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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 All Thoughts Podcast.

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Speaker 2: I'm Tracy Alloway.

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Speaker 3: And I'm Joe Wysenthal.

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Speaker 2: Joe, we're back on the multi strat beat.

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Speaker 3: I love this beat. I think it's really interesting. There's

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Speaker 3: a lot we've learned, but there's a lot we haven't learned.

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Speaker 3: I love this beat. If you said we're going to

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Speaker 3: just do ten episodes about this, I'd be like, yeah,

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Speaker 3: that's fine.

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Speaker 2: Well, yeah, I look forward to part six hundred and

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Speaker 2: seventy eight in our ongoing attempt to understand multi strat

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Speaker 2: hedge funds. But you know, we've been sort of learning

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Speaker 2: as we go along, and there are a bunch of

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Speaker 2: questions that I still have. One of them is there

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Speaker 2: seem to be a lot of different opinions and variation

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Speaker 2: pod shops right on how exactly they can be designed.

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Speaker 3: Right, So there's different sort of structures that I understand.

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Speaker 3: There's different compensation structures. There's different degrees to which the

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Speaker 3: different pods so to speak, coordinate with each other. There's

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Speaker 3: different degrees to which they like centralize ideas and research.

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Speaker 3: So like I get that, there's still some big questions

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Speaker 3: in my mind, and I'll just say one of the

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Speaker 3: big ones right off the bat, which is, if you

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Speaker 3: have a bunch of teams doing a bunch of different

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Speaker 3: strategies and trading a bunch of things, why are the

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Speaker 3: returns good instead of average? Because in my intuition, if

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Speaker 3: you have a bunch of teams like, okay, you're diversifying

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Speaker 3: alpha across a bunch of things, but great, But then

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Speaker 3: you have a bunch, my gut intuition will be like,

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Speaker 3: you don't get great returns, you get average returns, right,

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Speaker 3: And yet many of them put up really impressive returns

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Speaker 3: year after year after year, And I don't think I

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Speaker 3: totally have a grasp of life.

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Speaker 2: Well, yes, and this is a question that I have,

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Speaker 2: which is eventually the pod shop. Some of them are

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Speaker 2: getting very very big, right, and so if you have

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Speaker 2: one thousand pods working under your roof, that's a bit extreme.

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Speaker 2: But at some point aren't you just sort of replicating

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Speaker 2: the market and that alpha opportunity as you just described

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Speaker 2: kind of goes away. Well, on that note, I am

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Speaker 2: happy to say we have the perfect guests to discuss

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Speaker 2: all of this. So these sort of variations behind multistrap

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Speaker 2: funds and also the math that actually powers it. We're

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Speaker 2: going to be speaking with Dan Morillo. He is the

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Speaker 2: co founder of Freestone Grove Partners and also ex Citadel,

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Speaker 2: so again, the perfect person to be speaking to.

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Speaker 4: Dan.

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Speaker 2: Welcome to the show.

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Speaker 5: Thank you, thank you for having me.

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Speaker 2: I guess my first question is why are we talking

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Speaker 2: to you?

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Speaker 3: Yeah, why are we talking?

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Speaker 5: Well, you're probably in a better position to answer than me,

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Speaker 5: but I guess I'll tell you my background and hopefully

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Speaker 5: that helps a little bit. So I've been about twenty

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Speaker 5: five years now dating myself in the by side, on

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Speaker 5: the hedge fund by side in particular, and I grew

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Speaker 5: up on the quantitative side of the world. I thought

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Speaker 5: I was going to be a professor, and then I

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Speaker 5: realized that life is more exciting on the industry side

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Speaker 5: of things. And I've done a wide range of roles

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Speaker 5: in the quote quant side of the world, so everything

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Speaker 5: from you know, at some point I was the lead

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Speaker 5: of the global long short business at Parkley's Global Investors

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Speaker 5: before Black Crok required them. At Blackrok, I stuck around

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Speaker 5: for a bit. I at some point ran the research

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Speaker 5: group for I Shares. I also was one of the

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Speaker 5: founders of the model Solutions business there as you said,

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Speaker 5: I was etc.

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Speaker 4: Where I had.

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Speaker 5: Responsibility for the Equity Quantitariy Research Group that did a

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Speaker 5: lot of the stuff that you guys have talked about,

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Speaker 5: risk model stuff and the hedging stuff and all of

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Speaker 5: these sort of things. I also had responsibility for the

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Speaker 5: Center Book where a lot of that central stuff that

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Speaker 5: you also have talked about happens, and then most recently

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Speaker 5: have founded co founded that also does the pod long

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Speaker 5: short thing. So I'd like to think I have some expertise,

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Speaker 5: but I guess you'd tell me after you ask me

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Speaker 5: all these questions.

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Speaker 3: I have a really rudimentary question, what does the word

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Speaker 3: quant mean in finance?

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Speaker 5: Actually? So this is a good point, right, I think

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Speaker 5: it can mean lots of things. From my point of view.

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Speaker 5: The thing that has always been attracted to me about

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Speaker 5: the quant side of things is the idea that you

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Speaker 5: can be disciplined in how you make decisions.

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Speaker 4: Right.

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Speaker 5: You can be quantitative in the purely mathematical sense, like

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Speaker 5: you brand some code and there's lots of numbers, while

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Speaker 5: still not actually applying that much judgment. You can also

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Speaker 5: actually be quite disciplined and systematic without using a lot

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Speaker 5: of quant tools. Right. I think the right way of

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Speaker 5: doing quant is where you also mix these two together, right,

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Speaker 5: when you have the ability to bring in the judgment

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Speaker 5: that comes from understanding what the humans in the market

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Speaker 5: are doing, but to do certa in a way that

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Speaker 5: is repeatable and disciplined, and that tends to require quantitative

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Speaker 5: modeling tools, whether that's risk models, focusing, evaluation, attribution, all

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Speaker 5: of these sorts of things, right, And in fact, that's

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Speaker 5: the sort of thing that attracted me. That is sort

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Speaker 5: of a I guess a common threat through all of

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Speaker 5: these jobs that I mentioned that I've had is the

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Speaker 5: idea that you can do this this sort of systematic

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Speaker 5: modeling work not just with the numbers themselves, but also

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Speaker 5: with the humans that participate in the market. Are also

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Speaker 5: subject to analysis, right, whether you think about sentiment measurement

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Speaker 5: or the sort of questions you guys have asked in

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Speaker 5: this podcast, right, what is the right way to organize

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Speaker 5: a team? You know, how many teams should you have,

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Speaker 5: how should you pay them? What fees should you charge

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Speaker 5: with those? These are all subject to analysis, right. So

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Speaker 5: I like the idea that you can do the quant

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Speaker 5: thing on human behavior, right.

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Speaker 2: Oh, this is exactly what I wanted to ask you about. Actually.

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Speaker 2: So if you go to Freestone's website, you can see

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Speaker 2: that there are two partners on the front page, and

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Speaker 2: you are the quantitative one, and you do have a

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Speaker 2: large number of quant researchers. What's the value added by

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Speaker 2: those quants to a fundamental equities fund?

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Speaker 5: Yeah? I think the way you want to think about

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Speaker 5: it is that the insight that is associated with understanding

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Speaker 5: the mechanics of a firm, which is the fundamental in

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Speaker 5: this case for equities. You know, the job of the

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Speaker 5: PM analyst is to understand what drives revenue, earnings, margins.

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Speaker 4: Et cetera.

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Speaker 5: And in portucur what is likely to be surprising about

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Speaker 5: those next time they know earnings or over the next

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Speaker 5: couple of quarters. Right, the way you make money is

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Speaker 5: you have a view that is different from that of

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Speaker 5: the market and people come to agree with you. Right,

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Speaker 5: that's sort of success.

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Speaker 4: Right.

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Speaker 5: And in that effort, whether it's modeling the firms, whether

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Speaker 5: it's understanding what about that surprise is really surprising about

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Speaker 5: the firm versus something that's happening in the broader market.

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Speaker 5: The data that comes into all of this, right, alternative

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Speaker 5: data stuff. All of that requires a huge amount of

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Speaker 5: investment on the technology side, the analytics side, the forecasting side. Right.

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Speaker 5: It's no longer the case that you can be a

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Speaker 5: smart guy reading tank q's in ten case, as might

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Speaker 5: have been the case twenty five years ago, and just

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Speaker 5: kind of see what the surprise is going to be.

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Speaker 5: It requires a significant investment in being the most sophisticated

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Speaker 5: person at doing that job. And that's not a thing

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Speaker 5: you can do without all of that investment on the

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Speaker 5: quantitative tooling.

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Speaker 4: Right.

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Speaker 5: There's also all the behavioral stuff. Right. Humans have the

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Speaker 5: ability to really get into the detail of what the

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Speaker 5: firm is doing. Right. Many of the people who are

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Speaker 5: very good at there's are people who have been covering

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Speaker 5: the same firm literally for a decade. Right. They know

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Speaker 5: their CFO or the CEO, the product. You know, they've

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Speaker 5: visited the factories, and so they have this ability to

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Speaker 5: preak up on really subtle patterns. But they're also human, right,

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Speaker 5: and humans come with biases. Right. You project your own

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Speaker 5: patterns of sort of your view of the world into

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Speaker 5: what's happening on the ground, and so it is also

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Speaker 5: helpful to think about how do you become as disciplined

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Speaker 5: as possible in that process, right, So you think about

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Speaker 5: risk models, attribution questions, how can you tell luck versus skill? Right?

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Speaker 5: Most humans, if you do well, you tend to think

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Speaker 5: it's all about you. And if you do poorly, well,

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Speaker 5: there wasn't my fault, my fault, right, And so these

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Speaker 5: processes of how do you make sure that humans are

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Speaker 5: as discipline as possible again requires huge investment in that

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Speaker 5: quantitative analytical capability. So that's kind of what you bring

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Speaker 5: to the table.

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Speaker 3: Right, So we'll get into how you go about measuring

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Speaker 3: the skill of your portfolio managers and breaking all these

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Speaker 3: things down, and we'll talk about that a lot. When

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Speaker 3: you founded Freestone Growth, you and your co founder Todd Barker,

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Speaker 3: you must think there's an opportunity there, right, You must

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Speaker 3: think there's like some opportunity out there to make money,

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Speaker 3: to have a fund that's different than something that already

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Speaker 3: exists on the market, that you bring something to the

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Speaker 3: table that you could structure a company in some way

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Speaker 3: that's advantageous. What is the sort of theory or thesis

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Speaker 3: behind Freestone Growth such that you wanted to build something new?

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Speaker 5: Yeah, so you're correct, We do think we can compete

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Speaker 5: at the highest level of the industry, right, Otherwise we

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Speaker 5: would have started this time. The way in which we

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Speaker 5: think we can do this isn't some new magic thing, right, like, oh,

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Speaker 5: only we can do X, Y and Z.

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Speaker 4: Right.

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Speaker 5: A lot of how we and this is what we

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Speaker 5: tell our clients is that in having spent all this

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Speaker 5: time looking at what works and what doesn't in the

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Speaker 5: space I call it the multi strategy or MULTIPM space,

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Speaker 5: we have a view that you can sort of be

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Speaker 5: optimal around key business decisions, right, the number of analysts

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Speaker 5: and pms you have in your platform, the way you

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Speaker 5: organize them, the way you think about the incentives or

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Speaker 5: how they're compensated, the right mix of counnitata versus fundamental

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Speaker 5: in a way that sort of is the best of

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Speaker 5: what we've seen around. Right. So it's not so much oh,

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Speaker 5: there's this one thing that is massively different about us,

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Speaker 5: and instead lots of little things that we think you

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Speaker 5: can optimize in a way that many of the other

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Speaker 5: platforms for various reasons, haven't gotten to, particularly with the

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Speaker 5: advent of a lot of new ones. Right, where you

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Speaker 5: end up with business design that we happen to think

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Speaker 5: is not nearly as optimal as it could be. Right,

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Speaker 5: So it's sort of optimized the business as sort of

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Speaker 5: the pitch and then run each piece the best you can.

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Speaker 5: Does that make sense?

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Speaker 3: Yeah?

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Speaker 2: Well, on this note, so there's something that kept coming

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Speaker 2: up when we were preparing for this podcast. But people

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Speaker 2: keep talking about Dan's math. Can you put your professorial

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Speaker 2: hat on and explain to us what exactly is Dan's

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Speaker 2: math and how does it come into play when it

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Speaker 2: comes to designing and optimizing the size of your firm.

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Speaker 4: Yeah?

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Speaker 5: So, first, in my defense, I did not come up

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Speaker 5: with that. I believe it was actually somebody from BOOMEERG

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Speaker 5: that came up with that after some interview that they

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Speaker 5: did with us early on. But yeah, question. Look, the

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Speaker 5: point is that many of the things that you think about,

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Speaker 5: which range from how many people should you having a platform,

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Speaker 5: what sort of risk models should you run, what risks

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Speaker 5: should you take, how should you do capital location, these

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Speaker 5: are things that are subject to systematic analysis, right, and

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Speaker 5: so this idea of quote de math is that many

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Speaker 5: of these decisions you don't have to wave your hands around, right,

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Speaker 5: there's sort of reasonably clear answers about them, right, there's

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Speaker 5: a couple of ones that and we can chase down

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Speaker 5: whichever ones as you like. But one of the ones

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Speaker 5: that in my mind is the most important is there's

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Speaker 5: been this sort of press in the industry with this

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Speaker 5: idea that more is always better, Right. You want to

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Speaker 5: have more porfan managers, more as more assets like that

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Speaker 5: that scales a sort of underlying strength.

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Speaker 4: Right.

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Speaker 5: It actually goes back to your question around how come

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Speaker 5: do you get good results out of lots of people? Right?

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Speaker 5: And the answer is to listener, is actually not wrong.

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Speaker 1: Right.

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Speaker 5: There comes a point where adding more people actually doesn't

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Speaker 5: make any difference. Right, And so if you just allow

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Speaker 5: me two minutes to set up a little example. Right. So,

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Speaker 5: the way this business works is you're hiring individual risk

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Speaker 5: takers let's call them analysts. Right, So there's some potential

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Speaker 5: pool of people you can hire, and assuming you have

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Speaker 5: good hiring practices, you expect to hire people who have

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Speaker 5: some mean performance. Think of that as a sharp ratio.

