do that,
[12:23] Speaker 3: but also because it creates an atmosphere where people are
[12:28] Speaker 3: happy to work at CFM. I hope you know, I
[12:31] Speaker 3: don't want to put words in their mouth.
[12:34] Speaker 2: Well, you guys, open up a big, expanded, a big
[12:37] Speaker 2: New York officer. You don't seem to be having much
[12:39] Speaker 2: difficulty recruiting people there. What's the head count there? Now?
[12:43] Speaker 3: We have one hundred and fifteen researchers and fifteen percent
[12:47] Speaker 3: of them are in New York.
[12:50] Speaker 2: What motivated expanding the New York office as much as
[12:54] Speaker 2: you have?
[12:54] Speaker 3: Well, first of all, a lot of our investors are
[12:57] Speaker 3: in the US, so we need to be there interact
[13:00] Speaker 3: with them, and we need to have a presence, if
[13:05] Speaker 3: only for investor relation, but also because there's a lot
[13:09] Speaker 3: of talents in the US that we want to grab
[13:12] Speaker 3: and attract. There's a lot of data, a lot of brokers,
[13:15] Speaker 3: so it makes a lot of sense. So we've been
[13:17] Speaker 3: in New York for twenty years, and you know, it's
[13:22] Speaker 3: obvious that you know it is a hub and we
[13:25] Speaker 3: should expand there.
[13:26] Speaker 2: So you mentioned earlier your co founder Jean Pierre Aguilar.
[13:31] Speaker 2: He passed away in two thousand and nine. What was
[13:34] Speaker 2: the impact on the firm. How did you guys manage
[13:38] Speaker 2: around That's a big loss when you lose a founder.
[13:41] Speaker 3: Yeah, it was a tragedy because he died in a
[13:43] Speaker 3: glider accident. You know, we knew that he was gliding.
[13:46] Speaker 3: We knew that gliding was dangerous, but in a sense,
[13:51] Speaker 3: and it really means bad risk management, right, we never
[13:55] Speaker 3: thought that he could crash. It never occurred to us,
[13:59] Speaker 3: which was straight because these things happen, and so it
[14:03] Speaker 3: was tragic because we were not prepared. And it was
[14:06] Speaker 3: tragic because he was not only a friend, but he
[14:09] Speaker 3: was you know, the public figure of CFM. He was
[14:13] Speaker 3: not involved in constructing models. I mean quants in a way.
[14:16] Speaker 3: What's great about quant investing is that you don't need
[14:19] Speaker 3: star traders, you don't need you know, pms that know everything.
[14:24] Speaker 3: It's a collective effort and so when someone disappears or
[14:28] Speaker 3: resigns or dies, it's not a tragedy. But in the
[14:31] Speaker 3: case of Jean Pierre, it was even that he was
[14:33] Speaker 3: not really involved in the construction of models. He was
[14:37] Speaker 3: just very inspiring, generous, and he was really great. Was
[14:43] Speaker 3: he had a vision, you know when we met and
[14:45] Speaker 3: he thought, Okay, with that guy, we can build something,
[14:48] Speaker 3: build something great. It's amazing, you know, to think that
[14:53] Speaker 3: he was so enthusiastic about creating what we created together,
[14:57] Speaker 3: and so we owe him a lot. When he passed away,
[15:01] Speaker 3: it was really difficult to well, you know, there were
[15:05] Speaker 3: several issues. One is that he had fifty seven percent
[15:09] Speaker 3: of the company, so we had to negotiate with the
[15:13] Speaker 3: estate to get back control. That was pretty difficult, but
[15:18] Speaker 3: we went through that. And also we needed to reassure
[15:21] Speaker 3: our investors. You know, Jean Pierre was he seemed to
[15:24] Speaker 3: be the public figure. He was a public figure, and
[15:27] Speaker 3: it seemed to be the inspired inspiration behind everything, and
[15:30] Speaker 3: so we had to we you know, communicate that we
[15:33] Speaker 3: were at the helm and that we would navigate that
[15:37] Speaker 3: and it worked and so it was it was very stressful,
[15:42] Speaker 3: but it was very rewarding as well to go through
[15:45] Speaker 3: that and.
[15:46] Speaker 2: The firm carries on as his legacy.
[15:49] Speaker 3: Yeah.
[15:50] Speaker 2: Coming up, we continue our conversation with John Philip Schau,
[15:54] Speaker 2: head of Research and Chief Scientists at CFM, talking about
[15:58] Speaker 2: the growth of Capital Fund Management.
[16:01] Speaker 6: I'm Barry Redults.
[16:02] Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm
[16:16] Speaker 2: Barry Ridults. You're listening to Masters in Business on Bloomberg Radio.
[16:21] Speaker 2: My extra special guest this week is Jean Philip the show.
[16:24] Speaker 2: He is the head of Research, chairman and chief scientist
[16:28] Speaker 2: at Capital Fund Management Hedge fund managing over twenty billion dollars,
[16:33] Speaker 2: a quantitative shop specializing in managed futures and other quant
[16:39] Speaker 2: type funds. So let's talk a little bit about the
[16:45] Speaker 2: building of the fund. You meet you cofind found Science
[16:49] Speaker 2: and Finance in nineteen ninety four with Jean Pierre Aguilar.
[16:55] Speaker 2: What was the thought process did you think you were
[16:58] Speaker 2: building a quant fund research shop? What was the original plan.
[17:03] Speaker 6: A quand fund From day one.
[17:05] Speaker 3: From day one a quand fund. But from day one
[17:08] Speaker 3: we knew that we wanted to be strongly associated with academia.
[17:13] Speaker 3: We knew that the only way to innovate, you know,
[17:16] Speaker 3: again coming back to the fact that financial markets are
[17:19] Speaker 3: complex systems, it's really difficult to beat the market. We
[17:23] Speaker 3: know that everybody's trying to beat the market. If we
[17:26] Speaker 3: want to have something else to say and not follow
[17:29] Speaker 3: the crowd, we have to innovate, and innovating is hard.
[17:32] Speaker 3: You have to spend time, you have to have new
[17:35] Speaker 3: ideas that nobody else has, and so this means investing
[17:39] Speaker 3: heavily in research. So the two are not contradictory. We
[17:42] Speaker 3: really wanted to be a con fund. We had already.
[17:46] Speaker 3: We knew already about Renaissance. We knew that these guys
[17:49] Speaker 3: at Renaissance they were very close in spirit and in
[17:52] Speaker 3: culture to what we were, and so we thought we
[17:55] Speaker 3: were going to try to emulate them. Of course, they're
[17:59] Speaker 3: so great that there's no way to emulate them, but anyway,
[18:02] Speaker 3: this was the aim.
[18:03] Speaker 2: They had a forty year head start on you guys.
