Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Wait, Gapy Palio logo or.
Speaker 2: I'm so excited for you to tackle that and not me.
Speaker 3: Good luck.
Speaker 2: I thought I had it down, but then I heard
Speaker 2: you say.
Speaker 1: It, and I feel like when I first met you,
Speaker 1: I think I asked you if you go by Gappy
Speaker 1: because of your famous track record of taking gardening leave,
Speaker 1: like having gaps in your career.
Speaker 3: Oh okay, I didn't you remember that. Yeah, that's a
Speaker 3: great excuse for for a nickname. No, but the reason
Speaker 3: is when I came to the States for grad school,
Speaker 3: and this was a long time ago, in ninety five.
Speaker 3: So the first thing that you did was set up
Speaker 3: an email account. You still have the freedom to choose
Speaker 3: an email account. Now, they just give you your initials with
Speaker 3: the number, and so my initials are gap Gap, and
Speaker 3: of course it was taken. So I said, okay, well Gappy,
Speaker 3: and then everybody in grad school, and then my wife
Speaker 3: was Italian, everybody started to call me Gappy and that's stuck.
Speaker 3: And now at work they just have dispensed with my
Speaker 3: real name, like on all systems, I'm just Gappy Paliologo.
Speaker 3: So I expect that that will be you know, prosecuted
Speaker 3: for tax evasion because on my tax forms there is
Speaker 3: Gappy Palaiologo or something like that.
Speaker 1: Well, hello, and welcome to the Artistuff Podcast.
Speaker 2: I'm Matt Livian and I'm Katie Greifeld.
Speaker 1: And we have a guest today, Gabby pale Oligo, who
Speaker 1: is now at pali Asne and has been at most
Speaker 1: of the other couch funds and Hudson River Trading. I
Speaker 1: do want to start by talking.
Speaker 3: About gardening me, okay, natural.
Speaker 1: I think that we counted for your link Your LinkedIn
Speaker 1: is like famous for discussing your gardening live in some detail,
Speaker 1: and I think we counted three years of gardening. Leave No,
Speaker 1: I think it's a bit okay, it's not precise. Fifteen
Speaker 1: months from Citadel one you're Hudson River Trading and four
Speaker 1: months from millennium. Okay, so pretty close.
Speaker 3: Not terrible, though a bit less than two years.
Speaker 1: From my perspective, it seems very fun. Did you enjoy
Speaker 1: your three years of gardening?
Speaker 3: I do so. I try to keep myself busy, so
Speaker 3: I teach typically at some university. So the first time
Speaker 3: during my Seitaadel two Millennium, Guardian leve I was teaching
Speaker 3: at Cornell and in the HRT to Bam Guardian Leve,
Speaker 3: I was at NYU and I love teaching, and then
Speaker 3: what I do is it helps me focus on on stuff.
Speaker 3: Usually what I do in you know, whenever I read
Speaker 3: a book or paper that I like, I take notes.
Speaker 3: I take notes in Lattech and then I really arrive
Speaker 3: or think about things, and so that typically is the
Speaker 3: basis for my course material, and then it becomes the
Speaker 3: basis for my books. I've written a couple of books
Speaker 3: during my noncompetes.
Speaker 2: Interesting because thinking about gardening leave. Matt and I talk
Speaker 2: about it all the time because it's very alluring to me.
Speaker 2: Gardening leave doesn't really exist in journalism. I love to
Speaker 2: imagine what I would do. But one of the questions
Speaker 2: I had for you was, you know, do you ever
Speaker 2: have anxiety about losing your edge or falling behind? But
Speaker 2: it sounds like teaching is one of the ways.
Speaker 3: That particularly worried with that. I think that there is
Speaker 3: only a very specific subset of quantitative researchers who are
Speaker 3: afraid of losing their edges. And yeah, that's not been
Speaker 3: my case. I keep reading, I try to stay up
Speaker 3: to date.
Speaker 1: Feedback into the work like, do you get ideas or
Speaker 1: like deep in your understanding of techniques by teaching and
Speaker 1: writing the books? Or are they just sort of like.
Speaker 3: Extracurricular No, no, No, it's definitely I learn a lot
Speaker 3: from writing the books.
Speaker 1: How long do you I go to your next job
Speaker 1: and generate more profits by of.
Speaker 3: Course plenty more profits. Tell that to my employers. No,
Speaker 3: but I definitely I learn a lot from writing, from
Speaker 3: the first drafts, and then I rewrite and rewrite, and
Speaker 3: I learn a lot from discarding material too. It's very
Speaker 3: useful to discard material. It makes you really focus on
Speaker 3: what matters and what doesn't. So I try to give
Speaker 3: a narrative, like a logical connection between various topics, and
Speaker 3: that is something that is possible only when you write
Speaker 3: a book. I really do not like writing. That Nobody
Speaker 3: I think likes writing, maybe except for you.
Speaker 1: I understand that it's weird even among writers, but it is.
Speaker 3: Very I find it very painful. I find painful letting
Speaker 3: go of material, Yes, but I also like it. You know,
Speaker 3: it's some kind of strange delayed gratification.
Speaker 1: I guess one theory that I have written is that
Speaker 1: Hedge fund and quantitative research gardening leave is like a
Speaker 1: source of like human flourishing because you have all these
Speaker 1: like highly trained people who haven't enforced a year of
Speaker 1: And I've written that all the hedge fund researchers should
Speaker 1: go work at LM companies or like analytics departments of
Speaker 1: sports teams, and I'm like, partially kidding and partially not.
Speaker 1: How true is it for you? Like how much of
Speaker 1: like your quantitative skills at this point are really just
Speaker 1: for investing, and how much of it is like if
Speaker 1: you spent three months, you know, consulting for a soccer team,
Speaker 1: you would be able to tell them how to find
Speaker 1: better players.
Speaker 3: I'm not sure, so I'll say this right. I was
Speaker 3: thinking a few days ago if there was a kind
Speaker 3: of a common thread in my professional life, because it
Speaker 3: seems kind of random, And actually I think that there is,
Speaker 3: because I think that I was about fourteen when I
Speaker 3: realized that I had an aptitude for applied math. I
Speaker 3: discovered physics, and I liked math, and I also liked
Speaker 3: literature very much, so I loved reading. I read a lot.
Speaker 3: I was not a very social animal. And then basically
Speaker 3: since then, I've been doing the same thing in various forms. Right,
Speaker 3: I did physics, I did applied math. I didn't do
Speaker 3: applied math in finance. I did applied math in weird
Speaker 3: things like optimization and logistics. So I have been doing
Speaker 3: kind of the same thing over and over, which has
Speaker 3: been writing and applying math to something. So I think
Speaker 3: that I could do it. I would like to do it,
Speaker 3: but I also think that it's not that simple to
Speaker 3: go to a new field and oh, after three months,
Speaker 3: I know soccer. No, there is a lot of specificity.
