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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of The Odd Lots Podcast.

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Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 2: Tracy, do you know this? Sometimes I wonder, like, you know,

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Speaker 2: one in the morning, if I can't sleep, I think

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Speaker 2: to myself, in a different life, could I have been

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Speaker 2: the next Steve Cohen? Yeah? No, for real though, And

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Speaker 2: I don't, you know, need to talk about it. There's

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Speaker 2: a lot and I've brought it up before, you know.

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Speaker 2: I did get an offer at a prop trading shop

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Speaker 2: right after college to be a stock trader at this

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Speaker 2: place where they're gonna let use to your capital. And

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Speaker 2: I think Steve Cohen started off like as a prop

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Speaker 2: trader at some shop before being one of the great

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Speaker 2: hedge funders of all time. And I didn't take that

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Speaker 2: job for reasons I still can't explain to myself twenty

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Speaker 2: five years later, But I always wonder whether could have

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Speaker 2: cut it. Maybe I could have been a good trader.

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Speaker 2: I don't know.

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Speaker 3: It's good you have a healthy level of self confidence, Joe. No,

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Speaker 3: When I lay awake at night, I think like, oh, shoot,

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Speaker 3: what did I say? Something stickid on the podcast, and

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Speaker 3: that's what keeps me up. But yes, good for you, Joe.

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Speaker 2: No, I don't really think I could have, and I

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Speaker 2: actually do not think I would have been a good trader.

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Speaker 2: I don't think like that. I'm not that good at

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Speaker 2: poker other things. I'm not a natural better I don't

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Speaker 2: do like sports betting. I don't think that. But I

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Speaker 2: do sort of, you know, wonder about what my life

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Speaker 2: had been different if I had said yes to that.

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Speaker 3: Yeah, fair enough. I mean we know, we know from

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Speaker 3: multiple episodes of the podcast this year alone. Yes, like

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Speaker 3: there are a lot of hedge fund traders out there,

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Speaker 3: especially in multi strats, who seem to be making a

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Speaker 3: lot of money, and everyone's sort of talking about them

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Speaker 3: up until recently. Maybe I should say, we're recording this

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Speaker 3: on August seventh, so maybe those bonuses a little bit

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Speaker 3: less this year given the market sell off. But up

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Speaker 3: until this month, people seem to have been doing relatively well,

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Speaker 3: and there was all this intrigue and interest in the

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Speaker 3: world of traders. And I'm sort of curious. This has

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Speaker 3: come up a couple times now, but what makes a

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Speaker 3: good trader and how are traders actually evaluated? Because my

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Speaker 3: impression was always like, Okay, well it depends on how

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Speaker 3: much money you make, but what's the timeframe for making

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Speaker 3: that money? And then also what about people who are

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Speaker 3: working in for instance, these particular pods who are doing

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Speaker 3: one specific thing, like what is the benchmark against which

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Speaker 3: they are judged?

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Speaker 2: You mentioned that maybe they're not making so much money

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Speaker 2: this week or this month, But Tracy, I think we're

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Speaker 2: told all the time they're so neutral on everything. Their

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Speaker 2: market neutral, they're beta neutral, they're neutral every factor you

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Speaker 2: can think of. Why should they be losing money right

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Speaker 2: now They're supposed to like be neutral all.

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Speaker 4: Of this time.

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Speaker 3: Yeah, I'm sure they're making loads shore. I'm absolutely sure.

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Speaker 2: No, but you're right. And look, we've been doing a

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Speaker 2: lot on hedge fund structure, and we did that episode

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Speaker 2: with Giuseppe Polyoligo, and we did that episode with Rich

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Speaker 2: falk Wallace, various aspects of like how hedge funds measure

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Speaker 2: risk and try to isolate alpha and all this stuff.

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Speaker 2: But they're just like so many questions in my mind,

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Speaker 2: Like I feel like we're just scratching the surface because

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Speaker 2: you know, we haven't even really talked about like idea generation.

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Speaker 2: So it's one thing to you know, talk about like, okay,

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Speaker 2: here's how you like factor out all of these exposures

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Speaker 2: that you don't on have like market beta, et cetera.

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Speaker 2: It's another thing to talk about like okay, but like

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Speaker 2: how do you pick the stocks to buy or go short?

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Speaker 3: Well, yeah, we have gotten into this a little bit,

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Speaker 3: but you're right, there's more we could do. There are

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Speaker 3: all these questions about like how do you size your positions?

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Speaker 3: And if you're convinced that one thing is going to

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Speaker 3: be the next big thing, then why don't you just

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Speaker 3: have like one hundred percent positions?

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Speaker 4: Yeah?

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Speaker 2: Right, how do you make money if you can't just

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Speaker 2: go one hundred percent leverage long and video in video.

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Speaker 3: Yeah.

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Speaker 2: Anyway, so there's a lot more we can do. But

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Speaker 2: to my original, very egotistical start to this episode, I

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Speaker 2: do wonder like it's.

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Speaker 3: Okay, Joe, it's good to have self confidence. I'm being serious,

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Speaker 3: thank you.

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Speaker 2: I do wonder like this big question of like you know,

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Speaker 2: and a lot of people are probably interested in this

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Speaker 2: because these hedge one PM jobs or trader jobs seem

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Speaker 2: pretty great and as you mentioned, lucrative, and so it

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Speaker 2: would be interesting to know how a fund or anyone

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Speaker 2: goes about identifying like the next great trader who gets

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Speaker 2: to have that seat, so to speak.

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Speaker 3: Well, I also think if you can identify what makes

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Speaker 3: a good trader at a hedge fund, then you can

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Speaker 3: get more into the business model of what they're actually

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Speaker 3: doing on a day to day basis. It helps us

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Speaker 3: understand what.

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Speaker 2: They're really good at and what they can do specifically. Well,

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Speaker 2: I'm very excited today because we really do have the

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Speaker 2: perfect guest. We're going to be speaking with, Joe Peta.

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Speaker 2: He is the author of a recent book, Moneyball for

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Speaker 2: the money set, which is the name sort of implies

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Speaker 2: tries to, you know, figure out new ways or the

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Speaker 2: best ways to identify talent. I'm sure there's a lot

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Speaker 2: of old heuristics like they had in Bay, you know,

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Speaker 2: and they're like, well, this guy looks like he has

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Speaker 2: a good hustle, and then Moneyball came along. He's like, no, actually,

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Speaker 2: you want to really look at his like you know,

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Speaker 2: on base percentage or whatever it is, and stop looking

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Speaker 2: at like his like spirit or you know, his hustle

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Speaker 2: ahead of him. Anyway, and prior to that, in his career,

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Speaker 2: he's been in this industry for a long time. He

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Speaker 2: was the head of performance analytics at point seventy two.

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Speaker 2: So this speaks right to the question of how do

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Speaker 2: you evaluate traders. We also hit him on years ago

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Speaker 2: one of our really early episodes where he talked about

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Speaker 2: sports betting with some of these same ideas, et cetera.

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Speaker 2: So I'm thrilled to have Joe back to talk about

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Speaker 2: this basic question of how it's good trader. So thanks

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Speaker 2: for coming back, Joe.

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Speaker 4: Oh, it's great to be here Joe and Tracy, and

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Speaker 4: nice to do it in person. Seven years ago, Tracy,

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Speaker 4: I believe you were in Hong Kong and yeah, Joe,

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Speaker 4: you just had a garage band instead of selling out venues.

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Speaker 2: Now that's right, that's right. Still mentioned you were head

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Speaker 2: of performance analytics at point seventy two. How did you

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Speaker 2: get that job at a point seventy two? Steve Cohen's

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Speaker 2: big yes.

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Speaker 4: So that goes right back to my appearance seven years ago.

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Speaker 4: So when I was on in twenty seventeen, I had

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Speaker 4: written a book called Trading Basis, which really looked at

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Speaker 4: the critical reasoning overlap between asset management, sports betting, and

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Speaker 4: the moneyballization of baseball. And you had ask me, Joe,

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Speaker 4: I think it was you asked me a specific question

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Speaker 4: of well, I mentioned that somebody from all three of

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Speaker 4: those constituents could learn something from the other two. And Joe,

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Speaker 4: you asked me for a specific example of how they

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Speaker 4: look at things differently, and I said, well, if you

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Speaker 4: go onto a trading floor, or you go to a

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Speaker 4: mutual fund and you ask them, hey, who's your best

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Speaker 4: trader or who's your best PM, Inevitably they will point

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Speaker 4: to the individual who had the highest return in the

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Speaker 4: prior year, either the biggest P and L or the

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Speaker 4: highest return on capitol. But I contrasted that that if

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Speaker 4: you went into the front office of a baseball team

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Speaker 4: and asked them who their best player was, they wouldn't

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Speaker 4: look at you know, which picture necessarily had the lowest

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Speaker 4: ra or the most wins. They would answer that question

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Speaker 4: based on skill sets, and so they it's a subtle difference.

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Speaker 4: Instead of looking at results, they would look at skills

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Speaker 4: because they know that the skills, there's so much noise

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Speaker 4: and results that the skills. If you can identify the skills,

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Speaker 4: you have a better chance of predicting who will do

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Speaker 4: better going forward. And as it was told to me,

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Speaker 4: a member of the c suite, at zero point seventy

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Speaker 4: two listen regular listener heard that episode and played a

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Speaker 4: portion of it for Steve. In fact, I think it

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Speaker 4: was the part I just mentioned, And I was told,

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Speaker 4: as it was relayed to me that Steve said, find him.

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Speaker 4: I want to talk to him. And I guess that's

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Speaker 4: not a surprise because in twenty twelve, and this is

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Speaker 4: all public knowledge. In fact, there's a book by Molly

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Speaker 4: Knight called The Best Team Money Can Buy that chronicles

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Speaker 4: the Dodger's ownership through the turbulent court years Frank McCourt's ownership,

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Speaker 4: and that team was sold in twenty twelve to the

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Speaker 4: Guggenheim Group. But Steve also bid for that team and

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Speaker 4: came very close to buying the Dodgers in twenty twelve.

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Speaker 4: And of course we all know him now as New

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Speaker 4: Yorker's nome his uncle Steve owner of the New York Mets.

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Speaker 4: So he has, I believe, always had an interest in

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Speaker 4: an analytical approach, and I think he always wondered, well

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Speaker 4: could that work in the hedge Fund. And I came

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Speaker 4: away from those meetings with the bunch of different people

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Speaker 4: in the investment committee, and I kind of came away

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Speaker 4: with three queries that I thought could sort of be

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Speaker 4: my marching orders and how I could help, And that

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Speaker 4: was I think at all these pod shops, when somebody

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Speaker 4: has a good year, they ask for more money, and

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Speaker 4: in terms of buying power, not cash, but in terms

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Speaker 4: of buying power. And so the question that management would

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Speaker 4: have is, well, is what they did repeatable? And at

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Speaker 4: the same time, as you know, there's turnover at these firms, right,

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Speaker 4: And I think another question is, well, sometimes we let

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Speaker 4: people go too early that then thrive elsewhere just because

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Speaker 4: they had a bad start to their career in terms

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Speaker 4: of results. Is there a way that we can avoid

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Speaker 4: that mistake? And then finally, when a team does well,

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Speaker 4: inevitably there's a bit of way, right because we know

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Speaker 4: that these four or five huge firms are all very

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Speaker 4: competitive and they're trying to steal talent. And so the

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Speaker 4: question is I know what a PM and or her

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Speaker 4: team may have made me in the past, but what

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Speaker 4: are they worth going forward? And all of those queries

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Speaker 4: can be answered by looking at skills, which is a

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Speaker 4: little different than what the traditional quants do at these firms.

