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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. Wait, Gapy 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.

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Speaker 1: It, and I feel like when I first met you,

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Speaker 1: I think I asked you if you go by Gappy

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Speaker 1: because of your famous track record of taking gardening leave,

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Speaker 1: like having gaps in your career.

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Speaker 3: Oh okay, I didn't you remember that. Yeah, that's a

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Speaker 3: great excuse for for a nickname. No, but the reason

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Speaker 3: is when I came to the States for grad school,

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Speaker 3: and this was a long time ago, in ninety five.

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Speaker 3: So the first thing that you did was set up

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Speaker 3: an email account. You still have the freedom to choose

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Speaker 3: an email account. Now, they just give you your initials with

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Speaker 3: the number, and so my initials are gap Gap, and

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Speaker 3: of course it was taken. So I said, okay, well Gappy,

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Speaker 3: and then everybody in grad school, and then my wife

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Speaker 3: was Italian, everybody started to call me Gappy and that's stuck.

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Speaker 3: And now at work they just have dispensed with my

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Speaker 3: real name, like on all systems, I'm just Gappy Paliologo.

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

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Speaker 3: for tax evasion because on my tax forms there is

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Speaker 3: Gappy Palaiologo or something like that.

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Speaker 1: Well, hello, and welcome to the Artistuff Podcast.

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

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Speaker 1: And we have a guest today, Gabby pale Oligo, who

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Speaker 1: is now at pali Asne and has been at most

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Speaker 1: of the other couch funds and Hudson River Trading. I

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Speaker 1: do want to start by talking.

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Speaker 3: About gardening me, okay, natural.

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

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

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Speaker 1: and I think we counted three years of gardening. Leave No,

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Speaker 1: I think it's a bit okay, it's not precise. Fifteen

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Speaker 1: months from Citadel one you're Hudson River Trading and four

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Speaker 1: months from millennium. Okay, so pretty close.

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Speaker 3: Not terrible, 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. I try to keep myself busy, so

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Speaker 3: I teach typically at some university. So the first time

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Speaker 3: during my Seitaadel two Millennium, Guardian leve I was teaching

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Speaker 3: at Cornell and in the HRT to Bam Guardian Leve,

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Speaker 3: I was at NYU and I love teaching, and then

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Speaker 3: what I do is it helps me focus on on stuff.

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Speaker 3: Usually what I do in you know, whenever I read

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Speaker 3: a book or paper that I like, I take notes.

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Speaker 3: I take notes in Lattech and then I really arrive

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Speaker 3: or think about things, and so that typically is the

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Speaker 3: basis for my course material, and then it becomes the

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Speaker 3: basis for my books. I've written a couple of books

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Speaker 3: during my noncompetes.

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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.

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Speaker 3: That particularly worried with that. I think that there is

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Speaker 3: only a very specific subset of quantitative researchers who are

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Speaker 3: afraid of losing their edges. And yeah, that's not been

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Speaker 3: my case. I keep reading, I try to stay up

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Speaker 3: to date.

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Speaker 1: Feedback into the work like, do you get ideas or

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Speaker 1: like deep in your understanding of techniques by teaching and

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Speaker 1: writing the books? Or are they just sort of like.

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Speaker 3: Extracurricular No, no, No, it's definitely I learn a lot

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Speaker 3: from writing the books.

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Speaker 1: How long do you I go to your next job

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Speaker 1: and generate more profits by of.

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Speaker 3: Course plenty more profits. Tell that to my employers. No,

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Speaker 3: but I definitely I learn a lot from writing, from

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Speaker 3: the first drafts, and then I rewrite and rewrite, and

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Speaker 3: I learn a lot from discarding material too. It's very

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Speaker 3: useful to discard material. It makes you really focus on

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Speaker 3: what matters and what doesn't. So I try to give

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Speaker 3: a narrative, like a logical connection between various topics, and

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Speaker 3: that is something that is possible only when you write

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Speaker 3: a book. I really do not like writing. That Nobody

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Speaker 3: I think likes writing, maybe except for you.

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

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Speaker 3: Very I find it very painful. I find painful letting

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Speaker 3: go of material, Yes, but I also like it. You know,

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Speaker 3: 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 fund and quantitative research gardening leave is like a

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

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Speaker 1: like highly trained people who haven't enforced a year of

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Speaker 1: And I've written that all the hedge fund researchers should

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Speaker 1: go work at LM companies or like analytics departments of

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Speaker 1: sports teams, and I'm like, partially kidding and partially not.

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Speaker 1: How true is it for you? Like how much of

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Speaker 1: like your quantitative skills at this point are really just

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Speaker 1: for investing, and how much of it is like if

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Speaker 1: you spent three months, you know, consulting for a soccer team,

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Speaker 1: you would be able to tell them how to find

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Speaker 1: better players.

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Speaker 3: I'm not sure, so I'll say this right. I was

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Speaker 3: thinking a few days ago if there was a kind

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Speaker 3: of a common thread in my professional life, because it

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Speaker 3: seems kind of random, And actually I think that there is,

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Speaker 3: because I think that I was about fourteen when I

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Speaker 3: realized that I had an aptitude for applied math. I

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Speaker 3: discovered physics, and I liked math, and I also liked

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Speaker 3: literature very much, so I loved reading. I read a lot.

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Speaker 3: I was not a very social animal. And then basically

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Speaker 3: since then, I've been doing the same thing in various forms. Right,

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Speaker 3: I did physics, I did applied math. I didn't do

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Speaker 3: applied math in finance. I did applied math in weird

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Speaker 3: things like optimization and logistics. So I have been doing

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Speaker 3: kind of the same thing over and over, which has

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Speaker 3: been writing and applying math to something. So I think

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Speaker 3: that I could do it. I would like to do it,

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Speaker 3: but I also think that it's not that simple to

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Speaker 3: go to a new field and oh, after three months,

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Speaker 3: I know soccer. No, there is a lot of specificity.

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Speaker 3: And the beauty of I think being a good applied

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Speaker 3: mathematician is that they start with the problems and with

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Speaker 3: the domain first, and that they're sufficiently mature from a

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Speaker 3: mathematical standpoint that they are not making too much of

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Speaker 3: an effort in using math. So I think the good

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Speaker 3: art of being an applied mathematician is to study persistently

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Speaker 3: the application. So no, I don't think that after three

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Speaker 3: months it would be good enough. But after a year,

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Speaker 3: you know, about a year of being fully immersed in

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Speaker 3: an application, then you start getting a little bit better,

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Speaker 3: and then the math is not the problem, and then

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Speaker 3: you start 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 creativity and genuine interest in the problems more

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Speaker 1: than you know, math, coorse power. Yeah, this is a

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Speaker 1: dumb question. But how does one develop how does one identify,

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Speaker 1: you know, creativity and interest in financial topics? And is

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Speaker 1: the obvious answer those are where the money is? Or

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

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Speaker 1: finance as a topic, And is the answer because that's what.

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Speaker 3: The money is. So first of all, I think that

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Speaker 3: creativity is either a personality trait doesn't belong to You're

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Speaker 3: not creative in finance, you know, you're you're creative in

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Speaker 3: in cooking, you're creative in whatever. And it's a mix

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Speaker 3: I guess of extraversion opening and as to experience, and

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Speaker 3: I don't know what else. I'm not a psychologist, but

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Speaker 3: I do believe that people are genuinely creative. And in fact,

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Speaker 3: you see it right that sometimes you ask someone and

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Speaker 3: you find out that yes, they like writing, they play

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Speaker 3: some instrument if badly, and you know, and they paint

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Speaker 3: and they do whatever. And so I would say, if

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Speaker 3: you go to finance because where the money is, there's

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Speaker 3: nothing wrong with that. And in a way, that's my

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Speaker 3: story you know, I was, I was a researcher. I

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Speaker 3: wanted to have more money and whatnot. But eventually you

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Speaker 3: stay in finance, or at least in my you know,

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Speaker 3: little domain, because you're genuinely curious about finding out stuff, right, So.

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Speaker 1: Like why are the problems like why do they arouse curios?

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Speaker 1: Like why are the problems of finance intrigue you after

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Speaker 1: years of doing it? Right? Like what's interesting about those

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Speaker 1: problems as opposed to other domains.

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Speaker 3: It's really hard for me to say, Like I think

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Speaker 3: that I read once that a young songwriter asked Bob

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Speaker 3: Dylan how to become a good songwriter, and Bob Dylan

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Speaker 3: just answered, well, what's going on? I said, what do

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Speaker 3: you mean, what's going on? Yeah, what's going on? What's

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Speaker 3: going on in your life? Just you know, look around.

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Speaker 3: So sometimes I get these questions from investors, But you know,

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Speaker 3: how do you keep yourself interested? How do you find problems?

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Speaker 3: It's not a problem like the problems jump at you

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Speaker 3: like there are too many problems. There are too many

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Speaker 3: interesting problems. So if anything, the skill is in sorting

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Speaker 3: the problems in the right order, right. That is where

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Speaker 3: maybe having some maturity in doing research kicks in. But

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Speaker 3: 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 3: I don't like right now? What are we working on?

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Speaker 3: I mean, we are trying to understand how earnings are monetized? Right?

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Speaker 3: How do you make money in earnings? It's such a

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Speaker 3: basic thing in fundamental equities.

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

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Speaker 3: Earnings, yes, what are I mean without getting too much

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Speaker 3: into details, but you know there what are the relevant variables?

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Speaker 3: Imagine that you had an oracle who told you what

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Speaker 3: the variables are? What would you do with that? What

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Speaker 3: would you do if you'd had all the information in

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Speaker 3: the world right and everything in your world here in

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Speaker 3: existence would be like an approximation problem.

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Speaker 1: There's an incredible STYLI story of like the guys into

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Speaker 1: I think, like one of the newswire services and got

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

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

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Speaker 1: success rate, which is great, but also like it means

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Speaker 1: that had a thirty percent, like they traded the wrong way,

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Speaker 1: knowing earnings perfectly in advance. It's like a good yeah, yes,

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Speaker 1: so I had the you know, it's still hard.