[18:06] Speaker 2: So yeah, But so it's funny you mentioned renaissance technologies.
[18:10] Speaker 2: I think of when I'm doing my research for for
[18:14] Speaker 2: CFM kind of reminded of D. E. Shaw and AQR
[18:19] Speaker 2: and a few other little bit of Millennium, although they
[18:22] Speaker 2: do so much of everything. The thought process behind being
[18:29] Speaker 2: a QUND shop when there's so many other QUND shops.
[18:32] Speaker 2: If we don't create our own models, if we don't
[18:34] Speaker 2: create our own findings and innovations, we're just an also run.
[18:38] Speaker 2: Is that ran? That is that the thought process behind it,
[18:42] Speaker 2: like we have we have to do this otherwise because
[18:45] Speaker 2: it's all of these other cound shop that I've mentioned,
[18:50] Speaker 2: none of them are quite the academic lab that.
[18:53] Speaker 3: They AQR is. I think that closest to us in
[18:58] Speaker 3: that front. I think, you know, in the songs they
[19:00] Speaker 3: took the initially the completely different term. They thought we
[19:04] Speaker 3: have to be completely secretive about everything and be a
[19:08] Speaker 3: kind of black hole where everything goes in but nothing
[19:11] Speaker 3: goes out. And that was not our philosophy. We thought that,
[19:15] Speaker 3: you know, life is too short as well. This is
[19:17] Speaker 3: not only we want to make money for ourselves, for
[19:20] Speaker 3: our investors. We want to excel, but not at any costs.
[19:24] Speaker 3: We think that there's something else in life, that there's
[19:27] Speaker 3: a legacy that we want to leave. And this legacy
[19:30] Speaker 3: is intellectual as.
[19:31] Speaker 2: Well, pursuing the truth as to what drives markets and
[19:35] Speaker 2: what leads to alpha.
[19:36] Speaker 6: And returns exactly.
[19:38] Speaker 2: You know, every new discovery of alpha eventually gets arbitraged away.
[19:43] Speaker 2: Is that is that the thinking?
[19:44] Speaker 3: Yeah, not exactly, I mean transfollowing. You know, it's not
[19:47] Speaker 3: arbitrage the way at all. And actually, if you think
[19:50] Speaker 3: about it, it's very hard to arbitrage trend following. You know,
[19:54] Speaker 3: if if people trend follow is going to lead to
[19:58] Speaker 3: more trendfor.
[19:59] Speaker 2: Right, it's not only a farm of French factor, but
[20:02] Speaker 2: it takes on its own life.
[20:04] Speaker 3: Right, Well, Fama doesn't like momentum but anyway.
[20:06] Speaker 2: But isn't it part it's not part of the original
[20:09] Speaker 2: three factor model, but wasn't it one of the later models?
[20:14] Speaker 3: Reluctantly, I think he had to add it. But it's
[20:17] Speaker 3: a disgrace for efficient market theory. So he doesn't like
[20:20] Speaker 3: momentum at all.
[20:22] Speaker 2: Listen, you know, if the math is there, it doesn't
[20:25] Speaker 2: matter if you like it, if it works, if it's
[20:28] Speaker 2: a valid factor, it's a valid factor.
[20:30] Speaker 3: Agree, I agree, But you know that's again the physicists
[20:34] Speaker 3: point of view. Experiments is about everything else. But sometimes
[20:40] Speaker 3: when you talk to economists, they have a strange view
[20:43] Speaker 3: that theorems and axioms are supersede any empirical observation. I
[20:48] Speaker 3: was told that by an economist, and so there's a
[20:52] Speaker 3: very strong difference in perception.
[20:54] Speaker 2: I recently had Richard Thaylor and Alex Amus in the studio,
[21:00] Speaker 2: and I was shocked to learn from them they still
[21:04] Speaker 2: aren't teaching behavioral finance in economics course at a college level,
[21:10] Speaker 2: which is kind of shocking. I think everything we've learned.
[21:13] Speaker 2: So let's talk about another technology. There have been over
[21:17] Speaker 2: the past decade, but especially the past few years, huge
[21:21] Speaker 2: advances and artificial intelligence and machine learning, to say nothing
[21:25] Speaker 2: about large language models. How are you guys thinking about
[21:29] Speaker 2: real world investing driven by AI and what sort of
[21:33] Speaker 2: opportunities does this open up?
[21:35] Speaker 3: Well, you know, AI is really an advanced form of
[21:39] Speaker 3: data analysis, and in a way, we've been doing machine
[21:43] Speaker 3: learning forever. The thing is that techniques have evolved there.
[21:48] Speaker 3: It's now much more efficient. There many more things that
[21:52] Speaker 3: one can do, in particular reading text. For many years
[21:55] Speaker 3: we were just using numbers, and actually for many years
[21:58] Speaker 3: we were just using prices and volume and not anything else,
[22:02] Speaker 3: and well and fundamental information about companies. But now there's
[22:07] Speaker 3: so much data that you can use. There's new data
[22:10] Speaker 3: set every day that we're presented by the data vendors,
[22:13] Speaker 3: and so there's a need to handle that data to
[22:16] Speaker 3: read sometimes huge data files. For example, if you think
[22:19] Speaker 3: about microstructure high frequency data, you know there's events happening
[22:26] Speaker 3: in the other book of major exchanges at the millisecond
[22:30] Speaker 3: level or even faster. This generates a huge amount of
[22:33] Speaker 3: information that has to be dealt with, analyzed, and machine
[22:37] Speaker 3: learning helps you very much doing that, reading texts that
[22:42] Speaker 3: no human would be able to read and extracting information
[22:46] Speaker 3: statistical information from that text. So for us, it is
[22:50] Speaker 3: I wouldn't say a revolution, but it's an acceleration of
[22:54] Speaker 3: things that we were trying to do before, and obviously
[22:57] Speaker 3: we're much in tune with that. We've actually created to
[23:00] Speaker 3: the nomil ab at CFM to help transferring technology from
[23:05] Speaker 3: what mL people are constructing and what researchers at CFM
[23:09] Speaker 3: may be using, but also to try to understand how
[23:13] Speaker 3: these things work right, because we're very uncomfortable with the
[23:16] Speaker 3: idea of black boxes. Black box is something that can
[23:21] Speaker 3: improve the research process, but when you think about implementing
[23:26] Speaker 3: that in production and having models trading with these models,
[23:30] Speaker 3: you really want to be sure that the machine has
[23:32] Speaker 3: done something that makes sense, and so understanding what machine
[23:37] Speaker 3: learning is actually doing? Why are these things working? To
[23:41] Speaker 3: start with? You know, what is strange is that it
[23:44] Speaker 3: works so well, but nobody understands why. When you're driving
[23:48] Speaker 3: a car, the car works really well, but we know
[23:50] Speaker 3: exactly why it works how it works machine learning, nobody
[23:55] Speaker 3: really understands what's the magic, and I think it's a
[23:59] Speaker 3: huge intellect challenge and we want to be part of that.