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Speaker 3: Okay, so here's my question, who should we bill for

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Speaker 3: the finder's fee fee? Should we send it directly to Steez?

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Speaker 2: He do you right?

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Speaker 4: It would be the firm, right, they probably saved a

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Speaker 4: lot of money as opposed to going through a traditional headhunter.

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Speaker 3: Okay, I'm joking. Obviously that's fantastic to hear. I've loved

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Speaker 3: stories like that. Before we get into the existing model

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Speaker 3: of compensation. There's one question that I wonder because I

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Speaker 3: think we've done a number of Moneyball episodes at this point,

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Speaker 3: but it's been a while since we've talked about that approach,

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Speaker 3: and all I remember is the movie and Brad Pitt

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Speaker 3: kind of unconvincingly playing a guy that understands math. Could

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Speaker 3: you maybe explain, like what it is about the Moneyball

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Speaker 3: approach that seems to attract people in finance, Like why

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Speaker 3: is there that analogy that seems to come up again

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Speaker 3: and again.

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Speaker 4: Yeah, I think if you're attracted to critical reasoning, and

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Speaker 4: that's the big thing and all of this industry is

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Speaker 4: you know, Joe said, what have I succeeded here? And

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Speaker 4: I always think the biggest question is do you have

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Speaker 4: the mentality in the stomach to make decisions and commit

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Speaker 4: capital based on incomplete information? Whether you have the skills

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Speaker 4: to you know, build models for you know, and and

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Speaker 4: understand companies and read documents. It's really can you make

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Speaker 4: decisions based on income information? And it's true at the

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Speaker 4: poker table, all right, and it's certainly true when you're

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Speaker 4: building a sports team, right you're like, how much is

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Speaker 4: this free agent worth? And before there were a lot

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Speaker 4: of Joe like you say heuristics, and I even mentioned

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Speaker 4: that in the book. I feel that still goes on

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Speaker 4: at the allocator level in this industry. Allocators they do

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Speaker 4: the interviews and you will hear things like, well he

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Speaker 4: just got divorced, you know, or there's a Bentley in

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Speaker 4: the parking lot. He must not be hungry anymore. Oh absolutely.

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Speaker 4: And one of the reasons is because they don't have

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Speaker 4: they don't take a different approach that might be more databased.

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Speaker 4: The whole idea of the moneyball approach is to tease

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Speaker 4: out skill from or the signal from these very noisy results,

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Speaker 4: because both athletes and asset managers, their results are filled

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Speaker 4: with influences over which they have no control. To answer

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Speaker 4: this question later, you both were talking about like market

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Speaker 4: neutral PMS, neutral everything pms, Why would they be having

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Speaker 4: the worst week this week than before. And there's an

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Speaker 4: actual real answer to that that has nothing to do

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Speaker 4: with their skills. Yeah, so this can apply to any

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Speaker 4: time period. We're looking at days or months, a year.

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Speaker 4: Let's go to sort of an economics one OHO one

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Speaker 4: like holding all else equal. Let's say we have a

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Speaker 4: PM that has one long and one short, okay, and

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Speaker 4: that's their entire portfolio. And of course they never would

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Speaker 4: this goes to something else you said in the intro

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Speaker 4: because of career risk. Right, even if it's their best

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Speaker 4: idea long and best idea short, they'll still fill it.

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Speaker 4: But let's say this is their portfolio and on any

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Speaker 4: given day or any period we could measure, but let's

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Speaker 4: keep it at a day.

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Speaker 2: It's a perfect.

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Speaker 4: Portfolio in that the long produces alpha and the short

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Speaker 4: produces alpha. So the long outperforms the market and the

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Speaker 4: short underperforms the market. Right, So that's a perfect portfolio.

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Speaker 4: What is the expected re turn for that portfolio for

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Speaker 4: like I say, any period, but for a day, And

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Speaker 4: the answer is there's a way to figure out. And Tracey,

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Speaker 4: you're gonna love this because the answer is dispersion. And

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Speaker 4: I know you light up when when you have the

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Speaker 4: But this is a little different dispersion than the quants

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Speaker 4: and the derivative traders make their life around. This dispersion

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Speaker 4: is and it's going to be very context specific for

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Speaker 4: where the PM toils. Right, so we know what these

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Speaker 4: pod shops they tend to be. They have you know,

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Speaker 4: subject matter expertise in sectors. Right, So you might have

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Speaker 4: an energy PM, and so let's say this is a consumer

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Speaker 4: discretionary PM, and you would say, okay, well, I'm going

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Speaker 4: to look at his or her universe and maybe that's

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Speaker 4: the S and P fifteen hundred consumer discretionary Maybe it's

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Speaker 4: say a portfolio of just consumer discretionary stocks that he

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Speaker 4: has modeled, so there might only be eighty or so

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Speaker 4: he and his team, but whatever it is, we'll say

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Speaker 4: that it's the all the consumer discretionary stocks in the

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Speaker 4: S and P five hundred or fifteen hundred. Well, the

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Speaker 4: way to figure out what the expected return is is

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Speaker 4: to simply look at all those stocks and say, here's

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Speaker 4: the skill neutral return, which would be the average return

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Speaker 4: of all those holdings. And then you look at the

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Speaker 4: ones that outperformed what was their average And you look

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Speaker 4: at all the stocks that underperformed and what was their average.

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Speaker 4: And the difference between those two numbers is the dispersion

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Speaker 4: between outperformers and underperformers, and that varies greatly from day

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Speaker 4: to day, and it can very greatly from year to year.

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Speaker 3: It's not like the maximum that you can produce dispersions.

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Speaker 4: Not the maximum, because you could have the very best

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Speaker 4: outperformer and the very best underperformer. But if you're looking

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Speaker 4: at a POD, so I'm taking all the pod from

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Speaker 4: all the shops across the street that are focused on

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Speaker 4: consumer discretionary, I'm going to be dead on by saying

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Speaker 4: the average of all those of all those perfect portfolios

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Speaker 4: is going to be the average of all the outperformers

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Speaker 4: and the average of all the underperformers. And it's invisible

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Speaker 4: to investment commits, to CIOs, to the pms themselves. They

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Speaker 4: can be just as skilled from one day or one

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Speaker 4: period and one year to the next, but the payoff

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Speaker 4: is different. And this is sort of the moneyball. Look

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Speaker 4: at hey, once we get this all context neutral, we

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Speaker 4: might say that a neutral everything PM that had a

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Speaker 4: seven percent return one year and a five percent cent

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Speaker 4: return the next year, he may have even been more

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Speaker 4: skilled than the five percent year, but the dispersion wasn't

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Speaker 4: there to pay off that skill.

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Speaker 2: Oh, I see what you're saying. So in other words,

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Speaker 2: it's like, Okay, this person's up five percent. In order

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Speaker 2: to establish like whether that's good or bad or not,

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Speaker 2: you have to have some sort of like holistic view

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Speaker 2: of what dispersion on average looked like. In that that's

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Speaker 2: exactly well, that makes sense. It also seems kind of obvious,

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Speaker 2: you know, I know, the divorce and the Bentley is

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Speaker 2: probably like extreme examples. They're sort of funny. But you know,

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Speaker 2: thinking about the moneyball thing, and I mentioned in the

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Speaker 2: old days like oh, that guy looks he's a good

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Speaker 2: ey or whatever, just like all these sort of unquantified

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Speaker 2: his hustle, you know, his heart whatever. And then you know,

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Speaker 2: Brad Pitt or the being came along and actually put

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Speaker 2: some numbers on this. If they're not doing that, what

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Speaker 2: are the sort of like old heuristics that aren't the

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Speaker 2: extreme ones that the investment committees or the hiring committees

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Speaker 2: of the firing committees would have been using two aviliate.

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Speaker 4: Pro So there's no question that it seems obvious, and

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Speaker 4: it's just the first building block. And this isn't Black

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Speaker 4: Shawl stuff in terms of complexity.

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Speaker 2: Yeah.

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Speaker 4: I started this sort of journey and analytics by working

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Speaker 4: for a company called Novus, and Novas was one of

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Speaker 4: about fifteen years ago, was at the forefront of portfolio analytics,

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Speaker 4: and in fact, they had read my book and I'm like, hey,

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Speaker 4: this is what we try to do. And I worked

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Speaker 4: for them. So I've seen just about every package out there,

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Speaker 4: whether it is from a vendor in terms of analytics

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Speaker 4: or you know inside firms. I've you know, worked with allocators.

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Speaker 4: I have never seen dispersion quoted Michael Mobison has written

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Speaker 4: a paper on it. So there are academics who are

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Speaker 4: aware of it, but I don't think people realize that

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Speaker 4: is the calculation for the fruit on the tree, the

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Speaker 4: meat on the bone, for these pod shops, there has

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Speaker 4: to be dispersion to pay off a non factor you know,

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Speaker 4: a factor neutral portfolio. So what the quants really do

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Speaker 4: and this is like what Gappy touched on when he

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Speaker 4: talked about you know, the day in the life of

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Speaker 4: a quant and your other guest within the last month

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Speaker 4: whose name I can't read. Yes, there was lots of

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Speaker 4: talk about risk management, right because of course it's of

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Speaker 4: utmost importance when you have a leveraged firm, right, you

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Speaker 4: have to understand every factor that's bouncing around in there,

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Speaker 4: and that's really their job, and they will, of course

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Speaker 4: because draw downs in a leverage firm, drawdowns are to

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Speaker 4: be avoided as much as possible. So the sharp ratio

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Speaker 4: really drives the way the quants are looking at pms.

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Speaker 4: But they're all backwards looking, sort of in my view,

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Speaker 4: So they do strip out everything. But once they get alpha,

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Speaker 4: or as I know, one firm calls it idiosyncratic alpha.

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Speaker 4: What I then do is the next step. I don't

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Speaker 4: change the definition of alpha, but then I break that

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Speaker 4: into a skill framework so that once you get different skills,

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Speaker 4: you can say this one's more repeatable than another skill,

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Speaker 4: et cetera.

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Speaker 3: So like dispersion weighted basically like weighted by the opportunity

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Speaker 3: that's available.

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Speaker 4: To you, Yes, exactly, And that's what allows you Tracy

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Speaker 4: to compare the energy trader to the consumer discussionary trader

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Speaker 4: because and I make an analogy in the book, it's

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Speaker 4: like looking at NFL punt. You know, PM's job is

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Speaker 4: to make as much money as possible, and essentially a

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Speaker 4: punter's job is to kick the ball as far as possible.