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Speaker 5: Let's say that sharp ratio is point seventy five, right,

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Speaker 5: So have shot ratio point seventy five means that if

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Speaker 5: you take a risk of a dollar of risk, you

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Speaker 5: expect to januarate seventy five cents of per that amount

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Speaker 5: of risk that you deployed, right. And so you want

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Speaker 5: to think of performance in sharp ratio space, right, because

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Speaker 5: in different spaces people have different risk. Right. There's you know,

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Speaker 5: biotech names are riskier than say, bank names, and so

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Speaker 5: you want to adjust for that. So typically you want

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Speaker 5: to think in sharp ratio space. So you hire folks

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Speaker 5: you expect to have some mean distribution, some mean outcome. Right.

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Speaker 5: So I hire a person. I don't know what their

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Speaker 5: sharp ratio is going to be. I hope it's good.

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Speaker 5: And on avers I get people who are let's say

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Speaker 5: point seventy five right. Some people are going to be

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Speaker 5: better than that. Some people are going to be worse

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Speaker 5: than that. Maybe I end up needing to hire them, right,

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Speaker 5: But I get some distribution of them, right, and then

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Speaker 5: you give them capital and they run a couple over

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Speaker 5: the time. Right. And so the magic of the versification

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Speaker 5: is that you get a higher sharp ratio as you

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Speaker 5: add people. Right. If the correlation was exactly zero, then

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Speaker 5: the more people you add, essentially, the more your sharp

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Speaker 5: ratio increases. It increases where there's square root of end. Essentially,

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Speaker 5: if there's correlation. However, there's like a maximum limit of

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Speaker 5: how much your aggregator sharp ratio can be. Right. So

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Speaker 5: let's take a simple example. Let's say these point seventy

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Speaker 5: five people that you have on average, Let's say they're

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Speaker 5: correlated by ten percent, which most people will tell you

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Speaker 5: that sounds kind of low, not a lot of correlation.

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Speaker 5: Then there's a maximum limit of what your starbration can

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Speaker 5: be about two point three even if you have an

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Speaker 5: infinite number of people. So you're intuition that if you

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Speaker 5: add lots and lots of people, you add some gate

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Speaker 5: to some quote average return is correct, it's just what

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Speaker 5: is the scale of that average return, right, And so

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Speaker 5: if you add lots and lots of people, you get

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Speaker 5: to that sort of maximum level. And the thing that

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Speaker 5: really matters is the correlation, right, So it is incredibly

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Speaker 5: hard to get zero correlation like that just doesn't really happen.

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Speaker 3: So, just to be clear what we're talking about when

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Speaker 3: you say correlation, you hire one PM and they trade semiconductors.

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Speaker 3: You hire another PM and they trade interest rates, or

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Speaker 3: maybe they trade banks or something like that. Yeah, but

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Speaker 3: because things in the market are generally correlated, you could

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Speaker 3: have these different people all around the world, and implicitly,

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Speaker 3: even though it looks like they have their own focus

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Speaker 3: on the market, they might all implicitly be making money

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Speaker 3: based on their read of the FED or something like that,

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Speaker 3: and thus their returns are correlated. And therefore, even if

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Speaker 3: they're really all really good at their jobs, that caps

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Speaker 3: the amount of firm wide sharp by virtue of the

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Speaker 3: fact that they're not really adding diversification.

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Speaker 5: That is exactly correct. So, and it's as simple as

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Speaker 5: if you were to observe somebody's return literally every day, right,

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Speaker 5: and we observe the other persons return every day. You

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Speaker 5: can just computer correlation, put it in Excel computer correlation.

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Speaker 5: And if that number is low, you get more juice

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Speaker 5: out of adding more people. If that almost is hi,

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Speaker 5: you get less juice. To point, it matters enormously. So

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Speaker 5: in that example, that maximum is about two point four.

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Speaker 5: If your mean person is point seventy five, like with

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Speaker 5: an infinite number right at correlation of ten percent, Let's

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Speaker 5: say your correlation is actually twenty percent, right, you know,

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Speaker 5: it's obviously more, but it's still low in the grand

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Speaker 5: scheme of things, then that maximum number is only one

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Speaker 5: point six, right, So a little bit of correlation has

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Speaker 5: an enormous impact on how much you can deliver in

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Speaker 5: the end. Right, And more importantly, you get pretty close

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Speaker 5: to that maximum without a lot of people.

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Speaker 4: Right.

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Speaker 5: So, in the example of point seventy five, in a

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Speaker 5: correlation of ten percent, if I have forty five risk takers,

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Speaker 5: think of them as analysts. Let's say I put them

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Speaker 5: in teams of three. Right, PM team made out of

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Speaker 5: three risk takers. You know, there's not that many teams, right,

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Speaker 5: fifteen teams. That gives me about ninety five percent of

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Speaker 5: that ultimate maximum. Right, So I don't need to have

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Speaker 5: one hundred teams to get to my maximum. In fact,

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Speaker 5: there comes a point where it is actually more important.

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Speaker 5: Let's say you have a million dollars actually to spend on.

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Speaker 5: Something could be I hire another person, but something could

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Speaker 5: also be, Hey, I might produce a better piece of

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Speaker 5: software to help me manage that correlation. To teach people

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Speaker 5: to think about whatever their return is really independent of,

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Speaker 5: you know, for example, interest rates. As you highlighted them,

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Speaker 5: that actually might be a significantly better investment than adding

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Speaker 5: a team, because if I reduce my correlation by a

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Speaker 5: little bit, that actually gives me more juice than just

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Speaker 5: adding people. Right, And to look back to that original question,

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Speaker 5: what do we think might be different in terms of

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Speaker 5: how you set up your business. Is again that a

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Speaker 5: lot of people have gone from scale for scale, even

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Speaker 5: though you don't have to, at least not for performance reasons. Right.

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Speaker 5: There comes a point where you just kind of have

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Speaker 5: the right scale and you're better invest better off investing

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Speaker 5: in other things.

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Speaker 4: Right.

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Speaker 5: The reason people have gone for scale is because they

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Speaker 5: want to run more money. It's not because that gives

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Speaker 5: you more performance, right, at least a past a certain amount. Right.

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Speaker 5: And in fact, if you think about scale, scale comes

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Speaker 5: with lots of other issues. It comes with complexity. You

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Speaker 5: maybe end up with more management layers, You have to

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Speaker 5: worry a lot more about you know, offices and coordination

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Speaker 5: and you know management, etcetera. You might actually end up

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Speaker 5: reducing your performance. That that complexity costs money. Right, And

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Speaker 5: so one of the key things that we say to

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Speaker 5: our clients, just as an example, is we look to

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Speaker 5: cap our size so that we can run the right

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Speaker 5: number of people at the minimum complexity if possible, while

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Speaker 5: still delivering pretty much sad level of performance.

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Speaker 4: Right.

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Speaker 2: Why do hedge funds promise uncorrelated returns at all? Because

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Speaker 2: it feels to me, as you just said, it's very

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Speaker 2: hard to get correlation down to zero. But the pitch

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Speaker 2: to investors is always, here are a bunch of uncorrelated

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Speaker 2: returns that we can do over and over again. And

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Speaker 2: then what you see repeatedly is that when there is

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Speaker 2: a big event in the market, they all have drawdowns

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Speaker 2: at the same time. So why do they keep pitching

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Speaker 2: on correlated returns and why do you investors keep putting

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Speaker 2: money in them?

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Speaker 5: Okay, so there seems to be two questions then there,

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Speaker 5: which is how come are they correlated even though they

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Speaker 5: claim not to be a number? One? And two is

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Speaker 5: that why is that even a thing in the first place? Right,

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Speaker 5: So let me start with the second one. The reality

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Speaker 5: is most correlation is driven by some common effect.

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Speaker 4: Right.

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Speaker 5: You know you've had guests here talking about risk models

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Speaker 5: where you think about sort of common factors, right. And

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Speaker 5: a key reason why if you're an allocator, say you're

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Speaker 5: a pension fund, you know, in university endowment, is that

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Speaker 5: you get paid for taking risk. Right. A lot of

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Speaker 5: the allocation is into things that are risky, and you

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Speaker 5: expect to get paid for taking that risk.

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Speaker 1: Right.

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Speaker 5: That's sort of in a sense, that's the function of

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Speaker 5: a big endowment or a big punch of fund. Right.

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Speaker 5: The thing is, most of the risks that pay you

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Speaker 5: those returns, whether that's you know, market as a whole,

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Speaker 5: whether it's you know, individual factors like momentum that you

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Speaker 5: can buy separately, you know interest rate risk, you know

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Speaker 5: inflation risk. All of these things you can allocate to

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Speaker 5: those for like essentially like a tenth of a cent

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Speaker 5: on the dollar, right. And so if you're going to

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Speaker 5: make an allocation to something else, you don't want that

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Speaker 5: allocation to be the same thing you already have at

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Speaker 5: essentially no fees.

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Speaker 4: Right.

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Speaker 5: So let's say you have a hedgehund who charges you,

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Speaker 5: I don't know two and twenty, but that hedge fund

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Speaker 5: has you know, typically a beta of like say fifty

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Speaker 5: percent on average. Right. Then half of the money you're

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Speaker 5: giving that hedge run is beta that you could buy

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Speaker 5: for essentially no fees.

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Speaker 4: Right.

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Speaker 5: And so the advantage of a hedgehund that is able

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Speaker 5: to in fact deliver on coliter rator risk is that

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Speaker 5: now you can make cleaner allocation.

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Speaker 1: Right.

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Speaker 5: You can say, Okay, this is my market risk, this

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Speaker 5: is my interest rate risk, this is my you know,

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Speaker 5: I don't know housing premium, whatever it is. However, you've

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Speaker 5: sort of decided to do your allocation, and then there's

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Speaker 5: a piece that boosts my returns because it is not

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Speaker 5: correlated to those other things, right, and so it is

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Speaker 5: the right objective if you will right, if you're an

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Speaker 5: allocator right. Then the question is whether people can actually

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Speaker 5: execute and delivering that you know that outcome right, which

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Speaker 5: is a somewhat separate question.

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Speaker 3: I want to get into how you hire people at

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Speaker 3: Freestone Growth and why a talented PM would come to

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Speaker 3: Freestone Growth from somewhere else in the conversation, et cetera.

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Speaker 3: But before we get to that, I have to imagine

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Speaker 3: there's certain like information asymmetry challenges. You probably have a

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Speaker 3: limited visibility into not just a PM's returns, but exactly

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Speaker 3: how they achieved those returns, whether they achieve those returns

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Speaker 3: in a way that demonstrates their ability to actually extract

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Speaker 3: alpha rather than ride the various betas that you're trying

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Speaker 3: to extract out of them. I assume, if you're starting

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Speaker 3: a fund, do you think you're good at identifying the

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Speaker 3: people who will come to work for you? What information

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Speaker 3: do you have to use and when you're accumulating pms

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Speaker 3: or analysts, what is the basic process for identifying skill

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Speaker 3: before they show up on your door.

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Speaker 5: That's a really good question, and obviously it's it's partly

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Speaker 5: a systematic process. But you know, like with like with

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Speaker 5: hiring for everything, it's a bit of an art too, right,

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Speaker 5: whether you're hiring a portfin manager or you know, quantitative research,

440
00:19:58,880 --> 00:20:01,159
Speaker 5: there's there's always a bit of an art associated.

441
00:20:00,640 --> 00:20:01,120
Speaker 4: With it, right.

442
00:20:01,800 --> 00:20:04,159
Speaker 5: The I think the key objective that you should have

443
00:20:04,440 --> 00:20:07,119
Speaker 5: is do you understand via what mechanism do they deliver

444
00:20:07,240 --> 00:20:08,760
Speaker 5: this skill that they claim to deliver it?

445
00:20:08,840 --> 00:20:09,040
Speaker 4: Right?

446
00:20:09,600 --> 00:20:12,800
Speaker 5: And so it's a good thing that you typically can't see,

447
00:20:12,920 --> 00:20:15,400
Speaker 5: you know, a good tracker over returns, because then you'd

448
00:20:15,400 --> 00:20:17,680
Speaker 5: be tended to based it on past returns, which is

449
00:20:17,720 --> 00:20:19,359
Speaker 5: not a good idea. If it's a bad idea, we

450
00:20:19,359 --> 00:20:22,879
Speaker 5: can talk about that separately. It forces us to think about, Okay,

451
00:20:22,880 --> 00:20:25,080
Speaker 5: if you claim that you can generate good returns via

452
00:20:25,200 --> 00:20:28,120
Speaker 5: what mechanism do you do that? Right? For a typical analyst,

453
00:20:28,119 --> 00:20:30,639
Speaker 5: at least inequities, it tends to be some form of

454
00:20:30,840 --> 00:20:34,480
Speaker 5: I understand what the surprises and fundamentals are going to be, right?

455
00:20:34,960 --> 00:20:37,280
Speaker 5: I can tell that this firm is going to, you know,

456
00:20:37,400 --> 00:20:41,040
Speaker 5: announce a billion dollars worth of revenue, whereas everybody else

457
00:20:41,080 --> 00:20:43,200
Speaker 5: is expecting is going to be nine hundred or whatever. Right,

458
00:20:43,960 --> 00:20:45,639
Speaker 5: And if that's a claim which tends to be the

459
00:20:45,640 --> 00:20:48,480
Speaker 5: common claim, right almost by definition in that job, you

460
00:20:48,520 --> 00:20:50,719
Speaker 5: can then sort of back into what sort of process

461
00:20:50,840 --> 00:20:54,199
Speaker 5: leads you there? Right, what sort of modeling capability you

462
00:20:54,200 --> 00:20:54,520
Speaker 5: could do?

463
00:20:54,600 --> 00:20:54,760
Speaker 4: Right?

464
00:20:54,840 --> 00:20:56,520
Speaker 3: Does this sort of get to what you were saying

465
00:20:56,520 --> 00:20:58,000
Speaker 3: in the beginning when I ask you, like, what is

466
00:20:58,040 --> 00:21:00,399
Speaker 3: the definition of quant Where it's not an enough to

467
00:21:00,680 --> 00:21:03,040
Speaker 3: just be able to math that out. There has to

468
00:21:03,080 --> 00:21:05,880
Speaker 3: be some ability to like have the human intuition understand

469
00:21:05,920 --> 00:21:06,320
Speaker 3: how these.

