Jean Philippe Bachaud (3:18)

Jean Philippe in France, JP in Anglo Saxon countries.

Barry Ritholtz (3:22)

All right, it seems a little informal, but I'll go with JP. So, so JP, let, let's start with your background. PhD in theoretical physics from ENS. You spent some years at very prestigious research in this institutions. I mentioned Cavendish Labs. What was the original career plan?

Jean Philippe Bachaud (3:43)

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 was always fascinated by numbers. I've always liked statistics. And financial markets spit statistics every day. And I thought this is a very interesting complex system. There are crises, crashes, jumps. This system seems to be driven by its own dynamics. Physicists have to do something about this.

Barry Ritholtz (4:22)

And so sounds very similar to chaos theory.

Jean Philippe Bachaud (4:25)

Yeah, exactly. I mean that was the high days of chaos theory.

Barry Ritholtz (4:29)

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 which can be either physics or finance. Exactly. Sounds like, but what are the dynamics of glassy systems and granular media? That sounds fascinating.

Jean Philippe Bachaud (4:49)

Yeah, but it's all the problem is how do interacting elements give rise to something surprising? Granular matter is grains that 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 financial markets, right? I mean many things happen, nothing much follows and then sometimes there's a crash. And so this was really intriguing for physicists like me.

Barry Ritholtz (5:32)

So 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.

Jean Philippe Bachaud (5:54)

Yeah, I mean there are two ways to be unpredictable. One is that the system is by itself unpredictable. That even with deterministic laws like the three body problem, you can't say much after a few seconds, days or weeks. But there are other kinds of unpredictability. 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, I.e. traders, agents that trade and buy and sell to each other. And this can generate intrinsic randomness that is not of the same kind as the three body problem, but really comes from the interaction of a huge number of elements.

Barry Ritholtz (6:59)

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 econo physics?

Jean Philippe Bachaud (7:18)

Yeah, so as I said initially, I've always been excited by data and trying to make sense of data, so there was something there anyway. But what really drove the transition was in a sense, the 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 happen from time to time. And someone who was working in the banking industry called me and said, hey, it's really interesting because it resembles what happens in finance and in particular what just happened the 1987 crash. And there's this theory, the Black Scholes theory, that is a theory that only works in a world where there are no crashes, where all the motions are small and they're random, but they're kind of predictable, even if they're random in some strange way. And I thought, this is really weird. And this guy said, why don't you try to generalize Black 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 it could be extended to non Gaussian statistics, as they're called, 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 91, that was 94. And I started 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. And this was done in two weeks. It was amazing the way we met and there was a fluid that was flowing between us immediately. And so CFM then merged with science and finance in 1990, so it's now the same firm. But the idea he had at the time, he had this small CTA trading firm and he thought, I need to beef up my research team. And this guy seems to be interesting. So we just partnered and that's how it all started.

Barry Ritholtz (10:02)

And that CTA firm specialized in managed futures pretty much. 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?

Jean Philippe Bachaud (10:20)

Yeah, well, that was exactly Jean Pierre Aguilar's idea. He said, okay, I'm doing trend following. It's good, but it's not rocket science. Maybe we can do much better. And so he said, why don't we work on something more beefy than just trend following? And so that started the whole thing. And the idea of, the main idea is data. Physicists are good at looking at data and extracting structures, looking at data and imagining that from that data you can build theories, you can identify what's important and what's not. And that's, I think, the way it all works in physics, that you scrutinize data and then there's a flash and you think, okay, I can model that. And it's really the same process in finance. At least it's as far as we were concerned and we are concerned now. It hasn't changed. It's the same process.

Barry Ritholtz (11:16)

Really, really quite fascinating. You keep your professorships at ENS and you've maintained a foot in academia even as you're building and running an asset management firm. Tell us about that. You're still publishing papers. What keeps you interested in the academic side of finance?

Jean Philippe Bachaud (11:34)

Well, first of all, it is me. I feel I'm a researcher at heart and I need to continue. It's like people running the marathon. They are doing something else in life. And then there's an urge to run the marathon. For me, there's an urge to understand what I'm doing and understand also things that I'm not doing even now. Even physics problems, I can get excited about them or trying new things like the ML revolution. How does ML work? Why do large language models work so well, learn so well? I think it's fascinating. I want to understand. But there's another reason for doing this, is that to attract 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 CFM has been attracting talents. And I think part of that Only part of that, of course, it's a teamwork is due to the fact that I'm still very connected in academic circles. And young students, they've listened to me giving talks, lecturing, they've read my papers. And so they feel, let's go and work for that firm. Because it seems that they're really doing cool stuff.

