Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of The Odd Lots Podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, do you know this? Sometimes I wonder, like, you know,
Speaker 2: one in the morning, if I can't sleep, I think
Speaker 2: to myself, in a different life, could I have been
Speaker 2: the next Steve Cohen? Yeah? No, for real though, And
Speaker 2: I don't, you know, need to talk about it. There's
Speaker 2: a lot and I've brought it up before, you know.
Speaker 2: I did get an offer at a prop trading shop
Speaker 2: right after college to be a stock trader at this
Speaker 2: place where they're gonna let use to your capital. And
Speaker 2: I think Steve Cohen started off like as a prop
Speaker 2: trader at some shop before being one of the great
Speaker 2: hedge funders of all time. And I didn't take that
Speaker 2: job for reasons I still can't explain to myself twenty
Speaker 2: five years later, But I always wonder whether could have
Speaker 2: cut it. Maybe I could have been a good trader.
Speaker 2: I don't know.
Speaker 3: It's good you have a healthy level of self confidence, Joe. No,
Speaker 3: When I lay awake at night, I think like, oh, shoot,
Speaker 3: what did I say? Something stickid on the podcast, and
Speaker 3: that's what keeps me up. But yes, good for you, Joe.
Speaker 2: No, I don't really think I could have, and I
Speaker 2: actually do not think I would have been a good trader.
Speaker 2: I don't think like that. I'm not that good at
Speaker 2: poker other things. I'm not a natural better I don't
Speaker 2: do like sports betting. I don't think that. But I
Speaker 2: do sort of, you know, wonder about what my life
Speaker 2: had been different if I had said yes to that.
Speaker 3: Yeah, fair enough. I mean we know, we know from
Speaker 3: multiple episodes of the podcast this year alone. Yes, like
Speaker 3: there are a lot of hedge fund traders out there,
Speaker 3: especially in multi strats, who seem to be making a
Speaker 3: lot of money, and everyone's sort of talking about them
Speaker 3: up until recently. Maybe I should say, we're recording this
Speaker 3: on August seventh, so maybe those bonuses a little bit
Speaker 3: less this year given the market sell off. But up
Speaker 3: until this month, people seem to have been doing relatively well,
Speaker 3: and there was all this intrigue and interest in the
Speaker 3: world of traders. And I'm sort of curious. This has
Speaker 3: come up a couple times now, but what makes a
Speaker 3: good trader and how are traders actually evaluated? Because my
Speaker 3: impression was always like, Okay, well it depends on how
Speaker 3: much money you make, but what's the timeframe for making
Speaker 3: that money? And then also what about people who are
Speaker 3: working in for instance, these particular pods who are doing
Speaker 3: one specific thing, like what is the benchmark against which
Speaker 3: they are judged?
Speaker 2: You mentioned that maybe they're not making so much money
Speaker 2: this week or this month, But Tracy, I think we're
Speaker 2: told all the time they're so neutral on everything. Their
Speaker 2: market neutral, they're beta neutral, they're neutral every factor you
Speaker 2: can think of. Why should they be losing money right
Speaker 2: now They're supposed to like be neutral all.
Speaker 4: Of this time.
Speaker 3: Yeah, I'm sure they're making loads shore. I'm absolutely sure.
Speaker 2: No, but you're right. And look, we've been doing a
Speaker 2: lot on hedge fund structure, and we did that episode
Speaker 2: with Giuseppe Polyoligo, and we did that episode with Rich
Speaker 2: falk Wallace, various aspects of like how hedge funds measure
Speaker 2: risk and try to isolate alpha and all this stuff.
Speaker 2: But they're just like so many questions in my mind,
Speaker 2: Like I feel like we're just scratching the surface because
Speaker 2: you know, we haven't even really talked about like idea generation.
Speaker 2: So it's one thing to you know, talk about like, okay,
Speaker 2: here's how you like factor out all of these exposures
Speaker 2: that you don't on have like market beta, et cetera.
Speaker 2: It's another thing to talk about like okay, but like
Speaker 2: how do you pick the stocks to buy or go short?
Speaker 3: Well, yeah, we have gotten into this a little bit,
Speaker 3: but you're right, there's more we could do. There are
Speaker 3: all these questions about like how do you size your positions?
Speaker 3: And if you're convinced that one thing is going to
Speaker 3: be the next big thing, then why don't you just
Speaker 3: have like one hundred percent positions?
Speaker 4: Yeah?
Speaker 2: Right, how do you make money if you can't just
Speaker 2: go one hundred percent leverage long and video in video.
Speaker 3: Yeah.
Speaker 2: Anyway, so there's a lot more we can do. But
Speaker 2: to my original, very egotistical start to this episode, I
Speaker 2: do wonder like it's.
Speaker 3: Okay, Joe, it's good to have self confidence. I'm being serious,
Speaker 3: thank you.
Speaker 2: I do wonder like this big question of like you know,
Speaker 2: and a lot of people are probably interested in this
Speaker 2: because these hedge one PM jobs or trader jobs seem
Speaker 2: pretty great and as you mentioned, lucrative, and so it
Speaker 2: would be interesting to know how a fund or anyone
Speaker 2: goes about identifying like the next great trader who gets
Speaker 2: to have that seat, so to speak.
Speaker 3: Well, I also think if you can identify what makes
Speaker 3: a good trader at a hedge fund, then you can
Speaker 3: get more into the business model of what they're actually
Speaker 3: doing on a day to day basis. It helps us
Speaker 3: understand what.
Speaker 2: They're really good at and what they can do specifically. Well,
Speaker 2: I'm very excited today because we really do have the
Speaker 2: perfect guest. We're going to be speaking with, Joe Peta.
Speaker 2: He is the author of a recent book, Moneyball for
Speaker 2: the money set, which is the name sort of implies
Speaker 2: tries to, you know, figure out new ways or the
Speaker 2: best ways to identify talent. I'm sure there's a lot
Speaker 2: of old heuristics like they had in Bay, you know,
Speaker 2: and they're like, well, this guy looks like he has
Speaker 2: a good hustle, and then Moneyball came along. He's like, no, actually,
Speaker 2: you want to really look at his like you know,
Speaker 2: on base percentage or whatever it is, and stop looking
Speaker 2: at like his like spirit or you know, his hustle
Speaker 2: ahead of him. Anyway, and prior to that, in his career,
Speaker 2: he's been in this industry for a long time. He
Speaker 2: was the head of performance analytics at point seventy two.
Speaker 2: So this speaks right to the question of how do
Speaker 2: you evaluate traders. We also hit him on years ago
Speaker 2: one of our really early episodes where he talked about
Speaker 2: sports betting with some of these same ideas, et cetera.
Speaker 2: So I'm thrilled to have Joe back to talk about
Speaker 2: this basic question of how it's good trader. So thanks
Speaker 2: for coming back, Joe.
Speaker 4: Oh, it's great to be here Joe and Tracy, and
Speaker 4: nice to do it in person. Seven years ago, Tracy,
Speaker 4: I believe you were in Hong Kong and yeah, Joe,
Speaker 4: you just had a garage band instead of selling out venues.
Speaker 2: Now that's right, that's right. Still mentioned you were head
Speaker 2: of performance analytics at point seventy two. How did you
Speaker 2: get that job at a point seventy two? Steve Cohen's
Speaker 2: big yes.
Speaker 4: So that goes right back to my appearance seven years ago.
