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I'm so excited for you to tackle that and not me.
Good luck.
I thought I had it down, but then I heard you say.
It, and I feel like when I first met you, I think I asked you if you go by Gappy because of your famous track record of taking gardening leave, like having gaps in your career.
Oh okay, I didn't you remember that. Yeah, that's a great excuse for for a nickname. No, but the reason is when I came to the States for grad school, and this was a long time ago, in ninety five. So the first thing that you did was set up an email account. You still have the freedom to choose an email account. Now, they just give you your initials with the number, and so my initials are gap Gap, and
of course it was taken. So I said, okay, well Gappy, and then everybody in grad school, and then my wife was Italian, everybody started to call me Gappy and that's stuck. And now at work they just have dispensed with my real name, like on all systems, I'm just Gappy Paliologo. So I expect that that will be you know, prosecuted for tax evasion because on my tax forms there is Gappy Palaiologo or something like that.
Well, hello, and welcome to the Artistuff Podcast.
I'm Matt Livian and I'm Katie Greifeld.
And we have a guest today, Gabby pale Oligo, who is now at pali Asne and has been at most of the other couch funds and Hudson River Trading. I do want to start by talking.
About gardening me, okay, natural.
I think that we counted for your link Your LinkedIn is like famous for discussing your gardening live in some detail, and I think we counted three years of gardening. Leave No, I think it's a bit okay, it's not precise. Fifteen months from Citadel one you're Hudson River Trading and four months from millennium. Okay, so pretty close.
Not terrible, though a bit less than two years.
From my perspective, it seems very fun. Did you enjoy your three years of gardening?
I do so. I try to keep myself busy, so I teach typically at some university. So the first time during my Seitaadel two Millennium, Guardian leve I was teaching at Cornell and in the HRT to Bam Guardian Leve, I was at NYU and I love teaching, and then what I do is it helps me focus on on stuff. Usually what I do in you know, whenever I read a book or paper that I like, I take notes.
I take notes in Lattech and then I really arrive or think about things, and so that typically is the basis for my course material, and then it becomes the basis for my books. I've written a couple of books during my noncompetes.
Interesting because thinking about gardening leave. Matt and I talk about it all the time because it's very alluring to me. Gardening leave doesn't really exist in journalism. I love to imagine what I would do. But one of the questions I had for you was, you know, do you ever have anxiety about losing your edge or falling behind? But it sounds like teaching is one of the ways.
That particularly worried with that. I think that there is only a very specific subset of quantitative researchers who are afraid of losing their edges. And yeah, that's not been my case. I keep reading, I try to stay up to date.
Feedback into the work like, do you get ideas or like deep in your understanding of techniques by teaching and writing the books? Or are they just sort of like.
Extracurricular No, no, No, it's definitely I learn a lot from writing the books.
How long do you I go to your next job and generate more profits by of.
Course plenty more profits. Tell that to my employers. No, but I definitely I learn a lot from writing, from the first drafts, and then I rewrite and rewrite, and I learn a lot from discarding material too. It's very useful to discard material. It makes you really focus on what matters and what doesn't. So I try to give a narrative, like a logical connection between various topics, and that is something that is possible only when you write
a book. I really do not like writing. That Nobody I think likes writing, maybe except for you.
I understand that it's weird even among writers, but it is.
Very I find it very painful. I find painful letting go of material, Yes, but I also like it. You know, it's some kind of strange delayed gratification.
I guess one theory that I have written is that Hedge fund and quantitative research gardening leave is like a source of like human flourishing because you have all these like highly trained people who haven't enforced a year of And I've written that all the hedge fund researchers should go work at LM companies or like analytics departments of sports teams, and I'm like, partially kidding and partially not.
How true is it for you? Like how much of like your quantitative skills at this point are really just for investing, and how much of it is like if you spent three months, you know, consulting for a soccer team, you would be able to tell them how to find better players.
I'm not sure, so I'll say this right. I was thinking a few days ago if there was a kind of a common thread in my professional life, because it seems kind of random, And actually I think that there is, because I think that I was about fourteen when I realized that I had an aptitude for applied math. I discovered physics, and I liked math, and I also liked literature very much, so I loved reading. I read a lot. I was not a very social animal. And then basically
since then, I've been doing the same thing in various forms. Right, I did physics, I did applied math. I didn't do applied math in finance. I did applied math in weird things like optimization and logistics. So I have been doing kind of the same thing over and over, which has been writing and applying math to something. So I think that I could do it. I would like to do it, but I also think that it's not that simple to go to a new field and oh, after three months,
I know soccer. No, there is a lot of specificity. And the beauty of I think being a good applied mathematician is that they start with the problems and with the domain first, and that they're sufficiently mature from a mathematical standpoint that they are not making too much of an effort in using math. So I think the good art of being an applied mathematician is to study persistently the application. So no, I don't think that after three
months it would be good enough. But after a year, you know, about a year of being fully immersed in an application, then you start getting a little bit better, and then the math is not the problem, and then you start doing some good work.