[24:03] Speaker 2: How much of that is pattern recognition? Because when I
[24:06] Speaker 2: think about the work, whenever I read about LMS, it's
[24:10] Speaker 2: really just statistically what makes the most sense for the
[24:14] Speaker 2: next letter, the next word, the next sentence. It's kind
[24:17] Speaker 2: of hard to just think of crafting a document based
[24:21] Speaker 2: on probabilities of the most likely word if you have
[24:26] Speaker 2: these few words beginning. But apparently that's a big part
[24:30] Speaker 2: of how they work. Or am I grossly overse.
[24:33] Speaker 3: So why does it work? And can it work in
[24:36] Speaker 3: finance too? You know? So is it because the language
[24:40] Speaker 3: or images have such a strong structure that you know
[24:45] Speaker 3: there's an internal logic to language, or to pictures, or
[24:50] Speaker 3: to other things that the model is able to capture.
[24:53] Speaker 3: And using these breathively simple ideas of statistical prediction of
[24:59] Speaker 3: what's going to happen next is enough to generate meaningful sentences.
[25:04] Speaker 3: But maybe maybe this part of that the structure of
[25:08] Speaker 3: the of the data. Is it the case in finance too, well,
[25:12] Speaker 3: he may.
[25:12] Speaker 2: Be literally exactly where to go. If you're training an
[25:15] Speaker 2: LLM on billions and billions of documents, pages, books, whatever,
[25:19] Speaker 2: and it now has a giant data source of when
[25:22] Speaker 2: you get these first few words or these first few sentences,
[25:26] Speaker 2: here's what's most common, here's what's second most common. And
[25:30] Speaker 2: here's a reference check for you to say, how does
[25:33] Speaker 2: this compare grammatically structurally to the giant corpus we have.
[25:40] Speaker 2: I can see the math behind that, because there's only
[25:44] Speaker 2: so many trillions of combinations of letters where it sentences.
[25:48] Speaker 2: But when you now apply it to markets, which seem
[25:52] Speaker 2: to be so random tick to tick day to day,
[25:57] Speaker 2: can you apply the same sort of logic to to
[26:00] Speaker 2: invest there?
[26:01] Speaker 3: Well, that are two problems. One is fundamental, are there
[26:07] Speaker 3: structures that you can extract? And we believe that there are,
[26:12] Speaker 3: because otherwise we wouldn't be there. I mean, you know,
[26:14] Speaker 3: trend following is a structure. It's a pretty trivial one,
[26:17] Speaker 3: but it is a structure. Now, many other types of
[26:20] Speaker 3: structures in the data that we've extracted without using mL
[26:25] Speaker 3: or using mL now or recovering with mL, or even
[26:29] Speaker 3: more complicated one with emls. But the major difference between
[26:34] Speaker 3: finance and languages or pictures is one the amount of data.
[26:39] Speaker 3: Because in the end, you know, stock markets have only
[26:42] Speaker 3: existed since I don't know, nineteen hundred or eighteen.
[26:45] Speaker 6: Hundred year one small data set.
[26:47] Speaker 3: Small said, except if you go to high frequency, as
[26:50] Speaker 3: I said, you know, if you go to tick by
[26:52] Speaker 3: tick or the book data, there's huge amounts of data
[26:56] Speaker 3: and there you can think that there's more to but
[27:03] Speaker 3: what it was I saying, yeah, so so, so there's
[27:06] Speaker 3: the problem of the availability of data and the frequency
[27:10] Speaker 3: at which you want to predict. So for high frequency,
[27:12] Speaker 3: I think there's a lot of structure. For lower frequency,
[27:15] Speaker 3: it's not clear yet that it is going to be useful,
[27:19] Speaker 3: you know, used as a kind of technical model which
[27:25] Speaker 3: only looks at prices without reading texts. For reading text
[27:29] Speaker 3: we know that there's there's a lot of structure, which
[27:31] Speaker 3: corresponds to to the structure of language. But having said
[27:35] Speaker 3: everything you said, there's still something strange about lll m's
[27:39] Speaker 3: or you know, generative AI is that with this process
[27:42] Speaker 3: of constructing sentences that are statistic statistically valid, you can
[27:47] Speaker 3: invent new things. And that's the thing that really is
[27:51] Speaker 3: really strange, right, I mean, you can learn pictures, for example,
[27:55] Speaker 3: you know this celebrity database where you have made the
[27:59] Speaker 3: machine learn these these pictures and then you ask the
[28:02] Speaker 3: machine to generate new ones and it does, and these
[28:06] Speaker 3: are pictures that look exactly I mean that you look
[28:09] Speaker 3: and you think it's a celebrity, but the celebrity doesn't exist.
[28:13] Speaker 3: So there's something still weird about this that, as I said,
[28:17] Speaker 3: nobody really understand.
[28:19] Speaker 2: Really kind of interesting.
[28:21] Speaker 3: And so continuing on that, what we are trying to
[28:24] Speaker 3: do is to do the same thing with financial markets. So,
[28:28] Speaker 3: as I said, one hundred years of data is not
[28:30] Speaker 3: a lot, but maybe you can use these gen AI
[28:33] Speaker 3: models to generate a millionaires of fictitious financial markets. That's interesting,
[28:40] Speaker 3: very interesting.
[28:41] Speaker 2: So let's talk a little bit about trend following and
[28:43] Speaker 2: managed futures. It's had a few real standout years in particular,
[28:48] Speaker 2: twenty twenty two real challenging year and manage futures at
[28:56] Speaker 2: we're top of the asset quilt? What does that episode
[29:01] Speaker 2: tell us about what strategies work, why they work? And
[29:05] Speaker 2: the question I always find with trend following why did
[29:09] Speaker 2: so few investors tend to stay with them? They all
[29:13] Speaker 2: seem to get nervous and tap out right before things.
[29:17] Speaker 2: It's almost when you see people giving up. It's just
[29:21] Speaker 2: about when the turn occurs.
[29:23] Speaker 6: I agree, So what is it?
[29:25] Speaker 4: What?
[29:25] Speaker 2: First of all, why was twenty twenty two such a
[29:28] Speaker 2: standout year? I don't know just I mean, aside from
[29:31] Speaker 2: the fact that we had big FED rate hikes and
[29:35] Speaker 2: fixed income and equities both got sill act double digits
[29:40] Speaker 2: kind of rare occurs the same year. I think you
[29:44] Speaker 2: have to go back about forty to forty one years
[29:46] Speaker 2: to see both of them down significantly. How do you
[29:51] Speaker 2: think about how do you think about what environment leads
[29:57] Speaker 2: to best results for trend following.
