Speaker 1: Bloomberg Audio Studios, podcasts, radio news, Happy Thanksgiving.
Speaker 2: I hope you're having a great holiday. We are obviously
Speaker 2: not recording on this Thursday, so we're.
Speaker 1: Gonna rerun a favorite old episode. It's going to be great.
Speaker 2: You're going to enjoy it even more the second time. Bye.
Speaker 1: Wait gay 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 it, and I feel like.
Speaker 1: When I first met you, I think I asked you
Speaker 1: if you go by gappie because of your famous track
Speaker 1: record of taking gardening leave, like having gaps in your
Speaker 1: career as well.
Speaker 4: Okay I didn't remember. Yeah, that's a great excuse for
Speaker 4: for a nickname. No, But the reason is when I
Speaker 4: came to the States for grad school, and this was
Speaker 4: a long time ago, in ninety five. So the first
Speaker 4: thing that you did was set up an email account.
Speaker 4: You still had the freedom to choose an Emil account.
Speaker 4: Now they just give you your initials with the number,
Speaker 4: and so my initials are gap Gapo, and of course
Speaker 4: it was taken. So I said, okay, well Gappy, and
Speaker 4: then everybody in grad school and then my wife was Italian,
Speaker 4: everybody started to call me Gappy and that's stuck.
Speaker 3: And now at.
Speaker 4: Work they just have dispensed with my real name, like
Speaker 4: on all systems, I'm just Gappy Paliologo.
Speaker 3: So I expect that that will be, you know.
Speaker 4: Prosecuted for tax evasion because on my tax forms there
Speaker 4: is Gappy Paliologo or something like that.
Speaker 1: Well, hello, and welcome to the Stuff podcast.
Speaker 2: I'm at Livian and I'm Katie Greifeld.
Speaker 1: And we have a guest today, Gabby Paleo Logo, who
Speaker 1: is now at pali Asney has been at most of
Speaker 1: the other pig Catch funs and Hudsond River Trading. I
Speaker 1: do want to start by talking about.
Speaker 2: Gardening there, Okay, Natural Purse.
Speaker 1: I think that we counted from your link. Your LinkedIn
Speaker 1: is like famous for discussing your gardening leave in some detail,
Speaker 1: and I think we counted three years of gardening leave.
Speaker 4: No, I think it's a bit like Okay, it's not precise.
Speaker 4: Fifteen months from Citadel, one year Hudson River Trading, and
Speaker 4: four months from Millennium. Okay, so pretty close, not terrible,
Speaker 4: 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.
Speaker 4: I try to keep myself busy, so I teach, typically
Speaker 4: at some university. So the first time during my Seitaadel
Speaker 4: two millennium, Guardian Leve I was teaching at Cornell and
Speaker 4: in the HRT to Bam Guardian Leve was at NYU.
Speaker 4: And I love teaching. And then what I do is
Speaker 4: it helps me focus on stuff. Usually what I do
Speaker 4: in you know, whenever I read a book or read
Speaker 4: a paper that I like, I take notes, take notes
Speaker 4: in lattech, and then I read, arrive or think about things,
Speaker 4: and so that typically is the basis for my course material,
Speaker 4: and then it becomes the basis for my books. I've
Speaker 4: written a couple of books during my non competes.
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 that.
Speaker 3: Yeah, I'm not particularly worried with that.
Speaker 4: I think that there is only a very specific subset
Speaker 4: of quantitative researchers who are afraid of losing their edges.
Speaker 4: And yeah, that's not been my case. I keep reading.
Speaker 4: I try to stay up to date.
Speaker 1: To the feedback into the work. Like do you get
Speaker 1: ideas or like deep in your understanding of techniques by
Speaker 1: teaching and writing the books? Or are they just sort
Speaker 1: of like.
Speaker 4: Extracurricular No, no, no, it's definitely I learn a lot
Speaker 4: from writing the books.
Speaker 1: How long do you I get to hear your next job?
Speaker 1: And yeah, generate more profits by of.
Speaker 3: Course, plenty more profits. Sell that to my employers.
Speaker 4: No, but I definitely I learn a lot from writing
Speaker 4: from the first drafts, and then I rewrite and rewrite,
Speaker 4: and I learn a lot from discarding material too. It's
Speaker 4: very useful to discard material. It makes you really focus
Speaker 4: on what matters and what doesn't. So I try to
Speaker 4: give a narrative, like a logical connection between various topics,
Speaker 4: and that is something that is possible only when you
Speaker 4: write a book. I really do not like writing that.
Speaker 4: Nobody I think likes writing, maybe except for you.
Speaker 1: I I think I understand that it's weird even among writers, but.
Speaker 4: It is very I find it very painful. I find
Speaker 4: painful letting go of material, Yes, but I also like it.
Speaker 4: You know, it's some kind of strange delayed gratification.
Speaker 1: I guess one theory that I have written is that
Speaker 1: hedge funded quantitative research gardening leave is like a source
Speaker 1: of like human flourishing, because you have all these like
Speaker 1: highly trained people who haven't enforced the year of And
Speaker 1: I've written that all the Hedge fund researchers should go
Speaker 1: work at LM companies or like analytics departments of sports teams.
Speaker 1: And I'm like, partially kidding and partially not. How true
Speaker 1: is it for you? Like how much of like your
Speaker 1: quantitative skills at this point are really just for investing in?
Speaker 1: How much of it is like if you spent three months,
Speaker 1: you know, consulting for a soccer team, you would be
Speaker 1: able to tell them how to find better players.
Speaker 4: I'm not sure, so I'll say this right. I was
Speaker 4: thinking a few days ago if there was a kind
Speaker 4: of a common thread in my professional life because it
Speaker 4: seems kind of random, And actually I think that there is,
Speaker 4: because I think that I was about fourteen when I
Speaker 4: realized that I had an aptitude for applied math. I
Speaker 4: discovered physics, and I liked math, and I also liked
Speaker 4: literature very much, so I loved reading. I read a lot.