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Speaker 3: Yes, it's still very hard. Actually, shout out to Victor Hagan,

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Speaker 3: who wrote the paper about ten years ago on this.

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Speaker 3: He made a organize a simple controlled experiment where he

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Speaker 3: gave basically a biased coin where you, I think had

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Speaker 3: a success rate of sixty percent forty percent failure, and

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Speaker 3: you had some capital and you could invest it over

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Speaker 3: time on these informed predictions, and a lot of subjects

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Speaker 3: went bankrupt. Okay, now I think we are better than that,

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Speaker 3: but still there are lots of problems related to trading

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Speaker 3: around an event.

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Speaker 2: For example, before we get too far away, you mentioned

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Speaker 2: Bob Dylan. It actually reminded me of another Dylan quote

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Speaker 2: which I'm going to paraphrase poorly, but he basically said,

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Speaker 2: when asked about writing songs, do you think that you

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Speaker 2: could write whatever the work that was being referenced now,

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Speaker 2: And he said, I don't think so. It's like the

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

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Speaker 2: They were just sort of hanging in the air and

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Speaker 2: they came to me. And it kind of also rang

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Speaker 2: true with what you were saying about you didn't go

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Speaker 2: looking for problems, They're just there. Necessarily. I actually want

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Speaker 2: to go back to applied math if it doesn't interrupt

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Speaker 2: the course of conversation too much. You tweeted on June

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Speaker 2: twenty fourth that there's no child prodigies when it comes

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Speaker 2: to poetry, when it comes to applied mathematics. And I'm

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Speaker 2: not saying that. You said that you were a prodigy,

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Speaker 2: but you were a child at fourteen. I mean, how

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Speaker 2: at fourteen do you realize that you have an aptitude

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Speaker 2: for something like applied mathematics?

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Speaker 3: All right, I don't want to flex about this stuff, No,

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Speaker 3: you should. I think I'm honestly a little weird. I'm

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Speaker 3: just a little weird, I think, honestly, but I.

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Speaker 2: Like prodigy weird or.

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Speaker 3: I did have my share of yeah, adults telling me

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Speaker 3: that I was good at this or that or you know.

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Speaker 3: But yeah, I mean, okay, I'm just a little bit atypical. Also,

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Speaker 3: when I talk to investors, I think investors enjoy my

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Speaker 3: presence because I think I'm incredibly unfiltered for somebody who's

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Speaker 3: talking to them, so it's like fun for them. And

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Speaker 3: I was very unfiltered when I talk to my professors

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Speaker 3: in school. Sometimes I corrected them stuff like this, Yeah,

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Speaker 3: 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 3: I don't think they're quantity, but I think that they're

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Speaker 3: very analytical. So I don't think that they would make

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Speaker 3: great mathematicians, but I think they would make very very

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Speaker 3: decent applied mathematicians. Actually, they tend to be very analytical.

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Speaker 3: They tend to be very process oriented. And they have

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Speaker 3: also additional qualities that actually mentioned in that essay, like

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Speaker 3: they have very little disposition effect, so that's part of

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Speaker 3: being analytical. They have no sound cost fallacy in them.

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Speaker 3: So even though they don't do a lot of math,

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Speaker 3: but they do some math. Okay, So first of all,

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Speaker 3: they're fluent in a sense in basic literacy. But I

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Speaker 3: think it's more their process that is closer to if

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Speaker 3: 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 3: So I think that having an economics background is not

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Speaker 3: necessarily a benefit, might even be a disadvantage actually, But

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Speaker 3: just based on very few samples that I have a

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Speaker 3: lot of very good, outstanding quantitative researchers actually come from

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Speaker 3: physics and specifically from astrophysics. That's the experience that I've

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Speaker 3: 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 3: So I can answer the second question more easily. I

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Speaker 3: think that astrophysicists deal with large amounts of data, and

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Speaker 3: they deal with observational data, so they don't get to

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Speaker 3: do a lot of experiments. And that's good for finance, right,

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Speaker 3: you deal with a lot of data. You need to

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Speaker 3: know how to have good agen for observational data, and

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Speaker 3: you need to have very good theory, like you need

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Speaker 3: to have very good instruments without being falling in love

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Speaker 3: with those instruments. Whereas I think economists, Okay, first of all,

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Speaker 3: my statement is purely empirical. Okay, So I'm just really

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Speaker 3: guessing on a economists and I'm going to be hated

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Speaker 3: by all economists or economists in finance, but I do

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Speaker 3: have my issues with their methods.

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Speaker 1: Right.

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Speaker 3: So, first of all, I think that there is an

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Speaker 3: original scene in economics, which is I think a lot

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Speaker 3: of economics is informed by a desire to be as

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Speaker 3: rigorous as mathematics. Right, And so a lot of theoreticians

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Speaker 3: in economics are very deductive in their approach. If you

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Speaker 3: think of you know, the unrealistic assumptions behind the welfare

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Speaker 3: theorems or arrows impossibility theorem or whatnot, or just pick

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Speaker 3: up you know Samuelson textbooks, and I think this is

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Speaker 3: just axiomatic rather very axiomatic, very deductive. Whereas physicists are

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Speaker 3: very happy to think in terms of small idealized models

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Speaker 3: that apply to a specific domain, and if the model

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Speaker 3: doesn't work out, they will discard and make another one.

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Speaker 3: The grand theory behind physical theories exists, like there are

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Speaker 3: people who do this for a living. But many, many

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Speaker 3: good theoretical economists physicists starting the small and then they

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Speaker 3: expanded domain of their models. So economists tend to maybe

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Speaker 3: in a sense, fall in love with methods too much,

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Speaker 3: with techniques too much.

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Speaker 1: We had cliff Astness on the podcast a little while ago,

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

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Speaker 1: episode 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 3: Well, if I am being consistent with my book, investing

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Speaker 3: is really about problems and not about specific techniques or

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Speaker 3: anything like this, Right, So it's basically a way to

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Speaker 3: go through the whole investment process from let's say preparing

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Speaker 3: the ingredients to cooking to eating. That is very process driven. Ultimately,

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Speaker 3: you would imagine that one thing that you know, quant

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Speaker 3: investing has in common across multiple domains, you know, if

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Speaker 3: you do futures, stocks, event based and whatnot, is I

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Speaker 3: think the number of bets tends to be high in

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Speaker 3: systematic investing. Right, so you can be a very successful

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Speaker 3: microeconomic investor portfolio manager. And you you know, according to

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Speaker 3: even several statements by Buffett, you know, he made like

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Speaker 3: ten twelve very good bets. Okay, so that's great, and

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Speaker 3: that's not quant investing. You know, you could put enough

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Speaker 3: pms making you know, twenty bets in their lives, you

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Speaker 3: will get a few that have let's say twelve thirteen, right,

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Speaker 3: and they will be rich. We do not have that luxury, right.

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Speaker 3: We have to make millions of bets. You know, we

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Speaker 3: trade a portfolio with three thousand stocks sometimes in waves

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Speaker 3: of half an hour. You can't make a judgment on

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Speaker 3: all of these bets. So you need a method that

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Speaker 3: reduces the dimension of your problem to something that can

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Speaker 3: be treated in a systematic manner. I don't know if

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Speaker 3: that answers for you. You know that, but you know, basically,

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Speaker 3: basically the idea is, think about if you make a

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Speaker 3: lot of bets, you cannot bet individually. You have to

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Speaker 3: have some kind of juristic or some kind of method

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Speaker 3: 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 I'm quite investing

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

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Speaker 1: universal investments. You're shutting your.

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Speaker 3: Head, Yeah, I yes, and no, I think yes because

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Speaker 3: the book, you know, has maybe one hundred and fifty

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Speaker 3: pages on factor models, but also no, because maybe in

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Speaker 3: one hundred years from now, I suspect there will be

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Speaker 3: still something left. But you know, we might have better

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Speaker 3: techniques and not necessary factor models any longer.

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Speaker 1: I don't know, we don't want to go. Two attractions

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Speaker 1: of that, one is like, are the better techniques something

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Speaker 1: more neural netty unstructured?

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Speaker 3: Who knows? Yeah, something like that. I mean there is,

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Speaker 3: 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 the here are some

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

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Speaker 1: like some residual idiots and credit return. There are clearly

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

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Speaker 1: in factors. My impression is that at like the places

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Speaker 1: that you work, the business is the opposite of that

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Speaker 1: is to hedge out your factor risk as much as

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Speaker 1: possible and to get as much idiosyncratic risk as possible.

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Speaker 1: Is that right? And like, like, how do you discriminate

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Speaker 1: between like a factory return and idiosymcratic return, Like what

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Speaker 1: makes the thing a factor as opposed to another ring.

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Speaker 3: So that's a good question. So first, a lot of

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Speaker 3: systematic investing is still about factors, just not the factors

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Speaker 3: that get published in the literature, you know, not the

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Speaker 3: factors that Cliff maybe was talking about. And yet a

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Speaker 3: lot of successful systematic investing is really factor driven.

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Speaker 1: It in the sense that you have a model that

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

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

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Speaker 3: You do, and you do the rest. You have other

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Speaker 3: terms that matter. So that's one thing, But there are

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Speaker 3: two other things. There are sometimes sources of returns that

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Speaker 3: are factor like but not quite like factors. So you

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Speaker 3: may have a theme. For example, you may identify a

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Speaker 3: theme in the market that is not pervasive enough or

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Speaker 3: is alive only for a few months, but is there

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Speaker 3: and it's not only affecting let's say two stocks. Right,

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Speaker 3: So these brought thems can be invested on, but cannot

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Speaker 3: really model in the traditional way as a traditional factor model. Also,

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Speaker 3: there is a lot of good modeling in factors as

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Speaker 3: opposed to bad modeling, So it's it seems easy, but

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Speaker 3: it's not that easy. So there is a little bit

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Speaker 3: of craftsmanship in making these models, okay, And then the

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Speaker 3: third thing is that there are also returns that have

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Speaker 3: nothing to do with factors, or almost nothing to do

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Speaker 3: with factors. So if you really know how a company works,

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Speaker 3: and you have a little bit of an edge in

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Speaker 3: predicting its future performance, you can bet on it, and

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Speaker 3: you make enough bets and again you will make some

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Speaker 3: money if you repeat, and you know recycle. So even

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Speaker 3: discretionary investing in this sense has inherited a little bit

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Speaker 3: of the spirit of systematic investing.