Barry Ritholtz (12:55)

So is that the thinking behind establishing the research division at cfm? I mean, you run that as a full academic research department as opposed to, you know, a lot of asset management shops. They have a couple of CFPs and MBAs and their CFAs, and they're working on their quantitative models. You guys seem like you've taken it to a whole different level.

Jean Philippe Bachaud (13:20)

Yeah, I mean, most, maybe even all our researchers have a PhD. It doesn't mean that we're an academic lab. We're really working on concrete stuff. We're really there to make models that work, build portfolios that are robust, model risk, model execution, control costs. All these things are bread and butter for 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, how do markets work? Why are there crashes? Are market sufficient? What about the economy? Do people understand inflation? Do we need new theories to understand inflation, monetary policy and all these things? We believe it's our role also because we have access to so much data and we're privileged academics, they don't have access to so much data. And so we have to give back in a way. And the reason with 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.

Barry Ritholtz (14:39)

Well, you guys opened up a big or an expanded, a big New York office. You don't seem to be having much difficulty recruiting people there. What's the headcount there now?

Jean Philippe Bachaud (14:48)

We have 115 researchers and 15% of them are in New York.

Barry Ritholtz (14:56)

What motivated expanding the New York office as much as you have?

Jean Philippe Bachaud (15:00)

Well, first of all, a lot of our investors are in the US So we need to be there and interact with them. And we need to have a presence, if only for investor relation, but also because there's a lot of talents in the US that we want to grab and attract. There's a lot of data, a lot of brokers, so it makes a lot of sense. So we've been in New York for 20 years and, you know, it's obvious that, you know, it is a hub and we should expand there.

Barry Ritholtz (15:32)

So you mentioned earlier your co founder, Jean Pierre Aguilar, he passed away in 2009.

Jean Philippe Bachaud (15:39)

Yes.

Barry Ritholtz (15:39)

What was the impact on the firm? How did you guys grow, manage around? That's a big loss when you lose a founder.

Jean Philippe Bachaud (15:46)

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, you know, 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 the public figure of cfm. He was not involved in constructing models, quants. In a way, what's great about quant investing is that you don't need star traders, you don't need PMs that know everything. It's a collective effort. And so when someone disappears or resigns or dies, it's not a tragedy. But in the case of Jean Pierre, it was even that he was not really involved in the construction of models. He was just very inspiring, generous, and he was really great. He had a vision when we met, and he thought, okay, with that guy, we can build something great. It's amazing 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. Well, you know, there were several issues. One is that he had 57% of the company. So we had to negotiate with the estate to get back control. That was pretty difficult, but we went through that. And also we needed to reassure our investors. You know, Jean Pierre was. He seemed to be the public figure. He was a public figure, and he seemed to be the inspiration behind everything. And so we had to. We communicate that we were at the helm and that we would navigate that. And it worked. And so it was very stressful, but it was very rewarding as well to go through that.

Barry Ritholtz (17:51)

And the firm carries on as his legacy. Coming up, we continue our conversation with Jean Philippe Bachaud, head of research and chief scientist at cfm, talking about the growth of capital fund management. I'm Barry Ritholtz. You're listening to Masters of Business on Bloomberg Radio. Today's show is brought to you by Vanguard. To all the financial advisors listening, let's talk bonds for a minute. Capturing value in fixed income is not easy. Bond markets are massive and murky. Lots of firms throw a couple of flashy funds your way and call it a day. Vanguard takes a different approach. The Vanguard Lineup includes over 80 bond funds actively managed by a 200 person global squad of sector specialists, analysts and traders. Lots of firms love to highlight their star portfolio managers like it's all about that one brilliant mind that makes the magic happen. Vanguard's philosophy is different. They believe the best active strategy shouldn't be one person, it should be shared across the team. So if you're looking to offer your clients funds that are built to deliver consistent results, go see the record for yourself@vanguard.com audio that's vanguard.com audio all investing is subject to risk. Vanguard Marketing Corporation Distributor Support for the

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Barry Ritholtz (20:51)

I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week is Jean Philippe Basheux. He is the head of research, chairman and chief scientist at Capital Fund Management Hedge fund managing over $20 billion, a quantitative shop specializing in managed futures and other quant type funds. So 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? What. What was the original plan?