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Speaker 4: So before sports analytics came along, punters were judged on

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Speaker 4: and in fact, I think there was even award for

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Speaker 4: the punter that had the biggest average at the end

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Speaker 4: of the year, right the distance of all his punts

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Speaker 4: divided by total number of punts. But what sports analytics

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Speaker 4: people quickly figured out is that, well, hey, if the

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Speaker 4: best punter is averaging forty four yards to punt, and

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Speaker 4: you've got another punter whose coach is so conservative that

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Speaker 4: he's constantly punning from the opponent's thirty five yard line

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Speaker 4: or the opponent's forty yard line, he can't even get

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Speaker 4: a forty four yard punt off. So the way to

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Speaker 4: measure that is to say, okay, when a punts from

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Speaker 4: his own fifteen yard line, I'm going to measure that

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Speaker 4: against every other punt from the fifteen yard line. And

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Speaker 4: now you each punt is then evaluated. And I think

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Speaker 4: what's really important to the work I do too, is

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Speaker 4: or to note, you don't measure it now by the distance.

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Speaker 4: You measure it by plus or minus the average punter.

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Speaker 4: So you can say someone is on average one and

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Speaker 4: a half yards better per punt, and then you can

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Speaker 4: put a value on that. And that's the same way

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Speaker 4: a lot of you know, my framework is, it's not

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Speaker 4: saying you know it especially sort of like that that

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Speaker 4: canned package. You will see a canned batting average on

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Speaker 4: all analytics platform. It's meaningless. In fact, it's worthless. But

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Speaker 4: if you express it the way I just talked about punters,

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Speaker 4: sort of this skill neutral and to say, oh, his

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Speaker 4: batting average is one or two percent above, you know,

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Speaker 4: over the year he averages one percent a day. Well,

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Speaker 4: you know, in a fifty percent portfolio, that would be

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Speaker 4: you know, one more winner than expect it every other day.

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Speaker 4: Then you can compare that to the dispersion world that

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Speaker 4: he lives in, and you can put an absolute value

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Speaker 4: on his skill. Now it might differ from the actual,

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Speaker 4: but that's because of stuff out of the PM's control.

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Speaker 4: So that's the approach, and it's sort of marrying the

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Speaker 4: sports analytics approach. And again you kind of said, like,

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Speaker 4: why isn't this done? I do have some thoughts on

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Speaker 4: that because I got dropped into a fish out of

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Speaker 4: water quant division And they're brilliant, right, they are brilliant,

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Speaker 4: but they're not very flexible in they're thinking. They tend

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Speaker 4: to think the same way. And I found that when

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Speaker 4: I was interviewing for a quant developer, you know, because

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Speaker 4: I'm sort of building my framework on Excel and then

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Speaker 4: you need some production around it to make it usable

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Speaker 4: in a big firm or to clients. And I was,

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Speaker 4: you know, an interviewing for a quant developer. I couldn't

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Speaker 4: get them to stop talking about factors because that's sort

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Speaker 4: of the way they're trained. And I'm like, okay, right,

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Speaker 4: we're gonna strip out factors. How would you evaluate skill?

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Speaker 4: And again it get you know, it came down it

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Speaker 4: was very.

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Speaker 3: Good start talking about factors again.

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Speaker 4: Yeah, yeah, And like I say, they're brilliant, but I

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Speaker 4: think sort of an approach outside the industry, Yeah, it

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Speaker 4: can really help. You can uncover different stuff by sort

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Speaker 4: of marrying two different industries.

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Speaker 2: So a lot of this stuff, so far as you've

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Speaker 2: described it is intuitive as you describe it, like, yeah,

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Speaker 2: it makes a lot of sense that you know, you

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Speaker 2: have to if you're going to compare two different pods

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Speaker 2: that are trading consumer discretionary, you have to understand that

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Speaker 2: dispersion and how they compare to a.

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Speaker 3: Or comparing someone trading consumer discretionary versus like utilities.

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Speaker 2: Totally, and it makes sense to me that there's more

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Speaker 2: than just volatility adjusted return sharp ratios. And it makes

433
00:22:31,240 --> 00:22:33,760
Speaker 2: sense to me that punters shouldn't just be measured on

434
00:22:33,840 --> 00:22:35,879
Speaker 2: pure length because you don't know where their coaches have

435
00:22:36,000 --> 00:22:38,400
Speaker 2: them punt from. And maybe sometimes you want to punt

436
00:22:38,440 --> 00:22:40,600
Speaker 2: shorter for various reasons because you want to have a

437
00:22:40,680 --> 00:22:43,320
Speaker 2: chance that you know, if you're catcher something like that. Okay,

438
00:22:43,640 --> 00:22:46,080
Speaker 2: I get all of that. Talk to us a little

439
00:22:46,160 --> 00:22:51,000
Speaker 2: bit more about the art of measuring skill, specifically outside

440
00:22:51,440 --> 00:22:55,320
Speaker 2: of returns, because this is the moneyball thing, which is

441
00:22:55,440 --> 00:22:58,919
Speaker 2: like every day they're coming up with new metrics and

442
00:22:58,960 --> 00:23:02,080
Speaker 2: vanity metrics that they these conferences where it's like vorp

443
00:23:02,200 --> 00:23:04,639
Speaker 2: and all these things. And I know that vorp is

444
00:23:04,680 --> 00:23:07,639
Speaker 2: like that was like twenty years ago that someone invented vorp, right,

445
00:23:07,920 --> 00:23:09,879
Speaker 2: but you know, there's all of these new things that

446
00:23:09,920 --> 00:23:11,399
Speaker 2: I was trying to come up with something that will

447
00:23:11,520 --> 00:23:13,879
Speaker 2: unlock this is the guy who produces a lot of

448
00:23:13,920 --> 00:23:16,600
Speaker 2: extra wins or something for the baseball team. What are

449
00:23:16,640 --> 00:23:20,240
Speaker 2: some of the other techniques or maybe what are the

450
00:23:20,280 --> 00:23:23,679
Speaker 2: other skills sure that you can measure a traitor on

451
00:23:23,960 --> 00:23:27,280
Speaker 2: other than just looking at xpos factor returns to justify risk.

452
00:23:27,520 --> 00:23:30,000
Speaker 4: Right, Yes, so's that's a great question. And I'm laughing

453
00:23:30,040 --> 00:23:32,520
Speaker 4: as you talk about the acronyms because obviously the sport

454
00:23:32,560 --> 00:23:35,720
Speaker 4: channel at the community is famous for their acronyms. So

455
00:23:35,800 --> 00:23:41,080
Speaker 4: I in creating my framework, I have five skills that

456
00:23:41,359 --> 00:23:44,919
Speaker 4: explain alpha, okay, and it doesn't reinvent alpha or in

457
00:23:44,960 --> 00:23:49,000
Speaker 4: any way, it just breaks it down, and of course

458
00:23:49,040 --> 00:23:52,560
Speaker 4: I use acronyms to describe, and with a nod to

459
00:23:53,119 --> 00:23:57,080
Speaker 4: the industry that inspired them, I've named them after five

460
00:23:57,200 --> 00:24:00,760
Speaker 4: different baseball players from you know, the night teen seventies

461
00:24:00,760 --> 00:24:04,320
Speaker 4: when I was an impressionable baseball fan, And those skills

462
00:24:04,320 --> 00:24:09,119
Speaker 4: by name are sever Aaron, carew Rose, and then lumb

463
00:24:09,400 --> 00:24:12,000
Speaker 4: Lum is something you probably a name you haven't heard of,

464
00:24:12,400 --> 00:24:13,760
Speaker 4: but that is named for.

465
00:24:13,720 --> 00:24:16,840
Speaker 3: A Yeah, that is named for it.

466
00:24:18,240 --> 00:24:21,840
Speaker 2: Sorry, I'm not the other four Tom.

467
00:24:23,320 --> 00:24:26,720
Speaker 4: Aaron, Aaron so all Hall of Fame level players, even

468
00:24:26,760 --> 00:24:29,440
Speaker 4: though Pete Rose isn't Lan but so interestingly, and I

469
00:24:29,800 --> 00:24:31,120
Speaker 4: won't go in deeply into this.

470
00:24:31,080 --> 00:24:33,639
Speaker 2: But Rose measures the degree to which they're betting on

471
00:24:33,680 --> 00:24:34,879
Speaker 2: the side.

472
00:24:34,880 --> 00:24:37,520
Speaker 4: How good are they at yes, at being well, Pete Rose,

473
00:24:37,640 --> 00:24:40,800
Speaker 4: it's a good one. So this isn't actually a descriptive acronym.

474
00:24:41,040 --> 00:24:44,960
Speaker 4: So Rose stands for return on sector excellence. So why Rose?

475
00:24:45,000 --> 00:24:48,120
Speaker 4: And you know why this? Well, Pete Rose made more

476
00:24:48,119 --> 00:24:51,119
Speaker 4: All Star teams at different positions than anybody else in baseball.

477
00:24:51,160 --> 00:24:53,160
Speaker 4: He made an All Star team at second base, outfield,

478
00:24:53,160 --> 00:24:55,119
Speaker 4: third base, and first base. So he was good at

479
00:24:55,119 --> 00:24:57,800
Speaker 4: sector rotation, right, So that that's sort of what that

480
00:24:57,880 --> 00:25:03,000
Speaker 4: skill is, measuring the lumb for luck uncontrolled by the manager.

481
00:25:03,119 --> 00:25:06,800
Speaker 4: Lum and what that really references, Tracy, It's a lot

482
00:25:06,840 --> 00:25:09,000
Speaker 4: of what we were talking about in terms of the

483
00:25:09,040 --> 00:25:11,879
Speaker 4: dispersion and really sort of the average stock in a

484
00:25:11,920 --> 00:25:15,240
Speaker 4: portfolio versus what the benchmark might be, because the average

485
00:25:15,240 --> 00:25:18,200
Speaker 4: stock is really what the skill neutral performer. Well, those

486
00:25:18,280 --> 00:25:21,320
Speaker 4: differences are sort of luck that is either a tailwind

487
00:25:21,400 --> 00:25:24,440
Speaker 4: or head wind uncontrolled by the manager. And Mike Lum

488
00:25:24,480 --> 00:25:28,040
Speaker 4: was a journeyman player who happened to play on an

489
00:25:28,040 --> 00:25:31,359
Speaker 4: Atlanta Braves team with Hank Aaron and Davy Johnson Daryl

490
00:25:31,560 --> 00:25:33,520
Speaker 4: Evans when they all hit forty home runs. They're the

491
00:25:33,600 --> 00:25:36,080
Speaker 4: only team that did that, and that inflated all of

492
00:25:36,119 --> 00:25:39,399
Speaker 4: Mike Lum's performance too, and obviously it's something he couldn't control.

493
00:25:39,720 --> 00:25:43,359
Speaker 4: But so these skills, I think the So what they're

494
00:25:43,359 --> 00:25:46,680
Speaker 4: really measuring is one is luck, two is sector excellence.