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Speaker 1: I think of that as like that a pod job,

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Speaker 1: but like aval 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 creditnoledge

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Speaker 1: of the company. Is that like kind of right?

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Speaker 3: Kind of right?

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Speaker 1: Yeah?

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Speaker 3: I think that at this point it is very interesting

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Speaker 3: how the mind of professional portfolio managers has been remolded

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Speaker 3: in a factor based world, so that a modern portfolio

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Speaker 3: manager discretion The portfolio manager thinks in factors, you know,

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Speaker 3: so I don't even need to tell them, hey, this

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Speaker 3: is your exposure. They see their exposure, they have the

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Speaker 3: tools to see it, and they control it in real

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Speaker 3: time with minimal intervention from me. So what we do

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Speaker 3: is we have you know, a good team that models

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Speaker 3: factors in a way that is suitable for the investment

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Speaker 3: universe and style in which they operate. That's a very

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Speaker 3: very sophisticated and difficult and portfolio managers use that and

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Speaker 3: then neutralize It's become like second.

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

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Speaker 1: idiosyncratic alpha rather than factors. That's right. I feel like

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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 the like are known

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

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Speaker 1: of exposures to factors that are sort of in code

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Speaker 1: or unknown, but like ultimately, when you become really, really smart,

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Speaker 1: you'll know that, like, actually the bet they were making

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Speaker 1: was some you know particular knowing the company really well

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Speaker 1: means like they had exposure to like some you know,

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Speaker 1: personality factor in the CEO or something that like eventually

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Speaker 1: someone will be able to write that down and it'll

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Speaker 1: come out of like being idiosyncratic and become a factor.

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Speaker 1: And then I don't know, what happens.

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Speaker 3: I think that there is some truth to that. There

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Speaker 3: is definitely some some truth to that, in the sense

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Speaker 3: that sometimes for folio managers, especially in specific sectors, will

436
00:24:58,320 --> 00:25:03,520
Speaker 3: use some heuristics that you could call characteristics in a

437
00:25:03,800 --> 00:25:05,800
Speaker 3: factor model, but they are not in a factor model,

438
00:25:05,920 --> 00:25:09,960
Speaker 3: and then they trade that. However, it's also true that

439
00:25:10,359 --> 00:25:14,800
Speaker 3: the decision that enters a particular investment is usually not

440
00:25:14,960 --> 00:25:18,080
Speaker 3: that simple as taking a ration spreadsheet, so it's a

441
00:25:18,119 --> 00:25:21,280
Speaker 3: bit more complicated than that. You could still argue that

442
00:25:21,400 --> 00:25:24,480
Speaker 3: there is a factor, right, And what's the factor is

443
00:25:24,560 --> 00:25:29,720
Speaker 3: ultimately the set of feces that are highly correlated or

444
00:25:29,760 --> 00:25:34,840
Speaker 3: relatively highly correlated across portfolio managers across firms, because if

445
00:25:34,880 --> 00:25:38,240
Speaker 3: there is an expected return, and if you have skill,

446
00:25:39,000 --> 00:25:40,960
Speaker 3: and you have sufficient skill to be close to the

447
00:25:41,040 --> 00:25:44,720
Speaker 3: best possible portfolio, you have to be also relatively close

448
00:25:44,760 --> 00:25:48,600
Speaker 3: to other people approximating that best possible portfolio. Right, So

449
00:25:49,000 --> 00:25:51,679
Speaker 3: then it becomes a truism, Right, there is a factor,

450
00:25:51,720 --> 00:25:55,200
Speaker 3: and that's the factor of investor, of informed investors. So

451
00:25:55,480 --> 00:25:56,040
Speaker 3: it's true.

452
00:25:56,400 --> 00:25:58,719
Speaker 1: I think if it is like there's like a scientific

453
00:25:58,800 --> 00:26:01,879
Speaker 1: process that ever pursuing I hear are the best people

454
00:26:02,119 --> 00:26:03,840
Speaker 1: and they like do the best work to pursue that

455
00:26:03,880 --> 00:26:08,760
Speaker 1: scientific process, and so they'll eventually converge on something that

456
00:26:08,880 --> 00:26:11,840
Speaker 1: is like truth. But that means buying all the same stocks.

457
00:26:12,400 --> 00:26:14,439
Speaker 3: Yes, it's very difficult to get to that truth.

458
00:26:14,840 --> 00:26:19,399
Speaker 1: Sure it is. Yeah it's not.

459
00:26:20,080 --> 00:26:21,119
Speaker 3: Let's let's tire about it.

460
00:26:21,359 --> 00:26:23,000
Speaker 1: Weird if they weren't hurting among.

461
00:26:22,880 --> 00:26:25,520
Speaker 3: The best, yes, yes, but there is, there is And

462
00:26:25,520 --> 00:26:27,359
Speaker 3: by the way, and this brings to one of the

463
00:26:27,440 --> 00:26:32,040
Speaker 3: limitations of factor models, right, which is effectively a factor

464
00:26:32,080 --> 00:26:36,120
Speaker 3: model is a form of glorified regression over time. Right,

465
00:26:36,720 --> 00:26:39,479
Speaker 3: And behind a regression there is a bit of an

466
00:26:39,560 --> 00:26:45,119
Speaker 3: assumption to some extent, of independent observations over time. And

467
00:26:45,200 --> 00:26:49,720
Speaker 3: the market and hedge funds are not in dependent random variables.

468
00:26:49,720 --> 00:26:52,720
Speaker 3: They are super dependent random variables, and they are in

469
00:26:52,760 --> 00:26:56,160
Speaker 3: a sort of continuous in direct conversation through their portfolios.

470
00:26:56,280 --> 00:26:59,840
Speaker 3: And sometimes the conversation gets really nasty when one hedge

471
00:26:59,840 --> 00:27:02,920
Speaker 3: fun is in state of distress and all of a sudden,

472
00:27:03,440 --> 00:27:05,200
Speaker 3: or not even a hatch fund, it could be also

473
00:27:05,240 --> 00:27:08,720
Speaker 3: an institution investor and decide to liquidate part of their portfolio.

474
00:27:09,000 --> 00:27:11,320
Speaker 3: And then it becomes a process where you have a

475
00:27:11,400 --> 00:27:15,680
Speaker 3: lot of reflexivity and positive feedback and everybody suffers. And

476
00:27:15,720 --> 00:27:20,280
Speaker 3: in this case, factor models don't really You can still identify,

477
00:27:20,520 --> 00:27:25,080
Speaker 3: like if the system is running a temperature with some characteristics,

478
00:27:25,320 --> 00:27:29,199
Speaker 3: but they are not factors in the traditional sense. I

479
00:27:29,240 --> 00:27:29,760
Speaker 3: do want to.

480
00:27:29,720 --> 00:27:32,199
Speaker 2: Talk about before we move too far away, I do

481
00:27:32,280 --> 00:27:34,640
Speaker 2: want to talk a little bit about how and if

482
00:27:34,680 --> 00:27:39,200
Speaker 2: factors can die, because we've talked a bit about identifying factors.

483
00:27:39,240 --> 00:27:44,240
Speaker 2: But when do you decide that this doesn't work anymore?

484
00:27:44,320 --> 00:27:49,040
Speaker 2: Necessarily that the market has fundamentally changed and this worked

485
00:27:49,119 --> 00:27:52,159
Speaker 2: maybe ten years ago, maybe fifteen years ago, but maybe

486
00:27:52,160 --> 00:27:55,679
Speaker 2: now it's devolved.

487
00:27:56,680 --> 00:28:01,560
Speaker 3: Well, there is the good old reason, which is people

488
00:28:01,600 --> 00:28:03,840
Speaker 3: make mistakes in the sense that we think that there

489
00:28:03,920 --> 00:28:06,480
Speaker 3: is a factor and then we look back and there

490
00:28:06,520 --> 00:28:09,760
Speaker 3: is no factor. Right, So there are so many factors

491
00:28:09,800 --> 00:28:12,200
Speaker 3: that some of them have got to be a little

492
00:28:12,200 --> 00:28:15,919
Speaker 3: bit redundant. So that that's one reason, right, So just

493
00:28:16,560 --> 00:28:21,439
Speaker 3: pure in a sense research revisions. And then there is

494
00:28:21,480 --> 00:28:23,679
Speaker 3: also the fact that there are two other things that

495
00:28:23,760 --> 00:28:27,960
Speaker 3: can happen. One is the moment that you tell people

496
00:28:28,000 --> 00:28:30,160
Speaker 3: that there is a factor, the factor comes into being

497
00:28:30,400 --> 00:28:33,480
Speaker 3: to some extent, right, So it's never black and white

498
00:28:33,520 --> 00:28:36,000
Speaker 3: that the factor did not exist. Maybe the factor did

499
00:28:36,040 --> 00:28:39,240
Speaker 3: exist and then the moment you identify it, it becomes

500
00:28:39,760 --> 00:28:46,040
Speaker 3: more existent, like you know, speak yeah, yeah. So esg

501
00:28:46,240 --> 00:28:49,320
Speaker 3: is is one case where the focal point that it

502
00:28:49,440 --> 00:28:52,680
Speaker 3: became makes into an investible theme.

503
00:28:52,920 --> 00:28:55,000
Speaker 2: I thought that was just black rock pumping.

504
00:28:55,320 --> 00:28:59,479
Speaker 3: As possible, but everybody had to incorporate it in some sense, right,

505
00:28:59,520 --> 00:29:05,120
Speaker 3: so it became a major source of revenue for the vendors. Right.