You have a famous essay on like advice for quant careers and you say that like the things that matter the most creativity and genuine interest in the problems more than you know, math, coorse power. Yeah, this is a dumb question. But how does one develop how does one identify, you know, creativity and interest in financial topics? And is the obvious answer those are where the money is? Or like like why why did you fall in love with finance as a topic, And is the answer because that's what.
The money is. So first of all, I think that creativity is either a personality trait doesn't belong to You're not creative in finance, you know, you're you're creative in in cooking, you're creative in whatever. And it's a mix I guess of extraversion opening and as to experience, and I don't know what else. I'm not a psychologist, but
I do believe that people are genuinely creative. And in fact, you see it right that sometimes you ask someone and you find out that yes, they like writing, they play some instrument if badly, and you know, and they paint and they do whatever. And so I would say, if you go to finance because where the money is, there's nothing wrong with that. And in a way, that's my story you know, I was, I was a researcher. I
wanted to have more money and whatnot. But eventually you stay in finance, or at least in my you know, little domain, because you're genuinely curious about finding out stuff, right, So.
Like why are the problems like why do they arouse curios? Like why are the problems of finance intrigue you after years of doing it? Right? Like what's interesting about those problems as opposed to other domains.
It's really hard for me to say, Like I think that I read once that a young songwriter asked Bob Dylan how to become a good songwriter, and Bob Dylan just answered, well, what's going on? I said, what do you mean, what's going on? Yeah, what's going on? What's going on in your life? Just you know, look around. So sometimes I get these questions from investors, But you know, how do you keep yourself interested? How do you find problems? It's not a problem like the problems jump at you
like there are too many problems. There are too many interesting problems. So if anything, the skill is in sorting the problems in the right order, right. That is where maybe having some maturity in doing research kicks in. But there are lots of problems, infinite problems, weird problems.
What's your favorite problem right now?
I don't like right now? What are we working on? I mean, we are trying to understand how earnings are monetized? Right? How do you make money in earnings? It's such a basic thing in fundamental equities.
And you mean, if you're like correct about predicting.
Earnings, yes, what are I mean without getting too much into details, but you know there what are the relevant variables? Imagine that you had an oracle who told you what the variables are? What would you do with that? What would you do if you'd had all the information in the world right and everything in your world here in existence would be like an approximation problem.
There's an incredible STYLI story of like the guys into I think, like one of the newswire services and got earnings releases early like for hundreds of companies, and they traded on this and they had like a seventy percent success rate, which is great, but also like it means that had a thirty percent, like they traded the wrong way, knowing earnings perfectly in advance. It's like a good yeah, yes, so I had the you know, it's still hard.
Yes, it's still very hard. Actually, shout out to Victor Hagan, who wrote the paper about ten years ago on this. He made a organize a simple controlled experiment where he gave basically a biased coin where you, I think had a success rate of sixty percent forty percent failure, and you had some capital and you could invest it over time on these informed predictions, and a lot of subjects
went bankrupt. Okay, now I think we are better than that, but still there are lots of problems related to trading around an event.
For example, before we get too far away, you mentioned Bob Dylan. It actually reminded me of another Dylan quote which I'm going to paraphrase poorly, but he basically said, when asked about writing songs, do you think that you could write whatever the work that was being referenced now, And he said, I don't think so. It's like the words were in the air and I just plucked them out. They were just sort of hanging in the air and
they came to me. And it kind of also rang true with what you were saying about you didn't go looking for problems, They're just there. Necessarily. I actually want to go back to applied math if it doesn't interrupt the course of conversation too much. You tweeted on June twenty fourth that there's no child prodigies when it comes to poetry, when it comes to applied mathematics. And I'm not saying that. You said that you were a prodigy,
but you were a child at fourteen. I mean, how at fourteen do you realize that you have an aptitude for something like applied mathematics?
All right, I don't want to flex about this stuff, No, you should. I think I'm honestly a little weird. I'm just a little weird, I think, honestly, but I.
Like prodigy weird or.