[30:00] Speaker 3: It's very difficult to say, because otherwise we would have
[30:02] Speaker 3: a meta model that albutrages and you know, increases the
[30:06] Speaker 3: weight of trend following when it's going to work. Well,
[30:09] Speaker 3: I think it's there probably is more research to do,
[30:13] Speaker 3: and where we've been trying, we haven't found anything that's
[30:16] Speaker 3: very convincing. But you know, beginning of twenty twenty six
[30:19] Speaker 3: is also a very good period for trendsforward. Actually, since
[30:22] Speaker 3: we wrote a paper in twenty fourteen called two hundred
[30:25] Speaker 3: Years of trend Following, and we were reporting on the
[30:28] Speaker 3: fact that since eighteen hundred, if you pay per trade,
[30:32] Speaker 3: a very simple trend following strategy, you make money every
[30:36] Speaker 3: decade with ups and downs. You know, there are years
[30:39] Speaker 3: that are not so good years. But as you say,
[30:42] Speaker 3: I mean, what is striking and about the very point
[30:46] Speaker 3: you made about people getting out of trend following just
[30:50] Speaker 3: before it gets back on is I think it's ingrained
[30:54] Speaker 3: in people's behavior to chase performance. So if performance has
[30:59] Speaker 3: been bad for a few years, everybody declares. And that
[31:02] Speaker 3: was the case in twenty fourteen when we wrote our paper,
[31:06] Speaker 3: transfollowing had been flat for the last five years, and
[31:09] Speaker 3: people said, okay, well, trensfalling is dead now, and we
[31:11] Speaker 3: were actually convinced that it was not the case. That
[31:14] Speaker 3: you know, transfalling is such a strong behavioral bias that
[31:19] Speaker 3: performance chasing is so ingrained in every one of us
[31:23] Speaker 3: even rational, we can't help, and so we bet and
[31:28] Speaker 3: it was confirmed that trenfollowing would come back. And since
[31:31] Speaker 3: twenty twenty four is twenty fourteen, it's been very good.
[31:34] Speaker 2: Actually overall, well, you've had a market that very much
[31:38] Speaker 2: was trending, mostly in one direction. For I mean, you
[31:41] Speaker 2: have Q four of twenty eighteen, and I think twenty
[31:43] Speaker 2: sixteen was so so. But for the past fifteen years,
[31:47] Speaker 2: the bias has been pretty much in one direction. If
[31:50] Speaker 2: you're on the right side of that, you should do
[31:52] Speaker 2: pretty well.
[31:52] Speaker 3: Yeah, but I'm not speaking about being long. I mean
[31:55] Speaker 3: I'm really speaking about you know.
[31:56] Speaker 2: Different asset classes, different trends of your long and short.
[32:00] Speaker 3: Yeah.
[32:00] Speaker 2: Sure, so it doesn't matter as long as the trend
[32:03] Speaker 2: is in place you want.
[32:03] Speaker 6: To part exactly.
[32:06] Speaker 2: And for people who are not familiar with managed futures,
[32:10] Speaker 2: there's a decent amount of leverage used in that product,
[32:13] Speaker 2: so you have to really manage the risk of the downside.
[32:17] Speaker 2: But how do you think about, you know, the potential
[32:19] Speaker 2: upside relative to the risk you're taking in a futures product.
[32:23] Speaker 3: What do you mean exactly? I mean, I mean, we
[32:27] Speaker 3: just think about risk we want to risk. Is a
[32:30] Speaker 3: very complex object. Actually, you know, there's a volatility, but
[32:33] Speaker 3: that's also correlation. If you deal with the portfolio of
[32:35] Speaker 3: futures that has like one hundred and fifty futures, there's
[32:39] Speaker 3: a very subtle correlation structure between all the assets that
[32:44] Speaker 3: you have in your portfolio. So if you think about risk,
[32:47] Speaker 3: you really have to think about how all these products
[32:51] Speaker 3: interact with one another, talk to one another. And so
[32:54] Speaker 3: it's not only a question of volatility that goes up
[32:57] Speaker 3: and down that you have to control, but also a
[32:59] Speaker 3: question of how these assets co move together or anti
[33:03] Speaker 3: co move together. But the way we think about upside
[33:06] Speaker 3: risk is the same as the way we think about
[33:07] Speaker 3: downside risk is it's just a question of risk.
[33:10] Speaker 2: So you mentioned one hundred and fifty different of assets.
[33:14] Speaker 2: I'm assuming some of these are commodities with modities, bones, stockspawns,
[33:19] Speaker 2: interest rates, right, But what else is in the full
[33:22] Speaker 2: list of one hundred and fifty.
[33:24] Speaker 3: Well, you know there's different the maturities that different countries,
[33:28] Speaker 3: futures in China. I mean, if you count everything, it
[33:33] Speaker 3: goes up to you know, one hundred. I don't have
[33:35] Speaker 3: the exact number, but in the one hundred and fifties altogether.
[33:39] Speaker 2: Has CFN been looking at prediction markets things like CALCI
[33:43] Speaker 2: and polymarket.
[33:44] Speaker 3: No, we haven't. They're not liquid enough for us.
[33:47] Speaker 2: Really, you need size and they can't provide it exactly.
[33:51] Speaker 2: Really really quite fascinating. Coming up, we continue our conversation
[33:55] Speaker 2: with Jean Felipe Buschot, co founder and chief scientists at
[34:00] Speaker 2: Capital Fund Management, talking about how market structures are changing today.
[34:06] Speaker 6: I'm Barry Results.
[34:07] Speaker 2: You're listening to Master's Business on Bloomberg Radio.
[34:24] Speaker 6: I'm Bury Redults.
[34:25] Speaker 2: You're listening to Masters in Business on Bloomberg Radio. My
[34:29] Speaker 2: extra special guest this week, John Philip Buschow, chief scientist
[34:33] Speaker 2: and chairman at CFM, a quantitative hedge fund managing over
[34:37] Speaker 2: twenty billion dollars in assets. So let's just talk a
[34:41] Speaker 2: little bit about risk management. I know that when you're
[34:45] Speaker 2: dealing with leveraged or long short or futures, there's a
[34:51] Speaker 2: very robust thought process around risk management. Tell us a
[34:57] Speaker 2: little bit about how you think about out correlation and risk.
[35:03] Speaker 3: Yeah, we have a discipline and systematic approach not only
[35:06] Speaker 3: to alpha signals, to building prediction, but also to risk management.
[35:10] Speaker 3: We have a pretty sufisticated tool to predict the velocity
[35:15] Speaker 3: of tomorrow, the velocility of our portfolio tomorrow, and we're
[35:19] Speaker 3: pretty good at that. So of course, you know, we
[35:22] Speaker 3: know in financial markets are difficult beasts, and even if
[35:25] Speaker 3: you have the best model of the world, you can
[35:28] Speaker 3: still have an expected events that blow up your portfolio.