506
00:29:05,240 --> 00:29:08,000
Speaker 3: So that's that's one thing. And then there is the

507
00:29:08,040 --> 00:29:12,200
Speaker 3: adaptive nature of the market. So things that before generated

508
00:29:12,200 --> 00:29:15,200
Speaker 3: a priced return. So you run some risk, you made

509
00:29:15,240 --> 00:29:19,680
Speaker 3: some money, and then it becomes table stakes, it becomes

510
00:29:19,680 --> 00:29:24,600
Speaker 3: incorporated into factor models, it becomes it becomes a smart beet,

511
00:29:25,040 --> 00:29:27,400
Speaker 3: it becomes a smart patent, and then it becomes so

512
00:29:27,560 --> 00:29:30,160
Speaker 3: I think, you know, you could say definitely that medium

513
00:29:30,160 --> 00:29:34,080
Speaker 3: tonmamentum worked much better. You could say that even you know,

514
00:29:34,120 --> 00:29:37,920
Speaker 3: short term reversal worked better. There were years when short

515
00:29:37,960 --> 00:29:42,000
Speaker 3: interest was great, and there are factors or data sources

516
00:29:42,000 --> 00:29:44,640
Speaker 3: that work well now and then maybe in five years

517
00:29:44,760 --> 00:29:48,120
Speaker 3: will become known and become part of the I mean

518
00:29:48,440 --> 00:29:51,680
Speaker 3: credit card data. Right for consumer that was like there

519
00:29:51,680 --> 00:29:54,520
Speaker 3: were people who were making a lot of money in

520
00:29:54,680 --> 00:29:59,400
Speaker 3: two thousand and eleven through sixteen seventeen, and then it's

521
00:29:59,440 --> 00:30:01,760
Speaker 3: become it's very hard to make money in that.

522
00:30:02,280 --> 00:30:04,680
Speaker 1: You said the market is a conversation among catch funds.

523
00:30:05,080 --> 00:30:06,800
Speaker 1: One thing that I think might be true that I'm

524
00:30:06,840 --> 00:30:09,960
Speaker 1: not entirely sure of, is like, to what extent the

525
00:30:10,000 --> 00:30:14,280
Speaker 1: market is a conversation among four patch funds? Now? Like,

526
00:30:14,320 --> 00:30:16,240
Speaker 1: to what extent is like the marginal price or of

527
00:30:16,240 --> 00:30:20,640
Speaker 1: every stock a portfolio manager at you know, one of

528
00:30:20,680 --> 00:30:21,640
Speaker 1: the places you've worked.

529
00:30:22,520 --> 00:30:24,760
Speaker 3: It's a very good question. I don't really have the

530
00:30:24,800 --> 00:30:26,520
Speaker 3: answers to this. I'm not sure.

531
00:30:26,680 --> 00:30:29,200
Speaker 1: It's it's like, what is the intuition at places like that, Like,

532
00:30:29,320 --> 00:30:32,280
Speaker 1: is it like the market price is determined by like

533
00:30:32,800 --> 00:30:35,560
Speaker 1: the collective thought of like the top people at the

534
00:30:35,560 --> 00:30:39,080
Speaker 1: top hedge funds, Or is it like we are a

535
00:30:39,080 --> 00:30:41,480
Speaker 1: little bump on the market and we're trading against the

536
00:30:41,520 --> 00:30:42,560
Speaker 1: whole random universe.

537
00:30:42,640 --> 00:30:45,480
Speaker 3: I mean, you'd like to think that the prices are

538
00:30:45,520 --> 00:30:49,200
Speaker 3: determined by the marginal informed investor, Right, so by people

539
00:30:49,240 --> 00:30:52,800
Speaker 3: like us at the time horizon where we predict right,

540
00:30:52,840 --> 00:30:55,000
Speaker 3: which is not the same as at the time of

541
00:30:55,080 --> 00:30:57,440
Speaker 3: Rizon of half a day. Right, that's a different player.

542
00:30:57,720 --> 00:30:59,800
Speaker 1: What is your time horizon, like I think of it as.

543
00:30:59,760 --> 00:31:03,800
Speaker 3: Well, it depends well, yes, it depends. Within a hedge fund,

544
00:31:03,880 --> 00:31:06,520
Speaker 3: you have a variety of even within long shore equities,

545
00:31:06,640 --> 00:31:08,880
Speaker 3: you know, you have you know, portfolio managers who are

546
00:31:09,000 --> 00:31:11,800
Speaker 3: very tactical, and so they think in terms of they

547
00:31:11,800 --> 00:31:14,880
Speaker 3: have strong daily or intra day alpha, even though they're

548
00:31:14,880 --> 00:31:19,160
Speaker 3: fully discretionary up to pms that think easily in terms

549
00:31:19,200 --> 00:31:22,360
Speaker 3: of months. Also depends on the sector. So you know,

550
00:31:22,480 --> 00:31:26,080
Speaker 3: financials typically probably monetizes a little bit less on earnings

551
00:31:26,360 --> 00:31:30,280
Speaker 3: and tends to have a longer horizon. Banks are basically

552
00:31:30,560 --> 00:31:34,080
Speaker 3: modeling giant balance sheets, right, and then in a hedge

553
00:31:34,080 --> 00:31:37,200
Speaker 3: fund you also have systematic, but even in systematic there

554
00:31:37,200 --> 00:31:40,560
Speaker 3: are all sorts of time scales, and this cacophony makes

555
00:31:40,560 --> 00:31:43,080
Speaker 3: the prices. I really don't know, Like I said, another

556
00:31:43,200 --> 00:31:47,720
Speaker 3: question is basically, are how inefficient is the market? How

557
00:31:47,960 --> 00:31:50,200
Speaker 3: incorrect are the prices are within a factor of two?

558
00:31:50,280 --> 00:31:52,160
Speaker 3: Like Black used to say, or I don't know, Like

559
00:31:52,240 --> 00:31:55,960
Speaker 3: I don't think that the market is becoming so super efficient,

560
00:31:56,080 --> 00:31:58,280
Speaker 3: but it's getting it seems to be more efficient.

561
00:31:58,880 --> 00:32:00,560
Speaker 1: I do feel, like, you know, the big stories is

562
00:32:00,640 --> 00:32:03,000
Speaker 1: the rise of like these big multi strategy hedge funds,

563
00:32:03,040 --> 00:32:07,719
Speaker 1: like you would hope. Maybe you wouldn't hope because it's

564
00:32:07,720 --> 00:32:10,320
Speaker 1: sort of the economic and just, but like one might

565
00:32:10,360 --> 00:32:12,680
Speaker 1: hope that like the rise of these big multi strategy

566
00:32:12,720 --> 00:32:15,320
Speaker 1: hedge funds and a lot of capital being allocated to

567
00:32:15,360 --> 00:32:19,320
Speaker 1: them would observably make the market more efficient.

568
00:32:20,640 --> 00:32:23,680
Speaker 3: Yeah, I don't know if observably holds. I don't. It's

569
00:32:23,760 --> 00:32:29,320
Speaker 3: really hard to like, can you can you tell when

570
00:32:29,320 --> 00:32:30,440
Speaker 3: a bubble is forming?

571
00:32:31,600 --> 00:32:33,200
Speaker 2: A lot of people would say that they can.

572
00:32:34,080 --> 00:32:38,120
Speaker 3: Yeah, I can point you to a few papers, yeah

573
00:32:38,160 --> 00:32:42,200
Speaker 3: that you know made all the wrong calls. Okay, I

574
00:32:42,200 --> 00:32:43,840
Speaker 3: don't want to shame academics in public.

575
00:32:45,280 --> 00:32:47,120
Speaker 2: I do like the idea that the market is a

576
00:32:47,120 --> 00:32:50,360
Speaker 2: conversation between four hedge funds because I live in the

577
00:32:50,400 --> 00:32:54,280
Speaker 2: ETF world, and you know, the big thing is passive

578
00:32:54,360 --> 00:32:58,160
Speaker 2: is just distorting the market and there's no price discovery anymore.

579
00:32:58,240 --> 00:33:00,960
Speaker 2: And it sounds like that's on the opposite end of

580
00:33:01,000 --> 00:33:01,760
Speaker 2: that spectrum.

581
00:33:02,520 --> 00:33:06,200
Speaker 3: I didn't say, I think exactly that it's a conversation

582
00:33:06,280 --> 00:33:09,800
Speaker 3: between It's a beautiful thing to say, though. It sounds

583
00:33:09,840 --> 00:33:15,600
Speaker 3: really cool. It sounds good podcasts. Yeah, that's great. Yeah,

584
00:33:15,760 --> 00:33:18,880
Speaker 3: But I think your question is whether the rise of

585
00:33:19,120 --> 00:33:21,280
Speaker 3: passive has made markets less efficient.

586
00:33:21,080 --> 00:33:22,960
Speaker 2: More of a statement. I don't think I was a

587
00:33:23,000 --> 00:33:24,600
Speaker 2: bad podcaster and didn't actually.

588
00:33:24,400 --> 00:33:27,320
Speaker 3: Ask a question, But okay, how do you know?

589
00:33:28,200 --> 00:33:30,720
Speaker 2: How do I know that passive is the story in

590
00:33:30,760 --> 00:33:32,560
Speaker 2: the market? People on Twitter tell me?

591
00:33:32,600 --> 00:33:36,640
Speaker 3: So, oh, okay, don't trust people on Twitter.

592
00:33:37,280 --> 00:33:39,280
Speaker 2: That's true. Number one, real number one.

593
00:33:39,800 --> 00:33:42,600
Speaker 3: Now I don't know. I mean, the rise of passive

594
00:33:43,080 --> 00:33:47,480
Speaker 3: has made index rebalancing a weirder strategy, right, so where

595
00:33:47,560 --> 00:33:50,840
Speaker 3: the margins have compressed, but the size has become so

596
00:33:51,000 --> 00:33:54,400
Speaker 3: big that you can still make money in it and periodic.

597
00:33:54,520 --> 00:33:58,600
Speaker 3: It's a very you know, cyclical strategy. So I don't know.