I did have my share of yeah, adults telling me that I was good at this or that or you know. But yeah, I mean, okay, I'm just a little bit atypical. Also, when I talk to investors, I think investors enjoy my presence because I think I'm incredibly unfiltered for somebody who's talking to them, so it's like fun for them. And I was very unfiltered when I talk to my professors in school. Sometimes I corrected them stuff like this, Yeah, I don't know, honestly, I don't know.
When you talk to like fundamental equity portfolio managers, like how much like matrix algebras they're in your conversations like how quantity are the fundamental pms or whatever.
I don't think they're quantity, but I think that they're very analytical. So I don't think that they would make great mathematicians, but I think they would make very very decent applied mathematicians. Actually, they tend to be very analytical. They tend to be very process oriented. And they have also additional qualities that actually mentioned in that essay, like they have very little disposition effect, so that's part of being analytical. They have no sound cost fallacy in them.
So even though they don't do a lot of math, but they do some math. Okay, So first of all, they're fluent in a sense in basic literacy. But I think it's more their process that is closer to if not a mathematical one, but more of a scientific one.
And when it comes to being a quant does it basically boil down to being good at math and being interested in math? Are things such as statistics and physics? I mean, do you need to have any finance or economics background at all.
So I think that having an economics background is not necessarily a benefit, might even be a disadvantage actually, But just based on very few samples that I have a lot of very good, outstanding quantitative researchers actually come from physics and specifically from astrophysics. That's the experience that I've had in a couple of places.
In broad brushstrokes, could you talk about why economics in the small sample size you have, how could that possibly be a detriment good?
So I can answer the second question more easily. I think that astrophysicists deal with large amounts of data, and they deal with observational data, so they don't get to do a lot of experiments. And that's good for finance, right, you deal with a lot of data. You need to know how to have good agen for observational data, and you need to have very good theory, like you need to have very good instruments without being falling in love
with those instruments. Whereas I think economists, Okay, first of all, my statement is purely empirical. Okay, So I'm just really guessing on a economists and I'm going to be hated by all economists or economists in finance, but I do have my issues with their methods.
Right.
So, first of all, I think that there is an original scene in economics, which is I think a lot of economics is informed by a desire to be as rigorous as mathematics. Right, And so a lot of theoreticians in economics are very deductive in their approach. If you think of you know, the unrealistic assumptions behind the welfare theorems or arrows impossibility theorem or whatnot, or just pick up you know Samuelson textbooks, and I think this is
just axiomatic rather very axiomatic, very deductive. Whereas physicists are very happy to think in terms of small idealized models that apply to a specific domain, and if the model doesn't work out, they will discard and make another one. The grand theory behind physical theories exists, like there are people who do this for a living. But many, many good theoretical economists physicists starting the small and then they
expanded domain of their models. So economists tend to maybe in a sense, fall in love with methods too much, with techniques too much.
We had cliff Astness on the podcast a little while ago, and my father, not a finance person, listened to the episode and said, I still don't know what a quant is. I just read skimmed your new book which is called The Elements of Quantitative Investing, and as lays out the elements, what is a quant like? What are the elements? Like? What's the thing that makes someone a quant investor that, like someone reading a slim book about the Elements of quant investing needs to learn?
Well, if I am being consistent with my book, investing is really about problems and not about specific techniques or anything like this, Right, So it's basically a way to go through the whole investment process from let's say preparing
the ingredients to cooking to eating. That is very process driven. Ultimately, you would imagine that one thing that you know, quant investing has in common across multiple domains, you know, if you do futures, stocks, event based and whatnot, is I think the number of bets tends to be high in systematic investing. Right, so you can be a very successful microeconomic investor portfolio manager. And you you know, according to even several statements by Buffett, you know, he made like
ten twelve very good bets. Okay, so that's great, and that's not quant investing. You know, you could put enough pms making you know, twenty bets in their lives, you will get a few that have let's say twelve thirteen, right, and they will be rich. We do not have that luxury, right. We have to make millions of bets. You know, we trade a portfolio with three thousand stocks sometimes in waves of half an hour. You can't make a judgment on
all of these bets. So you need a method that reduces the dimension of your problem to something that can be treated in a systematic manner. I don't know if that answers for you. You know that, but you know, basically, basically the idea is, think about if you make a lot of bets, you cannot bet individually. You have to have some kind of juristic or some kind of method around that.
Right, and like to me, like the book sort of you know, the standard method I guess I'm quite investing is you build a factor model of what drives your universal investments. You're shutting your.
Head, Yeah, I yes, and no, I think yes because the book, you know, has maybe one hundred and fifty pages on factor models, but also no, because maybe in one hundred years from now, I suspect there will be still something left. But you know, we might have better techniques and not necessary factor models any longer.
