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Speaker 1: Bloomberg Audio Studios, podcasts, radio news, Happy Thanksgiving.

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Speaker 2: I hope you're having a great holiday. We are obviously

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Speaker 2: not recording on this Thursday, so we're.

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Speaker 1: Gonna rerun a favorite old episode. It's going to be great.

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Speaker 2: You're going to enjoy it even more the second time. Bye.

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Speaker 1: Wait gay palio logo or.

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Speaker 2: I'm so excited for you to tackle that and not me.

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Speaker 3: Good luck.

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Speaker 2: I thought I had it down, but then I heard

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Speaker 2: you say it, and I feel like.

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Speaker 1: When I first met you, I think I asked you

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Speaker 1: if you go by gappie because of your famous track

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Speaker 1: record of taking gardening leave, like having gaps in your

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Speaker 1: career as well.

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Speaker 4: Okay I didn't remember. Yeah, that's a great excuse for

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Speaker 4: for a nickname. No, But the reason is when I

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Speaker 4: came to the States for grad school, and this was

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Speaker 4: a long time ago, in ninety five. So the first

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Speaker 4: thing that you did was set up an email account.

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Speaker 4: You still had the freedom to choose an Emil account.

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Speaker 4: Now they just give you your initials with the number,

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Speaker 4: and so my initials are gap Gapo, and of course

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Speaker 4: it was taken. So I said, okay, well Gappy, and

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Speaker 4: then everybody in grad school and then my wife was Italian,

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Speaker 4: everybody started to call me Gappy and that's stuck.

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Speaker 3: And now at.

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Speaker 4: Work they just have dispensed with my real name, like

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Speaker 4: on all systems, I'm just Gappy Paliologo.

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Speaker 3: So I expect that that will be, you know.

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Speaker 4: Prosecuted for tax evasion because on my tax forms there

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Speaker 4: is Gappy Paliologo or something like that.

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Speaker 1: Well, hello, and welcome to the Stuff podcast.

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Speaker 2: I'm at Livian and I'm Katie Greifeld.

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Speaker 1: And we have a guest today, Gabby Paleo Logo, who

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Speaker 1: is now at pali Asney has been at most of

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Speaker 1: the other pig Catch funs and Hudsond River Trading. I

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Speaker 1: do want to start by talking about.

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Speaker 2: Gardening there, Okay, Natural Purse.

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Speaker 1: I think that we counted from your link. Your LinkedIn

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Speaker 1: is like famous for discussing your gardening leave in some detail,

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Speaker 1: and I think we counted three years of gardening leave.

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Speaker 4: No, I think it's a bit like Okay, it's not precise.

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Speaker 4: Fifteen months from Citadel, one year Hudson River Trading, and

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Speaker 4: four months from Millennium. Okay, so pretty close, not terrible,

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Speaker 4: though a bit less than two years.

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Speaker 1: From my perspective, it seems very fun. Did you enjoy

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Speaker 1: your three years of gardening?

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Speaker 3: I do so.

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Speaker 4: I try to keep myself busy, so I teach, typically

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Speaker 4: at some university. So the first time during my Seitaadel

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Speaker 4: two millennium, Guardian Leve I was teaching at Cornell and

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Speaker 4: in the HRT to Bam Guardian Leve was at NYU.

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Speaker 4: And I love teaching. And then what I do is

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Speaker 4: it helps me focus on stuff. Usually what I do

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Speaker 4: in you know, whenever I read a book or read

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Speaker 4: a paper that I like, I take notes, take notes

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Speaker 4: in lattech, and then I read, arrive or think about things,

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Speaker 4: and so that typically is the basis for my course material,

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Speaker 4: and then it becomes the basis for my books. I've

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Speaker 4: written a couple of books during my non competes.

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Speaker 2: Interesting because thinking about gardening leave. Matt and I talk

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Speaker 2: about it all the time, because it's very alluring to me.

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Speaker 2: Gardening leave doesn't really exist in journalism. I love to

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Speaker 2: imagine what I would do. But one of the questions

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Speaker 2: I had for you was, you know, do you ever

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Speaker 2: have anxiety about losing your edge or falling behind? But

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Speaker 2: it sounds like teaching is one of the ways that.

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Speaker 3: Yeah, I'm not particularly worried with that.

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Speaker 4: I think that there is only a very specific subset

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Speaker 4: of quantitative researchers who are afraid of losing their edges.

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Speaker 4: And yeah, that's not been my case. I keep reading.

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Speaker 4: I try to stay up to date.

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Speaker 1: To the feedback into the work. Like do you get

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Speaker 1: ideas or like deep in your understanding of techniques by

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Speaker 1: teaching and writing the books? Or are they just sort

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Speaker 1: of like.

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Speaker 4: Extracurricular No, no, no, it's definitely I learn a lot

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Speaker 4: from writing the books.

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Speaker 1: How long do you I get to hear your next job?

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Speaker 1: And yeah, generate more profits by of.

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Speaker 3: Course, plenty more profits. Sell that to my employers.

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Speaker 4: No, but I definitely I learn a lot from writing

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Speaker 4: from the first drafts, and then I rewrite and rewrite,

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Speaker 4: and I learn a lot from discarding material too. It's

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Speaker 4: very useful to discard material. It makes you really focus

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Speaker 4: on what matters and what doesn't. So I try to

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Speaker 4: give a narrative, like a logical connection between various topics,

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Speaker 4: and that is something that is possible only when you

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Speaker 4: write a book. I really do not like writing that.

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Speaker 4: Nobody I think likes writing, maybe except for you.

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Speaker 1: I I think I understand that it's weird even among writers, but.

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Speaker 4: It is very I find it very painful. I find

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Speaker 4: painful letting go of material, Yes, but I also like it.

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Speaker 4: You know, it's some kind of strange delayed gratification.

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Speaker 1: I guess one theory that I have written is that

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Speaker 1: hedge funded quantitative research gardening leave is like a source

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Speaker 1: of like human flourishing, because you have all these like

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Speaker 1: highly trained people who haven't enforced the year of And

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Speaker 1: I've written that all the Hedge fund researchers should go

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Speaker 1: work at LM companies or like analytics departments of sports teams.

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Speaker 1: And I'm like, partially kidding and partially not. How true

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Speaker 1: is it for you? Like how much of like your

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Speaker 1: quantitative skills at this point are really just for investing in?

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Speaker 1: How much of it is like if you spent three months,

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Speaker 1: you know, consulting for a soccer team, you would be

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Speaker 1: able to tell them how to find better players.

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Speaker 4: I'm not sure, so I'll say this right. I was

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Speaker 4: thinking a few days ago if there was a kind

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Speaker 4: of a common thread in my professional life because it

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Speaker 4: seems kind of random, And actually I think that there is,

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Speaker 4: because I think that I was about fourteen when I

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Speaker 4: realized that I had an aptitude for applied math. I

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Speaker 4: discovered physics, and I liked math, and I also liked

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Speaker 4: literature very much, so I loved reading. I read a lot.

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Speaker 4: I was not a very social animal. And then basically

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Speaker 4: since then, I've been doing the same thing in various forms. Right,

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Speaker 4: I did physics, I did applied math. I didn't do

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Speaker 4: applied math in finance. They did applied math in weird

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Speaker 4: things like optimization and logistics. So I have been doing

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Speaker 4: kind of the same thing over and over, which has

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Speaker 4: been writing and applying math to something. So I think

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Speaker 4: that I could do it. I would like to do it,

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Speaker 4: but I also think that it's not that simple to

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Speaker 4: go to a new field and say, oh, after three months,

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Speaker 4: I know soccer. No, there is a lot of specificity,

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Speaker 4: And the beauty of I think being a good applied

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Speaker 4: mathematician is that they start with the problems and with

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Speaker 4: the domain first, and that they're sufficiently mature from a

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Speaker 4: mathematical standpoint that they are not making too much of

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Speaker 4: an effort in using math. So I think the good

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Speaker 4: art of being an applied mathematician is to study persistently

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Speaker 4: the application. So no, I don't think that after three

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Speaker 4: months it would be good enough. But after a year,

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Speaker 4: you know, about a year of being fully in an application,

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Speaker 4: then you start getting a little bit better and then

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Speaker 4: the mass is not the problem, and then you start

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Speaker 4: doing some good work.

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Speaker 1: You have a famous essay on like advice for quant

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Speaker 1: careers and you say that, like, the things that matter

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Speaker 1: the most are creativity and genuine interest in the problems

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Speaker 1: more than you know, math course power. Yeah, this is

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Speaker 1: a dumb question, but how does one develop? How does

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Speaker 1: one identify you know, creativity and interest in financial topics?

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Speaker 1: And is the obvious answer those are where the money is,

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Speaker 1: or like like why why did you fall in love

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Speaker 1: with finance as a topic? And is the answer because

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Speaker 1: that's what the money is.

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Speaker 4: So first of all, I think that creativity is either

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Speaker 4: personality trait doesn't belong to You're not creative in finance,

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Speaker 4: you know, you're you're creative in in cooking, you're creative

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Speaker 4: in whatever. And it's a mix I guess of extraversion

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Speaker 4: open and as to experience, and I don't what else.

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Speaker 4: I'm not a psychologist, but I do believe that people

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Speaker 4: are genuinely creative, and in fact, you see it right

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Speaker 4: that sometimes you ask someone and you find out that, yes,

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Speaker 4: they like writing, they play some instrument, if badly, and

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Speaker 4: you know, and they paint and they do whatever. And

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Speaker 4: so I would say, if you go to finance because

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Speaker 4: that's where the money is, there's nothing wrong with that.

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Speaker 4: And in a way, that's my story. You know, I

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Speaker 4: was I was a researcher, and I wanted to have

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Speaker 4: more money and whatnot. But eventually you stay in finance,

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Speaker 4: or at least in my you know, little domain, because

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Speaker 4: you're genuinely curious about finding out stuff, right.

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Speaker 1: So, like why are the problems like why do they

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Speaker 1: arisk curiosity? Like why are the problems of finance intrigue

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Speaker 1: you after years of doing it right? Like what's interesting

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Speaker 1: about those problems as opposed to other domains.

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Speaker 4: It's really hard for me to say, Like I think

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Speaker 4: that I read once that a young songwriter asked Bob

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Speaker 4: Dylan how to become a good songwriter, and bub Bylan

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Speaker 4: just answered, well, what's going on? What do you mean,

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Speaker 4: what's going on? Yeah, what's going on? What's going on

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Speaker 4: in your life? Just you know, look around. So sometimes

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Speaker 4: I get these questions from investors or but you know,

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Speaker 4: how do you keep yourself interested. How do you find problems?