598
00:33:58,760 --> 00:34:03,960
Speaker 1: So you're an indexy balancing PM do take like eight

599
00:34:03,960 --> 00:34:09,120
Speaker 1: months of vacation a year and like all day rebalance.

600
00:34:09,400 --> 00:34:13,040
Speaker 3: Not the ones I know who probably listen to this podcast, Okay,

601
00:34:13,800 --> 00:34:14,719
Speaker 3: they work very hard.

602
00:34:14,800 --> 00:34:22,160
Speaker 1: Sure indexes aren't paying rebalanced all the time, planning more

603
00:34:22,160 --> 00:34:22,839
Speaker 1: than you would think.

604
00:34:23,040 --> 00:34:26,360
Speaker 3: Index rebalancing is another you know, poster child for a

605
00:34:26,400 --> 00:34:30,359
Speaker 3: strategy that seems so simple that everybody can talk about it,

606
00:34:30,719 --> 00:34:34,560
Speaker 3: and then it's full of nuances and it requires a

607
00:34:34,560 --> 00:34:36,319
Speaker 3: lot of skill to trade effectively.

608
00:34:37,160 --> 00:34:40,120
Speaker 1: I believe that just because like I thought a little

609
00:34:40,160 --> 00:34:42,080
Speaker 1: bit about like just like the sort of like accounting

610
00:34:42,120 --> 00:34:46,040
Speaker 1: of like you basically know how many index mounds there are,

611
00:34:47,080 --> 00:34:49,239
Speaker 1: let's say, can predict what will come in and out

612
00:34:49,280 --> 00:34:51,279
Speaker 1: of the index, and like what the so like there's

613
00:34:51,320 --> 00:34:54,920
Speaker 1: like some mechanics around, like you know, figuring out the market,

614
00:34:54,920 --> 00:34:57,319
Speaker 1: calves that will come in and whatever, but then it

615
00:34:57,320 --> 00:34:59,319
Speaker 1: feels like the unknown is like who else is doing

616
00:34:59,400 --> 00:35:01,239
Speaker 1: the rebalance strategy? Is that? Right?

617
00:35:01,600 --> 00:35:04,799
Speaker 3: I think you're mostly right because I don't want to say,

618
00:35:04,840 --> 00:35:09,440
Speaker 3: because you know, out of respect for for the CMS,

619
00:35:09,440 --> 00:35:09,799
Speaker 3: did I know?

620
00:35:10,760 --> 00:35:13,360
Speaker 1: I love? Yes? Like so we had Cliff Askiness on

621
00:35:13,560 --> 00:35:18,120
Speaker 1: a few weeks ago, and like, to me, Cliff Asness

622
00:35:18,200 --> 00:35:22,520
Speaker 1: is like a quantitative investor, like a systematic investor, but

623
00:35:22,640 --> 00:35:26,440
Speaker 1: like what he's doing is sort of recognizably what a

624
00:35:26,520 --> 00:35:28,960
Speaker 1: sort of traditional asset manager. He's like trying to find

625
00:35:29,239 --> 00:35:31,600
Speaker 1: companies that are undervalued, right, he talked about it's like

626
00:35:31,600 --> 00:35:33,400
Speaker 1: being a Grammar dot investor. You know, you want like

627
00:35:33,840 --> 00:35:36,239
Speaker 1: valuation plus a catalyst. And he's like, oh, or you

628
00:35:36,280 --> 00:35:39,600
Speaker 1: know trading, you know, value and momentum and like you

629
00:35:39,640 --> 00:35:42,520
Speaker 1: look at what eight or two is maybe a little different,

630
00:35:42,520 --> 00:35:44,719
Speaker 1: but there's like you know, the hypercacy trading firms, Like

631
00:35:44,760 --> 00:35:47,200
Speaker 1: you can model those as like those are quantitative versions

632
00:35:47,200 --> 00:35:50,200
Speaker 1: of like a voice market maker fifty years ago, where

633
00:35:50,200 --> 00:35:52,479
Speaker 1: they're like trying to keep inventory flat and like trying

634
00:35:52,520 --> 00:35:55,880
Speaker 1: to you know, make the bid asks bread. So like

635
00:35:55,920 --> 00:35:59,879
Speaker 1: those are like very traditional economic functions that have been

636
00:36:00,280 --> 00:36:05,319
Speaker 1: quantify like turned into systematic what's the intuition for like

637
00:36:05,400 --> 00:36:07,479
Speaker 1: what a bally Asni or a star doll or a

638
00:36:07,520 --> 00:36:11,919
Speaker 1: millennium does? Like what business are you in? Do you think?

639
00:36:12,680 --> 00:36:17,480
Speaker 1: Like as a philosophical matter, like one thing I think,

640
00:36:17,560 --> 00:36:18,239
Speaker 1: like I think about like.

641
00:36:18,800 --> 00:36:21,680
Speaker 3: You're asking from a social kind of point or.

642
00:36:21,760 --> 00:36:23,960
Speaker 1: Well, I think like the index rebalancing, Like to me,

643
00:36:24,880 --> 00:36:26,879
Speaker 1: it feels like the sort of trade and I think

644
00:36:26,920 --> 00:36:28,400
Speaker 1: to something that was the sort of trade that like

645
00:36:28,440 --> 00:36:30,440
Speaker 1: an investment bank would have done twenty years ago, thirty

646
00:36:30,520 --> 00:36:32,920
Speaker 1: years ago, and like some of that function I think

647
00:36:32,960 --> 00:36:36,640
Speaker 1: has moved to like the big multimanagers. But like I

648
00:36:36,680 --> 00:36:39,359
Speaker 1: wonder like from where you say, like how you see

649
00:36:39,400 --> 00:36:42,360
Speaker 1: the like role in the financial markets of those firms.

650
00:36:42,520 --> 00:36:45,280
Speaker 3: So at a very high level, we don't do anything

651
00:36:45,320 --> 00:36:47,880
Speaker 3: different than everybody else in the sense that what we

652
00:36:47,960 --> 00:36:52,399
Speaker 3: provide is always this, right, is we provide shifting time preferences,

653
00:36:52,440 --> 00:36:56,719
Speaker 3: which means we provide liquidity. We house you know, risk

654
00:36:57,280 --> 00:37:00,560
Speaker 3: for people who don't want to hold it right now.

655
00:37:01,000 --> 00:37:03,719
Speaker 3: And that's what you do when you do indext rebalancing, right,

656
00:37:03,800 --> 00:37:05,799
Speaker 3: that's what you do when you do merger ARB and

657
00:37:05,960 --> 00:37:09,120
Speaker 3: when you do the various subtypes of basis traits.

658
00:37:09,239 --> 00:37:09,359
Speaker 1: Right.

659
00:37:09,440 --> 00:37:13,040
Speaker 3: So we do provide liquidity, which is very important. And

660
00:37:13,080 --> 00:37:16,120
Speaker 3: then the second thing we again very high level, we

661
00:37:16,160 --> 00:37:19,600
Speaker 3: provide price discovery, right, So we study the firms and

662
00:37:19,760 --> 00:37:22,279
Speaker 3: we think, okay, this is at the margin mispriced and

663
00:37:22,360 --> 00:37:24,160
Speaker 3: we're going to short it or we're going to invest

664
00:37:24,160 --> 00:37:27,400
Speaker 3: in it, and that's a beautiful thing. So we do

665
00:37:27,440 --> 00:37:29,680
Speaker 3: it at a different time scale, right. So you always

666
00:37:29,760 --> 00:37:32,960
Speaker 3: want to do things at the margin where you don't

667
00:37:33,000 --> 00:37:36,040
Speaker 3: have a lot of other participants, and at the margin

668
00:37:36,200 --> 00:37:39,360
Speaker 3: of the let's say month to three month investment horizon,

669
00:37:40,200 --> 00:37:44,360
Speaker 3: there are not that many participants. So in the words

670
00:37:44,400 --> 00:37:48,160
Speaker 3: of another hatch fund manager I cannot name, but it said,

671
00:37:48,239 --> 00:37:51,440
Speaker 3: once you know, we don't invest in securities, we dated them,

672
00:37:51,560 --> 00:37:53,799
Speaker 3: and so we are in the dating service. Not that

673
00:37:53,840 --> 00:37:55,719
Speaker 3: many people are doing it, and so we do it.

674
00:37:55,760 --> 00:37:57,400
Speaker 3: But I would say also this right, not at the

675
00:37:57,440 --> 00:37:59,880
Speaker 3: social level. I just want to answer the like my

676
00:38:00,080 --> 00:38:02,760
Speaker 3: personal level. What we do. We are a massive filter

677
00:38:03,360 --> 00:38:06,480
Speaker 3: of talent, and the talent that we hire is a

678
00:38:06,520 --> 00:38:10,120
Speaker 3: massive filter of information. So it's like information squared.

679
00:38:12,360 --> 00:38:14,719
Speaker 1: Maybe this is like a bad question, but like, do

680
00:38:14,760 --> 00:38:18,000
Speaker 1: you think that like long only asset managers are worse

681
00:38:18,000 --> 00:38:20,759
Speaker 1: than they were thirty years ago because that filter has

682
00:38:20,800 --> 00:38:24,839
Speaker 1: been so successful? In other words, like there are lots

683
00:38:24,840 --> 00:38:26,880
Speaker 1: of jobs you could have gotten in finance in nineteen ninety,

684
00:38:26,920 --> 00:38:29,600
Speaker 1: but like, yeah, there's like a clear hierarchy now.