[35:31] Speaker 3: That's that's something that we can't say will never happen.
[35:36] Speaker 3: But in a way with you know, if you don't
[35:38] Speaker 3: want to make take any risk, you shouldn't be in
[35:41] Speaker 3: financial markets. You shouldn't be in that business. So we
[35:43] Speaker 3: accept that there might be I don't know, a completely
[35:47] Speaker 3: unexpected event that you know, breaks the whole financial markets
[35:52] Speaker 3: everywhere in the world and you know everything is going
[35:55] Speaker 3: to fail and there's nothing to do about that, So
[35:58] Speaker 3: that can happen. But barring these extreme events, we think
[36:03] Speaker 3: we're pretty good at predicting what's going to happen. And
[36:06] Speaker 3: over the last thirty five years of the existence of CFM,
[36:10] Speaker 3: we're actually anniversary is this year, we're celebrating our thirty
[36:16] Speaker 3: fifth anniversary in June in Paris. Very proud of that. So,
[36:22] Speaker 3: you know, it kind of resisted these thirty five years,
[36:25] Speaker 3: although we've become much better with time. But having said that,
[36:31] Speaker 3: there's always an element that you have to be ready
[36:35] Speaker 3: to intervene, even if you're a contrap and so on
[36:39] Speaker 3: several occasions in the past thirty five years, we decided
[36:44] Speaker 3: that our risk model couldn't know about things that we
[36:47] Speaker 3: humans knew, Like you know, I don't know the Brexit
[36:51] Speaker 3: votes or in the let's.
[36:53] Speaker 2: Talk about that because this year, just the past twelve months,
[36:58] Speaker 2: between the tariffs and venezuel Ella and now the ongoing
[37:02] Speaker 2: war and Iran. How does global market volatility around all
[37:06] Speaker 2: these geopolitical events, How does a quad shop deal with that?
[37:12] Speaker 2: What I'm hearing is the humans have to do what
[37:15] Speaker 2: humans do and sometimes override the machines.
[37:18] Speaker 6: Sometimes, yes, I mean when when.
[37:20] Speaker 2: Unexpected geopol political events disrupt the world, our models just
[37:28] Speaker 2: not built to really work their way through that.
[37:32] Speaker 3: You're right, some events are okay, and like the war
[37:37] Speaker 3: in Iran for the moment, is not, you know, something
[37:40] Speaker 3: that our risk models are completely blind to. Right, It
[37:44] Speaker 3: doesn't mean that they've predicted at all. It just means that,
[37:47] Speaker 3: you know, we're comfortable with the risk that our model
[37:50] Speaker 3: have predicted, and they've adapted sufficially fast toward to the events,
[37:54] Speaker 3: so that we're comfortable with the risk level no human intervention.
[37:58] Speaker 3: On the other hand, in some cases it is completely unexpected,
[38:01] Speaker 3: like tariffs and Liberation Day, this created havoc, although strangely enough,
[38:07] Speaker 3: Libration Day was announced. You know, everybody knew what it
[38:11] Speaker 3: was going to be said, and still everybody.
[38:14] Speaker 6: Was apprised of it.
[38:15] Speaker 2: I don't believe despite I'm tariff man, it's the most
[38:19] Speaker 2: beautiful in the dictionary. I think the one hundred, one
[38:23] Speaker 2: hundred and fifty percent tariffs on specific countries later found
[38:27] Speaker 2: to be completely unconstitutional, But at the time I think
[38:31] Speaker 2: people were genuinely shocked by this. And then a week later,
[38:37] Speaker 2: you know, the little bit of a taco trade where
[38:40] Speaker 2: let's just put a pin in this for ninety days. Again,
[38:44] Speaker 2: back to the volatility, how do you deal? I agree
[38:47] Speaker 2: oil is trending upwards, and then you have a tweet
[38:50] Speaker 2: the world the war's over, and then it resumes, and
[38:53] Speaker 2: then you have a tweet I think we've got a deal,
[38:56] Speaker 2: and then the other side says we're not even negotiating.
[39:00] Speaker 2: I don't recall a period in history where the president
[39:03] Speaker 2: of the United States just constantly disrupted the normal flow
[39:08] Speaker 2: of market activity. How disruptive is this to a quant model.
[39:14] Speaker 3: Well, as I said, the two cases seem to be
[39:16] Speaker 3: pretty different. Liberation Day was really a surprise and we
[39:20] Speaker 3: had to manually intervene. There was something in our models
[39:23] Speaker 3: that was completely blind to these things, and we had
[39:26] Speaker 3: to make a judge on the call. I think the
[39:28] Speaker 3: idea really is that humans should use their best judgment
[39:32] Speaker 3: in these cases and decide whether it's reasonable that the
[39:36] Speaker 3: risk model knows something about what's going on or not.
[39:40] Speaker 3: In some cases it does, in some cases it doesn't.
[39:42] Speaker 3: I think the the tricky part is not to overreact,
[39:47] Speaker 3: because you said you don't remember periods of the world
[39:50] Speaker 3: where things like this happened. But you know, looking back,
[39:54] Speaker 3: I've been in the markets for thirty five years and
[39:56] Speaker 3: everything every year there seems to be something unexpect that happens.
[40:00] Speaker 6: You know, just not every day, not every day, but
[40:03] Speaker 6: every year.
[40:03] Speaker 2: It seems like a lot.
[40:04] Speaker 3: Right, But in a way, every day means that it
[40:07] Speaker 3: becomes a new normal, I guess, and so it's not
[40:11] Speaker 3: that bad.
[40:11] Speaker 6: It's if it's every day.
[40:13] Speaker 3: But really, you know, this idea that this time is
[40:16] Speaker 3: different is something that's strange. If you look at the
[40:19] Speaker 3: world and the history of financial markets. It's really being normal.
[40:25] Speaker 3: That's not normal, and we've become used to that.
[40:30] Speaker 2: So let's stay with the idea of modeling. You've been
[40:34] Speaker 2: kind of skeptical of certain applications of deep learning in finance.
[40:39] Speaker 2: There's overfitting. You know, no one's ever seen a bad
[40:44] Speaker 2: back test because they all seem to work perfectly in
[40:48] Speaker 2: the past. You have a lot of signal and noise issues.
[40:52] Speaker 2: What are some of the problems with models that you're
[40:56] Speaker 2: focusing on improving.
[40:57] Speaker 3: Yeah, well, for example, this exactly what you just said.