Speaker 4: I was not a very social animal. And then basically
Speaker 4: since then, I've been doing the same thing in various forms. Right,
Speaker 4: I did physics, I did applied math. I didn't do
Speaker 4: applied math in finance. They did applied math in weird
Speaker 4: things like optimization and logistics. So I have been doing
Speaker 4: kind of the same thing over and over, which has
Speaker 4: been writing and applying math to something. So I think
Speaker 4: that I could do it. I would like to do it,
Speaker 4: but I also think that it's not that simple to
Speaker 4: go to a new field and say, oh, after three months,
Speaker 4: I know soccer. No, there is a lot of specificity,
Speaker 4: And the beauty of I think being a good applied
Speaker 4: mathematician is that they start with the problems and with
Speaker 4: the domain first, and that they're sufficiently mature from a
Speaker 4: mathematical standpoint that they are not making too much of
Speaker 4: an effort in using math. So I think the good
Speaker 4: art of being an applied mathematician is to study persistently
Speaker 4: the application. So no, I don't think that after three
Speaker 4: months it would be good enough. But after a year,
Speaker 4: you know, about a year of being fully in an application,
Speaker 4: then you start getting a little bit better and then
Speaker 4: the mass is not the problem, and then you start
Speaker 4: 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 are creativity and genuine interest in the problems
Speaker 1: more than you know, math course power. Yeah, this is
Speaker 1: a dumb question, but how does one develop? How does
Speaker 1: one identify you know, creativity and interest in financial topics?
Speaker 1: And is the obvious answer those are where the money is,
Speaker 1: or like like why why did you fall in love
Speaker 1: with finance as a topic? And is the answer because
Speaker 1: that's what the money is.
Speaker 4: So first of all, I think that creativity is either
Speaker 4: personality trait doesn't belong to You're not creative in finance,
Speaker 4: you know, you're you're creative in in cooking, you're creative
Speaker 4: in whatever. And it's a mix I guess of extraversion
Speaker 4: open and as to experience, and I don't what else.
Speaker 4: I'm not a psychologist, but I do believe that people
Speaker 4: are genuinely creative, and in fact, you see it right
Speaker 4: that sometimes you ask someone and you find out that, yes,
Speaker 4: they like writing, they play some instrument, if badly, and
Speaker 4: you know, and they paint and they do whatever. And
Speaker 4: so I would say, if you go to finance because
Speaker 4: that's where the money is, there's nothing wrong with that.
Speaker 4: And in a way, that's my story. You know, I
Speaker 4: was I was a researcher, and I wanted to have
Speaker 4: more money and whatnot. But eventually you stay in finance,
Speaker 4: or at least in my you know, little domain, because
Speaker 4: you're genuinely curious about finding out stuff, right.
Speaker 1: So, like why are the problems like why do they
Speaker 1: arisk curiosity? Like why are the problems of finance intrigue
Speaker 1: you after years of doing it right? Like what's interesting
Speaker 1: about those problems as opposed to other domains.
Speaker 4: It's really hard for me to say, Like I think
Speaker 4: that I read once that a young songwriter asked Bob
Speaker 4: Dylan how to become a good songwriter, and bub Bylan
Speaker 4: just answered, well, what's going on? What do you mean,
Speaker 4: what's going on? Yeah, what's going on? What's going on
Speaker 4: in your life? Just you know, look around. So sometimes
Speaker 4: I get these questions from investors or but you know,
Speaker 4: how do you keep yourself interested. How do you find problems?
Speaker 4: It's not a problem like the problems jump at you
Speaker 4: like there are too many problems. There are too many
Speaker 4: interesting problems. So if anything, the skill is in sorting
Speaker 4: the problems in the right order. Right, That is where
Speaker 4: maybe having some maturity in doing research kicks in. But
Speaker 4: there are lots of problems, infinite problems, weird problems.
Speaker 1: What's your favorite problem right now?
Speaker 4: I don't like right now, what are we working on?
Speaker 4: I mean, we are trying to understand how earnings are monetized? Right,
Speaker 4: how do you make money in earnings? It's such a
Speaker 4: basic thing in fundamental equities.
Speaker 1: And you mean, if you're like correct about predicting earnings?
Speaker 4: Yes, what are I mean? Without get into much into details,
Speaker 4: but you know there what are the relevant variables? Imagine
Speaker 4: that you had an oracle who told you what the
Speaker 4: variables are? What would you do with that? What would
Speaker 4: you do if you'd had all the information in the world, right,
Speaker 4: and everything in your world here in existence? Would be
Speaker 4: like an approximation problem.
Speaker 1: There's there's an incredible styled story of like the guys
Speaker 1: hacked into I think like one of the newswire services
Speaker 1: and got earnings releases early, like for hundreds of companies,
Speaker 1: and they traded on this and they had like a
Speaker 1: seventy percent success rate, which is great, but also like
Speaker 1: it means that had a thirty percent, Like they traded
Speaker 1: the wrong way, knowing earnings perfectly in advance. It's like
Speaker 1: a good.
Speaker 4: Yeah, yes, so they had the racle on it. You know,
Speaker 4: it's still hard, Yes, it's still very hard. Actually, shout
Speaker 4: out to Victor Hagan, who wrote the paper about ten
Speaker 4: years ago on this. He made a organize a simple
Speaker 4: controlled experiment where he gave basically a biased coin where
Speaker 4: you I think had a success rate of sixty percent
Speaker 4: forty percent failure, and you some capital and you could
Speaker 4: invest it over time on these informed predictions, and a
Speaker 4: lot of subjects went bankrupt. Okay, now I think we
Speaker 4: are better than that, but still there are lots of
Speaker 4: problems related to trading around an event. For example, before we.
Speaker 2: Get too far away, you mentioned Bob Dylan. It actually
Speaker 2: reminded me of another Bob Dylan quote which I'm going
Speaker 2: to paraphrase poorly, but he basically said, when asked about
Speaker 2: writing songs, do you think that you could write whatever
Speaker 2: the work that was being referenced now, and he said,
Speaker 2: I don't think so. It's like the words were in
Speaker 2: the air and I just plucked them out. They were
Speaker 2: just sort of hanging in the air and they came
Speaker 2: to me. And it kind of also rang true with
Speaker 2: what you were saying about you didn't go looking for problems.
Speaker 2: They're just there necessarily. I actually want to go back
Speaker 2: to applied math if it doesn't interrupt the course of
Speaker 2: conversation too much. You tweeted on June twenty fourth that
Speaker 2: there's no child prodigies when it comes to poetry, when
Speaker 2: it comes to applied mathematics. And I'm not saying that.
Speaker 2: You said that you were a prodigy, but you were
Speaker 2: a child at fourteen. I mean, how how fourteen do
Speaker 2: you realize that you have an aptitude for something like
Speaker 2: applied mathematics.