685
00:38:31,040 --> 00:38:33,839
Speaker 3: I think that the market and the set of investors

686
00:38:33,880 --> 00:38:37,879
Speaker 3: has learned right, and I think the distinction between VITA

687
00:38:37,960 --> 00:38:44,160
Speaker 3: and HALFA has been useful for investors, and so active

688
00:38:44,320 --> 00:38:49,000
Speaker 3: investors who are mostly long only, I think have suffered

689
00:38:49,040 --> 00:38:53,640
Speaker 3: from this distinction because the vast majority of them underperforms

690
00:38:53,680 --> 00:38:56,680
Speaker 3: their benchmarks and so there is no reason for them

691
00:38:56,680 --> 00:39:00,600
Speaker 3: to exist. And then what we do is we provide

692
00:39:00,800 --> 00:39:06,400
Speaker 3: really uncorrelated returns to the benchmarks to most factors, and

693
00:39:06,719 --> 00:39:11,400
Speaker 3: investors want that, right, So there is a future where

694
00:39:11,640 --> 00:39:16,359
Speaker 3: active investors, long on investors asset managers will become even

695
00:39:16,480 --> 00:39:19,440
Speaker 3: less influential, smaller, and also.

696
00:39:20,000 --> 00:39:22,200
Speaker 1: I think of that as like a customer demand side,

697
00:39:22,200 --> 00:39:23,800
Speaker 1: but also like a talent filter side.

698
00:39:23,680 --> 00:39:26,719
Speaker 3: Right, yes, Yeah, And then the interesting thing is and

699
00:39:26,760 --> 00:39:31,359
Speaker 3: then there is also a process where the multimanager platforms

700
00:39:32,480 --> 00:39:35,720
Speaker 3: are able to make the business model of a single

701
00:39:35,760 --> 00:39:39,960
Speaker 3: portfolio manager that is not sustainable in isolation working in

702
00:39:40,000 --> 00:39:43,600
Speaker 3: this kind of federated system. So why would you or

703
00:39:43,640 --> 00:39:47,320
Speaker 3: how could you survive as a single portfolio manager hedge

704
00:39:47,320 --> 00:39:50,719
Speaker 3: fund nowadays? It's really really difficult, but you can do

705
00:39:50,800 --> 00:39:53,840
Speaker 3: it in a multimanager platform provided that you have you know,

706
00:39:53,920 --> 00:39:55,760
Speaker 3: sufficient talent, sufficient edge.

707
00:39:56,400 --> 00:39:59,480
Speaker 2: That's also where you can blame the passive influence on Twitter.

708
00:39:59,480 --> 00:40:01,560
Speaker 2: If you're a long entry manager that you know it's

709
00:40:01,560 --> 00:40:03,440
Speaker 2: impossible to be the market now because you just have

710
00:40:03,480 --> 00:40:05,279
Speaker 2: this money constantly pouring in.

711
00:40:05,400 --> 00:40:08,160
Speaker 3: Yeah, I don't disagree. Yeah.

712
00:40:08,520 --> 00:40:10,839
Speaker 1: One more question, like social roles is just like you've

713
00:40:10,880 --> 00:40:12,840
Speaker 1: worked at most of the big pod jobs, but you

714
00:40:12,840 --> 00:40:16,280
Speaker 1: also worked at HRT, Like what's the difference in roles

715
00:40:16,360 --> 00:40:19,040
Speaker 1: and like what they do all day? Because HRT, I

716
00:40:19,080 --> 00:40:22,080
Speaker 1: think of is a classic like high frequency treating firm

717
00:40:22,120 --> 00:40:24,040
Speaker 1: where I don't know they're exactly a market maker, but

718
00:40:24,080 --> 00:40:26,520
Speaker 1: they're certainly on the higher frequency side, and then like

719
00:40:27,000 --> 00:40:30,520
Speaker 1: the pod jobs have a lower frequency and a you know,

720
00:40:30,640 --> 00:40:35,680
Speaker 1: they're not prop they're running hedgephones. Like what's the cultural

721
00:40:35,719 --> 00:40:37,680
Speaker 1: and role and differences?

722
00:40:37,920 --> 00:40:41,400
Speaker 3: Yeah, okay, So I briefly mentioned the HRT in a

723
00:40:41,560 --> 00:40:45,120
Speaker 3: in an interview with The Financial Times, and my manager

724
00:40:45,200 --> 00:40:48,000
Speaker 3: told me that, you know, people at HRT were both

725
00:40:48,080 --> 00:40:52,160
Speaker 3: annoyed and delighted by what I've had said about about HRT.

726
00:40:53,000 --> 00:40:55,839
Speaker 3: I think HRT is a really special place, even in

727
00:40:55,920 --> 00:40:58,960
Speaker 3: the in the context of proper training firms. So I'm

728
00:40:59,000 --> 00:41:04,120
Speaker 3: a little bit hesitant and to just being them in

729
00:41:04,760 --> 00:41:11,439
Speaker 3: as a representative, right, So they're not representative because there

730
00:41:11,560 --> 00:41:15,520
Speaker 3: is something in the culture of HRT that is special. Okay,

731
00:41:15,640 --> 00:41:19,560
Speaker 3: it's collaborative, it's truly kind. Yeah. So I think it's

732
00:41:19,600 --> 00:41:23,640
Speaker 3: a great place to work, and it is fundamentally monolithic,

733
00:41:23,920 --> 00:41:28,799
Speaker 3: so you have, you know, sharing of ideas and you

734
00:41:28,840 --> 00:41:31,560
Speaker 3: can work at the intersection of these ideas. It's also

735
00:41:31,600 --> 00:41:35,400
Speaker 3: a place that is very tech oriented, so it's a

736
00:41:35,480 --> 00:41:38,960
Speaker 3: bit of a technology firm operating in the financial space.

737
00:41:40,280 --> 00:41:43,120
Speaker 3: And because of that, it also attracts i think the

738
00:41:43,160 --> 00:41:46,759
Speaker 3: best technical talent that I've ever worked with. It's just

739
00:41:46,800 --> 00:41:49,719
Speaker 3: a pleasure to work with great technologists, people who are

740
00:41:49,840 --> 00:41:54,319
Speaker 3: very competent in that respect. So nothing against the hedge funds.

741
00:41:54,320 --> 00:41:56,759
Speaker 3: I love edge funds for different reasons. You know. I

742
00:41:56,800 --> 00:41:59,640
Speaker 3: love BAM, which is also very collaborative and it's an

743
00:41:59,640 --> 00:42:04,600
Speaker 3: investor meant company. But HRT has as a technical side

744
00:42:04,640 --> 00:42:07,080
Speaker 3: to it and also gain a cultural side to it.

745
00:42:07,080 --> 00:42:07,600
Speaker 3: It's great.

746
00:42:23,080 --> 00:42:26,480
Speaker 2: We didn't talk about AI AI.

747
00:42:26,840 --> 00:42:28,719
Speaker 3: Yeah, of course you have to talk about it.

748
00:42:29,200 --> 00:42:32,920
Speaker 1: Like I have like three models of how investment works

749
00:42:32,960 --> 00:42:35,440
Speaker 1: systematic about Like one is like you have like some

750
00:42:35,520 --> 00:42:37,600
Speaker 1: economic intuition and you build a model of like the

751
00:42:37,640 --> 00:42:40,000
Speaker 1: stock market that predicts prices. And in other way is a

752
00:42:40,080 --> 00:42:43,080
Speaker 1: sort of like neural netty ai E way, where like

753
00:42:43,600 --> 00:42:46,160
Speaker 1: you throw a lot of data at a neural net

754
00:42:46,160 --> 00:42:47,719
Speaker 1: and it build its own model of how to predict

755
00:42:47,760 --> 00:42:51,040
Speaker 1: stock prices. And then the third model is like you

756
00:42:51,080 --> 00:42:53,200
Speaker 1: get really good at prompt engineering and you get a

757
00:42:53,239 --> 00:42:55,680
Speaker 1: chat GPT and you say what stocks will go up?

758
00:42:55,760 --> 00:42:57,319
Speaker 1: But you ask it in the right way, and then

759
00:42:57,360 --> 00:43:00,759
Speaker 1: chat JPT tells me what stocks will go up? How

760
00:43:00,800 --> 00:43:04,480
Speaker 1: good is it? I see? The third model no one uses,

761
00:43:04,480 --> 00:43:05,560
Speaker 1: but like someone uses.

762
00:43:06,920 --> 00:43:08,279
Speaker 2: I think a lot of people use that.

763
00:43:08,400 --> 00:43:12,240
Speaker 3: All right, So first thing like, Okay, nobody knows anything,

764
00:43:12,280 --> 00:43:17,160
Speaker 3: and anybody saying the opposite, you know, should be heavily discounted. Okay,

765
00:43:17,280 --> 00:43:23,120
Speaker 3: so we agree on this, and so let's forget for

766
00:43:23,160 --> 00:43:26,239
Speaker 3: a second all the technical details of AI just from

767
00:43:26,239 --> 00:43:31,920
Speaker 3: a pure industrial organization standpoint, Right, So what's going to happen?

768
00:43:33,080 --> 00:43:36,920
Speaker 3: Consider AI just like another technology like Internet and whatnot. Right,

769
00:43:37,000 --> 00:43:40,000
Speaker 3: So you know, first of all, we're going to observe

770
00:43:40,160 --> 00:43:44,160
Speaker 3: economies of scale, So there's going to be concentration, and

771
00:43:44,200 --> 00:43:47,040
Speaker 3: there was going to be some kind of monopolistic competition.

772
00:43:47,120 --> 00:43:52,680
Speaker 3: I was thinking about Bloomberg specifically, which could be I

773
00:43:52,719 --> 00:43:56,160
Speaker 3: hope for you people to be among the winners, because

774
00:43:56,280 --> 00:43:59,200
Speaker 3: you have a good starting point, right, You have lots

775
00:43:59,200 --> 00:44:02,600
Speaker 3: of data, you have a customer base, and maybe in

776
00:44:02,640 --> 00:44:07,600
Speaker 3: the future we'll finally not see the good old Bloomberg terminal,

777
00:44:07,640 --> 00:44:10,200
Speaker 3: which has been kind of unchanged since I remember it,

778
00:44:10,560 --> 00:44:14,040
Speaker 3: and instead people will just prompt Bloomberg to conduct very

779
00:44:14,080 --> 00:44:18,560
Speaker 3: complex actions where it will act on a sequence of

780
00:44:18,760 --> 00:44:22,279
Speaker 3: keywords and connect them and give you, like a much

781
00:44:22,280 --> 00:44:25,520
Speaker 3: more valuable product for which Bloomberg will charge twice as

782
00:44:25,600 --> 00:44:28,480
Speaker 3: much as they do already. So this is going to

783
00:44:28,520 --> 00:44:30,920
Speaker 3: happen in one form or another. If it's not Bloomber,

784
00:44:30,960 --> 00:44:34,160
Speaker 3: somebody else will do it. Okay, But the same thing

785
00:44:34,200 --> 00:44:38,719
Speaker 3: applies to other areas of finance. So maybe once upon

786
00:44:38,760 --> 00:44:42,520
Speaker 3: a time, you know, a big sufficiently big fund could

787
00:44:42,560 --> 00:44:45,000
Speaker 3: build their own client for email.