Jean Philippe Bachaud (21:37)

A quant fund from day one. From day one, the quant fund. But from day one we knew that we wanted to be strongly associated with academia. We knew that the only way to innovate. Again, coming back to the fact that financial markets are complex systems. It's really difficult to beat the market. We know that everybody'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 so this means investing heavily in research. So the two are not contradictory. We really wanted to be a confound. We knew already about Renaissance. 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.

Barry Ritholtz (22:38)

They had a 40 year head start on you guys. So.

Jean Philippe Bachaud (22:41)

Yeah, but.

Barry Ritholtz (22:42)

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 AQR and a few other. A little bit of Millennium. Although they do so much of everything. The thought process behind being a quant shop, when there's so many other quant shops, if we don't create our own models, if we don't create our own findings and innovations, we're just an also run. Is that ran? Is that the thought process behind it? Like we have to do this otherwise because it's all of these other quantshop that I've mentioned, none of them are quite the academic lab that they created.

Jean Philippe Bachaud (23:29)

AQR is. I think they're closest to us in that front. I think, you know, Renaissance, they took the initial initially the completely different turn. 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. This is not only we want to make money for ourselves, for our investors, we want to excel, but not at any cost. We think that there's something else in life, that there's a legacy that we want to leave. And this legacy is intellectual as well,

Barry Ritholtz (24:06)

pursuing the truth as to what drives markets and what leads to alpha and returns.

Jean Philippe Bachaud (24:11)

Exactly.

Barry Ritholtz (24:12)

You know, every new discovery of alpha eventually gets arbitraged away. Is that the thinking?

Jean Philippe Bachaud (24:19)

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 trend following. You know, if, if people trend follow, it's going to lead to more trend following, not less.

Barry Ritholtz (24:34)

Right. Momentum is a, it's not only a fama French factor, but it takes on its own life.

Jean Philippe Bachaud (24:38)

Right. Well, fama doesn't like momentum, but anyway.

Barry Ritholtz (24:41)

But isn't it part, it's not part of the original three factor model, but wasn't it one of the later models?

Jean Philippe Bachaud (24:49)

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.

Barry Ritholtz (24:57)

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.

Jean Philippe Bachaud (25:04)

I agree, I agree, but that's again the physicist point of view. Experiments is above everything else. But sometimes when you talk to economists, they have a strange view that theorems and axioms supersede any empirical observation. I was told that by an economist and so there's a very strong difference in perception.

Barry Ritholtz (25:29)

I recently had Richard Thaler and Alex Emis in the studio and I was shocked to learn from them. They still aren't teaching behavioral finance in economics course at a college level, which is kind of shocking, I think, everything we've learned. So let's talk about another technology. There have been over the past decade, but especially the past few years, huge advances in artificial intelligence and machine learning, to say nothing about large language models. How are you guys thinking about real world investing driven by AI? And what sort of opportunities does this open up?

Jean Philippe Bachaud (26:10)

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 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 the data vendors. And so there's a need to handle that data to read sometimes huge data files. For example, if you think about Microstructure, high frequency data, 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 text that no human would be able to read, and extracting information, statistical information from that text. So for us, it is, I wouldn't say a revolution, but it's an acceleration of things that we were trying to do before, and obviously we're much in tune with that. We've actually created an email lab at CFM to help transferring technology from what ML people are constructing and what researchers at CFM may be using, but also to try to understand how these things work. Right. Because we, we're very uncomfortable with the idea of black boxes. Black box is something that can improve the research process, but when you think about implementing that in production and having models, trading with these models, you really want to be sure that the machine has done something that makes sense. And so understanding what machine learning is actually doing. Why are these things working? To start with, what is strange is that it works so well, but nobody understands why. When you're driving a car, the car works really well, but we know exactly why it works, how it works, machine learning, nobody really understands what's the magic. And I think it's a huge intellectual challenge and we want to be part of that.

Barry Ritholtz (28:37)

How much of that is pattern recognition? Because when I think about the work, I, whenever I read about LLMs, it's really just statistically what makes the most sense for the next letter, the next word, next sentence. It's kind of hard to just think of crafting a document based on probabilities of, of the most likely word. If you have these few words beginning. But apparently that's a big part of, of how they work. Or am I grossly oversight?

Jean Philippe Bachaud (29:07)

Yeah, so, so why is that? Why does it work? And can it work in finance too? 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 there's part of that, the structure of the data? Is it the case in finance too? Maybe not.