Speaker 3: And the beauty of I think being a good applied
Speaker 3: mathematician is that they start with the problems and with
Speaker 3: the domain first, and that they're sufficiently mature from a
Speaker 3: mathematical standpoint that they are not making too much of
Speaker 3: an effort in using math. So I think the good
Speaker 3: art of being an applied mathematician is to study persistently
Speaker 3: the application. So no, I don't think that after three
Speaker 3: months it would be good enough. But after a year,
Speaker 3: you know, about a year of being fully immersed in
Speaker 3: an application, then you start getting a little bit better,
Speaker 3: and then the math is not the problem, and then
Speaker 3: you start doing some good work.
Speaker 1: You have a famous essay on like advice for quant
Speaker 1: careers and you say that like the things that matter
Speaker 1: the most creativity and genuine interest in the problems more
Speaker 1: than you know, math, coorse power. Yeah, this is a
Speaker 1: dumb question. But how does one develop how does one identify,
Speaker 1: you know, creativity and interest in financial topics? And is
Speaker 1: the obvious answer those are where the money is? Or
Speaker 1: like like why why did you fall in love with
Speaker 1: finance as a topic, And is the answer because that's what.
Speaker 3: The money is. So first of all, I think that
Speaker 3: creativity is either a personality trait doesn't belong to You're
Speaker 3: not creative in finance, you know, you're you're creative in
Speaker 3: in cooking, you're creative in whatever. And it's a mix
Speaker 3: I guess of extraversion opening and as to experience, and
Speaker 3: I don't know what else. I'm not a psychologist, but
Speaker 3: I do believe that people are genuinely creative. And in fact,
Speaker 3: you see it right that sometimes you ask someone and
Speaker 3: you find out that yes, they like writing, they play
Speaker 3: some instrument if badly, and you know, and they paint
Speaker 3: and they do whatever. And so I would say, if
Speaker 3: you go to finance because where the money is, there's
Speaker 3: nothing wrong with that. And in a way, that's my
Speaker 3: story you know, I was, I was a researcher. I
Speaker 3: wanted to have more money and whatnot. But eventually you
Speaker 3: stay in finance, or at least in my you know,
Speaker 3: little domain, because you're genuinely curious about finding out stuff, right, So.
Speaker 1: Like why are the problems like why do they arouse curios?
Speaker 1: Like why are the problems of finance intrigue you after
Speaker 1: years of doing it? Right? Like what's interesting about those
Speaker 1: problems as opposed to other domains.
Speaker 3: It's really hard for me to say, Like I think
Speaker 3: that I read once that a young songwriter asked Bob
Speaker 3: Dylan how to become a good songwriter, and Bob Dylan
Speaker 3: just answered, well, what's going on? I said, what do
Speaker 3: you mean, what's going on? Yeah, what's going on? What's
Speaker 3: going on in your life? Just you know, look around.
Speaker 3: So sometimes I get these questions from investors, But you know,
Speaker 3: how do you keep yourself interested? How do you find problems?
Speaker 3: It's not a problem like the problems jump at you
Speaker 3: like there are too many problems. There are too many
Speaker 3: interesting problems. So if anything, the skill is in sorting
Speaker 3: the problems in the right order, right. That is where
Speaker 3: maybe having some maturity in doing research kicks in. But
Speaker 3: there are lots of problems, infinite problems, weird problems.
Speaker 1: What's your favorite problem right now?
Speaker 3: I don't like right now? What are we working on?
Speaker 3: I mean, we are trying to understand how earnings are monetized? Right?
Speaker 3: How do you make money in earnings? It's such a
Speaker 3: basic thing in fundamental equities.
Speaker 1: And you mean, if you're like correct about predicting.
Speaker 3: Earnings, yes, what are I mean without getting too much
Speaker 3: into details, but you know there what are the relevant variables?
Speaker 3: Imagine that you had an oracle who told you what
Speaker 3: the variables are? What would you do with that? What
Speaker 3: would you do if you'd had all the information in
Speaker 3: the world right and everything in your world here in
Speaker 3: existence would be like an approximation problem.
Speaker 1: There's an incredible STYLI story of like the guys into
Speaker 1: I think, like one of the newswire services and got
Speaker 1: earnings releases early like for hundreds of companies, and they
Speaker 1: traded on this and they had like a seventy percent
Speaker 1: success rate, which is great, but also like it means
Speaker 1: that had a thirty percent, like they traded the wrong way,
Speaker 1: knowing earnings perfectly in advance. It's like a good yeah, yes,
Speaker 1: so I had the you know, it's still hard.
Speaker 3: Yes, it's still very hard. Actually, shout out to Victor Hagan,
Speaker 3: who wrote the paper about ten years ago on this.
Speaker 3: He made a organize a simple controlled experiment where he
Speaker 3: gave basically a biased coin where you, I think had
Speaker 3: a success rate of sixty percent forty percent failure, and
Speaker 3: you had some capital and you could invest it over
Speaker 3: time on these informed predictions, and a lot of subjects
Speaker 3: went bankrupt. Okay, now I think we are better than that,
Speaker 3: but still there are lots of problems related to trading
Speaker 3: around an event.
Speaker 2: For example, before we get too far away, you mentioned
Speaker 2: Bob Dylan. It actually reminded me of another Dylan quote
Speaker 2: which I'm going to paraphrase poorly, but he basically said,
Speaker 2: when asked about writing songs, do you think that you
Speaker 2: could write whatever the work that was being referenced now,
Speaker 2: And he said, I don't think so. It's like the
Speaker 2: words were in the air and I just plucked them out.
Speaker 2: They were just sort of hanging in the air and
Speaker 2: they came to me. And it kind of also rang
Speaker 2: true with what you were saying about you didn't go
Speaker 2: looking for problems, They're just there. Necessarily. I actually want
Speaker 2: to go back to applied math if it doesn't interrupt
Speaker 2: the course of conversation too much. You tweeted on June
Speaker 2: twenty fourth that there's no child prodigies when it comes
Speaker 2: to poetry, when it comes to applied mathematics. And I'm
Speaker 2: not saying that. You said that you were a prodigy,
Speaker 2: but you were a child at fourteen. I mean, how
Speaker 2: at fourteen do you realize that you have an aptitude
Speaker 2: for something like applied mathematics?
Speaker 3: All right, I don't want to flex about this stuff, No,
Speaker 3: you should. I think I'm honestly a little weird. I'm
Speaker 3: just a little weird, I think, honestly, but I.
Speaker 2: Like prodigy weird or.
Speaker 3: I did have my share of yeah, adults telling me
Speaker 3: that I was good at this or that or you know.
Speaker 3: But yeah, I mean, okay, I'm just a little bit atypical. Also,
Speaker 3: when I talk to investors, I think investors enjoy my
Speaker 3: presence because I think I'm incredibly unfiltered for somebody who's
Speaker 3: talking to them, so it's like fun for them. And
Speaker 3: I was very unfiltered when I talk to my professors
Speaker 3: in school. Sometimes I corrected them stuff like this, Yeah,
Speaker 3: I don't know, honestly, I don't know.