Speaker 4: So when I was on in twenty seventeen, I had
Speaker 4: written a book called Trading Basis, which really looked at
Speaker 4: the critical reasoning overlap between asset management, sports betting, and
Speaker 4: the moneyballization of baseball. And you had ask me, Joe,
Speaker 4: I think it was you asked me a specific question
Speaker 4: of well, I mentioned that somebody from all three of
Speaker 4: those constituents could learn something from the other two. And Joe,
Speaker 4: you asked me for a specific example of how they
Speaker 4: look at things differently, and I said, well, if you
Speaker 4: go onto a trading floor, or you go to a
Speaker 4: mutual fund and you ask them, hey, who's your best
Speaker 4: trader or who's your best PM, Inevitably they will point
Speaker 4: to the individual who had the highest return in the
Speaker 4: prior year, either the biggest P and L or the
Speaker 4: highest return on capitol. But I contrasted that that if
Speaker 4: you went into the front office of a baseball team
Speaker 4: and asked them who their best player was, they wouldn't
Speaker 4: look at you know, which picture necessarily had the lowest
Speaker 4: ra or the most wins. They would answer that question
Speaker 4: based on skill sets, and so they it's a subtle difference.
Speaker 4: Instead of looking at results, they would look at skills
Speaker 4: because they know that the skills, there's so much noise
Speaker 4: and results that the skills. If you can identify the skills,
Speaker 4: you have a better chance of predicting who will do
Speaker 4: better going forward. And as it was told to me,
Speaker 4: a member of the c suite, at zero point seventy
Speaker 4: two listen regular listener heard that episode and played a
Speaker 4: portion of it for Steve. In fact, I think it
Speaker 4: was the part I just mentioned, And I was told,
Speaker 4: as it was relayed to me that Steve said, find him.
Speaker 4: I want to talk to him. And I guess that's
Speaker 4: not a surprise because in twenty twelve, and this is
Speaker 4: all public knowledge. In fact, there's a book by Molly
Speaker 4: Knight called The Best Team Money Can Buy that chronicles
Speaker 4: the Dodger's ownership through the turbulent court years Frank McCourt's ownership,
Speaker 4: and that team was sold in twenty twelve to the
Speaker 4: Guggenheim Group. But Steve also bid for that team and
Speaker 4: came very close to buying the Dodgers in twenty twelve.
Speaker 4: And of course we all know him now as New
Speaker 4: Yorker's nome his uncle Steve owner of the New York Mets.
Speaker 4: So he has, I believe, always had an interest in
Speaker 4: an analytical approach, and I think he always wondered, well
Speaker 4: could that work in the hedge Fund. And I came
Speaker 4: away from those meetings with the bunch of different people
Speaker 4: in the investment committee, and I kind of came away
Speaker 4: with three queries that I thought could sort of be
Speaker 4: my marching orders and how I could help, And that
Speaker 4: was I think at all these pod shops, when somebody
Speaker 4: has a good year, they ask for more money, and
Speaker 4: in terms of buying power, not cash, but in terms
Speaker 4: of buying power. And so the question that management would
Speaker 4: have is, well, is what they did repeatable? And at
Speaker 4: the same time, as you know, there's turnover at these firms, right,
Speaker 4: And I think another question is, well, sometimes we let
Speaker 4: people go too early that then thrive elsewhere just because
Speaker 4: they had a bad start to their career in terms
Speaker 4: of results. Is there a way that we can avoid
Speaker 4: that mistake? And then finally, when a team does well,
Speaker 4: inevitably there's a bit of way, right because we know
Speaker 4: that these four or five huge firms are all very
Speaker 4: competitive and they're trying to steal talent. And so the
Speaker 4: question is I know what a PM and or her
Speaker 4: team may have made me in the past, but what
Speaker 4: are they worth going forward? And all of those queries
Speaker 4: can be answered by looking at skills, which is a
Speaker 4: little different than what the traditional quants do at these firms.
Speaker 3: Okay, so here's my question, who should we bill for
Speaker 3: the finder's fee fee? Should we send it directly to Steez?
Speaker 2: He do you right?
Speaker 4: It would be the firm, right, they probably saved a
Speaker 4: lot of money as opposed to going through a traditional headhunter.
Speaker 3: Okay, I'm joking. Obviously that's fantastic to hear. I've loved
Speaker 3: stories like that. Before we get into the existing model
Speaker 3: of compensation. There's one question that I wonder because I
Speaker 3: think we've done a number of Moneyball episodes at this point,
Speaker 3: but it's been a while since we've talked about that approach,
Speaker 3: and all I remember is the movie and Brad Pitt
Speaker 3: kind of unconvincingly playing a guy that understands math. Could
Speaker 3: you maybe explain, like what it is about the Moneyball
Speaker 3: approach that seems to attract people in finance, Like why
Speaker 3: is there that analogy that seems to come up again
Speaker 3: and again.
Speaker 4: Yeah, I think if you're attracted to critical reasoning, and
Speaker 4: that's the big thing and all of this industry is
Speaker 4: you know, Joe said, what have I succeeded here? And
Speaker 4: I always think the biggest question is do you have
Speaker 4: the mentality in the stomach to make decisions and commit
Speaker 4: capital based on incomplete information? Whether you have the skills
Speaker 4: to you know, build models for you know, and and
Speaker 4: understand companies and read documents. It's really can you make
Speaker 4: decisions based on income information? And it's true at the
Speaker 4: poker table, all right, and it's certainly true when you're
Speaker 4: building a sports team, right you're like, how much is
Speaker 4: this free agent worth? And before there were a lot
Speaker 4: of Joe like you say heuristics, and I even mentioned
Speaker 4: that in the book. I feel that still goes on
Speaker 4: at the allocator level in this industry. Allocators they do
Speaker 4: the interviews and you will hear things like, well he
Speaker 4: just got divorced, you know, or there's a Bentley in
Speaker 4: the parking lot. He must not be hungry anymore. Oh absolutely.
Speaker 4: And one of the reasons is because they don't have
Speaker 4: they don't take a different approach that might be more databased.
Speaker 4: The whole idea of the moneyball approach is to tease
Speaker 4: out skill from or the signal from these very noisy results,
Speaker 4: because both athletes and asset managers, their results are filled
Speaker 4: with influences over which they have no control. To answer
Speaker 4: this question later, you both were talking about like market
Speaker 4: neutral PMS, neutral everything pms, Why would they be having
Speaker 4: the worst week this week than before. And there's an
Speaker 4: actual real answer to that that has nothing to do
Speaker 4: with their skills. Yeah, so this can apply to any
Speaker 4: time period. We're looking at days or months, a year.
Speaker 4: Let's go to sort of an economics one OHO one
Speaker 4: like holding all else equal. Let's say we have a
Speaker 4: PM that has one long and one short, okay, and
Speaker 4: that's their entire portfolio. And of course they never would
Speaker 4: this goes to something else you said in the intro
Speaker 4: because of career risk. Right, even if it's their best
Speaker 4: idea long and best idea short, they'll still fill it.
Speaker 4: But let's say this is their portfolio and on any
Speaker 4: given day or any period we could measure, but let's
Speaker 4: keep it at a day.
Speaker 2: It's a perfect.
Speaker 4: Portfolio in that the long produces alpha and the short
Speaker 4: produces alpha. So the long outperforms the market and the
Speaker 4: short underperforms the market. Right, So that's a perfect portfolio.