I don't know, we don't want to go. Two attractions of that, one is like, are the better techniques something more neural netty unstructured?
Who knows? Yeah, something like that. I mean there is, there is a revolution every five years.
So my other question is like I've never fully understood like a factor model is like the here are some factors that drive the returns of stocks, and then there's like some residual idiots and credit return. There are clearly people whose business is to identify factors and then invest in factors. My impression is that at like the places that you work, the business is the opposite of that is to hedge out your factor risk as much as possible and to get as much idiosyncratic risk as possible.
Is that right? And like, like, how do you discriminate between like a factory return and idiosymcratic return, Like what makes the thing a factor as opposed to another ring.
So that's a good question. So first, a lot of systematic investing is still about factors, just not the factors that get published in the literature, you know, not the factors that Cliff maybe was talking about. And yet a lot of successful systematic investing is really factor driven.
It in the sense that you have a model that has like twenty factors and like ten are like value, and you neutralize those and you try the other time.
You do, and you do the rest. You have other terms that matter. So that's one thing, But there are two other things. There are sometimes sources of returns that are factor like but not quite like factors. So you may have a theme. For example, you may identify a theme in the market that is not pervasive enough or is alive only for a few months, but is there and it's not only affecting let's say two stocks. Right, So these brought thems can be invested on, but cannot
really model in the traditional way as a traditional factor model. Also, there is a lot of good modeling in factors as opposed to bad modeling, So it's it seems easy, but it's not that easy. So there is a little bit of craftsmanship in making these models, okay, And then the third thing is that there are also returns that have nothing to do with factors, or almost nothing to do
with factors. So if you really know how a company works, and you have a little bit of an edge in predicting its future performance, you can bet on it, and you make enough bets and again you will make some money if you repeat, and you know recycle. So even discretionary investing in this sense has inherited a little bit of the spirit of systematic investing.
I think of that as like that a pod job, but like aval like you have discretionary investors who know a lot about a company make bets on the company, and then someone like you tells them, these are your factory exposers. You have to get those down to zero. That you're making pure bets on your idios and creditnoledge of the company. Is that like kind of right?
Kind of right?
Yeah?
I think that at this point it is very interesting how the mind of professional portfolio managers has been remolded in a factor based world, so that a modern portfolio manager discretion The portfolio manager thinks in factors, you know, so I don't even need to tell them, hey, this is your exposure. They see their exposure, they have the tools to see it, and they control it in real
time with minimal intervention from me. So what we do is we have you know, a good team that models factors in a way that is suitable for the investment universe and style in which they operate. That's a very very sophisticated and difficult and portfolio managers use that and then neutralize It's become like second.
Nature, and they've internalized that their goal is to create
idiosyncratic alpha rather than factors. That's right. I feel like a criticism that people sometimes have of like the pod shop model is that, like there's some universe of factors that exist in commercial models and the like are known in the literature, and then portfolio managers have a set of exposures to factors that are sort of in code or unknown, but like ultimately, when you become really, really smart, you'll know that, like, actually the bet they were making
was some you know particular knowing the company really well means like they had exposure to like some you know, personality factor in the CEO or something that like eventually someone will be able to write that down and it'll come out of like being idiosyncratic and become a factor. And then I don't know, what happens.
I think that there is some truth to that. There is definitely some some truth to that, in the sense that sometimes for folio managers, especially in specific sectors, will use some heuristics that you could call characteristics in a factor model, but they are not in a factor model, and then they trade that. However, it's also true that the decision that enters a particular investment is usually not that simple as taking a ration spreadsheet, so it's a
bit more complicated than that. You could still argue that there is a factor, right, And what's the factor is ultimately the set of feces that are highly correlated or relatively highly correlated across portfolio managers across firms, because if there is an expected return, and if you have skill, and you have sufficient skill to be close to the best possible portfolio, you have to be also relatively close
to other people approximating that best possible portfolio. Right, So then it becomes a truism, Right, there is a factor, and that's the factor of investor, of informed investors. So it's true.
I think if it is like there's like a scientific process that ever pursuing I hear are the best people and they like do the best work to pursue that scientific process, and so they'll eventually converge on something that is like truth. But that means buying all the same stocks.
Yes, it's very difficult to get to that truth.
Sure it is. Yeah it's not.
Let's let's tire about it.
Weird if they weren't hurting among.
The best, yes, yes, but there is, there is And by the way, and this brings to one of the limitations of factor models, right, which is effectively a factor model is a form of glorified regression over time. Right, And behind a regression there is a bit of an assumption to some extent, of independent observations over time. And the market and hedge funds are not in dependent random variables.