470
00:21:06,240 --> 00:21:09,360
Speaker 5: Things are correct. Right, So just to use these examples, right,

471
00:21:09,440 --> 00:21:11,280
Speaker 5: Let's say you tell me I'm having an interview, I'm

472
00:21:11,320 --> 00:21:13,640
Speaker 5: interviewing you for an analyst, and you tell me I'm

473
00:21:13,640 --> 00:21:15,960
Speaker 5: great at knowing what the fundamentals are going to be, right,

474
00:21:16,000 --> 00:21:18,320
Speaker 5: And I say, okay, well, do you have a track

475
00:21:18,359 --> 00:21:21,480
Speaker 5: record of your own estimates? Right? So presumably for having

476
00:21:21,880 --> 00:21:24,479
Speaker 5: many names you covered, you knew you had an estimate

477
00:21:24,520 --> 00:21:26,359
Speaker 5: in your head about what their revenue is going to be,

478
00:21:26,400 --> 00:21:27,640
Speaker 5: what the margins are going to be what their earners

479
00:21:27,680 --> 00:21:29,920
Speaker 5: are going to be. I could ask you, okay, what

480
00:21:30,000 --> 00:21:33,080
Speaker 5: were those estimates back in time three days before the

481
00:21:33,119 --> 00:21:35,880
Speaker 5: company announced their know the results for all the names

482
00:21:35,920 --> 00:21:38,360
Speaker 5: are covered back many years, right, And to be clear,

483
00:21:38,359 --> 00:21:40,080
Speaker 5: I'm not necessarily looking for you to have them and

484
00:21:40,119 --> 00:21:42,800
Speaker 5: give them to me. But what processes did you used

485
00:21:42,840 --> 00:21:45,480
Speaker 5: to think about even understanding whether you have skill in

486
00:21:45,480 --> 00:21:48,320
Speaker 5: the first place? Right? And it is not uncommon to

487
00:21:48,359 --> 00:21:50,680
Speaker 5: have folks answer that question by saying, well, I don't

488
00:21:50,680 --> 00:21:54,240
Speaker 5: really know, because I keep my model saying Excel, right,

489
00:21:54,280 --> 00:21:56,679
Speaker 5: And I have a very complicated Excel model with all

490
00:21:56,720 --> 00:21:58,640
Speaker 5: the income say madlines and all the balance sheet lines

491
00:21:58,680 --> 00:22:01,199
Speaker 5: and all these things. And as the firm evolves, I

492
00:22:01,280 --> 00:22:03,280
Speaker 5: changed that model, right, I change the numbers, I change

493
00:22:03,320 --> 00:22:06,119
Speaker 5: my assumptions. I maybe even add in supract lines. You

494
00:22:06,240 --> 00:22:08,919
Speaker 5: add more complexity in the model. And keeping track of

495
00:22:08,960 --> 00:22:11,000
Speaker 5: what it was at every point in time is horse right.

496
00:22:11,040 --> 00:22:13,280
Speaker 5: You and I have to save the file every day,

497
00:22:13,280 --> 00:22:14,879
Speaker 5: and you have some database to figure out what it

498
00:22:14,920 --> 00:22:16,720
Speaker 5: was every day and change them and do some analysis.

499
00:22:16,800 --> 00:22:16,960
Speaker 1: Right.

500
00:22:17,800 --> 00:22:20,400
Speaker 5: And you want to talk to the people who understand

501
00:22:20,400 --> 00:22:22,200
Speaker 5: that that's the thing they should be doing, and have

502
00:22:22,280 --> 00:22:24,800
Speaker 5: made some effort to move in that direction, right, Meaning

503
00:22:25,240 --> 00:22:28,720
Speaker 5: there's an interest in being disciplined and understanding your own skill, right.

504
00:22:28,840 --> 00:22:32,000
Speaker 5: Just that is an auto significant difference between somebody who

505
00:22:32,040 --> 00:22:34,320
Speaker 5: just does it for so somebody who's interested in understanding

506
00:22:34,440 --> 00:22:36,520
Speaker 5: how they do it and how they improve. Right.

507
00:22:36,800 --> 00:22:41,960
Speaker 2: So, on the flip side of identifying good portfolio managers,

508
00:22:42,040 --> 00:22:45,359
Speaker 2: how do good portfolio managers or why do good portfolio

509
00:22:45,400 --> 00:22:48,800
Speaker 2: managers want to come work for you? Because my impression

510
00:22:48,920 --> 00:22:52,439
Speaker 2: is there are giants in the multi strat world. You

511
00:22:52,520 --> 00:22:55,000
Speaker 2: used to work for one of them. They can pay

512
00:22:55,520 --> 00:23:00,119
Speaker 2: millions to a talented PM that they really want. How

513
00:23:00,200 --> 00:23:04,480
Speaker 2: do you compete with that kind of package? Is it autonomy?

514
00:23:04,640 --> 00:23:07,480
Speaker 2: Is it the culture of the firm? What is the

515
00:23:07,520 --> 00:23:09,440
Speaker 2: attraction for good traders?

516
00:23:09,800 --> 00:23:11,800
Speaker 5: Yeah? So it's a mix of things. Let me give

517
00:23:11,800 --> 00:23:14,160
Speaker 5: you sort of what I think are the key things

518
00:23:14,160 --> 00:23:16,640
Speaker 5: that might make you want to talk to us, right

519
00:23:16,680 --> 00:23:18,800
Speaker 5: as opposed to stay at your big job, you know,

520
00:23:19,040 --> 00:23:20,920
Speaker 5: at one of the sort of big name platforms.

521
00:23:20,960 --> 00:23:21,120
Speaker 4: Right.

522
00:23:21,560 --> 00:23:24,879
Speaker 5: So number one, because of this drive to scale, what

523
00:23:24,960 --> 00:23:27,520
Speaker 5: has sended to happen at many of the platforms is

524
00:23:27,560 --> 00:23:29,960
Speaker 5: that if you are, say a tech portfilmer manager, you're

525
00:23:30,000 --> 00:23:35,320
Speaker 5: one of ten, potentially fifteen. Right, and remember you're competing

526
00:23:35,359 --> 00:23:38,800
Speaker 5: for your ability to have the resources necessary to do

527
00:23:38,840 --> 00:23:41,639
Speaker 5: that job really well. Right, So rundown the sort of

528
00:23:41,640 --> 00:23:44,199
Speaker 5: thing you need, right, You need corporate access. Right, So

529
00:23:44,240 --> 00:23:47,080
Speaker 5: you would like to have the ability to talk to CFO, CEO,

530
00:23:47,840 --> 00:23:50,560
Speaker 5: you know, even IR for the companies you cover, you know,

531
00:23:50,600 --> 00:23:54,199
Speaker 5: go to the conferences, do the non deal roadtro and

532
00:23:54,840 --> 00:23:56,600
Speaker 5: it doesn't matter how big you are. At some point,

533
00:23:56,680 --> 00:23:59,000
Speaker 5: the CFO of some firm is not going to talk

534
00:23:59,040 --> 00:24:01,440
Speaker 5: to a million managers, right, so they're going to say

535
00:24:01,480 --> 00:24:03,880
Speaker 5: to the big names, Okay, I'll give you two slots.

536
00:24:04,119 --> 00:24:06,080
Speaker 5: They're not going to give you fifteen slots just because

537
00:24:06,119 --> 00:24:08,359
Speaker 5: you have fifteen pms. In fact, they really don't want

538
00:24:08,400 --> 00:24:10,439
Speaker 5: to talk to you, right, Most companies don't prefer not

539
00:24:10,520 --> 00:24:12,959
Speaker 5: to talk to the investors. And so you end up

540
00:24:12,960 --> 00:24:15,040
Speaker 5: in a situation where you're competing for corporate access, you're

541
00:24:15,040 --> 00:24:18,880
Speaker 5: also competing for data science resources, quantitative resources, PORTOFOIO, construction

542
00:24:18,920 --> 00:24:21,960
Speaker 5: and risk management resources. Meaning as that scale happens, it

543
00:24:22,000 --> 00:24:24,920
Speaker 5: becomes ever harder to get what I would describe as

544
00:24:24,920 --> 00:24:28,399
Speaker 5: a truly integrated in sort of partner like relationship with

545
00:24:28,440 --> 00:24:30,800
Speaker 5: the resources that you have, right, And so it is

546
00:24:30,840 --> 00:24:33,320
Speaker 5: not a typical to find folks in the big platforms

547
00:24:33,800 --> 00:24:35,679
Speaker 5: who might like their job, might like the way they

548
00:24:35,680 --> 00:24:38,320
Speaker 5: get paid, but are actually frustrated about the fact that

549
00:24:38,400 --> 00:24:40,080
Speaker 5: it's a bit like being a small cog in a

550
00:24:40,119 --> 00:24:43,000
Speaker 5: big place. Right. So that's one aspect of it. The

551
00:24:43,040 --> 00:24:45,639
Speaker 5: second aspect of it is again the fact that the

552
00:24:45,840 --> 00:24:48,919
Speaker 5: firm is really large doesn't mean that you necessarily are

553
00:24:48,960 --> 00:24:51,200
Speaker 5: running any more money at a large place than you

554
00:24:51,240 --> 00:24:55,800
Speaker 5: would with us. In fact, our refile managers run likely

555
00:24:55,840 --> 00:24:58,640
Speaker 5: more money than they would run in most other places, right,

556
00:24:58,680 --> 00:25:02,360
Speaker 5: because yes, we're small, but we also have fewer people, right,

557
00:25:02,640 --> 00:25:05,280
Speaker 5: And so we're looking to run as large a scale

558
00:25:05,320 --> 00:25:08,280
Speaker 5: a team as you could with fewer teams, if that

559
00:25:08,400 --> 00:25:10,200
Speaker 5: makes sense. It's a distinction. And so from the perfimer

560
00:25:10,200 --> 00:25:12,879
Speaker 5: manager's point of view, that's actually not that different in

561
00:25:12,960 --> 00:25:14,960
Speaker 5: terms of how a risk you might get, but you

562
00:25:15,040 --> 00:25:18,399
Speaker 5: get better resources, more integrated platform on the technology, risk,

563
00:25:18,800 --> 00:25:22,720
Speaker 5: corporate access, etc. There's other things that have this flavor, right.

564
00:25:23,200 --> 00:25:25,879
Speaker 5: And remember, because most folks get paid out of some

565
00:25:26,119 --> 00:25:28,239
Speaker 5: share of the return that they can generate from that

566
00:25:28,400 --> 00:25:31,080
Speaker 5: amount of assets. It's not like your comp is going

567
00:25:31,119 --> 00:25:33,040
Speaker 5: to be terribly different. Right, if you run just as

568
00:25:33,119 --> 00:25:35,920
Speaker 5: much justice and your returns are good or better because

569
00:25:35,920 --> 00:25:38,439
Speaker 5: you get better resources, more integration, and a better platform,

570
00:25:38,760 --> 00:25:43,120
Speaker 5: it's not obvious why it's necessarily an unattractive platform. In fact,

571
00:25:43,200 --> 00:25:47,320
Speaker 5: we have found that we have hired folks that we're

572
00:25:47,359 --> 00:25:50,080
Speaker 5: performer managers, are other placers that came to be analysts

573
00:25:50,080 --> 00:25:52,600
Speaker 5: with us because they understand the benefit of all of

574
00:25:52,600 --> 00:25:54,879
Speaker 5: those things, right, as opposed to be one of I

575
00:25:54,920 --> 00:25:57,160
Speaker 5: don't know, five hundred analysts in some really large place

576
00:25:57,240 --> 00:25:57,960
Speaker 5: doesn't make sense.

577
00:25:58,359 --> 00:26:00,760
Speaker 2: Wait, talk more about that, because I'm I'm curious. I

578
00:26:00,800 --> 00:26:04,040
Speaker 2: get the impression that a lot of multistrat firms or

579
00:26:04,080 --> 00:26:08,919
Speaker 2: podshops are always going after like the star portfolio managers

580
00:26:09,160 --> 00:26:12,880
Speaker 2: or people who have experience, and I'm curious, is their

581
00:26:13,040 --> 00:26:18,080
Speaker 2: scope for developing talent in house? For instance? Could you

582
00:26:18,200 --> 00:26:21,040
Speaker 2: hire me or Joe and train us to be a

583
00:26:21,119 --> 00:26:25,000
Speaker 2: really good portfolio manager. How much flexibility is there in

584
00:26:25,040 --> 00:26:25,879
Speaker 2: that career path.

585
00:26:26,359 --> 00:26:28,640
Speaker 5: There's actually a decent amount of flexibility. So your preference

586
00:26:28,680 --> 00:26:32,320
Speaker 5: would be not to have to rely on imperfect information,

587
00:26:32,520 --> 00:26:35,280
Speaker 5: particularly if you have to promise somebody lots of things

588
00:26:35,280 --> 00:26:37,000
Speaker 5: in order to come to your platform. Right. So you

589
00:26:37,000 --> 00:26:40,160
Speaker 5: should have a preference to develop talent internally. The question

590
00:26:40,200 --> 00:26:42,040
Speaker 5: is what sort of culture insistence do you have to

591
00:26:42,040 --> 00:26:44,280
Speaker 5: make that happen, right, And I fact, I think you've

592
00:26:44,280 --> 00:26:47,560
Speaker 5: had guests on in the poscat on this podcast talking

593
00:26:47,640 --> 00:26:51,159
Speaker 5: about those training grounds, right, And so people understand that

594
00:26:51,640 --> 00:26:54,400
Speaker 5: you should have a preference to bring in people who

595
00:26:54,440 --> 00:26:56,159
Speaker 5: you can shape into who you think are going to

596
00:26:56,160 --> 00:26:58,359
Speaker 5: be the best analysm, the best portfolio manager in a

597
00:26:58,400 --> 00:27:00,560
Speaker 5: way that really matches with you know, your culture and

598
00:27:00,560 --> 00:27:02,680
Speaker 5: the way you pay and the way the systems work. Right.