Speaker 4: What is the expected re turn for that portfolio for
Speaker 4: like I say, any period, but for a day, And
Speaker 4: the answer is there's a way to figure out. And Tracey,
Speaker 4: you're gonna love this because the answer is dispersion. And
Speaker 4: I know you light up when when you have the
Speaker 4: But this is a little different dispersion than the quants
Speaker 4: and the derivative traders make their life around. This dispersion
Speaker 4: is and it's going to be very context specific for
Speaker 4: where the PM toils. Right, so we know what these
Speaker 4: pod shops they tend to be. They have you know,
Speaker 4: subject matter expertise in sectors. Right, So you might have
Speaker 4: an energy PM, and so let's say this is a consumer
Speaker 4: discretionary PM, and you would say, okay, well, I'm going
Speaker 4: to look at his or her universe and maybe that's
Speaker 4: the S and P fifteen hundred consumer discretionary Maybe it's
Speaker 4: say a portfolio of just consumer discretionary stocks that he
Speaker 4: has modeled, so there might only be eighty or so
Speaker 4: he and his team, but whatever it is, we'll say
Speaker 4: that it's the all the consumer discretionary stocks in the
Speaker 4: S and P five hundred or fifteen hundred. Well, the
Speaker 4: way to figure out what the expected return is is
Speaker 4: to simply look at all those stocks and say, here's
Speaker 4: the skill neutral return, which would be the average return
Speaker 4: of all those holdings. And then you look at the
Speaker 4: ones that outperformed what was their average And you look
Speaker 4: at all the stocks that underperformed and what was their average.
Speaker 4: And the difference between those two numbers is the dispersion
Speaker 4: between outperformers and underperformers, and that varies greatly from day
Speaker 4: to day, and it can very greatly from year to year.
Speaker 3: It's not like the maximum that you can produce dispersions.
Speaker 4: Not the maximum, because you could have the very best
Speaker 4: outperformer and the very best underperformer. But if you're looking
Speaker 4: at a POD, so I'm taking all the pod from
Speaker 4: all the shops across the street that are focused on
Speaker 4: consumer discretionary, I'm going to be dead on by saying
Speaker 4: the average of all those of all those perfect portfolios
Speaker 4: is going to be the average of all the outperformers
Speaker 4: and the average of all the underperformers. And it's invisible
Speaker 4: to investment commits, to CIOs, to the pms themselves. They
Speaker 4: can be just as skilled from one day or one
Speaker 4: period and one year to the next, but the payoff
Speaker 4: is different. And this is sort of the moneyball. Look
Speaker 4: at hey, once we get this all context neutral, we
Speaker 4: might say that a neutral everything PM that had a
Speaker 4: seven percent return one year and a five percent cent
Speaker 4: return the next year, he may have even been more
Speaker 4: skilled than the five percent year, but the dispersion wasn't
Speaker 4: there to pay off that skill.
Speaker 2: Oh, I see what you're saying. So in other words,
Speaker 2: it's like, Okay, this person's up five percent. In order
Speaker 2: to establish like whether that's good or bad or not,
Speaker 2: you have to have some sort of like holistic view
Speaker 2: of what dispersion on average looked like. In that that's
Speaker 2: exactly well, that makes sense. It also seems kind of obvious,
Speaker 2: you know, I know, the divorce and the Bentley is
Speaker 2: probably like extreme examples. They're sort of funny. But you know,
Speaker 2: thinking about the moneyball thing, and I mentioned in the
Speaker 2: old days like oh, that guy looks he's a good
Speaker 2: ey or whatever, just like all these sort of unquantified
Speaker 2: his hustle, you know, his heart whatever. And then you know,
Speaker 2: Brad Pitt or the being came along and actually put
Speaker 2: some numbers on this. If they're not doing that, what
Speaker 2: are the sort of like old heuristics that aren't the
Speaker 2: extreme ones that the investment committees or the hiring committees
Speaker 2: of the firing committees would have been using two aviliate.
Speaker 4: Pro So there's no question that it seems obvious, and
Speaker 4: it's just the first building block. And this isn't Black
Speaker 4: Shawl stuff in terms of complexity.
Speaker 2: Yeah.
Speaker 4: I started this sort of journey and analytics by working
Speaker 4: for a company called Novus, and Novas was one of
Speaker 4: about fifteen years ago, was at the forefront of portfolio analytics,
Speaker 4: and in fact, they had read my book and I'm like, hey,
Speaker 4: this is what we try to do. And I worked
Speaker 4: for them. So I've seen just about every package out there,
Speaker 4: whether it is from a vendor in terms of analytics
Speaker 4: or you know inside firms. I've you know, worked with allocators.
Speaker 4: I have never seen dispersion quoted Michael Mobison has written
Speaker 4: a paper on it. So there are academics who are
Speaker 4: aware of it, but I don't think people realize that
Speaker 4: is the calculation for the fruit on the tree, the
Speaker 4: meat on the bone, for these pod shops, there has
Speaker 4: to be dispersion to pay off a non factor you know,
Speaker 4: a factor neutral portfolio. So what the quants really do
Speaker 4: and this is like what Gappy touched on when he
Speaker 4: talked about you know, the day in the life of
Speaker 4: a quant and your other guest within the last month
Speaker 4: whose name I can't read. Yes, there was lots of
Speaker 4: talk about risk management, right because of course it's of
Speaker 4: utmost importance when you have a leveraged firm, right, you
Speaker 4: have to understand every factor that's bouncing around in there,
Speaker 4: and that's really their job, and they will, of course
Speaker 4: because draw downs in a leverage firm, drawdowns are to
Speaker 4: be avoided as much as possible. So the sharp ratio
Speaker 4: really drives the way the quants are looking at pms.
Speaker 4: But they're all backwards looking, sort of in my view,
Speaker 4: So they do strip out everything. But once they get alpha,
Speaker 4: or as I know, one firm calls it idiosyncratic alpha.
Speaker 4: What I then do is the next step. I don't
Speaker 4: change the definition of alpha, but then I break that
Speaker 4: into a skill framework so that once you get different skills,
Speaker 4: you can say this one's more repeatable than another skill,
Speaker 4: et cetera.
Speaker 3: So like dispersion weighted basically like weighted by the opportunity
Speaker 3: that's available.
Speaker 4: To you, Yes, exactly, And that's what allows you Tracy
Speaker 4: to compare the energy trader to the consumer discussionary trader
Speaker 4: because and I make an analogy in the book, it's
Speaker 4: like looking at NFL punt. You know, PM's job is
Speaker 4: to make as much money as possible, and essentially a
Speaker 4: punter's job is to kick the ball as far as possible.
Speaker 4: So before sports analytics came along, punters were judged on
Speaker 4: and in fact, I think there was even award for
Speaker 4: the punter that had the biggest average at the end
Speaker 4: of the year, right the distance of all his punts
Speaker 4: divided by total number of punts. But what sports analytics
Speaker 4: people quickly figured out is that, well, hey, if the
Speaker 4: best punter is averaging forty four yards to punt, and
Speaker 4: you've got another punter whose coach is so conservative that
Speaker 4: he's constantly punning from the opponent's thirty five yard line
Speaker 4: or the opponent's forty yard line, he can't even get
Speaker 4: a forty four yard punt off. So the way to
Speaker 4: measure that is to say, okay, when a punts from
Speaker 4: his own fifteen yard line, I'm going to measure that
Speaker 4: against every other punt from the fifteen yard line. And
Speaker 4: now you each punt is then evaluated. And I think
Speaker 4: what's really important to the work I do too, is
Speaker 4: or to note, you don't measure it now by the distance.