They are super dependent random variables, and they are in a sort of continuous in direct conversation through their portfolios. And sometimes the conversation gets really nasty when one hedge fun is in state of distress and all of a sudden, or not even a hatch fund, it could be also an institution investor and decide to liquidate part of their portfolio. And then it becomes a process where you have a
lot of reflexivity and positive feedback and everybody suffers. And in this case, factor models don't really You can still identify, like if the system is running a temperature with some characteristics, but they are not factors in the traditional sense. I do want to.
Talk about before we move too far away, I do want to talk a little bit about how and if factors can die, because we've talked a bit about identifying factors. But when do you decide that this doesn't work anymore? Necessarily that the market has fundamentally changed and this worked maybe ten years ago, maybe fifteen years ago, but maybe now it's devolved.
Well, there is the good old reason, which is people make mistakes in the sense that we think that there is a factor and then we look back and there is no factor. Right, So there are so many factors that some of them have got to be a little bit redundant. So that that's one reason, right, So just pure in a sense research revisions. And then there is also the fact that there are two other things that
can happen. One is the moment that you tell people that there is a factor, the factor comes into being to some extent, right, So it's never black and white that the factor did not exist. Maybe the factor did exist and then the moment you identify it, it becomes more existent, like you know, speak yeah, yeah. So esg is is one case where the focal point that it became makes into an investible theme.
I thought that was just black rock pumping.
As possible, but everybody had to incorporate it in some sense, right, so it became a major source of revenue for the vendors. Right. So that's that's one thing. And then there is the adaptive nature of the market. So things that before generated
a priced return. So you run some risk, you made some money, and then it becomes table stakes, it becomes incorporated into factor models, it becomes it becomes a smart beet, it becomes a smart patent, and then it becomes so I think, you know, you could say definitely that medium tonmamentum worked much better. You could say that even you know,
short term reversal worked better. There were years when short interest was great, and there are factors or data sources that work well now and then maybe in five years will become known and become part of the I mean credit card data. Right for consumer that was like there were people who were making a lot of money in two thousand and eleven through sixteen seventeen, and then it's become it's very hard to make money in that.
You said the market is a conversation among catch funds. One thing that I think might be true that I'm not entirely sure of, is like, to what extent the market is a conversation among four patch funds? Now? Like, to what extent is like the marginal price or of every stock a portfolio manager at you know, one of the places you've worked.
It's a very good question. I don't really have the answers to this. I'm not sure.
It's it's like, what is the intuition at places like that, Like, is it like the market price is determined by like the collective thought of like the top people at the top hedge funds, Or is it like we are a little bump on the market and we're trading against the whole random universe.
I mean, you'd like to think that the prices are determined by the marginal informed investor, Right, so by people like us at the time horizon where we predict right, which is not the same as at the time of Rizon of half a day. Right, that's a different player.
What is your time horizon, like I think of it as.
Well, it depends well, yes, it depends. Within a hedge fund, you have a variety of even within long shore equities, you know, you have you know, portfolio managers who are very tactical, and so they think in terms of they have strong daily or intra day alpha, even though they're fully discretionary up to pms that think easily in terms of months. Also depends on the sector. So you know, financials typically probably monetizes a little bit less on earnings
and tends to have a longer horizon. Banks are basically modeling giant balance sheets, right, and then in a hedge fund you also have systematic, but even in systematic there are all sorts of time scales, and this cacophony makes the prices. I really don't know, Like I said, another question is basically, are how inefficient is the market? How incorrect are the prices are within a factor of two?
Like Black used to say, or I don't know, Like I don't think that the market is becoming so super efficient, but it's getting it seems to be more efficient.
I do feel, like, you know, the big stories is the rise of like these big multi strategy hedge funds, like you would hope. Maybe you wouldn't hope because it's sort of the economic and just, but like one might hope that like the rise of these big multi strategy hedge funds and a lot of capital being allocated to them would observably make the market more efficient.
Yeah, I don't know if observably holds. I don't. It's really hard to like, can you can you tell when a bubble is forming?
A lot of people would say that they can.
Yeah, I can point you to a few papers, yeah that you know made all the wrong calls. Okay, I don't want to shame academics in public.
I do like the idea that the market is a conversation between four hedge funds because I live in the ETF world, and you know, the big thing is passive is just distorting the market and there's no price discovery anymore. And it sounds like that's on the opposite end of that spectrum.
I didn't say, I think exactly that it's a conversation between It's a beautiful thing to say, though. It sounds really cool. It sounds good podcasts. Yeah, that's great. Yeah, But I think your question is whether the rise of passive has made markets less efficient.