Speaker 1: When you talk to like fundamental equity portfolio managers, like
Speaker 1: how much like matrix algebras they're in your conversations like
Speaker 1: how quantity are the fundamental pms or whatever.
Speaker 3: I don't think they're quantity, but I think that they're
Speaker 3: very analytical. So I don't think that they would make
Speaker 3: great mathematicians, but I think they would make very very
Speaker 3: decent applied mathematicians. Actually, they tend to be very analytical.
Speaker 3: They tend to be very process oriented. And they have
Speaker 3: also additional qualities that actually mentioned in that essay, like
Speaker 3: they have very little disposition effect, so that's part of
Speaker 3: being analytical. They have no sound cost fallacy in them.
Speaker 3: So even though they don't do a lot of math,
Speaker 3: but they do some math. Okay, So first of all,
Speaker 3: they're fluent in a sense in basic literacy. But I
Speaker 3: think it's more their process that is closer to if
Speaker 3: not a mathematical one, but more of a scientific one.
Speaker 2: And when it comes to being a quant does it
Speaker 2: basically boil down to being good at math and being
Speaker 2: interested in math? Are things such as statistics and physics?
Speaker 2: I mean, do you need to have any finance or
Speaker 2: economics background at all.
Speaker 3: So I think that having an economics background is not
Speaker 3: necessarily a benefit, might even be a disadvantage actually, But
Speaker 3: just based on very few samples that I have a
Speaker 3: lot of very good, outstanding quantitative researchers actually come from
Speaker 3: physics and specifically from astrophysics. That's the experience that I've
Speaker 3: had in a couple of places.
Speaker 2: In broad brushstrokes, could you talk about why economics in
Speaker 2: the small sample size you have, how could that possibly
Speaker 2: be a detriment good?
Speaker 3: So I can answer the second question more easily. I
Speaker 3: think that astrophysicists deal with large amounts of data, and
Speaker 3: they deal with observational data, so they don't get to
Speaker 3: do a lot of experiments. And that's good for finance, right,
Speaker 3: you deal with a lot of data. You need to
Speaker 3: know how to have good agen for observational data, and
Speaker 3: you need to have very good theory, like you need
Speaker 3: to have very good instruments without being falling in love
Speaker 3: with those instruments. Whereas I think economists, Okay, first of all,
Speaker 3: my statement is purely empirical. Okay, So I'm just really
Speaker 3: guessing on a economists and I'm going to be hated
Speaker 3: by all economists or economists in finance, but I do
Speaker 3: have my issues with their methods.
Speaker 1: Right.
Speaker 3: So, first of all, I think that there is an
Speaker 3: original scene in economics, which is I think a lot
Speaker 3: of economics is informed by a desire to be as
Speaker 3: rigorous as mathematics. Right, And so a lot of theoreticians
Speaker 3: in economics are very deductive in their approach. If you
Speaker 3: think of you know, the unrealistic assumptions behind the welfare
Speaker 3: theorems or arrows impossibility theorem or whatnot, or just pick
Speaker 3: up you know Samuelson textbooks, and I think this is
Speaker 3: just axiomatic rather very axiomatic, very deductive. Whereas physicists are
Speaker 3: very happy to think in terms of small idealized models
Speaker 3: that apply to a specific domain, and if the model
Speaker 3: doesn't work out, they will discard and make another one.
Speaker 3: The grand theory behind physical theories exists, like there are
Speaker 3: people who do this for a living. But many, many
Speaker 3: good theoretical economists physicists starting the small and then they
Speaker 3: expanded domain of their models. So economists tend to maybe
Speaker 3: in a sense, fall in love with methods too much,
Speaker 3: with techniques too much.
Speaker 1: We had cliff Astness on the podcast a little while ago,
Speaker 1: and my father, not a finance person, listened to the
Speaker 1: episode and said, I still don't know what a quant is.
Speaker 1: I just read skimmed your new book which is called
Speaker 1: The Elements of Quantitative Investing, and as lays out the elements,
Speaker 1: what is a quant like? What are the elements? Like?
Speaker 1: What's the thing that makes someone a quant investor that,
Speaker 1: like someone reading a slim book about the Elements of
Speaker 1: quant investing needs to learn?
Speaker 3: Well, if I am being consistent with my book, investing
Speaker 3: is really about problems and not about specific techniques or
Speaker 3: anything like this, Right, So it's basically a way to
Speaker 3: go through the whole investment process from let's say preparing
Speaker 3: the ingredients to cooking to eating. That is very process driven. Ultimately,
Speaker 3: you would imagine that one thing that you know, quant
Speaker 3: investing has in common across multiple domains, you know, if
Speaker 3: you do futures, stocks, event based and whatnot, is I
Speaker 3: think the number of bets tends to be high in
Speaker 3: systematic investing. Right, so you can be a very successful
Speaker 3: microeconomic investor portfolio manager. And you you know, according to
Speaker 3: even several statements by Buffett, you know, he made like
Speaker 3: ten twelve very good bets. Okay, so that's great, and
Speaker 3: that's not quant investing. You know, you could put enough
Speaker 3: pms making you know, twenty bets in their lives, you
Speaker 3: will get a few that have let's say twelve thirteen, right,
Speaker 3: and they will be rich. We do not have that luxury, right.
Speaker 3: We have to make millions of bets. You know, we
Speaker 3: trade a portfolio with three thousand stocks sometimes in waves
Speaker 3: of half an hour. You can't make a judgment on
Speaker 3: all of these bets. So you need a method that
Speaker 3: reduces the dimension of your problem to something that can
Speaker 3: be treated in a systematic manner. I don't know if
Speaker 3: that answers for you. You know that, but you know, basically,
Speaker 3: basically the idea is, think about if you make a
Speaker 3: lot of bets, you cannot bet individually. You have to
Speaker 3: have some kind of juristic or some kind of method
Speaker 3: around that.
Speaker 1: Right, and like to me, like the book sort of
Speaker 1: you know, the standard method I guess I'm quite investing
Speaker 1: is you build a factor model of what drives your
Speaker 1: universal investments. You're shutting your.
Speaker 3: Head, Yeah, I yes, and no, I think yes because
Speaker 3: the book, you know, has maybe one hundred and fifty
Speaker 3: pages on factor models, but also no, because maybe in
Speaker 3: one hundred years from now, I suspect there will be
Speaker 3: still something left. But you know, we might have better
Speaker 3: techniques and not necessary factor models any longer.
Speaker 1: I don't know, we don't want to go. Two attractions
Speaker 1: of that, one is like, are the better techniques something
Speaker 1: more neural netty unstructured?
Speaker 3: Who knows? Yeah, something like that. I mean there is,
Speaker 3: there is a revolution every five years.