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Speaker 4: It's not a problem like the problems jump at you

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Speaker 4: like there are too many problems. There are too many

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Speaker 4: interesting problems. So if anything, the skill is in sorting

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Speaker 4: the problems in the right order. Right, That is where

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Speaker 4: maybe having some maturity in doing research kicks in. But

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Speaker 4: there are lots of problems, infinite problems, weird problems.

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Speaker 1: What's your favorite problem right now?

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Speaker 4: I don't like right now, what are we working on?

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Speaker 4: I mean, we are trying to understand how earnings are monetized? Right,

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Speaker 4: how do you make money in earnings? It's such a

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Speaker 4: basic thing in fundamental equities.

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Speaker 1: And you mean, if you're like correct about predicting earnings?

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Speaker 4: Yes, what are I mean? Without get into much into details,

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Speaker 4: but you know there what are the relevant variables? Imagine

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Speaker 4: that you had an oracle who told you what the

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Speaker 4: variables are? What would you do with that? What would

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Speaker 4: you do if you'd had all the information in the world, right,

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Speaker 4: and everything in your world here in existence? Would be

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Speaker 4: like an approximation problem.

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Speaker 1: There's there's an incredible styled story of like the guys

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Speaker 1: hacked into I think like one of the newswire services

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Speaker 1: and got earnings releases early, like for hundreds of companies,

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Speaker 1: and they traded on this and they had like a

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Speaker 1: seventy percent success rate, which is great, but also like

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Speaker 1: it means that had a thirty percent, Like they traded

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Speaker 1: the wrong way, knowing earnings perfectly in advance. It's like

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Speaker 1: a good.

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Speaker 4: Yeah, yes, so they had the racle on it. You know,

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Speaker 4: it's still hard, Yes, it's still very hard. Actually, shout

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Speaker 4: out to Victor Hagan, who wrote the paper about ten

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Speaker 4: years ago on this. He made a organize a simple

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Speaker 4: controlled experiment where he gave basically a biased coin where

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Speaker 4: you I think had a success rate of sixty percent

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Speaker 4: forty percent failure, and you some capital and you could

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Speaker 4: invest it over time on these informed predictions, and a

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Speaker 4: lot of subjects went bankrupt. Okay, now I think we

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Speaker 4: are better than that, but still there are lots of

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Speaker 4: problems related to trading around an event. For example, before we.

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Speaker 2: Get too far away, you mentioned Bob Dylan. It actually

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Speaker 2: reminded me of another Bob Dylan quote which I'm going

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Speaker 2: to paraphrase poorly, but he basically said, when asked about

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Speaker 2: writing songs, do you think that you could write whatever

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Speaker 2: the work that was being referenced now, and he said,

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Speaker 2: I don't think so. It's like the words were in

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Speaker 2: the air and I just plucked them out. They were

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Speaker 2: just sort of hanging in the air and they came

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Speaker 2: to me. And it kind of also rang true with

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Speaker 2: what you were saying about you didn't go looking for problems.

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Speaker 2: They're just there necessarily. I actually want to go back

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Speaker 2: to applied math if it doesn't interrupt the course of

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Speaker 2: conversation too much. You tweeted on June twenty fourth that

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Speaker 2: there's no child prodigies when it comes to poetry, when

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Speaker 2: it comes to applied mathematics. And I'm not saying that.

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Speaker 2: You said that you were a prodigy, but you were

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Speaker 2: a child at fourteen. I mean, how how fourteen do

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Speaker 2: you realize that you have an aptitude for something like

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Speaker 2: applied mathematics.

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Speaker 4: I don't want to flex about this stuff, No you should.

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Speaker 4: I think I'm honestly a little weird. I'm just a

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Speaker 4: little weird, I think, honestly.

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Speaker 2: But I like prodigy weird or I did.

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Speaker 4: Have my share of yeah, adults telling me that I

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Speaker 4: was good at this or that or you know, but yeah,

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Speaker 4: I mean, okay.

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Speaker 3: I'm just a little bit atypical.

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Speaker 4: Also, when I talk to investors, I think investors enjoying

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Speaker 4: my presence because I think I'm incredibly unfiltered for somebody

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Speaker 4: who's talking to.

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Speaker 3: Them, so it's like fun for them.

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Speaker 4: And I was very unfiltered when I talked to my

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Speaker 4: professors in school. Sometimes I corrected them stuff like this. Yeah,

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Speaker 4: I don't know, Honestly, I don't know.

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Speaker 1: When you talk to like fundamental equity portfolio managers, like

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Speaker 1: how much like matrix algebras they're in your conversations like

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Speaker 1: how quantity are the fundamental pms or whatever.

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Speaker 4: I don't think they're quantity, but I think that they're

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Speaker 4: very analytical. So I don't think that they would make

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Speaker 4: great mathematicians, but I think they would make very very

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Speaker 4: decent applied mathematicians. Actually, they tend to be very analytical.

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Speaker 4: They tend to be very process oriented. And they have

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Speaker 4: also additional qualities that actually mentioned in that essay, like

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Speaker 4: they have very little disposition effect, so that's part of

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Speaker 4: being analytical. They have no sound cost fallacy in them.

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Speaker 4: So even though they don't do a lot of math,

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Speaker 4: but they do some math. Okay, So first of all,

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Speaker 4: they're fluent in a sense in basic literacy, but I

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Speaker 4: think it's more their process that is closer to if

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Speaker 4: not a mathematical one, but more of a scientific one.

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Speaker 2: And when it comes to being a quant does it

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Speaker 2: basically boil down to being good at math and being

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Speaker 2: interested in math? Are things such as statistics and physics?

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Speaker 2: I mean, do you need to have any finance or

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Speaker 2: economics background at all?

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Speaker 4: So I think that having an economics background is not

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Speaker 4: necessarily a benefit, might even be a disadvantage actually, But

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Speaker 4: just based on very few samples that I have a

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Speaker 4: lot of very good, outstanding quantitative researchers actually come from

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Speaker 4: physics and specifically from astrophysics. That's the experience that I've

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Speaker 4: had in a couple of places.

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Speaker 2: In broad brushstrokes, could you talk about why economics in

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Speaker 2: the small sample size you have, how could that possibly

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Speaker 2: be a detriment good?

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Speaker 4: So I can answer the second question more easily. I

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Speaker 4: think that astrophysicists deal with large amounts of data, and

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Speaker 4: they deal with observational data, so they don't get to

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Speaker 4: do a lot of experiments, and that's good for finance. Right,

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Speaker 4: you deal with a lot of data, you need to

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Speaker 4: know how to have good agen for observational data, and

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Speaker 4: you need to have very good theory, like you need

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Speaker 4: to have very good instruments without being falling in love

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Speaker 4: with those instruments. Whereas I think economists, Okay, first of all,

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Speaker 4: my statement is purely empirical. Okay, so I'm just really

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Speaker 4: guessing on economists, and I'm going to be hated by

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Speaker 4: all economists or economists in finance, but I do have

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Speaker 4: my issues with their methods.

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Speaker 3: Right.

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Speaker 4: So, first of all, I think that there is an

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Speaker 4: original scene in economics, which is I think a lot

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Speaker 4: of economics is informed by a desire to be as

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Speaker 4: rigorous as mathematics, right, and so a lot of theoreticians

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Speaker 4: in economics are very deductive in their approach. If you

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Speaker 4: think of you know, the unrealistic assumptions behind the welfare

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Speaker 4: theorems or Rows impossibility theorem or whatnot, or just pick

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Speaker 4: up you know Samuelson textbooks, and I think this is

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Speaker 4: just rather very acxiomatic.

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Speaker 3: Very deductive.

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Speaker 4: Whereas physicists are very happy to think in terms of small,

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Speaker 4: idealized models that apply to a specific domain, and if

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Speaker 4: the model doesn't work out, they will discard and make

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Speaker 4: another one. The grand theory behind physical theories exists, like,

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Speaker 4: there are people who do this for a living, but many,

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Speaker 4: many good theoretical economists physicists starting the small and then

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Speaker 4: they expand the domain of their models. So economists tend

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Speaker 4: to maybe in a sense, fall in love with methods

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Speaker 4: too much, with techniques too much.

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Speaker 1: We had clip Asness on the podcast a little while ago,

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Speaker 1: and my father, not a finance listened to the episode

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Speaker 1: and said, I still don't know what a quant is.

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Speaker 1: I just read skimmed your new book which is called

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Speaker 1: The Elements of Quantitative Investing, and as lays out the elements,

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Speaker 1: what is a quant like? What are the elements? Like?

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Speaker 1: What's the thing that makes someone a quant investor that,

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Speaker 1: like someone reading a slim book about the Elements of

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Speaker 1: quant investing needs to learn?

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Speaker 4: Well, if I am being consistent with my book, investing

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Speaker 4: is really about problems and not about specific techniques or

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Speaker 4: anything like this. Right, So it's basically a way to

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Speaker 4: go through the whole investment process from let's say preparing

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Speaker 4: the ingredients to cooking to eating that is very processed driven. Ultimately,

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Speaker 4: you would imagine that one thing that you know quantu

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Speaker 4: investing has in common across multiple domains you know, if

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Speaker 4: you do futures, stocks, event based and whatnot. Is I

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Speaker 4: think the number of bets tends to be high in

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Speaker 4: systematic right, so you can be a very successful microeconomic

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Speaker 4: investor portfolio manager. And you you know, according to even

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Speaker 4: several statements by Buffett, you know, he made like ten

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Speaker 4: twelve very good bets. Okay, so that's great, and that's

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Speaker 4: not quantu investing. You know, you could put enough pms

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Speaker 4: making you know, twenty bets in their lives and you

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Speaker 4: will get a few that have let's say twelve thirteen right,

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Speaker 4: and they will be rich. We do not have that luxury, right.

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Speaker 4: We have to make millions of bets. You know, we

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Speaker 4: trade a portfolio with three thousand stocks sometimes in waves

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Speaker 4: of half an hour. You can't make a judgment on

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Speaker 4: all of these bets. So you need a method that

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Speaker 4: reduces the dimension of your problem to something that can

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Speaker 4: be treated in a systematic manner. I don't know if

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Speaker 4: that answers for you. You know that, but you know, basically,

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Speaker 4: basically the idea is, think about if you make a

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Speaker 4: lot of bets, you cannot bet individually. You have to

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Speaker 4: have some kind of view res take or some kind

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Speaker 4: of method around that.

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Speaker 1: Right, and like to me, like the book sort of

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Speaker 1: you know the standard method I guess and quant investing

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Speaker 1: is you built a factor model of what drives your

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Speaker 1: universal investments. You're shaking your head.