599
00:27:03,200 --> 00:27:05,159
Speaker 5: Part of the problem though, is that humans are humans, right,

600
00:27:05,200 --> 00:27:07,280
Speaker 5: and so even if you train somebody, you can't guarantee

601
00:27:07,280 --> 00:27:09,080
Speaker 5: that they're going to stay with you, and vice versa.

602
00:27:09,200 --> 00:27:11,600
Speaker 5: You might, especially if you're really large and you have

603
00:27:11,640 --> 00:27:14,399
Speaker 5: to run lots of assets. In a sense, you're forced

604
00:27:14,400 --> 00:27:16,879
Speaker 5: into this turnover, right, because if you have to deploy

605
00:27:16,920 --> 00:27:19,480
Speaker 5: all those assets, and if somebody quits for whatever reason,

606
00:27:19,520 --> 00:27:21,280
Speaker 5: maybe they just have a personal thing they leave, not

607
00:27:21,280 --> 00:27:24,080
Speaker 5: because they're going somewhere else, you're sort of forcing into

608
00:27:24,200 --> 00:27:26,639
Speaker 5: this replacement process. And at some point, part of the

609
00:27:26,640 --> 00:27:29,080
Speaker 5: problem is you might not have the next person ready

610
00:27:29,080 --> 00:27:30,920
Speaker 5: to be promoted and therefore you've got to go outside,

611
00:27:31,000 --> 00:27:32,840
Speaker 5: right And I don't, to be entirely honest, I don't

612
00:27:32,840 --> 00:27:34,880
Speaker 5: think there's sertably different in this industry from any other

613
00:27:34,920 --> 00:27:37,960
Speaker 5: industry right where you need to hire very talented people

614
00:27:37,960 --> 00:27:39,800
Speaker 5: and there's a limited number of them, and you kind

615
00:27:39,800 --> 00:27:42,040
Speaker 5: of have to go through that mix of ingrown talent

616
00:27:42,280 --> 00:27:45,280
Speaker 5: hiring externally, you know, some mix of the two. And yes,

617
00:27:45,320 --> 00:27:48,960
Speaker 5: I could train you to be really good perfile managers.

618
00:27:49,320 --> 00:27:53,000
Speaker 3: I want to get into soon, like actual how the

619
00:27:53,080 --> 00:27:55,480
Speaker 3: comp part, because it's nice to talk about access to

620
00:27:55,560 --> 00:27:58,600
Speaker 3: teams and you know, lean management and all that, but

621
00:27:58,760 --> 00:28:01,880
Speaker 3: you know it's finance will care about paychecks a lot.

622
00:28:01,920 --> 00:28:04,800
Speaker 3: But before we do, there's something you said, and it's

623
00:28:04,840 --> 00:28:06,840
Speaker 3: come up before and I still have a hard time

624
00:28:06,920 --> 00:28:08,920
Speaker 3: wrapping my head around it. So I'd like to hear

625
00:28:08,960 --> 00:28:11,760
Speaker 3: how you clarify it. When you talk about a PM

626
00:28:11,880 --> 00:28:15,120
Speaker 3: having access to a company's management team, that makes sense.

627
00:28:15,160 --> 00:28:15,520
Speaker 5: I get it.

628
00:28:15,560 --> 00:28:17,720
Speaker 3: Investing, you want to talk to the CFO or whatever,

629
00:28:17,760 --> 00:28:20,960
Speaker 3: the CIO or whatever the CEO, But you know we're

630
00:28:21,000 --> 00:28:24,200
Speaker 3: not talking Berkshire Hathaway here where you're holding a stock

631
00:28:24,280 --> 00:28:26,399
Speaker 3: for twenty five years and you really get to know it.

632
00:28:26,440 --> 00:28:29,760
Speaker 3: In fact, the sort of hold times for a stock

633
00:28:29,960 --> 00:28:33,160
Speaker 3: within one of the within a firm like yours supposedly

634
00:28:33,280 --> 00:28:35,920
Speaker 3: is extremely short, and sometimes maybe five days or ten

635
00:28:36,000 --> 00:28:38,880
Speaker 3: days or one quarter or something like that, in which

636
00:28:38,960 --> 00:28:41,240
Speaker 3: it's not intuitive to me that if I'm holding a

637
00:28:41,320 --> 00:28:46,080
Speaker 3: stock for twenty days, it's particularly important to say no

638
00:28:46,240 --> 00:28:49,840
Speaker 3: the management team the way Warren Buffett gets to know

639
00:28:49,920 --> 00:28:52,920
Speaker 3: a management team. Can you explain to me the importance

640
00:28:52,960 --> 00:28:55,520
Speaker 3: of that sort of insight into a company given the

641
00:28:55,560 --> 00:28:58,480
Speaker 3: short holding periods, given the high amount of actual training

642
00:28:58,520 --> 00:28:58,840
Speaker 3: that you do.

643
00:28:59,360 --> 00:29:01,720
Speaker 5: Yeah, that's a really good question. I think it's just

644
00:29:01,840 --> 00:29:03,960
Speaker 5: like you're munging two things together that don't go together.

645
00:29:04,040 --> 00:29:04,120
Speaker 1: Right.

646
00:29:04,160 --> 00:29:04,800
Speaker 3: Yah, that's fine.

647
00:29:05,000 --> 00:29:07,680
Speaker 5: I think you want to separate the investment decision, which

648
00:29:07,760 --> 00:29:10,760
Speaker 5: might be a sure horizon, versus what drives the inside

649
00:29:10,960 --> 00:29:13,480
Speaker 5: that gets you to that investment decision. Right. And so

650
00:29:13,520 --> 00:29:16,880
Speaker 5: the reason you want to really understand the company is

651
00:29:16,920 --> 00:29:19,760
Speaker 5: because that allows you to pick up on subtle patterns

652
00:29:19,800 --> 00:29:23,320
Speaker 5: about what the likely misunderstandings about that company is from

653
00:29:23,360 --> 00:29:25,880
Speaker 5: everybody else. Right, So I'll repeat, the way you make

654
00:29:25,920 --> 00:29:28,080
Speaker 5: money is you have a view that is different from

655
00:29:28,120 --> 00:29:30,240
Speaker 5: the other marginal participant, and the way you make money

656
00:29:30,280 --> 00:29:33,720
Speaker 5: is you place the trade, and then over time people

657
00:29:33,760 --> 00:29:36,440
Speaker 5: come to agree with you. Right. And it's either because

658
00:29:36,440 --> 00:29:39,040
Speaker 5: they eventually see the same thing that you do, right,

659
00:29:39,120 --> 00:29:41,080
Speaker 5: They see the same data, they do the same analysis.

660
00:29:41,080 --> 00:29:42,640
Speaker 5: Maybe you got there because your data is better, your

661
00:29:42,640 --> 00:29:45,880
Speaker 5: analysis is more sophisticated, et cetera. Or the firm tells you.

662
00:29:45,960 --> 00:29:48,600
Speaker 5: The firm literally comes and says, here's our earnings and

663
00:29:48,640 --> 00:29:50,880
Speaker 5: here's our revenue. And you turn out to be correct

664
00:29:50,920 --> 00:29:53,400
Speaker 5: versus other folks. Right, So you need that catalyst, right,

665
00:29:54,000 --> 00:29:57,239
Speaker 5: And so you're playing in the same firm over and

666
00:29:57,280 --> 00:30:00,320
Speaker 5: over again. But the nature of the insight is what's changing, right.

667
00:30:00,360 --> 00:30:03,040
Speaker 5: And so because you know of the firm that well,

668
00:30:03,080 --> 00:30:04,720
Speaker 5: and because you've been following it for ten years and

669
00:30:04,760 --> 00:30:07,320
Speaker 5: go to the conferences and talk to the management, et cetera,

670
00:30:08,040 --> 00:30:10,200
Speaker 5: you are able to tell that g well, this quarter,

671
00:30:10,320 --> 00:30:12,920
Speaker 5: my suspicion is that people are underestimating their earnings. Maybe

672
00:30:12,920 --> 00:30:15,080
Speaker 5: the next quarter they're over in estiem many of the earnings. Right,

673
00:30:15,360 --> 00:30:17,640
Speaker 5: And if I can repeat that process, my trades are

674
00:30:17,640 --> 00:30:19,920
Speaker 5: short horizon. But it's not that I have a short

675
00:30:19,920 --> 00:30:22,960
Speaker 5: horison view of the firm. In fact, if you if

676
00:30:22,960 --> 00:30:25,320
Speaker 5: you're going to do this well, you should have a

677
00:30:25,400 --> 00:30:27,360
Speaker 5: long view of what the firm is likely to do.

678
00:30:27,480 --> 00:30:30,280
Speaker 5: In fact, some of your hippodicies might be, Hey, people

679
00:30:30,320 --> 00:30:34,040
Speaker 5: are thinking that the XYZ product is going to be

680
00:30:34,440 --> 00:30:37,160
Speaker 5: you know, enormously successful over the next five years of

681
00:30:37,200 --> 00:30:39,800
Speaker 5: ten years aka long term view. But if you think

682
00:30:39,840 --> 00:30:42,480
Speaker 5: that that's going to be slightly disappointing this quarter.

683
00:30:43,400 --> 00:30:45,760
Speaker 3: Why hold it Like a company, like a video, everyone

684
00:30:45,880 --> 00:30:48,840
Speaker 3: has a big tenure horizon corret so that's not that

685
00:30:48,920 --> 00:30:50,760
Speaker 3: you're not going to gain an edge just knowing that

686
00:30:50,800 --> 00:30:52,160
Speaker 3: AI is going to be bigger for the next time.

687
00:30:52,160 --> 00:30:54,400
Speaker 5: Correct, the edge is going to be You might want

688
00:30:54,440 --> 00:30:57,880
Speaker 5: to be long on average example and video, but if

689
00:30:57,920 --> 00:31:00,000
Speaker 5: you think that they're going to miss those very high

690
00:31:00,080 --> 00:31:02,520
Speaker 5: expectations and exporter, why are you holding it down? You

691
00:31:02,560 --> 00:31:05,160
Speaker 5: could shuder now and then by again, you know after there.

692
00:31:21,600 --> 00:31:24,560
Speaker 2: So one of the criticisms of multi strats and their

693
00:31:24,640 --> 00:31:27,960
Speaker 2: phenomenal growth has been this idea that we're getting more

694
00:31:27,960 --> 00:31:30,360
Speaker 2: crowding risk in the markets. And you brought up in

695
00:31:30,480 --> 00:31:32,640
Speaker 2: Vidia just then, and to some extent that's kind of

696
00:31:33,040 --> 00:31:36,440
Speaker 2: the perfect example of some of this. It feels like

697
00:31:36,480 --> 00:31:38,840
Speaker 2: whenever in Vidia has a big move, now there's some

698
00:31:38,920 --> 00:31:42,360
Speaker 2: talk about like oh there's a pod behind it. Yeah,

699
00:31:42,400 --> 00:31:45,880
Speaker 2: that's right, or like some sort of factor is changing.

700
00:31:46,160 --> 00:31:49,360
Speaker 2: Talk to us how you actually see the impact of

701
00:31:49,440 --> 00:31:52,680
Speaker 2: the growth of multistrats and factor investing on the market.

702
00:31:53,480 --> 00:31:56,000
Speaker 5: Yes, okay, So I'm going to separate this into two pieces.

703
00:31:56,040 --> 00:31:57,760
Speaker 5: One is it how do you think about it as

704
00:31:57,760 --> 00:31:59,800
Speaker 5: an individual manager? And then what impact that has in

705
00:31:59,840 --> 00:32:02,040
Speaker 5: the because I think it's important to make that distinction, right,

706
00:32:02,600 --> 00:32:05,480
Speaker 5: So on the first one, I think crowding is one

707
00:32:05,480 --> 00:32:07,560
Speaker 5: of those things that you should manage rather than be

708
00:32:07,640 --> 00:32:10,080
Speaker 5: worried about. Right. The analogy that we sometimes use is

709
00:32:10,120 --> 00:32:12,800
Speaker 5: this idea of sitting at a poker table. Right, If

710
00:32:12,800 --> 00:32:16,280
Speaker 5: there's the two of us playing poker, POD's not very big. Right.

711
00:32:16,320 --> 00:32:18,800
Speaker 5: If three more people come in, I'm not worried about,

712
00:32:19,000 --> 00:32:20,360
Speaker 5: Oh my bed's going to be the same I you.

713
00:32:20,560 --> 00:32:22,600
Speaker 5: If I think I'm better than you and the three

714
00:32:22,640 --> 00:32:25,080
Speaker 5: people who've shown up, having more people at the table

715
00:32:25,160 --> 00:32:28,440
Speaker 5: is great, right, Meaning the way in which you make

716
00:32:28,480 --> 00:32:30,400
Speaker 5: money again I'll repeat, which is you have a different

717
00:32:30,480 --> 00:32:32,360
Speaker 5: view from the rest of the market participants and they

718
00:32:32,360 --> 00:32:36,080
Speaker 5: come to agree with you. That looks like crowding. Remember,

719
00:32:36,120 --> 00:32:38,520
Speaker 5: I come into a position before it's crowded, and the

720
00:32:38,560 --> 00:32:41,280
Speaker 5: way I make money is it becomes crowded, and at

721
00:32:41,280 --> 00:32:43,400
Speaker 5: some point I say, Okay, I've gotten paid for my view,

722
00:32:43,400 --> 00:32:45,680
Speaker 5: and I rotate into the next thing, hopefully the next

723
00:32:45,680 --> 00:32:48,760
Speaker 5: thing also early and whatever the idea is, right, And

724
00:32:48,800 --> 00:32:51,760
Speaker 5: so crowding in a sense is the mechanical way in

725
00:32:51,800 --> 00:32:54,320
Speaker 5: which you get paid from being early in an idea.

726
00:32:54,480 --> 00:32:54,680
Speaker 4: Right.

727
00:32:55,280 --> 00:32:58,200
Speaker 5: And so for a manager, an individual portfolio manager, or

728
00:32:58,200 --> 00:33:00,920
Speaker 5: a firm like ours, we want to think about how

729
00:33:00,960 --> 00:33:02,719
Speaker 5: do you manage the crowd So I'll give you an example.