Speaker 1: So my other question is like I've never fully understood
Speaker 1: like a factor model is like the here are some
Speaker 1: factors that drive the returns of stocks, and then there's
Speaker 1: like some residual idiots and credit return. There are clearly
Speaker 1: people whose business is to identify factors and then invest
Speaker 1: in factors. My impression is that at like the places
Speaker 1: that you work, the business is the opposite of that
Speaker 1: is to hedge out your factor risk as much as
Speaker 1: possible and to get as much idiosyncratic risk as possible.
Speaker 1: Is that right? And like, like, how do you discriminate
Speaker 1: between like a factory return and idiosymcratic return, Like what
Speaker 1: makes the thing a factor as opposed to another ring.
Speaker 3: So that's a good question. So first, a lot of
Speaker 3: systematic investing is still about factors, just not the factors
Speaker 3: that get published in the literature, you know, not the
Speaker 3: factors that Cliff maybe was talking about. And yet a
Speaker 3: lot of successful systematic investing is really factor driven.
Speaker 1: It in the sense that you have a model that
Speaker 1: has like twenty factors and like ten are like value,
Speaker 1: and you neutralize those and you try the other time.
Speaker 3: You do, and you do the rest. You have other
Speaker 3: terms that matter. So that's one thing, But there are
Speaker 3: two other things. There are sometimes sources of returns that
Speaker 3: are factor like but not quite like factors. So you
Speaker 3: may have a theme. For example, you may identify a
Speaker 3: theme in the market that is not pervasive enough or
Speaker 3: is alive only for a few months, but is there
Speaker 3: and it's not only affecting let's say two stocks. Right,
Speaker 3: So these brought thems can be invested on, but cannot
Speaker 3: really model in the traditional way as a traditional factor model. Also,
Speaker 3: there is a lot of good modeling in factors as
Speaker 3: opposed to bad modeling, So it's it seems easy, but
Speaker 3: it's not that easy. So there is a little bit
Speaker 3: of craftsmanship in making these models, okay, And then the
Speaker 3: third thing is that there are also returns that have
Speaker 3: nothing to do with factors, or almost nothing to do
Speaker 3: with factors. So if you really know how a company works,
Speaker 3: and you have a little bit of an edge in
Speaker 3: predicting its future performance, you can bet on it, and
Speaker 3: you make enough bets and again you will make some
Speaker 3: money if you repeat, and you know recycle. So even
Speaker 3: discretionary investing in this sense has inherited a little bit
Speaker 3: of the spirit of systematic investing.
Speaker 1: I think of that as like that a pod job,
Speaker 1: but like aval like you have discretionary investors who know
Speaker 1: a lot about a company make bets on the company,
Speaker 1: and then someone like you tells them, these are your
Speaker 1: factory exposers. You have to get those down to zero.
Speaker 1: That you're making pure bets on your idios and creditnoledge
Speaker 1: of the company. Is that like kind of right?
Speaker 3: Kind of right?
Speaker 1: Yeah?
Speaker 3: I think that at this point it is very interesting
Speaker 3: how the mind of professional portfolio managers has been remolded
Speaker 3: in a factor based world, so that a modern portfolio
Speaker 3: manager discretion The portfolio manager thinks in factors, you know,
Speaker 3: so I don't even need to tell them, hey, this
Speaker 3: is your exposure. They see their exposure, they have the
Speaker 3: tools to see it, and they control it in real
Speaker 3: time with minimal intervention from me. So what we do
Speaker 3: is we have you know, a good team that models
Speaker 3: factors in a way that is suitable for the investment
Speaker 3: universe and style in which they operate. That's a very
Speaker 3: very sophisticated and difficult and portfolio managers use that and
Speaker 3: then neutralize It's become like second.
Speaker 1: Nature, and they've internalized that their goal is to create
Speaker 1: idiosyncratic alpha rather than factors. That's right. I feel like
Speaker 1: a criticism that people sometimes have of like the pod
Speaker 1: shop model is that, like there's some universe of factors
Speaker 1: that exist in commercial models and the like are known
Speaker 1: in the literature, and then portfolio managers have a set
Speaker 1: of exposures to factors that are sort of in code
Speaker 1: or unknown, but like ultimately, when you become really, really smart,
Speaker 1: you'll know that, like, actually the bet they were making
Speaker 1: was some you know particular knowing the company really well
Speaker 1: means like they had exposure to like some you know,
Speaker 1: personality factor in the CEO or something that like eventually
Speaker 1: someone will be able to write that down and it'll
Speaker 1: come out of like being idiosyncratic and become a factor.
Speaker 1: And then I don't know, what happens.
Speaker 3: I think that there is some truth to that. There
Speaker 3: is definitely some some truth to that, in the sense
Speaker 3: that sometimes for folio managers, especially in specific sectors, will
Speaker 3: use some heuristics that you could call characteristics in a
Speaker 3: factor model, but they are not in a factor model,
Speaker 3: and then they trade that. However, it's also true that
Speaker 3: the decision that enters a particular investment is usually not
Speaker 3: that simple as taking a ration spreadsheet, so it's a
Speaker 3: bit more complicated than that. You could still argue that
Speaker 3: there is a factor, right, And what's the factor is
Speaker 3: ultimately the set of feces that are highly correlated or
Speaker 3: relatively highly correlated across portfolio managers across firms, because if
Speaker 3: there is an expected return, and if you have skill,
Speaker 3: and you have sufficient skill to be close to the
Speaker 3: best possible portfolio, you have to be also relatively close
Speaker 3: to other people approximating that best possible portfolio. Right, So
Speaker 3: then it becomes a truism, Right, there is a factor,
Speaker 3: and that's the factor of investor, of informed investors. So
Speaker 3: it's true.
Speaker 1: I think if it is like there's like a scientific
Speaker 1: process that ever pursuing I hear are the best people
Speaker 1: and they like do the best work to pursue that
Speaker 1: scientific process, and so they'll eventually converge on something that
Speaker 1: is like truth. But that means buying all the same stocks.
Speaker 3: Yes, it's very difficult to get to that truth.
Speaker 1: Sure it is. Yeah it's not.
Speaker 3: Let's let's tire about it.
Speaker 1: Weird if they weren't hurting among.
Speaker 3: The best, yes, yes, but there is, there is And
Speaker 3: by the way, and this brings to one of the
Speaker 3: limitations of factor models, right, which is effectively a factor
Speaker 3: model is a form of glorified regression over time. Right,
Speaker 3: And behind a regression there is a bit of an
Speaker 3: assumption to some extent, of independent observations over time. And
Speaker 3: the market and hedge funds are not in dependent random variables.
Speaker 3: They are super dependent random variables, and they are in
Speaker 3: a sort of continuous in direct conversation through their portfolios.