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Speaker 4: Yeah, I yes, and no. I think yes because the book,

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Speaker 4: you know, has maybe one hundred and fifty pages on

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Speaker 4: factor models.

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Speaker 3: But also no, because maybe.

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Speaker 4: In one hundred years from now, I suspect there will

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Speaker 4: be still something left. But you know, we might have

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Speaker 4: better techniques and not necessary factor models any longer. I

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Speaker 4: don't know, we.

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Speaker 1: Don't want to go. Two attractions of that one is like,

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Speaker 1: are the better techniques something more neural netty unstructured?

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Speaker 4: Who knows? Yeah, something like that. I mean there is,

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Speaker 4: there is a revolution every five years.

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Speaker 1: So my other question is like I've never fully understood

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Speaker 1: like a factor model is like, here are some factors

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Speaker 1: that drive the returns of stocks, and then there's like

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Speaker 1: some residual idios and credit return There are clearly people

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Speaker 1: whose business is to identify factors and then invest in factors.

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Speaker 1: My impression is that at like the places that you work,

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Speaker 1: the business is the opposite of that is to pedge

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Speaker 1: out your factory risk as much as possible and to

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Speaker 1: get as much idiotsyncratic risk as possible. Is that right?

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Speaker 1: And like, like, how do you discriminate between like a

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Speaker 1: factory return and idiosyncratic return, Like what makes the thing

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Speaker 1: a factor as opposed to another thing.

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Speaker 3: So that's a good question.

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Speaker 4: So first, a lot of systematic investing is still about factors,

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Speaker 4: just not the factors that get published in the literature,

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Speaker 4: you know, not the factors that Cliff maybe was talking about.

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Speaker 4: And yet a lot of successful systematic investing is really

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Speaker 4: factor driven, see in.

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Speaker 1: The sense that you have a model that has like

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Speaker 1: twenty factors and like ten are like value, and you

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Speaker 1: neutralize those and you try the other time kind.

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Speaker 4: Of you do, and you do the rest. You have

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Speaker 4: other terms that matter. So that's one thing, but there

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Speaker 4: are two other things. There are sometimes sources of returns

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Speaker 4: that are factor like but not quite like factors. So

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Speaker 4: you may have a theme. For example, you may identify

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Speaker 4: a theme in the market that is not pervasive enough

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Speaker 4: or is alive only for a few months, but it

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Speaker 4: is there and it's not only affecting let's say two stocks, right,

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Speaker 4: So these brought themes can be invested on, but cannot

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Speaker 4: really model in the traditional way as a traditional factor model. Also,

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Speaker 4: there is a lot of good modeling in factors as

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Speaker 4: opposed to bad modeling. So it seems easy, but it's

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Speaker 4: not that easy. So there is a little bit of

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Speaker 4: craftsmanship in making these models. Okay, And then the third

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Speaker 4: thing is that there are also returns that have nothing

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Speaker 4: to do with factors, or almost nothing to do with factors.

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Speaker 4: So if you really know how a company works, and

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Speaker 4: you have a little bit of an edge in predicting

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Speaker 4: its future performance, and you can bet on it, and

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Speaker 4: you make enough bets and again you will make some

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Speaker 4: money if you repeat, and you know recycle. So even

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Speaker 4: discretionary investing in this sense has inherited a little bit

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Speaker 4: of the spirit of systematic investing.

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Speaker 1: I think of that as like that apod job, but

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Speaker 1: like a baliosni Like you have discretionary investors who know

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Speaker 1: a lot about a company, make bets on the company,

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Speaker 1: and then someone like you tells them, these are your

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Speaker 1: factory exposers. You have to get those down to zero

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Speaker 1: that you're making pure bets on your idios and creditnowledge

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Speaker 1: of the company. Is that like you're kind.

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Speaker 3: Of right, kind of right.

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Speaker 4: Yeah, I think that at this point, it is very

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Speaker 4: interesting how the mind of professional portfolio managers has been

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Speaker 4: remolded in a factor based world, so that a modern

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Speaker 4: portfolio manager, discretion portfolio manager thinks in factors. You know,

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Speaker 4: so I don't even need to tell them, hey, this

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Speaker 4: is your exposure. They see their exposure, they have the

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Speaker 4: tools to see it, and they control it in real

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Speaker 4: time with minimal intervention from me. So what we do

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Speaker 4: is we have, you know, a good team that models

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Speaker 4: factors in a way that is suitable for the investment

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Speaker 4: universe and style in which they operate. That's very very

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Speaker 4: sophisticated and difficult, and portfolio managers use that and then

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Speaker 4: neutralize it's it's become like second.

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Speaker 1: Nature, and they've internalized that their goal is to see

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Speaker 1: us and credit alpha rather than factors. That's right, I feelink.

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Speaker 1: A criticism that people sometimes have of like the pod

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Speaker 1: shop model is that, like, there's some universe of factors

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Speaker 1: that exist in commercial models and like are known in

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Speaker 1: the literature, and then portfolio managers have a set of

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Speaker 1: exposures to factors that are sort of encoded or unknown,

437
00:24:48,640 --> 00:24:52,040
Speaker 1: but like, ultimately, when you become really, really smart, you'll

438
00:24:52,119 --> 00:24:54,960
Speaker 1: know that like actually the bet they're making was some

439
00:24:55,280 --> 00:24:58,000
Speaker 1: you know, particular knowing the company really well means like

440
00:24:58,040 --> 00:25:00,680
Speaker 1: they had exposure to like some you know, person factor

441
00:25:00,680 --> 00:25:03,639
Speaker 1: in the CEO or something that eventually someone will be

442
00:25:03,680 --> 00:25:05,840
Speaker 1: able to write that down and it'll come out of

443
00:25:05,880 --> 00:25:09,119
Speaker 1: like being idiosyncratic and become a factor, and then I

444
00:25:09,119 --> 00:25:09,920
Speaker 1: don't know what happens.

445
00:25:11,119 --> 00:25:13,200
Speaker 4: I think that there is some truth to that. There

446
00:25:13,280 --> 00:25:15,760
Speaker 4: is definitely some truth to that, in the sense that

447
00:25:15,960 --> 00:25:20,920
Speaker 4: sometimes for folume managers, especially in specific sectors, will use

448
00:25:21,080 --> 00:25:26,760
Speaker 4: some heuristics that you could call characteristics in a factor model,

449
00:25:26,760 --> 00:25:28,919
Speaker 4: but they are not in a factor model, and then.

450
00:25:28,800 --> 00:25:29,600
Speaker 3: They trade that.

451
00:25:30,320 --> 00:25:34,719
Speaker 4: However, it's also true that the decision that enters a

452
00:25:34,760 --> 00:25:38,639
Speaker 4: particular investment is usually not that simple as taking a

453
00:25:38,760 --> 00:25:41,840
Speaker 4: ration spreadsheet, so it's a bit more complicated than that.

454
00:25:42,480 --> 00:25:44,920
Speaker 4: You could still argue that there is a factor, right,

455
00:25:45,080 --> 00:25:49,520
Speaker 4: and what's the factor is ultimately the set of thess

456
00:25:49,960 --> 00:25:54,680
Speaker 4: that are highly correlated or relatively highly correlated across portfolio

457
00:25:54,720 --> 00:25:58,639
Speaker 4: managers across firms, Because if there is an expected return,

458
00:25:59,119 --> 00:26:02,440
Speaker 4: and if you have skill, and you have sufficient skill

459
00:26:02,520 --> 00:26:05,399
Speaker 4: to be close to the best possible portfolio, you have

460
00:26:05,480 --> 00:26:09,119
Speaker 4: to be also relatively close to other people approximating that

461
00:26:09,200 --> 00:26:13,120
Speaker 4: best possible portfolio. Right, So then it becomes a truism. Right,

462
00:26:13,240 --> 00:26:15,400
Speaker 4: there is a factor and that's the factor of investor,

463
00:26:15,600 --> 00:26:18,320
Speaker 4: of informed investors. So it's true.

464
00:26:18,760 --> 00:26:21,560
Speaker 1: I think it is like there's like a scientific process

465
00:26:21,800 --> 00:26:24,520
Speaker 1: that everyone is pursuing. I hire the best people, and

466
00:26:24,560 --> 00:26:27,160
Speaker 1: they like do the best work to pursue that scientific process,

467
00:26:27,480 --> 00:26:31,320
Speaker 1: and so like they'll eventually converge on something that is

468
00:26:31,359 --> 00:26:33,960
Speaker 1: like truth. But that means buying all the same stocks.

469
00:26:34,720 --> 00:26:36,720
Speaker 4: Yes, it's very difficult to get to that truth.

470
00:26:37,160 --> 00:26:43,200
Speaker 3: Sure, but yeah it's not. Let's let's tale.

471
00:26:43,080 --> 00:26:45,040
Speaker 1: About it would be weird if they weren't hurting among

472
00:26:45,240 --> 00:26:45,600
Speaker 1: the best.

473
00:26:45,800 --> 00:26:47,480
Speaker 3: Yes, yes, but there is there is.

474
00:26:47,760 --> 00:26:49,719
Speaker 4: And by the way, this brings to one of the

475
00:26:49,760 --> 00:26:54,359
Speaker 4: limitations of factor models, right, which is effectively a factor

476
00:26:54,400 --> 00:26:58,440
Speaker 4: model is a form of glorified regression over time. Right,

477
00:26:59,040 --> 00:27:01,840
Speaker 4: And behind a regression there is a bit of an

478
00:27:01,880 --> 00:27:07,439
Speaker 4: assumption to some extent, of independent observations over time. And

479
00:27:07,560 --> 00:27:12,040
Speaker 4: the market and hedge funds are not in dependent random variables.

480
00:27:12,040 --> 00:27:15,040
Speaker 4: They are super dependent random variables, and they are in

481
00:27:15,080 --> 00:27:18,320
Speaker 4: a sort of continuous in direct conversation through their portfolios

482
00:27:18,600 --> 00:27:22,200
Speaker 4: and sometimes the conversation gets really nasty when one hedge

483
00:27:22,280 --> 00:27:24,760
Speaker 4: fund is in a state of distress and all of

484
00:27:24,760 --> 00:27:27,200
Speaker 4: a sudden, or not even a hatch fund, it could

485
00:27:27,200 --> 00:27:30,240
Speaker 4: be also an institution investor and decide to liquidate part

486
00:27:30,280 --> 00:27:33,200
Speaker 4: of their portfolio. And then it becomes a process where

487
00:27:33,240 --> 00:27:36,520
Speaker 4: you have a lot of reflexivity and positive feedback and

488
00:27:36,600 --> 00:27:40,679
Speaker 4: everybody suffers. And in this case, factor models don't really

489
00:27:41,280 --> 00:27:45,080
Speaker 4: You can still identify, like if the system is running

490
00:27:45,080 --> 00:27:49,080
Speaker 4: a temperature with some characteristics, but they are not factors

491
00:27:49,080 --> 00:27:50,399
Speaker 4: in the traditional sense.