[41:01] Speaker 3: Can you have indicators that tell you whether your back
[41:05] Speaker 3: test is overfitted or not? And for many years we
[41:10] Speaker 3: struggled with that, and we use judgment again to say
[41:14] Speaker 3: this is plausible, this is not plausible. We can believe
[41:17] Speaker 3: that we kind of replace the trader that trades every
[41:21] Speaker 3: day his signals or his beliefs to a higher level
[41:28] Speaker 3: where we are traders of models. We know we kind
[41:32] Speaker 3: of judge models. We say this model is good enough
[41:35] Speaker 3: to go in production, this model is not convincing enough,
[41:38] Speaker 3: but it would be great to have something more systematic.
[41:41] Speaker 3: And over the years we've been struggling, and I think
[41:44] Speaker 3: with some success, to have meta models that predict whether
[41:49] Speaker 3: your back test is really fudged or if it's decent
[41:54] Speaker 3: enough to go in production. So we're kind of industrial
[41:58] Speaker 3: and industrializing this process of selecting models that will go
[42:03] Speaker 3: into production. Does that make sense?
[42:04] Speaker 2: Yeah, No, that makes perfect sense. You're quant, and we've
[42:08] Speaker 2: seen some issues with a lot of quant shops in
[42:12] Speaker 2: the US where crowding became a structural risk. You have
[42:17] Speaker 2: all these system systematic strategies, and math is math, so
[42:21] Speaker 2: essentially you end up with a crowded trade. We had
[42:25] Speaker 2: what people called the quant quake way back when. How
[42:29] Speaker 2: do you think about that? How do you manage that
[42:32] Speaker 2: problem when you're constructing portfolio?
[42:34] Speaker 3: Sure it's something that we you know, every day we
[42:36] Speaker 3: think about this. We were in the quant quake in
[42:39] Speaker 3: two thousand and oh seven, and actually we were fortunate
[42:44] Speaker 3: enough to be out of the markets or to have
[42:47] Speaker 3: the leveraged already two weeks before the worst day of
[42:53] Speaker 3: the quont quake.
[42:54] Speaker 2: Was that an external signal or one of your model
[42:57] Speaker 2: signals or what like?
[42:58] Speaker 3: Was it was something in the perfour months already in July.
[43:01] Speaker 3: The one quick the really bad day happened maybe ninth
[43:05] Speaker 3: of August, I don't remember exactly but.
[43:07] Speaker 6: It was early August days of summer.
[43:09] Speaker 3: Right, but starting like tenth of July already there was
[43:14] Speaker 3: something really very strange in our portfolio. And we started
[43:18] Speaker 3: reflecting on what was going on and decided someone was
[43:21] Speaker 3: leveraging and you know, hitting us by shorting our lungs
[43:27] Speaker 3: and buying our shorts, and you know, this process, this
[43:30] Speaker 3: thought process of imagining that even if a fund that
[43:35] Speaker 3: was like ten percent correlated with ours, not a lot,
[43:38] Speaker 3: but ten percent and having every day a kind of
[43:42] Speaker 3: systematic deleveraging policy, it would create exactly the kind of
[43:46] Speaker 3: signals that we were seeing in our portfolio. So we thought, okay,
[43:49] Speaker 3: this is maybe going to lead to a crash because
[43:52] Speaker 3: people are going to you know, suffer suffering at one point,
[43:56] Speaker 3: they're going to cascades and skate and so on, and
[43:59] Speaker 3: so that was rationale for getting out. So in some
[44:02] Speaker 3: cases you have you're lucky enough to have strong enough
[44:06] Speaker 3: signals that tell you that your standard risk model is
[44:09] Speaker 3: wrong and you should do something else.
[44:13] Speaker 2: That's really that's really fascinating. So you mentioned almost thirty
[44:17] Speaker 2: five years of doing this, What do you think that
[44:21] Speaker 2: I'm gonna say that again? So you are now thirty
[44:25] Speaker 2: five almost thirty five years into being a market quant
[44:30] Speaker 2: what's the most important thing about markets that the mainstream
[44:36] Speaker 2: funds still get wrong?
[44:39] Speaker 3: Well, I think it's this question of what is price doing?
[44:44] Speaker 3: What are moving prices? And a lot of people still
[44:48] Speaker 3: believe that there's something like a fundamental value and that
[44:51] Speaker 3: the price is really moving because fundamentals are moving whereas
[44:57] Speaker 3: we believe, and this is going you know, this touches
[45:00] Speaker 3: very recent academic papers that Gabex and coaches and to
[45:05] Speaker 3: economists have put forward. They've called it the inefficient inelastic
[45:09] Speaker 3: market hypothesis, and we've contributed to that debate as well.
[45:14] Speaker 3: And the idea is really that markets are not driven
[45:16] Speaker 3: by fundamentals, or at least they are. There are to
[45:21] Speaker 3: some extent driven by fundamentals, but this is a small
[45:24] Speaker 3: long term effect on short run. Short run meaning from
[45:28] Speaker 3: one day to one year's pretty long already. It's really
[45:31] Speaker 3: flows that matter. That is people buying or selling stuff,
[45:36] Speaker 3: whatever the reason they buy or sell, is going to
[45:39] Speaker 3: move prices. It's not going to move prices on a
[45:41] Speaker 3: short timescale and then disappear. It's really going to leave
[45:45] Speaker 3: a trace in markets. And this is really a fundamental
[45:49] Speaker 3: change of point of view that I think that you
[45:53] Speaker 3: know is going to percolate and convince more and more
[45:58] Speaker 3: people looking for But having this change of tech is
[46:03] Speaker 3: really important because in one case, what you need to
[46:05] Speaker 3: do to make money is to predict fundamentals. In the
[46:09] Speaker 3: other case, you need to predict what people are going
[46:11] Speaker 3: to do. And so in a sense, crowding can be
[46:14] Speaker 3: a good thing because if there's crowding, it's easier to
[46:16] Speaker 3: predict what the crowd is going to do. And so
[46:20] Speaker 3: if whatever the reason, people do things, they move prices
[46:23] Speaker 3: and you're able to predict what people are going to
[46:25] Speaker 3: do because you have, you know, behavioral models and structural
[46:29] Speaker 3: models that tell you, you know, everything else being equal, people
[46:33] Speaker 3: are more likely to do this and that then you
[46:35] Speaker 3: can build models. And I think that's the reason why
[46:38] Speaker 3: we've been successful is this change of philosophy. We were
[46:42] Speaker 3: not kind of anchored to fundamentals. We're anchored to flows.
[46:46] Speaker 2: So this sounds a little bit like the ben Gram line.
[46:49] Speaker 2: I think it's Gram in the short run markets or
[46:52] Speaker 2: voting machines and the long run their weighing machines. Is
[46:55] Speaker 2: that is that the balance between flows and fundamentals.
[47:00] Speaker 3: Yeah, I think it's an old idea. I mean, you know,
[47:03] Speaker 3: Canes also said things like that. You know, but in
[47:06] Speaker 3: the long run, we're all dead, right, Panes dead was
[47:09] Speaker 3: saying that, So it's really a question of whether you're
[47:12] Speaker 3: going to be solvable. I'm getting tired. I'll take that again.