Speaker 4: I don't want to flex about this stuff, No you should.
Speaker 4: I think I'm honestly a little weird. I'm just a
Speaker 4: little weird, I think, honestly.
Speaker 2: But I like prodigy weird or I did.
Speaker 4: Have my share of yeah, adults telling me that I
Speaker 4: was good at this or that or you know, but yeah,
Speaker 4: I mean, okay.
Speaker 3: I'm just a little bit atypical.
Speaker 4: Also, when I talk to investors, I think investors enjoying
Speaker 4: my presence because I think I'm incredibly unfiltered for somebody
Speaker 4: who's talking to.
Speaker 3: Them, so it's like fun for them.
Speaker 4: And I was very unfiltered when I talked to my
Speaker 4: professors in school. Sometimes I corrected them stuff like this. Yeah,
Speaker 4: 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 4: I don't think they're quantity, but I think that they're
Speaker 4: very analytical. So I don't think that they would make
Speaker 4: great mathematicians, but I think they would make very very
Speaker 4: decent applied mathematicians. Actually, they tend to be very analytical.
Speaker 4: They tend to be very process oriented. And they have
Speaker 4: also additional qualities that actually mentioned in that essay, like
Speaker 4: they have very little disposition effect, so that's part of
Speaker 4: being analytical. They have no sound cost fallacy in them.
Speaker 4: So even though they don't do a lot of math,
Speaker 4: but they do some math. Okay, So first of all,
Speaker 4: they're fluent in a sense in basic literacy, but I
Speaker 4: think it's more their process that is closer to if
Speaker 4: 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 4: So I think that having an economics background is not
Speaker 4: necessarily a benefit, might even be a disadvantage actually, But
Speaker 4: just based on very few samples that I have a
Speaker 4: lot of very good, outstanding quantitative researchers actually come from
Speaker 4: physics and specifically from astrophysics. That's the experience that I've
Speaker 4: 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 4: So I can answer the second question more easily. I
Speaker 4: think that astrophysicists deal with large amounts of data, and
Speaker 4: they deal with observational data, so they don't get to
Speaker 4: do a lot of experiments, and that's good for finance. Right,
Speaker 4: you deal with a lot of data, you need to
Speaker 4: know how to have good agen for observational data, and
Speaker 4: you need to have very good theory, like you need
Speaker 4: to have very good instruments without being falling in love
Speaker 4: with those instruments. Whereas I think economists, Okay, first of all,
Speaker 4: my statement is purely empirical. Okay, so I'm just really
Speaker 4: guessing on economists, and I'm going to be hated by
Speaker 4: all economists or economists in finance, but I do have
Speaker 4: my issues with their methods.
Speaker 3: Right.
Speaker 4: So, first of all, I think that there is an
Speaker 4: original scene in economics, which is I think a lot
Speaker 4: of economics is informed by a desire to be as
Speaker 4: rigorous as mathematics, right, and so a lot of theoreticians
Speaker 4: in economics are very deductive in their approach. If you
Speaker 4: think of you know, the unrealistic assumptions behind the welfare
Speaker 4: theorems or Rows impossibility theorem or whatnot, or just pick
Speaker 4: up you know Samuelson textbooks, and I think this is
Speaker 4: just rather very acxiomatic.
Speaker 3: Very deductive.
Speaker 4: Whereas physicists are very happy to think in terms of small,
Speaker 4: idealized models that apply to a specific domain, and if
Speaker 4: the model doesn't work out, they will discard and make
Speaker 4: another one. The grand theory behind physical theories exists, like,
Speaker 4: there are people who do this for a living, but many,
Speaker 4: many good theoretical economists physicists starting the small and then
Speaker 4: they expand the domain of their models. So economists tend
Speaker 4: to maybe in a sense, fall in love with methods
Speaker 4: too much, with techniques too much.
Speaker 1: We had clip Asness on the podcast a little while ago,
Speaker 1: and my father, not a finance listened to the episode
Speaker 1: 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 4: Well, if I am being consistent with my book, investing
Speaker 4: is really about problems and not about specific techniques or
Speaker 4: anything like this. Right, So it's basically a way to
Speaker 4: go through the whole investment process from let's say preparing
Speaker 4: the ingredients to cooking to eating that is very processed driven. Ultimately,
Speaker 4: you would imagine that one thing that you know quantu
Speaker 4: investing has in common across multiple domains you know, if
Speaker 4: you do futures, stocks, event based and whatnot. Is I
Speaker 4: think the number of bets tends to be high in
Speaker 4: systematic right, so you can be a very successful microeconomic
Speaker 4: investor portfolio manager. And you you know, according to even
Speaker 4: several statements by Buffett, you know, he made like ten
Speaker 4: twelve very good bets. Okay, so that's great, and that's
Speaker 4: not quantu investing. You know, you could put enough pms
Speaker 4: making you know, twenty bets in their lives and you
Speaker 4: will get a few that have let's say twelve thirteen right,
Speaker 4: and they will be rich. We do not have that luxury, right.
Speaker 4: We have to make millions of bets. You know, we
Speaker 4: trade a portfolio with three thousand stocks sometimes in waves
Speaker 4: of half an hour. You can't make a judgment on
Speaker 4: all of these bets. So you need a method that
Speaker 4: reduces the dimension of your problem to something that can
Speaker 4: be treated in a systematic manner. I don't know if
Speaker 4: that answers for you. You know that, but you know, basically,
Speaker 4: basically the idea is, think about if you make a
Speaker 4: lot of bets, you cannot bet individually. You have to
Speaker 4: have some kind of view res take or some kind
Speaker 4: of method around that.
Speaker 1: Right, and like to me, like the book sort of
Speaker 1: you know the standard method I guess and quant investing
Speaker 1: is you built a factor model of what drives your
Speaker 1: universal investments. You're shaking your head.
Speaker 4: Yeah, I yes, and no. I think yes because the book,
Speaker 4: you know, has maybe one hundred and fifty pages on
Speaker 4: factor models.
Speaker 3: But also no, because maybe.
Speaker 4: In one hundred years from now, I suspect there will
Speaker 4: be still something left. But you know, we might have
Speaker 4: better techniques and not necessary factor models any longer. I
Speaker 4: don't know, we.
Speaker 1: Don't want to go. Two attractions of that one is like,
Speaker 1: are the better techniques something more neural netty unstructured?