495
00:25:46,800 --> 00:25:49,440
Speaker 4: Third is a consistency measure, and that's the Rod carew

496
00:25:49,560 --> 00:25:52,959
Speaker 4: and in great batting average. And then there's power. Like

497
00:25:53,040 --> 00:25:55,119
Speaker 4: I talk about what the expected return is of that

498
00:25:55,200 --> 00:25:59,320
Speaker 4: perfect portfolio, Well, if someone's return is above or below that,

499
00:25:59,800 --> 00:26:03,520
Speaker 4: what that's really measuring is their ability to identify the

500
00:26:03,560 --> 00:26:07,359
Speaker 4: best of the outperformers and crucially avoid the worst of

501
00:26:07,400 --> 00:26:09,919
Speaker 4: the outperformers. And I can quantify that. And then the

502
00:26:09,920 --> 00:26:13,040
Speaker 4: final one, the siver is a sizing thing, and you

503
00:26:13,119 --> 00:26:16,240
Speaker 4: put all five of those together and you might have someone, Well,

504
00:26:16,440 --> 00:26:19,600
Speaker 4: here's a great example of how it's useful on a

505
00:26:19,680 --> 00:26:22,720
Speaker 4: multi manager platform. And I should say that all my

506
00:26:22,840 --> 00:26:26,760
Speaker 4: work only deals with public equities. Yeah, public equity APMs

507
00:26:27,000 --> 00:26:30,760
Speaker 4: evaluating them. So on a POD platform, you might have

508
00:26:31,280 --> 00:26:36,840
Speaker 4: four dozen, five dozen different teams, right, and you generally

509
00:26:37,359 --> 00:26:40,720
Speaker 4: do not need a model to tell you who the

510
00:26:40,720 --> 00:26:43,320
Speaker 4: best two or three are, And to a little lesser extent,

511
00:26:43,359 --> 00:26:44,880
Speaker 4: you don't need a model tell you who the worst

512
00:26:44,920 --> 00:26:47,720
Speaker 4: two or three are. They're outliers, and they the ones

513
00:26:47,760 --> 00:26:50,080
Speaker 4: that are really good are out there every year. But

514
00:26:50,280 --> 00:26:53,240
Speaker 4: in the middle you might have three dozen pms that

515
00:26:53,280 --> 00:26:57,720
Speaker 4: are tightly bunched around sort of the average production of

516
00:26:57,760 --> 00:27:00,760
Speaker 4: all the pms. What the model is really good at

517
00:27:00,960 --> 00:27:03,959
Speaker 4: is looking at these very similar returns at the end

518
00:27:04,000 --> 00:27:07,399
Speaker 4: of the year, looking at the skills that make them up,

519
00:27:07,440 --> 00:27:10,760
Speaker 4: and say, well, I know sizing tends to have a

520
00:27:10,760 --> 00:27:13,480
Speaker 4: correlation of zero from year to year. It reverts back

521
00:27:13,520 --> 00:27:15,760
Speaker 4: to the mean. So if you have two people with

522
00:27:16,480 --> 00:27:19,040
Speaker 4: the same return, but one of them was adding alpha

523
00:27:19,119 --> 00:27:23,200
Speaker 4: via their sizing decisions versus someone who was more consistently

524
00:27:23,760 --> 00:27:27,600
Speaker 4: picking out performers, and this is what you don't see

525
00:27:27,680 --> 00:27:30,719
Speaker 4: if you're just looking at idiosyncratic alpha, even though you've

526
00:27:30,760 --> 00:27:33,359
Speaker 4: stripped out all the factors. That's how the framework comes about,

527
00:27:33,359 --> 00:27:36,080
Speaker 4: and that's how it is both backward looking in terms

528
00:27:36,080 --> 00:27:39,560
Speaker 4: of explaining alpha by skill, but then also it becomes

529
00:27:39,760 --> 00:27:43,399
Speaker 4: a forward predictor by knowing what the correlation is between

530
00:27:43,400 --> 00:27:44,560
Speaker 4: past and future periods.

531
00:27:45,000 --> 00:27:47,679
Speaker 3: I have so many questions for my next one, and

532
00:27:47,920 --> 00:27:50,359
Speaker 3: let me just add a caveat before I ask it,

533
00:27:50,400 --> 00:27:53,800
Speaker 3: which is everything I know about baseball I learned from

534
00:27:53,800 --> 00:27:56,520
Speaker 3: that one episode of The Simpsons. So that is to say,

535
00:27:56,640 --> 00:27:59,920
Speaker 3: I don't know very much at all other than don't

536
00:28:00,200 --> 00:28:04,520
Speaker 3: mean to Daryl Strawberry. But my impression, and again I

537
00:28:04,520 --> 00:28:07,280
Speaker 3: don't remember a lot about moneyball, but my impression was

538
00:28:07,320 --> 00:28:10,880
Speaker 3: like part of that strategy was finding players that are

539
00:28:11,000 --> 00:28:15,160
Speaker 3: underpriced by the market and capable maybe of doing one

540
00:28:15,240 --> 00:28:18,560
Speaker 3: specific thing very well, and then kind of putting them

541
00:28:18,600 --> 00:28:24,240
Speaker 3: together into a team that can work very well, like holistically,

542
00:28:24,520 --> 00:28:27,720
Speaker 3: rather than just going after the expensive players that hit

543
00:28:27,840 --> 00:28:31,119
Speaker 3: home runs a lot. Yeah, exactly. I guess my question

544
00:28:31,200 --> 00:28:34,960
Speaker 3: is I get the approach to evaluating individual traders, but

545
00:28:35,600 --> 00:28:38,160
Speaker 3: is part of your approach also looking at how they

546
00:28:38,240 --> 00:28:42,000
Speaker 3: like holistically work together and impact each other at all,

547
00:28:42,320 --> 00:28:45,280
Speaker 3: or because of the nature of multi strats and the

548
00:28:45,320 --> 00:28:48,080
Speaker 3: pod shops, doesn' not matter so much on that.

549
00:28:48,480 --> 00:28:51,840
Speaker 4: That's an insightful question, And I will pick up a

550
00:28:51,960 --> 00:28:55,560
Speaker 4: topic that Gappy talked about a couple months ago. He

551
00:28:55,640 --> 00:28:59,000
Speaker 4: talked about the different cultures and how like how these

552
00:28:59,040 --> 00:29:01,520
Speaker 4: pod shops and the and the multi manager platforms can

553
00:29:01,560 --> 00:29:04,000
Speaker 4: be different and a lot of times there's a big

554
00:29:04,040 --> 00:29:07,600
Speaker 4: culture difference. And I would say that that is absolutely true,

555
00:29:07,760 --> 00:29:09,960
Speaker 4: and I have a great sort of answer to your

556
00:29:10,040 --> 00:29:15,440
Speaker 4: question for that. So at some shops, the philosophy is,

557
00:29:15,680 --> 00:29:19,240
Speaker 4: we are going to strip out everything a PM does

558
00:29:19,760 --> 00:29:23,760
Speaker 4: and cynically they have so many factors, and we'll pay

559
00:29:23,800 --> 00:29:26,920
Speaker 4: them on what the idiosyncratic alpha that's left is. And

560
00:29:26,960 --> 00:29:30,440
Speaker 4: they have so many factors they're stripping out that you know,

561
00:29:30,440 --> 00:29:32,720
Speaker 4: they're trying to get that alpha number down as small

562
00:29:32,720 --> 00:29:34,400
Speaker 4: as possible so they don't have to pay off bonuses.

563
00:29:34,400 --> 00:29:37,480
Speaker 4: And I remember joking with a PM one time at

564
00:29:37,520 --> 00:29:39,400
Speaker 4: one of those shops and he's like, yeah, I feel

565
00:29:39,440 --> 00:29:41,960
Speaker 4: like every time I go in there, they tell me like, yeah,

566
00:29:42,000 --> 00:29:45,320
Speaker 4: you had a good year, but look year out performance

567
00:29:45,400 --> 00:29:48,560
Speaker 4: is due to investing in dividend paying companies where the

568
00:29:48,600 --> 00:29:51,320
Speaker 4: CEO went to an IVY League school and we can get.

569
00:29:51,120 --> 00:29:52,800
Speaker 2: That for free, right.

570
00:29:52,960 --> 00:29:58,520
Speaker 4: So that so at those shops, their philosophy is it

571
00:29:58,560 --> 00:30:03,480
Speaker 4: doesn't matter because we're taking out everything. I prefer a

572
00:30:03,600 --> 00:30:06,680
Speaker 4: little different approach, and there are shops that do it

573
00:30:06,680 --> 00:30:09,560
Speaker 4: this way, which is to say, my job as a

574
00:30:09,640 --> 00:30:14,320
Speaker 4: CIO is to build a multi manager platform where some

575
00:30:14,360 --> 00:30:18,160
Speaker 4: of these offset so that there are different skills and

576
00:30:18,400 --> 00:30:23,040
Speaker 4: then instead of stripping out factors at each portfolio level,

577
00:30:23,840 --> 00:30:26,880
Speaker 4: more stripping out the factors. Once you put them all together,

578
00:30:27,080 --> 00:30:29,200
Speaker 4: you've got this bully of base stew and then you

579
00:30:29,320 --> 00:30:31,680
Speaker 4: take the factors out. And that is a different approach

580
00:30:31,760 --> 00:30:35,040
Speaker 4: because I think the pms feel a little bit more freedom.

581
00:30:35,320 --> 00:30:37,160
Speaker 4: They still have their buffers they have to stay in,

582
00:30:37,840 --> 00:30:42,720
Speaker 4: but they don't see the ETFs or the factor anti

583
00:30:42,720 --> 00:30:45,800
Speaker 4: factor things getting shoved right into their portfolio. The approach

584
00:30:45,880 --> 00:30:48,719
Speaker 4: is more higher. So you can take either approach. I

585
00:30:48,800 --> 00:30:52,880
Speaker 4: do prefer the sort of roster construction idea that you

586
00:30:52,960 --> 00:30:56,520
Speaker 4: have that you have in sports, but that really is

587
00:30:56,560 --> 00:30:59,920
Speaker 4: a difference in you know, I think in from culture.

588
00:31:00,760 --> 00:31:20,880
Speaker 2: Yeah, that's super interesting. So in baseball, a general manager

589
00:31:21,320 --> 00:31:24,800
Speaker 2: looking for players can look at other teams, they can

590
00:31:24,800 --> 00:31:27,920
Speaker 2: look in the minor leagues, they can look at college sports.

591
00:31:27,960 --> 00:31:30,760
Speaker 2: They can start scouting at high school. Probably there's a

592
00:31:30,760 --> 00:31:33,200
Speaker 2: farm system and they call it a farm system. What

593
00:31:33,280 --> 00:31:37,680
Speaker 2: you've described so far makes sense for evaluating people in

594
00:31:37,800 --> 00:31:41,640
Speaker 2: existing seats, either at your shop or perhaps at another shop.

595
00:31:42,240 --> 00:31:45,280
Speaker 2: Is there a way to transfer it or to apply

596
00:31:45,400 --> 00:31:48,280
Speaker 2: some of these same ideas to people who don't have

597
00:31:48,720 --> 00:31:50,920
Speaker 2: the same because there I don't think there's the same

598
00:31:50,960 --> 00:31:55,360
Speaker 2: equivalent unless trading, you know, an Mari trader Schwab, which

599
00:31:55,400 --> 00:31:57,360
Speaker 2: actually I do think maybe is kind of a thing.

600
00:31:57,400 --> 00:31:59,040
Speaker 2: But is there a way to sort of think about,

601
00:31:59,080 --> 00:32:03,080
Speaker 2: like how you evaluate someone who just does not have

602
00:32:03,160 --> 00:32:04,640
Speaker 2: that much of a track record yet.