[47:25] Speaker 6: What's the word was that, Kip?
[47:27] Speaker 2: By the way, Knes or Graham? I initially thought it
[47:30] Speaker 2: was Canes, and then I checked myself.
[47:32] Speaker 3: I'm not sure, but what Kines said is that you
[47:36] Speaker 3: what was his quote? Markets can remain irrational longer than
[47:43] Speaker 3: you can remain solid.
[47:44] Speaker 2: So he never said that, but it's always attributed to him. Really,
[47:48] Speaker 2: there's a great website called Quote Investigator. I see, and
[47:52] Speaker 2: you give them a quote. Because people as a sort
[47:55] Speaker 2: of authority, you know, appeal to authority, they'll put somebody
[48:01] Speaker 2: sophisticated as the source. Einstein never said compounding is the most.
[48:06] Speaker 5: Powerful force in the universe, but they always attributed to
[48:09] Speaker 5: I didn't know that you would be I spend way
[48:12] Speaker 5: too much time perusing quotes on the site, but you
[48:15] Speaker 5: would be shocked who said markets are a voting machine
[48:20] Speaker 5: in the short run, but a weighing machine in the
[48:22] Speaker 5: long run.
[48:23] Speaker 3: I don't think it's Canes by the way, No, I
[48:25] Speaker 3: don't remember if.
[48:26] Speaker 2: It was Canes or Graham, but I thought it was
[48:28] Speaker 2: one or the other, and it'll tell me. Yeah, Benjamin Graham,
[48:31] Speaker 2: But the first thing that came to mind was Canes
[48:34] Speaker 2: because or Galbraith. They have so many favorite quotes from.
[48:37] Speaker 3: Yeah, well he said many relevant things today. Yes, absolutely,
[48:43] Speaker 3: but actually we have models that predict exactly that that
[48:46] Speaker 3: you know, on the short run you can have trends
[48:48] Speaker 3: and the irrational behavior, and on the long run it reverts
[48:51] Speaker 3: back to fundamentals. But the long run from our estimate
[48:54] Speaker 3: is like five ten years. I'm trying a long time scale.
[48:58] Speaker 2: I'm trying to remember I'd have been French of Farm
[49:01] Speaker 2: of French who said, you can't really tell if a
[49:04] Speaker 2: manager is skilled innt you until you have twenty years
[49:07] Speaker 2: of data, because it could just be good luck over
[49:10] Speaker 2: five or ten years, which is kind of fascinating. I
[49:13] Speaker 2: want to stay with the efficient market or the inelastic
[49:18] Speaker 2: market hypothesis. I'm curious as to your thoughts on EMH.
[49:24] Speaker 2: I've always thought markets were kind of sort of eventually efficient,
[49:29] Speaker 2: but not very efficient in the short run. What's your
[49:33] Speaker 2: criticism of EMH.
[49:35] Speaker 3: Well, It really depends what you mean my efficient. If
[49:39] Speaker 3: you mean that they're very close to unpredictable, then you're write.
[49:43] Speaker 3: But I think it's a very dumb down version of EMH.
[49:46] Speaker 3: The question is whether our prices reflect something from the
[49:49] Speaker 3: mental that is, in principle noble, that reflects reality or
[49:56] Speaker 3: not at all. And one I think smoking gun of
[50:01] Speaker 3: that is do you have long term mean reversion? That is,
[50:07] Speaker 3: can prices do random things in particular trending, which is
[50:10] Speaker 3: really completely against the EMH. On the short run, that
[50:14] Speaker 3: is from a week to six months, markets are trending
[50:18] Speaker 3: over six months, one year, and then on the longer
[50:21] Speaker 3: time scale they kind of hover around some long term
[50:27] Speaker 3: trend and I think this is true, but this is
[50:30] Speaker 3: really at odds with efficient market, which tells you that
[50:33] Speaker 3: every day markets are around the correct price and there's
[50:38] Speaker 3: no trend, no mean reversion.
[50:40] Speaker 2: Ever, which is which you know that that's not exactly
[50:45] Speaker 2: what your day to day experiences if you're in the markets.
[50:48] Speaker 2: It seems it's not magic that the collective votes of
[50:53] Speaker 2: all market participants don't magically bring you to the correct
[50:58] Speaker 2: in quotes.
[50:59] Speaker 3: But that's that's the assumption of efficient market, or actually
[51:04] Speaker 3: not even an assumption. It's the argument that collectively, you
[51:09] Speaker 3: know if you have. That's the difference between having rational
[51:13] Speaker 3: investors that all take decisions based on noisy observation, but
[51:18] Speaker 3: independent from one another. Then because they're independent, they realize
[51:23] Speaker 3: the mean I mean, some overpriced, some underprice, and then
[51:27] Speaker 3: it's a voting machine and the vote comes out right
[51:30] Speaker 3: because there's enough investors and they're uncorrelated to one another.
[51:34] Speaker 3: But the problem with markets is that it's not the
[51:36] Speaker 3: way it works. The people are influenced by what other
[51:40] Speaker 3: people are doing and what other people are saying. So
[51:43] Speaker 3: instead of having independent guys doing random stuff, they're kind
[51:47] Speaker 3: of one guy who's doing only one thing, which is
[51:54] Speaker 3: a fictitious body that aggregates everybody in the same way.
[51:59] Speaker 2: Let's jump to our favorite questions that we ask all
[52:01] Speaker 2: of our guests, starting with tell us about your mentors
[52:05] Speaker 2: who helped shape the direction of that career.
[52:08] Speaker 3: That's an easy one. I have, you know, several mentors,
[52:11] Speaker 3: but two of them are really close to my heart.
[52:14] Speaker 3: One is but man that brought of course, you know,
[52:17] Speaker 3: the fractal guy. I knew him personally, Oh really, yes,
[52:21] Speaker 3: and he did a lot of things in physics as well, sure,
[52:25] Speaker 3: and so he influenced a lot my wife. My wife
[52:29] Speaker 3: was a physicist before turning a playwright now, and and
[52:35] Speaker 3: she worked on fracture surfaces. The way you know, when
[52:37] Speaker 3: you break a material, what emerges from from from the
[52:42] Speaker 3: fracture is a kind of very rough landscape that is fractal.
[52:46] Speaker 3: And Manda Brought had worked on that, and there was
[52:48] Speaker 3: a lot of interaction and.
[52:50] Speaker 2: The fractal at a molecular level or at.