Barry Ritholtz (29:47)

Well, that's literally exactly where I want to go. If you're training an LLM on billions and billions of documents, pages, books, whatever, and it 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. 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 there?

Jean Philippe Bachaud (30:36)

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.

Barry Ritholtz (31:20)

If you want small data set.

Jean Philippe Bachaud (31:21)

Small data set, except if you go to high frequency, as I said, 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 do. But what was I saying? Yeah, so there's the problem of the availability of data and the frequency at which you want to predict. So for 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, used as a kind of technical model which only looks at prices without reading text. For reading text, we know that there's a lot of structure which corresponds to the structure of language. But having said everything you said, there's still something strange about LLMs or, you know, generative AI, is that with this process of constructing sentences that are statistically valid, you can invent new things. And that's the thing that is really strange, right? I mean, you can learn pictures, for example, you know, this celebrity database where you have, you make the machine learn these pictures and then you ask the machine to generate new ones, and it does. And these are pictures that look exactly, I mean, that you look and you think it's a celebrity, but the celebrity doesn't exist. So there's something still weird about this that, as I said, nobody really understands.

Barry Ritholtz (32:54)

Really, really kind of interesting.

Jean Philippe Bachaud (32:55)

And so continuing on that what we are trying to do is to do the same thing with financial markets. So As I said, 100 years of data is not a lot. But maybe you can use these gen AI models to generate a million years of fictitious financial markets. That's interesting.

Barry Ritholtz (33:15)

Very interesting. So let's talk a little bit about trend following and managed futures. It's had a few real standout years in particular 2022, real challenging year and managed futures at we're 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.

Jean Philippe Bachaud (33:58)

I agree.

Barry Ritholtz (33:59)

So, so what is it? First of all, why was 2022 such a standout year? I don't know, 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 trend following?

Jean Philippe Bachaud (34:34)

It's very difficult to say because otherwise we would have a meta model that arbitrage and increases the weight of trend following when it's going to work. Well, I think there probably is more research to do and we've been trying. We haven't found anything that's very convincing. But beginning of 2026 is also a very good period for trend following. Actually since we wrote a paper in 2014 called 200 Years of Trend following and we were reporting on the fact that since 1800, if you paper trade a very simple trend following strategy, you make money every decade with ups and downs. There are years that are not so good years. But as you say, 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 declares. And that was the case in 2014 when we wrote our paper. Trend following had been flat for the last five years and people said okay, well trend following is dead now. And we were absolutely convinced that it was not the case that 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. And so we bet. And it was confirmed that trend following would come back. And since 2024 is 2014, it's been very good actually overall.

Barry Ritholtz (36:10)

Well, you had a market that very much was trending mostly in one direction for, I mean, you have Q4 of 2018 and I think 2016 was so, so. But for the past 15 years, the bias has been pretty much in one direction. If you're on the right side of that, you do pretty well.

Jean Philippe Bachaud (36:27)

Yeah, but I'm not speaking about being long. I mean, I'm really speaking about different

Barry Ritholtz (36:31)

asset classes, different trends up. You're long and short.

Jean Philippe Bachaud (36:35)

Yeah, sure.

Barry Ritholtz (36:36)

So it doesn't matter as long as the trend is in place. You want to.

Jean Philippe Bachaud (36:38)

Exactly.

Barry Ritholtz (36:40)

And for people who are not familiar with managed futures, there's a decent amount of leverage used in that product. So you have to really manage the risk to the downside. But how do you think about, you know, the potential upside relative to the risk you're taking in a futures product?

Jean Philippe Bachaud (36:58)

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. There's volatility, but there's also correlation. If you deal with a portfolio of futures that has 150 futures, there's a very subtle correlation structure between all the assets that you have in your portfolio. So if you think about risk, you really have to think about how all these products interact with one another, talk to one another. And so it's not only a question of volatility that goes up and down that you have to control, but also a question of how these assets co move together or anti co move together. But the way we think about upside risk is the same as the way we think about downside risk. It's just a question of risk.

Barry Ritholtz (37:45)

So you mentioned 150 different assets. I'm assuming some of these are commodities. Bonds, stocks, bonds, interest rates. What else is in the full list of 150?

Jean Philippe Bachaud (37:58)

Well, you know, there's, there's different maturities that different countries. Futures in China, I mean, if you count everything, it goes up to, you know, 100. I don't have the exact number, but in the 150s altogether.