Speaker 3: And sometimes the conversation gets really nasty when one hedge
Speaker 3: fun is in state of distress and all of a sudden,
Speaker 3: or not even a hatch fund, it could be also
Speaker 3: an institution investor and decide to liquidate part of their portfolio.
Speaker 3: And then it becomes a process where you have a
Speaker 3: lot of reflexivity and positive feedback and everybody suffers. And
Speaker 3: in this case, factor models don't really You can still identify,
Speaker 3: like if the system is running a temperature with some characteristics,
Speaker 3: but they are not factors in the traditional sense. I
Speaker 3: do want to.
Speaker 2: Talk about before we move too far away, I do
Speaker 2: want to talk a little bit about how and if
Speaker 2: factors can die, because we've talked a bit about identifying factors.
Speaker 2: But when do you decide that this doesn't work anymore?
Speaker 2: Necessarily that the market has fundamentally changed and this worked
Speaker 2: maybe ten years ago, maybe fifteen years ago, but maybe
Speaker 2: now it's devolved.
Speaker 3: Well, there is the good old reason, which is people
Speaker 3: make mistakes in the sense that we think that there
Speaker 3: is a factor and then we look back and there
Speaker 3: is no factor. Right, So there are so many factors
Speaker 3: that some of them have got to be a little
Speaker 3: bit redundant. So that that's one reason, right, So just
Speaker 3: pure in a sense research revisions. And then there is
Speaker 3: also the fact that there are two other things that
Speaker 3: can happen. One is the moment that you tell people
Speaker 3: that there is a factor, the factor comes into being
Speaker 3: to some extent, right, So it's never black and white
Speaker 3: that the factor did not exist. Maybe the factor did
Speaker 3: exist and then the moment you identify it, it becomes
Speaker 3: more existent, like you know, speak yeah, yeah. So esg
Speaker 3: is is one case where the focal point that it
Speaker 3: became makes into an investible theme.
Speaker 2: I thought that was just black rock pumping.
Speaker 3: As possible, but everybody had to incorporate it in some sense, right,
Speaker 3: so it became a major source of revenue for the vendors. Right.
Speaker 3: So that's that's one thing. And then there is the
Speaker 3: adaptive nature of the market. So things that before generated
Speaker 3: a priced return. So you run some risk, you made
Speaker 3: some money, and then it becomes table stakes, it becomes
Speaker 3: incorporated into factor models, it becomes it becomes a smart beet,
Speaker 3: it becomes a smart patent, and then it becomes so
Speaker 3: I think, you know, you could say definitely that medium
Speaker 3: tonmamentum worked much better. You could say that even you know,
Speaker 3: short term reversal worked better. There were years when short
Speaker 3: interest was great, and there are factors or data sources
Speaker 3: that work well now and then maybe in five years
Speaker 3: will become known and become part of the I mean
Speaker 3: credit card data. Right for consumer that was like there
Speaker 3: were people who were making a lot of money in
Speaker 3: two thousand and eleven through sixteen seventeen, and then it's
Speaker 3: become it's very hard to make money in that.
Speaker 1: You said the market is a conversation among catch funds.
Speaker 1: One thing that I think might be true that I'm
Speaker 1: not entirely sure of, is like, to what extent the
Speaker 1: market is a conversation among four patch funds? Now? Like,
Speaker 1: to what extent is like the marginal price or of
Speaker 1: every stock a portfolio manager at you know, one of
Speaker 1: the places you've worked.
Speaker 3: It's a very good question. I don't really have the
Speaker 3: answers to this. I'm not sure.
Speaker 1: It's it's like, what is the intuition at places like that, Like,
Speaker 1: is it like the market price is determined by like
Speaker 1: the collective thought of like the top people at the
Speaker 1: top hedge funds, Or is it like we are a
Speaker 1: little bump on the market and we're trading against the
Speaker 1: whole random universe.
Speaker 3: I mean, you'd like to think that the prices are
Speaker 3: determined by the marginal informed investor, Right, so by people
Speaker 3: like us at the time horizon where we predict right,
Speaker 3: which is not the same as at the time of
Speaker 3: Rizon of half a day. Right, that's a different player.
Speaker 1: What is your time horizon, like I think of it as.
Speaker 3: Well, it depends well, yes, it depends. Within a hedge fund,
Speaker 3: you have a variety of even within long shore equities,
Speaker 3: you know, you have you know, portfolio managers who are
Speaker 3: very tactical, and so they think in terms of they
Speaker 3: have strong daily or intra day alpha, even though they're
Speaker 3: fully discretionary up to pms that think easily in terms
Speaker 3: of months. Also depends on the sector. So you know,
Speaker 3: financials typically probably monetizes a little bit less on earnings
Speaker 3: and tends to have a longer horizon. Banks are basically
Speaker 3: modeling giant balance sheets, right, and then in a hedge
Speaker 3: fund you also have systematic, but even in systematic there
Speaker 3: are all sorts of time scales, and this cacophony makes
Speaker 3: the prices. I really don't know, Like I said, another
Speaker 3: question is basically, are how inefficient is the market? How
Speaker 3: incorrect are the prices are within a factor of two?
Speaker 3: Like Black used to say, or I don't know, Like
Speaker 3: I don't think that the market is becoming so super efficient,
Speaker 3: but it's getting it seems to be more efficient.
Speaker 1: I do feel, like, you know, the big stories is
Speaker 1: the rise of like these big multi strategy hedge funds,
Speaker 1: like you would hope. Maybe you wouldn't hope because it's
Speaker 1: sort of the economic and just, but like one might
Speaker 1: hope that like the rise of these big multi strategy
Speaker 1: hedge funds and a lot of capital being allocated to
Speaker 1: them would observably make the market more efficient.
Speaker 3: Yeah, I don't know if observably holds. I don't. It's
Speaker 3: really hard to like, can you can you tell when
Speaker 3: a bubble is forming?
Speaker 2: A lot of people would say that they can.
Speaker 3: Yeah, I can point you to a few papers, yeah
Speaker 3: that you know made all the wrong calls. Okay, I
Speaker 3: don't want to shame academics in public.
Speaker 2: I do like the idea that the market is a
Speaker 2: conversation between four hedge funds because I live in the
Speaker 2: ETF world, and you know, the big thing is passive
Speaker 2: is just distorting the market and there's no price discovery anymore.
Speaker 2: And it sounds like that's on the opposite end of
Speaker 2: that spectrum.
Speaker 3: I didn't say, I think exactly that it's a conversation
Speaker 3: between It's a beautiful thing to say, though. It sounds
Speaker 3: really cool. It sounds good podcasts. Yeah, that's great. Yeah,
Speaker 3: But I think your question is whether the rise of
Speaker 3: passive has made markets less efficient.
Speaker 2: More of a statement. I don't think I was a
Speaker 2: bad podcaster and didn't actually.
Speaker 3: Ask a question, But okay, how do you know?
Speaker 2: How do I know that passive is the story in
Speaker 2: the market? People on Twitter tell me?