492
00:27:51,440 --> 00:27:54,040
Speaker 2: I do want to talk about before we move too

493
00:27:54,040 --> 00:27:55,359
Speaker 2: far away, I do want to talk a little bit

494
00:27:55,359 --> 00:27:58,680
Speaker 2: about how and if factors can die, because you know,

495
00:27:58,720 --> 00:28:03,080
Speaker 2: we've talked a bit about identify fying factors. But when

496
00:28:03,080 --> 00:28:07,359
Speaker 2: do you decide that this doesn't work anymore. Necessarily that

497
00:28:07,400 --> 00:28:11,880
Speaker 2: the market has fundamentally changed and this worked maybe ten

498
00:28:11,920 --> 00:28:16,840
Speaker 2: years ago, maybe fifteen years ago, but maybe now it's devolved.

499
00:28:19,000 --> 00:28:23,879
Speaker 4: Well, there is the good old reason, which is people

500
00:28:23,960 --> 00:28:26,119
Speaker 4: make mistakes in the sense that we think that there

501
00:28:26,240 --> 00:28:28,800
Speaker 4: is a factor and then we look back and there

502
00:28:28,880 --> 00:28:32,080
Speaker 4: is no factor. Right, So there are so many factors

503
00:28:32,119 --> 00:28:34,160
Speaker 4: that some of them have got to.

504
00:28:34,119 --> 00:28:35,240
Speaker 3: Be a little bit redundant.

505
00:28:36,119 --> 00:28:40,000
Speaker 4: So that's one reason, right, So just pure in a

506
00:28:40,040 --> 00:28:43,120
Speaker 4: sense research revisions.

507
00:28:43,160 --> 00:28:43,920
Speaker 3: And then there is.

508
00:28:43,840 --> 00:28:46,040
Speaker 4: Also the fact that there are two other things that

509
00:28:46,080 --> 00:28:50,320
Speaker 4: can happen. One is the moment that you tell people

510
00:28:50,320 --> 00:28:52,520
Speaker 4: that there is a factor. The factor comes into being

511
00:28:52,720 --> 00:28:55,840
Speaker 4: to some extent, right, so it's never black and white

512
00:28:55,840 --> 00:28:58,720
Speaker 4: that the factor did not exist. Maybe the factor did exist,

513
00:28:58,760 --> 00:29:04,160
Speaker 4: and then the moment you identify it, it becomes more existent,

514
00:29:04,520 --> 00:29:08,840
Speaker 4: like as you know speak, yeah, yeah, So ESG is

515
00:29:08,960 --> 00:29:12,920
Speaker 4: one case where the focal point that it became makes

516
00:29:13,000 --> 00:29:15,000
Speaker 4: into an investable theme.

517
00:29:15,280 --> 00:29:17,520
Speaker 2: I thought that was just black rock pumping as.

518
00:29:17,840 --> 00:29:20,920
Speaker 4: Possible, but you know, but everybody had to incorporate it

519
00:29:20,960 --> 00:29:24,040
Speaker 4: in some sense, right, so it became a major source

520
00:29:24,080 --> 00:29:28,720
Speaker 4: of revenue for the vendors, right. So that's that's one thing.

521
00:29:29,560 --> 00:29:31,800
Speaker 4: And then there is the adaptive nature of the market.

522
00:29:31,920 --> 00:29:35,920
Speaker 4: So things that before generated a priced return, So you

523
00:29:36,120 --> 00:29:39,440
Speaker 4: run some risk, you made some money, and then it

524
00:29:39,600 --> 00:29:43,640
Speaker 4: becomes table stakes, it becomes incorporated into factor models, it

525
00:29:43,640 --> 00:29:49,600
Speaker 4: becomes it becomes a smart and and then it becomes

526
00:29:49,680 --> 00:29:52,160
Speaker 4: so I think, you know, you could say definitely that

527
00:29:52,200 --> 00:29:56,200
Speaker 4: medium tomamentum worked much better. You could say that even

528
00:29:56,280 --> 00:29:59,160
Speaker 4: you know, short term reversal worked better. There were years

529
00:29:59,240 --> 00:30:03,480
Speaker 4: when short interest was great, and there are factors or

530
00:30:03,600 --> 00:30:06,440
Speaker 4: data sources that work well now and then maybe in

531
00:30:06,480 --> 00:30:09,600
Speaker 4: five years will become known and become part of the

532
00:30:10,200 --> 00:30:13,040
Speaker 4: I mean credit card data right for consumer. That was

533
00:30:13,280 --> 00:30:16,080
Speaker 4: like there were people who were making a lot of

534
00:30:16,160 --> 00:30:19,959
Speaker 4: money in two thousand and eleven through I don't know,

535
00:30:20,000 --> 00:30:23,400
Speaker 4: sixteen seventeen, and then it's become it's very hard to

536
00:30:23,440 --> 00:30:23,840
Speaker 4: make money.

537
00:30:23,840 --> 00:30:26,320
Speaker 1: In that you said the market is a conversation among

538
00:30:26,360 --> 00:30:28,840
Speaker 1: catch funds. One thing that I think might be true

539
00:30:28,880 --> 00:30:30,840
Speaker 1: that I'm not entirely sure of, is like, to what

540
00:30:30,960 --> 00:30:36,480
Speaker 1: extent the market is a conversation among four hedge funds? Now?

541
00:30:36,560 --> 00:30:38,440
Speaker 1: Like to what extent is like the marginal price or

542
00:30:38,480 --> 00:30:42,920
Speaker 1: of every stock a portfolio manager at you know, one

543
00:30:42,920 --> 00:30:44,320
Speaker 1: of the places you've worked.

544
00:30:44,880 --> 00:30:47,120
Speaker 4: It's a very good question. I don't really have the

545
00:30:47,120 --> 00:30:48,840
Speaker 4: answer to this. I'm not sure.

546
00:30:49,000 --> 00:30:51,400
Speaker 1: It's it's like, what is the intuition at places like that?

547
00:30:51,440 --> 00:30:54,400
Speaker 1: Like is it like the market price is determined by

548
00:30:54,480 --> 00:30:57,800
Speaker 1: like the collective thought of like the top people at

549
00:30:57,800 --> 00:31:00,480
Speaker 1: the top hedge funds, Or is it like we are

550
00:31:01,320 --> 00:31:03,600
Speaker 1: a little bump on the market and we're trading against

551
00:31:03,720 --> 00:31:04,880
Speaker 1: the whole random universe.

552
00:31:04,960 --> 00:31:07,800
Speaker 4: I mean, you'd like to think that the prices are

553
00:31:07,840 --> 00:31:11,560
Speaker 4: determined by the marginal informed investor, right, so by people

554
00:31:11,600 --> 00:31:15,160
Speaker 4: like us at the time horizon where we predict, right,

555
00:31:15,160 --> 00:31:17,320
Speaker 4: which is not the same as at the time a

556
00:31:17,320 --> 00:31:19,760
Speaker 4: horizon of alpha day. Right, that's a different player.

557
00:31:20,040 --> 00:31:21,000
Speaker 1: What is your time arising?

558
00:31:21,080 --> 00:31:24,080
Speaker 4: Like I think of it as well, it depends well, yes,

559
00:31:24,400 --> 00:31:26,880
Speaker 4: it depends. Within a hatch fund, you have a variety

560
00:31:27,000 --> 00:31:29,440
Speaker 4: of even within long shot equities, you know, you have

561
00:31:29,640 --> 00:31:32,520
Speaker 4: you know, portfolio managers who are very tactical, and so

562
00:31:32,560 --> 00:31:35,320
Speaker 4: they think in terms of they have strong daily or

563
00:31:35,360 --> 00:31:38,600
Speaker 4: intra day alpha, even though they're fully discretionary up to

564
00:31:39,560 --> 00:31:42,920
Speaker 4: pms that think easily in terms of months. Also depends

565
00:31:42,920 --> 00:31:46,840
Speaker 4: on the sector. So you know, financials typically probably monetizes

566
00:31:46,880 --> 00:31:49,760
Speaker 4: a little bit less on earnings and tends to have

567
00:31:50,320 --> 00:31:54,640
Speaker 4: a longer horizon. Banks are basically modeling giant balance sheets, right.

568
00:31:55,560 --> 00:31:57,640
Speaker 4: And then in a hedge fund you also have systematic

569
00:31:57,680 --> 00:32:01,040
Speaker 4: but even in systematic there are also of time scales,

570
00:32:01,520 --> 00:32:04,200
Speaker 4: and this cacophony makes the prices. I really don't know,

571
00:32:04,320 --> 00:32:08,400
Speaker 4: Like I said, another question is basically, are how inefficient

572
00:32:08,680 --> 00:32:11,880
Speaker 4: is the market, how incorrect the prices are within a

573
00:32:11,880 --> 00:32:13,760
Speaker 4: factor of two, like Black used to say, or I

574
00:32:13,800 --> 00:32:15,840
Speaker 4: don't know, Like I don't think that the market is

575
00:32:15,960 --> 00:32:19,680
Speaker 4: becoming so super efficient, but it's getting it seems to

576
00:32:19,680 --> 00:32:20,600
Speaker 4: be more efficient.

577
00:32:21,200 --> 00:32:22,240
Speaker 1: I do you feel like that. You know, one of

578
00:32:22,280 --> 00:32:24,000
Speaker 1: the big stories is the rise of like these big

579
00:32:24,120 --> 00:32:29,040
Speaker 1: multi strategy hedgehs. Like you would hope. Maybe you wouldn't

580
00:32:29,040 --> 00:32:31,680
Speaker 1: hope because it's sort of the economic and just, but

581
00:32:31,800 --> 00:32:34,160
Speaker 1: like one might hope that like the rise of these

582
00:32:34,200 --> 00:32:37,040
Speaker 1: big multi strategy hedgehus and a lot of capital being

583
00:32:37,080 --> 00:32:41,640
Speaker 1: allocated to them would observably make the market more efficient.

584
00:32:43,000 --> 00:32:46,040
Speaker 4: Yeah, I don't know if observably holds. I don't it's

585
00:32:46,120 --> 00:32:51,640
Speaker 4: really hard to Like, can you can you tell when

586
00:32:51,680 --> 00:32:52,760
Speaker 4: a bubble is forming?

587
00:32:53,960 --> 00:32:55,520
Speaker 2: A lot of people would say that they can.