Speaker 4: Who knows? Yeah, something like that. I mean there is,
Speaker 4: 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, here are some factors
Speaker 1: that drive the returns of stocks, and then there's like
Speaker 1: some residual idios and credit return There are clearly people
Speaker 1: whose business is to identify factors and then invest in factors.
Speaker 1: My impression is that at like the places that you work,
Speaker 1: the business is the opposite of that is to pedge
Speaker 1: out your factory risk as much as possible and to
Speaker 1: get as much idiotsyncratic risk as possible. Is that right?
Speaker 1: And like, like, how do you discriminate between like a
Speaker 1: factory return and idiosyncratic return, Like what makes the thing
Speaker 1: a factor as opposed to another thing.
Speaker 3: So that's a good question.
Speaker 4: So first, a lot of systematic investing is still about factors,
Speaker 4: just not the factors that get published in the literature,
Speaker 4: you know, not the factors that Cliff maybe was talking about.
Speaker 4: And yet a lot of successful systematic investing is really
Speaker 4: factor driven, see in.
Speaker 1: The sense that you have a model that has like
Speaker 1: twenty factors and like ten are like value, and you
Speaker 1: neutralize those and you try the other time kind.
Speaker 4: Of you do, and you do the rest. You have
Speaker 4: other terms that matter. So that's one thing, but there
Speaker 4: are two other things. There are sometimes sources of returns
Speaker 4: that are factor like but not quite like factors. So
Speaker 4: you may have a theme. For example, you may identify
Speaker 4: a theme in the market that is not pervasive enough
Speaker 4: or is alive only for a few months, but it
Speaker 4: is there and it's not only affecting let's say two stocks, right,
Speaker 4: So these brought themes can be invested on, but cannot
Speaker 4: really model in the traditional way as a traditional factor model. Also,
Speaker 4: there is a lot of good modeling in factors as
Speaker 4: opposed to bad modeling. So it seems easy, but it's
Speaker 4: not that easy. So there is a little bit of
Speaker 4: craftsmanship in making these models. Okay, And then the third
Speaker 4: thing is that there are also returns that have nothing
Speaker 4: to do with factors, or almost nothing to do with factors.
Speaker 4: So if you really know how a company works, and
Speaker 4: you have a little bit of an edge in predicting
Speaker 4: its future performance, and you can bet on it, and
Speaker 4: you make enough bets and again you will make some
Speaker 4: money if you repeat, and you know recycle. So even
Speaker 4: discretionary investing in this sense has inherited a little bit
Speaker 4: of the spirit of systematic investing.
Speaker 1: I think of that as like that apod job, but
Speaker 1: like a baliosni 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 creditnowledge
Speaker 1: of the company. Is that like you're kind.
Speaker 3: Of right, kind of right.
Speaker 4: Yeah, I think that at this point, it is very
Speaker 4: interesting how the mind of professional portfolio managers has been
Speaker 4: remolded in a factor based world, so that a modern
Speaker 4: portfolio manager, discretion portfolio manager thinks in factors. You know,
Speaker 4: so I don't even need to tell them, hey, this
Speaker 4: is your exposure. They see their exposure, they have the
Speaker 4: tools to see it, and they control it in real
Speaker 4: time with minimal intervention from me. So what we do
Speaker 4: is we have, you know, a good team that models
Speaker 4: factors in a way that is suitable for the investment
Speaker 4: universe and style in which they operate. That's very very
Speaker 4: sophisticated and difficult, and portfolio managers use that and then
Speaker 4: neutralize it's it's become like second.
Speaker 1: Nature, and they've internalized that their goal is to see
Speaker 1: us and credit alpha rather than factors. That's right, I feelink.
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 like are known in
Speaker 1: the literature, and then portfolio managers have a set of
Speaker 1: exposures to factors that are sort of encoded or unknown,
Speaker 1: but like, ultimately, when you become really, really smart, you'll
Speaker 1: know that like actually the bet they're making was some
Speaker 1: you know, particular knowing the company really well means like
Speaker 1: they had exposure to like some you know, person factor
Speaker 1: in the CEO or something that eventually someone will be
Speaker 1: able to write that down and it'll come out of
Speaker 1: like being idiosyncratic and become a factor, and then I
Speaker 1: don't know what happens.
Speaker 4: I think that there is some truth to that. There
Speaker 4: is definitely some truth to that, in the sense that
Speaker 4: sometimes for folume managers, especially in specific sectors, will use
Speaker 4: some heuristics that you could call characteristics in a factor model,
Speaker 4: but they are not in a factor model, and then.
Speaker 3: They trade that.
Speaker 4: However, it's also true that the decision that enters a
Speaker 4: particular investment is usually not that simple as taking a
Speaker 4: ration spreadsheet, so it's a bit more complicated than that.
Speaker 4: You could still argue that there is a factor, right,
Speaker 4: and what's the factor is ultimately the set of thess
Speaker 4: that are highly correlated or relatively highly correlated across portfolio
Speaker 4: managers across firms, Because if there is an expected return,
Speaker 4: and if you have skill, and you have sufficient skill
Speaker 4: to be close to the best possible portfolio, you have
Speaker 4: to be also relatively close to other people approximating that
Speaker 4: best possible portfolio. Right, So then it becomes a truism. Right,
Speaker 4: there is a factor and that's the factor of investor,
Speaker 4: of informed investors. So it's true.
Speaker 1: I think it is like there's like a scientific process
Speaker 1: that everyone is pursuing. I hire the best people, and
Speaker 1: they like do the best work to pursue that scientific process,
Speaker 1: and so like they'll eventually converge on something that is
Speaker 1: like truth. But that means buying all the same stocks.
Speaker 4: Yes, it's very difficult to get to that truth.
Speaker 3: Sure, but yeah it's not. Let's let's tale.
Speaker 1: About it would be weird if they weren't hurting among
Speaker 1: the best.
Speaker 3: Yes, yes, but there is there is.
Speaker 4: And by the way, this brings to one of the
Speaker 4: limitations of factor models, right, which is effectively a factor
Speaker 4: model is a form of glorified regression over time. Right,
Speaker 4: And behind a regression there is a bit of an
Speaker 4: assumption to some extent, of independent observations over time. And
Speaker 4: the market and hedge funds are not in dependent random variables.