Speaker 3: So, oh, okay, don't trust people on Twitter.
Speaker 2: That's true. Number one, real number one.
Speaker 3: Now I don't know. I mean, the rise of passive
Speaker 3: has made index rebalancing a weirder strategy, right, so where
Speaker 3: the margins have compressed, but the size has become so
Speaker 3: big that you can still make money in it and periodic.
Speaker 3: It's a very you know, cyclical strategy. So I don't know.
Speaker 1: So you're an indexy balancing PM do take like eight
Speaker 1: months of vacation a year and like all day rebalance.
Speaker 3: Not the ones I know who probably listen to this podcast, Okay,
Speaker 3: they work very hard.
Speaker 1: Sure indexes aren't paying rebalanced all the time, planning more
Speaker 1: than you would think.
Speaker 3: Index rebalancing is another you know, poster child for a
Speaker 3: strategy that seems so simple that everybody can talk about it,
Speaker 3: and then it's full of nuances and it requires a
Speaker 3: lot of skill to trade effectively.
Speaker 1: I believe that just because like I thought a little
Speaker 1: bit about like just like the sort of like accounting
Speaker 1: of like you basically know how many index mounds there are,
Speaker 1: let's say, can predict what will come in and out
Speaker 1: of the index, and like what the so like there's
Speaker 1: like some mechanics around, like you know, figuring out the market,
Speaker 1: calves that will come in and whatever, but then it
Speaker 1: feels like the unknown is like who else is doing
Speaker 1: the rebalance strategy? Is that? Right?
Speaker 3: I think you're mostly right because I don't want to say,
Speaker 3: because you know, out of respect for for the CMS,
Speaker 3: did I know?
Speaker 1: I love? Yes? Like so we had Cliff Askiness on
Speaker 1: a few weeks ago, and like, to me, Cliff Asness
Speaker 1: is like a quantitative investor, like a systematic investor, but
Speaker 1: like what he's doing is sort of recognizably what a
Speaker 1: sort of traditional asset manager. He's like trying to find
Speaker 1: companies that are undervalued, right, he talked about it's like
Speaker 1: being a Grammar dot investor. You know, you want like
Speaker 1: valuation plus a catalyst. And he's like, oh, or you
Speaker 1: know trading, you know, value and momentum and like you
Speaker 1: look at what eight or two is maybe a little different,
Speaker 1: but there's like you know, the hypercacy trading firms, Like
Speaker 1: you can model those as like those are quantitative versions
Speaker 1: of like a voice market maker fifty years ago, where
Speaker 1: they're like trying to keep inventory flat and like trying
Speaker 1: to you know, make the bid asks bread. So like
Speaker 1: those are like very traditional economic functions that have been
Speaker 1: quantify like turned into systematic what's the intuition for like
Speaker 1: what a bally Asni or a star doll or a
Speaker 1: millennium does? Like what business are you in? Do you think?
Speaker 1: Like as a philosophical matter, like one thing I think,
Speaker 1: like I think about like.
Speaker 3: You're asking from a social kind of point or.
Speaker 1: Well, I think like the index rebalancing, Like to me,
Speaker 1: it feels like the sort of trade and I think
Speaker 1: to something that was the sort of trade that like
Speaker 1: an investment bank would have done twenty years ago, thirty
Speaker 1: years ago, and like some of that function I think
Speaker 1: has moved to like the big multimanagers. But like I
Speaker 1: wonder like from where you say, like how you see
Speaker 1: the like role in the financial markets of those firms.
Speaker 3: So at a very high level, we don't do anything
Speaker 3: different than everybody else in the sense that what we
Speaker 3: provide is always this, right, is we provide shifting time preferences,
Speaker 3: which means we provide liquidity. We house you know, risk
Speaker 3: for people who don't want to hold it right now.
Speaker 3: And that's what you do when you do indext rebalancing, right,
Speaker 3: that's what you do when you do merger ARB and
Speaker 3: when you do the various subtypes of basis traits.
Speaker 1: Right.
Speaker 3: So we do provide liquidity, which is very important. And
Speaker 3: then the second thing we again very high level, we
Speaker 3: provide price discovery, right, So we study the firms and
Speaker 3: we think, okay, this is at the margin mispriced and
Speaker 3: we're going to short it or we're going to invest
Speaker 3: in it, and that's a beautiful thing. So we do
Speaker 3: it at a different time scale, right. So you always
Speaker 3: want to do things at the margin where you don't
Speaker 3: have a lot of other participants, and at the margin
Speaker 3: of the let's say month to three month investment horizon,
Speaker 3: there are not that many participants. So in the words
Speaker 3: of another hatch fund manager I cannot name, but it said,
Speaker 3: once you know, we don't invest in securities, we dated them,
Speaker 3: and so we are in the dating service. Not that
Speaker 3: many people are doing it, and so we do it.
Speaker 3: But I would say also this right, not at the
Speaker 3: social level. I just want to answer the like my
Speaker 3: personal level. What we do. We are a massive filter
Speaker 3: of talent, and the talent that we hire is a
Speaker 3: massive filter of information. So it's like information squared.
Speaker 1: Maybe this is like a bad question, but like, do
Speaker 1: you think that like long only asset managers are worse
Speaker 1: than they were thirty years ago because that filter has
Speaker 1: been so successful? In other words, like there are lots
Speaker 1: of jobs you could have gotten in finance in nineteen ninety,
Speaker 1: but like, yeah, there's like a clear hierarchy now.
Speaker 3: I think that the market and the set of investors
Speaker 3: has learned right, and I think the distinction between VITA
Speaker 3: and HALFA has been useful for investors, and so active
Speaker 3: investors who are mostly long only, I think have suffered
Speaker 3: from this distinction because the vast majority of them underperforms
Speaker 3: their benchmarks and so there is no reason for them
Speaker 3: to exist. And then what we do is we provide
Speaker 3: really uncorrelated returns to the benchmarks to most factors, and
Speaker 3: investors want that, right, So there is a future where
Speaker 3: active investors, long on investors asset managers will become even
Speaker 3: less influential, smaller, and also.
Speaker 1: I think of that as like a customer demand side,
Speaker 1: but also like a talent filter side.
Speaker 3: Right, yes, Yeah, And then the interesting thing is and
Speaker 3: then there is also a process where the multimanager platforms
Speaker 3: are able to make the business model of a single
Speaker 3: portfolio manager that is not sustainable in isolation working in
Speaker 3: this kind of federated system. So why would you or
Speaker 3: how could you survive as a single portfolio manager hedge
Speaker 3: fund nowadays? It's really really difficult, but you can do
Speaker 3: it in a multimanager platform provided that you have you know,
Speaker 3: sufficient talent, sufficient edge.
Speaker 2: That's also where you can blame the passive influence on Twitter.