More of a statement. I don't think I was a bad podcaster and didn't actually.
Ask a question, But okay, how do you know?
How do I know that passive is the story in the market? People on Twitter tell me?
So, oh, okay, don't trust people on Twitter.
That's true. Number one, real number one.
Now I don't know. I mean, the rise of passive has made index rebalancing a weirder strategy, right, so where the margins have compressed, but the size has become so big that you can still make money in it and periodic. It's a very you know, cyclical strategy. So I don't know.
So you're an indexy balancing PM do take like eight months of vacation a year and like all day rebalance.
Not the ones I know who probably listen to this podcast, Okay, they work very hard.
Sure indexes aren't paying rebalanced all the time, planning more than you would think.
Index rebalancing is another you know, poster child for a strategy that seems so simple that everybody can talk about it, and then it's full of nuances and it requires a lot of skill to trade effectively.
I believe that just because like I thought a little bit about like just like the sort of like accounting of like you basically know how many index mounds there are, let's say, can predict what will come in and out of the index, and like what the so like there's like some mechanics around, like you know, figuring out the market, calves that will come in and whatever, but then it feels like the unknown is like who else is doing the rebalance strategy? Is that? Right?
I think you're mostly right because I don't want to say, because you know, out of respect for for the CMS, did I know?
I love? Yes? Like so we had Cliff Askiness on a few weeks ago, and like, to me, Cliff Asness is like a quantitative investor, like a systematic investor, but like what he's doing is sort of recognizably what a sort of traditional asset manager. He's like trying to find companies that are undervalued, right, he talked about it's like being a Grammar dot investor. You know, you want like
valuation plus a catalyst. And he's like, oh, or you know trading, you know, value and momentum and like you look at what eight or two is maybe a little different, but there's like you know, the hypercacy trading firms, Like you can model those as like those are quantitative versions of like a voice market maker fifty years ago, where they're like trying to keep inventory flat and like trying
to you know, make the bid asks bread. So like those are like very traditional economic functions that have been quantify like turned into systematic what's the intuition for like what a bally Asni or a star doll or a millennium does? Like what business are you in? Do you think? Like as a philosophical matter, like one thing I think, like I think about like.
You're asking from a social kind of point or.
Well, I think like the index rebalancing, Like to me, it feels like the sort of trade and I think to something that was the sort of trade that like an investment bank would have done twenty years ago, thirty years ago, and like some of that function I think has moved to like the big multimanagers. But like I wonder like from where you say, like how you see the like role in the financial markets of those firms.
So at a very high level, we don't do anything different than everybody else in the sense that what we provide is always this, right, is we provide shifting time preferences, which means we provide liquidity. We house you know, risk for people who don't want to hold it right now. And that's what you do when you do indext rebalancing, right, that's what you do when you do merger ARB and when you do the various subtypes of basis traits.
Right.
So we do provide liquidity, which is very important. And then the second thing we again very high level, we provide price discovery, right, So we study the firms and we think, okay, this is at the margin mispriced and we're going to short it or we're going to invest in it, and that's a beautiful thing. So we do
it at a different time scale, right. So you always want to do things at the margin where you don't have a lot of other participants, and at the margin of the let's say month to three month investment horizon, there are not that many participants. So in the words of another hatch fund manager I cannot name, but it said, once you know, we don't invest in securities, we dated them, and so we are in the dating service. Not that many people are doing it, and so we do it.
But I would say also this right, not at the social level. I just want to answer the like my personal level. What we do. We are a massive filter of talent, and the talent that we hire is a massive filter of information. So it's like information squared.
Maybe this is like a bad question, but like, do you think that like long only asset managers are worse than they were thirty years ago because that filter has been so successful? In other words, like there are lots of jobs you could have gotten in finance in nineteen ninety, but like, yeah, there's like a clear hierarchy now.
I think that the market and the set of investors has learned right, and I think the distinction between VITA and HALFA has been useful for investors, and so active investors who are mostly long only, I think have suffered from this distinction because the vast majority of them underperforms their benchmarks and so there is no reason for them
to exist. And then what we do is we provide really uncorrelated returns to the benchmarks to most factors, and investors want that, right, So there is a future where active investors, long on investors asset managers will become even less influential, smaller, and also.
I think of that as like a customer demand side, but also like a talent filter side.
Right, yes, Yeah, And then the interesting thing is and then there is also a process where the multimanager platforms are able to make the business model of a single portfolio manager that is not sustainable in isolation working in this kind of federated system. So why would you or how could you survive as a single portfolio manager hedge fund nowadays? It's really really difficult, but you can do it in a multimanager platform provided that you have you know, sufficient talent, sufficient edge.