[52:52] Speaker 3: A yeah, well fractal from the kind of very fine
[52:57] Speaker 3: structure to microscopic length skill. And so Manelrad also did
[53:03] Speaker 3: his work on financial markets, and for me it was
[53:06] Speaker 3: really a revelation. It was something very influencing and out
[53:12] Speaker 3: of the dogma of Brownian statistics and Gaustian phenomena and
[53:16] Speaker 3: so on, and so it was also very close to
[53:18] Speaker 3: what I was doing myself in physics. So you know,
[53:22] Speaker 3: it was clear that he influenced me enormously on that.
[53:26] Speaker 2: Didn't he write a book on market crashes and how
[53:30] Speaker 2: there's a fractal.
[53:31] Speaker 3: Yeah, with the misbehavior of market that's right there, you go,
[53:35] Speaker 3: I'm actually quoted in that book.
[53:38] Speaker 2: Oh that's fascinating.
[53:39] Speaker 3: And then Pierre Gilldzene who was a Nobel Price in
[53:43] Speaker 3: physics of French physicists, who was so fantastic, and you
[53:48] Speaker 3: know both these two, and and also Phil Anderson who
[53:51] Speaker 3: was a Nobel Price in physics as well in the US.
[53:54] Speaker 3: These three people, they convinced me that you shouldn't be
[54:00] Speaker 3: stuck to your own field. You should broaden your scope,
[54:04] Speaker 3: and what you learn from one field can be very
[54:06] Speaker 3: useful understanding another field. The three of them, they're really
[54:10] Speaker 3: kind of hovered around and not got tied to their
[54:15] Speaker 3: specific initial field. And I think this creates well, at
[54:20] Speaker 3: least for me, this created this The inhibited me in
[54:26] Speaker 3: the sense that I thought, Okay, maybe I'm not legitimate
[54:29] Speaker 3: to speak about finance because I'm a physicist, but you know,
[54:33] Speaker 3: doesn't matter. If I have things that I strongly believe in,
[54:37] Speaker 3: I should better say them and go to the end
[54:40] Speaker 3: of them. So I think they were really influential in
[54:42] Speaker 3: that way.
[54:43] Speaker 2: So since we mentioned the misbehavior of markets, let's talk
[54:47] Speaker 2: about some books. What are some of your favorites? What
[54:49] Speaker 2: are you reading right now?
[54:50] Speaker 6: Wow? So many?
[54:51] Speaker 3: That's really a very broad question. So my last book
[54:55] Speaker 3: is a book on John and Paul By in Leslie Johnny.
[55:03] Speaker 3: It's a beautiful book. I really loved it.
[55:05] Speaker 6: What's the name of it, John and Paul?
[55:08] Speaker 2: Is it John and PAULA love story Memory?
[55:10] Speaker 6: Correct?
[55:11] Speaker 2: I'm a big Beatles fan, and.
[55:14] Speaker 3: Oh you should read it.
[55:15] Speaker 6: I'm very very I'm going to.
[55:17] Speaker 2: Make a recommendation to you for a YouTube channel called
[55:22] Speaker 2: you Can't Unhear This. They take apart Beatles songs in
[55:26] Speaker 2: ways that just little things that were done in the
[55:30] Speaker 2: recording process that in a million years you never would
[55:33] Speaker 2: have noticed, and then once you hear it, you just
[55:35] Speaker 2: can't unhear it. And if you're a Beatles fan, it's
[55:39] Speaker 2: a rabbit hole. You'll you'll love this. What else besides
[55:42] Speaker 2: John and Paul, Yeah.
[55:44] Speaker 3: I'm reading someone that's something that I actually have read
[55:46] Speaker 3: for years that Missus Dollaway Virginia or Wolf. Yeah, I'm
[55:51] Speaker 3: read a great admirer of Virginia.
[55:53] Speaker 2: Wolf really really interesting. Do you do much streaming TV
[56:01] Speaker 2: podcast anything like that or we can skip.
[56:03] Speaker 3: Both podcasts a bit, but they're kind of French podcast.
[56:07] Speaker 2: So let's let's you know, people are always looking for stuff.
[56:09] Speaker 2: I'm going to ask that, So what are you streaming today?
[56:12] Speaker 2: What sort of podcasts are you listening to.
[56:14] Speaker 3: Yeah, you know, in France, we were very fortunate we
[56:17] Speaker 3: have something called Hoskulture. It's a radio where there's an
[56:21] Speaker 3: enormal I mean, you could stay tuned all day if
[56:24] Speaker 3: you want it. There's so many interesting things going on
[56:27] Speaker 3: about everything cultural literature, but also you know, movies, politics,
[56:33] Speaker 3: and so.
[56:34] Speaker 2: We have NPR here.
[56:35] Speaker 6: It's very similar.
[56:36] Speaker 2: You can it's a rabbit hole you could fall down.
[56:39] Speaker 3: Yeah, okay, so life of big peeple of major celebrities
[56:44] Speaker 3: in culture, in in cinema and theater, all these things.
[56:49] Speaker 3: So I'm really a big fan of Hostalture.
[56:52] Speaker 2: And our final two questions, First, what sort of advice
[56:57] Speaker 2: would you give to a recent college grad interest did
[57:00] Speaker 2: in a career in either quantitative investing or theoretical physics.
[57:06] Speaker 3: Well, study theoretical physics and study everything that's related to data,
[57:12] Speaker 3: and pay attention to data. And think about something that
[57:17] Speaker 3: you strongly believe in and that you feel has not
[57:21] Speaker 3: been investigated, and it doesn't matter if it's big or small.
[57:25] Speaker 3: Make the effort of building something you strongly believe in.
[57:29] Speaker 2: Really good answer. And our final question, what do you
[57:34] Speaker 2: know about the world of investing today? Might have been
[57:36] Speaker 2: useful thirty five years or so ago when you were
[57:39] Speaker 2: first getting started.
[57:41] Speaker 3: That is extremely competitive, much more than we thought.
[57:44] Speaker 2: Really wow, that's really fascinating. Jean Philip, thank you so
[57:48] Speaker 2: much for being so generous with your time. We have
[57:52] Speaker 2: been speaking with Jean Philippe Boucheau, cfm's co founder, chairman,
[57:58] Speaker 2: and chief scientist. If you enjoy this conversation, well check
[58:02] Speaker 2: out any of the six hundred plus we've done over
[58:04] Speaker 2: the past twelve years. You could find those at Apple Podcasts, Spotify, YouTube, Bloomberg,
[58:11] Speaker 2: wherever you find your favorite podcasts. I would be remiss
[58:15] Speaker 2: if I didn't thank the crack team that helps me
[58:17] Speaker 2: put these conversations together each week. Meredith Frank is my
[58:21] Speaker 2: video producer. Anna Luke is my podcast producer. Sean Russo
[58:26] Speaker 2: is that my head of research. I'm Barry Ritoults. You've
[58:30] Speaker 2: been listening to Masters in Business on Bloomberg Radio.

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