730
00:33:03,520 --> 00:33:05,920
Speaker 5: Let's say two perficle measures. They both have the same quote,

731
00:33:05,960 --> 00:33:08,440
Speaker 5: crowding exposure right, measured in some way that we all

732
00:33:08,440 --> 00:33:11,440
Speaker 5: agree is a good way of measuring. If I got

733
00:33:11,440 --> 00:33:14,800
Speaker 5: there because I was early, and then I got paid

734
00:33:14,840 --> 00:33:17,200
Speaker 5: slowly as people came to be. In my view, that

735
00:33:17,280 --> 00:33:19,920
Speaker 5: is very different from somebody who's chasing the idea. Right,

736
00:33:19,920 --> 00:33:21,640
Speaker 5: They weren't early, They just see it happening and then

737
00:33:21,680 --> 00:33:25,760
Speaker 5: they chase. And is different because if there's a crowding online,

738
00:33:25,960 --> 00:33:29,040
Speaker 5: we both might have some negative returns, but I'd likely

739
00:33:29,040 --> 00:33:31,240
Speaker 5: have less negative returns because some of my ideas are new,

740
00:33:31,320 --> 00:33:33,280
Speaker 5: some part of my portfolio is not ask crowded. And

741
00:33:33,360 --> 00:33:36,200
Speaker 5: two I got paid on the way up, right, and

742
00:33:36,200 --> 00:33:38,400
Speaker 5: so how you get there is super critical right now

743
00:33:38,400 --> 00:33:41,840
Speaker 5: to market question. If there's more participants doing anything, whatever

744
00:33:41,880 --> 00:33:44,640
Speaker 5: it is, the mean return of course comes down. That

745
00:33:44,680 --> 00:33:46,760
Speaker 5: doesn't mean that the people who are at the high

746
00:33:46,840 --> 00:33:49,280
Speaker 5: end of skill are affected by it. In fact, they

747
00:33:49,320 --> 00:33:51,520
Speaker 5: might even make more money if there's enough people on

748
00:33:51,560 --> 00:33:53,840
Speaker 5: the other side of their skill, if that makes sense, right.

749
00:33:54,520 --> 00:33:56,320
Speaker 5: And the last thing that I would say is that

750
00:33:57,080 --> 00:34:00,800
Speaker 5: being a multi strategy fund is a way of organizing yourself, right.

751
00:34:00,840 --> 00:34:03,680
Speaker 5: It's a way of deciding that instead of running a

752
00:34:03,720 --> 00:34:07,640
Speaker 5: traditional integrated, single decision maker kind of fund, I am

753
00:34:07,680 --> 00:34:10,160
Speaker 5: going to think more carefully about how do I outcoupt capital,

754
00:34:10,239 --> 00:34:12,720
Speaker 5: hy do this thing? There's talent, how do I manage

755
00:34:12,760 --> 00:34:14,319
Speaker 5: all of these things that we talk about, the way

756
00:34:14,360 --> 00:34:16,080
Speaker 5: people get paid and all the incentives. It's a way

757
00:34:16,080 --> 00:34:19,520
Speaker 5: of organizing yourself. It's not an investment strategy. You could

758
00:34:19,640 --> 00:34:22,360
Speaker 5: organize itself that way and have lots of different ways

759
00:34:22,360 --> 00:34:25,960
Speaker 5: of investing. And it's the coincidence of the investment strategy

760
00:34:26,000 --> 00:34:28,680
Speaker 5: being the same that drives crowding. It's not the way

761
00:34:28,719 --> 00:34:31,560
Speaker 5: you're organizing yourself. So there's not obvious to me, and

762
00:34:31,800 --> 00:34:34,520
Speaker 5: I'm not sure that the data supports the idea that

763
00:34:34,600 --> 00:34:37,360
Speaker 5: somehow there's more crowding. In fact, the biggest crowding event

764
00:34:37,400 --> 00:34:39,759
Speaker 5: that we've ever had was back in two thousand and seven,

765
00:34:39,760 --> 00:34:41,600
Speaker 5: which is the Great crowding online.

766
00:34:41,719 --> 00:34:41,839
Speaker 1: Right.

767
00:34:41,920 --> 00:34:44,440
Speaker 5: Yeah, Crowding is a thing, no matter where it comes from. Right.

768
00:34:44,480 --> 00:34:46,800
Speaker 5: So if I have a bunch of long only active managers,

769
00:34:46,840 --> 00:34:49,840
Speaker 5: how liken video, that's just as mass crowding as you know,

770
00:34:49,880 --> 00:34:52,480
Speaker 5: some multi strategy liking and video. Does that make sense? Like, yeah,

771
00:34:52,719 --> 00:34:53,360
Speaker 5: different things.

772
00:34:53,480 --> 00:34:56,319
Speaker 2: I think the concern is more that, like the emphasis

773
00:34:56,400 --> 00:34:59,560
Speaker 2: on we talked about the short term horizon of some

774
00:34:59,600 --> 00:35:02,839
Speaker 2: of the stuff, and you talked about the focus on

775
00:35:02,880 --> 00:35:06,600
Speaker 2: the catalyst. I think the concern is that at turning points,

776
00:35:06,760 --> 00:35:12,040
Speaker 2: maybe you introduce more volatility because everyone starts shortly, yeah, exactly, shortleash.

777
00:35:12,080 --> 00:35:14,359
Speaker 3: Everyone knows these very tight stops they want to keep

778
00:35:14,400 --> 00:35:18,600
Speaker 3: their job, and that dad creates a specific type of

779
00:35:18,680 --> 00:35:20,960
Speaker 3: volatility because everyone the speed with which they have to

780
00:35:20,960 --> 00:35:22,399
Speaker 3: cut positions, etc.

781
00:35:23,160 --> 00:35:25,640
Speaker 5: Yeah. I don't disagree, but again, that's something that happens

782
00:35:25,680 --> 00:35:27,799
Speaker 5: at the individual level. Right. So let's say you have

783
00:35:28,480 --> 00:35:31,399
Speaker 5: you know, whatever your stop loss is. Some firms don't

784
00:35:31,440 --> 00:35:34,480
Speaker 5: even have that. They do their risk control differently. That

785
00:35:34,560 --> 00:35:38,520
Speaker 5: is specific to a particular strategy, right, And so whether

786
00:35:38,600 --> 00:35:41,239
Speaker 5: or not that adds volatility depends on whether that strategy

787
00:35:41,320 --> 00:35:44,279
Speaker 5: happens to be correlated with five, ten, fifteen others, right,

788
00:35:44,640 --> 00:35:47,160
Speaker 5: and as not obvious why that should happen just because

789
00:35:47,200 --> 00:35:49,160
Speaker 5: people have this view. Does that make sense? Right?

790
00:35:49,239 --> 00:35:49,560
Speaker 4: Yeah?

791
00:35:49,600 --> 00:35:52,439
Speaker 5: So let's say that there's one hundred people playing for

792
00:35:52,560 --> 00:35:54,719
Speaker 5: the next earnings from I'll make it up. I don't

793
00:35:54,760 --> 00:35:57,439
Speaker 5: know Bank of America, right, Like, they're going to report something,

794
00:35:57,440 --> 00:35:59,719
Speaker 5: and there's a lot of people playing them. Of course,

795
00:35:59,800 --> 00:36:03,240
Speaker 5: if everybody on of these hundred people that I'm describing

796
00:36:03,320 --> 00:36:04,799
Speaker 5: is on one side of it, you may get a

797
00:36:04,800 --> 00:36:07,319
Speaker 5: big ball move depending on what the results are. But

798
00:36:07,360 --> 00:36:09,279
Speaker 5: it's not obvious why they would be all in the

799
00:36:09,280 --> 00:36:12,279
Speaker 5: same side, right, just because they're organized aspopumps? Does that

800
00:36:12,280 --> 00:36:12,879
Speaker 5: make sense? Yeah?

801
00:36:12,920 --> 00:36:13,360
Speaker 4: Yeah.

802
00:36:13,920 --> 00:36:18,160
Speaker 3: Let's talk about comp and making money. You mentioned very

803
00:36:18,320 --> 00:36:21,279
Speaker 3: kindly that in theory you think you could mold me

804
00:36:21,400 --> 00:36:24,719
Speaker 3: and Tracy into decent traders or a lesser PMS maybe

805
00:36:24,760 --> 00:36:28,359
Speaker 3: anless that's fine, Okay, So Tracy and I are there

806
00:36:28,680 --> 00:36:33,120
Speaker 3: and we seem to deliver something that resembles alpha over time.

807
00:36:33,880 --> 00:36:36,200
Speaker 3: What's our paycheck? How is our paycheck derived?

808
00:36:36,760 --> 00:36:40,400
Speaker 5: Yeah, So, typically you want to have an incentive for

809
00:36:40,440 --> 00:36:43,040
Speaker 5: you to focus on the mechanics or your job, right,

810
00:36:43,080 --> 00:36:45,480
Speaker 5: and so typically there's a trade off between making your

811
00:36:45,520 --> 00:36:49,480
Speaker 5: compensation highly discussionary, I just decide because I like you

812
00:36:49,560 --> 00:36:52,840
Speaker 5: or don't like it, whatever, versus exactly formulaic right, fifteen

813
00:36:52,880 --> 00:36:55,000
Speaker 5: percent of your gross returns or whatever it is.

814
00:36:55,080 --> 00:36:55,279
Speaker 4: Right.

815
00:36:55,760 --> 00:36:58,200
Speaker 5: Typically, what you find is that the more you can

816
00:36:58,640 --> 00:37:01,600
Speaker 5: separate the job to be about these forty names in

817
00:37:01,640 --> 00:37:05,399
Speaker 5: the context of you know, some particular boundaries of risk

818
00:37:05,440 --> 00:37:08,120
Speaker 5: and capital deployment and concentration rules, et cetera, it becomes

819
00:37:08,200 --> 00:37:11,520
Speaker 5: easier to give that direct incentive, right. And so what

820
00:37:11,560 --> 00:37:13,640
Speaker 5: you'll find is that most places end up in a

821
00:37:13,680 --> 00:37:17,000
Speaker 5: circumstance where that incentive to be very focused on the

822
00:37:17,080 --> 00:37:20,000
Speaker 5: thing you're good at tends to drive better outcomes. Right now,

823
00:37:20,040 --> 00:37:21,640
Speaker 5: to be clear, there are trade offs on the other

824
00:37:21,680 --> 00:37:25,359
Speaker 5: side business wise, Right, So this is something that allocators

825
00:37:25,400 --> 00:37:28,600
Speaker 5: I suspect need to get better at really digging in.

826
00:37:29,200 --> 00:37:31,279
Speaker 5: So let's say you have thirty six risk takers. Let's

827
00:37:31,320 --> 00:37:34,560
Speaker 5: call them analyst right, and imagine three ways of potentially

828
00:37:34,600 --> 00:37:39,040
Speaker 5: paying them. One way is you net everybody's returns fair first,

829
00:37:39,239 --> 00:37:40,759
Speaker 5: and you know, some of them did well, some of

830
00:37:40,760 --> 00:37:43,360
Speaker 5: them they're poorly, maybe even negative. You get some total

831
00:37:43,400 --> 00:37:46,000
Speaker 5: return at the end across everybody, and then some fraction

832
00:37:46,120 --> 00:37:48,719
Speaker 5: of that is everybody's comp and then you sort of

833
00:37:48,719 --> 00:37:52,920
Speaker 5: paid discretionary. Right. It probably not as good from the

834
00:37:52,960 --> 00:37:55,080
Speaker 5: firm's point of view because it makes it hard to

835
00:37:55,120 --> 00:37:56,919
Speaker 5: have that sort of one to one incentive and really

836
00:37:57,000 --> 00:37:59,560
Speaker 5: focusing on the thing you're good at. But to be clear,

837
00:37:59,600 --> 00:38:01,840
Speaker 5: from the the allocator's point of view, it might be

838
00:38:01,880 --> 00:38:03,759
Speaker 5: the best because you're only paying for the returns that

839
00:38:03,760 --> 00:38:04,800
Speaker 5: were delivered in total.

840
00:38:05,160 --> 00:38:05,359
Speaker 4: Right.

841
00:38:06,000 --> 00:38:07,960
Speaker 5: Now, let's go to the oh, I say, yep. Now

842
00:38:08,040 --> 00:38:10,480
Speaker 5: let's go to the other extreme, which is typical now

843
00:38:10,560 --> 00:38:14,279
Speaker 5: with many platforms, which is eachrisk taker runs a small team,

844
00:38:14,440 --> 00:38:17,319
Speaker 5: each annalyst you know, has like an associate that helps them,

845
00:38:17,680 --> 00:38:19,839
Speaker 5: and each of them you pay, let's say the same

846
00:38:19,920 --> 00:38:22,840
Speaker 5: fifteen percent of whatever the share is. So now you

847
00:38:22,880 --> 00:38:24,759
Speaker 5: have this thing that people in the industry would called

848
00:38:24,760 --> 00:38:27,520
Speaker 5: netting risk, right, which is you pay fifteen percent of

849
00:38:27,600 --> 00:38:30,440
Speaker 5: the people who did well and the people who did poorly,

850
00:38:30,920 --> 00:38:33,440
Speaker 5: it's not that you're getting money back, right, And so

851
00:38:33,480 --> 00:38:35,920
Speaker 5: the total amount of compensation they're paying is larger than

852
00:38:36,000 --> 00:38:36,759
Speaker 5: in the first case.

853
00:38:37,120 --> 00:38:37,359
Speaker 4: Right.

854
00:38:37,880 --> 00:38:40,560
Speaker 5: In fact, in this example, imagine this thirty six people.

855
00:38:40,880 --> 00:38:43,040
Speaker 5: Let's say they each have THEO point seventy five that

856
00:38:43,200 --> 00:38:45,440
Speaker 5: example that I've been using before. If that's what's happening,

857
00:38:45,520 --> 00:38:48,160
Speaker 5: they pay, you pay about twenty five percent more in

858
00:38:48,239 --> 00:38:51,440
Speaker 5: comp costs in this second case as compared to the

859
00:38:51,480 --> 00:38:53,399
Speaker 5: first case. So if you say this is great because

860
00:38:53,400 --> 00:38:56,000
Speaker 5: everybody has a direct incentive of what they're doing, that's

861
00:38:56,040 --> 00:38:58,720
Speaker 5: not free. Right. It costs you literally twenty five percent

862
00:38:58,800 --> 00:39:02,960
Speaker 5: more cost Right. And in a situation where you're passing

863
00:39:02,960 --> 00:39:05,200
Speaker 5: through all this to your investors, your investors are worse

864
00:39:05,239 --> 00:39:06,240
Speaker 5: off by a decent amount.