Speaker 4: They are super dependent random variables, and they are in
Speaker 4: a sort of continuous in direct conversation through their portfolios
Speaker 4: and sometimes the conversation gets really nasty when one hedge
Speaker 4: fund is in a state of distress and all of
Speaker 4: a sudden, or not even a hatch fund, it could
Speaker 4: be also an institution investor and decide to liquidate part
Speaker 4: of their portfolio. And then it becomes a process where
Speaker 4: you have a lot of reflexivity and positive feedback and
Speaker 4: everybody suffers. And in this case, factor models don't really
Speaker 4: You can still identify, like if the system is running
Speaker 4: a temperature with some characteristics, but they are not factors
Speaker 4: in the traditional sense.
Speaker 2: I do want to talk about before we move too
Speaker 2: far away, I do want to talk a little bit
Speaker 2: about how and if factors can die, because you know,
Speaker 2: we've talked a bit about identify fying factors. But when
Speaker 2: do you decide that this doesn't work anymore. Necessarily that
Speaker 2: the market has fundamentally changed and this worked maybe ten
Speaker 2: years ago, maybe fifteen years ago, but maybe now it's devolved.
Speaker 4: Well, there is the good old reason, which is people
Speaker 4: make mistakes in the sense that we think that there
Speaker 4: is a factor and then we look back and there
Speaker 4: is no factor. Right, So there are so many factors
Speaker 4: that some of them have got to.
Speaker 3: Be a little bit redundant.
Speaker 4: So that's one reason, right, So just pure in a
Speaker 4: sense research revisions.
Speaker 3: And then there is.
Speaker 4: Also the fact that there are two other things that
Speaker 4: can happen. One is the moment that you tell people
Speaker 4: that there is a factor. The factor comes into being
Speaker 4: to some extent, right, so it's never black and white
Speaker 4: that the factor did not exist. Maybe the factor did exist,
Speaker 4: and then the moment you identify it, it becomes more existent,
Speaker 4: like as you know speak, yeah, yeah, So ESG is
Speaker 4: one case where the focal point that it became makes
Speaker 4: into an investable theme.
Speaker 2: I thought that was just black rock pumping as.
Speaker 4: Possible, but you know, but everybody had to incorporate it
Speaker 4: in some sense, right, so it became a major source
Speaker 4: of revenue for the vendors, right. So that's that's one thing.
Speaker 4: And then there is the adaptive nature of the market.
Speaker 4: So things that before generated a priced return, So you
Speaker 4: run some risk, you made some money, and then it
Speaker 4: becomes table stakes, it becomes incorporated into factor models, it
Speaker 4: becomes it becomes a smart and and then it becomes
Speaker 4: so I think, you know, you could say definitely that
Speaker 4: medium tomamentum worked much better. You could say that even
Speaker 4: you know, short term reversal worked better. There were years
Speaker 4: when short interest was great, and there are factors or
Speaker 4: data sources that work well now and then maybe in
Speaker 4: five years will become known and become part of the
Speaker 4: I mean credit card data right for consumer. That was
Speaker 4: like there were people who were making a lot of
Speaker 4: money in two thousand and eleven through I don't know,
Speaker 4: sixteen seventeen, and then it's become it's very hard to
Speaker 4: make money.
Speaker 1: In that you said the market is a conversation among
Speaker 1: catch funds. One thing that I think might be true
Speaker 1: that I'm not entirely sure of, is like, to what
Speaker 1: extent the market is a conversation among four hedge funds? Now?
Speaker 1: Like to what extent is like the marginal price or
Speaker 1: of every stock a portfolio manager at you know, one
Speaker 1: of the places you've worked.
Speaker 4: It's a very good question. I don't really have the
Speaker 4: answer to this. I'm not sure.
Speaker 1: It's it's like, what is the intuition at places like that?
Speaker 1: Like is it like the market price is determined by
Speaker 1: like the collective thought of like the top people at
Speaker 1: the top hedge funds, Or is it like we are
Speaker 1: a little bump on the market and we're trading against
Speaker 1: the whole random universe.
Speaker 4: I mean, you'd like to think that the prices are
Speaker 4: determined by the marginal informed investor, right, so by people
Speaker 4: like us at the time horizon where we predict, right,
Speaker 4: which is not the same as at the time a
Speaker 4: horizon of alpha day. Right, that's a different player.
Speaker 1: What is your time arising?
Speaker 4: Like I think of it as well, it depends well, yes,
Speaker 4: it depends. Within a hatch fund, you have a variety
Speaker 4: of even within long shot equities, you know, you have
Speaker 4: you know, portfolio managers who are very tactical, and so
Speaker 4: they think in terms of they have strong daily or
Speaker 4: intra day alpha, even though they're fully discretionary up to
Speaker 4: pms that think easily in terms of months. Also depends
Speaker 4: on the sector. So you know, financials typically probably monetizes
Speaker 4: a little bit less on earnings and tends to have
Speaker 4: a longer horizon. Banks are basically modeling giant balance sheets, right.
Speaker 4: And then in a hedge fund you also have systematic
Speaker 4: but even in systematic there are also of time scales,
Speaker 4: and this cacophony makes the prices. I really don't know,
Speaker 4: Like I said, another question is basically, are how inefficient
Speaker 4: is the market, how incorrect the prices are within a
Speaker 4: factor of two, like Black used to say, or I
Speaker 4: don't know, Like I don't think that the market is
Speaker 4: becoming so super efficient, but it's getting it seems to
Speaker 4: be more efficient.
Speaker 1: I do you feel like that. You know, one of
Speaker 1: the big stories is the rise of like these big
Speaker 1: multi strategy hedgehs. Like you would hope. Maybe you wouldn't
Speaker 1: hope because it's sort of the economic and just, but
Speaker 1: like one might hope that like the rise of these
Speaker 1: big multi strategy hedgehus and a lot of capital being
Speaker 1: allocated to them would observably make the market more efficient.
Speaker 4: Yeah, I don't know if observably holds. I don't it's
Speaker 4: really hard to Like, can you can you tell when
Speaker 4: a bubble is forming?
Speaker 2: A lot of people would say that they can.
Speaker 4: Yeah, I can point you to a few papers. Yeah
Speaker 4: that you know made all the wrong calls. I don't
Speaker 4: 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 4: I didn't say I think exactly that. It's a conversation
Speaker 4: between It's a beautiful thing to say though. It sounds
Speaker 4: really cool. It sounds good podcasts.
Speaker 3: That's great.