788
00:44:45,080 --> 00:44:45,279
Speaker 1: Right.

789
00:44:45,400 --> 00:44:47,799
Speaker 3: Of course, nobody builds a client for email anymore. Right,

790
00:44:47,880 --> 00:44:51,319
Speaker 3: so a lot of this stuff gets outsourced. We will

791
00:44:51,360 --> 00:44:54,520
Speaker 3: outsource at some point some of the functions that we

792
00:44:54,680 --> 00:44:59,880
Speaker 3: conduct internally using AI to other AI agents. It's perfectly fine.

793
00:45:00,400 --> 00:45:05,359
Speaker 3: So this will become a utility to some extent. Yes,

794
00:45:05,440 --> 00:45:11,200
Speaker 3: functions include well not stock picking. Not stock picking. I

795
00:45:11,239 --> 00:45:15,200
Speaker 3: think that the functions that we will see available are

796
00:45:15,640 --> 00:45:20,200
Speaker 3: essentially like another self, like another Mathlevin. Who can you

797
00:45:20,719 --> 00:45:22,200
Speaker 3: be a good baseline for you?

798
00:45:22,520 --> 00:45:22,880
Speaker 1: Okay?

799
00:45:22,960 --> 00:45:26,360
Speaker 3: You could feed a post train and AI system with

800
00:45:26,560 --> 00:45:31,120
Speaker 3: all your gazillions of words, right, and that agent will

801
00:45:31,160 --> 00:45:35,760
Speaker 3: reproduce your sense of humor, your investigative style and everything. Okay,

802
00:45:35,920 --> 00:45:38,240
Speaker 3: it's a good approximation. It's not going to be perfect,

803
00:45:38,960 --> 00:45:39,560
Speaker 3: but why not?

804
00:45:39,800 --> 00:45:39,960
Speaker 1: Right?

805
00:45:40,040 --> 00:45:42,120
Speaker 3: So I would be very happy to have a replica

806
00:45:42,160 --> 00:45:46,319
Speaker 3: of myself that can answer most simple questions. Now, I

807
00:45:46,360 --> 00:45:49,600
Speaker 3: think that the decision to invest in a particular stock

808
00:45:49,760 --> 00:45:53,520
Speaker 3: is a very demanding cognitive function, and I don't see

809
00:45:53,520 --> 00:45:56,719
Speaker 3: that really being replicated very well. But I think that

810
00:45:56,800 --> 00:45:58,880
Speaker 3: this will be baselined to some extent.

811
00:45:59,160 --> 00:46:03,240
Speaker 1: Is it many kind of function because because it exists

812
00:46:03,239 --> 00:46:07,200
Speaker 1: in a competitive market, So like the sort of like

813
00:46:07,400 --> 00:46:09,279
Speaker 1: whatever the kind of function is, is going to get

814
00:46:09,280 --> 00:46:11,279
Speaker 1: like the baseline is always going to get higher because

815
00:46:11,280 --> 00:46:13,440
Speaker 1: like someone else will will have the same information as

816
00:46:13,440 --> 00:46:14,320
Speaker 1: you do or the same.

817
00:46:14,160 --> 00:46:17,000
Speaker 3: Well, this is getting really in the highly speculative side

818
00:46:17,040 --> 00:46:20,799
Speaker 3: of you know things. I think that in order for

819
00:46:20,880 --> 00:46:24,080
Speaker 3: an AI agent to be good at this, they have

820
00:46:24,160 --> 00:46:28,520
Speaker 3: to be able to experience the world the same way

821
00:46:28,600 --> 00:46:32,919
Speaker 3: that an investor experiences it. And our inputs are much

822
00:46:32,960 --> 00:46:36,520
Speaker 3: more complex than just a string of text or YouTube videos.

823
00:46:36,600 --> 00:46:39,640
Speaker 3: Right we have a model of the world which comes

824
00:46:39,640 --> 00:46:43,960
Speaker 3: from visually experiencing the world, talking to humans, consuming the goods,

825
00:46:44,440 --> 00:46:49,520
Speaker 3: right anything, It's vastly more complex than the way an

826
00:46:49,560 --> 00:46:53,799
Speaker 3: AI system right now experiences the world and also influences

827
00:46:53,840 --> 00:46:57,839
Speaker 3: the world. So an investor has a fundamentally different experience

828
00:46:57,960 --> 00:47:02,240
Speaker 3: of a company than an LM that has an experience

829
00:47:02,280 --> 00:47:05,839
Speaker 3: thats is mediated by multiple layers of processing. You know,

830
00:47:05,960 --> 00:47:08,320
Speaker 3: they learn about a company through text that is written

831
00:47:08,360 --> 00:47:11,480
Speaker 3: by somebody. So I don't think that that's in danger

832
00:47:11,480 --> 00:47:13,360
Speaker 3: for the time being. But maybe, you know, again, in

833
00:47:13,400 --> 00:47:18,440
Speaker 3: five years, maybe we will have our glasses feeding our

834
00:47:18,520 --> 00:47:21,520
Speaker 3: experiences to AI agents. Who knows, right, But I don't

835
00:47:21,520 --> 00:47:24,960
Speaker 3: think that it's that close, and I don't think AI

836
00:47:25,040 --> 00:47:27,640
Speaker 3: is that's smart also, so I think that having a

837
00:47:27,680 --> 00:47:29,680
Speaker 3: baseline system would be already pretty good.

838
00:47:30,200 --> 00:47:34,120
Speaker 2: That's somewhat comforting that our experiences count for something, our

839
00:47:34,160 --> 00:47:35,840
Speaker 2: physical experience of the world.

840
00:47:36,239 --> 00:47:38,120
Speaker 1: It's interesting because I always think of like the comparison

841
00:47:38,160 --> 00:47:42,760
Speaker 1: as like investing in self driving cars, or like investors

842
00:47:42,800 --> 00:47:44,160
Speaker 1: do a lot of things, but like one thing they

843
00:47:44,200 --> 00:47:46,399
Speaker 1: do a lot is sit at a desk and read

844
00:47:46,440 --> 00:47:49,239
Speaker 1: computers and like look at numbers, right, and like those

845
00:47:49,280 --> 00:47:51,399
Speaker 1: things seem like things that a computer can do well,

846
00:47:51,520 --> 00:47:55,640
Speaker 1: whereas like you know, drivers like have physical reflexes and

847
00:47:55,719 --> 00:47:59,040
Speaker 1: like have a you know, complicated field division. I always thought,

848
00:47:59,040 --> 00:48:02,239
Speaker 1: like investing should be easier than self driving cars for

849
00:48:02,400 --> 00:48:06,960
Speaker 1: computer and a master, but you, I think you're learning this.

850
00:48:07,000 --> 00:48:09,160
Speaker 1: Think of like investing as like the great liberal art,

851
00:48:09,200 --> 00:48:11,560
Speaker 1: where it's like you incorporate all of human experience and

852
00:48:11,600 --> 00:48:12,640
Speaker 1: so the AI can't relate.

853
00:48:12,840 --> 00:48:17,080
Speaker 3: Okay, let's let's take the metaphor to you know, extreme consequences.

854
00:48:17,160 --> 00:48:19,680
Speaker 3: Imagine that you had a system that is the equivalent

855
00:48:19,719 --> 00:48:22,959
Speaker 3: of a perfect self driving car in investing. So now

856
00:48:23,040 --> 00:48:26,560
Speaker 3: I'm giving you a machine, a box that is telling

857
00:48:26,600 --> 00:48:30,640
Speaker 3: you the long term value, if not the returns, right,

858
00:48:30,680 --> 00:48:33,360
Speaker 3: because the moment that the value is known, you immediately

859
00:48:33,360 --> 00:48:34,920
Speaker 3: equilibrate to that level.

860
00:48:35,000 --> 00:48:35,160
Speaker 1: Right.

861
00:48:35,200 --> 00:48:37,760
Speaker 3: So imagine that you know the true value of everything

862
00:48:37,800 --> 00:48:40,759
Speaker 3: because a box tells you so, and it's invaluable. It's

863
00:48:40,760 --> 00:48:45,400
Speaker 3: an oracle. Okay. Would you think that finance stops existing.

864
00:48:47,040 --> 00:48:49,400
Speaker 3: I wouldn't say so, right, So I think that a

865
00:48:49,400 --> 00:48:53,799
Speaker 3: lot of arbitrush trades, you know, would maybe change significantly,

866
00:48:53,800 --> 00:48:57,760
Speaker 3: but every risk, right, every return would be correctly priced

867
00:48:58,120 --> 00:49:00,839
Speaker 3: by the risk of the agent's training it. So there

868
00:49:00,920 --> 00:49:03,680
Speaker 3: still would be trading because we still have different preferences,

869
00:49:04,000 --> 00:49:06,359
Speaker 3: but basically every risk would be priced. There would be

870
00:49:06,440 --> 00:49:09,760
Speaker 3: in a sense, less alpha, but finance will still exist.

871
00:49:09,920 --> 00:49:12,000
Speaker 1: It's a lot of like service provision, like liquid.