865
00:39:06,400 --> 00:39:06,520
Speaker 2: Right.

866
00:39:07,320 --> 00:39:10,480
Speaker 5: Now, imagine middle ground where you say, okay, I want

867
00:39:10,800 --> 00:39:13,560
Speaker 5: one to one incentives with the thing you're really focused on,

868
00:39:13,960 --> 00:39:15,560
Speaker 5: and so I'm going to put these thirty six people

869
00:39:15,560 --> 00:39:18,879
Speaker 5: onto teams. Right, So I'm going to make teams of three, right,

870
00:39:19,360 --> 00:39:21,440
Speaker 5: and within that team they net with each other. Right,

871
00:39:21,440 --> 00:39:22,840
Speaker 5: So maybe one of them has a poor year or

872
00:39:22,840 --> 00:39:25,080
Speaker 5: the other two do well, And now you pay the

873
00:39:25,239 --> 00:39:28,040
Speaker 5: team that same share of fifteen percent within the team,

874
00:39:28,080 --> 00:39:30,640
Speaker 5: there's maybe some you know, ability to have some discussion

875
00:39:30,719 --> 00:39:33,920
Speaker 5: art u comp. Right, it's still more expensive than netting everybody,

876
00:39:33,920 --> 00:39:36,360
Speaker 5: but it's only five percent five to six percent more expensive.

877
00:39:36,840 --> 00:39:39,120
Speaker 5: So that version of the world gets you almost all

878
00:39:39,120 --> 00:39:41,719
Speaker 5: of the benefit of that direct focus on your job

879
00:39:41,920 --> 00:39:45,960
Speaker 5: with much less cost. Right, And so if you're an allocator,

880
00:39:46,719 --> 00:39:48,879
Speaker 5: you should be asking this. Remember in this example, these

881
00:39:48,880 --> 00:39:51,160
Speaker 5: are the same thirty six people with the same skill,

882
00:39:51,280 --> 00:39:53,840
Speaker 5: with the same total capital matters, and from the allocator's

883
00:39:53,840 --> 00:39:55,880
Speaker 5: point of view, it makes a huge difference which of

884
00:39:55,920 --> 00:39:56,719
Speaker 5: these you're doing.

885
00:39:56,880 --> 00:39:59,719
Speaker 3: Tracy, I find this to be so fascinating that you

886
00:39:59,760 --> 00:40:03,320
Speaker 3: could basically have the same structure and that the math

887
00:40:03,400 --> 00:40:07,200
Speaker 3: works out so differently just if you sort of change

888
00:40:07,320 --> 00:40:09,840
Speaker 3: the size of the set where you do the netting

889
00:40:09,920 --> 00:40:11,080
Speaker 3: like this is really interesting.

890
00:40:11,160 --> 00:40:14,120
Speaker 2: Way. I have another money related question, but how much

891
00:40:14,160 --> 00:40:17,000
Speaker 2: money would you give us as pms, Not in terms

892
00:40:17,040 --> 00:40:19,600
Speaker 2: of direct comp but how would you decide how much

893
00:40:19,600 --> 00:40:23,080
Speaker 2: we actually have to play around with? And then related

894
00:40:23,160 --> 00:40:26,680
Speaker 2: to that one thing I'm always unclear on with multistrap firms,

895
00:40:26,800 --> 00:40:30,440
Speaker 2: it seems like the size of the available capital pool

896
00:40:30,640 --> 00:40:34,360
Speaker 2: is sometimes a draw for individual pms like oh, I

897
00:40:34,400 --> 00:40:37,600
Speaker 2: get to play with I don't know like fifty million

898
00:40:37,719 --> 00:40:40,640
Speaker 2: or I don't even know what a normal number is

899
00:40:40,680 --> 00:40:43,319
Speaker 2: for them. But on the other hand, you sometimes see

900
00:40:43,360 --> 00:40:46,960
Speaker 2: headlines about how you know, Citadel or Millennium have to

901
00:40:47,120 --> 00:40:51,200
Speaker 2: limit new investor funds. So I'm wondering, like, how do

902
00:40:51,239 --> 00:40:55,080
Speaker 2: you right size the available capital for trading?

903
00:40:55,440 --> 00:40:55,600
Speaker 1: Yeah?

904
00:40:55,640 --> 00:40:58,200
Speaker 5: Okay, so there's I think there's multiple questions in there.

905
00:40:58,320 --> 00:41:01,000
Speaker 5: One is like a capital location, So how do I differentiate?

906
00:41:01,080 --> 00:41:03,359
Speaker 5: Do I give you more than her advice versa?

907
00:41:03,480 --> 00:41:03,600
Speaker 1: Right?

908
00:41:03,640 --> 00:41:06,000
Speaker 5: So that's like whatever the amount I have, there's an

909
00:41:06,000 --> 00:41:07,799
Speaker 5: allocation question, so we can get there in a second.

910
00:41:07,880 --> 00:41:10,120
Speaker 5: And then there's also the is there such a thing

911
00:41:10,120 --> 00:41:13,080
Speaker 5: as like an optimal amount for an individual person?

912
00:41:13,160 --> 00:41:13,399
Speaker 4: Right?

913
00:41:13,800 --> 00:41:15,319
Speaker 5: Let me start with the second one. The answer is

914
00:41:15,360 --> 00:41:18,040
Speaker 5: generally yes, and I think would your previous I think

915
00:41:18,040 --> 00:41:20,759
Speaker 5: it was Kapi who made this point that there's a

916
00:41:20,920 --> 00:41:23,399
Speaker 5: human and sort of psychology aspect of how much money

917
00:41:23,440 --> 00:41:27,759
Speaker 5: you can comfortably run, right, and so typically past a

918
00:41:27,800 --> 00:41:31,200
Speaker 5: certain amount, Literally, the psychology of seeing however much you're

919
00:41:31,239 --> 00:41:34,400
Speaker 5: making or losing every day gets really large and uncomfortable

920
00:41:34,440 --> 00:41:35,200
Speaker 5: for a lot of people.

921
00:41:35,280 --> 00:41:38,120
Speaker 2: Right, I get anxious just looking at my four oh

922
00:41:38,120 --> 00:41:38,840
Speaker 2: one case.

923
00:41:38,719 --> 00:41:40,719
Speaker 5: Yes, exactly that, and that to be clear, that's a thing.

924
00:41:40,840 --> 00:41:43,920
Speaker 5: Right you Let's say you start somebody running, I'll make

925
00:41:43,960 --> 00:41:46,560
Speaker 5: it up one hundred million dollars of just dollars, right,

926
00:41:46,640 --> 00:41:48,560
Speaker 5: and they're you know, they're fifty of them are long,

927
00:41:48,600 --> 00:41:52,319
Speaker 5: fifty short, and maybe every day they go out by

928
00:41:52,560 --> 00:41:54,799
Speaker 5: you know, half a million. You know they're be done

929
00:41:54,800 --> 00:41:56,440
Speaker 5: by half a million. Right, that's sort of the range.

930
00:41:56,760 --> 00:42:00,920
Speaker 5: Now you make that ten x int space it might

931
00:42:01,000 --> 00:42:03,919
Speaker 5: be literally identical, but the psychology off you walk into

932
00:42:03,960 --> 00:42:05,839
Speaker 5: the morning and the market's open. Now you're down five

933
00:42:05,880 --> 00:42:09,000
Speaker 5: million dollars. There comes a point where people where that's

934
00:42:09,040 --> 00:42:09,279
Speaker 5: a thing.

935
00:42:09,400 --> 00:42:12,719
Speaker 3: Right, Like when I like play poker, I wonder if,

936
00:42:12,800 --> 00:42:14,040
Speaker 3: like it would be nice if they would just lie

937
00:42:14,080 --> 00:42:15,600
Speaker 3: to me and say you're playing a one to two game,

938
00:42:15,640 --> 00:42:17,680
Speaker 3: you're buying for two hundred, and then at the end

939
00:42:17,680 --> 00:42:19,200
Speaker 3: they're like, oh, it turns out you're playing for two

940
00:42:19,239 --> 00:42:20,880
Speaker 3: thousand because the chips are the safe.

941
00:42:21,000 --> 00:42:21,200
Speaker 4: Yeah.

942
00:42:21,239 --> 00:42:24,239
Speaker 5: And the psychology, the way the psychology plays is not

943
00:42:24,280 --> 00:42:25,919
Speaker 5: just on the amount of money you can comfortably run

944
00:42:26,000 --> 00:42:28,080
Speaker 5: and remember the bigger the amounts you have to worry

945
00:42:28,080 --> 00:42:31,200
Speaker 5: about things other than your say, fundamental views. You have

946
00:42:31,239 --> 00:42:33,880
Speaker 5: to worry more about tea costs and implementation questions and

947
00:42:33,920 --> 00:42:37,000
Speaker 5: liquidity questions, and you know, how can do you get

948
00:42:37,000 --> 00:42:39,719
Speaker 5: to play on smaller cap names where you maybe feel

949
00:42:39,719 --> 00:42:41,239
Speaker 5: you have an edge, but now you can't really do

950
00:42:41,280 --> 00:42:42,960
Speaker 5: as much of it. So there's all these sort of

951
00:42:43,000 --> 00:42:44,960
Speaker 5: things that have to do with scale. The other thing

952
00:42:45,000 --> 00:42:46,960
Speaker 5: that happens is a psychology and compensation.

953
00:42:47,120 --> 00:42:47,319
Speaker 4: Right.

954
00:42:47,600 --> 00:42:50,839
Speaker 5: It is not uncommon for folks to prefer I could

955
00:42:50,880 --> 00:42:53,720
Speaker 5: give you a billion dollars and pay you fifteen percent

956
00:42:53,760 --> 00:42:57,800
Speaker 5: of say your night returns, or maybe half a billion

957
00:42:57,800 --> 00:42:59,840
Speaker 5: dollars and pay your thirty percent. Right, it's the economics

958
00:42:59,840 --> 00:43:02,719
Speaker 5: are the same. Many people might prefer the latter rather

959
00:43:02,760 --> 00:43:06,080
Speaker 5: than the former, right, So psychology does play a significant

960
00:43:06,160 --> 00:43:08,600
Speaker 5: role in this. We tend to find that good perform

961
00:43:08,680 --> 00:43:11,560
Speaker 5: managers can actually run, assuming they have a good team

962
00:43:11,600 --> 00:43:14,680
Speaker 5: with them, in the billions of dollars, But it's not

963
00:43:14,719 --> 00:43:19,360
Speaker 5: necessarily the most common situation. Most platforms find themselves running

964
00:43:19,400 --> 00:43:22,080
Speaker 5: smaller teams with lots of littlecations. We then have all

965
00:43:22,080 --> 00:43:24,520
Speaker 5: these netting issues, right, so you do want to think

966
00:43:24,520 --> 00:43:26,960
Speaker 5: about that. The second question is, okay, how are big

967
00:43:27,000 --> 00:43:29,520
Speaker 5: eat port IFOI? You might get how do you separate? Like,

968
00:43:29,600 --> 00:43:31,720
Speaker 5: how do I give you more than other person?

969
00:43:31,840 --> 00:43:32,040
Speaker 4: Right?

970
00:43:32,520 --> 00:43:35,040
Speaker 5: The reality is you want to make your capital location

971
00:43:35,120 --> 00:43:36,920
Speaker 5: based on your expectation of return.

972
00:43:37,080 --> 00:43:37,319
Speaker 4: Right.

973
00:43:37,400 --> 00:43:39,839
Speaker 5: Will you have good sharp ratio in the future. Right?

974
00:43:40,160 --> 00:43:42,320
Speaker 5: The problem is you don't know the true sharp ratio.

975
00:43:42,560 --> 00:43:45,520
Speaker 5: Most people are tempted to use some realize sharp rasio.

976
00:43:45,520 --> 00:43:46,759
Speaker 5: What was your sharp ratio last year?

977
00:43:46,880 --> 00:43:47,000
Speaker 1: Right?

978
00:43:47,040 --> 00:43:49,240
Speaker 5: And the problem is there's a huge amount of noise

979
00:43:49,280 --> 00:43:49,560
Speaker 5: in that.

980
00:43:49,800 --> 00:43:50,000
Speaker 4: Right.

981
00:43:50,560 --> 00:43:52,680
Speaker 5: And I find the intuition of this really interesting. So

982
00:43:52,760 --> 00:43:55,160
Speaker 5: if you have a good basic way of thinking about it,

983
00:43:55,280 --> 00:43:58,239
Speaker 5: let's say you cover forty names in your views. About

984
00:43:58,239 --> 00:44:00,319
Speaker 5: these names, let's say I like this, I don't like this.

985
00:44:00,560 --> 00:44:04,520
Speaker 5: Every day are correlated with actual returns by what one percent?

986
00:44:05,080 --> 00:44:07,080
Speaker 5: So not a lot of predictability, like nine to nine

987
00:44:07,080 --> 00:44:08,560
Speaker 5: percent of what's happening you don't know, but you have

988
00:44:08,560 --> 00:44:12,359
Speaker 5: one percent predictability. If you do this and trade based

989
00:44:12,400 --> 00:44:14,560
Speaker 5: on these views, you will have a sharp person of

990
00:44:14,600 --> 00:44:16,120
Speaker 5: about one at the end of the year, which is

991
00:44:16,120 --> 00:44:20,080
Speaker 5: pretty good for forty names, right, meaning little amount of productility.

992
00:44:20,120 --> 00:44:22,120
Speaker 5: One percent in this case is what people call the ic.

993
00:44:22,400 --> 00:44:25,240
Speaker 5: The correlation between your views and next day of returns.