Speaker 4: You measure it by plus or minus the average punter.
Speaker 4: So you can say someone is on average one and
Speaker 4: a half yards better per punt, and then you can
Speaker 4: put a value on that. And that's the same way
Speaker 4: a lot of you know, my framework is, it's not
Speaker 4: saying you know it especially sort of like that that
Speaker 4: canned package. You will see a canned batting average on
Speaker 4: all analytics platform. It's meaningless. In fact, it's worthless. But
Speaker 4: if you express it the way I just talked about punters,
Speaker 4: sort of this skill neutral and to say, oh, his
Speaker 4: batting average is one or two percent above, you know,
Speaker 4: over the year he averages one percent a day. Well,
Speaker 4: you know, in a fifty percent portfolio, that would be
Speaker 4: you know, one more winner than expect it every other day.
Speaker 4: Then you can compare that to the dispersion world that
Speaker 4: he lives in, and you can put an absolute value
Speaker 4: on his skill. Now it might differ from the actual,
Speaker 4: but that's because of stuff out of the PM's control.
Speaker 4: So that's the approach, and it's sort of marrying the
Speaker 4: sports analytics approach. And again you kind of said, like,
Speaker 4: why isn't this done? I do have some thoughts on
Speaker 4: that because I got dropped into a fish out of
Speaker 4: water quant division And they're brilliant, right, they are brilliant,
Speaker 4: but they're not very flexible in they're thinking. They tend
Speaker 4: to think the same way. And I found that when
Speaker 4: I was interviewing for a quant developer, you know, because
Speaker 4: I'm sort of building my framework on Excel and then
Speaker 4: you need some production around it to make it usable
Speaker 4: in a big firm or to clients. And I was,
Speaker 4: you know, an interviewing for a quant developer. I couldn't
Speaker 4: get them to stop talking about factors because that's sort
Speaker 4: of the way they're trained. And I'm like, okay, right,
Speaker 4: we're gonna strip out factors. How would you evaluate skill?
Speaker 4: And again it get you know, it came down it
Speaker 4: was very.
Speaker 3: Good start talking about factors again.
Speaker 4: Yeah, yeah, And like I say, they're brilliant, but I
Speaker 4: think sort of an approach outside the industry, Yeah, it
Speaker 4: can really help. You can uncover different stuff by sort
Speaker 4: of marrying two different industries.
Speaker 2: So a lot of this stuff, so far as you've
Speaker 2: described it is intuitive as you describe it, like, yeah,
Speaker 2: it makes a lot of sense that you know, you
Speaker 2: have to if you're going to compare two different pods
Speaker 2: that are trading consumer discretionary, you have to understand that
Speaker 2: dispersion and how they compare to a.
Speaker 3: Or comparing someone trading consumer discretionary versus like utilities.
Speaker 2: Totally, and it makes sense to me that there's more
Speaker 2: than just volatility adjusted return sharp ratios. And it makes
Speaker 2: sense to me that punters shouldn't just be measured on
Speaker 2: pure length because you don't know where their coaches have
Speaker 2: them punt from. And maybe sometimes you want to punt
Speaker 2: shorter for various reasons because you want to have a
Speaker 2: chance that you know, if you're catcher something like that. Okay,
Speaker 2: I get all of that. Talk to us a little
Speaker 2: bit more about the art of measuring skill, specifically outside
Speaker 2: of returns, because this is the moneyball thing, which is
Speaker 2: like every day they're coming up with new metrics and
Speaker 2: vanity metrics that they these conferences where it's like vorp
Speaker 2: and all these things. And I know that vorp is
Speaker 2: like that was like twenty years ago that someone invented vorp, right,
Speaker 2: but you know, there's all of these new things that
Speaker 2: I was trying to come up with something that will
Speaker 2: unlock this is the guy who produces a lot of
Speaker 2: extra wins or something for the baseball team. What are
Speaker 2: some of the other techniques or maybe what are the
Speaker 2: other skills sure that you can measure a traitor on
Speaker 2: other than just looking at xpos factor returns to justify risk.
Speaker 4: Right, Yes, so's that's a great question. And I'm laughing
Speaker 4: as you talk about the acronyms because obviously the sport
Speaker 4: channel at the community is famous for their acronyms. So
Speaker 4: I in creating my framework, I have five skills that
Speaker 4: explain alpha, okay, and it doesn't reinvent alpha or in
Speaker 4: any way, it just breaks it down, and of course
Speaker 4: I use acronyms to describe, and with a nod to
Speaker 4: the industry that inspired them, I've named them after five
Speaker 4: different baseball players from you know, the night teen seventies
Speaker 4: when I was an impressionable baseball fan, And those skills
Speaker 4: by name are sever Aaron, carew Rose, and then lumb
Speaker 4: Lum is something you probably a name you haven't heard of,
Speaker 4: but that is named for.
Speaker 3: A Yeah, that is named for it.
Speaker 2: Sorry, I'm not the other four Tom.
Speaker 4: Aaron, Aaron so all Hall of Fame level players, even
Speaker 4: though Pete Rose isn't Lan but so interestingly, and I
Speaker 4: won't go in deeply into this.
Speaker 2: But Rose measures the degree to which they're betting on
Speaker 2: the side.
Speaker 4: How good are they at yes, at being well, Pete Rose,
Speaker 4: it's a good one. So this isn't actually a descriptive acronym.
Speaker 4: So Rose stands for return on sector excellence. So why Rose?
Speaker 4: And you know why this? Well, Pete Rose made more
Speaker 4: All Star teams at different positions than anybody else in baseball.
Speaker 4: He made an All Star team at second base, outfield,
Speaker 4: third base, and first base. So he was good at
Speaker 4: sector rotation, right, So that that's sort of what that
Speaker 4: skill is, measuring the lumb for luck uncontrolled by the manager.
Speaker 4: Lum and what that really references, Tracy, It's a lot
Speaker 4: of what we were talking about in terms of the
Speaker 4: dispersion and really sort of the average stock in a
Speaker 4: portfolio versus what the benchmark might be, because the average
Speaker 4: stock is really what the skill neutral performer. Well, those
Speaker 4: differences are sort of luck that is either a tailwind
Speaker 4: or head wind uncontrolled by the manager. And Mike Lum
Speaker 4: was a journeyman player who happened to play on an
Speaker 4: Atlanta Braves team with Hank Aaron and Davy Johnson Daryl
Speaker 4: Evans when they all hit forty home runs. They're the
Speaker 4: only team that did that, and that inflated all of
Speaker 4: Mike Lum's performance too, and obviously it's something he couldn't control.
Speaker 4: But so these skills, I think the So what they're
Speaker 4: really measuring is one is luck, two is sector excellence.