That's also where you can blame the passive influence on Twitter. If you're a long entry manager that you know it's impossible to be the market now because you just have this money constantly pouring in.
Yeah, I don't disagree. Yeah.
One more question, like social roles is just like you've worked at most of the big pod jobs, but you also worked at HRT, Like what's the difference in roles and like what they do all day? Because HRT, I think of is a classic like high frequency treating firm where I don't know they're exactly a market maker, but they're certainly on the higher frequency side, and then like the pod jobs have a lower frequency and a you know,
they're not prop they're running hedgephones. Like what's the cultural and role and differences?
Yeah, okay, So I briefly mentioned the HRT in a in an interview with The Financial Times, and my manager told me that, you know, people at HRT were both annoyed and delighted by what I've had said about about HRT. I think HRT is a really special place, even in the in the context of proper training firms. So I'm a little bit hesitant and to just being them in as a representative, right, So they're not representative because there is something in the culture of HRT that is special. Okay,
it's collaborative, it's truly kind. Yeah. So I think it's a great place to work, and it is fundamentally monolithic, so you have, you know, sharing of ideas and you can work at the intersection of these ideas. It's also a place that is very tech oriented, so it's a bit of a technology firm operating in the financial space. And because of that, it also attracts i think the best technical talent that I've ever worked with. It's just a pleasure to work with great technologists, people who are
very competent in that respect. So nothing against the hedge funds. I love edge funds for different reasons. You know. I love BAM, which is also very collaborative and it's an investor meant company. But HRT has as a technical side to it and also gain a cultural side to it. It's great.
We didn't talk about AI AI.
Yeah, of course you have to talk about it.
Like I have like three models of how investment works systematic about Like one is like you have like some economic intuition and you build a model of like the stock market that predicts prices. And in other way is a sort of like neural netty ai E way, where like you throw a lot of data at a neural net and it build its own model of how to predict stock prices. And then the third model is like you get really good at prompt engineering and you get a chat GPT and you say what stocks will go up?
But you ask it in the right way, and then chat JPT tells me what stocks will go up? How good is it? I see? The third model no one uses, but like someone uses.
I think a lot of people use that.
All right, So first thing like, Okay, nobody knows anything, and anybody saying the opposite, you know, should be heavily discounted. Okay, so we agree on this, and so let's forget for a second all the technical details of AI just from a pure industrial organization standpoint, Right, So what's going to happen?
Consider AI just like another technology like Internet and whatnot. Right, So you know, first of all, we're going to observe economies of scale, So there's going to be concentration, and there was going to be some kind of monopolistic competition.
I was thinking about Bloomberg specifically, which could be I hope for you people to be among the winners, because you have a good starting point, right, You have lots of data, you have a customer base, and maybe in the future we'll finally not see the good old Bloomberg terminal, which has been kind of unchanged since I remember it, and instead people will just prompt Bloomberg to conduct very complex actions where it will act on a sequence of keywords and connect them and give you, like a much
more valuable product for which Bloomberg will charge twice as much as they do already. So this is going to happen in one form or another. If it's not Bloomber, somebody else will do it. Okay, But the same thing applies to other areas of finance. So maybe once upon a time, you know, a big sufficiently big fund could build their own client for email.
Right.
Of course, nobody builds a client for email anymore. Right, so a lot of this stuff gets outsourced. We will outsource at some point some of the functions that we conduct internally using AI to other AI agents. It's perfectly fine. So this will become a utility to some extent. Yes, functions include well not stock picking. Not stock picking. I think that the functions that we will see available are essentially like another self, like another Mathlevin. Who can you be a good baseline for you?
Okay?
You could feed a post train and AI system with all your gazillions of words, right, and that agent will reproduce your sense of humor, your investigative style and everything. Okay, it's a good approximation. It's not going to be perfect, but why not?
Right?
So I would be very happy to have a replica of myself that can answer most simple questions. Now, I think that the decision to invest in a particular stock is a very demanding cognitive function, and I don't see that really being replicated very well. But I think that this will be baselined to some extent.
Is it many kind of function because because it exists in a competitive market, So like the sort of like whatever the kind of function is, is going to get like the baseline is always going to get higher because like someone else will will have the same information as you do or the same.
Well, this is getting really in the highly speculative side of you know things. I think that in order for an AI agent to be good at this, they have to be able to experience the world the same way that an investor experiences it. And our inputs are much more complex than just a string of text or YouTube videos.