603
00:32:05,000 --> 00:32:10,360
Speaker 4: Yes, because of the way these multi manager platforms are

604
00:32:10,560 --> 00:32:13,640
Speaker 4: formed now, they don't hire from the street anymore. I

605
00:32:13,680 --> 00:32:15,600
Speaker 4: think twenty years ago, thirty years ago, I know when

606
00:32:15,640 --> 00:32:18,479
Speaker 4: I was on the street, the researchers that were covering

607
00:32:18,560 --> 00:32:21,440
Speaker 4: the companies, they'd get plucked away, sometimes by the shops.

608
00:32:21,800 --> 00:32:25,080
Speaker 4: Sometimes traders would get plucked away. Right, that doesn't happen

609
00:32:25,120 --> 00:32:29,280
Speaker 4: as much anymore because what these huge firms have done,

610
00:32:29,320 --> 00:32:32,640
Speaker 4: and this also goes to their competitive advantage and their

611
00:32:33,120 --> 00:32:36,120
Speaker 4: ability to scale, is they are now training these people

612
00:32:36,520 --> 00:32:39,800
Speaker 4: right out of school, right. They have you know, universities

613
00:32:39,880 --> 00:32:43,960
Speaker 4: or academies or you know these schools essentially where they're

614
00:32:44,040 --> 00:32:49,760
Speaker 4: teaching people to be analysts or pms and again sort

615
00:32:49,800 --> 00:32:52,760
Speaker 4: of to a culture thing. My favorite ones are the

616
00:32:52,800 --> 00:32:56,720
Speaker 4: ones where the firms realize it used to just be

617
00:32:56,760 --> 00:32:59,040
Speaker 4: an upper out thing, right, Like you became an analyst

618
00:32:59,040 --> 00:33:00,960
Speaker 4: and then you became a PM, and if you weren't

619
00:33:00,960 --> 00:33:03,200
Speaker 4: a good analyst, you never became a good PM. And

620
00:33:03,400 --> 00:33:06,560
Speaker 4: I think that there are firms now that recognize and

621
00:33:06,760 --> 00:33:10,400
Speaker 4: analysts can be a career. You may be a great analyst,

622
00:33:10,440 --> 00:33:14,560
Speaker 4: but not necessarily united a capitol committee. You know, there's

623
00:33:14,600 --> 00:33:17,640
Speaker 4: a different skill set to being the PM. And they

624
00:33:17,720 --> 00:33:20,320
Speaker 4: find out some of these things in the academies and

625
00:33:20,760 --> 00:33:24,800
Speaker 4: in the universities. They're in house training schools. This is

626
00:33:24,880 --> 00:33:27,920
Speaker 4: the farm system that is coming up. Quite literally, this

627
00:33:28,080 --> 00:33:31,600
Speaker 4: is the bench and we see that and they don't

628
00:33:31,640 --> 00:33:35,280
Speaker 4: just get thrown in. They do tend to run paper

629
00:33:35,280 --> 00:33:38,880
Speaker 4: portfolios or portfolios that feel like they're real because they

630
00:33:38,920 --> 00:33:39,880
Speaker 4: are entering trading.

631
00:33:40,200 --> 00:33:42,520
Speaker 2: So in their careers depend on them doing well. So

632
00:33:42,600 --> 00:33:44,560
Speaker 2: they're taking risk even if it's paper money.

633
00:33:44,680 --> 00:33:47,640
Speaker 4: Yes, exactly, And you can run the same analytics on

634
00:33:47,680 --> 00:33:53,200
Speaker 4: these portfolios. And what I definitely have seen is some

635
00:33:53,280 --> 00:33:57,240
Speaker 4: of the newly graduated pms. These firms are good at

636
00:33:57,240 --> 00:34:01,080
Speaker 4: who they're training and those are the best pms to

637
00:34:01,360 --> 00:34:06,240
Speaker 4: find alpha signals from. Because they're portfolios, they tend to

638
00:34:06,280 --> 00:34:09,440
Speaker 4: be small so they can be replicated. It's it's and

639
00:34:09,480 --> 00:34:12,080
Speaker 4: this is another job of the quants too. If you

640
00:34:12,160 --> 00:34:15,279
Speaker 4: have a very senior PM, who's you know, who has

641
00:34:15,320 --> 00:34:18,160
Speaker 4: a contract that allows he or she to run a

642
00:34:18,200 --> 00:34:22,200
Speaker 4: two billion dollar biotech portfolio. There's not much left for

643
00:34:22,320 --> 00:34:24,920
Speaker 4: the quants to you know, because they're you know, they're

644
00:34:24,920 --> 00:34:27,520
Speaker 4: probably a little more thinly cat capitalized. There's not much

645
00:34:27,560 --> 00:34:31,719
Speaker 4: room to replicate that portfolio at another quant level in

646
00:34:31,760 --> 00:34:35,000
Speaker 4: the firm. But the new people that are coming up,

647
00:34:35,160 --> 00:34:38,440
Speaker 4: they're cheap, they're running small portfolios. But if they're skilled,

648
00:34:38,800 --> 00:34:42,040
Speaker 4: they're knowing what they're in is just as important as

649
00:34:42,520 --> 00:34:43,480
Speaker 4: a more senior PM.

650
00:34:43,600 --> 00:34:46,880
Speaker 2: Yeah, Tracy and listeners. There's a great piece on the

651
00:34:46,920 --> 00:34:50,319
Speaker 2: Bloomberg from June nineteenth by our colleagues Nishan Kumar and E.

652
00:34:50,320 --> 00:34:53,920
Speaker 2: Liza Tetley about exactly this hedge fund talent schools are

653
00:34:53,920 --> 00:34:56,560
Speaker 2: looking for the perfect trader, and it talks about zero

654
00:34:56,560 --> 00:34:59,279
Speaker 2: point seventy two and it talks about citadel building these

655
00:34:59,320 --> 00:35:02,560
Speaker 2: sort of in how training things. So all all these

656
00:35:02,600 --> 00:35:05,200
Speaker 2: pieces are coming together, building the own farm system in

657
00:35:05,280 --> 00:35:07,160
Speaker 2: house to see who's going to be good one day.

658
00:35:07,400 --> 00:35:10,120
Speaker 3: We should go to talent school. It's fun talent school,

659
00:35:10,160 --> 00:35:17,320
Speaker 3: that's to be clear. Okay, that was the joke. Yeah, Okay, Joe,

660
00:35:17,440 --> 00:35:20,320
Speaker 3: you've talked about sizing and you talked about the general

661
00:35:20,520 --> 00:35:23,720
Speaker 3: skill set that you're looking for one thing I'm still

662
00:35:23,920 --> 00:35:26,520
Speaker 3: unclear on. You alluded to it earlier, but I would

663
00:35:26,600 --> 00:35:28,680
Speaker 3: love for you to talk more about it in detail.

664
00:35:29,520 --> 00:35:33,239
Speaker 3: Time frame. What is the time frame by which you

665
00:35:33,320 --> 00:35:37,520
Speaker 3: are evaluating traders? And I guess how much runway do

666
00:35:37,600 --> 00:35:42,839
Speaker 3: you give people to either prove themselves correct or prove

667
00:35:42,880 --> 00:35:46,560
Speaker 3: themselves to be disastrously wrong because you know the correlation

668
00:35:46,640 --> 00:35:48,399
Speaker 3: they were betting on suddenly breaks down.

669
00:35:48,600 --> 00:35:52,280
Speaker 4: Yeah. So again, great question, and it became a point

670
00:35:52,280 --> 00:35:55,520
Speaker 4: of frustration for me from when I first started at

671
00:35:55,520 --> 00:35:57,640
Speaker 4: Novus and building this stuff because I was very used

672
00:35:57,640 --> 00:36:02,600
Speaker 4: to sports analytics, and specifically baseball, but some some other

673
00:36:02,800 --> 00:36:05,799
Speaker 4: sports as well. I'll touch on golf. When you're evaluating

674
00:36:05,800 --> 00:36:08,680
Speaker 4: the skill of a picture, and there's three skills that

675
00:36:08,719 --> 00:36:11,839
Speaker 4: a picture has that are not dependent on anything else,

676
00:36:11,840 --> 00:36:14,560
Speaker 4: not dependent on it's teammates, who's batting it, et cetera.

677
00:36:15,600 --> 00:36:18,440
Speaker 4: It's right, not dependent on fielding. Right would be the

678
00:36:18,480 --> 00:36:20,840
Speaker 4: strikeout rate of a picture, the walk rate of a picture,

679
00:36:21,120 --> 00:36:23,000
Speaker 4: and the ground ball rate of a picture. These are

680
00:36:23,040 --> 00:36:26,880
Speaker 4: things that the picture controls, and what happens is after

681
00:36:26,920 --> 00:36:30,600
Speaker 4: about fifty plate appearances, you get the strikeout rate for

682
00:36:30,640 --> 00:36:34,000
Speaker 4: a picture. That is predictive of you know, it's you know,

683
00:36:34,000 --> 00:36:38,000
Speaker 4: from a maths standpoint, the correlation is above zero point seven,

684
00:36:38,080 --> 00:36:40,720
Speaker 4: so squared it's above point five. Right, the past explains

685
00:36:40,719 --> 00:36:43,239
Speaker 4: more of the future than factors that we haven't identified.

686
00:36:43,600 --> 00:36:47,640
Speaker 4: But with PMS, there's much more noise in their result

687
00:36:47,719 --> 00:36:51,440
Speaker 4: and it takes a lot longer to find a meaningful correlation.

688
00:36:52,040 --> 00:36:55,719
Speaker 4: So although I can do work for like, I can

689
00:36:55,920 --> 00:36:58,000
Speaker 4: and I do this for a single day, right, So

690
00:36:58,200 --> 00:36:59,960
Speaker 4: every day I generate a report, and I do this

691
00:37:00,200 --> 00:37:04,000
Speaker 4: for clients now showing their PMS and exactly what they're

692
00:37:04,040 --> 00:37:06,080
Speaker 4: readings of all these skills were each day. And of

693
00:37:06,160 --> 00:37:09,200
Speaker 4: course for one day it's just trivia. It's no more

694
00:37:09,239 --> 00:37:11,680
Speaker 4: than trivia. But what it is doing is building a

695
00:37:11,760 --> 00:37:15,400
Speaker 4: data set. And at the point that you get to

696
00:37:15,480 --> 00:37:17,600
Speaker 4: six months, which is about one hundred and twenty five

697
00:37:17,920 --> 00:37:24,359
Speaker 4: days trading days. Bigger picture, the full model takes five

698
00:37:24,480 --> 00:37:27,960
Speaker 4: hundred the past five hundred results, and that's when you

699
00:37:28,000 --> 00:37:32,560
Speaker 4: start getting very different and but more persistent correlations between

700
00:37:32,600 --> 00:37:36,680
Speaker 4: all these skills. Right. But what I have found is

701
00:37:36,719 --> 00:37:39,279
Speaker 4: that even after one hundred and fifty days, if you

702
00:37:39,400 --> 00:37:42,120
Speaker 4: take for the other year and a half, a mean

703
00:37:42,160 --> 00:37:45,400
Speaker 4: reversion assumption, and then just every time a new day

704
00:37:45,440 --> 00:37:48,319
Speaker 4: comes in, you drop off an assumption you'd get you

705
00:37:48,400 --> 00:37:51,919
Speaker 4: have a pretty robust skill reading that starts to mean

706
00:37:52,040 --> 00:37:56,719
Speaker 4: something after six months, and after two years, that's when

707
00:37:56,760 --> 00:37:59,520
Speaker 4: it really has, you know, has some great predictive power

708
00:37:59,520 --> 00:38:02,759
Speaker 4: for the next quarter, and so you're constantly dropping off. Now,

709
00:38:02,760 --> 00:38:05,400
Speaker 4: why only two years? I talk about this in the book.