Speaker 4: Third is a consistency measure, and that's the Rod carew
Speaker 4: and in great batting average. And then there's power. Like
Speaker 4: I talk about what the expected return is of that
Speaker 4: perfect portfolio, Well, if someone's return is above or below that,
Speaker 4: what that's really measuring is their ability to identify the
Speaker 4: best of the outperformers and crucially avoid the worst of
Speaker 4: the outperformers. And I can quantify that. And then the
Speaker 4: final one, the siver is a sizing thing, and you
Speaker 4: put all five of those together and you might have someone, Well,
Speaker 4: here's a great example of how it's useful on a
Speaker 4: multi manager platform. And I should say that all my
Speaker 4: work only deals with public equities. Yeah, public equity APMs
Speaker 4: evaluating them. So on a POD platform, you might have
Speaker 4: four dozen, five dozen different teams, right, and you generally
Speaker 4: do not need a model to tell you who the
Speaker 4: best two or three are, And to a little lesser extent,
Speaker 4: you don't need a model tell you who the worst
Speaker 4: two or three are. They're outliers, and they the ones
Speaker 4: that are really good are out there every year. But
Speaker 4: in the middle you might have three dozen pms that
Speaker 4: are tightly bunched around sort of the average production of
Speaker 4: all the pms. What the model is really good at
Speaker 4: is looking at these very similar returns at the end
Speaker 4: of the year, looking at the skills that make them up,
Speaker 4: and say, well, I know sizing tends to have a
Speaker 4: correlation of zero from year to year. It reverts back
Speaker 4: to the mean. So if you have two people with
Speaker 4: the same return, but one of them was adding alpha
Speaker 4: via their sizing decisions versus someone who was more consistently
Speaker 4: picking out performers, and this is what you don't see
Speaker 4: if you're just looking at idiosyncratic alpha, even though you've
Speaker 4: stripped out all the factors. That's how the framework comes about,
Speaker 4: and that's how it is both backward looking in terms
Speaker 4: of explaining alpha by skill, but then also it becomes
Speaker 4: a forward predictor by knowing what the correlation is between
Speaker 4: past and future periods.
Speaker 3: I have so many questions for my next one, and
Speaker 3: let me just add a caveat before I ask it,
Speaker 3: which is everything I know about baseball I learned from
Speaker 3: that one episode of The Simpsons. So that is to say,
Speaker 3: I don't know very much at all other than don't
Speaker 3: mean to Daryl Strawberry. But my impression, and again I
Speaker 3: don't remember a lot about moneyball, but my impression was
Speaker 3: like part of that strategy was finding players that are
Speaker 3: underpriced by the market and capable maybe of doing one
Speaker 3: specific thing very well, and then kind of putting them
Speaker 3: together into a team that can work very well, like holistically,
Speaker 3: rather than just going after the expensive players that hit
Speaker 3: home runs a lot. Yeah, exactly. I guess my question
Speaker 3: is I get the approach to evaluating individual traders, but
Speaker 3: is part of your approach also looking at how they
Speaker 3: like holistically work together and impact each other at all,
Speaker 3: or because of the nature of multi strats and the
Speaker 3: pod shops, doesn' not matter so much on that.
Speaker 4: That's an insightful question, And I will pick up a
Speaker 4: topic that Gappy talked about a couple months ago. He
Speaker 4: talked about the different cultures and how like how these
Speaker 4: pod shops and the and the multi manager platforms can
Speaker 4: be different and a lot of times there's a big
Speaker 4: culture difference. And I would say that that is absolutely true,
Speaker 4: and I have a great sort of answer to your
Speaker 4: question for that. So at some shops, the philosophy is,
Speaker 4: we are going to strip out everything a PM does
Speaker 4: and cynically they have so many factors, and we'll pay
Speaker 4: them on what the idiosyncratic alpha that's left is. And
Speaker 4: they have so many factors they're stripping out that you know,
Speaker 4: they're trying to get that alpha number down as small
Speaker 4: as possible so they don't have to pay off bonuses.
Speaker 4: And I remember joking with a PM one time at
Speaker 4: one of those shops and he's like, yeah, I feel
Speaker 4: like every time I go in there, they tell me like, yeah,
Speaker 4: you had a good year, but look year out performance
Speaker 4: is due to investing in dividend paying companies where the
Speaker 4: CEO went to an IVY League school and we can get.
Speaker 2: That for free, right.
Speaker 4: So that so at those shops, their philosophy is it
Speaker 4: doesn't matter because we're taking out everything. I prefer a
Speaker 4: little different approach, and there are shops that do it
Speaker 4: this way, which is to say, my job as a
Speaker 4: CIO is to build a multi manager platform where some
Speaker 4: of these offset so that there are different skills and
Speaker 4: then instead of stripping out factors at each portfolio level,
Speaker 4: more stripping out the factors. Once you put them all together,
Speaker 4: you've got this bully of base stew and then you
Speaker 4: take the factors out. And that is a different approach
Speaker 4: because I think the pms feel a little bit more freedom.
Speaker 4: They still have their buffers they have to stay in,
Speaker 4: but they don't see the ETFs or the factor anti
Speaker 4: factor things getting shoved right into their portfolio. The approach
Speaker 4: is more higher. So you can take either approach. I
Speaker 4: do prefer the sort of roster construction idea that you
Speaker 4: have that you have in sports, but that really is
Speaker 4: a difference in you know, I think in from culture.
Speaker 2: Yeah, that's super interesting. So in baseball, a general manager
Speaker 2: looking for players can look at other teams, they can
Speaker 2: look in the minor leagues, they can look at college sports.
Speaker 2: They can start scouting at high school. Probably there's a
Speaker 2: farm system and they call it a farm system. What
Speaker 2: you've described so far makes sense for evaluating people in
Speaker 2: existing seats, either at your shop or perhaps at another shop.
Speaker 2: Is there a way to transfer it or to apply
Speaker 2: some of these same ideas to people who don't have
Speaker 2: the same because there I don't think there's the same
Speaker 2: equivalent unless trading, you know, an Mari trader Schwab, which
Speaker 2: actually I do think maybe is kind of a thing.
Speaker 2: But is there a way to sort of think about,
Speaker 2: like how you evaluate someone who just does not have
Speaker 2: that much of a track record yet.
Speaker 4: Yes, because of the way these multi manager platforms are
Speaker 4: formed now, they don't hire from the street anymore. I
Speaker 4: think twenty years ago, thirty years ago, I know when
Speaker 4: I was on the street, the researchers that were covering
Speaker 4: the companies, they'd get plucked away, sometimes by the shops.
Speaker 4: Sometimes traders would get plucked away. Right, that doesn't happen
Speaker 4: as much anymore because what these huge firms have done,
Speaker 4: and this also goes to their competitive advantage and their
Speaker 4: ability to scale, is they are now training these people
Speaker 4: right out of school, right. They have you know, universities
Speaker 4: or academies or you know these schools essentially where they're
Speaker 4: teaching people to be analysts or pms and again sort
Speaker 4: of to a culture thing. My favorite ones are the
Speaker 4: ones where the firms realize it used to just be
Speaker 4: an upper out thing, right, Like you became an analyst
Speaker 4: and then you became a PM, and if you weren't
Speaker 4: a good analyst, you never became a good PM. And
Speaker 4: I think that there are firms now that recognize and
Speaker 4: analysts can be a career. You may be a great analyst,
Speaker 4: but not necessarily united a capitol committee. You know, there's
Speaker 4: a different skill set to being the PM. And they
Speaker 4: find out some of these things in the academies and
Speaker 4: in the universities. They're in house training schools. This is
Speaker 4: the farm system that is coming up. Quite literally, this
Speaker 4: is the bench and we see that and they don't
Speaker 4: just get thrown in. They do tend to run paper
Speaker 4: portfolios or portfolios that feel like they're real because they
Speaker 4: are entering trading.
Speaker 2: So in their careers depend on them doing well. So
Speaker 2: they're taking risk even if it's paper money.