588
00:32:56,400 --> 00:33:00,440
Speaker 4: Yeah, I can point you to a few papers. Yeah

589
00:33:00,520 --> 00:33:04,680
Speaker 4: that you know made all the wrong calls. I don't

590
00:33:04,720 --> 00:33:06,160
Speaker 4: want to shame academics in public.

591
00:33:07,640 --> 00:33:09,440
Speaker 2: I do like the idea that the market is a

592
00:33:09,440 --> 00:33:12,719
Speaker 2: conversation between four hedge funds because I live in the

593
00:33:12,720 --> 00:33:16,600
Speaker 2: ETF world, and you know, the big thing is passive

594
00:33:16,680 --> 00:33:20,480
Speaker 2: is just distorting the market, and there's no price discovery anymore,

595
00:33:20,600 --> 00:33:23,320
Speaker 2: and it sounds like that's on the opposite end of

596
00:33:23,320 --> 00:33:24,120
Speaker 2: that spectrum.

597
00:33:24,840 --> 00:33:28,520
Speaker 4: I didn't say I think exactly that. It's a conversation

598
00:33:28,640 --> 00:33:32,120
Speaker 4: between It's a beautiful thing to say though. It sounds

599
00:33:32,160 --> 00:33:35,240
Speaker 4: really cool. It sounds good podcasts.

600
00:33:36,640 --> 00:33:37,240
Speaker 3: That's great.

601
00:33:37,680 --> 00:33:41,080
Speaker 4: Yeah, But I think your question is whether the rise

602
00:33:41,120 --> 00:33:44,120
Speaker 4: of passive has made markets less efficient more of a statement.

603
00:33:44,200 --> 00:33:44,680
Speaker 4: I don't think.

604
00:33:44,960 --> 00:33:47,080
Speaker 2: I was a bad podcaster and didn't actually ask a.

605
00:33:47,080 --> 00:33:49,680
Speaker 4: Question, But okay, how do you know?

606
00:33:50,560 --> 00:33:53,480
Speaker 2: How do I know that passive is destorying the market?

607
00:33:53,840 --> 00:33:55,080
Speaker 2: People on Twitter tell me so.

608
00:33:55,240 --> 00:34:00,680
Speaker 4: Oh, okay, don't trust people one number one.

609
00:34:00,960 --> 00:34:02,959
Speaker 3: Number one, No, I don't know.

610
00:34:03,200 --> 00:34:07,000
Speaker 4: I mean the rise of passive has made index rebalancing

611
00:34:07,120 --> 00:34:11,160
Speaker 4: a weirder strategy, right, so where the margins have compressed,

612
00:34:11,200 --> 00:34:14,120
Speaker 4: but the size has become so big that you can

613
00:34:14,160 --> 00:34:17,399
Speaker 4: still make money in it, and periodic. It's a very

614
00:34:17,480 --> 00:34:19,080
Speaker 4: you know, cyclical strategy.

615
00:34:19,160 --> 00:34:20,960
Speaker 3: So I don't know.

616
00:34:21,080 --> 00:34:25,040
Speaker 1: So if you're an indexy balancing PM, do take like

617
00:34:26,080 --> 00:34:27,399
Speaker 1: eight vacation a year?

618
00:34:27,480 --> 00:34:34,520
Speaker 4: And not the ones I know who probably listen to

619
00:34:34,560 --> 00:34:37,040
Speaker 4: this podcast, they work very hard.

620
00:34:37,600 --> 00:34:38,840
Speaker 2: They want to.

621
00:34:41,000 --> 00:34:44,719
Speaker 1: Indexes aren't paying rence all the time, planning more than.

622
00:34:44,640 --> 00:34:47,239
Speaker 4: You would think Index three balancing is another you know,

623
00:34:47,400 --> 00:34:50,560
Speaker 4: poster child for a strategy that seems so simple that

624
00:34:51,080 --> 00:34:54,320
Speaker 4: everybody can talk about it, and then it's full of

625
00:34:54,800 --> 00:34:58,640
Speaker 4: nuances and it requires a lot of skill to trade effectively.

626
00:34:59,480 --> 00:35:02,600
Speaker 1: I believe, just because like I thought a little bit

627
00:35:02,600 --> 00:35:04,640
Speaker 1: about like just like the sort of like accounting of

628
00:35:04,680 --> 00:35:08,399
Speaker 1: like you basically know how many index funds there are,

629
00:35:09,400 --> 00:35:11,560
Speaker 1: let's say, can predict what will come in and out

630
00:35:11,600 --> 00:35:13,600
Speaker 1: of the index, and like what the so like there's

631
00:35:13,640 --> 00:35:16,920
Speaker 1: like some mechanics around, like you know, figuring out the

632
00:35:16,960 --> 00:35:19,520
Speaker 1: market calves that will come in and whatever, but then

633
00:35:19,560 --> 00:35:21,440
Speaker 1: it feels like the unknown is like who else is

634
00:35:21,520 --> 00:35:23,560
Speaker 1: doing the rebalancing strategy? Is that right?

635
00:35:23,960 --> 00:35:27,120
Speaker 4: I think you're mostly right, because I don't want to say,

636
00:35:27,200 --> 00:35:31,760
Speaker 4: because you know, out of respect for for the CMS,

637
00:35:31,760 --> 00:35:33,439
Speaker 4: did I know fair enough?

638
00:35:33,880 --> 00:35:36,160
Speaker 1: Yess? Like, So we had Cliff Askiness on a few

639
00:35:36,160 --> 00:35:40,680
Speaker 1: weeks ago, and like, to me, Cliff Asness is like

640
00:35:40,719 --> 00:35:45,160
Speaker 1: a quantitative investor, like a systematic investor, but like what

641
00:35:45,239 --> 00:35:49,120
Speaker 1: he's doing is sort of recognizably what a sort of

642
00:35:49,120 --> 00:35:52,120
Speaker 1: traditional asset manager. He's like trying to find companies that

643
00:35:52,160 --> 00:35:52,840
Speaker 1: are undervalued.

644
00:35:52,880 --> 00:35:53,000
Speaker 3: Right.

645
00:35:53,120 --> 00:35:55,040
Speaker 1: He talked about it's like being a Grammar dot investor.

646
00:35:55,080 --> 00:35:57,800
Speaker 1: You know you want like valuation plus a catalyst, and

647
00:35:57,880 --> 00:36:00,440
Speaker 1: he's like, oh, were you know trading you value and

648
00:36:00,480 --> 00:36:04,000
Speaker 1: momentum and like you look at what eight or two

649
00:36:04,080 --> 00:36:05,719
Speaker 1: is maybe a little different, but there's like you know,

650
00:36:05,760 --> 00:36:08,000
Speaker 1: the hypercacy trading firms, Like you can model those as

651
00:36:08,040 --> 00:36:11,359
Speaker 1: like those are quantitative versions of like a voice market

652
00:36:11,440 --> 00:36:13,399
Speaker 1: maker fifty years ago, where they're like trying to keep

653
00:36:13,400 --> 00:36:15,480
Speaker 1: inventory flat and like trying to you know, make the

654
00:36:15,480 --> 00:36:19,719
Speaker 1: bid ask spread. So like those are like very traditional

655
00:36:19,920 --> 00:36:24,719
Speaker 1: economic functions that have been quantified, like turned into systematic

656
00:36:26,239 --> 00:36:28,759
Speaker 1: what's the intuition for like what a bally asthe or

657
00:36:28,800 --> 00:36:32,640
Speaker 1: a sedad doll or a millennium does? Like what business

658
00:36:32,960 --> 00:36:36,600
Speaker 1: are you in? Do you think? Like as a philosophical matter,

659
00:36:39,160 --> 00:36:40,680
Speaker 1: like one thing I think like I think about.

660
00:36:40,440 --> 00:36:43,640
Speaker 4: Like you're asking from a social Yeah, that's kind of point.

661
00:36:44,080 --> 00:36:46,160
Speaker 1: Like I think like so the index rebalancing like to

662
00:36:46,239 --> 00:36:49,239
Speaker 1: me feels like the sort of trade and I think

663
00:36:49,280 --> 00:36:50,719
Speaker 1: to something that was the sort of trade that like

664
00:36:50,760 --> 00:36:52,799
Speaker 1: an investment bank would have done twenty years ago, thirty

665
00:36:52,840 --> 00:36:55,279
Speaker 1: years ago, and like some of that function I think

666
00:36:55,280 --> 00:36:59,280
Speaker 1: has moved to like the big multimanagers. But like I wonder,

667
00:36:59,320 --> 00:37:01,800
Speaker 1: like from where you say, like how you see that

668
00:37:02,719 --> 00:37:04,680
Speaker 1: role in the financial markets of those firms.

669
00:37:04,840 --> 00:37:07,640
Speaker 4: So at a very high level, we don't do anything

670
00:37:07,680 --> 00:37:10,200
Speaker 4: different than everybody else in the sense that what we

671
00:37:10,280 --> 00:37:14,719
Speaker 4: provide is always this, right, is we provide shifting time preferences,

672
00:37:14,800 --> 00:37:20,280
Speaker 4: which means we provide liquidity, We house risk for people

673
00:37:20,280 --> 00:37:23,919
Speaker 4: who don't want to hold it right now. And that's

674
00:37:23,960 --> 00:37:26,319
Speaker 4: what you do when you do indextre balancing, right, That's

675
00:37:26,360 --> 00:37:28,440
Speaker 4: what you do when you do merger ARB and when

676
00:37:28,480 --> 00:37:31,480
Speaker 4: you do the various subtypes of basis traits.

677
00:37:31,560 --> 00:37:31,719
Speaker 1: Right.

678
00:37:31,760 --> 00:37:35,359
Speaker 4: So we do provide liquidity, which is very important. And

679
00:37:35,400 --> 00:37:38,440
Speaker 4: then the second thing we again very high level, we

680
00:37:38,480 --> 00:37:41,960
Speaker 4: provide price discovery. Right, So we study the firms and

681
00:37:42,080 --> 00:37:44,600
Speaker 4: we think, okay, this is at the margin mispriced and

682
00:37:44,680 --> 00:37:46,480
Speaker 4: we're going to short it or we're going to invest

683
00:37:46,480 --> 00:37:49,719
Speaker 4: in it, and that's a beautiful thing. So we do

684
00:37:49,800 --> 00:37:52,040
Speaker 4: it at a different time scale, right. So you always

685
00:37:52,080 --> 00:37:55,319
Speaker 4: want to do things at the margin where you don't

686
00:37:55,320 --> 00:37:58,360
Speaker 4: have a lot of other participants, and at the margin

687
00:37:58,520 --> 00:38:01,719
Speaker 4: of the let's say a month three month investment horizon,

688
00:38:02,520 --> 00:38:06,640
Speaker 4: there are not that many participants, so in the words

689
00:38:06,719 --> 00:38:10,520
Speaker 4: of another hatch fund manager I cannot name, but it said,

690
00:38:10,560 --> 00:38:13,800
Speaker 4: once you know, we don't invest in securities, we dated them,

691
00:38:13,880 --> 00:38:16,120
Speaker 4: and so we are in the dating service. Not that

692
00:38:16,200 --> 00:38:18,040
Speaker 4: many people are doing it, and so we do it.