Speaker 4: Yeah, But I think your question is whether the rise
Speaker 4: of passive has made markets less efficient more of a statement.
Speaker 4: I don't think.
Speaker 2: I was a bad podcaster and didn't actually ask a.
Speaker 4: Question, But okay, how do you know?
Speaker 2: How do I know that passive is destorying the market?
Speaker 2: People on Twitter tell me so.
Speaker 4: Oh, okay, don't trust people one number one.
Speaker 3: Number one, No, I don't know.
Speaker 4: I mean the rise of passive has made index rebalancing
Speaker 4: a weirder strategy, right, so where the margins have compressed,
Speaker 4: but the size has become so big that you can
Speaker 4: still make money in it, and periodic. It's a very
Speaker 4: you know, cyclical strategy.
Speaker 3: So I don't know.
Speaker 1: So if you're an indexy balancing PM, do take like
Speaker 1: eight vacation a year?
Speaker 4: And not the ones I know who probably listen to
Speaker 4: this podcast, they work very hard.
Speaker 2: They want to.
Speaker 1: Indexes aren't paying rence all the time, planning more than.
Speaker 4: You would think Index three balancing is another you know,
Speaker 4: poster child for a strategy that seems so simple that
Speaker 4: everybody can talk about it, and then it's full of
Speaker 4: nuances and it requires a lot of skill to trade effectively.
Speaker 1: I believe, just because like I thought a little bit
Speaker 1: about like just like the sort of like accounting of
Speaker 1: like you basically know how many index funds 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
Speaker 1: market calves that will come in and whatever, but then
Speaker 1: it feels like the unknown is like who else is
Speaker 1: doing the rebalancing strategy? Is that right?
Speaker 4: I think you're mostly right, because I don't want to say,
Speaker 4: because you know, out of respect for for the CMS,
Speaker 4: did I know fair enough?
Speaker 1: Yess? Like, So we had Cliff Askiness on a few
Speaker 1: weeks ago, and like, to me, Cliff Asness is like
Speaker 1: a quantitative investor, like a systematic investor, but like what
Speaker 1: he's doing is sort of recognizably what a sort of
Speaker 1: traditional asset manager. He's like trying to find companies that
Speaker 1: are undervalued.
Speaker 3: Right.
Speaker 1: He talked about it's like being a Grammar dot investor.
Speaker 1: You know you want like valuation plus a catalyst, and
Speaker 1: he's like, oh, were you know trading you value and
Speaker 1: momentum and like you look at what eight or two
Speaker 1: is maybe a little different, but there's like you know,
Speaker 1: the hypercacy trading firms, Like you can model those as
Speaker 1: like those are quantitative versions of like a voice market
Speaker 1: maker fifty years ago, where they're like trying to keep
Speaker 1: inventory flat and like trying to you know, make the
Speaker 1: bid ask spread. So like those are like very traditional
Speaker 1: economic functions that have been quantified, like turned into systematic
Speaker 1: what's the intuition for like what a bally asthe or
Speaker 1: a sedad doll or a millennium does? Like what business
Speaker 1: are you in? Do you think? Like as a philosophical matter,
Speaker 1: like one thing I think like I think about.
Speaker 4: Like you're asking from a social Yeah, that's kind of point.
Speaker 1: Like I think like so the index rebalancing like to
Speaker 1: me 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 wonder,
Speaker 1: like from where you say, like how you see that
Speaker 1: role in the financial markets of those firms.
Speaker 4: So at a very high level, we don't do anything
Speaker 4: different than everybody else in the sense that what we
Speaker 4: provide is always this, right, is we provide shifting time preferences,
Speaker 4: which means we provide liquidity, We house risk for people
Speaker 4: who don't want to hold it right now. And that's
Speaker 4: what you do when you do indextre balancing, right, That's
Speaker 4: what you do when you do merger ARB and when
Speaker 4: you do the various subtypes of basis traits.
Speaker 1: Right.
Speaker 4: So we do provide liquidity, which is very important. And
Speaker 4: then the second thing we again very high level, we
Speaker 4: provide price discovery. Right, So we study the firms and
Speaker 4: we think, okay, this is at the margin mispriced and
Speaker 4: we're going to short it or we're going to invest
Speaker 4: in it, and that's a beautiful thing. So we do
Speaker 4: it at a different time scale, right. So you always
Speaker 4: want to do things at the margin where you don't
Speaker 4: have a lot of other participants, and at the margin
Speaker 4: of the let's say a month three month investment horizon,
Speaker 4: there are not that many participants, so in the words
Speaker 4: of another hatch fund manager I cannot name, but it said,
Speaker 4: once you know, we don't invest in securities, we dated them,
Speaker 4: and so we are in the dating service. Not that
Speaker 4: many people are doing it, and so we do it.
Speaker 4: But I would say also this right, not at the
Speaker 4: social level. I just want to answer at the like
Speaker 4: my personal level. What we do. We are a massive
Speaker 4: filter of talent, and the talent that we hire is
Speaker 4: a 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
Speaker 1: ninety but like, yeah, there's like a clear hierarchy now.
Speaker 4: I think that the market and the set of investors
Speaker 4: has learned right, and I think the distinction between beten
Speaker 4: HALFA has been useful for investors and so active investors
Speaker 4: who are mostly long only, I think have suffered from
Speaker 4: this distinction because the vast majority of them underperforms their
Speaker 4: benchmarks and so there is no reason for them to exist.
Speaker 4: And then what we do is we provide really uncorrelated
Speaker 4: returns to the benchmarks to most factors, and investors want that, right,
Speaker 4: So there is a future where active investors, long on investors,
Speaker 4: asset managers will become even less influential, smaller.
Speaker 1: And also I think of that as like a customer
Speaker 1: demand side, but also like a talent filter side, right.
Speaker 4: Yes, Yeah, And then the interesting thing is and then
Speaker 4: there is also a process where the multimanager platforms are
Speaker 4: able to make the business model of a single portfolio
Speaker 4: manager that is not sustainabil and isolation working in this
Speaker 4: kind of federated system. So why would you or how
Speaker 4: could you survive as a single portfolio manager hedge fund nowadays?
Speaker 4: It's really really difficult, But you can do it in
Speaker 4: a multi manager platform provided that you have you know,
Speaker 4: 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 Enchey manager, then 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 a don't disagree.