Speaker 4: Yes, exactly, And you can run the same analytics on
Speaker 4: these portfolios. And what I definitely have seen is some
Speaker 4: of the newly graduated pms. These firms are good at
Speaker 4: who they're training and those are the best pms to
Speaker 4: find alpha signals from. Because they're portfolios, they tend to
Speaker 4: be small so they can be replicated. It's it's and
Speaker 4: this is another job of the quants too. If you
Speaker 4: have a very senior PM, who's you know, who has
Speaker 4: a contract that allows he or she to run a
Speaker 4: two billion dollar biotech portfolio. There's not much left for
Speaker 4: the quants to you know, because they're you know, they're
Speaker 4: probably a little more thinly cat capitalized. There's not much
Speaker 4: room to replicate that portfolio at another quant level in
Speaker 4: the firm. But the new people that are coming up,
Speaker 4: they're cheap, they're running small portfolios. But if they're skilled,
Speaker 4: they're knowing what they're in is just as important as
Speaker 4: a more senior PM.
Speaker 2: Yeah, Tracy and listeners. There's a great piece on the
Speaker 2: Bloomberg from June nineteenth by our colleagues Nishan Kumar and E.
Speaker 2: Liza Tetley about exactly this hedge fund talent schools are
Speaker 2: looking for the perfect trader, and it talks about zero
Speaker 2: point seventy two and it talks about citadel building these
Speaker 2: sort of in how training things. So all all these
Speaker 2: pieces are coming together, building the own farm system in
Speaker 2: house to see who's going to be good one day.
Speaker 3: We should go to talent school. It's fun talent school,
Speaker 3: that's to be clear. Okay, that was the joke. Yeah, Okay, Joe,
Speaker 3: you've talked about sizing and you talked about the general
Speaker 3: skill set that you're looking for one thing I'm still
Speaker 3: unclear on. You alluded to it earlier, but I would
Speaker 3: love for you to talk more about it in detail.
Speaker 3: Time frame. What is the time frame by which you
Speaker 3: are evaluating traders? And I guess how much runway do
Speaker 3: you give people to either prove themselves correct or prove
Speaker 3: themselves to be disastrously wrong because you know the correlation
Speaker 3: they were betting on suddenly breaks down.
Speaker 4: Yeah. So again, great question, and it became a point
Speaker 4: of frustration for me from when I first started at
Speaker 4: Novus and building this stuff because I was very used
Speaker 4: to sports analytics, and specifically baseball, but some some other
Speaker 4: sports as well. I'll touch on golf. When you're evaluating
Speaker 4: the skill of a picture, and there's three skills that
Speaker 4: a picture has that are not dependent on anything else,
Speaker 4: not dependent on it's teammates, who's batting it, et cetera.
Speaker 4: It's right, not dependent on fielding. Right would be the
Speaker 4: strikeout rate of a picture, the walk rate of a picture,
Speaker 4: and the ground ball rate of a picture. These are
Speaker 4: things that the picture controls, and what happens is after
Speaker 4: about fifty plate appearances, you get the strikeout rate for
Speaker 4: a picture. That is predictive of you know, it's you know,
Speaker 4: from a maths standpoint, the correlation is above zero point seven,
Speaker 4: so squared it's above point five. Right, the past explains
Speaker 4: more of the future than factors that we haven't identified.
Speaker 4: But with PMS, there's much more noise in their result
Speaker 4: and it takes a lot longer to find a meaningful correlation.
Speaker 4: So although I can do work for like, I can
Speaker 4: and I do this for a single day, right, So
Speaker 4: every day I generate a report, and I do this
Speaker 4: for clients now showing their PMS and exactly what they're
Speaker 4: readings of all these skills were each day. And of
Speaker 4: course for one day it's just trivia. It's no more
Speaker 4: than trivia. But what it is doing is building a
Speaker 4: data set. And at the point that you get to
Speaker 4: six months, which is about one hundred and twenty five
Speaker 4: days trading days. Bigger picture, the full model takes five
Speaker 4: hundred the past five hundred results, and that's when you
Speaker 4: start getting very different and but more persistent correlations between
Speaker 4: all these skills. Right. But what I have found is
Speaker 4: that even after one hundred and fifty days, if you
Speaker 4: take for the other year and a half, a mean
Speaker 4: reversion assumption, and then just every time a new day
Speaker 4: comes in, you drop off an assumption you'd get you
Speaker 4: have a pretty robust skill reading that starts to mean
Speaker 4: something after six months, and after two years, that's when
Speaker 4: it really has, you know, has some great predictive power
Speaker 4: for the next quarter, and so you're constantly dropping off. Now,
Speaker 4: why only two years? I talk about this in the book.
Speaker 4: I don't have a great answer for that.
Speaker 3: I suppose you have to start somewhere around.
Speaker 4: Yeah, well, here's what I knew. Two years was better
Speaker 4: than three years, which in one sense, why would that be?
Speaker 4: And I have talked to different quants about that, and
Speaker 4: they have approached this from a much different perspective, and
Speaker 4: they also have come to somewhat of a two year conclusion.
Speaker 4: The reason seems to be regimes within the stock market,
Speaker 4: just something about where you are skilled. You know, I
Speaker 4: haven't been able to identify it. And I also know
Speaker 4: that we could do a you know, we could we
Speaker 4: could run the numbers and find out that, oh, you know,
Speaker 4: it's not two years. It's the most predictive thing for
Speaker 4: the last three months would have been two years and
Speaker 4: forty three days. When you try to get that precise
Speaker 4: that's not going to be what the perfect So two
Speaker 4: years does seem to work because you're constantly rolling off
Speaker 4: whatever happened two years to go, and so there's some
Speaker 4: regime change that seems to work. But that is an
Speaker 4: unanswered to a question I have too.
Speaker 2: So one thing about baseball is that every GM in
Speaker 2: baseball has basically perfect visibility into the performance of every
Speaker 2: player on every other team because it's all out on
Speaker 2: the field and it's all measured, and we all have
Speaker 2: the same information. You know, one of the.
Speaker 3: Most measure heart Jack.
Speaker 2: Yeah, right right, you can't measure Harvey get we all
Speaker 2: could see players on base percentage and ops and slop
Speaker 2: and all of this stuff, right. You know some of
Speaker 2: the most popular alerts that always read spike on the
Speaker 2: terminal or it's like consumer Discretionary Manager, Palacity goes to
Speaker 2: citadel whatever. People love, people eat that stuff up. Just
Speaker 2: from an industry perspective, sitting aside, whether you want to
Speaker 2: use a traditional sharp ratio perspective or rose or lum
Speaker 2: or whatever, how much visibility does one shop have into
Speaker 2: the performance of a pod at another shop that can
Speaker 2: then be ported over, or how much insight can you
Speaker 2: we have if maybe there's an undervalued player somewhere else,
Speaker 2: if you want to bring them over and give them
Speaker 2: more capital.
Speaker 4: Than they're getting, extremely limited in terms of it. So well,
Speaker 4: and I'll tell you who can solve that problem. What
Speaker 4: you will have is, of course, if a team is
Speaker 4: marketing itself or being recruited by another firm, they bring
Speaker 4: over their returns, right, But they don't bring over They
Speaker 4: might talk about portfolio construction, but I'm pretty sure they
Speaker 4: shouldn't and probably don't bring over their two years right,
Speaker 4: what the portfolio, what their holdings have been for the
Speaker 4: last two years. So you don't get that type of visibility.