693
00:38:18,080 --> 00:38:19,759
Speaker 4: But I would say also this right, not at the

694
00:38:19,760 --> 00:38:22,080
Speaker 4: social level. I just want to answer at the like

695
00:38:22,200 --> 00:38:24,720
Speaker 4: my personal level. What we do. We are a massive

696
00:38:24,760 --> 00:38:28,680
Speaker 4: filter of talent, and the talent that we hire is

697
00:38:28,719 --> 00:38:32,440
Speaker 4: a massive filter of information. So it's like information squared.

698
00:38:34,680 --> 00:38:37,040
Speaker 1: Maybe this is like a bad question, but like, do

699
00:38:37,120 --> 00:38:40,320
Speaker 1: you think that like long only asset managers are worse

700
00:38:40,360 --> 00:38:43,080
Speaker 1: than they were thirty years ago because that filter has

701
00:38:43,120 --> 00:38:47,160
Speaker 1: been so successful? In other words, like there are lots

702
00:38:47,160 --> 00:38:48,880
Speaker 1: of jobs you could have gotten in finance in nineteen

703
00:38:48,920 --> 00:38:51,919
Speaker 1: ninety but like, yeah, there's like a clear hierarchy now.

704
00:38:53,400 --> 00:38:56,160
Speaker 4: I think that the market and the set of investors

705
00:38:56,239 --> 00:39:00,320
Speaker 4: has learned right, and I think the distinction between beten

706
00:39:00,440 --> 00:39:07,400
Speaker 4: HALFA has been useful for investors and so active investors

707
00:39:07,480 --> 00:39:11,560
Speaker 4: who are mostly long only, I think have suffered from

708
00:39:11,600 --> 00:39:16,200
Speaker 4: this distinction because the vast majority of them underperforms their

709
00:39:16,200 --> 00:39:19,560
Speaker 4: benchmarks and so there is no reason for them to exist.

710
00:39:20,200 --> 00:39:24,400
Speaker 4: And then what we do is we provide really uncorrelated

711
00:39:24,440 --> 00:39:30,719
Speaker 4: returns to the benchmarks to most factors, and investors want that, right,

712
00:39:30,760 --> 00:39:36,239
Speaker 4: So there is a future where active investors, long on investors,

713
00:39:36,400 --> 00:39:40,360
Speaker 4: asset managers will become even less influential, smaller.

714
00:39:41,040 --> 00:39:44,080
Speaker 1: And also I think of that as like a customer

715
00:39:44,080 --> 00:39:46,320
Speaker 1: demand side, but also like a talent filter side, right.

716
00:39:46,360 --> 00:39:49,239
Speaker 4: Yes, Yeah, And then the interesting thing is and then

717
00:39:49,280 --> 00:39:55,000
Speaker 4: there is also a process where the multimanager platforms are

718
00:39:55,120 --> 00:39:58,640
Speaker 4: able to make the business model of a single portfolio

719
00:39:58,640 --> 00:40:02,400
Speaker 4: manager that is not sustainabil and isolation working in this

720
00:40:02,520 --> 00:40:06,200
Speaker 4: kind of federated system. So why would you or how

721
00:40:06,200 --> 00:40:10,520
Speaker 4: could you survive as a single portfolio manager hedge fund nowadays?

722
00:40:10,560 --> 00:40:13,319
Speaker 4: It's really really difficult, But you can do it in

723
00:40:13,360 --> 00:40:16,200
Speaker 4: a multi manager platform provided that you have you know,

724
00:40:16,239 --> 00:40:18,040
Speaker 4: sufficient talent, sufficient edge.

725
00:40:18,719 --> 00:40:21,840
Speaker 2: That's also where you can blame the passive influence on Twitter.

726
00:40:21,840 --> 00:40:23,880
Speaker 2: If you're a long Enchey manager, then you know it's

727
00:40:23,920 --> 00:40:25,759
Speaker 2: impossible to be the market now because you just have

728
00:40:25,800 --> 00:40:27,560
Speaker 2: this money constantly pouring in.

729
00:40:27,719 --> 00:40:29,880
Speaker 3: Yeah, I a don't disagree.

730
00:40:30,000 --> 00:40:33,000
Speaker 1: Yeah, one more question, like social role is just like

731
00:40:33,000 --> 00:40:35,080
Speaker 1: you've worked at most of the big pod jobs, but

732
00:40:35,080 --> 00:40:38,160
Speaker 1: you also work at HRT, Like what's the difference in

733
00:40:38,280 --> 00:40:41,200
Speaker 1: roles and like what they do all day? Because HRT

734
00:40:41,360 --> 00:40:44,399
Speaker 1: I think of as a classic like high frequency treating firm,

735
00:40:44,440 --> 00:40:46,360
Speaker 1: where I don't know they're exactly a market maker, but

736
00:40:46,400 --> 00:40:48,840
Speaker 1: they're certainly on the higher frequency side. And then like

737
00:40:49,320 --> 00:40:52,880
Speaker 1: the pod shops have a lower frequency and a you know,

738
00:40:52,960 --> 00:40:58,000
Speaker 1: they're not prop they're running hedgephnes. Like what's the cultural

739
00:40:58,040 --> 00:40:59,880
Speaker 1: and role and difference?

740
00:41:00,200 --> 00:41:04,640
Speaker 4: Yeah, okay, So I briefly mentioned HRT in an interview

741
00:41:04,640 --> 00:41:08,120
Speaker 4: with The Financial Times, and my manager told me that,

742
00:41:08,520 --> 00:41:11,840
Speaker 4: you know, people at HIT were both annoyed and delighted

743
00:41:12,200 --> 00:41:15,600
Speaker 4: by what I've had said about about HRT. I think

744
00:41:15,719 --> 00:41:18,680
Speaker 4: HRT is a really special place, even in the in

745
00:41:18,719 --> 00:41:21,640
Speaker 4: the context of proper training firms. So I'm a little

746
00:41:21,640 --> 00:41:28,600
Speaker 4: bit hesitant to just be in them in as a representative, right,

747
00:41:28,680 --> 00:41:34,759
Speaker 4: So they're not representative because there is something in the

748
00:41:34,760 --> 00:41:39,200
Speaker 4: culture of HRT that is special. Okay, it's collaborative, it's

749
00:41:39,239 --> 00:41:39,960
Speaker 4: truly kind.

750
00:41:40,880 --> 00:41:43,040
Speaker 3: Yeah. So I think it's a great place.

751
00:41:42,840 --> 00:41:47,200
Speaker 4: To work, and it is fundamentally monolithic, so you have,

752
00:41:48,000 --> 00:41:51,719
Speaker 4: you know, sharing of ideas and you can work at

753
00:41:51,719 --> 00:41:53,120
Speaker 4: the intersection of these ideas.

754
00:41:53,480 --> 00:41:54,600
Speaker 3: It's also a place.

755
00:41:54,360 --> 00:41:58,120
Speaker 4: That is very tech oriented, so it's a bit of

756
00:41:58,160 --> 00:42:03,520
Speaker 4: a technology firm or in the financial space, and because

757
00:42:03,520 --> 00:42:06,200
Speaker 4: of that, it also attracts I think, the best technical

758
00:42:06,239 --> 00:42:09,640
Speaker 4: talent that I've ever worked with. It's just a pleasure

759
00:42:09,719 --> 00:42:13,279
Speaker 4: to work with great technologies. People were very competent in

760
00:42:13,280 --> 00:42:16,880
Speaker 4: that respect. So nothing against the hedge funds. I love

761
00:42:16,960 --> 00:42:19,680
Speaker 4: edge funds for different reasons. You know, I love BAM,

762
00:42:19,880 --> 00:42:23,080
Speaker 4: which is also very collaborative and it's an investment company,

763
00:42:23,880 --> 00:42:27,640
Speaker 4: but HRT has as a technical side to it and

764
00:42:27,680 --> 00:42:29,319
Speaker 4: also gain a cultural side to it.

765
00:42:29,400 --> 00:42:29,920
Speaker 3: It's great.

766
00:42:45,440 --> 00:42:48,800
Speaker 2: We didn't talk about ai AI.

767
00:42:49,200 --> 00:42:51,040
Speaker 3: Yeah, of course you have to talk about it.

768
00:42:51,719 --> 00:42:55,800
Speaker 1: So I have like three models of how investment works systematic.

769
00:42:56,080 --> 00:42:58,640
Speaker 1: Like one is like you have like some economic intuition

770
00:42:58,719 --> 00:43:00,640
Speaker 1: and you build a model of the stock market that

771
00:43:00,680 --> 00:43:02,880
Speaker 1: predicts prices. In other way is a sort of like

772
00:43:03,400 --> 00:43:06,440
Speaker 1: neural netty ai E way, where like you throw a

773
00:43:06,440 --> 00:43:08,959
Speaker 1: lot of data at a neural net and it build

774
00:43:08,960 --> 00:43:11,160
Speaker 1: its own model of how to predict stock prices. And

775
00:43:11,160 --> 00:43:14,000
Speaker 1: then the third model is like you get really good

776
00:43:14,000 --> 00:43:16,640
Speaker 1: at prompt engineering and you get a chat GPT and

777
00:43:16,680 --> 00:43:18,560
Speaker 1: you say what stocks will go up? But you ask

778
00:43:18,600 --> 00:43:20,600
Speaker 1: it in the right way, and then chat GPT tells

779
00:43:20,640 --> 00:43:24,960
Speaker 1: me what stocks will go up? How good is it? Okay,

780
00:43:25,320 --> 00:43:27,080
Speaker 1: I assume the third model no one uses, but like

781
00:43:27,120 --> 00:43:27,880
Speaker 1: someone uses.

782
00:43:29,280 --> 00:43:31,000
Speaker 4: I think a lot of people use that, all right,

783
00:43:31,040 --> 00:43:35,400
Speaker 4: So first thing, like, okay, nobody knows anything, and anybody

784
00:43:35,480 --> 00:43:39,520
Speaker 4: saying the opposite, you know, should be heavily discounted. Okay,

785
00:43:39,600 --> 00:43:45,440
Speaker 4: so we agree on this, and so let's forget for

786
00:43:45,480 --> 00:43:48,520
Speaker 4: a second all the technical details of AI just from

787
00:43:48,600 --> 00:43:54,240
Speaker 4: a pure industrial organization standpoint, Right, So what's going to happen?