Speaker 1: Yeah, one more question, like social role is just like
Speaker 1: you've worked at most of the big pod jobs, but
Speaker 1: you also work at HRT, Like what's the difference in
Speaker 1: roles and like what they do all day? Because HRT
Speaker 1: I think of as 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 shops have a lower frequency and a you know,
Speaker 1: they're not prop they're running hedgephnes. Like what's the cultural
Speaker 1: and role and difference?
Speaker 4: Yeah, okay, So I briefly mentioned HRT in an interview
Speaker 4: with The Financial Times, and my manager told me that,
Speaker 4: you know, people at HIT were both annoyed and delighted
Speaker 4: by what I've had said about about HRT. I think
Speaker 4: HRT is a really special place, even in the in
Speaker 4: the context of proper training firms. So I'm a little
Speaker 4: bit hesitant to just be in them in as a representative, right,
Speaker 4: So they're not representative because there is something in the
Speaker 4: culture of HRT that is special. Okay, it's collaborative, it's
Speaker 4: truly kind.
Speaker 3: Yeah. So I think it's a great place.
Speaker 4: To work, and it is fundamentally monolithic, so you have,
Speaker 4: you know, sharing of ideas and you can work at
Speaker 4: the intersection of these ideas.
Speaker 3: It's also a place.
Speaker 4: That is very tech oriented, so it's a bit of
Speaker 4: a technology firm or in the financial space, and because
Speaker 4: of that, it also attracts I think, the best technical
Speaker 4: talent that I've ever worked with. It's just a pleasure
Speaker 4: to work with great technologies. People were very competent in
Speaker 4: that respect. So nothing against the hedge funds. I love
Speaker 4: edge funds for different reasons. You know, I love BAM,
Speaker 4: which is also very collaborative and it's an investment company,
Speaker 4: but HRT has as a technical side to it and
Speaker 4: 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: So I have like three models of how investment works systematic.
Speaker 1: Like one is like you have like some economic intuition
Speaker 1: and you build a model of the stock market that
Speaker 1: predicts prices. In other way is a sort of like
Speaker 1: neural netty ai E way, where like you throw a
Speaker 1: lot of data at a neural net and it build
Speaker 1: its own model of how to predict stock prices. And
Speaker 1: then the third model is like you get really good
Speaker 1: at prompt engineering and you get a chat GPT and
Speaker 1: you say what stocks will go up? But you ask
Speaker 1: it in the right way, and then chat GPT tells
Speaker 1: me what stocks will go up? How good is it? Okay,
Speaker 1: I assume the third model no one uses, but like
Speaker 1: someone uses.
Speaker 4: I think a lot of people use that, all right,
Speaker 4: So first thing, like, okay, nobody knows anything, and anybody
Speaker 4: saying the opposite, you know, should be heavily discounted. Okay,
Speaker 4: so we agree on this, and so let's forget for
Speaker 4: a second all the technical details of AI just from
Speaker 4: a pure industrial organization standpoint, Right, So what's going to happen?
Speaker 4: Consider AI just like another technology like Internet and whatnot. Right,
Speaker 4: So you know, first of all, we're going to observe
Speaker 4: economies of scale, So there's going to be concentration, and
Speaker 4: there was going to be some kind of monopolistic competition.
Speaker 3: I was thinking about Bloomberg.
Speaker 4: Specifically, which could be I hope for you people to
Speaker 4: be among the winners, because you have a good starting point, right,
Speaker 4: you have lots of data, right, you have a customer base,
Speaker 4: and maybe in the future we'll finally not see the
Speaker 4: good old Bloomberg terminal, which has been kind of unchanged
Speaker 4: since I remember it, and instead people will just prompt
Speaker 4: Bloomberg to conduct very complex actions where it will act
Speaker 4: on a sequence of keywords and connect them and give you,
Speaker 4: like a much more valuable product for which Bloomberg will
Speaker 4: charge twice as much as they do already. So this
Speaker 4: is going to happen in one form or another. If
Speaker 4: it's not Bloomber, somebody else will do it. Okay, But
Speaker 4: the same thing applies to other areas of finance. So
Speaker 4: maybe once upon a time, you know, a big sufficiently
Speaker 4: big fund could build their own client for email. Right.
Speaker 4: Of course, nobody builds a client for email anymore. Right,
Speaker 4: So a lot of this stuff gets outsourced. We will
Speaker 4: outsource at some point some of the functions that we
Speaker 4: conduct internally using AI to other AI agents. It's perfectly fine.
Speaker 4: So this will become a utility to some extent.
Speaker 1: Yes, functions in clid like well.
Speaker 4: Not stockpicking, not stock picking. I think that the functions
Speaker 4: that we will see available are essentially like another self,
Speaker 4: like another mathlevin, who can you know be a good
Speaker 4: baseline for you?
Speaker 3: Okay?
Speaker 4: You could feed a post train an AI system with
Speaker 4: all your gazillions of words, right, and that agent will
Speaker 4: reproduce your sense of humor, your investigative style and everything Okay,
Speaker 4: it's a good approximation. It's not going to be perfect,
Speaker 4: but why not, right, So I would be very happy
Speaker 4: to have a replica of myself that can answer most
Speaker 4: simple questions. Now, I think that the decision to invest
Speaker 4: in a particular stock is a very demanding cognitive function,
Speaker 4: and I don't see that really being replicated very well.
Speaker 4: But I think that this will be baselined to some extent.
Speaker 1: Is it a many kind of function because because it
Speaker 1: exists in a competitive market, So like the sort of
Speaker 1: like whatever the kind of function is, is going to
Speaker 1: get like the baseline is always going to get higher
Speaker 1: because like someone else will will have the same information
Speaker 1: as you do or the same.
Speaker 4: Well, this is getting really in the highly speculative side
Speaker 4: of you know things. I think that in order for
Speaker 4: an AI agent to be good at this, they have
Speaker 4: to be able to experience the world the same way
Speaker 4: that an investor experiences it, and our inputs are much
Speaker 4: more complex than just a string of text or YouTube videos.
Speaker 1: Right.
Speaker 4: We have a the world which comes from visually experiencing
Speaker 4: the world, talking to humans, consuming the goods, right anything,
Speaker 4: It's vastly more complex than the way an AI system
Speaker 4: Right now, experiences the world and also influences the world.
Speaker 4: So an investor has a fundamentally different experience of a company.
Speaker 3: Than an LM that has.
Speaker 4: An experience that is mediated by multiple layers of processing.