994
00:44:25,719 --> 00:44:27,640
Speaker 5: Get to a pretty good outcome at the end of

995
00:44:27,680 --> 00:44:30,799
Speaker 5: the year. It also tells you that there's a huge

996
00:44:30,800 --> 00:44:32,759
Speaker 5: amount of noise. Right, So if you think about let's

997
00:44:32,760 --> 00:44:35,759
Speaker 5: say that we all three of us agree that you know,

998
00:44:35,800 --> 00:44:37,680
Speaker 5: we have a crystable, and we know for a fact

999
00:44:37,719 --> 00:44:39,959
Speaker 5: that there's a person that has one percent correlation between

1000
00:44:40,000 --> 00:44:43,400
Speaker 5: views and returns, and we observe a year worth of returns,

1001
00:44:43,800 --> 00:44:46,840
Speaker 5: and we observe that for one hundred years, the average

1002
00:44:46,840 --> 00:44:50,439
Speaker 5: sharp will be one, but some years will be low

1003
00:44:50,520 --> 00:44:53,160
Speaker 5: because you know of the ninety percent, you're not predicting.

1004
00:44:53,239 --> 00:44:55,000
Speaker 5: You might be unlucky some year and you end up

1005
00:44:55,000 --> 00:44:57,480
Speaker 5: with a SHARPO of zero. Some years you get really lucky,

1006
00:44:57,520 --> 00:45:00,360
Speaker 5: you end up with a sharp of two. So realize turns.

1007
00:45:00,360 --> 00:45:03,120
Speaker 5: Realize sharps have a huge amount of variation. So you

1008
00:45:03,120 --> 00:45:05,680
Speaker 5: don't know what the true sharp is. You only observe

1009
00:45:05,719 --> 00:45:07,239
Speaker 5: the real life sharp, and so if you make out

1010
00:45:07,280 --> 00:45:10,280
Speaker 5: locations based on the real life sharp, you're mostly allocating

1011
00:45:10,280 --> 00:45:12,440
Speaker 5: on noise, especially if you only do it over a

1012
00:45:12,480 --> 00:45:14,719
Speaker 5: short period of time. Right, And so the way you

1013
00:45:14,760 --> 00:45:16,719
Speaker 5: want to start is to say, look, I'm going to

1014
00:45:16,719 --> 00:45:20,680
Speaker 5: ignore the past returns and do equal risk. That's essentially

1015
00:45:20,719 --> 00:45:23,000
Speaker 5: the same as saying, I am going to assume that

1016
00:45:23,040 --> 00:45:25,160
Speaker 5: the two of you have the same I see the

1017
00:45:25,200 --> 00:45:27,799
Speaker 5: same sharp I don't because I don't know what it is. Right,

1018
00:45:27,800 --> 00:45:30,799
Speaker 5: It's sort of Abasian statistics kind of thing. Right, And

1019
00:45:30,840 --> 00:45:33,399
Speaker 5: then I deviate away from that benchmark of equal risk

1020
00:45:33,520 --> 00:45:35,920
Speaker 5: as I to learn not so much more about your returns,

1021
00:45:35,960 --> 00:45:38,640
Speaker 5: but what drives returns, so overy time I might be

1022
00:45:38,640 --> 00:45:41,799
Speaker 5: able to observe that. Actually, as it turns out, one

1023
00:45:41,840 --> 00:45:44,920
Speaker 5: of you is really good at the margin parts of

1024
00:45:44,960 --> 00:45:48,040
Speaker 5: thinking about earnings, right, and for kind where names where

1025
00:45:48,080 --> 00:45:49,960
Speaker 5: there's a lot of room to think about differences and

1026
00:45:50,000 --> 00:45:52,959
Speaker 5: views about margin, and you happen to do really well right,

1027
00:45:53,280 --> 00:45:57,680
Speaker 5: whereas somebody else might have high expertise on product questions, Right,

1028
00:45:57,719 --> 00:45:59,959
Speaker 5: will a product fly or not fly in a particular space?

1029
00:46:00,160 --> 00:46:00,239
Speaker 1: Right?

1030
00:46:00,320 --> 00:46:02,200
Speaker 5: And I collect data about the stuff. So let me

1031
00:46:02,200 --> 00:46:04,680
Speaker 5: give you an example. Let's say you tell me the

1032
00:46:04,719 --> 00:46:07,600
Speaker 5: reason I generate one percent correlation between my views and

1033
00:46:07,640 --> 00:46:12,440
Speaker 5: returns is because I'm good at predicting surprises, right, earning surprises, Okay,

1034
00:46:12,680 --> 00:46:15,000
Speaker 5: and you tell me that you can predict surprises at

1035
00:46:15,000 --> 00:46:17,480
Speaker 5: ten percent correlation. So every time you have a prediction

1036
00:46:17,560 --> 00:46:21,440
Speaker 5: for forty names, they are correlated ten percent with actual surprises.

1037
00:46:21,760 --> 00:46:23,760
Speaker 5: So this is not much better because if I collect

1038
00:46:23,800 --> 00:46:27,000
Speaker 5: data about your predictions of earnings, not returns, I can

1039
00:46:27,000 --> 00:46:30,120
Speaker 5: distinguish ten percent from zero much better than one percent

1040
00:46:30,120 --> 00:46:30,600
Speaker 5: from zero.

1041
00:46:30,960 --> 00:46:31,200
Speaker 4: Right.

1042
00:46:31,920 --> 00:46:33,920
Speaker 5: The second thing that is true is that I knew

1043
00:46:33,960 --> 00:46:37,600
Speaker 5: that returns are correlated with earning surprises by about ten percent,

1044
00:46:37,680 --> 00:46:39,959
Speaker 5: And to be clear that I can do with lots

1045
00:46:40,000 --> 00:46:41,400
Speaker 5: of data. I can go back in time and think

1046
00:46:41,400 --> 00:46:43,640
Speaker 5: about the correlation of returns and earning surprises for every style,

1047
00:46:43,760 --> 00:46:46,239
Speaker 5: going back in time for fifty years. Right, And these

1048
00:46:46,239 --> 00:46:48,600
Speaker 5: are transitive. So if you predict earnings by ten percent

1049
00:46:48,920 --> 00:46:51,160
Speaker 5: and returns are correlated with earning surprises by ten percent,

1050
00:46:51,280 --> 00:46:53,480
Speaker 5: you get the one percent that you're looking for. But

1051
00:46:53,680 --> 00:46:55,799
Speaker 5: I can look at your earnings and do much better

1052
00:46:55,840 --> 00:46:58,680
Speaker 5: analysis because those are ten percent correlated with actual earnings

1053
00:46:58,719 --> 00:47:01,759
Speaker 5: doesn't make sense. So as I get time, I can

1054
00:47:01,800 --> 00:47:04,399
Speaker 5: get to understand your investment the underlying things that drive

1055
00:47:04,480 --> 00:47:05,680
Speaker 5: those returns much better.

1056
00:47:05,840 --> 00:47:08,839
Speaker 3: This seems like a very big theme throughout this conversation

1057
00:47:09,000 --> 00:47:12,080
Speaker 3: that the more you can understand why things work correct,

1058
00:47:12,120 --> 00:47:16,680
Speaker 3: the better you are, the easier many other decisions become.

1059
00:47:16,920 --> 00:47:19,960
Speaker 3: And I have one last question for you say, we

1060
00:47:20,040 --> 00:47:23,200
Speaker 3: have some students. College students listen to odd lots from

1061
00:47:23,239 --> 00:47:26,520
Speaker 3: time to time. I'm a freshman in college. I'm interested

1062
00:47:26,560 --> 00:47:29,600
Speaker 3: in finance. It sounds like a fun career. I want

1063
00:47:29,600 --> 00:47:31,440
Speaker 3: to make a lot of money working for a multi

1064
00:47:31,440 --> 00:47:35,040
Speaker 3: strategy hedge fund one day. What's the best decision I

1065
00:47:35,040 --> 00:47:38,520
Speaker 3: could make right now as a freshman or sophomore in college.

1066
00:47:38,840 --> 00:47:41,200
Speaker 3: They would most likely open a future door for me

1067
00:47:41,400 --> 00:47:42,440
Speaker 3: for something of this career.

1068
00:47:42,640 --> 00:47:44,880
Speaker 5: Yeah, that's a that's a good question. You know, we

1069
00:47:45,080 --> 00:47:47,480
Speaker 5: run an internship program, so you get asked this thing

1070
00:47:47,520 --> 00:47:50,160
Speaker 5: all the time. I would say two things. Number one

1071
00:47:50,280 --> 00:47:53,040
Speaker 5: is you I think need to have a good mix

1072
00:47:53,239 --> 00:47:58,040
Speaker 5: of liking and being reasonably good at the I'm going

1073
00:47:58,120 --> 00:47:59,799
Speaker 5: to call it the data part of it. Right. These

1074
00:47:59,840 --> 00:48:03,400
Speaker 5: ares are all about do I understand the data that

1075
00:48:03,440 --> 00:48:04,800
Speaker 5: tells me something about this firms?

1076
00:48:04,880 --> 00:48:05,040
Speaker 4: Right?

1077
00:48:05,080 --> 00:48:07,680
Speaker 5: And so you know, whether it's you know, I cover

1078
00:48:07,760 --> 00:48:10,239
Speaker 5: consumer firms and I'm looking at kurk card data and

1079
00:48:10,320 --> 00:48:12,680
Speaker 5: you know, thinking about, you know, what is the color

1080
00:48:12,719 --> 00:48:14,640
Speaker 5: of the fall and how I might get you know,

1081
00:48:14,719 --> 00:48:16,680
Speaker 5: data about whose color is going to be the important one?

1082
00:48:16,719 --> 00:48:18,040
Speaker 5: And what story of am I running? And all these

1083
00:48:18,040 --> 00:48:19,640
Speaker 5: sorts of things. So there's a lot of data analysis

1084
00:48:19,680 --> 00:48:20,880
Speaker 5: that they have to do, and you have to be

1085
00:48:21,520 --> 00:48:23,359
Speaker 5: sort of both good at it and really like it

1086
00:48:23,400 --> 00:48:24,920
Speaker 5: because it becomes sort of your day to day.

1087
00:48:25,000 --> 00:48:25,160
Speaker 4: Right.

1088
00:48:25,320 --> 00:48:28,120
Speaker 5: The second thing is you have to be willing to

1089
00:48:28,280 --> 00:48:31,879
Speaker 5: understand that there's sort of a grind aspect of the job.

1090
00:48:32,040 --> 00:48:32,239
Speaker 1: Right.

1091
00:48:32,280 --> 00:48:34,520
Speaker 5: It sounds really exciting to think about predicting things and

1092
00:48:34,520 --> 00:48:36,480
Speaker 5: potentially making a lot of money, but the reality is

1093
00:48:36,520 --> 00:48:38,120
Speaker 5: that the data to day job can be a bit

1094
00:48:38,120 --> 00:48:40,120
Speaker 5: of a grind. Right. You're covering these forty names, and

1095
00:48:40,160 --> 00:48:43,239
Speaker 5: they are the same forty names every year, right, and

1096
00:48:43,280 --> 00:48:45,839
Speaker 5: you're listening to every conference call and listening to every

1097
00:48:45,840 --> 00:48:48,600
Speaker 5: airnings announcement, and you're looking for like tiny little bits

1098
00:48:48,600 --> 00:48:50,680
Speaker 5: of differences. It's like, well, you know, last Timmer around

1099
00:48:51,280 --> 00:48:54,640
Speaker 5: they describe the nature of the particular product that they're

1100
00:48:54,680 --> 00:48:57,040
Speaker 5: working on in this way. Now describing is slightly differently.

1101
00:48:57,080 --> 00:48:59,680
Speaker 5: I wonder if that means something about their strategy, and

1102
00:48:59,719 --> 00:49:02,600
Speaker 5: so is this sort of to partner uses the word

1103
00:49:02,600 --> 00:49:05,080
Speaker 5: of coal mining, right, it can be. It could be

1104
00:49:05,080 --> 00:49:05,960
Speaker 5: a bit of a grind right.

1105
00:49:06,120 --> 00:49:08,640
Speaker 2: Now in the minds of multi stress exactly right.

1106
00:49:08,920 --> 00:49:11,319
Speaker 5: It's not all the excitement of I show up in

1107
00:49:11,320 --> 00:49:12,680
Speaker 5: the morning and have an idea and now I make

1108
00:49:12,719 --> 00:49:14,720
Speaker 5: coup exactly.

1109
00:49:14,800 --> 00:49:18,680
Speaker 2: Yes, Wait, speaking of the grind and interns, is there

1110
00:49:18,760 --> 00:49:21,920
Speaker 2: a future where I know you spoke earlier about the

1111
00:49:21,920 --> 00:49:24,319
Speaker 2: importance of the human factor in a lot of this,

1112
00:49:24,560 --> 00:49:31,520
Speaker 2: but could you switch the emphasis to more AI.

1113
00:49:30,200 --> 00:49:32,400
Speaker 3: This other thing that I wasn't gonna get it?

1114
00:49:32,440 --> 00:49:34,959
Speaker 5: Yeah, because I'm thinking, I'm happy to talk about AI.

1115
00:49:35,000 --> 00:49:39,320
Speaker 2: Stock Want Funds were like the original users of machine learning,

1116
00:49:39,440 --> 00:49:43,360
Speaker 2: or one of the big original users, so it seems

1117
00:49:43,640 --> 00:49:46,439
Speaker 2: fairly natural for them to use more AI in order

1118
00:49:46,480 --> 00:49:49,560
Speaker 2: to spot potential patterns or potential catalysts for big moves.

1119
00:49:49,680 --> 00:49:51,840
Speaker 3: Tell us what's real and what's bs.