788
00:43:55,400 --> 00:43:59,279
Speaker 4: Consider AI just like another technology like Internet and whatnot. Right,

789
00:43:59,320 --> 00:44:02,320
Speaker 4: So you know, first of all, we're going to observe

790
00:44:02,520 --> 00:44:06,520
Speaker 4: economies of scale, So there's going to be concentration, and

791
00:44:06,560 --> 00:44:09,360
Speaker 4: there was going to be some kind of monopolistic competition.

792
00:44:09,440 --> 00:44:11,839
Speaker 3: I was thinking about Bloomberg.

793
00:44:11,760 --> 00:44:16,239
Speaker 4: Specifically, which could be I hope for you people to

794
00:44:16,320 --> 00:44:21,000
Speaker 4: be among the winners, because you have a good starting point, right,

795
00:44:21,040 --> 00:44:23,680
Speaker 4: you have lots of data, right, you have a customer base,

796
00:44:24,400 --> 00:44:27,919
Speaker 4: and maybe in the future we'll finally not see the

797
00:44:28,520 --> 00:44:31,560
Speaker 4: good old Bloomberg terminal, which has been kind of unchanged

798
00:44:31,600 --> 00:44:34,840
Speaker 4: since I remember it, and instead people will just prompt

799
00:44:34,840 --> 00:44:40,080
Speaker 4: Bloomberg to conduct very complex actions where it will act

800
00:44:40,120 --> 00:44:43,919
Speaker 4: on a sequence of keywords and connect them and give you,

801
00:44:44,000 --> 00:44:47,080
Speaker 4: like a much more valuable product for which Bloomberg will

802
00:44:47,120 --> 00:44:50,279
Speaker 4: charge twice as much as they do already. So this

803
00:44:50,440 --> 00:44:52,520
Speaker 4: is going to happen in one form or another. If

804
00:44:52,520 --> 00:44:55,880
Speaker 4: it's not Bloomber, somebody else will do it. Okay, But

805
00:44:55,960 --> 00:45:00,080
Speaker 4: the same thing applies to other areas of finance. So

806
00:45:00,320 --> 00:45:03,520
Speaker 4: maybe once upon a time, you know, a big sufficiently

807
00:45:03,560 --> 00:45:07,640
Speaker 4: big fund could build their own client for email. Right.

808
00:45:07,719 --> 00:45:10,160
Speaker 4: Of course, nobody builds a client for email anymore. Right,

809
00:45:10,200 --> 00:45:13,680
Speaker 4: So a lot of this stuff gets outsourced. We will

810
00:45:13,680 --> 00:45:16,880
Speaker 4: outsource at some point some of the functions that we

811
00:45:17,000 --> 00:45:22,080
Speaker 4: conduct internally using AI to other AI agents. It's perfectly fine.

812
00:45:22,719 --> 00:45:26,000
Speaker 4: So this will become a utility to some extent.

813
00:45:27,560 --> 00:45:30,320
Speaker 1: Yes, functions in clid like well.

814
00:45:30,080 --> 00:45:34,600
Speaker 4: Not stockpicking, not stock picking. I think that the functions

815
00:45:34,640 --> 00:45:39,760
Speaker 4: that we will see available are essentially like another self,

816
00:45:39,840 --> 00:45:43,560
Speaker 4: like another mathlevin, who can you know be a good

817
00:45:43,600 --> 00:45:44,520
Speaker 4: baseline for you?

818
00:45:44,840 --> 00:45:45,200
Speaker 3: Okay?

819
00:45:45,280 --> 00:45:48,680
Speaker 4: You could feed a post train an AI system with

820
00:45:48,920 --> 00:45:53,479
Speaker 4: all your gazillions of words, right, and that agent will

821
00:45:53,480 --> 00:45:58,080
Speaker 4: reproduce your sense of humor, your investigative style and everything Okay,

822
00:45:58,239 --> 00:46:00,640
Speaker 4: it's a good approximation. It's not going to be perfect,

823
00:46:01,320 --> 00:46:03,600
Speaker 4: but why not, right, So I would be very happy

824
00:46:03,640 --> 00:46:06,920
Speaker 4: to have a replica of myself that can answer most

825
00:46:07,040 --> 00:46:10,960
Speaker 4: simple questions. Now, I think that the decision to invest

826
00:46:11,000 --> 00:46:14,840
Speaker 4: in a particular stock is a very demanding cognitive function,

827
00:46:14,920 --> 00:46:18,480
Speaker 4: and I don't see that really being replicated very well.

828
00:46:18,520 --> 00:46:21,200
Speaker 4: But I think that this will be baselined to some extent.

829
00:46:21,520 --> 00:46:25,160
Speaker 1: Is it a many kind of function because because it

830
00:46:25,239 --> 00:46:29,359
Speaker 1: exists in a competitive market, So like the sort of

831
00:46:29,440 --> 00:46:31,360
Speaker 1: like whatever the kind of function is, is going to

832
00:46:31,440 --> 00:46:33,319
Speaker 1: get like the baseline is always going to get higher

833
00:46:33,360 --> 00:46:35,600
Speaker 1: because like someone else will will have the same information

834
00:46:35,640 --> 00:46:36,640
Speaker 1: as you do or the same.

835
00:46:36,480 --> 00:46:39,319
Speaker 4: Well, this is getting really in the highly speculative side

836
00:46:39,360 --> 00:46:43,120
Speaker 4: of you know things. I think that in order for

837
00:46:43,200 --> 00:46:46,399
Speaker 4: an AI agent to be good at this, they have

838
00:46:46,520 --> 00:46:50,839
Speaker 4: to be able to experience the world the same way

839
00:46:50,920 --> 00:46:55,279
Speaker 4: that an investor experiences it, and our inputs are much

840
00:46:55,320 --> 00:46:58,840
Speaker 4: more complex than just a string of text or YouTube videos.

841
00:46:58,920 --> 00:46:59,080
Speaker 1: Right.

842
00:46:59,400 --> 00:47:03,360
Speaker 4: We have a the world which comes from visually experiencing

843
00:47:03,400 --> 00:47:07,800
Speaker 4: the world, talking to humans, consuming the goods, right anything,

844
00:47:08,040 --> 00:47:13,160
Speaker 4: It's vastly more complex than the way an AI system

845
00:47:13,400 --> 00:47:16,600
Speaker 4: Right now, experiences the world and also influences the world.

846
00:47:16,640 --> 00:47:21,560
Speaker 4: So an investor has a fundamentally different experience of a company.

847
00:47:21,560 --> 00:47:24,160
Speaker 3: Than an LM that has.

848
00:47:23,960 --> 00:47:27,799
Speaker 4: An experience that is mediated by multiple layers of processing.

849
00:47:27,920 --> 00:47:30,120
Speaker 4: You know, they learn about a company through text that

850
00:47:30,239 --> 00:47:33,319
Speaker 4: is written by somebody. So I don't think that that's

851
00:47:33,320 --> 00:47:35,600
Speaker 4: in danger for the time being. But maybe, you know, again,

852
00:47:35,600 --> 00:47:39,719
Speaker 4: in five years, maybe we will have our glasses feeding

853
00:47:40,600 --> 00:47:43,640
Speaker 4: our experiences to AI agents. Who knows, right, But I

854
00:47:43,640 --> 00:47:47,040
Speaker 4: don't think that it's that close, And I don't think

855
00:47:47,080 --> 00:47:49,799
Speaker 4: AI is that smart also, so I think that having

856
00:47:49,880 --> 00:47:52,000
Speaker 4: a baseline system would be already pretty good.

857
00:47:52,560 --> 00:47:56,480
Speaker 2: That's somewhat comforting that our experiences count for something, our

858
00:47:56,520 --> 00:47:58,200
Speaker 2: physical experience of the world.

859
00:47:58,560 --> 00:48:00,640
Speaker 1: It's interesting. I always think of like the comparison is

860
00:48:00,680 --> 00:48:05,200
Speaker 1: like investing in self driving cars, or like investors do

861
00:48:05,200 --> 00:48:07,040
Speaker 1: a lot of things. Like one thing they do a

862
00:48:07,040 --> 00:48:09,480
Speaker 1: lot is sit at a desk and read computers and

863
00:48:09,520 --> 00:48:12,280
Speaker 1: like look at numbers, right, and like those things seem

864
00:48:12,320 --> 00:48:14,400
Speaker 1: like things that a computer can do well, whereas like

865
00:48:15,080 --> 00:48:18,480
Speaker 1: you know, drivers like have physical reflexes and like have

866
00:48:18,560 --> 00:48:21,480
Speaker 1: a you know, complicated field division. I always thought like

867
00:48:22,200 --> 00:48:24,680
Speaker 1: investing should be easier than self driving cars for a

868
00:48:24,719 --> 00:48:28,880
Speaker 1: computer and a master. But you and I think you're

869
00:48:28,960 --> 00:48:31,000
Speaker 1: learning this. Think of like investing as like the great

870
00:48:31,000 --> 00:48:33,279
Speaker 1: liberal art, where it's like you incorporate all of human

871
00:48:33,320 --> 00:48:35,040
Speaker 1: experience and so the AI can't.

872
00:48:34,800 --> 00:48:38,319
Speaker 4: Really Okay, let's let's take the metaphor to you know,

873
00:48:38,360 --> 00:48:41,360
Speaker 4: extreme consequences. Imagine that you had a system that is

874
00:48:41,400 --> 00:48:44,680
Speaker 4: the equivalent of a perfect self driving car in investing.

875
00:48:44,800 --> 00:48:45,760
Speaker 3: So now I'm.

876
00:48:45,600 --> 00:48:49,000
Speaker 4: Giving you a machine, a box that is telling you

877
00:48:50,000 --> 00:48:53,239
Speaker 4: the long term value, if not the returns, right, because

878
00:48:53,239 --> 00:48:56,279
Speaker 4: the moment that the value is known, you immediately equilibrate

879
00:48:56,400 --> 00:48:57,240
Speaker 4: to that level.

880
00:48:57,320 --> 00:48:57,480
Speaker 1: Right.

881
00:48:57,520 --> 00:49:00,120
Speaker 4: So imagine that you know the true value of everything

882
00:49:00,160 --> 00:49:02,880
Speaker 4: because a box tells you so, and it's infallible.

883
00:49:02,920 --> 00:49:04,520
Speaker 3: It's an oracle. Okay.