710
00:38:06,120 --> 00:38:07,600
Speaker 4: I don't have a great answer for that.

711
00:38:07,880 --> 00:38:09,680
Speaker 3: I suppose you have to start somewhere around.

712
00:38:09,560 --> 00:38:11,879
Speaker 4: Yeah, well, here's what I knew. Two years was better

713
00:38:11,920 --> 00:38:15,960
Speaker 4: than three years, which in one sense, why would that be?

714
00:38:16,719 --> 00:38:19,000
Speaker 4: And I have talked to different quants about that, and

715
00:38:19,040 --> 00:38:22,560
Speaker 4: they have approached this from a much different perspective, and

716
00:38:22,640 --> 00:38:25,560
Speaker 4: they also have come to somewhat of a two year conclusion.

717
00:38:26,080 --> 00:38:29,400
Speaker 4: The reason seems to be regimes within the stock market,

718
00:38:29,640 --> 00:38:34,480
Speaker 4: just something about where you are skilled. You know, I

719
00:38:34,520 --> 00:38:37,480
Speaker 4: haven't been able to identify it. And I also know

720
00:38:37,680 --> 00:38:41,960
Speaker 4: that we could do a you know, we could we

721
00:38:42,000 --> 00:38:44,319
Speaker 4: could run the numbers and find out that, oh, you know,

722
00:38:44,400 --> 00:38:47,680
Speaker 4: it's not two years. It's the most predictive thing for

723
00:38:47,719 --> 00:38:49,799
Speaker 4: the last three months would have been two years and

724
00:38:49,840 --> 00:38:53,080
Speaker 4: forty three days. When you try to get that precise

725
00:38:53,160 --> 00:38:55,160
Speaker 4: that's not going to be what the perfect So two

726
00:38:55,280 --> 00:38:58,719
Speaker 4: years does seem to work because you're constantly rolling off

727
00:38:58,760 --> 00:39:01,640
Speaker 4: whatever happened two years to go, and so there's some

728
00:39:01,719 --> 00:39:05,480
Speaker 4: regime change that seems to work. But that is an

729
00:39:05,560 --> 00:39:07,400
Speaker 4: unanswered to a question I have too.

730
00:39:07,760 --> 00:39:11,560
Speaker 2: So one thing about baseball is that every GM in

731
00:39:11,680 --> 00:39:15,719
Speaker 2: baseball has basically perfect visibility into the performance of every

732
00:39:15,719 --> 00:39:17,759
Speaker 2: player on every other team because it's all out on

733
00:39:17,800 --> 00:39:19,640
Speaker 2: the field and it's all measured, and we all have

734
00:39:19,680 --> 00:39:21,480
Speaker 2: the same information. You know, one of the.

735
00:39:21,400 --> 00:39:23,040
Speaker 3: Most measure heart Jack.

736
00:39:22,960 --> 00:39:25,120
Speaker 2: Yeah, right right, you can't measure Harvey get we all

737
00:39:25,160 --> 00:39:28,400
Speaker 2: could see players on base percentage and ops and slop

738
00:39:28,480 --> 00:39:30,360
Speaker 2: and all of this stuff, right. You know some of

739
00:39:30,360 --> 00:39:33,560
Speaker 2: the most popular alerts that always read spike on the

740
00:39:33,640 --> 00:39:38,080
Speaker 2: terminal or it's like consumer Discretionary Manager, Palacity goes to

741
00:39:38,080 --> 00:39:41,600
Speaker 2: citadel whatever. People love, people eat that stuff up. Just

742
00:39:41,600 --> 00:39:45,960
Speaker 2: from an industry perspective, sitting aside, whether you want to

743
00:39:46,200 --> 00:39:49,640
Speaker 2: use a traditional sharp ratio perspective or rose or lum

744
00:39:49,800 --> 00:39:53,640
Speaker 2: or whatever, how much visibility does one shop have into

745
00:39:53,680 --> 00:39:56,920
Speaker 2: the performance of a pod at another shop that can

746
00:39:56,960 --> 00:40:00,000
Speaker 2: then be ported over, or how much insight can you

747
00:40:00,000 --> 00:40:02,759
Speaker 2: we have if maybe there's an undervalued player somewhere else,

748
00:40:02,800 --> 00:40:04,080
Speaker 2: if you want to bring them over and give them

749
00:40:04,080 --> 00:40:04,959
Speaker 2: more capital.

750
00:40:04,640 --> 00:40:08,759
Speaker 4: Than they're getting, extremely limited in terms of it. So well,

751
00:40:08,800 --> 00:40:10,960
Speaker 4: and I'll tell you who can solve that problem. What

752
00:40:11,040 --> 00:40:13,560
Speaker 4: you will have is, of course, if a team is

753
00:40:13,600 --> 00:40:16,080
Speaker 4: marketing itself or being recruited by another firm, they bring

754
00:40:16,120 --> 00:40:19,440
Speaker 4: over their returns, right, But they don't bring over They

755
00:40:19,480 --> 00:40:22,239
Speaker 4: might talk about portfolio construction, but I'm pretty sure they

756
00:40:22,280 --> 00:40:26,839
Speaker 4: shouldn't and probably don't bring over their two years right,

757
00:40:26,880 --> 00:40:28,640
Speaker 4: what the portfolio, what their holdings have been for the

758
00:40:28,719 --> 00:40:31,400
Speaker 4: last two years. So you don't get that type of visibility.

759
00:40:31,680 --> 00:40:34,239
Speaker 4: But let me tell you who can. Okay, And this

760
00:40:34,600 --> 00:40:41,040
Speaker 4: is I think one of the most important constituents in

761
00:40:41,440 --> 00:40:44,520
Speaker 4: our industry because I think they have the purest motive,

762
00:40:44,560 --> 00:40:48,400
Speaker 4: and that is the allocators, right allocators. I'm talking about

763
00:40:48,400 --> 00:40:56,280
Speaker 4: the huge, multi billion dollar entities which provide the blood

764
00:40:56,840 --> 00:41:01,359
Speaker 4: that keeps the heart pumping right at all these hegge file. Sure,

765
00:41:01,480 --> 00:41:04,080
Speaker 4: we know that Ken Griffin has a tremendous amount of

766
00:41:04,160 --> 00:41:06,920
Speaker 4: the aum is his money, and and you know we

767
00:41:07,000 --> 00:41:09,400
Speaker 4: hear that about some other people too, But in general,

768
00:41:09,520 --> 00:41:13,360
Speaker 4: these firms it's outside money which which keep these firms afloat.

769
00:41:13,560 --> 00:41:17,440
Speaker 4: But the allocators, many of them though, and what I'm

770
00:41:17,480 --> 00:41:22,399
Speaker 4: talking about here are foundations, university endowments, sovereign wealth funds, right,

771
00:41:22,520 --> 00:41:25,719
Speaker 4: pension plans, and they have a very pure motive. Right,

772
00:41:26,000 --> 00:41:28,560
Speaker 4: they are trying to get returns for the retirees or

773
00:41:29,040 --> 00:41:31,759
Speaker 4: you know, reduced tuition for future students, et cetera, or

774
00:41:31,880 --> 00:41:33,920
Speaker 4: you know, in the case of Norway, the citizens of

775
00:41:34,200 --> 00:41:34,720
Speaker 4: the country.

776
00:41:34,800 --> 00:41:35,000
Speaker 2: Right.

777
00:41:35,840 --> 00:41:40,520
Speaker 4: So they are a treasured investor, right if you run

778
00:41:40,520 --> 00:41:44,160
Speaker 4: a hedge fund. So when they are doing manager selection,

779
00:41:44,760 --> 00:41:47,560
Speaker 4: they have the ability to go to hedge funds. Now,

780
00:41:47,560 --> 00:41:51,760
Speaker 4: maybe not Citadel and Millennium, but to all these non

781
00:41:51,920 --> 00:41:55,080
Speaker 4: multi manager platforms. They have the ability to go to

782
00:41:55,080 --> 00:41:57,040
Speaker 4: them and say, hey, if you want us to really

783
00:41:57,080 --> 00:42:00,640
Speaker 4: evaluate you, we need to see podil. Yeah, we need

784
00:42:00,640 --> 00:42:02,879
Speaker 4: to see we need position level transparency for the last

785
00:42:02,880 --> 00:42:04,239
Speaker 4: two years. Hey if you want, if you don't want

786
00:42:04,280 --> 00:42:06,960
Speaker 4: to give us yesterday, start a quarterback so that it's

787
00:42:06,960 --> 00:42:10,040
Speaker 4: on a lag. But now they have the leverage to

788
00:42:10,840 --> 00:42:14,279
Speaker 4: get those returns, especially if you're talking about emerging managers, right,

789
00:42:14,360 --> 00:42:16,879
Speaker 4: young managers are trying to that, you know, to build

790
00:42:16,920 --> 00:42:21,040
Speaker 4: a hege Fund, and I don't feel they use that leverage.

791
00:42:21,120 --> 00:42:23,560
Speaker 4: And this is to me is like, well, Joe, you're

792
00:42:23,600 --> 00:42:26,640
Speaker 4: you know you you talk about this framework and it's

793
00:42:26,680 --> 00:42:29,359
Speaker 4: it's applicable to multi manager platforms, and you know an

794
00:42:29,400 --> 00:42:33,080
Speaker 4: endowment isn't a leveraged portfolio, so how could they use it? Well,

795
00:42:33,120 --> 00:42:34,680
Speaker 4: this is how they could use it, because they can

796
00:42:34,760 --> 00:42:38,560
Speaker 4: get that, Joe, and they do ask those questions about

797
00:42:38,560 --> 00:42:39,920
Speaker 4: the Bentley and the.

798
00:42:40,320 --> 00:42:41,920
Speaker 2: In fact, can I give you an example? Can I?

799
00:42:42,840 --> 00:42:46,600
Speaker 4: This is I really like this. I've never worked with them.

800
00:42:46,640 --> 00:42:48,760
Speaker 4: I should say that I have worked with their brethren,

801
00:42:48,800 --> 00:42:52,840
Speaker 4: and I've worked with the endowments that they would measure

802
00:42:52,880 --> 00:42:58,359
Speaker 4: themselves against. But the MIT Endowment Matimco is the name

803
00:42:58,560 --> 00:43:02,359
Speaker 4: of the uh, the name of the entity. They have

804
00:43:02,400 --> 00:43:04,759
Speaker 4: something between twenty and thirty billion dollars under management. So

805
00:43:04,800 --> 00:43:08,680
Speaker 4: we know that a portion of that is dedicated to

806
00:43:08,960 --> 00:43:12,439
Speaker 4: public equities. And we know because and I won't mention

807
00:43:12,680 --> 00:43:14,160
Speaker 4: his name, so I'm not trying to call him out,

808
00:43:14,160 --> 00:43:18,240
Speaker 4: but we know that one of those gentlemen that looks

809
00:43:18,239 --> 00:43:21,799
Speaker 4: for equity managers is a presence on fin Twitt. He's

810
00:43:21,800 --> 00:43:26,200
Speaker 4: actually a great follow, very earnest, and so he'll talk

811
00:43:26,239 --> 00:43:29,480
Speaker 4: about things, and sometimes he'll post job postings.