Speaker 4: You know, they learn about a company through text that
Speaker 4: is written by somebody. So I don't think that that's
Speaker 4: in danger for the time being. But maybe, you know, again,
Speaker 4: in five years, maybe we will have our glasses feeding
Speaker 4: our experiences to AI agents. Who knows, right, But I
Speaker 4: don't think that it's that close, And I don't think
Speaker 4: AI is that smart also, so I think that having
Speaker 4: a 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. I always think of like the comparison is
Speaker 1: like investing in self driving cars, or like investors do
Speaker 1: a lot of things. Like one thing they do a
Speaker 1: lot is sit at a desk and read computers and
Speaker 1: like look at numbers, right, and like those things seem
Speaker 1: like things that a computer can do well, whereas like
Speaker 1: you know, drivers like have physical reflexes and like have
Speaker 1: a you know, complicated field division. I always thought like
Speaker 1: investing should be easier than self driving cars for a
Speaker 1: computer and a master. But you and I think you're
Speaker 1: learning this. Think of like investing as like the great
Speaker 1: liberal art, where it's like you incorporate all of human
Speaker 1: experience and so the AI can't.
Speaker 4: Really Okay, let's let's take the metaphor to you know,
Speaker 4: extreme consequences. Imagine that you had a system that is
Speaker 4: the equivalent of a perfect self driving car in investing.
Speaker 3: So now I'm.
Speaker 4: Giving you a machine, a box that is telling you
Speaker 4: the long term value, if not the returns, right, because
Speaker 4: the moment that the value is known, you immediately equilibrate
Speaker 4: to that level.
Speaker 1: Right.
Speaker 4: So imagine that you know the true value of everything
Speaker 4: because a box tells you so, and it's infallible.
Speaker 3: It's an oracle. Okay.
Speaker 4: Would you think that finance stops existing? I wouldn't say so, right,
Speaker 4: So I think that a lot of arbitrush trades, you know,
Speaker 4: would maybe change significantly, but every risk, right, every return
Speaker 4: would be correctly priced by the risk of the agents
Speaker 4: trading it. So there still would be trading because we
Speaker 4: still have different preferences, but basically every risk could be priced.
Speaker 4: There would be in a sense less alpha. But finance
Speaker 4: will still exist. It's a lot of service provision like
Speaker 4: liquidity provision. Yeah, and so the liquidity provision would still exist.
Speaker 4: The informational services maybe will stop existing in the current.
Speaker 3: Form, but that's okay.
Speaker 4: I think that we'll all still be employed.
Speaker 2: Mm hmm.
Speaker 1: It's an interesting I think about it, because I do think,
Speaker 1: like we talked about, like, one thing that the big
Speaker 1: hedgehunds to do is things that have the flavor of
Speaker 1: liquidity provisions basis trades and merger 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 one 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: at liquidity provision. Is that sort of right? You didn't
Speaker 1: know the value of the Yeah.
Speaker 4: I mean a short, short horizon. Liquidity provision and information
Speaker 4: tend to be very closely rated, Like you know, a limit.
Speaker 4: If you are good at crossing, even good at crossing,
Speaker 4: you should be pretty good at adding okay, adding liquidity,
Speaker 4: so you know, but I mean like you could make
Speaker 4: you know, a profit by posting a lot of limit
Speaker 4: orders and providing liquidity to the market, or crossing the
Speaker 4: spread and making money with predicting the future prices. If
Speaker 4: you're good at one, you're good at the other. Most likely,
Speaker 4: right at that time scale, I think that this though
Speaker 4: might I'm not sure because I haven't thought about this
Speaker 4: very very carefully, but I think this might be coupled
Speaker 4: at longer time scale, so you know, you're when you're out.
Speaker 4: I'm not sure. And in any case, at that time
Speaker 4: scale is really difficult for an AI or for a
Speaker 4: human being anyone, Like, there are not that many hard data,
Speaker 4: even the unstructured data are not that many. So it's
Speaker 4: a very difficult problem. It's the coupled it's it's complicated.
Speaker 4: So yeah, but I tend to believe at longer time
Speaker 4: scales you have more or less liquidit provisioning and you know,
Speaker 4: violations of law of one price on one side and
Speaker 4: predicting on the other side.
Speaker 1: But you combine both.
Speaker 4: But you can combine both, and it's a very potent mix.
Speaker 1: Right, there's normally different people.
Speaker 4: It is right, very different people for sure, different, very different,
Speaker 4: very different people, very different cultures.
Speaker 1: Yeah, can you summarize the difference in cultures between like I.
Speaker 4: Have, I guess, but well, as you said, people who
Speaker 4: typically trade in ARB trades, if not historically, but also
Speaker 4: historically come from banks. Right, whereas you still can see
Speaker 4: long only portfolio managers being recycled and reformatted into long
Speaker 4: short portfolio managers, you can have an excellent short specialist
Speaker 4: becoming a long short portfolio manager, like it happened.
Speaker 1: I mean my sense is that like that, people on
Speaker 1: the information version long short sider more academic and research oriented.
Speaker 1: In the people on the ARB side are more.
Speaker 4: Yeah, I think you can actually have very good long
Speaker 4: short portfolio managers who were journalists in their past lives.
Speaker 1: I've heard of some of these. I thought about it, No, just.
Speaker 4: Like real.
Speaker 2: Breaking news on your podcast.
Speaker 1: I've jumps that's better than podcasting. Not thought about it
Speaker 1: in the sonth that I'd be good at it, just
Speaker 1: 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 4: Yes, I think that you could interest a few huge funds.
Speaker 4: They might be listening. On a note, Kathy, thanks for
Speaker 4: coming on the pleasure, Thanks for having me.
Speaker 1: And that was the Money Stuff Podcast.
Speaker 2: I'm Matt Levian and I'm Katie Greifeld.
Speaker 1: You can find my work by subscribing to The Money
Speaker 1: Stuff newsletter on Bloomberg dot com.
Speaker 2: And you can find me on Bloomberg TV. Every day
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Speaker 1: The Money Stuff Podcast is produced by Anna Maserakus and Moses.
Speaker 2: Onam Our theme music was composed by Blake Maples.
Speaker 1: Brandon Francis Newdhim is our executive producer.
Speaker 2: And Stage Ballman is Bloomberg's head of Podcasts.
Speaker 1: Thanks for listening to The Money Stuff Podcast. We'll be
Speaker 1: back next week with more stuff