1120
00:49:51,560 --> 00:49:55,000
Speaker 5: There's always a mix. But I do want to say

1121
00:49:55,000 --> 00:49:57,520
Speaker 5: something before we get to a specifically, this sort of

1122
00:49:57,600 --> 00:49:59,680
Speaker 5: job is always a bit of an arms race, right,

1123
00:49:59,760 --> 00:50:02,200
Speaker 5: meaning this sort of thing that made you money, Let's

1124
00:50:02,200 --> 00:50:04,800
Speaker 5: say twenty years ago. Twenty years ago, you could have

1125
00:50:04,800 --> 00:50:07,759
Speaker 5: been an analyst that figured out that in order to

1126
00:50:07,880 --> 00:50:12,120
Speaker 5: understand particular, say retail firms, you could go look at

1127
00:50:12,200 --> 00:50:15,520
Speaker 5: footnotes about whether you know you owned or at least

1128
00:50:15,600 --> 00:50:17,880
Speaker 5: your retail space where you sold your T shirts or

1129
00:50:17,880 --> 00:50:20,680
Speaker 5: whatever it was, and that might have had some consequence, right,

1130
00:50:20,719 --> 00:50:23,040
Speaker 5: depending on how your finance and what that meant for

1131
00:50:23,080 --> 00:50:26,799
Speaker 5: you know, Etcaday early data stuff, Right, you don't do

1132
00:50:26,840 --> 00:50:28,359
Speaker 5: that now. And the reason you don't do that now

1133
00:50:28,400 --> 00:50:30,200
Speaker 5: is because that's all in the database that everybody can

1134
00:50:30,239 --> 00:50:32,080
Speaker 5: go mechanically look at it, right. And so there's this

1135
00:50:32,160 --> 00:50:34,319
Speaker 5: sort of sub get you need to become ever more

1136
00:50:34,360 --> 00:50:37,719
Speaker 5: sophisticated data and analytics wise, and AI is sort of

1137
00:50:37,760 --> 00:50:40,319
Speaker 5: one more step in that direction, right, So I don't

1138
00:50:40,360 --> 00:50:42,920
Speaker 5: think of it as something inherently different from this sort

1139
00:50:42,960 --> 00:50:46,520
Speaker 5: of constant evolution of always being more sophisticated and understanding

1140
00:50:46,520 --> 00:50:46,960
Speaker 5: the firms.

1141
00:50:47,000 --> 00:50:47,200
Speaker 4: Right.

1142
00:50:48,120 --> 00:50:51,439
Speaker 5: The one thing that I would say about AI is that,

1143
00:50:51,880 --> 00:50:54,120
Speaker 5: at least up until this point, if you think about

1144
00:50:54,160 --> 00:50:57,520
Speaker 5: how AI is trained, right, you feeded all this text

1145
00:50:57,600 --> 00:51:01,279
Speaker 5: essentially mostly from their Internet. And the job that it's

1146
00:51:01,320 --> 00:51:03,560
Speaker 5: trying to do is that it's trying to predict the

1147
00:51:03,680 --> 00:51:06,960
Speaker 5: most likely answer to a question, or the most likely

1148
00:51:07,000 --> 00:51:09,680
Speaker 5: thing that comes after some prompt. Right. That's essentially what

1149
00:51:09,719 --> 00:51:12,839
Speaker 5: you're doing. And what that means, by definition is that

1150
00:51:13,560 --> 00:51:16,560
Speaker 5: if you ask it, hey, what is different about company X?

1151
00:51:16,600 --> 00:51:19,200
Speaker 5: By definition, it's going to tell you what everybody else

1152
00:51:19,239 --> 00:51:21,520
Speaker 5: thinks is different about companyes, which means it's actually not

1153
00:51:21,560 --> 00:51:24,759
Speaker 5: the different thing. Aka, you're getting the consensus right, and

1154
00:51:24,800 --> 00:51:27,200
Speaker 5: so that could be quite useful in the way you

1155
00:51:27,280 --> 00:51:29,319
Speaker 5: think about doing data analysis as lots of ways. And

1156
00:51:29,320 --> 00:51:31,439
Speaker 5: we have a bunch of investment in AI work within

1157
00:51:31,520 --> 00:51:35,360
Speaker 5: the firm, but that is not the same as assuming

1158
00:51:35,600 --> 00:51:39,080
Speaker 5: that AI will have inside about the firm, because it's

1159
00:51:39,080 --> 00:51:41,960
Speaker 5: been trained on the average of things kind of by definition, right,

1160
00:51:42,000 --> 00:51:45,239
Speaker 5: And so the step of going from it helps me

1161
00:51:45,600 --> 00:51:48,840
Speaker 5: summarize or potentially, you know, kind of clarify what themes

1162
00:51:48,880 --> 00:51:50,520
Speaker 5: are people talking about. And there's lots of things that

1163
00:51:50,520 --> 00:51:52,200
Speaker 5: you might be able to do with it that is

1164
00:51:52,200 --> 00:51:55,560
Speaker 5: not quite the same as the jump to and therefore

1165
00:51:56,120 --> 00:51:59,560
Speaker 5: here's a difference in view versus everybody else's views. Does

1166
00:51:59,600 --> 00:52:00,239
Speaker 5: that make sense? Yeah?

1167
00:52:00,239 --> 00:52:03,440
Speaker 2: Absolutely, Dan, Thank you so much for coming on all thoughts.

1168
00:52:03,480 --> 00:52:06,960
Speaker 2: That was great, amazing You explained the maths perfectly.

1169
00:52:06,520 --> 00:52:09,759
Speaker 3: So Dan, Matt, Yeah, No, it was really great than like,

1170
00:52:09,800 --> 00:52:12,520
Speaker 3: I feel like a million questions we answered your very

1171
00:52:12,600 --> 00:52:15,279
Speaker 3: game to really work us through, work work through all

1172
00:52:15,280 --> 00:52:16,719
Speaker 3: of them with us. So appreciate you coming on.

1173
00:52:17,000 --> 00:52:30,400
Speaker 5: Thank you, Joe.

1174
00:52:30,400 --> 00:52:31,080
Speaker 2: That was fun.

1175
00:52:31,320 --> 00:52:32,080
Speaker 3: It was so fun.

1176
00:52:32,520 --> 00:52:36,160
Speaker 2: I like talking about maths and multi strap funds, DAN maths, Yeah,

1177
00:52:36,160 --> 00:52:38,440
Speaker 2: the DAN mats. So there are a few things to

1178
00:52:38,520 --> 00:52:41,239
Speaker 2: pick out of there. I really liked the emphasis, and

1179
00:52:41,520 --> 00:52:44,880
Speaker 2: this has come up before, but the idea that crowding

1180
00:52:45,000 --> 00:52:49,640
Speaker 2: in is not necessarily a bad thing for individual managers

1181
00:52:49,680 --> 00:52:53,040
Speaker 2: because what you're trying to do is identify that catalyst. Yeah,

1182
00:52:53,040 --> 00:52:54,680
Speaker 2: that will get everyone crowded.

1183
00:52:55,560 --> 00:52:58,320
Speaker 3: Crowding, crowding in how you get paid, Yeah, like you

1184
00:52:58,960 --> 00:53:01,160
Speaker 3: eventually you just want to be there before the crowding,

1185
00:53:01,200 --> 00:53:03,960
Speaker 3: But the crowding is ultimately what delivers the paycheck.

1186
00:53:03,719 --> 00:53:08,080
Speaker 2: Right now, does that maybe have a less desirable effect

1187
00:53:08,239 --> 00:53:10,759
Speaker 2: on the overall market? I mean I kind of take

1188
00:53:10,760 --> 00:53:12,600
Speaker 2: the point about, well, if you have a bunch of

1189
00:53:12,640 --> 00:53:15,640
Speaker 2: long only funds that are in something and then something

1190
00:53:15,680 --> 00:53:19,000
Speaker 2: bad happens, they'll all retreat. That that's like the same

1191
00:53:19,000 --> 00:53:22,560
Speaker 2: effect as multi strats crowding in. But it does feel

1192
00:53:22,600 --> 00:53:25,759
Speaker 2: to me, just observing the market in recent years, that

1193
00:53:25,920 --> 00:53:29,360
Speaker 2: you are getting these sort of shorter and sharper turning

1194
00:53:29,400 --> 00:53:30,520
Speaker 2: points or reactions.

1195
00:53:30,840 --> 00:53:34,400
Speaker 3: Totally, there are so many things that I took from

1196
00:53:34,440 --> 00:53:37,319
Speaker 3: that conversation. I thought that was fantastic, and all of

1197
00:53:37,360 --> 00:53:40,040
Speaker 3: our conversations about this topic have been good, but to

1198
00:53:40,120 --> 00:53:42,320
Speaker 3: talk to an actual founder of a fund though it

1199
00:53:42,360 --> 00:53:44,960
Speaker 3: was great. You know, there was the big conceptual thing

1200
00:53:44,960 --> 00:53:47,279
Speaker 3: that he kept coming back to, which is that the

1201
00:53:47,320 --> 00:53:50,160
Speaker 3: more you can know why something works, the better. I

1202
00:53:50,160 --> 00:53:52,760
Speaker 3: think I'm pretty good at my job of co hosting outlas.

1203
00:53:52,800 --> 00:53:54,160
Speaker 3: I think you are too.

1204
00:53:54,040 --> 00:53:54,720
Speaker 1: But I host.

1205
00:53:54,800 --> 00:53:56,560
Speaker 3: Yeah, but I do think and they're like, you know,

1206
00:53:56,600 --> 00:53:58,279
Speaker 3: I know other people are good at their jobs. But

1207
00:53:58,320 --> 00:54:00,920
Speaker 3: to be able to articulate why you are good at

1208
00:54:00,960 --> 00:54:04,520
Speaker 3: your jobs, and provably be able to articulate why you're

1209
00:54:04,560 --> 00:54:05,719
Speaker 3: good at your jobs, would you.

1210
00:54:05,760 --> 00:54:07,320
Speaker 2: Go why you didn't just get lucky?

1211
00:54:07,400 --> 00:54:09,840
Speaker 3: Yeah, why it's not lucky? Why you are able to

1212
00:54:09,960 --> 00:54:13,640
Speaker 3: identify something like, oh, I am very good at identifying

1213
00:54:13,680 --> 00:54:17,080
Speaker 3: earning surprises. Setting aside the question of am I good

1214
00:54:17,120 --> 00:54:20,399
Speaker 3: at picking stocks? That's a really interesting way to think

1215
00:54:20,400 --> 00:54:22,920
Speaker 3: about it, Like, Okay, we know that earning surprises are

1216
00:54:22,960 --> 00:54:26,040
Speaker 3: correlated to stock performance. If I could prove that I'm

1217
00:54:26,080 --> 00:54:28,719
Speaker 3: good at X, then I could probably prove that I'm

1218
00:54:28,719 --> 00:54:31,960
Speaker 3: good at stock selection. That is really interesting. I love

1219
00:54:32,080 --> 00:54:34,760
Speaker 3: like hearing about the math of like why you want

1220
00:54:34,760 --> 00:54:39,080
Speaker 3: to avoid correlation between managers and how powerful that effect

1221
00:54:39,120 --> 00:54:41,920
Speaker 3: is and how few pods you need to get optimal.

1222
00:54:42,600 --> 00:54:45,880
Speaker 3: So much good stuff. The part about compensation, yeah, super interesting.

1223
00:54:46,040 --> 00:54:48,960
Speaker 2: Well, I do think in general a good piece of

1224
00:54:49,040 --> 00:54:53,960
Speaker 2: life advice is identify your comparative advantage early on, right,

1225
00:54:54,080 --> 00:54:56,759
Speaker 2: and play up to it, Like figure out what you

1226
00:54:56,840 --> 00:54:59,719
Speaker 2: do well and why you do it well. That's a

1227
00:54:59,760 --> 00:55:02,200
Speaker 2: real good thing to do early in your career.

1228
00:55:02,880 --> 00:55:03,200
Speaker 4: All right.

1229
00:55:03,360 --> 00:55:07,440
Speaker 3: No, I figured out early in my career that my

1230
00:55:07,520 --> 00:55:10,480
Speaker 3: one competitive advantage in journalism was waking up at four

1231
00:55:10,520 --> 00:55:14,280
Speaker 3: am before everyone. And now I'm spending thousands of dollars

1232
00:55:14,360 --> 00:55:17,240
Speaker 3: a year on therapy to like allow myself to sleep

1233
00:55:17,280 --> 00:55:19,920
Speaker 3: in a little bit more. So there are some drawbacks

1234
00:55:19,920 --> 00:55:21,640
Speaker 3: depending on what thing you identify.

1235
00:55:22,200 --> 00:55:26,120
Speaker 2: All right, everyone stop asking Joe or stop telling Joe

1236
00:55:26,160 --> 00:55:29,239
Speaker 2: what he missed, because it's just compounding. That's this problem.

1237
00:55:29,320 --> 00:55:29,600
Speaker 4: All right.

1238
00:55:29,640 --> 00:55:30,279
Speaker 2: Shall we leave it there.

1239
00:55:30,400 --> 00:55:31,080
Speaker 3: Let's leave it there.

1240
00:55:31,120 --> 00:55:33,800
Speaker 2: This has been another episode of the au Thoughts podcast.

1241
00:55:33,920 --> 00:55:37,280
Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway.

1242
00:55:37,000 --> 00:55:39,800
Speaker 3: And I'm Joe Wisenthal. You can follow me at the Stalwart.

1243
00:55:40,120 --> 00:55:43,720
Speaker 3: Follow our producers Carmen Rodriguez at Carman Ermann Dashill, Bennett

1244
00:55:43,719 --> 00:55:46,799
Speaker 3: at Dashbot at kel Brooks at Kelbrooks. Thank you to

1245
00:55:46,840 --> 00:55:49,880
Speaker 3: our producer Moses ONMDAM. For more Oddlots content, go to

1246
00:55:49,880 --> 00:55:52,600
Speaker 3: Bloomberg dot com slash odd Lots, where you have transcripts,

1247
00:55:52,680 --> 00:55:55,359
Speaker 3: a blog, and a newsletter and you can chet about

1248
00:55:55,400 --> 00:55:57,719
Speaker 3: all of these topics twenty four to seven in our

1249
00:55:57,880 --> 00:56:00,920
Speaker 3: discord Discord dot ggs.

1250
00:56:01,280 --> 00:56:03,640
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1251
00:56:03,719 --> 00:56:07,879
Speaker 2: ongoing exploration of multi strategy hedge funds, then please leave

1252
00:56:07,960 --> 00:56:12,120
Speaker 2: us a positive review on your favorite podcast platform. And remember,

1253
00:56:12,239 --> 00:56:14,799
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1254
00:56:14,920 --> 00:56:18,200
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1255
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1256
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