Speaker 4: But let me tell you who can. Okay, And this
Speaker 4: is I think one of the most important constituents in
Speaker 4: our industry because I think they have the purest motive,
Speaker 4: and that is the allocators, right allocators. I'm talking about
Speaker 4: the huge, multi billion dollar entities which provide the blood
Speaker 4: that keeps the heart pumping right at all these hegge file. Sure,
Speaker 4: we know that Ken Griffin has a tremendous amount of
Speaker 4: the aum is his money, and and you know we
Speaker 4: hear that about some other people too, But in general,
Speaker 4: these firms it's outside money which which keep these firms afloat.
Speaker 4: But the allocators, many of them though, and what I'm
Speaker 4: talking about here are foundations, university endowments, sovereign wealth funds, right,
Speaker 4: pension plans, and they have a very pure motive. Right,
Speaker 4: they are trying to get returns for the retirees or
Speaker 4: you know, reduced tuition for future students, et cetera, or
Speaker 4: you know, in the case of Norway, the citizens of
Speaker 4: the country.
Speaker 2: Right.
Speaker 4: So they are a treasured investor, right if you run
Speaker 4: a hedge fund. So when they are doing manager selection,
Speaker 4: they have the ability to go to hedge funds. Now,
Speaker 4: maybe not Citadel and Millennium, but to all these non
Speaker 4: multi manager platforms. They have the ability to go to
Speaker 4: them and say, hey, if you want us to really
Speaker 4: evaluate you, we need to see podil. Yeah, we need
Speaker 4: to see we need position level transparency for the last
Speaker 4: two years. Hey if you want, if you don't want
Speaker 4: to give us yesterday, start a quarterback so that it's
Speaker 4: on a lag. But now they have the leverage to
Speaker 4: get those returns, especially if you're talking about emerging managers, right,
Speaker 4: young managers are trying to that, you know, to build
Speaker 4: a hege Fund, and I don't feel they use that leverage.
Speaker 4: And this is to me is like, well, Joe, you're
Speaker 4: you know you you talk about this framework and it's
Speaker 4: it's applicable to multi manager platforms, and you know an
Speaker 4: endowment isn't a leveraged portfolio, so how could they use it? Well,
Speaker 4: this is how they could use it, because they can
Speaker 4: get that, Joe, and they do ask those questions about
Speaker 4: the Bentley and the.
Speaker 2: In fact, can I give you an example? Can I?
Speaker 4: This is I really like this. I've never worked with them.
Speaker 4: I should say that I have worked with their brethren,
Speaker 4: and I've worked with the endowments that they would measure
Speaker 4: themselves against. But the MIT Endowment Matimco is the name
Speaker 4: of the uh, the name of the entity. They have
Speaker 4: something between twenty and thirty billion dollars under management. So
Speaker 4: we know that a portion of that is dedicated to
Speaker 4: public equities. And we know because and I won't mention
Speaker 4: his name, so I'm not trying to call him out,
Speaker 4: but we know that one of those gentlemen that looks
Speaker 4: for equity managers is a presence on fin Twitt. He's
Speaker 4: actually a great follow, very earnest, and so he'll talk
Speaker 4: about things, and sometimes he'll post job postings.
Speaker 2: Right, and what you will find is.
Speaker 4: Everybody who works in that division or and in fact,
Speaker 4: you can even see this publicly. I know this. Yale's
Speaker 4: Management company has the resumes of every person who's in
Speaker 4: that division, and they're all the same. Here's what they
Speaker 4: will say. They will say things like, you know, was
Speaker 4: president of the investment club at the University of Virginia, right,
Speaker 4: and they'd been investing in stock since I had a
Speaker 4: paper route.
Speaker 1: Right.
Speaker 4: They're always have this, right. So when they go to
Speaker 4: do managers selection, and I've been on that side too
Speaker 4: as a as a marketer, they will sit down with
Speaker 4: the PM and they'll ask they'll go over each position
Speaker 4: in the portfolio and make no mistake about it, they're
Speaker 4: passing judgment, right, because if they're not frustrated or want
Speaker 4: to be pms, this is how they think about the market.
Speaker 4: All right, So I'm gonna put full stop there. Now,
Speaker 4: let's go to a The general manager of the Philadelphia
Speaker 4: seventy six ers is a gentleman named Daryl Mory.
Speaker 2: Oh yeah, I like Daryl. Darryl.
Speaker 4: Right, he was with Houston and in fact, while he
Speaker 4: was at Houston. He really brought moneyball to the NBA.
Speaker 4: Mark Cuban was probably maybe the second, but Darryl Moury
Speaker 4: right down to the fact that Michael Lewis did a
Speaker 4: piece on him in the Sunday New York Times maybe
Speaker 4: twenty years ago. So Daryl Morey is the GM of
Speaker 4: the seventy six ers, and he has juniors too, right,
Speaker 4: And when they're doing their equivalent of manager selection, whether
Speaker 4: it'll be drafting a player or looking at free agents,
Speaker 4: can you imagine how absurd it would be for Darryl
Speaker 4: and the analysts to go down and shoot free throws
Speaker 4: with perspective player right, and to judge the player based
Speaker 4: on that. But I guarantee you at the endowments they
Speaker 4: go back and say, can you believe you know that
Speaker 4: managed your short netflix right? Like, so, now, why did
Speaker 4: I pick those two? And the example is this, before
Speaker 4: Daryl got into basketball, he is a proud graduate of
Speaker 4: MIT Sloan. He got his MBA at Sloan School of
Speaker 4: Management and he started along with a woman named Jessica Keelman,
Speaker 4: he started the Sloan Sports Conference, which started as Bill
Speaker 4: Simmons when he was at Grantline described at as Dorcapalooza. Right,
Speaker 4: it was just people, kids, guys, and it was almost
Speaker 4: all guys back then talking about sports analytics. And it
Speaker 4: has morphed into a massive event and it's a job
Speaker 4: fare where all these sports teams from all different leagues
Speaker 4: are looking for talent, right, and they're essentially looking for
Speaker 4: performance analytics people. Right.
Speaker 2: Voros McCracken was the one from what's.
Speaker 4: A exactly exactly, So this is think now, now, look
Speaker 4: across the campus at the MIT Sloan Endowment. What they're
Speaker 4: actually trying to find is performance analytics. Do you think
Speaker 4: there might be anybody right across the campus who may
Speaker 4: have never invested in stocks but gets the profit motive?
Speaker 4: They would take my work and probably take it three
Speaker 4: steps more. But I don't think there's an endowment out
Speaker 4: there that things like that. Like, I'm sure they've never
Speaker 4: walked across the campus, and even I'm sure Darryl has
Speaker 4: never thought to invite them over to the you know,
Speaker 4: to hey whyt to interview some of some of our people.
Speaker 4: So that's again sort of how I look at, like, Hey,
Speaker 4: this is how some of this work. How you somebody
Speaker 4: who doesn't have the data can get it at the
Speaker 4: allocator level.
Speaker 3: We've been talking very much about, you know, performance evaluation
Speaker 3: and metrics from a sort of managerial level. If I
Speaker 3: am a trader or quant, you know, a sort of
Speaker 3: junior medium level quant, I guess at one of these
Speaker 3: multi strat hedge funds, how am I viewing the performance
Speaker 3: of others and competition? Is it the case that I'm
Speaker 3: trying to move into a particular sector that maybe has
Speaker 3: more of an opportunity set in terms of dispersion, where
Speaker 3: maybe there's more volatility or more relative value opportunities or
Speaker 3: something like that. How am I like viewing my competition?