Barry Ritholtz (38:13)

Has CFN been looking at prediction markets, things like Kalshi and Poly Market?

Jean Philippe Bachaud (38:19)

No, we haven't. They're not liquid enough for us really.

Barry Ritholtz (38:23)

You need size and they can't provide.

Jean Philippe Bachaud (38:24)

Exactly.

Barry Ritholtz (38:25)

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 Rad.

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Barry Ritholtz (41:19)

I'm Barry Ritholtz. You're listening to Masters in Business on Bloomberg Radio. My extra special guest this week, Jean Philippe Bichaud, 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.

Jean Philippe Bachaud (41:57)

Yeah, we have a disciplined and systematic approach not only to alpha signals, so building prediction, but also to risk management. We have a pretty sophisticated tool to predict the volatility of tomorrow, the velocity of our portfolio tomorrow. And we're pretty good at that. So of course 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 something that we can't say will never happen. But in a way, if you don't want to take any risk, you shouldn't be in financial markets, you shouldn't be in that business. So we accept that there might be, I don't know, a completely unexpected event that breaks the whole financial markets everywhere in the world and everything is going to fail and there's nothing to do about that. So that can happen. But barring these extreme events, we think we're pretty good at predicting what's going to happen. And over the last 35 years of the existence of CFM, we're actually on anniversary is this year we're celebrating our third 35th anniversary in June in Paris. Very proud of that. So, you know, it kind of resisted these 35 years, although we've become much better with time. But having said that, there's always an element that you have to be ready to intervene, even if you're a quant shop. And so on several occasions in the past 35 years, we decided that our risk model couldn't know about things that we humans knew, like, I don't know, the Brexit votes.

Barry Ritholtz (43:47)

So let's talk about that, because this year, just the past 12 months, between the tariffs and Venezuela and now the ongoing war in Iran, how does global market volatility around all these geopolitical political events, how does a quant shop deal with that? What I'm hearing is the humans have to do what humans do and sometimes override the machines.

Jean Philippe Bachaud (44:13)

Sometimes, yes.

Barry Ritholtz (44:14)

I mean, when, when unexpected geopolitical political events disrupt the world, are models just not built to really work their way through that?

Jean Philippe Bachaud (44:26)

You're right, some events are okay. And like the war in Iran for the moment is not something that our risk models are completely blind to. It doesn't mean that they've predicted at all. It just means that we're comfortable with the risk that our model have predicted and they've adapted sufficiently fast to the events so that we're comfortable with the risk level. No human intervention. On the other hand, in some cases, it is completely unexpected, like tariffs and Liberation Day. This created havoc. Although, strangely enough, Liberation Day was announced. You know, everybody knew what was going to be said, and still everybody was surprised of it.

Barry Ritholtz (45:10)

I don't believe, despite I'm tariff man, it's the most beautiful 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. And then a week later, you know, a 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? I agree. 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?

Jean Philippe Bachaud (46:08)

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 tricky part is not to overreact because you said you don't remember periods of the world where things like this happened. But looking back, I've been in the markets for 35 years, and every year there seems to be something unexpected that happens.

Barry Ritholtz (46:55)

Just not every day. Not every day, but every day seems like a lot.

Jean Philippe Bachaud (46:59)

Right? But in a way, every day means that it becomes a new normal, I guess. And so it's not that bad if it's every day. But really, this idea that this time is different is something that's strange. If you look at the world and the history of financial markets, it's really being normal. That's not normal. And we've become used to that.

Barry Ritholtz (47:24)

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 to noise issues. What are some of the problems with models that you're focusing on improving?

Jean Philippe Bachaud (47:52)

Yeah, well, for example, this exactly what you just said. Can you have indicators that tell you whether your backtest 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 believe that we kind of replace the trader that trades every day, his signals or his beliefs to a higher level where we are traders of models, we kind of judge the models. We say this model is good enough to go in production, this model is not convincing enough, but it would be great to have something more systematic. And over the years we've been struggling and I think with some success to have meta models that predict whether your back test is really fudged or if it's decent enough to go in production. So. So we are kind of industrializing this process of selecting models that will go into production. Does that make sense?

Barry Ritholtz (48:59)

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 system 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?