Speaker 2: If you're a long entry manager that you know it's
Speaker 2: impossible to be the market now because you just have
Speaker 2: this money constantly pouring in.
Speaker 3: Yeah, I don't disagree. Yeah.
Speaker 1: One more question, like social roles is just like you've
Speaker 1: worked at most of the big pod jobs, but you
Speaker 1: also worked at HRT, Like what's the difference in roles
Speaker 1: and like what they do all day? Because HRT, I
Speaker 1: think of is a classic like high frequency treating firm
Speaker 1: where I don't know they're exactly a market maker, but
Speaker 1: they're certainly on the higher frequency side, and then like
Speaker 1: the pod jobs have a lower frequency and a you know,
Speaker 1: they're not prop they're running hedgephones. Like what's the cultural
Speaker 1: and role and differences?
Speaker 3: Yeah, okay, So I briefly mentioned the HRT in a
Speaker 3: in an interview with The Financial Times, and my manager
Speaker 3: told me that, you know, people at HRT were both
Speaker 3: annoyed and delighted by what I've had said about about HRT.
Speaker 3: I think HRT is a really special place, even in
Speaker 3: the in the context of proper training firms. So I'm
Speaker 3: a little bit hesitant and to just being them in
Speaker 3: as a representative, right, So they're not representative because there
Speaker 3: is something in the culture of HRT that is special. Okay,
Speaker 3: it's collaborative, it's truly kind. Yeah. So I think it's
Speaker 3: a great place to work, and it is fundamentally monolithic,
Speaker 3: so you have, you know, sharing of ideas and you
Speaker 3: can work at the intersection of these ideas. It's also
Speaker 3: a place that is very tech oriented, so it's a
Speaker 3: bit of a technology firm operating in the financial space.
Speaker 3: And because of that, it also attracts i think the
Speaker 3: best technical talent that I've ever worked with. It's just
Speaker 3: a pleasure to work with great technologists, people who are
Speaker 3: very competent in that respect. So nothing against the hedge funds.
Speaker 3: I love edge funds for different reasons. You know. I
Speaker 3: love BAM, which is also very collaborative and it's an
Speaker 3: investor meant company. But HRT has as a technical side
Speaker 3: to it and also gain a cultural side to it.
Speaker 3: It's great.
Speaker 2: We didn't talk about AI AI.
Speaker 3: Yeah, of course you have to talk about it.
Speaker 1: Like I have like three models of how investment works
Speaker 1: systematic about Like one is like you have like some
Speaker 1: economic intuition and you build a model of like the
Speaker 1: stock market that predicts prices. And in other way is a
Speaker 1: sort of like neural netty ai E way, where like
Speaker 1: you throw a lot of data at a neural net
Speaker 1: and it build its own model of how to predict
Speaker 1: stock prices. And then the third model is like you
Speaker 1: get really good at prompt engineering and you get a
Speaker 1: chat GPT and you say what stocks will go up?
Speaker 1: But you ask it in the right way, and then
Speaker 1: chat JPT tells me what stocks will go up? How
Speaker 1: good is it? I see? The third model no one uses,
Speaker 1: but like someone uses.
Speaker 2: I think a lot of people use that.
Speaker 3: All right, So first thing like, Okay, nobody knows anything,
Speaker 3: and anybody saying the opposite, you know, should be heavily discounted. Okay,
Speaker 3: so we agree on this, and so let's forget for
Speaker 3: a second all the technical details of AI just from
Speaker 3: a pure industrial organization standpoint, Right, So what's going to happen?
Speaker 3: Consider AI just like another technology like Internet and whatnot. Right,
Speaker 3: So you know, first of all, we're going to observe
Speaker 3: economies of scale, So there's going to be concentration, and
Speaker 3: there was going to be some kind of monopolistic competition.
Speaker 3: I was thinking about Bloomberg specifically, which could be I
Speaker 3: hope for you people to be among the winners, because
Speaker 3: you have a good starting point, right, You have lots
Speaker 3: of data, you have a customer base, and maybe in
Speaker 3: the future we'll finally not see the good old Bloomberg terminal,
Speaker 3: which has been kind of unchanged since I remember it,
Speaker 3: and instead people will just prompt Bloomberg to conduct very
Speaker 3: complex actions where it will act on a sequence of
Speaker 3: keywords and connect them and give you, like a much
Speaker 3: more valuable product for which Bloomberg will charge twice as
Speaker 3: much as they do already. So this is going to
Speaker 3: happen in one form or another. If it's not Bloomber,
Speaker 3: somebody else will do it. Okay, But the same thing
Speaker 3: applies to other areas of finance. So maybe once upon
Speaker 3: a time, you know, a big sufficiently big fund could
Speaker 3: build their own client for email.
Speaker 1: Right.
Speaker 3: Of course, nobody builds a client for email anymore. Right,
Speaker 3: so a lot of this stuff gets outsourced. We will
Speaker 3: outsource at some point some of the functions that we
Speaker 3: conduct internally using AI to other AI agents. It's perfectly fine.
Speaker 3: So this will become a utility to some extent. Yes,
Speaker 3: functions include well not stock picking. Not stock picking. I
Speaker 3: think that the functions that we will see available are
Speaker 3: essentially like another self, like another Mathlevin. Who can you
Speaker 3: be a good baseline for you?
Speaker 1: Okay?
Speaker 3: You could feed a post train and AI system with
Speaker 3: all your gazillions of words, right, and that agent will
Speaker 3: reproduce your sense of humor, your investigative style and everything. Okay,
Speaker 3: it's a good approximation. It's not going to be perfect,
Speaker 3: but why not?
Speaker 1: Right?
Speaker 3: So I would be very happy to have a replica
Speaker 3: of myself that can answer most simple questions. Now, I
Speaker 3: think that the decision to invest in a particular stock
Speaker 3: is a very demanding cognitive function, and I don't see
Speaker 3: that really being replicated very well. But I think that
Speaker 3: this will be baselined to some extent.
Speaker 1: Is it many kind of function because because it exists
Speaker 1: in a competitive market, So like the sort of like
Speaker 1: whatever the kind of function is, is going to get
Speaker 1: like the baseline is always going to get higher because
Speaker 1: like someone else will will have the same information as
Speaker 1: you do or the same.
Speaker 3: Well, this is getting really in the highly speculative side
Speaker 3: of you know things. I think that in order for
Speaker 3: an AI agent to be good at this, they have
Speaker 3: to be able to experience the world the same way
Speaker 3: that an investor experiences it. And our inputs are much
Speaker 3: more complex than just a string of text or YouTube videos.