Speaker 4: It's a good question. Even the work that I do well,
Speaker 4: I think there's definitely comparison, right. You douce again to
Speaker 4: culture some of the pods, and I think I think
Speaker 4: Yapy mentioned that some of the pods there is a
Speaker 4: there's a sharing of information, right, and at some shops
Speaker 4: there's not. This is a case where I actually prefer
Speaker 4: the not sharing of information right, because I would rather
Speaker 4: I think the quants would rather know that maybe two
Speaker 4: different pms came to the same conclusion independently, as opposed
Speaker 4: to they both went to the same idea. Dinner and
Speaker 4: then both decided to buy the stock. There's more of
Speaker 4: a signal in somebody coming to it independently, so I
Speaker 4: believe they're aware of what the returns are of their
Speaker 4: other pms. In addition, and I don't know if this
Speaker 4: was in that article Joe you just referenced, but these
Speaker 4: firms all have coaching teams too, and I certainly found
Speaker 4: that the older pms that you know had been in
Speaker 4: the business since the nineties, they're setting their ways right.
Speaker 4: They don't want a quant to come in with a
Speaker 4: laptop and start telling them that's spin rate, right, the
Speaker 4: spin rate of their pictures. But the younger people, I
Speaker 4: think there's more of a hey, if there's data that
Speaker 4: you can give me to help me get better, I
Speaker 4: think maybe in some ways they might be looking for that.
Speaker 2: Joe Peter, this is super fun. Thank you so much
Speaker 2: for coming on the podcast again.
Speaker 4: Oh it's always great to be here. And I'll see
Speaker 4: you in seven years.
Speaker 2: Yeah, exactly when whatever your next job is from.
Speaker 3: The Sun, Thanks so much.
Speaker 4: Joke.
Speaker 3: Yeah, even though there were baseball references, I enjoyed it.
Speaker 2: Yeah, Tracy, that was a really fun conversation.
Speaker 3: I love hearing stories when people get jobs off the
Speaker 3: back of Authoughts appearances.
Speaker 2: That's nothing sort of flatters are egos and sense of self.
Speaker 3: Well know, I also like it when people say they're
Speaker 3: listening to A Thoughts episodes while going to the gym.
Speaker 3: Oh yeah, because I hate going to the gym. I
Speaker 3: hate running and things like that. But it makes me
Speaker 3: feel nice that like people are listening to us or
Speaker 3: to offset something that's kind of like a chore.
Speaker 2: No, But beyond all that, it was very fun. I
Speaker 2: feel like we could just talk about these businesses forever.
Speaker 2: It seems so rich, you know, like we still have
Speaker 2: to do something on like compensation struggle. Yeah, but also
Speaker 2: like just the sort of like fundamental point that everyone knows,
Speaker 2: which is like manager identification is really difficult because and
Speaker 2: you know, first of all, there's all these questions about, well,
Speaker 2: it's beating the market really possible because of efficient markets
Speaker 2: and stuff. And then you could identify someone to all
Speaker 2: this person beat the market seven years in a row,
Speaker 2: but have a pool of one thousand managers. There's gonna
Speaker 2: be a lot of people who beat the market seven
Speaker 2: years in a row, and so it seems like a
Speaker 2: very interesting problem to solve.
Speaker 3: It's kind of funny that you're trying to like select
Speaker 3: traders on a factor neutral basis, who are themselves able
Speaker 3: to be factor neutral in some respects, Like you're kind
Speaker 3: of you're trying to separate them from like these circumstances
Speaker 3: that they are operating in, or trying to wait them
Speaker 3: against the value of the opportunity that they are currently facing.
Speaker 3: Right that dispersion that Joe was mentioning, that's kind of funny.
Speaker 3: I've thought about it. Not to go all media and
Speaker 3: naval games, so sure, but you know, it's sort of
Speaker 3: similar to journalist beats in some respects, where you can
Speaker 3: get really lucky and be on a really interesting beat
Speaker 3: where there's tons happening, and suddenly you know, all your
Speaker 3: stuff is getting read and you're getting all these major scoops,
Speaker 3: and then maybe two years later, to go back to
Speaker 3: that timeframe point, it's sort of faded into the distance
Speaker 3: and there's not as much to write about, And how
Speaker 3: do you judge the talent of a particular journalist or
Speaker 3: a trader from their particular set of circumstances.
Speaker 2: That's a great example. I remember, you know, like when
Speaker 2: I was in a business insider years ago. It's like the
Speaker 2: reporters who covered Apple on days of like iPhone announcement,
Speaker 2: they got we were like measured on traffic back then
Speaker 2: they got all the traffic.
Speaker 4: You know.
Speaker 2: It's like, oh, this isn't fair, Like I'm talking about
Speaker 2: like the Bank of England decision. This is nonsense. Okay.
Speaker 3: I just read a really good analysis of like US payrolls,
Speaker 3: and people only want to read about the next iPhone.
Speaker 2: I explained, I explained Mario Draggy's new OMT thing really
Speaker 2: well and like ten people ready, But no, like this
Speaker 2: is like it's all like version of the same problem.
Speaker 2: By the way, my hedge fund media metaphor that I
Speaker 2: use in my head is like alpha decay. So it's
Speaker 2: like the first person who ever came up with like
Speaker 2: here's what you need to know or the answer will
Speaker 2: shock you, like probably like did crazy. Well, but by
Speaker 2: the time that was you, wasn't it Yeah, And then
Speaker 2: by the million person who did like the answer will
Speaker 2: shock you, it stopped working. So it's like there's the
Speaker 2: same thing of like alpha decay, where it's like you
Speaker 2: can be the first on a strategy and then everyone
Speaker 2: discovers it and then the excess returns from.
Speaker 3: That move on the crowding in effect. Yeah, no, I
Speaker 3: did think actually that timeframe point was really interesting and
Speaker 3: the fact that Joe kind of I guess gravitated towards
Speaker 3: two years or five hundred trading days. But then he
Speaker 3: was talking about how others seem to have sort of
Speaker 3: alighted on that same time period. Yeah, I wonder why that.
Speaker 3: I mean, I get that you have to at some point,
Speaker 3: you just have to choose, like age horizon, but it is. Yeah,
Speaker 3: it's an interesting one.
Speaker 2: Very interesting stuff. Plenty more to come on this topic.
Speaker 3: All right, shall we leave there.
Speaker 2: Let's leave it there.
Speaker 3: This has been another episode of the Oudlots podcast. I'm
Speaker 3: Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Joe Peta, He's at Magic Rat SF
Speaker 2: and check out his book Moneyball for the money Set.
Speaker 2: Follow our producers Carmen Rodriguez at Carman Erman dash Ol
Speaker 2: Bennett at Dashbot, and Kilbrooks at cal Brooks. Thank you
Speaker 2: to our producer Moses One. More odd Lags content go
Speaker 2: to Bloomberg dot com slash odd Lots, where you have transcripts,
Speaker 2: a blog, and a newsletter, and you can chat about
Speaker 2: all of these topics twenty four to seven in our discord,
Speaker 2: discord dot gg, slash odlogs.
Speaker 3: And if you enjoy odd Lots, then please leave us
Speaker 3: a positive review on your favorite podcast platform. We will
Speaker 3: accept positive reviews in lieu of finders' fees. And remember,
Speaker 3: if you are a Bloomberg subscriber, you can listen to
Speaker 3: all of our episodes absolutely ad free. All you need
Speaker 3: to do is connect your Bloomberg account with Apple Podcasts.
Speaker 3: In order to do that, just find the Bloomberg channel
Speaker 3: on Apple Podcasts and follow the instructions there. Thanks for listening.