812
00:43:29,400 --> 00:43:31,279
Speaker 2: Right, and what you will find is.

813
00:43:32,880 --> 00:43:36,120
Speaker 4: Everybody who works in that division or and in fact,

814
00:43:36,160 --> 00:43:38,840
Speaker 4: you can even see this publicly. I know this. Yale's

815
00:43:38,920 --> 00:43:42,759
Speaker 4: Management company has the resumes of every person who's in

816
00:43:42,760 --> 00:43:45,560
Speaker 4: that division, and they're all the same. Here's what they

817
00:43:45,880 --> 00:43:48,560
Speaker 4: will say. They will say things like, you know, was

818
00:43:48,680 --> 00:43:52,080
Speaker 4: president of the investment club at the University of Virginia, right,

819
00:43:52,120 --> 00:43:55,080
Speaker 4: and they'd been investing in stock since I had a

820
00:43:55,080 --> 00:43:55,640
Speaker 4: paper route.

821
00:43:55,680 --> 00:43:55,759
Speaker 1: Right.

822
00:43:55,800 --> 00:43:59,360
Speaker 4: They're always have this, right. So when they go to

823
00:43:59,400 --> 00:44:02,080
Speaker 4: do managers selection, and I've been on that side too

824
00:44:02,120 --> 00:44:04,000
Speaker 4: as a as a marketer, they will sit down with

825
00:44:04,080 --> 00:44:06,280
Speaker 4: the PM and they'll ask they'll go over each position

826
00:44:06,320 --> 00:44:09,399
Speaker 4: in the portfolio and make no mistake about it, they're

827
00:44:09,480 --> 00:44:12,839
Speaker 4: passing judgment, right, because if they're not frustrated or want

828
00:44:12,880 --> 00:44:15,520
Speaker 4: to be pms, this is how they think about the market.

829
00:44:15,680 --> 00:44:18,120
Speaker 4: All right, So I'm gonna put full stop there. Now,

830
00:44:18,200 --> 00:44:21,400
Speaker 4: let's go to a The general manager of the Philadelphia

831
00:44:21,440 --> 00:44:23,440
Speaker 4: seventy six ers is a gentleman named Daryl Mory.

832
00:44:23,520 --> 00:44:26,040
Speaker 2: Oh yeah, I like Daryl. Darryl.

833
00:44:26,120 --> 00:44:28,359
Speaker 4: Right, he was with Houston and in fact, while he

834
00:44:28,440 --> 00:44:31,720
Speaker 4: was at Houston. He really brought moneyball to the NBA.

835
00:44:32,280 --> 00:44:35,280
Speaker 4: Mark Cuban was probably maybe the second, but Darryl Moury

836
00:44:35,640 --> 00:44:37,520
Speaker 4: right down to the fact that Michael Lewis did a

837
00:44:37,520 --> 00:44:39,640
Speaker 4: piece on him in the Sunday New York Times maybe

838
00:44:39,640 --> 00:44:43,440
Speaker 4: twenty years ago. So Daryl Morey is the GM of

839
00:44:43,480 --> 00:44:46,000
Speaker 4: the seventy six ers, and he has juniors too, right,

840
00:44:46,960 --> 00:44:50,080
Speaker 4: And when they're doing their equivalent of manager selection, whether

841
00:44:50,120 --> 00:44:52,880
Speaker 4: it'll be drafting a player or looking at free agents,

842
00:44:53,440 --> 00:44:56,400
Speaker 4: can you imagine how absurd it would be for Darryl

843
00:44:56,520 --> 00:44:59,720
Speaker 4: and the analysts to go down and shoot free throws

844
00:44:59,760 --> 00:45:04,040
Speaker 4: with perspective player right, and to judge the player based

845
00:45:04,120 --> 00:45:07,120
Speaker 4: on that. But I guarantee you at the endowments they

846
00:45:07,160 --> 00:45:09,279
Speaker 4: go back and say, can you believe you know that

847
00:45:09,320 --> 00:45:13,040
Speaker 4: managed your short netflix right? Like, so, now, why did

848
00:45:13,120 --> 00:45:16,440
Speaker 4: I pick those two? And the example is this, before

849
00:45:16,560 --> 00:45:20,560
Speaker 4: Daryl got into basketball, he is a proud graduate of

850
00:45:20,880 --> 00:45:24,840
Speaker 4: MIT Sloan. He got his MBA at Sloan School of

851
00:45:24,880 --> 00:45:29,120
Speaker 4: Management and he started along with a woman named Jessica Keelman,

852
00:45:29,480 --> 00:45:34,359
Speaker 4: he started the Sloan Sports Conference, which started as Bill

853
00:45:34,400 --> 00:45:37,120
Speaker 4: Simmons when he was at Grantline described at as Dorcapalooza. Right,

854
00:45:37,120 --> 00:45:40,960
Speaker 4: it was just people, kids, guys, and it was almost

855
00:45:41,000 --> 00:45:44,680
Speaker 4: all guys back then talking about sports analytics. And it

856
00:45:44,719 --> 00:45:50,160
Speaker 4: has morphed into a massive event and it's a job

857
00:45:50,239 --> 00:45:53,960
Speaker 4: fare where all these sports teams from all different leagues

858
00:45:54,120 --> 00:45:57,600
Speaker 4: are looking for talent, right, and they're essentially looking for

859
00:45:58,200 --> 00:45:59,959
Speaker 4: performance analytics people. Right.

860
00:46:00,280 --> 00:46:02,520
Speaker 2: Voros McCracken was the one from what's.

861
00:46:02,400 --> 00:46:08,759
Speaker 4: A exactly exactly, So this is think now, now, look

862
00:46:08,800 --> 00:46:11,759
Speaker 4: across the campus at the MIT Sloan Endowment. What they're

863
00:46:11,840 --> 00:46:15,680
Speaker 4: actually trying to find is performance analytics. Do you think

864
00:46:15,719 --> 00:46:19,560
Speaker 4: there might be anybody right across the campus who may

865
00:46:19,600 --> 00:46:22,960
Speaker 4: have never invested in stocks but gets the profit motive?

866
00:46:23,200 --> 00:46:25,399
Speaker 4: They would take my work and probably take it three

867
00:46:25,480 --> 00:46:28,239
Speaker 4: steps more. But I don't think there's an endowment out

868
00:46:28,280 --> 00:46:31,400
Speaker 4: there that things like that. Like, I'm sure they've never

869
00:46:31,440 --> 00:46:34,040
Speaker 4: walked across the campus, and even I'm sure Darryl has

870
00:46:34,120 --> 00:46:36,920
Speaker 4: never thought to invite them over to the you know,

871
00:46:37,000 --> 00:46:39,160
Speaker 4: to hey whyt to interview some of some of our people.

872
00:46:39,719 --> 00:46:42,919
Speaker 4: So that's again sort of how I look at, like, Hey,

873
00:46:42,960 --> 00:46:45,640
Speaker 4: this is how some of this work. How you somebody

874
00:46:45,640 --> 00:46:47,360
Speaker 4: who doesn't have the data can get it at the

875
00:46:47,400 --> 00:46:48,279
Speaker 4: allocator level.

876
00:46:49,000 --> 00:46:52,960
Speaker 3: We've been talking very much about, you know, performance evaluation

877
00:46:53,120 --> 00:46:57,080
Speaker 3: and metrics from a sort of managerial level. If I

878
00:46:57,120 --> 00:46:59,759
Speaker 3: am a trader or quant, you know, a sort of

879
00:47:00,600 --> 00:47:04,359
Speaker 3: junior medium level quant, I guess at one of these

880
00:47:04,400 --> 00:47:08,200
Speaker 3: multi strat hedge funds, how am I viewing the performance

881
00:47:08,239 --> 00:47:11,719
Speaker 3: of others and competition? Is it the case that I'm

882
00:47:11,800 --> 00:47:16,839
Speaker 3: trying to move into a particular sector that maybe has

883
00:47:16,960 --> 00:47:20,080
Speaker 3: more of an opportunity set in terms of dispersion, where

884
00:47:20,080 --> 00:47:24,680
Speaker 3: maybe there's more volatility or more relative value opportunities or

885
00:47:24,719 --> 00:47:29,279
Speaker 3: something like that. How am I like viewing my competition?

886
00:47:29,719 --> 00:47:32,960
Speaker 4: It's a good question. Even the work that I do well,

887
00:47:33,000 --> 00:47:36,880
Speaker 4: I think there's definitely comparison, right. You douce again to

888
00:47:36,960 --> 00:47:39,799
Speaker 4: culture some of the pods, and I think I think

889
00:47:39,880 --> 00:47:42,600
Speaker 4: Yapy mentioned that some of the pods there is a

890
00:47:44,040 --> 00:47:47,520
Speaker 4: there's a sharing of information, right, and at some shops

891
00:47:47,560 --> 00:47:50,920
Speaker 4: there's not. This is a case where I actually prefer

892
00:47:51,960 --> 00:47:55,040
Speaker 4: the not sharing of information right, because I would rather

893
00:47:55,840 --> 00:47:59,839
Speaker 4: I think the quants would rather know that maybe two

894
00:48:00,040 --> 00:48:04,840
Speaker 4: different pms came to the same conclusion independently, as opposed

895
00:48:04,840 --> 00:48:07,200
Speaker 4: to they both went to the same idea. Dinner and

896
00:48:07,239 --> 00:48:09,680
Speaker 4: then both decided to buy the stock. There's more of

897
00:48:09,719 --> 00:48:13,480
Speaker 4: a signal in somebody coming to it independently, so I

898
00:48:13,640 --> 00:48:17,040
Speaker 4: believe they're aware of what the returns are of their

899
00:48:17,080 --> 00:48:20,120
Speaker 4: other pms. In addition, and I don't know if this

900
00:48:20,239 --> 00:48:23,279
Speaker 4: was in that article Joe you just referenced, but these

901
00:48:23,280 --> 00:48:27,720
Speaker 4: firms all have coaching teams too, and I certainly found

902
00:48:27,800 --> 00:48:30,880
Speaker 4: that the older pms that you know had been in

903
00:48:30,880 --> 00:48:34,080
Speaker 4: the business since the nineties, they're setting their ways right.

904
00:48:34,160 --> 00:48:36,000
Speaker 4: They don't want a quant to come in with a

905
00:48:36,080 --> 00:48:39,600
Speaker 4: laptop and start telling them that's spin rate, right, the

906
00:48:40,000 --> 00:48:42,880
Speaker 4: spin rate of their pictures. But the younger people, I

907
00:48:43,080 --> 00:48:46,640
Speaker 4: think there's more of a hey, if there's data that

908
00:48:46,719 --> 00:48:49,719
Speaker 4: you can give me to help me get better, I

909
00:48:49,760 --> 00:48:52,239
Speaker 4: think maybe in some ways they might be looking for that.