Jean Philippe Bachaud (49:29)

Sure. It's something that we, you know, every day we think about this. We were in the Quant quake in 2007 and actually we were fortunate enough to be out of the markets or to have deleveraged already two weeks before. The worst day of the quant quake,

Barry Ritholtz (49:49)

was that an external signal or one of your model signals or what led you to.

Jean Philippe Bachaud (49:53)

It was something in the performance Already in July, the quant quake, the really bad day happened maybe 9th of August. I don't remember exactly, but yeah, it

Barry Ritholtz (50:02)

was early August, dog days of summer

Jean Philippe Bachaud (50:04)

for sure, but starting like 10th of July already there was something really, really very strange in our portfolio. And we started reflecting on what was going on and decided someone was deleveraging and hitting us by shorting our longs and buying our shorts. And this thought process of imagining that even if a fund that was like 10% correlated with ours, not a lot, but 10%. And having every day a kind of systematic 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 are going to suffer, suffer and at one point they're going to cascade, cascade and so on. And so that was the rationale for getting out. So in some cases you're lucky enough to have strong enough signals that tell you that your standard risk model is wrong and you should do something else.

Barry Ritholtz (51:07)

That's really, that's really fascinating. So you mentioned almost 35 years of doing this. What do you think that I'm going to say that again? So you are now 35, almost 35 years into being a market quant. What's the most important thing about markets that the mainstream funds still get wrong?

Jean Philippe Bachaud (51:33)

Well, I think it's this question of what is price doing? 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, this touches very recent academic papers that gabex and coidgent, two economists have put forward. They've called it the 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 to some extent driven by fundamentals. But this is a small long term effect on short run. Short run meaning from one day to one year, it's 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 view that I think that is going to percolate and convince more and more people looking forward. But having this change of tack is really important because in one case what you need to do to make money is to predict fundamentals. In the other case you need to predict what people are going to do. And so in a sense crowding can be a good thing because if 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 behavioral models and structural models that tell you 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're not anchored to fundamentals, we're anchored to flows.

Barry Ritholtz (53:41)

So this sounds a little bit like the Ben Graham line. I think it's Graham. 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?

Jean Philippe Bachaud (53:54)

Yeah, I think it's an old idea. I mean, you know, it's as a Keynes also things like that, you know, but in the long run, we're all dead. Right. Keynes, that was saying that. So it's really a question of whether you're going to be solvable. Ooh, I'm getting tired. I'll take that again. What's the word?

Barry Ritholtz (54:21)

Was that, by the way, Keynes or Graham? I initially thought it was Keynes and then I checked myself, I'm not sure.

Jean Philippe Bachaud (54:27)

But what Keynes said is that you. What was this quote? Markets can remain irrational longer than you can remain solid.

Barry Ritholtz (54:39)

So he never said that, but it's always attributed to him, really. There's a great website called Quote Investigator.

Jean Philippe Bachaud (54:46)

I see.

Barry Ritholtz (54:46)

And you give them a quote because people, as a sort of 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.

Jean Philippe Bachaud (55:04)

I didn't know that.

Barry Ritholtz (55:04)

And you would be, you would be. 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.

Jean Philippe Bachaud (55:18)

I don't think it's Keynes, by the way.

Barry Ritholtz (55:19)

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. They have so many favorite quotes from.

Jean Philippe Bachaud (55:33)

He said many relevant things, even today.

Barry Ritholtz (55:36)

Yes, absolutely.

Jean Philippe Bachaud (55:37)

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 reverts back to fundamentals. But the long run from our estimate is like five, 10 years. I'm trying long timescale, I'm trying to try to remember.

Barry Ritholtz (55:54)

It might have been French of French who said, you can't really tell if a manager is skilled until you. Until you have 20 years of data because it could just be good luck over five or 10 years, which is kind of, 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?

Jean Philippe Bachaud (56:30)

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, I.e. can prices do random things, in particular trending, which is really completely against EMH on the short run, that is for from a week to six months markets are trending over six months, one year and then on the longer timescale 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 everyday markets are around the correct price and there's no trend. Knowing your version ever, which is which,

Barry Ritholtz (57:38)

you know that 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.

Jean Philippe Bachaud (57:55)

That's the assumption of efficient market or actually even not, not even an assumption.

Barry Ritholtz (58:00)

It's.

Jean Philippe Bachaud (58:00)

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 investment and they're uncorrelated to one another. But the problem with markets is that it's not the way it works. The people are influenced by what other people are doing and what other people are saying. So instead of having independent guys doing random stuff, there are kind of one guy who's doing only one thing which is a fictitious body that aggregates everybody in the same way.