Right we have a model of the world which comes from visually experiencing the world, talking to humans, consuming the goods, right anything, It's vastly more complex than the way an AI system right now experiences the world and also influences the world. So an investor has a fundamentally different experience of a company than an LM that has an experience thats is mediated by multiple layers of processing. You know, they learn about a company through text that is written
by somebody. So I don't think that that's in danger for the time being. But maybe, you know, again, in five years, maybe we will have our glasses feeding our experiences to AI agents. Who knows, right, But I don't think that it's that close, and I don't think AI is that's smart also, so I think that having a baseline system would be already pretty good.
That's somewhat comforting that our experiences count for something, our physical experience of the world.
It's interesting because I always think of like the comparison as like investing in self driving cars, or like investors do a lot of things, but like one thing they do a lot is sit at a desk and read computers and like look at numbers, right, and like those things seem like things that a computer can do well, whereas like you know, drivers like have physical reflexes and
like have a you know, complicated field division. I always thought, like investing should be easier than self driving cars for computer and a master, but you, I think you're learning this. Think of like investing as like the great liberal art, where it's like you incorporate all of human experience and so the AI can't relate.
Okay, let's let's take the metaphor to you know, extreme consequences. Imagine that you had a system that is the equivalent of a perfect self driving car in investing. So now I'm giving you a machine, a box that is telling you the long term value, if not the returns, right, because the moment that the value is known, you immediately equilibrate to that level.
Right.
So imagine that you know the true value of everything because a box tells you so, and it's invaluable. It's an oracle. Okay. Would you think that finance stops existing. I wouldn't say so, right, So I think that a lot of arbitrush trades, you know, would maybe change significantly, but every risk, right, every return would be correctly priced by the risk of the agent's training it. So there still would be trading because we still have different preferences,
but basically every risk would be priced. There would be in a sense, less alpha, but finance will still exist.
It's a lot of like service provision, like liquid.
Liquidity provision and yeah, and so the liquidity provision would still exist. The informational services maybe will stop existing in the current form, but that's okay. I think that we'll all still be employed.
Mhmm.
It's interesting I think about it because I do think, like we talked about, like, one thing that the big hedgehunds do is things that have the flavor of liquidity provisions basis trades and merger urb and whatever. Things that like I think of as like something that a bank would have done thirty years ago, and then now a big hedge fund does. And then another thing they do has the flavor of information provision, where it's getting prices right.
Like to me, those things seem quite intellectually separate, but I guess they feed each other in the sense that the better you are prices, the better you can be a liquidity provision.
The value yeah, I mean a short, short horizon. Liquidity provision and information tend to be very closely rated. Like you know, a limit if you're good at if you're either good at crossing even good at crossing, you should be pretty good at adding okay, adding liquidity, so you know. But I mean like you could make you know a profit by posting a lot of limit orders and providing liquidity to the market or crossing the spread and making
money with predicting the future prices. If you're good at one, you're good at the other, most likely, right at that time scale, I think that this though might I'm not sure because I haven't thought about this very very carefully, but I think this might decoupled at longer time scale, so you know you're when you're out. I'm not sure.
And in any case, at that time scale is really difficult for an AI or for a human being anyone, like, there are not that many hard data, even the unstructured data are not that any So it's a very difficult problem. It's the coupled it's it's complicated. So yeah, but I tend to believe at longer time scales you have more or less liquid provisioning and you know, violations of law of one price on one side and predicting on the other side.
But you combine both.
But you can combine both, and it's a very potent mix.
Right. There is normally different people, it is, right.
Very different people for sure, different parts, very different very different people, very different cultures.
Yeah, can you summarize the difference in cultures between like I have a guess.
But well, as you said, people who typically trade in arbitrades, if not historically but also historically come from banks.
Yeah.
Right, Whereas you still can see long only portfolio managers being recycled and reformatted into long short portfolio managers, you can have an excellent short specialist becoming a long short portfolio manager like it happened, I.
Mean makes sense. Is that like the people on the information version long short side are more academic and research orientered, and the people on the ARB side are.
More Yeah, I think you can actually have a very good long short portfolio managers who were journalists in their past lives.
I've heard of some of these thought about this about it no, just like.
No not breaking news on your podcast.
I've noticed that's that's better than podcasting. Not thought about it in the sense that I'd be good at it, just in the sense that the money is good.
You could be bad at it and paid really well for a short amount of time.
I don't know that that's true. Actually, they're they're an excellent talent filter or so I hear.
Yes, I think that you could interest a few huge funds. They might be listening.
On a note, Kathy, thanks for coming on the.
Thanks for having me, And that.
Was the money Stuff podcast.
I'm Matt Levian and I'm Katie Greifeld.
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Mm hmm