910
00:48:52,880 --> 00:48:55,719
Speaker 2: Joe Peter, this is super fun. Thank you so much

911
00:48:55,800 --> 00:48:57,319
Speaker 2: for coming on the podcast again.

912
00:48:57,400 --> 00:48:59,239
Speaker 4: Oh it's always great to be here. And I'll see

913
00:48:59,239 --> 00:48:59,839
Speaker 4: you in seven years.

914
00:49:00,040 --> 00:49:03,240
Speaker 2: Yeah, exactly when whatever your next job is from.

915
00:49:02,800 --> 00:49:04,080
Speaker 3: The Sun, Thanks so much.

916
00:49:04,200 --> 00:49:04,359
Speaker 4: Joke.

917
00:49:04,560 --> 00:49:07,759
Speaker 3: Yeah, even though there were baseball references, I enjoyed it.

918
00:49:07,840 --> 00:49:22,920
Speaker 2: Yeah, Tracy, that was a really fun conversation.

919
00:49:23,120 --> 00:49:25,480
Speaker 3: I love hearing stories when people get jobs off the

920
00:49:25,520 --> 00:49:27,279
Speaker 3: back of Authoughts appearances.

921
00:49:26,800 --> 00:49:31,279
Speaker 2: That's nothing sort of flatters are egos and sense of self.

922
00:49:31,520 --> 00:49:34,479
Speaker 3: Well know, I also like it when people say they're

923
00:49:34,520 --> 00:49:37,239
Speaker 3: listening to A Thoughts episodes while going to the gym.

924
00:49:37,320 --> 00:49:39,279
Speaker 3: Oh yeah, because I hate going to the gym. I

925
00:49:39,320 --> 00:49:41,759
Speaker 3: hate running and things like that. But it makes me

926
00:49:41,800 --> 00:49:45,080
Speaker 3: feel nice that like people are listening to us or

927
00:49:45,360 --> 00:49:48,319
Speaker 3: to offset something that's kind of like a chore.

928
00:49:48,600 --> 00:49:51,080
Speaker 2: No, But beyond all that, it was very fun. I

929
00:49:51,120 --> 00:49:54,880
Speaker 2: feel like we could just talk about these businesses forever.

930
00:49:55,000 --> 00:49:57,239
Speaker 2: It seems so rich, you know, like we still have

931
00:49:57,320 --> 00:50:01,200
Speaker 2: to do something on like compensation struggle. Yeah, but also

932
00:50:01,440 --> 00:50:05,239
Speaker 2: like just the sort of like fundamental point that everyone knows,

933
00:50:05,280 --> 00:50:10,680
Speaker 2: which is like manager identification is really difficult because and

934
00:50:10,800 --> 00:50:13,520
Speaker 2: you know, first of all, there's all these questions about, well,

935
00:50:13,560 --> 00:50:16,560
Speaker 2: it's beating the market really possible because of efficient markets

936
00:50:16,560 --> 00:50:19,000
Speaker 2: and stuff. And then you could identify someone to all

937
00:50:19,120 --> 00:50:21,240
Speaker 2: this person beat the market seven years in a row,

938
00:50:21,719 --> 00:50:24,480
Speaker 2: but have a pool of one thousand managers. There's gonna

939
00:50:24,480 --> 00:50:26,160
Speaker 2: be a lot of people who beat the market seven

940
00:50:26,239 --> 00:50:27,880
Speaker 2: years in a row, and so it seems like a

941
00:50:27,960 --> 00:50:29,400
Speaker 2: very interesting problem to solve.

942
00:50:29,520 --> 00:50:32,960
Speaker 3: It's kind of funny that you're trying to like select

943
00:50:33,040 --> 00:50:37,240
Speaker 3: traders on a factor neutral basis, who are themselves able

944
00:50:37,360 --> 00:50:40,399
Speaker 3: to be factor neutral in some respects, Like you're kind

945
00:50:40,400 --> 00:50:44,200
Speaker 3: of you're trying to separate them from like these circumstances

946
00:50:44,200 --> 00:50:47,359
Speaker 3: that they are operating in, or trying to wait them

947
00:50:47,520 --> 00:50:51,800
Speaker 3: against the value of the opportunity that they are currently facing.

948
00:50:51,880 --> 00:50:55,240
Speaker 3: Right that dispersion that Joe was mentioning, that's kind of funny.

949
00:50:55,440 --> 00:50:58,120
Speaker 3: I've thought about it. Not to go all media and

950
00:50:58,200 --> 00:51:01,400
Speaker 3: naval games, so sure, but you know, it's sort of

951
00:51:01,440 --> 00:51:05,279
Speaker 3: similar to journalist beats in some respects, where you can

952
00:51:05,280 --> 00:51:07,960
Speaker 3: get really lucky and be on a really interesting beat

953
00:51:08,040 --> 00:51:12,000
Speaker 3: where there's tons happening, and suddenly you know, all your

954
00:51:12,040 --> 00:51:15,120
Speaker 3: stuff is getting read and you're getting all these major scoops,

955
00:51:15,560 --> 00:51:18,480
Speaker 3: and then maybe two years later, to go back to

956
00:51:18,520 --> 00:51:21,600
Speaker 3: that timeframe point, it's sort of faded into the distance

957
00:51:21,640 --> 00:51:23,480
Speaker 3: and there's not as much to write about, And how

958
00:51:23,520 --> 00:51:26,759
Speaker 3: do you judge the talent of a particular journalist or

959
00:51:26,800 --> 00:51:30,000
Speaker 3: a trader from their particular set of circumstances.

960
00:51:30,120 --> 00:51:32,480
Speaker 2: That's a great example. I remember, you know, like when

961
00:51:32,480 --> 00:51:35,600
Speaker 2: I was in a business insider years ago. It's like the

962
00:51:35,719 --> 00:51:39,719
Speaker 2: reporters who covered Apple on days of like iPhone announcement,

963
00:51:40,120 --> 00:51:42,040
Speaker 2: they got we were like measured on traffic back then

964
00:51:42,239 --> 00:51:43,160
Speaker 2: they got all the traffic.

965
00:51:43,360 --> 00:51:43,480
Speaker 4: You know.

966
00:51:43,600 --> 00:51:45,719
Speaker 2: It's like, oh, this isn't fair, Like I'm talking about

967
00:51:45,760 --> 00:51:48,520
Speaker 2: like the Bank of England decision. This is nonsense. Okay.

968
00:51:48,560 --> 00:51:51,480
Speaker 3: I just read a really good analysis of like US payrolls,

969
00:51:51,480 --> 00:51:53,480
Speaker 3: and people only want to read about the next iPhone.

970
00:51:53,520 --> 00:51:58,000
Speaker 2: I explained, I explained Mario Draggy's new OMT thing really

971
00:51:58,040 --> 00:52:00,160
Speaker 2: well and like ten people ready, But no, like this

972
00:52:00,320 --> 00:52:02,640
Speaker 2: is like it's all like version of the same problem.

973
00:52:02,680 --> 00:52:06,319
Speaker 2: By the way, my hedge fund media metaphor that I

974
00:52:06,360 --> 00:52:08,600
Speaker 2: use in my head is like alpha decay. So it's

975
00:52:08,640 --> 00:52:10,839
Speaker 2: like the first person who ever came up with like

976
00:52:11,440 --> 00:52:14,319
Speaker 2: here's what you need to know or the answer will

977
00:52:14,360 --> 00:52:16,560
Speaker 2: shock you, like probably like did crazy. Well, but by

978
00:52:16,600 --> 00:52:18,759
Speaker 2: the time that was you, wasn't it Yeah, And then

979
00:52:18,800 --> 00:52:21,399
Speaker 2: by the million person who did like the answer will

980
00:52:21,400 --> 00:52:24,160
Speaker 2: shock you, it stopped working. So it's like there's the

981
00:52:24,239 --> 00:52:26,239
Speaker 2: same thing of like alpha decay, where it's like you

982
00:52:26,280 --> 00:52:28,240
Speaker 2: can be the first on a strategy and then everyone

983
00:52:28,280 --> 00:52:30,720
Speaker 2: discovers it and then the excess returns from.

984
00:52:30,520 --> 00:52:33,200
Speaker 3: That move on the crowding in effect. Yeah, no, I

985
00:52:33,239 --> 00:52:36,839
Speaker 3: did think actually that timeframe point was really interesting and

986
00:52:36,880 --> 00:52:39,520
Speaker 3: the fact that Joe kind of I guess gravitated towards

987
00:52:39,640 --> 00:52:42,440
Speaker 3: two years or five hundred trading days. But then he

988
00:52:42,560 --> 00:52:44,759
Speaker 3: was talking about how others seem to have sort of

989
00:52:44,800 --> 00:52:49,120
Speaker 3: alighted on that same time period. Yeah, I wonder why that.

990
00:52:49,440 --> 00:52:51,239
Speaker 3: I mean, I get that you have to at some point,

991
00:52:51,280 --> 00:52:55,279
Speaker 3: you just have to choose, like age horizon, but it is. Yeah,

992
00:52:55,480 --> 00:52:56,760
Speaker 3: it's an interesting one.

993
00:52:56,640 --> 00:52:59,120
Speaker 2: Very interesting stuff. Plenty more to come on this topic.

994
00:52:59,200 --> 00:53:00,239
Speaker 3: All right, shall we leave there.

995
00:53:00,320 --> 00:53:01,080
Speaker 2: Let's leave it there.

996
00:53:01,719 --> 00:53:04,879
Speaker 3: This has been another episode of the Oudlots podcast. I'm

997
00:53:04,920 --> 00:53:07,640
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway.

998
00:53:07,760 --> 00:53:10,759
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.

999
00:53:11,000 --> 00:53:14,400
Speaker 2: Follow our guest Joe Peta, He's at Magic Rat SF

1000
00:53:14,480 --> 00:53:16,839
Speaker 2: and check out his book Moneyball for the money Set.

1001
00:53:17,040 --> 00:53:20,440
Speaker 2: Follow our producers Carmen Rodriguez at Carman Erman dash Ol

1002
00:53:20,440 --> 00:53:23,840
Speaker 2: Bennett at Dashbot, and Kilbrooks at cal Brooks. Thank you

1003
00:53:23,880 --> 00:53:27,040
Speaker 2: to our producer Moses One. More odd Lags content go

1004
00:53:27,120 --> 00:53:29,960
Speaker 2: to Bloomberg dot com slash odd Lots, where you have transcripts,

1005
00:53:29,960 --> 00:53:32,479
Speaker 2: a blog, and a newsletter, and you can chat about

1006
00:53:32,520 --> 00:53:35,200
Speaker 2: all of these topics twenty four to seven in our discord,

1007
00:53:35,600 --> 00:53:37,840
Speaker 2: discord dot gg, slash odlogs.

1008
00:53:38,200 --> 00:53:41,640
Speaker 3: And if you enjoy odd Lots, then please leave us

1009
00:53:41,719 --> 00:53:45,359
Speaker 3: a positive review on your favorite podcast platform. We will

1010
00:53:45,400 --> 00:53:50,000
Speaker 3: accept positive reviews in lieu of finders' fees. And remember,

1011
00:53:50,080 --> 00:53:52,440
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1012
00:53:52,640 --> 00:53:55,839
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1013
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1014
00:53:59,440 --> 00:54:02,200
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1015
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