872
00:49:12,680 --> 00:49:15,919
Speaker 3: Liquidity provision and yeah, and so the liquidity provision would

873
00:49:15,960 --> 00:49:20,360
Speaker 3: still exist. The informational services maybe will stop existing in

874
00:49:20,400 --> 00:49:23,120
Speaker 3: the current form, but that's okay. I think that we'll

875
00:49:23,160 --> 00:49:24,240
Speaker 3: all still be employed.

876
00:49:24,880 --> 00:49:25,320
Speaker 2: Mhmm.

877
00:49:26,000 --> 00:49:27,799
Speaker 1: It's interesting I think about it because I do think,

878
00:49:27,920 --> 00:49:31,960
Speaker 1: like we talked about, like, one thing that the big

879
00:49:32,000 --> 00:49:35,160
Speaker 1: hedgehunds do is things that have the flavor of liquidity

880
00:49:35,200 --> 00:49:38,839
Speaker 1: provisions basis trades and merger urb and whatever. Things that

881
00:49:38,880 --> 00:49:40,480
Speaker 1: like I think of as like something that a bank

882
00:49:40,480 --> 00:49:42,080
Speaker 1: would have done thirty years ago, and then now a

883
00:49:42,080 --> 00:49:44,319
Speaker 1: big hedge fund does. And then another thing they do

884
00:49:44,880 --> 00:49:48,600
Speaker 1: has the flavor of information provision, where it's getting prices right.

885
00:49:49,480 --> 00:49:53,520
Speaker 1: Like to me, those things seem quite intellectually separate, but

886
00:49:53,560 --> 00:49:55,319
Speaker 1: I guess they feed each other in the sense that

887
00:49:56,640 --> 00:49:58,360
Speaker 1: the better you are prices, the better you can be

888
00:49:58,400 --> 00:49:59,840
Speaker 1: a liquidity provision.

889
00:50:04,239 --> 00:50:07,920
Speaker 3: The value yeah, I mean a short, short horizon. Liquidity

890
00:50:07,960 --> 00:50:12,520
Speaker 3: provision and information tend to be very closely rated. Like

891
00:50:12,560 --> 00:50:15,640
Speaker 3: you know, a limit if you're good at if you're

892
00:50:16,000 --> 00:50:18,520
Speaker 3: either good at crossing even good at crossing, you should

893
00:50:18,520 --> 00:50:21,920
Speaker 3: be pretty good at adding okay, adding liquidity, so you know.

894
00:50:22,040 --> 00:50:24,360
Speaker 3: But I mean like you could make you know a

895
00:50:24,440 --> 00:50:27,320
Speaker 3: profit by posting a lot of limit orders and providing

896
00:50:27,360 --> 00:50:31,040
Speaker 3: liquidity to the market or crossing the spread and making

897
00:50:31,080 --> 00:50:34,880
Speaker 3: money with predicting the future prices. If you're good at one,

898
00:50:34,960 --> 00:50:37,080
Speaker 3: you're good at the other, most likely, right at that

899
00:50:37,120 --> 00:50:40,200
Speaker 3: time scale, I think that this though might I'm not

900
00:50:40,239 --> 00:50:42,800
Speaker 3: sure because I haven't thought about this very very carefully,

901
00:50:43,040 --> 00:50:46,480
Speaker 3: but I think this might decoupled at longer time scale,

902
00:50:46,560 --> 00:50:50,160
Speaker 3: so you know you're when you're out. I'm not sure.

903
00:50:50,200 --> 00:50:52,600
Speaker 3: And in any case, at that time scale is really difficult

904
00:50:52,640 --> 00:50:55,840
Speaker 3: for an AI or for a human being anyone, like,

905
00:50:55,880 --> 00:50:59,160
Speaker 3: there are not that many hard data, even the unstructured

906
00:50:59,200 --> 00:51:02,760
Speaker 3: data are not that any So it's a very difficult problem.

907
00:51:03,000 --> 00:51:07,400
Speaker 3: It's the coupled it's it's complicated. So yeah, but I

908
00:51:07,520 --> 00:51:11,399
Speaker 3: tend to believe at longer time scales you have more

909
00:51:11,480 --> 00:51:16,040
Speaker 3: or less liquid provisioning and you know, violations of law

910
00:51:16,080 --> 00:51:19,400
Speaker 3: of one price on one side and predicting on the

911
00:51:19,440 --> 00:51:20,160
Speaker 3: other side.

912
00:51:20,960 --> 00:51:21,920
Speaker 1: But you combine both.

913
00:51:22,800 --> 00:51:25,560
Speaker 3: But you can combine both, and it's a very potent mix.

914
00:51:25,960 --> 00:51:28,320
Speaker 1: Right. There is normally different people, it is, right.

915
00:51:28,480 --> 00:51:31,279
Speaker 3: Very different people for sure, different parts, very different very

916
00:51:31,280 --> 00:51:32,960
Speaker 3: different people, very different cultures.

917
00:51:33,920 --> 00:51:37,160
Speaker 1: Yeah, can you summarize the difference in cultures between like

918
00:51:37,480 --> 00:51:38,760
Speaker 1: I have a guess.

919
00:51:38,400 --> 00:51:43,160
Speaker 3: But well, as you said, people who typically trade in arbitrades,

920
00:51:43,520 --> 00:51:47,360
Speaker 3: if not historically but also historically come from banks.

921
00:51:47,440 --> 00:51:48,080
Speaker 1: Yeah.

922
00:51:48,200 --> 00:51:53,040
Speaker 3: Right, Whereas you still can see long only portfolio managers

923
00:51:53,600 --> 00:51:58,160
Speaker 3: being recycled and reformatted into long short portfolio managers, you

924
00:51:58,200 --> 00:52:02,200
Speaker 3: can have an excellent short specialist becoming a long short

925
00:52:02,320 --> 00:52:05,239
Speaker 3: portfolio manager like it happened, I.

926
00:52:05,200 --> 00:52:07,560
Speaker 1: Mean makes sense. Is that like the people on the

927
00:52:07,640 --> 00:52:13,440
Speaker 1: information version long short side are more academic and research orientered,

928
00:52:13,440 --> 00:52:14,880
Speaker 1: and the people on the ARB side are.

929
00:52:14,719 --> 00:52:19,799
Speaker 3: More Yeah, I think you can actually have a very

930
00:52:19,840 --> 00:52:24,359
Speaker 3: good long short portfolio managers who were journalists in their

931
00:52:24,400 --> 00:52:25,040
Speaker 3: past lives.

932
00:52:25,400 --> 00:52:28,520
Speaker 1: I've heard of some of these thought about this about

933
00:52:28,560 --> 00:52:29,759
Speaker 1: it no, just like.

934
00:52:31,520 --> 00:52:35,480
Speaker 2: No not breaking news on your podcast.

935
00:52:35,719 --> 00:52:41,799
Speaker 1: I've noticed that's that's better than podcasting. Not thought about

936
00:52:41,800 --> 00:52:43,040
Speaker 1: it in the sense that I'd be good at it,

937
00:52:43,120 --> 00:52:45,959
Speaker 1: just in the sense that the money is good.

938
00:52:46,200 --> 00:52:48,799
Speaker 2: You could be bad at it and paid really well

939
00:52:48,880 --> 00:52:49,919
Speaker 2: for a short amount of time.

940
00:52:50,160 --> 00:52:53,080
Speaker 1: I don't know that that's true. Actually, they're they're an

941
00:52:53,120 --> 00:52:54,920
Speaker 1: excellent talent filter or so I hear.

942
00:52:57,040 --> 00:53:00,680
Speaker 3: Yes, I think that you could interest a few huge funds.

943
00:53:01,239 --> 00:53:08,919
Speaker 3: They might be listening.

944
00:53:07,800 --> 00:53:13,040
Speaker 1: On a note, Kathy, thanks for coming on the.

945
00:53:14,680 --> 00:53:21,759
Speaker 3: Thanks for having me, And that.

946
00:53:21,880 --> 00:53:23,000
Speaker 1: Was the money Stuff podcast.

947
00:53:23,160 --> 00:53:25,160
Speaker 2: I'm Matt Levian and I'm Katie Greifeld.

948
00:53:25,520 --> 00:53:27,560
Speaker 1: You can find my work by subscribing to The Money

949
00:53:27,600 --> 00:53:30,120
Speaker 1: Stuff newsletter on Bloomberg dot com.

950
00:53:29,760 --> 00:53:32,239
Speaker 2: And you can find me on Bloomberg TV every day

951
00:53:32,280 --> 00:53:35,400
Speaker 2: on Open Interest between nine to eleven am Eastern.

952
00:53:35,640 --> 00:53:37,319
Speaker 1: We'd love to hear from you. You can send an

953
00:53:37,320 --> 00:53:40,560
Speaker 1: email to Moneypot at Bloomberg dot net, ask us a

954
00:53:40,600 --> 00:53:42,040
Speaker 1: question and we might answer it on air.

955
00:53:42,480 --> 00:53:44,680
Speaker 2: You can also subscribe to our show wherever you're listening

956
00:53:44,719 --> 00:53:46,719
Speaker 2: right now and leave us a review. It helps more

957
00:53:46,760 --> 00:53:47,640
Speaker 2: people find the show.

958
00:53:48,360 --> 00:53:51,160
Speaker 1: The Money Stuff Podcast is produced by Anna Masarakus and

959
00:53:51,239 --> 00:53:52,200
Speaker 1: Moses on Them.

960
00:53:52,200 --> 00:53:54,400
Speaker 2: Our theme music was composed by Blake Maples.

961
00:53:54,600 --> 00:53:56,960
Speaker 1: Brandon Francis Nunim is our executive.

962
00:53:56,480 --> 00:53:59,200
Speaker 2: Producer, and Stage Bollman is Bloomberg's head of Podcasts.

963
00:53:59,480 --> 00:54:01,840
Speaker 1: Thanks for listening to The Money Stuff Podcast. We'll be

964
00:54:01,920 --> 00:54:03,360
Speaker 1: back next week with more stuff.

965
00:54:08,960 --> 00:54:09,439
Speaker 2: Mm hmm