Barry Ritholtz (58:53)

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 their career.

Jean Philippe Bachaud (59:02)

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, of course, you know, the fractal guy. I knew him personally.

Barry Ritholtz (59:14)

Oh, really?

Jean Philippe Bachaud (59:15)

Yes. And he did a lot of things in physics as well.

Barry Ritholtz (59:18)

Sure.

Jean Philippe Bachaud (59:20)

And so he influenced a lot. My wife. My wife was a physicist before turning, a playwright now, and. And she works on fracture surfaces. The way, you know, when you break a material, what emerges from. 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 interaction.

Barry Ritholtz (59:45)

And fractal at a molecular level or at a larger level?

Jean Philippe Bachaud (59:48)

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. 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.

Barry Ritholtz (60:21)

Didn't he write a book on market crashes and how there's a fractal nature within those?

Jean Philippe Bachaud (60:28)

The misbehavior of market.

Barry Ritholtz (60:29)

That's right. There you go.

Jean Philippe Bachaud (60:30)

I'm actually quoted in that book.

Barry Ritholtz (60:32)

Oh, get out. Oh, that's fascinating.

Jean Philippe Bachaud (60:34)

And then Pierre Gilles de Jean, who was a Nobel Prize in physics, a French physicist who was so fantastic, and you know, both these two, and also Phil Anderson, who was a Nobel Prize in physics as well in the U.S. 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 of another field. The three of them, they've 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 de. Inhibited me in the sense that I thought, okay, maybe I'm not legitimate to speak about finance because I'm a physicist, but, you know, 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.

Barry Ritholtz (61:38)

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?

Jean Philippe Bachaud (61:45)

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, Paul McCartney. McCartney. It's a beautiful book. I really Loved it.

Barry Ritholtz (62:00)

What's the name of it?

Jean Philippe Bachaud (62:01)

John and Paul.

Barry Ritholtz (62:02)

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.

Jean Philippe Bachaud (62:08)

Oh, you should read it. I'm very, very emotional.

Barry Ritholtz (62:11)

I'm gonna make a recommendation to you for a YouTube channel called you can't unhear this. They take apart Beatles songs in ways that just little things that were done in the recording process that in a million years, you never would have noticed. And then once you hear it, you just can't unhear it. And if you're a Beatles fan, it's a rabbit hole. You'll. You'll love this. What else besides John and Paul?

Jean Philippe Bachaud (62:38)

I'm reading someone something that actually read for years. Mrs. Dalloway. Virginia Woolf. Yeah, I'm really a great admirer of Virginia Woolf.

Barry Ritholtz (62:49)

Really, really interesting. Do you do much streaming, tv, podcasts, anything like that, or.

Jean Philippe Bachaud (62:57)

We can skip over podcast a bit, but they're kind of French podcasts, so let's.

Barry Ritholtz (63:02)

Let's. You know, people are always looking for stuff. I'm gonna ask that. So what are you streaming today? What sort of podcasts are you listening to?

Jean Philippe Bachaud (63:09)

Yeah, you know, in frostbite, we were 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 we have NPR here.

Barry Ritholtz (63:30)

It's very similar. You can. It's a rabbit hole. You could fall down.

Jean Philippe Bachaud (63:33)

Yeah. Okay. So life of big people, of. Of major celebrities in culture, in cinema and theater, all these things. So I'm really a big fan of horse culture.

Barry Ritholtz (63:47)

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?

Jean Philippe Bachaud (64:01)

Well, study theoretical physics and study everything that's related to data. Pay attention to data and think about something that you strongly believe in and that you feel has not been investigated. And it doesn't matter if it's big or small. Make the effort of building something you strongly believe in.

Barry Ritholtz (64:24)

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.

Jean Philippe Bachaud (64:35)

That is extremely competitive. Much more than we thought.

Barry Ritholtz (64:38)

Really?

Jean Philippe Bachaud (64:39)

Yeah.

Barry Ritholtz (64:40)

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 Boucher, CFM's co founder, chairman and Chief Scientist. If you enjoy this conversation, well, check out any of the 600 plus we've done over the past 12 years. You could find those at Apple Podcasts, Spotify, YouTube, Bloomberg. Wherever you find your favorite 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 Luke is my podcast producer. Sean Russo is my head of Research. I'm Barry Ritholtz. You've been listening to Masters in Business on Bloomberg Radio.

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