Speaker 3: Right we have a model of the world which comes
Speaker 3: from visually experiencing the world, talking to humans, consuming the goods,
Speaker 3: right anything, It's vastly more complex than the way an
Speaker 3: AI system right now experiences the world and also influences
Speaker 3: the world. So an investor has a fundamentally different experience
Speaker 3: of a company than an LM that has an experience
Speaker 3: thats is mediated by multiple layers of processing. You know,
Speaker 3: they learn about a company through text that is written
Speaker 3: by somebody. So I don't think that that's in danger
Speaker 3: for the time being. But maybe, you know, again, in
Speaker 3: five years, maybe we will have our glasses feeding our
Speaker 3: experiences to AI agents. Who knows, right, But I don't
Speaker 3: think that it's that close, and I don't think AI
Speaker 3: is that's smart also, so I think that having a
Speaker 3: baseline system would be already pretty good.
Speaker 2: That's somewhat comforting that our experiences count for something, our
Speaker 2: physical experience of the world.
Speaker 1: It's interesting because I always think of like the comparison
Speaker 1: as like investing in self driving cars, or like investors
Speaker 1: do a lot of things, but like one thing they
Speaker 1: do a lot is sit at a desk and read
Speaker 1: computers and like look at numbers, right, and like those
Speaker 1: things seem like things that a computer can do well,
Speaker 1: whereas like you know, drivers like have physical reflexes and
Speaker 1: like have a you know, complicated field division. I always thought,
Speaker 1: like investing should be easier than self driving cars for
Speaker 1: computer and a master, but you, I think you're learning this.
Speaker 1: Think of like investing as like the great liberal art,
Speaker 1: where it's like you incorporate all of human experience and
Speaker 1: so the AI can't relate.
Speaker 3: Okay, let's let's take the metaphor to you know, extreme consequences.
Speaker 3: Imagine that you had a system that is the equivalent
Speaker 3: of a perfect self driving car in investing. So now
Speaker 3: I'm giving you a machine, a box that is telling
Speaker 3: you the long term value, if not the returns, right,
Speaker 3: because the moment that the value is known, you immediately
Speaker 3: equilibrate to that level.
Speaker 1: Right.
Speaker 3: So imagine that you know the true value of everything
Speaker 3: because a box tells you so, and it's invaluable. It's
Speaker 3: an oracle. Okay. Would you think that finance stops existing.
Speaker 3: I wouldn't say so, right, So I think that a
Speaker 3: lot of arbitrush trades, you know, would maybe change significantly,
Speaker 3: but every risk, right, every return would be correctly priced
Speaker 3: by the risk of the agent's training it. So there
Speaker 3: still would be trading because we still have different preferences,
Speaker 3: but basically every risk would be priced. There would be
Speaker 3: in a sense, less alpha, but finance will still exist.
Speaker 1: It's a lot of like service provision, like liquid.
Speaker 3: Liquidity provision and yeah, and so the liquidity provision would
Speaker 3: still exist. The informational services maybe will stop existing in
Speaker 3: the current form, but that's okay. I think that we'll
Speaker 3: all still be employed.
Speaker 2: Mhmm.
Speaker 1: It's interesting I think about it because I do think,
Speaker 1: like we talked about, like, one thing that the big
Speaker 1: hedgehunds do is things that have the flavor of liquidity
Speaker 1: provisions basis trades and merger urb and whatever. Things that
Speaker 1: like I think of as like something that a bank
Speaker 1: would have done thirty years ago, and then now a
Speaker 1: big hedge fund does. And then another thing they do
Speaker 1: has the flavor of information provision, where it's getting prices right.
Speaker 1: Like to me, those things seem quite intellectually separate, but
Speaker 1: I guess they feed each other in the sense that
Speaker 1: the better you are prices, the better you can be
Speaker 1: a liquidity provision.
Speaker 3: The value yeah, I mean a short, short horizon. Liquidity
Speaker 3: provision and information tend to be very closely rated. Like
Speaker 3: you know, a limit if you're good at if you're
Speaker 3: either good at crossing even good at crossing, you should
Speaker 3: be pretty good at adding okay, adding liquidity, so you know.
Speaker 3: But I mean like you could make you know a
Speaker 3: profit by posting a lot of limit orders and providing
Speaker 3: liquidity to the market or crossing the spread and making
Speaker 3: money with predicting the future prices. If you're good at one,
Speaker 3: you're good at the other, most likely, right at that
Speaker 3: time scale, I think that this though might I'm not
Speaker 3: sure because I haven't thought about this very very carefully,
Speaker 3: but I think this might decoupled at longer time scale,
Speaker 3: so you know you're when you're out. I'm not sure.
Speaker 3: And in any case, at that time scale is really difficult
Speaker 3: for an AI or for a human being anyone, like,
Speaker 3: there are not that many hard data, even the unstructured
Speaker 3: data are not that any So it's a very difficult problem.
Speaker 3: It's the coupled it's it's complicated. So yeah, but I
Speaker 3: tend to believe at longer time scales you have more
Speaker 3: or less liquid provisioning and you know, violations of law
Speaker 3: of one price on one side and predicting on the
Speaker 3: other side.
Speaker 1: But you combine both.
Speaker 3: But you can combine both, and it's a very potent mix.
Speaker 1: Right. There is normally different people, it is, right.
Speaker 3: Very different people for sure, different parts, very different very
Speaker 3: different people, very different cultures.
Speaker 1: Yeah, can you summarize the difference in cultures between like
Speaker 1: I have a guess.
Speaker 3: But well, as you said, people who typically trade in arbitrades,
Speaker 3: if not historically but also historically come from banks.
Speaker 1: Yeah.
Speaker 3: Right, Whereas you still can see long only portfolio managers
Speaker 3: being recycled and reformatted into long short portfolio managers, you
Speaker 3: can have an excellent short specialist becoming a long short
Speaker 3: portfolio manager like it happened, I.
Speaker 1: Mean makes sense. Is that like the people on the
Speaker 1: information version long short side are more academic and research orientered,
Speaker 1: and the people on the ARB side are.
Speaker 3: More Yeah, I think you can actually have a very
Speaker 3: good long short portfolio managers who were journalists in their
Speaker 3: past lives.
Speaker 1: I've heard of some of these thought about this about
Speaker 1: it no, just like.
Speaker 2: No not breaking news on your podcast.
Speaker 1: I've noticed that's that's better than podcasting. Not thought about
Speaker 1: it in the sense that I'd be good at it,
Speaker 1: just in the sense that the money is good.
Speaker 2: You could be bad at it and paid really well
Speaker 2: for a short amount of time.
Speaker 1: I don't know that that's true. Actually, they're they're an
Speaker 1: excellent talent filter or so I hear.
Speaker 3: Yes, I think that you could interest a few huge funds.
Speaker 3: They might be listening.
Speaker 1: On a note, Kathy, thanks for coming on the.
Speaker 3: Thanks for having me, And that.
Speaker 1: Was the money Stuff podcast.
Speaker 2: I'm Matt Levian and I'm Katie Greifeld.
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Speaker 1: Moses on Them.
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Speaker 1: Thanks for listening to The Money Stuff Podcast. We'll be
Speaker 1: back next week with more stuff.
Speaker 2: Mm hmm