884
00:49:04,800 --> 00:49:10,399
Speaker 4: Would you think that finance stops existing? I wouldn't say so, right,

885
00:49:10,480 --> 00:49:13,920
Speaker 4: So I think that a lot of arbitrush trades, you know,

886
00:49:14,080 --> 00:49:18,560
Speaker 4: would maybe change significantly, but every risk, right, every return

887
00:49:18,680 --> 00:49:21,920
Speaker 4: would be correctly priced by the risk of the agents

888
00:49:22,040 --> 00:49:24,600
Speaker 4: trading it. So there still would be trading because we

889
00:49:24,640 --> 00:49:28,160
Speaker 4: still have different preferences, but basically every risk could be priced.

890
00:49:28,160 --> 00:49:31,320
Speaker 4: There would be in a sense less alpha. But finance

891
00:49:31,360 --> 00:49:33,719
Speaker 4: will still exist. It's a lot of service provision like

892
00:49:33,800 --> 00:49:38,960
Speaker 4: liquidity provision. Yeah, and so the liquidity provision would still exist.

893
00:49:39,520 --> 00:49:43,440
Speaker 4: The informational services maybe will stop existing in the current.

894
00:49:43,160 --> 00:49:44,600
Speaker 3: Form, but that's okay.

895
00:49:44,760 --> 00:49:46,560
Speaker 4: I think that we'll all still be employed.

896
00:49:47,239 --> 00:49:47,640
Speaker 2: Mm hmm.

897
00:49:48,360 --> 00:49:50,160
Speaker 1: It's an interesting I think about it, because I do think,

898
00:49:50,239 --> 00:49:54,279
Speaker 1: like we talked about, like, one thing that the big

899
00:49:54,320 --> 00:49:57,000
Speaker 1: hedgehunds to do is things that have the flavor of

900
00:49:57,000 --> 00:50:01,160
Speaker 1: liquidity provisions basis trades and merger and whatever. Things that

901
00:50:01,200 --> 00:50:02,799
Speaker 1: like I think of as like something that a bank

902
00:50:02,800 --> 00:50:04,400
Speaker 1: would have done thirty years ago, and then now a

903
00:50:04,400 --> 00:50:06,640
Speaker 1: big hedge one does. And then another thing they do

904
00:50:07,200 --> 00:50:10,960
Speaker 1: has the flavor of information provision, where it's getting prices right.

905
00:50:11,800 --> 00:50:15,840
Speaker 1: Like to me, those things seem quite intellectually separate, but

906
00:50:15,920 --> 00:50:17,680
Speaker 1: I guess they feed each other in the sense that

907
00:50:18,960 --> 00:50:20,719
Speaker 1: the better you are prices, the better you can be

908
00:50:20,719 --> 00:50:26,480
Speaker 1: at liquidity provision. Is that sort of right? You didn't

909
00:50:26,480 --> 00:50:27,640
Speaker 1: know the value of the Yeah.

910
00:50:27,480 --> 00:50:31,960
Speaker 4: I mean a short, short horizon. Liquidity provision and information

911
00:50:32,560 --> 00:50:35,880
Speaker 4: tend to be very closely rated, Like you know, a limit.

912
00:50:36,320 --> 00:50:40,400
Speaker 4: If you are good at crossing, even good at crossing,

913
00:50:40,480 --> 00:50:43,759
Speaker 4: you should be pretty good at adding okay, adding liquidity,

914
00:50:43,840 --> 00:50:45,919
Speaker 4: so you know, but I mean like you could make

915
00:50:46,360 --> 00:50:48,520
Speaker 4: you know, a profit by posting a lot of limit

916
00:50:48,680 --> 00:50:52,520
Speaker 4: orders and providing liquidity to the market, or crossing the

917
00:50:52,560 --> 00:50:56,560
Speaker 4: spread and making money with predicting the future prices. If

918
00:50:56,600 --> 00:50:58,719
Speaker 4: you're good at one, you're good at the other. Most likely,

919
00:50:58,800 --> 00:51:01,520
Speaker 4: right at that time scale, I think that this though

920
00:51:01,880 --> 00:51:03,799
Speaker 4: might I'm not sure because I haven't thought about this

921
00:51:04,000 --> 00:51:07,320
Speaker 4: very very carefully, but I think this might be coupled

922
00:51:07,480 --> 00:51:11,080
Speaker 4: at longer time scale, so you know, you're when you're out.

923
00:51:11,880 --> 00:51:13,759
Speaker 4: I'm not sure. And in any case, at that time

924
00:51:13,800 --> 00:51:16,279
Speaker 4: scale is really difficult for an AI or for a

925
00:51:16,360 --> 00:51:19,640
Speaker 4: human being anyone, Like, there are not that many hard data,

926
00:51:20,400 --> 00:51:23,200
Speaker 4: even the unstructured data are not that many. So it's

927
00:51:23,200 --> 00:51:27,640
Speaker 4: a very difficult problem. It's the coupled it's it's complicated.

928
00:51:27,719 --> 00:51:32,160
Speaker 4: So yeah, but I tend to believe at longer time

929
00:51:32,200 --> 00:51:36,360
Speaker 4: scales you have more or less liquidit provisioning and you know,

930
00:51:36,760 --> 00:51:40,880
Speaker 4: violations of law of one price on one side and

931
00:51:40,960 --> 00:51:42,920
Speaker 4: predicting on the other side.

932
00:51:43,280 --> 00:51:44,239
Speaker 1: But you combine both.

933
00:51:45,120 --> 00:51:47,880
Speaker 4: But you can combine both, and it's a very potent mix.

934
00:51:48,280 --> 00:51:49,800
Speaker 1: Right, there's normally different people.

935
00:51:49,840 --> 00:51:53,280
Speaker 4: It is right, very different people for sure, different, very different,

936
00:51:53,440 --> 00:51:55,279
Speaker 4: very different people, very different cultures.

937
00:51:56,239 --> 00:51:59,920
Speaker 1: Yeah, can you summarize the difference in cultures between like I.

938
00:51:59,840 --> 00:52:03,439
Speaker 4: Have, I guess, but well, as you said, people who

939
00:52:03,560 --> 00:52:07,520
Speaker 4: typically trade in ARB trades, if not historically, but also

940
00:52:07,560 --> 00:52:13,320
Speaker 4: historically come from banks. Right, whereas you still can see

941
00:52:13,719 --> 00:52:18,680
Speaker 4: long only portfolio managers being recycled and reformatted into long

942
00:52:18,719 --> 00:52:23,239
Speaker 4: short portfolio managers, you can have an excellent short specialist

943
00:52:23,320 --> 00:52:26,880
Speaker 4: becoming a long short portfolio manager, like it happened.

944
00:52:27,400 --> 00:52:29,759
Speaker 1: I mean my sense is that like that, people on

945
00:52:29,800 --> 00:52:35,720
Speaker 1: the information version long short sider more academic and research oriented.

946
00:52:35,760 --> 00:52:39,759
Speaker 1: In the people on the ARB side are more.

947
00:52:40,080 --> 00:52:42,640
Speaker 4: Yeah, I think you can actually have very good long

948
00:52:42,680 --> 00:52:47,360
Speaker 4: short portfolio managers who were journalists in their past lives.

949
00:52:47,760 --> 00:52:51,919
Speaker 1: I've heard of some of these. I thought about it, No, just.

950
00:52:51,840 --> 00:52:53,719
Speaker 4: Like real.

951
00:52:55,880 --> 00:52:57,760
Speaker 2: Breaking news on your podcast.

952
00:52:58,080 --> 00:53:04,200
Speaker 1: I've jumps that's better than podcasting. Not thought about it

953
00:53:04,200 --> 00:53:05,520
Speaker 1: in the sonth that I'd be good at it, just

954
00:53:05,560 --> 00:53:08,279
Speaker 1: in the sense that the money is good.

955
00:53:08,520 --> 00:53:11,160
Speaker 2: You could be bad at it and paid really well

956
00:53:11,239 --> 00:53:12,239
Speaker 2: for a short amount of time.

957
00:53:12,480 --> 00:53:15,440
Speaker 1: I don't know that that's true. Actually, they're they're an

958
00:53:15,440 --> 00:53:17,400
Speaker 1: excellent talent filter or so I hear.

959
00:53:19,360 --> 00:53:23,000
Speaker 4: Yes, I think that you could interest a few huge funds.

960
00:53:23,560 --> 00:53:34,880
Speaker 4: They might be listening. On a note, Kathy, thanks for

961
00:53:34,920 --> 00:53:37,759
Speaker 4: coming on the pleasure, Thanks for having me.

962
00:53:43,880 --> 00:53:45,320
Speaker 1: And that was the Money Stuff Podcast.

963
00:53:45,480 --> 00:53:47,440
Speaker 2: I'm Matt Levian and I'm Katie Greifeld.

964
00:53:47,840 --> 00:53:49,919
Speaker 1: You can find my work by subscribing to The Money

965
00:53:49,920 --> 00:53:52,480
Speaker 1: Stuff newsletter on Bloomberg dot com.

966
00:53:52,080 --> 00:53:54,560
Speaker 2: And you can find me on Bloomberg TV. Every day

967
00:53:54,640 --> 00:53:57,720
Speaker 2: on Open Interest between nine to eleven am Eastern.

968
00:53:57,960 --> 00:53:59,600
Speaker 1: We'd love to hear from you. You can send an

969
00:53:59,640 --> 00:54:03,200
Speaker 1: email Moneypot at Bloomberg dot net, ask us a question

970
00:54:03,239 --> 00:54:04,359
Speaker 1: and we might answer it on air.

971
00:54:04,800 --> 00:54:07,000
Speaker 2: You can also subscribe to our show wherever you're listening

972
00:54:07,040 --> 00:54:09,080
Speaker 2: right now and leave us a review. It helps more

973
00:54:09,080 --> 00:54:09,959
Speaker 2: people find the show.

974
00:54:10,719 --> 00:54:14,279
Speaker 1: The Money Stuff Podcast is produced by Anna Maserakus and Moses.

975
00:54:13,920 --> 00:54:16,640
Speaker 2: Onam Our theme music was composed by Blake Maples.

976
00:54:16,920 --> 00:54:19,600
Speaker 1: Brandon Francis Newdhim is our executive producer.

977
00:54:19,400 --> 00:54:21,520
Speaker 2: And Stage Ballman is Bloomberg's head of Podcasts.

978
00:54:21,840 --> 00:54:24,120
Speaker 1: Thanks for listening to The Money Stuff Podcast. We'll be

979
00:54:24,280 --> 00:54:25,760
Speaker 1: back next week with more stuff